ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-12991-2018Source regions contributing to excess reactive nitrogen deposition in the
Greater Yellowstone Area (GYA) of the United StatesSource regions contributing to excess reactive nitrogen depositionZhangRuiThompsonTammy M.BarnaMichael G.HandJennifer L.https://orcid.org/0000-0002-4644-2459McMurrayJill A.BellMichael D.https://orcid.org/0000-0003-3248-3265MalmWilliam C.SchichtelBret A.bret_schichtel@nps.govCooperative Institute for Research in the Atmosphere, Colorado State
University, Fort Collins, CO 80523, USAAmerican Association for the Advancement of Science, Washington DC
20005, USANational Park Service, Air Resources Division, Lakewood, CO 80235,
USAUS Forest Service, Bozeman, MT 59771, USABret A. Schichtel (bret_schichtel@nps.gov)10September20181817129911301110April201827April20184August201816August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/12991/2018/acp-18-12991-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/12991/2018/acp-18-12991-2018.pdf
Research has shown that excess reactive nitrogen (Nr) deposition in
the Greater Yellowstone Area (GYA) of the United States has passed critical
load (CL) thresholds and is adversely affecting sensitive ecosystems in this
area. To better understand the sources causing excess Nr
deposition, the Comprehensive Air Quality Model with Extensions (CAMx), using
Western Air Quality Study (WAQS) emission and meteorology inputs, was used to
simulate Nr deposition in the GYA. CAMx's Particulate Source
Apportionment Technology (PSAT) was employed to estimate contributions from
agriculture (AG), oil and gas (OG), fire (Fire), and other (Other) source
sectors from 27 regions, including the model boundary conditions (BCs) to the
simulated Nr for 2011. The BCs were outside the conterminous United
States and thought to represent international anthropogenic and natural
contributions. Emissions from the AG and Other source sectors are
predominantly from reduced N and oxidized N compounds, respectively. The
model evaluation revealed a systematic underestimation in ammonia (NH3)
concentrations by 65 % and overestimation in nitric acid concentrations
by 108 %. The measured inorganic N wet deposition at National Trends
Network sites in the GYA was overestimated by 31 %–49 %, due at
least partially to an overestimation of precipitation. These uncertainties
appear to result in an overestimation of distant source regions including
California and BCs and an underestimation of closer agricultural source
regions including the Snake River valley. Due to these large uncertainties,
the relative contributions from the modeled sources and their general
patterns are the most reliable results. Source apportionment results showed
that the AG sector was the single largest contributor to the GYA total
Nr deposition, contributing 34 % on an annual basis. A total of
74 % of the AG contributions originated from the Idaho Snake River
valley, with Wyoming, California, and northern Utah contributing another
7 %, 5 %, and 4 %, respectively. Contributions from the OG sector
were small at about 1 % over the GYA, except in the southern Wind River
Mountain Range during winter where they accounted for more than 10 %,
with 46 % of these contributions coming from OG activities in Wyoming.
Wild and prescribed fires contributed 18 % of the total Nr
deposition, with fires within the GYA having the highest impact. The Other
source category was the largest winter contributor (44 %) with high
contributions from California, Wyoming, and northern Utah.
Introduction
The Greater Yellowstone Area (GYA) (see Fig. 1) of the United States, with
Yellowstone National Park (YNP) and Grand Teton National Park (GTNP) at its
core, is one of the largest remaining intact ecosystems in the northern
temperate zone and features diverse wildlife, alpine lakes, forests, and
geologic wonders (Keiter and Boyce, 1994; NPS, 2017). Increasing
concentrations of reactive nitrogen (Nr) compounds in air, rain,
and snowpack samples over the GYA have been reported in the past 30 years and
linked to Nr emissions from human activities (Clow et al., 2003;
Blett et al., 2011; IMPROVE, 2011; Sullivan et al., 2011; USGS, 2014; NADP,
2016; Nanus et al., 2017; also, see Fig. S1). The inorganic wet Nr
deposition rates measured at high-elevation National Trends Network (NTN)
sites within the GYA in 2010 were 2.5–3.5 kg N ha-1 yr-1,
compared with 1.5–2.5 kg N ha-1 yr-1 in 2000 (NADP, 2016).
This is relevant to the long-term conservation of the area because as
Nr deposition levels increase, they can cross critical load (CL)
thresholds, at which negative effects to sensitive ecosystem components can
occur (Porter et al., 2005; Pardo et al., 2011). Additional concerns posed by
enhanced Nr deposition include lake acidification, loss of lichen
biodiversity, and eutrophication (Baron, 2006; Blett et al., 2011; NADP,
2016). While ecosystem changes due to excess Nr deposition over
Class I areas including the GYA have been documented (e.g., Baron et al.,
2011; Saros et al., 2011; Sullivan et al., 2011; Spaulding et al., 2015;
Nanus et al., 2017), the origins, chemical composition, and spatial and
temporal changes in the deposition over this region are not as well
understood.
Total Nr is a mix of oxidized and reduced inorganic nitrogen (N)
and organic N compounds that are chemically and biologically active in the
Earth's biosphere and atmosphere and deposited through wet and dry processes.
These compounds arise from a variety of sources, with inorganic oxidized N
primarily emitted as nitrogen oxides (NOx) from fossil fuel
combustion, with approximately 25 % from power plants, 50 % from
automobiles, and 10 % from other mobile sources, based on annual
county-level estimations (EPA, 2015a). Atmospheric reactions of
NOx result in nitric acid (HNO3), particulate
nitrate (PNO3), and other compounds. Reduced N arises primarily
from ammonia (NH3) gas emissions from agricultural activities,
which can react with acidic aerosols to form ammonium (NH4+)
compounds (Galloway et al., 2004). Mobile sources are also an important
source of NH3 and can be the primary emitter in urban areas (Sun et
al., 2014, 2017). Emissions from this sector have large uncertainties and a
recent study suggests that on-road NH3 emissions in the 2011
National Emissions Inventory (NEI) were underestimated by a factor of 2.9
(Fenn et al., 2018). There are hundreds of organic N compounds, including
reduced (e.g., amines) and oxidized forms (e.g., alkyl nitrates). Sources of
organic N are less well known, but increasing evidence suggests that biomass
burning and agriculture (AG) are significant contributors, as are atmospheric
reactions of NOx with volatile organic compounds (Cape et
al., 2011; Reay et al., 2012). With the steady decline of
NOx emissions in the United States during past decades as a
result of the implementation of the Clean Air Act, the importance of reduced
N to the total N deposition budget has increased (Li et al., 2016). Specific
to the GYA, local anthropogenic emissions are small, but upwind sources,
including agricultural activities in the Snake River valley and northern
Utah, wildfires throughout the western United States, energy development in
the Upper Green River basin, and anthropogenic activities at urban centers
such as Salt Lake City, are larger and likely to be significant contributors
to regional N emissions (Prenni et al., 2014).
To better understand the levels and composition of the Nr compounds
deposited in the GYA and to help guide strategies to reduce N deposition, the
National Park Service (NPS) initiated the Grand Teton Reactive Nitrogen
Deposition Study (GrandTReNDS), which included spatially and temporally
detailed measurements of N compounds during April to September 2011 (Benedict
et al., 2013a; Prenni et al., 2014). It was found that during summer months
at the high-elevation sites (e.g., Grand Targhee; see Fig. 2), 62 % of
the N deposition was from reduced N and about equally split between dry and
wet deposition, and oxidized N only accounted for 27 % of the N
deposition budget, with the remaining N in the form of wet-deposited, organic
N. Study findings indicate a significant west-to-east gradient in atmospheric
NH3 concentrations, with higher concentrations west of the Teton
mountain range. Concurrently measured concentrations of HNO3,
PM2.5 (particulate matter with an aerodynamic diameter of less than
2.5 µm) nitrate, and NH4+ showed relatively small
west-to-east gradients inside GTNP (Benedict et al., 2013a; Prenni et al.,
2014).
