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Modeling Trans-Pacific Transport and Stratospheric Intrusion of
Tropospheric Ozone using Hemispheric CMAQ during April 2010:
Part 2. Examination of Emission Impacts based on the Higher-order
Decoupled Direct Method
Syuichi Itahashi1, Rohit Mathur 2, Christian Hogrefe 2, Sergey L. Napelenok 2, and Yang Zhang 3
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1 Environmental Science Research Laboratory, Central Research Institute of Electric Power Industry (CRIEPI), 1646 Abiko,
Abiko, Chiba 270–1194, Japan
2 Environmental Protection Agency (EPA), Computational Exposure Division, National Exposure Research Laboratory, Office
of Research and Development, Research Triangle Park, NC 27711, USA
3 Department of Marine, Earth, and Atmospheric Sciences (MEAS), North Carolina State University (NCSU), Campus Box
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8208, Raleigh, NC 27695, USA
Correspondence to: Syuichi Itahashi (isyuichi@criepi.denken.or.jp)
Abstract.
The state-of-the-science Community Multiscale Air Quality (CMAQ) Modeling System which has recently been extended for
hemispheric-scale modeling applications (referred to as H-CMAQ), is applied to study the trans-Pacific transport, a
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phenomenon recognized as a potential source of air pollution in the U.S.A., during April 2010. The results of this analysis are
presented in two parts. In the previous part 1 paper, model evaluation for tropospheric ozone (O3) was presented and an air
mass characterization method was developed. Results from applying this newly established method pointed to the importance
of emissions as the factor to enhance surface O3 mixing ratio over the U.S.A. In this subsequent part 2 paper, emission impacts
are examined based on mathematically rigorous sensitivity analysis using the higher-order decoupled direct method (HDDM)
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implemented in H-CMAQ. The HDDM sensitivity coefficients indicate the presence of a NOx-sensitive regime during April
2010 over most of the northern hemisphere. By defining emission source regions over the U.S.A. and East Asia, impacts from
these emission sources are examined. At the surface during April 2010, the emission impacts of the U.S.A. and East Asia are
comparable over the western U.S.A. with a magnitude of about 3 ppbv impacts on a monthly mean of all hours basis whereas
the impact of domestic emissions dominates over the eastern U.S.A. with a magnitude of about 10 ppbv impacts on a monthly
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mean basis. The positive correlation (r=0.63) between surface O3 mixing ratios and domestic emission impacts is confirmed.
In contrast, the relationship between surface O3 mixing ratios and emission impacts from East Asia exhibits a flat slope when
considering the entire U.S.A. However, this relationship has strong regional differences between the western and eastern
U.S.A.; the western region exhibits a positive correlation (r=0.36-0.38) whereas the latter exhibits a flat slope (r<0.1). Based
on the comprehensive evaluation of H-CMAQ, we extend the sensitivity analysis for O3 aloft. The results reveal the significant
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impacts of emissions from East Asia on the free troposphere (defined as 750 to 250 hPa) over the U.S.A. (impacts of more
than 5 ppbv), and the dominance of stratospheric intrusions on upper model layer (defined as 250 to 50 hPa) over the U.S.A.
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(impacts greater than 10 ppbv). Finally, we estimate changes of trans-Pacific transport by taking into account recent emission
trends from 2010 to 2015 assuming the same meteorological condition. The analysis suggests that the impact of recent emission
changes on changes in the contribution of trans-Pacific transport to U.S.A. O3 levels was insignificant at the surface level and
was small (less than 1 ppbv) over the free troposphere.
1 Introduction
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Tropospheric ozone (O3) is a secondary air pollutant produced through photochemical reactions including nitrogen
oxides (NOx) and volatile organic compounds (VOCs) (Haagen-Smit and Fox, 1954). Tropospheric O3 plays an important role
by producing hydroxyl radicals (OH) which control the oxidizing capacity (Logan, 1985). O3 at the surface level poses
significant human health impacts; hence many countries have an air quality standard for its ambient mixing ratios. The National
Ambient Air Quality Standard (NAAQS) of O3 in the U.S.A. is set on the annual 4th highest maximum daily 8-h concentration
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(MD8O3) averaged over three years. Its threshold value was set at 70 ppbv in 2015 (EPA, 2018). An analysis of trends in
surface O3 observation during the period of 1998 and 2013 in the U.S.A. indicated that the highest O3 mixing ratio have been
decreasing responsive to reductions in O3 precursor emissions (Simon et al., 2015). Regarding O3 pollution in the U.S.A.,
sources enhancing O3 mixing ratios are not limited to national emissions. One issue of potential concern is the dramatic
variation of anthropogenic emissions in East Asia which has been recognized as an important source for the U.S.A. through
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previous research on trans-Pacific transport (e.g., Jacob et al., 1999; Fiore et al., 2002; Wang et al., 2009, 2012; Lin et al.,
2012a; Huang et al., 2017; Guo et al., 2018; Jaffe et al., 2018). Stratosphere-to-troposphere transport (STT) is another process
affecting tropospheric O3 pollution (Lelieveld and Dentener, 2000). The fraction of stratospheric origin on tropospheric O3
varies by location and season, is strongly dependent on the tropopause altitudes and is an active research area (e.g., Fiore et
al., 2003; Lin et al., 2012b; Mathur et al., 2017). Literature estimates of the contributions of these two factors are summarized
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in the part 1 paper (see, Table 1 of Itahashi et al., 2019). The occurrence of these trans-Pacific transport and stratospheric
intrusion can be related to the mid-latitude jet stream, and this is controlled by La Niña and El Niño. The springtime trans-
Pacific transport may be enhanced following an El Niño winter due to the eastward extension of the atmospheric circulation
over the Pacific-North America sector and the southward shift of the subtropical jet stream. The stratospheric intrusions may
be enhanced following a La Niña winter due to a meandering of the jet stream (Lin et al., 2015). Because enhancement of
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trans-Pacific transport is expected after the 2009-2010 El Niño winter, April 2010 is selected as the study period in the current
analysis.
