assessment report: air quality in europe in 2012€¦ · grant agreement n°283576 macc-ii...

Grant agreement n°283576

MACC-II Deliverable D113.4

Assessment Report: Air quality in Europe in 2012

Date: 07/2014 Authors: Laurence ROUÏL (INERIS), and the MACC/EVA teams

File: MACCII_EVA_DEL_D_113.4_AssessmentReport2012_final.docx.doc/.pdf

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Date 07/2014

Status Final version

Authors Laurence Rouïl (INERIS), Emmanuele Emili (CERFACS) Matthias Beekman, Gilles Foret (CNRS/LISA) Mikhail Sofiev, Julius Vira (FMI), Henk Eskes (KNMI) Frédérik Meleux, Anthony Ung (INERIS) Virginie Marécal (Meteo France) Alvaro Valdebenito, Anna Carlin-Benedictow (Met.no) Hendrik Elbern, Elmar Friese, Achim Strunk (RIUKK) Lennart Robertson(SMHI), Arjo Segers (TNO) And the European Aeroallergen Network (EAN)

Use and reproduction of this report or parts of it may be restricted. Appropriate non-commercial use will normally be granted under the condition that reference is made to MACC-II. Please enquire with: [email protected]

This document has been produced in the context of the MACC-II project (Monitoring Atmospheric Composition and Climate - Interim Implementation). The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7 THEME [SPA.2011.1.5-02]) under grant agreement n° 283576. All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission has no liability in respect of this document, which is merely representing the authors view.

mailto:[email protected]


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Executive Summary / Abstract This report is the MACC-II/EVA assessment report for the year 2012 delivered by the air quality re-analysis multi-model system implemented in the MACC and MACC-II projects from the Copernicus program. It results from the regional air quality re-analysis chains developed by the MACC-II regional air quality modelling teams. Actually, seven European regional air quality modelling systems were run to provide simulations of air pollutant concentrations over the year 2012, with an hourly resolution throughout Europe. When possible (depending on the pollutant and the maturity of the modelling chain), data assimilation procedures were activated to calculate re-analyses of the air pollution fields, combining raw simulation outputs with validated observations available at monitoring stations running in air quality networks. So-called “Ensemble” estimations of the concentrations were built up mixing the available results from the various models in a median average. Compare to the previous air quality assessment reports, more modelling teams provided re-analyses from their data assimilation system. Therefore the “Ensemble” results are more consistent and more robust, leading to an overall improvement of the quality of the MACC-II air quality assessment services. Operational production of ensemble re-analysed air pollutant concentration fields is the main objective of the EVA subproject within MACC-II, for deriving, on a yearly basis, air pollution regulatory and usual exposure indicators. It is expected that very comprehensive information on air pollution patterns and levels, considered as the “best estimate” can be derived. A systematic evaluation of the model and data assimilated results against a relevant set of dedicated observation data (not used for to be assimilated in the models) has been conducted. It showed very satisfactory performances compared to the state of the art, and better results than for the previous years. The strength of the multi-model approach is the capacity of highlighting the most sensitive geographical areas where re-analyses are the most uncertain, either because of model weaknesses (uncertainties on emissions, complex topography, and uncertainty in the dynamic and chemical parametrizations...) or because of a lack of available representative observations. Uncertainty is linked to the range of model responses. Therefore we hope to be able to provide policy-oriented users with high quality maps associated with spatio-temporal uncertainty linked to the variability of the model responses. MACC-II regional air quality re-analyses are relevant for assessing rural and urban background concentrations and where limit values are exceeded1 in such areas. This information must be reported according to the air quality Directive of the European Commission (2008/50/EC). Because the overall European domain is considered in MACC-II, in countries where no mapping or modelling systems are available to simulate air pollution patterns, MACC-II products can be an alternative to get data to be reported, or to get

1 It should be noted that the MACC-II assessments are not suited for mapping local exceedances (street canyons, industrial sites). Model resolution is too coarse to simulate correctly such situations that must be considered with appropriate models.


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assessments of pollution episodes. Beyond graphical maps, numerical data issued from model results with or without assimilation process can be delivered to interested users on request. Finally for the first time within the MACC and MACC-II projects, the assessment report for a given year is released one year and a half later (2012 assessment report released by mid 2014). The previous assessments reports were published more than two years later. This is a significant improvement achieved by the end of MACC-II. Actually use of validated in-situ data available in the AIRBASE database maintained by the European Environment Agency makes challenging to reduce more the time for publication. Validated database is generally updated by the EEA by the beginning of the year Y+2 for data related to the year Y, and time is necessary to run the re-analyses. The year 2012 was considered by the World Meteorological Organization (WMO) as the previous ones, among the warmest year in the world and also in some parts of Europe. Mild temperatures occurred at the beginning of the year and rather higher temperatures were recorded in March in Western, Southern and even Northern Europe. During summer the situation was much contrasted, with rather cool temperatures in Scandinavia and in the UK, and high ones in Southern and Eastern countries. Some countries like France experienced a little heat wave at the end of August with temperatures even higher than in August 2003. Ozone concentration levels were still high, especially in Mediterranean countries and in the Balkans, where annual mean of daily ozone concentrations were above 100 µg/m3. A large part of South of France, Slovenia, Austria, Czech Republic, Croatia, Slovakia, and a part of Hungary were the most exposed countries in 2012 considering indicators relevant for human health. Investigating the AOT40 vegetation exposure indicator which refers to the early summer period, shows that the target value of the Air quality Directive is exceeded in many places in Europe. Exceedances of ozone information threshold occured around the Mediterranean Sea, and especially in Spain and Italy, but also Central Europe (Slovakia, Czech Republic). Therefore, ground level ozone remains an issue in Europe. Annual PM10 concentrations ranged in 2012 between 5 µg/m3 (in Scandinavia) and 40 µg/m3 which is the limit annual average value set by the air quality Directive. The limit value was exceeded in North part of France (including Paris), Benelux, Poland, Slovakia, Czech Republic, North of Italy, and the Balkans. PM10 high concentrations occurred during winter periods because of stable meteorological conditions combined with residential heating extra emissions (compared to the rest of the year). Concerning the number of days exceeding the 50 µg/m3 limit value (daily average), it is still too high. The cold spell that occurred in a large part of Europe in February 2012 contributed to increase the number of days when the daily limit value was exceeded. This indicator is partially reported in the MACC-II/EVA assessment reports for background levels. But the model resolution is not sufficient to catch exceedances at traffic or industrial stations that are representative of local air pollution sources. However the MACC-II/EVA assessment work gives an idea of the European areas which are the most sensitive to PM10 pollution. Benelux, Paris area, Poland, Slovakia, Czech Republic, the Pô Valley, and other local places in Western, Central and Eastern Europe are the most concerned. The 35 limit number of days exceeding the daily limit value is likely to be exceeded in all these regions.


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PM2.5 simulations reported areas where the target value of 20 µg/m3 (annual average) for 2020 is already exceeded. Locations concerned are hot spot areas being driven by high anthropogenic activity. This report synthesizes those results with a selection of illustrations. All numerical data are available for users on request to the MACC-II/EVA sub-project leader: [email protected].



