Supplemental Material

Aspects of uncertainty in total reactive nitrogen deposition estimates for North American critical load applications

John Walker1*, Michael D. Bell2, Donna Schwede1, Amanda Cole3, Greg Beachley4, Gary Lear4**, Zhiyong Wu1

1U.S. EPA, Office of Research and Development, Durham, NC2National Park Service, Air Resources Division, Lakewood, CO3Environment and Climate Change Canada, Air Quality Research Division, Toronto, ON4U.S. EPA, Office of Air Programs, Washington, DC

*Corresponding author walker.johnt@epa.gov**Retired

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1 Measurements and modeling platforms used in North American Nr deposition budgets

1.1 Measurement networks

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Figure S1. Maps of wet deposition and other air monitoring (atmospheric concentrations) sites

in Canada and the conterminous U.S. and Alaska.

NADP/NTN, NADP/AIRMoN, CASTNET, and CAPMoN are the primary networks supporting Nr

deposition assessments in North America. Other air monitoring networks measuring atmospheric Nr

include the NADP Ammonia Monitoring Network (AMoN, http://nadp.slh.wisc.edu/amon/), the

Interagency Monitoring of Protected Visual Environments (IMPROVE,

http://vista.cira.colostate.edu/Improve/), the Canadian National Air Pollution Surveillance Program

(NAPS; https://www.canada.ca/en/environment-climate-change/services/air-pollution/monitoring-

networks-data/national-air-pollution-program.html) and several other networks that collectively feed data

into the EPA Air Quality System (AQS, https://www.epa.gov/aqs; https://www.epa.gov/amtic/amtic-

ambient-air-monitoring-networks), briefly summarized here. The NCore Multipollutant Monitoring

Network measures NO and NOy in urban and rural locations. The Near-road NO2 Monitoring

program measures NO2 at urban locations. The Photochemical Assessment Monitoring Stations

(PAMS) measure HNO3, NO2 and NOx in urban and suburban locations. The Chemical

Speciation Network (CSN) measures NH4+ and total NO3

- in PM2.5 in urban and suburban

locations.

1.2 Modeling platforms

1.2.1 Chemical transport models

CTMs require input for meteorology, land use and vegetation characterization, and emissions.

Meteorological inputs are provided by a meteorological model such as the Weather Research and

Forecasting model (WRF) or the Global Environmental Multiscale model (GEM), which may be

run as a standalone program or coupled with the CTM as in the case of WRF-CMAQ and GEM-

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MACH. Meteorological models sometimes use data assimilation to improve model performance

but still often have errors in the predictions of parameters such as temperature, surface wetness,

and timing and location of precipitation which are important to correctly predict deposition

(Gilliam et al., 2015; Ran et al., 2016).

Both the meteorological models and CTMs require information on the type of land use within a

model grid cell and vegetation characteristics including the canopy height, surface roughness,

minimum stomatal resistance, and characteristic leaf dimension. Models vary in how this sub-

grid information is used, with some using only a dominant land use category (e.g., WRF-CHEM)

or averaging surface characteristics for land use types within the grid (e.g., Pleim-Xiu land

surface model used in CMAQ). Others use the sub-grid information to calculate deposition

values which are subsequently area-weighted to calculate the grid value (e.g., GEM-MACH,

CAMx). Most models, including CMAQ, GEM-MACH, CAMx, and GEOS-CHEM use land use

specific values for parameters such as canopy height and minimum stomatal resistance when, in

fact, these parameters are species specific. For example, canopy height for a coniferous forest in

North Carolina (average canopy height ~30 m) would be the same in some models as the

Redwoods in California (average canopy height ~70 m). Minimum stomatal resistance is species

dependent and using a land use category average value of 200 s m-1, for example, would give a

different deposition value than using the value of 100 s m-1 typical of maples and oaks versus a

value of 300 s m-1 typical of white birch (Meyers et al., 1998). Data sets can be developed that

provide this level of detail, but they are not currently used by most models. Additionally, there is

lack of agreement between models on what the land use specific values should be, which adds to

the variability of predicted deposition.