As illustrated in the part 1 paper, the objective of this sequential research is to better understand the relative
contributions of precursor emissions from the U.S.A. and East Asia and also the impacts of STT on air quality in the U.S.A.
during spring time. To quantify these contributions, we used the model of Community Multiscale Air Quality (CMAQ) version
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5.2 applied for hemispheric-scale analysis (H-CMAQ) (Mathur et al., 2017). The current study extends our previous analysis
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(Itahashi et al., 2019; hereafter referred to as Part 1). A brief summary of the findings from that analysis and the motivation
for this study is presented subsequently.
2 Summary of Part 1 and Motivation for Part 2
The model of H-CMAQ was configured with a horizontal grid spacing of 108 km with 187×187 grids to cover the
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entire Northern Hemisphere on 44 terrain-following vertical layers from the surface to 50 hPa (Mathur et al., 2017). The
emission inputs are based on the modeling experiments of Hemispheric Transport of Air Pollution version 2 (HTAP2), and the
description of this emission dataset can be found in relevant studies (Janssens-Maenhout et al., 2015; Pouliot et al., 2015;
Galmarini et al., 2017; Hogrefe et al., 2018). For gas-phase and aerosol chemistry representation, cb05e51 and aero6 with
nonvolatile primary organic aerosol (POA) were used, respectively (Simon and Bhave, 2012; Appel et al., 2017), and further
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included a condensed representation of halogen chemistry which relate to O3 loss in maritime environments (Sarwar et al.,
2015). In terms of the stratospheric O3 behavior, a robust indicator to distinguish between stratospheric and tropospheric air
masses is potential vorticity (PV). A value of 2 PVU (1 PVU = 10-6 m2 K kg-1 s-1) is suggested as the identification of
stratospheric air (e.g., Hoskins et al., 1985). O3 mixing ratios and PV are correlated, and O3/PV ratios are used in H-CMAQ to
specify the model top O3 mixing ratio. Starting with H-CMAQ version 5.2, a dynamic O3/PV function has been implemented
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to account for the seasonal, latitudinal, and altitude dependencies of this relationship (Xing et al., 2016). The H-CMAQ
simulation in this study started from 1 March 2010 and was initialized by three-dimensional chemical fields from prior model
simulations for 2010 described in Hogrefe et al. (2018); March was discarded as a spin-up period and April was selected as
analysis period.
To evaluate the performance of H-CMAQ simulations, the part 1 paper computed the Pearson’s correlation coefficient
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(R) with student’s t-test for the statistical significance level, the normalized mean bias (NMB), and the normalized mean error
(NME). The analysis of ground-based mixing ratios included observations at 52 sites of the World Data Centre for Greenhouse
Gases (WDCGG) over the northern hemisphere (WDCGG, 2018), 9 sites of the Acid Deposition Monitoring Network in East
Asia (EANET) over Japan (EANET, 2018), and 81 sites of the Clean Air Status and Trends Network (CASTNET) over the
U.S.A. (CASTNET, 2018). Based on more than 4000 observation-model pairs of MD8O3, the results of this analysis showed
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good model performance with R around 0.5-0.6, NMBs around −10%, and NMEs around 10-20%. In addition to this ground-
based analysis, vertical O3 profiles were evaluated for three vertical layer ranges: from surface to approximately 750 hPa (i.e.,
boundary layer), approximately 750-250 hPa (i.e., free troposphere), and approximately 250-50 hPa (i.e., upper model layers)
following the previous work of Hogrefe et al. (2018). Comparisons of vertical O3 profile with ozonesonde observations
revealed that H-CMAQ can capture O3 behavior well over the boundary layer. However, systematic underestimations by H-
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CMAQ over free troposphere were found with NMBs up to −30%, especially during strong STT events. Comparisons of
modeled tropospheric O3 columns with observed satellite data (NASA, 2018) indicate that H-CMAQ can generally capture the
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northern hemispheric tropospheric O3 column distributions with lower column amounts over the Pacific Ocean near the equator
and higher column amounts over the mid-latitudes.
For the estimation of STT, a air mass characterization technique was newly developed. This was derived based on
the ratio of modeled O3 mixing ratios and an those of inert tracer for stratospheric O3 to judge the relative importance of
photochemistry and then determine whether an air mass is of stratospheric origin if the photochemistry is weak. The estimated
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STT showed day-to-day variations both in the impact magnitude and the air mass origin. The relationship between surface O3
levels and estimated stratospheric air mass in the troposphere showed a negative slope, indicating that high surface O3 mixing
ratios at most locations were driven by other factors (e.g., emissions). In contrast, the relationship at elevated sites exhibits a
slight positive slope, indicating a steady STT contribution to O3 levels.
Because high surface O3 mixing ratios were determined to be caused by emissions, this subsequent part 2 paper
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focuses on the analysis of emission impacts from the U.S.A. and East Asia. To examine these emission impacts, the traditional
brute force method (BFM) approach of varying input parameters (e.g., emission) one-at-a-time is frequently used (e.g.,
Clappier et al., 2017). The application of the decoupled direct method (DDM) in H-CMAQ has been initiated to investigate
the trends of O3 distribution (Mathur et al., 2018a). In this study, we use the higher-order decoupled direct method (HDDM)
implemented in H-CMAQ, which enables accurate and computationally efficient calculations of the sensitivity coefficients
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required for evaluation of the impact of input parameters variations on output chemical concentrations (Hakami et al., 2003;
Cohan et al., 2005; Napelenok et al., 2008; Kim et al., 2009; Napelenok et al., 2011; Itahashi et al., 2013; Itahashi et al., 2015).
The manuscript is organized as follows. The HDDM is described in Section 3. Analysis of O3 sensitivity regimes over the
entire northern hemisphere is presented in Section 4.1. By defining source regions over the U.S.A. and East Asia, the impacts
of emissions from these regions on surface level O3 over the U.S.A. are examined in Section 4.2. We then extend the analysis
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to O3 aloft and present the results in Section 4.3. Trans-Pacific transport may have changed due to recent emission changes in
East Asia, and the effects of these changes are estimated by considering the emission changes after 2010. This is discussed in
Section 4.4. Finally, Section 5 summarizes the conclusions of our sequential papers.