The origins of Nr transported to the GYA and other remote locations
in the western United States have been examined in past modeling studies.
Back trajectory analyses have shown that air mass transport to GTNP is
predominantly from the west through the Snake River valley and from the
southwest through northern Utah (Prenni et al., 2014). Zhang et al. (2012)
applied the global chemical transport model (CTM) GEOS-Chem (Bey et al.,
2001) using zero-out sensitivity
simulations and found that in 2006 natural sources, including lightning and
wildfires, contributed more than 10 % of the total Nr
deposition over the Teton area. Lee et al. (2016) used the adjoint version of
GEOS-Chem to quantify the sources of Nr deposition in eight
selected federal Class I areas in 2010 and found a nonnegligible footprint
(>20 %) of Nr deposition in the western United States, including
GTNP and Rocky Mountain National Park (RMNP), attributed to long-range
transport from sources in California, especially during summertime. Mobile
NOx and livestock NH3 were also found to be major
sources of Nr deposition in GTNP. Similar modeling studies focusing
on RMNP also suggested the important contributions of distant sources
including those from California and other counties and the fact that the
contributions from sources of reduced Nr were larger than those from
sources of oxidized Nr (Thompson et al., 2015; Malm et al., 2016).
In this work, we add to the growing body of Nr modeling source
apportionment studies by conducting a detailed analysis using the Particulate
Source Apportionment Technology (PSAT) module within the CAMx (Comprehensive
Air Quality Model with extensions) (ENVIRON,
2014) CTM to quantify the seasonal contributions
from different source regions and source sectors to Nr throughout
the GYA. Compared with previous Nr deposition simulation studies in United
States, this work uses tagged reactive tracers to attribute the contributions
from four designated emission sectors and 27 designated emission regions to
Nr deposition in the GYA with a much higher horizontal grid
resolution (12 km) and an up-to-date emission inventory instead of using a
zero-out approach (e.g., Zhang et al., 2012) or an adjoint model (e.g., Lee
et al., 2016). The model simulation of Nr and its constituents were
first evaluated against routine measured data as well as the unique data
measured during the GrandTReNDS campaign period (Benedict et al., 2013a;
Prenni et al., 2014). Nr deposition from CAMx simulations was also
compared with total deposition maps (TDEPs), which were developed for
deposition trend analysis and ecological impact assessment (Schwede and Lear,
2014). The detailed source apportionment results are presented here, focusing
on seasonal variations and the relative importance to CL exceedance in
sensitive ecosystems within the GYA. The discussion of identified model bias
and uncertainties in the interpretation of source apportionment results, including
the model lateral boundary conditions, the impact of model precipitation to
wet deposition simulation, and the impact of ammonium dry deposition velocity
on dry deposition are also presented.
Source region partition for CAMx PSAT simulation in this study. The
27 tagged regions are (1) NW Colorado; (2) NE Colorado; (3) SE Colorado;
(4) SW Colorado; (5) Upper Green River, Wyoming; (6) Jackson, Wyoming;
(7) eastern Wyoming; (8) western Wyoming; (9) Yellowstone; (10) northern
Idaho; (11) Snake River valley, Idaho; (12) northern Utah; (13) southern
Utah; (14) Nevada; (15) Montana; (16) Washington; (17) Oregon;
(18) California; (19) Mexico; (20) New Mexico; (21) Arizona; (22) Texas &
Oklahoma; (23) Canada; (24) North Dakota; (25) the Pacific; (26) the far east US;
and (27) South Dakota, Kansas, and Nebraska.
Annual NH3 emission for the 12 km inner modeling domain at
focused tagged regions (see Table S2 and Fig. 1 for the details of the 27
source region partition) as well as locations of the monitoring sites at
different networks (a Ammonia Monitoring Network; b Clean
Air Status and Trends Network; c Grand Teton Reactive Nitrogen
Deposition Study; d Interagency Monitoring of Protected Visual
Environments; e National Trends Network) used in the model
performance evaluation of CAMx nitrogen species concentration and dry–wet
deposition in the GYA (the black boundary line). The numbers in the figure
are locations for the three sampling sites during GrandTReNDS and the eight
Class I areas within the area: (1) Driggs, (2) Grand Targhee, (3) NOAA
climate station, (4) Grand Teton National Park, (5) John D.
Rockfeller Jr. Memorial Parkway, (6) Yellowstone National Park, (7) Teton
Wilderness, (8) Washakie Wilderness, (9) North Absaroka Wilderness,
(10) Fitzpatrick Wilderness, and (11) Bridger Wilderness.
Modeling system for Nr source apportionment
Modeling simulations for 2011 were conducted using the CAMx version 6.10
(ENVIRON, 2014) with two nested grids. The outer
domain (36 km) covered the contiguous United States (CONUS), as well as
portions of Canada and Mexico, while the inner domain (12 km) encompassed
the western United States and focused on states within the Western Regional
Air Partnership (WRAP) (see Fig. 1).
The hourly meteorological inputs for 2011 were generated by the Weather
Research and Forecasting (WRF) model (WRF-ARW, version 3.5.1) (Skamarock et
al., 2008) and were obtained from the Intermountain West Data Warehouse
(IWDW) (http://views.cira.colostate.edu/tsdw/, last access: 30
August 2018). This meteorological simulation performed
in comparison to other recent prognostic model
applications used in air quality planning (UNC-Chapel Hill and ENVIRON,
2014a).
The emission inventory used by CAMx was primarily derived from the 2011 NEI
version 2 (NEI2011v2) (EPA, 2015b) with the Sparse Matrix Operator Kernel
Emissions (SMOKE) processing system version 3.0 (Houyoux et al., 2002) for
anthropogenic emissions, the Model of Emissions of Gases and Aerosols from
Nature (MEGAN) version 2.10 (Guenther et al., 2012) for biogenic emissions,
and the WRAP Windblown Dust Model (WRAP-WBD) to estimate wind-driven dust
emissions (UNC-Chapel Hill and ENVIRON, 2014b). Emissions from the oil and
gas (OG) sector were further updated by the IWDW to represent the
best-available inventory for OG activity in the western United States at the
time of modeling (UNC-Chapel Hill and ENVIRON, 2014b). The emissions for fire
activities (Fire) include agricultural fires, prescribed fires, and wildfires
and were generated by the Particulate Matter Deterministic and Empirical
Tagging and Assessment of Impacts on Levels (PMDETAIL) study (Moore et al.,
2012). PMDETAIL developed 2011 fire emissions using satellite data, ground
detection, and burn scar and estimated the plume rise, depending on fire size
and type. The hourly, nonsurface fire emissions were allocated to the proper
CAMx vertical layers based on the model-predicted planetary boundary layer
(PBL) height and the spanning of the plume top and bottom above the ground
(Mavko and Morris, 2013).
The boundary conditions for the 36 km domain were estimated from a 2011
global model run using the Model for Ozone and Related chemical Tracers
(MOZART) version 4.6 (Emmons et al., 2010). The simulation year of 2011 was
preceded by 15 days of spin-up time to minimize the effects of initial
conditions. A more-detailed description of the WRF–SMOKE–CAMx modeling
platform applied in this study is summarized in Table S1 in the Supplement as
well as the 2011 Three-State Air Quality Study (3SAQS) (UNC-Chapel Hill and
ENVIRON, 2014b).
