atmospheric composition change: ecosystems - atmosphere ... · 03/02/2009 accent s&i –...

03/02/2009 ACCENT S&I – Ecosystems (D Fowler) 1

Atmospheric Composition Change: Ecosystems - Atmosphere interactions

D. Fowler1*, K. Pilegaard2, M.A. Sutton1, P. Ambus2, M. Raivonen3, J. Duyzer4, D. Simpson5, H.

Fagerli6, J.K. Schjoerring7, A. Neftel8, J. Burkhardt9, U. Daemmgen10, J. Neirynck11, E.

Personne12, R. Wichink-Kruit13, K. Butterbach-Bahl14, C. Flechard8, J.P. Tuovinen15, M. Coyle1,

G. Gerosa16, B. Loubet17, N. Altimir18, L. Gruenhage19, C. Ammann8, S. Cieslik20, E. Paoletti21,

T.N. Mikkelsen2, H. Ro-Poulsen22, P. Cellier17, J.N. Cape1, L. Horváth23, F. Loreto24, Ü.

Niinemets25, P. I. Palmer26, J. Rinne27, P. Misztal1, E. Nemitz1, D. Nilsson28, S. Pryor29, M.W.

Gallagher30, T. Vesala27, U. Skiba1, N. Brüeggemann14, S. Zechmeister-Boltenstern31, J.

Williams32, C. O’Dowd33, M. C. Facchini34, G. de Leeuw35, A. Flossman36, N. Chaumerliac36,

J.W. Erisman37

1Centre of Ecology and Hydrology, Bush Estate, Edinburgh, UK 2Risø National Laboratory, Technical University of Denmark, Frederiksborgvej 399, DK-4000 Roskilde, Denmark 3Department of Forest Ecology, University of Helsinki, Finland 4TNO Institute of Environmental Sciences, The Netherlands 5Chalmers University of Technology, Hörsalsvg, Gothenburg, Sweden 6Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, Norway 7Royal and Veterinary and Agricultural University, Bülowsvej 17, Frederiksberg C, Denmark 8Agroscope FAL Reckenholz, Federal Research Station for Agroecology and Agriculture, PO Box, CH 8046 Zurich, Switzerland 9University of Bonn, Institute of Crop Science and Resource Conservation - Plant Nutrition, Karlrobert-Kreiten-Straße 13, 53115 Bonn, Germany 10 Bundesforschungsanstalt für Landwirtschaft (FAL) Institut für Agrarökologie, Bundesallee 50, 38116 Braunschweig, Germany 11 Research Institute for Nature and Forest, Gaverstraat 4, B-9500 Geraardsbergen, Belgium 12 INRA, INA PG, UMR Environm & Grandes Cultures, F-78850 Thiverval Grignon, France 13Department of Meteorology and Air Quality, Wageningen University and Research Centre (WUR), Postbus 47, 6700 AA Wageningen, The Netherlands 14Institute of Meteorology and Climate Research, Atmos. Environ. Research (IMK-IFU), Research Centre Karlsruhe GmbH, Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany 15Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland 16Dipartimento di Biologia Vegetale, Università di Firenze, Piazzale Cascine 28, I-50144 Firenze, Italy 17 INRA Unité Mixte de Recherche, 78850 Thiverval-Grignon, France 18 Department of Forest Ecology, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland 19 Institute for Plant Ecology, Justus-Liebig-University of Giessen, Heinrich-Buff-Ring 26-32, 35392, Giessen, Germany 20Joint Research Centre, I-21027 Ispra, Italy 21IPP-CNR, Via Madonna del Piano 10, I-50019 Sesto Fiorentino, Firenze, Italy 22Botanical Institute, University of Copenhagen, Øster Farimagsgade 2D, 1353 Copenhagen K, Denmark 23Hungarian Meteorological Service, PO Box 39, H-1675 Budapest, Hungary 24Consiglio Nazionale delle Ricerche – Istituto di Biologia Agroambientale e Forestale, Via Salaria Km 29.300, 00015 Monterotondo Scalo, Italy 25Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 1, 51014 Tartu, Estonia 26School of GeoSciences, University of Edinburgh, King's Buildings, West Mains Road, Edinburgh, EH9 3JN, UK 27Department of Physical Sciences, University of Helsinki, Helsinki, Finland


28 Stockholm Univ, Dept Appl Environm Sci, Atmospher Sci Unit, S-10691 Stockholm, Sweden 29Atmospheric Science Program, Dept of Geography, Indiana University, Bloomington, IN, USA 30School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Williamson Building, Oxford Road, Manchester, M13 9PL, UK 31Department of Forest Ecology, Federal Research and Training Centre for Forests, Natural Hazards and Landscape, Seckendorff-Gudent-Weg 8, 1131 Vienna, Austria 32Max-Planck-Institut für Chemie, J.J.-Becher-Weg 27, D-55128 Mainz, Germany 33Department of Experimental Physics and Environmental Change Institute, National University of Ireland, Galway University Road, Galway, Ireland 34Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy 35Climate and Global Change Unit, Research and Development, Finnish Meteorological Institute, 00560 Helsinki, Finland 36Laboratoire de Météorologie Physique, Université Blaise Pascal-CNRS-OPGC, 24, avenue des Landais, F-63177 Aubière Cedex, France 37Energy Research Centre of The Netherlands, The Netherlands

* Author to whom correspondence addressed


Table of Contents Page Abstract 6

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7

Introduction Scale Reactivity of natural surfaces Frameworks for analysis and interpretation of trace gas and aerosol exchange Bi-directional exchange Aerosols Ocean Atmosphere Exchange Wet deposition

7 9

10 10 11 12 12 13

2 2.1 2.2 2.3 2.4 2.5 2.6 2.7

Reactive Gaseous Nitrogen Compounds Introduction Emissions from soils Emissions of NOy from plant surfaces Canopy atmosphere interactions Models and measurements Exchange of HNO3, HONO, PAN Upscaling and regional and global trends

14 14 14 18 19 20 22 26

3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

Reduced Nitrogen (NH3, amines) Introduction Advances in Measurement Methods Key controls on biosphere-atmosphere exchange of Ammonia Effects of ecosystem type on Ammonia biosphere-atmosphere exchange Modelling surface-atmosphere exchange of Ammonia Dynamic simulation ecosystem C-N cycling of Ammonia fluxes Integrating Ammonia exchange processes Future challenges for Ammonia exchange

28 28 29 34 35 39 42 42 44

4 4.1 4.2 4.2.1 4.2.1.1 4.2.1.2 4.2.1.3 4.2.2 4.2.3 4.2.3.1 4.2.3.2 4.3 4.4 4.5

Sulphur Dioxide Introduction Worldwide advances in SO2 flux monitoring & modelling Asia Sulphur dioxide deposition to soils Micrometeorological measurements over vegetated areas Long term deposition studies and inferential modelling North America Europe Long-term flux monitoring in the UK Other recent European datasets Control of surface uptake rates by leaf cuticular chemistry Advances in deposition modelling Future challenges

46 46 47 47 47 48 49 50 51 51 52 54 56 58

5 5.1 5.2 5.2.1 5.2.2 5.2.3 5.2.4 5.2.5 5.3 5.4 5.5 5.6

Ozone Introduction Deposition rates European Forests Crops Grasslands Other vegetated surfaces Non-vegetated surfaces Non-stomatal deposition processes Model development and validation Risk Assessment Methods Potential effects of climate change

59 59 61 61 63 64 65 66 67 69 70 72

6 6.1

Biogenic Volatile Organic Compounds (BVOC) Introduction

75 75


6.1.1 6.1.2 6.2 6.2.1 6.2.2 6.2.3 6.2.4 6.2.5 6.3 6.4 6.4.1 6.4.2 6.4.3 6.4.4 6.4.5 6.4.6 6.4.7 6.5 6.6

Volatile Isoprenoids Oxygenated volatile compounds Environmental controls on BVOC emission Physiological and physico-chemical controls of emissions Physico-chemical controls of emission in species lacking specific storage structures Uptake and release of volatile compounds by vegetation CO2-dependence of emissions Induced emissions Contemporary difficulties in scaling BVOC emissions from leaf to ecosystem BVOC fluxes over Europe, by compound and in relation to the needs of photochemical oxidant models Flux measurement techniques Isoprene Monoterpenes Sesquiterpenes Methanol Acetone and Acetaldehyde Other compounds The EU large field campaigns in the Mediterranean area: from BEMA to ACCENT Remote sensing of BVOC

75 76 77 77 78

79 80 80 81 82

82 82 83 84 84 85 85 85 87

7 7.1 7.2 7.2.1 7.2.2 7.3 7.3.1 7.3.2 7.4 7.4.1 7.4.2 7.4.3 7.5 7.5.1 7.5.2 7.5.3 7.6 7.6.1 7.6.2 7.7

Aerosols Introduction Review of new measurement approaches and instrumentation Flux measurements of particle numbers (size-resolved or total), without information on chemical composition Flux measurements of individual aerosol chemical species Area sources of particles Resuspension Urban emissions of aerosols Dry deposition of particles Dry deposition rates to vegetation Parameterising and modelling deposition rates Dry deposition rates to urban areas Uncertainties Uncertainties in the application of micrometeorological flux measurement techniques for deriving the local flux Relating measured fluxes to surface exchange: flux divergence and the effect of chemical interactions Interpretation of measurements for model verification Future research needs Deposition measurements and reporting Deposition models Conclusions – Aerosols

91 91 92 92

94 95 95 96 99 99

104 104 105 105

106

109 109 109 111 113

8 8.1 8.2 8.3 8.3.1 8.3.2 8.3.3 8.3.4 8.3.4.1 8.3.4.2 8.4 8.4.1

Ecosystem-Atmosphere exchange of Radiatively Active Gases – N2O & CH4 Introduction Global budgets of N2O and CH4 Biological Sources of N2O and CH4 The biology of production and consumption of N2O and CH4 in soils and sediments Distribution of active microbial populations in soils N2O and CH4 fluxes from the main global ecosystems Plant mediated transport and production of N2O and CH4 Methane from vegetation Nitrous Oxide from vegetation New developments in measurements of N2O and CH4 and denitrification Flux Chambers

115 115 115 116 116

117 118 119 119 120 121 121


8.4.2 8.4.3 8.4.4 8.5 8.6 8.7

Micrometeorological methods Comparison of eddy covariance with chamber methods Recent methodological advances in measurements of total denitrification rates Modelling of N2O and CH4 fluxes on site and regional scale: approaches, applications and uncertainties Validation of models by landscape and regional scale measurements Conclusions

122 122 122 124

127 128

9 9.1 9.1.1 9.1.2 9.2 9.2.1 9.2.2 9.2.3

Exchange of trace gases & aerosols over the oceans New trace gas interactions at the air-sea interface Introduction Case Studies Aerosols Primary Marine Aerosol (PMA) Source functions Chemical Composition of primary sea spray Secondary Aerosol Production

129 129 129 131 136 136 138 141

10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8

Processes of wet scavenging of aerosols and trace gases from the atmosphere Introduction Nucleation scavenging of drops and ice crystals Impaction scavenging of aerosol particles Scavenging of gases Clouds Orographic precipitation Snow Chemistry Conclusions & some priority areas of future research

143 143 143 145 146 146 149 149 153

11 Ecosystem-Atmosphere exchange – Conclusions 154 12 Acknowledgements 157 13 References 158


Abstract

Ecosystems and the atmosphere

This review describes the state of understanding the processes involved in the exchange of trace

gases and aerosols between the earth’s surface and the atmosphere. The gases covered include

NO, NO2, HONO, HNO3, NH3, SO2, DMS, Biogenic VOC, O3, CH4, N2O and aerosols in the

size range 1nm to 10um including organic and inorganic chemical species. The main focus of the

review is on the exchange between terrestrial ecosystems, both managed and natural and the

atmosphere, although ocean-atmosphere exchange is included. The material presented is biased

towards the last decade, but includes earlier work, where more recent developments are limited

or absent.

Methodology and new instrumentation have enabled, if not driven technical advances in

measurement, and these are described, including the application of new mass spectrometric

methods, such as AMS and PTRMS adapted for field measurement of vertical fluxes using

micrometeorological methods for chemically resolved aerosols and for a wide range of VOC

respectively. Also briefly described are advances in theory and techniques in micrometeorology.

For some of the compounds there have been paradigm shifts in approach and application of both

techniques and assessment. These include flux measurements over marine surfaces and urban

areas using micrometeorological methods and the up-scaling of flux measurements using aircraft

and satellite remote sensing. The application of a flux based approach in assessment of O3 effects

on vegetation at regional scales of ozone is an important policy linked development secured

through improved quantification of fluxes. The coupling of monitoring modelling and intensive

flux measurement at a continental scale within the NitroEurope network represents a quantum

development in the application of research teams to address the underpinning science of reactive

nitrogen in the cycling between ecosystems and the atmosphere in Europe.

Some important developments of the science have been applied to assist in addressing policy

questions, which have been the main driver of the research agenda, while other developments in

understanding have not been applied to their wider field especially in CTM models through

deficiencies in linking data to enable application or inertia within the modelling community. The

paper identifies some of these applications, gaps and research questions that have remained

intractable at least since 2000, within the specialized sections of the paper rather than collated

together within the conclusions and where possible these have been focussed on research

questions for the coming decade.


1 Introduction

The composition of the earth’s atmosphere is unique in the solar system in being largely

determined by biological processes in soils, vegetation and the oceans interacting with physical

and chemical processes within the atmosphere. The surface–atmosphere exchange of most gases

contributing major and trace constituents of the atmosphere is coupled to biological production

processes and transferred through the surface–atmosphere interface. Thus, developing a

mechanistic understanding of the production and exchange processes is a core activity in

understanding the earth system. The subject of this review is much narrower than the scope of

these opening lines, and is restricted to the trace gases and aerosols exchanged between the

atmosphere and the earth’s surface. However, as is clear from much of the international

assessment of changes in atmospheric composition since the industrial revolution, these trace

atmospheric constituents are changing the earth’s climate (IPCC 2007), global biodiversity

(Millenium Ecosystem Assessment 2005) and the biogeochemical cycling of major nutrients

including nitrogen, carbon, and sulphur. The earth's surface is a sink for some atmospheric trace

gases and aerosols, and a source for many others and for most, the surface–atmosphere interface

represents a zone within which a substantial fraction of the overall control of fluxes occur. An

understanding of the rate controlling processes at this interface is therefore vital in describing the

overall exchange process and understanding the global biogeochemical cycles. Applications of

science in this field, in addition to their intrinsic value, are necessary to quantify and model

responses to human perturbation of many of the biogeochemical cycles (C, N, S, halogens and

metals to name but a few). These perturbations include changes in land use or emissions of trace

gases to the atmosphere, through combustion and industrial activities. Taking as an example the

global nitrogen cycle, human activity through combustion processes for oxidized nitrogen and

the Haber Bosch process for reduced nitrogen now dominates the cycling of reactive nitrogen

through the atmosphere and back to terrestrial and marine ecosystems (Galloway et al 2004). The

total emission of reactive nitrogen (Nr) from human activities at the end of the 20th century

exceeds that from natural processes by a factor of 4 (20.7 Tg of oxidized and reduced reactive

nitrogen Nr from natural sources within a total of 104 Tg-N in 1993, Galloway et al 2004). As

nitrogen is a limiting nutrient in many ecosystems, these modifications of the natural cycling

have profound effects on ecosystem function, biodiversity and atmospheric composition

(Erisman et al 2008). The human disturbance of the global carbon cycle is also extensive, and the

quantities involved are very large. Global emissions of CO2 from fossil fuels since 1700 amount


to approximately 600 Gt-C, which have increased the atmospheric CO2 mixing ratio from 280

ppm to 380 ppm in 2006, an increase of about 30%, (IPCC 2007).

These high level indicators of human influence provide essential context for this review paper,

but conceal the detailed changes taking place and the range of chemical species and interactions

involved. The subject area includes many different chemical species, and it is not possible to be

comprehensive in this review for all gases. In particular the subject of the global carbon cycle

and CO2 in particular are much too large to cover in this review. The focus of this review is on

the reactive trace gases and for the greenhouse gases, CH4 and N2O. The different gases are

associated with a range of biological sources and have varied chemical reactivity in the

atmosphere and at surfaces. These differences reveal the range of controls and temporal and

spatial variability in rates of exchange, which are the focus of the review. The review moves

through a wide range of chemical species, identifying the current state of knowledge and, where

possible the applications of the new developments in a policy context.

The gases emitted from terrestrial and ocean ecosystems include all of the major greenhouse

gases, H2O, CO2, CH4 and N2O, the nitrogen gases (both in reduced and oxidized forms), sulphur

compounds, volatile organic compounds (VOC) and halogens.

Quantifying the fluxes of these trace components of the atmosphere is clearly a prerequisite

within an assessmemnt process leading to the development of policy within the context of

climate change, eutrophication, acidification and photochemical oxidant formation. Many

research groups have become involved in the measurement and modelling of emission and

uptake (deposition) fluxes of trace gases and particles. The mechanistic understanding developed

mainly within the last 30 years from two different and quite narrow fields of study. The first was

concerned with the sources of atmospheric trace constitiuents, and the greenhouse gases were

among the first compounds for which surface fluxes were quantified directly by field

measurements. These included small scale (0.1 m to 0.5 m2) measurements of fluxes from soils

and vegetation using chamber methods for CO2, CO, CH4, N2O (Junge, 1963). The

measurements showed large spatial and temporal variability so that up-scaling to regions

generated very large uncertainties. The other development was mainly associated with the

atmospheric transport and deposition of pollutants, including nitrogen and sulphur compounds

and the photochemical oxidants in the 1960s and 1970s (Husar et al 1978). These early studies

were made to determine the importance of surface removal which is better known as dry

deposition (to distinguish the process from removal by precipitation) as a sink for reactive trace


gases (NO2, SO2, O3). Advances in understanding and computing resources have allowed more

sophisticated approaches to be adopted, in which the processes at the surface recognised

different sinks and interactions with other trace gases, allowing rates of dry deposition to change

with time and with surface characteristics.

1.1 Scale

Emission or deposition schemes to quantify trace gas fluxes operate at a range of scales

depending on the applications (Fig 1.1). For hourly integration the application is primarily for

research purposes and mechanistic study at the small scale (<102 m2). For landscape scale

measurements and for assessment of the fate of pollutants at the regional scale (106 km2) the

application has both research and policy application. At this scale spatial and temporal

integration provides robust parameterization. The application in global models to quantify

sources and sinks is restricted in spatial resolution, typically to 1o x 1o, and likewise has research

and policy application. For the landscape scale, flux measurements may be made directly, using

micrometeorological methods above canopies of vegetation, soil, or even ocean surfaces and

have become the method of choice for long term flux measurement. These techniques provide, in

addition to the target trace gas flux the turbulent exchange characteristics of the underlying

surface and the partitioning of available radient energy which enables the processes to be

investigated at a sufficiently large scale to integrate canopy scale fluxes over typically 105 m2.

Figure 1.1 A diagrammatic representation of the scales of measurement of trace gas fluxes for process studies (a

transverse section through a Phaseolus vulgaris leaf, showing the palisade and mesophyll cellsbounded by epidermal

cells and the air spaces for internal exchange between trace gases and intercellular fluid). The field scale at which

most of the micrometeorological flux measurements are made and the continental scale where models provide the

emission and deposition fluxes. In this case the emission fluxes of oxides of nitrogen over Europe are shown,

revealing the importance of international shipping to the continental fluxes.


The sections focus mainly on individual trace gases or classes of atmospheric particles, and each

considers the surface-atmosphere exchange over specific ecosystems. The exceptions are the

ocean surfaces and wet deposition, within which a range of relevant compounds are considered.

1.2 Reactivity of natural surfaces

For many of the short lived gases (<2 days in the boundary layer) there are multiple sinks at the

surface and these include foliar surfaces and soil whose properties as sinks for a range of gases

vary with humidity and the presence of surface water and are influenced, sometimes strongly, by

the presence of other gases (Fig 1.2). The chemical and physical complexity of terrestrial

surfaces, illustrated in Figures 1.1 and 1.2 at the microscopic scale is greatly simplified in the

parameterisations used in models. This is necessary in part due to the nature of the flux

measuring systems, which integrate the net fluxes, and fail to reveal the microscopic scale of

variability of the true exchange.

Figure 1.2 Illustrating the importance of different sinks for reaction of trace gases at the terrestrial surfaces, notably

the external surfaces of vegetation often as in this case covered by complex layers of epicuticular wax and illustrated

in figure 1.1, the internal structure of leaves, following uptake through the stomatal apertures and soils greatly

simplified in this illustration

1.3 Frameworks for analysis and interpretation of trace gas and aerosol exchange

The measurements of surface-atmosphere exchange provide at the simplest level the mass

exchange per unit area of surface, which may be ground, water or leaf area, per unit time. To

extract useful information on the underlying processes it is necessary to quantify the

contributions each step in the transfer pathway makes to the overall exchange between defined


points, which in this scheme is simplified to vertical levels between a source and a sink. The

most widely applied transfer scheme is a resistance analogue (Monteith and Unsworth 2007), in

which the flux of trace gas or particle is treated as an analogue of electrical current in a simple

network of resistances, which may act in series if there is just one sink at the surface, such as a

pure water surface, or may have several sinks at the surface, acting in parallel, each representing

a distinct chemical component of the underlying surface. A simple resistance network

representing three different sinks at the surface, and the two atmospheric resistances (Ra and Rb,

respectively the turbulent transfer resistance and the leaf boundary layer resistance) are

illustrated in Figure 1.3.

⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜

⎝

⎛

++=

++=

c3c2c1

c

cba

R1

R1

R1

1R

RRRtR

χO3(z0’) = 0

χO3(z-d)

FO3

Rc3soil

RbO3

Rc2cuticle Rc1

stomata

Ra

Figure 1.3 A simple resistance analogy for a trace gas with sinks in stomata, on foliar surfaces and in soil.

The atmospheric resistances may be separated from the total resistance using independent

measures of the turbulence above the vegetation. The overall flux may be measured by a variety

of micrometeorological methods (Monteith 1975), and thus the total of the surface or canopy

resistances to transfer between the source and sink may be quantified as the residual, as shown in

Figure 1.3.

1.4 Bi-directional exchange

For many of the trace gases, regardless of their reactivity, the exchange fluxes may be shown to

vary in sign as well as magnitude, with emission and deposition being commonly observed. The

most widely known example of bidirectional exchanges is CO2, which exhibits both deposition

and emission fluxes due to photosynthesis and respiration respectively. In this case the concept

of compensation points as mixing ratios at which no net exchange takes place is now widely

recognised for a range of trace gases (NH3, NO, CO2).


The recognition of bi-directional exchange requires modelling approaches to simulate the

process for application in surface-atmosphere exchange models, as illustrated for NH3 in Fig 1.4.

Figure 1.4. A diagrammatic representation of bi-directional exchange, for NH3 exchange between the atmosphere

and vegetation.

1.5 Aerosols

The understanding of deposition and emissions of aerosols over terrestrial surfaces has advanced

considerably in the last decade, after a long period in which application of a model developed by

Slinn (1984) has been a standard for many modelling approaches. Likewise, the emission of

aerosols by resuspension from terrestrial surfaces has advanced following innovative new

measurement approaches.

1.6 Ocean atmosphere exchange

For many years the ocean atmosphere exchange of trace gases has been treated in a more

simplistic way (Liss et al 1981), in part due to the relative simplicity of the ocean surface relative

to terrestrial surfaces, but also due to the difficulty in making measurements of fluxes of trace

gases over the open ocean and the focus on the more polluted regions. However there has been

an accelerating interest in ocean –atmosphere exchange as new techniques have become

available to make the flux measurements and as very new issues have been identified. Current

interest in ocean acidification and ocean eutrophication further raise the profile of ocean –

atmosphere exchange, and given that these surfaces cover 71% of the earth’s surface, the


relatively small section of this review paper on this topic belies its importance in understanding

atmospheric composition change.

1.7 Wet deposition

Understanding of underlying processes of precipitation scavenging has continued to develop,

with important improvements, justifying the inclusion of this important subject in the review.

The applications of wet deposition schemes are very important in the LRT models (EMEP 2007)

and increasingly in global chemistry-transport models (CTM) (Stevenson et al). These two

applications have very different demands on available knowledge and understanding. In the case

of LRT models in Europe (eg. EMEP), the applications form part of the integrated assessment

process and within the user community the pressure to provide ever finer spatial scale estimates

for the assessment of inputs presents challenges to both the scientific understanding but even

more to the capability of LRT models and the meteorological models on which they depend.

Current demand for assessments of effects at the 1km x 1km scale allows the scale of the input

estimate to approach the scale of an individual nature reserve, for example.


2 Reactive gaseous nitrogen compounds – oxidized nitrogen 2.1 Introduction

Developments in understanding surface-atmosphere exchanges of NO and NO2 over the last

decade have focussed on three specific issues:- the long term emission of NO from soils; the

interaction of chemical processing of nitrogen oxides in and above plant canopies; and the

deposition of NO2 and HNO3 to foliar and soil surfaces. The measurements have been made over

different vegetation, but the recent focus has been on forests, in part because the interactions

between these processes are greatest for forests, but also because some of the measurements are

simpler to make and interpret for mature forests. This chapter outlines the developments in

understanding NOy exchange between terrestrial ecosystems and the atmosphere, concentrating

on developments during the last decade.

The consequence of soil emissions of NO and within canopy conversion of NO to NO2 by

reaction with O3, is that this produces a within canopy source of NO2. The within canopy source

of NO2 interacts with the NO2 from above the forest canopy to determine the net exchange above

the forest. At very small ambient NO2 concentrations, the forest may therefore be a source of

NOx to the lower atmosphere, as the emission of NO from soil and conversion to NO2 within

canopy exceeds the uptake by the canopy and NO2 is emitted by the forest. At larger ambient

NO2 concentrations, the forest becomes a net sink, as stomatal uptake of NO2 from above canopy

sources exceeds the NO emission from the soil.

2.2 Emissions from soils

Soil surface emissions of NO are the result of several biological and abiotic processes in the soil

producing and consuming NO. Production and consumption of NO occurs predominantly via the

biological nitrification and denitrification processes. Nitrification is the oxidation of soil NH4+ to

NO3-, and denitrification is the anaerobic reduction of soil NO3

- to N2O and N2. In nitrification

NO is formed as a by-product during the oxidation of NH4+ to NO2

- and possibly also as a result

of nitrifier reduction of NO2- leading to a NO production of 1-4% of the NH4

+ being oxidized

(Skiba et al., 1997). The NO produced may be transformed within the soil profile by oxidation to

NO3- or it may be released to the atmosphere following diffusion to the soil surface. In

denitrification, NO occurs as an intermediate in the cascade of reductive processes, and in the

soil profile NO reduction may contribute to the formation of N2O. Abiotic production of NO

occurs from oxidation of nitrous acid (HONO) that has been produced by protonation of

biologically formed NO2- (Venterea et al., 2005). Under certain conditions e.g. after application

of anhydrous ammonia to agricultural soils or acidic forest soils, the coupled biological-abiotic


production of NO may constitute the dominant process for soil NO emissions (Venterea and

Rolston, 2000; Gödde and Conrad, 1998). Factors that increase nitrification and denitrification,

e.g. substrate and O2 availability, temperature and pH are thus predicted to influence NO

formation. Likewise, factors affecting transport processes in the soil are predicted to regulate

emissions of NO (and other gases). It has been hypothesized (Davidson, 1991) that where WFPS

(water filled pore space) is less than 0.6, nitrification is the dominant process and relatively high

emissions of NO may be observed. Under more reducing conditions, 0.6<WFPS<0.9,

denitrification dominates which has a higher potential for NO production compared to

nitrification (Skiba et al., 1997); however under conditions where anoxic conditions are

generated by high soil water content or by compaction of fine textured soil the probability of NO

being re-consumed by the denitrifying community is greatly enhanced. Soil water may also play

a central role in mediating chemical processes leading to NO formation (Venterea et al., 2005).

Under most soil conditions, both nitrification and denitrification occur simultaneously and the

net flux of NO between soil and atmosphere is the result of both processes together. As current

views of controls over NO gas emissions are still incomplete and need revision e.g. with

emphasis on the role of abiotic formation (Venterea et al., 2005) there is a continuous need to

further develop and improve methodologies to identify and characterize the NO formation

processes. Gödde and Conrad (1998) achieved this by a combined modelling and experimental

approach to determine the net NO flux in relation to NO concentration in order to quantify

production and consumption rate constants and compensation concentration. Recent advances in

methodological approaches to deepen our understanding of soil based NO emissions have

include application of stable isotope techniques. Stark et al. (2002) applied a 15N-isotope pool

dilution method to obtain the simultaneous gross rates of NO forming processes combined with

soil emissions, and Russow et al. (2000) adopted a kinetic isotope method (KIM) to study the

complex N transformation processes involved in soil NO emissions.

NO and N2O emissions were measured continuously at 15 forest sites as part of the EU-funded

project NOFRETETE (Pilegaard et al., 2006) including coniferous and deciduous forests in

different European climates, ranging from boreal to temperate continental forests and from

Atlantic to Mediterranean forests. Furthermore the sites differ in atmospheric N-deposition

ranging from low deposition (0.2 g N m2 a-1) to high deposition (4 g N m-2 a-1).

The relationships of the emissions of NO and N2O, with the parameters nitrogen deposition,

forest type, age, C/N, pH, soil temperature and water-filled pore space (WFPS) were investigated


by means of stepwise multiple regression analysis. NO emission was dependent on forest type

and positively correlated with nitrogen deposition (Fig 2.1). WFPS was tested for curvature by

including a quadratic term, but this was not significant. Separately performed regression analyses

for deciduous and coniferous forests showed, however, that the relationship between nitrogen

deposition and NO emission was only significant for the coniferous forests: (NO (µg N m`2 h`1)

= `13.9 + 25.5 [N deposition (g m`2 a`1)], r2=0.82) The N2O emission was significantly

negatively correlated with both the C/N ratio and the age of the stands; a logarithmic

transformation of N2O emission improved the significance of the correlation.

Figure 2.1 Left: NO emission (µg N m`2 h`1) as a function of nitrogen deposition (g N m`2 a`1).

Regression lines (solid = significant, dashed = non significant) for coniferous and deciduous sites,

respectively. Right: N2O emission (µg N m`2 h`1) as a function of C/N ratio. The full line represents a

linear regression and the dotted line a logarithmic regression.

Soil temperature is a key variable affecting the emission rates of both gases (Fig 2.2). Emissions

of both NO (Slemr and Seiler, 1984) and N2O (Skiba et al., 1998) increase with rising soil

temperature due to the fact that rates of enzymatic processes generally increase with temperature

as long as other factors (e.g. substrate or moisture) are not limiting. Soil water acts as a transport

medium for NO3- and NH4

+ and influences the rate of O2 supply and thereby controls whether

aerobic processes such as nitrification or anaerobic processes such as denitrification dominate

within the soil. While N2O emissions are known to increase at higher water contents through

larger losses from denitrification (Papen and Butterbach-Bahl, 1999) the relationship between

the NO flux and the soil water is more complex. Due to limited substrate diffusion at very low

water content and limited gas diffusion at high water content, nitric oxide emissions are

suspected to have a maximum at low to medium soil water content.


-5 0 5 10 15 200

100

200

300

400

NO

em

issi

on [µ

g N

O-N

m-2 h

-1]

Forest floor temperature [°C]

Figure 2.2 Relationship between daily mean forest floor temperature and daily mean NO emissions at the

Höglwald Forest (spruce, control) for the observation period January 1, 2004 – December 31, 2006. For

details on measurement and site characteristics see Gasche & Papen (1999)

The effects of soil moisture and temperature on NO and N2O emission were studied in a

laboratory experiment with soil cores from some of the NOFRETETE field sites (Schindlbacher

et al., 2004). Soil moisture and temperature explained most of the variations in NO (up to 74 %)

and N2O (up to 86 %) emissions for individual soils. NO and N2O were emitted from all soils

except from a boreal pine forest soil in Finland, where the laboratory experiment showed that

NO was consumed. NO emissions from a German spruce forest ranged from 1.3 to over 600 µg

NO-N m-2 h-1 and greatly exceeded emissions from other soils. Average N2O emissions from this

soil tended also to be highest (170 ± 40 µg N2O-N m-2 h-1), but did not differ significantly from

other soils. NO and N2O emissions showed a positive exponential relationship to soil

temperature.

The results from the annual averages of field data did not show any significant relationship with

soil temperature for either NO or for N2O emission. Schindlbacher et al. (2004) showed that N2O

emissions increased with increasing WFPS or decreasing water tension, respectively. Maximum

N2O emissions were measured between 80 and 95 % WFPS or 0 kPa water tension. The optimal

moisture for NO emission differed significantly between the soils, and ranged between 15 %

WFPS in sandy Italian floodplain soil and 65 % in loamy Austrian beech forest soils. For the

field data WFPS was not a significant parameter for N2O emission, but had a positive significant

effect on NO emission (Fig 2.3). The annual average WFPS in the field was higher than the


optima found for NO in the laboratory experiment, but since not all field sites were studied in the

laboratory it is difficult to provide a general conclusion. The interannual variation within single

sites clearly showed relationships to both temperature and soil moisture. An important factor for

N2O emission is freeze-thaw events which can produce a significant outburst of N2O [Kitzler et

al., 2006].

0 20 40 60 80 100

020

4060

8010

0

WFPS(%)

NO

em

issi

on (%

of m

axim

um)

Sandy loamSilty loamSandy clay loamLoam(1)Loam(2)

Figure 2.3 The relationship of NO emission and water filled pore space at different localities in the

NOFRETE project (based on data in Schindlbacher et al. 2004).

In general, relationships between nitrogen oxides emission and soil moisture and soil

temperature can be found within a single locality when studying short-term variations. However,

using the same parameters when comparing annual values from different localities within a large

region as in this study does not necessarily reveal comparable relationships since other factors

such as soil properties, stand age, and site hydrological conditions interfere.

2.3 Emissions of NOy from plant surfaces

Production of NOy on Scots pine branch surfaces by ultraviolet radiation has been observed in

Hyytiälä, southern Finland (Hari et al., 2003) (Fig 2.4). Other studies have shown that irradiance-

dependent NOy emissions from snow and different chamber surfaces have been observed to

originate from HNO3 or nitrate photolysis. In Hyytiälä, Raivonen et al (2006) investigated

whether the NOy emitted from pine shoots could originate from photolysis of HNO3 attached to

the needle surface. Field data of several years from Hyytiälä were used to test this hypothesis.

The HNO3 deposition, estimated for the Hyytiälä site, has been high enough to account for the

NOy emission rates observed from the chambers. The particular characteristics of the daily


pattern of CO2 exchange or stomatal control was not reflected in the NOy flux. When a pine

branch was rinsed, which reduced the amount of water-soluble nitrogen compounds (e.g., HNO3,

nitrates and HONO) from the needle surface, NOy emissions from that branch decreased

compared to another non-rinsed branch. Therefore, it was concluded that the results support the

hypothesis and that HNO3 photolysis on plant surfaces needs to be taken into account both from

air chemistry and plant sciences point of view.

Figure 2.4 Effect of UV radiation on the NOy concentration in a small Teflon chamber that enclosed a clean pine

branch or a branch that had been treated with NH4NO3 solution. The branches were dead and dry, cut from the tree.

UV wavelengths were filtered away using a Plexiglas plate (Raivonen et al., 2006).

2.4 Canopy atmosphere interactions

The interaction between chemical reactions of nitrogen oxides taking place in the canopy and

trunk space of a forest is a special case because in this area chemical and turbulent timescales

change substantially leading to a very complex situation in which even the direction of fluxes

may change (Duyzer et al., 1995) (Fig 2.5).

This makes it nearly impossible to interpret measurements of the turbulent fluxes of some

reactive trace species above the canopy from single point eddy covariance measurements.

Several models have been developed to simulate the overall exchange and show the magnitudes

of the different competing processes. These models describe the coupled processes of

atmospheric transport and chemical processes above and in canopies in detail. Over forests the

situation is even more complex. Flux measurements are usually carried out near the top of rough

canopies leading to potential inaccuracies in the K theory approximation. This theory is

relatively easy to combine with vertical atmospheric transport phenomena with fast chemical


reactions. A probably more realistic description of atmospheric transport, using Lagrangian

models, (Nemitz et al [date]) is much more difficult to combine with chemical reactions.

Figure 2.5. Complete schematic of the various canopy interactions in the case of the exchange of nitrogen oxides

with forests.

2.5 Models and measurements

Measurements of small fluxes of NO and NO2 have shown spurious results especially at low

concentrations due to a lack of specificity of monitors and a lack of instrument sensitivity, but

other problems may well have contributed, including violation of conditions under which such

fluxes may be measured above canopies and the complexity of interactions; soils and sunlight

driven reactions may both be sources of NOy and these interact with the stomatal sinks and the

chemical processing within the canopy trunk space.

As a result of these limitations there are only limited data available for verification of models.

Duyzer et al (2004) described the analyses of a data set acquired in the framework of a European

project from an experiment carried out in a 20 m high coniferous forest (Speulderbos, The

Netherlands). A 1D multilayer model of a forest canopy was used to analyze the field data. In

each layer vertical transport was described using K-theory; canopy uptake was described using a

resistance layer model. Simple chemical reactions between ozone and nitric oxide and photolysis

of nitrogen dioxide were described. The coupled differential equations were solved numerically.

Input to the model calculations were concentrations of nitrogen oxides and ozone at the highest

level above the forest, levels of radiation, temperature, humidity, wind speed, turbulence

parameters and an estimate of the emission of nitric oxide. Output of the model is the

concentration and fluxes of the relevant components at the height of each level in and above the

canopy. These may be compared with measured fluxes of these components at two levels above


and one below the canopy. It is fair to say that the comparison between measured and modelled

fluxes is not impressive. There are many possible explanations for this observation but no clear

single cause has been identified.

Depending on the magnitude of this soil flux the NO2 flux is either downward or upward. In the

case of the coniferous forest described here and the conditions during the experiment the result

was that when the NO flux from the soil exceeded 10 ng/m2/s, the NO2 emission was upward

(i.e. away from the forest).

At high concentrations the NO2 flux is directed towards the forest and at small concentrations the

flux is more likely to be directed towards the free atmosphere. This may be interpreted as a

compensation point above which the flux is directed towards the surface and below which the

flux is away from the surface.

In summary the flux of NO2 above a forest can be described with the following function:

where all variables have their common meaning and CNO2 denotes the concentration of nitrogen

dioxide above the canopy. This equation is rather qualitative but indicates the sensitivity of the

flux of NO2 above the canopy. More quantitative model runs are needed, but these require a

large amount of input data and the results are still uncertain.

A simple resistance model (Duyzer et al 2005) was tested in a deciduous forest (Sorø, Denmark)

and is illustrated in Figure 2.6. Generally the understanding of the various processes and their

interaction is increasing. Nevertheless many uncertainties remain and there is a need for further

improvement of models, especially for lagrangian models incorporating chemical reactions. On

the other hand, the accuracy of the results of field measurements has been rather low. It should

be noted that although the interaction between atmospheric chemical reactions and exchange

between the canopy and the atmosphere is easy to understand its importance may be limited. In

cases where fluxes of nitrogen oxides are small the corrections could be large in a relative sense

but still rather small in an absolute sense. The currently available models could very well be

capable of making estimates of the magnitude of these effects. In view of all the uncertainties

hindering improved estimates in testing of models the limited quality of the description of

atmospheric transport processes within the canopy may not be a serious problem here.


Figure 2.6 Profiles of CO2, NO2, O3 and NO in a beech forest near Sorø, Denmark. The profiles clearly show the

effects of stable conditions during night and daytime turbulence mixing the full air column. CO2 is built up during

night due to soil respiration; O3 is depleted during night due to deposition and chemical reactions. At the soil surface

high concentrations of NO are seen due to emission from the soil.

2.6 Exchange of HNO3, HONO, PAN

The deposition of HNO3 to terrestrial surfaces has been shown to be primarily controlled by the

rates of turbulent exchange in the atmospheric boundary layer and the leaf boundary layer

(Huebert et al 1982). The highly reactive and soluble nature of gaseous HNO3 leads to large rates

of deposition, approaching the maximum rates of deposition limited by turbulent exchange when

each molecule arriving at terrestrial surfaces is immediately absorbed at the surface. In these

conditions the surface is considered to be acting as a perfect sink, canopy resistance is zero and

the numerical value for the deposition velocity becomes:

Vg(NHO3) =vmax=1/ra+rb

The values for deposition velocity in these conditions are very sensitive to wind velocity values

and approach several cm s-1 even over relatively short vegetation. The consequence of these

large rates of deposition are that even in areas with small HNO3 concentrations, dry deposition of

HNO3 contributes a substantial quantity if nitrogen. Taking an ambient concentration of 0.1 ppb

HNO3, the annual deposition of N for a forest would be of the order 3 kg N ha-1 annually from

HNO3 alone.


The close coupling between rates of turbulent exchange and dry deposition rates for HNO3 also

generates substantial spatial variability in N deposition in the landscape, with hot spots for N

deposition being forests and especially forest edges, hedgerows and isolated, exposed hills,

where wind speeds are larger.

Several studies have recently attempted to measure total oxidized nitrogen (NOy) fluxes or even

total reactive nitrogen (Nr = NOy + NHx) to ecosystems (Turnipseed et al., 2006). These

approaches offer the prospect to apply eddy-covariance techniques for the robust and relatively

cost effective determination of total atmospheric N deposition, but they do not provide the

chemical speciation needed to further process understanding. There have, however, also been

advances in the understanding of individual N compounds other than NH3, NO and NO2:

A recent lab study (Sparks et al., 2003) has confirmed that PAN deposition through the stomata

can make a significant contribution to plant uptake of atmospheric N. In addition, recent

instrument developments in chemical ionization mass spectroscopy (CIMS) and thermal-

dissociation laser induced fluorescence (TD-LIF) have enabled the application of eddy-

covariance to the biosphere / atmosphere exchange of preoxy acyl nitrates (PANs such as PAN,

PPN and MPAN). Measurements were made over two contrasting US pine forests at Duke

Forest, North Carolina, (RH > 75%) and Blodgett Forest, California, (RH < 30 %) (Farmer et al.,

2006; Turnipseed et al., 2006; Wolfe et al., 2008).

At Duke Forest fluxes of PAN, PPN and MPAN were measured with a CIMS technique

(Turnipseed et al., 2006). There were no significant differences in the Vd of the three different

PAN compounds, but all three species deposited about four times faster than predicted by the

model of Wesely (1989) during the day, and nearly an order of magnitude faster during the night,

indicating that aqueous solubility considerations are insufficient to predict the behaviour of PAN

on surfaces. The average Vd was 2.5 mm s-1 during day and 8 mm s-1 during night. In contrast to

the considerations of Wesely (1989), wet surfaces showed a smaller non-stomatal resistance (Rns

= 125 s m-1) than dry surfaces (Rns = 250 s m-1).

At the much drier Blodgett forest site, the flux of the sum of all PANs was measured by TD-LIF,

based on thermal conversion and NO2 detection (Farmer et al., 2006). PAN was derived as the

difference between the ambient temperature and 180°C channel. They found upward fluxes in

summer and on average bi-directional exchange with afternoon deposition in winter, when noon-

time deposition velocities averaged 8 mm s-1. More recently, these measurements have been


repeated with the more selective CIMS technique (Wolfe et al., 2008). Here measurements

indicated larger average midday values of Vd for PPN (12 mm s-1) than for the PAN and MPAN

(4 mm s-1), while both compounds deposited slowly at night (Vd < 2 mm s-1). The authors of this

study attribute the difference in the Vd between compounds to MPAN and PAN production

inside the canopy and suggest that the PPN fluxes are a better descriptor of the surface

deposition. They suggest that the non-stomatal uptake is dominated, but not fully explained, by

thermochemical decomposition, and thus strongly linked to canopy temperature.

In summary, this recent measurement evidence suggests that deposition rates of PANs in warm

climates are at least a factor of 5 larger than predicted by commonly used models and non-

stomatal deposition is larger to wet and humid surfaces than to dry surfaces.

During the same TD-LIF study, Farmer et al. (2006) measured fluxes of total alkyl nitrates (gas

and aerosol phase), from the difference between the 180°C and 330°C channels. These

compounds showed large winter-time midday deposition velocities of 20 mm s-1, approaching

those of HNO3 (25 mm s-1) (Farmer and Cohen, 2008). Even higher Vd of 30 mm s-1 was derived

by Horii et al. (2005) for what they interpret as isoprene-derived hydroxyalkyl nitrates.

Nitric acid (HNO3) has traditionally been believed to deposit at the maximum rate possible

according to turbulence (Vmax) and its flux measurement continues to be used to derive quasi-

laminar boundary-layer resistances for vegetation (e.g. Pryor and Klemm, 2004). This view has

been challenged by recent measurements that indicated non-negligible canopy resistances in the

range of 50 to > 200 s m-1 during midday (Nemitz et al., 2004b; Wolff et al., 2007; Nemitz et al.,

2008). This has been attributed to non-zero chemical compensation points governed by the

thermodynamic equilibrium with NH4NO3 on leaf surfaces or fertilizer pellets. Figure 2.7 shows

an example of reduced deposition of HNO3 and HCl over a Dutch heathland.


15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

F χ [n

g m

-2 s

-1]

-40

-30

-20

-10

0

10

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

Rc [

s m

-1]

0

100

200

300

400

500

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

u * [m

s-1

]

0.0

0.1

0.2

0.3

0.4

0.5

RH

(z0')

[%]

0

20

40

60

80

100

120

RH u*

15:00 18:00 21:00 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

Vd [

mm

s-1

]

0

5

10

15

20

25

30

Vmax(HNO3)Vmax(HCl)Vd(HNO3)Vd(HCl)

HNO3

HCl

HNO3 HCl

Figure 2.7 Example time series of HNO3 and HCl exchange measured above a Dutch heathland with a denuder

gradient system with online analysis by ion chromatography. The panels show: (a) fluxes, (b) deposition velocities

of HNO3 and HCl in comparison with their maximum values and (c) Rc for HNO3 and HCl, (d) friction velocity (u*)

and relative humidity (h). Data represent 2.5 hr. running means of 30 min. Vd(HNO3 and Vd(HCl) are reduced

compared with their maximum values, presumably due to non-zero chemical compensation points originating from

deposited NH4+ salts. From Nemitz et al. (2004a).

The view that HNO3 normally deposits with a near zero canopy resistance still holds. There is an

increasing measurement database of HNO3 concentrations in national and regional networks

suitable for inferential modelling of HNO3 deposition (Tang et al., 2008), which now provides

independent confirmation from the model results, that HNO3 deposition makes a very significant

contribution to nitrogen deposition across Europe. In addition, Europe-wide monitoring activities

have produced the first hourly monitoring datasets of HNO3, which allows for a much more in-

depth assessment of the performance of oxidized nitrate chemistry in atmospheric transport

models (Tarrason and Nyiri, 2008).


Much development has occurred in measurement techniques for nitrous acid (HONO), e.g. based

on long path absorption photometry (LOPAP) and differential optical absorption spectrometry

(DOAS). This has contributed to the improvement of process understanding of sources of HONO

in the atmosphere, e.g. revealing larger daytime sources than previously thought and identifying

NO2 reactions with humic acid as a novel production mechanism (Kleffmann et al., 2005;

Stemmler et al., 2006). By contrast, applications of these approaches to flux gradient

measurements are still rare, which nevertheless confirm surface sources of HONO (Vitel et al.,

2002; Kleffmann et al., 2003).

2.7 Upscaling and regional and global trends

The complexity of processes involved in NO emissions from soils has resulted in a significant

uncertainty in the regional and global source strength of soils for NO. However, different

methodologies have been developed, e.g. relatively simple statistical models as well as process

based model approaches, to cope with the problem of regionalisation of soil NO fluxes. The most

widely used approach for calculating regional NO emissions from soils is based on the work of

Yienger and Levy (1995). These authors consider land use and respective background emission

strengths, nitrogen fertilization rate (2.5% loss of applied nitrogen), temperature effects (three

classes: cold-linear, exponential and optima) as well as the pulsing of NO emissions following

prolonged dry periods (four classes, based on intensity of rainfall) to estimate soil NO emissions.

Yienger and Levy also provide a so called canopy reduction factor in order to consider chemical

conversion and re-deposition of NO as NO2 within the canopy. Compared to the methodology by

Yienger and Levy, the Skiba-EMEP/CORINAIR approach is more simplistic. Based on a

literature review by Skiba et al. (1997) this approach postulates that 0.3% of any form of

nitrogen is volatilized as NO, i.e. regardless whether it originates from inorganic or organic

fertilization or atmospheric N deposition. Furthermore, a background emission of 0.1 ng NO-N

m-2 s-1 (≈0.032 kg NO-N ha-1 a-1) was assumed (Simpson et al., 1999). In addition, EMEP/

CORINAIR also use a more detailed methodology (BEIS-2), which originates from the work of

Novak and Pierce (1993) and considers soil temperature as well as different land use classes. A

statistical summary model was developed by Stehfest and Bouwman (2006), which is based on

the most extensive literature review currently available. This methodology for calculating soil

NO emissions on global and regional scales considers land-use, N fertilization rate [Fertilizer],

soil N content (three different classes, estimated as 1:10 of soil organic carbon content) [SON]

and climatic regions. The methodology was recently adapted to calculate a European wide

inventory of NO emissions from forest soils (Kesik et al., 2006, 2007). Kesik et al. used the


process oriented ecosystem model Forest-DNDC. The model was extensively tested for its

performance to predict NO emissions at the various NOFRETETE field sites, which were

located across Europe and, thus, were covering different climatic conditions (Pilegaard et al.,

2006). Regionalization was finally achieved by linking the model to a detailed GIS database

holding all relevant information for initializing and driving the model such as data on vegetation

(e.g. forest type) and soil properties (e.g. texture, soil pH, organic C content) and climate (either

present day conditions or projected future climate predictions). This approach demonstrated for

the first time the huge regional differences in NO emissions from forest soils across Europe as

shown in Figure 2.8, to estimate its significance on a regional scale and to unravel the

importance of atmospheric N deposition for the magnitude of forest soil NO emissions.

Fig. 2.8 Importance of atmospheric N deposition for NO emissions from forests soils. Shown is the difference in NO

emissions for a scenario with zero atmospheric N deposition and present day atmospheric N deposition. In huge

parts of central Europe but also Scandinavia forest NO emissions are likely to decrease significantly if atmospheric

N deposition can be reduced to background levels.


3 Biosphere atmosphere exchange of ammonia 3.1 Introduction

Substantial progress has been made during the last five years in understanding ammonia

biosphere-atmosphere exchange. Experimental studies have included controlled laboratory

analysis, while a series of micrometeorological studies have assessed net fluxes occurring under

field conditions. In particular, major advances have been made in modelling the different aspects

of ammonia exchange. This has included not just analysis of the drivers of the vertical flux

densities, but also a consideration of non-stationarities, such as advection effects and chemical

interactions. Traditionally, micrometeorological experiments were designed to avoid these

effects, focusing as far as possible on ‘ideal’ micrometeorological conditions, so as to better

quantify the vertical exchange processes, and develop parametrizations for ‘dry deposition

schemes’ in regional models (Fowler and Duyzer 1989; Fowler et al., 2001; Sutton et al. 1994;

Simpson et al. 2006). However, for ammonia, it has become increasingly clear that these non-

stationarities represent important effects that are widespread in the real environment and need to

be quantified (Sutton et al., 2007).

The developments in the last years have arisen from a wide range of national and international

projects. National studies have particularly addressed exchange with key ecosystems of regional

importance, such as ammonia losses from agricultural systems (e.g., Milford et al., 2001a;

Walker et al., 2006; Wichink Kruit et al., 2007) and the ammonia inputs into semi-natural

ecosystems of conservation value (e.g., Wyers and Erisman 1998; Neiyrink and Ceulemans,

2008). Collaborative international projects have sought to integrate and extend these interests,

making the comparison between ecosystem types and looking at the interactions (e.g., Sutton et

al., 2008d).

The first European collaborative project dedicated to ammonia exchange was ‘EXAMINE’.

Attention was given to quantifying ammonia exchange with a range of European ecosystems,

under both experimental and field conditions (e.g., Sutton et al. 1995; Schjoerring et al. 1998;

Neftel et al. 1998; Meixner et al. 1996; Nemitz et al., 2004), including analysis of the surface

gas-particle interactions between ammonia, nitric acid and hydrochloric acid (e.g., Nemitz et al.

1996; Nemitz and Sutton 2004). As part of EXAMINE a major collaborative analysis was made

in the North Berwick experiment, which provided a uniquely detailed examination of the

processes controlling ammonia exchange with an oilseed rape canopy (Husted et al. 2000;

Nemitz et al. 2000a,b; Sutton et al. 2000a,b).


In the second major European collaboration dedicated to ammonia, the GRAMINAE project

analyzed the processes controlling ammonia exchange with grassland ecosystems across Europe

(Sutton et al. 2001). This included assessment of both the bi-directional fluxes of ammonia with

agricultural grassland – as these affect atmospheric ammonia balance (e.g., Milford et al. 2001a;

Mosquera et al. 2001), and with semi-natural grasslands as these are impacted by the atmosphere

(e.g., Horvath et al. 2005). These studies were complemented by the LIFE project, which added

long term ammonia flux data for a number of grassland, moorland and forest ecosystems (e.g.,

Flechard and Fowler 1998; Erisman et al. 2001; Spindler et al. 2001).

As understanding of ammonia exchange has improved and scientific ambition developed,

increased attention has been given to integrating the different drivers of ammonia exchange

processes. This has, for example, been reflected in the Braunschweig Integrated Experiment of

GRAMINAE (e.g., Sutton et al. 2002, 2008a,b), which linked a wide range of biospheric,

atmospheric and management interactions as these control ammonia exchange with managed

grassland. This integration has developed substantially under the NitroEurope Integrated Project

(Sutton et al. 2007), which is currently addressing how the different components of nitrogen

fluxes, including ammonia and oxidized nitrogen, integrate and interact to influence net

greenhouse gas balance. In parallel, major advances have been made in spatial modelling of

ammonia fluxes, from individual forest edges to global scales (Theobald et al. 2005; Dentener et

al. 2006; Hertel et al., 2006; Loubet et al., 2008a; Sutton et al. 2008c).

3.2 Advances in measurement methods

Before considering the developments outlined above in more detail, it is important to highlight

that the advances have been critically dependent on improvements in measurement technology

(See Table 3.1). At the start of the 1990s, ammonia flux measurements were still being made

using wet chemistry and manual batch sampling with time integration of typically 2 hours (e.g.,

Sutton et al. 1993; Duyzer 1994). The most important key advance has been the introduction of

continuous wet chemistry methods for measuring ammonia profiles, including the AMANDA

wet rotating denuder (Wyers et al. 1993) and the mini-Wet Effluent Diffusion Denuder (e.g.,

Blatter et al., 1993; Neftel et al., 1999). Although these techniques are liable to malfunction,

with effort and careful operation they have produced many key datasets over the last 15 years

(e.g., Erisman and Wyers, 1993; Sutton and Fowler 1993; Sutton et al., 1995; 1998; Fowler et al.

1998; Flechard and Fowler 1998; Neftel et al., 1998; Milford et al. 2001a,b; Nemitz et al., 2001b,

2004) and still represent the state-of-the-art as regards precise measurement of small ammonia

fluxes (Wichink Kruit et al. 2007; Neiyrink and Ceulemans, 2008; Sutton et al., 2008c).


A unique inter-comparison of four continuous wet chemical systems was made at the

GRAMINAE Braunschweig Experiment (Sutton et al., 2002, 2007, 2008b; Milford et al., 2008),

which highlights the potential and limitations of the approach. Figure 3.1 shows the ammonia

flux measured before and after cutting of an agricultural grassland, as well as after subsequent

fertilization with calcium ammonium nitrate. The measurement systems were able to detect the

wide range of ammonia fluxes, but the degree of agreement varied greatly between days. This

was a result of varying performance of the different analyzers, highlighting the need for highly

intensive instrument maintenance.

06/06/00 07/06/00 08/06/00 09/06/00

NH

3 flu

x (n

g m

-2 s-1

)

0

2000

4000

600031/05/00 01/06/00 02/06/00 03/06/00

NH

3 flu

x (n

g m

-2 s-1

)

0

500

22/05/00 23/05/00 24/05/00 25/05/00

NH

3 flu

x (n

g m

-2 s-1

)

-75

-50

-25

0

25

50CEHFRI

Pre-cut

Post-cut

Post-fert

CEH FRI FAL-D FAL-CH

CEH FRI FAL-D FAL-CH

Figure 3.1: Inter-comparison of continuous profile systems for measuring ammonia fluxes by the aerodynamic

gradient method (AGM), from the GRAMINAE Braunschweig Experiment. Although highly scattered, this flux

inter-comparison is unique and represents the current state-of-the-art in chemical detection systems for ammonia

fluxes. Increased emissions due to cutting of the underlying grass sward (29 May) and the effect of N fertilization

with (100 kg N ha-1, 5 June) are clearly shown (Sutton et al., 2002; 2007, 2008; Milford et al., 2008).

Despite the good improvements that have been made in the automation and reliability of the

continuous wet chemical gradient methods (e.g., Wichink Kruit et al. 2007; Flechard et al. 2007;


Sutton et al., 2007), their remain several limitations, which have encouraged researchers to seek

alternative ammonia flux measurement approaches. In principle, many refinements have allowed

the automated wet chemical methods to become more reliable and comprehensive (such as being

able to measure aerosol and acid gas gradients simultaneously, Oms et al. 1996; Trebs et al.,

2006). However, the use of many moving parts can be considered as inherently liable to faults.

Similarly, the response times of these instruments are typically >5 minutes, which means that

they are normally limited to the measurement of mean concentration differences and vertical

gradients.

The benefits of quantifying ammonia fluxes using the gradient technique have been clearly

demonstrated by the many papers published using this approach. In terms of informing our

understanding of ammonia exchange processes and model development, this has almost

exclusively been provided by measurements using the aerodynamic gradient method (90%), with

a few studies (in continental climates) applying the modified Bowen Ratio method (5%). By

contrast, the key disadvantage of this method is that it depends on good stationarity, with no

change in the vertical flux with height. However these methods are not suitable for the study of

exchange fluxes where advection of ammonia from local sources is of interest (e.g., Loubet et

al., 2001, 2006; Milford et al. 2001b) and where gas-particle ammonia - ammonium interactions

are significant (e.g., Brost et al., 1988; Nemitz et al. 1996; 2004).