3 Description of HDDM
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Response of chemical concentrations to perturbations in model parameters (e.g., emissions, initial condition,
boundary condition, reaction rate constants, etc.) can be investigated through sensitivity analysis. A perturbed sensitivity
parameter, pi, has the following relationship with the unperturbed sensitivity parameter, Pi, in the base-case simulation:
!"#$%"$&"#
'
( )$ *$%"
+
$&"$$$$$$$$$$$$$$$'(+
where εi is a scaling factor with a nominal value of 1, and Δεi is a perturbed scaling factor (e.g., εi is 0 and then Δεi is −1 for
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zero emission simulation). Here, the response of a chemical concentration, C, against the perturbations in a sensitivity
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parameter, pi, is defined as sensitivity coefficients, Si. The semi-normalized first- and second-order sensitivity coefficients,
Si(1) and Si,j(2) are defined as follows:
,"
'-+ #$ &"
.$/
.$!"#$ &"
.$/
.$'%"$&"+#.$/
.$%"$$$$$$$$$$$$$$$$'0+
,"12
'3+ #$ &"
.$/
.$!"&
2
.$/
.$!2# &"
.$/
.$'%"$&"+&
2
.$/
.$'%2$&
2+#.3/
.%".%2$$$$$$$$$$$$$$$$'4+
5
Because εi and εj are unitless, Si(1) and Si,j(2 ) have the same units with the chemical concentration, C. Physically, Si(1) represents
the impact of one variable pi on the concentration, C, and Si,j(2) measures how a first-order sensitivity of Si(1) changes under the
changes of another variable pj, and can be used to explore the nonlinearities in a system. When i=j, Si,i(2) represents the local
curvature of the relationships between concentration and one parameter. HDDM calculates semi-normalized first- and second-
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order sensitivity coefficients simultaneously in a single model simulation based on a governing set of sensitivity equations
which have a formulation analogous to the atmospheric species equations in the CMAQ modeling system.
To project the fractional perturbation from the base-case simulation, the corresponding concentration can be
approximated by a Taylor series expansion of the sensitivity coefficient:
/
5
!"1 !2
6
# $/
5
&"1 &
2
6
)$,"
'
-
+
$*%"$) ,2
'
-
+
*%2) $ (
07$,"1"
'
3
+
$$*%$"
3)$ (
07$ ,212
'
3
+
$$*%$2
3) ,"12
'
3
+
$$*%"
$*%2) 89 :9 ; 9$$$$$$$$$$'<+
15
where C(Pi,Pj) is concentration in the base-case simulation, and the higher order term greater than third-order were summarized
into h.o.t. The zero-out contribution (ZOC) is defined as the difference between the base-case simulation and the concentration
that would occur if the sensitivity parameter did not exist (Cohan et al., 2005). It is derived as follows:
20
=>/$
5
&"1 &
2
6
# /
5
&"1 &
2
6
$? $/
5
!"# @1 !2# $@
6
A$ ,"
'
-
+
) ,2
'
-
+
?$(
0$,"1"
'
3
+
$
$?$(
0$,212
'
3
+
$
$?$,"12
'
3
+
$
$$$$$$$$$$$'B+
Throughout this study, we investigate the emission impacts based on this ZOC formulation in Eq. (5). The emissions of the O3
precursor species NOx and non-methane volatile organic compounds (NMVOCs; hereafter simply referred to as VOCs) are
used as sensitivity parameters (i and j). For example, the expression of
,CDE
'-+
means the first-order sensitivity of O3 to NOx
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emission.
In addition, HDDM was extended to examine the sensitivity of O3 mixing ratios towards stratospheric O3. A dynamic
O3/PV function considering the seasonal, latitudinal and altitude dependencies is constructed at three vertical levels of 58, 76,
and 95 hPa fitted as a 5th order polynomial function, and applicable between the range of 50 and 100 hPa (Xing et al., 2016).
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The sensitivity to this stratospheric O3 can be directly calculated, and quantify the effects of STT on tropospheric O3. This
sensitivity is hereafter referred to as O3VORT.
4 Results and Discussion of Sensitivity Analysis by HDDM
4.1 Sensitivity Regime in April 2010
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Sensitivity coefficients towards domain-wide emissions (i.e., emissions across the entire simulation domain)
calculated by HDDM are shown in Fig. 1; these values represent monthly-means and in turn are computed from hourly
sensitivity coefficients output by the CMAQ model configured with HDDM. Generally, the response of O3 to NOx emissions
exhibits positive first-order sensitivities (Fig. 1 (a)) and negative second-order sensitivities (Fig. 1 (c)) because of the concave
response of O3 to NOx emissions. Exceptions are found over eastern China to the Korean Peninsula, some parts of Europe, and
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some cities in the western U.S.A. (e.g., Seattle, San Francisco, and Los Angeles), around the Great Lakes, and the northeastern
U.S.A. (e.g., New England region). These regions show negative first-order sensitivity to NOx emissions due to the NO titration
effect by dense NOx emission sources. The values of sensitivity coefficients to VOC emissions (Figs. 1 (b) and (d)) are small
compared to those to NOx emissions. In addition, the second-order sensitivity coefficients of O3 to VOCs emissions are also
smaller, indicating that the non-linear response of large-scale O3 distributions to VOC emissions is negligible. A positive
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second-order cross sensitivity of O3 to domain-wide NOx and VOC emissions (Fig. 1 (e)) demonstrates O3 will become less
responsive to NOx emissions with a concurrent reduction of VOCs emissions, and vice versa. While these sensitivities were
calculated towards total (i.e., both anthropogenic and biogenic) emissions, the main interest from a policy making perspective
is on the sensitivities towards anthropogenic emissions. To estimate these sensitivities, we recalculated sensitivity coefficients
of O3 to isoprene emissions as a proxy for biogenic emissions (Fig. S1). By comparing these sensitivities to isoprene emissions
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(Fig. S1) to the sensitivities towards all emissions (Fig. 1), it can be concluded that the impacts of biogenic VOCs emissions
during April 2010 are small compared to the impacts of NOx emissions.
