ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-10965-2015Ozone and NOx chemistry in the eastern US: evaluation of CMAQ/CB05 with satellite (OMI) dataCantyT. P.tcanty@atmos.umd.eduHembeckL.VinciguerraT. P.AndersonD. C.https://orcid.org/0000-0002-9826-9811GoldbergD. L.CarpenterS. F.AllenD. J.https://orcid.org/0000-0003-3305-9669LoughnerC. P.SalawitchR. J.DickersonR. R.https://orcid.org/0000-0003-0206-3083Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USAEarth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USANASA Goddard Space Flight Center, Greenbelt, MD, USADepartment of Chemistry and Biochemistry, University of Maryland, College Park, MD, USAT. P. Canty (tcanty@atmos.umd.edu)2October2015151910965109826December201417February201513August201519August2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/10965/2015/acp-15-10965-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/10965/2015/acp-15-10965-2015.pdf
Regulatory air quality models, such as the Community Multiscale Air Quality model (CMAQ), are used by federal and state agencies
to guide policy decisions that determine how to best achieve adherence with National Ambient Air Quality Standards for surface
ozone. We use observations of ozone and its important precursor NO2 to test the representation of the photochemistry and
emission of ozone precursors within CMAQ. Observations of tropospheric column NO2 from the Ozone Monitoring Instrument
(OMI), retrieved by two independent groups, show that the model overestimates urban NO2 and underestimates rural
NO2 under all conditions examined for July and August 2011 in the US Northeast. The overestimate of the urban to rural
ratio of tropospheric column NO2 for this baseline run of CMAQ (CB05 mechanism, mobile NOx emissions from the
National Emissions Inventory; isoprene emissions from MEGAN v2.04) suggests this model may underestimate the importance of
interstate transport of NOx. This CMAQ simulation leads to a considerable overestimate of the 2-month average of 8 h
daily maximum surface ozone in the US Northeast, as well as an overestimate of 8 h ozone at AQS sites during days when the
state of Maryland experienced NAAQS exceedances. We have implemented three changes within CMAQ motivated by OMI NO2 as
well as aircraft observations obtained in July 2011 during the NASA DISCOVER-AQ campaign: (a) the modeled lifetime of organic
nitrates within CB05 has been reduced by a factor of 10, (b) emissions of NOx from mobile sources has been reduced by
a factor of 2, and (c) isoprene emissions have been reduced by using MEGAN v2.10 rather than v2.04. Compared to the baseline
simulation, the CMAQ run using all three of these changes leads to considerably better simulation of column NO2 in
both urban and rural areas, better agreement with the 2-month average of daily 8 h maximum ozone in the US Northeast, fewer
number of false positives of an ozone exceedance throughout the domain, as well as an unbiased simulation of surface ozone at
ground-based AQS sites in Maryland that experienced an ozone exceedance during July and August 2007. These modifications to CMAQ
may provide a framework for use in studies focused on achieving future adherence to specific air quality standards for surface
ozone by reducing emission of NOx from various anthropogenic sectors.
Introduction
The spatial scale of tropospheric ozone production is of enormous
consequence, especially for the eastern US where cross-state transport of air
pollutants is a major policy concern (EPA vs. EME, 2014). As early as the
1980's (Logan, 1989), analysis of measurements indicated that surface ozone
episodes covered areas approaching 106km2. Numerous
observational studies have demonstrated the westerly transport of ozone and
its precursors and the impact of upwind emissions that lead to high
concentrations of surface ozone in the eastern United States (Brent et al.,
2013; Hains et al., 2008; He et al., 2014, 2013a, b; Ryan et al., 1998;
Taubman et al., 2004, 2006). Dramatic improvements in air quality have been
observed due to reductions in the emission of ozone precursors (Butler
et al., 2011; Fiore et al., 1998; Gego et al., 2007; He et al., 2013b; Marufu
et al., 2004; Walsh et al., 2008). Numerical simulations of ozone show
a dependence on NOx (NO +NO2) concentrations, but do not
always capture the regional nature of photochemical smog events nor the
strength of the response to NOx emission controls (Fujita et al., 2013;
Gilliland et al., 2008; Godowitch et al., 2008a, b; Hogrefe et al., 2011;
Pollack et al., 2013; Wilson et al., 2012; Yegorova et al., 2011; Zhou
et al., 2013). Recognizing that long-range transport affects local compliance
of federally mandated standards, the EPA enacted the Cross State Air
Pollution Rule (CSAPR) http://www.epa.gov/crossstaterule which reduces
the emissions of ozone precursors from power plants. Phase 1 of the CSAPR
emissions budgets is scheduled for implementation in 2015.
The Community Multiscale Air Quality model (CMAQ) is used extensively for
regulatory purposes (Byun and Schere, 2006). State Implementation Plans
(SIPs) quantify future emission reductions that will bring nonattainment
areas into compliance with the National Ambient Air Quality Standard (NAAQS)
for surface ozone. The recommendations submitted in the SIPs are based on
analysis of output from air quality models such as CMAQ that simulate
meteorology and the nonlinearities inherent in tropospheric ozone chemistry.
Oxides of nitrogen play a controlling role in ozone production in the eastern
US and comparison of monitor records with CMAQ output suggests that this
model overestimates NOx concentrations in urban areas, but underestimates
NOx in rural areas (Castellanos et al., 2011). The overestimate of NOx
in urban areas, coupled with the underestimate in rural areas, means CMAQ may
keep NOx too closely confined to source regions, thereby underestimating
the interstate transport of ozone precursors. Such disagreement may stem from
the complicated nature of NOx emissions, recycling, and removal though
proper representation of dynamics (e.g. vertical mixing) may also play a
role. Organo-nitrate compounds can act as reservoirs or sinks for NOx and
have received substantial attention for many years (Atlas, 1988; Beaver
et al., 2012; Day et al., 2003; Farmer et al., 2006; Lockwood et al., 2010;
Neff et al., 2002; Perring et al., 2013, 2010, 2009; Xie et al., 2013). The
production and loss mechanisms that govern the concentrations of
organo-nitrate species are largely simplified in most air quality models.
Ground-based and aircraft measurements of ozone and its precursors provide
important constraints on the concentrations of trace gasses and can be
used to validate model output. However, these data are geographically
limited. In contrast, satellites provide a measure of the spatial variation
of these species over a much larger domain. In this study, we investigate the
regional representation of NO2 in CMAQ by comparing model output to
satellite observations from the Ozone Monitoring Instrument (OMI) (e.g.,
Boersma et al., 2011; Bucsela et al., 2013) during July and August 2007.
Modifications to the chemical mechanism and emissions inventories used by the
model are suggested to improve agreement between model and observations. More
relevant to policy makers is the ability of air quality models to reproduce
surface ozone. We extend our analysis to include comparisons between model
output and ozone observed at ground-based stations. The model domain for this
study is the eastern United States (i.e., most of the states eastward of the
Mississippi river), a region that has been the focus of intense SIP modeling
efforts by mid-Atlantic states and for which detailed emissions inventories
and meteorological fields are readily available.
Data description: OMI satellite
OMI is one of four instruments onboard the NASA Aura satellite, now in its
tenth year http://aura.gsfc.nasa.gov/. The satellite is in a sun
synchronous orbit, providing OMI with a daytime overpass at ∼ 13:40
local time (LT). NO2 columns are retrieved using differential optical
absorption spectroscopy (DOAS) in the 405–465 nm range. There are
two operational retrievals of tropospheric column NO2 based on
radiances measured by OMI; the Derivation of OMI tropospheric NO2
(DOMINO) product (Boersma et al., 2007, 2011) provided by the Royal
Netherlands Meteorological Institute (KNMI) and the NASA Goddard Space Flight
Center (GSFC) product (Bucsela et al., 2013).
Retrievals of tropospheric column NO2 (hereafter, column NO2)
from OMI are available for the width of the atmosphere observed along the
viewing track (or swath) of the instrument. OMI tropospheric column
NO2 is calculated by subtracting the stratospheric signal from the
observations of total column NO2. The method of determining the
stratospheric component varies between the GSFC and DOMINO product and is
explained in Bucsela et al. (2013) and Boersma et al. (2007), respectively.
GSFC assumes that total column NO2 over regions with little expected
tropospheric influence represents the stratospheric column. This field of
stratospheric column NO2 is interpolated over nearby regions of high
surface pollution and removed from the total column NO2 retrieval to
determine tropospheric column NO2. For the DOMINO product, data
assimilation of OMI slant columns with the TM4 model (Tracer Model version 4,
van Noije et al., 2006) determines the stratospheric subtraction. These level
2 (L2) observations do not occur on a regularly spaced grid. For a more
meaningful comparison between satellite retrievals and model output, we
generate level 3 (L3) products on a 0.25∘×0.25∘
(latitude, longitude) grid for both observed and calculated column
NO2. Column NO2 from OMI is weighted based on satellite
viewing angle using the formulation of Bucsela et al. (2013). These same
weights are applied to column NO2 from CMAQ to assure a meaningful
comparison.