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Models require information on anthropogenic and biogenic emissions, which also contain

uncertainty. Anthropogenic emissions are derived from emissions inventories in the U.S.,

Canada, and Mexico and source specific models such as the EPA’s Motor Vehicle Emission

Simulator (MOVES), which provides mobile source emissions. In the U.S., NH3 emissions from

confined animal feeding operations (CAFOs) are estimated by applying animal and management

specific emission factors to county-level animal population data to produce county-level

emissions (National Emissions Inventory, U.S. EPA, 2014; McQuilling et al., 2015; Pinder et al.,

2004) at daily resolution. The Sparse Matrix Operator Kernel Emissions (SMOKE) model

(https://www.cmascenter.org/smoke/) processes county-level data to provide gridded hourly

emissions for use in CTMs. For NH3 emissions from soils, the Fertilizer Emission Scenario Tool

for CMAQ (FEST-C, https://www.cmascenter.org/fest-c/) simulates daily fertilizer application

using the Environmental Policy Integrated Climate (EPIC) model (Cooter et al., 2012). EPIC

simulates soil biogeochemistry to provide temporally and spatially resolved estimates of the soil

NH3 emission potential, which are used within the CMAQ bi-directional modeling framework

(Bash et al., 2013) to provide NH3 emissions from fertilized soils at the hourly timescale.

Biogenic emissions are modeled using primarily the Biogenic Emissions Inventory System

(BEIS) (Bash et al., 2016) or the Model of Emissions of Gases and Aerosols from Nature

(MEGAN) (Guenther et al., 2012). These models rely on land cover information and species or

plant type specific parameters. Biogenic emissions can vary depending on the model chosen.

Wildfire emissions are also important inputs to CTMs and there is still a great deal of uncertainty

in the characterization of these emissions.

The U.S. EPA provides emission inventories on a 3-year cycle (e.g. 2011, 2014, 2017) with

updates to inventories occurring occasionally. Thus, models from the same year can vary based

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on the timing of the inventory. Emissions from Canada and Mexico are updated less frequently.

Models from some major point sources are year specific, but other sources must be interpolated

for non-inventory years.

Another important aspect of model uncertainty is in the treatment of atmospheric chemistry. To

provide reasonable runtimes, CTMs often use chemical mechanisms that are condensations of

the Master Chemical Mechanism (http://mcm.leeds.ac.uk/MCM/). Two mechanisms commonly

used in North America are the Carbon Bond (Yarwood et al., 2010) and SAPRC (Carter, 2010).

In the condensation, species are lumped together and treated with the same chemical properties.

Sometimes the lumping is performed due to lack of knowledge of chemistry or existence of

certain species. For example, in early versions of the Carbon Bond and SAPRC mechanisms,

organic nitrates were treated very simply. Recent studies have provided expanded information

and the mechanisms have been updated to include more explicit treatment enabling consideration

of a range of properties such as chemical solubility and diffusivity (Luecken et al., 2019; Pye et

al., 2015). However, field and chamber studies continue to expand the knowledge of atmospheric

chemistry and the models will continue to require updating.

The parameterizations for wet and dry deposition contained in CTMs also contribute to the

overall uncertainty in deposition. To the extent possible, deposition parameterizations are based

on process level understanding. However, there are not sufficient experimental studies to enable

understanding of all processes over all surface and vegetation types. For example, much is

known about the stomatal exchange of many gases while much less is known about the exchange

with plant cuticles or the ground (e.g., non-stomatal pathways) or the impact of in-canopy

chemistry on net exchange with the atmosphere. Surface wetness can be an important non-

stomatal driver of air-surface exchange for Nr compounds such as PAN and NH3 (Turnipseed et

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al., 2012; Wentworth et al., 2016). Upward fluxes of Nr compounds, including NO2 and peroxy-

nitrates, observed above forests (Min et al, 2014; Farmer et al., 2008) illustrate the importance of

in-canopy chemistry in the regulation of net atmosphere-biosphere exchange.