Determining the O3 sensitivity regime can provide useful information to policy makers designing emission reduction
strategies by clarifying the relative importance of precursor emissions. Based on the relationships between the sensitivity
coefficients, we determined O3-sensitivity regimes from threshold values revised from previous studies (Wang et al., 2011;
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Itahashi et al., 2013) as follows.
(@$
F
GGHI
J
K$ ,LDMN
'-+ $OPQ$,CDE
'-+ K$ ,LDMN
'-+ R $STU$VWPVXYXIW
(@$
F
GGHI
J
K$ ,CDE
'-+ $OPQ$,LDMN
'-+ K$ ,CDE
'-+ R $ZT[$VWPVXYXIW
Grid cells meeting neither of these two criteria are considered to be in a transition regime. This classification is applied to all
hourly HDDM results during April 2010 and then averaged. The O3-sensitivitity regimes obtained through this analysis are
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shown in Fig. 2. The shading of NOx (purple) or VOC (green) sensitive indicates the high frequency of occurrence of sensitivity
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to NOx or VOC Regime. As already suggested by the relative magnitudes of the sensitivity coefficients towards NOx and
VOCs emissions shown in Fig. 1, O3 during April 2010 is in a NOx sensitive regime over the mid-latitude northern hemisphere
with the exception over the locations that had negative first-order sensitivity to NOx emission and were classified as VOC
sensitive. Therefore, controls on NOx emissions can be an effective way to reduce surface O3 across almost the entire northern
hemisphere but it may cause an increase of O3 mixing ratios over eastern China and some areas in Europe and the U.S.A.
5
Through the analysis of HDDM results for domain-wide emissions, this section provided an overview of O3 sensitivities and
the response of O3 to precursor emissions over the northern hemisphere. The following section further investigates the
sensitivity of surface O3 over the U.S.A. by defining different emission source regions over the U.S.A. and East Asia.
4.2 Emission Impacts from U.S.A. and East Asia at Surface Level
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To investigate the emission impacts from the U.S.A. and East Asia, we defined two source regions as shown in Fig.
3. In this study, East Asia includes China, Taiwan, Mongolia, the Korean Peninsula (North and South Korea), and Japan. We
conducted additional HDDM simulations using these two source regions and then calculated their sensitivity coefficients which
are shown in Figs. S2 and S3 in the supplemental material. Based on these sensitivity coefficients, ZOC of emissions from the
U.S.A. and East Asia are derived according to Eq. (5) and the resulting emission impacts are shown in Fig. 3. The ZOC of
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emissions from the U.S.A. show more than 10 ppbv over the southeastern U.S.A. and relatively small impacts around 2-8 ppbv
in the western U.S.A. In some areas that are characterized by a VOC-sensitive regime in Fig. 2 (e.g., Seattle, San Francisco,
Los Angeles, around the Great Lakes, and New England regions), emissions from the U.S.A. have small negative impacts. The
U.S.A. emission impacts extend to the Atlantic Ocean with impacts of more than 2 ppbv which are comparable to those found
over the western U.S.A., and then decrease over Africa. The ZOC of emissions from East Asia also shows positive impacts
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greater than 10 ppbv over China, Taiwan, Japan, and the western Pacific Ocean with the exception of negative impacts over
eastern China. These negative impacts indicate that the elimination of emissions can lead to O3 increase, because NO titration
works to reduce O3 mixing ratio over these areas that have a high emission density. The analysis of the ZOC from East Asian
emissions clearly illustrates the presence of trans-Pacific transport of O3. This transport on a monthly mean basis is estimated
to be more than 2 ppbv over almost the entire Pacific Ocean, and reaches many parts of North America, i.e., almost the entire
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U.S.A. and Canada and western Mexico.
Detailed analyses of these impacts over the U.S.A. are conducted by focusing on longitudinal differences. In this
study, we use four time zones of Pacific, Mountain, Central, and Eastern standard time (abbreviated as PST, MST, CST, and
EST, respectively) in the U.S.A. and investigate O3 mixing ratio and ZOC of emission from U.S.A. and East Asia in these
zones. Results for monthly and daily means are shown in Fig. 4 and are also listed in Table 1. Consistent with previous studies
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(e.g., Simon et al., 2015), O3 mixing ratios have a longitudinal gradient with lower values in the West and higher values in the
East (modeled monthly mean concentrations are 35.8, 39.3, 39.1, and 40.6 ppbv over PST, MST, CST, and EST, respectively;
see Table 1). The results of the ZOC analysis reveal varying impacts from U.S.A. and East Asia emissions across the four
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regions. For the U.S.A on a monthly-mean domain-wide basis, the impact of domestic emissions surpasses that of East Asian
emissions. Over the PST region, the monthly averaged impact of domestic emissions is 3.2 ppbv while that of East Asian
emissions is 2.8 ppbv, i.e., the impacts from both source regions over the PST zone are comparable. It should be noted that the
daily averaged impact of East Asian emissions can exceed that of U.S.A. emissions on some days (e.g., in early April and
during April 27-30), suggesting the significant role of episodic trans-Pacific transport on air quality over the western U.S.A.
5
In contrast to the situation over the PST zone, the impact from domestic emissions always clearly exceeds the impact from
East Asian emissions in the MST, CST, and EST zones; this feature strengthens towards the east. For example, the temporal
variations of daily averaged O3 mixing ratios and the impacts of domestic emissions are well correlated over the EST zone.
