We investigated the ozone pollution trend and its sensitivity to key
precursors from 1990 to 2015 in the United States using long-term EPA Air Quality System (AQS)
observations and mesoscale simulations. The modeling system, a coupled
regional climate–air quality model (CWRF-CMAQ; Climate-Weather Research Forecast and
the Community Multiscale Air Quality), captured well the summer
surface ozone pollution during the past decades, having a mean slope of
linear regression with AQS observations of ∼0.75. While the
AQS network has limited spatial coverage and measures only a few key
chemical species, CWRF-CMAQ provides comprehensive simulations to enable
a more rigorous study of the change in ozone pollution and chemical
sensitivity. Analysis of seasonal variations and diurnal cycle of ozone
observations showed that peak ozone concentrations in the summer afternoon
decreased ubiquitously across the United States, up to 0.5 ppbv yr-1 in major
non-attainment areas such as Los Angeles, while concentrations at certain
hours such as the early morning and late afternoon increased slightly.
Consistent with the AQS observations, CMAQ simulated a similar decreasing
trend of peak ozone concentrations in the afternoon, up to 0.4 ppbv yr-1, and
increasing ozone trends in the early morning and late afternoon. A monotonically
decreasing trend (up to 0.5 ppbv yr-1) in the odd oxygen (Ox=O3+NO2) concentrations are simulated by CMAQ at all daytime hours.
This result suggests that the increased ozone in the early morning and late
afternoon was likely caused by reduced NO–O3 titration, driven by
continuous anthropogenic NOx emission reductions in the past decades.
Furthermore, the CMAQ simulations revealed a shift in chemical regimes of
ozone photochemical production. From 1990 to 2015, surface ozone production
in some metropolitan areas, such as Baltimore, has transited from a
VOC-sensitive environment (>50 % probability) to a
NOx-sensitive regime. Our results demonstrated that the long-term
CWRF-CMAQ simulations can provide detailed information of the ozone
chemistry evolution under a changing climate and may partially explain the
US ozone pollution responses to regional and national regulations.
Introduction
Tropospheric ozone (O3) is one of the major air pollutants, regulated
by the US Environmental Protection Agency (EPA), that pose myriad threats
to public health and the environment (Adams et al., 1989; WHO, 2003;
Ashmore, 2005; Anderson, 2009; Jerrett et al., 2009). It is also an
important greenhouse gas due to the absorption of thermal radiation,
affecting the climate (Fishman et al., 1979; Ramanathan and Dickinson, 1979;
IPCC, 2013). The major source of tropospheric ozone is photochemical
production from ozone precursors such as carbon monoxide (CO), volatile
organic compounds (VOCs) and nitrogen oxides (NOx) in the presence of
sunlight (Crutzen, 1974; National Research Council, 1991; Jacob, 2000; USEPA, 2006), while
downward transport of stratospheric air mass contributes substantially to
ozone concentrations in the upper troposphere (Levy et al., 1985; Holton et al.,
1995; Stevenson et al., 2006). In the past decades, ozone pollution in the
United States has been reduced substantially due to regulations on
anthropogenic emissions of ozone precursors (Oltmans et al., 2006; Lefohn et
al., 2008, 2010; Cooper et al., 2012, 2014; He et al., 2013),
although some studies suggested no trend or slight increases in some rural
areas (Jaffe and Ray, 2007; Lefohn et al., 2010; Cooper et al., 2012). Most
of these analyses focused on peak ozone concentrations, e.g., daily maximum
8 h average ozone (MDA8), during summer, but studies of trends in
seasonal and diurnal patterns of ozone pollution are limited. He et al. (2019) analyzed measurements from four monitoring sites in the eastern
United States and found different ozone trends between rural and urban sites
from the late 1990s to the early 2010s, including some increases at certain
hours, suggesting effects of national regulations could be regionally
dependent. Thus, it is important to extend our study to other regions of the
United States in a longer time period.