To address some aspects of advection and air chemistry interactions, determination of fluxes at a

single height offers a way forward. If this can be achieved, in principle, deployment of replicate

measurement systems at several heights could then be able to determine vertical flux divergences

(Sutton et al., 2007, 2008a). Both the Eddy Covariance (EC) method and Relaxed Eddy

Accumulation (REA) allow fluxes to be determined from measurements at one height, and have

therefore been the subject of several recent studies. The advantage of REA is that slow response

ammonia measurements can be combined with fast response switching, as has recently been

demonstrated in an inter-comparison of 4 REA systems for ammonia (Hensen et al., 2008). A

further advantage is that programmed periods of random switching between air up- and down-

drafts allows automatic zero checks and the correction of any biases (Nemitz et al., 2001a;

Hensen et al. 2008). By contrast, the challenge for REA and ammonia is that the concentration

differential to be measured is typically much smaller than for the gradient method, which to a

large extent cancels out the precision benefit of the auto-referencing.


Several recent studies have demonstrated the potential of fast response tunable diode laser

absorption spectroscopy (TDLAS) for measurement of ammonia fluxes by eddy covariance

(Shaw et al., 1998; Famulari et al., 2004; Whitehead et al., 2008). In principle, reliable flux

measurements can now be made for periods of large ammonia fluxes (e.g., after manured

application), as has recently been demonstrated in an intercomparison of two laser systems

(Figure 3.2). However, there was little correlation for fluxes <50 ng m-2 s-1, while the AMANDA

systems have been shown to be able to measure <10 ng m-2 s-1 (e.g., Sutton et al., 1995, 1998).

Table 3.1 provides an overview of these and other systems for measuring ammonia fluxes. In

principle, TDL and EC has the potential to be rated as high as the continuous gradient methods,

but this still needs to be demonstrated by a more substantial body of published measurements,

particularly over longer time periods and of a suitable quality for testing of models.

-200

0

200

400

600

800

1000

1200

29/04/200514:24

29/04/200520:24

30/04/200502:24

30/04/200508:24

30/04/200514:24

30/04/200520:24

TDLASQC-TDLAS

Flux

NH

3(n

gm

-2s-1

)

Date, Time (GMT)

-200

0

200

400

600

800

1000

1200

-200

0

200

400

600

800

1000

1200

29/04/200514:24

29/04/200520:24

30/04/200502:24

30/04/200508:24

30/04/200514:24

30/04/200520:24

TDLASQC-TDLAS

Flux

NH

3(n

gm

-2s-1

)

Date, Time (GMT)

Figure 3.2: Fluxes of NH3 measured by eddy covariance over intensively managed grassland (Easter Bush,

Scotland) several days after the application of liquid manure to the grassland (Sutton et al. 2007; Whitehead et al.

2008)


Table 3.1: Practical suitability of systems to measure ammonia biosphere-atmosphere exchange.

Chemical approach

Advantage Disadvantage Application References

Box AGM REA EC Batch filterpacks

Simple, cheap, high air vol.

Uncertain gas – aerosol split, Batch

☺ Harrison et al. (1989); Sutton et al. (1993).

Batch denuders

Simple, cheap good gas-aerosol split

Low air volume Batch

☺ Duyzer (1994); Andersen et al. (1999).

Automated batch annular denuders

Automated in field, medium cost, Precise, High air volume

High laboratory processing cost, Only hourly, Need two syst-ems for fluxes.

☺ ☺ Keuken et al. (1988); Loubet et al. (2006, 2008b).

Continuous annular denuders

Automatic Sensitive, Precise, High air volume

Cost, Complexity, Fault liable, Gradient only

☺☺ ☺☺ Wyers et al. (1993); Erisman and Wyers (1993); Sutton et al. (1995, 2000b, 2001a); Nemitz et al. (2001b)

Continuous parallel plate denuders

Automatic, Sensitive, High air volume REA

Cost, Complexity, Fault liable

☺ ☺☺ Nemitz et al. (2001a); Hensen et al. (2008).

Continuous mini-WEDD

Automatic, Sensitive, Precise

Cost, Complexity, Fault liable

☺ ☺☺ ☺ Neftel et al. (1999); Hensen et al. (2008)

Continuous membrane denuder AIRmonia

Automatic, Sensitive, Precise, reliable

Cost Medium complexity

☺ ☺☺ ☺ Flechard et al. (2007); Hensen et al. (2008)

Photo-accoustic

Automatic, Sensitive, In principle reliable

Cost, Complexity, not reliable

☺☺ ☺ Whitehead et al. (2008)

Tunable Diode Laser

Automatic, Sensitive, Fast response (>10 Hz)

Very high cost Complexity, Maintenance

☺ ☺ ☺ Shaw et al. (1999) Famulari et al (2004) Twigg et al. (2005)Whitehead et al. (2008)

Notes: AGM: Aerodynamic Gradient Method; REA: Relexed Eddy Accumulation, EC: Eddy Covariance.


3.3 Key controls on biosphere atmosphere exchange of ammonia

Figure 3.3 summarizes the main processes affecting the net exchange of ammonia with the

atmosphere (Sutton et al., 2007). The primary driver of ammonia exchange is the difference

between the atmospheric ammonia concentration and the average concentration in the ecosystem

canopy, both of which vary in time and in space (e.g., Sutton et al., 1995, Asman et al., 1998).

Within the canopy, several sources and sinks combine together to determine the average

ammonia concentration in the canopy, including exchange with plant tissues through stomata,

with leaf cuticles and with decomposing leaf litter and the soil surface (e.g., Denmead et al.,

1976; Sutton et al., 1993b, 1998). Ammonia within or immediately above the canopy air space

may undergo chemical reactions, for example forming particulate matter, while depletion of

gases within a plant canopy coupled with altered microclimate can lead to evaporation of

ammonium containing aerosol (Brost et al., 1988; Nemitz et al. 1996, 2004, 2008a). Finally, the

complex nature of ammonia sources and sinks in rural landscapes means that strong horizontal

gradients of ammonia occur. The result is that ammonia is not simply deposited from above, but

fluxes are often significantly influenced by advection effects, for example where advection from

a ground level source beneath a micrometeorological reference height adds substantially to a net

deposition flux (Loubet et al., 2001, 2008a,b, Milford et al., 2001b).

Advection from local sources

Within-canopy sources & sinks

Within-canopy chemistry

Above-canopychemistry

Ideal: flux measurements resolved with height, sufficiently accurate to quantify these effects

Figure 3.3. Summary of the key issues affecting the net land-atmosphere exchange of ammonia. Each of these

interactions can lead to ammonia fluxes changing with height above the ground. Ideally, flux measurements, based

on e.g. relaxed eddy accumulation or eddy covariance, made at several heights above the canopy would be used to

quantify these effects, though until now such assessments have had to focus on the use of vertical profiles in mean

ammonia concentration.

It is relevant to summarize the main influences on the primary drivers of exchange, the

atmospheric ammonia concentration and the mean concentration of ammonia within the canopy.

The first of these is influenced partly by dispersion from adjacent ammonia sources and partly by

exchange with the surface itself. Over a surface which acts as an ammonia sink, above-canopy


ammonia concentrations are depleted compared with background concentrations, while, the

above canopy concentrations may be significantly enhanced if the surface is a net source (e.g.,

Sutton et al., 1997, 2000a).

The mean ammonia concentration of the canopy itself results from the resolution of competing

emission and deposition processes with leaf cuticles, through stomata and with the ground

surface. The concept of ‘compensation point’ concentrations has often been used to describe

these relationships. The earliest view of a compensation point for ammonia related it to exchange

through plant stomata with the leaf apoplast (Lemon and van Houtte, 1980; Farquhar et al.,

1980). Under this interpretation, net ammonia fluxes would depend on the difference between

what has since been termed the ‘stomatal compensation point’ (χs) and the atmospheric

concentration (χa). By contrast, subsequent studies highlighted the fact that ammonia deposition

rates were often faster than feasible by stomatal uptake, demonstrating the importance of

ammonia deposition to leaf cuticles (e.g., Sutton et al., 1993a,b; Duyzer 1994). The resolution

between these positions was provided in the development of the concept of the ‘canopy

compensation point’ (χc), which accounts for both bi-directional stomatal exchange and

deposition to leaf cuticles (Sutton and Fowler, 1993; Sutton et al., 1995). Such canopy

compensation point concepts have since been further developed to include bi-directional

exchange with leaf surfaces and exchange with the ground surface under the canopy (e.g., Sutton

et al., 1998, Flechard et al., 1999; Nemitz et al., 2001b).

These inter-relationships are summarized in Figure 3.4. The figure illustrates the resistance

framework of the two-layer canopy compensation point approach (Nemitz et al., 2001b). One of

the key points to note about ammonia compensation points is that they depend on the net

solubility of ammonia in aqueous solution, which is largely dependent on its equililbrium with

ammonium ions. By combining the temperature dependence of the Henry equilibrium and the

ammonium dissociation equilibrium, the gaseous ammonia concentration can be compared with

a given ratio of [NH4+]/[H+], which has been termed Γ (Nemitz et al., 2000b, 2001a; Sutton et

al., 2000). On this basis, Γ can be used to provide temperature-normalized compensation points,

for example χs = f(T, Γs) where Γs = [NH4+]apoplast/[H+]apoplast.

3.4 Effects of ecosystem type on Ammonia biosphere-atmosphere exchange

It has long been established that ecosystem type affects net ammonia fluxes (cf., Denmead et al.,

1976; Horvath, 1983; Sutton et al., 1993b, 1995; Duyzer, 1994). Overall, unfertilized

ecosystems, such as forest and moorlands have mostly shown ammonia deposition, while


fertilized and grazed agricultural ecosystems tend to show bi-directional fluxes with some

periods of deposition and some periods of emission. Of course, the distinction is not absolute, as

smaller ammonia emissions may also occur from semi-natural ecosystems (e.g., Sutton et al.,

1995; Flechard and Fowler, 1998). However, such a general difference is clear, and can be

explained by the increase in χs and χground that occurs in fertilized and grazed ecosystems. Two

recently published examples of ammonia exchange provide a useful basis to highlight these

differences.

Neirynck et al. (2005) report ammonia flux measurements made using the AMANDA technique

(Wyers et al., 1993) over a coniferous forest in Belgium. Their forest site occurs in an area of

intensive livestock rearing, so that ammonia concentrations from some wind directions are very

large (5-25 ug m-3) while for other wind sectors ammonia concentrations were more moderate

(2-4 ug m-3). Even considering the effects of canopy wetness, in all conditions the mean diurnal

profiles show consistent net deposition to the forest canopy. Curiously, the largest deposition

fluxes occurred in dry conditions, which is counter intuitive, as Rw would be expected to be

smaller when the canopy is wet (Sutton et al., 1995; 1998; Nemitz et al., 2001b). Although this

difference is partly explained by different values of Fmax during conditions of different canopy

wetness, this appears not to fully explain the difference. Further analysis by Neiyrinck et al.

(2005) showed differences in the overall canopy resistance (Rc) for ammonia deposition with

different canopy wetness and temperature, and with larger values of Rc occurring at higher

ammonia concentrations.

Neirynck et al. (2005) did however find some periods of net ammonia emission from their forest

canopy (Figure 3.4). These were recorded during periods with winds from the high ammonia

wind sector and found to only happen at very large ammonia concentrations, which occurred

when air temperatures were larger than 15 ºC and relative humidity less than 60%. Figure 3.4

presents an intriguing result, since according to the concepts of ammonia compensation points a

different picture should emerge, namely that periods of ammonia emission occur when

atmospheric ammonia concentrations are small. By contrast, such a relationship is possible when

emissions from a canopy are strong (and not compensation point driven), so that it is the

emissions from the surface that generate increased ammonia air concentrations. For example,

this phenomenon was observed following harvest of an oilseed rape field (Sutton et al., 2000a,b).

However, in the present case, the air concentrations are extremely high (25-56 µg m-3),

combined with large ammonia fluxes (0.1 -1.5 µg m-2 s-1). For example, if Ra+Rb were 40 s m-1,


this would imply minimum emission potentials from the canopy (χ(zo’)) in the range 30 – 120 µg

m-3, which represent extremely high values for a forest canopy.

It is perhaps feasible that these emission periods represent events of desorption of previously

deposited ammonia occurring in dry conditions. Conversely, it is also feasible that they represent

apparent ‘emissions’, being an artefact whereby horizontal ammonia concentration gradients

away from an adjacent ground-based ammonia source (e.g. manure spreading, farms etc) lead to

an advection error. This would reduce the measured deposition rate and could explain apparent

ammonia upward fluxes in this context. This illustration emphasizes the complexity of

measuring ammonia exchange processes and highlights the need for further investigation of each

option.

Fig. 3.4. Dependence of ammonia flux on concentration in the high ammonia wind sector during warm daytime

conditions with dry canopy. (Neirynck et al., 2005, Reproduced by permission Elsevier). [Permission should be

sought]

The above example of mainly ammonia deposition to a forest ecosystem may be contrasted with

recently published measurements of ammonia fluxes over an intensively managed grassland in

the Netherlands (Wichink Kruit et al., 2007). The diurnal patterns in ammonia concentration and

net exchange flux are illustrated in Figure 3.5. Hourly ammonia concentrations in the air at this

site were again very large, 1-50 µg m-3, with an overall mean of around 10 µg m-3. In this case,

net emission occurred for around 40% of the diurnal period (10:00-20:00), with net deposition at

other times.


Figure 3.5. NH3-concentration (upper panel) and NH3-flux (lower panel) measurements above managed grassland

in The Netherlands from 18 July until 15 August 2004 (summer period). The horizontal axis represents time of the

day (UTC). Local time is UTC+2. The vertical axis represents the NH3-concentration (µg m−3) or NH3-flux

(ng m−2 s−1). Diamonds are calculated values for the half-hourly NH3-concentration or NH3-flux; the solid line (—

—) (with vertical 25 and 75 percentile bars) is the median of all half-hourly fluxes for that time. The dashed line (- -

-) in the lower panel is the mean leaf wetness signal during this period (Wichink Kruit et al., 2007. Reproduced by

permission Elsevier). [Permission from Elsevier should be sought].

Wichink Kruit et al. (2007) also estimated the canopy compensation point (χc) based on profile

estimation of χ (zo’). They then combined this with estimates of surface temperature to estimate

Γ( zo’) or ‘Γc’ from the measurements (Figure 3.6). Estimated values of χc were in the range 1-

30 ug m-3, which is comparable with other studies for managed grassland (e.g., Milford et al.

2001a, Sutton et al., 2001; Loubet et al. 2006), and substantially smaller than the upper values

implied for the forest in Figure 2.4. Normalized for canopy temperature, the values of Γc were in

the range 200-11,000 through a period of May to October 2004, with a mean value of just over

2000. These values are comparable with other estimates which have elsewhere been shown to

vary substantially with grassland management practice (e.g. Milford et al., 2001b; Sutton et al.,

2001), and may be in the range 10000-30000 for some days after fertilization (Sutton et al.,

2001, 2008b). It must be remembered, however, that Γ(zo’) only represents a crude indicator of

the actual controlling values. During emission situations, in the presence of additional stomatal


or within canopy resistances, Γ(zo’) represents a minimum estimate, while during periods of

deposition it represents a maximum estimate of the actual mean value of Γ at the surface.

Fig. 3.6. Derived canopy compensation points (χc = χ(zo’)) (upper panel) and ratios between NH4+ and H+

concentration (Γc = Γ(zo’)) (lower panel) from the end of May until the end of October 2004 (diamonds) and the

constant value (2200) that is normally assumed for modeling (line). (Wichink Kruit et al., 2007. Reproduced by

permission Elsevier). [Permission from Elsevier should be sought].

3.5 Modelling surface –atmosphere exchange of ammonia

Over recent years, the canopy compensation point approach has become the standard basis to

model bi-directional ammonia surface atmosphere exchange. Starting with the 1-layer models

offsetting bi-directional stomatal exchange against deposition to leaf surfaces (Sutton and

Fowler, 1993; Sutton et al., 1995), subsequent models have developed in several directions. The

main subsequent developments can be summarized as follows:

Treatment of multiple canopy layers In addition to ammonia exchange with the top part of the

canopy, leaf litter and the soil surface have been shown to be important sources of ammonia

emission into the plant canopy (e.g., Nemitz et al. 2000a). For an oilseed rape canopy Nemitz et

al. (2000b) also highlighted the importance of an upper and lower part of the main foliage,

distinguishing the main foliage from an over canopy of oilseed ‘siliques’. In practice this three

layer model becomes complex to parametrize, and there has now developed consensus that a


two-layer model (as shown in Figure 3.4) represents an appropriate balance of realistic

description while avoiding too much complexity. A recent implementation of the 2-layer model

is that of Personne et al. (2008) for the GRAMINAE Integrated Experiment (Sutton et al.,

2008b). They used measured bioassay estimates of Γs and Γlitter (Mattsson et al., 2008a,b;

Herrmann et al., 2008) combined with an energy balance approach to calculate component

resistances, showing close agreement with measured ammonia fluxes (Figure 3.7).

-500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

22-May 29-May 05-Jun 12-Jun

NH

3 flu

xes

(ng

m-2

s-1)

Measured flux

Modelled flux (Gamma Litter)

Figure 3.7: Comparison of ammonia fluxes simulated by a two-layer canopy compensation point model

(SURFATM-NH3) with measured fluxes (Fmg) during the GRAMINAE Braunschweig Experiment. For this model

scenario, the ground emission is assumed to originate from leaf litter based on measured Γlitter (Personne et al., 2008;

Sutton et al., 2008b).

Treatment of cuticular fluxes. The initial parametrizations of the cuticular resistance (Rw)

allowed only for deposition, dependent on relative humidity (Sutton and Fowler, 1993; Sutton et

al., 1995) or vapour pressure deficit (Nemitz et al., 2000b, 2001b). As noted above for the forest

example, ammonia deposited to a canopy surface may also be re-emitted to the atmosphere,

particularly under drying conditions. A first approach to simulate this effect treated the leaf

surface as a humidity dependent capacitance (Qd), which would be in equilibrium with a non

zero leaf surface concentration (χd) (Sutton et al., 1998). In this case an adsorption/desorption

resistance (Rd) is also defined. This first dynamic approach had the advantage of being able to

simulate ammonia charging and discharging of the cuticle, but had the disadvantage that the leaf

surface pH needed to be specified as an input. The approach was further developed by Flechard

et al. (1999) who considered the full aqueous chemistry on leaf surfaces, dependent on multiple

air pollutant inputs and potential leaching of base cations from leaf surfaces. In this model, leaf

surface pH is solved by ion balance, and the model is able to take account of the effects of other


trace components (like SO2 concentrations) on ammonia fluxes. Burkhardt et al. (2008) have

recently extended this model to incorporate the two-layer approach with bi-directional exchange

for each of the leaf surface, stomata and ground surface. The model is able to qualitatively

reproduce the bidirectional fluxes during the pre-cut period of the GRAMINAE Braunschweig

Experiment, but at present overestimates the emission and deposition peaks. The cuticular

resistance clearly responds to the chemistry of the liquid film on vegetation and the combination

of reactive gases present (Flechard et al 1999). Even in the absence of additional reactive trace

gases, the cuticular resistance declines with increasing NH3 concentration. In a series of chamber

experiments Jones et al (2007) quantified the relationships between ambient NH3 concentration

and the bulk canopy resistance for a range of moorland vegetation as shown in figure 3.8 in

which the non-stomatal ‘cuticular’ resistance is seen to increase lineary with NH3 concentration,

leading to much smaller deposition at high concentrations than if deposition velocity remained

constant with concentration as is usually assumed.

Figure 3.8. Relationship between ammonia concentrations and resistances to deposition to moorland vegetation. A

significant difference was found between day and night for the bulk canopy resistance (Rc), which included both

stomatal uptake and deposition to the leaf surfaces. Once the effect of the stomatal resistance (Rs) was accounted for,

the cuticular resistance (Rw) was found to be not significantly different between day and night (Jones et al., 2007).


3.6 Dynamic simulation of ecosystem C-N cycling and ammonia fluxes.

A disadvantage of the basic scheme for simulating ammonia fluxes outlined above is that

empirical values of Γ must be provided. The only way forward from this position is to develop

models of carbon-nitrogen cycling that can simulate Γvalues for the different pools based on an

understanding of the pool dynamics (cf. Massad et al., 2008). To date, the only such model to

attempt this coupling is the PaSim model of Riedo et al. (2002). To do this the model

distinguished plant nitrogen pools into structural nitrogen, substrate nitrogen and apoplastic

nitrogen (a sub-pool of substrate nitrogen), linking these with plant uptake and growth processes.

The model was parametrized based on measured fluxes for a Scottish grassland (Milford et al.,

2001b) and has recently been tested for the Braunschweig Experiment (Sutton et al., 2008b).

Overall, the model was able to simulate the larger net emissions that occurred after cutting and

after fertilization, as well as the decline in the 10 day period following fertilization. By contrast,

the component fluxes were less well described. Bioassays, chamber measurements and within-

canopy profiles during the Braunschweig Experiment (Mattsson et al., 2008a,b; Herrmann et al.,

2008; David et al., 2008a,b; Nemitz et al. 2008b, Sutton et al., 2008b) highlighted leaf litter as

being a key source of emission following cutting. This source is currently not simulated in

PaSim, which simulated that increased emissions after cutting were due to an increase in

apoplastic ammonium. The bioassays indicated that the foliage was more likely to be a sink of

soil/litter ammonia emissions, highlighting the need for improved ecosystem modelling of

ammonia exchange that accounts for litter decomposition processes (Sutton et al., 2008b).

3.7 Integrating ammonia exchange processes

The preceding sections have highlighted the many processes and interactions that define

ammonia fluxes between vegetation and the atmosphere. It thus becomes a major challenge to

integrated each of these processes to develop a holistic view. It is necessary to quantify the

interactions in each case in order that valid conclusions can be obtained. This creates a major

challenge for experimentalists to be able to address all the questions in the field. For example, in

the absence of measurements of horizontal concentration profiles, it is difficult to quantify the

potential for advection effects to have influenced the results presented in Figure 3.4. Similarly, it

remains an open question in most studies whether gas-particle interactions have a significant

influence on measured ammonia fluxes.


It was with such interactions in mind that the GRAMINAE Integrated Experiment was designed.

A number of findings from this experiment have already been mentioned, but the experiment

demonstrates both the challenges and the power of developing an integrated approach. Figure 3.9

summarizes each of the issues and measurement methods that were investigated during this

experiment (Sutton et al., 2008a). The key conclusions have been summarized by Sutton et al.

(2008b) and include:

• That unreplicated ammonia flux measurements in most studies are highly uncertain and

need to be considered with great caution when being compared with model estimates.

• That advection effects can significantly influence measured ammonia fluxes, both due to

dispersion away from nearby point sources (correction for advection effects increases net

deposition) and due to emissions from an emitting field itself (correction for advection

increases net emission).

• That gas-particle interactions had a minor effect on measured ammonia fluxes, though the

relative effect on calculated aerosol deposition rates was significant (being the cause of

apparent aerosol emissions).

• That reasonable agreement can be found between relaxed eddy accumulation for

ammonia and the aerodynamic gradient method, though measurements are not

sufficiently precise to detect flux divergence (except for possible cases of extreme

advection errors).

• That net emissions from a grassland canopy are controlled by the recapture of leaf litter

ammonia emissions by overlying foliage and the interaction of cuticular exchange pools

with mainly stomatal uptake of ammonia from the leaf litter emissions. Net emissions

increase following cutting due to exposure of the litter and cutting induced senescence,

with a similar recapture process affecting net emission following fertilization.

• A range of models is able to simulate the dynamics of net ammonia exchange with the

managed grassland, but further attention is needed to develop dynamic treatments of

ammonia emissions from leaf litter decomposition.


NH3 release from litter decomposition

Soil chemistry interactions with plant N uptake & NH3 fluxes

Advection of NH3 from nearby sources & effectson vertical fluxes

Interactions with acid gases and ammonium

particles & effectson net NH3 fluxes

Effects of cutting& N fertilization events & choices

Estimation offarm-scale NH3

emissionsfrom plume

measurements

NH3 compensationpoints of foliage

Continuous measurement of NH3fluxes by gradient

and REA approaches

Effects of leaf senescenceand plant species on

NH3 emission potential

Quantification of energy balance &

environ controls onNH3 exchange

Within-canopy cycling of NH3 fluxes

Determination ofwithin-canopy

turbulent exchange

Plant bioassay determination of


Effects of dew& leaf surface

chemistryon NH3 fluxes

NH3 release from litter decomposition

Soil chemistry interactions with plant N uptake & NH3 fluxes

Advection of NH3 from nearby sources & effectson vertical fluxes

Interactions with acid gases and ammonium

particles & effectson net NH3 fluxes

Effects of cutting& N fertilization events & choices

Estimation offarm-scale NH3

emissionsfrom plume

measurements

NH3 compensationpoints of foliage

Continuous measurement of NH3fluxes by gradient

and REA approaches

Effects of leaf senescenceand plant species on


Quantification of energy balance &

environ controls onNH3 exchange

Within-canopy cycling of NH3 fluxes

Determination ofwithin-canopy

turbulent exchange

Plant bioassay determination of


Effects of dew& leaf surface

chemistryon NH3 fluxes

Figure 3.9 Overview of issues addressed by the GRAMINAE Integrated Experiment (Sutton et al. 2008a).

3.8 Future challenges for ammonia exchange

The results from the GRAMINAE Integrated Experiment provide a microcosm of some of the

key challenges to measure ammonia fluxes and model the process interactions. In a wider

perspective key challenges include the climate dependence of net ammonia emission and

deposition, and the characteristic fluxes of other ecosystems in the world.

In principle the models of ammonia exchange incorporate the main features of environmental

conditions and could therefore be applied in different climates. Here the limitations faced are

ones of lack of available data for empirical factors such as Γ values and on extrapolation to

conditions with rather different climates. Currently, the estimates of Γ have mainly been derived

for cool European conditions for a very limited number of ecosystems. Although there have

been many studies on ammonia emission rates from fertilized tropical systems, such as rice and

maize, there are hardly any published studies on ammonia fluxes over semi-natural unfertilized

tropical ecosystems. The rates of ammonia deposition/exchange in these situations are thus

highly uncertain. Given the differences in biology of these systems, there can be no substitute

for direct measurements.

A modest degree of climate change (e.g. + 2 ºC) is a much easier matter to simulate, for example

based on the analysis of temperature effects within existing datasets. The thermodynamics of

ammonia solubility and dissociation are rather straightforward, indicating for example a


doubling in χs every 5 ºC increase for a given value of Γ (Sutton et al., 2001). However, caution

is needed before making climate change simulations on this basis. Analysis of the PaSim model

under different temperature regimes showed that net ammonia fluxes for Easter Bush in Scotland

(cf to measured fluxes of Milford et al., 2001b) were rather insensitive to temperature. For

example, increased temperature (in the absence of moisture limitation) led to increased grass

growth which diluted available nitrogen pools, thereby reducing Γ values (Sutton and Milford,

unpublished simulations). Similarly, increases in wetness, while favouring smaller values of Rw

may also lead to increased rates of leaf litter decomposition, favouring ammonia emissions. To

take another example, in colder conditions, NH3 from manure application to the land surface

tends to be emitted at smaller rates, but the emission lasts longer, especially if a waterlogged or

frozen soil conditions prevent infiltration. With these illustrations in mind, it becomes a major

future challenge to generalize how ammonia fluxes might change in the future under different

climatic regimes.


4 Sulphur Dioxide 4.1 Introduction

There are three very different spatial scales relevant to the exchange of SO2 at terrestrial

surfaces, first the micro-scale, at which the chemical and biological interactions occur (Figure

1.1). Second is the spatial scale at which most of measurement and interpretation takes place,

which is the field scale (103 to 105 m2) for measurements using micrometeorological methods.

Lastly, the application of knowledge of the surface exchange process is primarily at regional to

continental scales to characterise the fluxes and budgets within chemical transport models

(CTM) and comparisons with the concentration fields observable from satellites (Richter et al

[date]).

The measurements have primarily been at the field scale using micrometeorological methods,

although there have been some laboratory studies, mainly in the early days of SO2 dry deposition

research. The initial measurements were used primarily to estimate the regional scales of dry

deposition, often using a fixed deposition velocity as a key variable within long range transport

models (eg Fisher, 1978). With larger data sets of measurements covering a wide range of

conditions, it is clear that rates of dry deposition vary considerably in time and space (Fowler

and Unsworth, 1976) in particular because the sinks available at terrestrial surfaces, including the

stomata in vegetation, leaf surfaces and the presence of liquid water on vegetation from dew or

rain, present a variable absorbing surface. The data have shown the role of atmospheric

composition and surface leaf water chemistry in controlling canopy resistance.

Most dry deposition measurements of sulphur dioxide over the last 30 years have been made in

N. America and Europe, and have served as a basis for the parameterisation of dry deposition

models (Erisman, 1994; Smith et al., 2000; Zhang et al., 2002), which in turn have been applied

to ecosystems in different parts of the globe. However, most SO2 emission and deposition now

occurs outside N. America and Europe. Asia’s contribution in 1985 of 20 % to global

anthropogenic SO2 emissions has doubled since then, reaching 37% by the year 2000, of which

23% is emitted by China alone and 5% by India.

Southern China is one of the world’s most sulphur polluted areas. Paradoxically, in Northern

China, where ambient SO2 concentrations are very large, rainfall is generally alkaline, and the

areas polluted by acid rain do not necessarily correspond to the areas of high SO2 emissions. One

of the reasons for this discrepancy is the presence of alkaline soils (yellow sand) distributed over

the arid areas of N.W. China (e.g. the loess plateau and Gobi desert), the windborne erosion of


particles with high base cation concentrations can neutralize atmospheric acidity (Utiyama et al.,

2005). Loess soil, which covers vast areas of the Eurasian continent extending from N.E. China

to Central Asia, contains Ca in large quantities, and calcium carbonate (CaCO3) reacts with

atmospheric SO2, to form calcium sulphate (CaSO4). Thus, even bare soil without vegetation

may be a significant sink (Sorimachi et al., 2004), which may affect the regional SO2 budget if

the process is inadequately quantified in dry deposition models.

In this section we review research and monitoring from the last decade, including SO2 dry

deposition measurements from Asia, North America and Europe, as well as findings from long-

term flux monitoring experiments. The current state of knowledge concerning mechanisms of

SO2 dry removal from the atmosphere is reviewed, with consequences for temporal trends in

atmospheric concentration and deposition, and key future research areas are identified.

4.2 Worldwide advances in SO2 flux monitoring and modeling

4.2.1 Asia

Sulphur dioxide dry deposition to vegetated surfaces is largely controlled by non-stomatal

processes, but in many arid ecosystems and deserts of the world where vegetation is sparse, the

nature and pH of soils determine the sink strength. In Asia, substantial efforts have for example

gone into the characterization of SO2 uptake by loess soils, given their large geographical

representation in Northern China, their alkaline nature and their ability to neutralize atmospheric

acidity and to serve as an oxidation medium for SO2. Both micrometeorological and laboratory-

or field-based flow reactor methods were deployed. New micrometeorological measurements

over forests and short vegetation have also been reported over the last 10 years in the region,

reflecting the growing concern over increasing sulphur emissions and deposition to ecosystems.

4.2.1.1 Sulphur dioxide deposition to soils

Utiyama et al. (2005) measured dry deposition to loess soil and dead grass in Beijing using the

aerodynamic gradient method, though in neutral conditions 22% of the time. In stable or unstable

thermal stratification, they used a surface reaction concept for inferring dry deposition. Two

surface kinetics models were considered: either i) the reaction occurs in soil pores and SO2

molecules diffuse through porosity while reacting with alkaline sites on the pore surface; or ii)

the adsorption mechanism is of Langmuir-Hinshelwood type, where the partial pressure of SO2

and its desorption pressure from the site are in equilibrium. The model parameters are then fitted

so that the resulting (modelled) vertical SO2 concentration gradient matches the observations.

Measured deposition velocities (Vd) were in the range 1-12 mm s-1.


Sorimachi et al. (2004) used a combination of laboratory flow-reactor methods and field-based

passive collectors to determine SO2 dry deposition to 6 Chinese loess soils originating from arid

areas in N. China such as the loess plateau and Gobi desert. They found that the uptake rate

increased with soil alkalinity and relative humidity (RH). Mean canopy resistances (Rc) of 160 (±

60) s m-1 for RH <10%, and Rc of 90 (± 50) s m-1 for RH = 60%, were measured. Likewise,

Sorimachi and Sakamoto (2007) conducted laboratory-based flow-reactor measurements of SO2

deposition to soil samples from 12 sites in the arid loess plateau and deserts of Northern China.

Canopy resistances in the range 28-650 s m-1 (with a mean of around 200 s m-1) were found to be

dependent on RH, as was S(IV) oxidation to S(VI). It was hypothesized that Northern China

soils, which are much more alkaline than in Southern China, are a greater sink for SO2 and a

neutralizing buffer for acidifying atmospheric deposition. By comparison, in modelling SO2

deposition to Asia, Xu and Carmichael (1998) used a fixed Rc for deserts of 500 s m-1, which is

clearly too high in the case of Northern China deserts. A flow-reactor was also used by

Sakamoto et al. (2004) to determine SO2 dry deposition to yellow sand and soil-mediated SO2

oxidation by O3. The deposition velocity for SO2 increased with RH due to the positive effect of

RH on the SO2 oxidation rate.

4.2.1.2 Micrometeorological measurements over vegetated areas

Matsuda et al. (2006) reported micrometeorological (aerodynamic gradient) flux measurements

of SO2 and O3 over a tropical (teak) forest in Northern Thailand in dry and wet seasons. The

deposition velocity for SO2 in the dry season was rather low, in the range 1-3.1 mm s-1 in

daytime and 0.8-1.1 mm s-1 in night-time. In the wet season, however, Vd was much higher due

to enhanced non-stomatal uptake in wet conditions, with values in the range 9.5-13.9 mm s-1 in

daytime and 2.6-4.2 mm s-1 in night-time. The data were compared with a recent non-stomatal

resistance scheme (Zhang et al., 2003a), and it was concluded that extended experimental SO2

dry deposition studies are needed in the tropics, while Zhang et al. (2003a) recommend more

studies to quantify the different effects of dew and rain on SO2 deposition.

Sulphur dioxide dry deposition was also measured by Matsuda et al. (2002) over a red pine

forest located in Oshiba Highland, Nagano, Japan, using a Bowen ratio technique. The median

daytime (12:00 to 14:00) deposition velocity was 9 mm s-1. Measurements compared favourably

with estimates by an inferential model for wet conditions, but for dry or mixed wet-dry surfaces

there were large differences between model and measurements. The authors ascribed the

discrepancy to a relative humidity threshold value used in the inferential scheme to characterize


canopy wetness, and pointed to the need for a refined parameterisation of the cuticle or external

leaf surface resistance.

In a study of SO2 and O3 dry deposition to short grassy vegetation over an alkaline soil (pH=9.2)

near Beijing, using the aerodynamic gradient method, Sorimachi et al. (2003) measured mean Vd

values of 2 (±1) mm s-1 and 4 (±2) mm s-1 in late summer and early winter, respectively.

Although the grass was lush and thick in the late summer, and senescent and leafless in the early

winter observation period, there was no difference in the mean Rc (180 ±270 s m-1 and 180 ±300

s m-1, respectively), but the uncertainties given reveal a large variability in measured Rc. The

difference in Vd stemmed from the higher aerodynamic (Ra) and quasi-laminar sub-layer (Rb)

resistances in late summer than in early winter. The absence of vegetation and stomatal uptake in

early winter, which might otherwise have reduced the SO2 sink strength, seems to have been

compensated for by the soil alkalinity. As the soil was more exposed and the in-canopy

aerodynamic resistance was reduced, the soil surface offered more adsorption and reaction sites

for SO2, with the result that the field was an equally efficient SO2 sink in early winter as in

summer.

The deposition velocity for SO2 was measured by Jitto et al. (2007) during a 1-year experiment

over a canopy of irrigated rice paddy in Thailand using the Bowen ratio technique. The

deposition velocity was highest around noon and lowest at night. Seasonally-averaged values of

Vd were 6.7, 12.5, and 15.1 mm s-1 in the winter, summer, and rainy seasons, respectively.

4.2.1.3 Long-term deposition studies and inferential modelling

As alternatives to costly and labour-intensive micrometeorological measurements of dry

deposition, several authors in Asia have estimated long-term SO2 deposition using monitored

concentration data and inferential models, or long-term artificial collection devices. The latter

can only provide crude estimates of deposition rates, as surrogate surfaces do not adequately

account for the complexity of natural surfaces, but they do allow continuous monitoring at a

number of sites and help to detect trends.

Ta et al. (2005) thus provided long-term sulphur dioxide dry deposition estimates across Gansu

Province, China, using K2CO3-coated surrogate sulfation plates. Samples were taken monthly for

11 years at 48 sites distributed across 11 cities in the province. The data showed that cumulative

SO2 dry deposition fluxes were closely related to local SO2 emissions, and had seasonal

variations with maxima in winter and minima during summer as a result of higher winter and


lower summer SO2 emissions and concentrations. Monthly average SO2 deposition velocities,

however, peaked in April-July at 11–27 mm s-1, and minimum values were observed in January

at 2-10 mm s-1.

Inferential models (Erisman, 1994; Smith et al., 2000; Zhang et al., 2002) may be used to

estimate dry deposition at observation sites, where single-height ambient concentration

measurements are available together with standard meteorological data. Model parameters,

however, have been largely derived from European and N. American studies and may not

necessarily be adequate for Asian vegetation and soils, and numerical evaluations need to be

carried out. Thus Takahashi et al. (2002) simulated the dry deposition of SO2 to a Japanese cedar

(Cryptomeria japonica) forest located in Gumma Prefecture, based on the results of 1-year’s

concentration measurements. The mean modelled Vd at this site was 8.8 mm s-1 (Takahashi et al.,

2001). The inferential estimate of the dry sulphur deposition flux was 11.1 mmol m-2 yr-1 (3.6 kg

S ha-1 yr-1), which compared well with the net throughfall flux (12.4mmol m-2 yr-1, or 4.0 kg S

ha-1 yr-1). Over a broadleaf forest on typical red soil of Southern China, Xu et al. (2004)

simulated Vd for SO2 and particulate SO42-, as well as their atmospheric deposition fluxes. The

simulations indicated that about 99% of the dry sulphur deposition flux in the forest resulted

from SO2, which contributed over 69% of the total (wet + dry) annual sulphur deposition.

By comparison, Wang et al. (2003) computed dry deposition fluxes of SO2 and SO42- for 1 year

to agricultural land over red soil (pH = 5.3 to 5.8) in the Jiangxi province of Central China. The

crops grown were rice paddies and oilseed rape. Sulphur dioxide concentrations were measured

8 times day-1, 7 days month-1, using a bubbler method. Annual mean modelled estimates of Vd

were 3.8 (± 0.16) mm s-1 for SO2 and 0.20 (± 0.12) mm s-1 for SO42-. Measured monthly mean

concentrations ranged from 9 to 163 µg S m-3 (6.7-121 ppb), with an annual mean of 64 µg S m-3

(47 ppb). Estimates of total monthly wet and dry deposition of SO2 and SO42- ranged from 2.2 to

20.3 kg S ha-1 with an annual total deposition of over 100 kg S ha-1, of which 83% was via dry

deposition, accounting for over 90% of total S input to farmland in this area.

4.2.2 North America

Few long-term datasets of SO2 dry deposition monitoring have emerged over the last 10 years,

reflecting the declining importance of SO2 as an acidifying input relative to NOy and NHx.

Advances have nonetheless been made in inferential modelling of SO2 uptake, especially

regarding the quantification of the non-stomatal (external) leaf surface resistance, which serve as

a basis for simulating regional patterns of SO2 deposition (Zhang et al., 2002, 2003b).


Micrometeorological SO2 flux data from 5 sites (2 forests, a corn field, a soybean field and a

pasture) in eastern USA (Finkelstein et al., 2000; Meyers et al., 1998) were compared with

modelled data by Zhang et al. (2003a), with the specific objective of evaluating the new non-

stomatal resistance scheme of the new Canadian model (Zhang et al., 2002, 2003b). Over the

forest sites, Finkelstein et al. (2000) had noted that wetness tended to increase deposition

velocity, but that the nature of wetness (rain or dew) and its chemistry also controlled canopy

resistance. Non-stomatal surfaces like leaf surface, stem, trunk and ground were important sinks

for SO2, and the authors concluded that a better understanding of surface chemistry and water

film chemistry was needed.

Dew formation has long been recognised as an important sink for SO2 (Fowler et al, 1974). In

more recent work Meyers et al (1998) show that dew is the reason for the relatively high early

morning deposition rates at 2 of the 3 low vegetation sites studied in Eastern USA. Recognizing

the weakness of existing North American parameterisations (e.g. Meyers et al., 1998) in

predicting SO2 deposition rates to non-stomatal surfaces, especially in wet canopies, Zhang et al.

(2003a) demonstrate that the AURAMS scheme (Zhang et al., 2002) performed well at these 5

sites, using different resistance values for dew and rain. The revised non-stomatal resistance

scheme (Zhang et al., 2003b) includes a treatment of in-canopy transport, soil and cuticle terms,

and is a function of relative humidity, leaf area index and friction velocity. For wet canopies, the

cuticular resistance is treated differently for dew and rain.

4.2.3 Europe

4.2.3.1 Long-term flux monitoring in the UK

Sulphur dioxide fluxes have been monitored continuously since the mid 90’s at two rural sites in

the UK, over agricultural land at Sutton Bonnington in the English Midlands, and over moorland

at Auchencorth Moss in S. Scotland. The dry deposition measurements have continued to bring

surprises over the last 10 years. At Sutton Bonnington, the ambient concentrations have declined

from about 2.8 ppb in 1996 to current values close to 1.4 ppb and yet the deposition velocity

continues to increase due to continued reduction in the canopy resistance (Rc) (Fig. 4.1). Over

the monitoring period the canopy resistance has almost halved and is now about 70sm-1. The

consequence of the steady decline in the canopy resistance along with a decline in ambient

concentration is that the flux remains nearly constant. The measurements of the atmospheric

terms (Ra and Rb) show that the trend is not caused by changes in turbulence, and thus the

interpretation of cause in changes in the surface processing of the deposited SO2 is secure.


These dry deposition measurements have proved valuable in explaining the consistently larger

decline in ambient SO2 concentration than in emissions in Europe. In the absence of these flux

measurements it would be a matter of speculation as to the underlying cause of the faster decline

in ambient concentration than emission. Even with these measurements there remains the

possibility that SO2 oxidation rates have increased due to the growing oxidizing capacity of the

atmosphere and have contributed to the relative changes in emission and deposition (non-

linearity). It will be necessary in the further analysis and interpretation of European pollution

climate data to carefully examine the relative importance of the different contributors to the

observed trends in concentration and deposition and quantify the relative importance of changes

in dry deposition and oxidation rates in the long term trends.

0123456789

10

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Mix

ing

ratio

(ppb

)

NH3SO2

0

0.1

0.2

0.3

0.4

0.5

0.6

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

SO2/N

H3 m

olar

rat

io (p

pb p

pb-1

)

0

20

40

60

80

100

120

140

Rc S

O2 (

s m-1

)SO2/NH3Rc (Wheat)

0123456789

10

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Mix

ing

ratio

(ppb

)

NH3SO2

0

0.1

0.2

0.3

0.4

0.5

0.6

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

SO2/N

H3 m

olar

rat

io (p

pb p

pb-1

)

0

20

40

60

80

100

120

140

Rc S

O2 (

s m-1

)SO2/NH3Rc (Wheat)

Figure 4.1 Changes in the mean concentrations (ppbV) and ratio of ammonia and sulphur dioxide and in

the May-July canopy resistance for SO2 deposition on Wheat at Sutton Bonnington between 1996 and

2003 .

4.2.3.2 Other recent European datasets

The SO2 flux-gradient data obtained over short vegetation by Feliciano et al. (2001), collected

over a period of 3 years in the mid-nineties at 3 different sites in Portugal, were important in

providing Rc estimates for the Mediterranean region of Southern Europe. The 3 sites had


contrasting pollution climates, with a coastal, oceanic, humid meadow in N. Portugal, a hot and

semi-arid pseudo-steppe and a site located in a mostly dry, intensive agricultural area, both in S.

Portugal. Median canopy resistances varied from 140 s m-1 to 200 s m-1 and although stomatal

uptake was important when vegetation was biologically active, the annual deposition was

dominated by non-stomatal mechanisms on wet surfaces. The night-time canopy resistance, a

proxy for the non-stomatal resistance, increased with decreasing relative humidity at all 3 sites.

A comparison of nocturnal Rc for the southern sites showed that, for a given level of relative

humidity, the Rc at the intensive agricultural site was systematically lower than at the pseudo-

steppe site, which is used more extensively for grazing and hay production. Although the authors

make no mention of NH3 being measured at these sites, it might be hypothesized that a higher

NH3 concentration at the intensive agricultural site may have been responsible for the observed

lower nocturnal Rc, compared with the extensively-managed, steppe-like grassland.

The development of low-cost systems for the long-term monitoring of SO2, NH3 and other trace

gas fluxes holds promise for widening the range of dry deposition datasets for comparison with

inferential models. Hole et al. (2008) present an 18-month dataset of SO2 fluxes acquired with a

conditional time-averaged gradient (COTAG) system (Fowler et al., 2001; Famulari et al., 2008)

in a semi-alpine ecosystem in Southern Norway. The mean annual SO2 deposition velocity was

4.0 mm s-1, although the dataset included some negative deposition velocities (upward fluxes),

and the annual mean Vd was 13.0 mm s-1 if only the positive values were included. The authors

report evidence of enhanced SO2 deposition rates during an episode in November 2005 when the

NH3/SO2 ratio was high, and conversely of decreased SO2 uptake and increased NH3 uptake in

November 2004 when the NH3/SO2 ratio was low. Comparison with the inferential model by

Zhang et al. (2002, 2003b) was satisfactory but the model could not reproduce the large observed

variability in exchange rates, which may result from NH3-SO2 co-deposition processes not being

included in their resistance scheme.

More experimental evidence of the mutual influences of NH3 and SO2 concentrations on their

deposition rates was obtained by Derome et al. (2004), though not by micrometeorological

measurements but using bulk precipitation collectors and throughfall measurements in Scots pine

canopies in SW Finland. The study was conducted over a 6-year period (1993-1998) in the

vicinity of a Cu-Ni smelter, which emitted large amounts of gaseous NH3. These emissions were

shown to have strongly enhanced the scavenging of atmospheric SO2 by the pine canopy,

resulting in increased levels of N and S deposition and increased foliar N and S concentrations.

In an NH3 fumigation experiment, Cape et al. (1998) had previously described similar findings


over a Scots pine forest in Central Scotland, with the canopy resistance for SO2 decreasing with

elevated NH3 concentration. Although NH3 concentrations were not measured in the Finnish

study (Derome et al., 2004), they were likely higher than normally encountered in the

countryside, except near animal housing in areas of intensive agriculture, where such processes

could be significant.

4.3 Control of surface uptake rates by leaf cuticular chemisty

A number of authors have addressed the issue of the chemical control of surface pollutant uptake

rates (eg. Flechard et al, 1999). The most important finding for SO2 deposition is that the rates of

deposition are controlled mainly by the chemistry at the vegetation-atmosphere interface, and

that as the surfaces are wet most of the time, the processes are regulated by the chemistry of the

thin film of moisture. In principle, many compounds influence the chemistry of this surface

layer, including plant exudates and soil derived compounds, but the key reactant for SO2 is NH3.

Thus the ambient concentrations of SO2 and NH3 essentially regulate the pH of the surface

moisture and thus control the uptake of SO2. The full surface chemistry of the process has been

incorporated into a dynamic mechanistic model shown in Fig. 4.2 (Flechard et al 1999). The

chemistry of the surface water film is initialised in the model using measured precipitation

chemistry, the model then simulates the dynamic responses of the net land-atmosphere exchange

of SO2 as the ambient concentrations of the reactive trace gases and meteorological conditions

change. The model has been shown to provide good agreement with observed 30 min average

fluxes for several days. An example is provided in Figure 4.3, for a five-day period at

Auchencorth Moss in the Scottish Borders. The general agreement between measured and

modelled fluxes is excellent during the three day period, 21st March to 23rd March 1995. From

the 24th March, the observed NH3 concentrations are increased to 2ug m-3 to provide sufficient

NH3 to neutralise the acidity from the ambient SO2 oxidation in solution. The consequence is to

decrease the canopy resistance for SO2 and increase the deposition rate of SO2 to the maximum

under the prevailing atmospheric conditions. Demonstrating a close link between SO2 deposition

and ambient NH3 is not new, as this was predicted in earlier work within the first phase of

BIATEX. However, this work quantified the process correctly for the first time, demonstrated

the effects in field conditions at ambient concentrations and provided a mechanistic model

incorporating the full chemical scheme.


KhsKha KhhonoKhc

Kn1 Ks1Kc1Ka

NH3,aq CO2,aq HNO2,aq

NH3 CO2 HNO2 SO2

SO2,aq

CO32- SO3

2- SO42-

NH4+ HCO3

- NO2- HSO3

- HSO4-

Ks2Kc2 Ks3

Kw

H2O H++OH-

χd

Rd

AQUEOUS

GASEOUS

Kha

χs(NH3)

Rs

Rcut

Fcut

Rb

Ra{z-d}

χ{z-d}

χ{z0’}=χc

χ{z0}

WET DRY

HNO3

HCl

Cl-

NO3- H2O2

O3

APOPLAST

Ft

Fd

FsK+Mg2+

Na+ Ca2

+

K+Mg2+

Na+ Ca2

+

KaNH3,aqNH4

+

Kw

H2O H++OH-

Figure 4.2. A schematic representation of the dynamic canopy compensation pollution model for SO2 and NH3 exchange over vegetation (from Flechard et al 1999).

21/0

3/95

12:

00

22/0

3/95

00:

00

22/0

3/95

12:

00

23/0

3/95

00:

00

23/0

3/95

12:

00

24/0

3/95

00:

00

24/0

3/95

12:

00

25/0

3/95

00:

00

25/0

3/95

12:

00

26/0

3/95

00:

00

26/0

3/95

12:

00

SO2 f

lux

(ng

m-2

s-1 S

O2)

-60

-50

-40

-30

-20

-10

0

10

Meas. FSO2

FSO2, max

Mod. FSO2 (χNH3 = ambient)Mod. FSO2 (χNH3 = 2 µg m-3)

Figure 4.3. A comparison between measured and modelled SO2 fluxes at Auchencorth Moss over the

period 21-3-95 to 26-3-95 showing the influence of increasing ambient NH3 concentration on SO2 flux

(from Flechard et al., 1999).


4.4 Advances in deposition modelling

The magnitude of dry deposition at the national and regional scales requires that process-based,

rather than empirical, parameterisations be implemented in atmospheric models, accounting for

variations in surface chemical characteristics driven by local pollution climates. The observation

of changes in deposition velocity from the European SO2 deposition studies of the 90’s is now

widely known and is being used by EMEP to explain growing discrepancies in the model-

measurement comparisons over Europe. The work has led to modifications of the EMEP model

(Simpson et al., 2003) to simulate the temporal trends, resulting in an increase in Vd for SO2 over

many parts of the continent, and driven by the long term, large scale decrease in the SO2/NH3

ratio (Fig. 4.4). The new scheme, for non-stomatal resistance of both NH3 and SO2, incorporates

an acidity to alkalinity (SO2/NH3) molar ratio as a scaling factor for resistances. For SO2, two

non stomatal resistances Rns,wet and Rns,dry are calculated as a function of the SO2/NH3 ratio, and a

function of relative humidity is used for the transition from dry to wet when the surface cannot

be considered fully wet nor fully dry.

.

Figure 4.4. Modelled (EMEP) dry deposition velocity of SO2 (cm s-1) over Europe for 1980 and 2000,

taking into account the effect of the change in the SO2/NH3 ratio on the canopy resistance (Ref to Fagerli

et al., in press).

The EMEP non-stomatal scheme has also been used in field-scale inferential modelling of N and

S dry deposition as part of the NitroEurope project, using low-cost, long-term atmospheric trace

gas and aerosol DELTA samplers (Tang et al. 2009). Another implementation of the

parameterisation was made by Zimmermann et al. (2006) for the simulation of atmospheric

deposition to Norway spruce, using the SPRUCEDEP SVAT model, and comparison with

throughfall measurements and a canopy base cation budget model. The agreement between

(inferential & canopy budget) modelling and observations was very good for S and oxidised N.


Here, the contribution of dry to total (dry+wet) deposition was around 60% for S and for both

reduced and oxidised N.

The Dutch IDEM model (Bleeker et al., 2004; Erisman et al., 1994) also uses an NH3/SO2 molar

ratio as a proxy for surface acidity. For NH3, a range of default Rext values are used for 3 classes

of the N/S ratio (very low, low and high), depending on surface wetness, land-use and time of

day, while for SO2 the only effect implemented is to add 50 s m-1 to the non-stomatal resistance

when the N/S ratio is very low (<0.02).

The large reduction in European SO2 emissions and ambient concentrations over the last 25

years, and the relative stagnation in NH3 emissions and concentrations in Western Europe over

the same period has meant that the SO2/NH3 ratio has decreased dramatically, resulting in a

reduced Rc for SO2 (Figure 4.1). While ambient SO2 was a relatively good proxy for total

atmospheric and leaf surface acidity 15 or 25 years ago, the relative share of SO2 compared to

other inorganic atmospheric acids (e.g. HNO3 and HCl) is now much lower. The NitroEurope

network of 56 DELTA samplers across the European continent currently provides monthly mean

concentrations of HNO3 and HCl as well as SO2 and NH3 and aerosol NH4+, NO3

- and SO42-

(Tang et al., 2009), with a view to validating European concentration fields of concentration and

deposition for these species. The data show (Fig. 4.5) that the geometric mean mixing ratios of

SO2, HNO3 and HCl across the network are 0.4, 0.35 and 0.15 ppb, respectively, so that, on

average, SO2 makes up only about 40% of the sum of acids (SO2 + HNO3 + HCl). Further, the

data indicate that at some sites (e.g. most Danish, French and Italian sites), the acidity is largely

dominated by HNO3 and HCl, which are considered in most models (e.g. Simpson et al., 2003)

to be deposited at the maximum rates allowed by turbulence (Rc ~ 0 s m-1). Under such

conditions, the proxy (SO2+HNO3+HCl) / NH3 would seem more appropriate to quantify the

relative importance of surface acidity and alkalinity in model parameterisations, than the ratio of

SO2 alone to NH3. Clearly the surface affinity for SO2 uptake will depend whether fast-

depositing, strong acids are present, as the acidity is no longer SO2-dominated, and this needs to

be accounted for in extended surface resistance parameterisations.


0

0.5

1

1.5

2

2.5

BE-

Bra

BE-

Lon

BE-

Vie

CH

-Lae

CH

-Oe1

CZ-

BK

1D

E-G

ebD

E-G

riD

E-H

aiD

E-K

liD

E-Th

aD

E-W

etD

K-L

vaD

K-R

isD

K-S

orES

-ES1

ES-L

Ma

ES-V

DA

FI-H

yyFI

-Kaa

FI-S

odFR

-Fon

FR-G

riFR

-Hes

FR-L

Br

FR-L

q2FR

-Pue

HU

-Bug

IE-C

a2IE

-Dri

IT-A

mp

IT-B

Ci

IT-C

olIT

-MB

oIT

-Ren

IT-R

o2IT

-SR

oN

L-C

a1N

L-H

orN

L-Lo

oPL

-wet

PT-E

spPT

-Mi1

RU

-Fyo

SE-N

orSE

-Sk2

UK

-AM

oU

K-E

Bu

UK

-ESa

UK

-Gri

UA

-Pet

DE-

Hoe

FI-L

omIT

-Cas

NL-

Spe

DE-

Meh

Con

cent

ratio

n (p

pb) HCl

HNO3SO2

0

0.5

1

1.5

2

2.5

3

3.5

4

BE-

Bra

BE-

Lon

BE-

Vie

CH

-Lae

CH

-Oe1

CZ-

BK

1D

E-G

ebD

E-G

riD

E-H

aiD

E-K

liD

E-Th

aD

E-W

etD

K-L

vaD

K-R

isD

K-S

orES

-ES1

ES-L

Ma

ES-V

DA

FI-H

yyFI

-Kaa

FI-S

odFR

-Fon

FR-G

riFR

-Hes

FR-L

Br

FR-L

q2FR

-Pue

HU

-Bug

IE-C

a2IE

-Dri

IT-A

mp

IT-B

Ci

IT-C

olIT

-MB

oIT

-Ren

IT-R

o2IT

-SR

oN

L-C

a1N

L-H

orN

L-Lo

oPL

-wet

PT-E

spPT

-Mi1

RU

-Fyo

SE-N

orSE

-Sk2

UK

-AM

oU

K-E

Bu

UK

-ESa

UK

-Gri

UA

-Pet

DE-

Hoe

FI-L

omIT

-Cas

NL-

Spe

DE-

Meh

Aci

d/N

H3 r

atio

(ppb

ppb

-1)

SO2/NH3(SO2+HNO3+HCl) / NH3

0

0.5

1

1.5

2

2.5

BE-

Bra

BE-

Lon

BE-

Vie

CH

-Lae

CH

-Oe1

CZ-

BK

1D

E-G

ebD

E-G

riD

E-H

aiD

E-K

liD

E-Th

aD

E-W

etD

K-L

vaD

K-R

isD

K-S

orES

-ES1

ES-L

Ma

ES-V

DA

FI-H

yyFI

-Kaa

FI-S

odFR

-Fon

FR-G

riFR

-Hes

FR-L

Br

FR-L

q2FR

-Pue

HU

-Bug

IE-C

a2IE

-Dri

IT-A

mp

IT-B

Ci

IT-C

olIT

-MB

oIT

-Ren

IT-R

o2IT

-SR

oN

L-C

a1N

L-H

orN

L-Lo

oPL

-wet

PT-E

spPT

-Mi1

RU

-Fyo

SE-N

orSE

-Sk2

UK

-AM

oU

K-E

Bu

UK

-ESa

UK

-Gri

UA

-Pet

DE-

Hoe

FI-L

omIT

-Cas

NL-

Spe

DE-

Meh

Con

cent

ratio

n (p

pb) HCl

HNO3SO2

0

0.5

1

1.5

2

2.5

3

3.5

4

BE-

Bra

BE-

Lon

BE-

Vie

CH

-Lae

CH

-Oe1

CZ-

BK

1D

E-G

ebD

E-G

riD

E-H

aiD

E-K

liD

E-Th

aD

E-W

etD

K-L

vaD

K-R

isD

K-S

orES

-ES1

ES-L

Ma

ES-V

DA

FI-H

yyFI

-Kaa

FI-S

odFR

-Fon

FR-G

riFR

-Hes

FR-L

Br

FR-L

q2FR

-Pue

HU

-Bug

IE-C

a2IE

-Dri

IT-A

mp

IT-B

Ci

IT-C

olIT

-MB

oIT

-Ren

IT-R

o2IT

-SR

oN

L-C

a1N

L-H

orN

L-Lo

oPL

-wet

PT-E

spPT

-Mi1

RU

-Fyo

SE-N

orSE

-Sk2

UK

-AM

oU

K-E

Bu

UK

-ESa

UK

-Gri

UA

-Pet

DE-

Hoe

FI-L

omIT

-Cas

NL-

Spe

DE-

Meh

Aci

d/N

H3 r

atio

(ppb

ppb

-1)

SO2/NH3(SO2+HNO3+HCl) / NH3

Fig. 4.5. Top: Annual mean concentrations of SO2, HNO3 and HCl across the NitroEurope network;

Bottom: Annual mean Acid/NH3 molar ratios calculated from SO2 alone or from SO2+HNO3+HCl.

