The National Emission Inventory (NEI) of the US Environmental
Protection Agency (EPA) reports a steady decrease in US NOx emissions
over the 2005–2017 period at a rate of 0.1 Tg N a-1 (53 % decrease
over the period), reflecting sustained efforts to improve air quality.
Tropospheric NO2 columns observed by the satellite-based Ozone
Monitoring Instrument (OMI) over the US show a steady decrease until 2009
but a flattening afterward, which has been attributed to a flattening of
NOx emissions, contradicting the NEI. We show here that the
steady 2005–2017 decrease in NOx emissions reported by the NEI is in
fact largely consistent with observed network trends of surface NO2 and
ozone concentrations. The OMI NO2 trend is instead similar to that
observed for nitrate wet deposition fluxes, which is weaker than that for
anthropogenic NOx emissions, due to a large and increasing relative
contribution of non-anthropogenic background sources of NOx (mainly
lightning and soils). This is confirmed by contrasting OMI NO2 trends
in urban winter, where the background is low and OMI NO2 shows a
2005–2017 decrease consistent with the NEI, and rural summer, where the
background is high and OMI NO2 shows no significant 2005–2017 trend. A
GEOS-Chem model simulation driven by NEI emission trends for the 2005–2017
period reproduces these different trends, except for the post-2009 flattening
of OMI NO2, which we attribute to a model underestimate of free
tropospheric NO2. Better understanding is needed of the factors
controlling free tropospheric NO2 in order to relate satellite
observations of tropospheric NO2 columns to the underlying NOx
emissions and their trends. Focusing on urban winter conditions in the
satellite data minimizes the effect of this free tropospheric background.
Introduction
Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel
combustion harm air quality by catalyzing ozone production and by producing
nitrate particulate matter. They also contribute to acid and nitrogen
deposition. Starting in the early 2000s, the US Environmental Protection
Agency (EPA) implemented increasingly stringent NOx emission controls
targeted principally at improving ozone air quality. The EPA National
Emission Inventory (NEI) reports a steady decrease in US NOx emissions
over the 2005–2017 period at a rate of 0.10 Tg N a-1 or 53 % overall
(EPA, 2018). However, Jiang et al. (2018) showed that tropospheric NO2
columns observed by the OMI satellite instrument over the US stopped
decreasing after 2009, and they concluded that NOx emissions have been
decreasing much less than reported by the NEI. Here we show that the
flattening of the OMI NO2 trend is in fact not inconsistent with the
sustained decrease in NOx emissions reported by the NEI and that the
NEI emission trend is consistent with other atmospheric observations of
NOx and ozone trends. Our results demonstrate the importance of
accounting for the free tropospheric NO2 background when using
satellite observations of NO2 columns to infer NOx emissions and
their trends.
The Ozone Monitoring Instrument (OMI) aboard the US National Aeronautics and
Space Administration (NASA) Aura satellite has been making continuous daily
global observations of NO2 since late 2004 (Levelt et al., 2006, 2018).
The NO2 retrieval (Boersma et al., 2011; Bucsela et al., 2013) involves
spectral fitting of measured nadir solar backscatter at 400–500 nm, yielding
“slant” NO2 columns along the line of sight from which the
contribution from the stratosphere is removed (Martin et al., 2002; Richter
and Burrows, 2002; Bucsela et al., 2013). The slant tropospheric columns are
then converted to actual tropospheric NO2 columns by accounting for
surface and atmospheric scattering, and assuming a vertical distribution of
NO2 within the column (“shape factor”). In polluted regions with high
NOx emissions, most of the information in the NO2 tropospheric
column is presumed to originate from the boundary layer. Thus, the column is
commonly viewed as a proxy for NOx emissions.
Satellite observations of tropospheric NO2 columns have been used
extensively to infer NOx emissions and their trends (Leue et al., 2001;
Martin et al., 2003; Richter et al., 2005; Boersma et al., 2008). OMI
NO2 observations from the early part of the record showed decreasing
trends over the US consistent with the decreases in NOx emissions
reported by the NEI (Russell et al., 2012; Duncan et al., 2013, 2016; Streets et
al., 2013; de Foy et al., 2015; Krotkov et al., 2016)
and also consistent with trends in NO2 concentrations observed from
surface networks (Kharol et al., 2015; Lamsal et al., 2015; Lu et al., 2015;
Tong et al., 2015; Zhang et al., 2018). Several studies reported a
steepening of the OMI NO2 decrease during the Great Recession of
2007–2009 and a subsequent flattening attributed to economic recovery
(Russell et al., 2012; Tong et al., 2015; de Foy et al., 2016). However, the
analysis of the 2005–2015 record by Jiang et al. (2018) shows that the
post-2009 flattening of the NO2 trend extends well beyond the initial
economic recovery period.
The NEI is a “bottom-up” national inventory compiled by the EPA every 3 years using continuous emission monitoring systems (CEMS) for large point
sources, and estimates derived from activity data and emission factors
(NOx emitted per unit of activity) for smaller and distributed sources.