The nonmonotonic trends in United States ozone pollution could be caused by
the complex nonlinear chemistry of ozone production involving NOx and
VOCs (Logan et al., 1981; Finlayson-Pitts and Pitts, 1999; Seinfeld and
Pandis, 2006). With continuous reduction in anthropogenic emissions of ozone
precursors mainly NOx and VOCs in the United States, we need to better
understand the photochemical regime change for local ozone production (i.e.,
ozone production sensitivity), because air pollution regulations could have
different effects under NOx-sensitive and VOC-sensitive environments
(Dodge, 1987; Kleinman, 1994). For instance, under a VOC-sensitive
photochemical regime, the decrease in NOx emissions has limited impacts
on improving ozone pollution. Previous studies have developed photochemical
indicators to identify the ozone production sensitivity (Sillman, 1995;
Sillman et al., 1997; Tonnesen and Dennis, 2000b, a; Sillman and He,
2002). Sillman (1999) found the ratio of VOCs and NOx (VOC/NOx)
has a typical value of less than 4 for the VOC-sensitive environment and higher
than 15 for the NOx-sensitive regime. Observation-based studies of
ozone production sensitivity relied on research-grade measurements of ozone
precursors and photochemical intermediates that are not routinely measured
by air quality management agencies such as the US EPA. These species
include reactive nitrogen compounds (NOy), nitric acid (HNO3) and hydrogen peroxide (H2O2), normally observed during field campaigns
(e.g., Shon et al., 2007; Peng et al., 2011), which only covered limited
areas in certain periods. Studies based on air quality models (AQMs) could
identify the ozone production regimes at regional scales (Sillman et al.,
1997; Sillman and He, 2002; Zhang et al., 2009a, b; Xie et
al., 2011), but the simulation periods were usually short (less than 1
year) and thus could not capture the long-term change in ozone production
sensitivity.
Regional AQMs are widely used for investigating the US air quality
(Tagaris et al., 2007; Tang et al., 2009; Hogrefe et al., 2011; Pour-Biazar
et al., 2011; He et al., 2016a, 2018). They incorporate finer
resolutions, more detailed emissions and more explicit chemical mechanism
than global chemical transport models to better resolve characteristics of
tropospheric and surface dynamics, physical and chemical processes that are essential
for air quality. Our group has developed and used coupled regional
climate–air quality models to study air quality variations under a changing
regional climate (Huang et al., 2007; Zhu and Liang, 2013; He et al., 2016a, 2018). Our previous studies showed the ability of the model to capture
the decadal US air quality change (e.g., Zhu and Liang, 2013). In this
study, we coupled the latest Climate-Weather Research Forecast (CWRF) and
the EPA Community Multiscale Air Quality (CMAQ) models. CWRF has
demonstrated substantial improvement in downscaling regional climate and
extremes (Liang et al., 2012, 2019; Chen et al., 2016; Liu et al., 2016; Sun and Liang, 2020a, b) and thus can provide
more realistic weather conditions for AQMs in order to produce more credible air
quality simulations.
To supplement the limited observations in both spatial coverage and chemical
species, we conducted a continuous 26-year CWRF-CMAQ simulation from 1990 to
2015 for a more rigorous analysis of long-term US ozone trend. The model
performance of the US air quality was first evaluated against gridded
ozone observations. The ozone seasonal variations and diurnal cycles were
then extracted to determine the observed long-term trend. The model
simulations were subsequently analyzed to explain the observed ozone trends
and change in ozone production sensitivity.
Observations and model simulationsLong-term EPA observations
Hourly measurements of surface ozone concentrations from 1990 to 2015 were
available from the EPA Air Quality System (AQS) database (https://www.epa.gov/outdoor-air-quality-data, last access: May 2016). They have been examined
following EPA guidance, including for quality assurance and quality
control. The locations and durations of AQS monitoring sites have changed
substantially due to logistics and requirements to cover the regions
sensitive to air pollution. Figure 1 shows that more than 2000 sites
reported ozone measurements during the period of 1990 to 2015. To alleviate
the impacts from missing data and short durations, we selected 640 sites
that had ozone observation records of longer than 20 years. Hourly ozone
observations were processed following the approach described in He et al. (2019) in order to create the long-term seasonal and diurnal records for these
stations.
Locations of EPA AQS sites for surface ozone monitoring during
1990–2015. Red dots stand for monitoring sites with
records of more than 20 years. Black dots show the locations of monitoring sites have short data
records, which are not used in this study. The map shows the CWRF-CMAQ 30 km
domain and five subdomains sensitive to air pollution. CA: California
(including nearby parts of Nevada, Arizona and Oregon); TX: Texas (including
nearby parts of Louisiana, Arkansas and Oklahoma); SE: Southeast; NE:
Northeast; and MW: Midwest. Please note that our CA and TX subdomains include
more area than the states of California and Texas.