4.5 Future challenges

The principal controls over SO2 deposition to terrestrial surfaces have been identified from field,

mainly micrometeorological measurements. These studies have enabled the controlling steps in

the deposition pathway to be separated and their response to environmental variables quantified.

In turn the data and responses have been used to develop process based models and applied to

quantify regional deposition budgets at country and continental scales. There have been

surprises, notably in the last decade. The largest surprise has been the recognition that long term

(~1 year) average deposition velocities change with time due to changes in the chemical

climatology at the regional scale. Thus a few measurements of SO2 deposition rates to

parameterise models will not necessarily be satisfactory in the long term. It is necessary to

underpin estimates of regional SO2 deposition with measured deposition fluxes and estimates of

the surface resistance to quantify the long term trends. The same logic means that deposition

velocities from one region will not necessarily apply elsewhere. The most important region

globally for sulphur emissions and deposition is currently East Asia, and China specifically.

While the methodologies developed in Europe and North America are applicable for

measurements of SO2 deposition, the use of parameters deduced in Europe or North America are

not applicable, and measures fluxes and surface resistances in these regions are necessary to

underpin the assessments.


5. Ozone 5.1 Introduction

Ozone is a gaseous, phytotoxic secondary air pollutant with widespread effects on human health,

vegetation and materials. It is also a greenhouse gas (GHG), affecting the radiation balance of

the earth, and it interacts with other GHGs such as methane. Its deleterious effects on plants pose

a large-scale risk to crop production and forest vitality, which has been widely documented in

Europe and North America (e.g. Hayes et al., 2007; Karnosky et al., 2007), and there is also

evidence of ozone impacts in Asia, Africa and Latin America (e.g. Ashmore, 2005).

Ozone deposition to external surfaces of vegetation is important as a removal pathway for

ground level ozone but is of little consequence for plant effects. The primary potential for injury

to vegetation, requires stomatal uptake of ozone molecules (Fig. 5.1) followed by reaction with

the internal plant tissue generating highly reactive oxidants that interfere with physiological

processes (e.g. Matyssek et al., 2008). As ozone is a strong oxidant, it can also react with leaf

cuticles and other external plant surfaces or with volatile compounds emitted by vegetation and

non-stomatal ozone deposition is a substantial fraction of the total flux. In addition to vegetation,

ozone molecules may be deposited at any surface providing a chemical sink or acting as a

surface for heterogeneous decomposition (Cape et al 2009). Quantifying the stomatal uptake

rates is central to understanding the ozone-induced risk to vegetation, but the non-stomatal

deposition needs to be quantified to correctly partition the total deposition flux.

ATMOSPHERE

SOIL

CANOPY

turbulent transfer to the surface

stomatal uptake, ozone enters the plant through stomata and reacts with internal plant tissues and

fluids non-stomatal uptake to

leaf cuticles, stems, soil or

any other materials

In-canopy chemistry: reactions of ozone with plant VOCs or soil NO emissions

Fig 5.1 The sinks for ozone at terrestrial surfaces and process regulatory fluxes.


Over terrestrial surfaces ozone, confined within the atmospheric boundary layer, has a relatively

short lifetime scale of the order of one day due to dry deposition at the surface (Wesely and

Hicks, 2000). Thus surface removal represents an important control on the near-surface ozone

concentrations, and is the main cause of diurnal variation in rural areas (Garland and Derwent,

1979). Dry deposition constitutes a major term in the global mass balance of tropospheric ozone,

the mean global dry deposition sink calculated with 20 chemistry-transport models (CTMs)

(1000 Tg yr-1) clearly exceeding the net stratospheric input (550 Tg yr-1) (Stevenson et al., 2006).

The first measurements of ozone deposition fluxes were made in the 1950s using the

micrometeorological gradient method (Regener, 1957). The earliest investigations were aimed at

quantifying the surface sink term of the tropospheric ozone budget. Based on these, Galbally

(1971) concluded that bulk surface resistance (Rc) of dry soil and short grass surfaces was

approximately 100 s m-1. These and other pioneering studies (e.g. Turner et al., 1974) showed

that vegetation and soil constitute important pathways by which ozone is removed from the

atmosphere, while water and snow surfaces are rather inefficient sinks. However, the early

studies provide rather little information about the processes that control ozone deposition to

terrestrial or marine surfaces.

There has also been an interest in measuring surface fluxes prompted by ecological concerns.

The importance of environmental conditions on the plant injury through the regulation of

stomatal uptake was recognised in 1960’s by Mukammal (1965), who observed that the presence

of high concentrations was not a sufficient condition for plant injury. Indeed, a few years later

the close coupling between ozone and water vapour fluxes was demonstrated by Rich et al.

(1970). However, while ozone effects on vegetation have been closely associated with stomatal

uptake for decades, only recently have practical risk assessment methods been formulated in

terms of stomatal uptake rather than ambient concentration (UNECE, 2004).

Micrometeorological techniques have been in use since the first flux measurements. In the late

1970s, Eastman and Stedman (1977) developed a fast-response ozone sensor that facilitated

direct ozone flux density measurements by the eddy covariance method. This resulted in a series

of measurement campaigns in the eastern United States, including various vegetated and other

surface types (Wesely, 1983). These studies improved the understanding of deposition processes

and formed the basis for the detailed surface resistance parameterisation of Wesely (1989). This

parameterisation has been implemented into numerous CTMs. In Europe, the eddy covariance


technique was adopted somewhat later, but rapidly became popular with the introduction of a

commercial fast-response ozone sensor (Güsten et al., 1992).

5.2 Deposition rates

Early estimates of ozone dry deposition were obtained from measurements of the diurnal cycle

of ozone in rural areas (Garland and Derwent, 1979). The first long-term measurements showed

a seasonal cycle over vegetated surfaces that followed the growing season of the plants (Colbeck

& Harrison, 1985), leading to the assumption that ozone deposition is mainly controlled by

stomatal uptake and deposition to non-vegetated surfaces is constant, only depending on the

material and surface area. More recent studies have shown that although deposition rates are

partly governed by stomatal uptake over a plant canopy, it only accounts for ca 40 to 60% of

total deposition on average and that the non-stomatal component is not constant (Coyle et al

2008b, Hogg et al 2007). These observations are described in more detail in the following

sections.

5.2.1 European forests

Ozone deposition fluxes to forests, as well as other vegetated surfaces, are largely controlled by

the physiological activity and associated gas exchange of the vegetation, with solar radiation, air

temperature, air humidity and soil moisture as the main controlling variables. Thus the

deposition velocities (Vd) observed above forests typically exhibit diurnal and seasonal cycles

that depend on the structure, physiological responses and phenological state of the trees.

According to present understanding, however, there are other significant processes in addition to

stomatal regulation of gas exchange that control the magnitude and variation of the ozone

deposition efficiency of forests (Altimir et al., 2006; Cieslik, 2004; Dorsey et al., 2004;

Goldstein et al., 2004; Hogg et al., 2007; Lamaud et al., 2002; Tuovinen et al., 2001; Zhang et

al., 2002).

The temporal patterns are clearly demonstrated by the long-term flux measurement data

available from a few sites, such as those reported for a temperate spruce forest by Mikkelsen et

al. (2004) and a boreal pine forest by Keronen et al. (2003) and Altimir et al. (2006). In the

boreal region in winter, the dormancy of vegetation and below-zero temperatures result in low

and relatively stable deposition rates (Vd ≈ 0.1 cm s-1, Figure 5.2a), while the mean diurnal cycle

at the temperate forest shows a weak midday enhancement related to gas exchange superimposed

on a relatively high and seasonally invariable base level (Vd ≈ 0.5 cm s-1). The decrease of Rc in

spring correlates with the onset of photosynthesis, and a well-defined, symmetrical diurnal cycle


ensues in summer, with a Vd maximum of 0.7–0.9 cm s-1 (Keronen et al., 2003; Mikkelsen et al.,

2004). However, Altimir et al. (2006) observed that the correlation with physiological activity is

poorer in autumn and concluded that the deposition rate is modified by the frequent wetting of

the forest canopy. Both Mikkelsen et al. (2004) and Altimir et al. (2006) attribute a major part of

the total annual ozone deposition to non-stomatal pathways.

Boreal Pine Forest

00:0

0 02:0

0 04:0

0 06:0

0 08:0

0 10:0

0 12:0

0 14:0

0 16:0

0 18:0

0 20:0

0 22:0

0 00:0

0

v d [

cm s

-1]

0.00.10.20.30.40.50.60.7

MedianWinter (Nov-Feb)Summer (May-July)

Potatoes

01:0

0 0

3:0

0 0

5:0

0 0

7:0

0 0

9:0

0 1

1:0

0 1

3:0

0 1

5:0

0 1

7:0

0 1

9:0

0 2

1:0

0 2

3:0

0

v d [

cm s

-1]

0.00.20.40.60.81.01.21.4

July-Aug

Temperate Oak Forest

01:0

0 03:0

0 05:0

0 07:0

0 09:0

0 11:0

0 13:0

0 15:0

0 17:0

0 19:0

0 21:0

0 23:0

0

v d [

cm s

-1]

0.00.20.40.60.81.01.21.4

July-Aug

Temperate Grassland

00:0

0 0

2:0

0 0

4:0

0 0

6:0

0 0

8:0

0 1

0:0

0 1

2:0

0 1

4:0

0 1

6:0

0 1

8:0

0 2

0:0

0 2

2:0

0 0

0:0

0

v d [

cm s

-1]

0.00.10.20.30.40.50.60.7

Apr-SepOct-Mar

a

d

b

c

Figure 5.2 Median diurnal cycles in deposition velocity at (a) boreal scots pine forest, Hyytiala, Finland, 2002-2003 (Altimir et al 2006), (b) oak forest, Alice Holt, England, 16th July-05 – 18th August-05 (Coyle et al 2006), (c) potatoes, Gilchriston, Scotland, 9th July-06 – 3rd Aug-06, (Coyle et al 2008b) (d) intensively managed lolium perene grassland, Easter Bush Scotland, 2002-03 (Coyle 2005)

High non-stomatal fluxes are also observed in Mediterranean forests (Gerosa et al., 2005, 2008).

However, the diurnal and seasonal variations differ from those of the northern forests in many

aspects. The measurements above oak forests in central Italy (Gerosa et al., 2005, 2008; Cieslik,

2008) and south-eastern France (Michou et al., 2005) show that dry and hot conditions can

significantly affect the diurnal courses of Vd and Rc. On the other hand, in this region there is a

potential for high deposition rates throughout the year, and higher stomatal uptake may take

place during winter than summer months, in spite of the lower ozone concentrations in winter

(Cieslik, 2008).

During dry periods stomata are either almost completely closed or the cycle of stomatal

conductance (Gst = Rst-1) is less symmetrical, with a rapid increase from the nocturnal levels to a

maximum in the morning, rather than around noon, and a gradual decrease towards the evening.

The latter behaviour may take place in more northern forests as well, if leaf temperatures reach


sufficiently high levels (e.g. Dorsey et al., 2004; Tuovinen et al., 2001). At the French site, Vd

starts increasing very early (at 3–4 a.m. local time) and it was suggested that this could be related

to either stomatal response to blue light, resulting in uptake already in the predawn hours, or to

non-stomatal deposition enhanced by surface wetness (Michou et al., 2005). At the Italian site,

however, the maximum (in Gst) occurs later and seems to have a non-stomatal origin, possibly

due to the reaction with the NO accumulated within the forest canopy (Gerosa et al., 2005) or

leaf wetness (Gerosa et al., 2008). Similarly, the measurements taken by Coyle et al. (2006) in an

oak forest in England in summer show a steep increase to the maximum at 8 a.m. (Vd ≈ 1.0 cm s-

1) and a approximately linear decrease to a nocturnal level (Vd ≈ 0.1 cm s-1), Figure 5.2b.

In addition to the phenological development of plants, the seasonal cycle can be strongly

influenced by the soil moisture conditions, especially in southern Europe. Consequently, there

may be large annual variation in the ozone uptake rates depending on the occurrence and

persistence of drought. For example, in August 2003 the mean Gst derived for the Italian oak

forest by Gerosa et al. (2008) was only 35% of that in the cooler and wetter August of 2004, and

the effect of drought persisted even after soil water was replenished by rainfall.

5.2.2 Crops

For agricultural crops, ozone deposition rates exhibit pronounced seasonality that results from

the distinct phenological stages of the growing season. The flux measurements above an Italian

barley field by Gerosa et al. (2004) demonstrate how Vd increases during the first growth stages

(seedling growth, tillering, stem elongation). The maximum is reached soon after anthesis,

during the grain filling period, when photosynthetic activity is at its highest level. Gerosa et al.

(2004) observed an average Rc of about 75 s m-1 for this period, while the bulk stomatal

resistance (Rst) was about 150 s m-1. After that, deposition rates decrease gradually with ripening

of the crop and leaf senescence, as Rst increases, and are further reduced by harvest. A slightly

higher minimum Rc but a similar decrease was observed at the same site for wheat. In this case,

the decline was amplified by the rapid drying of soil (Gerosa et al., 2003).

For both barley and wheat, the diurnal cycles of Vd and surface conductances were symmetrical

during the photosynthetically active period with a maximum (Vd ≈ 0.7–0.9 cm s-1) around noon

(Gerosa et al., 2003, 2004). During the latter part of the growing season, the midday deposition

rates are strongly reduced, due to increased Rst, resulting in an earlier maximum and a diurnal

course that is skewed towards morning. In the afternoon, values comparable to those after

harvest were observed for the barley field (Gerosa et al., 2004).


For other crops, data are rather limited. Michou et al. (2005) report a symmetrical diurnal cycle

for the Vd measured over a rapidly growing maize field, with a mean minimum of 0.05 cm s-1

and a mean maximum of 0.50 cm s-1. In an earlier North American study, a similar diurnal cycle,

with slightly higher values throughout the day, was observed over a maize field during the period

of most active growth (Meyers et al., 1998). During senescence, the hourly mean Vd only reached

0.2 cm s-1 in the morning. The patterns were also similar for soybean, but the Rc of soybean

seems to be significantly lower than that of maize, as the maximum mean hourly Vd was almost 1

cm s-1 during the active growth period (Meyers et al., 1998). Coyle et al. (2008b) reported an

asymmetrical diurnal cycle in Vd over a potato field during the phase of tuber initiation and

growth through to harvest, with a median value of 0.6 cm s-1, day-time values from 0.5 to 2 cm s-

1 decreasing to 0.4 cm s-1, or less, at night (Figure 5.2c).

As was the case with forests, stomatal uptake rates derived from the evapotranspiration fluxes

only explain a part of the total ozone flux and a significant proportion must be attributed to non-

stomatal sinks. Even during the active growth period, no more than 50–60% of the total flux to a

wheat field was stomatal, and this fraction decreased during the senescence (Gerosa et al., 2003).

The same was true for barley, and even though the day-to-day variation in the inferred non-

stomatal flux fraction is large, the corresponding non-stomatal surface conductance seems to

remain relatively stable throughout the summer (Gerosa et al., 2004). Similar results were

obtained for onion, but in this case the variation can be explained by irrigation that clearly

enhanced both stomatal and total fluxes (Cieslik, 2008). In the case of potatoes, the non-stomatal

component was only ~15% when the canopy was well-watered but increased to ~80% when the

crop dried out (Coyle et al., 2008b).

5.2.3 Grasslands

Grasslands can be used to describe a wide variety of habitats from intensively managed pastures

that are usually dominated by a single species, such as Lolium perenne, to natural grasslands

which contain a rich diversity of grasses, forbs and legumes and are often of high conservation

value. The canopies that have been studied to date all exhibit similar patterns of deposition as

forests and agricultural crops in that they have phenologically driven seasonal and diurnal cycles.

There are few long-term measurements of ozone deposition to grasslands reported in the

literature at present (Colbeck and Harrison, 1985; Grünhage et al., 1994; Pio et al., 2000). All

these studies measured during winter and summer so that the growing season and dormant

periods were observed. The results of several short-term campaigns over active and dormant or


dry grasslands have also been reported and are summarised below (Bassin et al., 2004; De

Miguel and Bilbao, 1999; Droppo 1985; Duyzer et al., 1983; Garland and Derwent, 1979;

Meyers et al., 1998; Pederson et al., 1995; Sorimachi et al., 2003).

Over active, green grasslands the daytime Vd is only 0.5 cm s-1 on average, decreasing to ~0.1-

0.2 cm s-1 at night, although peaks over 1 cm s-1 are often observed (Figure 5.2d). The diurnal

cycle is often symmetrical with a steady increase in the morning after sunrise and decrease is the

afternoon as light and temperatures decrease. However (Grünhage et al., 1994) reported an

asymetrical diurnal cycle in Vd with a steep increase after 6 a.m. until 9 a.m. when it steadily

declined. This is attributed to an increase in water vapour pressure deficit (VPD) at midday and

the afternoon causing stomata to close, as has been observed for many other canopies. Where

measurements have been made over dormant (i.e. during winter) or dry grasslands the diurnal

cycle is far less pronounced with daytime Vd only reaching ~0.2 cm s-1 although night-time

values are similar at all times of year (Figure 5.2d).

Coyle, (2005) showed that non-stomatal uptake was ~60% of the total budget over an improved

grassland in Central Scotland. Pleijel et al, (1995) found that the non-stomatal sink is enhanced

by surface wetness while the work of Coyle (2005) also demonstrated that the non-stomatal

component was not constant but varied with wetness, surface temperature, solar radiation and

wind speed as has been suggested for other canopies.

5.2.4 Other vegetated surfaces

Measurements have been made over a variety of other plant canopies from moorlands and

subartic mires to tropical forests. They all exhibit diurnal and seasonal cycles driven by

variations in stomatal activity and climate, as described for the previous canopies. For example

Fowler et al (2001) for moorland reported summer diurnal cycles ranging from ~0.3 cm s-1 at

night to ~0.6 cm s-1 during the day while in the winter the afternoon peak was only ~0.4 cm s-1

with night-time values also ~0.3 cm s-1. Tuovinen et al (1998) measured a small diurnal cycle

over flark fen, 300 km north of the Artic circle in the late summer, ranging from 0.1 to 0.15 cm s-

1 at night to only ~0.2 cm s-1 during the day. For tropical forests there are often two seasons, wet

and dry: During the wet season, Rummel et al (2007) reported diurnal cycles over Amazonian

rain forest ranging from ~0.4 cm s-1 during the night to ~1 cm s-1 at day, with a symmetrical

diurnal cycle while Matsuda et al (2006) reported values ranging from 0.26 to 0.63 cm s-1 for

night and day respectively over a deciduous forest (teak) in Thailand; during the dry season both

canopies exhibited asymmetrical diurnal cycles with a peak of ~1.5 cm s-1 at 4 am over the


Amazon rain forest, declining to ~0.4 cm s-1 at night, and ~0.5 cm s-1 at 9 am over Thai teak

forest, declining to ~0.1 cm s-1 at night which are attributed to the effect large afternoon vpd.

5.2.5 Non-vegetated surfaces

Deposition to the soil underlying the vegetation layer may significantly contribute to the vertical

ozone flux observed above the canopy (e.g. Dorsey et al., 2004). Especially in arid regions, the

surface resistance of soil (Rsoil) is the key determinant of the surface removal of ozone (e.g.

Michou et al., 2005). As the literature review by Massman (2004) and the more recent data of

Sorimachi and Sakamoto (2007) indicate, Rsoil is highly variable, ranging from 10 to >1000 s m-1.

It has been observed that Rsoil decreases with increasing organic content and porosity of soil.

Clearly, wet soils have a considerably higher Rsoil (~500 s m-1) than dry soils (~100 s m-1)

(Galbally and Roy, 1980; Massman, 2004), as increasing the moisture content of soil decreases

its porosity and thus reduces the area of reactive surface available to ozone molecules (Sorimachi

and Sakamoto, 2007). However, it is difficult to disentangle individual effects based on the

current data, and the situation is further complicated by biogenic NO emissions from soils.

Removal through the reaction with NO potentially constitutes a significant sink for ozone,

especially in forests at night (Pilegaard, 2001; Dorsey et al., 2004), even though this reaction

does not take place specifically at the air–soil interface.

Snow

The dry deposition velocity over snow- and ice-covered surfaces and water is known to be

relatively small. However, the measurement data are highly variable. It is possible that the

measured Vd is affected by chemical reactions taking place in the snowpack, which, combined

with dynamic transport processes, can result in the observed variability. Helmig et al. (2007)

modelled ozone concentrations in high northern latitudes with different values of Rc and

demonstrated how even small deposition rates, if effective over large areas, can significantly

affect the near-surface concentrations and that a high Rc is required for snow in order to

reproduce the observed concentrations; the best agreement was obtained when limiting Vd to

0.01 cm s-1.

Water

For water surfaces, the understanding of controlling processes is more coherent, involving both

turbulent and molecular mixing, and chemical reactions. It has been observed that wind-induced

turbulence greatly enhances deposition rates by increasing atmospheric mixing, surface

roughness, wave breaking and spray generation (e.g. Gallagher et al., 2001). . The observations


of Rc range from 1000 to 10,000 s m-1 (Gallagher et al., 2001). At low wind speeds, Vd

increasingly depends on the molecular gas transfer near the air–water interface. Even in the

absence of turbulence, significant deposition rates are possible, since the chemical reactions

taking place in the aqueous phase enable more efficient deposition than the solubility of ozone

alone would suggest. The modelling results of Fairall et al. (2007) show that the oceanic

turbulent mixing also plays an important role in enhancing ozone deposition by up to a factor of

three. Iodide (Chang et al., 2004) and chlorophyll (Clifford et al., 2008) have been suggested as

the main reagents controlling ozone destruction in the organic surface microlayer. As the

distribution of these compounds in water bodies is related to that of phytoplankton, the chemical

enhancement of ozone deposition can be expected to be highly variable both temporally and

spatially. Similarly, coastal ozone deposition to the iodide-rich macroalgae surfaces depends on

the tidal phase, and fluxes can be further enhanced by photochemical destruction of ozone during

the iodine-mediated particle formation events (Whitehead et al., 2008).

5.3 Non-stomatal deposition processes

Although the stomatal uptake of ozone is an important sink over vegetated surfaces it accounts

for only a fraction (typically 1/3 to 2/3) of the total deposition. In most studies it has been

assumed that the non-stomatal sink is constant, depending only on the surface material and area,

although it was expected that the presence of surface water would inhibit deposition as the

solubility of ozone is quite small. However, field measurements have indicated that surface

temperature, solar radiation, surface wetness and wind speed may all have an influence on the

magnitude of the non-stomatal flux. The influence of wind speed is simply explained, as when

wind speed increases more air will penetrate the canopy, increasing the surface area available for

deposition. The mechanisms that have been proposed for the other parameters are:

• Temperature: thermal decomposition ozone on plant surfaces, mediated by waxes and

other substances on the surface (Coyle et al 2008b, Hogg et al 2007, Fowler et al 2001,

Cape et al 2009)

• Solar radiation: ozone photolysis also mediated by the surface (Coyle et al 2008b, Hogg

et al 2007 and references therein) and reaction with VOCs emitted by vegetation (Hogg

et al 2007, Coyle 2005)

• Surface wetness: aqueous reactions in water-films on plant surfaces (Coyle et al 2008b,

Altimir et al)

Principal component analysis of data from several field campaigns indicated that parameters had

the following order of precedence: temperature, surface wetness (measured as vapour pressure),


solar radiation then wind speed although for forests temperature and wetness were of almost

equal importance (Coyle et al, 2008a).

Some preliminary work exposing wax coated and metal surfaces to ozone in controlled

environment chambers (Cape et al in press) showed that Vd is temperature dependent. An

Arrhenius type plot of the change in reaction rate with temperature is shown in Fig. 5.3 (the

natural logarithm of the surface resistance in s m-1 is taken as the inverse reaction rate). The

results for four vegetated canopies are also plotted in Fig. 5.3 and the slope of their linear

regression lines is of the same order of magnitude as the artificial surfaces. This shows that the

dependence on temperature and activation energies are similar for all surfaces but the absolute

reaction rates differ. The simplest conclusion is that heterogeneous decomposition of ozone to O2

is responsible; hence the similar activation energies in Fig. 5.3 and the variation in reaction rates

can be attributed to differences in effective surface area (Cape et al, 2009).

y = 40.1x - 3.28, R2 = 0.84

y = 24.9x + 0.14, R2 = 0.29y = 22.8x + 0.85, R2 = 0.18

y = 16.1x + 2.18, R2 = 0.44

4

6

8

10

12

14

0.39 0.4 0.41 0.42 0.43

1000/RT

ln(R

ns)

stainless steel

aluminium foil

paraffin wax

beeswax

moorland vegetation

Potatoes

Grassland

Forests

y = 36.4x - 9.62, R2 = 1

y = 49.9x - 14.84, R2 = 0.06

y = 52.6x - 16.26, R2 = 0.06

y = 56.2x - 16.56, R2 = 0.14

Figure 5.3 Arrenhius reaction rate plots for ozone deposition to various surfaces: stainless steel, Aluminium foil, paraffin wax

and beeswax from Cape et al in press; moorland Fowler et al 2001; potatoes Coyle et al 2008b; Grassland and Forests, Coyle et al

2008a.

Cape et al (2009) also tested the hypothesis that the surface reactivity of vegetation may be

enhanced by reaction biogenic volatile organic compounds (BVOCs) dissolved in cuticular

waxes and showed no enhancement due to surface reaction. However ozone does react in the

gase phase with BVOCs emitted by vegetation, with gas-phase reaction rate coefficients varying

between 10-18 to 10-16 cm3 molec-1 s-1, potentially leading to apparent non-stomatal deposition

velocities of similar magnitude to those measured in the field (Coyle 2005). The emission of

BVOCs by vegetation is also light and temperature dependent, increasing with both parameters

which may explain some of the variation in non-stomatal deposition. Nevertheless, most

measurements indicate that the concentrations or reactivity of the emitted BVOCs are not

sufficient to explain the magnitude or variation in non-stomatal deposition in all circumstances.

In can be concluded that although BVOCs may play a part in the non-stomatal deposition they


are only significant for species that emit currently unidentified compounds that react very rapidly

with ozone (Hogg et al, 2007).

Although some studies have shown surface water inhibits ozone deposition to a vegetated

canopy the consensus is now that it increases deposition (Hogg et al 2007, Coyle et al 2008a) in

most circumstances. Although Fuentes (1992) found more organic compounds in water from

maple leaves compared to poplar, specific compounds that may be responsible have not been

identified. Coyle et al (2008b) suggested that non-stomatal deposition is governed by three main

regimes: ozone deposition increasing as the temperature and solar radiation increases on a dry

surface due to thermal decomposition; decreased deposition on surfaces with a thin film of water

present as thermal decomposition is blocked by the water film; enhanced deposition on a fully

wetted surface due to aqueous reactions in the water.

5.4 Model development and validation

The recent European measurement data on ozone fluxes have been used for testing and

improving the DO3SE deposition module, which has been incorporated into the Unified EMEP

CTM developed for European policy-making applications (Tuovinen et al., 2001, 2004;

Emberson et al., 2007). DO3SE makes it possible to calculate stomatal and non-stomatal ozone

fluxes to different vegetation types, taking into account both plant phenological and

meteorological factors (irradiance, temperature, humidity, soil moisture) on an hourly basis

(Simpson et al., 2007; Emberson et al., 2000, 2007). Many of the validation studies have been

focussed on the leaf-scale stomatal conductance of a specific plant species only, since

parameterisations of this kind are needed to relate the ozone uptake to plant response (e.g. Pleijel

et al., 2007). There are also smaller-scale CTMs covering a part of Europe, which have been

used for high-resolution mapping of ozone fluxes (e.g. Lagzi et al., 2004), and local-scale soil–

vegetation–atmosphere–transfer models that include ozone (e.g. Grünhage and Haenel, 2008).

Inferential modelling (IM) has been used for calculating regional ozone budgets (Coyle et al.,

2003) and mapping exposures and doses on a high (1–2-km) spatial resolution for national-scale

risk assessment of ozone effects (e.g. Keller et al., 2007).

The global-scale CTMs (Stevenson et al., 2006), as well as many regional models (e.g. Vautard

et al., 2005), typically include a variant of the parameterisation of Wesely (1989). This

parameterisation does not include the stomatal effect of soil moisture conditions, which has been

shown to be a significant modifier of ozone fluxes, especially in the Mediterranean region

(Gerosa et al., 2008), yet proved challenging to model within CTMs (Emberson et al., 2007).


Another key problem with large-scale CTMs is related to the aggregated land cover classes,

making it difficult to parameterise sub-grid processes. For example, the dry deposition module of

the recently developed global Modular Earth Submodel System (MESSy) does not differentiate

between different vegetation types, even though a soil moisture stress function is included

(Kerkeweg et al., 2006).

The non-stomatal deposition processes are parameterised in CTMs and IM systems in a much

cruder way than the stomatal component, typically by defining constant values for the relevant

resistances. However, a preliminary parameterisation has been developed for the EMEP CTM

for surface wetness effects for northern European coniferous forests (Tuovinen et al., 2008). This

parameterisation is derived from the observations of Altimir et al. (2006) and represents

enhancing surface sink with increasing surface wetness, parameterised as a function of relative

humidity. This results in higher and more variable ozone removal rates within the model. In the

future, integrated models are needed for coupling the surface exchange of energy, carbon and

trace gases. In particular, a multi-layer structure facilitating an explicit simulation of vertical

mixing and other in-canopy processes would be useful for interpreting flux measurements (e.g.

Duyzer et al., 2004; Simon et al., 2005).

5.5 Risk assessment methods

European abatement strategies are founded on effects-based approaches, which involves

different numerical indicators for different air pollution effects on vegetation and human health

(UNECE, 2004). For potential ozone effects on vegetation, the AOTX (Accumulated exposure

Over a Threshold of X ppb) exposure index replaced simpler concentration averages in the 1990s

(Fuhrer et al., 1997). Present definitions also include a metric based on the stomatal uptake flux,

AFstY (Accumulated stomatal flux Fst above a threshold of Y nmol m-2 s-1), which represents the

absorbed dose and is thus considered biologically more meaningful than the concentration-based

AOTX (UNECE, 2004). AFstY is more complex than AOTX in that it entails modelling the

stomatal conductance.

AOT40 is still the most common risk indicator used in Europe for setting environmental

objectives and defining the so-called critical levels. For a proper application of AOT40, ozone

concentration must be known at the height of the canopy top (UNECE, 2004). This means that

the concentration determined above this height (as is typically the case with measurements) must

be transformed to the correct reference height, because of the deposition-sink generated vertical

concentration gradient; a failure to correct for this gradient may seriously overestimate the risk


metrics (Tuovinen and Simpson, 2008). Even if the profile correction compensates for a

significant bias, the near-surface concentration may prove a poor surrogate for the effect-

inducing flux. High ozone concentrations are frequently connected with conditions that

potentially limit the stomatal uptake, such as high temperature and VPD (e.g. Solberg et al.,

2008). This co-variation means that the concentration at leaf surface is not necessarily connected

with a proportional stomatal uptake and therefore plant response (Cieslik, 2004). This is one

reason for the different accumulation rates of AOT40 and stomatal uptake observed in many

studies (Fig. 5.4).

Figure 5.4 Accumulation of AOT40 and stomatal dose over a Holm oak forest (6 August – 11 October) in 2003 and

2004 in Italy (Gerosa et al., 2008).

As a compromise between the less data-intense exposure indices and the more realistic dose

metrics, solutions based on an ‘effective’ concentration have been suggested (e.g. Gerosa et al.,

2004; Pihl Karlsson et al., 2004; Pleijel et al., 2004). Common to all these is the idea that the

concentrations for AOTX are first modified to accommodate environmental factors controlling

stomatal uptake, such as VPD in the definition of the modified AOT30 (Pihl Karlsson et al.,

2004). In some cases, all the main modifiers, such as those in the DO3SE model, are considered

(Gerosa et al., 2004; Pleijel et al., 2004). However, this approach is very close to actually

calculating the stomatal flux, as it involves a multiplicative stomatal conductance model. Related

to this observation, it is worth noticing that even the profile corrections required for AOTX are

based on flux–gradient relationships of ozone and thus entail deposition modelling (Tuovinen

and Simpson, 2008). From a micrometeorological point of view, it would thus appear natural to

aim at developing accurate parameterisations for partitioning the total ozone flux into the

stomatal and non-stomatal components and applying flux-based risk metrics because of their

superior biological basis.

The model calculations of Simpson et al. (2007) indicate that AOT40 and AFstY (for both crops

and forests) show very different regional patterns of exceedance of critical levels across Europe


with much smaller south–north gradients and larger exceedance area for AFstY (Fig. 5.5). Even

though there are still numerous uncertainties involved in this kind of modelling, there is evidence

that the flux-based risk maps better correlate with observed plant damage (Hayes et al., 2007).

Figure 5.5 Ratio of the AOT40 (left) and AFst1.6 (right) risk metric to the corresponding critical level for forests

(not shown for values < 1) (Simpson et al., 2007).

5.6 Potential effects of climate change

Changes in the climatic conditions and chemical composition of the atmosphere are expected to

have a wide range of effects on the interactions between tropospheric ozone, itself a GHG, and

the biosphere. Firstly, ozone exposures are changing globally due to changes in its precursor

emissions. Secondly, the long-term changes in meteorological conditions affect atmospheric

transport patterns and the rates of tropospheric chemical reactions and dry deposition processes,

and also modify plant phenology. In addition to the rising temperature and changes in

precipitation distribution, elevated CO2 and ozone concentrations may act as significant

modifiers to stomatal exchange (e.g. Ashmore, 2005). Finally, the characteristics of vegetation

cover and land use may be altered on various scales as a result of human activities, effects of

climate and ozone on plant species composition and ecosystem function, and natural

disturbances, all of which potentially generate feedbacks to the surface removal of ozone.

So far only a few studies have addressed the projected changes beyond the ozone precursor

emissions and atmospheric dynamics. In the multi-model ensemble simulations of future

concentrations by Stevenson et al. (2006), the projected Vd was only altered by the

meteorological responses, neglecting the effects of water stress and elevated CO2 concentrations,

for example. In some studies, individual, uncoupled effects have been included in the models.

For example, Sanderson et al. (2007) investigated the impact of the stomatal conductance


changes as directly induced by the rising CO2 levels. In this experiment, a global CTM was run

with an assumed reduction in Gst due to a doubling of CO2 concentrations, but with no changes

in the meteorological input data. This resulted in a decreased dry deposition sink, which

increased the near-surface ozone concentrations by 2–8 ppb on a seasonal basis. However, a

reduction in Gst, due to increased CO2 or any other effect, does not proportionally reduce the

stomatal uptake flux. This results from the effect of decreased surface removal on the overall

mass balance, with higher concentrations partly compensating for the suppressed Gst.

While it would seem plausible that in warm climates increasing temperatures reduce stomatal

exchange, the opposite is true for cooler regions. However, based on a modified version of the

DO3SE model, Karlsson et al. (2007) concluded that in northern Scandinavia the most significant

impact of the higher temperatures may be related to an earlier onset of the growing season and

the associated phenological development, rather than their direct enhancement of Gst. This

together with elevated and increasing ozone concentrations in spring may amplify the risk of

negative ozone effects on vegetation in these areas. On the other hand, there may be a

counteracting effect on the stomatal uptake mediated by the concurrent higher VPD (Karlsson et

al., 2007; Harmens et al., 2007).

The summer of 2003 was exceptionally warm in Europe, especially in the central part of the

continent, and may be taken as an analogue of the future summers to be expected in the latter

part of the 21st century. A series of heat waves produced meteorological conditions highly

favourable for the net formation of ozone and its build-up over large areas; indeed, record-high

near-surface concentrations were observed in many locations (Solberg et al., 2008). It is very

likely that reduced dry deposition played a significant role in the formation of the ozone episodes

in 2003. The high temperatures and soil moisture deficits (SMDs) most probably decreased the

Gst of vegetation and thus ozone removal from the atmosphere in central and southern Europe, as

indicated by the Italian eddy covariance measurements (Gerosa et al., 2008) discussed above.

Fig. 5.4 shows that the ozone dose absorbed by a Holm oak forest in August–September 2003

was less than 50% of that during the same period in 2004, in spite of the much higher

concentrations in 2003.

The sensitivity runs with a global CTM by Solberg et al. (2008) demonstrate the potentially large

effect of dry deposition on near-surface concentrations. Similarly, doubling of Rc within the

modelling study of Vautard et al. (2005), partly because of the expected increase in SMD which

is not taken into account in the deposition parameterisation, improved the model performance by


increasing the modelled concentrations. Considering the accumulation of ozone dose of plants

over the whole growing season, the significance of drought periods much depends on their

timing with respect to the phenological stage of plants and the occurrence of elevated ozone

concentrations (Harmens et al., 2007). In addition, prolonged drought stress may result in

sustained impairment of the hydraulic conductivity of plants, challenging the traditional dry

deposition models (Gerosa et al., 2008).

It has been estimated that exposure of plants to even the current levels of ozone may

significantly increase water use of forest trees (McLaughlin et al., 2007) and reduce plant

productivity in the most polluted areas of the world, with exacerbating effects projected for the

future (Felzer et al., 2005). With elevated ozone concentrations, the reductions in carbon

sequestration may also lower soil carbon formation rates and alter the below-ground carbon

cycling (Loya et al., 2003). Sitch et al. (2007) suggest that the ozone-induced suppression of the

global land-carbon sink gives rise to additional accumulation of anthropogenic CO2 emissions in

the atmosphere and thus should be considered an indirect radiative forcing, which could exceed

the direct radiative forcing due to ozone increases.


6. Biogenic volatile organic compounds (BVOC) 6.1 Introduction

Biogenic volatile organic compounds (BVOC) are emitted by almost all plants. In higher plants,

emissions range from close to zero to 10-20% of the carbon fixed by photosynthesis. Global

emission is estimated at around 800 Tg C y-1 although this figure has been regularly revised

based on improvements in scaling up of laboratory results, spatial and temporal integrations, and

large scale monitoring at whole ecosystem levels (Lathiere et al., 2006; Arneth et al. 2008).

About half of the emissions are believed to be isoprene (Guenther et al. 2006). Monoterpenes,

the other large class of volatile isoprenoids, contribute another 10-15 % of the total BVOC

emissions. Sesquiterpenes, a third class of isoprenoids, are emitted in small quantities from non-

stressed vegetation, except from flowers. The remainder is emitted as oxygenated volatile

compounds, including alcohols, aldehydes and ketones, particularly during certain periods of

plant development or in response to environmental stress (Heiden et al., 2003; Seco et al., 2007).

6.1.1 Volatile isoprenoids

Volatile isoprenoids have been extensively studied compounds because of their relevant

functions in plant vs. environment interactions and their role in the atmosphere. Isoprene and

monoterpenes are formed in plastids via methylerythritol phosphate (MEP) pathway

(Lichtenthaler, 1999).

While the isoprenoid emissions mainly rely on newly synthesized photosynthetic metabolites in

chloroplasts, extra-chloroplastic sources can feed carbon to sustain isoprene or monoterpene

biosynthesis, including xylem-transported sugars and chloroplastic starch (Karl et al., 2002a;

Kreuzwieser et al., 2002;) as well as refixation of respired CO2 (Loreto et al., 2004). These

additional carbon sources of isoprenoid biosynthesis can become significant especially when

photosynthesis is constrained by environmental stresses. Under extreme stress conditions, such

as drought stress, the leaf carbon budget can become negative, as leaves release more carbon in

the form of isoprenoids and respiratory CO2 than they gain through photosynthesis (e.g. Brilli et

al., 2007).

Monoterpenes and sesquiterpenes are active compounds in plant interactions with other

organisms. Monoterpenes may either attract or deter herbivores or carnivores, and attract

pollinators (Gershenzon and Dudareva, 2007). Indirect evidence suggests that isoprene and

monoterpenes as lipid soluble molecules may protect plant membranes from thermal and


oxidative stress (reviewed by Sharkey and Yeh, 2001). This favourable action can assist in

adapting isoprenoid-emitters to increasing oxidative pressures (Lerdau, 2007).

6.1.2 Oxygenated volatile compounds

While the production and emission of volatile isoprenoids, in particular isoprene and

monoterpenes, is strongly species-specific, all plants emit oxygenated volatiles. At the global

scale, the emission source strength of these compounds is generally lower than that for volatile

isoprenoids, and many of these oxygenated compounds are less reactive than isoprenoids in the

atmosphere. However, the emissions of oxygenated compounds, which may be induced by

developmental and stress factors, may be large at certain periods of the year and by certain

vegetation types (see also flux measurements below).

Methanol, acetaldehyde and C-6 compounds are often emitted in large quantities, especially in

the presence of mechanical wounding or other stresses (Loreto et al. 2006). Methanol formation

is likely due to the demethylation of pectins in cell walls (Galbally and Kirstine, 2002) and does

not have any known protective role for plants. The release of methanol into the atmosphere is

therefore associated with cell wall damage occurring because of wounding (Karl et al., 2001a).

Methanol is also emitted by growing plant tissues (Nemecek-Marshall et al., 1995; Harley et al.,

2007; Hüve et al., 2007), and senescing tissues (Fall, 2003). Large fluxes of methanol could be

measured, as an example, from rapidly expanding leaves of the Mediterranean vegetation during

the spring ACCENT-VOCBAS 2007 campaign (see below).

Large fluxes of acetaldehyde have been observed in conditions of root anoxia such as under

waterlogging stress. Short-lived bursts of acetaldehyde are sometimes also observed from

darkened leaves (Karl et al., 2002b). Interestingly, acetaldehyde is also emitted following

wounding (Fall et al., 1999) and ozone stress episodes, and large fluxes of this compound can be

observed under natural conditions (Lathiere et al. 2006).

Finally, C-6 oxygenated compounds are emitted from leaves subject to various stresses such as

wounding, e.g. as a consequence of cutting hay, insect feeding, ozone stress and heat stress

(Hatanaka, 1993). Aldehydes, (Z)-3-hexenal, (E)-3-hexenal and (E)-2-hexenal with characteristic

green leaf (‘cut grass’) odor are formed first, and then transformed into corresponding alcohols

by alcohol dehydrogenases. Esters such as hexenolacetates can be further formed and emitted.

Proton-transfer reaction mass-spectrometry has provided detailed insight into the associated

time-sequence of events (Beauchamp et al., 2005).


6.2 Environmental controls on BVOC emissions

6.2.1 Physiological and physico-chemical controls of emissions

Models of BVOC emissions consider that emissions are controlled either by physiological

factors (“isoprene” algorithm) or by physico-chemical factors (“monoterpene” algorithm)

(Guenther et al., 1993). By considering only physiological factors, the synthesized compounds

are immediately released from the foliage. In contrast, by considering only physico-chemical

factors, the compounds are emitted from specialized storage structures such as resin ducts

present in conifers, glandular trichomes in species from Lamiaceae (peppermint), and oil glands

in Rutaceae (lemon, orange) and Myrtaceae (eucalypts).

Physiological controls operate at the level of compound synthesis. Light and temperature, the

key environmental drivers, affect the rate of intermediate production; temperature also affects the

activity of flux controlling enzymes (Fig. 6.1, Niinemets et al., 1999;) Over longer term, the

synthesis and turnover of rate-limiting enzymes can control the flux rate (Monson et al., 1994;

Lehning et al., 2001; Fischbach et al., 2002). In species with large storage pools, the emissions

can be independent of the rate of compound synthesis, being controlled by temperature effects on

the evaporation and diffusion of compounds from the storage pools (Fig. 6.1, e.g. Tingey et al.,

1991).

Often there is no clear-cut separation between physiological and physico-chemical controls.

Although it is generally belived that only evaporation from pools controls the emissions in

species with specialized storage pools, there is increasing evidence that the emissions can be

partly controlled by physiological factors also in classical “storage” species. On the other hand,

physico-chemical controls on the emissions often interact with physiological controls in species

without specialized storage pools for BVOC (Fig. 6.1)


Figure 6.1 The emission of volatile organic compounds from plants is limited by both physiological and physico-

chemical characteristics. Physiological factors control the rate of synthesis of VOC and operate at the level of

intermediate production and the activity of flux controlling enzymes such as isoprene synthase or terpene synthases

for volatile isoprenoids. Physico-chemical factors, in particular, low compound volatility and rate of diffusion, can

limit the release of synthesized VOC from the leaves and determine the degree to which the synthesized compound

can be stored in the leaves.

6.2.2 Physico-chemical controls of emission in species lacking specific storage structures

Every BVOC species can be non-specifically stored in leaf liquid and lipid phases, with the non-

specific storage capacity depending on the compound physico-chemical traits such as the

gas/liquid aqueous phase partition coefficient (Henry’s law constant, H) and lipid/liquid phase

partition coefficient (octanol/water partition coefficient, KOW). For instance, compounds that are

highly water-soluble such as methanol and ethanol can accumulate in leaf aqueous phase,

especially if gas-phase diffusion out of the leaves is hindered due to limited opening of stomatal

pores. Although the rate of compound synthesis may respond very quickly to environmental

perturbations, a build up of water-soluble compounds reduces the sensitivity of the emission

responses to variation in environmental factors.

Analyses of the emissions of strongly lipid-soluble BVOC species such as non-oxygenated

monoterpenes indicates that the non-specific storage of these compounds, mainly in leaf lipid-

phase, significantly alters the emission kinetics in species lacking specialized monoterpene

storage pools. In these species, to reach steady-state rates of monoterpene emission can take

from minutes to hours depending on monoterpene physico-chemical characteristics (Niinemets

and Reichstein, 2002; Noe et al., 2006).

The delayed emission responses due to non-specific storage can also result in modified

sensitivity of the emissions to environmental factors. For instance, a sigmoidal light-response

curve can result if non-specific storage pools have not yet reached a steady-state with each light

level. Under such experimental conditions, the emission rate recorded is lower than the

monoterpene synthesis rate. Given the long time-periods, often on the order of 1 h required to

reach the steady-state, non-steady-state conditions are common in monoterpene measurements.

In addition to the alteration of the immediate environmental controls, non-specific storage can

modify the emissions at daily and weekly time-scales. Another important implication of the non-

specific storage is significant nocturnal emissions, which was observed e.g. in the case of

monoterpenes (Loreto et al. 2000; Niinemets et al., 2002b). Simulation analyses indicate


substantial nocturnal emissions at the ecosystem scale (Fig. 6.2). Significant night-time BVOC

emissions from non-specific storage can have large influence on OH-radical concentration and

atmospheric reactivity in morning hours. Current steady-state models predict zero nightime

monoterpenes emissions for species lacking specialized storage structures.

Figure 6.2 Monoterpene emissions from Quercus ilex dominated forest in Castelporziano, Italy for six days in June

simulated using the standard Guenther et al. (1993) model and a model considering non-specific monoterpene

storage in leaf liquid and lipid pools (modified from Niinemets and Reichstein, 2002; Niinemets, 2008). The

standard emission model predicts that the emission rate responds immediately to changes in light and temperature,

but non-specific storage of lipid-soluble non-oxygenated and water-soluble oxygenated monoterpenes results in

time-lags between terpene synthesis and emission. As the result of these time-lags, the emissions are predicted to

continue also at night, although synthesis has ceased.

6.2.3 Uptake and release of volatile compounds by vegetation

A relevant implication of the non-specific storage is the uptake of volatile compounds from

ambient air when air concentrations are higher than those in equilibrium with plant liquid and

lipid phases. The compounds taken up during the periods with high atmospheric BVOC

concentrations may be released back into the ambient air when air concentrations are small if

they are not metabolized or translocated to the roots (Bimmeet et al. [date]). The uptake of

water-soluble compounds is expected to scale with leaf water content, while the uptake of lipid-

soluble compounds with leaf lipid content (Noe et al., 2008a). Thus, even species considered

“non-emitting” can emit several BVOCs at trace level from the non-specific storage built up

from ambient sources.


6.2.4 CO2-dependence of emissions

The classical Guenther (1993) algorithm considers strong light and temperature dependencies of

isoprene and “non-stored” monoterpene emissions. CO2 was not considered as an important

factor in controlling the emission of volatile isoprenoids. More recent laboratory and field

studies have established that volatile isoprenoids are sensitive to ambient CO2 and that the

emissions decrease in plants grown at CO2 concentrations higher than ambient (Loreto et al.,

2001; Rosenstiel et al., 2003). This effect may be observed at CO2 concentrations that are likely

to be reached in the future (double or less than double the current concentrations) and should be

therefore addressed in future modeling efforts.

There is now sufficient information to believe that the negative effect of CO2 on isoprene

emission is ubiquitous and not species-specific. Arneth et al. (2008) included empirically CO2-

dependence into Niinemets et al. (1999) model, and predicted that CO2 reduction of isoprene

emission could partially compensate for the emission increases with rising temperature.

However, elevated CO2 will enhance photosynthesis and growth of plants, in particular

vegetation leaf area, which may in turn increase the emission of isoprene by vegetation. The net

effect of these interactions await experimental confirmation.

Monoterpene emissions are also likely to be influenced by CO2, but there is less experimental

evidence than for isoprene (e.g. Loreto et al., 2001). More studies are clearly needed to assess

whether the different physico-chemical controls (see above) and the presence of small internal

pools buffer the effect of CO2 and make monoterpenes less sensitive to CO2 control.

6.2.5 Induced emissions

In addition to the constitutive emissions, recent work demonstrates that synthesis of volatile

isoprenoids is induced in many species in response to biotic (e.g. attack of herbivores and

pathogens) and abiotic (e.g. ozone stress, heat stress) stresses (Beauchamp et al., 2005; Blande et

al., 2005; Loreto et al. 2006).

Previous work has shown that the emission of volatile organic compounds is induced in response

to stress practically in all plant species, also in those not emitting volatile isoprenoids under

optimal growth conditions (e.g. tobacco and sunflower Heiden et al., 1999; Beauchamp et al.,

2005). In constitutively emitting species, the volatile isoprenoid blend differs between induced

and constitutive emissions. For instance, European aspen (Populus tremula) emits isoprene as


the main product in non-stressed conditions, but induced emissions are dominated by

monoterpenes limonene and α-pinene and sesquiterpenes (Blande et al., 2005). In non-stressed

conditions, the emissions of the monoterpene-emitting species Pinus pinea are dominated by

limonene, but the emissions in conditions of high temperature and low water availability are

dominated by linalool and ocimene; these emissions significantly exceed the emission in non-

stressed conditions (Staudt et al., 2000).

Current emission models do not consider “non-emitting” species, which, in addition to emissions

from non-specific storage, may have significant induced emissions. Furthermore, modification of

gene expression profiles in response to stress and upon adaptation to stress may in many cases

explain the modified emission compositions. Accordingly, understanding induction mechanisms

and consideration of induced emissions is crucial in explaining and predicting emission profiles.

6.3 Contemporary difficulties in scaling BVOC emissions from leaf to ecosystem

Parameterization of models at ecosystem scales is bound by a series of uncertainties. A key

uncertainty is currently the lack of reliable information of emission potentials (Arneth et al.,

2008). Although the available emission information has been recently collated, data for many

key emitting species are still lacking. Recent observations have indicated that several important

species such as cork oak (Quercus suber) (Staudt et al., 2004; Pio et al., 2005) and European

beech (Fagus sylvatica) (Moukhtar et al., 2005; Holzke et al., 2006) previously considered non-

emitters are moderate to strong emitters of monoterpenes.

The emission potentials of many ornamental, alien species used in urban landscaping and in

gardens are missing (Owen et al., 2003; Noe et al., 2008b). Understanding the emissions of

ornamental species is further important in light of potential changes in vegetation in urban

landscapes driven by global change and the growth of urban areas. Global warming and

associated increase of evergreen emitting exotic species in northern urban landscapes of northern

hemisphere can importantly enhance the winter emissions (Niinemets and Peñuelas, 2008).

In addition, stress- and time-dependent modifications of emission potentials are only partly

understood, but such adaptive responses can vastly affect ecosystem fluxes. Apart from gradual

changes in BVOC emission capacity in response to day-to-day and seasonal differences in

weather conditions (Guenther, 1999; Sharkey et al., 1999), emissions triggered by biotic stresses

such as herbivory or pathogen attack or by abiotic stress factor such as elevated ozone


concentrations (e.g. Beauchamp et al., 2005), can potentially greatly influence whole ecosystem

fluxes.

6.4 BVOC fluxes over Europe, by compound and in relation to the needs of

photochemical oxidant models

6.4.1 Flux measurement techniques

The development of disjunct eddy covariance (DEC) methods (Rinne et al., 2001;) alongside the

development of proton transfer reaction – mass spectrometry (PTR-MS, Lindinger et al., 1998)

has enabled direct flux measurements of multiple VOC species simultaneously. The PTR-MS

has also been used in a more traditional continuously sampling eddy covariance technique, in

this mode the flux of just one VOC species can be measured at a time (Karl et al., 2001bc). The

DEC methods have been utilized in different European ecosystems (Rinne et al., 2007; Davidson

et al., 2008ab). Intercomparison experiments to validate these new techniques have been

conducted partly under ACCENT-BIAFLUX (Ammann et al., 2006; Neftel et al., 2007; Rinne et

al., 2008). For isoprene there also exists a fast isoprene sensor (FIS) based on

chemiluminescence enabling the application of eddy covariance measurements (Guenther and

Hills, 1998).

High reactivity of several plant BVOCs imposes significant difficulties in determining whole

canopy terpene emission fluxes by micrometeorological techniques. In particular, some

monoterpenes and most sesquiterpenes have atmospheric lifetimes on the order of minutes. High

reactivity of these compounds can imply that before reaching the BVOC detector, a significant

fraction of the emitted compounds has already reacted in the atmosphere, resulting in

underestimated emission fluxes (Rinne et al., 2007). To determine the emissions of reactive

terpenes, atmospheric chemistry models have been inverted (Bonn et al., 2007). However, the

lack of reactive rate coefficients for many terpenes and the dependencies of these on humidity

and temperature seriously hamper the overall assessment of the rates of emission and

contribution to atmospheric reactivity.

6.4.2 Isoprene

Isoprene is the most studied biogenic VOC. European isoprene emissions have been studied at

the ecosystem scale for various ecosystems. In some European ecosystems considerable isoprene

emission fluxes have been measured (Ciccioli et al., 1997; Davidson et al., 2008b). In contrast

European boreal coniferous forests are generally very low emitters (Rinne et al., 1999;). In


measurements conducted above homogeneous conifer forest, the isoprene emissions from

isoprene emitters concentrated in e.g. ditches, lakeshores and roadsides, will not be observed.

Landscape scale emissions over southern Finland, estimated by a boundary layer budget method,

show much smaller isoprene than monoterpene emissions (Spirig et al., 2004). In general,

measuring forest sites, characterised by mixed composition, must account for the plant species

composition within the flux footprint. This has been shown by Spirig et al., (2005), who

observed the variation in normalized ecosystem scale isoprene emission from a central European

mixed broadleaf forest to be dependent on the abundance of Quercus robur, which is a high

isoprene emitter, in the flux footprint area.

Flux measurements of isoprene often correlate well with leaf-level measurements, reflecting the

relatively long atmospheric lifetime of isoprene (~1 hour) compared with other emitted BVOCs.

However, when the ecosystems are not homogenous, correspondence between emissions at plant

and ecosystem levels may not be straightforward. For example, the study conducted at Siikaneva

fen ecosystem using soil chambers (Hellén et al., 2006) and REA technique (Haapanala et al.,

2006) shows a considerable discrepancy between the isoprene emissions measured by these two

techniques. The fluxes measured by Haapanala et al. (2006) under low CTCL values were

typically lower than the model values, which may imply deeper penetration of PAR into the

moss carpet at high light conditions; a simple light penetration model slightly improved the

correlation (Haapanala et al. 2006).

6.4.3 Monoterpenes

Many European ecosystems emit considerable amounts of monoterpenes into the atmosphere and

thus many flux measurement experiments have been concentrated on these compounds. The

diurnal variations of the monoterpene fluxes above boreal coniferous forests appear to be

relatively well reproduced by the temperature dependent Tingey-Guenther emission algorithm

(Guenther et al., 1993). Monoterpene emissions from Mediterranean ecosystems are better

described by the light and temperature dependent isoprene emission algorithm (Seufert et al.,

1997; Schween et al., 1997), with important discrepancies likely reflecting non-specific storage

and stress-dependent changes in emissions (Niinemets et al., 2002a; b, see above).

At coniferous forest sites a diurnal concentration cycle with highest concentrations at night are

typical (e.g. Hakola et al., 2000; Steinbrecher et al., 2000; Rinne et al., 2005). This is due to the

emission continuing during night time, although at a lower rate than during the day, and to

considerably reduced turbulent mixing at night. On the contrary, in the Mediterranean region, as


well as in Amazonian tropics, and in European mixed broadleaf forest, where the night time

monoterpene emission is practically zero due to the light dependence of the monoterpene

emission, daytime maxima are typical (Zimmermann et al., 1988; Schween et al., 1997; Rinne et

al., 2002, Spirig et al., 2005).

