Introduction
Photochemistry in the atmosphere is a well-known source of tropospheric
ozone (O3) (e.g., Haagen-Smit and Fox, 1954) and is determined by
ambient levels of O3 precursors (i.e., NOx and VOC) and
photolysis rates, which are largely influenced by meteorological factors such
as solar irradiance and temperature. It is well known that aerosols
influence radiation through light scattering and absorption, thereby
modulating atmospheric radiation and temperature. These aerosol direct
effects (ADEs) can then impact thermal and photochemical reactions leading to
the formation of O3 (Dickerson et al., 1997). Recent studies suggest that
the aerosol-induced reduction in solar irradiance leads to lower photolysis
rates and less O3 (e.g., Benas et al., 2013), and therefore extensive
aerosol reductions, particularly in developing regions such as in East Asia,
may pose a potential risk by enhancing O3 levels (Bian et al., 2007;
Anger et al., 2016; Wang et al., 2016). For example, Wang et al. (2016) found
that because of ADEs, the surface 1 h maximum ozone (noted as DM1O3) was
reduced by up to 12 % in eastern China during the EAST-AIRE campaign,
suggesting that the benefits of PM2.5 reductions may be partially offset by
increases in ozone associated with reducing ADEs.
Ambient O3 levels are influenced by several sources and sinks. The
modulation of photolysis rates by ADEs is only one manifestation of ADEs
impacts on O3. In addition, ADEs modulate the temperature (e.g., Hansen
et al., 1997; Mitchell et al., 1995), atmospheric ventilation (e.g.,
Jacobson et al., 2007; Mathur et al., 2010), cloud and rainfall (e.g.,
Albrecht, 1989; Liou and Ou, 1989; Twomey, 1977), which also influence the
O3 concentrations. Therefore, ADEs can impact air quality through
multiple pathways and process chains (Jacobson, 2002, 2010; Jacobson et al.,
2007; Wang et al., 2014; Xing et al., 2015a; Ding et al., 2016). For
example, Xing et al. (2015a) suggested that the O3 response to ADEs is
largely contributed by the increased precursor concentrations which enhance
the photochemical reaction, presenting an overall positive response of
O3 to ADEs by up to 2–3 % in eastern China. The assessment of a separate
contribution from individual processes is necessary for fully understanding
how ADEs impact O3.
In China, atmospheric haze is currently one of the most serious
environmental issues of concern. Over the next decade, the national
government plans to implement stringent control actions aimed at lowering
the PM2.5 concentrations (Wang et al., 2017). Ideas on whether
such extensive aerosol controls will enhance O3 and oxidation capacity
needs to be carefully assessed and quantified. Many studies suggest that
aerosols may have substantial impacts on ozone through heterogeneous
reactions including hydrolysis of N2O5, irreversible absorption of
NO2 and NO3, as well as the uptake of HO2 (Tang et al.,
2004; Tie et al., 2005; Liao and Seinfeld, 2005; Pozzoli et al., 2008; Li et al., 2011; Xu et al., 2012; Lou et al., 2014). While our model
contains comprehensive treatment of the heterogeneous hydrolysis of
N2O5 (Davis et al., 2008; Sarwar et al., 2012,
2014), we have not quantified its impacts on ozone in this study. However,
ADE impacts on ozone have not been well evaluated previously. Accurate
assessment of the multiple ADE impacts is a prerequisite for accurate policy
decision. The process analysis (PA) methodology is an advanced probing tool
that enables quantitative assessment of integrated rates of key processes
and reactions simulated in the atmospheric model (Jang et al., 1995; Zhang et
al., 2009; Xu et al., 2008; Liu et al., 2010; Xing et al., 2011). In this
study, we apply the PA methodology in the two-way coupled meteorology and
atmospheric chemistry model, i.e., the Weather Research and Forecasting (WRF) model
coupled with the Community Multiscale Air Quality (CMAQ) model developed by
U.S. Environmental Protection Agency (Pleim et al., 2008; Mathur et al.,
2010, 2014; Wong et al., 2012; Yu et al., 2014; ; Xing et al.,
2015b) to examine the process chain interactions arising from ADEs and
quantify their impacts on O3 concentration.
