In the present study, we use a specific version of the WRF-CHEM model (Grell et al., 2005) to investigate the O3 formation in eastern China. The model is developed by Li et al. (2010, 2011a, b, 2012) at the Molina Center for Energy and the Environment, including a new, flexible gas-phase chemical module and the Models-3 community multiscale air quality (CMAQ) aerosol module developed by the US EPA (Binkowski and Roselle, 2003). The wet deposition of chemical species is calculated using the method in the CMAQ module and the dry deposition parameterization follows Wesely (1989). The fast radiation transfer model (FTUV) is used to calculate photolysis rates (Tie et al., 2003; Li et al., 2005), considering the impacts of aerosols and clouds on the photochemistry (Li et al., 2011b). The ISORROPIA version 1.7 is used to calculate the inorganic aerosols (Nenes et al., 1998). The secondary organic aerosol (SOA) is predicted using a non-traditional SOA module, including the volatility basis set (VBS) modeling approach and SOA contributions from glyoxal and methylglyoxal. Detailed information about the WRF-CHEM model can be found in Li et al. (2010, 2011a, b, 2012).

A high O3 pollution episode from 22 to 28 May 2015 in eastern China is simulated using the WRF-CHEM model. The WRF-CHEM model adopts one grid with horizontal resolution of 10 km and 35 sigma levels in the vertical direction, and the grid cells used for the domain are 350×350 (Fig. 1). The physical parameterizations include the microphysics scheme of Hong and Lim (2006), the Mellor–Yamada–Janjic (MYJ) turbulent kinetic energy (TKE) planetary boundary layer scheme (Janjić, 2002), the unified Noah land-surface model (Chen and Dudhia, 2001), the Goddard longwave (Chou and Suarez, 2001) and shortwave parameterization (Chou and Suarez, 1999). The National Centers for Environmental Prediction (NCEP) 1∘×1∘ reanalysis data are used to obtain the meteorological initial and boundary conditions, and the meteorological simulations are not nudged in the study. The chemical initial and boundary conditions are interpolated from the 6 h output of MOZART (Horowitz et al., 2003). The spin-up time of the WRF-CHEM model is 28 h, which is generally long enough for simulations considering that the initial and boundary conditions are adopted from MOZART, a global chemical transport model. The SAPRC-99 chemical mechanism is used in the present study. The anthropogenic emissions are developed by Zhang et al. (2009) and Li et al. (2017), including contributions from agriculture, industry, power generation, residential, and transportation sources. The biogenic emissions are calculated online using the MEGAN (Model of Emissions of Gases and Aerosol from Nature) model developed by Guenther et al. (2006). Detailed model configurations are given in Table 1. The simulation domain is shown in Fig. 1.

For discussion convenience, eastern China is divided into four sections: (1) northeast China (including Heilongjiang, Jilin, Liaoning, and the east part of Inner Mongolia, hereafter referred to as NEC), (2) the North China Plain and surrounding areas (including Beijing, Tianjin, Hebei, Shandong, Henan, Shanxi, and the north part of Jiangsu and Anhui, hereafter referred to as NCPs), (3) the YRD and surrounding areas (including the south part of Jiangsu and Anhui, Shanghai, Zhejiang, and Hubei, hereafter referred to as YRDs), and (4) the PRD and surrounding areas (including Fujian, Jiangxi, Hunan, Guangxi, and Guangdong, hereafter referred to as PRDs) (shown in the Supplement, Fig. S1).

We use the mean bias (MB) and the index of agreement (IOA) to assess the WRF-CHEM model performance in simulating air pollutants against measurements. MB=1N∑i=1NPi-OiIOA=1-∑i=1NPi-Oi2∑i=1NPi-O‾+Oi-O‾2, where Pi and Oi are the calculated and observed pollutant concentrations, respectively. N is the total number of the predictions used for comparisons, and O‾ represents the average of the prediction and observation, respectively. The IOA ranges from 0 to 1, with 1 showing perfect agreement of the prediction with the observation.

The hourly near-surface CO, NO2, SO2, and PM2.5 mass concentrations from April to September 2015 in eastern China are released by China MEP and can be downloaded from the website http://www.aqistudy.cn/. China MEP releases the pollutant observations using the mass concentration (µg m-3 or mg m-3) as the unit. Therefore, in order to keep consistent with the observations, the mass concentration is used in the paper, although the mixing ratio (such as ppbv) is a more common unit used in the literature for air pollutants.