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1.2.2 Measurement-model fusion

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Figure S2. Overview of TDep (Schwede and Lear, 2014) and ADAGIO MMF methodologies.

2 Characterizing uncertainty and bias in deposition estimates

2.1 Measurement to model comparisons

2.1.1 Precipitation amount and wet deposition

Knowledge of precipitation amount is key for calculating wet deposition from measured

precipitation chemistry and in CTM predictions of wet deposition. NADP annual deposition

maps combine spatially interpolated measurements of chemistry with precipitation amount. For

the TDep procedure, wet deposition grids are calculated by combining the NADP precipitation-

weighted concentrations with a modified version of the precipitation estimates obtained from the

Parameter-elevation Regressions on Independent Sloped Model (PRISM, Daly et al., 2008,

http://www.prism.oregonstate.edu/). Concentration grids are created using inverse distance

weighting (IDW) interpolation of NADP/NTN and AIRMoN data that meet completeness

criteria. PRISM 4-km precipitation grids are adjusted to the precipitation amounts measured at

NADP monitoring network sites. The adjustment is made proportionally using an IDW

weighting procedure over a 0 to 30 km gradient from the measurement location; the PRISM

estimate is used at a distance of 30 km and greater. Deposition is calculated using the IDW

interpolated concentration and adjusted PRISM precipitation grids.

Precipitation measurements made at NADP NTN locations provide point values that may not be

representative of nearby locations due to influences of orography or local processes such as

convection. Use of the PRISM data helps account for the effects of orography in developing

deposition maps. PRISM relies on a combination of measurements and topographic data to create

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a regression surface and evaluations have shown that the method produces a very close match

with measured values at sites not included in the regression (Daly et al., 2017). Fusion of the

NADP NTN data with the PRISM model values results in a surface that reproduces the values at

the NADP NTN sites and appropriate spatial gradients between sites. However, spatial

variability coupled with sparse measurements can make interpolation in complex terrain more

uncertain (Latysh and Wetherbee, 2012).

In CTMs, the precipitation rate and amount are calculated by a meteorological model such as

WRF. Evaluations of WRF predictions against observed data have highlighted the difficulty in

predicting convective rainfall events (Otte et al., 2012; Bullock et al., 2014) while recent updates

to convective parameterizations (e.g. Alapaty et al., 2012; Herwehe et al., 2014) have improved

model predictions. Additionally, a method for assimilating lightning data into the model for

retrospective runs (Heath et al., 2016) has shown promise for reducing uncertainties. Accurate

predictions of orographic precipitation are also challenging for meteorological models and the

accuracy of the predictions is impacted by model scale. Comparisons of WRF precipitation fields

and PRISM + NTN fields shows that meteorological models adequately capture the overall

spatial patterns of precipitation but may have errors in areas of complex terrain or extreme

weather events.

It is noted that PRISM is only available in the U.S. ADAGIO combines precipitation amounts

from the Canadian Precipitation Analysis (CaPA, Mahfouf et al., 2007), itself a fusion of forecast

precipitation from the Global Environmental Multiscale (GEM) meteorological model and

surface and radar observations, with modeled precipitation and concentrations of ions in

precipitation from GEM-MACH. CaPA reduces some of the bias in the GEM model but does not

fully resolve the issue of orographic precipitation (Lespinas et al., 2015). However, given the

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sparser precipitation network in much of Canada, a model-based estimate of wet deposition is

used.

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Figure S3. Comparison of CMAQ V5.2.1 versus CASTNET (HNO3, NO3-, NH4

+) and AMoN

(NH3) weekly air concentrations across the continental U.S. during 2015. RMSE = root mean

square error, RMSEu = unsystematic root mean square error, NMdnB = normalized median bias

(%), MdnB = median bias.

