Widespread and persistent ozone pollution in eastern China during the non-winter season of 2015: observations and source attributions

Abstract

Rapid growth of industrialization, transportation, and urbanization has caused increasing emissions of ozone (O${}_{\mathrm{3}})$ precursors recently, enhancing the O${}_{\mathrm{3}}$ formation in eastern China. We show here that eastern China has experienced widespread and persistent O${}_{\mathrm{3}}$ pollution from April to September 2015 based on the O${}_{\mathrm{3}}$ observations in 223 cities. The observed maximum 1 h O${}_{\mathrm{3}}$ concentrations exceed 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in almost all the cities, 400 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in more than 25 % of the cities, and even 800 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in six cities in eastern China. The average daily maximum 1 h O${}_{\mathrm{3}}$ concentrations are more than 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in 45 % of the cities, and the 1 h O${}_{\mathrm{3}}$ concentrations of 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ have been exceeded on over 10 % of days from April to September in 129 cities. Analyses of pollutant observations from 2013 to 2015 have shown that the concentrations of CO, SO${}_{\mathrm{2}}$, NO${}_{\mathrm{2}}$, and PM${}_{2.5}$ from April to September in eastern China have considerably decreased, but the O${}_{\mathrm{3}}$ concentrations have increased by 9.9 %. A widespread and severe O${}_{\mathrm{3}}$ pollution episode from 22 to 28 May 2015 in eastern China has been simulated using the Weather Research and Forecasting model coupled to chemistry (WRF-CHEM) to evaluate the O${}_{\mathrm{3}}$ contribution of biogenic and various anthropogenic sources. The model generally performs reasonably well in simulating the temporal variations and spatial distributions of near-surface O${}_{\mathrm{3}}$ concentrations. Using the factor separation approach, sensitivity studies have indicated that the industry source plays the most important role in the O${}_{\mathrm{3}}$ formation and constitutes the culprit of the severe O${}_{\mathrm{3}}$ pollution in eastern China. The transportation source contributes considerably to the O${}_{\mathrm{3}}$ formation, and the O${}_{\mathrm{3}}$ contribution of the residential source is not significant generally. The biogenic source provides a background O${}_{\mathrm{3}}$ source, and also plays an important role in the south of eastern China. Further model studies are needed to comprehensively investigate O${}_{\mathrm{3}}$ formation for supporting the design and implementation of O${}_{\mathrm{3}}$ control strategies, considering rapid changes of emission inventories and photolysis caused by the Atmospheric Pollution Prevention and Control Action Plan released by the Chinese State Council in 2013.

Introduction

In the urban planetary boundary layer (PBL), ozone (O${}_{\mathrm{3}})$ is formed as a result of photochemical reactions involving volatile organic compounds (VOCs) and nitrogen oxide (NO${}_x)$ in the presence of sunlight (Brasseur et al., 1999): $$\begin{array}{cc}{\displaystyle }& {\displaystyle }{\text{NO}}_{\mathrm{2}}+h\mathit{\upsilon }\to \text{NO}+\text{O}\left({}^{\mathrm{3}},\text{P}\right)\left(290,,\text{nm},<,\mathit{\lambda },<,420,,\text{nm}\right)\\ {\displaystyle }& {\displaystyle }\text{O}\left({}^{\mathrm{3}},\text{P}\right)+{\text{O}}_{\mathrm{2}}+\text{M}\to {\text{O}}_{\mathrm{3}}+\mathrm{M}\\ {\displaystyle }& {\displaystyle }{\mathrm{O}}_{\mathrm{3}}+h\mathit{\upsilon }\to {\text{O}}_{\mathrm{2}}+\text{O}\left({}^{\mathrm{1}},\text{D}\right)\left(290,,\text{nm},<,\mathit{\lambda },<,329,,\text{nm}\right)\\ {\displaystyle }& {\displaystyle }\text{O}\left({}^{\mathrm{1}},\text{D}\right)+{\text{H}}_{\mathrm{2}}\mathrm{O}\to \mathrm{2}\text{OH}\\ {\displaystyle }& {\displaystyle }\text{OH}+\text{VOCs}+{\text{O}}_{\mathrm{2}}\to {\text{RO}}_{\mathrm{2}}+\text{others}\\ {\displaystyle }& {\displaystyle }{\text{RO}}_{\mathrm{2}}+\text{NO}\to \text{RO}+{\text{NO}}_{\mathrm{2}}\end{array}$$ where $h\mathit{\upsilon }$ represents the energy of a photon; O$\left({}^{\mathrm{3}},\text{P}\right)$ and O$\left({}^{\mathrm{1}},\text{D}\right)$ represent the ground state and electronically excited oxygen atoms, respectively; RO${}_{\mathrm{2}}$, RO, and OH denote peroxy, oxy-, and hydroxyl radicals, respectively. High O${}_{\mathrm{3}}$ concentrations ([O${}_{\mathrm{3}}$]) are of major environmental concern due to its deleterious impacts on ecosystems (e.g., National Research Council, 1991) and human health (Lippmann, 1993; Weinhold, 2008).