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Table of content 1. Rationale ........................................................................................................................... 11

2. Ozone concentrations in 2012 .......................................................................................... 14

Ozone indicators .................................................................................................................. 14

Ozone episode during summer 2012 ................................................................................... 19

Uncertainty analysis ............................................................................................................. 21

3. Nitrogen dioxide indicators .............................................................................................. 24

Annual average ..................................................................................................................... 24


4. PM10 indicators ................................................................................................................. 27

Annual and daily indicators compared to limit values ......................................................... 27

PM10 episodes in 2012 ......................................................................................................... 29


5. PM2.5 indicators ................................................................................................................. 33

6. Pollens............................................................................................................................... 34

Introduction to the experiment ........................................................................................... 34

Birch pollen impact in 2012.................................................................................................. 34


7. Conclusions ....................................................................................................................... 39


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List of figures Figure 1. Reanalysis of the annual daily mean ozone concentrations in 2012. (Ensemble data assimilated chain of the MACC-II /EVA systems) ____________________________________________________________ 15 Figure 2. Reanalysis of the annual daily mean ozone concentrations in 2011. (Ensemble data assimilated chain of the MACC-II /EVA systems) ____________________________________________________________ 15 Figure 3. Reanalysis of the summer daily mean ozone concentrations in 2012 (Ensemble data assimilated chain of the MACC-II/EVA systems) ____________________________________________________________ 16 Figure 4. Number of days in 2012 when the 8-hour daily average exceeded 120 µg/m3(Ensemble data assimilated chain of the MACC-II/EVA systems) __________________________________________________ 16 Figure 5. Re-analyses of SOMO35 in (µg/m3)·days for the year 2012 ______________________________ 18 Figure 6. Re-analyses of AOT40 in (µg/m3)·hours for the year 2012 _______________________________ 18 Figure 7. Re-analyses of SOMO35 in (µg/m3)·days for the year 2011 ______________________________ 19 Figure 8. Re-analyses of AOT40 in (µg/m3)·hours for the year 2011 _______________________________ 19 Figure 9. Evolution of ozone episode in Europe from the 20th to 27th August 2014. Representation of daily maximum concentrations ____________________________________________________________________ 20 Figure 10. Correlation coefficient comparing MACC-II/EVA reanalyses of ozone daily mean concentrations to observations (AIRBASE)- period: summer 2012 ___________________________________________________ 21 Figure 11. Root mean square Error comparing MACC-II/EVA reanalyses of ozone daily mean concentrations to observations (AIRBASE)- period: summer 2012 _________________________________________________ 22 Figure 12. Taylor diagram of MACC-II models’ simulations of the ozone daily mean concentrations over the year 2012. Data assimilated results are noted with the “a” index. ____________________________________ 23 Figure 13. Taylor diagram of MACC-II models’ simulations of the ozone daily maxima concentrations over the year 2012. Data assimilated results are noted with the “a” index. _________________________________ 23 Figure 14. 2012 re-analysed annual mean concentrations of Nitrogen dioxide throughout Europe simulated by the RIUUK/EURAD modelling system assimilating AIRBASE data and satellite information ______________ 25 Figure 15. Taylor diagram for MACC-II data assimilated Ensemble model responses to simulate annual means concentrations of nitrogen dioxide over the year 2012 _______________________________________ 26 Figure 16. Taylor diagram for MACC-II data assimilated EURAD model responses to simulate annual means concentrations of nitrogen dioxide over the year 2012 _____________________________________________ 26 Figure 17. 2012 Reanalysis of the PM10 annual average in Europe (Ensemble data assimilated chain of the MACC-II/EVA systems) ______________________________________________________________________ 28 Figure 18. 2012 Reanalysis of the PM10 winter average in Europe; (Ensemble data assimilated chain of the MACC-II/EVA systems). ______________________________________________________________________ 28 Figure 19. Number of days exceeding the 50 µg/m3 threshold (daily average) simulated by the MACC-II/EVA system in 2012 _____________________________________________________________________________ 29 Figure 20. PM10 daily averages from the 2nd to 16th February 2012 _________________________________ 30 Figure 21. Number of days when the 50 µg/m3 threshold for PM10 daily average was exceeded during from the 25th January to the 16th February 2012 ______________________________________________________ 31 Figure 22. Organic carbon concentrations in PM10 measured in 2012 at the Melpitz supersite from the ACTRIS network. Source: EBAS database; www.ebas.nilu.no ________________________________________ 31 Figure 23. Taylor diagram of MACC-II models’ simulations of the PM10 daily mean concentrations over the year 2012. Data assimilated results are noted with the “a” index. ____________________________________ 32 Figure 24. PM2.5 annual average concentrations for the year 2012, assessed with the data assimilation Ensemble Copernicus/MACC-II system (re-analysed fields) __________________________________________ 33 Figure 25. Propagation of the flowering season in 2012: 10-days mean concentrations from 1.3.2012 till 1.7.2012. [pollen m-3] _______________________________________________________________________ 35 Figure 26. Total pollen load (cumulative daily count over the whole season), [pollen day m-3], left-hand panel, and the number of hours when the pollen concentrations exceeded 50 pollen m-3 _________________ 36 Figure 27. Season start, end and duration following the 2.5%-97.5% criterion ________________________ 36 Figure 28. Correlation coefficient for daily concentrations calculated with initial guess of emission (upper row) and assimilated birch pollen emission correction factor (lower row). Assimilated (left column) and evaluation (right column) stations are shown ____________________________________________________ 37


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Figure 29. RMSE for daily concentrations calculated with initial guess of emission (upper row) and assimilated birch pollen emission correction factor (lower row). Assimilated (left column) and evaluation (right column) stations are shown. Unit [pollen m-3]. Obs different scales ___________________________________ 38 Figure 30. European temperature anomalies relative to 1961-1990 for March 2012. Source : WMO regional Association VI(Europe), regional Climate centre on climate monitoring, Deutscher Wetterdienst, Germany __ 42 Figure 31. Location of the NO2 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes ______________________________________________________________________ 45 Figure 32. Location of the O3 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes ______________________________________________________________________ 46 Figure 33. Location of the PM10 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes ______________________________________________________________________ 47 Figure 34. Location of the PM2.5 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes ______________________________________________________________________ 48


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Glossary AIRBASE European Air Quality database (http://air-

climate.eionet.europa.eu/databases/airbase/) Analyses Maps of air pollutant concentrations fields issued from numerical

model results combined with up-to-date available observation data to improve their accuracy in the vicinity of measurement points. In MACC-II, they are produced routinely on a daily basis.

AOT 40 Accumulated Ozone over the 40 ppb Threshold

AQD Air Quality Directive Assessments Quantitative evaluation of air quality fields based on validated data

and numerical model results CERFACS Centre Européen de Recherche et de Formation Avancée en Calcul

Scientifique (France) Data assimilation Mathematical process to incorporate observations in a numerical

model of physical systems EDA Air Quality data assimilation sub-project in the MACC and MACC-II

projects EEA European Environment Agency ENS Air quality forecasting and analysis sub-project in the MACC and

MACC-II projects Ensemble Model Combination of various results from various models. This can be a

simple average (median), or a weighted average resulting from analysis of models’ behaviour over past periods. The models building-up the ensemble can correspond to different systems (multi-model approach) or to the same modelling system fed with different input datasets.

EVA: Air quality validated assessments sub-project in the MACC and MACC-II projects

FMI Finnish meteorological Institute KNMI Royal Netherlands Meteorological Institute LISA Laboratoire Interuniversitaire des Systèmes Atmosphérique (France) Météo France French Weather Services Met.no Norwegian Meteorological institute (Norway) Raw model data Model results directly issued from the modelling chain, without any

post-treatment process Re-analyses Maps of air pollutant concentrations fields issued from numerical

model results combined with validated observation data to improve their accuracy in the vicinity of measurement points

RIUKK Rhenish Institute for Environmental research at the University of Cologne (Germany)

RMSE Root Mean Square Error. It gives the standard deviation of the model prediction error. A smaller value indicates better model performance.

SMHI Swedish Meteorological and Hydrological Institute (S)

http://air-climate.eionet.europa.eu/databases/airbase/



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SOMO35 Ozone concentrations accumulated dose over a threshold of 35 ppb TNO Netherlands Organisation for applied Scientific Research (NL)

VOC Volatile Organic Compound

WHO World Health Organization

WMO World Meteorological Organization


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1. Rationale The Copernicus/MACC-II project aims at delivering a number of services dedicated to global atmospheric composition and air quality monitoring in Europe. Air quality issues are covered by two services (http://www.copernicus-atmosphere.eu/services/aqac/) :

- The so-called ENS services are focussed on routine and near real time products (forecasts, Near Real Time analyses). Up to four days forecasts of ozone, nitrogen dioxide and particulate matter concentrations (PM10 and PM2.5) throughout Europe are available every day. Daily analyses of the same variables (simulated fields are improved with observations thanks to data assimilation techniques) are proposed as well. They are available on the Copernicus atmosphere services website (http://macc-raq.gmes-atmosphere.eu/som_regrid_ens3D.php ).

- The so-called EVA services relate to detailed analysis of past situations thanks to validated material issued from observation networks and modelling. Therefore, a posteriori validated air quality assessments for Europe, based on re-analysed air pollutant concentration fields are proposed. Simulations of past years are performed and “improved” thanks to the assimilation of available validated in-situ and satellite observations. They are available on the Copernicus atmosphere services website (http://www.gmes-atmosphere.eu/services/raq/raq_reanalysis/).

