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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
2
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
8
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
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