Evaluation of simulated Nr deposition
Figure shows the spatial distribution of total, reduced,
and oxidized annual Nr deposition in the contiguous US in 2010 calculated
with GEOS-Chem. Total Nr deposition consists of all chemical species included
in the cost function, reduced Nr deposition is the sum of wet and dry
deposition of NH3 and NH4+, and oxidized Nr deposition is total minus
reduced. Total Nr deposition ranges from 2 to 5 kgNha-1yr-1 in the West,
except in some parts of California where it is >12 kgNha-1yr-1, and from 6 to
20 kgNha-1yr-1 in the East. Annual total Nr deposition over the contiguous US
is 5.6 TgN (3.2 oxidized, 2.4 reduced). Oxidized Nr is higher than reduced
Nr overall, while reduced Nr is higher in mid-California, Iowa, and eastern
North Carolina.
The spatial distribution of reduced and oxidized Nr deposition is comparable
with other studies yet a few differences and uncertainties are worth
considering. found greater wet deposition of
NH4+
compared to wet deposition of NO3- over the contiguous US except in the
Northeast region. The larger fraction of reduced wet Nr deposition in their
work may be related to the year being analyzed (increased NH3 and
decreased NOx emissions in their study period of 2011–2012 compared to
ours in 2010) and to the overestimation of HNO3 in our study that is
discussed below.
, using the same model we use but with the
different emissions, found that wet and dry HNO3 deposition is
overestimated compared to observations when the model's isoprene nitrate is
treated as HNO3, as in our simulation, rather than being treated
separately as organic nitrate. Further, comparison of modeled to measured
HNO3 deposition in required consideration of
sub-grid concentration gradients near the surface. Simulated ambient
HNO3
concentrations are also overestimated ,
possibly owing to excessive N2O5 hydrolysis. This suggests that
oxidized Nr may be overestimated in our study.
generated maps of Nr deposition for multiple years, including 2010. These
maps display localized hotspots in parts of Colorado and Idaho that are not
evident in our results. The high Nr deposition in these regions is attributed
to dry deposition of reduced nitrogen , whereas in
our result the contribution of reduced nitrogen deposition is generally less
than that of oxidized nitrogen deposition (Fig. ), possibly
owing to the aforementioned overestimation of HNO3.
For the eight selected Class I areas, we compare seasonal average values from
measurements provided by NADP/NTN and CASTNET versus GEOS-Chem model
estimates (Fig. ). Total modeled Nr deposition in each
Class I area (Jp, which includes non-measured species) is also plotted in
Fig. as blue diamonds to show the role of non-measured
species. Seasonal averages are calculated from monthly values. Measured Nr
corresponds to the sum of modeled wet deposition of NH3, NH4+, HNO3,
and NO3-, and dry deposition of NH4+, NO3-, and HNO3. The
squared correlation coefficient (R2) of measured and modeled Nr is shown
in each plot. For SQ, R2 is calculated with spring, fall, and winter data.
The model well reproduces the seasonality of measurements (R2>0.6)
except at JT. For all sites, measurements and model estimates have maximum
values in the summer. Seasonally averaged measured Nr range from 0 to 0.6 kgNha-1month-1
(monthly value 0 to 1.3 kgNha-1month-1), modeled Nr range from 0.0
to 1.2 kgNha-1month-1 (monthly value 0 to 1.3 kgNha-1month-1) and Jp
(modeled Nr including non-measured species) range from 0.1 to 1.3 kgNha-1month-1
(monthly value 0 to 1.4 kgNha-1month-1). Modeled Nr deposition is
also higher than the measured Nr in the spring and summer in SM and SD,
likely owing to overestimated HNO3 as discussed above. Additionally, our
model grid-cell size (∼3350 km2) is larger than the largest Class I
area (BB, 2866 km2). Representational error may thus also contribute to
the discrepancy between the model and the measurement for regions with large
emissions within grid cells containing the Class I area (e.g., SM and SD).
Lastly, comparison to dry deposition measurements warrants some additional
considerations. The MLM model used for deriving the CASTNET dry deposition
values is subject to uncertainty in estimating dry deposition velocities
because of a height-dependent
non-physical component that can lead to an overestimation of HNO3 deposition by
10–30 % . Additionally,
found that measurements of HNO3 dry deposition in a clearing, such as the
CASTNET sites in SM and SD from which dry deposition measurements are
derived, are lower than measurements of dry deposition to the surrounding
forest canopy. Thus, measured Nr deposition in Class I areas that have large
forested areas (such as SM, SD, RM, GT, and SQ, see Fig. )
is likely underestimated.