The so-called MACC-II regional air quality assessment reports describe, with a yearly frequency, the state and the evolution of background concentrations of air pollutants in European countries. Special care is given to pollutants characterised by the influence of long range transport, correctly caught by European scale modelling systems: ozone, nitrogen dioxide, particulate matter (PM10 and PM2.5). Focus on specific pollution episodes that happened during the year will be proposed. The MACC-II air quality assessment initiative is conceived to be an actual tool for European policy and decision makers in charge of air quality monitoring and reporting. Indeed, according to the Directive an Ambient Air quality and Cleaner Air for Europe (the CAFE Directive 2008/50/EC), the Member states have to report to the European Commission and to inform their citizens on air pollution they are exposed to. In particular, situations (or episodes) when nitrogen dioxide and particulate matter concentrations exceed regulatory limit values (or target values for ozone) must be carefully analysed (geographical extension, duration, intensity, population exposed...) and action plans to limit their impact and to avoid their future development must be proposed. Member states usually base their investigations on observations available from national air quality monitoring networks, modelling tools and national expertise. In its operational stage (foreseen in mid-2014) the Copernicus atmospheric services will propose complementary and comprehensive information established on the basis of state of art chemistry-transport models run operationally by modelling teams with a long experience in the field of air pollution, and extended in-situ and satellite observation sets gathered and made available. Both sources of information (modelling and measurement)

http://www.copernicus-atmosphere.eu/services/aqac/

http://macc-raq.gmes-atmosphere.eu/som_regrid_ens3D.php

http://www.gmes-atmosphere.eu/services/raq/raq_reanalysis/


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are smartly mixed by the MACC-II scientists to elaborate the so-called “re-analyses” of past periods and support national experts in the Member states in their interpretation of air pollution trends and events that touch their country. According to users‘requirements, special care will be accorded in future services to the analysis of particulate air pollution episodes. With the MACC-II project this new generation of tools and information based on indicators geographically distributed on comprehensive maps becomes available. It combines the capacities of chemistry transport models to represent air pollutant patterns and the accuracy of observations in re-analyses. They are compiled and presented in the MACC-II/EVA assessment reports, being considered as the “best available or most realistic” representations of air pollution patterns: more relevant than interpolation of observations and more accurate than raw simulations. Moreover the multi-model functionalities developed in the MACC-II services allow the provision of “ensemble” model estimations. They relate to the combination of the various model results to obtain an average with improved skills compared to the individual models’ ones. This combination can be a simple median average or more sophisticated averages, weighted by coefficients depending on the model, the geographical location, the simulated period.... The 2012 ensemble assessments are built upon the median of the MACC-II models results. The present document is the fifth MACC-II assessment report on European air Quality for the year 2012. It is established on model outputs provided by seven modelling teams involved in the regional operational production, and the in-situ observations available from the AIRBASE database (from the European Environment Agency, http://air-climate.eionet.europa.eu/databases/airbase/)2. Notice The technical descriptions of the modelling and data assimilation systems that have been used for the 2012 assessment report are described in detail in annex 2. It should be noted that following recommendations from the European Environment Agency, since last year, the simulation domain significantly extended to encompass more countries. Iceland, Turkey, Scandinavia now long the MACC domain, as western part of Russia and North Africa. A specific report is edited to describe the model performances (individual models and ensemble). The performances of the models and data assimilation systems were systematically evaluated by INERIS running a common model evaluation framework. The maps and graphs proposed in the present report correspond to the best available estimation of the targeted indicators amongst the individual model results and the ensemble, according to the evaluation that have been conducted and reported in the validation report. 2 Data being available on http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8



http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8



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Content of the report This report describes the air quality situation in Europe during the year 2012. A set of indicators characterizing the state of regulatory air pollutant concentrations is established. It includes annual and seasonal indicators: average values and indicators related to situations when regulatory thresholds for the considered pollutants are exceeded, are proposed, for a better qualification of the year 2012 in terms of air quality exposure in Europe. These indicators are presented in the following sections for the ozone, nitrogen dioxide and particulate matter (PM10 and PM2.5). All the indicators presented in the report have been calculated using the MACC-II data issued from the available assimilation chains developed by the regional modelling teams within the MACC and MACC-II projects. Two distinct sets of observation data that were used for building up the re-analyses on one side and performing the score calculations on the other side. Both were issued from the AIRBASE database maintained by the European Environment Agency (EEA). No satellite information was used at this stage of the project, except for the production of NO2 concentration fields with the German EURAD model and the Dutch LOTOS-EUROS model. Moreover, the air quality indicators presented in this report result from data assimilated results from the individual model chains combined in a median average to process the “MACC-II Ensemble”. In the following section the Ensemble assimilated fields are referred as “ENSa”3. Finally, whatever the considered indicators, the availability of various models4 results allows to assess the range of variability of model responses which is considered as a quantification of the modelling uncertainty. So it is possible to consider the situations when the model results are the most uncertain (when the range of variability of the model responses is the highest) and should be taken with caution. Below graphical displays, the model numerical results are available on request. Please contact Laurence Rouïl the leader of the MACC-II/EVA subproject ([email protected]) .

3 In comparison to the Ensemble results from the raw simulations that are simply referred as “ENS” 4 These models were acknowledged for their correct performances they showed during the preliminary GEMS (2006-2009) and MACC (2009-2011) precursor projects



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2. Ozone concentrations in 2012

Ozone indicators The year 2012 was exceptionally warm in Central and Southern Europe, but cooler-than-average temperatures were recorded in Northern Europe. According to the world meteorological organization 2012 report5, France, Spain, Czech Republic experiences short heat-wave periods comparable to 2003. Conversely, some countries in Europe (United Kingdom, Sweden) had a relatively mild summer. The beginning of the year and spring were exceptionally warm in most parts of Europe. This contrasted situation is reflected in ozone daily mean concentrations over the year 2012 and throughout the MACC domain. Figure 1 maps those concentrations: rather high ozone levels are displayed in countries around the Mediterranean Sea and in the Balkans (70 µg/m3), and lower levels in the North part of Europe (35-40 µg/m3). Figure 2 presents, as a reminder, the same indicator in 2011 . The patterns of ozone daily mean concentrations are very similar. Considering summer situation, Figure 3 highlights highest levels of ozone concentrations with “hot spots” in all countries around the Mediterranean Sea, exceeding 100 µg/m3. In Spain, South of France, Italy, Greece, The Balkans, Turkey, summer ozone remains an actual issue. The most exposed areas during the year 2012, considering health issues, appear clearly considering Figure 4 that maps the number of days when the 120 µg/m3 threshold (8-hours average) was exceeded. It must be noted that the target value set in the air quality Directive (2008/50/EC) is 25 days. This air quality objective was logically exceeded in several Southern places (Italy, Spain, South of France, Greece and Turkey) and locally in Croatia, Austria, Slovakia and Czech Republic where severe heat waves occurred. On the other hand, no critical level for this indicator was recorded in Ireland and the United Kingdom, Benelux, Scandinavia and a large part of Central Europe. But it is interesting to note that these high temperatures (and summer heat waves) did not impact ozone levels as it was the case in 2003. High ozone levels slightly increased compared to the previous years (2009-2010), but no unexpected high values had been reported. At this stage more studies are necessary to assign this signal either to the impact of emissions control strategies of ozone precursors (NOx and Volatile Organic Compounds especially) or to the economical crisis or the meteorological variability.

5 From WMO statement on the Status of the Climate in 2012, WMO report N°1108, http://www.wmo.int/pages/prog/wcp/wcdmp/documents/WMO_1108.pdf

http://www.wmo.int/pages/prog/wcp/wcdmp/documents/WMO_1108.pdf


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Figure 1. Reanalysis of the annual daily mean ozone concentrations in 2012. (Ensemble data assimilated chain of the MACC-II /EVA systems)

Figure 2. Reanalysis of the annual daily mean ozone concentrations in 2011.

