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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.