Regional climate modeling
CWRF (Liang et al., 2012) was driven by the European Centre for Medium-Range
Weather Forecasts ERA-Interim reanalysis (ERI; Dee et al., 2011) in order to
downscale regional climate variations during 1989–2015, with the first year
as the spin-up and not used. We adopted the well-established CWRF North
American domain with a 30 km grid spacing (Fig. 1), covering the contiguous
United States (CONUS) and neighboring southern Canada, northern Mexico and
adjacent oceans. The CWRF was developed as a climate extension of the WRF
model (Skamarock et al., 2008), incorporating numerous improvements in
representation of physical processes and integration of external forcings
that are crucial to climate scales, including interactions between
land–atmosphere–ocean, convection–microphysics and cloud–aerosol–radiation,
and system consistency throughout all process modules (Liang et al., 2012;
Qiao and Liang, 2015,
2016; Chen et al., 2016; Liu et al., 2016). CWRF is built with a comprehensive ensemble of many alternate
mainstream parameterization schemes for each of key physical processes. It
has been vigorously tested in North America and Asia, showing an outstanding
ability to capture regional climate characteristics (Yuan and Liang,
2011; Chen et al., 2016; Liu et al., 2016; Liang et al., 2019). The CWRF
downscaling has been shown to provide realistic meteorological fields and
regional climate signals that can be cordially used to drive the CMAQ for
long air quality simulations. Major CWRF physics configurations include the
semiempirical cloudiness parameterization of Xu and Randall (1996), the
cloud microphysics scheme of Tao et al. (1989), the shortwave and longwave
radiation scheme of Chou et al. (2001), the ensemble cumulus
parameterization (Qiao and Liang, 2015, 2016, 2017) and the
planetary boundary layer scheme of Holtslag and Boville (1993). Hourly CWRF
outputs were processed using a modified Meteorology-Chemistry Interface
Processor (MCIP, version 4.3) for CMAQ simulations.
Emissions preparation
To prepare anthropogenic emissions, we chose 2014 as the baseline year. The
emissions from this year were modified from the National Emissions Inventory 2011
(NEI2011). The modifications were based on measurements from the Ozone
Monitoring Instrument (OMI) on board the satellite Aura, the ground-based AQS
network and the in situ continuous emissions monitoring in power plants (Tong et
al., 2015, 2016). The so-modified NEI2011 inventory was
processed using the Sparse Matrix Operator Kernel Emissions (SMOKE) version 3.7 (Houyoux et al., 2000). Emissions from on-road, off-road and area
sources were placed at the model layer closest to the surface. Emissions
from point sources, e.g., stacks from power plants, were distributed
vertically based on stack height and plume rise. The plume rise was
estimated based on the method in Briggs (1972). The inventory pollutants
were speciated according to the Carbon Bond Mechanism version 5 (CB05) and AERO5 aerosol mechanism. To fill the gap where NEI2011 data were
not available, the Emissions Database for Global Atmospheric Research (EDGAR
v3; http://edgar.jrc.ec.europa.eu/, last access: October 2016) at a 1∘×1∘ resolution developed by the Joint Research Centre of the
European Commission was adapted. Figure 2 shows an example of 2010–2015 mean
NOx emissions distribution over the modeling domain. Daily mean
NOx emissions have high values in urban areas of cities such as Los Angeles and Chicago and the Northeast corridor from Washington DC to Boston.
Averaged daily NOx emissions between 2010 and 2015 in the modeling domain (in moles per square kilometer).
To project emissions from the baseline year into all individual years, we
used the scaling factors from Air Pollutant Emissions Trends (APETs) data
compiled by the US EPA (https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data, last access: October 2016).
Emissions of the baseline year are based on EPA NEI2011 inventory, which can
provide the best available anthropogenic emissions to the CONUS and are
currently used in the operational US national air quality forecast. The
usage of APET scaling factors can guarantee the domain total emissions are
consistent with the US EPA emissions trend, although assuming the same
spatial distribution of anthropogenic emissions from year to year may not be
realistic. Without a reasonable observation of actual spatiotemporal
variations, it is the cost-effective approach as a first-order approximation
to simulate long-term US air quality driven by consistent CONUS total
anthropogenic emissions that account interannual trends. Figure 3 shows the
emission evolution from 1990 to 2015. Since 1990 anthropogenic emissions of
NOx, CO, sulfur dioxide (SO2) and VOCs have steadily decreasing
trends, with SO2 experiencing the largest reduction. On the other hand,
anthropogenic PM2.5 and NH3 emissions have stayed mostly flat since the
early 2000s.
Anthropogenic emission evolution relative to 2014 in the modeling
domain from 1990 to 2015.