(Ensemble data assimilated chain of the MACC-II /EVA systems)


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Figure 3. Reanalysis of the summer daily mean ozone concentrations in 2012

(Ensemble data assimilated chain of the MACC-II/EVA systems)

Figure 4. Number of days in 2012 when the 8-hour daily average exceeded 120

µg/m3(Ensemble data assimilated chain of the MACC-II/EVA systems)


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The indicator generally used in regulatory reporting to assess ozone impact on vegetation according to the Air Quality Directive is the AOT40. AOT 40 (Accumulated dose over a threshold of 40 ppb) requires for its calculation hourly ozone data. It is the sum of the differences between the hourly ozone concentration (in ppb) and 40 ppb, for each hour when the concentration exceeds 40 ppb, accumulated during daylight hours (8:00-20:00 UTC). AOT40 has a dimension of (µg/m3)·hours. In the AQD, the target value of AOT40 calculated from May to July is 18.000 (µg/m3)·hours, with a long term objective of 6.000 (µg/m3)·hours. For quantification of the ozone health impacts the World Health Organisation recommends the use of the SOMO35 (ozone concentrations accumulated dose over a threshold of 35 ppb) indicator. SOMO35 is the sum of the differences between maximum daily 8-hour running mean concentrations greater than 35 ppb and 35 ppb. SOMO35 has a dimension of (µg/m3)·days. This indicator is used as an impact constraint in impact assessment modelling used to define national emission ceilings set in the NEC Directive (2001/81/EC). Ensemble re-analyses performed by the MACC-II platforms allowed to map AOT40 and SOMO35 indicators for the year 2012 (Figure 5 and Figure 6). As a comparison, SOMO35 and AOT40 indicators displayed for the year 2011 are given on Figure 7 and Figure 8 respectively. The expected North-South gradient exists but SOMO35 values are higher than in 2011 especially in the Mediterranean countries. As a consequence European people were more exposed to ozone during the year 2012 than in 2011 and 2010. Almost all countries in Western, Southern and Central Europe had SOMO35 values higher than 4 000 (µg/m3).days. Investigating the AOT40 vegetation exposure indicator which refers to the early summer period shows that the target value of the Air quality Directive is exceeded in many places in Europe. Exceedances are huge around the Mediterranean Sea, and especially in Spain and Italy, but also Central Europe (Slovakia, Czech Republic). As usually, health and ecosystems exposure to ozone levels was higher in Italy (and especillay the Po Valley), South of France, South of Spain, Greece, Balkans and Turkey. As noted in the previous assessment reports, it is not possible to conclude that exposure to ozone significantly decreases in Europe, and clearly 2011-2012 situations counter-balances the slight improvement in terms of ozone exposure noted in 20096.

6 See 2009 MACC air quality validated assessment report


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Figure 5. Re-analyses of SOMO35 in (µg/m3)·days for the year 2012

Figure 6. Re-analyses of AOT40 in (µg/m3)·hours for the year 2012


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Figure 7. Re-analyses of SOMO35 in (µg/m3)·days for the year 2011

Figure 8. Re-analyses of AOT40 in (µg/m3)·hours for the year 2011

Ozone episode during summer 2012 It is interesting to note that a little (few days) warmer period occurred in Southern and Central Europe at the end of August 2012, with temperatures locally higher than those recorded in summer 2003. Raising temperature impacted ozone concentrations. Figure 9 shows evolution of this episode, with the daily ozone maxima concentrations. Mediterranean countries but also western (at the beginning) and central Europe were concerned by concentrations exceeding the information threshold value of 180 µg/m3. However they were lower than those recorded during previous episodes (2003) and the episode was not very huge in geographical extension and intensity. However temperatures were high, as reported by the


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WMO in their “Status of Climate 2012 report”7 : “Overall, the Northern Hemisphere summer of 2012 is ranked as one of the warmest summers for several countries in the southern half of Europe, including Bosnia and Herzegovina (warmest), Serbia (warmest), Montenegro (warmest), Macedonia (warmest), Hungary (2nd warmest), Slovenia (2nd warmest), Austria (3rd warmest), and Spain (4th warmest)”. The report also mentions that in France the short heat wave that occurred at the end of August was even worst in intensity than in 2003, and records of temperature were reached during this period in Czech Republic, and Slovakia. It is therefore difficult to compare, in terms of ozone concentrations, to previous episodes. Heat wave occurred quite late in summer, which can explain that ozone levels reached are not so high, and there is certainly an impact of decreasing emissions of precursors, as a result of emission reductions strategies or economical crisis. But it is difficult to discriminate reasons furthermore. Preliminary data from research networks (see www.ebas.nilu.no) confirm data there is no large drop in ozone concentrations in the countries mostly impacted by the heat wave.

Figure 9. Evolution of ozone episode in Europe from the 20th to 27th August 2014.

Representation of daily maximum concentrations

7 Status of the Climate in 2012, WMO report N°1108, http://www.wmo.int/pages/prog/wcp/wcdmp/documents/WMO_1108.pdf

20120821 20120823

20120825 20120827

http://www.ebas.nilu.no/



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Uncertainty analysis The twin report “Validation of the MACC assessment products for the year 2012” contains a number of scores that allow the qualification of skills and performances of the MACC-II products. Indeed, to build up confidence in the Copernicus air quality regional modelling system, special care should be according to the evaluation of its performance in simulating right concentrations and in catching correctly situations when regulatory threshold values are exceeded. In this paragraph, only main conclusions are reported to help in the interpretation of the previous results. The correlation coefficient (Figure 10) between simulations and observations of ozone daily mean concentrations is high for the ensemble estimations (about 0.9) whatever the station typology. The model performances are rather consistent from a geographical area to another. In particular, good results for Southern Europe (rural stations) are a noticeable improvement of the MACC-II modelling system achieved over the period of the project. The Root mean square error (RMSE) gives the standard deviation of the model prediction error. A smaller value indicates better model performance. RMSE for the Ensemble results is displayed on Figure 11 for various station typologies. The results are very convincing as well with values lower than 15 µg/m3 (and even 10 µg/m3) in many locations. Few isolated stations show poorer performance especially in the Central Europe (Hungary, East Austria), in mountainous areas and central Europe where models have generally more difficulties to reproduce correctly dynamic and chemical complex processes.

Figure 10. Correlation coefficient comparing MACC-II/EVA reanalyses of ozone daily

mean concentrations to observations (AIRBASE)- period: summer 2012


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Figure 11. Root mean square Error comparing MACC-II/EVA reanalyses of ozone daily

mean concentrations to observations (AIRBASE)- period: summer 2012

So called “Taylor Diagrams” are conceived to represent on the same diagram various statistical performance indicators: correlation coefficient, RMSE and standard deviation. Figure 12 presents the Taylor diagram of the various models responses to simulate daily ozone maximum over the year 2012. The assimilated model results correspond to those with a “a” index in their name. The ensemble models are noted “ENS” and “ENSa”. The Ensemble assimilated (ENSa) chain gave the best results, and a clear improvement can be observed for the data assimilated results from all the individual modelling chains (inside the bule circle). Performances are actually very satisfactory whatever the station typology with correlation coefficient higher than 0.85, and RMSE ranging between 10 and 15 µg/m3. Variability of the model responses seems to reproduce correctly the variability of observations as well. As usually, performance score are even better when considering ozone daily maxima (Figure 13).


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Figure 12. Taylor diagram of MACC-II models’ simulations of the ozone daily mean

concentrations over the year 2012. Data assimilated results are noted with the “a” index.

Figure 13. Taylor diagram of MACC-II models’ simulations of the ozone daily maxima



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3. Nitrogen dioxide indicators Warning note: It should be noted that the MACC-II/EVA mapping system is not fitted to deal with local hot spot situations as those that develop near busy roads or on industrial sites. Actually, the model resolution is about 20-25 km2, and is not sufficient to catch actual NO2 concentrations at traffic and industrial sites. For the year 2012, and as for 2011, six teams over seven assimilated in an operational way NO2 observations. This is a significant improvement compare to assessment exercises prior to 2011. RIUUK (Rhenish Institute for Environmental Research at the University of Cologne) assimilated NO2 ground-level observations from the AIRBASE database and also NO2 columns retrieved from satellites observations (OMI, GOME-2, SCHIAMACHI). The consortium KNMI/TNO assimilated ground level concentrations from AIRBASE, and satellite observations from IASI. The FMI assimilated NO2 in-situ data from AIRBASE in its results. For a pollutant driven by local sources like NO2, data assimilation improved significantly the simulation of concentration patterns (see warning note above) and regional model results with observed data assimilation should be preferred. Therefore, an ensemble based on the results of these six models has been built-up. The analysis of the scores will be discussed in detail in the 2012 validation report. Because the results presented in this report should be based on the best estimates provided by the MACC-II suites, we have chosen to present NO2 indicators simulated by the best model, which is not, in the case of the nitrogen dioxide, the Ensemble. The EURAD assimilated chain from the RIUUK performed the best results in that sense (see the Taylor diagram in) and those are commented below.