Continuous deterioration of air quality in China has engendered the implementation of the Atmospheric Pollution Prevention and Control Action Plan (hereafter referred to as APPCAP), released by Chinese State Council in September 2013 to reduce PM2.5 by up to 25 % by 2017 relative to 2012 levels. Therefore, variations of air pollutants from 2013 to 2015 demonstrate the mitigation effects of implementation of the APPCAP on the air quality to a considerable degree. A total of 65 cities, with 427 monitoring sites, have air pollutant observations from 2013 to 2015 during April to September in eastern China (Fig. 1). Considering the occurrence of high [O3] in the afternoon (12:00–18:00 Beijing time (BJT)), Table 2 provides the average concentrations of air pollutants in the afternoon from April to September in the 65 cites of eastern China in 2013 and 2015. Apparently, implementation of the APPCAP has decreased the mass concentrations of CO, SO2, NO2, and PM2.5 in eastern China, particularly with regard to SO2, with a reduction of close to 40 % from 2013 to 2015. The [O3] however exhibit an increasing trend, enhanced by 9.9 % from 2013 to 2015. Additionally, if the O3 exceedance is defined as hourly [O3] exceeding 200 µg m-3 (the second grade of National Ambient Air Quality Standards in China), the O3 exceedance frequency in the afternoon had increased from 5.2 % in 2013 to 6.8 % in 2015, enhanced by about 31.5 %. The ozone monitoring instrument (OMI) satellite observations have also shown that the annual O3 concentration has increased by 1.6 % per year over central and eastern China from 2005 to 2014 (Shan et al., 2016).

There are several possible reasons for the O3 pollution deterioration in eastern China since implementation of the APPCAP. Firstly, if the O3 production regime in eastern China is VOC sensitive, the decrease of NOx due to implementation of the APPCAP likely enhances the O3 formation. Secondly, mitigation of PM2.5 or aerosols directly or indirectly increases the photolysis rates and expedites the O3 formation. Thirdly, increasing transportation activities enhances the emissions of VOCs and semi-VOCs, facilitating the O3 formation. In addition, variability of meteorological situations also leads to the [O3] fluctuation (Calkins et al., 2016). Hence, implementation of the APPCAP does not help mitigate [O3], and unfortunately, severe O3 pollution has been looming in eastern China.

In 2015, O3 observations had been performed in 223 cities with 1064 monitoring sites in eastern China, which were used to analyze the O3 pollution situation from April to September. For comparisons, Fig. 2 shows the distribution of observed maximum 1 h [O3] in mainland China from April to September in 2015. The cities with the maximum 1 h [O3] exceeding 300 µg m-3 are mainly concentrated in NCPs, YRDs, and PRD. In eastern China, there are only two cities with the maximum 1 h [O3] less than 200 µg m-3. About 28 % of cities have observed more than 400 µg m-3 [O3] (about 200 ppb), showing widespread O3 pollution in eastern China. Furthermore, it is worth to note that the observed maximum 1 h [O3] in six cites exceed 800 µg m-3 (about 400 ppb) at a very dangerous level.

Figure 3 presents the distribution of average daily maximum 1 h [O3] in mainland China from April to September 2015. The average daily maximum 1 h [O3] are more than 120 µg m-3 in more than 95 % of the cities, and 160 µg m-3 in 46 % of the cities in eastern China. Particularly, there are seven cities with the average daily maximum 1 h [O3] exceeding 200 µg m-3 during 6 months. Figures 4 and 5 show the distributions of exceedance days with the maximum 1 h [O3] exceeding 160 and 200 µg m-3 in mainland China from April to September 2015, respectively. There are more than 60 days with the maximum 1 h [O3] exceeding 160 µg m-3 in 114 cities, and even more than 90 days in 62 cites in eastern China from April to September. The 1 h [O3] of 200 µg m-3 have been exceeded on over 10 % of days in 129 cities, and on 30 % of days in 38 cities (Fig. 5). Hence, persistent O3 pollution has occurred in eastern China from April to September in 2015.