The emissions of O${}_{\mathrm{3}}$ precursors, VOCs and NO${}_x$, have been significantly increased recently in China due to rapid industrialization and urbanization, and increasing transportation activity (e.g., Zhang et al., 2009; Kurokawa et al., 2013; Yang et al., 2015). Satellite measurements have demonstrated that NO${}_x$ emissions have been increased by a factor of 2 in central and east China from 2000 to 2006 (Richter et al., 2005). Zhang et al. (2009) have also shown an increasing trend of NO${}_x$ emissions with an enhancement of 55 % in China from 2001 to 2006. NO${}_x$ emissions have still continued to increase since 2006, due to increasing power plants and vehicles (S. W. Wang et al., 2012; Y. Wang et al., 2013; Yang et al., 2015). In addition, the agriculture has been proposed to have a large potential to produce NO${}_x$ (Oikawa et al., 2015). VOC emissions have been estimated to increase by 29 % during 2001–2006 in China (Zhang et al., 2009), and predicted to increase by 49 % by 2020 relative to 2005 levels (Xing et al., 2011). Additionally, modeling studies have been performed to investigate the O${}_{\mathrm{3}}$ pollution in eastern China (Wang et al., 2010; Liu et al., 2012; Situ et al., 2013; Huang et al., 2015). For example, Tie et al. (2013) have analyzed the characteristics of regional O${}_{\mathrm{3}}$ formation to explain the O${}_{\mathrm{3}}$ pollution in Shanghai and its surrounding area using the Weather Research and Forecasting model coupled to chemistry (WRF-CHEM). Using the observation-based chemical model, Xue et al. (2014) have provided insights into the ozone pollution in Beijing, Shanghai, and Guangzhou by analyzing the O${}_{\mathrm{3}}$ precursors and the potential impacts of heterogeneous chemistry.

Increasing O${}_{\mathrm{3}}$ precursor emissions have caused O${}_{\mathrm{3}}$ to be one of the most serious air pollutants of concern during summertime, particularly in eastern China, including the North China Plain (NCP), Yangtze River Delta (YRD), and Pearl River Delta (PRD) (e.g., Xu et al., 2011; Tie et al., 2013; Li et al., 2013; Feng et al., 2016). For example, a maximum O${}_{\mathrm{3}}$ concentration of 286 ppb has been observed in urban plumes from Beijing (Wang et al., 2006). Chen et al. (2015) have reported that the average maximum daily [O${}_{\mathrm{3}}$] exceed 150 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in the summer of 2015 at most of monitoring sites in Beijing. Wu et al. (2017) have also shown that, during summertime in 2015 in Beijing, the average O${}_{\mathrm{3}}$ concentration in the afternoon was 163.2 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, and the frequency of the O${}_{\mathrm{3}}$ exceedance with hourly [O${}_{\mathrm{3}}$] exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ was 31.8 %. In addition, Cheng et al. (2016) have demonstrated an increasing trend of daily maximum 1 h [O${}_{\mathrm{3}}$] from 2004 to 2015 in Beijing, and Ma et al. (2016) have reported a significant increase of surface O${}_{\mathrm{3}}$ at a rural site in NCP. In the PRD region, the annual average near-surface O${}_{\mathrm{3}}$ level has been reported to increase from 24 ppbv in 2006 to 29 ppbv in 2009, and the maximum 1 h [O${}_{\mathrm{3}}$] can be up to 150–200 ppb in the summer and fall (Ou et al., 2016). Numerous studies have been performed to examine the severe O${}_{\mathrm{3}}$ pollution in China, but were primarily confined to megacities or industrial complexes. Few studies have been conducted for all of eastern China to investigate the O${}_{\mathrm{3}}$ pollution situation and formation.