Annual average The annual mean of nitrogen dioxide concentrations should not exceed the limit value 40 µg/m3 to be in compliance with the 2008 AQ Directive. This indicator mapped throughout Europe is presented below (Figure 14). The cities’ footprint is clearly marked with annual background concentrations ranging from 20 to 30 µg/m3 in most of the cases, and reaching or being close to the limit value in few ones (London, Paris, Marseille, several cities in Italy and the Pô Valley, in Germany, in Spain). Several countries were concerned, and although it is difficult to focus on the hot spot emission areas with the MACC-II model resolution, one can suspect that traffic stations are the most concerned by exceedances of the annual limit value for NO2.The ship traffic influence is caught as well, especially in the Mediterranean Sea and in the Channel where NO2 annual concentrations ranged between 20 and 30 µg/m3. The Pô Valley, Germany, the UK, France, and the Benelux nad local places in central and eastern Europe were the most exposed areas to NO2 in Europe in 2012.


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Figure 14. 2012 re-analysed annual mean concentrations of Nitrogen dioxide

throughout Europe simulated by the RIUUK/EURAD modelling system assimilating AIRBASE data and satellite information

Another indicator to be considered as a regulatory flag is the limit value of 200 µg/m3 hourly average that should not to be exceeded more than 18 times a year. Generally this indicator is representative of “hot spots” situations near high emission sources (typically dense traffic areas), and refers to local pollution. As mentioned above, this parameter is not compatible with the spatial resolution of the model re-analyses conducted in the MACC-II/EVA project (20km in the best case). Therefore this indicator has not been calculated within the 2012 MACC-II/EVA re-analyses.

Uncertainty analysis Figure 15 and Figure 16 present the synthesis of the ENSa and EURADa models performances with the Taylor diagram corresponding to annual mean concentrations of nitrogen dioxide. Best results are obtained for EURAD model from the University of Cologne, especially for urban and suburban stations. However for the first time since the publication of the EVA assessment report, the skills of the data assimilated Ensemble are not too far for


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NO2. This is a very positive signal showing that the individual modelling chains performed better than the previous and become more mature and operational. More information is available on the model performance in the “Validation of the MACC assessment products for the year 2012”. However few elements are given below to help in the interpretation of the results.

Figure 15. Taylor diagram for MACC-II data assimilated Ensemble model responses to simulate annual means concentrations of nitrogen dioxide over the year 2012

Figure 16. Taylor diagram for MACC-II data assimilated EURAD model responses to

simulate annual means concentrations of nitrogen dioxide over the year 2012


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4. PM10 indicators For the year 2012, six teams over seven assimilated in an operational way PM10 observed concentrations (one more than for the 2011 report): EURAD from RIUUK (Rhenish Institute for Environmental Research at the University of Cologne), CHIMERE from INERIS (France), MATCH from SMHI (Sweden), LOTOS-EUROS from the KNMI/TNO consortium (Netherlands) and MOCAGE from Météo France (France) assimilated in their raw model results in-situ observation data gathered in the AIRBASE database. Getting six models over seven in the re-analysis process is a significant improvement of the MACC-II system, ensuring robustness and accuracy of the air quality assessment service. It is acknowledged that data assimilation process improves significantly the simulation of concentration patterns and regional model results with observed data assimilation should be preferred. Therefore, an ensemble based on the results of these four models has been built-up and the results are presenting in the report.

Annual and daily indicators compared to limit values Annual PM10 concentrations ranged in 2012 between 5 µg/m3 (in Scandinavia) to 40 µg/m3 (in large cities areas), the limit annual average value, set by the air quality Directive in very few local areas (Figure 17). Globally, PM10 concentrations ranged from 15 to 30 µg/m3 in most of the European places. Despite the fact that local exceedances are missed with the current MACC-II/EVA system (because of the spatial resolution), MACC-II/EVA re-analysis systems gave for PM10 average indicators results that should be consistent with the actual observed values reported to the EEA in AIRBASE (according to the performance analysis). Figure 18 shows winter averages map for the year 2012. Highest winter concentrations were obtained in the Pô Valley and in Central Europe (Poland, Czech Republic, Slovakia), in the North part of France (including Paris) and in the Benelux. It is not surprising that highest concentrations are generally observed in winter when meteorological stagnant conditions and inversion avoid dispersion. They are also more influenced by residential heating emissions which are well-known to generate particulate matter. The first half of February 2012 was characterized by a cold spell that impacted the whole of Europe (see section below). In such situations, PM concentrations increase drastically due to cold and stagnant meteorological conditions and higher residential heating emissions. The geographical area mentioned above, exceeded the limit value of 35 days with a daily PM10 average higher than 50 µg/m3 (Figure 19). The MACC-II/EVA re-analyses report whether the number of 35 days of exceedances of the PM10 regulatory threshold for daily average is exceeded at background locations. The model resolution is not sufficient to catch exceedances at traffic or industrial stations that are influenced by local air pollution sources. However the overview proposed in Figure 19 gives an idea of the European areas which are the most sensitive to PM10 pollution. Poland, Slovakia, Czech Republic, the Pô Valley, Benelux and Paris and large cities in France were the most concerned in 2012.


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Figure 17. 2012 Reanalysis of the PM10 annual average in Europe (Ensemble data assimilated chain of the MACC-II/EVA systems)

Figure 18. 2012 Reanalysis of the PM10 winter average in Europe;

(Ensemble data assimilated chain of the MACC-II/EVA systems).


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Figure 19. Number of days exceeding the 50 µg/m3 threshold (daily average)

simulated by the MACC-II/EVA system in 2012

PM10 episodes in 2012 A remarkable cold spell developed in Europe at the beginning of the year 2012. According to the WMO Climate 2012 statement report8 : “(...) in France, the exceptional cool conditions were the most severe since January 1987, while it was the most significant cold spell in 27 years for Switzerland. In Austria, temperatures were 10°C below average during the first half of February, contributing to its coolest February since 1986.” Very cold meteorological conditions in winter are generally associated with PM episodes, because of stagnant meteorological conditions avoiding air pollutant dispersion, and increasing emissions from residential heating (especially wood burning in some European countries). Figure 20 shows the evolution of this episode during the first half of February 2012. It developed over the whole of Europe, being more critical in Western Europe at the beginning of February, and growing up in intensity over Central Europe at the end of the period. PM10 daily concentrations exceeded the limit value (50 µg/m3) everywhere in Europe. This is shown on the Figure 21. North of France, Benelux, Pô Valley, Austria, Germany, Czech Republic, Poland, were the most exposed countries. Available complementary measurement data from research networks (see the EBAS data base from NILU www.ebas.nilu.no) showed that, during this period, organic carbon concentrations increased (Figure 22). This can be a clue of the contribution of residential heating sources to explain PM10 episode.

8 https://www.wmo.int/pages/mediacentre/press_releases/documents/966_WMOstatement.pdf


https://www.wmo.int/pages/mediacentre/press_releases/documents/966_WMOstatement.pdf


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Figure 20. PM10 daily averages from the 2nd to 16th February 2012


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Figure 21. Number of days when the 50 µg/m3 threshold for PM10 daily average was

exceeded during from the 25th January to the 16th February 2012

Figure 22. Organic carbon concentrations in PM10 measured in 2012 at the Melpitz

supersite from the ACTRIS network. Source: EBAS database; www.ebas.nilu.no

Uncertainty analysis More information is available on the model performance in the “Validation of the MACC assessment products for the year 2012”. However few elements are given below to help in the interpretation of the results. Figure 23 presents the Taylor diagram issued from all models results for PM10 daily mean. It shows that the data assimilated results are always the best ones. The added-value brought by the data-assimilation process to improve PM10 concentrations is particularly significant for three models: CHIMERE, EMEP and EURAD. The cases of the models LOTOS-EUROS and SMHI will need further investigation. The data assimilation



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process does not significantly improve the modelled PM10 concentrations. This point will be reviewed as a priority point for future runs.