The major monoterpenes emitted by forest ecosystems in Europe seem to be α- and β-pinene and

∆3-carene. For example, monoterpene emissions from Norway spruce forest consist of α- and β-

pinene (Christensen et al., 2000; Gallagher et al., 2000). However, in some Mediterranean

ecosystems limonene can form a significant part of the monoterpene emission (Schween et al.,

1997) and, in the case of orange orchards, be the dominant monoterpene emitted (Christensen et

al., 2000; Darmais et al., 2000).

6.4.4 Sesquiterpenes

Due to the enhanced chemical reactivity of sesquiterpenes, the atmospheric concentrations of

these compounds are very low, making flux measurements extremely difficult. Enclosure

measurements at an orange orchard revealed substantial emission of β-caryophyllene, while the

flux measurements conducted by REA method show the fluxes to be close to zero indicating

rapid within and below canopy chemical degradation (Ciccioli et al., 1999).

6.4.5 Methanol

The development of the DEC-PTR-MS has recently enabled ecosystem scale flux measurements

of methanol leading to considerable progress in our knowledge of ecosystem scale emission of

this compound. Methanol seems to be emitted from all ecosystems and also from drying and

decaying plant material. Methanol is typically the second or third most abundantly emitted

biogenic VOC after isoprene and monoterpenes in most ecosystems.

The measured ecosystem scale methanol emissions have generally not been fitted to emission

algorithms, in the same way isoprene and monoterpene fluxes have been modelled. Only a recent

empirical temperature dependent formulation has been presented (Harley et al., 2007). Based on

a mechanistic understanding of the physico-chemical control (see above), methanol emission

should be regulated by stomata, in contrast to isoprene and monoterpene emission. In field flux

experiments, this kind of behaviour has not been generally observed. Brunner et al. (2007) have

observed the methanol emissions from agricultural grassland ecosystem to be relatively high in

the morning as compared to the emission in the evening. A similar observation was found in the


recent ACCENT-VOCBAS campaign on the Mediterranean macchia of Castelporziano (Davison

et al. 2008b).

6.4.6 Acetone and acetaldehyde

Acetone and acetaldehyde in the atmosphere are the result of both primary emissions from

biogenic and anthropogenic sources and secondary formation from other gaseous precursors.

Fluxes of these carbonyls have been observed to be emitted from coniferous forests in Europe

(Rinne et al., 2007) as well as in the US (Schade and Goldstein, 2001;). Also the Mediterranean

macchia ecosystem is observed to emit these compounds (Davidson et al., 2008b). No emission

of these compounds from broadleaf deciduous forests has been reported. Anaerobic conditions in

root system have been observed to enhance the emission of acetaldehyde from plant foliage

(Kreuzwieser et al., 1999) as also mechanistically explained above. However, no ecosystem

scale flux measurement of this compound, in conditions where the root systems was anaerobic,

has been reported.

6.4.7 Other compounds

The emissions of many compounds, other than isoprene, monoterpenes, methanol, acetone and

acetaldehyde, have been too small to be measured by micrometeorological flux measurement

techniques. However, there are a few other compounds which have been observed to be emitted

by micrometeorological flux measurement techniques in certain ecosystems. Some western US

pine forests have been shown to emit 2-Methyl-3-buten-2-ol (MBO) in considerable amounts

(e.g. Baker et al., 1999; Schade et al., 2000). From European pines no significant emissions of

MBO have been observed (e.g. Hakola et al., 2006). It is noteworthy that drying hay was

observed to emit (Z)-3-hexenal and (Z)-3-hezenol and hexenyl acetates (Davidson et al., 2008a).

These compounds appear to be reliable markers of membrane damage and lipoxygenation, as

previously indicated (Loreto et al. 2006).

6.5 The EU large field campaigns in the Mediterranean area: from BEMA to ACCENT

Pioneering campaigns around the world (e.g. the SOS 1999 campaign in North America, the

LBA/CLAIRE 1999 campaign over the Amazon, the SAFARI 2000 campaign in Southern

Africa) highlighted the predominance of isoprene as the most commonly emitted VOC by

vegetation, over different environments, and by different ecosystems, spanning from tropical to

boreal forests. In Europe, however, a different picture emerged. The BIPHOREP 1996-1997

campaign in boreal Europe revealed the dominance of monoterpenes as the most abundantly

emitted VOC from European boreal ecosystems. In otherwise clean northerly air masses, at least


the growth, if not the formation, of organic aerosol, seems to be heavily affected by the presence

of biogenic VOCs. The most likely candidates contributing to this aerosol growth are

monoterpene oxidation products (Lee et al. 2006). The BEMA (Biogenic Emission in the

Mediterranean Area) 1996 campaign highlighted the remarkable peculiarity of Mediterranean

coastal vegetation that is almost uniquely characterized by monoterpene-emitting species (Loreto

et al., 1998a). The reason for this distinctive trait of Mediterranean vegetation remains unknown.

A new campaign was organized during spring 2007 in the Mediterranean area, funded by the

European Commission project ACCENT and by the European Science Foundation programme

VOCBAS. The campaign was deliberately held on the same site of the BEMA campaign, the

large peri-urban natural preserved area of Castelporziano, in the conurbation of Roma. This site

has two main characteristics that make it an excellent case of study for biosphere-atmosphere

interactions. First, the 6000 ha wide preserved area of Castelporziano is a hot spot for

biodiversity in the Mediterranean, with more than 1000 plant species represented in the flora of

the area. The main ecosystems going toward the sea are characterized by oak (Quercus ilex,

Quercus suber, Quercus cerris) and pine (Pinus pinea) forests, often associated with a rich

understory vegetation. The part of the Estate facing the Tyrrhenian sea is characterized by sand

dunes and a humid retrodunal area, with a large and extremely well preserved area covered by

Mediterranean “macchia” vegetation, prevalently shrubs and small evergreen trees, such as

Juniperus communis, Quercus ilex, Phillyrea latifolia, Arbutus unedo, Rosmarinus officinale,

Erica arborea, and Cistus incanus. Second, the preserved area is only distant 25 km from the

centre of Roma in the S-E direction. It is exposed to a constant wind circulation that favours

transport of air masses from the city center during night-time, and from the sea during daytime.

This periodically exposes vegetation to urban pollutants and may trigger formation of secondary

pollutants that are contributed by BVOC precursors (Chameides et al., 1988; Di Carlo et al.,

2004).

This ACCENT-VOCBAS campaign (i) provided fluxes of BVOC from Mediterranean

vegetation, in a season during which plants are in optimal physiological conditions prior to

drought and heat stress conditions experienced later in the year, but during which BVOC

emisions are thought to be constrained by leaf development limitations; (ii) investigated, by

coupling concentration and flux measurements, the in situ extent of BVOC reactivity, and in

particular whether in situ BVOC oxidation could drive formation of secondary organic

compounds in the atmosphere; (iii) identified whether BVOCs can act as precursors of

photochemical smog; (iv) provided a second assessment, ten years after the campaign organized


by the EC-BEMA project (Seufert et al. 1997), of BVOC emission in an area largely affected by

anthropogenic and climatic changes, thus creating the foundation for a historical series of

measurements which may be especially important in view of current and future climate change

factors, and, in particular, of the simultaneous and strong increase of temperature, drought and

pollutants in the Mediterranean area; and (v) fostered interdisciplinary collaboration between the

communities of biologists, atmospheric chemists and physicists, further catalyzing research on

the important roles of BVOCs in the environment.

The campaign was run in the macchia strip placed between the dunes and the main forested land

inside the Castelporziano Estate. The oak and pine forested area was extremely well

characterized during the BEMA campaign (1997) but the macchia vegetation received only

limited attention (Owen et al., 1997). The macchia strip is characterized by a modest roughness

of the terrain due to the presence of small sandy dunes, and by a gradient of soil humidity due to

the presence of freshwater sources. Both features were considered to be irrelevant for flux

measurements during the experimental period that was characterized by optimal water supply

conditions for the plants. The campaign was organized and coordinated by the Consiglio

Nazionale delle Ricerche of Italy (CNR) and was run by ten European groups with different

tasks, spanning from measurements of CO2, H2O and BVOC exchanges at leaf level, to flux

measurements of the same parameters and of pollutants above the entire ecosystem. To achieve

flux measurements, a series of scaffolds were erected, in the middle of the fetch used for flux

measurements, which were equipped with sensors and inlets for instrumentations that was

located on shelters at the bottom of the scaffolds.

6.6 Remote sensing of BVOC

Over the last decade space-based instrumentation, capable of probing the lower troposphere, has

reached the level of accuracy necessary to quantify surface sources and sinks of trace gases from

observed variations in trace gas concentrations (Palmer, 2008). The only non-methane BVOC

measured in the lower troposphere from space is formaldehyde (HCHO), a high yield product of

VOC oxidation that is measured from clear-sky backscattered solar radiation at ultraviolet

wavelengths. The main sinks of HCHO are oxidation by OH and photolysis leading to a

tropospheric lifetime of several hours. Figure 5.3 shows the global distribution of HCHO

columns observed by the Ozone Monitoring Instrument during August 2006.


Figure 6.3 Formaldehyde columns (1016 molec cm-2) retrieved from the NASA Aura Ozone Monitoring Instrument

(OMI) for August 2006, courtesy of Thomas Kurosu, Harvard Smithsonian Center for Astrophysics.

Oxidation of methane (CH4) by OH, the largest global source of HCHO, provides a uniform

HCHO background of ~100 pptv, reflecting the 8-year lifetime of CH4. The limit of detection of

HCHO from current space-borne instrument is approximately 4x1015 molec cm-2 (Chance et al,

2001), which is close to the source of HCHO from CH4 oxidation. Over the continental boundary

layer, oxidation of anthropogenic and biogenic VOCs provide an additional source of HCHO

that can reach on a local scale up to several ppbv, equating to columns over an order of

magnitude determined by CH4 (Figure 6.3). Observed variations in HCHO, determined by the

oxidation of VOCs, therefore provide constraints on emissions of the parent VOCs. Horizontal

transport smears the local relationship between VOC emissions and HCHO columns, the extent

of which is determined by wind speed and the time-dependent yield of HCHO from the VOC

oxidation (Palmer et al, 2003). Over a number of global regions, variations in HCHO columns

are determined by isoprene (Palmer et al, 2006;), due to its rapid production and high molar yield

of HCHO (Palmer et al, 2006). Other reactive biogenic VOCs, such as monoterpenes, also have

short atmospheric lifetimes but they quickly produce acetone with a high yield that has an

atmospheric lifetime of weeks and consequently slows down the production of HCHO (Palmer et

al, 2006). Long-lived VOCs such as CH4 and CH3OH, while being the largest sources of HCHO,

only contribute to the slowly varying background of HCHO.


Early work showed that the magnitude and distribution of GOME-derived isoprene emissions

based on HCHO measurements were more consistent with in situ measurements than either the

GEIA or BEIS2 isoprene inventories, based on Guenther et al, 1995 (Palmer et al, 2003).

Monthly mean distributions of HCHO, and inferred isoprene emissions, during summertime are

dominated by high values over the southeastern states of the USA (Chance et al, 2001) due to a

large density of isoprene-emitting oak trees over the Ozarks Plateau ( Wiedinmyer et al, 2005).

Examination of GOME orbital data revealed large variations in HCHO, explained by changes in

surface temperature, which led to inferred monthly mean isoprene emissions that were

significantly lower than those predicted by bottom-up models (Palmer et al, 2003). Later work

showed that the observed seasonal and year-to-year variability was consistent with the MEGAN

model (Guenther et al, 2006), but GOME-derived isoprene emissions were 25% higher (lower) at

the beginning (end) of the growing season (Abbot et al, 2003; Palmer et al, 2006). Both MEGAN

and GOME show a maximum over the Southeast US but disagree in the precise location, with

implications for modelling surface ozone (Fiore et al, 2005).

Isoprene emissions from tropical ecosystems have been estimated to contribute 75% of the

global isoprene budget, but are not well quantified. Over tropical South America, the widespread

extent of biomass burning in the dry season means that without high-resolution data the only

practical approach is to use data over west Amazonia, which is largely unaffected by fires

(Barkley et al, 2008). There is a strong seasonal cycle of GOME HCHO columns over this

region reproduced each year during 1996-2001, characterized by large values in the wet and dry

seasons, separated by low values in the wet-to-dry transition period (May-July); this is consistent

with in situ isoprene concentration measurements (Palmer et al, 2007). This large-scale reduction

in isoprene emissions suggests a major temporary shift in underlying meteorology or phenology,

but its origin remains unclear. This study found that MEGAN and GOME were in better

agreement in the dry season, when GOME isoprene emissions could not be explained by changes

in surface temperature. GOME isoprene emissions in the wet season could be not explained by

changes in surface temperature, precipitation or soil moisture, suggesting either an unexplained

process that determines isoprene during this season or noisy data (Barkley et al, 2008). These

substantial open questions will be readdressed with data from newer sensors and should be the

subject of extensive year-long ground-based measurement campaigns.

There are a number of uncertainties associated with the HCHO measurement and the approach

used to infer surface emissions of isoprene from these measurements (Palmer et al, 2003, Millet

et al, 2006), which lead to uncertainties that total more than 100% of estimated emissions


(Palmer et al, 2006). Work related to the analysis of GOME data has suggested that HCHO

production predicted by chemical mechanisms typically used by large-scale chemistry transport

models are in error by more than 25% (Palmer et al, 2006).

Current studies that determine VOC emissions using HCHO column data are already focusing on

data from newer space-borne sensors (e.g., Aura OMI and MetOp GOME-2) that have better

spatial (100s km2) and daily temporal resolution, enabling more detailed testing of current

bottom-up inventories. The advances in resolution, in particular, improve: 1) the probability of

cloud-free scenes and consequently lead a greater number of useful data points; 2) spatial

disaggregation of different HCHO sources, e.g., biomass burning and biogenic VOC emissions

over tropical regions, which can lead a better description of land-surface processes; and 3) their

usefulness in planning and executing measurement campaigns. For example, analysis of GOME-

2 and OMI HCHO data, which have local overpass times of 09:30 and 13:30, will allow us to

develop a crude understanding of the diurnal cycle of BVOC emissions on continental scales.

Knowledge of the complex organic chemistry associated with BVOC oxidation will only

improve with a concerted effort to increase the number of laboratory and field measurements.

With the rapid increase in available remotely sensed datasets that could be brought to bear on

estimating BVOC emissions (e.g., HCHO, leaf phenology, land-cover) there should be scope to

develop new functional descriptions of isoprene emission that are independent of the

assumptions made in traditional bottom-up models derived from in situ measurements.


7. Deposition and resuspension of aerosols onto and from terrestrial

surfaces 7.1 Introduction

Aerosols present a complex multi-variate and multi-scale problem to environmental research.

They have strong impacts on climate indeed they represent the largest uncertainties in our ability

to predict climate change (IPCC, 2007), human health and provide a means for pollutant

deposition to sensitive ecosystems (transporting e.g. nitrogen, sulphur and toxic metals). A vital

component of the global aerosol cycle is deposition of many critical secondary process-derived

condensed phase compounds as well as primary generated aerosols, both in the ultrafine and

coarse modes. The majority arise from the consequences of anthropogenic activities associated

with global industrialisation and urbanization where large emissions of reactive nitrogen species

lead to an increase in nitrogen aerosol formation which is eventually removed by wet or dry

deposition. Sulphate aerosol remains an important issue in the Eastern US and China. Over the

last 150 years the atmospheric particulate loading has changed from coal and other solid fuel

burning to modern combustion processes that liberate a greater preponderance of sub-micron,

ultrafine particles. This has initiated a paradigm shift in some quarters of the relative importance

of coarse mode as opposed to fine mode particulates and their behaviour for mainly health

related reasons. This shift will likely move into a third phase where emissions from nano-particle

technologies are starting to play an increasing role. In addition, it is becoming obvious that

particulate matter provides an area of policy conflict: efforts to curb PM concentrations to

protect human health are likely to reduce global dimming and thus further accelerate climate

change, although there are also components, such as soot, that have a negative impact on both

human health and the climate system (cf. Raes et al., this issue). The primary aims of research

into the biosphere / atmosphere exchange of particles are:

(a) to improve our estimates of primary aerosol emissions from diffuse sources and their

parametrisations,

(b) to measure directly the contribution of particle deposition to the deposition of compounds

that are detrimental to ecosystems or may accumulate in water, soils or crops (e.g. N and S

compounds, heavy metals and nano particles),

(c) to derive parametrisations of the deposition velocity (Vd) of particles, for inclusion into

CTMs e.g. aimed at predicting deposition, human health impacts and climate impacts.

(d) to study aerosol formation and dynamics in the atmosphere.

If the models predict the size distribution of the aerosol explicitly, Vd needs to be parametrised as

a function of particle diameter (Dp). By contrast, where the models only deal with the bulk


species, Vd may be derived from measurements of the individual compounds (e.g. Ruijgrok et al.,

1997) or, more commonly, from a weighting of Vd(Dp) with a typical size distribution of the

aerosol component.

7.2 Review of New Measurement Approaches and Instrumentation

Progress in the quantification and parameterisation of surface/atmosphere exchange of particles

and aerosol compounds is closely related to developments in the measurement technology. In the

1970s and 80s measurements of aerosol dry deposition were made in the wind tunnel or using

surrogate surface collectors, such as knife-edge collectors, inverted frisbee type dust deposition

gauges and moss bags. Although these techniques are still being used, they have attracted serious

criticism as their aerodynamic properties are usually not representative for the surface for which

deposition is to be estimated. More recently, non-intrusive micrometeorological flux

measurement techniques have increasingly been extended to measure surface / atmosphere

exchange fluxes at the field scale. Measurements of particle fluxes fall into two categories:

particle number fluxes (total or size-segregated) and chemically resolved aerosol fluxes.

7.2.1 Flux measurements of particle numbers (size-resolved or total), without information on

chemical composition.

These measurements are usually used to derive (size-dependent) deposition velocities which can

be used in atmospheric transport and deposition models. Since the 1980s, fast optical particle

counters (e.g. OPCs such as ASASP-X/555X, Particle Measurement Systems; FAST, Droplet

Measurement Technologies) have been used for eddy-covariance (EC) measurements of size-

resolved aerosol number fluxes (e.g. Duan et al., 1988; Gallagher et al., 1997; Nemitz et al.,

2002a; Sievering, 1983; Vong et al., 2003). The measurements usually cover the size spectrum

between 0.1 to 0.5 µm, but even in this size range the particle statistics of these instruments have

often been marginal in deriving statistically significant fluxes. Several studies have attempted to

extend measurements to smaller and also to larger sizes. Particles significantly < 0.06 µm cannot

be sized with current optical techniques. Instead, recent studies have attempted to measure size-

segregated fluxes of smaller particles, either using the relaxed eddy accumulation (REA)

technique combined with size selection using a differential mobility analyser or interpreting total

particle number fluxes during periods where a certain size-range dominated the flux (Grönholm

et al., 2007; Pryor, 2006). Due to limited counting statistics, fluxes had to be averaged over many

days to obtain robust statistics and these measurement methods are therefore not yet suitable to

study short-term processes (Fig. 7.1). Variability between measurements is likely to be linked to


differences in turbulence, canopy morphology (including leaf area index) and surface roughness

length (cf. Section 7.4.1).

1

2

3

4

56789

10

2

3

Vd

[mm

/s]

7 8 910

2 3 4 5 6 7 8 9100

2

Dp [nm]

Beech forest (mean)dep. only, CPC (Pryor, 2006)

Beech forest (median), dep. only, CPC (Pryor, 2006)

Scots pine, CPC nucleation event (Gaman et al., 2004)

Scots pine, DMA REA (Gronholm et al., 2007)

Figure 7.1. Summary of measured size-segregated particle deposition velocities to forest for particles with

diameters < 100 nm.

Size-segregated EC fluxes of larger particles are only possible when these particles are abundant

and occur as the result of specific mechanisms, e.g. in dust storms, biomass burning or industrial

processes. High volume, closed path aerodynamic Mie scattering time of flight optical particle

counters, for sizes 0.5 < Dp < 20 µm, and open path forward scattering optical particle counters

have been used to measure deposition rates of super-micron particles and fog droplets (e.g.

Beswick et al., 1991; Burkhard et al., 2002; Klemm and Wrzesinski, 2007; Kowalski and Vong,

1999). As a result of current instrument limitations, measurement evidence is sparse on

deposition rates in the important accumulation mode (0.3 – 2 µm), which contains much of the

mass of sulphur, nitrogen and secondary organics. Recently developed mass-based flux

measurement approaches based on time-of-flight aerosol mass spectrometry (see below) may go

some way towards filling this gap in the future.

It is sometimes easier to measure super-micron emission fluxes. For example, Fratini et al.

(2007) reported measurements of desert dust resuspension, and Nemitz et al. (2000b) presented

urban EC flux measurements in the range 0.8 to 10 µm, made with aerodynamic particle sizers

(APS 3320, TSI Instruments), both made in conditions where fluxes were large.

In general, better counting statistics can be achieved when integrated particle number fluxes are

measured over larger size ranges. For example, condensation particle counters (CPCs) are now


more commonly used to measure total particle number fluxes with a lower cut-off between 2.5

and 20 nm over ice, sea water, short and tall vegetation canopies, as well as urban areas (e.g.

Buzorius et al., 1998; Buzorius et al., 2001; Dorsey et al., 2002; Held et al., 2006; Martensson et

al., 2002; Nemitz et al., 2002a; Nilsson and Rannik, 2001). CPC derived flux measurements are

dominated by particles in the range 10 to 100 nm, with smaller particles dominating during

nucleation events (Buzorius et al., 2001). Depending of the model used, CPCs have a response

time of around 1 s, which is sufficient to measure fluxes from taller towers, but requires flux

corrections for shorter vegetation. An alternative EC flux measurement approach that integrates

over all particles was implemented by Fontan et al. (1997) based on particle counting through a

combination of corona charging and detection by electrometers. Only very few gradient

measurements of small particle number fluxes have been reported in the literature (Hummelshoj,

1994) due to the large errors associated with these measurements. Work has started to extend EC

approaches to the measurement of particle fluxes from moving platforms such as aircraft (e.g.

Buzorius et al., 2006) and ships (Norris et al., 2008).

7.2.2 Flux measurements of individual aerosol chemical species

Measurements of chemically resolved aerosol fluxes can be used to quantify deposition inputs

directly, to investigate effects of aerosol composition on exchange rates and to understand

apparent emission fluxes. The number of studies that have applied micrometeorological

approaches to measure fluxes of aerosol compounds has been surprisingly limited. Up to the

1990s, the main option was gradient measurement with labour intensive manual sampling

techniques based on filter packs or denuder/filter combinations (Duyzer, 1994; Rattray and

Sievering, 2001; Wyers and Duyzer, 1997), where a key challenge is to achieve the precision

required to resolve the very small aerosol gradients, which are often < 3%. Manual sampling

techniques have also been used in REA approaches to measure fluxes, e.g. of sulphate to a maize

crop (Meyers et al., 2006) and of ions and heavy metal above a city (Nemitz et al., 2000c). A

family of automated real-time gradient monitors, based on gas and aerosol capture by

continuously flushed wet rotating denuders and steam jet aerosol collectors, respectively, and

online analysis by ion chromatography and/or flow injection analysis (for NH4+) (Thomas et al.,

2009) has been used in a number of studies to measure deposition of water soluble inorganic

aerosol components (Nemitz et al., 2004b; Nemitz et al., 2000a).

Eddy-covariance measurements of aerosol chemical species were first presented for SO42-

deposition to grassland, based on an analyser with thermal conversion to SO2 (Wesely et al.,

1985), with no further studies until the advent of aerosol mass spectrometry offered the prospect


for fast measurement of a number of aerosol compounds. Held et al. (2003) theoretically

investigated the suitability of aerosol mass spectrometers based on single-particle analysis by

laser ablation and ionisation for disjunct eddy-covariance flux measurements, dealing with the

limited counting statistics and quantification issues related to this instrument. An alternative

instrument, the Aerodyne Aerosol Mass Spectrometer (AMS), which is based on thermal

vapourisation coupled with electron impact ionisation, averages over a much larger aerosol

population and is quantitative for sub-micron aerosol components that are non-refractory, i.e.

volatilise at the vapouriser temperature of ~600 °C. An operational EC system using the

quadrupole-based AMS (Q-AMS) (Nemitz et al., 2008b) has been used to measure fluxes of

NO3-, SO4

2- and organic aerosol to urban areas and forests (Nemitz et al., 2008a; Phillips et al.,

2008; Thomas, 2007; Thomas et al., 2008; Thomas et al., 2007). The Q-AMS monitors only one

single mass/charge ratio (m/z) at a time. However, the quadrupole MS can be switched very

rapidly so that quasi-continuous time series of concentrations can be established at typically 10

different m/z at 10 Hz, similar to the use of the PTRMS for VOC measurements. Since >100

different m/z contribute to the organic mass spectrum, with the Q-AMS, the total organic aerosol

mass flux has to be estimated from fast response measurements at a few m/z. The arrival of the

next generation AMS based on a high-resolution time-of-flight mass spectrometers (HR-ToF-

AMS) (DeCarlo et al., 2006) provides the prospect of monitoring all m/z continuously at 10 Hz.

This should enable a fully quantitative flux measurement of the organic fraction and provide data

to apply statistical approaches, currently used to deconvolve the organic mass concentration in

different organic aerosol classes (e.g. Ulbrich et al., 2008), to the flux measurement.

7.3 Area sources of particles

7.3.1 Resuspension

Resuspension of particles has been studied extensively, both theoretically, in the wind tunnel or

through concentration measurements (Braaten and Paw U, 1992; Harrison et al., 2001;

Nicholson, 1988; Nicholson, 1993; Nicholson et al., 1989). A full review of the understanding of

this process is beyond the scope of this paper. Instead, we here focus on a recent development

involving the first application of micrometeorological flux measurement techniques to the direct

measurement of resuspension fluxes, and their potential to derive new parametrisations of the

process. Nemitz et al. (2009) measured super-micron size-segregated aerosol fluxes measured

with an Aerodynamic Particle Sizer (APS 3320, TSI Inc.) from a tower, some 65 m above the

city of Edinburgh, Scotland. The measurements, binned according to diameter and wind speed

lead to a parameterisation of the form: dFm(Dp)/dlog(Dp) = am(Dp) U b(Dp)


am(Dp) =exp(-68.69 + 73.39(1-exp(-0.730 Dp [µm]))) (1)

b(Dp) = 20.12 exp(-0.506 Dp [µm])

where U is wind speed measured at a height of 65 m.

The measurements show that above U(65 m) = 6 m s-1 resuspension becomes an important

particle source in the urban environment, with a mode centred around a Dp of 2.8 µm, which

increases with decreasing wind speed. An attempt to include traffic activity in the

parametrisation failed due to the overriding effect of wind speed. This suggests that, although

vehicle induced resuspension may be important to lift particles off the surface at street level,

high wind speeds are nevertheless required to flush these out of the street canyons. It should be

noted that the windy periods with largest coarse particle emissions did not result in the largest

concentrations, due to increased dispersion during these periods.

Similarly, Fratini et al. (2007) measured coarse aerosol fluxes during desert storms in the

Alashan desert in Nothern China, using an optical particle counter. The authors found that the

dependence of the resuspension flux (in µg cm-2 s-1) of PM1, PM2.5 and PM10 on u* (in m s-1)

could be described by the power relationships of F1 = 469 u*3.11, F2.5 = 6220 u*

3.34 and F10 =

47500 u*3.36, respectively. It should be noted that these two studies address different resuspension

processes, reflecting road dust resuspension with vehicle contribution, and natural saltation

processes, respectively.

7.3.2 Urban emissions of aerosols

In recent years, flux measurement techniques have been extended to the urban environment to

quantify emission fluxes of trace gases such as CO2, N2O, CO and VOCs (Nemitz et al., 2002b;

Velasco et al., 2005; Vesala et al., 2007), but also of particles. Total number fluxes have been

measured with condensation particle counters over several cities (Dorsey et al., 2002;

Martensson et al., 2006; Nemitz et al., 2008b). Martin et al. (2008) recently compared the pattern

of particle number fluxes measured at four different locations, three of which are shown in Fig.

7.2. Fluxes, typically covering the diameter range 0.01 (or 0.003) to 2 µm, ranged from 5000 to

70,000 # cm-2 s-1 and showed a clear dependence on traffic activity confirming the role of traffic

emissions as the major source of particles in the urban area. They derived a parametrisation for

the flux (FPred in cm-2 s-1) over each city based on friction velocity (u* in m s-1), sensible heat flux

(H in W m-2) and traffic activity (TA in veh s-1) of the form

[ ] 0trafficheat*frictionPred FTAEFHEFuEFCF −×+×+×= , (2)


which is shown in Fig. 6.3 for comparison. Here C is a site-specific factor in the range 0.12 to

0.55. The emission factors are EFfriction = 4500 cm-3, EFheat = 6.53 × 106 W-1 s-1 and EFtraffic lies

in the range 9000 to 12,600 veh-1 cm-2, depending on the location of the traffic census site. The

sink flux (F0) ranged from 13,000 to 57,000 cm-2 s-1.

Figure 7.2. Summary of diurnal patterns of measured particle number fluxes (dotted lines) and their

parametrisation (solid lines) for three UK cities: M – Manchester; L – London; E – Edinburgh; Win06 –

winter 2006 etc.

In the Edinburgh study of Nemitz et al. (2009) aerosol number fluxes were dominated by traffic

activity, while the aerosol mass emission fluxes were dominated by the wind-driven

resuspension described in the previous section This may be different for less windy locations as

demonstrated by Schmidt and Klemm (2008), who presented flux measurements of super-micron

particles made with a novel disjunct eddy covariance system, based on an Electronic Low

Pressure Impactor (ELPI, Dakati, Finland), indicating net coarse-mode deposition to the German

town of Münster. During the measurement periods, wind speed averaged 8.0 m s-1 in Edinburgh

and 4.4 m s-1 in Münster (O. Klemm, pers. communication). Donateo et al. (2006) reported flux

measurements of PM2.5 above an urban area made with an optical detector calibrated against

gravimetric PM2.5 measurements. These measurements indicated continuous net upward fluxes

and represent a combination of Aitken, accumulation mode particles and super-micron particles.


Chemically resolved flux measurements using the AMS EC technique described above have now

been made over a range of cities (Boulder, US; Mexico City; Gothenburg, Sweden; Manchester,

Edinburgh and London, UK) (Grivicke et al., 2007; Nemitz et al., 2008b; Phillips et al., 2007;

Thomas, 2007). The AMS measures total organic aerosol mass contained in particles with 60 to

800 nm vacuum aerodynamic diameter and volatilizes at typically 600°C. More information on

the organic aerosol classes can be obtained from the organic mass spectrum, with statistical

techniques (e.g. Ulbrich et al., 2008), which can be used to separate the organic mass flux into

fluxes of (primary) hydrocarbon-like organic aerosol (HOA) and (secondary) oxygenated

organic aerosol (OOA), where the OOA can often be divided into a more (OOA-I) and a less

(OOA-II) oxidized component. The measurements to date show clear diurnal fluxes of HOA

reflecting the pattern of the surface sources (Fig 7.3). However, the flux ratios of HOA/CO and

HOA/CO2 vary over the day , indicating that either (a) some of the HOA evaporates at rates that

vary over the day (e.g. Robinson et al., 2007) or (b) that the fuel mix contributing to the

emissions of HOA and CO varies over the night. Measurements suggest that food cooking may

contribute to HOA emissions in the evening in London. Fluxes of OOA-I appear to be mainly

downwards consistent with its production during long-term transport, while small upward fluxes

of OOA-II were measured, indicating that some OOA-II formation occurs below the

measurement height of typically 30 to 200 m. The urban flux measurements indicate that SO42- is

deposited to most city centres. Apparently, with the introduction of ultra low sulphur fuels, there

are no primary sources of this compound. Fluxes of NO3- were more variable: in Gothenburg,

Edinburgh, London and Boulder, net emission was observed, but fluxes were dominated by

individual, often cool or foggy days, indicating urban NO3- formation under these conditions. By

contrast, above Manchester and Mexico City, the average flux was downwards.


Figure 7.3. Averaged diurnal cycles of the fluxes of hydrocarbon like organic aerosol (HOA) and nitrate (NO3

-) over a range of cities as measured by eddy-covariance using an Aerodyne Aerosol Mass Spectrometer (data from Nemitz et al., 2008a (Boulder), Phillips et al., 2007 (Manchester, London) and Thomas, 2007 (Gothenburg)). The grey range indicates the 5th to 95th percentile.

7.4 Dry deposition of particles

7.4.1 Dry deposition rates to vegetation

Dry deposition of atmospheric particles can account for a large fraction, sometimes more than

half, of the total deposition of many important chemical compounds in the atmosphere, (e.g.

nitrate; Lovett, 1994), contributing significantly to global biogeochemical cycling.

Understanding atmospheric deposition processes in relation to the ever increasing sources of fine

particles in particular is therefore becoming a research priority. In general, both aerosol mass and

number, and, increasingly, surface area, must be determined to assess the environmental impacts

of anthropogenic activities. Mapping between number and mass fluxes however requires either a

detailed knowledge of the aerosol mass size distribution at relatively high temporal resolution or

by direct measurement of the aerosol deposition flux as a function of both size and chemical

composition. In this short summary we will critique the present level of understanding from an

observational perspective and identify the current gaps in our knowledge. There have been a

number of recent reviews on the subject of atmospheric aerosol deposition which have attempted

to collate the sparse experimental results available from the previous two and half decades.

These consist of many disparate and difficult to compare or even reconcile, methodologies. The

reviews, whilst achieving this difficult process in some respects, have succeeded mainly in

highlighting the general lack of a systematic approach towards improved understanding of

mechanistic deposition processes and, despite best efforts, have simply reinforced the view of

200

100

0

-100

24181260

1200

800

400

0

HOA flux [ng m

-2 s-1]

400300200100

0-100

HO

A flu

x [n

g m

-2 s

-1]

120

80

40

0

Manchester

London

Boulder

Gothenburg

40

20

0

24181260

200150100500

-50

NO

3 - flux [ng m-2 s

-1]

-40-20

02040

NO

3- flux

[ng

m-2

s-1

]

40

20

0

-20

Manchester

London

Boulder

Gothenburg


continued large uncertainties. Reviews have been very much focused on either a modeling

(Petroff et al., 2007b; Zhang and Vet, 2006) or a measurement perspective (Petroff et al., 2007a;

Pryor et al., 2007).

Many regional pollutant deposition models are currently struggling to correctly incorporate

physico-chemical properties of aerosols using realistic coupled sectional approaches, particularly

with respect to secondary organic aerosols. However, it could be stated that the scientific

community has not delivered any significant improvement in the accuracy of model predictive

capabilities for the atmospheric aerosol deposition pathway over the last two decades, (compare

for example one of the first reviews of model uncertainty, Ruijrok et al. (1997), with e.g. Petroff

et al. (2007b)). The aerosol modelling and composition community are pushing ahead with such

developments whilst seemingly unaware of the poor state of knowledge of deposition processes

and caution is required to avoid simply wasting research effort here. Whether the current level of

understanding and uncertainty is acceptable depends on the compound of interest and its position

with the aerosol mass size distribution prevalent in the atmosphere. Little in the way of detailed

sensitivity studies has been available since Ruijgrok et al. (1997). Feedback of such sensitivity

studies to the measurement community would also appear to be an area that requires

improvement (Zhang and Vet, 2006).

There has also been little in the way of any new laboratory investigation, at least for

atmospherically relevant conditions, that can usefully inform these communities on specific gaps

in knowledge that need to be pursued. While some progress is being made to highlight gaps in

knowledge with respect to models (Petroff et al., 2007b), these again show that different model

descriptions of atmospheric particle deposition, which rely on very limited semi-empirical data

and highly tunable, collection efficiency parameterisations, are as variable or more so than the

atmospheric observations that do exist (e.g. Zhang and Vet, 2006). Some improvements in

relating natural surface morphology descriptions to wind tunnel studies have been made.

In the following we explore the dependence of particle deposition velocity (Vd) on key

parameters, in comparison with measurements. Most of these measurements were made over

forests, where they are generally easier to obtain than over short vegetated surfaces for many

reasons related to the micrometeorological flux technique. The fluxes have been determined

using mainly but not exclusively direct micrometeorological techniques (cf. e.g. Pryor et al.,

2008b). The data must be treated with circumspection as (a) little account of particle composition

is provided in many of the studies reporting these data, (b) the sizes reported are an ad hoc


mixture of optical, electrical mobility, mass and aerodynamic diameters with little quantitative

information on the influence individual measurement techniques may have in altering actual

depositing particle size (which will be particle growth factor and hence composition dependent)

and (c) systematic detailed information on the morphology of the surfaces is not always reported

which often hinders useful model development.

Friction velocity (u*). Increased turbulence increases transport in the turbulent part of the

atmosphere, decreases the effective thickness of the quasi-laminar sub-layer and increases the

drag coefficient. It is therefore not surprising that Vd increases with increasing u*. Most modeling

approaches and measurements indicate a near-proportion relationship between Vd and u* for sub-

micron particles (Fig. 7.4).

Figure 7.4: Dependence of small particle deposition velocity on friction velocity (u*) for a range of surfaces; from

Pryor et al. (2008b).

Surface roughness length (z0) and canopy morphology. The effect of the surface roughness

length (for closed canopies: z0 ≈ 0.1 × canopy height,) extends beyond its effects on increasing

u*. Vd for forest tends to be by a factor of 5 to 10 larger than Vd for grass, due to the increased

height of the canopy and leaf area index and the enhanced turbulence induced by forest canopies.

Davidson et al. (1982) showed theoretically that even for the same vegetation type (grassland),

Vd may change within a factor of 5, depending on the exact morphology of the vegetation.

Similar results were more recently obtained by Petroff et al. (2007b) for forests, emphasizing the

influence of leaf dimensions and orientation on Vd. There is measurement based evidence as

well: Ould-dada (2002) used wind tunnel studies to investigate the dry deposition velocity of


sub-micron particles to model Norway spruce (Picea abies). A total canopy deposition velocity

of 5 mm s-1 was found which is in line with previous micrometeorological measurements to real

forest canopies reported in the literature. However, the deposition pattern was found to be a

highly complex function of height within the canopy. More studies such as these are needed to

improve model development and in-canopy gradient measurements are needed to validate multi-

layer deposition models.

Particle diameter (Dp). Both theoretical approaches and measurements show an effect of

particle size on Vd: the main deposition processes (Brownian diffusion, interception, impaction

and gravitational settling) are all size-dependent. Brownian diffusion is responsible for high Vd

for small particles (Dp < 100 nm), while gravitational settling is the dominant process for Dp > 5

µm. Interception and impaction are most effective in the intermediate size range, but less

effective than the other two processes. The resulting trough in Vd(Dp) (Fig. 7.5) is partially

responsible for the survival of the accumulation mode in the atmosphere.

Figure 7.5: Evolution of the deposition velocity Vd with the particle diameter Dp on grass and grass-like canopies

(lhs) and coniferous canopies (rhs) for friction velocity between 0.35 and 0.56 m s-1, as given by various

measurement campaigns and six existing models from the literature. Canopy characteristics used by models are hc =

0.07 m, z0 = 0.01 m, LAI = 4, dn = 3 mm, a = 1.78 for grass and h = 17 m, hc = 7 m, z0 = 1 m, LAI = 22, dn = 1 mm,

a = 3.81 for forest. Deposition velocities are recalculated at the same reference height zR = = 100z0. The parameters

of Slinn’s model (1982) are fIN = 0.01, dr = 20 mm, cv/cd = 1/3, b = 2. The model of Zhang et al. (2001) is applied on

Land Use Categories #6 (grass) and #1 (evergreen-needle-leaf trees), the corresponding parameters being,

respectively, fIM = 1.2 and 1, and fB = 0.52 and 0.56; from Petroff et al. (2007b).


When comparing measurements of Vd(Dp) with each other or with model results it needs to be

borne in mind that different instruments measure different types of diameter (i.e. geometric,

aerodynamic, vacuum-aerodynamic, electro-mobility and optical), which are often difficult to

compare without exact information on particle shape and composition. Some instruments dry out

the particles, while others measure the wet size. The wet size at the measurement height may not

reflect the size at which they impact with the surface, due to water uptake or release during the

deposition process, in a response to humidity gradients. Despite recent advances in measurement

technology, it is still not known how particle composition, particle shape and particle

hygroscopicity e.g. might influence microscale deposition mechanisms and collection

efficiencies onto and by different surface types with very different microstructures, which are

known to significantly influence e.g. aqueous phase aerosol contact angle and therefore the likely

collection efficiency.

Atmospheric stability (ζ = 1/L). There is strong evidence from a range of studies that Vd can be

greatly enhanced in unstable conditions. Figure 7.6 summarises the findings from several field

studies, by exploring the dependence of Vd, normalised by u*, on the inverse of the Monin-

Obukhov length (L), a standard measure of atmospheric stability. The cause for this enhancement

is not fully understood and therefore difficult to reproduce in numerical models.

0.04

0.03

0.02

0.01

0.00

Vd

/ u*

-0.04 -0.02 0.00 0.021/L [m-1]

unstable stable

grass; Wesely et al. (1985) forest; Gallagher et al. (1995) heathland; Nemitz et al. (2004) Dp = 0.1 µm 0.2 µm 0.3 µm 0.4 µm 0.5 µm

Figure 7.6: Summary of the dependence of aerosol deposition velocity on the Monin-Obukhov length (L),

indicating a sharp increase of normalized deposition velocity (Vd/u*) in unstable conditions; from Pryor et al.

(2008b), modified.


7.4.2 Parameterising and modelling deposition rates

The first detailed sensitivity study of aerosol deposition models compared to field observations

was conducted by Ruijgrok et al. (1997). They reported a factor of 5 uncertainty in model

predictions for sub micron aerosol deposition velocities based on input uncertainty. Despite this

there was general consensus that dry deposition measurements (mainly by gradient filter pack

and throughfall techniques) yielded deposition rates significantly larger than analytical models

were predicting for forested surfaces compared to grasslands (Erisman, 1993), which led to some

improvement in aerosol collection efficiency descriptions in models. Later the first eddy

covariance measurements at the same locations tended to confirm this, notwithstanding the

sampling issues associated with aerosol growth factors mentioned below (Erisman et al., 1996).

The latest review (Petroff et al., 2007b) suggests this uncertainty has now been reduced, to

around a factor of 3. Efforts to reduce this uncertainty further are restricted by the quality of the

measurements (as discussed in Section 6.4 below) and the completeness in the metadata reported

with measurements (in particular on canopy structure). The high sensitivity of the models to the

canopy structure further adds uncertainty to the application of simple generalised

parametrisations at the regional scale, where input parameters are limited.

7.4.3 Dry deposition rates to urban areas

Very little is currently known about deposition rates to urban areas, despite their importance for

estimating the contribution of aerosol deposition to the soiling and weathering of buildings, and

for the atmospheric lifetime in the atmosphere (Pesava et al., 1999). Although aerosol deposition

measurements have been made in cities, these were usually made with surrogate collectors (e.g.

Yun et al., 2002), which are unlikely to be representative of the uptake by urban structures.

Alternatively, the soiling of building has been studied, which provides information of the aerosol

deposition to a particular receptor, but not on the net removal rate from the atmosphere to the

urban matrix (Horvath et al., 1996).

Application of micrometeorological flux measurement techniques to the urban environment has

now been demonstrated (see Section 7.2.2 above). However, the net flux of particles above urban

areas is dominated by emission sources from the city and deposition rates can only be applied if

a chemical aerosol species or size-class is found which is not emitted from the city. Nemitz et al.

(2000c) presented initial measurements of chemically resolved aerosol fluxes at a coastal site and

showed that chloride was deposited at low wind speeds, presumably reflecting deposition of sea

salt, while it was emitted during windy periods, probably reflecting wind-driven resuspension of


previously deposited material. Similarly, Nemitz et al. (2008b) measured SO42- deposition fluxes

to urban environment with the Q-AMS EC system. These measurements suggest deposition

velocities in the range of 2 to 6 mm s-1, but will need to be supported with data from other cities.

As mentioned above, Schmidt and Klemm (2008) detected net deposition of PM2.5 to a German

town, but these fluxes almost certainly contain an upward component.

7.5 Uncertainties

7.5.1 Uncertainties in the application of micrometeorological flux measurement techniques for

deriving the local flux

Technical challenges in applying micrometeorological techniques to the measurement of aerosol

fluxes go beyond those encountered for gas flux measurements. Aerosol fluxes are often small

and deposition rates slow, resulting in small concentration differences that need to be resolved

for gradient and REA measurements. Similarly, the relative corrections, e.g. for density

fluctuations (Webb et al., 1980), may become large and can easily result in reversal of the sign of

the flux. A continuing challenge of particle flux measurements is that, due to the limited

counting statistics of the measurement, standard data processing techniques, such as tests and

corrections based on co-spectral analysis, and non-stationarity tests are difficult to implement.

While most gas analysers respond to many thousands of molecules per tenth of a second, aerosol

counters may only detect tens of particles resulting in statistical uncertainties (Fairall, 1984).

Similarly, in mass-based measurements the contribution from a few large particles may greatly

affect gradients and EC results. Different aerosol measurement instruments respond to different

parameters, not all of which are conserved. For example, artificial fluxes may be introduced if

particles are sized according to their wet size which responds to humidity fluctuations

(Kowalski, 2001), and similar problems are caused by volatilisation or formation in the

atmosphere which is discussed in more detail below. The exact effect on the measurement

depends on the setup and requires careful consideration: most previously reported sub-micron

aerosol number fluxes have relied on optical particle counting techniques that use closed path,

high power active laser cavity scattering cells. Furthermore most of these, but not all, adopt

recycling dry particle free sheath air flow with significantly lower RH than the ambient aerosol

flow to minimise contamination of the instrument optics. These instruments therefore most likely

provide a measure of the dry or partially dry particle size (e.g. O’Dowd 1992) and not the

ambient particle size. Hence measurements of sub-micron particle fluxes reported in the

literature are likely representative of “dry or near dry aerosol deposition velocities” of optical

particle size and not the actual ambient deposition velocity, whereas most large particle fluxes

are likely a combination of both, some being derived from closed path and other open path


instruments. Flux measurements using photometric techniques to determine total PM2.5 and PM10

mass fluxes may not be subject to these effects and for routine measurement of total mass fluxes

e.g. for network applications may be the most appropriate when combined with additional

composition measurements.

Care needs to be taken that the inlet system does not respond in a way that may be correlated

with w. This could occur during non-isokinetic sampling of coarse particles or during fast

switching of inlet flows in REA systems. There is also evidence that non-stationarities may

affect aerosol exchange particularly often (Fontan et al., 1997).

7.5.2 Relating measured fluxes to surface exchange: flux divergence and the effect of chemical

interactions

A further uncertainty of the flux estimation with micrometerological techniques is that, although

the local flux at the measurement height may be correct, it may differ from the actual surface /

atmosphere exchange. The most commonly applied form of the scalar conservation equation is

(Pryor et al., 2008b):

( ) SCvz

Cx

DCux

Cuxt

Cg

ii

ii

i+

∂∂

−∂

∂=′′

∂∂

+∂∂

+∂∂

2

2

(1) (2) (3) (4) (5) (6)

(3)

Here term (1) is the local change in concentration, term (2) advection by the mean flow, term (3)

represents the divergence of the turbulent flux, term (4) vertical transport by diffusion, term (5)

vertical transport by sedimentation and term (6) concentration changes due to sources or sinks.

Methods of estimating particle (and other scalar) fluxes at the air-surface interface have typically

relied on the assumptions of horizontal homogeneity, steady state, the absence chemical source

or sink of the scalar, that the constant flux layer assumption applies to the lowest tens of meters

above the surface (Businger et al., 1971), and that the turbulence responsible for transporting the

scalar of interest is locally-induced (Monin and Obukhov, 1954; Monin and Zilitinkevich, 1974).

However, as described in this sub-section, there are multiple causes of flux-divergence (i.e. that

the flux observed at some height above the surface is not equal to that at the surface). Three

dominant sources of particle flux divergence are described below along with methods for their

identification and quantification:

Non-conservative behaviour of the scalar under study (i.e. particles) due to the interaction of

other particle dynamics processes with the vertical exchange (i.e. ( )0≠S ). The degree to which S

deviates from 0 (i.e. the magnitude of the vertical flux divergence due to phase transitions) is


determined by; the chemical climate (Nemitz and Sutton, 2004; Nemitz et al., 2004b; Sutton et

al., 2007), particle ensemble (Pryor and Binkowski, 2004), and specific aspect of the particle

ensemble being observed. If Eq. (3) is applied to consideration of the mass of the entire particle

ensemble, then only mass transfer (i.e. evaporation and/or condensation) can result in flux

divergence. While if Eq. (3) is applied to a size-resolved number particle ensemble for any given

particle diameter, 0=S could derive from concentration changes resulting from nucleation,

coagulation, and condensation/evaporation. If Eq. (3) is applied to a chemically resolved (but not

size resolved) particle ensemble, evaporation/condensation and/or heterogeneous chemistry in/on

particle surfaces could cause flux divergence. It can also modify fluxes if it leads to growth or

shrinkage across the cut-off size of the particle probe. Nemitz et al. (to be submitted, 2008)

observed apparent emission fluxes with a CPC setup above a grassland fertilised with NH4NO3,

attributed these fluxes to aerosol growth due to NH3 and HNO3 uptake and used the fluxes to

derive particle growth rates across the 11 nm cut-off of the CPC (Nemitz et al., 2008). This

demonstrates that flux measurements can be used to infer information on S and thus on aerosol

processing, if the true deposition rate can be estimated independently.

The partitioning of species between the gas and particle phase is also associated with gas flux

divergence (Soerensen et al., 2005) and can change the net rate of surface uptake of, for

example, nitrate if the deposition velocities of the gas and particle phase species differ

substantially (Pryor and Soerensen, 2000). The likelihood of flux contamination due to non-

conservative behaviour can be estimated using time-scale analysis (De Arellano and Duynkerke,

1992), and can be quantified by deploying eddy covariance measurement systems at multiple

heights.

(i) Horizontal advection, due to the presence of large spatial gradients in particle number, mass

and/or composition. The importance of horizontal advection has been extensively evaluated

in the carbon dioxide flux community (Baldocchi et al., 2001; Hong et al., 2008) and is likely

to be important to particle fluxes in regions close to large particle emissions, but with few

exceptions (Vong et al., 2003) the horizontal advection term has generally been neglected in

most particle flux studies. The potential influence of horizontal advection can be quantified

using a horizontally dispersed measurement array.

(ii) The influence of non-local or ‘top-down’ processes in dictating vertical exchange. Observed

scalar fluxes near to the ground are derived from two components: local surface-driven

turbulence, and non-local or ‘top-down’ processes such as entrainment of air from above the

mixed-layer which can cause fluxes that are counter to local gradients (Holtslag and Moeng,

1991). The importance of non-locally induced turbulence in dictating observed fluxes has


been documented in gas exchange studies (Gao et al., 1989), but received less attention in the

aerosol community, despite evidence that it plays a substantial role in dictating flux

magnitudes and may provide an explanation for upward fluxes in environments that have

traditionally been viewed as solely particle sinks (Pryor et al., 2008a). The potential

influence of ‘top-down’ processes on observed near-surface fluxes can be identified using

scalar correlations (Sempreviva and Gryning, 2000) and quadrant analysis.

With the advent of techniques to measure compound-resolved aerosol mass fluxes, there is

growing evidence that particle deposition velocities of NO3- and NH4

+ to semi-natural vegetation

tend to exceed those derived for SO42- or from particle number flux measurements (Nemitz et al.,

2004b; Thomas et al., 2008) (Fig. 7.7). The deposition rate of these compounds measured over

heathland and forest greatly exceed those predicted theoretically for short and tall vegetation,

respectively (Fig. 7.2). The likely cause is evaporation of NH4NO3 near the ground during the

deposition process, where thermodynamic equilibrium favours the gas phase, due to the

depletion of NH3 and HNO3 by deposition of these reactive gases to foliar surfaces and warm

surface temperatures. Thus, the flux measured well above the canopy is not limited by the

physical interaction of the particles with the vegetation surface, but reflects the evaporation sink

in the airspace above. This is supported by the fact that the relationship between Vd and u* does

not differ between surfaces (Fig. 7.7), which implies that turbulent transport (which scales with

u*) is the main constraint on the flux. Since NH3 and HNO3 deposited to semi-natural vegation

much more effectively than NH4NO3 aerosol, this shift to the gas phase increases the deposition

rate of total ammonium and total nitrate. Since this evaporation only occurs close to the canopy,

it represents a non-resolvable (subgrid) process in traditional transport models. Future

parameterisations of Vd should account for this additional sink for highly volatile aerosol

components.

40

30

20

10

0

Vd

[mm

/s]

1.00.80.60.40.20.0u* [m/s]

NH4+ to heathland (Nemitz et al., 2004)

NO3- to oak forest (Thomas, 2007)


Figure 7.7: Apparent nitrate and ammonium deposition velocities derived from chemically speciated

micrometeorological flux measurements. The large values are indicative of an additional loss of ammonium nitrate

near the surface, due to evaporation.

7.5.3 Interpretation of measurements for model verification

Deposition is effectively a number dominated process which, using current methods, is subject to

large uncertainties. Mapping from number to mass space requires detailed knowledge of

composition, size, shape and hygroscopicity. The hygroscopicity of aerosols can potentially

generate the largest uncertainty, not just in the measurement, but also in interpreting the

measurements. For example, the size at which a particle interacts with the vegetation surface

often differs from its actual size at the measurement height, which again may differ from the size

reported by a given instrument (dry vs. wet; geometric vs. optical diameter etc.). Most previous

studies of deposition have not been sufficiently complete to address any of these uncertainties

with respect to a complete closure of aerosol number and mass for model comparison through

use of growth factors. As a consequence, size-segregated and chemically speciated eddy

covariance (and related) aerosol flux measurement techniques cannot currently provide

unambiguous results of particle number or mass fluxes to surfaces as a consequence of

fluctuations in aerosol size distributions on time scales that can lead to sampling biases. This

sampling ambiguity and the potential for bi-directionality in aerosol fluxes limit the accuracy

with which Vd can be determined and hence will hamper improvement in model mechanistic

descriptions of particle deposition to natural vegetated surfaces.

7.6 Future research needs 7.6.1 Deposition measurements and reporting Standardisation of eddy-covariance approaches and data analysis procedures

Comparisons between different measurement systems are currently made difficult by the diverse

approach used to measure the fluxes as well as to analyse and present the data. For example,

some authors have derived parameterisations of Vd(Dp) averaging over the negative (deposition)

fluxes only, while other authors have averaged over the entire datasets. The difference can be

large (Nemitz et al., 2002a). Often important parameters (e.g. leaf dimensions) are not provided

in the scientific papers to provide the input parameters to apply the models to the measurement

datasets. Thus there is an urgent need to harmonise flux measurement approaches as much as

possible and to provide guidance for auxiliary parameters that should be measured and provided

with the measurement datasets to maximise their potential for model evaluation.


Improved measurements in the accumulation mode

There are still significant gaps in observations, particularly with respect to the critical size range

associated with the transition between Brownian and turbulent impaction dominated particle

capture regimes. This size range is still poorly resolved by eddy covariance techniques (~ 0.5 <

Dp < 2 µm) as a consequence of few measurements being available by any suitably characterised

techniques and the large errors associated with these. The predicted minimum in Vd as a function

of size in this transition regime can vary widely between different models (Fig. 7.5) and this will

have significant consequences for long term integrated dry deposited mass fluxes. This is

therefore seen as a key area in need of attention by both models and field observations.

Understanding the effect of stability and leaf properties on deposition velocities

Another serious issue is the lack of any detailed testable hypothesis in models explaining the link

between increasing Vd and atmospheric stability and which most measurements have reported in

the literature for particle sizes Dp< 0.5 µm, most clearly seen for Aitken and small accumulation

mode sizes. So far there are no wind tunnel studies of aerosol deposition to vegetated surfaces

that take account of atmospheric stability and these are needed to allow further model

development. Studies of the hydrophobicity and anti-adhesion of non-smooth leaf surfaces show

that the morphology of plant epidermal cells and the morphology and distribution density of

epicuticular waxes significantly affect their hydrophobicity and anti-adhesion properties and

potentially the adhesion of aerosol particles following impaction and interception, Ren et al.

(2007). The microstructure of plant surfaces has been well documented but an interesting

phenomenon which might have potentially serious implications for some studies of dry and wet

deposition is the so-called self-cleaning mechanism of some leaf structures (referred to as the

“Lotus Effect”). Some plant leaves are completely lacking in microstructures while others can

have sunken or raised nervatures which as a consequence cause super hydrophobic behaviour.

This in turn leads to a remarkable self-cleaning process whereby fog droplets e.g. rolling down

the leaf surface pick up aerosols and remove them from the leaf surface. Experiments whereby

leaf surfaces have been artificially contaminated with radioactively tagged aerosols and then

subjected to artificial fog droplets have been used to determine the retention rate of aerosols to

plant surfaces and these can range from over 90% to less than 10% depending on the species

examined and which were linked to differences in leaf microstructure and orientation (Neinhuis

and Barthlott, 1997).

Considering the many inherent uncertainties in field flux measurements more wind tunnel

studies are needed under better controlled conditions of stability, surface morphology and


aerosol composition. The question is how detailed should a model be to describe adequately the

surface interactions with aerosols (whether they be “dry” or “wet” aerosols)? And more

importantly how can their importance be measured? What information should be reported on

surface morphology for future model development in order to attempt inclusion of these effects

in future? These questions are best tackled by revisiting wind tunnel studies coupled with

modern particle measurement techniques.

Filtering or accounting for chemical interactions and water uptake.

A particular aspect of the data processing is the correction for aerosol dynamics due to water

equilibration and / or chemical interactions. A growing number of datasets indicates that size-

segregated particle number fluxes are affected by chemical effects (Nemitz et al., 2004a; Nemitz

et al., 2004b) and this is confirmed by the first results from the chemically resolved mass fluxes

from the Q-AMS eddy covariance system, which indicates that often some chemical aerosol

components may be emitted at the same time as others are being deposited. Although some

modelling studies have been successful in qualitatively reproducing the observations both for

bulk chemical fluxes and size-segregated fluxes (e.g. Nemitz and Sutton, 2004; Van Oss et al.,

1998), standardized operational procedures for correction have not yet been developed. Indeed,

we do not currently have the strategies in place to test whether a particular dataset may be

affected by chemical interactions. It is unclear whether correction procedures will ever be

sufficiently accurate to fully correct for these effects, given the small value of the deposition

rates.