Column NO2 measurements are only considered for the gridding
procedure when solar zenith angle is less than 85∘ and effective
cloud fraction (Acarreta et al., 2004) is less than 30 %. Observations
are only considered valid and used for our analysis if the flag
“xtrackqualityflag”, provided in the data files for both retrievals, is
equal to zero. This flag indicates which OMI data products can be used in
a manner that minimizes the influence of the row anomaly along the observing
swath (see
http://www.knmi.nl/omi/research/product/rowanomaly-background.php for
more information). Summary quality flags, also provided in the data files,
provide quality assurance that at least 50 % of the tropospheric column
is determined by observed information. In our study, retrievals are only used
when this flag also equals zero. Comparison with model output is facilitated
through the use of averaging kernels (DOMINO) or scattering weights (GSFC).
No uncertainties are provided explicitly for the averaging kernels or
scattering weights. Instead, the DOMINO retrieval team provides uncertainties
(“VCDTropErrorUsingAVKernel”) that account for errors in both the
NO2 column and the averaging kernel. The precision in the GSFC
NO2 product is provided in the variable “ColumnAmountNO2TropStd”.
We use these uncertainties as the defacto way of comparing the two different
retrievals of column NO2 to model output of this quantity.
The production of ozone in our region of study is NOx-limited for nearly
all of the domain. An analysis of satellite observations acquired in 2005 to
2007 shows that ozone production is VOC-limited for only a small part of New
York City, with the rest of the domain being NOx-limited (Duncan et al.,
2010). Hence, the general focus of this manuscript is on model representation of
nitrogen oxides. The results of this study may not be applicable to regions
where production of ozone is VOC dominant, which occurs primarily in rural
regions such as the so-called isoprene volcano of the Ozarks (Carlton and
Baker, 2011) or regions of intense hydrocarbon processing such as Houston,
Texas (Li et al., 2007).
Model description
For this analysis we use the Community Multiscale Air Quality (CMAQ) modeling
system version 4.7.1 (https://www.cmascenter.org/cmaq/). This model has
been used extensively by states that are members of the Ozone Transport
Commission (OTC) in preparation for the 2015 ozone SIP call. For this
analysis, CMAQ simulations were performed for the Eastern US with
a 12km×12km horizontal resolution and a 34 layer
(σ coordinate) vertical grid from the surface to ∼ 20 km
with hourly output. CMAQ does not include stratospheric processes so the
upper layers of the model atmosphere should not be used for research
purposes. The analysis presented here is limited to altitudes below the
tropopause. Simulated meteorology is driven by output from the Weather
Research Forecasting (WRF v3.1.1) model for year 2007 and processed for use
in CMAQ by the Meteorological Chemistry Interface Processor (MCIP).
Emissions inventories for year 2007 were developed by the Mid-Atlantic
Regional Air Management Association, Inc. (MARAMA) specifically for use in
OTC modeling efforts. Biogenic emissions were estimated using the Model of
Emissions of Gases and
Aerosols in Nature (MEGAN v2.04) (Guenther et al., 2006). Emissions from
on-road mobile sources were developed using the Motor Vehicle Emission
Simulator (MOVES) (USEPA, 2010) while off–road emissions were supplied by
the National Mobile Inventory Model (NMIM) (USEPA, 2005). Emissions due to
aircraft are included in the inventories based on take-off and landing data
for individual airports. Emission inventories and WRF/MCIP meteorology are
merged and gridded using the Sparse Matrix Operator Kernel Emissions (SMOKE
v3.1, https://www.cmascenter.org/smoke/) model to generate
time-varying, three dimensional CMAQ ready emission fields. CMAQv4.7.1 uses
the 2005 Carbon Bond (CB05) chemical mechanism (Yarwood et al., 2005). Though
there is a more recent version of the Carbon Bond chemical mechanism (CB6) it
is not available for use in publicly available versions of CMAQ as of the
submission of this manuscript.
We have added lightning-generated NOx (LNOx), assuming
an NOx production rate of 500 moles flash-1, to the merged
emissions files following the parameterization described in Allen
et al. (2012). The production of LNOx is correlated to lightning
flashes observed by the National Lightning Detection network (NLDN), which
records cloud to ground flashes (CG). The amount of intercloud lightning (IC)
is calculated based on a climatological IC / CG ratio (Boccippio et al.,
2001). In general, the amount of NOx supplied by lightning is much smaller
than anthropogenic sources, especially in urban areas. This may not
necessarily be true in the future as further pollution control measures are
enacted to reduce the emissions of NOx from power plants, vehicles, etc.
Neglecting LNOx may result in modeled NOx being biased low,
especially in rural regions where NOx levels are already much lower than
in urban regions.
Average OMI tropospheric column NO2 observations for July
and August 2007 for both the DOMINO (left) and GSFC
(middle) retrievals. The level 2 swath data for both retrievals have been screened, weighted based on viewing angle and gridded
onto a 0.25∘×0.25∘ lat/lon grid. Only observations where cloud fraction is less than 30 % are used
in the gridding process. Both data sets indicate that the highest levels of tropospheric NO2 occur over urban
regions. A scatter plot of DOMINO vs. GSFC observations (right) indicates that the DOMINO retrieval is roughly 20 % higher,
on average, than the GSFC satellite product. Error bars indicate the uncertainty in the satellite retrievals.
Modeled tropospheric column NO2 is calculated by integrating the CMAQ
NO2 profiles from the surface to the level of the tropopause provided
by either the DOMINO or GSFC retrieval teams. Model output at the hour
closest to the OMI overpass time are convolved with the air mass factors and
averaging kernels (DOMINO) or scattering weights (GSFC) appropriate for the
two satellite retrievals. We interpolate the CMAQ output to the pressure grid
of the averaging kernel/scattering weight. The DOMINO tropospheric averaging
kernel is calculated by taking the product of the averaging kernel and the
ratio of the air mass factor to the tropospheric air mass factor (Boersma
et al., 2011). CMAQ output is multiplied by the tropospheric averaging kernel
and then integrated over pressure. In a similar fashion, GSFC scattering
weights are applied to the model profiles of NO2 to generate CMAQ
tropospheric column NO2 for direct comparison with the GSFC
tropospheric NO2 product (Bucsela et al., 2013; Lamsal et al., 2014).
Comparison of satellite observations of tropospheric column
NO2 (left panels) to output from a baseline CMAQ model run
(CMAQBSE) convolved with the satellite-averaging kernels for the DOMINO retrieval (top middle) and GSFC retrieval
(bottom middle). Model output is screened, weighted, and gridded in the same manner as the satellite retrievals. Scatter plots
of model and satellite NO2 provide a more quantitative comparison (right panels). Error bars represent the
uncertainty in the satellite retrievals. Metrics are provided to test the level of agreement between model and
observations. The variance (r2), reduced chi-squared (χ2), and the mean ratio are calculated for all points within the
model domain (black points). r2 indicates the model can explain ∼ 50–70 % of the satellite
observations. The relatively low value of the mean ratio is driven by the large concentration of points below 5×1015cm-2,
where the model underpredicts the satellite. χ2 is better for the GSFC retrieval (0.72) than for DOMINO (1.28). The same metrics are
calculated for grid cells where the simulations are at least 25 % greater than observations (called “Urban”, red points). While the variance
is good, the ratio is high and χ2 implies that baseline modeled tropospheric column NO2 falls outside the uncertainties of
the observations.
Only those model grid points closest to the center of each pixel along the
satellite swath are considered. To generate a model L3 gridded product, CMAQ
output at each model grid cell is only used if the co-located satellite
retrieval satisfies the quality flags and cloud fraction limits described
above (i.e., we only use those CMAQ points where the closest satellite
retrieval is also considered “valid”). CMAQ output is screened and gridded
in an identical fashion as the respective OMI retrievals, to generate model
L3 products (one for the DOMINO retrieval, the other for the GSFC retrieval)
appropriate for comparison to the satellite data.
Analysis and resultsModel/satellite comparisons
Gridded, L3 satellite retrievals of tropospheric column NO2 over the
Eastern US for the July/August 2007 period of study are shown in Fig. 1.
Both the DOMINO and GSFC retrievals exhibit similar patterns of elevated
NO2 over urban centers and lower, but substantial and quantifiable
NO2 in rural regions. The two retrieval products are well correlated
(r2=0.82) and generally fall within the error bars of the observations.
Column NO2 found by the DOMINO retrieval is ∼20 % higher
than column NO2 found using the GSFC retrieval. It is beyond the
scope of this paper to probe the cause of this disagreement. As such, we will
compare both satellite retrievals to model output separately. This difference
between data sets may affect attempts to estimate emissions of NOx from
satellite retrievals. Our focus is on the ratio of column
NO2 between urban and rural regions; both retrievals yield similar
scientific conclusions.