The paper is organized as following. A brief description of the model
configuration, scenario design and PA method is presented in Sect. 2. The
O3 response to ADEs is discussed in Sect. 3.1. PA analyses are
discussed in Sect. 3.2–3.3. The summary and conclusion is provided in
Sect. 4.
Method
Modeling system
The two-way coupled WRF-CMAQ model has been detailed and fully evaluated in
our previous papers (Wang et al., 2014; Xing et al., 2015a, b). The
meteorological inputs for WRF simulations were derived from the NCEP FNL
(Final) Operational Global Analysis data which has 1∘ spatial and
6 h temporal resolution. NCEP Automated Data Processing (ADP) Operational
Global Surface Observations were used for surface reanalysis and four-dimensional data assimilation. We have tested and chosen the proper strength of
nudging coefficients; i.e., 0.00005 s-1 is used for nudging both
u/v-wind and potential temperature and 0.00001 s-1 is used for nudging the water vapor mixing ratio to improve model performance without dampening
the effects of radiative feedbacks (Hogrefe et al., 2015; Xing et al.,
2015b). In the model version used here, concentrations of gaseous species
and primary and secondary aerosols are simulated by using Carbon Bond 05
gas-phase chemistry (Sarwar et al., 2008) and the sixth-generation CMAQ
modal aerosol model (AERO6) (Appel et al., 2013). The aerosol optical
properties were estimated by the coated-sphere module (i.e., BHCOAT; Bohren
and Huffman, 1983) based on simulated aerosol composition and size
distribution (Gan et al., 2015). In the coupled model, the estimated aerosol
optical properties are fed to the RRTMG radiation module in WRF, thus
updating the simulated atmospheric dynamics which then impact the simulated
temperature, photolysis rate, transport, dispersion, deposition, cloud
mixing and removal of pollutants. Due to large uncertainties associated with
the representation of aerosol impacts on cloud droplet number and optical
thickness, the indirect radiative effects of aerosols are not included in
the current calculation.
Simulation domain and locations of five selected regions in
China. Note: JJJ: Jing-Jin-Ji area; YRD: Yangzi River Delta area; PRD: Pearl River Delta area; SCH: Sichuan Basin area; HUZ: Hubei–Hunan area.
The gridded emission inventory and initial and boundary conditions are
consistent with our previous studies (Zhao et al., 2013a, b; Wang et al.,
2014), while the simulated domain is extended slightly to cover all of China, as shown in Fig. 1. A better model performance in the simulation of
dynamic fields including total solar radiation, planetary boundary layer (PBL) height data as well as
PM2.5 concentrations was suggested after the inclusion of ADEs (Wang et
al., 2014). In this study, the model performance in the simulation of
O3 will be evaluated through the comparison with observations from 74
cities across China from the China National Urban Air Quality Real-time
Publishing Platform (http://113.108.142.147:20035/emcpublish/). The
simulation period is selected as 1 to 31 January and 1 to 31 July
in 2013 to represent winter and summer conditions,
respectively. Five regions are selected for analysis, including the Jing-Jin-Ji
area (denoted JJJ), the Yangzi River Delta (denoted YRD), the Pearl River Delta
(denoted PRD), the Sichuan Basin (denoted SCH) and the Hubei–Hunan area (denoted
HUZ), as shown in Fig. 1.
Simulation design
Description of sensitivity simulations in this study.
Short name
Simulation description
Aerosol impacts on
Aerosol impacts on
photolysis calculations
radiation calculations
SimBL
Baseline simulation
No
No
SimNF
No aerosol feedback simulation
Yes
No
SimSF
Aerosol feedback simulation
Yes
Yes
Table 1 summarizes the scenario design in this study. In the baseline
simulation (denoted SimBL), no aerosol feedbacks either on photolysis rates
or radiation were taken into account. In simulation SimNF, only aerosol
feedbacks on photolysis rates were considered by embedding an inline
photolysis calculation in the model which accounted for the modulation of
photolysis due to ADEs. Finally, in simulation SimSF aerosol feedbacks were
considered on both photolysis rates and radiation calculations. Differences
between the simulations of SimNF and SimBL are considered as ADE impacts on
O3 through photolysis (denoted ΔPhotolysis). Similarly,
differences between the simulations of SimSF and SimNF are considered as the
ADE impacts on O3 through dynamics (denoted ΔDynamics), and
differences between the simulations of SimSF and SimBL represent the
combined ADE impacts on O3 due to both photolysis and dynamics (denoted
ΔTotal).
