Impacts of meteorology and emissions on summertime surface ozone increases over central eastern China between 2003 and 2015

Recent studies have shown that surface ozone (O₃) concentrations over central eastern China (CEC) have increased significantly during the past decade. We quantified the effects of changes in meteorological conditions and O₃ precursor emissions on surface O₃ levels over CEC between July 2003 and July 2015 using the GEOS-Chem model. The simulated monthly mean maximum daily 8 h average O₃ concentration (MDA8 O₃) in July increased by approximately 13.6 %, from 65.5±7.9 ppbv (2003) to 74.4±8.7 ppbv (2015), comparable to the observed results. The change in meteorology led to an increase in MDA8 O₃ of 5.8±3.9 ppbv over the central part of CEC, in contrast to a decrease of about $-\mathrm{0.8}\pm \mathrm{3.5}$ ppbv over the eastern part of the region. In comparison, the MDA8 O₃ over the central and eastern parts of CEC increased by 3.5±1.4 and 5.6±1.8 ppbv due to the increased emissions. The increase in averaged O₃ in the CEC region resulting from the emission increase (4.0±1.9 ppbv) was higher than that caused by meteorological changes (3.1±4.9 ppbv) relative to the 2003 standard simulation, while the regions with larger O₃ increases showed a higher sensitivity to meteorological conditions than to emission changes. Sensitivity tests indicate that increased levels of anthropogenic non-methane volatile organic compounds (NMVOCs) dominate the O₃ increase over the eastern part of CEC, and anthropogenic nitrogen oxides (NO_x) mainly increase MDA8 O₃ over the central and western parts and decrease O₃ in a few urban areas in the eastern part. Budget analysis showed that net photochemical production and meteorological conditions (transport in particular) are two important factors that influence O₃ levels over the CEC. The results of this study suggest a need to further assess the effectiveness of control strategies for O₃ pollution in the context of regional meteorology and anthropogenic emission changes.

1 Introduction

Tropospheric ozone (O₃) is a major atmospheric oxidant and the primary source of hydroxyl radicals (OH), which control the atmospheric oxidizing capacity (Seinfeld and Pandis, 2016). In the troposphere, O₃ is produced by the photochemical oxidation of hydrocarbons, carbon monoxide (CO) and nitrogen oxides (NO_x) in the presence of sunlight and can be transported from the stratosphere (Crutzen, 1973; Danielsen, 1968). It is an important greenhouse gas with a positive radiative forcing of 0.4 (0.2–0.6) W m⁻² (IPCC, 2013), and it has adverse effects on human health and ecosystem productivity (Monks et al., 2015).

Surface O₃ concentrations increased globally during the 20th century. Almost all available monitoring data from 1950–1979 until 2000–2010 for the Northern Hemisphere indicate an increase of 1–5 ppbv per decade (Cooper et al., 2014; Gaudel et al., 2018; Monks et al., 2015), although the trends have varied regionally since the 1990s. The O₃ concentrations in rural and remote areas of Europe showed an increasing trend until 2000, but then tended to level off or decline (Oltmans et al., 2013; Parrish et al., 2014; Yan et al., 2018b). In the eastern US, summertime O₃ has continued declining since 1990, whereas springtime O₃ in the western US shows large interannual variability (Lin et al., 2015). At some remote sites in the western US, only small increases (0.00–0.43 ppbv yr⁻¹) have been recorded (Cooper et al., 2012). In comparison with Europe and North America, the O₃ concentrations in China have shown significant increasing trends since the 1990s (Ding et al., 2008; Ma et al., 2016, Sun et al., 2016; X. Xu et al., 2008; W. Xu et al., 2016, 2018). Ding et al. (2008) reported an increase of 3 ppbv yr⁻¹ in the afternoon boundary-layer O₃ concentrations in summer over Beijing using aircraft data obtained by the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) program during 1995–2005. The maximum daily 8 h average O₃ concentration (MDA8 O₃) at Shangdianzi (SDZ), a rural site near Beijing, showed a significant increase at a rate of about 1.1 ppbv yr⁻¹ from 2003 to 2015 (Ma et al., 2016). Sun et al. (2016) reported an increase of 1.7–2.1 ppbv yr⁻¹ at Mt. Tai during summertime from 2003 to 2015. In recent years, high O₃ concentrations have been widely observed in China, especially in central eastern China (CEC: 103 to 120^∘ E, 28 to 40^∘ N) during the summertime (Lu et al., 2018; Wang et al., 2006, 2017; Xue et al., 2014). All of these results indicate that CEC might continue to experience worsening O₃ air pollution. In this study, we quantify the effects of several factors on O₃ changes and propose some suggestions to control surface O₃ in the future.