Figure 2 shows comparisons of the baseline CMAQ model
(CMAQBSE) value of column NO2 to the satellite data
product found by both retrievals. Regions of elevated column NO2 are
calculated over urban regions, roughly similar to observation, but the
modeled column NO2 is significantly smaller over rural areas than
reported by OMI. The correlation between model and observed NO2,
shown for both retrievals in Fig. 2, is largely driven by the high density of
points where column NO2 is below 5×1015cm-2
(Fig. 2, right panels). In this region of the distribution, observed
NO2 is larger than the baseline air quality model for both satellite
retrievals. The mean ratio of model to observations across the domain, which
is predominantly rural, is 0.34 and 0.37 for DOMINO and GSFC, respectively.
In the predominantly urban regions (red points, right panels of Fig. 2) the
opposite is true; the mean ratio of model to observed NO2 rises to
1.56 and 1.62 for the two retrievals.
To highlight areas where the model is higher than observations we restrict
our maps to only show those places where model calculations are at least
25 % higher than observed by satellite (Fig. 3). The regions that meet
the 25 % criteria are generally large urban centers for which CMAQ
NO2 is usually biased high by 50 to 60 % (DOMINO and GSFC
retrievals, respectively). This overestimate of column NO2 is not
true for all highly populated urban centers, such as the I-95 corridor from
Washington, DC through Philadelphia, PA. Over the entire domain shown in
Fig. 2, NO2 found using CMAQBSE is ∼60 % lower
than observed. Overall, the ratio of urban NO2 (those areas shown in
Fig. 3) to rural NO2 (all other areas) in the model is at least
a factor of 2 larger than the same ratio calculated from space-based
observations. Calculations of reduced chi squared, χ2, for these
urban regions (GSFC = 1.69, DOMINO = 1.4) indicates that the elevated
modeled column NO2 can not be reconciled with the observations using
the uncertainty estimates in the satellite retrievals. These results agree
with reported discrepancies based on comparison of CMAQ output to
observations of column NO2 from SCIAMACHY (Napelenok et al., 2008) as
well as surface NO2 (Castellanos, 2009). Use of the BEHR (Russell
et al., 2011) retrieval of column NO2 (not shown) gives similar
results. Table 1 summarizes the numerical comparisons between the DOMINO
satellite retrievals and all model simulations presented in this study.
Table 2 shows similar comparisons but for the GSFC retrieval. In a recent
study, Lamsal et al. (2014) compared OMI NO2 to in situ and surface
observations and reported OMI retrievals may be lower than observations in
urban regions and higher in rural regions, on the order of 20 %. This is
the opposite of our results, however, the OMI/observation biases are not
enough to explain the model/OMI disagreement presented here.
The same model output as Fig. 2 except results are only shown for
regions where the simulations are at least 25 % greater than observations
(left panels). This clearly shows that model is biased high over urban
regions indicated by the red
points in the scatter plots (right panels).
Long-lived NOx precursors
The CB05 chemical mechanism represents all organic nitrate species, such as
alkyl nitrates (e.g., isopropyl nitrate, n-propyl nitrate, isobutyl nitrate,
isoprene nitrates), as a single species called NTR (Yarwood et al., 2005). In
CB05, NTR is created by the breakdown of isoprene and isoprene products such
as methacrolein (MACR) and methyl vinyl ketone (MVK) and is lost through
photolytic and oxidation processes. The photolysis of NTR is calculated in
the model using the cross section of isopropyl nitrate to represent all
organic nitrate species and produces NO2 and HO2, important
precursors to surface O3 formation (Yu et al., 2010).
Values of the variance (r2), mean ratio, and reduced
chi-squared (χ2) calculated for all model simulations compared to
the DOMINO retrievals of tropospheric column NO2.
Model simulation BSENTRMGNN50TOTDomain:r20.670.710.710.730.78Ratio0.340.400.340.560.55χ21.281.041.210.670.63Urban:r20.960.960.910.960.92Ratio1.561.601.211.751.39χ21.401.500.282.150.55
Values of the variance (r2), mean ratio, and reduced
chi-squared (χ2) calculated for all model simulations compared to
the GSFC retrievals of tropospheric column NO2.
Model simulation BSENTRMGNN50TOTDomain:r20.540.600.590.580.64Ratio0.370.440.380.620.60χ20.720.590.660.420.37Urban:r20.870.880.810.900.83Ratio1.621.651.241.801.41χ21.691.800.392.440.68
We have diagnosed the lifetime of NTR due to photolysis to be ∼10 days
in CMAQ during summer, in agreement with the lifetime of C2 to
C4 alkyl nitrates in the mixed layer (Luke et al., 1989). The CMAQ
lifetime for NTR is based on photolysis frequencies calculated by the
photolysis rates preprocessor module (jproc). Within CMAQ, NTR often
constitutes 20 to 40 % of the total NOy budget. The long lifetime of
NTR results in sequestration of nitrogen compounds far from the emission
source, perhaps accounting for the low predicted values of CMAQ NO2
in rural areas. Analysis of aircraft observations, however, indicates the
speciation of NTR is not well described in CMAQ using CB05, with the most
abundant species in this family being hydroxynitrates with lifetimes on order
∼1 day or less (Horowitz et al., 2007; Perring et al., 2009; Beaver
et al., 2012). Furthermore, a recent analysis of laboratory studies that
evaluated absorption cross sections and photolysis frequencies indicates that
the photolysis of carbonyl nitrates may be 3 and 20 times faster than
previously reported (Müller et al., 2014).
A comparison of NTR from a baseline CMAQ run to measurements obtained during
the 2011 NASA DISCOVER-AQ field mission shows modeled NTR to be
2–4 times greater than observed (Fig. 4, top).
This version of the model uses emissions inventories and meteorological
fields appropriate for 2011 (Loughner et al., 2011; Anderson et al., 2014;
Goldberg et al., 2014; Flynn et al., 2014; He et al., 2014). To investigate
the source of this discrepancy we have increased the photolysis frequency of
NTR by a factor of 10, reducing the lifetime to ∼1 day. This model
scenario will be referred to as CMAQNTR for the remainder of
this study. Values of NTR found using a shorter lifetime are in much better
agreement with observed NTR, indicating a significant improvement in the
model representation of alkyl nitrate chemistry (Fig. 4, bottom). We
recognize that decreasing the lifetime of NTR with respect to photolysis by
a factor of 10 may be too simplistic, but this calculation is meant to
illustrate how a thorough representation of the NTR family of
gasses
may lead to overall improvements to the model framework. A full revision of
alkyl nitrate is being undertaken by the EPA (Leucken and Schwede, 2014).
(Top panel) Comparison of baseline CMAQ simulations of organic
nitrates (NTR) (colored contours) to NTR observed during
the DISCOVER–AQ field mission on 29 July 2011 (colored points). (Bottom panel) As in the top panel, except the CB05 chemical
mechanism in CMAQ has been modified such that the lifetime of NTR is reduced by a factor of 10.
The breakdown of NTR, having a lifetime of ∼1 day, increases local
NO2 both by direct production of NO2 and by shifting the
partitioning of NOx toward NO2 via the HO2+NO
reaction. A comparison of CMAQ tropospheric column NO2 (convolved
with the averaging kernels) found for the CMAQNTR simulation and
OMI column NO2 is shown in Fig. 5. There is slightly better
agreement between modeled and column NO2 and both satellite
retrievals for the entire domain compared to the baseline simulation,
generally due to increased NO2 in rural areas for
CMAQNTR. However, for CMAQNTR, modeled column
NO2 in urban regions (red points, Fig. 5) lies further from observed
NO2 than found for the baseline simulations.
Similar to Fig. 2 except the chemical mechanism in the CMAQ model
has been modified such that the lifetime of NTR is reduced by a factor of 10
(CMAQNTR). Gray points on the scatter plots represent the results
shown in Fig. 2 (right panels). As in Fig. 3, red points represent urban
regions.
In CB05, 100 % of the NOx from photolysis of NTR or its products is
recycled. Loss through OH attack, also fast for isoprene nitrates, yields
HNO3, a NOx sink in the troposphere. The model output presented
here can be considered a bounding scenario since this treatment of NOx is
an over-simplification. If NOx recycling were not 100 % efficient, we
would expect a decrease in column NO2 throughout the domain, and
a corresponding reduction in O3. It is beyond the scope of this
study to assess the sensitivity of NO2 and O3 to this level
of detail of the CB05 mechanism.
Emissions from airplanes en route are not considered in the emissions
inventories. However, the overall contribution from aircraft aloft to the
tropospheric column is relatively minor (i.e., less than 1 % of
tropospheric column) and would not explain the urban / rural discrepancy
between satellite observations and model output (Jacobson et al., 2013).
Emissions of NOx from mobile sources
A comparison of NOx from emission inventories for 2011 to observations
taken during DISCOVER-AQ (July 2011) has quantified a potential
overestimation of mobile NOx (Anderson et al., 2014). The ratio of
CO / NOy from observations was roughly a factor of 2 greater than the
ratio based on the National Emissions Inventory data used in CMAQ. Model CO
is only ∼ 15 % greater than observed for this time period,
indicative of a large overestimate of mobile NOx emissions (Anderson
et al., 2014). This conclusion is in agreement with a study by Yu
et al. (2012) who compared CMAQ simulations, using the CB4.2 chemical
mechanism, to aircraft data acquired during the TexAQS/GoMACCS campaign.