Figure 23. Taylor diagram of MACC-II models’ simulations of the PM10 daily mean



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5. PM2.5 indicators PM2.5 concentrations should be monitored since the adoption of the Unified Air quality Directive (2008/50/EC), with specific recommendations in urban areas to estimate the so-called “Average Exposure Indicator” in populated areas. Indeed, this pollutant is not regulated like the other air pollutants because “there is no identifiable threshold below which PM2.5 would not pose a [health] risk”. A target value for the annual average of PM2.5 concentrations over three years that should no longer be exceeded by the 1st January 2010, is fixed to 25 µg/m3. It will become a limit value in 2015, and will be reduced to 20 µg/m3 in 2020. Five teams provided results assimilating PM2.5 observed concentrations: met.no, Météo France, RIUUK, SMHI and TNO/KNMI. Current regional chemistry-transport models results improve to reproduce past periods assimilating observations even if only few stations are available. The Ensemble built on the basis of those five model results is used to present the 2012 assessment for PM2.5 concentrations. Figure 24 shows PM2.5 annual average concentrations simulated with the assimilated ensemble system. Highest concentrations are found in the Benelux, Paris area, Pô Valley, Central Europe (Poland, Czech Republic, Slovakia, Hungary), Turkey and in Eastern Europe. Cities in those areas are potential critical areas regarding the likelihood of exceedance of the current target value and a fortiori the 2020 limit value if emissions do not decrease.

Figure 24. PM2.5 annual average concentrations for the year 2012, assessed with the

data assimilation Ensemble Copernicus/MACC-II system (re-analysed fields)


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6. Pollens

Introduction to the experiment In addition to regulatory air pollutants, the natural atmospheric allergens, such as birch pollen, are considered as one of the developments within the scope of MACC -II. Birch pollen is among the species affected by the long-range transport and exhibiting extremely strong inter-annual variability. Therefore, its systematic re-analysis combining the observational information and numerical modelling is of direct interest for European allergy sufferers and public-health institutes and authorities. Re-analysis of birch pollen faces several challenges: (i) under-developed methodology of utilization of existing observational information in the model-based re-analysis: the bulk of observations are daily, whereas the concentrations exhibit very strong diurnal cycle; (ii) comparatively low-density and inhomogeneous observational network with poorly covered vast birch forests in Eastern Europe; (iii) poor knowledge of emission, model, and observational covariance matrices; (iv) insufficient knowledge on absolute emission level and its regionalization. These difficulties require a dedicated data assimilation methodology to be developed for the re-analysis purposes. Within MACC-II pollen re-analysis experiments are based on observations of European Aeroallergen Network and experimental 4D-VAR setup of SILAM model. Methodologies and data are described in annex 3.

Birch pollen impact in 2012 Propagation of the flowering season predicted by SILAM is presented Figure 25, which covers 5 months from March till June presenting 10-day average pollen concentrations. The season started already in the second decade of March but emission picked up over most of Central Europe only 10 days later. Within 2 weeks, pollination extended towards the east and in the second part of April high concentrations extended to Eastern Europe and to the north. In the beginning of May the flowering has finished over most of Central Europe and moved to Scandinavia and central and northern Russia. Until the end of June the model still predicted significant pollen concentrations over Northern-most areas of Scandinavia and Finland. Important characteristics for sensitive people are the total number of hours with the pollen concentrations exceeding the 50 grain m-3 threshold and the total pollen load over the season for a specific location (Figure 26b). With regard to these parameters, 2012 was quite high year: the total load exceeded 100,000 pollen day m-3 over large areas in Southern Finland and Western Russia and the number of hours with the concentrations higher than 50 pollen m-3 exceeds 600 hours over parts of Scandinavia. The pollen season duration is traditionally computed following the so-called 2.5% criterion: the season is considered started when the cumulative daily pollen count reaches 2.5% of the total seasonal count and finished when the cumulative count reaches 97.5% of the seasonal total (Figure 26). Other options include, for instance, the season duration for 1%-97.5% or 5%-95% basis. Still, in the current report we stick to 2.5% as a criterion for the season start. Evidently, this criterion is applicable only in the re-analysis exercise because it requires the knowledge of the concentrations over the whole season to calculate its starting date. Figure 27 presents the start and end dates (as the day number within 2012) of the main pollen season, as well as its duration in days.


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The season duration (Figure 27, panel c) should not be mixed with the length of the flowering season, which is shorter. The extension of the pollen season in comparison the flowering season is due to regional and long-range transport of grains emitted elsewhere.

Figure 25. Propagation of the flowering season in 2012: 10-days mean concentrations from 1.3.2012 till 1.7.2012. [pollen m-3]


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Figure 26. Total pollen load (cumulative daily count over the whole season), [pollen day m-3], left-hand panel, and the number of hours when the pollen concentrations

exceeded 50 pollen m-3Error! Reference source not found.

a) Pollen season start: the first day when cumulative count reaches 2.5% of the seasonal total Error! Reference source not found.. Contour line highlights the 90-th day (1.4.2010).

b) Pollen season end: the first day when cumulative count reaches 97.5% of the seasonal total Error! Reference source not found.. Contour line highlights the 120-th day (1.5.2010)

c) Pollen season duration: the difference between the last and the first days of the season (panels b and a of this figure, respectively). Contour line highlights the 30-day season duration.

Figure 27. Season start, end and duration following the 2.5%-97.5% criterion

Uncertainty analysis Correlation of the daily observed and predicted time series is presented in Figure 28 and RMSE in for first guess (upper row) and the final analysis fields (lower row) for assimilated (left-hand panels) and evaluation sites (right-hand panels). The median of correlation is about 0.5, with the bulk of poorly correlated sites located in the south of Europe where the abundance of birch is low and its mapping is difficult. In the North-Eastern Europe it is close to or above 0.6-0.7. Agreement is also high over the areas with large birch tree populations, such as Switzerland, Central and Eastern Germany, east of Austria, etc. Over the areas with poor birch stand information the time series appear essentially uncorrelated but the level of pollen concentrations there is very low. The effect of assimilation on the correlation coefficient should be limited because the whole season was assimilated in one 4D-VAR window, thus leading to time-independent emission correction field. However, comparison of upper and lower rows of Figure 28 shows substantial improvement of this parameter, largely because of reduction of the number of


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poorly captured sites. This effect is an illustration of importance of regional and long-range transport: improvement of absolute emission intensity pattern leads to higher temporal correlation. Importantly, such improvement is visible also for the evaluation sites, albeit not so large as in case of assimilated sites. In some cases, such as Latvian site in Riga, the assimilated field actually scores worse than the first guess due to lower main peak of the season, which lasted just two days. It is, however, located near the coastline and quite far from the neighbouring sites, which are mostly terrestrial and thus not necessarily representative for Riga conditions. The effect of assimilation on RMSE is seen from Figure 29 (obs different scales). Expectedly, by far the largest contribution to total RMSE comes from the northern sites, where the absolute concentrations are also very high (1-3 orders of magnitude higher than in Central or Southern Europe). The RMSE in assimilated stations has reduced from 319 pollen m-3 down to 241 pollen m-3, i.e. by ~25%. The reduction for evaluation stations mounted to about 15-20%.