For the source apportionment estimates, 27 source regions (Fig. 1), as well
as the lateral boundary conditions (BCs), were “tagged” in the CAMx PSAT
simulation. In addition, the emissions for each region were further
subdivided into four source sectors: (1) AG, (2) OG, (3) Fire, including wildfires and prescribed fires, and (4) the remaining sources labeled as Other. The Other source
sector primarily comes from mobile and large point sources, with smaller
contributions from natural sources such as lightning. Table S2 provides the
annual NH3 and NOx emissions used in this modeling
study with a breakdown by tagged source regions and source sectors. Figure 2
provides the annual emissions of NH3 in the inner 12 km domain as
well as the monitoring sites or receptor areas used for the model evaluation
and analysis. For NH3 emissions, the AG sector contributed 84.1 %
of the total emissions within the 12 km domain, while the OG, Fire, and
Other sectors contributed 0.1 %, 4.5 %, and 11.4 %, respectively
(Table S2). In the Snake River valley, the AG sector emissions dominate the
emission budget. For NOx emissions, the contribution
rankings from the four tagged emission sources are Other (83.8 %), OG
(12.8 %), Fire (3.2 %), and AG (0 %). The regions were selected
to highlight important source sector contributions to Nr deposition
in the GYA. For example, the state of Wyoming was partitioned into five
regions (YNP, Jackson, Upper Green River, eastern Wyoming, and western
Wyoming) to differentiate the possible source impacts from urban activity in
Jackson from energy development in southwestern Wyoming (Blett et al., 2011;
NPS, 2017). Significant agricultural operations in the Snake River valley in
Idaho, northern Utah, and northeastern Colorado were tagged due to their high
ammonia emissions (see Fig. 2) associated with fertilizer application and
confined animal feeding operations (Fenn et al., 2003; Clarisse et al., 2009;
Prenni et al., 2014). Lastly, wildfires are episodic events
(http://wrapfets.org/map.cfm, last access: 30 August 2018) that can have large intermittent contributions to Nr
deposition, but they can mask important contributions from other sources that
are significant in nonfire years.
CAMx model performance for nitrogen species concentrations as well
as nitrogen dry–wet depositions evaluated at sites in the AMoN, CASTNet,
IMPROVE, and NTN networks as well as the three sites during the GrandTReNDS
campaign over the GYA region (see Fig. 1 for site locations) in
2011.
1 AMoN samples are collected for 2
weeks. 2 CASTNet samples are collected for 1 week. 3 GrandTReNDS
samples are collected daily. 4 IMPROVE 24 h samples are collected every
3 days. 5 NHx=NH3+PNH4. 6 NTN samples are
collected for 1 week. 7 Average observation. 8 Average simulation.
9 Number of sites. 10 Number of samples; the values in the
parentheses are the percentage of valid samples used for model performance
evaluation. 11 Pearson's correlation coefficient. 12 Normalized
mean bias. 13 Normalized mean error. 14 Fractional bias.
15 fractional errors.
CAMx–PSAT treats nitrogen-containing compounds as one of seven species:
gaseous NH3; particulate ammonium (PNH4); reactive gaseous
nitrogen (RGN), which includes primary emissions of NOx,
nitrous acid (HONO), nitrate radical (NO3), and dinitrogen
pentoxide (N2O5); gaseous nitric acid (HNO3); gaseous
peroxy nitrogen (TPN), including peroxyacetyl nitrate (PAN) and peroxynitric
acid (PNA); gas-phase organic nitrate (NTR); and particulate nitrate
(PNO3). PSAT maintains the source-group identity (i.e., source
region and source sector) by apportioning the secondary species to the
precursor emissions (ENVIRON, 2014). In the source apportionment comparison
results, we report the reduced Nr deposition as the sum of
NH3 and PNH4 and the oxidized Nr deposition as
the sum of RGN, HNO3, PNO3, TPN, and NTR in units of
kg N ha-1.
Evaluation of CAMx-simulated Nr concentration and deposition
rates
Acceptable model performance of the regional air quality modeling system is a
prerequisite for a credible source apportionment interpretation (Boylan and
Russell, 2006; EPA, 2014; Emery et al., 2017). In this work, the CAMx
simulation was extensively evaluated against routine monitoring data as well
as data collected in the GrandTReNDS special field study (Benedict et al.,
2013a; Prenni et al., 2014) and against the nitrogen deposition estimates
from the National Atmospheric Deposition Program (NADP,
http://nadp.slh.wisc.edu/, last access: 30 August 2018) TDEP hybrid modeling results (Schwede and Lear, 2014). Performance
metrics recommended by the EPA's modeling guidance for ozone, PM2.5, and
regional haze attainment demonstrations (Yu et al., 2006; EPA, 2014) were
used (see Table 1).
The variables and routine monitoring networks used in the model evaluation
were NH3 concentrations from the Ammonia Monitoring Network (AMoN)
(http://nadp.sws.uiuc.edu/AMoN/, last access: 30 August 2018); nitric acid (HNO3), PNO3, and PNH4
concentrations as well as estimated dry deposition fluxes from the Clean Air
Status and Trends Network (CASTNet)
(https://www.epa.gov/castnet, last access: 30 August 2018); PNO3 and PNH4 concentrations from the Chemical
Speciation Network (CSN)
(https://www3.epa.gov/ttnamti1/speciepg.html, last access: 30 August 2018); PNO3 concentrations from the Interagency
Monitoring of Protected Visual Environments (IMPROVE) network; and
wet-deposited inorganic oxidized (NO3-) and reduced
(NH4+) nitrogen and associated precipitation rates from the NADP
NTN sites. Each network had a unique sampling frequency and duration
(Table 1). The hourly CAMx outputs were aggregated to match the timescales of
the measured data. All measurement data flagged as questionable, either due
to mal-operation or to insufficient samples to calculate representative
values, were excluded from the analysis. Table 1 reports the percentage of
valid measurements used for statistical analysis during evaluation time. For
most of the N species, the percentage of valid samples is more than 80 %.
In general, the CASTNet, IMPROVE, AMoN, and NADP networks sample in rural
areas, while the data from the CSN network primarily represent the air
quality in urban and suburban settings. Although organic N species were also
measured in the GrandTReNDS campaign, we focus on the inorganic N budget
comparison, given the large uncertainties for organic N prediction (Jickells
et al., 2013) and its incomplete treatment in the model's chemical mechanism.
For example, the modeling system does not account for primary emissions of
organic N compounds but does include the formation of organic N from the
alkylperoxy radical and secondary alkoxy radical (ENVIRON, 2014).
Evaluation against data in the GYA
The 3SAQS study performed photochemical grid modeling using the same modeling
platform and input files as this study (UNC-Chapel Hill and ENVIRON, 2014b)
and evaluated the model performance for the western United States. A subset
of these results is presented in the Supplement for reference. Model
performance statistics for the N species within the GYA area at AMoN,
CASTNet, IMPROVE, and NTN network sites (Fig. 1) at different periods in 2011
are presented in Table 1. The biases at the GYA sites are similar to those
throughout the west (Table S1) in that the CAMx simulation significantly
overestimated HNO3 with a normalized mean bias (NMB) of 108 %
and significantly underestimated NH3 concentrations with a NMB =-65 %. While the model had skill in reproducing the daily variation in
HNO3, with a correlation coefficient of r=0.71, it had little
skill for NH3, with r=0.2. The overestimation of HNO3 has
also been reported in other regional-scale modeling simulations over the
United States (e.g., Baker and Scheff, 2007; Foley et al., 2010; Thompson et
al., 2015) with the carbon bond mechanism used in this study. The possible
reason for the overestimation of HNO3 may be due to the uncertainty
for the N2O5 uptake coefficient setting for heterogeneous
reactions (Foley et al., 2010). The poor NH3 results may be related
to the high uncertainty in the NH3 emission inventory (Clarisse et
al., 2009) and important missing physical mechanisms in the model, including
the lack of bidirectional NH3 deposition (Zhang et al., 2010; Bash et
al., 2013; Zhu et al., 2015). The GYA area is located downwind of the major
agriculture sources in the Snake River valley and northern Utah (Table S2).