2.1.2 Throughfall deposition

While measurement networks such as NTN offer the best opportunity for comparisons across

large spatial scales, comparisons of deposition over smaller domains and in specific ecosystems

using non-network measurements can be helpful for understanding patterns of CTM sub-grid

variability and impacts of downscaling gridded model estimates. Examples are throughfall,

which is a measurement of the amount of deposition reaching the ground below a vegetation

canopy, and bulk deposition (Draaijers et al., 1996). Throughfall reflects the combined processes

of wet and dry deposition to the canopy, as well as uptake, transformations, and leaching within

the canopy. Due to canopy interactions, throughfall is not equivalent to the total N deposition to

the top of the vegetation canopy. Bulk collectors are placed in open areas and capture a mix of

wet and some dry deposition. Bulk and throughfall collectors may be deployed for short periods

to correlate with the wet/dry or snow/rain precipitation or for much longer periods of time to

integrate deposition at a single collector over seasonal to annual scales (Fenn and Poth, 2004;

Fenn et al., 2018). Throughfall deposition can be highly spatially variable and the number and

location of samplers within a study area is therefore important (Robson et al., 1994). Due to

these differences in methodology, as well as the processes reflected by the measurements versus

the model, throughfall samplers have rarely been compared with CTMs. Where they have been

evaluated, TDep estimated deposition varied between -2% to 550% of throughfall measurements

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in forests in the western U.S. (Schwede and Lear, 2014), and the wet Nr deposition bias between

model and observations in the Pacific Northwest approached or exceeded regional diatom and

lichen critical load values at several NADP-NTN monitoring sites (Williams et al., 2017). These

studies highlight the utility of wider evaluation of throughfall across a range of ecosystems to

better understand how throughfall processes can be appropriately compared to deposition from

CTMs.

2.2 Model to model comparisons

2.2.1 Measurement-model fusion

In comparing gridded values and point estimates, the incommensurability of volume-average

concentrations and pointwise observations needs to be considered (Swall and Foley, 2009). For

some chemicals, there can be a high degree of spatial variability within a model grid cell that is

not captured by the model but may be captured by the point value. If multiple observations

across a grid were averaged and then compared to the model value, a very different value for

model bias might be determined. This issue is particularly important when incorporating

measurement data that may be near emission sources, such as at the urban and residential sites in

the AQS and NAPS networks that are included in ADAGIO.

Temporal inconsistency between data sets is another source of uncertainty in MMF procedures.

Modeled values are typically available on an hourly basis while network data may integrate over

longer time periods. Important cross-correlations can exist between concentration values and

deposition velocities. If sampling periods (e.g. 3-day, weekly, biweekly) differ from modeled

deposition velocities, this correlation needs to be considered to avoid biases in deposition

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estimates (Matt and Meyers, 1993). While meteorology is usually available in near real time, the

release of emission inventories often lags several years which delays model runs. For TDep, the

most recent CASTNET and NADP measurement data for 2013 to 2017 are fused with CMAQ

model estimates for the 2012 V5.0.2 time-series, creating a divergence between model and

observation years. As this divergence grows, the uncertainty increases. Error evaluations

performed by Lear (2017) compared 2005 CMAQ runs with CMAQ runs from “lag” years 2006

through 2010 to assess the stand-alone error caused by the model-measurement year divergence.

Results suggest that additional error due to the discordant CMAQ model years is relatively low

(2.1 to 3.6% median absolute relative percent difference; MARPD) for dry deposition and less

(1.3 to 2.3% MARPD) for total deposition of Nr compounds, increasing slightly with each

discordant year.

2.3 Spatial representativeness

Figure S4 shows an example of land-use specific dry deposition fluxes for HNO3 and NH3 during

July 2014 derived from the CMAQ v5.3 model run with the Surface Tiled Aerosol and Gaseous

Exchange (STAGE, Bash et al., 2018) deposition module compared with grid cell average

fluxes. The grid cell average fluxes are calculated by summing the product of the land use

specific flux and the fraction of the grid covered by that land use type. For HNO3, the largest

differences between the deposition values for the deciduous forest and the grid-cell average

occur where the grid contains only a small fraction of deciduous forest, consistent with the

results of Schwede et al. (2018). For NH3, the spatial variation is complicated by the

bidirectional flux of ammonia. Taking crops as an example in Figure S4, the higher nitrogen

status of vegetation and soil relative to other land-use types results in a higher NH3 compensation

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point and net emission to the atmosphere, a feature that is less apparent for the grid-cell average

NH3 flux. In CMAQ v5.0.2, emissions from CAFOs are treated as point sources. In eastern North

Carolina, high atmospheric NH3 concentrations driven by emissions from CAFOs result in

modeled net deposition (i.e., atmospheric NH3 > surface compensation point) to all surfaces

within the grid cell.