Furthermore, in the urban PBL, high [O3] generally take place under calm or stable circumstances with strong solar radiation. From April to September, the east Asian summer monsoon influences eastern China, causing intensified precipitation which inhibits the high O3 formation by washing out O3 precursors and decreasing photolysis rates. Thus, if excluding rainy days in the analysis, the O3 pollution becomes more severe in eastern China. For example, in Beijing, there are 54 rainy days and 65 days with the maximum 1 h [O3] exceeding 200 µg m-3 from May to August in 2015. If it does not rain in Beijing, the occurrence possibility of the maximum 1 h [O3] exceeding 200 µg m-3 is around 94 %, showing severe and persistent O3 pollution.

The hourly measurements of O3 and NO2 in eastern China are used to validate the WRF-CHEM model simulations. Figure 6 presents the distributions of calculated and observed near-surface [O3] along with the simulated wind fields at 15:00 BJT from 22 to 27 May 2015. In order to interpret the effect of meteorological and synoptic conditions on the air quality in eastern China, Fig. S2 presents the average geopotential height wind filed at 500 hPa from 22 to 27 May 2015. During the study episode, the NCPs and NEC are generally located behind the trough whose center is located between 120 and 130∘ E. At the end of May, the main part of subtropical high at 500 hPa was located in the western Pacific, with the ridgeline moving around the 10–15∘ N. With the onset of the summer monsoon, the subtropical high gradually moves northwards and affects southern China, with more precipitation occurrence over YRDs and PRDs. Figure 6 presents the distributions of calculated and observed near-surface [O3] along with the simulated wind fields at 15:00 BJT from 22 to 27 May 2015. On 22 May, eastern China is influenced by the high pressure whose center, located over the Yellow Sea, was induced by the high-level trough. The east winds in the south of the high transport humid air into PRDs, causing rainfall weather that substantially decreases [O3]. The WRF-CHEM model well reproduces the observed low [O3] in the south of PRDs. In NCPs and YRDs, calm winds, clear sky, and high temperature, induced by the high, facilitate the O3 formation, and the simulated [O3] generally exceed 160 µg m-3, which is consistent with the observations. On 23 May, the subtropical high moves northward, also causing the rainfall belt in the south of PRDs to extend northward. The simulated O3 pollution in NCPs is deteriorated and also extended to NEC, in good agreement with the measurements. From 24 to 25 May, the high pressure located at the Yellow Sea continuously deteriorates the O3 pollution in eastern China. The simulated and observed O3 pollution on 25 May is almost widespread in eastern China, and northwest China also experiences high O3 pollution. On 26 and 27 May, the simulated and observed [O3] in the north of NCPs and NEC are still high, but in PRDs and YRDs, the [O3] have been significantly decreased due to the precipitation caused by the subtropical high and summer monsoon.

Generally, the simulated O3 spatial patterns are consistent with observations, but the model underestimation or overestimation still exists. For example, the model remarkably overestimates the observed [O3] on 24 May, and also cannot well reproduce the high [O3] on 25 May in PRD. There are several reasons for the model biases in simulating [O3] distribution. Firstly, the meteorological situations play a key role in air pollution simulations (Bei et al., 2010, 2012), determining the formation, transformation, diffusion, transport, and removal of the air pollutants. Therefore, uncertainties in meteorological field simulations significantly influence the air pollutant simulations. On 24 May, the model fails to predict the rainy or overcast weather, leading to remarkable overestimation of [O3] in PRD. Secondly, the 10 km horizontal resolution is used in simulations, which cannot resolve cumulus clouds well. The model overestimates the [O3] observed in some cities with [O3] much lower than their surrounding cities, which is primarily caused by the model failure in resolving convections. Thirdly, the fast changes in emissions are not reflected in the emission inventories used in the present study.