WRF-CHEM simulation domain with topography. The filled circles represent centers of cities with ambient monitoring sites and the size of circles denotes the number of ambient monitoring sites of cities. The red and blue filled circles show the cities with air pollutant observations since 2013 and 2015, respectively.

[Figure omitted. See PDF]

WRF-CHEM model configurations.

Regions	Eastern China
Simulation period	22–28 May 2015
Domain size	$350\times 350$
Domain center	35${}^{\circ }$ N, 114${}^{\circ }$ E
Horizontal resolution	10 km $\times$ 10 km
Vertical resolution	35 vertical levels with a stretched vertical grid with spacing ranging
	from 30 m near the surface to 500 m at 2.5 and 1 km above 14 km
Microphysics scheme	WSM six-class graupel scheme (Hong and Lim, 2006)
Boundary layer scheme	MYJ TKE scheme (Janjić, 2002)
Surface layer scheme	MYJ surface scheme (Janjić, 2002)
Land-surface scheme	Unified Noah land-surface model (Chen and Dudhia, 2001)
Longwave radiation scheme	Goddard longwave scheme (Chou and Suarez, 2001)
Shortwave radiation scheme	Goddard shortwave scheme (Chou and Suarez, 1999)
Meteorological boundary and initial conditions	NCEP ${\mathrm{1}}^{\circ }\times {\mathrm{1}}^{\circ }$ reanalysis data
Chemical initial and boundary conditions	MOZART 6 h output (Horowitz et al., 2003)
Anthropogenic emission inventory	SAPRC-99 chemical mechanism emissions (Zhang et al., 2009)
Biogenic emission inventory	MEGAN model developed by Guenther et al. (2006)
Model spin-up time	28 h

The China Ministry of Environmental Protection (China MEP) has commenced to release real-time hourly observations of pollutants, including O${}_{\mathrm{3}}$, NO${}_{\mathrm{2}}$, CO, SO${}_{\mathrm{2}}$, PM${}_{2.5}$, and PM${}_{10}$ (particulate matter with aerodynamic diameter less than 2.5 and 10 $\mathrm{\mu }$m, respectively) since 2013. In eastern China, there were 65 cities with air pollutant observations in 2013 during summertime, mainly concentrated in Beijing–Tianjin–Hebei (BTH), YRD, and PRD (Fig. 1). In 2015, a total of 223 cities had air pollutant observations in eastern China, providing a good opportunity to explore the O${}_{\mathrm{3}}$ pollution distributions. Therefore, in the present study, the O${}_{\mathrm{3}}$ pollution situation in 2015 is first analyzed from April to September when [O${}_{\mathrm{3}}$] are high in eastern China. A high O${}_{\mathrm{3}}$ episode that occurred in eastern China in 2015 is simulated using the WRF-CHEM model to evaluate the O${}_{\mathrm{3}}$ formation from biogenic and various anthropogenic sources. The WRF-CHEM model configuration and methodology are described in Sect. 2. Data analysis and model results are presented in Sect. 3, and conclusions and discussions are given in Sect. 4.

Model and methodology

WRF-CHEM model and configurations

In the present study, we use a specific version of the WRF-CHEM model (Grell et al., 2005) to investigate the O${}_{\mathrm{3}}$ 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).

Observed hourly mass concentrations of pollutants averaged in the afternoon from April to September 2013 and 2015 in 65 cities of eastern China.

Pollutants	CO (mg m${}^{-\mathrm{3}}$)	SO${}_{\mathrm{2}}$ ($\mathrm{\mu }$g m${}^{-\mathrm{3}})$	NO${}_{\mathrm{2}}$ ($\mathrm{\mu }$g m${}^{-\mathrm{3}})$	O${}_{\mathrm{3}}$ ($\mathrm{\mu }$g m${}^{-\mathrm{3}})$	PM${}_{2.5}$ ($\mathrm{\mu }$g m${}^{-\mathrm{3}})$
2013	1.05	24.8	27.7	100.5	46.9
2015	0.77	15.4	23.9	110.5	38.2
Change (%)	$-26.7$	$-37.8$	$-13.5$	$+9.9$	$-18.5$

A high O${}_{\mathrm{3}}$ 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 $350\times 350$ (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) ${\mathrm{1}}^{\circ }\times {\mathrm{1}}^{\circ }$ 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).