Figure S4. Land use specific fluxes and grid average fluxes during July, 2014 From CMAQ v5.3

with the STAGE deposition module. Negative values of NH3 flux indicate emission.

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3 Using WDUM in assessment of CL exceedances

Table S1. Examples of total deposition (TDep, Total N 2013-2015, kg N ha-1 yr-1), WDUM, and

percent contribution of deposition components to WDUM (% of WDUM) for select federal lands

where ecosystems are at risk of critical load exceedance. % of WDUM > 20 are in bold.

Bitterroot National Forest

Columbia River Gorge National

Scenic Area

Santa Monica Mountains National

Recreation AreaJoshua Tree

National ParkMean Max Mean Max Mean Max Mean Max

TDep 2.8 4.4 3.6 5.6 11.9 18.5 4.2 5.5WDUM 2.9 3.4 2.9 3.3 3.2 3.4 2.7 2.9

% of WDUM % of WDUM % of WDUM % of WDUMHNO3_dry 10.6 15.9 8.2 11.7 12.1 15.0 31.2 35.1NH3_dry 41.0 59.5 20.6 35.2 28.6 35.7 24.4 31.0NH4_dry 3.3 5.4 2.9 4.4 8.1 12.6 5.5 7.4NH4_wet 5.3 9.9 7.8 15.1 1.1 1.7 1.8 2.9NO3_dry 6.0 10.5 3.3 5.5 15.6 25.8 16.7 20.6NO3_wet 3.1 5.6 3.7 7.4 0.9 1.3 0.9 1.5NOM_dry 16.9 20.4 34.5 50.6 30.3 41.8 14.9 21.0NOM_wet 14.0 25.8 19.2 37.5 3.3 4.9 4.5 7.3

4 References

Alapaty, K., Herwehe, J.A., Otte, T.L., Nolte, C.G., Bullock, O.R., Mallard, M.S., Kain, J.S.,

Dudhia, J. 2012. Introducing subgrid scale cloud feedbacks to radiation for regional

meteorological and climate modeling. Geophysical Research Letters, 39, L24809.

Bash, J.O., Baker, K.R., Beaver, M.R. 2016. Evaluation of improved land use and canopy

representation in BEIS v3.61 with biogenic VOC measurements in California.

Geoscientific Model Development, 9, 2191-2207.

16

Bash, J.O., Schwede, D., Campbell, P., Spero, T., Appel, W., Pinder, R. 2018. Introducing the

Surface Tiled Aerosol and Gaseous Exchange (STAGE) dry deposition option for CMAQ

v5.3. 2018 Community Modeling and Analysis System (CMAS) Conference, October 22

– 24, 2018, Chapel Hill, NC.

Bullock, O. R., Jr., Alapaty, K., Herwehe, J.S., Mallard, M.S., Otte, T.L., Gilliam, R.C., Nolte,

C.G. 2014. An observation-based investigation of nudging in WRF for downscaling

surface climate information to 12-km grid spacing. Journal of Applied Meteorology and

Climatology, 53, 20–33.

Carter, W.P.L., 2010. Development of the SAPRC-07 chemical mechanism. Atmospheric

Environment, 44, 5324-5335.

Cooter, E. J., Bash, J.O., Benson, V., Ran, L., 2012. Linking agricultural crop management and

air quality models for regional to national-scale nitrogen assessments. Biogeosciences, 9,

4023–4035.

Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J.,

Pasteris, P.A. 2008. Physiographically-sensitive mapping of temperature and

precipitation across the conterminous United States. International Journal of Climatology,

28: 2031-2064.