Annual modeled Nr deposition in each Class I area (Jp) ranges from 2.2 to
10.7 kgNha-1yr-1, and is highest in SD and SM and lowest in BB. The dotted
lines in Fig. show the annual CLs from
divided by 12. Class I areas considered to be
in CL exceedance on an annual basis based on simulated values are VY, SM, SD,
RM, GT, and SQ and those in exceedance based on measurement are VY, SM, SD,
RM, and SQ. Within California, annual Nr deposition in SQ is about 70 %
larger than that in JT. This is influenced by the position of these parks
relative to large upwind anthropogenic sources, as well as different
vegetation types of the two parks (Fig. ). JT is 80 % desert
where very low Nr deposition is expected; in contrast, SQ has narrow conifers
and mediterranean scrub. The lowest annual Nr deposition in BB is explained,
in part, by the large fraction of desert (60 %) and succulent and thorn scrub
(18 %); it is also far from large anthropogenic sources.
Stacked bar of modeled seasonal Nr deposition showing speciation.
Others includes dry deposition of NO2, PANs, alkyl nitrate, and
N2O5. Blueish: oxidized N, reddish: reduced N, dark: wet deposition,
light: dry deposition.
Annual-averaged monthly footprint (χ) of Nr deposition in each
Class I area and pie chart of fractional contribution from emission sectors.
ls: livestock, fe: fertilizer, na: natural, sf: surface inventory, eg:
electric generating units, ne: non-eg industrial stacks, ac: aircraft, li:
lightning, so: soil. Inset numbers are cost function (Jp), annual Nr
deposition in each Class I area. Site locations are shown with open circles.
Footprint values are scaled for visibility with numbers in parenthesis.
Annual averaged monthly cumulative contribution as a function of
distance from the site. Vertical lines are for 50 % (blue) and 90 % (red) of
total Nr deposition. Note the change in scale of the y axis for SM and
SD.
Figure shows the model speciation of Jp. Non-measured
species are dry deposition of NO2, PANs, alkyl nitrate, N2O5 (lumped
as others in Fig. ) and dry NH3. Non-measured species
account for 0.5 % (winter, SM) to 55.6 % (summer, SQ) of seasonally averaged
Jp values in the model. Dry deposition of NH3 accounts for 14 % of
contiguous US total annual Nr deposition. The summer maximum of Jp is
mainly driven by wet deposition of HNO3 (VY, SM, SD, BB, RM) and dry
deposition of HNO3 (VY, GT, JT, SQ). Dry deposition of NH3 is a major
contributor in SQ. Organics (PANs and alkyl nitrate) make only a small
contribution (<5 %) to Nr deposition in the model. While it is known that
organics account for ∼30 % of total Nr deposition
, we expect organics to be
underestimated in our model because only dry deposition is included for these
species and isoprene nitrate is not explicitly treated
.
Source attribution using GEOS-Chem adjoint
Spatial and sectoral footprints of Nr deposition
The sensitivity of total annual Nr deposition (Jp) to emission sources is
calculated by the GEOS-Chem adjoint model. The results can be understood as
the contribution of emissions in each grid cell to the Nr deposition in each
Class I area. Figure shows spatial distributions of the
sensitivities of Nr deposition to NOx and NH3 emissions – the so
called source footprint (Eq. ) – for each region. Inset
numbers are the annual Nr deposition in each area from all sources (Jp).
Pie charts show the relative contributions to this value from specific
emission sectors (sectors contributing <1 % are not shown).
The source attribution results show significant variability in terms of the
sectors contributing to Nr deposition in different Class I areas. Livestock
NH3 and surface source NOx, i.e., mobile sources, are the major sources
of Nr deposition, contributing more than 65 % to SM, SD, RM, GT, JT, and SQ.
Livestock NH3 contributions are largest for SQ (54 %) and smallest for BB
(15 %). Mobile NOx is the major emission source for JT (63 %), SM (40 %)
and SD (38 %). Fertilizer NH3 is the third most important source of Nr
deposition for VY (14 %), GT (11 %), and SQ (8 %). In contrast to the other
sites, for BB the contribution of natural sources of Nr (the sum of natural
NH3, lightning and soil NOx equal to 47 %) is comparable to that of
anthropogenic contributions. NOx from EGUs is the third most important
source for RM (12 %) and SD (9 %). Lightning is a considerable source not
only for BB but for SM (9 %). Aircraft emissions have a noticeable impact
only for JT (2 %).