National Emissions Inventory (NEI) point and area sources for year 2001 were
projected to 2006 and mobile emissions were generated from the EPA MOBILE6
model. Yu et al. (2012) report modeled CO ∼ 10 % greater than
measured. However, the ratio of observed CO / NOy= 26.9
(determined using values from Table 4, Yu et al., 2012) is roughly a factor
of 2 higher than the CMAQ calculated ratio of CO / NOy= 13.1.
A separate analysis of 2011 DISCOVER-AQ observations has shown that CMAQ
consistently overestimated NOy by a factor of 2 from the surface to
3000 m altitude (Goldberg et al., 2014).
Airborne observations analogous to those taken during DISCOVER-AQ in summer
2011 do not exist for summer 2007. We have examined observations of CO and
NOx from six surface air quality monitoring sites in Maryland, Northern
Virginia, and the District of Columbia acquired during summer 2007. The
chemiluminescence method of detecting NOx suffers from known interferences
(Castellanos et al., 2011; Dunlea et al., 2007; Fehsenfeld et al., 1987). It
is reasonable to assume that the surface sites are actually reporting
NOx*= NOy-HNO3. For the summer of 2007, CO
observations also seem to have calibration issues. Nonetheless, we have
compared the observed ratio of CO / NOx* from these six sites to the
CMAQ value of CO / NOx*, where CMAQ is sampled at the time and
location of each surface observation. The mean and 1σ standard
deviation of the observed divided by modeled value of CO / NOx* is
1.97±1.5 for summer 2007; the large SD reflects a great deal of noise in
the surface observations. The fact that observed CO / NOx* exceeds
the modeled value of this quantity supports the notion that mobile emissions
in the 2007 inventory exceed the actual emissions from this source by an
amount comparable to that reported by Anderson et al. (2014). Doraiswamy
et al. (2009) also report a possible over-prediction in area source
emissions.
Following Anderson et al. (2014), we assume that there is a similar
overestimation of NOx in the 2007 emissions inventories used in our
analysis and we test for this discrepancy by reducing on-road mobile NOx
emissions by a factor of 2. Results
from a CMAQ model run that considers this change as well as the reduction in
NTR lifetime in the CB05 chemical mechanism, termed CMAQN50
(i.e., a factor of 2 or 50 % reduction), are shown in Fig. 6. The most
noticeable difference between these results and those presented in Figs. 2
and 5 is the reduction in column NO2 over urban regions, which is now
in much better agreement with satellite observations (ratios between model
and observations of 1.21 and 1.24 for DOMINO and GSFC, respectively). While
the modifications to the CB05 chemical mechanism and emissions inventories
have somewhat reconciled the differences between modeled and remotely sensed
urban tropospheric column NO2, rural NO2 is still
underestimated by the model. Mean ratios for the domain (0.34 and 0.38) are
similar to results from the baseline model. Essentially, these modifications
to the model have improved the urban disagreement, but overall model
performance is unchanged.
Same as Fig. 2 except model results include both changes to NTR
chemistry and a 50 % reduction in the emissions of
NOx from mobile sources (CMAQN50). Gray points on the scatter plots represent the results shown in Fig. 2
(right panels).
Biogenic emissions
As stated above, the original emissions inventories generated by MARAMA
include biogenic sources from the Model of Emissions of
Gases and Aerosols in Nature
(MEGAN v2.04) (Guenther et al., 2006). Since the 2007 emissions inventories
were made available, an updated version of MEGAN was released (v2.10,
Guenther et al., 2012). One of the main differences in biogenic emissions
between the two versions of MEGAN is that isoprene, the dominant VOC in the
mid-Atlantic region, has decreased by about 25 % in the latest version.
Emissions of biogenic isoprene calculated using MEGAN v2.10 are significantly
less sensitive to high levels of photochemically active radiation (PAR)
compared to earlier versions of MEGAN, resulting in lower isoprene during
midday near the time of OMI overpass. An in-depth analysis of the sensitivity
of regulatory air quality models to simulation of biogenic emissions will be
the subject of a forthcoming paper.
Fundamentally, isoprene oxidation is initiated by reaction with OH to
generate isoprene peroxy radical intermediates, termed RO2. In the
presence of NOx, these RO2 intermediates react with NO, producing
MVK, MACR, HCHO, and a small amount of organic nitrates (Paulot et al.,
2009). Overall, when output from the latest version of MEGAN is incorporated
into the emissions inventories (CMAQMGN) there is an overall
increase in column NO2 across the model domain due to the reduction
in NOx sinks (Fig. 7).
Same as Fig. 2 except model results include updated biogenic
emissions from MEGANv2.10 (CMAQMGN). Gray points on the
scatter plots represent the results shown in Fig. 2 (right panels).
Combining the modifications to the CB05 chemical mechanism with the changes
to mobile and biogenic emissions yields the best overall agreement between
model and observations (CMAQTOT, Fig. 8) over the model
domain. The correlation between measured and modeled NO2 is larger
for CMAQTOT (r2=0.78, 0.64 for DOMINO, GSFC) than for any
other model scenario. Modeled NO2 for urban regions is in even better
agreement with the satellite retrievals for all of the calculated metrics.
While further work is needed, we have succeeded in decreasing the urban
overestimate and rural underestimate of tropospheric column NO2
compared to satellite data by: (a) using the latest version of MEGAN,
(b) prescribing a factor of 2 reduction in mobile NOx emissions throughout
the domain, and (c) reducing the lifetime of NTR within CB05 by a factor of
10.
Same as Fig. 2 except model results include changes to NTR
chemistry, a 50 % reduction in the emissions of
NOx from mobile sources, and updated biogenic emissions (CMAQTOT). Gray points on the scatter plots
represent the results shown in Fig. 2 (right panels).
Model uncertainties
There are other possible sources of error within the model framework. Updates
to the kinetics based on recent studies may affect the loss of NO2.
Mollner et al. (2010) report the reaction rate of
OH +NO2+M is slower than what has been recommended by
prior work (Atksinson et al., 2004; Sander et al., 2006). The uptake of
N2O5in aerosols is likely overestimated in models (Han
et al., 2015; Stavrakou et al., 2013; Vinken et al., 2014; Yegorova et al.,
2011). We have performed two model simulations that consider the slower
reaction rate of OH +NO2+M
(CMAQOH+NO2) and assume the heterogeneous removal of
N2O5 is zero (CMAQN2O5). Both of these
scenarios also include all of the changes made to the model framework in
CMAQTOT. Results are presented in the Supplement. Overall, both
of these changes to the model show very little difference for column
NO2 compared to that found in CMAQTOT simulation. This
is in agreement with prior studies (Han et al., 2015; Stavrakou et al., 2013;
Vinken et al., 2014; Yegorova et al., 2011).
Average daily maximum 8 h daily ozone for July and August 2007 for
four model cases: CMAQBSE (top left),
CMAQNTR (top middle left), CMAQN50 (top middle right), and CMAQTOT (top right) and ground
based observations. Colored contours represent the CMAQ model output. Colored points denote average daily maximum 8 h daily
ozone from ground-based AQS sites for the same time period. Scatter plots of each CMAQ model output vs. observations are shown
(bottom panels). Gray horizontal and vertical lines indicate the 75 ppb ozone exceedance level. The number of times the
model output is greater than 75 ppb while the observations are less than 75 ppb is shown (false positive). The
mean ratio between CMAQ surface ozone and observations at air quality sites is indicated for each model simulation.
The production of HNO3 from the reaction of NO +HO2
(Butkovslaya et al., 2007) would lead to a decrease in NO2. This
channel of the NO +HO2 reaction is not included in the CB05
chemical mechanism. Inclusion of this NOx loss mechanism will have the
largest impact in the tropics (Cariolle et al., 2008; Stavrakou et al.,
2013). While changing the CB05 chemical mechanism may lead to better
agreement between model output and satellite retrievals over the urban
regions described in this study, it would exacerbate the model/measurement
discrepancy in rural areas.
Future work will investigate the importance of soil emissions of HONO, which
are not included in the current versions of MEGAN and the chemical kinetics
of other species that are important precursors to surface ozone. Prior
studies indicate that soil emissions may account for 7 % of column
NO2 during the summer ozone season (Choi et al., 2008) and that HONO
could be an important morning source of OH in an urban, VOC-rich environment
(Ren et al., 2013).
Ozone
The focus of state implementation plans is attainment of the 8 h standard
for surface ozone. It should be noted that CMAQ is most often used in
a relative sense for this purpose. For instance, modifications to emissions
inventories from a wide variety of sources, which represent future conditions
based on possible proposed regulations, are run through CMAQ. Output from
this model is compared to a base scenario to determine how much surface ozone
is expected to decrease if these policy measures were to be enacted. For this
study, we instead focus on the ability of CMAQ to reproduce observed surface
ozone. In Fig. 9, we compare average maximum daily 8 h average ozone from
the four modeling scenarios considered above (CMAQBSE,
CMAQNTR, CMAQN50, and CMAQTOT) to
measured surface ozone from ground-based air quality observing sites (AQS).