Process analysis
In this study the PA methodology is used in the WRF-CMAQ model to analyze
processes impacting simulated O3 level. The integrated process rates
(IPRs) track hourly contributions to O3 from seven major modeled
atmospheric processes that act as sinks or sources of O3. These
processes are gas-phase chemistry (denoted CHEM), cloud processes (i.e., the
net effect of aqueous-phase chemistry, below- and in-cloud scavenging, and
wet deposition, together denoted CLDS, dry deposition (denoted DDEP), horizontal
advection (denoted HADV), horizontal diffusion (denoted HDIF), vertical
advection (denoted ZADV) and turbulent mixing (denoted VDIF). The
difference in IPRs among SimBL, SimNF and SimSF represents the response of
individual process to ADEs. To enable the consistent examination of changes
in the process due the ADEs across all concentration ranges, we examine
changes in the IPRs normalized by the O3 concentrations. The
differences in these process rates (expressed in units h-1) between
the SimBL, SimSF and SimNF then provide estimates of the changes in process
rates resulting from ADEs and are shown in the column (b) of Figs. 4 and 6 and (b)–(d) of Fig. 5.
Integrated reaction rates (IRRs) are used to investigate the relative
importance of various gas-phase reactions in O3 formation. Following
the grouping approach of previous studies (Zhang et al., 2009; Liu et al.,
2010; Xing et al., 2011), the chemical production of total odd oxygen
(Ox) and the chain length of hydroxyl radical (OH) are calculated.
Additionally, the ratio of the chemical production rate of H2O2 to
that of HNO3 (PH2O2/PHNO3) is an estimated indicator of
NOx- or VOC- limited conditions for O3 chemistry.
Results
O3 response to ADEs
Observed and simulated O3 and its
response to ADEs (monthly average of daily 1 h maxima, µg m-3).
The simulated surface DM1O3 in SimBL, SimNF and SimSF is compared in
Fig. 2a–c. In January, higher DM1O3 concentrations are seen in PRD, where solar radiation is stronger than in the north. The model generally
captured the spatial pattern with highest DM1O3 in PRD over the
simulated domain. Simulated DM1O3 in YRD, SCH and HUZ is higher than
observations. Such overestimation might be associated with the relatively coarse spatial resolution in the model. NO titration effects in urban areas
were not well represented in the model. In July, high DM1O3 areas are
located towards the north, especially in the JJJ and YRD regions, which have
relatively larger NOx and VOC emission density and favorable meteorological
conditions (e.g., less rain and moderate solar radiation).
In January, O3 production in north China is occurs in a VOC-limited
regime (e.g., Liu et al., 2010); thus, increases in NOx at the surface
stemming from the stabilized atmosphere by ADEs (Jacobson et al., 2007;
Mathur et al., 2010; Ding et al., 2013; Xing et al., 2015) inhibit O3
formation due to enhanced titration by NO. As seen in Fig. 2d, the ΔDynamics reduced DM1O3 in eastern China by up to
24 µg m-3 but slightly increased DM1O3 in parts of southern China by up to 7 µg m-3. The decrease in incoming solar radiation due to ADEs
significantly reduces the photolysis rates in east China. As seen in Fig. 2e, the ΔPhotolysis reduced DM1O3 domain-wide by up to 16 µg m-3. The combined effect of both ΔDynamics and ΔPhotolysis results in an overall reduction in DM1O3 as evident across
the JJJ and SCH regions with monthly-average reductions of up to 39 µg m-3.