Statistical methods for comparisons

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. $$\begin{array}{cc}{\displaystyle }& {\displaystyle }\text{MB}={\displaystyle \frac{\mathrm{1}}{N}}\sum _{i=\mathrm{1}}^N\left(P_i,-,O_i\right)\\ {\displaystyle }& {\displaystyle }\text{IOA}=\mathrm{1}-{\displaystyle \frac{\sum _{i=\mathrm{1}}^N{\left(P_i,-,O_i\right)}^{\mathrm{2}}}{\sum _{i=\mathrm{1}}^N{\left(\left|P_i,-,\stackrel{\mathrm{‾}}{O}\right|,+,\left|O_i,-,\stackrel{\mathrm{‾}}{O}\right|\right)}^{\mathrm{2}}}},\end{array}$$ where $P_i$ and $O_i$ are the calculated and observed pollutant concentrations, respectively. $N$ is the total number of the predictions used for comparisons, and $\stackrel{\mathrm{‾}}{O}$ 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.

Air pollutants measurements

The hourly near-surface CO, NO${}_{\mathrm{2}}$, SO${}_{\mathrm{2}}$, and PM${}_{2.5}$ 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 ($\mathrm{\mu }$g m${}^{-\mathrm{3}}$ or mg m${}^{-\mathrm{3}})$ 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.

Results and discussions

O${}_{\mathrm{3}}$ pollution in eastern China

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 PM${}_{2.5}$ 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 [O${}_{\mathrm{3}}$] 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, SO${}_{\mathrm{2}}$, NO${}_{\mathrm{2}}$, and PM${}_{2.5}$ in eastern China, particularly with regard to SO${}_{\mathrm{2}}$, with a reduction of close to 40 % from 2013 to 2015. The [O${}_{\mathrm{3}}$] however exhibit an increasing trend, enhanced by 9.9 % from 2013 to 2015. Additionally, if the O${}_{\mathrm{3}}$ exceedance is defined as hourly [O${}_{\mathrm{3}}$] exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ (the second grade of National Ambient Air Quality Standards in China), the O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ pollution deterioration in eastern China since implementation of the APPCAP. Firstly, if the O${}_{\mathrm{3}}$ production regime in eastern China is VOC sensitive, the decrease of NO${}_x$ due to implementation of the APPCAP likely enhances the O${}_{\mathrm{3}}$ formation. Secondly, mitigation of PM${}_{2.5}$ or aerosols directly or indirectly increases the photolysis rates and expedites the O${}_{\mathrm{3}}$ formation. Thirdly, increasing transportation activities enhances the emissions of VOCs and semi-VOCs, facilitating the O${}_{\mathrm{3}}$ formation. In addition, variability of meteorological situations also leads to the [O${}_{\mathrm{3}}$] fluctuation (Calkins et al., 2016). Hence, implementation of the APPCAP does not help mitigate [O${}_{\mathrm{3}}$], and unfortunately, severe O${}_{\mathrm{3}}$ pollution has been looming in eastern China.

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

Distribution of observed maximum 1 h [O${}_{\mathrm{3}}$] in mainland China from April to September 2015.

[Figure omitted. See PDF]

Distribution of average daily maximum 1 h [O${}_{\mathrm{3}}$] in mainland China from April to September 2015.

[Figure omitted. See PDF]

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

Distribution of days with the maximum 1 h [O${}_{\mathrm{3}}$] exceeding 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in mainland China from April to September 2015.

[Figure omitted. See PDF]

Distribution of days with the maximum 1 h [O${}_{\mathrm{3}}$] exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in mainland China from April to September 2015.

[Figure omitted. See PDF]

Furthermore, in the urban PBL, high [O${}_{\mathrm{3}}$] 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 O${}_{\mathrm{3}}$ formation by washing out O${}_{\mathrm{3}}$ precursors and decreasing photolysis rates. Thus, if excluding rainy days in the analysis, the O${}_{\mathrm{3}}$ pollution becomes more severe in eastern China. For example, in Beijing, there are 54 rainy days and 65 days with the maximum 1 h [O${}_{\mathrm{3}}$] exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ from May to August in 2015. If it does not rain in Beijing, the occurrence possibility of the maximum 1 h [O${}_{\mathrm{3}}$] exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ is around 94 %, showing severe and persistent O${}_{\mathrm{3}}$ pollution.