The impact of East Asian emissions is small compared to that of U.S.A. emissions over the CST and EST zones, but it is not
negligible. These impacts are 2.1 ppbv on a monthly average basis (ranging between 1.2 ppbv and 3.0 ppbv on a daily basis)
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over CST and 1.9 ppbv on a monthly average basis (ranging between 1.2 ppbv and 2.8 ppbv on a daily basis) over EST through
April 2010.
To illuminate the relationship between surface O3 mixing ratio and impacts from U.S.A. and East Asian emissions,
in Figure 5 scatter plots were constructed using model derived estimates at all CASTNET sites and at elevated CASTNET
sites only (refer Fig. 12 of Itahashi et al., 2019). The statistical analysis of correlation coefficient (R) and its significance level
15
by Student’s t-test between surface O3 mixing ratio and these impacts by emissions are listed in Table 1. At all CASTNET
sites, the relationship between the modeled MD8O3 and the impact of emissions from the U.S.A. shows positive slope with R
of 0.63 and p < 0.001; confirming that domestic emissions are generally the cause of high surface O3 mixing ratios. On the
other hand, the relationship between modeled MD8O3 and the impact of emissions from East Asia is flat with R of −0.03 and
not significance; suggesting that constant impacts are found in the U.S.A. but do not directly relate to high surface O3 mixing
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ratios. A noticeable result is that the relationship varies across the regions. Each point in the scatter plots is shaded by time
zone, and it can be seen that high O3 mixing ratios over the CST and EST zones (darker black in Fig. 5 (c)) are not linked to
the impacts of East Asian emissions (R were 0.06 and −0.03 respectively, and not significant) while moderately higher O3
mixing ratio found over PST and MST (lighter black in Fig. 5(c)) appear to be linked to higher impacts from East Asian
emissions (R were 0.36 and 0.36 respectively, and p < 0.001). These analyses are repeated using data from sites with an
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elevation higher than 1000 m (see Table S1 in the supplemental material). At this subset of stations, the O3 mixing ratio shows
a positive relationship with emissions from both the U.S.A. (R of 0.52 with p < 0.001) and East Asia (R of 0.22 with p < 0.001).
This might be partly because most of the elevated CASTNET sites are located in the western U.S. A. (17 of 21 elevated sites
are located in the PST or MST zones). Since long-range transport occurs aloft and since changes in pollutant concentrations
influence their ground-level values (e.g., Mathur et al., 2018b) in the next section we specifically investigate the impacts of
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emissions from different source regions on O3 aloft.
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4.3 Emission Impacts on O3 aloft
In this section we focus on the impacts of U.S.A. and East Asian emissions on O3 distributions through the troposphere
over the U.S.A. Monthly averaged O3 mixing ratios and ZOC of emissions from the U.S.A. and East Asia at different altitudes
in the free troposphere are shown in Fig. 6. Throughout this study, we define the free troposphere to range from 750 to 250
hPa and refer to pressure levels of 750 hPa, 500 hPa, and 250 hPa as the bottom, middle, and top of the free troposphere,
5
respectively. The results of this analysis are also summarized in Table 2. O3 mixing ratios are larger over continents from the
surface to 750 hPa (i.e., boundary layer), but are more dispersed over mid to high latitudes at 500 and 250 hPa (Fig. 6). O3
mixing ratios at the surface exhibit a longitudinal gradient with lower values over the western U.S.A. and higher values over
the eastern U.S.A., and the same gradient is seen at 750 hPa. However, there are no longitudinal gradients at 500 hPa with 54
ppbv over the entire U.S.A., and a reversed longitudinal gradient with western highs and eastern lows is found at 250 hPa
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(Table 1).
Once O3 is lofted to free troposphere, its sinks are not effective and consequently it can be transported further. For
ZOC of U.S.A. emissions, the largest contribution is found over the southeast U.S.A. at 750 hPa but the impacts of U.S.A.
emissions stretch far across the Atlantic to Europe, North Africa, Eurasia, and even to Japan with values above 2 ppbv. Areas
where the impact of U.S.A. emissions exceeds 2 ppbv are shown over the entire northern hemisphere at 500 and 250 hPa (Fig.
15
6). It should also be noted that the impacts of U.S.A. emissions on U.S.A. remained constant or declined with increasing
altitude. In particular, constant impacts from U.S.A. emissions with increasing altitude are found over the PST zone, whereas
decreasing impacts are found over the MST, CST, and EST zones. From the middle to the top of the free troposphere, the
impacts of U.S.A. emissions on U.S.A. are around 2-3 ppbv (Table 2). For ZOC of East Asian emissions, extended impacts on
U.S.A. when increasing altitude are shown (Fig. 6). At 750 hPa, the impacts are found over the entire Pacific Ocean with more
20
than 10 ppbv around Hawaii and contribution as high as 4-8 ppbv over the entire U.S.A. At 500 hPa, its impacts are smaller
over the Pacific Ocean with less than 8 ppbv; however, the impacts are above 6 ppbv almost across the entire U.S.A., surpassing
the impacts found at 750 hPa. At 250 hPa, the impacts are slightly decreased beyond the U.S.A., but stretched across a broader
range to Europe and western Russia (Fig. 6). It is shown that the impacts of East Asian emissions are around 5 ppbv or more
over the entire free troposphere over U.S.A. (Table 2). From the middle to the top of the free troposphere, the impacts of
25
emissions from East Asia are twice or more those of U.S.A. emissions over the eastern and western U.S.A., respectively.