The incorporation of the bidirectional NH3 flux mechanism in the
model should increase ambient NH3 concentrations in the GYA and thus
decrease the large model underestimation of NH3 concentrations.
For PNO3 and PNH4 simulations in the GYA, CAMx overestimated
both species, with better performance for PNH4 than PNO3
(3 % vs. 37 %, respectively, in terms of NMB) and better agreement
for PNO3 at CASTNet sites vs. IMPROVE sites (37 % vs.
58 % for NMB, respectively). The errors and biases in the dry deposition
fluxes compared to CASTNet values follow the same patterns as in the ambient
concentrations, but it should be noted that CASTNet and CAMx use different
algorithms to estimate dry deposition velocities, and these model-to-model
discrepancies will manifest themselves in the performance evaluations.
Wet deposition measurements from the five NTN sites with sufficient data were
available from within the GYA (Fig. S2). Comparisons to CAMx showed that the
model captured the general trends in these data with r∼0.32–0.34 but
were somewhat biased, with a NMB = 31 % for NO3- and
NMB = 49 % for NH4+. The precipitation simulations were
consistently 100 %–200 % higher than the rain gauge measurements at
the NTN sites, showing that WRF overestimated the frequency and intensity of
precipitation events over the GYA in 2011 (Table 1). However, note that 2011
was a large snowpack year; by May, much of the GYA had 100 %–180 %
of the normal snow weather equivalent (USGS, 2014). Precipitation
measurements tend to be low during high-snow events.
Model performance for (a, b) seasonal average Nr
concentration, (c, d) seasonal accumulated Nr deposition
budget, and (e, f) seasonal accumulated precipitation amount
at collocated location sites (YNP and Pinedale) over the GYA in 2011.
1 Clean Air Status and Trends Network. 2 Comprehensive Air Quality
Model with extensions. 3 National Trends Network.
4 Parameter-elevation Regressions on Independent Slopes Model.
5 Weather Research and Forecasting model.
Inorganic nitrogen deposition budgets as an absolute (a) and
as a percentage (c) as well as precipitation (e), measured
at the three core sites during the GrandTReNDS study period (April to
September in 2011) with corresponding CAMx simulations (b, d, e).
1 Grand Teton Reactive Nitrogen Deposition Study. 2 Comprehensive
Air Quality Model with extensions. 3 Weather Research and Forecasting
model.
The seasonal, simulated ambient concentrations and deposition rates are
compared against measured CASTNet and NADP data at the YNP and Pinedale
monitoring sites in Fig. 3. Seasons refer to winter (December, January,
February, DJF), spring (MAM), summer (JJA), and fall (SON). The significant
overestimation of HNO3 is evident in all seasons. Also evident is the
poor simulation of the seasonality in Nr deposition, primarily due
to the poor reproduction of wet deposition, which is at least partly due to
the large errors in the simulated precipitation.
Table S3 provides a comparison of regional CTM performance evaluations
against measured N-containing species over the United States from
peer-reviewed studies in recent years (e.g., Simon et al., 2012; Bash et al.,
2013; Zhang et al., 2013; Yu et al., 2014; Thompson et al., 2015; Li et al.,
2017). The model performance results in this study are comparable to these
past studies, including the overestimation of HNO3 and underestimation
of NH3. Resolution of these biases requires additional research and
these biases need to be taken into account when interpreting the source
attribution of Nr deposition within the GYA.
Annual nitrogen deposition budgets as an absolute (a) and as a
percentage (b) as well as annual precipitation amounts (c)
from the NADP Total Deposition Map (TDEP) and corresponding CAMx
(Comprehensive Air Quality Model with extensions) and WRF (Weather Research
and Forecasting model) simulation results in 2011 at eight Class I areas
across the GYA (the receptor sites on the x axis are arranged from west to
east in the GYA; see Fig. 2). The reported CAMx dry and wet Nr
deposition values at the eight Class I areas are the average of the
simulation values at corresponding grid cells for each area.
Evaluation against GrandTReNDS data
The GrandTReNDS campaign provides a unique opportunity to evaluate the
capability of CAMx to simulate the Nr compounds and deposition
budget. Detailed measurements, including NH3, were made at three
sites that crossed GTNP from west to east: Driggs, in the foothills just west
of GTNP (43.74∘ N, -111.87∘ W, elevation 1947 m); Grand
Targhee, an upper-elevation site on the western edge of GTNP
(43.78∘ N, -110.94∘ W, elevation 2722 m); and the
National Oceanic and Atmospheric Administration (NOAA) climate station site
on the eastern edge of GTNP (43.66∘ N, -110.71∘ W,
elevation 1978 m) (also see Fig. 2). Figure 4 presents the monthly
deposition budgets for these three sites during the sampling periods, and
Table 1 provides the model performance statistics for the N species
concentration and deposition. As shown, the simulation does a poor job of
reproducing the total Nr deposition rates both in the
month-to-month variation and across the sites. The difference in the dry
NH3 deposition monthly variation between measurements and simulation
is mainly due to the difference in associated dry deposition velocity used
for calculation. However, consistent with the observations, the simulation
shows that wet deposition is larger than dry deposition and that the
contribution from reduced N deposition was larger than from the oxidized N
deposition at all three sites, although the observed range of
70 %–80 % reduced N was
more than the 55 %–68 % simulated in CAMx. The primary cause of this
bias was the overestimation in the HNO3 dry deposition rates, which
were 2–3 times larger than those derived from the measured data. This is
consistent with the systematic overestimation of HNO3 concentrations
(NMB = 106 % in Table 1). Other biases also exist, including an
underestimation in the NH3 dry deposition, which was somewhat
balanced by an overestimation in the NH4+ wet deposition
(NMB = 60 %). The underestimation of NH3 concentration still
existed (NMB =-16 %), and one of the possible reasons may be due
to the overestimation of HNO3 in the model pushing excessive
partitioning of NH3 into the particle phase, which can be shown by
the better model performance for NHx
(NHx=NH3+PNH4) simulation (NMB =-7 %) without splitting the gas-particle partition bias.
An additional challenge that affected model performance was the difficulty in
estimating precipitation rates. This is shown in Fig. 4, in which the simulated
precipitation rates do not reproduce the month-to-month variation and
generally were highly overestimated. For example, on average the simulated
precipitation at Driggs was more than double the measured precipitation, and
it was more than a factor of 4 higher at the NOAA climate station site.
Evaluation against NADP TDEP
TDEP maps (Schwede and Lear, 2014) are widely used in the land management
community to assess total Nr deposition throughout the United
States and estimate the critical load exceedances in sensitive ecosystems
(Saros et al., 2011; Nanus et al., 2017). TDEP employs a hybrid approach to
integrate measurements from multiple networks, including CASTNet and NTN,
with Community Multiscale Air Quality (CMAQ) modeling (Byun and Schere, 2006)
results for deposition velocities and unmeasured species' dry deposition, as
well as PRISM (Parameter-elevation Regressions on Independent Slopes Model)
(Daly et al., 1994) high-resolution precipitation estimates for mapping total
deposition in the United States (Schwede and Lear, 2014). Both the CAMx
simulation in this study and the TDEP results are derived from model
simulations and subject to similar errors in emissions and physical and
chemical processes. However, with the incorporation of measured wet
Nr deposition and N concentration data into the TDEP results, they
are expected to be less biased than the deposition results from a purely CAMx
simulation.