Figure 28. Correlation coefficient for daily concentrations calculated with initial guess of emission (upper row) and assimilated birch pollen emission correction factor

(lower row). Assimilated (left column) and evaluation (right column) stations are shown


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Figure 29. RMSE for daily concentrations calculated with initial guess of emission

(upper row) and assimilated birch pollen emission correction factor (lower row). Assimilated (left column) and evaluation (right column) stations are shown. Unit [pollen

m-3]. Obs different scales


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7. Conclusions The 2012 MACC-II/EVA assessment report presents a number of results related to ozone, nitrogen dioxide and particulate matter (PM10 and PM2.5) concentrations that held during the year. They result from the “decentralised” regional air quality modelling systems implemented in the Copernicus MACC-II project, the same that are routinely run for the provision of forecasts and near real time analyses in the ENS sub-project. Almost all models were run in a “re-analysis” mode, with data assimilation procedures which allowed an accurate representation of the air pollution patterns “combining” simulation and observations. It is a significant improvement compared to the material available for the previous assessment reports, and a major evolution of the operational air quality modelling chains involved in the MACC-II services. In-depth evaluation of these products is the aim of another report, edited under the title “Validation of the MACC assessment products for the year 2012”. It is shown in this report that an overall improvement of the performances of the individual modelling chains has been achieved for almost all models. Some question remains for some models and will be investigated for the next report. The year 2012 was rather contrasted, in terms of temperatures, with regions concerned by rather cool temperatures all over the year and other with relatively high temperatures. Annex 1 provides some insights about the climate situation in 2012 from WMO analysis. Ozone concentration levels were still high, especially in Mediterranean countries and in the Balkans, where annual mean of daily ozone concentrations were above 100 µg/m3. A large part of South of France, Slovenia, Austria, Czech Republic, Croatia, Slovakia, and a part of Hungary were the most exposed countries in 2012 considering indicators relevant for human health. Investigating the AOT40 vegetation exposure indicator which refers to the early summer period, we found that the target value of the Air quality Directive is exceeded in many places in Europe. Exceedances are huge around the Mediterranean Sea, and especially in Spain and Italy, but also Central Europe (Slovakia, Czech Republic). Therefore, such results show that ground level ozone remains an actual issue in Europe. Annual PM10 concentrations ranged in 2012 between 5 µg/m3 (in Scandinavia) and 40 µg/m3 which the limit annual average value set by the air quality Directive. The limit value was exceeded in North part of France (including Paris), Benelux, Poland, Slovakia, Czech Republic, North of Italy, and the Balkans. PM10 high concentrations occurred during winter periods because of stable meteorological conditions combined with residential heating extra emissions (compared to the rest of the year). Concerning the number of days exceeding the 50 µg/m3 limit value (daily average), it is still too high. The cold spell that occurred in a large part of Europe in February 2012 contributed to increase the number of days when the daily limit value was exceeded. This indicator is partially reported in the MACC-II/EVA assessment reports for background levels. But the model resolution is not sufficient to catch exceedances at traffic or industrial stations that are representative of local air pollution sources. However the MACC-II/EVA assessment work gives an idea of the European areas


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which are the most sensitive to PM10 pollution. Benelux, Paris area, Poland, Slovakia, Czech Republic, the Pô Valley, and other local places in Western, Central and Eastern Europe are the most concerned. The 35 limit number of days exceeding the daily limit value is likely to be exceeded in all these regions. PM2.5 simulations reported areas where the target value of 20 µg/m3 (annual average) for 2020 is already exceeded. Locations concerned are hot spot areas being driven by high anthropogenic activity. However, it is reminded that the MACC-II/EVA mapping system is not fitted to deal with local hot spot situations as those that develop near busy roads or on industrial sites. Actually, the model resolution is about 20-25 km2, and is not sufficient to catch actual NO2 concentrations at traffic and industrial sites. The availability of a set of several model results allows a qualitative estimation of the model uncertainty which is linked to the variability of the model results. Southern Europe and Eastern Europe are the European sub-regions where the model responses are the most variable and uncertain, even with the data assimilation systems. This is not a surprising result; the difficulties for simulating the dynamic and chemical atmospheric processes that occur in these geographical areas are well-known from the scientific communities and are still the subject of research project. Moreover, lack of measurements in Eastern Europe (compared to other geographical areas, see in annex 2) can also result in poor performance when modelled indicators are compared to observations. But this must be interpreted with caution. However the global uncertainty of the modelling systems is reduced compared to the one reported in the previous assessment reports. Finally, feedback from the users and the experts from the Member States on this report is expected and highly welcome. It will be the best way for improving the overall system. Therefore all the numerical values related to the results discussed in this report are available on request to the MACC/EVA subproject leader : [email protected] .



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ANNEX 1: European and global climate in 2012

According to the World Meteorological organization (WMO) the year 2012, as the ten previous ones, and this year was one of the warmest one over the last decades in Europe, despite the cooling influence of a la Nina episode at the beginning of the year. Europe had been strongly affected by a cold spell in early February with extreme low temperatures in Central, western and Southern Europe. For instance, according to the WMO climate 2012 assessment report9, “ (...) in France, the exceptional cool conditions were the most severe since January 1987, while it was the most significant cold spell in 27 years for Switzerland. In Austria, temperatures were 10°C below average during the first half of February, contributing to its coolest February since 1986”. However, except during this period, most of European countries experienced winter temperatures slightly higher than the average: “(...) March brought summer-like temperatures to parts of Europe. Many countries had March temperatures that ranked among the top-three warmest: Norway, Switzerland, and the Netherlands, Austria, the United Kingdom, France and Germany.” During summer contrasted situations were observed among the European countries. Southern and South-Eastern Europe had rather warm temperatures while Western and and Northern countries recorded cooler-than-average temperatures. According to WMO report, Denmark, Sweden and the United Kingdom had their coolest summer since 2000. It was the warmest one in Bosnia-Herzegovina, Bulgaria, Serbia, Hungary, and the third warmest one in Austria and the fourth warmest one in Spain. The WMO details : “ France experienced a short, yet significant, heat wave during the second half of August. The event was noteworthy for its lateness and for breaking records of maximum temperatures that were set in the August 2003 major heat wave. The Czech Republic also experienced heat waves during most of the summer.” Regarding exceptional dry conditions that occurred in 2012, the WMO report mentioned that several European countries reported their driest month in several years : Portugal (February), France (February), United Kingdom (March), Czech republic (March), Spain (most of the year). This situation was responsible for important forest fires, especially in the Southern part of Europe.

9 From WMO statement on the Status of the Climate in 2012, WMO report N°1108, http://www.wmo.int/pages/prog/wcp/wcdmp/documents/WMO_1108.pdf



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Figure 30. European temperature anomalies relative to 1961-1990 for March 2012.

Source : WMO regional Association VI(Europe), regional Climate centre on climate monitoring, Deutscher Wetterdienst, Germany


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ANNEX 2: methodologies and assumptions

Models: The models that provided raw simulations and data assimilated fields of air pollutant concentrations for the year 2012 are the ones involved in the MACC-II regional cluster dedicated to the provision of air quality information (near-real time and in delayed mode) at the European scale. Seven models running operationally on their own modelling platform perform re-analyses since the end of the MACC project (October 2011) for establishing yearly assessment reports. These models are described in the QA/QC dossiers available and regularly updated on the MACC website (http://www.gmes-atmosphere.eu/documents/deliverables/r-ens/). It is important to note that differences can occur between the modelling chain run routinely for the provision of daily air quality forecasts and near real time analyses, and the modelling chain used for re-analyses calculations. The later requires larger computational resources (computational time and storage space) to deal with a whole year on an hourly basis. Some teams did not achieve the development of their data assimilation system and in this cases reported only raw simulation values. The table below gives an overview of each data assimilation system developed by the regional air quality modelling partners in MACC, and its status at the time when the 2011 runs have been performed. This synthesis can facilitate the interpretation of some results reports in this report and in the validation report. Model DA process Pollutants

concerned Data sources Operational production

CHIMERE Optimal interpolation : kriging observation data with CHIMERE as external drift Ensemble Kalman filter

O3, PM10 O3

AIRBASE AIRBASE Under evaluation : O3 partial tropospheric columns (IASI)

Yes Yes

EMEP 3D-VAR NO2 O3, PM

OMI NO2 tropospheric column AIRBASE

Yes

EURAD Intermittent 3DVAR O3, NO2, NO, CO, SO2, PM10

AIRBASE in situ measurements MOSAIC air borne in situ measurements NO2 tropospheric column

Yes

http://www.gmes-atmosphere.eu/documents/deliverables/r-ens/).%20It


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retrievals from OMI, GOME-2, SCIAMACHY MOPITT CO profiles

LOTOS-EUROS

Ensemble Kalman filter

O3 NO2, PM10, AOD, SO2, SO4

AIRBASE OMI : observation operator developed and used for the 2009 report

Yes, However further evaluation is needed

MATCH 3D-VAR with transform into spectral space

O3, NO2 PM10, PM2.5

AIRBASE Yes, However further evaluation is needed

MOCAGE 3D-VAR O3 NO2

AIRBASE Yes

SILAM 3D-VAR 4D-VAR

O3, NO2, SO2

AIRBASE Yes

Table 1: Synthesis of the current data assimilation capacities developed in the regional air quality models involved in MACC. They must be operational by the end of the MACC project