The wildfire emissions were based on the Global Fire Emissions Database,
Version 4, with small fires (GFEDv4s; Randerson et al., 2017; van der Werf et
al., 2017). The 0.25∘×0.25∘ degree resolution
GFEDv4s data were projected onto the modeling domain and speciated into the
CB05 and AERO5 species. GFEDv4s had a monthly resolution from 1997 to 2000
and daily resolution from 2000 onward. Figure 4 illustrates the fire
emissions evolution during 1990 to 2015 relative to 2014. Fire emissions
have large interannual variations, with high emissions in 1998, 2002, 2013
and 2015 and low emissions in 2001, 2004 and 2014. We developed a method
to merge the aforementioned anthropogenic and wildfire emissions into the
temporalized, gridded and speciated data ready for CMAQ.
Fire emission evolution relative to 2014 in the modeling domain
from 1990 to 2015. Note that Global Fire Emissions Database (GFED) fire emissions are not available before
1997.
Summary of multiyear mean average of daily CO, NOx and NMVOCs
emissions in the CONUS and five subdomains (in moles per square kilometer per second).
Please note that our California and Texas subdomains include more area than
the states of California and Texas.
The biogenic emissions were calculated on-line within CMAQ based on the
Biogenic Emissions Landuse Database, Version 3 (BELD3; https://www.epa.gov/air-emissions-modeling/biogenic-emissions-landuse-database-version-3-beld3, last access: October 2016).
The 1 km resolution BELD3 data with spatial distribution of 230 vegetation
classes over the North America were processed through the Spatial Allocator
developed by the Community Modeling and Analysis System (CMAS) center
(https://www.cmascenter.org/sa-tools/, last access: October 2016) to generate the gridded
vegetation distribution over the study domain. Table 1 lists the 5-year mean
variations of daily major ozone precursor (CO, NOx and NMVOCs: non-methane VOCs)
emissions in the modeling domain and five subdomains. The emission data show
regionally dependent reductions. For instance, compared with 2000–2004, the
NOx emissions in 2005–2009 decreased by ∼36 % on average
in the CONUS, while 38 % and 35 % reductions existed in states of
California and Texas.
Air quality modeling
The EPA CMAQ model version 5.2 (EPA, 2017) was selected to simulate the US
air quality variations driven by CWRF meteorological fields (Sect. 2.2)
and constructed emissions (Sect. 2.3). Major chemical mechanisms include
the Carbon Bond 6 revision 3 (CB6r3) gas phase chemical scheme with updated
secondary organic aerosol (SOA) and nitrate chemistry (Yarwood et al., 2010)
and the latest AERO6 aerosol scheme (EPA, 2017), which improved US air
quality simulations over previous chemical mechanisms (Appel et al., 2016).
Chemical initial and boundary conditions were obtained from the default
concentration profiles built in CMAQ (EPA, 2017). Simulations were conducted
continuously for each 5-year segment (1990–1994, 1995–1999, etc.) with a
2-week spin-up in December prior to each starting year in order to speed up
simulation turn around. Hourly concentrations of ozone and its key
precursors such as nitric oxide (NO) and nitrogen dioxide (NO2) were
saved for subsequent analyses.
ResultsEvaluation of CMAQ performance
Our previous studies showed that the direct comparison of observation data
from monitoring sites and CMAQ results in 30 km grid could introduce
inconsistency for evaluating the model performance (He et al., 2016a). The
direct comparison is usually conducted by sampling the grid of CMAQ
where the AQS site is located, but the distribution of AQS monitoring
sites is usually uneven with more sites concentrated in populous urban and
suburban areas where high ozone levels prevail. Sampling 30 km CMAQ grids
over the locations of AQS measurements, i.e., direct comparison of averaged
concentrations in the 900 km2 CMAQ grid and pointwise AQS observations,
could introduce notable biases. So we applied the EPA Remote Sensing
Information Gateway (RSIG) software (available at https://www.epa.gov/rsig, last access: July 2016) to map the site observations onto our CMAQ grid.
The RSIG has the capability to “re-grid” the AQS observations on a selected
model grid using the inverse-distance-weighted method to calculate the
gridded mean concentrations (https://www.epa.gov/hesc/how-rsig-regrids-data, last access: July 2016). Figure 5 compares summer
(JJA) mean MDA8 ozone in 2014 between gridded AQS observations and CMAQ
outputs and shows that the model can capture well the US ozone pollution,
except for an underestimation in urban areas such as the Los Angeles basin.
Comparison of summer MDA8 ozone concentrations from EPA AQS
observations and CMAQ simulations in 2014. AQS station data were gridded to
the CMAQ grid using the EPA RSIG software. (a) Contour plot, the background
stands for the CMAQ outputs and the dots stand for gridded AQS observations;
(b) scatter plot of the gridded AQS observations and collocated CMAQ outputs.