7.6.2 Deposition models

Migration to a probabilistic approach

Comparison of measured deposition velocities as a function of size with different regional scale

model descriptions show large differences. Hence, unacceptable errors will very likely be

incurred in annual cumulative mass deposition values. A detailed sensitivity analysis between

different deposition schemes used in current regional models with observations has not yet been

undertaken. Given the large apparent difference between measurements and model schemes, it

may be more appropriate to move towards a probabilistic approach in deriving deposition

estimates, by exploring a range of possible solutions, together with statements on their

probability. For this purpose, probability density function distributions for aerosol deposition

velocities need to be developed which can be used to test sensitivities to these factors in regional

transport and global climate models


Improvement of modelling approaches

New modelling approaches compare favourably with the available measurement database, with

the caveats on the data quality mentioned above. Figure 7.5 demonstrates that the model of

Petroff et al. (2007b) in particular appears to be successful, similar to earlier modelling results of

Davidson et al. (1982). Both models have in common that they include a detailed description of

the canopy morphology. This introduces additional requirements for input parameters and further

degrees of freedom for adjustments to make the model match the measurements. However, good

agreement is achieved with measured canopy characteristics, increasing the confidence in the

modelling approach. However, the model of Petroff et al. (2007b) needs to be simplified for

application in operational chemical transport models and standardized characterizations for the

different vegetation classes need to be developed.

Impact of surface anisotropy on suspension & deposition

Spatial organisation of vegetation on sub-grid scales can influence aerosol surface exchange

properties by introducing significant perturbations to mean wind flows by altering the probability

density functions for turbulence velocities above that surface which in turn can alter the

magnitudes of aerosol surface exchange fluxes. The impact of this surface anisotropy is often

seen in observations of dust suspension fluxes over surfaces where elongated regions may occur

which are free of vegetation (e.g. Gillette and Chen, 2001). As a result of this, neighbouring

surfaces, which have the identical vegetative indexes, can produce dust fluxes that differ from

one another by as much as a factor of 4-8 (Okin, 2005). Recently models have been developed

that capture and demonstrate the importance of sub-grid cell isotropic spatial variability however

these have focussed on dust suspension and the subsequent impact on both horizontal and

vertical dust mass fluxes, which can be considerable (Gillette and Chen, 2001).

As deposition mechanisms for sub micron aerosols are controlled by turbulent interaction with

surfaces, and are highly sensitive to particle size and micro-scale structures, it is likely that

deposition velocities too are also affected by surface anisotropy and the manner in which this

links to the microstructure. The notion of a mean aerosol deposition velocity in this context has

little value (much as it is now thought to be for suspension fluxes) (Okin, 2005) and a

probabilistic approach must be used. Unfortunately, unlike dust emission mass fluxes, there are

virtually no observations of the impact of sub-grid scale surface anisotropy on aerosol

deposition. Initially, a theoretical model study could explore the likely impact of anisotropy on

effective dry deposition rates, for example adopting the concept of a lateral cover parameter (λ)


as a measure of the vegetative canopy area intercepted by the wind and its contribution through

surface drag to the surface roughness, from which an effective aerodynamic roughness length for

the landscape can be calculated (1993). In parallel, as a first step to improving understanding in

this area (which has been relatively moribund for some considerable time) (Pryor et al., 2008b)

the community needs to collect high quality micrometeorological aerosol flux measurements

over a number of different surfaces with very different anisotropic variability. The observations

should focus on determining the frequency distribution function, f(Vd (Dp)) of aerosol deposition

velocities.

7.7 Conclusions – Aerosols

After little progress in the understanding of surface/atmosphere exchange of aerosols in the

1980s and early 1990s, the development of novel instrumentation suitable for flux measurements

has led to new investigations into the surface exchange, extending micrometeorological flux

measurements to the urban environment and the sea. For example, new developments in mass

spectrometry have enabled the first eddy-covariance flux measurements of aerosol components

(NO3-, SO4

2- and organics) above urban areas and vegetation, providing new information on

sources, sinks and chemical processing, together with deposition rates of the accumulation mode

and the potential of studying deposition rates in relation to particle composition. Furthermore,

size-segregated particle flux measurement approaches have now been extended to the sub-100

nm size range, providing the first data for model evaluation. The first long-term flux

measurements of total aerosol number provide increasingly robust datasets of removal rates.

As more detailed flux measurements as a function of size and composition have become

available, it is becoming clear that size-segregated particle number flux measurements are often

influenced by hygroscopic growth and chemical processing. This highlights the need to minimize

or to filter/correct for these effects when the data are used for model validation. Apparent

upward fluxes have been used to study the formation of NH4NO3 or biogenic SOA formation.

Measured effective deposition rates of NH4NO3 to semi-natural vegetation greatly exceed those

of other aerosol compounds, indicating that new sub-grid parameterizations need to be developed

to account of the additional deposition mediated through the evaporation of volatile aerosol

components (e.g. NH4NO3).

Theoretical developments demonstrate that models of dry deposition need to account for canopy

structure and small-scale morphology. Existing models are now capable of reproducing selected

measurements, but will need to be simplified for operational application in transport models and


incorporate effects of atmospheric stability. The data available for model validation are highly

disperse in terms of quality, approaches, diameter measured and auxiliary information provided.

Harmonised approaches in data processing and presentation are needed. More sensitivity studies

and probabilistic approaches are needed to explore the range of possible deposition estimates. In

addition, the potential of modelling concepts that account for small-scale spatial variability

(increasingly applied for resuspension) should be explored to estimate dry deposition. Here a

new series of targeted, high-quality wind-tunnel experiments, coupled with the improved

measurement technology would help decrease remaining uncertainties.


8. Ecosystem-atmosphere exchange of the radiatively active gases - N2O and

CH4 8.1 Introduction

Atmospheric concentrations of the three main greenhouse gases CO2, CH4 and N2O have

increased since the industrial revolution in the 18th century due to anthropogenic activities.

Increased fossil fuel burning, land use change and the intensification of agriculture facilitated by

the manufacture of synthetic nitrogen and consequently population growth are the main causes.

Increased fossil fuel combustion is the main cause for rises in CO2, whereas microbial processes

in soils, sediments, and waters and rumens of animals, are responsible for the bulk of the

observed increased atmospheric CH4 and N2O concentrations. In this section current

understanding of CH4 and N2O, and especially of the biological processes, measurement

methodologies and models, are reviewed.

8.2 Global budgets of N2O and CH4

Atmospheric N2O and CH4 concentrations have risen from background levels prior

industrialisation from 270 to 320 ppb N2O and from 700 to 1782 ppb CH4 in 2006

(http://www.esrl.noaa.gov/gmd/aggi/). Nitrous oxide concentration has increased at a relatively

uniform rate, with a mean annual growth rate over the last 10 years of 0.76 ppb/year (Hirsch et

al., 2006). By contrast, the growth rate in CH4 concentration has changed considerably since the

early 1990s from a steady monotonic increase of approximately 15 ppb year-1 declining to some

years with no net change over the year, but with very large and unexplained inter-annual

variations in growth rate, (IPCC 2007).

(www.wmo.int/pages/prog/arep/gaw/ghg/documents/ghg-bulletin-3.pdf).

The global budget of N2O is constrained by the sink strength in the stratosphere and atmospheric

increase (http://www.esrl.noaa.gov/gmd/aggi); hence, the global source strength is 15. 8 – 16 Tg

N2O-N y-1 (Crutzen et al, 2008, Hirsh et al, 2006). There are some indications that also soils may

significantly act as sink for atmospheric N2O and that the soil N2O reduction has decreased

within the last decades (Chapuis-Lardy et al., 2007; Conen and Neftel, 2007). However, this is so

far not considered in any global estimate.

The atmospheric increase is largely attributed to agricultural activity (Table 8.1). Natural sources

of N2O, the oceans, tropical and temperate forests and grasslands/savannahs, are unlikely to have

changed much since pre-industrial times except where land use has changed significantly.


The CH4 budget is constrained by measurements of the major sources and δ13C signature to a

global total of 430 - 600 Tg y-1 (Wuebbles and Hayhoe, 2002, IPCC, 2007). Anthropogenic

sources contribute 70% of the total budget. Natural sources (wetlands, oceans, termites) are also

large and dominated global emissions until the 20th century (Table 8.1). Increased livestock

production and fossil fuel use are the main reasons for the atmospheric increase of CH4 (IPCC

2007). Soils are are a minor sink for CH4 and accounts for approximately 6% of the global

budget; the dominant removal process for atmospheric CH4 is oxidation by OH, mainly in the

troposphere.

8.3 Biological sources of N2O and CH4

8.3.1 The biology of production and consumption of N2O and CH4 in soils and sediments

Microorganims are the dominant sources and sinks of N2O and CH4 in the troposphere. A good

knowledge of the underlying processes and microbial community structure is essential for

improving global estimates of N2O and CH4. Fortunately the main microbial reactions involved

(nitrification, denitrification, methanogenesis and CH4 oxidation) are ubiquitous to all live

containing ecosystems and are all sensitive to anthropogenic activities (e.g. irrigation, drainage,

fertilisation) and climate (temperature and precipitation).

Table 8.1 Estimates of global N2O and CH4 budgets (Tg y-1)

N2O source a Tg N2O-N y-1 CH4 source b Tg CH4 y-1

Natural sources

Oceans 3.8 (1.8 – 5.8) Oceans 4 (0.2 – 20)

Atmosphere 0.6 (0.3 – 1.2) Termites 20 (2 – 22)

Soils 6.6 (3.3 – 9) Wetlands 100 (92 – 232)

Others c 21 (10.4 – 48.2)

Anthropogenic sources

Agriculture 2.8 (1.7 – 4.8) Rice cultivation 60 (25 – 90)

Biomass burning 0.7 (0.2 – 1) Biomass burning 50 (27 – 80)

Energy & Industry 0.7 (0.2 – 1.8) Energy d 106 (46 – 174)

Others e 2.5 (0.9 – 4.1) Ruminants 81 (65 – 100)

Waste disposal 61 (40 – 100)

Total sources 17.7 (8.5 – 27.7) 503 (410 - 660)

Sinks Stratosphere 12.5 (10-15) f Stratosphere 40 (32 – 48) Soils 1.5 – 3 g Soils 30 (15 – 45)


Tropospheric OH 445 (360 – 530) Total sinks 14 (11.5 – 18) 515 (430 – 600)

a sources are estimates for the 1990’s as provided by IPCC 2007, Table 8.7, b from Wuebbles and Hayhoe, 2002, c others = marine sediments, geological sources and wild fires, d energy = natural gas, coal mining and other fuel

related sources. e atmospheric deposition, aquatic systems, sewage, f Hirsh et al, 2006, g Cicerone et al., 1989.

Nitrous oxide is a by- product of aerobic nitrification and an obligate intermediate in the

denitrification pathway, and is emitted by both nitrifiers and denitrifiers. Production and

consumption of N2O is regulated by oxygen partial pressure; nitrification is additionally

controlled by the concentration of NH4+, while denitrification is also controlled by availability of

carbon and NO3- (Conrad 1996). Denitrification is the main biological process responsible for

returning fixed N to the atmosphere as N2, thus closing the N cycle (Philippot et al., 2008). This

reduction of soluble N to gaseous N is negative for agriculture, since it can deplete the soil of

NO3- , an essential plant nutrient. The denitrification N2O/N2 product ratio is variable, and N2O

may even be the dominant end product. However, denitrification also provides a valuable

ecosystem service by mediating N removal from NO3- polluted waters in sediments and

other water-saturated soils. Denitrifiers can be sinks for N2O. Sink activity appears to be

stimulated by low availability of mineral N (Capuis-Lardy et al., 2007, Conen and Neftel, 2007).

Methane is produced by methanogenic archaea in anaerobic soil (Philipot et al, 2008). The

organisms require low redox conditions as well as on the fermentative production of precursors

for the methanogens. The main terrestrial CH4 sources are wetland ecosystems, where both

methanogens and methanotrophs are present and active. Methane is consumed by methanotrophs

active in the aerobic layers of most soils; undisturbed soils are largest CH4 sinks.

8.3.2 Distribution of active microbial populations in soils

Although many different microbial species can produce and consume N2O and CH4, information

on the microbial biodiversity can provide useful insight into the health and functioning of the

soil. The development and recent automation of molecular methods have made it possible to

characterise the abundance and function of soil microbial populations relatively quickly. One of

these methods is the analysis of the Phospholipid Fatty Acids (PLFAs) composition of the

microbial membrane (Bach et al, 2008). This method, together with analysis of microbial

biomass carbon, Gram staining, N mineralisation rates, N2O, NO and CH4 fluxes, was applied to

soils from arable, grassland, wetland and forest ecosystems from the main climate zones in

Europe as part of the NitroEurope Project (http://www.nitroeurope.eu). The PLFA composition

provided an overview on the distribution of functional microbial groups in soils of different


landuses. There was a good separation between microbial communities from wetlands and

forests, but a closer similarity between microbes from grasslands and croplands (Fig. 8.1). The

ratio of two marker PLFAs, cyclic fatty acids and precursor monounsaturated fatty acids, is an

index of bacterial stress. In this study, the stress parameter correlated with soil NO emissions and

these were related to N-deposition rates and soil acidity (Pfeffer et al., pers com.). N2O

emissions correlated positively with the abundance of gram-negative bacteria, potential N-

mineralization rates and microbial biomass carbon. This can be explained by the fact that gram-

negative bacteria contain many microbial groups important for the N-cycle, such as nitrifiers,

free-living N2-fixers and several denitrifiers.

forestcroplandwetlandgrassland

UK-AMo

FI-Lom

FI-HyyDE-Hog

NL-SpeDK-Sor

IT-BCiDE-Geb

FR-Gri

IT-Cas

UK-Ebu

HU-Bug

CH-Oen


UK-AMo

FI-Lom

FI-HyyDE-Hog

NL-SpeDK-Sor

IT-BCiDE-Geb

FR-Gri

IT-Cas

UK-Ebu

HU-Bug

CH-Oen





UK-AMo

FI-Lom

FI-HyyDE-Hog

NL-SpeDK-Sor

IT-BCiDE-Geb

FR-Gri

IT-Cas

UK-Ebu

HU-Bug

CH-Oen

Figure. 8.1 Principal component analysis of microbial communites, determined as PLFAs (nmol g-1 soil dry weight)

of 13 NitroEurope sites representing different landuses. Abbreviations: AM: arbuscular mycorhiza fungi; Sites:

Forests: FI-Hyy = Hyytiälä, FIN; DK-Sor = Sorø, DK; NL_Spe = Speulder Bos, NL; DE-Hog = Högelwald, DE;

grasslands: UK-Ebu = Easter Bush, UK; CH-Oen = Oensingen, CH; HU-Bug =Bugac, HU; croplands: DE-Geb =

Gebesee, DE; FR-Gri = Grignon, FR; IT-Cas = Castellaro, I; IT-BCi = Borgo Cioffi, I; wetlands: FI_Lom:

Lompolojänkkä, FIN; UK-Amo = Auchencorth Moss, UK; Figure provided by B.Kitzler and M.Pfeffer (BWF,

Austria).

8.3.3 N2O and CH4 fluxes from the main global ecosystems

Biosphere atmosphere exchange of N2O and CH4 has been studied for over 30 years. The data

available are biased towards the large N2O and CH4 emitting ecosystems in highly developed

countries, principally northern Europe, the USA, Canada and Japan. For N2O, studies on N

fertilised agricultural soils dominate and for CH4 studies on rice paddy fields and northern

wetlands. Studies from Asia, where N demand is increasing at a faster rate than elsewhere, are

now emerging. There are insufficient data from agricultural systems in Central and South

American and African countries, from new emerging cropping systems, especially biofuel crops,

and land use change in temperate as well as tropical countries to provide the detailed

understanding required for model validation and for inclusion in emission inventories.


8.3.4 Plant mediated transport and production of N2O and CH4

The general view is that soil-based microbial production and consumption of CH4 and N2O are

considered to be the major processes involved in biosphere-atmosphere exchange of the two

greenhouse-gases. Based on this perception, and combined with the lack of appropriate

methodologies, our current knowledge about their exchange rates is almost exclusively based on

observations achieved using shallow, soil anchored enclosures. For many ecosystems such

enclosures may exclude the vegetation (e.g. tall crops and forests) and biases in emission

estimates according to contributions from vegetation may occur.

It is well documented that soil-atmosphere transport of both CH4 and N2O is mediated by

aerenchymatic wetland herbaceous species such as rice (e.g. Yan et al., 2000). Mangrove prop

roots and also wetland and flood-tolerant trees have been shown to mediate CH4 and N2O

transport from the soil to the atmosphere, e.g. through the bark of black alder or from hybrid

poplar seedlings (McBain et al., 2004), but only under conditions when the root zone was

exposed to above ambient concentrations of the gas.

The role of non-aerenchymatic plants and in particular trees in the exchange of CH4 and N2O

between the soil-plant system and the atmosphere has only been sparsely investigated. Recent

investigations, however, have emphasized a non-negligible role of vegetation in the biosphere-

atmosphere exchange of greenhouse gases.

8.3.4.1 Methane from vegetation

In 2006 Keppler et al reported a very surprising observation that higher plants had the capability

to emit CH4 under aerobic conditions with a mean emission rate of 374 ng CH4 g-1 dw h-1. From

their findings they calculated a global CH4 source strength of 62–236 Tg yr-1 for living plants

and 1–7 Tg y-1 for plant litter, the sum of which equals c. 10–40% of the total global CH4 source

strength. Methyl-ester groups of pectin, an abundant polysaccharide in cell walls of non-woody

plant tissue, served as a precursor for CH4 (Keppler et al., 2008). UV light appears to be

important in emissions of CH4 from plant material. Vigano et al., (2008) demonstrated that in

the absence of UV light CH4 was not produced until the temperature reached 70-80 oC; with UV

light emissions were significant already at room temperature with rates up to 67 ng CH4 g-1 dw h-

1. McLeod et al. (2008) provided further evidence that not only CH4 but also ethane, ethylene

and CO2 are produced from methyl-ester groups of pectin under UV irradiance, and that reactive


oxygen species (ROS) arising from environmental stress may have a role in the formation of CH4

from pectin. By contrast, Dueck et al. (2007) observed no significant CH4 emissions from

photosynthesizing or dark respiring leaves, adding evidence to speculations that plant derived

CH4 originates from abiotic processes.

8.3.4.2 Nitrous oxide from vegetation

In a number of experiments, especially crops, plant mediated emission of N2O have been

observed. Chen et al. (1999) found N2O emissions up to 2.8 mg m-2 d-1 from the plants in a soil-

rye grass (Lolium perenne) system, and plant-mediated N2O emissions from maize, soybean and

wheat contributed up to 11, 16 and 62% to the total sum of N2O emissions, respectively (Zou et

al., 2005). In contrast, Müller (2003) found that presence of plants in old grassland could induce

N2O uptake. However, these studies did not identify the mechanisms underlying the plant based

N2O production or reduction processes. Chang et al., (1998) observed that barley (Hordeum

vulgare) and oil-seed rape (Brassica napus) emitted N2O from the shoots upon irrigation with

water containing N2O, and hypothesized that N2O was conveyed by the plants from the soil to

the atmosphere via the transpiration stream. In contrast, Smart and Bloom (2001) found that N2O

emissions from wheat (Triticum aestivum) leaves was correlated with leaf NO3- assimilation

activity. They found that N2O was formed during in vitro NO2--reductase activity of the leaves

and suggested that N2O formation during NO2- photo-assimilation could be an important global

biogenic N2O source. Conversion of 15NO3- to 15N2O in a range of aseptically grown plant

species was reported by Hakata et al. (2003), and increased N2O emission from soybean was

observed concomitant with an herbicide induced accumulation of plant NO2- (Zhang et al., 2000)

providing further evidence for in planta production of N2O.

Figure 7.2 Nitrous oxide emissions (µg N2O-N m-2 h-1) from beech (Fagus sylvatica) leaves after

exposing the beech roots to different concentrations of N2O in the root compartment solution. Bars

indicate average (+SE) of two different beech seedlings. Modified after Pihlatie et al. (2005).


The potential for tree species to act as conduits for N2O emissions were demonstrated in the

work by Pihlatie et al. (2005). In a laboratory experiment with beech (Fagus sylvatica) seedlings

it was found that fertilization with 15N-ammonium-nitrate (15NH415NO3) induced foliage 15N2O

emissions and exposing the beech roots to elevated N2O concentrations induced significant

emissions of N2O from shoots and leaves (Figure 8.2). Pihlatie et al. also found that

concentrations of dissolved N2O in leaves in a beech forest canopy exceeded ambient

atmospheric concentrations, indicating a potential for canopy N2O emissions.

In summary, substantial evidence exist that plants contribute directly to the emission of CH4 and

N2O. Yet, most work is hitherto based on small-scale laboratory work and the scale of the fluxes

appears small. However, there is an urgent need for field based measurements and more detailed

explanation of the underlying processes.

8.4 New developments in measurements of N2O and CH4 and denitrification

N2O and CH4 fluxes are measured at scales ranging from a few grams of soil to several km. Each

scale and method has contributed to our current understanding of biosphere atmosphere

exchange of N2O and CH4 (Denmead, 2008). Our global understanding of N2O and CH4 fluxes

and their control by physical, chemical and microbial processes has largely arisen from flux

chamber measurements. Recent development of high frequency instruments, that detect very

small concentration changes, has improved our knowledge of N2O and CH4 biosphere

atmosphere exchange at the field/landscape scale and at a high temporal resolution.

8.4.1 Flux chambers

Usually closed (non steady state) chambers are used for N2O and CH4 flux measurements, e.g.

Butterbach-Bahl et al. (1997), Conen and Smith (1998). Advantages of chambers over

micrometeorological techniques are that chambers are low cost and can be used on small fields/

plots. Disadvantages include limited spatial averaging of a spatially variable quantity due to

small area (usually <1 m) of most of these enclosures. Recent developments in chamber

methodologies include 1) an intercomparison of the main chambers types employed for N2O and

CH4 chambers used within the European community (Philatie et al. pers. comm.), similar to the

intercomparison of soil respiration chambers (Pumpanen et al., 2004). ACCENT has contributed

towards the funding of this exercise; 2) the validity of the commonly used linear regression

equation to calculate fluxes from non steady state chambers was questioned, as it may

underestimate the true flux. An exponential approach may be more accurate (Kroon et al., 2008),


3) development of the fast box method (Hensen et al., 2006) facilitates chamber measurements

from many spots within the field and establish a picture of the spatial heterogeneity of N2O and

CH4 emissions very quickly. This method requires combining manual chambers with sensitive

fast response analysis of N2O and CH4, for example tunable diode lasers.

8.4.2 Micrometeorological methods

The development of tunable diode lasers for CH4 and N2O provides a method of measuring N2O

and CH4 biosphere atmosphere exchange by micrometeorological methods at high temporal

frequency (30 min) over surfaces where fluxes are reasonably large (approx 20 ng m-2 s-1 of N2O

or CH4). This measurement approach is particularly valuable for heterogeneous ecosystems, i.e.

grazed grasslands, and soft surfaces, where compaction by walking to a flux chamber may

release gases into the chamber, e.g. peat wetlands or dung heaps. Eddy covariance

measurements of CH4 for example were made over northern wetlands in Finland (Rinne et al.,

2007) and of N2O for example over grazed grasslands in Scotland (Di Marco et al, 2004). For

well defined point sources, like dung heaps and landfill sites the Gaussian plume method has

been used to calculate the emission strength, by either walking or driving through the emission

plume (Skiba et al., 2006, Hensen et al., 2006).

8.4.3 Comparison of eddy covariance with chamber methods

Scaling up to the field and regional scale is usually based on data from small flux chambers.

Several studies have been conducted to establish the validity of this approach. It is interesting

that for N2O fluxes from grasslands and arable soils (Christensen et al., 1996) chambers

strategically placed within the footprint of the micrometeorological tower are in reasonable

agreement with eddy covariance. However for CH4 fluxes from rice paddies discrepancies of a

factor of 2 to 3 between chamber and micrometeorological method, the chambers giving lower

emissions, were reported by Kanemasu et al. (1995) from the Philippines and Hargreaves (pers.

com.) from a rice paddy field in the Po Valley, Italy. These different observations suggest that

more comparisons need to be carried out and that chambers may well be suitable for relatively

firm surfaces, but not those of low bulk density or complete water logged.

8.4.4 Recent methodological advances in measurements of total denitrification rates

For a full understanding of the processes and accurate simulation of observations using models,

we need to know the removal rate of N2O in the ecosystem. The only natural process of

permanent removal of excess N from ecosystems is denitrification to N2. The very high natural

background of atmospheric N2 hampers direct quantification of total denitrification. A wealth of


methods has been developed in the past decades for quantification of total denitrification

(Groffman et al., 2006). Unfortunately, none is without drawbacks and even today, there is no

method that can be used at the field scale or at high temporal resolution. The most common

method is the acetylene inhibition method (Balderston et al., 1976), by which the terminal step of

denitrification, i.e. the reduction of N2O to N2 is inhibited by acetylene. Major drawbacks are

that it is not easy to achieve 100% diffusion of C2H2 to the active denitrification sites, that

nitrification is also inhibited by C2H2 and that C2H2 interacts with NO in oxic environments. To

overcome these problems a completely new concept of replacing the background N2 during soil

core incubations with a noble gas (e.g. with a He:O2 mixture) has been developed and facilitates

direct measurements of N2O and N2 (Scholefield et al., 1997, Butterbach-Bahl et al., 2002). The

major drawback is the high capital investment in equipment and the time-consuming flushing

procedure to remove N2.

The use of stable isotope analysis either in tracer studies with isotopically enriched tracer

compounds or at the natural abundance level offer promising alternatives, but very little progress

has been made in the last 5 years. Application of 15NO3- containing fertiliser and monitoring 15N-

labelled N2O and N2 provides a suitable tracer for denitrification to N2 for agricultural N

fertilised soils, but not in N-poor environments. For these 15N tracers can artificially stimulate N

turnover, microbial immobilisation or dissimilatory reduction of NO3- to NH4

+. For N poor

environments natural abundance of N and O isotopes may offer an alternative, as due to kinetic

isotope fractionation the intermediates and the end product of denitrification become

increasingly depleted in 15N, whereas the remaining soil NO3- becomes increasingly enriched in

15N and 18O. If substrate is not limiting, large kinetic N isotope fractionation factors of up to -

40‰ can be observed during denitrification (Groffman et al., 2006). However, if denitrification

is limiting or rates are small, as in the case for N poor ecosystems, the apparent N isotope

fractionation is too small to provide unambiguous interpretation of the data.

Dual-isotope labelling with 15N and 18O-enriched NO3- can identify nitrification or denitrification

as source of N2O; this information is desirable for the models (Wrage et al., 2005). However,

there are problems. At low pH NO2-, intermediate of nitrification and denitrification, rapidly

undergoes O-isotope exchange with water (Casciotti et al., 2007). This O-isotope exchange

might lead to misinterpretation of the results when stoichiometric relationships in the different

N2O formation pathways are assumed, and is very likely the cause of O-isotope exchange

between N2O and water, as reported by Kool et al. (2009).


The most recent approach of quantifying denitrification rates and differentiating between

nitrification and denitrification as sources of N2O is the analysis of N2O isotopomers.

Intramolecular physico-chemical site differences between terminal and central N atom lead to

differences in N isotope ratios between the two positions during N2O formation and

consumption. Differences in this so-called 15N site preference have been attributed to N2O

production during nitrification and denitrification, respectively (Pérez et al., 2001), unfortunately

microbial populations have a larger impact on the isotopic and isotopomer signatures of N2O

than the production pathway itself (Sutka et al., 2003). Very recently, the 15N isotopic abundance

of soil-emitted NO was determined for the first time (Li & Wang, 2008). The authors found very 15N-depleted NO with δ15N values down to –50‰ and could identify both nitrification and

denitrification as sources of soil-emitted NO.

It is concluded that many challenges of quantifying total denitrification and differentiating

between N2O produced by nitrification or denitrification remain.

8.5 Modelling of N2O and CH4 fluxes on site and regional scales: approaches,

applications and uncertainties

Signatory states to the United Nations Framework on Climate Change (UNFCC) are required to

produce annual national inventories of greenhouse gas emissions from all anthropogenic sources,

including emissions from soils. With regard to CH4 and N2O, soils are the dominating sources in

the respective global atmospheric budgets of both trace gases. The IPCC (2006) recommends

three different approaches (Tier 1 to Tier 3) to provide emission inventories. Tier 1 represents

the simplest way to model or estimate GHG fluxes on site and regional scales. It is a purely

statistical approach, relating e.g. soil N2O emissions to the amount of applied fertilizer. In the

2007 IPPC reporting guidelines (IPCC, 2006) the default emission factor for direct N2O losses

from soils following N fertilisation is 1%. However, even if one assumes that this factor is

representative on a global scale, which has been questioned in the recent past (Crutzen et al.,

2008), a fixed emission factor cannot consider reported effects of climate, management or soil

properties on the magnitude of GHG exchange. Therefore, based on a detailed survey on

reported soil N2O emissions worldwide, Stehfest and Bouwman (2006) developed a more

detailed statistical approach for calculation of emission inventories, which also considers general

environmental factors such as climate, texture and soil organic carbon contents and management

related factors such as fertilisation rates and crop types. However, beside the fact that the

demand on required input information is much larger, this approach also has its weaknesses: a)

the high uncertainty of the developed statistical model, b) the rough classification scheme (which


is due to the limited availability of field data sets describing GHG emissions for different

environmental conditions) and c) incomplete coverage of pulse events, which may dominate

annual site budgets.

To account for the huge spatial and temporal variability of GHG fluxes on site to global scales

the development and use of process-oriented models may at present be the most promising

approach (Butterbach-Bahl et al., 2004). These models simulate the GHG exchange at a given

site based on the underlying processes, i.e. by simulating the dominant physico-chemical, plant

and microbial processes involved in ecosystem C and N cycling and associated GHG exchange

(Li et al., 2000). As a general assumption, one defines that the controlling factors for e.g.

microbial C and N turnover such as temperature, moisture and substrate responses, are

comparable across different climatic zones and land uses and that by capturing the major

biogeochemical processes within an ecosystem it is possible to predict the temporal variability of

fluxes. Such models require a thorough process understanding of the coupled C and N (P) cycles,

even though the level of process description may vary between the models currently in use (e.g.

Li et al., 2000). However, these models do also have the drawback, that modelling of ecosystem

processes involves a huge data set of parameters needed to describe heat transfer, water

movement, plant and microbial growth or anthropogenic management. Since each parameter has

its specific, though often unknown uncertainty, the uncertainty of simulation results is often

significant, if at all measurable. In comparison to statistical approaches mechanistic models often

show an improved performance with regard to reproducing observed differences in GHG fluxes

between sites, seasons and management practices (e.g. Kesik et al., 2006).

However, the increasing use of mechanistic models also shows that we still need to improve our

process understanding, e.g. the regulation of microbial processes and its dependence on microsite

variability of environmental conditions such as redox potential or the feedback of temperature on

organic matter decomposition. A good example of the deficiencies in understanding N2 versus

N2O production during denitrification is provided by (Groffman et al., 2006). This gap in

knowledge also precludes the parameterisation of the denitrification process in biogeochemical

models such as DNDC or DayCent, which in consequence leads to a systematic underestimation

of N2 losses using either model.


Figure 8.3: N2O emissions from agricultural soils in Europe using the GIS coupled DNDC model. For further details

on databases and methodology see Butterbach-Bahl et al., (2008)

Biogeochemical process models have recently been used in a number of studies for calculating

regional soil GHG emission inventories (Figure 8.3). Thereby regionalisation is achieved by

coupling of the models to GIS databases holding all the relevant information needed for

initialising (soil and vegetation properties, management) and driving (meteorological conditions)

the models (Kesik et al., 2006). Such an approach partly neglects landscape processes, such as

e.g. lateral flow and transport of nutrients and sediments via leaching or erosion, even though an

increasing number of groups are working on fully coupled landscape models, which do allow to

consider interactions between the biosphere, hydrosphere and atmosphere on landscape scales.

National inventories for N2O and/ or CH4 emissions from soils using DNDC or DayCent have

been calculated for US, UK, China, Germany, India or Europe. Even on a global scale, the GIS

coupled Forest-DNDC model was used to estimate N2O emissions from tropical rain forest soils

(Werner et al. 2007). Increasingly biogeochemical models have been used to study potential

strategies for mitigating GHG emissions from soils on site (Li et al., 2005) as well as on regional

scales (e.g. Li et al., 2006) or to improve our understanding how future changes in climate or

land use may feedback on biosphere-atmosphere exchange of GHG (Parton et al., 2007).

Uncertainty in such emission inventories and mitigation/ feedback studies is associated with the

uncertainties in input parameters as well as with the uncertainties in model parameters. However,

in present studies the uncertainties in input parameters have mainly been addressed, e.g. by use

of Monte Carlo techniques (Kesik et al., 2006; Werner et al., 2007), whereas model parametric

uncertainty is often neglected (Van Oijen et al., 2005). This is a shortcoming, which needs to be

properly addressed in coming studies and which is already in the focus of model development


and application within the NitroEurope project (www.nitroeurope.eu). Nevertheless,

biogeochemical models offer a great chance to prove our understanding of ecosystem processes

and GHG exchange and they will play an important role in identifying and predicting

consequences and feedbacks of global changes (climate and land sue change) for ecosystem

functioning and biosphere-atmosphere trace gas exchange.

8.6 Validation of models by landscape and regional scale measurements

Developments in inverse modeling and direct large-scale measurements provide very powerful

tools to constrain and verify our bottom up models and inventories. For example, Bergamaschi et

al., (2005) compared inverse models with national bottom-up inventories for CH4. These

developments are taken further within the NitroEurope project (www.nitroeurope.eu).

The development of instruments sensitive enough to measure very small concentration

differences has made it possible to directly measure CH4 and N2O concentrations from aircraft

and satellites. For example, in the UK aircraft based N2O and CH4 concentrations measurements

downwind of the British coast have delivered unique measurements of CH4 and N2O at the

country scale and provided independent top-down estimates of UK emissions. Measurements

were interpreted by using a simple boundary-layer budget approach and the dispersion model

NAME. This approach suggests that the bottom up national emission inventory underestimates

CH4 emissions by a factor of two and N2O emissions by a factor of three (Poulsen et al.,

submitted). An underestimation of the UK national CH4 inventory was also reported by

Bergamaschi et al.’s (2005) comparison of bottom up and inverse modelling approaches.

Satellite-borne instruments, such as SCIAMACHY, are able to provide CH4 concentration

measurements at the global scale. SCIAMACHY can clearly detect spatial and temporal

variations in CH4 concentrations in the boundary layer, a considerable achievement given the

small enhancements in a large background signal. Using these methods emissions due to

coalfields, rice cultivation, ruminants and wetlands are visible for China and India and the Po

valley in Italy (Buchwitz et al., 2006). Comparisons between SCIAMACHY observations of CH4

concentrations and those derived from simple emission inventories revealed large regional and

seasonal differences, especially over tropical rainforests. To some extent these differences were

caused by overestimating CH4 concentrations when water vapour concentrations were high

(Frankenberg et al., 2008). In spite of this correction, SCIAMACHY still estimates a larger CH4

budget for the tropics than previously estimated. Unfortunately, validation of the global N2O


budget using satellites is currently not possible as sufficiently accurate and precise N2O satellite

data with high sensitivity near the earth’s surface do not exist.

8.7 Conclusions

The key developments and gaps in knowledge are:

o New molecular tools are now available to link soil microbial biodiversity with soil

function and can provide an overview of the distribution of functional microbial groups

in soils of different landuses, and assign trace gas emissions to the active microbial

population.

o Instrument development has facilitated CH4 and N2O flux measurements at the field and

landscape scale and provides long-term measurements at large spatial scale and high

temporal resolution at key sites.

o New methods to study denitrification rates to N2 and isotope studies to elucidate the

microbial pathway responsible for N2O production and removal are being developed, but

none of these methods can currently be used at the fieldscale or high frequency temporal

scales.

o There are insufficient data to scale up CH4 and N2O emissions to the global scale or to

include ‘new’ crops, i.e. bioenergy crops.

o There is a gap in knowledge of the contribution and quantification of plants, especially

trees, in producing and transporting N2O, CH4 from soil to atmosphere.

Biogeochemical models have been developed and synthesize our understanding of ecosystem

processes and GHG exchange. They play an important role in identifying and predicting

consequences and feedbacks of global changes (climate and land use change) for ecosystem

functioning and biosphere-atmosphere trace gas exchange.

Inverse modelling, tower and aircraft based boundary layer budget studies have been developed

to validate bottom up models and inventories.


9. Exchange of trace gases and aerosols over the oceans

9.1 New trace gas interactions at the air-sea interface

9.1.1 Introduction

Considering the size and potential importance of the air-ocean interface, it is surprisingly poorly

characterised for most organic trace gases. These organic species are known to play important

roles in the Earth’s atmosphere, impacting ozone chemistry and aerosol formation, thereby

influencing the Earth’s overall oxidation capacity and radiative budget (Williams 2004 and

references therein). It should be noted that the net primary production (NPP) of the ocean is

comparable in size to that of the terrestrial biosphere (ca. 45 PgC yr-1), even though there is

approximately 100 times less biomass in the ocean than on land. The relative paucity of ocean

based data compared to terrestrial sites is due partly to accessibility and partly to the high spatial

and temporal variation within the limited oceanic biomass. Moreover, there has been a

perception from earlier studies of oceanic alkanes and alkenes that the global ocean is a

relatively minor source term. Over the period of the ACCENT project, this view has changed

remarkably and recent studies are beginning to recognise the profound effects of the ocean-air

interface on global chemical budgets. For many important chemical species in the atmosphere

the role of the ocean remains the greatest uncertainty in the budget.

The sunlit regions of the oceans are home to a myriad tiny plant species and bacteria. These

organisms photosynthesise carbon dioxide (CO2) from the atmosphere into biomass, and a

fraction of the carbon “leaks” out into the surrounding seawater in the form of organic

compounds. Some small volatile species with low Henry´s Law coefficients are known to escape

directly to the atmosphere (e.g. dimethyl sulphide, DMS) while larger species will remain in the

water phase. Subsequent photo-oxidation in both air and seawater phases generates a multitude

of photochemical breakdown products. These compounds may affect the hygroscopicity and

reflectivity of the marine boundary layer aerosol, either by condensing onto existing aerosol

surfaces, or by being ejected directly with primary aerosol from the sea surface.


Figure 9.1 A schematic diagram of the processes affecting organic species at the air-sea interface.

Several important organic emissions from the ocean have been identified previously. The best

known is dimethyl sulphide (DMS) which is produced biogenically in the ocean (e.g. Keller et

al., 1989; Liss et al., 1997, and references therein), and yields the inorganic aerosol component

sulphate upon complete oxidation in the atmosphere (Kiene and Bates, 1990). It is also well

established that organohalogens are emitted in various forms (e.g. methyl iodide, bromoform,

methyl bromide) from phytoplankton, bacteria, molluscs and worms (e.g. Gribble, 1992,

Carpenter et al. 2000). Following atmospheric oxidation, these can affect either tropospheric or

stratospheric ozone, depending on the lifetime of the species. However, over the period of the

ACCENT project (2003-2008), there has come a realisation that the surface ocean can play an

important role in the budgets of many more organic trace gases. For example, the surface ocean

has been shown recently to be a large reservoir for oxygenated organic species e.g. acetone

(Singh et al., 2003, Williams et al., 2004). The possible influence of oceanic isoprene on marine

clouds has also been hotly debated (Meskhidze and Nenes, 2006). Finally a surface ocean source

of methanol first speculated in mesocosm studies (Sinha et al. 2005) has been implemented in a

global model assessment of methanol, thereby generating an improved fit between model and

measurement data (Millet et al. 2008). Therefore this article has been focussed on the more

recent discoveries related to acetone (CH3COCH3), methanol (CH3OH), isoprene (C5H8),

monoterpenes (C10H16) and alkyl nitrates (RONO2) in order to highlight the new developments in

air/ocean interactions.


9.1.2 Case studies

Acetone (Ocean uptake)

Over the past 5-6 years our understanding of the role of the ocean in the global acetone budget

has changed remarkably. Acetone is ubiquitous in the troposphere and found at relatively high

mixing ratios (ca. 200 ppt) even in the remote Pacific atmosphere (Singh et al. 2001). Since

acetone is recognised as an important precursor for PAN, ozone and HOx, especially in the cold,

dry, upper troposphere, there has been interest in determining the sources and sinks worldwide.

In 2002, Jacob et al. published a global budget of acetone (Jacob et al. 2002) which differed from

all previous budget attempts in that it considered the role of the ocean for the first time. Through

inverse modelling, they estimated that the ocean was an important net source of acetone. Indeed,

from the total global source of 95 Tg, some 25 Tg was estimated to originate from the ocean in

order to balance the known sources and sinks. This was pioneering work since at that time no

seawater acetone measurements, or air-sea fluxes were available. However, in the space of just 2

years this view changed drastically. In 2004 the model developed by Jacob et al. 2002 was tested

against measurements over the remote Pacific (Singh et al. 2004). It was found that the model

consistently overpredicted the measured acetone mixing ratios in the marine boundary layer and

the authors concluded that the ocean was a net global sink for 15 Tg, and that the sources and

sinks were not balanced. In 2004, the first open ocean measurements of acetone in air and

seawater were made (Williams et al. 2004). The interhemispheric gradients and depth profiles

shown by Williams et al. 2004 were consistent with uptake of acetone from the air to the sea and

a microbial sink in the seawater.

In 2004, two important new publications emerged concerning acetone. The first was a laboratory

based study which re-determined the photolysis quantum yield of acetone as a function of

temperature and pressure (Blitz et al. 2004). It was found that the accepted acetone photolysis

rates were significantly overestimated (factor 3-5), particularly for the cold, low pressure

conditions of the upper troposphere. Within the global budget of acetone this represented a

reduction in the effectiveness of the photolysis sink term (Arnold et al. 2004). In the same year, a

new shipborne measurement study was published in which the authors directly measured the flux

of acetone at the ocean surface for the first time using an eddy correlation method (Marandino et

al. 2005). Interestingly, the authors consistently found uptake fluxes (from the air to the ocean)

for acetone over the oligotrophic Pacific ocean which became stronger further from the equator.

For comparison Marandino et al. 2005 also determined the flux of acetone by making separate

measurements in the seawater (5m depth) and air (18m height). Similar to the results from the

Tropical Atlantic (Williams et al. 2004), these water and air measurements led to highly variable


flux results at the surface, whereas the direct flux measurement was more consistent. This

strongly suggested that for acetone, the actual air/ocean flux is being driven by processes in the

uppermost layer (0-5m).

Although these two new studies (Blitz et al. 2004 and Marandino et al. 2005) had a strong impact

on the original budget of Jacob et al. 2005,the overall the sources and and sinks for acetone were

still not balanced. To understand these processes better a new approach to investigate acetone

ocean fluxes was made using so-called “mesocosms” (Sinha et al. 2005). The mesocosms are

light permeable Teflon tent-like structures which float on the surface ocean enclosing a volume

of air near the surface, and with walls that extend some 20m below the surface to restrict the

advection of the water mass below. The airspace in the top of the mesocosm was continually

flushed with ambient air to give a residence time of approximately 3 hours in contact with the

water surface. By measuring at the inlet and outlet, the flux could be calculated while

phytoplankton in the water column were monitored. In the case of methanol a clear uptake flux

(from the air to the ocean) was observed throughout the experiment, whereas for DMS the flux

was always from the ocean to the air. Interestingly, for acetone the flux was found to be variable

but systematic. In strong daylight and in the presence of significant biological activity, acetone

was emitted from the ocean to the air. In low light or biologically poor regimes, however,

acetone was taken up by the water. These results are consistent with the results of Marandino et

al. 2005 and the ocean being a net sink for acetone on a global scale, since most of the ocean is

oligotrophic. However, biologically active regions (e.g. upwelling zones, ocean fronts, or large

natural phytoplankton blooms) can be strong sources in daylight and depending on their size

could to some extent offset the general sink. It is therefore important to investigate these

biological hotspots in future to better constrain the global budget.

Methanol (Ocean uptake)

In many respects the global methanol budget is similar to that of acetone discussed above. Plant

growth accounts for most of the estimated global source (40-80%) and again the role of the

ocean is one of the largest uncertainties in the budget. Studies of methanol have consistently

indicated an ocean uptake of methanol (Williams et al. 2004, Lewis et al. 2005, Sinha et al. 2007

and references therein.) Recently, a global 3-D chemical transport model (GEOS-Chem) was

used to integrate and interpret new aircraft, surface, and oceanic observations of methanol in

terms of the constraints that they place on the atmospheric methanol budget (Millet et al. 2008).

It was shown that for methanol, although overall the ocean represents a net sink, a separate light

dependent oceanic source needs to be introduced in order to correctly simulate regional


distributions in the atmosphere. This in-water source has the effect of tempering the uptake flux

particularly in the Tropics. It was deduced that that the ocean contains a large primary source (85

Tg y-1) of methanol to the atmosphere and also a large sink (101 Tg y-1), comparable in

magnitude to atmospheric oxidation by OH (88 Tg y-1). Thus the ocean is a net sink overall, but

the in-water source term must be included to match with available atmospheric measurements

datasets.

Isoprene (Ocean emission)

Isoprene, the strongest terrestrial biogenic emission, has also been observed as an oceanic

emission (Bonsang et al., 1992) and in laboratory based studies of plankton (Shaw et al., 2003

and references therein). It has been suggested recently that marine isoprene emissions are the

cause of cloud droplet radius changes in marine clouds situated directly over phytoplankton

blooms (Meskhidze and Nenes, 2006). However, an impact of the isoprene on cloud properties

appears unlikely given that concentrations of isoprene measured over the Southern Oceans do

not impact the organic carbon aerosol concentrations significantly (Arnold et al. 2008). In the

aforementioned paper an aerosol production efficiency of 2% was assumed for isoprene, and the

modelled contribution of isoprene to organic carbon (OC) was found to be less than a 1%.

Moreover, since time is required to oxidise isoprene to nucleating products, a superpositioning of

a cloud effect over a bloom in a region of high wind speeds again appears unlikely. Typical

mixing ratios of isoprene over phytoplankton rich areas are 200-300 ppt, approximately an order

of magnitude less than over the rainforest (Williams et al. 2001). However, since isoprene reacts

rapidly in air, terrestrial emissions will not impact the open ocean. Marine isoprene emissions

could influence the local ozone production efficiency in regions where ship emissions of NOx

occur. This may be significant for fishing fleets, as the fish, and hence the fleet, follow the

isoprene producing phytoplankton.

Halogenated Organics (Ocean emission and uptake)

The ocean acts as a huge reservoir for chlorine, bromine and iodine and volatile organic halogen

species (e.g. halocarbons) provide a pathway to transport halogens from the water phase to the

atmosphere. Previous studies revealed that halocarbons like CH3Cl, CH3Br, CH3I, CHBr3 and

CH2Br2 are emitted from various marine organisms, especially macro- and microalgae (Ekdahl et

al., 1998, Scarratt and Moore, 1999 and references therein). The global sources of CH3I, CH2Br2

and CHBr3 are dominated by marine contributions. Algal emissions of halogenated compounds

vary considerably, not only from species to species, but also as a function of age, temperature,

time of day, nutrition, partial desiccation, grazing, light and tidal movement (Ekdahl et al.,


1998). Polybrominated species (e.g. bromoform) are primarily emitted by macroalgae which

occur only in coastal regions, whereas monohalgenated compounds can be produced from

various open ocean biomes.

Monoterpenes (Ocean emission)

Recent laboratory incubation experiments and shipboard measurements in the Southern Atlantic

Ocean have provided first evidence for marine production of monoterpenes (Yassaa et al. 2008).

Nine marine phytoplankton monocultures were investigated using a GC-MS equipped with an

enantiomerically selective column and found to emit at rates, expressed as nmol C10H16

(monoterpene). g [Chl_a]-1. day-1, from 0.3 nmol g [chl_a]-1 day -1 for Skeletonema costatum and

Emiliania huxleyi to 225.9 nmol g [chl_a]-1 day -1 for Dunaliella tertiolecta. Nine monoterpenes

were identified in the sample and not in the control, namely; (-)-/(+)-pinene, myrcene, (+)-

camphene, (-)-sabinene, (+)-3-carene, (-)-pinene, (-)-limonene and p-ocimene.

The laboratory measurements are also supported by shipboard measurements of monoterpenes in

air were made between January and March 2007, while crossing the South Atlantic Ocean, see

Figure 9.2. Monoterpenes were detected in air over high ocean chlorophyll regions sufficiently

far from land as to exclude influence from terrestrial sources. Maximum levels of 100-200 pptv

total monoterpenes were encountered when the ship crossed an active phytoplankton bloom,

whereas over the oligotrophic ocean monoterpenes were mostly below detection limit. The

monoterpenes/isoprene ratio reached 21% in laboratory experiments (the ratio between the

highest production rates of total monoterpenes and isoprene) and ranged between 7 to 60% in the

Southern Atlantic Ocean.

Figure 9.2 MODIS chlorophyll picture of the Southern Atlanic ocean in January, inset the Research vessel Marion

Dufresne.


Alkyl nitrates (Ocean emission)

Alkyl nitrates were assumed until recently to be exclusively of anthropogenic origin, being

emitted directly from combustion or chemical processes (Simpson et al., 2002), or being

produced at low yield in the photooxidation of organic compounds in the presence of NOx via

the reaction of an organic peroxy radical (RO2) and NO (Roberts, 1990). However,

measurements of MeONO2 and EtONO2 both in equatorial air and seawater (Chuck et al., 2002)

have revealed positive saturation anomalies, and high levels of RONO2 which correlate strongly

with species of known marine origin such as bromoform (Blake et al., 1999). The mechanism of

formation of marine alkyl nitrates still remains somewhat unclear. Production in seawater

through aqueous phase photochemistry (Dahl et al., 2003) has been shown to occur via the

reaction of ROO + NO, where photolysis of coloured dissolved organic matter (CDOM)

generates the peroxy radicals and nitrate (NO2-) photolysis generates the NO. Interestingly, the

yield of the reaction ROO + NO in seawater was found to be significantly higher than in the gas

phase. Alternatively, alkyl nitrates may be emitted directly from marine biota, although direct

evidence for enzymatically mediated production has not yet been found. Using a chemical

transport model Neu et al. (2008) found the maximum impact of the oceanic alkyl nitrates to be

over the Western Pacific, where they were responsible for of increase of up to 20 % of the ozone

column.


9.2 Aerosols

9.2.1 Primary Marine Aerosol (PMA) Source functions

Primary marine aerosol (PMA), or sea-spray aerosol is a major source of global natural aerosol

mass budgets and is important for global climate. Mass is dominated by the supermicron size

range and traditionally source functions have been derived in this size regime. The supermicon

size range also contributes significantly to aerosol scattering (Kleefeld et al., 2002) and optical

depth (Mulcahy et al., 2008) and thus the direct climate forcing effect. In terms of the submicron

size range, number concentration rather than mass becomes important in terms of the indirect

radiative forcing effect through the production of cloud nuclei (O’Dowd et al., 1999). Only quite

recently it has become accepted that submicron sea spray aerosol exists, and as a result,

submicron source functions are relatively new in terms of development.

The PMA source function describes the flux of sea spray aerosol, i.e. the number of droplets

produced per unit surface area and per unit of time, evaluated typically at 10 m above the ocean.

Hence the function describes an effective flux, parameterized in terms of ambient parameters

such as wind speed and water temperature. Measurements may provide total fluxes, i.e. the total

number of particles in a given size interval, or spectral fluxes. The latter are expressed in number

of droplets for a range of size intervals, i.e. µm-1 m-2 s-1. In this review, we focus on a selection

of recently developed or improved source functions which span both sub-micron and super-

micron sizes. Particular emphasis is focused on the submicron spray flux and chemical

characteristics. A comprehensive historical review, focused primarily on sea-salt aerosol

production, is provided by Lewis & Schwartz (2005) with some additions and description of

specific source function formulation in O’Dowd and de Leeuw (2007).

It is often assumed that the dependences on droplet size and environmental parameters can be

separated, i.e. the source function is presented as the product of a size dependent function, g(r),

and a function that describes the parameterization as function of of environmental parameters,

f(a,b,…), where r is the droplet size at a specified relative humidity (dry, RH=80%, or wet) and

a, b, … are, e.g., wind speed, water temperature, atmospheric stability, etc. Scaling arguments

show that droplet production varies approximately with the third power of the wind speed.

However, other types of paramterization have been proposed as well. Selected source functions

are shown in Figure 9.1 for a wind speed of 8 m s-1. There are two main developments to report

on: the first in terms of the supermicron sizes where the data in Figure 9.1 show that the

discrepancy between different formulations is much reduced with respect to the review situation

reported by Andreas (2002). With respect to Lewis and Schwartz (2004), the uncertainty has


been reduced by a factor of 2. For small particles a definite size dependence emerges varying

roughly as r80-1.5. The source functions shown in Figure 9.1 were obtained using different

methods and different physical principles but leading to consistent results.

The second main development is

the extension of the source

function well into the submicron

size range. The Mårtensson et al

(2003) laboratory based study

extended the size resolved source

function down to r80=20 nm and

found that the production as a

function of size was also

dependent on temperature.

Clarke et al. (2006) provide a

source function for particles

down to 10 nm. These studies

combine experiments in the laboratory or over the surf zone, to determine the spectral shape of

the flux, with whitecap coverage which in turn is paramterized as function of wind speed. Direct

and in-situ measurements of sea spray total number fluxes (D> 10 nm) are provided by the eddy

covariance (EC) method that was first attempted by Nilsson et al. (2001) in the Arctic Ocean.

The advantage of this method, as opposed to the whitecap method, is that all particles within the

detectable size range may be measured, and hence there is no restriction to bubble-mediated

production. The technique was also used at a coastal station over the North East Atlantic by

Geever et al., (2005), who quantified total number concentration over two size ranges covering

the Aitken mode (10-100 nm) and the Accumulation mode (100-500 nm), and by Norris et al.

(2008) who provided a size-segregated source function from eddy covariance measurements

aboard a ship at the North Atlantic.

Overall, the most recent schemes agree quite well (e.g. Clarke et al. surf zone study compares

very well to the Mårtensson et al. laboratory based parameterization), providing an improved

level of confidence in PMA source functions over sizes from 0.01 µm to ~10 µm. In addition, the

Mårtensson et al. (2003) parameterization provides a water temperature dependence that

compares favorably with independent measurements (e.g., Clarke et al. (2006) for 25oC, Vignati

et al (2001) for ca. 15 oC).

Radius (microns)

10-2 10-1 100 101 102

dF/d

Log(

r) m

-2 s

-1

100

101

102

103

104

105

106

107

108

Monahan et al 1986Monahan ExtrapolMartensson et al., JGR, 2003 Vignati et al., JGR, 2001Gong, JGR, 2003Clarke et al., JGR, 2006de Leeuw et al., JGR, 2000de Leeuw et al., AMS, 2003Reid et al., JGR, 2001,

Figure 9.1. Compilation of sea-spray source functions. Flux values

are for a wind speed of 8 m s-1.


9.2.2 Chemical Composition of primary sea spray

Although the dominant mass fraction of sea-spray aerosol is sea-salt, organic matter also

contributes to the overall mass and it has long been known that marine aerosols contain organic

material (i.e. Blanchard, 1964). Field measurements suggested a significant biogenic primary

source of marine organic components (O’Dowd et al., 2004; Cavalli et al., 2004). In particular a

dominant water-insoluble organic fraction in fine marine aerosol collected during periods of

phytoplankton bloom in the North Atlantic was observed and it was hypothesized that these

insoluble organic components could have a mainly primary origin. Similar results supporting a

biologically driven oceanic OC source have been recently reported by Spracklen et al.,(2008)

Figure 9.2 (Left) Chemical and mass size distributions for

North Atlantic marine aerosol during periods of low

biological activity and high biological activity.

(Right) oceanic chlorophyll-a concentrations over the

North Atlantic for low and high biological activity

periods.

The most comprehensive study to date on the organic fraction of sea-spray aerosol has been

conducted by O’Dowd et al., (2004). They found a significant and dominating fraction of

organic matter in submicron sizes, while the supermicron size range was predominately

inorganic sea-salt. It should be noted, however, that the absolute magnitudes of organic mass in

the sub and super micron size ranges were equivalent (with one third of the total organic mass

residing in the coarse mode), and that it was their relative concentrations that differed


significantly. Figure 9.2 illustrates the chemical composition of clean marine aerosol over the

north east Atlantic during winter and summer periods, corresponding to low and high biological

activity periods (O’Dowd et al., 2004). Also shown is the distribution of chlorophyll-a derived

from the SeaWifs satellite. During periods of high biological activity, the organic fraction ranged

from 40-60% of the submicron mass, while during low biological activity periods, the fraction

reduced to about 10-15%. O’Dowd et al, (2004) argued that the water insoluble organic fraction,

dominating the organic composition in the fine size fraction, was likely to be derived from bubble

mediated production..

Fig 9.3. (Left) Average chemical composition relative

concentration from bubble bursting tank samples.

(Bottom) average ratio of WIOC to sea-salt from

atmospheric samples at Mace Head. Data for the 0.06 –

0.125 µm size range are not reported from the bubble

tank because the total carbon analyses in this stage were

below detection limit. The bars are the standard

deviation of the mean.

(Right) Visible satellite image of plankton bloom off the

west coast of Ireland during the MAP cruise June-July

2006. Bubble-bursting tank experiments were

conducted in and around the plankton bloom.

Later experiments, using the gradient technique to determine aerosol chemical fluxes at Mace

Head (Ceburnis et al., 2008) showed that the water-insoluble organic carbon (WIOC) mass

invariably had an upward mass flux associated with it and followed similar trends to sea-salt

gradients, while water-soluble organic carbon (WSOC) mass possessed a downward flux profile


identical to nss-suphate. They concluded from the gradients that WSOC must be formed from

secondary aerosol formation processes while WIOC must be formed from primary production.