The TDEP total Nr deposition and the CAMx 2011 simulation in this
work exhibited similar spatial and temporal patterns across the western
United States; for example, both sets of results show high Nr
deposition in the Snake River valley, northern Utah, and across the Wyoming
state border area near GTNP, with values >5 kg N ha-1 yr-1.
Within the GYA (Fig. S3), the CAMx simulation had higher dry Nr
deposition, which was more spatially heterogeneous than the corresponding
TDEP results, with significantly higher Nr deposition in the
agricultural lands to the west of the GYA and hot spots due to wildfires that
are not evident in the TDEP results. Both sets of results showed higher
Nr wet deposition at the higher-elevation sites in the interior of
the GYA, which was associated with higher precipitation rates. However, the
TDEP Nr wet deposition was generally higher throughout the GYA,
with an annual average Nr wet deposition rate of
2.0 N ha-1 yr-1 vs. 1.3 N ha-1 yr-1 from CAMx.
Precipitation maps generated by WRF and PRISM across the GYA had similar
spatial patterns, with hot spots located in high-elevation mountain ranges,
though the WRF annual precipitation rates were on average 73 % higher
than the PRISM estimates.
The annual Nr deposition budget and the annual precipitation rate
from TDEP and the CAMx simulations for eight Class I areas over the GYA are
compared in Fig. 5. The reported CAMx dry and wet Nr deposition
values in Fig. 5 are the averages of the simulation values at corresponding
grid cells for each area. Generally, results from the CAMx model agreed well
with TDEP results in terms of replicating spatial gradients and ratios of
oxidized vs. reduced N deposition. The TDEP 2011 annual Nr
deposition at the GYA receptor sites was in the range of
2.8–5.4 kg N ha-1 yr-1, while the corresponding values for
CAMx were 2.2–4.3 kg N ha-1 yr-1. Both results showed the
west-to-east gradient (Prenni et al., 2014) with higher Nr
deposition at the western side of the GYA and relatively low values in the
Fitzpatrick Wilderness. Also, both models showed the importance of reduced
Nr in the GYA, with a nearly 50 % or higher contribution to the
total Nr deposition budget. However, the two models differed on the
ratio of dry vs. wet Nr deposition, with CAMx simulating a
higher fraction from dry Nr deposition than TDEP.
Source apportionment of Nr deposition over the GYA in
2011
The seasonal modeled Nr deposition budgets averaged over the GYA
are presented in Fig. 6. As shown, the total Nr deposition rates
peaked in the summer (1.12 kg N ha-1 season-1) with somewhat
lower rates in the spring (0.91 kg N ha-1 season-1) and fall
(0.81 kg N ha-1 season-1) and with winter rates
(0.29 kg N ha-1 season-1) being about a factor of 3 smaller
than in the other seasons. These patterns are similar to the measured and
modeled data presented in Fig. 3. In total, the annual model Nr
deposition was 3.13 kg N ha-1 yr-1, with wet deposition
accounting for only ∼40 %. Reduced N compounds were the largest
contributor, except in winter, which is consistent with past studies (Li et
al., 2017). Contributions from organic N compounds are not measured in
routine monitoring programs. Together they accounted for <10 % of the
Nr deposition, suggesting a small but significant contribution.
This is also less than has been measured in field studies conducted at GTNP
(Benedict et al., 2013a; Prenni et al., 2014) and in RMNP (Benedict et al.,
2013b), where the GrandTReNDS study showed on average a 8 %–18 %
contribution from organic N to total Nr deposition budgets during
the whole campaign period and up to 39 % in June at the NOAA Climate
Station site (Fig. 7 in Benedict et al., 2013a).
Seasonal CAMx simulated Nr deposition budgets averaged
over the GYA in 2011. The left axis is the relative contribution of different
Nr species to seasonal Nr deposition while the right axis
corresponds to the black diamonds for seasonal total Nr
deposition as an absolute (kg N ha-1).
Contributions of source sectors to the mean total Nr
deposition, dry Nr deposition, and wet Nr deposition over
the GYA in different seasons in 2011. (a) The source sector
contributions as an absolute and (b) the corresponding contributions
as a percentage.
Seasonal patterns of different source sectors' (agriculture, oil and
gas activities, fires, others (e.g., anthropogenic, biogenic, lightning, and
boundary conditions)) contributions to total Nr deposition over the
GYA in 2011. The first column is the total seasonal Nr deposition
patterns in kg N ha-1 while the following five columns are the
seasonal patterns of relative contributions from different source sectors.
The relative contributions from the four modeled source sectors (AG, OG,
Fire, and Other) and the BCs averaged over the GYA are presented in Fig. 7,
while Fig. 8 presents the seasonal and spatial patterns of their
contributions over the GYA. As shown in Table S2, the AG source sector was
composed of almost all reduced N compounds (>99 %), while the Other
source sector was primarily composed (97 %) of oxidized N compounds, with
about 88 % originating from anthropogenic combustion emissions, including
point and mobile sources, and the remainder originating from the natural emissions from
soil and lightning. Contributions from the Fire and the BCs sectors were more
evenly split between reduced and oxidized N contributions.
Reduced N from the AG source sector was the largest contributor in the spring
(40 %) and fall (41 %) seasons, while oxidized N from the Other
source sector was the largest contributor in summer (29 %) and winter
(44 %) (Fig. 7). In terms of geographic impact (Fig. 8), AG emissions
contributed as much as 80 % of the total Nr deposition in the
western portion of the GYA during the spring and fall, which was associated
with the outflow from the Snake River valley. In the model, NH3 from
regional agriculture activities was treated as being from surface area
sources (i.e., emitted into the first model layer, which is approximately
24 m thick). These low-level emissions can be quickly deposited to the
surface unless there is sufficient vertical mixing to inject the NH3
into the upper levels of the atmosphere (Ferm, 1998; Fenn et al., 2003) or if
it reacted with acidic gases and aerosols. Consequently, it is likely that a
higher fraction of the modeled NH3 emissions from the AG sector will
be deposited in the lower-elevation periphery of the GYA near the
agricultural lands and not impact the more distant mountainous interior
(Fig. 2). The incorporation of the bidirectional NH3 flux could
extend the NH3 emission footprint (Bash et al., 2013; Zhu et al.,
2015).
The OG source sector contributed only about 1 % of the total Nr
deposition over the GYA, with contributions of 10 % or more occurring
during winter in the southeastern corner of the GYA where nearby OG activity
in the Jonah Field and Pinedale Anticline was taking place. Wildfires are
episodic and their locations and magnitudes vary significantly from year to
year (Westerling and Swetnam, 2003; Parisien et al., 2012). In 2011, fire
events contributed on average 18 % of the total Nr deposition
in the GYA. Most of the wildfire happened in summer and fall, while
agriculture and prescribed burning occurred in winter and spring. Near the
fire activities, the contribution to Nr deposition could be more
than 90 %, as seen in Fig. 8. The footprint of fire emission impacts
depends on the simulated injection height of the fire plumes. The emissions
from fires that occurred within the GYA during the summer and fall likely
remained within the mixed layer and had less of a chance to be transported far
downwind to impact more distant areas (Fig. S4). The Other source sector had
relatively uniform contributions throughout the GYA, indicative of
contributions from regional sources. The Other sector accounted for 26 %
of the annual Nr deposition, with its largest absolute
contributions in the summer, but had the highest relative contribution in the
winter at 44 % when AG contributions were at their lowest. Finally, the
BCs had high contributions, often over 20 %, with the highest
contributions occurring in the northern part of the GYA and at
higher-elevation sites.