In characterizing the dominant sources of O3 aloft, the role of stratospheric air masses also needs to be considered. In
our part 1 paper, we developed an air mass characterization technique, but it was limited to estimate the air mass burden on
column O3. In this part 2 paper, to unify the methodology investigating sensitivities to model parameters, the sensitivity towards
O3 specification near the tropopause based on a potential vorticity scaling, hereafter referred to as O3VORT, is directly
30
calculated. The results of the O3 sensitivity towards O3VORT are shown in Fig. 7 at the surface, 750 hPa, 500 hPa, and 250
hPa with different color scales. Not surprisingly, the sensitivity of O3 to O3VORT shows increasing values with increasing
altitude. At the surface level and on a monthly averaged time scale, the impact of STT is less than 1 ppbv except over the Tibet
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plateau because of its elevation. In other regions, smaller impacts of STT are noted over the western U.S.A. and north Africa;
the former due to the high elevation of Rocky mountain whereas the latter is likely related to active convection. Impacts of
STT exceeding 1 ppbv are found over mid-latitudes areas at 750 hP, and stronger impacts exceeding 5 ppbv are found at 500
hPa. At 250 hPa, the impacts of STT are shifted towards high-latitudes and exceed 25 ppbv, reflective of the lower tropopause
height at higher latitudes (Fig. 7). Over the U.S.A., the monthly averaged impacts of STT are below 1 ppbv at the surface and
5
750 hPa and increase from around 2 ppbv at 500 hPa to more than 10 ppbv at 250 hPa (Table 1). At 250 hPa, the impacts of
STT range from more than 20 ppbv in the west and a low of around 10 ppbv in the east.; therefore, these differences partly
account for the longitudinal gradient of the O3 mixing ratio modeled at the top of free troposphere.
To illustrate altitude dependencies of the impacts of U.S.A. and East Asian emission and STT, vertical cross-sections
(“curtain plots”) of these impacts at six ozonesonde sites across the U.S.A. are examined in Fig. 8 (refer Figs. 4 and 10 of
10
Itahashi et al., 2019). In these curtain plots, the pressure levels of 750, 500, and 250 hPa are marked to indicate the
representative altitude of the bottom, middle, and top of the free troposphere. The comparison of the ZOC from U.S.A. and
East Asian emissions clearly shows the differences of their vertical structures. Over these ozonesonde sites except Hilo (Fig.
8 (a)), the emission impacts from the U.S.A. greater than 10 ppbv are mostly confined below 750 hPa (within the boundary
layer) and occasionally extend into the free troposphere. In contrast, the emission impacts from East Asia can predominantly
15
be found in the free troposphere and sometimes extend into the boundary layer (below 750 hPa) and/or the upper model layers
(above 250 hPa). These patterns further confirm that pollution lofted to the free-troposphere over Asia can undergo efficient
transport across the Pacific and entrain to the lower troposphere and boundary layer over the U.S. The sensitivity towards
O3VORT is the dominant factor over the upper model layers (above 250 hPa) and downward into the upper part of the free
troposphere, but most of its episodic impact does not reach to the middle of the free troposphere (500 hPa) or below. The
20
strong STT events seen in these cross-sections, i.e., as the events in early and late April at Trinidad Head (Fig. 8 (b)), early
April at Boulder (Fig. 8 (c)), late April at Huntsville (Fig. 8 (d)), and middle April at Wallops Island (Fig. 8 (e)) and Rhode
Island (Fig. 8 (f)) are generally consistent with the results inferred from the airmass classification technique presented in the
part 1 paper. It should be however noted that a more robust quantification of the fraction of ground level O3 that originated in
the stratosphere and its seasonal and spatial distributions would require conduct of longer-term sensitivity simulations than
25
those examined here.
4.4 Perspective on the Changes in Trans-Pacific Transport
As has been shown in previous studies and affirmed in the current work, that trans-Pacific transport can impact air
quality in the U.S.A. April 2010 was used as the target period for our analysis because El Niño conditions during that time
30
period favored trans-Pacific transport. In this section, we estimate the variation of trans-Pacific transport caused by recent
emission changes. According to the NOAA Climate Prediction Center (CPC), strong and long-lasting El Niño conditions
occurred from late 2014 to the middle of 2016 (NOAA, 2018). Observed average MD8O3 over the U.S.A. was 46.9 ppbv in
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April 2015, a decline from its April 2010 values of 52.2 ppbv, and the number of sites exceeding the NAAQS declined from
39 sites in April 2010 to 7 sites in April 2015. From 2010 to 2015, annual NOx (VOCs) emissions in the U.S.A. decreased
from 13.4 (13.6) Tg to 10.6 (12.9) Tg (see, Fig. S1 of supplemental material of Itahashi et al., 2019). These emission reductions
likely contributed to the decline of the observed O3 mixing ratios relative to the 2010 values. How about the trans-Pacific
transport? Anthropogenic emissions in China grew during the 2000s (Itahashi et al., 2014), and reached the highest levels in
5
the world in 2010; however, substantial reductions have been measured by satellites since then (Irie et al., 2016; Krotkov et
al., 2016; van der A et al., 2017; Itahashi et al., 2018). In addition, bottom up emission inventories indicate that Chinese NOx
emissions were reduced as consequence of clean air actions (Zheng et al., 2018). In particular, Zheng et al. (2018) report that
annual NOx emissions were reduced from 26.5 Tg in 2010 to 23.7 Tg in 2015 while annual VOC emissions increased from
25.9 Tg in 2010 to 28.5 Tg in 2015. While NOx emissions have been regulated and subsequently declined after reaching a peak
10
of 29.2 Tg in 2012, the situation is more complex for VOCs emissions which show decreases from the residential and
transportation sectors but increases from the industrial sector and solvent use. Applying the percentage changes in Chinese
emissions from 2010 to 2015 to the HDDM sensitivities for East Asian emissions (assuming that changes in East Asian
emissions are dominated by changes in China), we estimated their impacts on tropospheric O3 mixing ratios.
The changes in O3 mixing ratio caused by emission changes between 2010 and 2015 over the U.S.A. and East Asia
15
can be investigated via Eq. (4). Based on the emission changes noted above, the resulting values of εi and εj in Eq. (4) are -
20.9% and -5.1% for NOx and VOC emissions from the U.S.A., and -10.6% and 10.0% for NOx and VOC emissions from East
Asia, respectively. The estimated spatial changes in O3 mixing ratios at the surface and aloft are shown in Fig. 9, and estimates
for monthly and daily means over four time zones in the U.S.A. are shown in Fig. 10 in a similar manner to Fig. 4. The U.S.A.
emission reductions between 2010 and 2015 resulted in generally reducing surface O3 mixing ratios with changes of around at
20
least −0.5 ppbv across the entire U.S. A. and up to −5.0 ppbv over the southeast U.S.A. Exceptions are found over Seattle, San
Francisco, Los Angeles, around the Great Lakes, and in New England regions that were characterized as VOC sensitive in
Section 3.2. These changes are expected because reductions in NOx emission were greater than those in VOC emissions across
the U.S. It is also shown that the U.S.A. emission reductions cause a reduction of O3 mixing ratio over the free troposphere.