Emissions in 2017 estimated by EPA (2018) included 35 % from on-road
mobile sources, 25 % from off-road mobile sources, 12 % from industrial
point sources, and 27 % from electricity generating units (EGUs). Mobile
emissions are estimated with the Motor Vehicle Emission Simulator (MOVES)
model using vehicle population, vehicle miles traveled (VMT), and
operating modes as inputs. Long-term trends in NOx emissions are recomputed with
each new NEI release using updated emission models so that national trends
are self-consistent for a given NEI version.
Many recent studies using near-source, urban, and regional observations of
atmospheric NOx have found that the NEI greatly overestimates US
NOx emissions (Castellanos et al., 2011; Brioude et al., 2013; Anderson
et al., 2014; Goldberg et al., 2016; Souri et al., 2016; Travis et al.,
2016). CEMS measurements of point sources are considered reliable but tunnel
and roadside measurements show that the MOVES inventory for mobile sources
may be too high (Fujita et al., 2012). Fuel-based approaches for estimating
emissions from mobile sources appear to be more reliable than the MOVES VMT
approach (Dallmann and Harley, 2010; McDonald et al., 2012; Kim et al.,
2016). McDonald et al. (2018) showed that on-road gasoline NOx emission
factors used by NEI are a factor of 2 too high compared to roadside
observations and their fuel-based inventory. All these studies were
conducted under summertime or warm conditions. By contrast, atmospheric
observations of NOx and related species during the WINTER campaign over
the northeastern US during February–March 2015 showed good agreement with the
NEI (Jaeglé et al., 2018; Salmon et al., 2018).
The uncertainty regarding NEI NOx emissions suggests that the trend in
these emissions could be uncertain as well. However, a flattening out of US
NOx emissions over the past decade, as inferred by Jiang et al. (2018)
from the OMI data, would be difficult to reconcile with observations of
steady improvement in ozone air quality (Astitha et al., 2017; Chang et al.,
2017), which has been attributed specifically to NOx emission controls
(Hidy and Blanchard, 2015; Simon et al., 2015; Strode et al., 2015; Xing et
al., 2015; Blanchard and Hidy, 2018; Li et al., 2018). Here we conduct a
more comprehensive analysis of 2005–2017 trends in US NOx emissions
by using the GEOS-Chem chemical transport model (Travis et al., 2016) to
concurrently interpret the trends observed in OMI NO2 columns, nitrogen
wet deposition fluxes, and surface observations of NO2 and ozone.
The 2005–2017 trends of OMI tropospheric NO2 columns
Figure 1 shows the 2005–2017 trends of OMI tropospheric NO2 columns
averaged spatially and annually over the contiguous US. The observations are
from the NASA operational retrieval (level 2, version 3.0; Krotkov et al., 2017)
after removing cloudy scenes (cloud radiance fraction >0.5),
bright surfaces (surface reflectivity >0.3), and observations
affected by the so-called row anomaly (Dobber et al., 2008). OMI is in a
sun-synchronous orbit with overpass at 13:30 LT. It measures
backscattered solar radiation in the nadir and off-track, with 13×24 km2 nadir pixel resolution and global daily coverage. The retrieval
fits the backscattered radiance spectrum to obtain the total slant NO2
column along the line of sight from the Sun to the satellite. The
stratospheric contribution to the total slant column is estimated using OMI
observations over clean background and cloudy areas and applying an
interpolating–filtering–smoothing algorithm (Bucsela et al., 2013). The
remaining tropospheric slant column is then converted to a vertical column
with an air mass factor (AMF; Palmer et al., 2001) that convolves the
altitude-dependent sensitivity from atmospheric scattering (scattering
weights) with the local relative vertical distribution of NO2 from the
Global Modeling Initiative (GMI) model (shape factor). Over continental
source regions, the AMF dominates the overall retrieval error due to
uncertainties in a priori NO2 profiles, surface albedo, and
aerosol and cloud parameters (Kleipool et al., 2008; Boersma et al., 2011;
Lamsal et al., 2014; Lorente et al., 2017). We recomputed the AMFs using
GEOS-Chem rather than GMI shape factors and found little difference in the
mean (Fig. 1).
The 2005–2017 trends in tropospheric NO2 columns and NOx
emissions over the contiguous US. Panel (a) shows OMI observations
averaged over the contiguous US and the corresponding GEOS-Chem simulation.
The OMI observations are from the NASA retrieval (Krotkov et al., 2017) with
air mass factors (AMFs) computed from the original GMI model NO2
vertical profiles or GEOS-Chem vertical profiles. Panel (b) shows
percent changes in tropospheric NO2 columns relative to 2005. Panel (c) shows 2005–2017 annual total NOx emissions from the GEOS-Chem
model, including anthropogenic fuel combustion emissions from the National
Emission Inventory (NEI), with a 60 % decrease for non-EGU sources (see
text and Appendix).