These conclusions were further supported by Facchini et al., (2008a) who conducted bubble-

bursting experiments amidst a plankton bloom over the NE Atlantic during the MAP (Marine

Aerosol Production) cruise in 2006 (Figure 9.3). During these experiments, it was found that

spray particles exhibited a progressive increase in the organic matter content from 3 ± 0.4% up to

77 ± 5% with decreasing particle diameter from 8 to 0.125 microns (Figure 9.3). Submicron

organic matter was almost entirely water insoluble (WIOM) and consisted of colloids and

aggregates exuded by phytoplankton. Facchini et al (2008a) found that the WIOC to sea-salt

mass ratio fingerprint as a function of particle size in the bubble tank experiments matched that

observed in atmospheric samples both at Mace Head (shown in Figure 9.3) and on the MAP

cruise over the open ocean. These results conclusively confirmed that the WIOC component

observed in marine air samples relate to primary aerosol production. Electron microscopy

observations of individual particles collected at the ocean surface in a number of sites support the

hypothesis that complex exopolimers and the microgels forming from these, produced by bacteria

and algae, are involved in bubble bursting processes (Bigg and Leck., 2008). Keene et al., (2007)

also measured sea salt and organic carbon in water extracts of nascent marine aerosol, showing a

strong enrichment of organic in all size fractions, with the highest enrichment in the smallest size

fractions. However, the authors did not distinguish between water soluble and insoluble organics.

These results indicate that a sea-spray source function should not only consider size resolved

mass, but also chemical composition. The first attempt at a combined sub-micron organic-

inorganic sea-spray source function, implemented in a regional climate mode, was produced by

O’Dowd et al (2008). They combined the Geever et al, (2005) accumulation mode number flux,

combined with the Yoon et al., (2007) seasonal modal diameter (minus secondary aerosol mass),

and integrated with the seasonal trend in WIOM/sea-salt ratios to produce a physico-chemical

flux function driven by wind speed and satellite-derived chlorophyll-a concentrations over the

North East Atlantic. The model predicted results compare well to seasonal observations at Mace

Head and are illustrated in Figure 9.4 for winter and summer seasons.


Figure 9.4. (Top panel) Near surface sea-spray mass concentrations around the European regions and average wind

vectors. (Bottom Panel) Percentage primary organic contribution to sea-spray mass.

9.2.3 Secondary Aerosol Production

In recent years, significant effort has been made into the study of new particle formation in the

coastal zone in the hope that it would elucidate key processes associated with nucleation over the

open ocean. Most of these studies have focused on nucleation in coastal zones (e.g. at Mace

Head) and revealed regular particle bursts, with burst concentrations often exceeding 106 cm-3.

These events have been linked to release of biogenic iodine vapours from coastal algae followed

by the photochemical production of iodine oxide aerosols (O’Dowd et al., 2002; McFiggans et

al., 2005). A detailed review of studies into these processes is found in O’Dowd and Hoffmann

(2005). Further studies revealed that the nucleation mode particles could also contain some

organic aerosol mass suggesting that secondary organic aerosol production also occurs in marine

air and contributes to aerosol growth (Vaattovaara et al., 2006). The findings of O’Dowd et al

(2004) and Ceburnis et al., (2008) also point to significant contributions of secondary organic


aerosol, manifesting itself in the WSOC component. The most relevant organic secondary

component (SOA) is methanesulphonic acid (MSA). Some dicarboxylic acids were associated to

secondary formation mechanisms in previous papers (i.e. Kawamura et al., 1999) but a relevant

fraction of the observed concentrations of oxidized organic matter in marine aerosol still remains

unaccounted. Modelling studies by Meskhidze and Nenes, (2006), proposed that isoprene

emissions from plankton were sufficient to produce enough water soluble organic aerosol to

significantly enhance CCN concentrations and cloud albedo; however, it was later revealed that

the isoprene fluxes were inadvertently overestimated by a factor of 100. Nevertheless, Zorn et

al., (2008) also confirmed the dominance of organic aerosol mass in air overlying plankton

blooms over the southern ocean but no detail on speciation was elucidated. Recently a new

secondary organic aerosol component, produced through the reaction of gaseous amines with

sulphuric acid has been found in marine aerosol over the North Atlantic.(Facchini et al., 2008b).

Dimethyl and diethyl ammonium salts (DMA+ and DEA+) are the most abundant organic species,

second only to MSA, detected in fine marine particles in North Atlantic and represent on average

11% of SOA and a dominant part (35% on average) of the aerosol water soluble organic nitrogen

(WSON). Several evidences support the hypothesis that DMA+ and DEA+

have a biogenic

oceanic source even if the formation mechanism of these biogenic amines remains unclear..

In conclusion, apart from MSA and a few dicarboxylic acids and amine salts, the vast majority of

secondary organic marine aerosol remains to be identified, suggesting that other formation

mechanisms and alternative SOA components should be studied.


10. The processes of wet scavenging of aerosols and trace gases from the

atmosphere 10.1 Introduction

Precipitation or wet scavenging is an efficient cleaning mechanism of the atmosphere. It

combines all the in and below cloud processes that take up trace gases and particles into liquid

drops or crystals forming a cloud and deposits the material to terrestrial or marine surfaces in

rain or snow.

10.2 Nucleation scavenging of drops and ice crystals

Droplets form on a subset of the aerosol particles present in every air mass (CCN=cloud

condensation nuclei). This mechanism is probably the most important to incorporate pollutants

into the cloud phase. Depending on their size, chemical composition and the ambient relative

humidity, aerosol particles take up a certain amount of water (Pruppacher and Klett, 1997) and

when exceeding their critical size they activate to cloud droplets. In the classical Köhler theory,

only their composition with respect to insoluble material and inorganic salts is considered.

Recent studies (e.g., Anttila, and Kerminen, 2002; Sorjamaa et al, 2004; Romakkaniemi et al,

2005; Kokkola et al, 2006; Topping et al, 2007) have highlighted the importance of soluble trace

gases and partly soluble organic substances which often coat the surface of the particles for the

activation properties.

Even though our knowledge of the formation of droplets is now reasonably satisfactory, the

nucleation of ice crystals is a subject still quite poorly understood. In the atmosphere, significant

numbers of ice particles start to form only below −5 ◦C coexisting still with liquid drops.

Homogeneous freezing of liquid droplets depends on the size; large droplets can freeze

homogeneously at temperatures of around −33 ◦C, whereas at −40 ◦C even the smallest droplets

freeze homogeneously. New insight into the homogeneous nucleation of ice crystals under these

conditions which correspond to cirrus clouds was obtained in the AIDA chamber (Benz et al,

2005; Möhler et al, 2006). In the temperature range between −5 and −40 ◦C, the presence of

insoluble nuclei is necessary to initiate the formation of an ice crystal. These ice nuclei (IN) are

aerosol particles that can act in four main ways:

– Deposition mode: water is adsorbed directly from the vapor phase onto the surface of an

IN where it is transformed into ice


– Condensation–freezing mode: this is a hybrid process that requires supersaturation with

respect to water. Here, the CCN that has formed the drop acts now as an IN. This process

seems far more effective than the deposition mode.

– Freezing mode: the IN, scavenged by the drop, initiates the ice phase from within a

supercooled water droplet

– Contact mode: the IN initiates the ice phase at the moment of contact with the

supercooled drop

The number of IN depends on the chemical properties of the aerosol particles. It has been found

that there exists a dependency on supersaturation (Meyers et al., 1992) and also on temperature

(Fletcher, 1962). In contrast to CCN, a good IN should be insoluble and have a crystalline-type

structure to facilitate the formation of the ice lattice (e.g. silicate).

Recently, the role of primary biological aerosols for nucleation of drops and ice crystals has been

highlighted (Deguillaume et al, 2008). These particles can be viable organisms capable of

metabolic reactions which can involve atmospheric organic compounds and oxidants (airborne

micro-organisms) (Ariya and Amyot, 2004; Sun and Ariya, 2006). They also comprise either

biological particles including alive, dead cells and cell fragments, capable of nucleating cloud

droplets and ice particles via physical processes (Möhler et al., 2007) or any kind of organic

substances deriving from biomolecules and contributing to aerosol masses. Airborne micro-

organisms are incorporated into cloud droplets and raindrops by nucleation scavenging as they

have CCN or IN potential (e.g., Lee et al., 2002; Bauer et al., 2003; Möhler et al., 2007) or by

washout processes. Some investigations clearly show that most of these micro-organisms are

able to develop at low temperatures (between −5 and 5°C) encountered in clouds. Furthermore,

measurements of concentrations of adenosine triphosphate (ATP) in cloud water indicate that

most micro-organisms are still metabolically active (Amato et al., 2007). For a comprehensive

review of the biogenic versus anthropogenic sources of IN, see also Szyrmer and Zawadzki

(1997).

Despite the presence of ice in many cloud systems, interactions between trace chemicals and ice

are not well understood (Abbatt, 2003). Chemical solutes originally dissolved in a supercooled

drop may be retained or expelled from the drop as it freezes. Non-volatile species, such as

sulfate, are efficiently retained during freezing but this retention process is not well characterized

for many soluble gases found in clouds (Voisin et al., 2000). Cloud modeling studies have found


that partitioning of solutes during hydrometeor freezing may significantly affect chemical

distributions in the troposphere and deposition to the ground (Audiffren et al., 1999; Mari et al.,

2000; Crutzen and Lawrence, 2000; Yin et al., 2005, Kärcher and Basko, 2004). A better

understanding of the partitioning of volatile chemical solutes during freezing is needed to

quantify their effects on tropospheric gas-phase and precipitation chemistry.

Bacteria which have entered the liquid phase find therein a solution of organic compounds which

may serve as nutrients. Recent studies show that living and active microorganisms, including

bacteria, yeasts and fungi, are present in the atmospheric water phase (Alfreider et al., 1996;

Fuzzi et al., 1997; Skidmore et al., 2000; Sattler et al., 2001; Bauer et al., 2003; Segawa et al.,

2005; Amato et al., 2005; 2007a). These microorganisms could play an active role in chemistry

and microphysics of clouds as discussed by a growing number of scientists (Ariya and Amyot,

2004; Amato et al., 2005, 2007b; Morris et al., 2008a; Möhler et al., 2007; Deguillaume et al.,

2008). Indeed, living microorganisms are clearly biocatalysts which could transform organic

compounds as an alternative route to photochemistry. Many unresolved questions remain on this

topic and long term observations can be used to evaluate the diurnal and seasonal variations of

structure and activity of microorganisms as a function of environmental conditions (ie humidity,

light, temperature, pH...).

10.3 Impaction scavenging of aerosol particles

Inside the cloud unactivated aerosol particles remain between the nucleated drops as interstitial

aerosol. These particles can collide with the hydrometeors and become incorporated into the

cloud particles. However, due to the fact that already the main part of the particle mass was

scavenged by nucleation, inside cloud this process does not contribute significantly to the

pollution mass in precipitation (Flossmann, 1998a and b; Flossmann and Wobrock, 1996). An

importance can be attributed to this process in combination with the contact mode freezing of the

previous section. Once the hydrometeors fall and leave cloud base, on their way to the earth’s

surface they meet an unperturbed aerosol particles population. Here, the collision with aerosol

particles can contribute a significant portion to the aerosol particle loading of the rain on the

ground, depending also on the height of cloud base.

During the cloud lifetime, chemical processes lead to the formation of new chemical species with

relatively low volatility such as inorganic and organic acids, which can modify the physico-

chemical properties of aerosol particles after the cloud dissipates (Feingold and Kreidenweis,


2002; Yin et al., 2005) and lead to secondary organic aerosols formation (Gelencsér and Varga,

2005). For some chemical species, aerosol particle dissolution is the only source in cloud

droplets; for instance, transition metal ions, and in particular iron, which is well known to play a

major role in the oxidizing capacity of clouds (Deguillaume et al. 2005). Study of such complex

interactions needs process modelling efforts integrating in-situ measurements.

10.4 Scavenging of gases

In addition to the particles, numerous trace gases are present in the atmosphere. Gases are taken

up into drops according to their solubility. The maximum amount of a gas that can be taken up

into water is a function of the Henry’s law coefficient. A comprehensive compilation of updated

Henry’s law coefficients is available at http://www2.mpch-

mainz.mpg.de/~sander/res/henry.html. Henry’s law describes the equilibrium between the

concentration in the air and the liquid, however, once in the liquid phase most gases are

destroyed by chemical reactions and, thus, an equilibrium will never be achieved. Consequently,

more and more gas can be taken up into the cloud drops. Only the droplet lifetime (max. 30 min)

will limit the gas scavenging. Recently, our knowledge of the uptake and reaction coefficients of

the ambient trace gases has significantly increased and quite complex aqueous phase reaction

schemes have become available (Hermann et al., 2005), including an extended reaction

mechanism for atmospherically important hydrocarbons containing more than two and up to six

carbon atoms.

The complexities of the cloud processes involved in pollutant scavenging have discouraged

investigators from simultaneously treating all aspects of multiphase chemistry and microphysics

with equal rigor. However, efforts made to develop sophisticated cloud models with complex

multiphase chemistry allow more detailed studies on the interaction between microphysical and

chemical multiphase processes (Leriche et al., 2007; Ervens et al., 2004). One important feature

lies in a detailed representation of the microphysical as well as multiphase chemical processes.

These developments really distance themselves from the other attempts of coupling multiphase

chemistry in 3D models, which are often restricted to the study of inorganic species and basic

organic species wet deposition (Tost et al., 2007).

10.5 Clouds

Clouds form when air ascends and following expansion and cooling the water vapour condenses.

The droplets grow further by condensation, then, collide and coalesce with each other, until they

become sufficiently heavy to fall against the updraft velocity that has suspended them until now.


Depending on the height of cloud base and the temperature conditions they might reach the

ground as rain. If the temperature in the clouds reaches temperatures sufficiently below zero,

then ice crystals develop. They also grow by water vapour deposition, and collide with each

other. If they become sufficiently heavy, they fall to the ground in solid or liquid form, as a

function of the below-cloud temperature. During their entire life time, these cloud hydrometeors

(=drops or ice particles) take up pollution in particulate and gaseous form and deposit it on the

ground together with precipitation. A schematic display for liquid clouds is shown in Figure

10.1.

Figure 10.1: Schematic display of the microphysical and scavenging processes in all liquid clouds

Only few clouds form locally due to convection and, thus, have only a limited geographical

impact. Most clouds are embedded in large scale system and cover areas of several thousand

km2. They incorporate the local pollution when the droplets nucleate and then transport them

over larger distances, processing the material during transport..

The problem of correctly describing this process is coupled to the problem of scales. As shown

below, the nucleation of hydrometeors and all subsequent reactions take place on the scale of the

individual drop or ice crystal. The formation, transport and dissipation mechanisms of clouds,

however, act over a much larger region and require description on a synoptic or even

hemispheric scale. In the past, this fact imposed severe constraints in the accuracy of the

modelling of these processes and resulted either in highly parameterized dynamical models with

detailed treatments of the chemistry (Wolke et al., 2005, Sander et al., 2005) or in highly

simplified chemical schemes in sophisticated meteorological models (Mari et al.,2000). Only

recently have 3-D dynamic codes with detailed microphysical treatment and aerosol particles

(Leroy et al, 2008) and chemistry (Tost et al., 2007) become available due to developments in


computers. These models obviously are restricted to rather limited modelling domains, however,

they highlight e.g. the importance of the background aerosol population for the development of

the cloud (Leroy et al, 2008).

Cloud drops :

0.01 g m-3

Raindrops : 1 g m-3

Ice crystals :

0.01 g m-3

Clean boundary layer:

NAP ≈ 400 cm-3

Cloud drops :

0.01 g m-3

Raindrops : 0.03 g m-3

Ice crystals :

0.01 g m-3

Polluted boundary layer:

NAP ≈ 6500 cm-3 Figure 10.2: Sensitivity study concerning the number concentration of boundary layer aerosol particles (Leroy et al,

2008) after 40 min of cloud development; the displayed domain is a 2-D cross section of the 3-D domain restricted

to 30km in the horizontal and 15km in the vertical; the envelopes of the different hydrometeors are specified for

each figure

Figure 10.2 displays the results of a sensitivity test for the CRYSTAL-FACE cloud (Leroy et al,

2008). The simulation shows a clean boundary layer in which rain develops readily while

precipitation formation is suppressed in the polluted case by the large population of aerosols

derived from air pollutants. Not only has the precipitation been suppressed but the horizontal and

vertical structure of the cloud is substantially modified.

By including as many as possible of these processes in larger scale models parameterisations

have been developed to yield the first reliable maps on critical loads (e.g. Hoose et al, 2008;

Pozzoli et al, 2008). One emphasis of these models has been the aspect of topography.


10.6 Orographic precipitation

At mid-latitudes, mountainous terrain is commonly associated with high annual precipitation due

to the forced ascent of air resulting in cloud formation and precipitation. At a sub-grid scale,

however, there can be significant variations in pollutant deposition due to local emissions and

variation in topography and vegetation. A need, therefore, arises for fine scale process models to

investigate pollutant deposition at the kilometer scale (Dore et al., 2006).

The aerosol population (size distribution and composition) has a major influence on the

dynamics and microphysics of orographic cloud development. The cloud condensation nuclei

(CCN) population entering cloud base determines the extent and onset of warm rain produced by

collision coalescence. These, along with the presence of heterogeneous ice nuclei affect the onset

of the glaciation process and the efficiency of secondary ice processes such as the Hallett-

Mossop process of ice splintering. These in turn determine the release of latent heat of fusion in

the cloud, which has a major influence on the vigour and structure of the cloud dynamics. The

initiation and development of the ice phase is crucial to the precipitation formation and its

location within the cloud. More detailed process studies are needed to understand such complex

feedbacks. In the case of orographic clouds, it is shown that aerosol-cloud interactions may

cause a displacement of precipitation from the upslope side of a hill towards the downslope side

when the number of aerosols is increased (Mühlbauer and Lohmann, 2008). Inverse relations

between air pollution and orographic precipitation could be of major interest for weather

prediction and hydrological budget evaluation.

The initial physical and chemical state of aerosol entering the clouds is strongly influenced by

the prevailing oxidant climate and airmass history. The degree of in-particle oxidation and

resultant hygroscopic properties has been seen previously to be closely tied to the degree of gas

phase photochemical ageing (Cubison et al. 2006). The specificity, in terms of time since surface

emission and oxidative exposure, which can be made from direct aerosol measurements, is

however relatively poor. This may be inferred much more accurately however by making

coincident measurements of gas phase volatile organic tracers (VOCs).

10.7 Snow Chemistry

Snow lying on the Earth‘s surface has traditionally been viewed as a chemically inert medium,

whose influence on the overlying atmosphere was exerted through its albedo effect, and by

restricting exchange of gases between the air and land/sea surfaces. The a priori view was that


the boundary layer and troposphere over Antarctica would be somewhat uninteresting, with low

concentrations of reactive radicals such as OH, HO2, NO and NO2, and a composition

dominated by longer-lived chemical species. The equivalent regions of the Arctic atmosphere

were assumed dominated by long-range transport of anthropogenic emissions from lower

latitudes. However, recent research has shown that this picture is far from the truth, and that

snow is a highly photochemically active medium (see Grannas et al., 2007 for a comprehensive

review). Snow-pack impurities, of which there are many, can be photolysed to release reactive

trace gases to the atmosphere. These processes are likely to be active anywhere that sunlight

irradiates snow. The importance of these processes to boundary layer composition varies with

geographical location; in regions with a high background of radicals, for example arising from

anthropogenic pollution, emissions from snow are of lesser importance. But in the remote polar

regions, emissions from snow can be the dominant source of reactive trace gases and have a

major influence on boundary layer chemical composition. This conclusion was first reached for

NOx (NO + NO2), which was measured in the boundary layer at Summit, Greenland at

surprisingly high concentrations, and with a ratio to NOy that suggested a local source (Honrath

et al., 1999). Measurements of NOx within the snow-pack interstitial air revealed concentrations

that were higher still, suggesting that the snow-pack itself was the source, with a gradient to the

atmosphere. Subsequent measurements made in Antarctica confirmed that NOx production

within the snow-pack was a feature of both polar regions (Jones et al., 2000). Additional

measurements confirmed that NOx generated within the snow-pack was released to the overlying

boundary layer (Jones et al., 2001), contributing to the higher than expected NOx concentrations

that were encountered. Production and emission of NOx from the snow-pack has now been

measured at many polar locations (Beine et al., 2002; Dibb et al., 2002; Honrath et al., 2002;

Oncley et al., 2004), and also during one study in the mid-latitudes of the US (Honrath et al.,

2000). The NOx is produced by the photolysis of nitrate impurities within the snow (Dubowski

et al., 2001; Jacobi and Hilker, 2007), and as nitrate is a ubiquitous snow-pack impurity, this

appears to be a process occurring wherever there is sunlight irradiating snow. The most extreme

case is that of South Pole, where NO mixing ratios exceeding 1000 pptv (parts per trillion by

volume) have been measured on occasions (Wang et al., 2008). This puts them in a league with a

polluted mid-latitude troposphere, rather than a remote clean atmosphere. The exceedingly high

mixing ratios are driven by numerous factors including a sometimes very shallow and stable

boundary layer into which emissions are concentrated, high concentrations of nitrate impurities

in the top layers of the snow-pack, intense solar radiation at such a high elevation (South Pole is

at 2835 m asl) and the fact that South Pole is downslope of the polar plateau, and therefore

receives air from a large snow-covered catchment (Neff et al., 2008). Not surprisingly, the effect


of these emissions is measured in other trace gases whose chemistry is influenced by NOx. For

example, the OH:HO2 ratio is shifted towards OH as a result of the reaction NO + HO2 -> NO2

+ OH Another example is ozone, whose only chemical source in the troposphere is from the

photolysis of NO2: NO2 + hν -> NO + O (3P). O(3P) + O2 -> O3 Normally this suite of

reactions is associated with polluted regions, but mixing ratios of NO2 at South Pole are

sufficiently large for in situ production of ozone in the summertime boundary layer to occur

(Crawford et al., 2001; Helmig et al., 2008). Other trace gases are also produced by the action of

sunlight on snow, and their production and release into the boundary layer have been studied at a

number of locations. For example, HONO can be generated from the photolysis of nitrate

depending upon a number of factors including the snow pH (Beine et al., 2003, 2005; Amoroso

et al., 2005; Jacobi and Hilker, 2007). HCHO can be produced from snow, either through

photolysis of snow impurities (e.g. Sumner and Shepson, 1999; Sumner et al., 2002; Grannas et

al., 2002, 2004; Dassau et al., 2002) or by volatilitic release driven by changes in temperature

(e.g. Hutterli et al., 1999, 2002, 2003; Couch et al., 2000; Burkhart et al., 2002). Hydrogen

peroxide, H2O2, is similarly lost from the snow-pack as a result of physical processes (Hutterli

et al., 2003). These three trace gases are particularly interesting as they are all direct sources of

OH, so will influence the oxidative capacity of the atmosphere in that region. Indeed, as a result

of snow-pack emissions, OH at South Pole has been measured at concentrations of the order 106

(Mauldin et al., 2001), more typical of tropical regions. In addition to HCHO, there is evidence

that other organic trace gases also have a snow-pack source. For example, fluxes of carbonyls,

alkyl halides, alkenes and alkyl nitrates have been measured at various polar locations (Sumner

and Shepson, 1999; Grannas et al., 2002, 2004) Frozen surfaces are also a key source of

halogens. For example, elevated concentrations of reactive bromine compounds (BrO, Br2,

BrCl) have been observed in polar regions (e.g. Richter et al., 1998; Foster et al., 2001; Spicer et

al., 2002; Saiz-Lopez et al., 2007a) with a source associated with sea salt/sea ice/snow-pack. For

example, as new sea ice forms, sea salt is expelled from the ice lattice such that concentrations

can build up in brine pools and on the surface of associated frost flowers (Rankin et al., 2002;

Kaleschke et al., 2004; Jacobi et al., 2006). Sea salt aerosol, either suspended or deposited to the

snow surface, can also act as a source of bromine. Furthermore, recent measurements have

detected significant concentrations of iodine monoxide in the Antarctic boundary layer.

Measurements from a ground-based station (Saiz-Lopez et al., 2007a) as well as from satellites

(Saiz-Lopez et al., 2007b; Schönhardt et al., 2008) have revealed a seasonal maximum in spring,

but with higher than expected concentrations sustained into the summer months. Although the

source of iodine is not confirmed, it is postulated to originate from algae that colonises the

underside of sea ice. The presence of even relatively small concentrations of these reactive


halogens has a marked effect on boundary layer chemistry. Bromine is primarily responsible for

the dramatic boundary layer ozone depletion events that are observed in coastal regions of both

the Arctic and Antarctica (Oltmans, 1981; Barrie et al., 1988; Helmig et al., 2007). It is also

instrumental in the oxidation of gaseous elemental mercury (Hg0) to forms of reactive gaseous

mercury (Schroeder et al., 1998; Steffen et al., 2007), as well as providing an additional, and

efficient, pathway for oxidation of dimethylsulphide (von Glasow and Crutzen, 2004). In

addition, bromine and iodine compounds in the summertime Antarctic boundary layer have been

shown to profoundly impact the baseline photochemistry, acting as the major sink of NOx, and

shifting the ratios of both NO2:NO and HO2:OH. A full review of halogen chemistry in polar

regions is provided by Simpson et al. (2007). So, rather than being chemically uninteresting,

snow at the Earth‘s surface is a source of reactive trace gases to the surrounding atmosphere,

either through photolytic or volatilitic processes. Whether this source is a significant influence

on boundary layer chemical composition depends greatly upon background concentrations.

Certainly in the remote polar regions, boundary layer composition has been found to be very

different from what was anticipated.

Organic N in air and rain

Better understanding of the processes that link the chemical and biological properties of aerosols

with cloud formation and droplet growth has indicated a need for better knowledge of the

organic components. Although transport or organic C, and deposition to the earth’s surface, has

not been regarded as quantitatively important for ecosystem health, organic N has the potential to

add to the known effects of inorganic N wet deposited from the atmosphere especially in remote

areas. Studies of precipitation chemistry have highlighted our lack of knowledge of the organic

nitrogen constituents (both gaseous and particulate) in the atmosphere. Recent reviews (Cornell

et al., 2003, Neff et al., 2002) have indicated that the contribution of water-soluble organic

nitrogen (WSON) in precipitation to wet deposition may be up to one-third of the total, yet little

is known about the chemical composition, form or sources of this material. Initial scepticism

about the nature of WSON has to some extent been dispelled (Cape et al., 2001), but the broad

range of possible composition and emission sources means that the transfer pathways are still

somewhat uncertain. It is known, for example, that biological processes interconvert inorganic

and organic nitrogen in forest canopies (Fang et al., 2008), but it is not clear how much

biological activity may occur in the atmosphere or on the surfaces of sampling equipment. The

presence of both gaseous and particulate WSON in the atmosphere implies that dry deposition is

an important but unquantified pathway for transfer of organic nitrogen to the earth’s surface.


10.8 Conclusions and some priority areas of future research

Atmospheric pollution is made of aerosol particles and trace gases. Both are taken up into cloud

hydrometeors during the life time of a cloud, processed and either released during evaporation or

deposited onto the ground with the liquid or solid precipitation. Knowledge of the underlying

processes has greatly advanced concerning the liquid phase. Here, the gap concerning the role of

the organic material is almost closed now. The greatest uncertainty nowadays lies with the ice

phase. Generally, the ice phase is chemically less active than the liquid phase. Thus, the uptake

and processing is reduced. Furthermore, the role of the ice phase in the precipitation formation

and the deposition is not completely known and this factor still attaches a large uncertainty to the

values that are obtained by current models.

The second challenge in the modelling of wet deposition is linked to role that bacteria and other

living organic matter can play in the microphysics of a cloud and in atmospheric aqueous phase

chemistry. Thus, their impact on the oxidizing capacity of the atmosphere still needs to be

quantified.


11. Ecosystem-Atmosphere exchange – Conclusions

This paper has reviewed the state of knowledge of atmosphere - surface interactions of a broad

range of trace gases and particles. Given the wide range of chemical species reviewed and the

reasonably self contained sections within the paper, this concluding section does not attempt to

provide a summary of the sections. The following commentary reflects on the overall direction

of the science which, like the subject material, is becoming ever more global and searching for

integrating mechanisms. There has been substantial progress over the last decade in process

understanding, field measurement and in modelling. Models have been developed incorporating

the process understanding to generalize the ecosystem - atmosphere exchange over regions and

are currently able to describe the fluxes with uncertainties of the order of 30% in wet deposition

and 50% in dry deposition. However, there is a lack of measurements to evaluate the models. For

the future, extensive measuring campaigns and monitoring are necessary to further develop this

important field, such as the development of super sites in the EMEP network with a full

spectrum of gas and aerosol phase trace atmospheric constituents and continuous measurements

of surface – atmospheric fluxes. Such long term flux measurements of reactive pollutants to test,

develop and validate models represent an important development, which, in turn will be

expanded regionally.

Policy needs

In the policy development there is a need to address environmental priority issues, among which

none are currently greater than climate change. However, human health, ecosystem quality and

the sustainability of land use uses are growing in importance.

There is a current tendency to group the environmental impacts because they are linked through

the actors and receptor which are often the same. New more integrated directions are Global

change, or reactive nitrogen, air quality and climate change, quality of life, bringing together a

range of related issues in the search for more sustainable solutions to the underlying problems.

This is however for from being implemented in policies because it involves a bigger scale and

therewith more actors (global), but aims are being developed, such as the Millennium Goals

(and/or the CO2 concentration in the atmosphere). In the end it will be the actors that will change

their activities and the receptors (humans, plants, animals and ecosystems) need to adapt. The

link between surface and atmosphere for the exchange persists to exist and the need for

quantification. The extension is the scale that needs to be linked from very local to global (Earth


system approach) and that different components of biogeochemical cycles need to be integrated

(Carbon, nitrogen, oxygen, hydrogen, phosphorus cycles).

Current understanding

It is questionable whether our current understanding of the atmosphere - surface exchange is

keeping pace with these more integrated needs. The processes are very variable and the

interactions of the different components are not yet studied mainly because of instrument and

resource limitations. In the 1950s the work started with single component fluxes to a few

ecosystems. Currently, as presented in this chapter, the state of knowledge is grouped into

families of components (VOC, reactive nitrogen, GHG, particles), with an extension of some

ecosystems (oceans). Within the area of reactive nitrogen it has been shown that there is a

dynamic exchange between the atmosphere and surface, regulated by the surface and stomatal

chemical interactions; deposition, re-emission and re-deposition processes; and by the exchange

of different forms of nitrogen in interaction with the status of the system (saturation, carbon,

phosphorus, water filled pore space, etc) (Sutton et al., 2008; Pilegaard et al., 2009). Sulphur has

a large impact on the uptake and release of ammonia at the surface. This in all is part of the

nitrogen cascade, where one molecule of reactive nitrogen that enters a system in oxidised or

reduced state is used and transformed in that system, can be leached to the groundwater as

nitrate, entering the river and in the estuaries where it can be emitted as N2O contributing to

climate change and into the stratosphere, where eventually it is broken down depleting the ozone

layer (Galloway et al., 2003). There is evidence that increased nitrogen deposition leads to NO

emissions from the soil. This links the nitrogen with the oxidants surface - atmosphere exchange.

Oxidants in their turn affect the ecosystem health and therewith the nitrogen uptake and use

efficiency. These are examples of the strong interaction between the components in the surface -

atmosphere exchange.

Future developments

Agriculture is a major source of emissions to the atmosphere, which relative to industry has been

regulated substantially less (Aneja et al., 2008). Until now policy has regarded agriculture as

necessary to produce our food and animal feed. More and more, however, it is recognized that

the production should be within the limits of sustainability, limiting losses of pollution to surface

or groundwater and emission to the atmosphere of reactive nitrogen compounds, greenhouse

gases, persistent organic pollutants, H2S and odour. Agriculture is a collection of diffuse sources

with many uncertainties in the emissions. Greenhouse gas emissions and ammonia are uncertain

because the emissions depend on farm management practices, soil type, fertilizer use, type of


crop or animal breeding, size and location of the farm, etc. This makes quantification of the

sources and successful targeted measures and policies for control very difficult.

The next step will be to understand the strong interactions between the different

cycles/components and the regional interactions in urban and rural landscapes. Studying the

landscape interactions is a new topic, where just a few exercises have been done (Cellier et al., ..;

Nemitz et al., ..). The landscape scale, such as agricultural areas, complex terrain, urban areas,

etc. have their own dynamic and interactions which differ from the normally studied stationary

conditions. It is necessary to study this scale because of the need to determine the contribution of

the individual sources which need to be controlled. In rural, agriculture dominated areas there is

a large contribution of different reactive nitrogen sources, such as animals in- or outside housing

systems, application of fertilizers or manure, storage of manure, traffic and small industries.

Within such an area deposition and re-emission takes place with a high spatial and temporal

dynamic. Especially for nitrogen the use efficiency can be improved if the individual

contribution of the different sources and resulting losses can be quantified.

In urban areas there is a concentration of sources of gases and pollutants from industry, traffic

and households. More than 50% of people live in urban areas and therefore the emissions to the

atmosphere and the resulting exposure in these areas is a growing concern over the world. It is

necessary to understand the dynamics in emissions and deposition of primary and secondary

particles. Trees or treated surfaces might act as depositing surfaces improving air quality in cities

(REF) or decreasing the net ammonia emissions around farms. It is necessary to determine the

effect of these policies against other measures.

A diverse range of natural and anthropogenic particles are capable of initiating the ice phase, but

the most active naturally occurring ice nuclei (IN) are biological in origin and have the capacity

to catalyze freezing at temperatures near -2°C. Based on the ubiquitous distribution of biological

IN in snow and rain from global locations, they are likely to encounter the appropriate conditions

to affect atmospheric processes leading to precipitation. The cloud nucleation processes are

currently the most uncertain in the global climate change modelling and predictions. Surface

fluxes of biological components and atmosphere - surface exchange of primary and secondary

particles is a research area that needs to be taken forward quickly.


Acknowledgements We gratefully acknowledge financial support from the European Commission for the ACCENT,

NitroEurope and GRAMINAE projects, from COST for ACTION 729 and the European Science

Foundation for the Nitrogen in Europe (NinE) program. This work was partly supported by the

UK Department for Environment Food and Rural Affairs and the NERC Centre for Ecology and

Hydrology.


References: Abbatt, J.P.D., 2003. Interactions of Atmospheric Trace Gases with Ice Surfaces: Adsorption and Reaction, Chem.

Rev., 103, 4783 Abbot, D.S., Palmer, P.I., Martin, R.V., Chance, K.V., Jacob, D.J., Guenther, A., 2003. Seasonal and interannual

variability of isoprene emissions as determined by formaldehyde column measurements from space. Geophysical Research Letters; doi:10.1029/2003GL017336.

Affek, H.P., Yakir, D., 2003. Natural abundance carbon isotopes composition of isoprene reflects incomplete coupling between isoprene synthesis and photosynthesis carbon flow. Plant Physiology 131, 1727–1736.

Altimir, N., Kolari, P., Tuovinen, J.-P., Vesala, T., Bäck, J., Suni, T., Kulmala, M., Hari, P., 2006. Foliage surface ozone deposition: a role for surface moisture? Biogeosciences 3, 209–228.

Amato, P., Ménager, M., Sancelme, M., Laj, P., Mailhot, G., and Delort, A-M. 2005. Microbial population in cloud water the Puy de Dôme: implications for the chemistry of clouds, Atmos. Environ., 39, 4143–4153

Amato, P., Parazols, M., Sancelme, M., Laj, P., Mailhot, G., and Delort, A.-M., 2007a. Micro-organisms isolated from the water phase of tropospheric clouds at the Puy de Dˆome: major groups and growth abilities at low temperatures, FEMS Microbiol. Ecol., 59(2), 242–254

Amato, P., Hennebelle, R., Magand, O., Sancelme, M., Delort, A.-M., Barbante, C., Boutron, C., and Ferrari, C., 2007b. Bacterial characterization of the snow cover at Spitzberg, Svalbard, FEMS Microbiol. Ecol., 59(2), 255–264

Amato P., Demeer F., Melaouhi A., Martin-Biesse A-S., Sancelme M., Laj P. and Delort A-M., 2007c. A fate for organic acids, formaldehyde and methanol in cloud water: their biotransformation by micro-organisms, Atmos. Chem. Phys., 7, 4159–4169

Amato, P., Parazols, M., Sancelme, M., Mailhot, G., Laj, P., and Delort, A.-M., 2007d. An important oceanic source of micro-organisms for cloud water at the puy de Dˆome (France), Atmos. Environ., 41, 8253–8263

Ammann, C., Brunner, A., Spirig, C., Neftel, A., 2006. Technical note: water vapour concentration and flux measurements with PTR-MS. Atmospheric Chemistry and Physics. 6, 4643-4651.

Andersen H.V., Hovmand M.F., Hummelshøv and Jensen N.O., 1999. Measurements of ammonia concentrations, fluxes and dry deposition velocities to a spruce forest 1991–1995. Atmospheric Environment, 33, 1367-1383.

Andreas, E.L., 2002. A review of the sea spray generation function for the open ocean. Atmosphere-Ocean Interactions, Volume 1, W.A. Perrie, Ed., WIT Press, Southampton, UK, pp. 1-46

Anttila, T., Veli-Matti Kerminen, 2002. Influence of organic compounds on the cloud droplet activation: A model investigation considering the volatility, water solubility, and surface activity of organic matter, Journal of Geophysical Research D, VOL. 107, NO. D22, 4662, doi:10.1029/2001JD001482

Ariya, P. A., Amyot, M. 2004. New directions: the role of bioaerosols in atmospheric chemistry and physics, Atmos. Environ., 38, 1231–1232

Arneth, A., Monson, R., Schurgers, G., Niinemets, Ü, P. I. Palmer. 2008. Why are estimates of global isoprene emissions so similar (and why is this not so for monoterpenes)? Atmospheric Chemistry and Physics, In Press.

Arnold, S. R., Chipperfield, M. P., Blitz, M. A., Heard, D. E. and Pilling, M. J. (2004) Photodissociation of acetone: Atmospheric implications of temperature-dependent quantum yields. Geophysical Research Letters, 31, L07110, doi:10.1029/2003GL019099.

Ashmore, M.R. 2005. Assessing the future global impacts of ozone vegetation. Plant Cell and Environment 28, 949–964.

Ashmore, M., Fuhrer, J. 2000. Use and abuse of the AOT40 concept. Atmospheric Environment 34, 1157–1158. Asman W.A.H., Sutton M.A. and Schjoerring J.K. (1998) Ammonia: emission, atmospheric transport and

deposition. New Phytologist, 139, 27-48. Audiffren, N., Cautenet, S., Chaumerliac, N. 1999. A Modeling Study of the Influence of Ice Scavenging on the

Chemical Composition of Liquid-Phase Precipitation of a Cumulonimbus Cloud, J. Appl. Meteor. Vol. 38, No. 8, pp. 1148-1160 (AMS)

Bach , L. H., Frostegård, Å., Ohlson, M. 2008. Variation in soil microbial communities across a boreal spruce forest landscape. Canadian J, Forest Research, 38, 1504 – 1516.

Baker, B., Guenther, A., Greenberg, J., Goldstein, A., Fall, R. 1999. Canopy fluxes of 2-methyl-3-buten-2-ol over a ponderosa pine forest by relaxed eddy accumulation: Field data and model comparison. Jornal of Geophysical Research 104, 26107-26114.

Balderston, W.L., Sherr B., Payne, W.J. 1976. Blockage by acetylene of nitrous oxide reduction in Pseudomonas perfectomarinus. Applied and Environmental Microbiology 31:504–508.

Baldocchi, D. et al. 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapour, and energy flux densities. Bulletin of the American Meteorological Society, 82: 2415-2434.

Barkley, M. P., Palmer, P. I., Kuhn, U., Kesselmeier, J., Chance, K., Kurosu, T. P., Martin, R. V., Helmig, D., Guenther, A. 2008. Net ecosystem fluxes of isoprene over tropical South America inferred from GOME observations of HCHO columns. Journal of Geophysical Research, submitted.


Bauer, H., Giebl, H., Hitzenberger, R., Kasper-Giebl, A., Reischl, G., Zibuschka, F., and Puxbaum, H. 2003. Airborne bacteria as cloud condensation nuclei, J. Geophys. Res., 108(D21), 4658, doi:10.1029/2003JD003545

Beauchamp, J., Wisthaler, A., Hansel, A., Kleist, E., Miebach, M., Niinemets, Ü., Schurr, U., Wildt, J. 2005. Ozone induced emissions of biogenic VOC from tobacco: relations between ozone uptake and emission of LOX products. Plant Cell Environment 28, 1334-1343.

Benz, S., Megahed, K., Möhler, O., Saathoff, H., Wagner, R., Schurath, U. 2005. T-dependent rate measurements of homogeneous ice nucleation in cloud droplets using a large atmospheric simulation chamber, Journal of Photochemistry and Photobiology A: Chemistry , Volume 176, Issue 1-3 SPEC. ISS., 208-217

Bergamaschi, P., Krol, M., Dentener, F., Vermeulen, A., Meinhardt, F., Graul, R.., Ramonet, M., Peters, W., Dlugokencky, E. J. 2005. Onverse modelling of national and European CH4 emissions using the atmospheric zoom model TM5Atmospheric Chemistry and Physics 5, 2431-2460.

Beswick, K. M., Hargreaves, K. J., Gallagher, M. W., Choulartion, T. W. and Fowler, D. 1991. Size-resolved measurements of cloud droplet deposition velocity to a forest canopy using an eddy-correlation technique. Quart. J. Roy. Met. Soc. 117(499): 623-645.

Bigg E.K., Leck, C. 2008. The composition of fragments of bubble bursting at ocean surface. J. Geophys. Res. , 113, D11209, doi:10.1029/2007JD009078

Blake et al., J. Geophys. Res. 104, 21803 1999. Blanchard, D.C. 1964. Sea to air transport of surface active material, Science, 146, 396-397 Blande, J.D., Tiiva, P., Freiwald, V., Oksanen, E., Holopainen, J.K. 2005. The effects of herbivore damage and

elevated ozone concentration on the volatile terpenoids produced by two hybrid aspen (Populus tremula x tremuloides) clones. Abstracts. XVII International Botanical Congress, Vienna, Austria, 17-23 July 2005, Vienna, p. 544.

Blatter, A., Neftel, A., Dasgupta, P.K. and Simon P.K. 1993. A combined wet effluent denuder and mist chamber system for deposition measurements of NH3, NH4

+, HNO3 and NO3

- 6th European Symposium of Physico-

Chemical behavior of atmospheric Pollutants, Varese , Italy. Blitz, M. A., Heard, D. E., Pilling, M. J., Arnold, S. R., and Chipperfield, M. P. 2004. Pressure and temperature-

dependent quantum yields for the photodissociation of acetone between 279 and 327.5 nm, Geophysical Research Letters, 31, L06111, doi:10.1029/2003GL018793.

Bollmann, A., Conrad, R. 1997. Acetylene blockage technique leads to underestimation of denitrification rates in oxic soils due to scavenging of intermediate nitric oxide. Soil Biology and Biochemistry 29:1067–1077.

Bonn, B., Hirsikko, A., Hakola, H., Kurtén, T., Laakso, L., Boy, M., Dal Maso, M., Mäkelä, J.M., Kulmala, M., 2007. Ambient sesquiterpene concentration and its link to air ion measurements. Atmospheric Chemistry and Physics 7, 2893-2916. Böttcher J, Strebel O, Voerkelius S, Schmidt HL. 1990. Using isotope fractionation of nitrate-nitrogen and nitrate-oxygen for evaluation of microbial denitrification in a sandy aquifer. Journal of Hydrology 114:413–424.

Bonsang, B., Polle, C. and Lambert, G. 1992. Evidence for Marine Production of Isoprene. Geophysical Research Letters, 19, 1129-1132.

Braaten, D.A., Paw U.K.T. 1992. A stochastic particle resuspension and deposition model, in Fifth International Conference on Precipitation Scavenging and Atmosphere-Surface Exchange Processes, Richland, Washington, 15-19 July 1991, 1143-1152.

Brenninkmeijer, C.A.M., Röckmann, T. 1999. Mass spectrometry of the intramolecular nitrogen isotope distribution of environmental nitrous oxide using fragment-ion analysis. Rapid Communications in Mass Spectrometry 13:2028–2033.

Brilli, F., Barta, C., Fortunati, A., Lerdau, M., Loreto, F., Centritto, M. 2007. Response of isoprene emission and carbon metabolism to drought in white poplar (Populus alba) saplings. New Phytologist 175, 244-254.

Brost, R. A., Delany, A. C. and Huebert, B.J. 1988. Numerical modeling of concentrations and fluxes of HNO3, NH3, and NH4NO3 near the surface. J. Geophys. Res. 93, 7137-7152.

Brunner, A., Ammann, C., Neftel, A., Spirig, C. 2007. Methanol exchange between grassland and the atmosphere, Biogeosciences 4, 395-410.

Buchwitz, M., de Beek, R., Burrows, J. P., Bovensmann, H., Warneke, T., Notholt, J., Meirink, J. F., Goede, A. P. H., Bergamaschi, P., Körner, S., Heimann, M., Schulz, A. 2005. Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models, Atmos. Chem. Phys., 5, 941–962 .

Burkhard, R., Eugster, W., Wrzesinski, T., Klemm, O. 2002. Vertical divergence of fogwater fluxes above a spruce forest. Atmospheric Reserach 64:133-145.

Burkhardt J., Flechard C.R., Gresens F., Mattsson M.E., Jongejan, P.A.C., Erisman J.W., Weidinger T., Meszaros R., Nemitz E., Sutton, M.A. 2008. Modelling the chemical interactions of atmospheric ammonia and other trace gases fluxes with measured leaf surface wetness in a managed grassland canopy. Biogeosciences Discuss., 5, 2505-2539.

Businger, J.A., Wyngaard, J., Izumi, Y. and Bradley, E.F. 1971. Flux-profile relationships in the atmospheric surface layer. Journal of the Atmospheric Sciences, 28: 181-189


Butterbach-Bahl, K., Gasche, R., Breuer, L., Papen, H. (1997) Fluxes of NO and N2O from temperate forest soils: impact of forest type, N deposition and of liming on the NO and N2O emissions. Nutrient Cycl. Agroecosys. 48, 1385-1314.

Butterbach-Bahl K, Willibald G, Papen H. (2002) Soil core method for direct simultaneous determination of N2 and N2O emissions from forest soils. Plant and Soil 240:105–116.

Butterbach-Bahl K, Kesik M, Miehle P, Papen H, Li C, (2004) Quantifying the regional source strength of N-trace gases across agricultural and forest ecosystems with process based models. Plant and Soil, 260, 311-329.

Buzorius, G., Rannik, U., Makela, J. M., Vesala, T. and Kulmala, M. (1998) Vertical aerosol particle fluxes measured by eddy covariance technique using condensational particle counter. Journal of Aerosol Science 29:157-171.

Buzorius, G., Rannik, U., Nilsson, D. and Kulmala, M. (2001) Vertical fluxes and micrometeorology during aerosol particle formation events. Tellus Series B-Chemical and Physical Meteorology 53:394-405.

Buzorius, G., Kalogiros, J., Varutbangkul, V. (2006) Airborne aerosol flux measurements with eddy correlation above the ocean in a coastal environment. Journal of Aeorosol Science 37:1267-1286.

Calogirou, A., Larsen, B.R., Kotzias, D. (1999) Gas-phase terpene oxidation products: a review. Atmospheric Environment 33, 1423-1439.

Cape J.N., Kirika A., Rowland A.P., Wilson D.R., Jickells T.D., Cornell S. (2001) Organic nitrogen in precipitation: real problem or sampling artefact? The Scientific World 1, 230-237.

Carpenter, L.J., Lewis, A.C.; Hopkins, J.R., et al., (2004) Uptake of methanol to the North Atlantic Ocean surface. Global Biogeochemical Cycles, 18, 4, GB4027.

Casciotti KL, Böhlke JK, McIlvin MR, Mroczkowski SJ, Hannon JE. (2007) Oxygen isotopes in nitrite: analysis, calibration, and equilibration. Analytical Chemistry 79:2427–2436.

Cavalli, F., Facchini, M.C., Decesari, S., Mircea, M., Emblico, L., Fuzzi, S., Ceburnis, D., Yoon, Y.J., O’Dowd, C.D., Putaud, J-P., Dell’Acqua, A. (2004) Advances in characterization of size resolved organic matter in marine aerosol over the North Atlantic, J. Geophys. Res, doi:10.1029/2004JD0051377

Ceburnis, D., O’Dowd, C.D., Jennings, S.G., Facchini, M.C., Emblico, L., Decesari, S., Fuzzi, S., Sakalys, J. (2008) Elucidation of production mechanisms of water soluble and insoluble organic carbon in marine air and associated gradient fluxes over the Northeast Atlantic, Geophys. Res. Letts. VOL. 35, L07804, doi:10.1029/2008GL033462

Chameides, W.L., Lindsay, R.W., Richardson, J., Kiang, C.S. (1988) The role of biogenic hydrocarbons in urban photochemical smog: Atlanta as a case study. Science 241, 1473–1475.

Chance, K., Palmer, P.I., Martin, R.V., Spurr, R.J.D., Kurosu, T.P., Jacob, D.J. (2000) Satellite observations of formaldehyde over North America from GOME. Geophysical Research Letters 27, 3461-3464.

Chang, C., Janzen, H.H., Cho, C.M. and Nakonechny, E.M. (1998) Nitrous oxide emission through plants. Soil Science Society of America Journal 62: 35-38.

Chang, W., Heikes, B.G., Lee, M. (2004) Ozone deposition to the sea surface: chemical enhancement and wind speed dependence. Atmospheric Environment 38, 1053–1059.

Chapuis-Lardy, L.E., Wrage, N., Metay, A., Chotte, J.L., Bernoux, M. (2007) Soils, a sink for N2O? A review, Global Change Biology 13, 1–17.

Christensen, S., Ambus, P., Arah, J. R. M., Clayton, H., Galle, B., Griffith, D. W. T., Hargreaves, K. J., Klemedtsson, L., Lind, A.-M., Maag, M., Scott, A., Skiba, U., Smith, K. A., Welling. M., Wienhold, F. G., (1996) Nitrous oxide emission from an agricultural field: Comparison between measurements by flux chamber and micrometerological techniques Atmospheric Environment, 30, 4183-4190.

Christensen, C.S., Hummelshøj, P., Jensen, N.O., Larsen, B., Lohse, C., Pilegaard, K., Skov, H. (2000) Determination of the terpene flux from orange species and Norway spruce by relaxed eddy accumulation. Atmospheric Environment 34, 3057-3067.

Chuck, A. L., Turner, S.M., Liss, P.S. (2002) Direct evidence of a Marine Source of C1 and C2 alkyl nitrates. Science, 297, 1151-1154.

Ciccioli, P., Brancaleoni, E., Frattoni, M., Di Palo, V., Valentini, R., Tirone, G., Seufert, G., Bertin, N., Hansen, U., Csiky, O., Lenz, R., Sharma, M. (1999) Emission of reactive terpene compounds from orange orchards and their removal by within-canopy processes. Journal of Geophysical Research 104, 8077-8094.

Ciccioli, P., Fabozzi, C., Brancaleoni, E., Cecinato, A., Frattoni, M., Cieslik, S., Kotzias, D., Seufert, G., Foster, P., Steinbrecher, R. (1997) Biogenic emission from the Mediterranean pseudosteppe ecosystem present in Castelporziano. Atmospheric Environment 31, 167-175.

Cicerone, R. (1989) Analysis of sources and sinks of atmospheric nitrous oxide (N2O), Journal of Geophysical Research 94, 18265 – 18271

Cieslik, S. (2008) Ozone fluxes over various plant ecosystems in Italy: A review. Environmental Pollution, in press. doi:10.1016/j.envpol.2008.09.050.

Cieslik, S.A. (2004) Ozone uptake by various surface types: a comparison between dose and exposure. Atmospheric Environment 38, 2409–2420.

Clarke, A.D., Owens, S.R., Zhou, J. (2006) An ultrafine sea-salt flux from breaking waves: implications for cloud condensation nuclei in the remote marine atmosphere. J. Geophys. Res., 111, D06202, doi:10.1029/2005JD006565, 14 pp


Clifford, D., Donaldson, D.J., Brigante, M., D’Anna, B., George, D., 2008. Reactive uptake of ozone by chlorophyll at aqueous surfaces. Environment Science & Technology 42, 1138–1143.

Conen, F and Smith, K. A., 1998 A re-examination of closed flux chamber methods for the measurement of trace gas emissions from soils to the atmosphere, European J. Soil Sci. 49, 701-707

Conen, F. and Neftel A. 2007 Do increasingly depleted d15N values of atmospheric N2O indicate a decline in soil N2O reduction? Biogeochemistry 82, 321-326.

Cornell S.E., Jickells T.D., Cape J.N., Rowland A.P., Duce R.A., 2003. Organic nitrogen deposition on land and coastal environments: a review of methods and data. Atmospheric Environment 37, 2173-2191.

Coyle M., 2005. The Gaseous Exchange of Ozone at Terrestrial Surfaces: Non-stomatal Deposition to Grassland, School of Geosciences, Faculty of Science and Engineering, PHD Thesis, The University of Edinburgh, Edinburgh, pp. 270.

Coyle M., Altimir N., Grunhage L., Gerosa G., Tuovinen J.P., Loubet B., Ammann C., Cieslik S., Paoletti E., Fowler D., Mikkleson T., Ro-Poulsen H., Cellier P., 2008a. Non-Stomatal Ozone Deposition to Vegetation: New Insights and Models, EGU General Assembly 2008, European Geophysical Union, Vienna, Austria.

Coyle M., Nemitz E., Storeton-West R., Fowler D., Cape J.N., 2008b. Measurements of ozone deposition to a potato canopy. Agricultural and Forest Meteorology in press.

Coyle, M., Smith, R., Fowler, D., 2003. An ozone budget for the UK: using measurements from the national ozone monitoring network; measured and modelled meteorological data, and a ‘big-leaf’ resistance analogy model for dry deposition. Environmental Pollution 123, 115–123.

Coyle, M., Fowler, D., Nemitz, E., Philips, G., Storeton-West, R., Thomas, R., 2006. Field measurements of the ozone flux to vegetation. In: Ozone Umbrella: Effects of Ground-Level Ozone on (Upland) Vegetation in the UK. CEH C02158 Report No. AS 06/02. Centre of Ecology and Hydrology, UK, pp. 68–104.

Crutzen, P. J. and Lawrence, M. G., 2000: The impact of precipitation scavenging on the transport of race gases: A 3-D dimensional model sensitivity study. J. Atmos. Chem. 37, 81–112.

Crutzen, P.J., Mosier, A.R., Smith, K.A., Winiwarter, W. 2008 N2O release from agro-biofuel production negates global warming reduction by replacing fossil fuels Atmos. Chem. Phys., 8, 389–395, 2008

Cubison et al. The characterisation of pollution aerosol in a changing photochemical environment. ACP 6, 1-15, 2006.

Dahl, E. E., E. S. Saltzman, and W. J. de Bruyn (2003), The aqueous phase yield of alkyl nitrates from ROO + NO: Implications for photochemical production in seawater, Geophys. Res. Lett., 30(6), 1271, doi:10.1029/2002GL016811.

Darmais, S., Dutaur, L., Larsen, B., Cieslik, S., Luchetta, L., Simon, V., Torres, L., 2000. Emission fluxes of VOC by orange trees determined by both relaxed eddy accumulation and vertical gradient approaches. Chemosphere, Global Change Science, 2, 47-56.

Davidson, C. I., Miller, J. M. and Pleskow, M. A. (1982). The influence of surface structure on predicted particle dry deposition to natural grass canopies. Water Air and Soil Pollution 18:25-43.

Davidson, E.A. 1991 Fluxes of nitrous oxide and nitric oxide from terrestrial ecosystems. In: Microbial production and consumption of greenhouse gases: Methane, nitrogen oxides, and halomethanes. (J.E. Rogers and W.B. Whitman eds). Washington, American Society for Microbiology: 219-235.

Davidson, B., Brunner, A., Ammann, C., Spirig, C., Jocher, M., Neftel, A., 2008. Cut-induced VOC emissions from agricultural grasslands. Plant Biology 10, 76-85.

Davidson, B., Taipale, R., et al., 2008. Ecosystem scale VOC emissions from Mediterranean macchia. In preparation.

De Arellano, J. and Duynkerke, P. (1992). Influence of chemistry on flux-gradient relationships in the NO-O3-NO2 system. Boundary-Layer Meteorology 28:181-189.

De Carlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T., Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K., Worsnop, D. R. and Jimenez, J. L. (2006). Field-deployable, high-resolution, time-of-flight aerosol mass spectrometer. Analytical Chemistry 78:8281-8289.

De Leeuw, G., M. Moerman, L. Cohen, B. Brooks, M. Smith and E. Vignati, Aerosols, bubbles and sea spray production studies during the RED experiments, Proceedings AMS conference, Long Beach, CA, 9-13 Feb., 2003.

Deguillaume, L., M. Leriche, K Desboeufs., G. Mailhot, C. George and N. Chaumerliac: Transition Metals in Atmospheric Liquid Phases: Sources, Reactivity, and Sensitive Parameters, Chem. Rev., 105-9 ,3388-3431, 2005.

Deguillaume, L., Leriche, M., Amato, P., Ariya, P. A., Delort, A.-M., Pöschl, U., Chaumerliac, N., Bauer, H., Flossmann, A. I., and Morris, C. E.: Microbiology and atmospheric processes: chemical interactions of primary biological aerosols, Biogeosciences, 5, 1073-1084, 2008.

Denmead O.T., Freney J.R. and Simpson J.R. (1976) A closed ammonia cycle within a plant canopy. Soil Sci. Biochem. 8, 161-164.

Denmead, O. T. 2008. Approaches to measuring fluxes of methane and nitrous oxide between landscapes and the atmosphere,. Plant and Soil 309, 5-24.

Dentener F., Drevet J., Lamarque J.F., Bey I., Eickhaut B., Fiore A.M., Hauglustaine D., Horowitz L.W., Krok M., Kulshrestha U.C., Lawrence M., Galy-Lacaux C., Rast S., Shindell D., Stevenson D., van Noije T., Atherton C.,


Bell N., Bergman D., Butler T., Cofala J., Collins B., Docherty R., Ellingsen K., Galloway J., Gauss M., Montanaro V., Müller J.F., Pitari G., Rodriguez J., Sanderson M., Solmon F., Strahan S., Schulz M., Sudo K., Szopa S. and Wild O. (2006) Nitrogen and sulfur deposition on regional and global scales: a multi-model evaluation. Global Biogeochem. Cycles 20 (4), GB4003.