We have chosen to show the average of the daily maximum 8 h ozone for July
and August 2007 at each model grid point. While it may seem that there were
no violations of the 75 ppb standard, based on the scatter plots of
model and observed ozone (Fig. 9, bottom), this is certainly not the case.
There were exceedances of the 75 ppb ozone standard during this time;
however, we have averaged these events with days having lower ozone. There
were no locations where the 62-day average of daily maximum 8 h ozone
exceeded 75 ppb for this time period. An additional comparison, shown
at the end of this section, will focus on days when the surface ozone
standard was exceeded in Maryland.
All of the CMAQ simulations presented in Fig. 9 generally overestimate
observed surface ozone. Ozone from CMAQBSE is 26 % greater
than observed (ratio of 1.26 denoted on Fig. 9), with 87 locations averaging
higher than 75 ppb within the model. CMAQ results for
CMAQNTR lead to a further increase (32 %) in modeled ozone
(an ozone dis-benefit from this model change). While the changes made to CB05
for CMAQNTR provide a much more realistic representation of
NOx chemistry (see Fig. 4), comparison of ozone found using
CMAQNTR and the baseline run suggests the presence of
compensating errors in the chemistry and/or dynamics that control ozone. The
inclusion of a 50 % reduction of mobile NOx (CMAQN50)
(Anderson et al., 2014) leads to a decrease in modeled ozone compared to
results from the CMAQNTR run. Simulated ozone is still 24 %
greater than measured, although there is slight improvement compared to the
baseline run. Improved agreement between model and AQS observations of ozone
occurs when updated biogenic emissions are also included, together with the
aforementioned changes to mobile NOx and the lifetime of NTR. In this
scenario, CMAQTOT, the model/measurement discrepancy for ozone is
reduced to 19 % and there are only 40 false positives. The reason for
this model behavior is that the reduction in isoprene emission upon use of
MEGANv2.10 leads to decreases in HO2 and RO2, important ozone
precursors.
Comparison of average 8 h surface ozone calculated from baseline
CMAQ model output (top left), CMAQNTR model
output (top right), CMAQN50 model output (bottom left panel), and CMAQTOT (bottom right) to observations
from ground-based AQS sites for those days and locations in Maryland that experienced exceedances (8 h average ozone greater
than 75 ppb) during July and August 2007.
If a ground-based ozone monitor reports 8 h average ozone exceeding the
NAAQS standard, currently 75 ppb, it is considered to be in
non-attainment. These sites are the subject of intense focus by state
agencies to determine the fundamental causes of the ozone exceedance (e.g.,
local sources of pollution vs. out of state upwind sources). A comparison of
ozone on days when observed ozone exceeded 75 ppb in Maryland during July
and August 2007 to CMAQ model output is shown in Fig. 10. It should be
noted that the ozone standard in 2007 was 80 ppb. However, the state
of Maryland provides historic ozone data for the current, 75 ppb
standard. These data can be found at
https://data.maryland.gov/Energy-and-Environment/Maryland-Ozone-Exceedance-Days-in-2007/iyzm-8pqd.
The model values of daily 8 h average ozone represent the closest points,
spatially, to the monitor sites on the day that the exceedance occurred. For
all model cases, CMAQ shows greater variability than the ground-based
monitors during times of ozone exceedance. The overall analysis of the model
runs for these days/times leads to results similar to the analysis in Fig. 9.
The decrease in the lifetime of NTR (13 % overestimate) leads to an
increase in model ozone compared to baseline (6 % overestimate), while
the model that considers both reduced NTR lifetime and a 50 % decrease in
mobile NOx emission yields an improved representation (2 %
overestimate) of ozone compared to the baseline. Best agreement between
modeled and observed ozone, for the times and place of exceedance, occurs
when MEGANv.2.10 is considered along with the other model modifications.
While the comparison still exhibits significant scatter about the 1:1 line,
CMAQ is now on average in agreement with observed surface ozone (ratio of
1.00). This represents significant progress for the modeling of surface
ozone, since we have prescribed a scenario for which the CMAQ air quality
model is unbiased during a period of air quality exceedance.
Conclusions
We examine the ability of CMAQ to simulate reactive nitrogen
over the eastern US by comparing model output to column content NO2
observed by the OMI instrument on the AURA satellite. For July and
August 2007, CMAQ consistently overestimates NO2 over urban areas and
underestimates NO2 over rural areas relative to OMI observations;
this finding is insensitive to the choice of GSFC or DOMINO retrieval
product. Neither inclusion of lightning NOx nor consideration of the
averaging kernel/scattering weights for OMI alters this conclusion. While the
absolute value of NO2 column content is subject to errors from
a variety of sources, the urban / rural ratio provides a rigorous test of
CMAQ's ability to simulate the photochemistry of NO2 and the
transport of this important criteria air pollutant. Because CMAQ using the
CB05 mechanism overestimates the urban to rural ratio of tropospheric column
NO2, this model may underestimate the interstate transport of NOx
and/or NOx reservoirs.
The CB05 chemical mechanism represents all alkyl nitrates as isopropyl
nitrate – a reasonable simplification given the state of knowledge at the
time of creation. However, there is now substantial evidence of a larger
variety of alkyl nitrates and substituted alkyl nitrates (Horowitz et al.,
2007; Perring et al., 2009; Beaver et al., 2012). These can have lifetimes as
short as hours and recycle NOx much faster than short chain alkyl
nitrates. Reducing the simulated lifetime of NTR in CB05 from ∼10 days
to ∼1 day improves the agreement between measured and modeled
tropospheric column NO2 for the Baltimore–Washington area. This
modification to CB05 reduces, but does not eliminate the urban / rural
NO2 bias within CMAQ. Implementation of a factor of 2 reduction in
NOx emissions from mobile sources, based on Anderson et al. (2014) and
analysis of ground-based observations for summer 2007, decreases NO2
in urban regions that have a high-density of vehicular traffic, which
improves the CMAQ representation of the ratio of urban to rural NO2.
Use of the latest MEGAN emissions for VOCs, v2.10 (Guenther et al., 2012),
increases NO2 throughout the domain and further improves the CMAQ
representation of urban to rural NO2, due to smaller levels of
RO2. The reduction in isoprene, which leads to the decrease in
RO2, is caused by the diminished sensitivity of isoprene emissions to
PAR in the newer version of MEGAN.
We have also examined the effect of these various model runs on the CMAQ
representation of surface ozone. Reducing the lifetime of NTR by a factor of
10 increases the average daily 8 h maximum ozone by ∼ 5 %;
decreasing mobile source NOx emissions by a factor of 2 decreases ozone by
about 6 %. The use of MEGAN v2.10 causes surface ozone to fall by
2.5 % relative to a simulation that is identical, except for use of MEGAN
2.04. Combining all three of these model changes (i.e., factor of 10
reduction in NTR lifetime, factor of 2 reduction in mobile NOx, MEGAN
v2.10) leads to the best simulation of surface ozone for the mid-Atlantic.
This model run leads to agreement with the 2-month average of daily 8 h
maximum ozone at the ±20 % level, fewest number of false positives of
an ozone exceedance throughout the domain, as well as an unbiased simulation
of surface ozone at ground-based AQS sites that experienced an ozone
exceedance (8 h ozone greater than 75 ppb) within Maryland during
July and August 2007. The prescription of an unbiased simulation of ozone,
coupled with a fairly accurate simulation of the urban to rural ratio of
column NO2, may provide a framework for use in studies focused on
achieving future adherence to specific air quality standards for surface
ozone by reducing emission of NOx from various anthropogenic sectors.
The Supplement related to this article is available online at doi:10.5194/acp-15-10965-2015-supplement.
Acknowledgements
We greatly appreciate the financial support for this research provided by the
NASA Aura Science Team, the NASA ACMAP, AQAST and MAP programs, as well as
the Maryland Department of the Environment. We appreciate the willingness of
Jim Crawford, Ken Pickering, Ron Cohen, and Andy Weinheimer to provide
support for our use of NASA DISCOVER-AQ measurements acquired in the
mid-Atlantic during summer 2011.