Model performance

The hourly measurements of O${}_{\mathrm{3}}$ and NO${}_{\mathrm{2}}$ in eastern China are used to validate the WRF-CHEM model simulations. Figure 6 presents the distributions of calculated and observed near-surface [O${}_{\mathrm{3}}$] 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${}^{\circ }$ 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${}^{\circ }$ 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 [O${}_{\mathrm{3}}$] 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 [O${}_{\mathrm{3}}$]. The WRF-CHEM model well reproduces the observed low [O${}_{\mathrm{3}}$] in the south of PRDs. In NCPs and YRDs, calm winds, clear sky, and high temperature, induced by the high, facilitate the O${}_{\mathrm{3}}$ formation, and the simulated [O${}_{\mathrm{3}}$] generally exceed 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, 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 O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ pollution in eastern China. The simulated and observed O${}_{\mathrm{3}}$ pollution on 25 May is almost widespread in eastern China, and northwest China also experiences high O${}_{\mathrm{3}}$ pollution. On 26 and 27 May, the simulated and observed [O${}_{\mathrm{3}}$] in the north of NCPs and NEC are still high, but in PRDs and YRDs, the [O${}_{\mathrm{3}}$] have been significantly decreased due to the precipitation caused by the subtropical high and summer monsoon.

Pattern comparison of simulated versus observed near-surface O${}_{\mathrm{3}}$ at 15:00 BJT from 22 to 27 May 2015. Colored circles: O${}_{\mathrm{3}}$ observations; color contour: O${}_{\mathrm{3}}$ simulations; black arrows: simulated surface winds.

[Figure omitted. See PDF]

Generally, the simulated O${}_{\mathrm{3}}$ spatial patterns are consistent with observations, but the model underestimation or overestimation still exists. For example, the model remarkably overestimates the observed [O${}_{\mathrm{3}}$] on 24 May, and also cannot well reproduce the high [O${}_{\mathrm{3}}$] on 25 May in PRD. There are several reasons for the model biases in simulating [O${}_{\mathrm{3}}$] 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 [O${}_{\mathrm{3}}$] in PRD. Secondly, the 10 km horizontal resolution is used in simulations, which cannot resolve cumulus clouds well. The model overestimates the [O${}_{\mathrm{3}}$] observed in some cities with [O${}_{\mathrm{3}}$] 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 [O${}_{\mathrm{3}}$] 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 [O${}_{\mathrm{3}}$] compared to observations, e.g., peak [O${}_{\mathrm{3}}$] in the afternoon due to active photochemistry and low [O${}_{\mathrm{3}}$] during nighttime caused by the NO${}_x$ 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 [O${}_{\mathrm{3}}$] in these three provinces. The model underestimates considerably the observed [O${}_{\mathrm{3}}$] in the three provinces (Fig. 7a, c, d), with MBs exceeding 19 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. The model generally exhibits good performance in simulating [O${}_{\mathrm{3}}$] 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 [O${}_{\mathrm{3}}$] 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 [O${}_{\mathrm{3}}$] variations compared to measurements in PRDs (Fig. 7p–t) with IOAs more than 0.7, but overestimates the observed [O${}_{\mathrm{3}}$] with MBs varying from 3.8 to 16.7 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, showing model biases in modeling precipitation processes.

 

[Figure omitted. See PDF]

Comparison of measured (black dots) and predicted (blue line) diurnal profiles of near-surface O${}_{\mathrm{3}}$ averaged over all ambient monitoring stations in provinces of eastern China from 22 to 28 May 2015.

[Figure omitted. See PDF]

The comparisons of simulated versus observed distributions and temporal variations of NO${}_{\mathrm{2}}$ mass concentrations ([NO${}_{\mathrm{2}}$]) are shown in the Supplement (Figs. S3 and S4). The simulated high near-surface [NO${}_{\mathrm{2}}$] are mainly concentrated in NCP, YRD, and PRD, which is generally consistent with the measurements. The model also reasonably yields temporal variations of [NO${}_{\mathrm{2}}$] compared to measurements, but the simulations of [NO${}_{\mathrm{2}}$] are not as good as those of [O${}_{\mathrm{3}}$], 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 NO${}_x$ biases in simulations is lack of consideration of the NO${}_x$ emissions in the agricultural region, which has been proposed to generate high NO${}_x$ emissions under high-temperature conditions (Oikawa et al., 2015). In general, the calculated distributions and variations of [O${}_{\mathrm{3}}$] and [NO${}_{\mathrm{2}}$] are consistent with the corresponding observations, showing that the simulations of meteorological fields and emission inventories are reasonable, providing the base for sensitivity studies.