The OMI data show an evident flattening of NO2 columns after 2009, as
pointed out by Jiang et al. (2018), who also find the same flattening in
alternative OMI NO2 retrievals produced by KNMI (Boersma et al., 2011) and UC Berkeley (Laughner et al., 2018). NO2 tropospheric columns decrease at a mean rate of 6±0.5 % a-1 over the 2005–2009 period but then do not change significantly post-2009. We find that data for the western, central, northeastern, and southeastern US all show similar
trends. Hence, we focus our analysis on the mean trends over the contiguous
US, following Jiang et al. (2018).
Also shown in Fig. 1 are trends from a 13-year simulation (2005–2017) with
the GEOS-Chem global chemical transport model at 0.5∘× 0.625∘ nested horizontal resolution over North America. The model is
driven by NEI NOx emissions for fuel combustion, decreased by 60 %
for non-EGU sources following Travis et al. (2016). It also includes
NOx emissions from background (nonfuel combustion) sources, including
open fires (Darmenov and da Silva, 2013), lightning (Murray et al., 2012),
and soil and fertilizer (Hudman et al., 2012). Further details on the model
are in the Appendix. The model NO2 column averages 28 % lower than
observed, due to both an underestimate in background NO2, discussed
below, and because the Travis et al. (2016) correction to the NEI is
excessive, which we will address in a separate paper. More to the point
here, the model shows a sustained decrease, averaging 3.3±0.1 % a-1 over the 2005–2017 period, at odds with the OMI observations,
though lower than the NEI reported decrease of 5.9 % a-1 over the
same period. Here and throughout this paper we derive linear trends by
ordinary regression and express them in units of percent per annum (% a-1) relative to
the mean over the data period, following Jiang et al. (2018). We compute
uncertainty using the bootstrapping method as the error standard deviation
of the linear trend.
The weaker relative trend in the model compared to the NEI is because of the
contribution from background NOx sources. Figure 1c
shows the annual total US NOx emissions for 2005–2017 in the GEOS-Chem
simulation. Anthropogenic emissions from fuel combustion decrease at a rate
of 5.9 % a-1, following the NEI trend. But these emissions account
for only 61 % of total US emissions in 2005 and 42 % in 2017. Natural
emissions from lightning and soils play a relatively increasing role as
anthropogenic emissions decrease. They have interannual variability but no
significant 2005–2017 trend. The trend of total US NOx emissions for
2005–2017 in GEOS-Chem is -3.5 % a-1, closely matching the simulated
NO2 column trend.
Trends in the NOx chemical lifetime over the 2005–2017 period would
affect the relationship between trends in NOx emissions and atmospheric
NO2. Many factors could contribute to a trend in NOx lifetime
(Laughner, 2018; Laughner and Cohen, 2018). We find in GEOS-Chem that the
daily tropospheric NO2 column lifetime over the contiguous US is 8.1 h
in 2005 (annual mean) and 7.7 h in 2017. In the model at least, the trend in
NOx lifetime is much weaker than the trend in emissions, so that the
trend in concentrations mainly follows that of emissions.
The 2005–2017 trends of surface observations
Long-term records of surface NO2 concentrations over the US are
available at a large number of monitoring sites from the US EPA Air Quality
System (AQS) (https://www.epa.gov/aqs, last access: 4 September 2018; Demerjian, 2000)
and at additional sites in the southeast from the Southeastern Aerosol
Research and Characterization Study (SEARCH) network (https://www.dropbox.com/sh/o9hxoa4wlo97zpe/AACbm6LetQowrpUgX4vUxnoDa?dl=0, last access: 27 July 2018; Hansen et al., 2003; Edgerton et al., 2006). AQS sites are mainly urban
and measure NO2 with a chemiluminescence analyzer equipped with a
molybdenum converter, known to have positive interferences from NOx
oxidation products including peroxyacetyl nitrate (PAN) and nitric acid
(HNO3; Dunlea et al., 2007; Steinbacher et al., 2007; Reed et al.,
2016). SEARCH sites are both urban and rural and use a more specific
photolytic converter instrument in which broadband photolysis of NO2 is
followed by chemiluminescence detection of the NO product with accuracy
better than 10 % (Ryerson et al., 2000; Pollack et al., 2010).
Figure 2a–d show annual average trends in daily
surface NO2 concentrations at the 132 AQS sites with continuous
yearlong records for 2005–2017 and the 2 rural SEARCH sites (Centreville, AL,
and Yorkville, GA) with continuous records for 2005–2016 (SEARCH was
discontinued in 2017). Also shown for the AQS sites are the values corrected
for interferences based on local GEOS-Chem monthly mean NO2, alkyl
nitrate, PAN, and HNO3 concentrations and following the correction
factor in Lamsal et al. (2008). The AQS data show decreasing trends
throughout the 2005–2017 period, generally consistent with the NEI. The
rural SEARCH sites also show a steady decrease but are more noisy (only two sites). One would expect the trend in the urban AQS data to be most
indicative of the trend in anthropogenic NOx emissions from fuel
combustion. GEOS-Chem underestimates the AQS observations because of the
urban nature of the sites, but the model relative decreases agree closely
with observations for both the AQS and the SEARCH data. This is in sharp
contrast to the OMI NO2 data.