Edited by: C. H. Song
ReferencesAcarreta, J. R., De Haan, J. F., and Stamnes, P.: Cloud pressure retrieval
using the O2–O2 absorption band at 477 nm, J. Geophys. Res., 109,
D05204,
doi:10.1029/2003JD003915,
2004.Allen, D. J., Pickering, K. E., Pinder, R. W., Henderson, B. H.,
Appel, K. W., and Prados, A.: Impact of lightning-NO on eastern United States
photochemistry during the summer of 2006 as determined using the CMAQ model,
Atmos. Chem. Phys., 12, 1737–1758,
doi:10.5194/acp-12-1737-2012,
2012.Anderson, D. C., Loughner, C. P., Weinheimer, A., Diskin, D., Canty, T. P.,
Salawitch, R. J., Worden, H., Freid, A., Mikoviny, T., Wisthaler, A., and
Dickerson, R. R.: Measured and modeled CO and NOy in DISCOVER-AQ: an
evaluation of emissions and chemistry over the eastern US, Atmos. Environ.,
96, 78–87, 2014.Atlas, E.: Evidence for greater than or equal to C3 alkyl nitrates in
rural and remote atmospheres, Nature, 331, 426–428, 1988.Atkinson, R., Baulch, D. L., Cox, R. A., Crowley, J. N., Hampson, R. F.,
Hynes, R. G., Jenkin, M. E., Rossi, M. J., and Troe, J.: Evaluated kinetic
and photochemical data for atmospheric chemistry: Volume I – gas phase
reactions of Ox, HOx, NOx and SOx species, Atmos. Chem. Phys., 4,
1461–1738, 10.5194/acp-4-1461-2004, 2004.Beaver, M. R., Clair, J. M. St., Paulot, F., Spencer, K. M., Crounse, J. D.,
LaFranchi, B. W., Min, K. E., Pusede, S. E., Wooldridge, P. J.,
Schade, G. W., Park, C., Cohen, R. C., and Wennberg, P. O.: Importance of
biogenic precursors to the budget of organic nitrates: observations of
multifunctional organic nitrates by CIMS and TD-LIF during BEARPEX 2009,
Atmos. Chem. Phys., 12, 5773–5785,
doi:10.5194/acp-12-5773-2012,
2012.
Boccippio, D., Cummins, K., Christian, H., and Goodman, S.: Combined
satellite- and surface-based estimation of the intra-cloud-to-ground
lightining ratio over the continental United States, Mon. Weather Rev., 129,
108–122, 2001.Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A,
R. J., Sneep, M., van den Oord, G. H. J., Levelt, P. F., Stammes, P.,
Gleason, J. F., and Bucsela, E. J.: Near-real time retrieval of tropospheric
NO2 from OMI, Atmos. Chem. Phys., 7, 2103–2118,
10.5194/acp-7-2103-2007, 2007.Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J.,
Veefkind, J. P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M.,
Claas, J., Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An
improved tropospheric NO2 column retrieval algorithm for the Ozone
Monitoring Instrument, Atmos. Meas. Tech., 4, 1905–1928,
doi:10.5194/amt-4-1905-2011,
2011.Brent, L. C., Thorn, W. J., Gupta, M., Leen, B., Stehr, J. W., He, H.,
Arkinson, H. L., Weinheimer, A., Garland, C., Pusede, S. E.,
Wooldridge, P. J., Cohen, R. C., and Dickerson, R. R.: Evaluation of the use
of a commercially available cavity ringdown absorption spectrometer for
measuring NO2 in flight, and observations over the Mid-Atlantic States,
during DISCOVER-AQ, J. Atmos. Chem.,
doi:10.1007/s10874-013-9265-6,
2013.Bucsela, E. J., Krotkov, N. A., Celarier, E. A., Lamsal, L. N., Swartz, W. H., Bhartia, P. K., Boersma, K. F.,
Veefkind, J. P., Gleason, J. F., and Pickering, K. E.: A new stratospheric and tropospheric NO2 retrieval algorithm for
nadir-viewing satellite instruments: applications to OMI, Atmos. Meas. Tech., 6, 2607–2626,
doi:10.5194/amt-6-2607-2013,
2013.Butkovskaya, N. I., Kukui, A., and Le Bras, G.: HNO3 forming channel of the HO2+ NO reaction as a
function of pressure and temperature in the ranges of 72–600 Torr and
223–323 K, J. Phys. Chem. A, 111, 9047–9053, 2007.Butler, T. J., Vermeylen, F. M., Rury, M., Likens, G. E., Lee, B., Bowker, G. E., and McCluney, L.: Response of ozone
and nitrate to stationary source NOx emission reductions in the eastern USA, Atmos. Environ., 45, 1084–1094,
doi:10.1016/j.atmosenv.2010.11.040, 2011. Byun, D. and Schere, K. L.: Review of the governing equations, computational algorithms, and other components of the
models-3 Community Multiscale Air Quality (CMAQ) modeling system, Appl. Mech. Rev., 59, 51–77, 2006.Carlton, A. G. and Baker, K.: Photochemical modeling of the Ozark isoprene volcano: MEGAN, BEIS, and their impacts on
air quality predictions, Environ. Sci. Technol., 45, 4438–4445,
doi:10.1021/es200050x, 2011.Cariolle, D., Evans, M., Chipperfield, M., Butkovskaya, N., Kukui, A., and LeBras, G.: Impact
of the new HNO3–forming channel of the HO2+ OH reaction on
tropospheric HNO3, NOx, HOx, and ozone, Atmos. Chem. Phys., 8,
4061–4068,
doi:10.5194/acp-8-4061-2008,
2008. Castellanos, P.: Analysis of Air Quality with Numerical Simulations (CMAQ), and Observations of Trace Gases,
The University of Maryland, College Park, 168 pp., 2009.Castellanos, P., Marufu, L. T., Doddridge, B. G., Taubman, B. F., Schwab, J. J., Hains, J. C., Ehrman, S. H., and
Dickerson, R. R.: Ozone, oxides of nitrogen, and carbon monoxide during pollution events over the eastern United States: an
evaluation of emissions and vertical mixing, J. Geophys. Res., 116, D16307,
doi:10.1029/2010JD014540, 2011.Choi, Y., Wang, Y., Zeng, T., Cunnold, D., Yang, E.-S., Martin, R.,
Chance., K., Thouret, V., and Edgerson, E.: Springtime transitions of NO2,
CO, and O3 over North America: model evaluation and analysis, J.
Geophys. Res.,
113, D20311,
doi:10.1029/2007JD009632, 2008.Day, D. A., Dillon, M. B., Wooldridge, P. J., Thornton, J. A., Rosen, R. S., Wood, E. C., and Cohen, R. C.: On alkyl
nitrates, O3, and the “missing NOy”, J. Geophys. Res., 108, 4501,
doi:10.1029/2003JD003685, 2003.Doraiswamy, P., Hogrefe, C., Hao, W., Henry, R. F., Civerolo, K., Ku, J.-Y.,
Sistla, G., Schwab, J. J., and Demerjian, K. L.: A diagnostic comparison of
measured and model-predicted speciated VOC concentrations, Atmos. Environ.,
43, 5759–5770,
doi:10.1016/j.atmosenv.2009.07.056,
2009.Duncan, B. N., Yoshida, Y., Olson, J. R., Sillman, S., Martin, R. V.,
Lamsal, L., Hu, Y., Pickering, K. E., Retscher, C., Allen, D. J., and
Crawford, J. H.: Application of OMI observations to a space-based indicator
of NOx and VOC controls on surface ozone formation, Atmos. Environ., 44,
2213–2223, 2010.Dunlea, E. J., Herndon, S. C., Nelson, D. D., Volkamer, R. M.,
San Martini, F., Sheehy, P. M., Zahniser, M. S., Shorter, J. H.,
Wormhoudt, J. C., Lamb, B. K., Allwine, E. J., Gaffney, J. S., Marley, N. A.,
Grutter, M., Marquez, C., Blanco, S., Cardenas, B., Retama, A., Ramos
Villegas, C. R., Kolb, C. E., Molina, L. T., and Molina, M. J.: Evaluation of
nitrogen dioxide chemiluminescence monitors in a polluted urban environment,
Atmos. Chem. Phys., 7, 2691–2704,
doi:10.5194/acp-7-2691-2007,
2007.
EPA v. EME: Homer City Generation, 12–1183, US, 11–1302, 2014.Farmer, D. K., Wooldridge, P. J., and Cohen, R. C.: Application of
thermal-dissociation laser induced fluorescence (TD-LIF) to measurement of
HNO3, Σalkyl nitrates, Σperoxy nitrates, and NO2
fluxes using eddy covariance, Atmos. Chem. Phys., 6, 3471–3486,
doi:10.5194/acp-6-3471-2006,
2006.Fehsenfeld, F. C., Dickerson, R. R., Hubler, G., Luke, W. T.,
Nunnermacker, L. J., Williams, E. J., Roberts, J. M., Calvert, J. G.,
Curran, C. M., Delany, A. C., Eubank, C. S., Fahey, D. W., Fried, A.,
Gandrud, B. W., Langford, A. O., Murphy, P. C., Norton, R. B.,
Pickering, K. E., and Ridley, B. A.: A Ground-Based Intercomparison of NO,
NOx, and NOy Measurement Techniques, J. Geophys. Res., 92,
14710–14722, 1987.
Fiore, A. M., Jacob, D. J., Logan, J. A., and Yin, J. H.: Long-term trends in
ground level ozone over the contiguous United States, 1980–1995, J. Geophys.
Res., 103, 1471–1480, 1998.Flynn, C. M., Pickering, K. E., Crawford, J. H., Lamsal, L. N.,
Krotkov, N. A., Herman, J., Weinheimer, A., Chen, G., Liu, X., Szykman, J.,
Tsay, S. C., Laughner, C. P., Hains, J., Lee, P., Dickerson, R. R.,
Stehr, J. W., and Brent, L.: The relationship between column-density and
surface mixing ratio: statistical analysis of O3 and NO2 data from
the July 2011 Maryland DISCOVER-AQ mission, Atmos. Environ., 92, 429–441,
2014.