The seasonal contributions from the modeled source regions and sectors to the
average total Nr deposition over the GYA are summarized in Fig. 9.
As shown, the Snake River valley in Idaho was the largest contributor (in all
seasons), with annual mean contributions of 38 % and a maximum
contribution of 43 % in fall. Most (74 %) of the Nr from
this region was from the AG source sector and was composed of reduced N
(Table S4). The next four largest contributors, on average, were the BCs
(21 %), western Wyoming (8 %), California (7 %), and northern
Utah (6 %). The impact of emissions from Wyoming on the GYA during summer
and fall (14 % and 16 %, respectively) was more pronounced than
winter and spring (5 % and 7 %, respectively). The contributions of
long-range transport from California and the BCs were higher during spring and
winter.
Seasonal source apportionment results of the average dry and wet Nr
deposition over the GYA are shown in Figs. 7 and 9. Compared to the results
for total Nr deposition, the dry Nr deposition had higher
contributions from closer sources, such as the Snake River valley (46 %
for dry vs. 38 % for total), with emissions primarily from AG sources.
Similarly, contributions to dry Nr deposition from Wyoming were
15 % compared to 12 % for total Nr deposition and ranked as
the second-largest contributor. The contributions from distant source regions
decreased. For example, the BCs decreased from 21 % for total Nr
deposition to 12 % for dry Nr deposition.
Contributions of source regions to the mean total Nr
deposition, dry Nr deposition, and wet Nr deposition over
the GYA in different seasons in 2011. (a) The source region
contributions as an absolute and (b) the corresponding contributions
as a percentage.
Contributions of different source sectors as well as boundary
conditions for total Nr deposition in 2011 at 10 points of interest
for critical load exceedance (see Table 2 for site locations and ecosystem
impacts). The black-and-white pies are the contributions by source sector
while the color pies are the contributions by source region. The color
contour for the GYA boundary is the terrain heights with the legend at
at the right.
Total reactive nitrogen (Nr) deposition and critical loads
for receptor points in the Greater Yellowstone Area in
Wyoming.
Total Nrdeposition (kg N ha-1)Critical load (kg N ha-1)3SiteSite nameLatitude/ElevationSensitiveCAMx1TDEP2RangeConfidenceID(state)longitude(m)ecosystemlevel1Absaroka-Beartooth Wilderness (MT)45.49∘ N, 110.51∘ W2536Lichen1.932.803.02–4.89Reliable2Twin Island(MT)45.07∘ N, 109.81∘ W2829Lake chemistry1.533.992.5–7.1Fairly reliable3Tower Falls(WY)44.92∘ N, 110.42∘ W2457Snowpack3.81.872.93–4.814Reliable4Moose Meadow(ID)44.63∘ N, 111.24∘ W1885Snowpack3.382.363.52–5.404Reliable5Biscuit Basin(WY)44.46∘ N, 110.83∘ W2050Snowpack2.693.493.39–5.274Reliable6Jedediah Smith Wilderness(WY)43.79∘ N, 110.94∘ W1944Lichen3.036.363.40–5.27Reliable7Holly Lake(WY)43.79∘ N, 110.79∘ W2230Lake chemistry3.155.502.5–7.1Fairly reliable8Fitzpatrick Wilderness(WY)43.40∘ N, 109.66∘ W2890Lichen1.791.863.41–5.29Reliable9Pinedale(WY)42.93∘ N, 109.79∘ W2246Lichen3.392.672.66–4.53Reliable10Black Joe Lake(WY)42.74∘ N, 109.16∘ W3133Lake chemistry2.323.562.5–7.1Fairly reliable
1 Comprehensive Air Quality Model
with extensions. 2 NADP total deposition maps. 3 The range of
critical loads (CLs) for different effects on the selected sensitive
ecosystem receptor is from US CLAD (Critical Loads for Sulfur and
Nitrogen Access Database), version 2.5 (Lynch et al., 2015). The level of
confidence is based on the work of Pardo et al. (2011). The lower ends of the
range were used in this study as a measured CL. 4 The CL values were for
lichen response at sites with snowpack as a sensitive ecosystem.
The opposite pattern is seen for wet Nr deposition, where the
contributions from the distant source regions increased relative to the
neighboring ones. The annual contributions from the BCs increased to 34 %
and peaked in spring and summer at 37 %, associated with higher
precipitation amounts than the other two seasons. Annual contributions from
sources in California (10 %) and Utah (8 %) surpassed Wyoming
(7 %). Furthermore, the seasonal variation for wet Nr
deposition was different from dry and total Nr deposition, with the
highest deposition rates occurring in spring as opposed to summer.
The GYA has been the focus of several ecological assessments of the response
of ecosystems to changing Nr deposition levels (Spaulding et al.,
2015; Nanus et al., 2017). Figure 10 presents the source attribution results
for 10 sites within the GYA where either ecosystem response studies or
deposition monitoring has been conducted for lichen diversity, alpine lake
chemistry, and snowpack analysis. In Table 2, the CL values are provided as
a range of lower-end and upper-end estimates of the annual total inorganic
Nr deposition values (Lynch et al., 2015) with confidence levels
(Pardo et al., 2011). The simulated Nr deposition exceeded the
lower CL values at three of the 10 sites, specifically, Holly Lake, Pinedale,
and Tower Falls. Comparatively, the 2011 TDEP Nr deposition results
exceeded the CL at 6 out of 10 sites (Black Joe Lake, Biscuit Basin, Holly
Lake, Jedediah Smith Wilderness, Pinedale, and Twin Island). As shown in
Fig. 10, the sites that exceeded the CL tend to be in high-alpine locations,
with four of these sites on the western slope of the mountains, which are
downwind of the Snake River valley. These results are consistent with another
modeling study to access CL exceedances in Class I areas using GEOS-Chem
(Ellis et al., 2013; Lee et al., 2016). In addition, in one study (Nanus et
al., 2017) over 30 % of the GYA was estimated to potentially exceed lower
Nr deposition CL thresholds, with the greatest impacts in sensitive
high-elevation basins, including areas within national parks and
wildernesses.
In terms of emission sectors and source regions contributing to the total
annual Nr deposition at CL exceedance sites, emission sources from
the Snake River valley were the largest contributors (27 %–32 %),
and AG emissions were the largest source of this subset. The next three
largest contributors were transport from the BCs (23 %–25 %) and
emissions from northern Utah (8 %–15 %) and California
(7 %–8 %). Wyoming emissions associated with the OG and Fire
emission sectors contributed around 3 %–5 % and 14 %–23 %,
respectively, of the Nr budget for receptor sites at the
southeastern corner of the GYA.
The influence of model bias on source apportionment results
It is evident from the results in Sect. 4 that the attribution of total
Nr deposition to source regions and sectors is sensitive to
NH3 dry deposition rates, the relative contributions of dry and wet
deposition, and the concentrations of N compounds from the BCs. However, the
model evaluation revealed a significant underestimation of NH3
concentrations and overestimation of HNO3 concentrations and
precipitation rates; thus, these modeling errors could bias the source
attribution results. To better understand the potential effects of these
biases, sensitivity analyses of the source attributions to changes in
NH3 dry deposition rates and average precipitation rates as well as
potential biases in the BCs were evaluated.