Figure 7 provides the diurnal profiles of calculated and observed near-surface [O3] averaged over the ambient monitoring sites in provinces and municipalities in eastern China during the episode. The model reasonably well reproduces the temporal variations of surface [O3] compared to observations, e.g., peak [O3] in the afternoon due to active photochemistry and low [O3] during nighttime caused by the NOx titration. Three provinces in NEC (Jilin, Liaoning, and Inner Mongolia) are apparently impacted by the transboundary transport from NCPs when the south winds are prevailing (Fig. 6). Thus, the uncertainties of wind field simulations constitute one of the most important reasons for the model biases in modeling [O3] in these three provinces. The model underestimates considerably the observed [O3] in the three provinces (Fig. 7a, c, d), with MBs exceeding 19 µg m-3. The model generally exhibits good performance in simulating [O3] variations in the provinces of NCPs (Fig. 7e–l) with IOAs exceeding 0.90, but is subject to underestimating the observations, particularly in Beijing which is also significantly influenced by the transboundary transport (Wu et al., 2017). In YRDs, the model cannot well predict the observed [O3] in Shanghai, which is affected by the sea breeze when the large-scale wind fields are weak. In general, however, current numerical weather prediction models, even in research mode, still have difficulties in producing the location, timing, depth, and intensity of the sea-breeze front (Banta et al., 2005; J. Wang et al., 2013). The model reasonably predicts the [O3] variations compared to measurements in PRDs (Fig. 7p–t) with IOAs more than 0.7, but overestimates the observed [O3] with MBs varying from 3.8 to 16.7 µg m-3, showing model biases in modeling precipitation processes.

The comparisons of simulated versus observed distributions and temporal variations of NO2 mass concentrations ([NO2]) are shown in the Supplement (Figs. S3 and S4). The simulated high near-surface [NO2] are mainly concentrated in NCP, YRD, and PRD, which is generally consistent with the measurements. The model also reasonably yields temporal variations of [NO2] compared to measurements, but the simulations of [NO2] are not as good as those of [O3], and the IOAs in Liaoning, Tianjin, and Shanghai are lower than 0.5. The difference between simulations and observations is frequently rather large during nighttime, which is perhaps caused by the model biases in modeling nighttime PBL or the complexity of nighttime chemistry. Another possible reason for NOx biases in simulations is lack of consideration of the NOx emissions in the agricultural region, which has been proposed to generate high NOx emissions under high-temperature conditions (Oikawa et al., 2015). In general, the calculated distributions and variations of [O3] and [NO2] are consistent with the corresponding observations, showing that the simulations of meteorological fields and emission inventories are reasonable, providing the base for sensitivity studies.

O3 formation in the PBL is a complicated nonlinear process, depending on its precursors of NOx and VOCs from biogenic and various anthropogenic sources. It is imperative to evaluate the O3 contribution from various sources for devising the O3 control strategy. Rapid growth of industries, transportation, and urbanization has caused increasing emissions of NOx and VOCs in eastern China (e.g., Zhang et al., 2009; Huang et al., 2011; Z. Wang et al., 2012; Y. Wang et al., 2013; Yang et al., 2015). Numerous studies have also demonstrated that biogenic VOCs, such as isoprene and monoterpenes, play a considerable role in the O3 formation in the PBL (e.g., Chameides et al., 1988; Tao et al., 2003; Li et al., 2007, 2014). Therefore, sensitivity studies are used to evaluate the O3 contributions of biogenic, industry, residential, and transportation sources in eastern China, respectively. It is worth to note that emissions of power plants are directly associated with residential living and industrial activities. Thus, in the study, 75 % of emissions from power plants are assigned to the industry source and the rest are assigned to the residential source according to the ratio of the power consumption used in industrial activities to residential living (Wang et al., 2012).

The factor separation approach (FSA) is used to evaluate the contribution of some emission source to the O3 concentration by differentiating two model simulations: one with all emission sources and the other without some emission source. Therefore, except the control simulations with all emissions, an additional four sensitivity simulations are performed in which the biogenic, industry, residential, and transportation emissions are excluded, respectively, to assess their corresponding contributions to the O3 formation in eastern China.

Figure 8 shows the contribution of near-surface [O3] averaged in the afternoon during the whole episode from industry, residential, transportation, and biogenic emissions. The industry source plays a more important role in the O3 formation than the other three sources, with the O3 contribution of 10–50 µg m-3 in the afternoon in eastern China. In highly industrialized areas, such as Hebei, Tianjin, Shandong, Zhejiang, etc., the O3 contribution of the industry source exceeds 30 µg m-3. The residential source is not important in the O3 formation and contributes about 2–15 µg m-3 O3 generally. The transportation source plays a considerable role in the O3 formation, accounting for about 5–30 µg m-3 O3 in eastern China. The O3 enhancement due to biogenic emissions is mainly concentrated in NCPs and PRDs, particularly in PRDs, with the O3 contribution of around 5–50 µg m-3.