The 2005–2017 trends in annual mean surface NO2 concentrations
and nitrate wet deposition fluxes over the contiguous US. Observations are
compared to GEOS-Chem model values sampled at the corresponding sites. The
map in the right panel (g) shows the observation sites for the AQS, SEARCH, and NADP
measurements networks with continuous annual records for 2005–2017 (2016 for
SEARCH). Panels (a) and (b) show surface NO2 observed at AQS sites (mainly
urban). The measurements are affected by positive interference from NOx
oxidation products and the gray line shows the data corrected as in Lamsal
et al. (2008). Panels (c) and (d) show surface NO2 at the two rural SEARCH
sites in the southeastern US. Panels (e) and (f) shows nitrate wet deposition
fluxes at NADP sites. Panels (b), (d) and (f) show trends relative to 2005 values
and the mean ± standard deviation percent change per year is shown
inset. All trends shown are statistically significant.
Jiang et al. (2018) reported AQS surface NO2 trends of -6.6±1.4 % a-1 for 2005–2009 and -2.6±1.5 % a-1 for
2011–2015, indicating a significant weakening of the trend with time. But
they used all AQS sites in that analysis including those with incomplete
records. We find that when using only sites with continuous records, the
slope is steeper for the latter time period. Specifically, we find the AQS
trend to be -6.6±1.2 % a-1 for 2005–2009 and -4.5±1.7 % a-1 for 2011–2015. In comparison, the NEI emission trend is
-6.4 % a-1 for 2005–2009 and -5.3 % a-1 for 2011–2015. Thus,
the surface data suggest a slight weakening of the NOx emission trend
relative to the NEI but not the flattening implied by the OMI data. Jiang et
al. (2018) presented an alternative fuel-based NOx emission inventory
to the NEI, featuring a slowdown in the trend of US NOx emissions after
2009 due to a slower rate of reduction for industrial, off-road mobile, and
on-road diesel sources as well as a smaller relative contribution of on-road
gasoline. That inventory shows a -2.9 % a-1 trend for 2011–2015. The
AQS trend is in somewhat better agreement with the NEI inventory but could
accommodate either inventory within its error standard deviation.
Figure 2e–f show observed and simulated trends in nitrate
(NO3-) wet deposition fluxes for the 138 National Acid Deposition
Program (NADP; https://nadp.slh.wisc.edu/data/NTN/, last access: 14 August 2018) sites with
continuous yearlong records for 2005–2017. Nitric acid gas and nitrate
aerosol are both efficiently scavenged by precipitation and the lifetime of
NOx is sufficiently short that nitrate wet deposition fluxes should
relate to total NOx emissions. The relationship is not one-to-one
because of competition from dry deposition but one would not expect a
long-term trend in the wet/dry deposition ratio. GEOS-Chem model values for
individual years are corrected for precipitation bias using the
high-resolution PRISM precipitation data (http://prism.oregonstate.edu, last access: 14 August 2018; Di Luzio et al., 2008), as described by
Paulot et al. (2014) and Travis et al. (2016). Model values average 17 %
lower than observed values, again because the model may underestimate emissions,
but the trends are consistent. The fluxes show a decrease over the 2005–2017
time period (averaging 2.7±0.3 % a-1 observed, 2.9±0.3 % a-1 modeled), weaker than for surface NO2 concentrations.
After 2012, there is still a significant decrease in nitrate wet deposition,
though it is less than during the earlier time period (averaging 1.3±0.9 % a-1 observed and 1.7±0.7 % a-1 modeled).
Nitrate wet deposition is more sensitive to background (nonfuel combustion)
influences than NO2 concentrations because (1) the wet deposition sites
are prevailingly rural and (2) precipitation scavenges a deeper column. Indeed,
in GEOS-Chem, the mean nitrate wet deposition trend is more consistent with
the -3.5 % a-1 trend of total NOx emissions (including lightning
and soils) than that of emissions from fuel combustion (-5.9% a-1).
The relative contribution from background sources to nitrate wet deposition
would be expected to increase over time as fuel combustion emissions
decrease. In order to quantify this, we performed GEOS-Chem sensitivity
simulations for 2005 and 2017 with only background NOx emissions
(shutting off NOx emissions from US fuel combustion). We find that
background contributed 50 % of nitrate wet deposition at NADP sites in
2005 but 69 % in 2017. In contrast, background only contributed 5 % to
surface NO2 at AQS sites in 2005 and 10 % in 2017.