Daly, C., Slater, M.E., Roberti, J.A., Laseter, S.H., Swift, L.W. Jr. 2017. High-resolution

precipitation mapping in a mountainous watershed: ground truth for evaluation

uncertainty in a national precipitation dataset. Internation Journal of Climatology, 37, 124

– 137.

17

Draaijers, G.P.J., Erisman, J.W., Spranger, T., Wyers, G.P. 1996. The application of throughfall

measurements for atmospheric deposition monitoring. Atmospheric Environment, 30,

3349-3361.

Farmer, D.K., Cohen, R.C. 2008. Observations of HNO3, PP AN, PN and NO2 fluxes: evidence

for rapid HOx chemistry within a pine forest canopy. Atmospheric Chemistry and

Physics, 8, 3899–3917.

Fenn, M.E., Poth, M.A. 2004. Monitoring nitrogen deposition in throughfall using ion exchange

resin columns: A field test in the San Bernardino Mountains. Journal of Environmental

Quality, 33, 2007-2014.

Fenn, M.E., Bytnerowicz, A. and Schilling, S.L. 2018. Passive monitoring techniques for

evaluating atmospheric ozone and nitrogen exposure and deposition to California

ecosystems. Gen. Tech. Rep. PSW-GTR-257. Albany, CA: U.S. Department of

Agriculture, Forest Service, Pacific Southwest Research Station. 129 p.

Gilliam, R. C., Hogrefe, C., Godowitch, J.M., Napelenok, S., Mathur, R., Rao, S.T. 2015. Impact

of inherent meteorology uncertainty on air quality model predictions. Journal of

Geophysical Research - Atmospheres, 120, 12,259–12,280.

Guenther, A.B., Jiang, X., Heald, C.L., Sakulyanontvittaya, T., Duhl, T., Emmons, L.K., Wang,

X., 2012. The Model of Emissions of Gases and Aerosols from Nature version 2.1

(MEGAN2.1): an extended and updated framework for modeling biogenic emissions.

Geoscientific Model Development, 5, 1471-1492.

Heath, N.K., Pleim, J.E., Gilliam, R.C., Kang, D. 2016. A simple lightning assimilation

technique for improving retrospective WRF simulations. Journal of Advances in

Modeling Earth Systems, 8, 1806–1824.

18

Herwehe, J.A., Alapaty, K., Spero, T.L., Nolte, C.G. 2014. Increasing the credibility of regional

climate simulations by introducing subgrid-scale cloud-radiation interactions. Journal of

Geophysical Research - Atmospheres, 119, 5317–5330.

Latysh, N.E., Wetherbee, G.A. 2012. Improved mapping of National Atmospheric Deposition

Program wet-deposition in complex terrain using PRISM-gridded data sets.

Environmental Monitoring and Assessment, 184, 913-928.

Lear, G.G. 2017, April. Quantifying a few uncertainties in TDep. Presented for TDep Maps

Update at Spring 2017 NADP meeting, Louisville, KY.

Luecken, D.J., Yarwood, G., Hutzell, W.H., 2019. Multipollutant of ozone, reactive nitrogen and

HAPs across the continental US with CMAQ-CB6. Atmospheric Environment 201, 62-

72.

Lee, H.-M., Paulot, F., Henze, D. K., Travis, K., Jacob, D. J., Pardo, L. H., Schichtel, B. A. 2016

Sources of nitrogen deposition in Federal Class I areas in the US, Atmospheric Chemistry

and Physics, 16, 525-540.

Lespinas, F., Fortin, V., Roy, G., Rasmussen, P., and Stadnyk, T., 2015. Performance Evaluation

of the Canadian Precipitation Analysis (CaPA). Journal of Hydrometeorology, 16, 2045-

2064.

Mahfouf, J.-F., Brasnett, B. and Gagnon, S. 2007. A Canadian PrecipitationAnalysis (CaPA)

project: Description and preliminary results. Atmosphere–Ocean, 45, 1–17

Matt D.R., Meyers, T.P., 1993. On the use of the inferential technique to estimate dry deposition

of SO2. Atmospheric Environment, 27A, 493–501.