Di Carlo, P., Brune, W.H., Martinez, M., Harder, H., Lesher, R., Ren, X., Thornberry, T., Carroll, M.A., Young, V., Shepson, P.B., Riemer, D., Apel, E., Campbell, C., 2004. Missing OH reactivity in a forest: evidence for unknown reactive biogenic VOCs. Science 304, 722-724.

Di Marco C., Skiba U., Weston K. and Hargreaves, K. 2004, Field scale N2O flux measurements from grassland using eddy covariance, Water, Air & Soil Pollution: Focus, 4, 143-149.

Donateo, A., Contini, D. and Belosi, F. (2006). Real time measurements of PM2.5 concentrations and vertical turbulent fluxes using an optical detector. Atmospheric Environment 40:1346-1360.

Dore A.J., M. Mousavi-Baygi, R.I. Smith, J. Hall, D. Fowler, and T.W. Choularton: A model of annual orographic precipitation and acid deposition and its application to Snowdonia, Atmos. Env., 40, 3316–3326, 2006.

Dorsey, J. R., Nemitz, E., Gallagher, M. W., Fowler, D., Williams, P. I., Bower, K. N. and Beswick, K. M. (2002). Direct measurements and parameterisation of aerosol flux, concentration and emission velocity above a city. Atmospheric Environment 36:791-800.

Dorsey, J.R., Duyzer, J.H., Gallagher, M.W., Coe, H., Pilegaard, K., Weststrate, J.H., Jensen, N.O., Walton, S., 2004. Oxidized nitrogen and ozone interaction with forest. I: Experimental observations and analysis of exchange with Douglas fir. Quarterly Journal of the Royal Meteorological Society 130, 1941–1955.

Duan, B., Fairall, C. W. and Thomson, D. W. (1988). Eddy-covariance measurements of the dry deposition of particles in wintertime. Journal of Applied Meteorology 28:642-652.

Dueck, T.A., de Visser, R., Poorter, H., Persijn, S., Gorissen, A., de Visser, W., Schapendonk, A., Verhagen, J., Snel, J., Harren, F.J.M., Ngai, A.K.Y., Verstappen, F., Bouwmeester, H., Voesenek, L.A.C.J. and van der Werf, A. 2007. No evidence for substantial aerobic methane emission by terrestrial plants: a 13C-labelling approach. New Phytologist 175: 29-35.

Duyzer, J. (1994). Dry Deposition of Ammonia and Ammonium Aerosols over Heathland. Journal of Geophysical Research-Atmospheres 99:18757-18763.

Eastman, J.A., Stedman, D.H., 1977. A fast sensor for ozone eddy-correlation flux measurements. Atmospheric Environment 11, 1209–1211.

Emberson, L.D., Ashmore, M.R., Cambridge, H.M., Simpson, D., Tuovinen, J.-P., 2000b Modelling stomatal ozone flux across Europe. Environmental Pollution 109, 403–413.

Emberson, L.D., Büker, P., Ashmore M.R., 2007. Assessing the risk caused by ground level ozone to European forest trees: A case study in pine, beech and oak across different climate regions. Environmental Pollution 147, 454–466.

Erisman, J.W., Wyers, G.P. (1993) Continuous measurements of surface exchange of SO2 and NH3: implications for their possible interaction in the deposition process. Atmos. Environ. 27A, 1937-1949.

Erisman, J. W. (1993). Acid deposition onto nature areas in the Netherlands. Part II: Throughfall measurements compared to deposition estimates. Water Air Soil Poll. 71:81-99.

Erisman, J. W., Draaijers, G., Duyzer, J., Hofschreuder, P., Van Leeuwen, N., Roemer, F., Ruijgrok, W., Wyers, P. and Gallagher, M. (1996). Particle deposition to forests - summary of results and application. Atmospheric Environment 31:321-332.

Erisman, J.W., Hensen, A., Fowler, D., Flechard, C., Grüner, A., Spindler, G., Duyzer, J., Weststrate, H., Römer, F., Vonk, A., van Jaarsveld, H., 2001. Dry deposition monitoring in Europe. WASP. Focus 1, 17-27.

Erisman et al 2008 Nature Ervens, B., George, C., Williams, J. E., Boxton, G. V., Salmon, G. A., Bydder, M., Wilkinsons, F., Dentener, F.,

Mirabel, P., Wolke, R., and Herrmann, H.: CAPRAM 2.4 (MODAC mechanism): An extended and condensed tropospheric aqueous phase mechanism and its application, J. Geophys. Res., 108, doi:10.1029/2002JD002202, 2003.

Ervens, B., Feingold, G., Frost, G. J., and Kreidenweis, S. M.: A modeling study of aqueous production of dicarboxylic acids: 1. Chemical pathways and speciated organic mass production, J. Geophys. Res., 109, D15205, doi:10.1029/2003JD004387, 2004.

Facchini, M.C., S. Decesari, M. Rinaldi, C. Carbone, E. Finessi, M. Mircea, S. Fuzzi, F. Moretti, E. Tagliavini, D. Ceburnis and C. D. O’Dowd, An important source of marine secondary organic aerosol from biogenic amines, Env. Sci. & Tech., 10.1021/es8018385, 2008b

Fairall, C. W. (1984). Interpretation of Eddy-Correlation Measurements of Particulate Deposition and Aerosol Flux. Atmospheric Environment 18:1329-1337.

Fairall, C.W., Helmig, D., Ganzeveld, L., Hare, J., 2007. Water-side turbulence enhancement of ozone deposition to the ocean. Atmospheric Chemistry and Physics 7, 443–451.

Fall, R., 2003. Abundant oxygenates in the atmosphere: a biochemical perspective. Chemical Reviews 103, 4941–4951.

Fall, R., Karl, T., Hansel, A., Jordan, A., Lindinger, W., 1999. Volatile organic compounds emitted after leaf wounding: online analysis by proton transfer-reaction mass spectrometry. Journal of Geophysical Research 104, 15963–15974.


Famulari, D., Fowler, D., Hargreaves, K., Milford, C., Nemitz, E., Sutton, M., Weston, K., 2004. Measuring eddy covariance fluxes of ammonia using tunable diode laser absorption spectroscopy. WASP. Focus 4(6), 151-158.

Fang Y.T., Gundersen P., Mo J.M., Zhu W.X., 2008. Input and output of dissolved organic and inorganic nitrogen in subtropical forests of South China under high air pollution. Biogeosciences 5, 339-352.

Farmer, D. K.; Woolridge, P. J.; Cohen, R. C.: Application of thermal-dissociation laser induced fluorescence (TD-LIF) to measurement of HNO3, aklyl nitrates, peroxy nitrates and NO2 fluxes using eddy covariance, Atmospheric Chemistry and Physics, 6, 3471-3486, 2006.

Farquhar G.D., Firth P.M., Wetselaar R. and Wier B. (1980) On the gaseous exchange of ammonia between leaves and the environment: determination of the ammonia compensation point. Plant Physiol. 66, 710-714.

Feingold, G. and Kreidenweis, S. M.: Cloud processing of aerosol as modeled by a large eddy simulation with coupled microphysics and aqueous chemistry, J. Geophys. Res., 107, 4687, doi:10.1029/2002JD002054, 2002.

Felzer, B., Reilly, J., Melillo, J., Kicklighter, D., Sarofim, M., Wang, C., Prinn, R., Zhuang, Q., 2005. Future effects of ozone on carbon sequestration and climate change policy using a global biogeochemical model. Climatic Change 73, 345–373.

Fiore, A.M., Horowitz, L.W., Purves, D.W., Levy II, H., Evans, M.J., Wang, Y., Li, Q., Yantosca, R.M., 2005. Evaluating the contribution of changes in isoprene emissions to surface ozone trends over the eastern United States. Journal of Geophysical Research 110, D12303, doi:10.1029/2004JD005485.

Fischbach, R.J., Staudt, M., Zimmer, I., Rambal, S., Schnitzler, J.P., 2002. Seasonal pattern of monoterpene synthase activities in leaves of the evergreen tree Quercus ilex. Physiologia Plantarum 114, 354-360.

Fisher, J.B., Baldocchi, D.D., Misson, L., Dawson, T.E., Goldstein, A.H., 2007. What the towers don’t see at night: nocturnal sap flow in trees and shrubs at two AmeriFlux sites in California. Tree Physiology 27, 597-610.

Flechard, C. R. and Fowler, D. (1998) Atmospheric ammonia at a moorland site. II: Long-term surface-atmosphere micrometeorological flux measurements. Quart. J. Roy. Meteor. Soc, 124: 759-791.

Flechard C., Fowler D., Sutton M.A. and Cape J.N. (1999) A dynamic chemical model of bi-directional ammonia exchange between semi-natural vegetation and the atmosphere. Q. J. R. Met. Soc. 125, 2611-2641.

Flechard, C., Neftel, A., Jocher, M., Amman, C. and Fuhrer, J 2005. .Bi-directional soil/atmosphere N2O exchange over two mown grassland systems with contrasting management practices. Glob. Change Biol. 11, 2114 – 2127.

Flechard, C. R., Neftel, A., Jocher, M., Ammann, C., Leifeld, J. 2007. Temporal changes in soil pore space CO2 concentration and storage under permanent grassland. Agricultural and Forest Meteorology 142 , 66–84

Flossmann, A.I., and W. Wobrock, 1996: Venting of Gases by Convective Clouds. J. Geophys.

Res. , 101, 18639-18649.

Flossmann, A., 1998a : Clouds and Pollution. Pure &Appl. Chem. 70, 1345-1352.

Flossmann, A.I., 1998b: Interaction of aerosol particles and clouds. J. Atmos. Sci., 55, 879-887. Fontan, J., Lopez, A., Lamaud, E. and Druilhet, A. (1997). Vertical flux measurements of the submicronic aerosol

particles and parameterization of the dry deposition velocity, in Biosphere-atmosphere exchange of pollutants and trace substances., J. Slanina, ed., Springer Verlag, Heidelberg, Germany, 381-390.

Fowler D. and Duyzer J.H. (1989) Micrometeorological techniques for the measurement of trace gas exchange. In: Exchange of trace gases between terrestrial ecosystems and the atmosphere. (Eds: Andreae M.O. and Schimel D.S.) pp 189-207. Wiley, Chichester, UK.

Fowler D., Flechard C.R., Sutton M.A. and Storeton-West R.L. (1998) Long term measurements of land-atmosphere exchange of ammonia over moorland. Atmospheric Environment (Ammonia Special Issue) 32, 453-459.

Fowler, D., Coyle, M., Flechard, C., Hargreaves, K.J., Nemitz, E., Storeton-West, R., Sutton, M.A., Erisman, J.W., 2001. Advances in micrometeorological methods for the measurement and interpretation of gas and particle nitrogen fluxes. Plant and Soil, 228, 117-129.

Frankenberg, C., Bergamaschi, P., Butz,A., Houweling, S., Meirink, J. F., Notholt, J., Petersen, A. K., Schrijver, H., Warneke, T., Aben, I., 2008. Tropical methane emissions: A revised view from SCIAMACHY onboard ENVISAT Geophys. Res. Let. 35, LI15811.

Fratini, G., Ciccioli, P., Febo, A., Forgione, A. and Valentini, R. (2007). Size-segregated fluxes of mineral dust from a desert area of northern China by eddy covariance. Atmos. Chem. Phys. 7:2839-2854.

Fu, T-M., Jacob, D.J., Palmer, P.I., Chance, K., Wang, Y.X., Barletta, B., Blake, D.R., Stanton, J.C., Pilling, M.J., 2007. Space-based formaldehyde measurements as constraints on volatile organic compound emissions in East and South Asia. Journal of Geophysical Research 112, doi:10.1029/2006JD007853.

Fuentes J.D., 1992. Effect of foliage surface wetness on the deposition of ozone, PHD Thesis, The University of Geulph, pp. 145.

Fuhrer, J., Skärby, L., Ashmore, M.R., 1997. Critical levels for ozone effects on vegetation in Europe. Environmental Pollution 97, 91–106.

Fuzzi, S., Mandrioli, P., and Perfetto, A.: Fog droplets – An atmospheric source of secondary

biological aerosol particles, Atmos. Environ., 31, 287–290, 1997.


Galbally, I.E., Kirstine, W., 2002. The production of methanol by flowering plants and the global cycle of methanol. Journal of Atmospheric Chemistry 43, 195–229.

Galbally, I.E., 1971. Ozone profiles and fluxes in the atmospheric surface layer. Quarterly Journal of the Royal Meteorological Society 97, 18–29.

Galbally, I.E., Roy, C.R., 1980. Destruction of ozone at the earth's surface. Quarterly Journal of the Royal Meteorological Society 106, 599–620.

Gallagher, M. W., Beswick, K. M., Duyzer, J., Westrate, H., Choularton, T. W. and Hummelshoj, P. (1997). Measurements of aerosol fluxes to Speulder forest using a micrometeorological technique. Atmospheric Environment 31:359-373.

Gallagher, M.W., Beswick, K.M., Coe, H., 2001. Ozone dry deposition to coastal waters. Quarterly Journal of the Royal Meteorological Society 127, 539–558.

Galloway, J. N., Dentener, F. J., Capone, D. G., Boyer, E. W., Howarth, R. W., Seitzinger, S. P., Asner, G. P., Cleveland, C. C., Green, P. A., Holland, E. A., Karl, D. M., Michaels, A. F., Porter, J. H., Townsend, A. R. and Vorosmarty, C. J.(2004) Nitrogen cycles: Past, present, and future, Biogeochemistry, 70, 153–226

Gao, W., Shaw, R.H. and Paw U, K.T., (1989). Observation of organized structures in turbulent flow within and above a forest canopy. Boundary-Layer Meteorology, 47: 349-377

Garland, J.A., Derwent, R.G., (1979). Destruction at the ground and the diurnal cycle of concentration of ozone and other gases. Quarterly Journal of the Royal Meteorological Society 105, 169–183.

Geever, M., C. O’Dowd, S. van Ekeren, R. Flanagan, D. Nilsson, G. de Leeuw and U. Rannik, Sub-micron sea-spray fluxes. GRL, 32, 2005, L15810. 2005.

Gerosa, G., Cieslik, S., Ballarin-Denti, A. (2003) Micrometeorological determination of time-integrated stomatal ozone fluxes over wheat: a case study in Northern Italy. Atmospheric Environment 37, 777–788.

Gerosa, G., Marzuoli, R., Cieslik, S., Ballarin-Denti, A., 2004. Stomatal ozone uptake by barley in Italy. "Effective exposure" as a possible link between concentration- and flux-based approaches. Atmospheric Environment 38, 2421–2432.

Gerosa, G., Vitale, M., Finco, A., Manes, F., Ballarin Denti, A., Cieslik, S., 2005. Ozone uptake by an evergreen Mediterranean forest (Quercus ilex) in Italy. Part I: Micrometeorological flux measurements and flux partitioning. Atmospheric Environment 39, 3255–3266.

Gerosa, G., Finco, A., Mereu, S., Vitale, M., Manes, F., Ballarin Denti, A., 2008. Comparison of seasonal variations of ozone exposure and fluxes in a Mediterranean Holm oak forest between the exceptionally dry 2003 and the following year. Environmental Pollution, in press, doi:10.1016/j.envpol.2007.11.025.

Gershenzon, J., Dudareva, N., 2007. The function of terpene natural products in the natural world. Nature Chemical Biology 3, 408-414

Gillette, D. A. and Chen, W. (2001). Particle production and aeolian transport from a "supply-limited" source area in the Chihuahuan desert, New Mexico, United States. Journal of Geophysical Research-Atmospheres 106:5267-5278.

Gödde, M., Conrad, R. 1998. Simultaneous measurement of nitric oxide production and consumption in soil using a simple static incubation system, and the effect of soil water content on the contribution of nitrification. Soil Biology and Biochemistry 30: 433-442.

Goldstein, A.H., McKay, M., Kurpius, M.R., Schade, G.W., Lee. A., Holzinger, R., Rasmussen, R.A., 2004. Forest thinning experiment confirms ozone deposition to forest canopy is dominated by reaction with biogenic VOCs. Geophysical Research Letters 31, L22106, doi:10.1029/2004GL021259.

Grabmer, W., Graus, M., Lindinger, C., Wisthaler, A., Rappenglück, B., Steinbrecher, R., Hansel, A., 2004. Disjunct eddy covariance measurements of monoterpene fluxes from a Norway spruce forest using PTR-MS, International Journal of Mass Spectrometry 239, 111–115.

Gribble, G.W., 1992. Sources of Halogenated Alkanes. Chemical & Engineering News 70, 3-3. Grivicke, R., Pressley, S., Allwine, G., Jobson, T., Westberg, H., Lamb, B., Jimenez, J. L., Nemitz, E., Alexander,

L., Velasco, E., Molina, L. and Ramos, R. (2007). Eddy covariance flux measurements of urban aerosols and related urban gaseous pollutants during the MILAGRO Mexico City Field Campaign., in AAAR 2007, American Aerosol Association, Reno, Nevada, September 24-28, 2007.

Groffman PM, Altabet MA, Böhlke JK, Butterbach-Bahl K, David MB, Firestone MK, Giblin AE, Kana TM, Nielsen LP, Voytek MA. 2006. Methods for measuring denitrification: diverse approaches to a difficult problem. Ecological Applications 16:2091–2122.

Grönholm, T., Aalto, P. P., Hiltunen, V., Rannik, U., Rinne, J., Laakso, L., Hyvonen, S., Vesala, T. and Kulmala, M. (2007). Measurement of aerosol particle dry deposition velocities using relaxed eddy accumulation technique. Tellus 59B:381-386.

Grote et al., 2006 Grote, R., Niinemets, Ü., 2008. Modeling volatile isoprenoid emission – a story with split ends. Plant Biology 10, 8-

28. Grünhage, L., Haenel, H.-D., 2008. Detailed documentation of the PLATIN (PLant-ATmosphere INteraction)

model. vTI Agriculture and Forestry Reseach, Special Issue 319, 85 pp.


Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C., 2006. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmospheric Chemistry and Physics 6, 107–173.

Guenther, A., Pierce, T., Lamb, B., Harley, P., Fall, R., 2000. Natural emissions of non-methane volatile organic compounds, carbon monoxide, and oxides of nitrogen from North America. Atmospheric Environment 34, 2205-2230.

Guenther, A., 1999. Modeling biogenic volatile organic compound emissions to the atmosphere. In: Hewitt, C.N. (Ed.). Reactive hydrocarbons in the atmosphere. Academic Press, San Diego, pp. 41-94.

Guenther, A.B., Hills, A.J., 1998. Eddy covariance measurement of isoprene fluxes. Journal of Geophysical Research 103, 13145-13152.

Guenther, A.B., Zimmerman, P.R., Harley, P.C., Monson, R.K., Fall, R., 1993. Isoprene and monoterpene emission rate variability: model evaluations and sensitivity analyses. Journal of Geophysical Research 98, 12609-12617.

Güsten, H., Heinrich, G., Schmidt, R.W.H., Schurath, U., 1992. A novel sensor for direct eddy flux measurements. Journal of Atmospheric Chemistry 14, 73–84.

Haapanala, S., Rinne, J., Pystynen, H-K., Hellén, H., Hakola, H., Riutta T., 2006. Measurements of biogenic hydrocarbon emissions from a boreal fen in the southern Finland with a REA system. Biogeosciences, 3, 103-112.

Hakata, M., Takahashi, M., Zumft, W., Sakamoto, A. and Morikawa, H. 2003. Conversion of the nitrate nitrogen and nitrogen dioxide to nitrous oxides in plants. Acta Biotechnologica 23: 249-257.

Hakola, H., Laurila, T., Rinne, J., Puhto, K., 2000. The ambient concentrations of biogenic hydrocarbons at a North-European site. Atmospheric Environment 34, 4971-4982.

Hakola, H., Tarvainen, V., Bäck, J., Ranta, H., Bonn, B., Rinne, J., Kulmala, M., 2006. Seasonal variation of mono- and sesquiterpene emission rates of Scots pine. Biogeosciences 3, 93-101.

Harley, P., Greenberg, J., Niinemets, Ü., Guenther, A., 2007. Environmental controls over methanol emission from leaves. Biogeosciences 4, 1083-1099.

Harmens, H., Mills, G., Emberson, L.D., Ashmore, M.R., 2007. Implications of climate change for the stomatal flux of ozone: A case study for winter wheat. Environmental Pollution 146, 763–770.

Harrison R.M., Rapsomanikis S. and Turnbull A. (1989) Land-surface exchange in a chemically reactive system; surface fluxes of HNO3, HCl and NH3. Atmos. Environ. 23, 1795-1800

Harrison, R. M., Yin, J. X., Mark, D., Stedman, J., Appleby, R. S., Booker, J. and Moorcroft, S. (2001). Studies of the coarse particle (2.5-10 mu m) component in UK urban atmospheres. Atmospheric Environment 35:3667-3679.

Hatanaka, A., 1993. The biogeneration of green odour by green leaves. Phytochemistry 34, 1201–1218. Hayes, F., Mills, G., Harmens, H., Norris, D., 2007. Evidence of widespread ozone damage to vegetation in Europe

(1990–2006). Programme Coordinating Centre for the ICP Vegetation, Centre for Ecology and Hydrology, Bangor, UK, 60 pp. Available from: <icpvegetation.ceh.ac.uk>.

Heiden, A.C., Kobel, K., Langebartels, C., Schuh-Thomas, G., Wildt, J., 2003. Emissions of oxygenated volatile organic compounds from plants. Part I: Emissions from lipoxygenase activity. Journal of Atmospheric Chemistry 45, 143-172.

Heiden, A.C., Kobel, K., Wildt, J., 1999. Einfluß verschiedener Streßfaktoren auf die Emission pflanzlicher flüchtiger organischer Verbindungen. Institut für Chemie und Dynamik der Geosphäre 2: Chemie der Belasteten Atmosphäre, Forschungszentrum Jülich, Jülich.

Held, A., Hinz, K.-P., Trimborn, A., Spengler, B. and Klemm, O. (2003). Towards direct measurement of turbulent vertical fluxes of compounds in atmospheric aerosol particles. Geophysical Research Letters 30:Article No. 10.1029/2003GL017854.

Held, A., Nowak, A., Wiedensohler, A. and Klemm, O. (2006). Field measurements and size-resolved model simulations of turbulent particle transport to a forest canopy J. Aerosol Sci. 37:786-798.

Hellén, H., Hakola, H., Pystynen, K-H., Rinne, J., Haapanala, S., 2006. C2-C10 hydrocarbon emissions from a boreal wetland and forest floor. Biogeosciences 3, 167-174.

Helmig, D., Ganzeveld, L., Butler, T., Oltmans, S.J., 2007. The role of ozone atmosphere-snow gas exchange on polar, boundary-layer tropospheric ozone – a review and sensitivity analysis. Atmospheric Chemistry and Physics 7, 15–30.

Hensen, A., Groot, T.T., van den Bulk, W.C.M., Vermeulen, A.T., Olesen J.E and Schelde K. 2006. Dairy farm CH4 and N2O emissions, from one square metre to the full farm scale Agric. Ecosys & Environ. 112, 146-152.

Hensen, A., Nemitz, E., Flynn, M.J., Blatter, A., Jones S. K., Sørensen, L. L., Hensen, B., Pryor, S., Jensen, B., Otjes, R.P., Cobussen, J., Loubet, B., Erisman, J.W., Gallagher, M.W., Neftel, A. and Sutton M.A. (2008) Inter-comparison of ammonia fluxes obtained using the Relaxed Eddy Accumulation technique. Biogeosciences Discuss., 5, 3965-4000.

Herrmann B., Mattsson M., Jones S.K., Cellier P., Milford C., Sutton M.A., Schjoerring J.K. and Neftel A. (2008) Vertical structure and diurnal variability of ammonia emission potential within an intensively managed grass canopy. Biogeosciences Discuss., 5, 2897-2921.


Herrmann, H., Tilgner, A., Barzaghi, P., Majdik, Z., Gligorovski, S., Poulain, L., and Monod, A.: Towards a more detailed description of tropospheric aqueous phase organic chemistry: CAPRAM 3.0, Atmos. Environ., 39, 4351–4363, 2005.

Hertel, O., Skjøth, C. A., Lofstrøm, P., Geels, C., Frohn, L. M., Ellermann, T. and Madsen, P. V. (2006) Modelling nitrogen deposition on a local scale - A review of the current state of the art. Environ. Chem. 3, 317-337.

Hirsch, A. I., Michalak, A. M.,, Bruhwiler, L. M., Peters, W., Dlugokencky, E. J. , Tans, P. P. 2006. Inverse modelling estimates of the global nitrous oxide surface flux from 1998 – 2001. Global Biogeochem. Cycl 20, GB1008.

Hogg, A., Uddling, J., Ellsworth, D., Carroll, M.A., Pressley, S., Lamb, B., Vogel, C., 2007. Stomatal and non-stomatal fluxes of ozone to a northern mixed hardwood forest. Tellus B 59, 514–525

Holtslag, A.A.M. and Moeng, C.H., 1991. Eddy diffusivity and countergradient transport in the convective atmospheric boundary-layer. Journal of the Atmospheric Sciences, 48: 1690-1698

Holzke, C., Dindorf, T., Kesselmeier, J., Kuhn, U., Koppmann, R., 2006. Terpene emissions from European beech (Fagus sylvatica L.): pattern and emission behaviour over two vegetation periods. Journal of Atmospheric Chemistry 55, 81-102.

Hong, J., Kim, J., Lee, D. and Lim, J.H., 2008. Estimation of the storage and advection effects on H2O and CO2 exchanges in a hill KoFlux forest catchment. Water Resources Research, 44([in press])

Hoose, C., Lohmann, U., Stier, P., Verheggen, B., Weingartner, E., 2008: Aerosol processing in mixed-phase clouds in ECHAM5-HAM: Model description and comparison to observations Journal of Geophysical Research D: Atmospheres , Volume 113, Issue 7, D07210

Horii, C. V.; Munger, J. W.; Wofsky, S. C.; Zahniser, M.; Nelson, D.; McManus, J. B.: Atmospheric reactive nitrogen concentration and flux budgets at a Northeastern U.S. forest site, Agricultural Forest Meteorology, 133, 210-225, 2005.

Horváth L. (1983) Concentration and near surface vertical flux of ammonia in the air in Hungary. Idöjárás (Journal of the Hungarian Meteor. Service) 87, 65-70.

Horvath, H., Pesava, P., Toprak, S. and Aksu, R. (1996). Technique for measuring the deposition velocity of particulate matter to building surfaces. Science of the Total Environment 189-190:255-258.

Horváth, L., Asztalos, M., Führer, E., Mészáros, R. and Weidinger, T.: Measurement of ammonia exchange over grassland in the Hungarian Great Plain. Agric. For. Meteor. 130, 282-298, 2005.

Hummelshoj, P. (1994). Dry deposition of particles and gases. Riso, R-658(EN); ISBN 87-550-1864-5, RISO National Laboratory, Roskilde, 109.

Husar, R.B., Lodge, J.P., Moore, D.J. (1978) Sulfur in the Atmosphere: Proceedings of the International Symposium, Yugoslavia. Pergamon Press.

Husted, S., Hebbern, C. A., Mattsson, M., Schjoerring, J.K. (2000) A critical experimental evaluation of methods for determination of NH4

+ in plant tissue, xylem sap and apoplastic fluid. Physiol. Plantarum, 109 (2), 167-179. Hüve, K., Christ, M.M., Kleist, E., Uerlings, R., Niinemets, Ü., Walter, A., Wildt, J., 2007. Simultaneous growth

and emission measurements demonstrate an interactive control of methanol release by leaf expansion and stomata. Journal of Experimental Botany 58, 1783-1793.

IPCC, 2006. IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. Eggleston, H.S.; Buendia, L.; Miwa, K.; Ngara, T; Tanabe, K. (eds)., Published: IGES, Japan., Vol. 4, Agriculture, Forestry and other Land Use.

IPCC (2007). Climate Change 2007 - The Physical Science Basis; Working Group I Contribution to the Fourth Assessment Report of the IPCC, Intergovernmental Panel on Climate Change.

Jacob, D.J., Field, B.D., Jin, E.M., Bey, I., Li, Q.B., Logan, J.A., Yantosca, R.M., Singh, H.B., 2002. Atmospheric budget of acetone. Journal of Geophysical Research-Atmospheres. 107, D10, 10.1029.

Jones, M.R., I.D. Leith, D. Fowler, J.A. Raven, M.A. Sutton, E. Nemitz, J.N. Cape, L.J. Sheppard, R.I. Smith and M.R. Theobald. (2007). Concentration-dependent NH3 deposition processes for mixed moorland semi-natural vegetation. Atmospheric Environment 41, 2049–2060. doi:10.1016/j.atmosenv.2006.11.003.

Junge, C.E. (1963) Air Chemistry and Radioactivity. Academic Press, New York, London Jungkunst HF, Freibauer A, Neufeldt H, Bareth G, 2006, Nitrous oxide emissions from agricultural land use in

Germany—a synthesis of available annual field data. J. Plant Nutr. Soil Sci. 169, 341-351. Kanemasu, E. T., Flitcroft, I. D., Shah, T. D. H., Kidd, G., Nie, D., Simpson, I., Lin, M., Neue, H.-U., Bronson, K.,

1995. Comparison of micrometeorological and chamber measurements of methane emisisns from rice fields. In: Climate Change and Rice (edt S. Peng, K. J. Ingram, K. J. Lampe, H.-U. Neue), IRRI, pp 91-101.

Kärcher, B., and M.M. Basko, Trapping of trace gases in growing ice crystals. J. Geophys. Res. 109, D22204, doi:10.1029/2004JD005254, 2004.

Karl, T., Fall, R., Jordan, A., Lindinger, W., 2001a. On-line analysis of reactive VOCs from urban lawn mowing. Environmental Science and Technology 35, 2926–2931.

Karl, T., Guenther, A., Jordan, A., Fall, R., Lindinger, W., 2001b. Eddy covariance measurement of biogenic oxygenated VOC emissions from hay harvesting. Atmospheric Environment 35, 491-495.

Karl, T., Guenther, A., Lindinger, C., Jordan, A., Fall, R., Lindinger, W., 2001c. Eddy covariance measurements of oxygenated volatile organic compound fluxes from crop harvesting using a redesigned proton-transfer-reaction mass spectrometer. Journal of Geophysical Research 106, 24157-24167.


Karl, T., Fall, R., Rosenstiel, T.N., Prazeller, P., Larsen, B., Seufert, G., Lindinger, W., 2002a. On-line analysis of the 13CO2 labeling of leaf isoprene suggests multiple subcellular origins of isoprene precursors. Planta 215, 894–905.

Karl, T., Curtis, A.J., Rosenstiel, T.N., Monson, R.K., Fall, R., 2002b. Transient releases of acetaldehyde from tree leaves – products of a pyruvate overflow mechanism? Plant Cell Environment 25, 1121–1131.

Karl, T, Spirig, C., Rinne, J., Stroud, C., Prevost, P., Greenberg, J., Fall, R., Guenther, A., 2002c. Virtual disjunct eddy covariance measurements of organic trace compound fluxes from a subalpine forest using proton transfer reaction mass spectrometry. Atmospheric Chemistry and Physics 2, 279-291.

Karl, T., Guenther, A., Spirig, C., Hansel, A., Fall, R., 2003. Seasonal variation of biogenic VOC emissions above a mixed hardwood forest in northern Michigan. Geophysical Research Letters 30, 2186, doi:10.1029/2003GL018432.

Karl, T., Harley, P., Guenther, A., Rasmussen, R., Baker, B., Jardine, K., Nemitz, E., 2005. The bi-directional exchange of oxygenated VOCs between a loblolly pine (Pinus taeda) plantation and the atmosphere. Atmospheric Chemistry and Physivs 5, 3015-3031.

Karlsson, G. Pihl, Karlsson, P.E., Soja, G., Vandermeiren, K., Pleijel, H., 2004. Test of the short-term critical levels for acute ozone injury on plants – improvements by ozone uptake modelling and the use of an effect threshold. Atmospheric Environment 38, 2237–2245.

Karlsson, P.E., Tang, L., Sundberg, J., Chen, D., Lindskog, A., Pleijel, H., 2007. Increasing risk for negative ozone impacts on vegetation in northern Sweden. Environmental Pollution 150, 96–106.

Karnosky, D.F., Skelly, J.M., Percy, K.E., Chappelka, A.H., 2007. Perspectives regarding 50 years of research on effects of tropospheric ozone air pollution on US forests. Environmental Pollution 147, 489–506.

Kawamura, K. and F. Sakaguchi, Molecular distributions of water-soluble dicarboxylic acids in marine aerosols over the Pacific Ocean including tropics, J. Geophys. Res., 104, 3501-3509, 1999.

Keene, D. W.; Pszenny, A.; Maben, J. R.; Stevenson, E.; Wall, A. Closure evaluation of size resolved aerosol pH in the New England coastal atmosphere during summer. J. Geophys. Res. 109, D23307; doi: 10.129/2004JD004801, 2004,

Keller, M.D., Bellows, W.K., Guillard, R.R.L., 1989. Dimethyl Sulfide Production in Marine-Phytoplankton. In E. S. Saltzmann and W. J. Cooper [eds.], Biogenic sulfur in the environment. American Chemical Society, pp. 167-182.

Keller, F., Bassin, S., Ammann, C., Fuhrer, J., 2007. High-resolution modelling of AOT40 and stomatal ozone uptake in wheat and grassland: A comparison between 2000 and the hot summer of 2003 in Switzerland. Environmental Pollution 146, 671–677.

Keppler, F., Hamilton, J.T.G., Braß, M., Röckmann, T., 2006. Methane emissions from terrestrial plants under aerobic conditions. Nature 439, 187-191.

Keppler, F., Hamilton, J.T.G., McRoberts, W.C., Vigano, I., Brass, M. and Röckmann, T. 2008. Methoxyl groups of plant pectin as a precursor of atmospheric methane: evidence from deuterium labelling studies. New Phytologist 178: 808-814.

Kerkeweg, A., Buchholz, J., Ganzeveld, L., Pozzer, A., Tost, H., Jöckel P., 2006. Technical Note: An implementation of the dry removal processes DRY DEPosition and SEDImentation in the Modular Earth Submodel System (MESSy), Atmospheric Chemistry and Physics 6, 4617–4632.

Keronen, P., Reissell, A., Rannik, Ü., Pohja, T., Siivola, E., Hiltunen, V., Hari, P., Kulmala, M., Vesala, T., 2003. Ozone flux measurements over a Scots pine forest using eddy covariance method: performance evaluation and comparison with flux-profile method. Boreal Environment Research 8, 425–443.

Kesik, M., Brüggemann N., Forkel R., Knoche R., Li C., Seufert G., Simpson D., Butterbach-Bahl K., 2006. Future Scenarios of N2O and NO emissions from European forest soils. J. Geophys. Res., 111, G02018, doi:10.1029/2005JG000115

Keuken, M.P., Schoonebeek, C.A.M., van Wensveen-Louter, A., Slanina, J., 1988. Simultaneous sampling of NH3, HNO3, HCl, SO2 and H2O2 by a wet annular denuder system. Atmos. Environ. 22, 2541-2548.

Kiene, R.P., and Bates, T.S., 1990. Biological removal of dimethyl sulphide from seawater. Nature 345, 702-705. Kleefeld, Ch., C.D. O’Dowd, S. O’Riely, S.G. Jennings, P. Aalto, E. Becker, G. Kunz, and G. de Leeuw. The

relative scattering of sub and super micron particles to aerosol light scattering in the marine boundary layer (MBL)., J. Geophys. Res., 107, 10.1029/2000JD000262, 2002

Kleffmann, J.; Kurtenbach, R.; Loerzer, J.; Wiesen, P.; Kalthoff, N.; Vogel, B.; Vogel, H.: Measured and simulated vertical profiles of nitrous acid - Part I: Field measurements, Atmospheric Environment, 37, 2949-2955, 2003.

Kleffmann, J.; Gavriloaiei, T.; Hofzumahaus, A.; Holland, F.; Koppmann, R.; Rupp, L.; Schlosser, E.; Siese, M.; Wahner, A.: Daytime formation of nitrous acid: a major source of OH radicals in a forest, Geophysical Research Letters, 32, L05818, doi:05810.01029/02005GL022524, 2005.

Klemm, O. and Wrzesinski, T. (2007). Fog deposition fluxes of water and ions to a mountainous site in Central Europe. Tellus B 59B:705.

Kokkola, H. Sorjamaa, R., Peräniemi, A., Raatikainen, T., Laaksonen, A., 2006: Cloud formation of particles containing humic-like substances, Geophysical Research Letters,Volume 33, Issue 10

Kool, D.M., Wrage, N., Oenema, O., Harris, D. and Van Groenigen, J.W., 2009. The 18O signature of biogenic nitrous oxide is determined by O exchange with water. Rapid Commun. Mass Spectrom., 23, 104–108.


Kowalski, A. S. and Vong, R. J. (1999). Near-surface fluxes of cloud water evolve vertically. Quart. J. Roy. Met. Soc. 125:2663-2684.

Kowalski, A. S. (2001). Deliquescence induces eddy covariance and estimable dry deposition errors. Atmospheric Environment 35:4843-4851.

Kreuzwieser, J., Graus, M., Wisthaler, A., Hansel, A., Rennenberg, H., Schnitzler, J.P., 2002. Xylem-transported glucose as additional carbon source for leaf isoprene formation in Quercus robur. New Phytologist 156, 171–178.

Kreuzwieser, J., Schnitzler, J.P., Steinbrecher, R., 1999: Biosynthesis of organic compounds emitted by plants. Plant Biology 1, 149-159.

Kroeze, C., Mosier, A., Bouwman, L. 1999. Closing the Global N2O Budget: a Retrospective Analysis 1500 – 1994. Global Biogeochemical Cycles 13, 1-8.

Kroon, P. S., Hensen, A., van den Bulk, W. C. M., Jongejan, P. A. C. and Vermeulen, A. T. 2008. The importance of reducing the systematic error due to non-linearity in N2O flux measurements by static chambers. Nutr Cycl Agroecosyst 82:175-186.

Lagzi, I., Mészáros, R., Horváth, L., Tomlin, A., Weidinger, T., Turányi, T., Ács, F., Haszpra, L., 2004. Modelling ozone fluxes over Hungary. Atmospheric Environment 38, 6211–6222.

Lamaud, E., Carrara, A., Brunet, Y., Lopez, A., Druilhet, A., 2002. Ozone fluxes above and within a pine forest canopy in dry and wet conditions. Atmospheric Environment 36, 77–88.

Lathiere, J., Hauglustaine, D.A., Friend, A.D., De Noblet-Ducoudre’, N., Viovy, N., Folberth, G. A., 2006. Impact of climate variability and land use changes on global biogenic volatile organic compound emissions. Atmospheric Chemistry and Physics 6, 2129–2146.

Lee, B. U., Kim, S. H., and Kim, S. S.: Hygroscopic growth of E. coli and B. subtilis bioaerosols, J. Aerosol Sci., 33, 1721–1723, 2002.

Lee, A., Goldstein, A.H., Kroll, J.H., Ng, N.L., Varutbangkul, V., Flagan, R.C., Seinfeld, J.H., 2006. Gas-phase products and secondary aerosol yields from the photooxidation of 16 different terpenes. Journal of Geophysical Research 111, D17305, doi:10.1029/2006JD007050.

Lehning, A., Zimmer, W., Zimmer, I., Schnitzler, J.P., 2001. Modelling of annual variations of oak (Quercus robur L.) isoprene synthase activity to predict isoprene emission rates. Journal of Geophysical Research 106, 3157-3166.

Lemon E. and Van Houtte R. (1980) Ammonia exchange at the land surface. Agron. J. 72, 876-883. Lerdau, M., 2007. A positive feedback with negative consequences. Science 316, 212-213. Leriche, M., R.L. Curier, L. Deguillaume, D. Caro, K. Sellegri and N. Chaumerliac: Numerical quantification of

sources and phase partitioning of chemical species in cloud: application to wintertime anthropogenic air masses at the Puy de Dôme station, J. Atmos. Chem., 57-3, 281-297, 2007.

Leroy, D., W. Wobrock and A. I. Flossmann, 2008: The role of boundary layer aerosol particles for the development of deep convective clouds: a high-resolution 3D model with detailed (bin) microphysics applied to CRYSTAL-FACE , Atmos. Res.; DOI: 10.1016/j.atmosres.2008.06.001

Lewis, A.C., Hopkins, J.R., Carpenter, L.J., et al. 2005. Sources and sinks of acetone, methanol, and acetaldehyde in North Atlantic marine air. Atmospheric Chemistry and Physics, 5, 1963-1974.

Lewis, E.R., and S. E. Schwartz, Sea salt aerosol production. Mechanisms, methods, measurements, and models. American Geophysical Union, Washington, D.C. USA, 2005.

Li C, Aber J, Stange F, Butterbach-Bahl K, Papen H, 2000, A process oriented model of N2O and NO emissions from forest soils: 1, Model development. Journal of Geophysical Research, 105, 4369-4384.

Li C, Salas W, DeAngelo B, Rose S, 2006, Assessing alternatives for mitigating net greenhouse gas emissions and increasing yields from rice production in China over the next twenty years. J. Environ. Qual. 35, 1554-1565.

Li, D., Wang, X. 2008. Nitrogen isotopic signature of soil-released nitric oxide (NO) after fertilizer application. Atmospheric Environment 42, 4747-4754.

Lichtenthaler, H.K., 1999. The 1-deoxy-D-xylulose-5-phosphate pathway of isoprenoid biosynthesis in plants. Annu. Rev. Plant Physiol. Plant Molec. Biol. 50, 47-65.

Lindinger, W., Hansel, A., Jordan, A., 1998. Review: On-line monitoring of volatile organic compounds at pptv levels by means of Proton-Transfer-Reaction Mass Spectrometry (PTR-MS), Medical applications, food control and environmental research, Int. J. Mass Spec. Ion Proc., 173, 191–241.

Liss, P. S., Hatton, A.D., Malin, G., Nightingale, P.D., Turner, S.M., 1997. Marine sulphur emissions. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 352, 159-168.

Liss, P.S., Balls, P.W., Martinelli, F.N., Coantic, M. (1981) The effect of evaporation and condensation on gas transfer across an air-water interface. Oceanol. Acta 4

Loreto, F., Ciccioli, P., Brancaleoni, E., Valentini, R., De Lillis, M., Csiky, O., Seufert, G., 1998. (a) A hypothesis on the evolution of isoprenoid emission by oaks based on the correlation between emission type and Quercus taxonomy. Oecologia 115, 302-305.

Loreto, F., Ciccioli, P., Brancaleoni, E., Frattoni, M., Delfine, S., 2000. Incomplete 13C labeling of alpha-pinene content in Quercus ilex leaves and appearance of unlabeled C in alpha-pinene emission in the dark. Plant Cell Environment 23, 229-234.


Loreto, F., Fischbach, R.J., Schnitzler, J-P., Ciccioli, P., Brancaleoni, E., Calfapietra, C., Seufert, G., 2001. (a) Monoterpene emission and monoterpene synthase activities in the Mediterranean evergreen oak Quercus ilex L. grown at elevated CO2 concentrations. Global Change Biology 7, 709-717.

Loreto, F., Pinelli, P., Brancaleoni, E., Ciccioli, P,. 2004. (a) 13C labelling reveals chloroplastic and extra-chloroplastic pools of dimethylallyl pyrophosphate and their contribution to isoprene formation. Plant Physiology 135, 1903–1907.

Loreto F., Barta, C., Brilli, F., Nogues, I. 2006. On the induction of volatile organic compound emissions by plants as consequence of wounding or fluctuations of light and temperature. Plant Cell Environment 29, 1820-1828.

Loubet, B., Milford, C., Sutton, M.A., Cellier, P., 2001. Investigation of the interaction between sources and sinks of atmospheric ammonia in an upland landscape using a simplified dispersion-exchange model, J. Geophys. Res. 106(D20): 24183-24196.

Loubet, B., Cellier, P., Milford, C. and Sutton, M. A. (2006) A coupled dispersion and exchange model for short-range dry deposition of atmospheric ammonia. Quart. J. Royal. Meteor. Soc. 132, 1733-1763.

Loubet B., Asman W.A.H., Theobald M.R., Hertel O., Tang Y.S., Robin P., Hassouna M., Daemmgen U., Genermont S., Cellier P. and Sutton M.A. (2008a) Ammonia deposition near hot spots: processes, models and monitoring methods. In: Atmospheric Ammonia: Detecting emission changes and environmental impacts (eds. M.A. Sutton, S. Baker and S. Reis), Springer.

Loubet, B., Milford, C., Hensen, A., Dämmgen, U., Cellier, P. and Sutton, M.A. (2008b) Advection of ammonia over a pasture field, and its effect on gradient flux measurements. Biogeosciences Discuss. (under review).

Lovett, G. M. (1994). Atmospheric deposition of nutrients and pollutants to North America: An ecological perspective. Ecological Applications 4:629-650.

Loya, W.M., Pregitzer, K.S., Karberg, N.J., King, J.S., Giardina, C.P., 2003. Reduction of soil carbon formation by tropospheric ozone under increased carbon dioxide levels. Nature 425, 705–707.

Marandino, C.A., De Bruyn, W.J., Miller, S.D., et al., 2005. Oceanic uptake and the global atmospheric acetone budget. Geophysical Research Letters, 32, 15, L15806.

Mari, C., Jacob, D. J., and Bechtold, P., 2000: Transport and scavenging of soluble gases in a deep convective cloud. J. Geophys. Res. 105, 22255–22267.

Martensson, E. M., Nilsson, E. D. and Johansson, C. (2002). Emissions of aerosol particles in the urban environment measured by eddy correlation, in 6th International Aerosol Conference, Taiwan, 9-13 September 2002.

Mårtensson, M., E. D. Nilsson, G. de Leeuw, L.H. Cohen, and H-C Hansson, Laboratory simulations of the primary marine aerosol generated by bubble bursting, JGR-Atmospheres 108 (D9), 10.1029/2002JD002263, 2003.

Martensson, E. M., Nilsson, E. D., Buzorius, G. and Johansson, C. (2006). Eddy covariance measurements and parameterisaton of traffic related emissions in an urban environment. Atmospheric Chemistry and Physics 6:769-785.

Martin, C. L., Longley, I. D., Dorsey, J. R., Thomas, R. M., Gallagher, M. W. and Nemitz, E. (2008). Ultrafine particle fluxes above four major European cities. Atmospheric Environment [in press].

Massad R. S., Loubet B., Tuzet A. and Cellier P. (2008) Relationship between ammonia stomatal compensation point and nitrogen metabolism in arable crops: Current status of knowledge and potential modelling approaches. Environ. Poll., 154, 390-403.

Massman, W.J., 2004. Toward an ozone standard to protect vegetation based on effective dose: a review of deposition resistances and a possible metric. Atmospheric Environment 38, 2323–2337.

Mattsson M., Herrmann B., David M., Loubet B., Riedo M., Theobald M.R., Sutton M.A., Bruhn D., Neftel A. and Schjoerring J.K. (2008a) Temporal variability in bioassays of ammonia emission potential in relation to plant and soil N parameters in intensively managed grassland. Biogeosciences Discuss., 5, 2749-2772.

Mattsson M., Herrmann B., Jones, S.K, Neftel A., Sutton M.A. and Schjoerring J.K. (2008b) Contribution of different grass species to plant-atmosphere ammonia exchange in intensively managed grassland. Biogeosciences Discuss., 5, 2583-2605.

Matyssek, R., Sandermann, H., Wieser, G., Booker, F., Cieslik, S., Musselman, R., Ernst, D., 2008. The challenge of making ozone risk assessment for forest trees more mechanistic. Environmental Pollution, in press, doi:10.1016/j.envpol.2008.04.017.

McBain, M.C., Warland, J.S., McBride, R.A. and Wagner-Riddle, C. 2004. Laboratory-scale measurements of N2O and CH4 emissions from hybrid poplars (Populus deltoides x Populus nigra). Waste Management Research 22: 454-465.

McFiggans, G., H. Coe, R. Burgess, J. Allan, M. Cubison, M.R. Alfarra, R. Saunders, A. Saiz-Lopez, J.M.C. Plane, D.J. Wevill, L.J. Carpenter, A.R: Rickard, P.S. Monks, Atmos. Chem. Phys. 4, 701-713, 2004.

McLaughlin, S.B., Wullschleger, S.D., Sun, G., Nosal, M., 2007. Interactive effects of ozone and climate on water use, soil moisture content and streamflow in a southern Appalachian forest in the USA. New Phytologist 174, 125–136.

McLeod, A.R., Fry, S.C., Loake, G.J., Messenger, D.J., Reay, D.S., Smith, K.A. and Yun, B.-W. 2008. Ultraviolet radiation drives methane emissions from terrestrial plant pectins. New Phytologist 9999(9999).


Meixner, F. X., Ammann, C., Arenz, R., Nathaus, F. J., Neftel, A., Blatter, A., Gut, A., Hoegger, D., Wyers, G.P., Jongejan, P., Loesch, R., Busch, J., Guesten, H., Sprung, D. and Baumann, M. (1996) The Bellheim '95 experiment: bi-directional fluxes of ammonia. Annales Geophysicae 14, Suppl II, C467.

Meskhidze, N., Nenes, A. 2006. Phytoplankton and cloudiness in the Southern Ocean Science ,314, 5804, 1419-1423.

Meyers, T.P., Finkelstein, P.L., Clarke, J., Ellestad, T.G., Sims, P., 1998. A multilayer model for inferring dry deposition using standard meteorological measurements. Journal of Geophysical Research 103, 22645–22661

Meyers, T. P., Luke, W. T. and Meisinger, J. J. (2006). Fluxes of ammonia and sulfate over maize using relaxed eddy accummulation. Agricultural Forest Meteorology 136:203-213.

Michou, M., Laville, P., Serça, D., Fotiadi, A., Bouchou, P., Peuch, V.-H., 2005. Measured and modeled dry deposition velocities over the ESCOMPTE area. Atmospheric Research 74, 89–116.

Mikkelsen, T.N., Ro-Poulsen, H., Hovmand, M.F., Jensen, N.O., Pilegaard, K., Egeløv, A.H., 2004. Five-year measurements of ozone fluxes to a Danish Norway spruce canopy. Atmospheric Environment 38, 2361–2371.

Milford, C., Theobald, M. R., Nemitz, E. and Sutton, M. A. (2001a) Dynamics of ammonia exchange in response to cutting and fertilizing in an intensively-managed grassland. Water, Air and Soil Pollution: Focus, 1, 167-176.

Milford, C., Hargreaves, K. J., Sutton, M. A., Loubet, B. and Cellier P. (2001b) Fluxes of NH3 and CO2 over upland moorland in the vicinity of agricultural land. J. geophys Res. (Atmospheres) 106, 24,169-24,181.

Milford, C., Theobald, M. R.., Nemitz, E., Hargreaves, K. J., Horvath, L., Raso, J., Dämmgen, U., Neftel, A., Jones, S. K., Hensen, A., Loubet, B. and Sutton, M.A. (2008) Ammonia fluxes in relation to cutting and fertilization of an intensively managed grassland derived from an inter-comparison of gradient measurements. Biogeosciences Discuss. (in press).

Millenium Ecosystem Assessment (2005) Millet, D.B., Jacob, D.J., Turquety, S., Hudman, R.C., Wu, S., Fried, A., Walega, J., Heikes, B.G., Blake, D.R.,

Singh, H.B., Anderson, B.E., Clarke, A.D., 2006. Formaldehyde distribution over North America: Implications for satellite retrievals of formaldehyde columns and isoprene emission. Journal of Geophysical Research 111, D24S02, doi:10.1029/2005JD006853.

Millet, D. B., Jacob, D. J., Custer, T. G., de Gouw, J. A., Goldstein, A. H., Karl, T., Singh, H. B., Sive, B. C., Talbot, R. W., Warneke, C. and Williams, J., 2008. New constraints on terrestrial and oceanic sources of atmospheric methanol. Atmospheric Chemistry and Physics Discussions, 8, 7609-7655.

Möhler, O., Bunz, H., Stetzer, O., 2006: Homogeneous nucleation rates of nitric acid dihydrate (NAD) at simulated stratospheric conditions - Part II: Modelling, Atmospheric Chemistry and Physics , Volume 6, Issue 10, 2006, Pages 3035-3047

Möhler, O., DeMott, P. J., Vali, G. and Levin, Z.: Microbiology and atmospheric processes: The role of biological particles in cloud physics, Biogeosciences Discuss., 4, 2559–2591, 2007, http://www.biogeosciences-discuss.net/4/2559/2007/

Monin, A.S. and Obukhov, A.M., 1954. Basic turbulent mixing laws in the atmospheric surface layer. Tr. Geofiz. Inst. Akad. Nauk. SSSR, 24: 163-187.

Monin, A.S. and Zilitinkevich, S., 1974. Similarity theory and resistance laws for the planetary layer. Boundary-Layer Meteorology, 7: 391-397

Monson, R.K., Harley, P.C., Litvak, M.E., Wildermuth, M., Guenther, A.B., Zimmerman, P.R., Fall, R., 1994. Environmental and developmental controls over the seasonal pattern of isoprene emission from aspen leaves. Oecologia 99, 260-270.

Monteith J.L., Unsworth, M.H. (2007) Principles of Environmental Physics. Academic Press Mosquera, J., Hensen, A., van den Bulk, W.C.M., Vermeulen, A.T., Erisman, J.W., 2001. Long term NH3 flux

measurements at two locations in the Netherlands. Water, Air and Soil Pollution: Focus 1, 203-212. Moukhtar, S., Bessagnet, B., Rouil, L., Simon, V., 2005. Monoterpene emissions from beech (Fagus sylvatica) in a

French forest and impact on secondary pollutants formation at regional scale. Atmospheric Environment 39, 3535-3547.

Muhlbauer, A. and Lohmann, U. (2008): Sensitivity studies of the role of aerosols in warm-phase orographic precipitation in different dynamical flow regimes, J. Atmos. Sci., 65, 2522–2542.

Mukammal, E.I., 1965. Ozone as a cause of tobacco injury. Agricultural Meteorology 2, 145–165. Mulcahy, J. P., C. D. O’Dowd, S. G. Jennings and D. Ceburnis, Significant Enhancement of Aerosol Optical Depth

in Marine Air under High Wind Conditions, Geophys. Res. Letts., Vol. 35, L16810, doi:10.1029/2008GL034303, 2008

Müller, C. 2003. Plants affect the in situ N2O emissions of a temperate grassland ecosystem. Journal of Plant Nutrition and Soil Science-Zeitschrift Für Pflanzenernährung Und Bodenkunde 166: 771-773.

Neff J.C., Holland E.A., Dentener F.J., McDowell W.H., Russell K.M., 2002. The origin, composition and rates of organic nitrogen deposition: A missing piece of the nitrogen cycle? Biogeochemistry 57, 99-136.

Neftel, A., Blatter, A., Gut, A., Hoegger, D., Meixner, F.X., Ammann, C., Nathaus, F.J., 1998. NH3 soil and soil surface gas measurements in a triticale wheat field. Atmos. Environment (Ammonia Special Issue) 32 (3), 499-506.


Neftel, A., Blatter, A., Otjes, R., Erisman, J. W. and Hansen. A.: State of the art REA NH3 flux measurements. in: Proc. 10th Nitrogen Workshop, Copenhagen, August 1999. II.49. Royal Veterinary and Agricultural University, Copenhagen, 1999.

Neftel, A., Ammann, C., Brunner, A., Jocher, M., Spirig, M., Davidson, B., Rinne, J., Dommen, J., 2007. Comparison of different approaches to measure VOC exchange over grassland by PTR-MS. In Hansel, A., and Märk, T. (Eds.): 3rd International Conference on Proton Transfer Reaction Mass Spectrometry and its applications - Contributions. January 27th - February 1st, 2007, Obergurgl, Austria. Conference Series, Innsbruck University Press. 80-84.

Neinhuis, C. and Barthlott, W. (1997). Characterization and distribution of water-repellent, self-cleaning plant surfaces. Annals of Botany 79:667-677.

Neirynck J. and Ceulemans R. (2008) Bidirectional ammonia exchange above a mixed coniferous forest. Environ. Pollut. (in press).

Neirynck J., Kowalski A.S., Carrara A., Ceulemans R. (2005) Driving forces for ammonia fluxes over mixed forest subjected to high deposition loads. Atmospheric Environment, 39, 5013-5024

Nemecek-Marshall, M., MacDonald, M.C., Franzen, J.J., Wojciechowski, C.L., Fall, R., 1995. Methanol emission from leaves: Enzymatic detection of gas-phase methanol and relation of methanol fluxes to stomatal conductance and leaf development. Plant Physiology 108, 1359-1368.

Nemitz E, Sutton M.A., Fowler D. and Choularton T. (1996) Application of a NH3 gas-to-particle conversion model to measurement data. In: Poster Proceedings of Atmospheric Ammonia: Emission, Deposition and Environmental Impacts. (Eds. Sutton M.A., Lee D.S., Dollard G.J. and Fowler D.) 98-103, Institute of Terrestrial Ecology, Edinburgh.

Nemitz, E., Theobald, M. R., McDonald, A. G., Fowler, D., Dorsey, J. R., Bower, K. N. and Gallagher, M. W. (2000b). Direct micrometeorological eddy-correlation measurements of size-dependent particle emission above a city, in EUROTRAC Symposium 2000, P. M. Midgley, M. Reuther and M. Williams (Editors), Springer Verlag, Heidelberg, Garmisch-Parenkirchen.

Nemitz, E., Sutton, M. A., Gut, A., San José, R., Husted, S. and Schjoerring, J. K. (2000a) Sources and sinks of ammonia within an oilseed rape canopy. Agric. For. Meteorol. (Ammonia Special Issue) 105 (4), 385-404.

Nemitz, E., Sutton, M. A., Schjoerring, J. K., Husted, S. and Wyers, G. P. (2000b) Resistance modelling of ammonia exchange over oilseed rape. Agric. For. Meteorol. (Ammonia Special Issue) 105 (4), 405-425.

Nemitz, E., Williams, P. I., Theobald, M. R., McDonald, A. G., Fowler, D. and Gallagher, M. W. (2000c). Application of two micrometeorological techniques to derive fluxes of aerosol components above a city, in EUROTRAC-2 Symposium 2000, P. M. Midgley, M. Reuther and M. Williams (Editors), Springer Verlag, Berlin, Garmisch-Partenkirchen, Germany.