Fujita, E. M., Campbell, D. E., Stockwell, W. R., and Lawson, D. R.: Past and
future ozone trends in California's South Coast Air Basin: reconciliation of
ambient measurements with past and projected emission inventories, J. Air
Waste Manage., 63, 54–69, 2013.
Gego, E., Porter, P. S., Gilliland, A., and Rao, S. T.: Observation-based
assessment of the impact of nitrogen oxides emissions reductions on ozone air
quality over the eastern United States, J. Appl. Meteorol. Clim., 46,
994–1008, 2007.Gilliland, A. B., Hogrefe, C., Pinder, R. W., Godowitch, J. M., Foley, K. L.,
and Rao, S. T.: Dynamic evaluation of regional air quality models: assessing
changes in O3 stemming from changes in emissions and meteorology, Atmos.
Environ., 42, 5110–5123, 2008.Godowitch, J. M., Hogrefe, C., and Rao, S. T.: Diagnostic analyses of
a regional air quality model: changes in modeled processes affecting ozone
and chemical-transport indicators from NOx point source emission
reductions, J. Geophys. Res., 113, D19303,
doi:10.1029/2007JD009537,
2008a.Godowitch, J. M., Gilliland, A. B., Draxler, R. R., and Rao, S. T.: Modeling
assessment of point source NOx emission reductions on ozone air quality in
the eastern United States, Atmos. Environ., 42, 87–100, 2008b.
Goldberg, D. L., Loughner, C. P., Tzortziou, M., Stehr, J. W.,
Pickering, K. E., Marufu, L. T., and Dickerson, R. R.: Higher surface ozone
concentrations over the Chesapeake Bay than over the adjacent land:
observations and models from the DISCOVER-AQ and CBODAQ campaigns, Atmos.
Environ., 84, 9–19, 2014.Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and
Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN
(Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys.,
6, 3181–3210,
doi:10.5194/acp-6-3181-2006,
2006.Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T.,
Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols
from Nature version 2.1 (MEGAN2.1): an extended and updated framework for
modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492,
doi:10.5194/gmd-5-1471-2012,
2012.
Hains, J. C., Taubman, B. F., Thompson, A. M., Stehr, J. W., Marufu, L. T.,
Doddridge, B. G., and Dickerson, R. R.: Origins of chemical pollution derived
from Mid-Atlantic aircraft profiles using a clustering technique, Atmos.
Environ., 42, 1727–1741, 2008.Han, K. M., Lee, S., Chang, L. S., and Song, C. H.: A comparison study
between CMAQ-simulated and OMI-retrieved NO2 columns over East Asia for
evaluation of NOx emission fluxes of INTEX–B, CAPSS, and REAS
inventories, Atmos. Chem. Phys., 15, 1913–1938,
doi:10.5194/acp-15-1913-2015,
2015.He, H., Hembeck, L., Hosley, K. M., Canty, T. P., Salawitch, R. J., and
Dickerson, R. R.: High ozone concentrations on hot days: the role of electric
power demand and NOx emissions, Geophys. Res. Lett., 40, 5291–5294,
2013a.He, H., Stehr, J. W., Hains, J. C., Krask, D. J., Doddridge, B. G.,
Vinnikov, K. Y., Canty, T. P., Hosley, K. M., Salawitch, R. J.,
Worden, H. M., and Dickerson, R. R.: Trends in emissions and concentrations
of air pollutants in the lower troposphere in the Baltimore/Washington
airshed from 1997 to 2011, Atmos. Chem. Phys., 13, 7859–7874,
doi:10.5194/acp-13-7859-2013,
2013b.He, H., Loughner, C. P., Stehr, J. W., Arkinson, H. L. Brent, L. C.,
Follette-Cook, M. B., Tzortziou, M. A. Pickering, K. E., Thompson, A. M.,
Martins, D. K., Diskin, G. S., Anderson, B. E., Crawford, J. H.,
Weinheimer, A. J., Lee, P., Hains, J. C., and Dickerson, R. R.: An elevated
reservoir of air pollutants over the Mid-Atlantic States during the 2011
DISCOVER-AQ campaign: airborne measurements and numerical simulations, Atmos.
Environ., 85, 18–30,
doi:10.1016/j.atmosenv.2013.11.039,
2014.
Hogrefe, C., Isukapalli, S. S., Tang, X. G., Georgopoulos, P. G., He, S.,
Zalewsky, E. E., Hao, W., Ku, J. Y., Key, T., and Sistla, G.: Impact of
biogenic emission uncertainties on the simulated response of ozone and fine
particulate matter to anthropogenic emission reductions, J. Air Waste
Manage., 61, 92–108, 2011.Horowitz, L. W., Fiore, A. M., Milly, G. P., Cohen, R. C., Perring, A.,
Wooldridge, P. J., Hess, P. G., Emmons, L. K., and Lamarque, J.:
Observational constraints on the chemistry of isoprene nitrates over the
eastern United States, J. Geophys. Res., 112, D12S08,
doi:10.1029/2006JD007747,
2007.
Jacobson, M. Z., Wilkerson, J. T., Naiman, A. D., and Lele, S. K.: The
effects of aircraft on climate and pollution. Part II: 20-year impacts of
exhaust from all commercial aircraft worldwide treated individually at the
subgrid scale, Faraday Discuss., 165, 369–381, 2013.Lamsal, L. N., Krotkov, N. A., Celarier, E. A., Swartz, W. H.,
Pickering, K. E., Bucsela, E. J., Gleason, J. F., Martin, R. V., Philip, S.,
Irie, H., Cede, A., Herman, J., Weinheimer, A., Szykman, J. J., and
Knepp, T. N.: Evaluation of OMI operational standard NO2 column
retrievals using in situ and surface-based NO2 observations, Atmos.
Chem. Phys., 14, 11587–11609,
doi:10.5194/acp-14-11587-2014,
2014.
Leucken, D. and Schwede, D.: Improving the treatment of oxidized nitrogen in
CMAQ influence of gas phase chemical and physical parameterizations, talk
presented at: Community Modeling and Analysis System conference, Friday
Center, UNC-Chapel Hill, USA, 2014.Li, G., Zhang, R., Fan, J., and Tie, X.: Impacts of biogenic emissions on
photochemical ozone production in Houston, Texas, J. Geophys. Res., 112,
D10309,
doi:10.1029/2006JD007924,
2007.Lockwood, A. L., Shepson, P. B., Fiddler, M. N., and Alaghmand, M.: Isoprene
nitrates: preparation, separation, identification, yields, and atmospheric
chemistry, Atmos. Chem. Phys., 10, 6169–6178,
doi:10.5194/acp-10-6169-2010,
2010.
Logan, J. A.: Ozone in rural-areas of the United States, J. Geophys. Res.,
94, 8511–8532, 1989.
Loughner, C. P., Allen, D. J., Pickering, K. E., Zhang, D. L., Shou, Y. X.,
and Dickerson, R. R.: Impact of fair-weather cumulus clouds and the
Chesapeake Bay breeze on pollutant transport and transformation, Atmos.
Environ., 45, 4060–4072, 2011.
Luke, W. T., Dickerson, R. R., and Nunnermacker, L. J.: Direct measurements
of the photolysis rate coefficients and Henry Law constants of several alkyl
nitrates, J. Geophys. Res., 94, 14905–14921, 1989.Marufu, L. T., Taubman, B. F., Bloomer, B., Piety, C. A., Doddridge, B. G.,
Stehr, J. W., and Dickerson, R. R.: The 2003 North American electrical
blackout: an accidental experiment in atmospheric chemistry, Geophys. Res.
Lett., 31, L13106,
doi:10.1029/2004GL019771, 2004.Mollner, A. K., Valluvadasan, S., Feng, L., Sprague, M. K., Okumura, M., Milligan, D. B., Bloss, W. J., Sander, S. P.,
Martien, P. T., Harley, R. A., McCoy, A. B., and Carter, W. P. L.: Rate of
Gas Phase Association of Hydroxyl Radical and Nitrogen Dioxide, Science, 330,
646–649,
doi:10.1126/science.1193030,
2010.Müller, J.-F., Peeters, J., and Stavrakou, T.: Fast photolysis of carbonyl nitrates from isoprene,
Atmos. Chem. Phys., 14, 2497–2508,
doi:10.5194/acp-14-2497-2014, 2014.Napelenok, S. L., Pinder, R. W., Gilliland, A. B., and Martin, R. V.: A method for evaluating spatially-resolved
NOx emissions using Kalman filter inversion, direct sensitivities, and
space-based NO2 observations,
Atmos. Chem. Phys., 8, 5603–5614,
doi:10.5194/acp-8-5603-2008, 2008.