The results of the adjoint sensitivity calculations show that the spatial
footprint of emissions affecting different Class I regions can vary by
several hundred kilometers. Even though NOx and NH3, by themselves,
have very short lifetimes (<1 day), in the form of aerosol species they can
influence Nr deposition over quite large distances, which is reflected in the
maps in Fig. . To provide a quantitative means of
evaluating the spatial extent of the footprint for each region,
Fig. shows cumulative contributions of annual average
monthly Nr deposition by radial distance from each site. Blue and red lines
indicate distances for which the cumulative influence is 50 and 90 % of the
total, respectively. For reference, the greatest distance within the
contiguous US, from Florida to Washington, is about 4500 km. It can be
inferred from the shape of the plot that VY, SM, and BB have broad source
regions spreading ∼1500 km from the site. In contrast, JT and SQ are
mostly (90 %) influenced by sources within 700 km (JT) and 400 km (SQ). Local
sources (within 50 km) contribute more than 20 % of total Nr deposition for
SD, while the rest are from more distant regions spread across ∼1100 km.
For RM and GT, there is a jump in the cumulative distribution around
1200 km
which is due to sources in California. Steep initial rises for JT and SQ
correspond to the influence of local urban centers (Los Angeles and San
Francisco, respectively).
Same as Fig. but for oxidized and reduce Nr
deposition in RM. Units for the pie charts and colorbar are kgNha-1yr-1. The
sum of the oxidized and reduced Nr deposition is smaller than the inset
number in Fig. because the number here excludes Nr from
“other species”.
Additional analysis was performed for RM, given the prevalence of studies on
Nr deposition in this area . Figure shows the
source distributions of oxidized and reduced Nr deposition. Our results
suggest that reduced Nr deposition originates primarily from east of the
park, while in contrast a large fraction of oxidized Nr deposition originates
from west of that park. This is consistent with the spatial distributions of
the emissions of NH3 compared to those of NOx surrounding the park. The
high sensitivity of reduced Nr to sources west of RM in California and Idaho
agrees with other recent studies .
Efficiencies of impacts on Nr deposition showing cost function
(Jp) change per kg N or kg S emission for the tracer and season indicated
in the plot. (a) Joshua Tree, (b) Rocky Mountain, (c) Shenandoah national
parks. Wind-roses for each site show fraction of wind frequencies based on
daily surface winds during the season.
Efficiency of emission impacts on Nr deposition
For each Class I area, we also calculate non-normalized
adjoint sensitivities as defined in Eq. () using the cost
function defined in Eq. (). These provide estimates of the response
of Nr deposition (Jp) in each park per kg emissions of NH3-N, NOx-N,
and SO2-S in each month. These are a measure of transport efficiency of
each species, largely determined by meteorology and aerosol partitioning.
Figure shows a few select results with unique seasonal
features in JJA and DJF.
In JT, there is a clear seasonal trend (Fig. a). Nr
deposition in the park is impacted most efficiently by sources in the NW-SE
direction during the summer and by sources in the NE-SW direction in the
winter, due to changes in wind patterns. In RM, Nr deposition is owing to the
sources from California during the summer, whereas the source footprints are
much more localized during the winter (Fig. b). While
stronger winds (≥6 ms-1) are actually more frequent in the winter, larger
NH3 emissions in the summer facilitate formation of NH4NO3 and thus
long-range Nr transport. In SD, NH3 emissions make a positive contribution
to Nr deposition during the summer, while emissions north of the park
contribute negatively during the winter (Fig. c). These
negative sensitivities occur because NH4NO3 formation is limited by
NH3 in the winter in SD. In these conditions, emissions of NH3 promote
formation of NH4NO3. Since NH4NO3 has a longer lifetime in the
atmosphere than gas-phase NH3 or HNO3, formation of NH4NO3 causes
Nr to be transported further away, and thus less Nr deposits in the park.
Thus, the deposition of Nr in the park has a negative sensitivity with
respect to NH3 emissions. This tradeoff is also manifested by
SO2
emissions having positive sensitivities during winter and negative
sensitivities during summer. In NH3 limited conditions (winter), increased
SO2 emissions would tie up NH3 as aerosol (NH4)2SO4 or
NH4HSO4, leaving less NH3 available to form NH4NO3.
CL exceedance in Class I areas; color indicates magnitude of
exceedance. The size of Class I areas is not reflected. Grid cells
containing Class I areas are shown as colored regardless of the fraction of
Class I areas. Bold line divides Western and Eastern US.
Analysis of all Class I areas in critical load exceedance
CL exceedance in Class I areas are shown in
Fig. . In order to see the number of grid cells in CL
exceedance, the area of the regions is not reflected in this map; it are
shown as filled cells if the fraction that the region occupies in the cell is
greater than zero (although fractional grid cell areas, βi, are
considered in the model simulations themselves). The West/East contrast is
clear. The number of cells in CL exceedance is larger in the West while the
magnitude of the CL exceedance is larger in the East. This is not surprising
considering the spatial distribution of Nr deposition
(Fig. ) and Class I areas. Among the 149 Class I areas in
the contiguous US only 38 are located in the East. Figure a
shows the sensitivity of Ja to NOx and NH3 emissions. This
sensitivity indicates the regions where reducing emission will result in the
largest decrease in the extent of Class I areas in CL exceedance.