Nemitz, E., Sutton, M. A., Wyers, G. P., Otjes, R. P., Schjoerring, J. K., Gallagher, M. W., Parrington, J., Fowler, D. and Choularton, T. W. (2000a). Surface/atmosphere exchange and chemical interaction of gases and aerosols over oilseed rape. Agricultural and Forest Meteorology 105:427-445.

Nemitz, E., Flynn, M., Williams, P.I., Milford, C., Theobald, M.R., Blatter, A., Gallagher, M.W., Sutton, M.A., 2001. (a) A relaxed eddy accumulation system for the automated measurement of atmospheric ammonia fluxes. Water, Air and Soil Pollution: Focus 1, 189-202.

Nemitz, E., Milford, C., Sutton, M.A., 2001. (b) A two-layer canopy compensation point model for describing bi- directional biosphere-atmosphere exchange of ammonia. Quart. J. Royal Meteor. Soc. 127, 815-833.

Nemitz, E., Gallagher, M. W., Duyzer, J. H. and Fowler, D. (2002a). Micrometeorological measurements of particle deposition velocities to moorland vegetation. Quarterly Journal of the Royal Met. Soc. 128:2281-2300.

Nemitz, E., Hargreaves, K. J., McDonald, A. G., Dorsey, J. R. and Fowler, D. (2002b). Micrometeorological measurements of the urban heat budget and CO2 emissions on a city scale. Environmental Science and Technology 36:3139-3146.

Nemitz, E., Sutton, M.A., Wyers, G.P., Jongejan, P.A.C., 2004. Gas-particle conversions above a Dutch heathland: I. Surface exchange fluxes of NH3, SO2, HNO3 and HCl. Atmos. Chem. Phys. 4, 989-1005.

Nemitz, E., Sutton, M.A., Wyers, G.P., Otjes, R.P., Mennen, M.G., van Putten, E.M., Gallagher, M.W., (2004b) Gas-particle interactions above a Dutch heathland: II. Concentrations and surface exchange fluxes of atmospheric particles. Atmospheric Chemistry and Physics 4, 1007-1024.

Nemitz E. and Sutton M.A. (2004) Gas-particle conversions above a Dutch heathland: III. Modelling of size-dependent NH4

+ fluxes as modified by the NH3-HNO3-NH4NO3 equilibrium. Atmos. Chem. Phys. 4, 1025-1045. Nemitz, E., Anderson, D., Baker, B., Karl, T., Stroud, C., Jimenez, J. L., Huffman, A., Delia, A. E., Canagaratna,

M., Worsnop, D. and Guenther, A. B. (2004a). The use of aerosol flux measurements to study biogenic secondary aerosol formation above a pine forest., in International Global Atmospheric Chemistry (IGAC) Conference, 4-9 September 2004, Christchurch, New Zealand.

Nemitz, E., Jimenez, J. L., Huffman, A., Canagaratna, M., Worsnop, D. R. and Guenther, A. B. (2008b). An eddy-covariance system for the measurement of surface / atmosphere exchange fluxes of submicron aerosol chemical species - first application above an urban area. Aerosol Science Technology [in press].

Nemitz E., Dorsey J.R., Flynn M.J., Gallagher M.W., Hensen A., Owen S.M., Dämmgen U. and Sutton M.A. (2008a) Aerosol fluxes and particle growth above managed grassland. Biogeosciences Discussions (in press)


Nemitz, E., Huffman, A., Jimenez, J. L., Baker, B., Anderson, D., Canagaratna, M., Worsnop, D. R., Walker, J. T., Delia, A. E., Karl, T. G. and Guenther, A. B. (2008a). Speciated and size-resolved aerosol fluxes above a pine forest. Journal of Geophysical Research-Atmospheres [in preparation].

Nemitz, E.; Dorsey, J. R.; Flynn, M.; Gallagher, M. W.; Hensen, A.; Erisman, J. W.; Owen, S.; Daemmgen, U.; Sutton, M. A.: Aerosol fluxes and particle growth above grassland following the application of NH4NO3 fertilizers, Biogeosciences Discussions, 2008 (to be submitted)

Nemitz, E., Theobald, M. R., McDonald, A. G., Dorsey, J. R., Gallagher, M. W. and Fowler, D. (2009). Size-segregated particle emissions above a city derived by a micrometeorological technique. Environmental Science and Technology [under review].

Neu, J. L., M. J. Lawler, M. J. Prather, and E. S. Saltzman (2008), Oceanic alkyl nitrates as a natural source of tropospheric ozone, Geophys. Res. Lett., 35, L13814, doi:10.1029/2008GL034189.

Nicholson, K. W. (1988). A Review of Particle Resuspension. Atmospheric Environment 22:2639-2651. Nicholson, K. W., Branson, J. R., Giess, P. and Cannell, R. J. (1989). The effect of vehicle activity on particle

resuspension. Journal of Aerosol Science 20:1425-1528. Nicholson, K. W. (1993). Wind-Tunnel Experiments on the Resuspension of Particulate Material. Atmospheric

Environment Part a-General Topics 27:181-188. Niinemets, Ü., Tenhunen, J.D., Harley, P.C., Steinbrecher, R., 1999. A model of isoprene emission based on

energetic requirements for isoprene synthesis and leaf photosynthetic properties for Liquidambar and Quercus. Plant Cell Environment 22, 1319-1336.

Niinemets, Ü., Reichstein, M., 2002. A model analysis of the effects of nonspecific monoterpenoid storage in leaf tissues on emission kinetics and composition in Mediterranean sclerophyllous Quercus species. Global Biogeochemical Cycles 16, 1110, DOI 1110.1029/2002GB001927.

Niinemets, Ü., Seufert, G., Steinbrecher, R., Tenhunen, J.D., 2002. (a) A model coupling foliar monoterpene emissions to leaf photosynthetic characteristics in Mediterranean evergreen Quercus species. New Phytologist 153, 257-276.

Niinemets, Ü., Reichstein, M., Staudt, M., Seufert, G., Tenhunen, J.D., 2002. (b) Stomatal constraints may affect emission of oxygenated monoterpenoids from the foliage of Pinus pinea. Plant Physiology 130, 1371-1385.

Niinemets, Ü., Loreto, F., Reichstein, M., 2004. Physiological and physico-chemical controls on foliar volatile organic compound emissions. Trends in Plant Science 9, 180-186.

Niinemets, Ü., Peñuelas, J., 2008. Gardening and urban landscaping: significant players in global change. Trends in Plant Science 13, 60-65.

Niinemets, Ü., 2008. Getting hold of terpene emissions from vegetation. ILeaps Newsletter 5, 40-42. Nilsson, E. D. and Rannik, U. (2001). Turbulent aerosol fluxes over the Arctic Ocean 1. Dry deposition over sea and

pack ice. Journal of Geophysical Research-Atmospheres 106(D23):32125-32137. Nilsson, E. D., U. Rannik, E. Swietlicki, C., Leck, P.P., Aalto, J., Zhou, J., and M. Norman, J. Geophys. Res., 106,

D23, 32139-32154, 2001. Noe, S.M., Ciccioli, P., Brancaleoni, E., Loreto, F., Niinemets, Ü., 2006. Emissions of monoterpenes linalool and

ocimene respond differently to environmental changes due to differences in physico-chemical characteristics. Atmospheric Environment 40, 4649-4662.

Noe, S.M., Copolovici, L., Niinemets, Ü., Vaino, E., 2008. (a) Foliar limonene uptake scales positively with leaf lipid content: “non-emitting” species absorb and release monoterpenes. Plant Biology 10, 129-137.

Noe, S.M., Peñuelas, J., Niinemets, Ü., 2008. (b) Monoterpene emissions from ornamental trees in urban areas: a case study of Barcelona, Spain. Plant Biology 10, 163-169.

Norris SJ, Brooks IM, Hill MK, Brooks BJ. 2008b. Eddy covariance measurments of sea spray aerosol fluxes over the open ocean. Submitted for publication in Geophys. Res. Lett.

O'Dowd, C.D., J. Lowe, M.H. Smith and A.D. Kaye, The relative importance of sea-salt and nss-sulphate aerosol to the marine CCN population: An improved multi-component aerosol-droplet parameterisation. Q. J. Roy. Met. Soc., 125, 1295-1313. 1999

O’Dowd, C.D., J.L. Jimenez, R. Bahreini, R.C. Flagan J.H. Seinfeld, L. Pirjola, M. Kulmala, S.FG. Jennings and T. Hoffmann, Marine particle formation from biogenic iodine emissions, Nature, 417, 632-636, 2002.

O'Dowd, C.D., M.C. Facchini, F. Cavalli, D. Ceburnis, M. Mircea, S. Decesari, S. Fuzzi, Y.J. Yoon, and J.P. Putaud, Biogenically-driven organic contribution to marine aerosol, Nature, doi:10.1038/nature02959,. 2004.

O’Dowd, C.D., & T. Hoffmann, Coastal New Particle Formation: A review of the current state-of-the-art. Environmental Chemistry, 2, 245-255, doi:10.1071/EN05077, 2005.

O’Dowd, C.D., & G. de Leeuw, Marine Aerosol Production: a review of the current knowledge,, Phil. Trans. R. Soc. A, doi:10.1098/rsta.2007.2043, 2007

O’Dowd, C.D., B. Langmann, S. Varghese, C. Scannell, D. Ceburnis, and M. C. Facchini., A Combined Organic-Inorganic Sea-Spray Source Function, Geophys. Res. Letts., 35, L01801, doi:10.1029/2007GL030331, 2008

Okin, G. S. (2005). Dependence of wind erosion and dust emission on surface heterogeneity: stochastic modelling. Journal of Geophysical Research-Atmospheres 110:doi:10.1029/2004JD005288.

Oms, M.T., Jongejan, P.A.C., Veltkamp, A.C., Wyers, G.P., Slanina, J., 1996. Continuous monitoring of atmospheric HCl, HNO2, HNO3, and SO2 by wet-annular denuder air sampling with on-line chromatographic analysis. Intern. J. Environ. Anal. Chem. 62: 207-218.


Ould-Dada, Z. (2002). Dry deposition profile of small particle within a model spruce canopy. Science of the Total Environment 286:83-96.

Owen, S., Boissard, S., Street, R.A., Duckham, S.C., Csiky, O., Hewitt, N., 1997. Screening of 18 Mediterranean plant species for volatile organic compound emission. Atmospheric Environment 31, 101-117.

Owen, S.M., MacKenzie, A.R., Stewart, H., Donovan, R., Hewitt, C.N., 2003. Biogenic volatile organic compound (VOC) emission estimates from an urban tree canopy. Ecological Applications 13, 927-938.

Palmer, P.I., Jacob, D.J., Fiore, A.M., Martin, R.V., Chance, K., Kurosu, T.P., 2003. Mapping isoprene emissions over North America using formaldehyde column observations from space. Journal of Geophysical Research doi: 10.1029/2002JD002153.

Palmer et al, 2006 Palmer et al, 2007 Palmer, P.I., 2008. Quantifying sources and sinks of trace gases using space-borne measurements: current and future

science. submitted to Phil. Trans. R. Soc. A. trienniel issue. Parton WJ, Morgan JA, Wang GM, Del Grosso S, 2007, Projected ecosystem impact of the Prairie Heating and CO2

Enrichment experiment. New Phytol., 174, 823-834. Pesava, P., Aksu, R., Toprak, S., Horvath, H. and Seidl, S. (1999). Dry deposition of particles to building surfaces

and soiling. The Science of the Total Environment 235:25-35. Petroff, A., Mailliat, A., Amielh, M. and Anselmet, F. (2007a). Aerosol dry deposition on vegetative canopies. Part

I: Review of present knowledge. Atmospheric Environment 42:3625-3653. Petroff, A., Mailliat, A., Amielh, M. and Anselmet, F. (2007b). Aerosol dry deposition on vegetative canopies. Part

II: A new modelling approach and applications. Atmospheric Environment 42:3654-3683. Pérez T, Trumbore SE, Tyler SC, Matson PA, Ortiz-Monasterio I, Rahn T, Griffith DWT. 2001. Identifying the

agricultural imprint on the global N2O budget using stable isotopes. Journal of Geophysical Research 106:9869–9878.

Philippot L, Hallin S, Börjesson G, Baggs EM (2008) Biochemical cycling in the rhizosphere having an impact on global change. Plant and Soil Special Issue S28 - RHIZO-BOOK: in press.

Phillips, G. J., Thomas, R., Famulari, D., Williams, P. I., Crosier, J., Allan, J., Coe, H., Gallagher, M. W., Flynn, M. and Nemitz, E. (2007). Fluxes of submicron organic particles above three UK cities, in Measurement of Speciated Aerosol Fluxes; Ph.D. Thesis, R. Thomas, ed., University of Manchester, Manchester.

Phillips, G., Thomas, R., Famulari, D., Williams, P. I., Bower, K. N., Allan, J. D., Gallagher, M. W., Flynn, M. and Nemitz, E. (2008). Fluxes of submicron aerosol above three UK cities. Atmos. Chem. Phys. (in preparation)

Pihlatie, M., Ambus, P., Rinne, J., Pilegaard, K. and Vesala, T. 2005. Plant-mediated nitrous oxide emissions from beech (Fagus sylvatica) leaves. New Phytologist 168: 93-98.

Pio, C.A., Silva, P.A., Cerqueira, M.A., Nuñes, T.V., 2005. Diurnal and seasonal emissions of volatile organic compounds from cork oak (Quercus suber) trees. Atmospheric Environment 39, 1817-1827.

Pleijel, H., Danielsson, H., Ojanperä, K., De Temmerman, L., Högy, P., Badiani, M., Karlsson, P.E., 2004. Relationship between ozone exposure and yield loss in European wheat and potato – a comparison of concentration- and flux-based exposure indices. Atmospheric Environment 38, 2259–2269.

Pleijel, H., Danielsson, H., Emberson, L., Ashmore, M.R., Mills G., 2007. Ozone risk assessment for agricultural crops in Europe: Further development of stomatal flux and flux–response relationships for European wheat and potato. Atmospheric Environment 41, 3022–3040.

Polson, D (2009) Boundary-layer-budget measurements of greenhouse gas emissions at the country scale indicate national inventories underestimate nitrous oxide and methane. Quarterly Journal of Royal Met. Soc. (submitted January 2009)

Pozzoli, L., Bey, I., Rast, S., Schultz, M.G., Stier, P., Feichter, J. , 2008: Trace gas and aerosol interactions in the fully coupled model of aerosol-chemistry-climate ECHAM5-HAMMOZ: 2. Impact of heterogeneous chemistry on the global aerosol distributions, Volume 113, Issue 7, D07309

Pruppacher H.R. and J.D. Klett, 1997: Microphysics of clouds and precipitation; second revised and enlarged edition; Kluwer Academic Publisher, 953 pp

Pryor, S.C. and Soerensen, L.L., 2000. Nitric acid - sea salt reactions: implication for nitrogen deposition to wate surfaces. Journal of Applied Meteorology, 39: 725-731

Pryor, S.C. and Binkowski, F.S., 2004. An analysis of the time scales assocaited with aerosol processes during dry deposition. Aerosol Science Technology, 38: 1091-1098

Pryor, S. C.; Klemm, O. (2004): Experimentally derived estimates of nitric acid dry deposition velocity and viscous sub-layer resistance at a conifer forest, Atmos. Environ., 38, 2769-2777

Pryor, S. C. (2006). Size resolved particle deposition velocities of sub-100 nm diameter particles over a forest. Atmospheric Environment 40:6192-6200.

Pryor, S. C., Larsen, S. E., Sorensen, L. L., Barthelmie, R. J., Gronholm, T., Kulmala, M., Lauiainen, S., Rannik, Ü. and Vesala, T. (2007). Particle fluxes over forests: analyses of flux methods and functional dependencies. Journal of Geophysical Research 112(D07205):doi:10.1029/2006JD008066.

Pryor, S. C., Barthelmie, R. J., Larsen, S. E., Sorensen, L. L., Sempreviva, A. M., Groenholm, T., Kulmala, M., Rannik, Ü. and Vesala, T. (2008a). Upward particle number fluxes over forest: Where, when, why? Tellus B 60:372-380.


Pryor, S. C., Gallagher, M. W., Sievering, H., Larsen, S. E., Barthelmie, R. J., Birsan, F., Nemitz, E., Rinne, J., Kulmala, M., Groenholm, T., Taipale, R. and Vesala, T. (2008b). A review of measurement and modelling results of particle atmosphere-surface exchange. Tellus B 60:42-75.

Pumpanen J., Kolari P., Ilvesniemi H., Minkkinen K., Vesala T., Niinistö S., Lohila A., Larmola T., Morero M.,Pihlatie M., Janssens I., Curiel Yuste J., Grünzweig J. M., Reth S., Subke J.-A., Savage K., Kutsch W., Østreng G., Ziegler W., Anthoni P., Lindroth A. and Hari P. 2004. Comparison of different chamber techniques for measuring soil CO2 efflux. Agricultural and Forest Meteorology 123. 159-176.

Rattray, G. and Sievering, H. (2001). Dry deposition of ammonia, nitric acid, ammonium, and nitrate to alpine tundra at Niwot Ridge, Colorado. Atmospheric Environment 35:1105-1109.

Raupach, M. R., Gillette, D. A. and Leys, J. F. (1993). The effect of roughness elements on wind erosion threshold. Journal of Geophysical Research-Atmospheres 98:3023-3029.

Regener, V.H., 1957. The vertical flux of atmospheric ozone. Journal of Geophysical Research 62, 221–228. Reid, J.S., H.H. Jonsson, M.H. Smith and A. Smirnov, Evolution of the vertical profile and flux of large sea-salt

particles in the coastal zone, J. Geophys. Res. 106, 12,039-12,053, 2001. Ren, L.-q., Wang, S.-j., Tian, X.-m., Han, Z.-w., Yan, L.-n. and Qui, Z.-m. (2007). Non-smooth morphologies of

typical plant leaf surfaces and their anti-adhesion effects. J. Bionic Engineering 4:33-40. Rich, S., Waggoner, P.E., Tomlinson, H., 1970. Ozone uptake by bean leaves. Science 169, 79–80. Riedo, M., Milford, C., Schmid, M., Sutton, M.A., 2002. Coupling soil-plant-atmosphere exchange of ammonia

with ecosystem functioning in grasslands. Ecol.Mod. 158, 83-110. Rinne, J., Hakola, H., Laurila, T., 1999. Vertical fluxes of monoterpenes above a Scots pine stand in the boreal

vegetation zone. Physics and Chemistry of Earth, 24, 711-715. Rinne, J., Hakola, H., Laurila, T., Rannik, U., 2000. (a) Canopy scale monoterpene emissions of Pinus sylvestris

dominated forests. Atmospheric Environment 34, 1099-1107. Rinne, J., Tuovinen, J.P., Laurila, T., Hakola, H., Aurela, M., Hypén, H., 2000. (b) Measurements of isoprene and

monoterpene fluxes by a gradient method above a northern boreal forest. Agricultural and Forest Meteorology 102, 25-37.

Rinne, H.J.I., Guenther, A.B., Warneke, C., de Gouw, J.A., Luxembourg, S.L., 2001. Disjunct eddy covariance technique for trace gas flux measurements. Geophysical Research Letters 28, 3139-3142.

Rinne, H.J.I., Guenther, A.B., Greenberg, J.P., Harley, P.C., 2002. Isoprene and monoterpene fluxes measured above Amazonian rainforest and their dependence on light and temperature. Atmospheric Environment 36, 2421-2426.

Rinne, J., Ruuskanen, T.M., Reissell, A., Taipale, R., Hakola, H., Kulmala, M., 2005. On-line PTR-MS measurements of atmospheric concentrations of volatile organic compounds in a European boreal forest ecosystem. Boreal Environ. Res. 10, 425–436.

Rinne, J., Taipale, R., Markkanen, T., Ruuskanen, T.M., Hellén, H., Kajos, M.K., Vesala, T., Kulmala, M., 2007. Hydrocarbon fluxes above a Scots pine forest canopy: measurements and modeling. Atmospheric Chemistry and Physics 7, 3361-3372.

Rinne, J., Douffet, T., Prigent, Y., Durand, P., 2008. Field comparison of disjunct and conventional eddy covariance techniques for trace gas flux measurements. Environmental. Pollution 152, 630-635.

Roberts, J.M., 1990 Atmos. Environ. 24A, 243 Robinson, A. L., Donahue, N. M., Shrivastava, M. K., Weitkamp, E. A., Sage, A. M., Grieshop, A. P., Lane, T. E.,

Pierce, J. R. and Pandis, S. N. (2007). Rethinking organic aerosols: semivolatile emissions and photochemical aging. Science 2:1259-1262.

Romakkaniemi, S., Kokkola, H. and Laaksonen, A., 2005: Soluble trace gas effect on cloud condensation nuclei activation: Influence of initial equilibration on cloud model results, Journal of Geophysical Research D,Volume 110, Issue 15, DOI: 10.1029/2004JD005364

Rosenstiel, T.N., Potosnak, M.J., Griffin, K.L., Fall, R., Monson, R.K., 2003. Increased CO2 uncouples growth from isoprene emission in an agriforest ecosystem. Nature 421, 256–259, 2003.

Ruijgrok, W., Tieben, H. and Eisinga, P. (1997). The dry deposition of particles to a forest canopy: a comparison of model and experimental results. Atmospheric Environment 31:399-415.

Russow, R., Sich, I. and Neue, H.-U. 2000. The formation of the trace gases NO and N2O in soils by the coupled processes of nitrification and denitrification: results of kinetic 15N tracer investigations. Chemosphere - Global Change Science 2: 359-366.

Sachs, T., Giebels, M., Wille, C., Kutzbach, L., Boike, J. 2008. Methane Emission from Siberian Wet Polygonal Tundra on Multiple Spatial Scales: Vertical Flux - Ninth International Conference on Permafrost, Fairbanks, USA, 2008 - epic.awi.de

Sander, R., Kerkweg, A., J¨ockel, P., and Lelieveld, J.: Technical Note: The new comprehensive atmospheric chemistry module MECCA, Atmos. Chem. Phys., 5, 445–450, 2005, http://www.atmos-chem-phys.net/5/445/2005/

Sanderson, M.G., Collins, W.J., Hemming, D.L., Betts, R.A., 2007. Stomatal conductance changes due to increasing carbon dioxide levels: Projeced impact on surface ozone levels. Tellus 59 B, 404–411.

Scarratt, M. G., and Moore, R. M.: Production of chlorinated hydrocarbons and methyl iodide by the red microalga Porphyridium purpureum, Limnology and Oceanography, 44, 703-707, 1999.


Schade, G.W., Goldstein, A.H., 2001. Fluxes of oxygenated volatile organic compounds from a ponderosa pine plantation. Journal of Geophysical Research 106, 3111-3123.

Schade, G.W., Goldstein, A.H., Gray, D.W., Lerdau, M.T., 2000. Canopy and leaf level 2-methyl-3-buten-2-ol fluxes from a ponderosa pine plantation. Atmospheric Environment 34, 3535-3544.

Schjoerring, J. K., Husted, S. and Mattsson, M.: Physiological parameters controlling plant-atmosophere ammonia exchange. Atmos. Environ. (Ammonia Special Issue) 32 (3), 491-498, 1998.

Schmidt, A. and Klemm, O. (2008). Direct determination of highly size-resolved turbulent particle fluxes with the disjunct eddy covariance method and a 12-stage electrical low pressure impactor. Atmospheric Chemistry and Physics Discussions 8:8997-9034.

Schnitzler, J.P., Graus, M., Kruezwieser, J., Heizmann, U., Renneberg, H., Wisthaler, A., Hansel, A., 2004. Contribution of different carbon sources to isoprene biosynthesis in poplar leaves. Plant Physiology 135, 152–160.

Scholefield D, Hawkins JMB, Jackson SM. 1997. Development of a helium atmosphere soil incubation technique for direct measurement of nitrous oxide and dinitrogen fluxes during denitrification. Soil Biology and Biochemistry 29:1345–1352.

Schween, J.H., Dlugi, R., Hewitt, C.N., Foster, P., 1997. Determination and accuracy of VOC-fluxes above the pine/oak forest at Castelporziano. Atmospheric Environment 31, 199-215.

Seco, R., Peñuelas, J., Filella, I., 2007. Short-chain oxygenated VOCs: emission and uptake by plants and atmospheric sources, sinks, and concentrations. Atmospheric Environment 41, 2477-2499.

Seinfeld, J.H., 1986: Atmospheric Chemistry and Physics of air pollution. Wiley and Sons. 738pp. Sempreviva, A.M.,Gryning, S.-E. (2000) Mixing height over water and its role on the correlation between

temperature and humidity fluctuations in the unstable surface layer. Boundary-Layer Meteorology, 97: 273-291 Seufert, G., Bartzis, J., Bomboi, T., Ciccioli, P., Cieslik, S., Dlugi, R., Foster, P., Hewitt, C.N., Kesselmeier, J.,

Kotzias, D., Lenz, R., Manes, F., Perez Pastor, R., Steinbrecher, R., Torres, L., Valentini, R., Versino, B., 1997. An overview of the Castelporziano experiments. Atmospheric Environment 31, 5-17.

Sharkey, T.D., Singsaas, E.L., Lerdau, M.T., Geron, C.D., 1999. Weather effects on isoprene emission capacity and applications in emissions algorithms. Ecological Applications 9, 1132-1137.

Sharkey, T.D., Yeh, S.S., 2001. Isoprene emission from plants. Annual Review Plant Physiology and Molecular Biology 52, 407–436

Shaw, W. J., Spicer, C.W., Kenny, D.V., 1998. Eddy correlation fluxes of trace gases using a tandem mass spectrometer, Atmos. Environ., 32, 2887-2898.

Shaw, S.L., Chisholm, S.W. and Prinn, R.G., 2003. Isoprene production by Prochlorococcus, a marine cyanobacterium, and other phytoplankton. Marine Chemistry, 80, 227-245.

Sievering, H. (1983). Eddy flux and profile measurements of small particle dry-deposition velocity at the Boulder Atmospheric Observatory (BAO), in Precipitation Scavenging, Dry Deposition and Resuspension, H. R. Pruppacher, R. G. Semonin and W. G. N. Slinn, eds., Elsevier, Amsterdam, NL, 963-977.

Simon, E., Meixner, F.X., Ganzeveld, L., Kesselmeir, J., 2005. Coupled carbon-water exchange of the Amazon rain forest, II. Comparison of predicted and observed seasonal exchange of energy, CO2, isoprene and ozone at a remote site in Rondônia. Biogeosciences 2, 255–275.

Simon, V., Dumergues, L., Bouchou, P., Torres, L., Lopez, A., 2005. Isoprene emission rates and fluxes measured above a Mediterranean oak (Quercus pubescens) forest. Atmospheric Research 74, 49-63.

Simpson, D., Fagerli, H., Jonson, J. E., Tsyro, S., Wind, P., and Tuovinen J-P. (2006) Transboundary acidification, eutrophication and ground level ozone in Europe, Part I, Unified EMEP Model description. 1, 1-104. 1-8-2003, Norwegian Meteorological Office, Blindern.

Simpson, D., Ashmore, M., Emberson, L., Tuovinen, J.-P., 2007. A comparison of two different approaches for mapping potential ozone damage to vegetation. A model study. Environmental Pollution 146, 715–725.

Simpson, I. J., S. Meinardi, D. R. Blake, N. J. Blake, F. S. Rowland, E. Atlas, and F. Flocke, 2002: A biomass burning source of C-1-C-4 alkyl nitrates. Geophys. Res. Lett., 29, art. no.-2168.

Singh, H. B. Salas, L. J., Chatfield, R. B., Czech, E., Fried, A., Walega, J.,Evans, M. J., Field, B. D., Jacob, D. J., Blake, D., Heikes, B., Talbot, R., Sachse, G., Crawford, J. H., Avery, M. A., Sandholm, S. Fuelberg, H., 2004. Analysis of the atmospheric distribution, sources, and sinks of oxygenated volatile organic chemicals based on measurements over the Pacific during TRACE-P, Journal of Geophysical Research-Atmospheres., 109, D15S07, doi:10.1029/2003JD003883.

Singh, H. B., Tabazadeh, A., Evans, M. J., Field, B. D., Jacob, D. J., Sachse, G., Crawford, J. H., Shetter, R., Brune, W. H., 2003. Oxygenated volatile organic chemicals in the oceans: Inferences and implications based on atmospheric observations and air-sea exchange models. Geophysical Research Letters. 30, No. 16, 1862 10.1029/2003GL017933.

Sinha, V., Williams, J., Meyerhofer, M., et al. 2007. Air-sea fluxes of methanol, acetone, acetaldehyde, isoprene and DMS from a Norwegian fjord following a phytoplankton bloom in a mesocosm experiment. Atmospheric Chemistry and Physics, 7, 739-755.

Sitch, S., Cox, P.M., Collins, W.J., Huntingford, C., 2007. Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature 488, 791–794.


Skiba, U., Fowler, D. and Smith, K.A. 1997. Nitric oxide emissions from agricultural soils in temperate and tropical climates: sources, controls and mitigation options. Nutrient Cycling in Agroecosystems 48: 139-153.

Skiba, U., Di Marco, C., Hargreaves, K., Sneath, R. and McCartney, L. 2006. Nitrous oxide emissions from a dung heap measured by chambers and plume methods. Agric. Ecosys. & Environ. 112, 135-139.

Slinn, W.G.N. (1982) Predictions for Particle Deposition to Vegetative Canopies. Atmospheric Environment. 16, 1785-1794

Smart, D.R. and Bloom, A.J. 2001. Wheat leaves emit nitrous oxide during nitrate assimilation. Proceedings of the National Academy of Sciences of the United States of America 98: 7875-7878.

Soerensen, L. L., Pryor, S. C., De Leeuw, G. and Schulz, M. (2005). Flux divergence of nitric acid in the marine atmospheric surface layer. Journal of Geophysical Research-Atmospheres 110:doi:10.1029/2004JD005403.

Solberg, S., Hov, Ø., Søvde, A., Isaksen, I.S.A., Coddeville, P., De Backer, H., Forster, C., Orsolini, Y., Uhse, K., 2008. European surface ozone in the extreme summer 2003. Journal of Geophysical Research 113, D07307, doi:10.1029/2007/JD009098.

Sorimachi, A., Sakamoto, K., 2007. Laboratory measurement of the dry deposition of sulfur dioxide onto northern Chinese soil samples. Atmospheric Environment 41, 2862-2869.

Sorjamaa, R., Svenningsson, B., Raatikainen, T., Henning, S., Bilde, M., Laaksonen, A., 2004: The role of surfactants in Köhler theory reconsidered, Atmospheric Chemistry and Physics, 4 (8), pp. 2107-2117.

Spanke, J., Rannik, Ü., Forkel, R., Nigge, W., Hoffmann, T., 2001. Emission fluxes and atmospheric degradation of monoterpenes above a boreal forest: field measurements and modelling. Tellus, B53, 406–422.

Sparks, J. P.; Roberts, J. M.; Monson, R. K.: The uptake of gaseous organic nitrogen by leaves: A significant global nitrogen transfer process, Geophysical Research Letters, 30, 2189, doi:2110.1029/2003GL018578, 2003.

Spindler, G., Teichmann, U. and Sutton, M. A.: Ammonia Dry Deposition over Grassland: Micrometeorological Flux Gradient Measurements and Bi-directional Flux Calculations using an Inferential Model. Q. J. Roy. Meteor. Soc. 127, 795-814, 2001.

Spirig, C., Guenther, A., Greenberg, J.P., Calanca, P., Tarvainen, V., 2004. Tethered balloon measurements of biogenic volatile organic compounds at a Boreal forest site. Atmospheric Chemistry and Physics 4, 215–229.

Spirig, C., Neftel, A., Ammann, C., Dommen, J., Grabmer, W., Thielmann, A., Schaub, A., Beauchamp, J., Wisthaler, A., Hansel, A., 2005. Eddy covariance flux measurements of biogenic VOCs during ECHO 2003 using proton transfer reaction mass spectrometry. Atmospheric Chemistry and Physics 5, 465–481.

Spracklen, D. V., S. R. Arnold, K. S. Carslaw, J. Sciare, and C. Pio (2008), Globally significant oceanic source of organic carbon aerosol, Geophys. Res. Lett., 35, L12811, doi:10.1029/2008GL033359.

Stark, J.D., Smart, D.R., Hart, S.C. and Haubensak, K.A. 2002. Regulation of nitric oxide emissions from forest and rangeland soils of western North America. Ecology 83: 2278-2292.

Staudt, M., Bertin, N., Frenzel, B., Seufert, G., 2000. Seasonal variation in amount and composition of monoterpenes emitted by young Pinus pinea trees - implications for emission modeling. Journal of Atmospheric Chemistry 35, 77-99.

Staudt, M., Mir, C., Joffre, R., Rambal, S., Bonin, A., Landais, D., Lumaret, R., 2004. Isoprenoid emissions of Quercus spp. (Q. suber and Q. ilex) in mixed stands contrasting in interspecific genetic introgression. New Phytologist 163, 573-584.

Stehfest E, Bouwman L, 2006, N2O and NO emission from agricultural fields and soils under natural vegetation: summarizing available measurement data and modeling of global annual emissions. Nutr. Cycl. Agroecosys. 74, 207-228.

Steinbrecher, R., Klauer, M., Hauff, K., Stockwell, W.R., Jaeschke, W., Dietrich, T., Herbert, F., 2000. Biogenic and anthropogenic fluxes of non-methane hydrocarbons over an urban-impacted forest, Frankfurter Stadtwald, Germany. Atmospheric Environment 34, 3779-3788.

Stemmler, K.; Ammann, M.; Donders, C.; Kleffmann, J.; George, C.: Photosensitized reduction of nitrogen dioxide on humic acid as a source of nitrous acid, Nature, 440, 195-198, 2006.

Stevenson, D.S., Dentener, F.J., Schulz, M.G., Ellingsen, K., van Noije, T.P.C., Wild, O., Zeng, G., Amann, M., Atherton, C.S., Bell, N., Bergmann, D.J., Bey, I., Butler, T., Cofala, J., Collins, W.J., Derwent, R.G., Doherty, R.M., Drevet, J., Eskes, H.J., Fiore, A.M., Gauss, M., Hauglustaine, D.A., Horowicz, L.W., Isaksen, I.S.A., Krol, M.C., Lamarque, J.-F., Lawrence, M.G., Montanaro, V., Müller, J.-F., Pitari, G., Prather, M.J., Pyle, J.A., Rast, S., Rodriguez, J.M., Sanderson, M.G., Savage, N.H., Shindell, D.T., Strahan, S.E., Sudo, K., Szopa, S., 2006. Multimodel ensemble simulations of present-day and near-future tropospheric ozone. Journal of Geophysical Research 111, D08301, doi:10.1029/2005JD006338.

Sun, J. and Ariya, P. A.: Atmospheric organic and bio-aerosols as cloud condensation nuclei (CCN): A review, Atmos. Environ., 40, 795–820, 2006.

Sutka RL, Ostrom NE, Ostrom PH, Gandhi H, Breznak JA. 2003. Nitrogen isotopomer site preference of N2O produced by Nitrosomonas europaea and Methylococcus capsulatus Bath. Rapid Communications in Mass Spectrometry 17:738–745.

Sutton, M. A., Fowler, D. and Moncrieff, J. B. (1993a) The exchange of atmospheric ammonia with vegetated surfaces. I: Unfertilized vegetation. Quart. J. Roy. Meteor. Soc. 119, 1023-1045.

Sutton M.A., Pitcairn C.E.R. and Fowler D. (1993b) The exchange of ammonia between the atmosphere and plant communities. Adv. Ecol. Research. 24, 301-393.


Sutton M.A. and Fowler D. (1993) A model for inferring bi-directional fluxes of ammonia over plant canopies. In: Proceedings of the WMO conference on the measurement and modelling of atmospheric composition changes including pollutant transport. World Meteorological Organization, Geneva, 179-182.

Sutton, M.A., Asman, W.A.H., Schjørring, J.K., 1994 Dry deposition of reduced nitrogen. Tellus 46B, 255-273. Sutton, M.A., Schjørring, J.K., Wyers, G.P., 1995. Plant Atmosphere Exchange of Ammonia. Phil. Trans. Roy. Soc.

London A351 (1656), 261-276. Sutton M.A. Perthue E., Fowler D., Storeton-West R.L., Cape J.N., Arends B.G. and Möls J.J. (1997) Vertical

distribution and fluxes of ammonia at Great Dun Fell. In: Special Issue on the Great Dun Fell Cloud Experiment 1993, (Ed. S. Fuzzi) Atmos. Environ. 31, 2615-2625.

Sutton, M.A., Burkhardt, J.K., Guerin, D., Nemitz, E., Fowler, D., 1998. Development of resistance models to describe measurement of bi-directional ammonia surface-atmosphere exchange. Atmospheric Environment 32, 473-480.

Sutton, M. A., Nemitz, E., Fowler, D., Wyers, G. P., Otjes, R. P., Schjoerring, J. K., Husted, S., Nielsen, K., San José, R., Moreno, J., Gallagher, M. W., Gut, A.: Fluxes of ammonia over oilseed rape: Overview of the EXAMINE experiment Agric. For. Meteorol. (Ammonia Special Issue) 105 (4), 327-349, 2000a.

Sutton M.A., Nemitz E., Milford C., Fowler D., Moreno J., San Jose R., Wyers G.P., Otjes R., Harrison R., Husted S. and Schjoerring J.K. (2000b) Micrometeorological measurements of net ammonia fluxes over oilseed rape during two vegetation periods. Agric. For. Meteorol. (Ammonia Exchange Special Issue) 105 (4), 351-369.

Sutton, M.A., Milford, C., Nemitz, E., Theobald, M.R., Hill, P.W., Fowler, D., Schjoerring, J.K., Mattsson, M.E., Nielsen, K.H., Husted, S., Erisman, J.W., Otjes, R., Hensen, A., Mosquera, J., Cellier, P., Loubet, B., David, M., Genermont, S., Neftel, A., Blatter, A., Herrmann, B., Jones, S.K., Horvath, L., Führer, E., Mantzanas, K., Koukoura, Z., Gallagher, M., Williams, P., Flynn, M., Riedo, M., 2001. (a) Biosphere-atmosphere interactions of ammonia with grasslands: Experimental strategy and results from a new European initiative. Plant Soil 228 131-145.

Sutton, M.A., Milford, C., Nemitz, E., Theobald, M.R., Riedo, M., Hargreaves, K.J., Hill, P.W., Dragosits, U., Fowler, D., Schjoerring, J.K., Mattsson, M.E., Husted, S., Erisman, J.W., Hensen, A., Mosquera, J., Otjes, R., Cellier, P., Loubet, B., David, M., Neftel, A., Hermann, B., Jones, S.K., Blatter, A., Horvath, L., Weidinger, T., Meszaros, R., Raso, J., Mantzanas, K., Koukoura, Z., Gallagher, M., Dorsey, J., Flynn, M., Lehman, B., Burkhardt, J., Daemmgen, U., 2002. Ammonia exchange with vegetation: measurement, modelling & application. In: Transport and chemical transformation in the troposphere. (Midgley, P.M., Reuther, M.) pp 49-60, Margraf Verlag.

Sutton, M. A., Nemitz, E., Erisman, J. W., Beier, C., Butterbach Bahl, K., Cellier, C., de Vries, W., Cotrufo, F., Skiba, U., Di Marco, C., Jones, S., Laville, P., Soussana, J. F., Loubet, B., Twigg, M., Famulari, D., Whitehead, J., Gallagher, M. W., Neftel, A., Flechard, C., Herrmann, B., Calanca, P. L., Schjoerring, J. K., Daemmgen, U., Horvath, L., Tang, Y.S., Emmett, B. A., Tietema, A., Peñuelas, J., Kesik, M., Brueggemann, N., Pilegaard, K., Vesala, T., Campbell, C. L., Olesen, J. E., Dragosits, U., Theobald, M. R., Levy, P., Mobbs, D. C., Milne, R., Viovy, N., Vuichard, N., Smith, J. U., Smith, P.E., Bergamaschi, P., Fowler, D. and Reis, S. (2007) Challenges in quantifying biosphere-atmosphere exchange of nitrogen species. Environ. Pollution 150, 125-139.

Sutton M.A., Nemitz E., Theobald M.R., Milford C., Dorsey J.R., Gallagher M.W., Hensen A., Jongejan P.A.C., Erisman J.W., Mattsson M.E., Schjoerring J.K., Cellier P., Loubet B., Roche R., Neftel A., Herrmann B., Jones S.K., Lehman B.E., Horvath L., Weidinger T., Rajkai K., Burkhardt J., Löpmeier F.J., Dämmgen U. (2008a) Dynamics of ammonia exchange with cut grassland: Strategy and implementation of the GRAMINAE Integrated Experiment. Biogeosciences Discuss., 5, 3347-3407.

Sutton M.A., Nemitz E., Milford C., Campbell C., Erisman J.W., Hensen A., Cellier P., David M., Loubet B., Personne E., Schjoerring J.K., Mattsson M.E., Dorsey J.R., Gallagher M.W., Horvath L., Weidinger T., Meszaros R., Dämmgen U., Neftel A., Herrmann B., Lehman B., Flechard C. and Burkhardt J. (2008b) Dynamics of ammonia exchange with cut grassland: Synthesis of results and conclusions of the GRAMINAE Integrated Experiment. Biogeoscience Discussions (under review).

Sutton M.A., Erisman J.W., Dentener F. and Moeller D. (2008c) Ammonia in the environment: from ancient times to the present. Environmental Pollution. (In press).

Sutton M.A., Reis S. and Baker S.M.H. (Eds.) (2008d) Atmospheric ammonia: detecting emission changes and environmental impacts. Springer.

Tang, Y. S.; Flechard, C. R.; van Dijk, N.; Simmons, I.; Daemmgen, U.; Fauvel, Y.; Djuricic, Y.; Vidic, S.; Gliha, Z.; Borovecki, D.; Mitosinkova, M.; Hanssen, J. E.; Uggerud, T. H.; Sanz, M. J.; Sanz, P.; Chorda, J. V.; Ferm, M.; Perrino, C.; Sutton, M. A.: European scale application of atmospheric reactive nitrogen measurements in a low-coast approach to infer dry deposition fluxes, Agriculture, Ecosystems Environment, [under review], 2008.

Tarrason, L.; Nyiri, A. "Transboundary Acidification, Eutrophication and Ground Level Ozone in Europe in 2006," Norwegian Meterological Institute, 2008.

Theobald, M.R., Dragosits, U., Place, C.J., Smith, J.U., Sozanska, M., Brown, L., Scholefield, D., Del Prado, A., Webb, J., Whitehead, P.G., Angus, A., Hodge, I.D., Fowler, D., Sutton, M.A., 2004. Modelling nitrogen fluxes at the landscape scale. WASP Focus 4(6), 135-142.


Thomas, R. M., Hallquist, M., Janhill, S., House, E., Coe, H. and Nemitz, E. (2007). Fluxes of submicron aerosol components above an urban canopy: measurements during the GOTE-2005 campaign. Atmospheric Chemistry and Physics [in preparation].

Thomas, R. (2007). Measurement of speciated aerosol fluxes. PhD Thesis, in Physics Department, University of Manchester, Manchester, 298.

Thomas, R., Phillips, G., House, E., Broadmeadow, M., Wilkinson, M., Coe, H. and Nemitz, E. (2008). Measurement of concentrations and fluxes of chemically speciated submicron aerosol over an English oak woodland. Atmos. Chem. Phys. [in preparation].

Thomas, R., Trebs, I., Otjes, R., Jongejan, P. A. C., ten Brink, H., Phillips, G., Korner, M., Meixner, F. X. and Nemitz, E. (2009). An automated analyser to measure surface-atmosphere exchange fluxes of water soluble inorganic aerosol compounds and reactive trace gases. Environmental Science Technology [accepted].

Tingey, D.T., Turner, D.P., Weber, J.A., 1991. Factors controlling the emissions of monoterpenes and other volatile organic compounds. In: Sharkey, T.D., Holland, E.A., Mooney, H.A. (Eds.). Trace gas emissions by plants. Academic Press, Inc., San Diego/New York/Boston/London/Sydney/Tokyo/Toronto, pp. 93-119.

Topping, D.O., McFiggans, G.B., Kiss, G., Varga, Z., Facchini, M.C., Decesari, S., Mircea, M., 2007: Surface tensions of multi-component mixed inorganic/organic aqueous systems of atmospheric significance: Measurements, model predictions and importance for cloud activation predictions, Atmospheric Chemistry and Physics, Volume 7, Issue 9, 2007, Pages 2371-2398

Tost, H., Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., and Lelieveld, J.: Global cloud and precipitation chemistry and wet deposition: tropospheric model simulations with ECHAM5/MESSy1, Atmos. Chem. Phys., 7, 2733–2757, 2007, http://www.atmos-chem-phys.net/7/2733/2007/.

Trebs, I., Lara, L. L., Zeri, L. M. M., Gatti, L. V., Artaxo, P., Dlugi, R., Slanina, J., Andreae, M. O., and Meixner, F. X. (2006) Dry and wet deposition of inorganic nitrogen compounds to a tropical pasture site (Rondonia, Brazil), Atmos. Chem. Phys., 6, 447-469.

Tuovinen, J.-P., Simpson, D., Mikkelsen, T.N., Emberson, L.D., Ashmore, M.R., Aurela, M., Cambridge, H.M., Hovmand, M.F., Jensen, N.O., Laurila, T., Pilegaard, K., Ro-Poulsen, H., 2001. Comparisons of measured and modelled ozone deposition to forests in Northern Europe. Water, Air and Soil Pollution: Focus 1, 263–274.

Tuovinen J.-P., Ashmore M., Emberson L., Simpson D., 2004. Testing and improving the EMEP ozone deposition module. Atmospheric Environment 38, 2373–2385.

Tuovinen, J.-P., Simpson, D., Altimir, N., 2008. Modelling the stomatal and non-stomatal ozone fluxes to coniferous forests in Northern Europe. EGU General Assembly, 14–18 April 2008, Vienna, Austria, Geophysical Research Abstracts 10, EGU2008-A-08272.

Tuovinen, J.-P., Simpson, D., 2008. An aerodynamic correction for the European ozone risk assessment methodology. Atmospheric Environment 42, 8371–8381.

Turner, N.C., Waggoner, P.E., Rich, S., 1974. Removal of ozone from the atmosphere by soil and vegetation. Nature 250, 486–489.

Turnipseed, A. A.; Huey, L. G.; Nemitz, E.; Stickel, R.; Higgs, J.; Tanner, D. J.; Slusher, D. L.; Sparks, J. P.; Flocke, F.; Guenther, A.: Eddy covariance fluxes of peroxyacetyl nitrates (PANs) and NOy to a coniferous forest, Journal of Geophysical Research-Atmospheres, 111, D09304, doi:09310.01029/02005JD006631, 2006.

Twigg, M.M., Famulari, D., Nemitz, E., Sutton, M.A., Fowler, D., Rippey, B. (2005) Principles and development of tunable diode laser spectroscopy for measuring fluxes of ammonia and nitric acid. In: The Changing Chemical Climate of the Atmosphere, First ACCENT Symposium, Urbino, Sep 12-16, 2005, Programme and Abstracts, p. 94

Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R. and Jimenez, J. L. (2008). Interpretation of Organic Components from Positive Matrix Factorization of Aerosol Mass Spectrometric Data. Atmos. Chem. Phys. Discuss. submitted Feb 2008.

UNECE, 2004. Mapping Critical Levels for Vegetation. Manual on Methodologies and Criteria for Modelling and Mapping Critical Loads & Levels and Air Pollution Effects, Risks and Trends. UNECE Convention on Long-range Transboundary Air Pollution, Geneva, 254 pp. Available from: <www.icpmapping.org>.

Vaattovaara, P., P.E. Huttunen, Y.J. Yoon, J. Joutsensaari, K.E.J. Lehtinen, C.D. O’Dowd, and A. Laaksonen, The composition of nucleation and Aitken modes particles during coastal nucleation events: evidence for marine secondary organic contribution, Atmos. Chem. Phys., 6, 4601–4616, 2006.

Van Oijen M, Rougier J, Smith R, 2005, Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol., 25, 915-927

Van Oss, R., Duyzer, J. and Wyers, P. (1998). The influence of gas-to-particle conversion on measurements of ammonia exchange over forest. Atmospheric Environment 32:465-471.

Vautard, R., Honoré, C., Beekmann, M., Rouil, L., 2005. Simularion of ozone during the August 2003 heat wave and emission control scenarios. Atmospheric Environment 39, 2957–2967.

Velasco, E., Lamb, B., Pressley, S., Allwine, E., Westberg, H., Jobson, B. T., Alexander, M., Prazeller, P., Molina, L. and Molina, M. (2005). Flux measurements of volatile organic compounds from an urban landscape. Geophysical Research Letters 32(20)

Venterea, R.T. and Rolston, D.E. 2000. Mechanisms and kinetics of nitric and nitrous oxide production during nitrification in agricultural soil. Global Change Biology 6: 303-316.


Venterea, R.T., Rolston, D.E. and Cardon, Z.G. 2005. Effects of soil moisture, physical, and chemical characteristics on abiotic nitric oxide production. Nutrient Cycling in Agroecosystems 72: 27-40.

Vesala, T., Jarvi, L., Launiainen, S., Sogachev, A., Rannik, Ü., Mammarella, I., Siivola, E., Keronen, P., Rinne, J., Riikonen, A. and Nikinmaa, E. (2007). Surface-atmosphere interactions over complex urban terrain in Helsinki, Finland. Tellus B 60:188-199.

Vigano, I., van Weelden, H., Holzinger, R., Keppler, F., McLeod, A. and Rockmann, T. 2008. Effect of UV radiation and temperature on the emission of methane from plant biomass and structural components. Biogeosciences 5: 937-947.

Vitel, H.; Kromer, B.; Moessner, M.; Platt, U.: New technique for measurements of atmospheric vertical trace gas profiles using DOAS, Environmental Science and Pollution, 9, 17-26, 2002.

Voisin, D. ; Legrand, M. ; Chaumerliac, N., Scavenging of acidic gases (HCOOH, CH3COOH, HNO3, HCl, and SO2) and ammonia in mixed liquid-solid water clouds at the Puy de Dôme mountain (France), J. Geophys. Res. Vol. 105 , No. D5 , p. 6817-6836, 2000.

Vong, R. J., Vickers, D. and Covert, D. S. (2003). Eddy correlation measurements of aerosol deposition to grass. Tellus Series B-Chemical and Physical Meteorology [under review].

Walker, J. T., Robarge, W. P., Wuc, Y. and Meyers, T. P. (2006) Measurement of bi-directional ammonia fluxes over soybean using the modified Bowen-ratio technique. Agric.For. Meteor. 138, 54-68.

Warneck, P., 1988: Chemistry of the Natural Atmosphere; International Geophysics Series, Volume 41, Academic Press, 757pp.

Webb, E. K., Pearman, G. I. and Leuning, R. (1980). Correction of flux measurements for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society 106:85-100.

Werner C, Butterbach-Bahl K, Haas E, Hickler T, Kiese R, 2007, A global inventory of N2O emissions from tropical rainforest soils using a detailed biogeochemical model. Global Biogeochemical Cycles, 21, GB3010, doi:10.1029/2006GB002909.

Wesely, M.L., 1983. Turbulent transport of ozone to surfaces common in the eastern half of the United States. Advances in Environmental Science and Technology 12, 345–370.

Wesely, M. L., Cook, D. R., Hart, R. L. and Speer, R. E. (1985). Measurements and parameterization of particulate sulfur dry deposition over grass. Journal of Geophysical Research-Atmospheres 90:2131-2143.

Wesely, M.L., 1989. Parameterization of surface resistances to gaseous dry deposition to regional-scale numerical models. Atmospheric Environment 23, 1293–1304.

Wesely, M.L, Hicks, B.B., 2000. A review of the current status of knowledge on dry deposition. Atmospheric Environment 34, 2261–2282.

Whitehead, J.D., McFiggans, G.B., Gallagher, M.W., Flynn, M.J., 2008. Direct linkage between tidally driven coastal ozone deposition fluxes, particle emission fluxes and subsequent CCN formation. Geophysical Research Letters, accepted.

Whitehead J.D., Twigg M., Famulari D., Nemitz E., Sutton M.A., Gallagher M.W. and Fowler D. (2008) Evaluation of laser absorption spectroscopic techniques for eddy covariance flux measurements of ammonia. Environ. Sci. Technol., 10.1021/es071596u.

Wichink Kruit, R. J., van Pul, W. A. J., Otjes, R. P., Hofschreuder, P., Jacobs, A. F. G. and Holtslag A. A. M. (2007) Ammonia fluxes and derived canopy compensation points over non-fertilized agricultural grassland in The Netherlands using the new gradient ammonia—high accuracy—monitor (GRAHAM) Atmos.Environ. 41, 1271-1287.

Wiedinmyer, C., Greenberg, J., Guenther, A., Hopkins, B., Baker, K., Geron, C., Palmer, P.I., Long, B.P., Turner, J.R., Petron, G., Pierce, T.E., Lamb, B., Westberg, H., Baugh, W., Harley, P., Koerber, M., Jannsen, M., 2005. The Ozarks Isoprene Experiment (OZIE): measurements and model interpretations of the 'isoprene volcano'. Journal of Geophysical Research. doi:10.1029/2005JD005800.

Williams, J., U. Pöschl, P.J. Crutzen, A. Hansel, R. Holzinger, C. Warneke, W. Lindinger, J. Lelieveld: An Atmospheric Chemistry Interpretation of Mass Scans obtained from a Proton Transfer Mass spectrometer flown over the Tropical Rainforest of Surinam. J. Atmospheric Chemistry, (2001) 38, 133-166.

Williams, J., 2004. Organic trace gases: an overview. Environmental Chemistry, 1, 125-136. Williams, J., Holzinger, R., Gros, V., Xu, X., Atlas, E. and Wallace, D.W.R. 2004. Measurements of organic species

in air and seawater from the tropical Atlantic. Geophysical Research Letters. 31, 23, L23S06. Wolfe, G. M.; Yatavelli, R. L. N.; Thornton, J. A.; McKay, M.; Goldstein, A. H.; LaFranchi, B.; Min, K.-E.; Cohen,

R. C.: Eddy covariance fluxes of acyl preoxy nitrates (PAN, PPN, and MPAN) above a Ponderosa pine forest, Atmospheric Chemistry and Physics Discussions, 8, 17495-17548, 2008.

Wolke, R., Sehili, A.M., Simmel, M., Knoth, O., Tilgner, A., Herrmann, H.: SPACCIM: A parcel model with detailed microphysics and complex multiphase chemistry. Atmos. Environ. 39, 4375–4388 (2005)

Wrage N, van Groenigen JW, Oenema O, Baggs EM. 2005. A novel dual-isotope labelling method for distinguishing between soil sources of N2O. Rapid Communications in Mass Spectrometry 19:3298–3306.

Wuebbles, D. J. and Hayhoe, K. 2002 Atmospheric methane and global change. Earth-Science reviews 57, 177-210 Wyers, G.P., Otjes, R.P., Slanina, J., 1993. A continuous-flow denuder for the measurement of ambient

concentrations and surface-exchange fluxes of ammonia. Atmos. Environ. 27, 2085-2090.


Wyers, G. P. and Duyzer, J. H. (1997). Micrometeorological measurement of the dry deposition flux of sulphate and nitrate aerosols to coniferous forest. Atmospheric Environment 31:333-343.

Wyers, G.P., Erisman, J.W., 1998. Ammonia exchange over coniferous forest. Atmos. Environ. 32(3), 441-451. Yan, X., Shi, S., Du, L. and Xing, G. 2000. Pathways of N2O emission from rice paddy soil. Soil Biology and

Biochemistry 32: 437-440. Yassaa, N., I. Peeken, , E. Zöllner, K. Bluhm, S. Arnold, D. Spracklen, H.Wernli, J. Williams. Evidence for

marineproduction of monoterpenes, Environmental Chemistry, In Press. Yin, Y., Carslaw, K.S., Feingold, G.: Vertical transport of aerosols in a mixed-phase convective cloud and the

feedback on cloud development. Q. J. R. Meteorol. Soc. 131, 221–245 (2005) Yoon, Y.J., D. Ceburnis, F. Cavalli, O. Jourdan, J.P. Putaud, M.C. Facchini, S. Decesari, S.Fuzzi , S.G. Jennings,

C.D. O’Dowd. (2007) Seasonal characteristics of the physico-chemical properties of North Atlantic marine atmospheric aerosols. J. Geophys. Res., 112 D04206, doi: 0.1029/2005JD007044

Yun, H. J., Yi, S. M. and Kim, Y. P. (2002). Dry deposition fluxes of ambient particulate heavy metals in a small city, Korea. Atmospheric Environment 36:5449-5458.

Zhang, L.F., Boeckx, P., Chen, G.X. and Van Cleemput, O. 2000. Nitrous oxide emission from herbicide-treated soybean. Biology and Fertility of Soils 32: 173-176.

Zhang, L., Gong, S., Padro, J. and Barrie, L. (2001). A size-segregated particle dry deposition scheme for an atmospheric aerosol module. Atmospheric Environment 35:549-560.

Zhang, L., Brook, J.R., Vet, R., 2002a. On ozone dry deposition – with emphasis on non-stomatal uptake and wet canopies. Atmospheric Environment 36, 4787–4799.

Zhang, L. and Vet, R. (2006). A review of current knowledge concerning size-dependent aerosol removal. China Particuology 4(6):272-282.

Zimmerman, P.R., Greenberg, J.P., Westberg, C.A., 1988. Measurements of atmospheric hydrocarbons and biogenic emission fluxes in the Amazon boundary layer. Journal of Geophysical Research 93, 1407-1416.

Zorn, S. R., F. Drewnick1, M. Schott, T. Hoffmann, and S. Borrmann, Characterization of the South Atlantic marine boundary layer aerosol using an aerodyne aerosol mass spectrometer, Atmos. Chem. Phys., 8, 4711–4728, 2008

Zou, J.W., Huang, Y., Sun, W.J., Zheng, X.H. and Wang, Y.S. 2005. Contribution of plants to N2O emissions in soil-winter wheat ecosystem: pot and field experiments. Plant and Soil 269: 205-211.

atmospheric composition change: ecosystems - atmosphere ... · 03/02/2009 accent s&i –...

Documents