On the time zone averaged basis, the changes in monthly-mean O3 mixing ratio are −0.5, −1.1, −1.8, and −1.5 ppbv over PST,
25
MST, CST, and EST, respectively (Fig. 10). The maximum reduction are found over CST because EST contains the complex
sensitivity over New England regions. In contrast, the changes in East Asian emissions between 2010 and 2015 do not cause
a noticeable reduction in surface O3 mixing ratios over the U.S.A. while they lead to O3 mixing ratio increases of more than 1
ppbv over eastern China, the Korean Peninsula, and some parts of Japan on monthly average (Fig. 9). These increases are
expected both because these areas were shown to be VOC-sensitive in Section 3.2 and because of the increase in VOC
30
emissions. On time zone averaged basis, changes in East Asia emissions between 2010 and 2015 are estimated to change
monthly mean O3 mixing ratio across the U.S. by about −0.1 to −0.3 ppbv. The corresponding changes in daily average surface-
level O3 mixing ratio were also less than −0.5 ppbv (Fig. 10). A slight reduction in monthly mean O3 mixing ratios of around
−0.5 ppbv was estimated across large parts of the Northern Hemisphere free troposphere, indicating that the reductions in East
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Asian emissions that occurred between 2010 and 2015 can partly contribute to a weakening of trans-Pacific O3 transport over
the free troposphere. However, the reductions in Asian emissions during 2010 and 2015 did not appear to alter monthly means
surface levels O3 mixing ratio across the U.S.A.
5 Conclusions
5
In this study, the regional chemical transport model extended for hemispheric applications, H-CMAQ, is applied to
investigate trans-Pacific transport during April 2010. A previous part 1 manuscript demonstrated that STT can cause impacts
on tropospheric O3, but did not relate to the enhancement of surface O3 mixing ratios. Therefore, in this part 2 manuscript,
emission impacts are investigated based on the sensitivity analysis through HDDM. The sensitivities to domain-wide emissions
indicate NOx-sensitive conditions during April 2010 for tropospheric O3 across most of the Northern Hemisphere except over
10
eastern China and a few urban areas over the U.S. and Europe. Contributions of emissions from source regions covering the
U.S.A. and East Asia were examined through propagation of emission sensitivities in HCMAQ. Analysis of estimated zero-
out contributions from the computed sensitivities demonstrate comparable impacts of U.S.A. and East Asian emissions on
surface level O3 over the western U.S.A. during April 2010 whereas contributions from U.S.A. emissions dominate O3
distributions over the eastern U.S.A. The analyses also reveal the significant impacts of East Asian emissions on free
15
tropospheric O3 over the U.S.A. which surpass the estimated impacts of U.S.A. emissions, further confirming the long-range
pollution transport conceptual view wherein pollution from source regions is convectively lofted to the free troposphere and
efficiently transported intercontinentally. Finally, the effects of recent emission changes on the trans-Pacific transport of O3
are estimated. Under the assumed similar meteorological condition on 2010 and 2015, it can be concluded that trans-Pacific
transport resulting from emission changes did not lead to significant changes in O3 mixing ratio over U.S.A. at the surface
20
level even on a daily mean basis in April. The year 2015 was selected because of El Niño conditions favorable to trans-Pacific
transport, however, the impacts of changes in year specific meteorological conditions are not investigated here. The possible
impacts of changing climate on trans-Pacific transport (e.g., Glotfelty et al., 2014) should however be further examined. Long-
term trend analysis taking into accounts both emission and meteorological changes (e.g., Mathur et al., 2018a) will be studied
in future work to further understand variability in trans-Pacific transport patterns and contributions. While the one-month
25
simulation period and analysis of a representative spring-time month helped characterize aspects of trans-Pacific transport,
longer term simulations need to be conducted to further quantify the seasonal source region contributions to trans-Pacific
transport. The results presented here are based on monthly or daily mean ozone during April and are not expected to be
consistent with other metrics (e.g., MD8O3) or times of the year when transport is less favorable and local ozone production
is more favorable. The longer-term calculations will also help better quantify the STT contributions to surface-level O3 which
30
appear to be lower in the current analysis relative to previous studies (e.g., Lelieveld and Dentener, 2000; Lin et al., 2015;
Mathur et al., 2017).
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Code availability
Source code for version 5.2 of the CMAQ model can be downloaded from https://github.com/USEPA/CMAQ/tree/5.2. For
further information, please visit the US Environmental Protection Agency website for the CMAQ system:
https://www.epa.gov/cmaq.
5
Data availability
The code of decoupled direct method on the version 5.2 of the CMAQ model can be downloaded from
https://github.com/USEPA/CMAQ/tree/5.2. For further information, please visit the US Environmental Protection Agency
website for the CMAQ system: https://www.epa.gov/cmaq.
10
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The views expressed in this paper are those of the authors and do not necessarily reflects the views or policies of the U.S.
15
Environmental Protection Agency.
Author contributions
Syuichi Itahashi performed the analysis of observation and model simulation and prepared the manuscript with contributions
from all co-authors. Rohit Mathur and Christian Hogrefe contributed to establish the hemispheric modeling application for this
20
study and prepared the emission dataset and initial condition from previous long-term simulation results. Sergey L. Napelenok
contributed to the discussion of sensitivity analysis based the higher-order decoupled direct method. Yang Zhang contributed
to the literature review of trans-Pacific transport and refined this research through simulation designs, and results interpretation.