Table 2 summarizes the statistics of CMAQ performance while simulating the summer
ozone concentrations during 2000–2015 in the CONUS and subdomains. Linear
regression analyses of MDA8 ozone result in a mean slope value of 0.75 for the
CONUS; i.e., CMAQ slightly underestimates ozone over the United States. In
subdomains, CMAQ performance exhibits large interannual variations. For
instance, in Texas the linear regression slope and correlation coefficient
ranges from 0.58 to 0.97 and 0.55 to 0.86, respectively. With a gradual
reduction in anthropogenic emissions, the fluctuations of CMAQ performance
could be related to climate signals that control the regional ozone
pollution. Future work is needed to identify the relationship between these
regional climate variations and the US ozone pollution. Generally, this
modeling system has substantially improved performance in the Southeast,
California and Texas and moderately improved performance in the Northeast
and Midwest as compared with our previous modeling system (He et al.,
2016a), which significantly underestimated the US ozone pollution. One
reason is that CWRF with a more sophisticated representation of physical
processes have the capability to better simulate the US climate, especially
surface temperature and precipitation (Liang et al., 2012; Chen et al.,
2016; Liu et al., 2016; Sun and Liang, 2020a, b), which
are key to accurate air quality simulations. The evaluation of CMAQ
performance demonstrates the capability of CWRF-CMAQ to credibly simulate
historical air quality.
Summary of the comparison of JJA MDA8 ozone concentrations from
AQS observations and CMAQ simulations during 2000–2015 in the CONUS and
subdomains. Slope and correlation (corr. R) are calculated for each year
based on linear regression analysis. Please note that our California and
Texas subdomains include more area than the states of California and Texas.
NMB: normalized mean bias (in percent);
RMSE: root mean square error (in parts per billion by volume).
Long-term ozone trend in AQS observations
We applied a box-averaging technique (He et al., 2016b, 2019) to
analyze ozone measurements at the selected AQS monitoring sites (Fig. 1).
This approach used an hour by month box to calculate the mean 24 h diurnal
cycle of ozone for each month. Then we calculated the climatology mean over
24 h by 12 months and the respective anomaly for each month at each AQS
site. Figure 6 shows samples of long-term mean ozone concentrations and
anomalies at four non-attainment cities: Baltimore, Maryland; Los Angeles,
California; Denver, Colorado; and New York City (NYC), New York. The hour by
month climatology (left column of Fig. 6) shows that the peak ozone
concentrations in the afternoon during the ozone season (April to September)
have been reduced significantly in these cities. However, ozone
concentrations in the morning (08:00 LT to 12:00 LT) and
at night (20:00 LT to 08:00 LT) increased slightly. These results confirm the
effectiveness of recent emission controls, which were designed to reduce the
peak ozone. But the expansion of ozone at moderate levels (40–50 ppbv),
which are higher than the natural background of US ozone (Fiore et al.,
2002, 2003; Wang et al., 2009; Lefohn et al., 2014), could
cause negative health impacts.
The box-averaging analyses of AQS ozone observations at selected
sites from 1990 to 2015. (a) Essex, Maryland (suburban Baltimore; AQS ID 240053001); (b) Pasadena, California (downtown Los Angeles; AQS ID 060372005); (c) Denver, Colorado (downtown Denver; AQS ID 080310014); (d) Staten Island, New York (suburban New York City; AQS ID 360850067). Left
column shows the monthly mean, and right column shows the anomaly values. White
patches stand for missing data or not sufficient data for the box-averaging
analysis.
The anomaly (right column of Fig. 6) shows large variabilities in ozone
concentrations because the ozone production is significantly impacted by
regional climate (e.g., temperature, precipitation) with interannual and
decadal variations. A large ozone reduction occurred after 2003 when the EPA
NOx State Implementation Plan (SIP) call was implemented (He et al., 2013).
The anomalies at Los Angeles (Fig. 6b) and NYC (Fig. 6d) show decreases in
the peak ozone in the afternoon in summer and increases in other times and
seasons. For Baltimore and Denver, the peak ozone was not monotonically
reduced but rather increased in some years after 2002. Given the continuous
reduction of anthropogenic emissions in the past decades, the increased
ozone pollution in these areas could be caused by other factors that need
further investigation in the future.