Figure 3 shows summertime ozone trends for 2005–2017 as further evidence of
a sustained decrease in anthropogenic NOx emissions. Data are from the
AQS and Clean Air Status and Trends Network (CASTNET; https://www.epa.gov/castnet, last access: 27 July 2018) networks. We show records for the 47 CASTNET
and 427 AQS sites with continuous summertime records for 2005–2017. The
trends are for the 95th percentiles in the maximum daily 8 h average
(MDA8) values determined at individual sites and then averaged across all
sites for each summer. We excluded high-elevation (>1.5 km)
CASTNET sites in the western US because they have different trends driven in
part by the larger influence from background ozone (Cooper et al., 2011; Lin
et al., 2017; Jaffe et al., 2018). Much of the interannual variability in
ozone concentrations in Fig. 3 can be explained by surface temperatures,
including the 2012 peak in ozone in the observations and captured by
GEOS-Chem, which is due to anomalously high temperatures (Fiore et al.,
2015; Jia et al., 2016; Lin et al., 2017). Nonetheless, the surface
observations do show overall decreases over the 2005–2017 time period. On a
national scale, the observations show declines of 1.11±0.08 ppb a-1 (CASTNET) and 1.04±0.03 ppb a-1 (AQS), with no indication of a post-2009 flattening. The GEOS-Chem model shows similar
trends. The sustained (post-2009) decrease in ozone pollution over the past
decade provides additional evidence of a continued decrease in anthropogenic
NOx emissions.
Summertime surface ozone trends for 2005–2017 at the CASTNET and
AQS networks in the contiguous US. The trends are for the 95th
percentile of the maximum daily 8 h average (MDA8) ozone concentrations
computed for individual sites (shown in the map on the right) and then
averaged over all sites from the network. High-elevation (>1.5 km) CASTNET sites in the western US are excluded. The slope and standard
deviation of the linear regressions are shown inset, and all trends shown
are statistically significant.
Comparative analysis of trends
Figure 4 combines the relative trends since 2005 of NEI
NOx emissions, OMI tropospheric NO2 columns, surface NO2
concentrations, and nitrate wet deposition fluxes into a single plot. Observed surface NO2
concentrations follow the NEI emissions trend, showing consistency with a
sustained decline of emissions over the 2005–2017 time period. This behavior
is well captured by GEOS-Chem, which confirms the 1:1 relationship expected
between surface NO2 concentrations and NOx emissions. Nitrate wet
deposition observations show a much weaker trend, which we attributed in
Sect. 3 to a larger contribution of the background. The GEOS-Chem trend
for nitrate wet deposition and tropospheric NO2 columns is similarly
weaker than for surface NO2, reflecting the influence of the
background, but shows a steeper decrease than observed after 2009. This
suggests that GEOS-Chem may underestimate the background contribution.
Relative trends since 2005 of NEI NOx emissions and relevant
atmospheric quantities averaged over the contiguous US. Panel (a) shows
observations and (b) shows the GEOS-Chem simulation. NEI
NOx emissions are the same in both panels. The SEARCH network was
discontinued in 2017.
Satellite-based tropospheric NO2 columns show trends remarkably similar
to those of nitrate wet deposition fluxes, both in the OMI observations and
in GEOS-Chem, suggesting that the post-2009 flattening of the OMI trend is
due to a large and increasing relative influence of the background rather
than to a leveling of US NOx emissions.
Background contribution to OMI NO2 trends
We showed in Sect. 4 that the 2005–2017 trend of OMI NO2 columns over
the US is similar to that of nitrate wet deposition and much weaker than
that of surface NO2 concentrations, pointing to the importance of
background in affecting the NO2 column. To further examine this effect,
we segregated the OMI observations into winter and summer as well as urban and rural. Urban
conditions are defined as the top 10 % NOx-emitting 0.5∘× 0.625∘ grid squares in the US according to the NEI. We
expect background influences to be relatively higher at rural than urban
sites, and higher in summer (lightning, soil, intercontinental transport;
Fischer et al., 2014) than in winter. Thus, background influences should be
at a minimum in winter urban conditions and a maximum under summer rural
conditions.
Figure 5 shows the results. OMI NO2 observations in urban winter show a
steady decline at a mean rate of 3.3±0.5 % a-1, with no
post-2009 flattening, though there is some suggestion of a slightly weaker
trend after 2009 when compared to GEOS-Chem driven by NEI. By contrast, the
OMI NO2 observations in rural summer show no significant trend over the
2005–2017 period. GEOS-Chem for rural summer shows a significant decreasing
trend for 2005–2017 but weaker than for urban winter and become
insignificant for the 2009–2017 period. The winter rural and summer urban
conditions in Fig. 5 show trends that are intermediate between these two limiting
cases. The ability of GEOS-Chem to capture the observed post-2009 weakening
of the trend in the summer urban case argues against a seasonal flattening
of emissions that would affect summer but not winter.
OMI tropospheric NO2 column trends over the contiguous US
relative to 2005, separated into urban and rural as well as summer (JJA) and winter (DJF).