Figure b is the sensitivity of Jc to emissions. This
sensitivity shows the sources that are causing the largest values of Nr
deposition, relative to the CLs (i.e., excessive or severe values).
Comparison of the two types of sensitivity analysis suggests how different
emission control strategies might be considered to meet different
objectives. Decreasing Nr emissions in California and regions surrounding RM
and SM would be useful for reducing both the extent of Class I areas in CL
exceedance (Fig. a) and the amount of excessive Nr in
Class I areas (Fig. b).
Nr originating from Idaho, Utah, Washington, and Arizona contribute more to
reduce the extent of Class I areas in CL exceedance but less to the amount of
excessive Nr in Class I areas, as the Nr deposition in these regions is not
as excessive as it is in other regions, as shown in Fig. .
Reducing Nr emissions from the tip of Florida would reduce the area of
regions in CL exceedance, while reductions to emissions in this area are not
as beneficial for avoiding excessively high deposition, as this region has
the highest CL (5 kgNha-1yr-1) of those considered here. For reduction of
excessive Nr above the CL, sources with the largest impact are located in the
East (i.e., Tennessee, Alabama, and Georgia) and the San Joaquin Valley in
California. Interestingly, the distribution of contributions across sectors
is similar for both Ja and Jc; surface NOx and livestock NH3 are
the major emission sectors contributing to both the extent and severity of CL
exceedances.
Same figure as Fig. but with different cost
functions. (a) Ja, the sum of Nr deposition in all Class I areas in CL
exceedance, (b) Jc, the sum of square of the difference of annual Nr
deposition and CL in all Class I areas in CL exceedance. Sensitivities of
(a) are scaled by ×2 to share the colorbar with (b).
Sum of NH3 emissions from anthropogenic, natural, biomass
burning, and biofuel sources. Inset numbers are contiguous US total NH3
emissions in each month.
Map of sensitivities of Jp to NH3 emissions for three selected
Class I areas (VY, SD, and RM) for two different NH3 emission inventories
(optimized NEI2005 and default NEI2008) in each month.
Uncertainty caused by NH3 emissions
To evaluate the robustness of our source attribution analysis with respect to
NH3 emissions uncertainties we compare our base case results using NEI2008
emissions to sensitivity results using NEI2005 NH3 emissions optimized
using remote-sensing observations from
. This is of interest not only because the
magnitude of NH3 emissions may change the contribution of NH3 to Nr
deposition, but also because Nr deposition is sensitive to long-range
transport of ammonium and nitrate aerosol and NH3 abundance exerts a
strong, nonlinear, influence on nitrate partitioning. As shown in
, in the optimized NEI2005 the overall NH3
emissions have increased compared to the original NEI2005 inventory;
emissions in California, the central US, and the Midwest are especially
enhanced. Figure shows the NH3 emissions from the
optimized NEI2005 and those used in this study, NEI2008. The NEI2008
inventory has even larger NH3 emissions over the Midwest compared to the
optimized NEI2005 in all 3 months shown here. In July, NH3 emissions
in the central US (Kansas, Nebraska, eastern Colorado, and Texas) and
Washington are higher with the optimized NEI2005.
Case studies are performed for VY, SD, and RM, whose Nr deposition footprint
(Fig. ) includes regions showing noticeable differences
between the two NH3 emission inventories (Fig. ). The
non-normalized sensitivity, λi,j, remains constant with the
changes in emissions but the semi-normalized sensitivity, χi,j,k, is
perturbed by the differences in Ei,j,k. Figure
shows the sensitivities of Jp (total modeled Nr deposition in individual
Class I areas) to NH3 emissions for these sites. Overall, when using
NEI2008 the contribution of NH3 emissions to Jp is larger than when
using the optimized NEI2005 inventory in all cases. Differences in NH3
emissions clearly affect sensitivities in VY. Differences in emissions
between the two inventories in Minnesota and Iowa mainly contribute to
changes in the sensitivities for Nr deposition in VY. The source footprint
for VY site gradually accumulates to 90 % of the total Nr deposition at a
distance of 1700 km from VY (see Figs.
and ), which encompasses the regions in Iowa where the
emissions have changed, which are ∼840 km away. SD is not affected much
by different NH3 inventories in July and October as up to 50 % of total Nr
deposition is owing to sources within 250 km (Fig. ).
However, NEI2008 leads to broader estimates of the source footprints in
April. Local influences become more pronounced for SD in the footprints
estimated using the NEI2005 emissions. For the base case, Nr deposition was
found to have significant long range influences for RM. However, when using
the optimized NEI2005 emissions, where NH3 sources in eastern Colorado are
estimated to be much larger, the relative role of long-range influence from
east of the park is reduced.