In July, the O3 chemistry changes from a VOC-limited to an NOx-limited regime across most of China. Therefore, an increase in
NOx concentration due to the stabilization of the atmosphere associated
with the ADEs, facilitates O3 formation. The ΔDynamics increased
DM1O3 across most areas of China, particularly in JJJ, YRD and SCH by
up to 5 µg m-3, with the exception of the PRD region where
DM1O3 decreased. The ΔPhotolysis results in contrasting impacts
in July compared to January, as it increased DM1O3 in most polluted
areas including JJJ, YRD, PRD, HUZ, although the solar radiances were
reduced due to ΔPhotolysis. This behavior is likely due to enhanced
aerosol scattering associated with higher summertime SO42- levels (He and Carmichael, 1999; Jacobson, 1998). Similar
results were found in Tie et al. (2005), who reported that surface-layer
photolysis rates in eastern China were reduced less significantly in summer
than in winter. The resultant enhancements in photolysis rates can then
cause the noted higher concentrations. More importantly, the diurnal
analysis (discussed in the next section) suggested that the reduced
photolysis during the early morning in SimNF enhances the ambient precursor
concentrations (due to less reaction in the early morning) at noon when O3
reaches the daily maximum. This increase in precursor concentrations then
leads to enhanced O3 formation later in the day which compensates for
or even outweighs the disbenefit from the reduced solar radiances. In
summer, ΔDynamics results in a much stronger influence on DM1O3
than ΔPhotolysis, and the combined impact of ADEs increased O3
in most of regions in China by up to 4 µg m-3.
The impact of the ADEs on O3 is further explored by examining the
relationship between the observed and simulated O3 concentrations
(DM1O3, daily values of the cities located in China) as a function of
the observed PM2.5 concentrations (observed daily averaged values in
those cities), as displayed in Fig. 3. The predicted ozone concentrations
under both low and high PM2.5 levels are compared in Table 2. In
regards to model performance for DM1O3 simulations, the model generally
exhibits a slight high bias in January but a low bias in July across the five regions. The inclusion of ADEs moderately reduced O3 concentrations in
January and slightly increased O3 in July, resulting in a reduction in
bias and improved performance for DM1O3 simulation in both January and
July for most of the regions. Comparing the O3 responses to ADEs (see
Δ-ADE in Table 2) under low and high PM2.5 levels reveals
that the O3 responses to ADEs are larger under high PM2.5 levels,
indicating the positive correlations between O3 responses and
PM2.5 levels.
Observed and simulated surface O3 concentration against PM2.5 concentration (O3 is daily 1 h maximum of monitoring sites over China – unit: µg m-3; PM2.5 is the daily average of those site – unit: µg m-3).
Comparison of model performance in ozone prediction across three
simulations (monthly average of daily 1 h maxima).
Low PM2.5 (< 60 µg m-3)
High PM2.5 (< 60 µg m-3)
Region
OBS
Normalized mean bias
Δ-ADE*
OBS
Normalized mean bias
Δ-ADE
(µg m-3)
SimSF
SimNF
SimBL
(µg m-3)
(µg m-3)
SimSF
SimNF
SimBL
(µg m-3)
January
JJJ
62.52
3 %
4 %
5 %
-1.05
37.02
22 %
36 %
53 %
-11.36
YRD
63.89
38 %
41 %
43 %
-2.76
66.74
54 %
59 %
67 %
-8.85
PRD
97.25
25 %
26 %
29 %
-4.52
122.61
6 %
5 %
9 %
-4.63
HUZ
47.67
172 %
173 %
193 %
-10.17
67.29
107 %
125 %
142 %
-23.9
SCH
88.63
-43 %
-40 %
-38 %
-3.85
111.19
-5 %
2 %
8 %
-13.78
China
76.61
30 %
31 %
34 %
-2.96
62.68
42 %
48 %
56 %
-8.61
July
JJJ
159.27
-29 %
-28 %
-28 %
-0.51
178.54
-25 %
-25 %
-25 %
1.02
YRD
171.04
-31 %
-31 %
-32 %
0.84
233.13
-24 %
-25 %
-23 %
-0.51
PRD
129.02
-20 %
-19 %
-20 %
-0.09
312.21
-44 %
-45 %
-46 %
4.92
HUZ
187.44
-36 %
-37 %
-37 %
1.39
208.99
-27 %
-28 %
-29 %
4.19
SCH
163.81
-38 %
-38 %
-39 %
0.77
191.19
-30 %
-31 %
-31 %
1.18
China
145.24
-28 %
-28 %
-28 %
0.3
181.65
-25 %
-25 %
-25 %
0.9
* Δ-ADE represents the O3 response to ADEs, which is calculated
from the difference between SimSF and SimBL.