Sensitivity studies

O${}_{\mathrm{3}}$ formation in the PBL is a complicated nonlinear process, depending on its precursors of NO${}_x$ and VOCs from biogenic and various anthropogenic sources. It is imperative to evaluate the O${}_{\mathrm{3}}$ contribution from various sources for devising the O${}_{\mathrm{3}}$ control strategy. Rapid growth of industries, transportation, and urbanization has caused increasing emissions of NO${}_x$ 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 O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ 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).

Distributions of the contribution to near-surface [O${}_{\mathrm{3}}$] averaged in the afternoon during the whole episode from (a) industry, (b) residential, (c) transportation, and (d) biogenic emissions.

[Figure omitted. See PDF]

O${}_{\mathrm{3}}$ contributions of industry (red line), residential (brown line), transportation (blue line), and biogenic emissions (green line) in NEC, NCPs, YRDs, and PRDs, as a function of simulated [O${}_{\mathrm{3}}$] in the control case.

[Figure omitted. See PDF]

The factor separation approach (FSA) is used to evaluate the contribution of some emission source to the O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ formation in eastern China.

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

In order to further evaluate the contribution of various sources to the [O${}_{\mathrm{3}}$], the hourly near-surface [O${}_{\mathrm{3}}$] in the control simulation are first subdivided into 16 bins with the interval of 20 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. [O${}_{\mathrm{3}}$] are assembled in the control and sensitivity simulations as the bin [O${}_{\mathrm{3}}$], respectively, and an average of [O${}_{\mathrm{3}}$] in each bin are calculated. Figure 9 shows the contributions of various emission sources to [O${}_{\mathrm{3}}$] in the four sections of eastern China during the episode. The industry emission plays the most important role in the O${}_{\mathrm{3}}$ formation, and is the culprit of the high O${}_{\mathrm{3}}$ pollution. When the [O${}_{\mathrm{3}}$] in the control simulation are less than 100 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, the industry source generally decreases [O${}_{\mathrm{3}}$]. However, when the simulated [O${}_{\mathrm{3}}$] are more than around 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, the O${}_{\mathrm{3}}$ contribution from the industry emissions generally exceeds 50 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, and when the simulated [O${}_{\mathrm{3}}$] are more than 300 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, the industrial O${}_{\mathrm{3}}$ contribution can be up to 100 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, constituting one-third of the [O${}_{\mathrm{3}}$]. The O${}_{\mathrm{3}}$ contribution from the residential source is not significant (generally less than 20 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$). The transportation source plays the second most important role in the O${}_{\mathrm{3}}$ formation in NEC, NCPs, and YRDs, but its O${}_{\mathrm{3}}$ contribution is much less than that from the industry source when the simulated [O${}_{\mathrm{3}}$] are more than 150 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. VOCs from the biogenic source generally enhance the O${}_{\mathrm{3}}$ formation, providing a background O${}_{\mathrm{3}}$ source. The biogenic source contributes about 10–50 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ O${}_{\mathrm{3}}$ when simulated [O${}_{\mathrm{3}}$] are more than 150 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in NEC, NCPs, and YRDs. However, in PRDs, the biogenic emissions constitute the second most important O${}_{\mathrm{3}}$ source, with the O${}_{\mathrm{3}}$ contribution exceeding 50 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ when simulated [O${}_{\mathrm{3}}$] are more than 250 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. Apparently, controlling the industry emissions can substantially mitigate the more severe O${}_{\mathrm{3}}$ pollution in eastern China. If the industry emissions are not considered in model simulations, on average, the [O${}_{\mathrm{3}}$] are generally not more than 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in NEC, YRDs, and PRDs, but still can exceed 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. In addition, excluding the industry source in NCPs does not mitigate [O${}_{\mathrm{3}}$] as remarkably as in the other regions, indicating that other emission sources also play an important role in the O${}_{\mathrm{3}}$ formation. Although the transportation emission is the second most important O${}_{\mathrm{3}}$ source in NEC, NCPs, and PRDs, its O${}_{\mathrm{3}}$ contribution is much less than that from the industry source. Table S1 in the Supplement further presents the emission rates of major O${}_{\mathrm{3}}$ precursors from different emission sources in the model domain during the study episode. The industrial source dominates the VOC and NO${}_x$ emissions, playing a key role in the O${}_{\mathrm{3}}$ formation. The transportation source emits more NO${}_x$ and active VOCs, such as olefins and aromatics, than the residential source, contributing considerably to the O${}_{\mathrm{3}}$ formation.