Trend in ozone observations at selected EPA AQS sites during
1990–2015 (in parts per billion by volume per year) at (a) 08:00 LT, (b) 12:00 LT, (c) 16:00 LT and (d) 20:00 LT. We only show the sites with a statistically significant
linear trend in the plots.
We used the linear regression analysis to calculate the slope, correlation
(R) and p value of ozone trend at each local hour. Figure 7 shows ozone
trends (slope; in parts per billion per year) at AQS sites that are statistically
significant (R2>0.5; p<0.05) in the early
morning (08:00 LT), at noon (12:00 LT), in the afternoon (16:00 LT) and in the evening
(20:00 LT). Consistent results with the four cities (Fig. 6) are found
ubiquitously. The peak ozone at noon and in the afternoon generally had a
decreasing trend in the CONUS, up to 0.5 ppbv yr-1, confirming the improved air
quality due to regulations, while ozone in the early morning and late
afternoon increased slightly at most of monitoring sites. However, AQS sites
in the Bay Area (San Francisco, California) and Denver had stronger positive
trends in the daytime. The possible explanations include the trans-Pacific
transport of ozone and its precursors to the US West Coast (Hudman et al.,
2004; Huang et al., 2010; Lin et al., 2012b) and stratosphere–troposphere
exchange of ozone to high altitude regions (Langford et al., 2009; Lin et
al., 2012a).
Ozone trends derived from CMAQ simulations
We applied the same box-averaging technique to hourly surface ozone
simulations of the CONUS and conducted the linear regression analysis to
estimate the ozone trend at each model grid (Fig. 8). Compared with ozone
trends derived from AQS observations (Fig. 7), the CMAQ model successfully
captured the spatial pattern and magnitude of change in ozone pollution. For
instance, at 16:00 LT, CMAQ simulated an up to 0.4 ppbv yr-1 decrease in surface
ozone in the eastern United States and southern region of California state.
However, CMAQ simulated statistically insignificant trends (white color in
Fig. 8c) at 16:00 LT in the Bay Area, Los Angeles and Denver where AQS
observations showed increasing trends (Fig. 7c). The discrepancy occurred
because our model used the static chemical lateral boundary conditions (LBCs) that
did not include the change in trans-Pacific transport of air pollutants,
which were known to elevate the background ozone on the West Coast. Also
CMAQ does not contain stratospheric chemistry and hence cannot account the
contribution of downward transport of stratospheric ozone to the high
altitude region.
Trends in ozone simulations from CMAQ during 1990-2015 (in parts per billion by volume per year) at (a) 08:00 LT, (b) 12:00 LT, (c) 16:00 LT and (d) 20:00 LT.
We only show CMAQ grids with a statistically significant linear trend in the
plots.
Consistent with trends derived from AQS observations, CMAQ also simulated
increasing ozone trends in the early morning (08:00 LT, Fig. 8a) and late
afternoon (20:00 LT, Fig 8d), especially in urban regions such as Los Angeles
and Chicago. He et al. (2019) found ozone increases from observations at
four sites in the eastern United States and a possible cause suggested by
the reduced NO–O3 titration through examining the trend in odd oxygen
(Ox=O3+NO2). Due to known interferences from nitrogen
compounds such as NOx and organic nitrates to standard NO2
measurements employed by EPA (Fehsenfeld et al., 1987; Dunlea et al., 2007;
Dickerson et al., 2019), the analysis of Ox required a research-grade
NO2 analyzer (e.g., photolytic NO2 conversion) which is not
available in current AQS network. Thus, our simulations provide a unique
opportunity to expand such a study to the whole CONUS.
Trend in Ox (Ox=O3+NO2) simulated by
CMAQ during 1990–2015 at (a) 08:00 LT, (b) 12:00 LT, (c) 16:00 LT and (d) at 20:00 LT. We only show CMAQ grids with a statistically significant linear
trend in the plots.
Trends in Ox concentrations simulated by CMAQ at 08:00 LT, 12:00 LT, 16:00 LT and
20:00 LT show a consistent decreasing trend over the modeling domain, up to 0.5 ppbv yr-1 reductions in the eastern United States (Fig. 9). The result
confirms our hypothesis that the reduced NO–O3 titration elevated
surface ozone concentrations in the early morning and late afternoon when
the photochemical production of ozone is low or not active. Nowadays, the
EPA ozone standard focuses on peak ozone concentrations, i.e., MDA8 ozone,
which usually has maximum values at noon or in the early afternoon, so the
damage from additional ozone exposure from these elevated ozone
concentrations in the early morning and late afternoon is not considered
under the current environment policy. These increased ozone levels could
offset the benefit from reduced peak ozone in past decades, which needs
further investigations to provide scientific evidence for future policy
decision.