Interestingly, from low to moderate PM2.5 levels (i.e., PM2.5<120 µg m-3), higher O3 concentration occur with
higher PM2.5 concentrations, which is evident in both observations and
simulations, suggestive of common precursors (e.g., NOx), source
sectors and/or transport pathways contributing to both O3 and
PM2.5 in these regions. However, a negative correlation between O3
and PM2.5 is evident in winter when PM2.5 can reach high
levels larger than 120 µg m-3, indicating the strong ADE impacts
on O3 through both feedbacks to dynamics and photolysis which
significantly reduced O3.
IPRs response to ADEs
Diurnal variation in selected integrated process
contributions to surface O3 concentration in JJJ. The
calculation is based on the average of grid cells in JJJ; (a) baseline is the
simulated O3 in SimBL (unit: ppb h-1); (b) Δ-ADE is the difference in
normalized IPRs between simulations (unit: h-1). Delta_Dynamic is the difference between SimSF and
SimNF; delta_Photolysis is the difference between SimNF and SimBL; delta_Total is the difference between SimSF and SimBL).
To further explore the ADE impacts on simulated O3, the integrated
process contributions are further analyzed in three ways: (a) 24 h
diurnal variations in process contributions to simulated surface O3
(Fig. 4); (b) vertical profiles from ground up to 1357 m a.g.l. (above ground
level, in model layers 1–10) at noon (Fig. 5); and (c) correlations with
near-ground PM2.5 (average concentrations between the ground and 355 m a.g.l.; model layers 1–5) (Fig. 6). In the following, we limit our discussion
to the analysis of model results for the JJJ region, which exhibited the
strongest ADEs among the regions; similar results were found for the other four regions and can be found in the Supplement.
Vertical profile of integrated process contributions to
surface O3 concentration at noon in JJJ. Full-layer
heights above ground are 40, 96, 160, 241, 355, 503, 688, 884, 1100 and 1357 m;
(a) baseline is the simulated O3 in SimBL (unit: ppb h-1); (b) ΔDynamic is the difference
in normalized IPRs between SimSF and SimNF (unit:
h-1); (c) ΔPhotolysis is the
difference in normalized IPRs between SimNF and SimBL (unit:
h-1); (d) ΔTotal is the difference in
normalized IPRs between SimSF and SimBL (unit: h-1).
Diurnal variation in process contributions from chemistry (CHEM), dry
deposition (DDEP) and vertical turbulent mixing (VDIF), which together
contribute to more than 90 % of the O3 rate of change for the JJJ
region, are illustrated in Fig. 4. The diurnal variation in IPRs for other
processes and their response to ADEs are displayed in Fig. S1 in the Supplement for JJJ and
Figs. S2–S5 for the other four regions.
For surface-level O3, VDIF is the major source and DDEP is the major
sink (Fig. S1). The stabilization of the atmosphere due to ΔDynamics
leads to lower dry deposition rates (due to lower dry deposition velocity
from the enhanced aerodynamic resistance) and thus increases surface
O3. The largest impact of ΔDynamics on DDEP occurs during early
morning and late afternoon, which is consistent with the response of the PBL
height to ADEs noted in our previous analysis (Xing et al., 2015a).
As expected, CHEM is the second-largest sink for surface O3 during
January but a source of surface O3 during the daytime in July. The
ΔDynamics increased the surface O3 around noon in both January
and July for almost all regions (no impacts in PRD and YRD in January; see
Figs. S2–S3), since increased stability due to ΔDynamics
concentrated more precursors locally, leading to enhanced O3 formation
during the photochemically most active period of the day. The ΔDynamics reduced the surface O3 around late afternoon in January in all regions. This is because the increased atmospheric stability during late
afternoon and evening hours increased NOx concentration, which titrated
more O3. The ΔPhotolysis reduced surface O3 in all regions
in January. These reductions were more pronounced during the early morning
hours when the photolysis rate are most sensitive to the radiation
intensity. The ΔPhotolysis resulted in comparatively larger
reductions in surface O3 during the early morning and late afternoon
hours in July but slightly increased surface O3 at noon for most of
the regions. This increase in O3 can be hypothesized to result from the
following sequence of events. Slower photochemical reaction in the morning
in the ΔPhotolysis case leads to higher levels of precursors, whose
accumulation then enhances O3 formation at noon. This hypothesis is
further confirmed by the changes in the diurnal variation in NO2, which
suggest that higher NO to NO2 conversion during early morning results
in enhanced daytime NO2 levels (see Fig. S6), consequently leading to
higher noontime O3.