OMI observations are shown in black, the standard GEOS-Chem model simulation
with EPA National Emission Inventory (NEI) trends (EPA, 2018) is in red, and
the GEOS-Chem sensitivity simulation with additional NO2 background (50 ppt above 5 km in winter and above 10 km in summer, up to the local
tropopause) is shown in blue. Slopes and standard deviation of the linear
regressions are shown inset. Urban conditions are defined as the top 10 %
NOx-emitting 0.5∘× 0.625∘ grid squares in
the NEI.
It thus appears that the post-2009 flattening of the OMI NO2 trend over
the US is due to increasing relative importance of the NO2 background,
rather than to flattening of US NOx emissions. Satellite observations
of tropospheric NO2 columns are more sensitive to the free troposphere
than to the boundary layer because of atmospheric scattering; the
sensitivity increases by a factor of 3 from the surface to the upper
troposphere for clear sky and by much more for a cloudy atmosphere (Martin
et al., 2002). For the OMI NO2 data set used here, the sensitivity
increases by over a factor of 4 from the surface to the upper
troposphere on average, as given by the scattering weights (Krotkov et al., 2017). The
AMF is intended to correct for this effect but relies on an assumed model
vertical distribution of NO2 that may not correctly account for free
tropospheric levels or for the changing ratio between the free troposphere
and the boundary layer as anthropogenic NOx emissions decrease.
There is indeed evidence that free tropospheric NO2 makes a large
contribution to OMI NO2 columns and that models underestimate this
contribution. Measurements of NO2 vertical profiles during the
SEAC4RS aircraft campaign over the southeastern US in August–September 2013 showed a median concentration of 300 ppt near the surface, dropping to a
50 ppt background in the free troposphere at 2–10 km, and rising back to 130 ppt at the 12 km aircraft ceiling (Silvern et al., 2018). By applying OMI
scattering weights to this median vertical profile, most representative of a
rural profile, Travis et al. (2016) found that the boundary layer below 1.5 km contributed only 19 %–28 % of the OMI NO2 tropospheric column. A
GEOS-Chem simulation of the SEAC4RS conditions matched the observed 50 ppt background (mostly from lightning) but could not reproduce the
enhancement above 10 km (Travis et al., 2016; Silvern et al., 2018). The GMI
model used to compute AMFs in the NASA OMI NO2 retrievals also has
little NO2 in the upper troposphere (Lamsal et al., 2014). Measurements
of NO2 in the upper troposphere are prone to positive interferences
because of inlet decomposition of labile reservoirs (Reed et al., 2016), but
the measurements in SEAC4RS were designed to minimize and correct for
these interferences (Thornton et al., 2000; Day et al., 2002; Wooldridge et
al., 2010; Nault et al., 2015). Silvern et al. (2018) suggested that errors
in the kinetics of NO–NO2–O3 cycling reactions could explain model
underestimates of NO2 concentrations in the upper troposphere.
Choi et al. (2014) and Belmonte Rivas et al. (2015) used the so-called
cloud-slicing method to isolate the upper tropospheric contribution to the
OMI NO2 observations by comparing neighboring cloudy scenes with cloud
tops at different altitudes. They report in this manner partial NO2
columns at 6–10 km altitude. Marais et al. (2018) evaluated these data in
comparison with aircraft observations and found large uncertainties but
concluded that GEOS-Chem underestimates NO2 at 6–10 km over North
America by 20–30 ppt in winter with no significant bias in summer. The good
agreement in summer is consistent with the comparison to SEAC4RS
observations, which shows, however, a low model bias above 10 km.
We conducted a sensitivity test, adding 50 ppt of background NO2 to the
GEOS-Chem vertical profiles above 5 km altitude in winter and above 10 km in
summer, up to the local tropopause. The resulting normalized vertical
profiles (shape factors) were convolved with the vertical distribution of
sensitivities (scattering weights) provided by the NASA retrieval to
recompute the AMFs. The implications for the model trends are shown in
Fig. 5 as the blue lines. The effect is large for winter rural conditions,
where the added free tropospheric background is particularly important and
largely reconciles the model trend with the OMI observations. It is much
less in summer, where the addition is only above 10 km and there is already
substantial background NO2 present. The discrepancy between the model and the
observations in summer is largely driven by the uptick in the summer
rural observations for 2016–2017.