Change in photochemical regime
With the continuous reduction in ozone precursor emissions, changes in the
complex O3–NOx–VOC chemistry are anticipated. We used the
O3/NOy ratio as the indicator to study the photochemical regime
change in the US surface ozone production. The usage of the O3/NOy
ratio was first proposed by Sillman (Sillman, 1995; Sillman et al., 1997).
Sillman et al. (1997) conducted a case study of observations
in urban areas (Atlanta, New York and Los Angeles) and modeling results
from the Urban Airshed Model and suggested the threshold of 7 as the
transition region from a VOC-sensitive environment to a NOx-sensitive
environment. Zhang et al. (2009a, b) expanded this method to the CONUS
with 1-year observations and CMAQ simulations (36 km spatial resolution) and
suggested a threshold of 15 for ozone pollution at the national scale. In
this study, we did not have access to the long-term research-grade NOy
observations from the AQS network and did not conduct sensitivity
experiments (due to computational resource limit) with reduced NOx
emissions following Sillman et al. (1997), so we have to reply on the
O3/NOy threshold from literature. We conducted a simple evaluation
of our CMAQ results and found the threshold of 7 would be more appropriate for
urban areas, and the threshold of 15 is more applicable to our study
of the whole United States (Fig. S1 in the Supplement). Please
note that the O3/NOy ratio could depend on the modeling framework,
so due to the similarity of our modeling system (30 km CMAQ) and the model
used in Zhang et al. (2009a, b), our analysis suggest the same
threshold of 15.
The threshold of 15 proposed by Zhang et al. (2009b) was adopted to identify
the VOC-sensitive or NOx-sensitive regime, i.e., O3/NOy<15, indicating the VOC-sensitive regime. For each local hour, we
calculated the probability that O3/NOy is lower than 15 in every month. Figure 10 shows the probability of VOC-sensitive regime at 14:00 LT in
July of 1995, 2005 and 2015. Most regions dominated by the VOC-sensitive
chemistry are urban or suburban, where anthropogenic NOx emissions are
relatively high as compared with anthropogenic and/or biogenic VOCs
emissions, areas such as in the Los Angeles basin, the Northeast corridor (Washington
DC–Baltimore–Philadelphia–NYC) and the Chicago metropolitan area. Note
that these maps are created based on ozone photochemical production
simulated at the surface level, so the distributions are slightly different
from recent studies using satellite data (Duncan et al., 2010; Jin et al.,
2017; Ring et al., 2018).
Probability of VOC-sensitive photochemical ozone production
(i.e., O3/NOy<15) in the CONUS simulated by CMAQ at 14:00 LT in July of (a) 1995, (b) 2005 and (c) 2015.
We calculated the mean probability of VOC-sensitivity (14:00 LT in July) in a 3×3 CMAQ grid in metropolitan areas of Baltimore, Los Angeles and
NYC from 1990 to 2015 (Fig. 11). CMAQ simulations suggest the transition
from a VOC-sensitive regime to a NOx-sensitive regime in these urban areas.
There were interannual variabilities in the probability of VOC-sensitive
photochemistry in Baltimore (∼50 %) and NYC
(∼80 %) in the 1990s and the early 2000s. After the EPA
2003 NOx SIP call, anthropogenic NOx emissions decreased
substantially, leading to reduced ozone pollution in the eastern United
States (He et al., 2013), so the photochemical production of surface ozone
is expected to gradually become NOx-sensitive. In 2015, ozone
photochemical production in Baltimore was dominated by NOx emissions
(only ∼20 % probability of VOC-sensitive), while NYC had
higher probability (>50 %) of VOC-sensitive chemistry. In Los
Angeles, ozone chemistry slowly leaned toward being NOx-sensitive, but until 2015
the local ozone production was still controlled by VOCs emissions. In
regions with VOC-sensitive photochemistry in summer, reduction in NOx
emissions had a limited impact on the local rate of ozone production until
the photochemistry of ozone production became NOx-sensitive. Our
analysis can partially explain the different responses of ozone pollution in
major US cities to national air quality regulations during the past
decades (Cooper et al., 2012) and can provide some insights for future
policy decision.
Long-term trends in probability of VOC-sensitive photochemical
production of surface ozone in three major urban areas at 14:00 LT in July.
Probability is calculated using averages of 3×3 grids centered at
downtown.