To test the sensitivity of the apportionment to NH3 dry deposition
rates, the deposition velocities were reduced by increasing the NH3
resistance scaling factor by 10 %, following the methodology used in
Thompson et al. (2015). The Zhang et al. (2003) dry deposition scheme was
used in the CAMx simulations (Table S1), and this resistance scaling factor
is designed to address the rapid removal of “sticky” compounds such as
HNO3 and NH3 and can yield a nonlinear response in the
estimated dry deposition velocity. July and August 2011 were simulated using
the modified deposition velocity, and these results will be referred to as
“DV_0.1”. The 10 % change in the resistance factor slowed the
NH3 deposition velocity from 2.5–4 to 1–1.5 cm s-1 over the
GYA, resulting in values more comparable to those used in the GrandTReNDS
study (Benedict et al., 2013a; Prenni et al., 2014). The simulated
NH3 concentrations for the DV_0.1 case increased throughout the GYA
compared to the base case. This resulted in better agreement with NH3
measurements at the Grand Targhee and NOAA climate station sites but poorer
agreement at the Driggs monitoring site (Fig. S5). The slower dry deposition
velocities result in a longer NH3 lifetime, allowing it to travel
farther from nearby source regions, e.g., the Snake River valley, into the
GYA and cause a more homogeneous concentration pattern throughout the GYA
(Fig. S6). As shown in Fig. 11, the slower deposition velocities also
somewhat altered the source attribution results. The contribution from the AG
emission sector increased with the DV_0.1 simulation to 23 % compared to
19 % in the base case, with a smaller decrease in the contributions from
the Other and the Fire sectors. This change was due to small increases in the
contributions from the Snake River valley and northern Utah and decreases
from Wyoming. Overall, decreasing the NH3 dry deposition rate by
about a factor of 2 had only a small impact on the Nr deposition
budget and source apportionment results in the GYA. It is important to note
that, although this was a significant reduction in the simulated dry
deposition velocity for NH3, it still represents a relatively rapid
removal rate compared to other species, and NH3 is quickly lost
from the atmosphere in either case. It is known that NH3 deposition
in many environments is a bidirectional as opposed to a unidirectional
process, and modeling the NH3 flux as a bidirectional process may
further decrease the bias for ambient NHx concentration
simulations (Bash et al., 2013; Wen et al., 2014; Whaley et al., 2018). The
key process in air quality models to represent the re-emission of NH3
from soil and plants to the atmosphere is the estimation of the available
soil NHx pool and the parameterization of compensation
points for the conditions to re-emit NH3 (Zhang et al., 2010; Whaley
et al., 2018). In the CMAQ model, the bidirectional NH3 deposition
was realized by coupling with the United States Department of Agriculture's
(USDA) Environmental Policy Integrated Climate (EPIC) agroecosystem model to
provide the fertilization timing, rate, and composition (Bash et al., 2013).
There is no similar parameterization available in the current CAMx model.
Furthermore, the CAMx source apportionment tools cannot properly account for
the origin of NH3 concentrations at a receptor that has been
deposited and then re-emitted.
The sensitivity of NH3 dry deposition velocity
(a, b “base” case; c, d “DV_0.1” case with NH3
dry deposition velocity slowing down) to source apportionment results over
the GYA during July–August 2011. Panels (a) and (c) show the
contributions by source region as an absolute and as a percentage while
(b) and (d) show the contributions by source sector.
Ratio of simulated vs. measured particulate nitrate
(PNO3) concentrations against the boundary contributions to simulated
PNO3 at IMPROVE sites over a 12 km domain.
The CAMx simulation overestimated the wet Nr deposition at measured
sites, which was likely associated with an overestimation in the
precipitation rates from WRF, especially at high-elevation sites. This
precipitation rate bias was large, with the annual precipitation over the GYA
more than 73 % higher than the PRISM estimates. We used the Noah
land-surface model and Kain–Fritsch scheme cumulus parametrization in the
WRF simulations (Table S1), and those physical module configurations were
reported to have the tendency to overestimate precipitation (Warrach-Sagi et
al., 2013). To evaluate the impact of the overestimation in precipitation on
the source attribution results, the seasonal wet deposition rates were scaled
to the measured precipitation rates at all NADP NTN and GrandTReNDS
monitoring sites, following the procedures by Appel et al. (2011). This was
equivalent to scaling the modeled wet deposition rates by the ratio of the
measured to modeled precipitation rates. This approach assumes that the
concentrations of Nr in the precipitation were the same in the
model and measured data, which was not the case. After the precipitation
adjustment, the correlation between the simulated and measured Nr
wet deposition improved (Fig. S7). Within the GYA, however, the scaled
Nr wet deposition underestimated the measured N deposition by about a factor of
2 and significantly underestimated the ratio of wet to dry deposition.
Consequently, scaled wet deposition results were not used in this assessment.
The overestimation of HNO3 concentrations in the GYA is another
reason for the wet Nr deposition overestimation. However, its
impact on source apportionment results was not conducted here due to unclear
reasons for the model bias (emission, chemistry, meteorology, deposition
scheme) and limited computational resources.
The BC used in this work was derived from a MOZART global model simulation.
An alternative set of BCs from the GEOS-Chem global model was also evaluated.
Both sets of BCs resulted in high contributions to the total Nr
deposition in the GYA, with the GEOS-Chem results having a slightly higher
average contribution of 23 % compared to 21 % for MOZART (Figure S8).
However, the GEOS-Chem BCs resulted in higher relative contributions of
oxidized N to the total Nr deposition rate compared to the MOZART
BCs (51 % and 45 %, respectively). The poor correspondence in the
oxidized to reduced Nr split is reflective of the large
uncertainties in the BC contributions to the Nr deposition and
suggests that more evaluation of the global model results is warranted.
To examine the potential bias in the BC contributions, the simulated
PNO3 concentrations were compared to measurements from the IMPROVE
monitoring program over the western United States for 2011. This comparison
is shown in Fig. 12, in which the ratio of the simulated to measured
PNO3, i.e., an estimate of the bias, is plotted against the relative
fraction of the contribution of the BCs to the simulated PNO3. The
data were first segregated by the fractional contribution of the BCs and then
averaged together. As shown, for the MOZART BCs, the bias increased with
larger relative contributions from the BCs, and when the BC fraction was
60 %, the bias was more than a factor of 2. This suggests that at least
the particulate nitrate concentrations from the BCs are overestimated and
possibly other Nr compounds from the BCs as well. In a CMAQ
simulation using BCs derived from a GEOS-Chem simulation, Baker et al. (2015)
also found that the contributions from the BCs to PNO3 were
overestimated when compared to IMPROVE data.
Summary and discussion
The CAMx model and its PSAT source apportionment tool were used to examine
and quantify the contributions of four different source sectors and 27 source
regions and the boundary conditions (BCs) to the 2011 total inorganic
Nr deposition within the GYA. The source sectors were agriculture
(AG), oil and gas activities (OG), wild and prescribed fires (Fire), and
remaining contributions labeled as Other. The Other sector was primarily
composed of oxidized N originating from anthropogenic combustion sources,
including mobile and point sources, and the AG sector was almost entirely
composed of reduced N compounds. Fire and the BCs were a mix of reduced and
oxidized N compounds. This assessment focused on only the inorganic N
fraction. There is measured evidence that organic N (Benedict et al., 2013a;
Prenni et al., 2014) is a significant contributor to Nr deposition,
and the inability to assess its origin in the current CTM is an important
uncertainty in this work. Nevertheless, this Nr source
apportionment work is the first thorough analysis of the origin of inorganic
Nr in the GYA using a regional air quality modeling platform. The
detailed source sector and source region configurations in PSAT enabled
quantitative, though uncertain, estimates of their relative importance. This
information is needed by stakeholders and regulators to understand the causes of excess Nr
deposition in the GYA, monitor changes in Nr deposition, and
develop possible future mitigation strategies.