It is possible that additional background NO2 missing from the model in
summer could be present in the tropopause region and lower stratosphere. The
deepest convection in summertime over the US can reach 17 km in the
lowermost stratosphere (Randel et al., 2012; Huntrieser et al., 2016b;
Anderson et al., 2017; Herman et al., 2017; Smith et al., 2017). Such a deep
convective injection could conceivably deliver substantial lightning
NO2 above the tropopause. Although delivered above the tropopause, this
NO2 would be counted as tropospheric in retrievals because it would
represent an enhancement above background NO2 columns in the
stratospheric separation. It could have a particularly important effect on
the AMF by being delivered above clouds. High NOx mixing ratios in the
lowermost stratosphere were observed over the central and southeastern US
during the DC3 aircraft campaign in May–June 2012 and were attributed to
lightning (Huntrieser et al., 2016a, b), and higher
lightning flash rates have been observed in tropopause-penetrating
above-anvil cirrus plumes (Bedka et al., 2018). There is suggestive evidence
that convective injection into the lowermost stratosphere over the US may
have increased during the 2004–2013 period (Cooney et al., 2018), which
could further affect the OMI NO2 column trend, although the Lightning
Imaging Sensor (LIS) satellite data do not show a 2003–2012 trend in total
lightning over the US (Koshak et al., 2015). While tropopause heights in the
GEOS MERRA-2 meteorological data driving GEOS-Chem agree well with
SEAC4RS observations of water vapor and ozone (Kuang et al., 2017;
Smith et al., 2017), models in general do not properly capture the observed
convective injections into the lowermost stratosphere (Smith et al., 2017;
Anderson et al., 2019). The 0.5∘× 0.625∘
resolution of the MERRA-2 meteorological data would be too coarse to resolve
convective overshoots.
Conclusions
US emissions of nitrogen oxides (NOx≡NO+NO2) from
fuel combustion steadily declined over 2005–2017 at a mean rate
of 5.9 % a-1 according to the National Emission Inventory (NEI) of
the US EPA. Tropospheric NO2 columns over the US observed by OMI aboard the Aura satellite instead show a leveling off after 2009, leading to
the suggestion that the NEI emission trend is in error and that related air
quality gains have halted. Here we re-examined this issue by using trends in
surface observations together with a 2005–2017 GEOS-Chem chemical transport
model simulation to better understand the relationship between satellite
NO2 observations, NOx emissions, and their trends.
We started by comparing the 2005–2017 GEOS-Chem simulation driven by NEI
emission trends to the OMI observations. The model shows a sustained
decrease in the tropospheric NO2 column at a mean rate of 3.3±0.1 % a-1 over the period. The rate is less than the NEI trend
because of natural NOx emissions (mainly from lightning and soils) that
account in GEOS-Chem for 58 % of total NOx emissions over the US by
2017. Nevertheless, the GEOS-Chem simulation cannot capture the post-2009
flattening in the OMI observations.
We then examined 2005–2017 US trends in surface observations of NO2
concentrations and nitrate wet deposition fluxes from surface networks (AQS,
SEARCH, NADP). Surface NO2 concentrations measured by the AQS (urban)
and SEARCH (rural) surface networks show a decline over the 2005–2017 time
period that closely follows the NEI emissions trend, and the same is found
in GEOS-Chem. Some deviation between AQS NO2 and the NEI towards the
later part of the time period suggests that the rate of decrease in
emissions may have slowed slightly. Nitrate wet deposition shows a much
weaker 2005–2017 trend than surface NO2 and NEI emissions, both in the
observations and the model, reflecting a large and increasing relative
contribution from background sources (69 % in the model in 2017) as
anthropogenic emissions decrease. Surface ozone concentrations from the
CASTNET and AQS networks show sustained 2005–2017 decreases, consistent with
the model; such sustained decreases would be hard to reconcile with a
flattening of NOx emissions.
Bringing together these observed trends, we see two different patterns: (1) a 2005–2017 decrease in surface NO2 that supports the steady decrease
in NOx emissions reported by the EPA NEI and (2) a weaker trend and
post-2009 flattening of OMI NO2 and nitrate wet deposition that
reflects a growing influence from the background, rather than large error in
NEI NOx emissions.
We confirmed the importance of background NO2 in driving the post-2009
flattening of OMI NO2 trends over the US by segregating the OMI
observations into urban and rural as well as winter and summer. There is a steady 2005–2017
decrease in the urban winter data where background influence is lowest. By
contrast, there is no significant 2005–2017 trend in rural summer (where
background influence is highest). The failure of GEOS-Chem to reproduce the
observed post-2009 flattening then points to a model underestimate of the
NO2 background. Cloud-sliced OMI NO2 data indicate a GEOS-Chem
underestimate of the upper tropospheric background in winter. Deep
convective injections of lightning NOx above the tropopause might add
to the NO2 background in summer. Observations from the NASA
SEAC4RS aircraft campaign show lower NO /NO2 ratios than
simulated by GEOS-Chem, which could reflect errors in the kinetics of
NO–NO2–O3 chemical cycling (Silvern et al., 2018). While such
errors would be most important in summertime, chemistry important for
wintertime NOx not being comprehensively included in models may help to
explain the winter background NO2 underestimate. Observations of
short-chained alkyl nitrates show higher concentrations in the northern
extratropical free troposphere in winter than captured by GEOS-Chem and may
represent an increasing reservoir of background NOx (Fisher et al.,
2018). Measurements from the WINTER campaign suggest models may also
overestimate NOx loss via N2O5 hydrolysis (Jaeglé et al.,
2018; Kenagy et al., 2018; McDuffie et al., 2018), and recent laboratory data
suggest that models using the recommended NASA-JPL kinetics for the
NO2+OH reaction may overestimate NOx loss at cold temperatures
(Amedro et al., 2019).