Integrated process contributions to daytime
near-ground-level O3 under different
PM2.5 levels in JJJ (between the ground and 350 m a.g.l.; model layers 1–5).
For O3 aloft (from 100 to 1600 m above ground), as seen in Fig. 5, CHEM is the major source of O3 at noon both in January and in July.
The ΔDynamics increased near-surface O3 (below 500 m; model layers 1–6) but reduced upper-level O3 (above 500 m; model layers 7–10) because increased stability of the atmosphere concentrated precursor emissions within a shallower layer resulting in higher O3 production.
The ΔPhotolysis case considerably reduced near-surface O3 at
noon in January. In July, ΔPhotolysis increased upper-level O3
at noon. Higher levels of precursors at noon might be the reason for such
enhancement (see Fig. S6).
The daytime near-ground-averaged (between the ground and 350 m a.g.l.; layers
1–5) IPR responses to ADEs are shown in Fig. 6 for JJJ and in Fig. S7 for
other regions. The IPR and its responses are presented as a function of
near-ground-averaged PM2.5 concentrations. As shown in Fig. 6, as
PM2.5 concentrations increase, the positive contribution of CHEM in
July becomes larger, while the negative contribution of CHEM in January becomes smaller. The overall ADEs enhanced CHEM and thus increased O3
concentration in July, and such enhancement is generally larger for higher
PM2.5 loading. In contrast, in January overall ADEs resulted in higher
rates of O3 destruction due to chemistry (negative contribution of
CHEM), and the magnitude of this sink increased as PM2.5 concentrations
increase. The reduction of O3 stemming from the enhancements in the
chemical sinks is the dominant impact of ADEs in January. The enhanced
positive contribution of CHEM due to ΔDynamics was partially
compensated for by the reduction from ΔPhotolysis (see Fig. S7),
resulting in a slight increase in the positive CHEM contribution to O3
in July.
DDEP is the major sink of daytime O3 during both January and July. The
increased stability due to ADEs reduced deposition velocity and thus
increased O3. These effects become larger with increasing PM2.5
concentrations. Thus, weaker removal of O3 from DDEP associated with
ADEs contributed to higher O3 in most regions during both January and
July. An enhanced O3 source of CHEM and reduced O3 sink of DDEP is
the dominant impact of ADEs in July.
IRR response to ADEs
Impacts of ADEs on surface Ox and OH
(monthly average of noon time 11:00–13:00 local time).
The simulated midday average (11:00–13:00 local time) surface Ox
(defined as the sum of O, O3, NO2, NO3, N2O5,
HNO3, peroxynitric acid, alkyl nitrates and peroxyacyl nitrates) and OH and their responses to ADEs is shown in Fig. 7. Both Ox and OH are significantly reduced in the
ΔPhotolysis case in January throughout the modeling domain. Both
Ox and OH also show reductions in the middle portions of east China in
the ΔDynamics case in January. Together, the combined ADE impacts
result in reduced Ox and OH in January, with widespread reductions
primarily due to ADEs on photolysis. In July, ΔPhotolysis increased
midday OH across most of China (Fig. 7), which is consistent with the
increase in O3 at noon stemming from a higher level of precursor accumulation due to ΔPhotolysis. The overall ADE impact on OH is
controlled by ΔPhotolysis and results in increased midday OH across
most of China. For Ox, however, the impact of ΔDynamics
outweighs the impact from ΔPhotolysis, resulting in increase in
Ox concentrations in east China including YRD, SCH and HUZ.