O${}_{\mathrm{3}}$ contributions when only the industry (red line), residential (brown line), and transportation emissions (blue line) are considered in NEC, NCPs, YRDs, and PRDs, as a function of simulated [O${}_{\mathrm{3}}$] in the control case.

[Figure omitted. See PDF]

Distributions of the average O${}_{\mathrm{3}}$ concentration during peak time with (a) all anthropogenic emissions, (b) industry emissions alone, (c) residential emissions alone, and (d) transportation emissions alone on May 2015.

[Figure omitted. See PDF]

Another three sensitivity studies are conducted to further explore the high O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ precursors and cannot be anthropogenically controlled. Figure 10 presents the O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ pollution. The O${}_{\mathrm{3}}$ contribution of the residential and transportation sources is less than 60 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ on average, further showing the important role of the industry source in the O${}_{\mathrm{3}}$ pollution. When the industry and residential sources are not considered in the simulation, the transportation source still causes the simulated [O${}_{\mathrm{3}}$] to exceed 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, 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 O${}_{\mathrm{3}}$ 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 O${}_{\mathrm{3}}$ pollution is significantly mitigated and the simulated [O${}_{\mathrm{3}}$] are less than 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ on average. Figure 11 provides the distribution of the [O${}_{\mathrm{3}}$] averaged during the peak time on 25 May when the most serous O${}_{\mathrm{3}}$ pollution occurs during the simulated episode. When only the industry emissions are considered, the O${}_{\mathrm{3}}$ pollution is mitigated considerably in eastern China, but still widespread in NCPs and PRDs. If only considering the transportation source, the O${}_{\mathrm{3}}$ pollution still occurs in NCPs, with the [O${}_{\mathrm{3}}$] exceeding 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. When the industry and transportation sources are excluded, the O${}_{\mathrm{3}}$ pollution is generally under control. Hence, reducing the emissions from industry and transportation is key to mitigating O${}_{\mathrm{3}}$ pollution in eastern China.

Summary and conclusions

In the present study, air pollutant observations, released by China MEP, have been analyzed to explore the O${}_{\mathrm{3}}$ pollution situation in eastern China. Analyses of air pollutant observations in 66 cities from 2013 to 2015 have shown that, although implementation of the APPCAP has considerably decreased the CO, SO${}_{\mathrm{2}}$, NO${}_{\mathrm{2}}$, and PM${}_{2.5}$ mass concentrations from April to September in eastern China, the [O${}_{\mathrm{3}}$] have increased by 9.2 % and the frequency of O${}_{\mathrm{3}}$ exceedance with hourly [O${}_{\mathrm{3}}$] exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ has increased by about 25 % in the afternoon. Mitigation of NO${}_x$ and PM${}_{2.5}$ due to implementation of the APPCAP, increasing transportation activities, or variability of meteorological situations perhaps contributes to the deterioration of the O${}_{\mathrm{3}}$ pollution in eastern China.

O${}_{\mathrm{3}}$ observations from April to September in 2015 have shown that eastern China has experienced widespread and persistent O${}_{\mathrm{3}}$ pollution. Only two cities in eastern China have observed the maximum 1 h [O${}_{\mathrm{3}}$] of less than 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. Over 25 % of cities have observed the maximum 1 h [O${}_{\mathrm{3}}$] exceeding 400 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$; particularly, more than 800 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ [O${}_{\mathrm{3}}$] have been observed in six cities in eastern China. The average daily maximum 1 h [O${}_{\mathrm{3}}$] from April to September exceed 160 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in 45 % of cities in eastern China, and the 1 h [O${}_{\mathrm{3}}$] of 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ have been exceeded on over 10 % of days from April to September in 129 cities, and on 40 % of days in 10 cities.

A widespread and severe O${}_{\mathrm{3}}$ pollution episode from 22 to 28 May 2015 in eastern China has been simulated using the WRF-CHEM model. The model generally simulates reasonably well the temporal variations and spatial distributions of near-surface [O${}_{\mathrm{3}}$], but the uncertainties of meteorological fields or emission inventories still cause model overestimation or underestimation. The model performs reasonably in simulating NO${}_{\mathrm{2}}$, but the model biases are rather large during nighttime.