We conclude that the sustained 2005–2017 decrease in US NOx emissions
reported by the EPA is supported by observations and that better
understanding of the free tropospheric background is needed to interpret
satellite observations of NO2 tropospheric columns in terms of their
implications for NOx emissions and their trends. The concern is minor
in highly polluted areas where NOx emissions are sufficiently high to
dominate over the background influence. In the US, however, NOx
emissions have now decreased to the point that NO2 columns over
nonurban areas are mostly contributed by the free tropospheric background.
Accounting for this poorly understood background will become increasingly
important as NOx emissions continue to decrease in the developed world
and in tropical regions that are undergoing rapid development but have a
deep troposphere and intense lightning.
Data availability
OMI NO2 observations are available from https://mirador.gsfc.nasa.gov/ (last access: 31 January 2019).
AQS NO2 and ozone observations are available from https://www.epa.gov/aqs (last access: 4 September 2018).
SEARCH NO2 observations are available from https://www.dropbox.com/sh/o9hxoa4wlo97zpe/AACbm6LetQowrpUgX4vUxnoDa?dl=0 (last access: 27 July 2018).
NADP nitrate wet deposition observations are available from https://nadp.slh.wisc.edu/data/NTN/ (last access: 14 August 2018).
CASTNET ozone observations are available from https://www.epa.gov/castnet (last access: 27 July 2018).
GEOS-Chem output from this work is available upon request.
The GEOS-Chem model
We conducted a 13-year simulation (2005–2017) with the GEOS-Chem global 3-D
chemical transport model version 11-02c (http://www.geos-chem.org, last access: 14 August 2018) using NASA MERRA-2 assimilated meteorological
data (Gelaro et al., 2017). We use the nested North American version of
GEOS-Chem at the native MERRA-2 0.5∘× 0.625∘
horizontal resolution over North America and adjacent oceans
(10–70∘ N, 140–40∘ W) with dynamic boundary conditions
from a global simulation with 4∘× 5∘
horizontal resolution. The simulation includes detailed
NOx–hydrocarbon–aerosol chemistry as described in Travis et al. (2016),
Fisher et al. (2016) and Marais et al. (2016). US anthropogenic emissions
are distributed spatially following the NEI2011 inventory (EPA, 2018).
NEI2011 is scaled for individual years using national annual totals (EPA,
2018), and we decrease non-EGU NOx emissions by 60 %, as in Travis et
al. (2016), for all years. Open fire emissions are from the daily Quick Fire
Emissions Database (QFED; Darmenov and da Silva, 2013) with diurnal
variability from the Western Regional Air Partnership (Air Sciences, 2005).
Soil NOx emissions, including emissions from fertilizer application,
are computed according to Hudman et al. (2012), with a 50 % reduction in
the midwestern US for summertime based on a previous comparison with OMI
NO2 observations (Vinken et al., 2014). Lightning NOx emissions
are described by Murray et al. (2012) with a horizontal distribution
matching climatological observations of lightning flashes, interannual
variability driven by MERRA-2 convection, and most of the release at the top
of convective updrafts (Ott et al., 2010). The NOx yield per flash is
260 mol to the south of 35∘ N and 500 mol to the north (Hudman et
al., 2007; Huntrieser et al., 2008, 2009; Ott et al., 2010; Travis et al.,
2016).
The GEOS-Chem simulation of NOx and related species over the US has
been evaluated in a number of recent papers including Zhang et al. (2012),
Ellis et al. (2013), and Lee et al. (2016) for nitrogen deposition; Travis
et al. (2016) for NOx concentrations over the southeastern US during the
SEAC4RS campaign; Fisher et al. (2016) for organic nitrates during that
same campaign; Jaeglé et al. (2018) for the WINTER campaign; and Fischer
et al. (2014) for the ensemble of PAN observations. These evaluations find
that the model is overall successful with no indication of systematic bias.
Author contributions
DJJ, LJM, and RFS designed the study. RFS and MPS
conducted model simulations. RFS analyzed satellite, surface, and model
data. KRT contributed NEI emissions in GEOS-Chem and supported data
analysis. LJM, EAM, RCC, and JLL helped with scientific interpretation and
discussion. SC, JJ, and LNL provided OMI data and supporting guidance. RFS
and DJJ wrote the manuscript and all authors provided input on the paper for
revision before submission.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
This study's contents are solely the responsibility of the grantee and do
not necessarily represent the official views of the US EPA. Further, US EPA
does not endorse the purchase of any commercial products or services
mentioned in the publication.
Financial support
This research has been supported by the US Environmental Protection Agency (grant no. 83587201). Daniel J. Jacob was supported by the NASA Earth Science
Division.
Review statement
This paper was edited by Qiang Zhang and reviewed by two anonymous referees.
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