19

McQuilling, A.M., Adams, P.J. 2015. Semi-empirical process-based models for ammonia

emissions from beef, swine, and poultry operations in the United States. Atmospheric

Environment, 120, 127–136.

Meyers, T.P., Finkelstein, P., Clarke, J., Ellestad, T.G., Sim, P.F. 1998. A multilayer model for

inferring dry deposition using standard meteorological measurements. Journal of

Geophysical Research – Atmospheres, 103, 22645-22661.

Min, K.-E., Pusede, S.E., Browne, E.C., LaFranchi, B.W., Cohen, R.C., 2014. Eddy covariance

fluxes and vertical concentration gradient measurements of NO and NO2 over a

ponderosa pine ecosystem: observational evidence for within-canopy chemical removal

of NOx. Atmospheric Chemistry and Physics, 14, 5495-5512.

Otte, T.L., Nolte, C.J., Otte, M.J., Bowden, J.H. 2012. Does nudging squelch the extremes in

regional climate modeling? Journal of Climatology,25, 7046–7066.

Pinder, R., Strader, R., Davidson, C., Adams, P. 2004. A temporally and spatially resolved

ammonia emission inventory for dairy cows in the United States. Atmospheric

Environment, 38.23, 3747–3756.

Pye, H.O.T., Luecken, D.J., Xu, L., Boyd, C.M., Ng, N.L., Baker, K.R., Ayres, B.R., Bash, J.O.,

Baumann, K., Carter, W.P.L., Edgerton, E., Fry, J.L., Hutzell, W.T., Schwede, D.B.,

Shepson, P.B. 2015. Modeling the current and future roles of particulate organic nitrates

in the southeastern United States. Environmental Science & Technology, 49, 14195-

14203

Ran, L., Pleim, J., Gilliam, R., Binkowski, F.S., Hogrefe, C., Band, L. 2016. Improved

meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation

and albedo. Journal of Geophysical Research - Atmospheres, 121, 2393–2415.

20

Robson, A.J., Neal., C., Ryland, G.P., Harrow, M. 1994. Spatial variations in throughfall

chemistry at the small plot scale. Journal of Hydrology, 158, 107-122.

Schwede, D.B., Lear, G.G. 2014. A novel hybrid approach for estimating total deposition in the

United States. Atmospheric Environment, 92, 207-220.

Schwede, D., Simpson, D., Tan, J., Fu, J., Dentener, F., Du, E., deVries, W. 2018. Spatial

variation of modelled total, dry and wet nitrogen deposition to forest at global scale.

Environmental Pollution, 243, 1287-1301.

Swall, J.L., Foley, K.M. 2009. The impact of spatial correlation and incommensurability on

model evaluation. Atmospheric Environment, 43, 1204-1217.

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. 2006. Eddy covariance fluxes of peroxyacetyl

nitrates (PANs) and NOy to a coniferous forest. Journal of Geophysical Research –

Atmospheres, 111, D09304.

Wentworth, G.R., Murphy, J.G., Benedict, K.B., Bangs, E. J., Collett, J.L. 2016. The role of dew

as a night-time reservoir and morning source for atmospheric ammonia. Atmospheric

Chemistry and Physics, 16, 7435-7449.

Williams, J.J., Chung, S.H., Johansen, A.M., Lamb, B.K., Vaughan, J.K., Beutel, M. 2017.

Evaluation of atmospheric nitrogen deposition model performance in the context of U.S.

critical load assessments. Atmospheric Environment, 150, 244-255.

Yarwood, G., Jung, J., Whitten, G.Z., Heo, G., Mellberg, J., Estes, M., 2010. Updates to the

Carbon Bond Mechanism for Version 6 (CB6). Presented at the 9th Annual CMAS

Conference, Chapel Hill, NC, October 11-13, p. 2. Available online:

http://www.cmascenter.org/conference/2010/abstracts/emery_updates_carbon_2010.pdf.

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