25
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Acknowledgement
Yang Zhang acknowledges support from the 2017-2018 NC State Internationalization Seed Grant and the 2019-2020 NC State
Kelly Memorial Fund for US-Japan Scientific Cooperation.
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Webpages
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https://www.epa.gov/castnet, Last Access: 26 April 2018.
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national-ambient-air-quality-standards-naaqs, Last Access: 1 April 2018.
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Figure 1. Spatial distribution of the sensitivity coefficients of O3 to domain-wide emissions. (a) first-order sensitivity to NOx emissions,
(b) first-order sensitivity to VOCs emissions, (c) same as (a) but as second-order, (d) same as (b) but as second-order, and (e) second-
order sensitivity to NOx and VOCs emissions during April 2010.
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Figure 2. Spatial distributions of the ozone-sensitive regime during April 2010.
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Figure 3. Source regions of U.S.A. and East Asia (left), and zero-out contribution of emissions from U.S.A (center) and East Asia
(right) during April 2010. East Asia are defined as China, Taiwan, Mongolia, Korean Peninsula, and Japan.
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Figure 4. Daily and monthly averaged O3 mixing ratio (left axis; black circles and thick lines) and zero-out contribution from U.S.A.
and East Asia (right axis; light blue and light red bars respectively) summarized over four time zones of Pacific, Mountain, Central,
and Eastern Standard Time (PST, MST, CST, and EST) in U.S.A. The units of left and right-axis is ppbv. On monthly averaged
(center panel), whiskers indicates daily minimum and maximum. Note that the axis of zero-out contributions is different in left (PST
5
and MST) and right panels (CST and EST).
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Figure 5. Relationship between modeled MD8O3 at the surface and zero-out contribution of emissions from (a, b) U.S.A. and (c, d)
East Asia. The points are shaded by four time zones in U.S.A. (a, c) All CASTNET sites, and (b, d) elevated CASTNET sites defined
as having an elevation greater than 1000 m (see also Table S1).
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Figure 6. Monthly averaged O3 concentration (first row) and zero-out contribution from U.S.A. (second row) and East Asia (third
row) at bottom of free troposphere (750 hPa; left column), middle of free troposphere (500 hPa; center column), and top of free
troposphere (250 hPa; right column).
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Figure 7. Monthly averaged sensitivity of O3VORT at surface, bottom of free troposphere (750 hPa), middle of free troposphere
(500 hPa), and top of free troposphere (250 hPa) from left to right.
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Figure 8. Curtain plots of (left) ZOC of emissions from U.S.A., (center) ZOC of emissions from East Asia, and (right) sensitivity of
O3VORT at U.S. ozonesonde sites of (a) Hilo (HI), (b) Trinidad Head (CA), and (c) Boulder (CO) during April 2010. Yellow stars
indicate the time of available ozonesonde measurements. Thick lines from bottom to top indicate 750, 500, and 250 hPa as a
representative bottom, middle, and top of free troposphere.
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Figure 8. Continued, but at (d) Huntsville (AL), (e) Wallops Island (VA), and (f) Rhode Island (RI).
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Figure 9. Changes in O3 concentration caused by emission changes from (top) U.S.A. and (bottom) East Asia at surface, bottom of
free troposphere (750 hPa), middle of free troposphere (500 hPa), and top of free troposphere (250 hPa) from left to right.
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Figure 10. Daily and monthly averaged changes in O3 mixing ratio caused by emission changes from U.S.A. (light blue bars) and
East Asia (light red bars) summarized over four time zones of Pacific, Mountain, Central, and Eastern Standard Time (PST, MST,
CST, and EST) in U.S.A. The units is ppbv. On monthly averaged (center panel), whiskers indicates daily minimum and maximum.
Note that the axis is different in left (PST and MST) and right panels (CST and EST).
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Table 1. Summary of correlation between modeled MD8O3 and zero-out contribution of emissions from U.S.A. and
East Asia.
Note: Significance levels by Students’ t-test for correlation coefficients between observations and simulations are remarked
as *p < 0.05, **p < 0.01, and ***p < 0.001, and lack of a mark indicates no significance.
5
N
Emission impacts from U.S.A.
Emission impacts from East Asia
All CASTNET sites
2286
0.63***
−0.03
−Pacific Standard Time (PST)
238
0.52***
0.38***
−Mountain Standard Time (MST)
359
0.65***
0.36***
−Central Standard Time (CST)
489
0.55***
0.06
−Eastern Standard Time (EST)
1200
0.64***
−0.02
Elevated CASTNET sites
587
0.52***
0.22***
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Table 2. Summary of O3 concentration and zero-out contribution of emissions from U.S.A. and East Asia, and
sensitivity of O3VORT over four time zones in U.S.A. during April 2010.
Note: All units are ppbv.
O3 concentration
Emission impacts
from U.S.A.
Emission impacts
from East Asia
Impacts by
stratospheric
intrusion
Pacific Standard Time (PST)
−Surface
35.8
3.2
2.8
0.2
−Bottom of free troposphere
47.3
2.7
6.1
0.7
−Middle of free troposphere
54.0
2.4
7.3
2.0
−Top of free troposphere
108.3
3.0
6.5
22.3
Mountain Standard Time (MST)
−Surface
39.3
5.5
3.3
0.4
−Bottom of free troposphere
50.3
4.8
5.7
0.9
−Middle of free troposphere
54.8
2.7
7.2
2.2
−Top of free troposphere
119.7
3.3
6.4
28.3
Central Standard Time (CST)
−Surface
39.1
9.2
2.1
0.2
−Bottom of free troposphere
50.9
6.6
4.9
0.6
−Middle of free troposphere
53.9
3.3
6.6
2.0
−Top of free troposphere
79.9
2.9
6.1
12.9
Eastern Standard Time (EST)
−Surface
40.6
9.6
1.9
0.1
−Bottom of free troposphere
52.0
7.9
5.0
0.6
−Middle of free troposphere
53.2
3.9
6.2
2.0
−Top of free troposphere
78.9
3.4
6.0
12.8
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