Neff, J., Holland, E., Dentener, F., McDowell, W., and Russell, K.: The origin, composition and rates of organic
nitrogen deposition: a missing piece of the nitrogen cycle?, Biogeochemistry, 57–58, 99–136, 2002.Paulot, F., Crounse, J. D., Kjaergaard, H. G., Kroll, J. H., Seinfeld, J. H., and Wennberg, P. O.: Isoprene
photooxidation: new insights into the production of acids and organic nitrates, Atmos. Chem. Phys., 9, 1479–1501,
doi:10.5194/acp-9-1479-2009, 2009.Perring, A. E., Bertram, T. H., Wooldridge, P. J., Fried, A., Heikes, B. G., Dibb, J., Crounse, J. D.,
Wennberg, P. O., Blake, N. J., Blake, D. R., Brune, W. H., Singh, H. B., and Cohen, R. C.: Airborne observations of total
RONO2: new constraints on the yield and lifetime of isoprene nitrates, Atmos. Chem. Phys., 9, 1451–1463,
doi:10.5194/acp-9-1451-2009, 2009.Perring, A. E., Bertram, T. H., Farmer, D. K., Wooldridge, P. J., Dibb, J., Blake, N. J., Blake, D. R., Singh, H. B.,
Fuelberg, H., Diskin, G., Sachse, G., and Cohen, R. C.: The production and persistence of ΣRONO2 in the Mexico City
plume, Atmos. Chem. Phys., 10, 7215–7229,
doi:10.5194/acp-10-7215-2010, 2010.Perring, A. E., Pusede, S. E., and Cohen, R. C.: An observational perspective on the atmospheric impacts of alkyl and
multifunctional nitrates on ozone and secondary organic aerosol, Chem. Rev., 113, 5848–5870,
doi:10.1021/cr300520x, 2013. Pollack, I. B., Ryerson, T. B., Trainer, M., Neuman, J. A., Roberts, J. M., and Parrish, D. D.: Trends in ozone, its
precursors, and related secondary oxidation products in Los Angeles, California: a synthesis of measurements from 1960 to 2010,
J. Geophys. Res., 118, 5893–5911, 2013.Ren, X., Van Duin, D., Cazorla, M., Chen, S., Brune, W. H., Flynn, J. H., Grossberg, N., Lefer, B. L.,
Rappengluck, B., Wong, K. W., Tsai, C., Stutz, J., Dibb, J. E., Jobson, B. T., Luke, W., and Kelley, P.: Atmospheric oxidation
chemistry and ozone production: results from SHARP 2009 in Houston, Texas, J. Geophys. Res., 118, 5770–5780,
doi:10.1002/jgrd.50342, 2103.Russell, A. R., Perring, A. E., Valin, L.`C., Bucsela, E. J., Browne, E. C.,
Wooldridge, P. J., and Cohen, R. C.: A high spatial resolution retrieval of
NO2 column densities from OMI: method and evaluation, Atmos. Chem. Phys.,
11, 8543–8554, 10.5194/acp-11-8543-2011, 2011.Ryan, W. F., Doddridge, B. G., Dickerson, R. R., Morales, R. M., Hallock, K. A., Roberts, P. T., Blumenthal, D. L.,
Anderson, J. A., and Civerolo, K. L.: Pollutant transport during a regional O3 episode in the mid-Atlantic states,
J. Air Waste Manage., 48, 786–797, 1998. Sander, S. P. , Friedl, R. R., Golden, D. M., Kurylo, M. J., Moortgat, G. K., Keller-Rudek, H., Wine, P. H., Ravishankara, A. R.,
Kolb, C. E., Molina, M. J., Finlayson-Pitts, B. J., Huie, R. E., and Orkin, V. L.: Chemical Kinetics and Photochemical Data for Use in
Atmospheric Studies, JPL Publication 06-2, Evaluation no. 15, 2006.Stavrakou, T., Müller, J.-F., Boersma, K. F., van der A, R. J., Kurokawa, J., Ohara, T., and Zhang, Q.: Key chemical NOx
sink uncertainties and how they influence top-down emissions of nitrogen oxides, Atmos. Chem. Phys., 13, 9057–9082,
doi:10.5194/acp-13-9057-2013, 2013. Taubman, B. F., Marufu, L. T., Piety, C. A., Doddridge, B. G., Stehr, J. W., and Dickerson, R. R.: Airborne
characterization of the chemical, optical, and meteorological properties, and origins of a combined ozone/haze episode over the
eastern US, J. Atmos. Sci., 61, 1781–1793, 2004.Taubman, B. F., Hains, J. C., Thompson, A. M., Marufu, L. T., Doddridge, B. G., Stehr, J. W., Piety, C. A., and
Dickerson, R. R.: Aircraft vertical profiles of trace gas and aerosol pollution over the mid-Atlantic United States: statistics
and meteorological cluster analysis, J. Geophys. Res., 111, D10S07,
doi:10.1029/2005JD006196, 2006. USEPA (United States Environmental Protection Agency): EPA's National Inventory Model (NMIM), A Consolidated Emissions Modeling
System for MOBILE6 and NONROAD, Office of Transportation and Air Quality, EPA420-B-09-015, 2005. USEPA (United States Environmental Protection Agency): Technical Guidance on the Use of MOVES2010 for Emission Inventory
Preparation in State Implementation Plans and Transportation Conformity, EPA-420-B-10-023, 2010.van Noije, T. P. C., Eskes, H. J., Dentener, F. J., Stevenson, D. S., Ellingsen, K., Schultz, M. G., Wild, O., Amann, M.,
Atherton, C. S., Bergmann, D. J., Bey, I., Boersma, K. F., Butler, T.,
Cofala, J., Drevet, J., Fiore, A. M., Gauss, M., Hauglustaine, D. A.,
Horowitz, L. W., Isaksen, I. S. A., Krol, M. C., Lamarque, J. F.,
Lawrence, M. G., Martin, R. V., Montanaro, V., Muller, J. F., Pitari, G.,
Prather, M. J., Pyle, J. A., Richter, A., Rodriguez, J. M., Savage, N. H.,
Strahan, S. E., Sudo, K., Szopa, S., and van Roozendael, M.: Multi-model
ensemble simulations of tropospheric NO2 compared with GOME retrievals for
the year 2000, Atmos. Chem. Phys., 6, 2943–2979, 2006,
doi:10.5194/acp-6-2943-2006,
2006.Vinken, G. C. M., Boersma, K. F., van Donkelaar, A., and Zhang, L.: Constraints on ship NOx emissions in Europe
using GEOS–Chem and OMI satellite NO2 observations, Atmos. Chem. Phys., 14, 1353–1369,
doi:10.5194/acp-14-1353-2014, 2014.
Walsh, K. J., Milligan, M., Woodman, M., and Sherwell, J.: Data mining to characterize ozone behavior in Baltimore
and Washington, DC, Atmos. Environ., 42, 4280–4292, 2008.Wilson, R. C., Fleming, Z. L., Monks, P. S., Clain, G., Henne, S.,
Konovalov, I. B., Szopa, S., and Menut, L.: Have primary emission reduction
measures reduced ozone across Europe? An analysis of European rural
background ozone trends 1996–2005, Atmos. Chem. Phys., 12, 437–454,
doi:10.5194/acp-12-437-2012,
2012.
Xie, Y., Paulot, F., Carter, W. P. L., Nolte, C. G., Luecken, D. J., Hutzell, W. T., Wennberg, P. O., Cohen, R. C.,
and Pinder, R. W.: Understanding the impact of recent advances in isoprene photooxidation on simulations of regional air
quality, Atmos. Chem. Phys., 13, 8439–8455,
doi:10.5194/acp-13-8439-2013, 2013.
Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z.: Updates to the Carbon
Bond Chemical Mechanism: CB05, ENVIRON International Corp, 2005.Yegorova, E. A., Allen, D. J., Loughner, C. P., Pickering, K. E., and
Dickerson, R. R.: Characterization of an eastern US severe air pollution
episode using WRF/Chem, J. Geophys. Res., 116, D17306,
doi:10.1029/2010JD015054,
2011.Yu, S., Mathur, R., Sarwar, G., Kang, D., Tong, D., Pouliot, G., and
Pleim, J.: Eta-CMAQ air quality forecasts for O3 and related species using
three different photochemical mechanisms (CB4, CB05, SAPRC-99): comparisons
with measurements during the 2004 ICARTT study, Atmos. Chem. Phys., 10,
3001–3025,
doi:10.5194/acp-10-3001-2010,
2010.Yu, S. C., Mathur, R. Pleim, J., Pouliot, G., Wong, D., Eder, B., Schere, K.,
Gilliam, R., and Rao, S. T.: Comparative evaluation of the impact of WRF-NMM
and WRF-ARW meteorology on CMAQ simulations for O3 and related species
during the 2006 TexAQS/GoMACCS campaign, Atmos. Pollut. Res., 3, 149–162,
doi:10.5094/APR.2012.015,
2012.Zhou, W., Cohan, D. S., and Napelenok, S. L.: Reconciling NOx emissions
reductions and ozone trends in the US, 2002–2006, Atmos. Environ., 70,
236–244, 2013.