FSA is utilized to assess the O${}_{\mathrm{3}}$ contribution of biogenic and various anthropogenic sources. Sensitivity studies have shown that the industry source plays the most important role in the O${}_{\mathrm{3}}$ pollution formation. When the simulated [O${}_{\mathrm{3}}$] are more than around 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, the O${}_{\mathrm{3}}$ contribution from the industry emissions generally exceeds 50 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ in eastern China, particularly when the simulated [O${}_{\mathrm{3}}$] exceed 300 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, the industrial O${}_{\mathrm{3}}$ contribution constitutes one-third of the [O${}_{\mathrm{3}}$]. The transportation emission is the second most important O${}_{\mathrm{3}}$ source in NEC, YRDs, and PRDs, but its O${}_{\mathrm{3}}$ contribution is much less than that from the industry source when the simulated [O${}_{\mathrm{3}}$] exceed 150 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. The biogenic source plays a more important role in O${}_{\mathrm{3}}$ formation than the transportation source in PRDs, with the O${}_{\mathrm{3}}$ contribution exceeding 50 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ when simulated [O${}_{\mathrm{3}}$] are more than 250 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. In general, the O${}_{\mathrm{3}}$ contribution from the residential source is not significant. Further sensitivity studies have also indicated that if only considering the residential source or excluding the industry and transportation sources in simulations, the O${}_{\mathrm{3}}$ pollution in eastern China could be significantly improved. Only the industry or transportation sources still cause O${}_{\mathrm{3}}$ pollution, particularly with regard to the industry source.

Widespread and persistent O${}_{\mathrm{3}}$ pollution poses adverse impacts on ecosystems and human health. Considering the key role of the industry source in the high O${}_{\mathrm{3}}$ formation, mitigation of the industry source becomes the top choice to improve the O${}_{\mathrm{3}}$ pollution in eastern China, particularly with regard to the VOC emissions that are still not fully considered in the current air pollutant control strategy. Rapid increase of vehicles also enhances the VOC and NO${}_x$ emissions and the transportation source plays an increasingly important role in the O${}_{\mathrm{3}}$ pollution. In addition, the rapid decrease of PM${}_{2.5}$ due to implementation of the APPCAP reduces the aerosol and cloud optical depth, which is subject to enhance the O${}_{\mathrm{3}}$ formation by increasing the photolysis. Hence, stringent control strategies of VOCs and NO${}_x$ need to be designed comprehensively and implemented to avoid the looming severe O${}_{\mathrm{3}}$ pollution in eastern China.

Although the model performs generally well in simulating O${}_{\mathrm{3}}$ and NO${}_{\mathrm{2}}$ during a 7-day O${}_{\mathrm{3}}$ pollution episode in eastern China, uncertainties from meteorological field simulations and emission inventory still cause model biases. Meteorological conditions play a key role in the formation of air pollution, determining the formation, transformation, diffusion, transport, and removal of the air pollutants in the atmosphere (Bei et al., 2010, 2012). A nudging of wind and temperature fields using observations generally improves the simulation of meteorological fields, reducing the model biases in reproducing the O${}_{\mathrm{3}}$ temporal variation and spatial distribution. Thus, future studies are needed to improve the meteorological fields using the data assimilation, such as the four-dimension data assimilation (FDDA). Taking into consideration the complexity of the O${}_{\mathrm{3}}$ formation and rapid changes of emission inventories, further model studies need to be performed to investigate the O${}_{\mathrm{3}}$ formation for supporting the design and implementation of emission control strategies, based on the improved meteorological field simulations.

Data availability

The real-time O${}_{\mathrm{3}}$ and PM${}_{2.5}$ data are accessible to the public at http://106.37.208.233:20035/ (MEP China, 2013a). One can also access the historic profile of observed ambient pollutants through visiting http://www.aqistudy.cn/ (MEP China, 2013b).

The Supplement related to this article is available online at doi:10.5194/acp-17-2759-2017-supplement.

The authors declare that they have no conflict of interest.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (no. 41275153) and by the “Strategic Priority Research Program” of the Chinese Academy of Sciences, grant no. XDB05060500. Guohui Li is also supported by the “Hundred Talents Program” of the Chinese Academy of Sciences. Naifang Bei is supported by the National Natural Science Foundation of China (no. 41275101). Wenting Dai is supported by the National Natural Science Foundation of China (no. 41503117). Edited by: L. Molina Reviewed by: two anonymous referees