Vertical profile of integrated reaction rates in JJJ at
noon. Full-layer heights above ground are 40, 96, 160, 241, 355, 503, 688,
884, 1100 and 1357 m; baseline is the simulation in SimBL; ΔDynamic is the difference between SimSF and SimNF; ΔPhotolysis is the difference between SimNF and SimBL; ΔTotal is the difference between SimSF and SimBL;
PtotalOx is total Ox production rate (unit: ppb h-1); OH_CL is OH chain
length; PNewOH is the production rate of new OH (unit: ppb h-1); PReactedOH is
the production rate of reacted OH (unit: ppb h-1);
PH2O2 is the production rate of
H2O2 (unit: ppb h-1); PHNO3 is the
production rate of HNO3 (unit: ppb h-1); the ratio of
PH2O2/PHNO3 is only shown for
layers 1–5.
To further examine the response of Ox to ADEs, in Fig. 8 we examine
vertical profiles of the integrated reaction rates at noon for the JJJ
region. The stabilization of the atmosphere due to ΔDynamics
concentrates precursors within a lower PBL, resulting in an increased total
Ox production rate (PtotalOx) mostly in near-ground model layers
(below 500 m; model layers 1–6); in magnitude aloft (above 500 m; model layers
7–10), this change in PtotalOx is smaller in January and
becomes decreasing in July. The reduction of PtotalOx due to ΔPhotolysis is greatest at the surface in January and declines with
altitude and even becomes reversed at high layers (about 1300 m; model layer
10) (Fig. 8a). The overall ADE impact in January is mainly dominated by
ΔPhotolysis, which largely outweighs the impact of ΔDynamics
(Fig. 8a). However, in July (Fig. 8b), ΔPhotolysis enhanced
PtotalOx
across all layers. The PtotalOx shows small decreases at high altitudes
but a significant increase in near-ground model layers (below 500 m; model layers 1–6) due to the combined ADEs in July.
The changes in vertical profiles of production rates of new OH (PNewOH)
and reacted OH (PReactedOH) are similar to those of PtotalOx, with
the noted decreases in January dominated by ΔPhotolysis. In
contrast, the increases in July result from contributions from both ΔPhotolysis and ΔDynamics.
An analysis of the chain length is important to understand the characteristics
of chain reaction mechanisms. The OH chain length (denoted OH_CL) is determined by the ratio of PReactedOH to PNewOH. ΔDynamics concentrated more NOx at the surface, thus leading to an
increased OH_CL (i.e., more reacted OH than new OH) in the
near-ground layers but a decreased OH_CL in the upper
layers. In January, the ΔPhotolysis reduced PNewOH more than
PReactedOH (probably because of more abundance of NOx resulting
from photolysis attenuation and consequently reduced photochemistry),
thereby leading to an increased OH_CL. In July, ΔPhotolysis enhanced both PNewOH and PReactedOH, particularly in
the upper layers. The OH_CL is increased by ΔPhotolysis because higher NOx levels (see Fig. S6) cause more OH to be reacted. Thus the surface OH_CL at noon is
increased in both January and July from combined ADEs of ΔPhotolysis
and ΔDynamics, indicating a stronger propagation efficiency of the
chain.
The production rates of H2O2 (PH2O2) and HNO3
(PHNO3) and their responses to ADEs are also summarized in Fig. 8
(average for midday hours) for the JJJ region (similar illustrations for
the other regions can be found in the supplemental Figs. S8–S11. Smaller
ratios of PH2O2/PHNO3 are noted in January compared to July,
indicating a stronger VOC-limited regime in January for all regions. The
ΔDynamics increases PHNO3 but decreases PH2O2 in both
January and July because the enhanced NOx at the surface in a more
stable atmosphere likely shifts O3 chemistry towards NOx-rich
conditions. The ΔPhotolysis reduced both PH2O2 and
PHNO3, but the ratio of PH2O2/PHNO3 is decreased due to a larger reduction
in PH2O2 than PHNO3. The combined impacts of ΔDynamics and
ΔPhotolysis result in a shift towards more VOC-limited conditions in
the near-surface layers during both January and July.
Summary
The impacts of ADEs on tropospheric ozone were quantified by using the
two-way coupled meteorology and atmospheric chemistry WRF-CMAQ model
using a process analysis methodology. Two manifestations of
ADE impacts on O3 – changes in atmospheric dynamics (ΔDynamics)
and changes in photolysis rates (ΔPhotolysis) – were systematically
evaluated through simulations that isolated their impacts on modeled process
rates over China for winter and summer conditions (represented by the months
of January and July in 2013, respectively). Results suggest that the model
performance for surface DM1O3 simulations improved after the inclusion
of ADEs, which moderately reduced the high bias in January and low bias in
July. In winter, the inclusion of ADE impacts resulted in an overall
reduction in surface DM1O3 across China by up to 39 µg m-3.