Conclusions and discussion
EPA AQS observations in the United States from 1990 to 2015 were analyzed in order to
study the trend in surface ozone seasonal variations and diurnal cycles. We
found that the peak ozone concentrations in the afternoon decreased
significantly, especially in major non-attainment regions, but the
concentrations in the early morning and late afternoon increased slightly.
Regional climate-air quality model captured the long-term records of US
ozone pollution and suggested that the increased ozone was caused by reduced
NO–O3 titration due to the continuous reduction in NOx emissions.
Model simulations also showed changes in ozone photochemical regime. The
US urban and suburban areas generally transited from the VOC-sensitive regime
in the early 1990s to a more NOx-sensitive regime recently. But ozone
production in some cities such as NYC and Los Angeles are still
substantially impacted by VOC emissions. The current national and regional
regulations focus on the MDA8 ozone concentrations, mainly determined by the
peak ozone in the afternoon. Our study revealed the elevated ozone
concentrations in the early morning and late afternoon, which must be
considered for their impacts on public health. Because NOx emissions are
currently the main target of national and regional control measures, our
study suggests that regulations on anthropogenic VOCs emissions could be
important in certain regions. This study can improve our understanding about
the effectiveness of regulations in the past decades and will provide
scientific evidence for future policy decision.
Ozone production is highly nonlinear, so accurate emissions are essential
to simulations of its long-term variations. Due to limited resources, we scaled
the anthropogenic emissions from a baseline year (2014) to the 1990s using
factors derived from the national trend data in order to construct consistent
emissions for the CONUS with respect to the EPA data. This scaling cannot
accurately reflect the detailed regional-dependent regulations for
individual state such as the 2012 Health Air Act in Maryland (He et al.,
2016b). Also, because the GFED data were only available after 1997, the
contribution of wildfire emissions to ozone pollution was not included in
model simulations between 1990 and 1996. Thus, we anticipated some
uncertainties in ozone simulations in the early 1990s. Our model also has
limitations in reproducing ozone records in high altitude regions such as
Denver because of the lack of stratospheric chemistry in CMAQ and missing
the effect of stratosphere–troposphere exchange on surface ozone. Lastly,
due to limited resources, our experiments used static chemical LBCs for
CMAQ, which excluded the long-range transport of air pollutants into the
United States. So our current modeling system cannot take the historical
changes in air pollution outside the United States into account. That is, the
effect of long-range transport of air pollutants through model domain
boundaries is presumed to be secondary to the long-term trends over the
United States. For some West Coast regions such as the state of California,
the trans-Pacific transport had been enhanced in the past decades and could
play a more important role in determining the local air quality. With these
increased air pollutant transported into the United States, our study may
underestimate the impacts of domestic emission reductions on US ozone
pollution, especially on the West Coast and in the Southwest. To accurately
evaluate the contribution from transboundary emission, dynamic LBCs from a
global chemical transport model are needed in future studies.
Code and data availability
EPA AQS observation data are available at https://www.epa.gov/outdoor-air-quality-data (US EPA, 2016). The CMAQ is a community
model and is freely available at https://www.cmascenter.org/cmaq/ (US EPA, 2017). The ECMWF ERI data are distributed by
NCAR (at https://rda.ucar.edu/datasets/ds627.0/, ECMWF, 2016). The CWRF
model source code is available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-3191-2020-supplement.
Author contributions
HH, XZL and ZT designed the experiment; HH and CS developed the
CWRF-CMAQ system and performed the CWRF modeling; ZT and DQT prepared the
emission data; HH conducted the CMAQ simulations; HH, ZT and CS
analyzed the data; HH prepared the manuscript with contributions from all
co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
This work has not been formally reviewed by
EPA. The views expressed in this document are solely those of the authors
and do not necessarily reflect those of the funding agency. EPA does not
endorse any products or commercial services mentioned in this publication.
Acknowledgements
This work was supported by the US Environmental Protection Agency under
Assistance Agreement no. RD-83587601.
We thank the support of University of Illinois at Urbana-Champaign
(UIUC)/EPA award 20110150701. We also acknowledge partial support from the US National Science Foundation Innovations at the Nexus of Food, Energy and Water Systems (EAR-1639327). We thank the National Center for
Supercomputing Applications (NCSA) and the National Center for Atmospheric
Research (NCAR) Computation and Information System Laboratory for
supercomputing support. We thank Plessel Todd for the help on the RSIG
software (https://www.epa.gov/rsig).
Financial support
This research has been supported by the US Environmental Protection Agency (grant no. RD83587601).
Review statement
This paper was edited by Hailong Wang and reviewed by three anonymous referees.
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