Observation datasets: The AIRBASE-v8 database gathering all observations reported from the regulatory air quality monitoring networks is available for the year 2012 and had been used for both re-analysis and evaluation processes (http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8 ) . However same data cannot be used for both (it would not make sense to evaluate re-analyses against the same observation dataset that was used for data assimilation in the models). So the set of observations available in AIRBASE had been split in two subsets, one for DA and the other for validation (about one third of the total number of stations). Randomness and homogeneous spatial coverage with respect with the station typology (rural, suburban, and urban) were the principles considered for the station classification. The table below presents the number of stations selected in each category and their location is mapped on the following figures. Rural/

DA Rural/ Validation

Suburban/ DA

Suburban/ Validation

Urban/ DA

Urban/ Validation

O3 404 106 343 80 561 121 Total O3: 1615

NO2 309 66 325 77 648 178 Total NO2: 1 603

PM10 146 44 163 44 469 111 Total PM10: 977

PM2.5 62 15 53 13 172 53 Total PM2.5: 368

Number of stations selected in the AIRBASE database for 2012 for DA and validation




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Figure 31. Location of the NO2 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes


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Figure 32. Location of the O3 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes


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Figure 33. Location of the PM10 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes


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Figure 34. Location of the PM2.5 AIRBASE stations selected for data assimilation (red dots) and validation (green dots) processes

Although there is much more PM2.5 observation data reported in AIRBASE than in the previous year, the number of PM2.5 stations is still reduced in Europe. It will increase significantly in the coming year with the implementation of the air quality Directive. However PM2.5 data from AIRBASE has been checked and processed for further used in the MACC/EVA systems. The figures below show the station locations according to their typology: rural, suburban, urban. In some countries, no rural stations are reported: this is not mandatory according to the Air Quality Directive that only requests data representative of human exposure to calculate the national average exposure indicator (AEI). Assumptions on input data: Emission data, meteorological re-analyses and boundary conditions emissions have been provided by the other MACC-II sub-project or by the MACC-II partners. Indeed, meteorological re-analyses were provided by ECMWF. The emission inventory used for running the models is the high resolution one provided for the year 2009 by the TNO within the “Emissions” MACC subproject. Finally boundary conditions come, for the gaseous compounds from the Global “reactive gases” sub-project (MOZART re-analyses).


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Annex 3 Methodologies and data for pollen

reanalyses Observational dataset The observations for birch pollen concentrations in air are collected with daily temporal resolution (bi-hourly for some stations, not used in the current calculations) by the European Aeroallergen Network EAN – the only existing pollen observation network in Europe (http://www.polleninfo.org). The total number of active stations in 2012 used in the study was 313, of which 249 were used in the data assimilation, 35 reserved for evaluation of the assimilation outcome and 29 excluded due to representativeness concerns (see the methodological and discussion sections). The temporal coverage was uneven but the overall coverage was acceptable: totally 42526 / 4140 daily records in the assimilation and evaluation subsets, respectively. Model The report is built on the basis of the European-scale calculations of birch emission and dispersion by SILAM model. Its reference birch source term has been taken as described in (Sofiev et al., 2012) and evaluated in a series of studies (Siljamo et al., 2012, 2008; Sofiev et al., 2012a; Veriankaitė et al., 2010) The simulations used the MACC standard domain with 0.15°×0.15° resolution and 8 vertical layers up to ~4.7km above the surface. Following (Sofiev et al., 2012), the model setup was the following: birch pollen grains were 22 µm in diameter, 800 kg m-3 density; uncertainties of the temperature sum threshold was assumed to be 10%; high relative humidity threshold suppressing the flowering was set to 80%; analogous threshold for rain rate was 0.5 mm hr-1; promoting wind speed saturation was taken to be 5 m s-1 with maximum impact factor to the pollen release equal to 1.5. Finally, the injection of pollen was taken to be homogeneous from 2m to 50m. Initial total seasonal emission from pure birch forest was set to 109 pollen m-2. Data assimilation The data assimilation of the observed pollen concentrations in its standard interpretation is not feasible for pollen because the bulk of the monitoring data are daily, whereas the diurnal variation exceeds an order of magnitude. With a typical life time of pollen particles of a few hours (~22 µm in diameter, 800 kg m-3 density leads to sedimentation velocity about 1.2 cm sec-1 (Sofiev et al., 2006)), daily observations provide very limited constraint for model predictions. Certain simplification comes from existence of strong external drivers of pollen emission: temperature, ambient humidity, and precipitation rate, which are all accounted for in the SILAM birch source term. Their utilization leads to comparatively high correlation between the daily-mean model predictions and observations over the regions with major birch forests: it exceeds 0.5 for about 50% of the sites in these areas. Thus, the birch forest mapping and total seasonal release become the biggest contributors to the overall uncertainty of the re-analysis. Additionally, the regional specific of the trees flowering in particular year has to be taken into account.

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Therefore, the primary goal of the utilization of the observational data in the current study was to estimate the total seasonal amount of pollen released in different parts of Europe. This task, being quite different from the standard data-assimilation objectives, required a specific model setup. The procedure was constructed in two steps:

- The model was run through the whole season from 1.3.2012 till 30.6.2012 and pollen emission and concentration fields were stored with hourly temporal resolution. The daily concentrations were compared with the observations and emission was scaled with the ratio of the seasonal total counts, grand-total from all stations. This step was applied to crudely account for the 2012 season severity over the whole Europe.

- The modified 4D-VAR was applied to obtain a space-resolving emission correction factor as described in [Vira and Sofiev, 2012]. Since the primary target was the seasonal total pollen release, a very long assimilation window was used: from 20.3.2012 till 20.6.2012, i.e. the whole period of flowering in Europe. With that long window, the assimilation does not affect the season development, correcting only the total emitted amount. This step accounted for the regional specifics of the flowering and transport patterns in 2012.

The whole observational dataset has been split to three parts. Firstly, 29 stations in mountain regions or immediately at the coastline have been excluded as non-representative at the current model resolution (see also discussion). The remaining set of stations was randomly split into ~85% of assimilated stations (249 sites) and ~15% of evaluation stations (35 sites). References Siljamo, P., Sofiev, M., Filatova, E., Grewling, L., Jäger, S., Khoreva, E., Linkosalo, T., Ortega Jimenez, S., Ranta, H., Rantio-Lehtimäki, A., Svetlov, A., Veriankaite, L., Yakovleva, E., Kukkonen, J., 2012. A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis. Int. J. Biometeorol. e-pub. doi:10.1007/s00484-012-0539-5

Siljamo, P., Sofiev, M., Severova, E., Ranta, H., Kukkonen, J., Polevova, S., Kubin, E., Minin, A., 2008. Sources, impact and exchange of early-spring birch pollen in the Moscow region and Finland. Aerobiologia (Bologna). 24, 211–230. doi:10.1007/s10453-008-9100-8

Sofiev, M., Siljamo, P., Ranta, H., Linkosalo, T., 2012. A numerical model of birch pollen emission and dispersion in the atmosphere . Description of the emission module. Int. J. Biometeorol. doi:10.1007/s00484-012-0532-z

Sofiev, M., Siljamo, P., Ranta, H., Linkosalo, T., Jaeger, S., Jaeger, C., Rassmussen, A., Severova, E., Oksanen, A., Karppinen, A., Kukkonen, J., 2012. From Russia to Iceland: an evaluation of a large-scale pollen and chemical air pollution episode during April and May, 2006, in: Clot, B., Comtois, P., Escamilla-Garcia, B. (Eds.), Aerobiological Monographs. Towards a Comprehensive Vision. pp. 95–113.

Sofiev, M., Siljamo, P., Ranta, H., Rantio-Lehtimaki, A., 2006. Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study. Int. J. Biometeorol. 50, 392–402. doi:10.1007/s00484-006-0027-x

Veriankaitė, L., Siljamo, P., Sofiev, M., Sauliene, I., Kukkonen, J., 2010. Modelling analysis of source regions of long-range transported birch pollen that influences allergenic seasons in Lithuania. Aerobiologia (Bologna). 26, 47–62. doi:10.1007/s10453-009-9142-6

Vira, J., Sofiev, M., 2011. On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations. Atmos. Environ. 46, 318–328. doi:10.1016/j.atmosenv.2011.09.066

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