Changes both in photolysis and atmospheric dynamics due to ADEs contributed
to the reductions in DM1O3 in winter. In contrast during July, the
impact of ADEs increased surface DM1O3 across China by up to 4 µg m-3. The summertime increase in DM1O3 results primarily from ADE-induced effects on atmospheric dynamics. It can thus be postulated that
reducing ADEs will have the potential risk of increasing O3 in winter but
will benefit the reduction in maximum O3 in summer.
Results from IPR analysis suggest that the ADE impacts exhibit strong
vertical and diurnal variations. The ADE-induced decrease in modeled
DM1O3 in January primarily results from ΔPhotolysis, which
reduced the chemical production of O3 in the near-ground layers. The
increase in DM1O3 in July due to ADEs results from a weaker dry
deposition sink as well as a stronger chemical source due to higher
precursor concentrations in a more stable and shallow PBL. These impacts
become stronger under higher PM2.5 concentrations when ADEs are larger.
The combined ADE impacts reduce Ox in January due to ΔPhotolysis but slightly increase Ox in July due to ΔDynamics.
OH is reduced by ADEs in January. However, midday OH concentrations during
summertime show enhancements associated with both ΔPhotolysis and
ΔDynamics, indicating a stronger midday atmospheric oxidizing
capacity in July. An increased OH chain length in the near-ground layers is
modeled both in January and July, indicating a stronger propagation
efficiency of the chain reaction. In both January and July, PHNO3 is
increased and PH2O2 is decreased due to ΔDynamics, and both are
reduced due to ΔPhotolysis. The ratio of PH2O2/PHNO3 is
decreased due to the combined impacts of ΔDynamics and ΔPhotolysis, indicating a shift towards more VOC-limited conditions due to
ADEs in the near-ground layers during both January and July.
Thus aerosol direct effects on both photolysis rates as well as atmospheric
dynamics can impact O3 formation rates and its local and regional
distributions. Comparisons of integrated process rates suggest that the
decrease in DM1O3 in January results from a larger net chemical sink
due to ΔPhotolysis, while the increase in DM1O3 in July is
mostly associated with the slower removal due to reduced deposition velocity
as well as a stronger photochemistry due to ΔDynamics. The IRR
analyses confirm that the process contributions from chemistry to DM1O3 can be influenced by both ΔDynamics and ΔPhotolysis.
Reduced ventilation associated with ΔDynamics enhances the precursor
levels, which increase the chemical production rate of Ox and OH, resulting
in greater O3 chemical formation at noon during both January and July.
One the other hand, reduced photolysis rates in ΔPhotolysis result in lower O3 in January. However, in July lower photolysis rates result
in the accumulation of precursors during the morning hours, which eventually lead
to higher O3 production at noon.
The comparison of integrated reaction rates from the various simulations
also suggest that the increased OH_CL and the shift towards
more VOC-limited conditions are mostly associated with the higher NO2
levels due to ADEs. This further emphasizes the importance of NOx
controls in air pollution mitigation. Traditionally, the co-benefits from
NOx control for ozone and PM reduction are mostly because NOx
is a common precursor for both O3 and PM2.5. This study suggests
that effective controls on NOx will not only gain direct benefits for
O3 reduction but can also indirectly reduce peak O3 through
weakening the ADEs from the reduced PM2.5, highlighting co-benefits from
NOx controls for achieving both O3 and PM2.5 reductions.
Reducing aerosols will have substantial impacts on ozone. The quantification of
the aerosol influence on ozone is important to understand co-benefits
associated with reductions in both particulate matter and ozone. This study
focused on the evaluation of ADE impacts, which were not well quantified
previously. However, the heterogeneous reactions associated with aerosols,
as well as the impacts of emission controls of gaseous precursors on both
aerosols and ozone also need to be studied in order to fully understand the
influence of reducing aerosols on ambient ozone.