In order to further evaluate the contribution of various sources to the [O3], the hourly near-surface [O3] in the control simulation are first subdivided into 16 bins with the interval of 20 µg m-3. [O3] are assembled in the control and sensitivity simulations as the bin [O3], respectively, and an average of [O3] in each bin are calculated. Figure 9 shows the contributions of various emission sources to [O3] in the four sections of eastern China during the episode. The industry emission plays the most important role in the O3 formation, and is the culprit of the high O3 pollution. When the [O3] in the control simulation are less than 100 µg m-3, the industry source generally decreases [O3]. However, when the simulated [O3] are more than around 200 µg m-3, the O3 contribution from the industry emissions generally exceeds 50 µg m-3, and when the simulated [O3] are more than 300 µg m-3, the industrial O3 contribution can be up to 100 µg m-3, constituting one-third of the [O3]. The O3 contribution from the residential source is not significant (generally less than 20 µg m-3). The transportation source plays the second most important role in the O3 formation in NEC, NCPs, and YRDs, but its O3 contribution is much less than that from the industry source when the simulated [O3] are more than 150 µg m-3. VOCs from the biogenic source generally enhance the O3 formation, providing a background O3 source. The biogenic source contributes about 10–50 µg m-3 O3 when simulated [O3] are more than 150 µg m-3 in NEC, NCPs, and YRDs. However, in PRDs, the biogenic emissions constitute the second most important O3 source, with the O3 contribution exceeding 50 µg m-3 when simulated [O3] are more than 250 µg m-3. Apparently, controlling the industry emissions can substantially mitigate the more severe O3 pollution in eastern China. If the industry emissions are not considered in model simulations, on average, the [O3] are generally not more than 200 µg m-3 in NEC, YRDs, and PRDs, but still can exceed 160 µg m-3. In addition, excluding the industry source in NCPs does not mitigate [O3] as remarkably as in the other regions, indicating that other emission sources also play an important role in the O3 formation. Although the transportation emission is the second most important O3 source in NEC, NCPs, and PRDs, its O3 contribution is much less than that from the industry source. Table S1 in the Supplement further presents the emission rates of major O3 precursors from different emission sources in the model domain during the study episode. The industrial source dominates the VOC and NOx emissions, playing a key role in the O3 formation. The transportation source emits more NOx and active VOCs, such as olefins and aromatics, than the residential source, contributing considerably to the O3 formation.

Another three sensitivity studies are conducted to further explore the high O3 formation in eastern China, in which only the industry, residential, and transportation sources are considered, respectively. It is worth to note that biogenic emissions are included in all the three sensitivity simulations considering that the biogenic emissions provide natural O3 precursors and cannot be anthropogenically controlled. Figure 10 presents the O3 contributions from individual anthropogenic sources averaged in the afternoon during the whole episode in the four sections of eastern China. If only the industry source is considered or the residential and transportation sources are excluded in the simulation, eastern China still experiences high O3 pollution. The O3 contribution of the residential and transportation sources is less than 60 µg m-3 on average, further showing the important role of the industry source in the O3 pollution. When the industry and residential sources are not considered in the simulation, the transportation source still causes the simulated [O3] to exceed 160 µg m-3, particularly in NCPs. Taking into consideration the very fast increase of vehicles in China recently (X. Wu et al., 2016), the transportation source increasingly constitutes a more important O3 source, particularly when the industry source is under control. Apparently, when the industry and transportation sources are excluded or only the residential source is included, the high O3 pollution is significantly mitigated and the simulated [O3] are less than 160 µg m-3 on average. Figure 11 provides the distribution of the [O3] averaged during the peak time on 25 May when the most serous O3 pollution occurs during the simulated episode. When only the industry emissions are considered, the O3 pollution is mitigated considerably in eastern China, but still widespread in NCPs and PRDs. If only considering the transportation source, the O3 pollution still occurs in NCPs, with the [O3] exceeding 160 µg m-3. When the industry and transportation sources are excluded, the O3 pollution is generally under control. Hence, reducing the emissions from industry and transportation is key to mitigating O3 pollution in eastern China.