Overall, the model simulation had a reasonable capacity to reproduce the
measured seasonal and annual total Nr deposition levels throughout
the GYA. However, the model simulation underestimated the measured
NH3 concentrations by 65 % on average and overestimated the
measured HNO3 by 108 %. Therefore the model tended to
overestimate the contributions from oxidized N compounds and underestimate
those from reduced N compounds to total Nr deposition. In addition,
both reduced and oxidized Nr wet depositions were overestimated by
20 %–30 %, which was due, at least partially, to the simulated
precipitation frequency and magnitude being too high in the model. These
biases suggest that the modeled contributions from the AG emission sector
were underestimated, while those from the Other sector's activities were
overestimated.
The simulated annual total Nr deposition over the GYA in 2011 was
3.13 kg N ha-1 yr-1 and exceeded the CL estimates for lichen
and lake chemistry primarily at high-elevation sites on the western slope and
southern portion of the GYA. This finding is consistent with other studies
using global models. Ellis et al. (2013) used the GEOS-Chem model to estimate
the Nr deposition to Class I areas for 2006 and showed that the
simulated total Nr deposition at GTNP
(2.9 kg N ha-1 yr-1) and YNP (2.6 kg N ha-1 yr-1)
exceeded the low end of CL for lichens (2.5 kg N ha-1 yr-1).
Emissions from the AG sector within the modeling domain were the largest
contributor to the GYA total Nr deposition budget at
34 % year-1. The contributions from the Other sector were also
large at 26 %. The OG emission sector generally had a small contribution,
except at the southern edge of the GYA, where it could contribute over
10 % of the total Nr deposition during winter months, with
almost half of the OG contributions originating from emissions in the
neighboring Jonah Field in western Wyoming. The Fire emission sector also had
a significant contribution of 18 % over the year. This was due to
regional contributions from fires throughout the west and large contributions (>90 %) in areas within the
GYA where several wildfires occurred (Fig. 8). The large impact from fires
within the GYA is notable since the episodic nature of fire will result in
differing year-to-year contributions from this uncontrollable sector.
The largest impact from the AG emission sector originated from sources
relatively close to the GYA, and the Snake River valley accounted for
74 % of the annual agricultural contribution. The agricultural
contribution from Wyoming was 7 %, and more-distant source regions in
northern Utah, California, and the northwestern United States each accounted
for 4 %–5 % of the agricultural contribution. Nearly half (45 %)
of the Nr deposition from the OG emission sector originated within
Wyoming, especially the Upper Green River (27 %). The largest impact from
the Fire emission sector originated from the Snake River valley (33 %) and
within the GYA (25 %). The Other emission sector was more evenly
distributed among near and distant regions, with the Snake River valley 23 %, Wyoming 17 %, and northern Utah, California, and
the northwestern United States accounting for 14 %–16 % of the
Nr deposition.
Long-range transport of N species from the BCs, which primarily originated
from international sources, contributed 21 % of the total Nr
deposition within the GYA during 2011 and had the largest absolute
contribution during the summer. Several studies have shown the importance of
international source contributions to particulates and N deposition within
the continental United States (Park et al., 2004; Brewer and Moore, 2009;
Zhang et al., 2012; Fann et al., 2013; Baker et al., 2015; Thompson et al.,
2015). However, the BCs contribution in this work is on the high end of the
reported values. For example, in a similar modeling study by Thompson et al.
(2015), the estimated contribution of BCs to Nr deposition in Rocky
Mountain National Park in 2009 was 13 %. Zhang et al. (2012) used the
GEOS-Chem model to evaluate N deposition in the United States during
2006–2008 and showed that foreign anthropogenic contributions were generally
<10 % but could rise up to 30 % near the Canadian and
Mexican borders. In addition, our evaluations of the BCs suggest that the
contribution of the BCs to ambient PNO3 and possibly other
Nr compounds was overestimated (Fig. 12), clearly suggesting that
more research is needed on the role of distant emission sources in impacting
N deposition in remote areas, as well as further investigations into model
biases.
The observed precipitation in 2011 was ∼30 %–50 % higher than the
historical average (NOAA, 2012), with the largest bias occurring at the
eastern sites in the GYA (Fig. S9). This suggests that dry deposition of
NH3 may be a more important contributor to total Nr
deposition during spring than that observed during GrandTReNDS. Also,
considering that the wet deposition in the GYA tended to be overestimated and
the precipitation amount in 2011 was anomalously high, the source regions
identified as having a higher weighting on the annual wet Nr
deposition budget (e.g., California) may not have such a significant impact
as the current PSAT results suggested.
As discussed, source apportionment assessments of Nr and its
deposition to remote, ecologically sensitive areas such as the GYA have large
uncertainties. Many of these uncertainties are known to the air quality
modeling community, including the challenges of simulating precipitation in
complex terrain, adequately characterizing NH3 emissions from
agricultural operations, the occurrence of wildfires, and the difficulty in
simulating the NH3 bidirectional flux and the deposition flux of the
other Nr compounds. Contributions from long-range transport of
international emissions can also play a significant role in deposition in
remote locations in the western United States. Further refinement in all of
these areas is required to better understand and estimate the relative
contributions of emission sources to excess N deposition within the GYA.
Nevertheless, the modeling assessment showed that reduced N contributed more
than 50 % of the total Nr deposition over the GYA, with
>90 % of the NH3 emissions originating from agriculture
sources. In addition, the Snake River valley in Idaho accounted for 74 %
of the agricultural contribution to the total Nr deposition.
Significant contributions from more distant sources, e.g., California and
international sources, to both oxidized and reduced Nr deposition
illustrate the regional nature of the Nr deposition problem.
Emissions of oxidized N compounds are projected to continue to decrease,
while emissions of ammonia are projected to remain relatively constant or
increase (Li et al., 2016). This will further increase the importance of the
AG sector. However, exceedances of CL are still relatively small, and it is
possible that decreased oxidized N deposition could reduce the Nr
deposition sufficiently to bring total Nr deposition below the CL in some GYA
ecosystems.
Model data have been made available through the Mountain
Scholar repository service. The nitrogen deposition source apportionment
results over the Greater Yellowstone Area can be accessed at
https://hdl.handle.net/10217/191136 (Zhang et al., 2018). Contact the
corresponding author for any additional data requests.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-12991-2018-supplement.
RZ, TMT conducted the source apportionment simulations.
RZ, TMT, MGB, JLH, BAS performed results analysis and interpretation. RZ, MGB, JLH, BAS
prepared the manuscript. MDB contributed the critical load excess assessment. JAM and WCM
were involved in the discussion of model uncertainties and edited the drafts of the paper.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the National Park Service Air Resources Division
under cooperative agreement P17AC00773. The assumptions, findings,
conclusions, judgments, and views presented herein are those of the authors
and should not be interpreted as necessarily representing the National Park
Service policies. The Interagency Monitoring of Protected Visual Environments
(IMPROVE) is a collaborative association of state, tribal, and federal
agencies and international partners. The U.S. Environmental Protection
Agency is the primary funding source, with contracting and research support
from the National Park Service. The Air Quality Group at the University of
California, Davis, is the central analytical laboratory, with ion analysis
provided by the Research Triangle Institute and carbon analysis provided by
the Desert Research Institute. We acknowledge the Total Deposition (TDEP)
Science Committee of the National Atmospheric Deposition Program (NADP) for
their role in making the TDEP data and maps available. Edited by: Leiming Zhang Reviewed by: two
anonymous referees
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