The level of O₃ in the troposphere is mainly determined by the abundance of its precursors, including both anthropogenic and natural emissions, and the meteorological conditions (Logan, 1985). The anthropogenic NO_x emissions in China continued rising until the launch of the Twelfth Five-Year Plan (2011–2015), which enforced a series of stringent NO_x emission control measures (China State Council, 2011). However, anthropogenic emissions of non-methane volatile organic compounds (NMVOCs) continue to increase unabated (Li et al., 2017a; Zheng et al., 2018). Biomass burning also makes an important contribution to O₃ formation (Real et al., 2007; Yamaji et al., 2010), and biogenic emissions of isoprene and monoterpenes contribute to O₃ levels, which are influenced by meteorological variations (Fu and Liao, 2012). Meteorological parameters, such as wind, temperature and humidity, can influence O₃ concentrations via mechanisms related to transport, chemical production and loss, and deposition (Monks, 2000; Zhao et al., 2010). Studies in the past 2 decades have shown that O₃ and its precursors can be transported across regions and even hemispheres, as it has a lifetime of several days to weeks in the troposphere (Jacob et al., 1999; Lin et al., 2008; Verstraeten et al., 2015). For example, Ni et al. (2018) showed significant foreign contributions to springtime O₃ over China. In addition, the stratosphere–troposphere exchange (STE) is another important process affecting the tropospheric O₃ burden, especially in the midlatitudes of the Northern Hemisphere during springtime (Hess and Zbinden, 2013). However, currently there is still large variation in quantifying the contribution of each factor to the O₃ trends among different models and study regions (Zhang et al., 2014a).

Previous studies have revealed the important effects of changing emission levels and varying climate conditions on tropospheric O₃ in different regions. Lou et al. (2015) found that the effect of variations in meteorological conditions on the interannual variability in surface O₃ was larger than that of variations in anthropogenic emissions in eastern China from 2004 to 2012. Using the GEOS-Chem model, Yan et al. (2018a) found that interannual climate variability is the main driver of daytime O₃ variability in the US, although the reduction of anthropogenic emissions of NO_x increased the nighttime O₃ concentrations due to reduced O₃ titration. The effects of the East Asian summer monsoon on surface O₃ have been analyzed by observational and modeling studies (He et al., 2008; Wang et al., 2011; Zhao et al., 2010). Given the scarcity of previous research, it is necessary to further quantify the contributions of emissions and meteorological conditions to surface O₃ levels to deepen our understanding of the factors influencing O₃ changes in China.

This is a follow-up study of Sun et al. (2016), who found a significant increase in summertime O₃ at a regional site in north China from 2003 and 2015. We integrate the global GEOS-Chem model and its Asian nested model to investigate the spatial distributions of surface O₃ over the whole CEC region and to quantify the relative contributions from changes in meteorological and anthropogenic emissions between 2003 and 2015. We identify the key factors that affect O₃ changes and make a policy recommendation for O₃ control in CEC in the future. Section 2 briefly introduces the GEOS-Chem model and simulation scenarios. Comparisons of the simulated and observed O₃ concentrations are made in Sect. 3. We quantify the individual effects of meteorological conditions and emissions on O₃ changes in Sects. 4 and 5, respectively. In Sect. 6, we discuss important processes influencing O₃ changes. Section 7 concludes the paper.

2 Model and simulations

2.1 Model description

A nested model coupled with the global chemical transport model GEOS-Chem v11-01 (http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_v11-01\#v11-01_public_release, last access: 30 September 2018) is used to simulate the surface O₃ concentrations and distributions over CEC in July of 2003 and 2015. The meteorological field is taken from MERRA-2 as assimilated by the Goddard Earth Observing System (GEOS) at NASA's Global Modeling and Assimilation Office. The global model and its nested model, covering China and Southeast Asia (60 to 150^∘ E, 11^∘ S to 55^∘ N), are configured to have horizontal spatial resolutions of $\mathrm{2}{}^{\circ }\times \mathrm{2.5}{}^{\circ }$ and $\mathrm{0.5}{}^{\circ }\times \mathrm{0.625}{}^{\circ }$, respectively, by latitude and longitude, and 47-layer reduced grids in the vertical direction with 10 layers (each ∼130 m in thickness) below 850 hPa. The models are run with the full standard NO_x–O_x–hydrocarbon–aerosol tropospheric chemistry (Mao et al., 2013) for January to August of 2003 and 2015, including the spin-up time of 6 months (January to June) for each simulation, but only the results for July are discussed in this paper. The results of August 2003 and 2015 are discussed in the Supplement to confirm the result of this study. Since the crop residue burning usually lasts from late May to late June over CEC and the emissions had varied greatly over the past decade, which introduces large uncertainty in the evaluation of impacts from anthropogenic emissions (Chen et al., 2017; Wu et al., 2018), we do not focus on the O₃ change simulations in June. For comparison, we also conducted model simulations for July 2004 and July 2014, and the results supported the major findings obtained from 2003 and 2015 (see results in the Supplement). We use the Linoz stratospheric ozone chemistry mechanism for stratospheric O₃ production (McLinden et al., 2000) and the nonlocal planetary boundary layer (PBL) mixing scheme for vertical mixing of air tracers in the PBL (Holtslag and Boville, 1993; Lin and McElroy, 2010).

Global anthropogenic emissions of NO_x and CO for 2003 and 2008 are taken from EDGAR v4.2 (Emission Database for Global Atmospheric Research, http://edgar.jrc.ec.europa.eu/overview.php?v=42, last access: 5 June 2018). NMVOC emissions are taken from the RETRO (REanalysis of TROpospheric chemical composition) inventory for 2000, but the emissions of C₂H₆ and C₃H₈ follow Xiao et al. (2008). For Europe, the US, Asia, China, Canada and Mexico, the anthropogenic emissions are taken from EMEP (from 2003 to 2012; Auvray et al., 2005), NEI2011 (base year: 2011; annual scale factors: 2006–2013; ftp://aftp.fsl.noaa.gov/divisions/taq/, last access: 5 June 2018), MIX (from 2008 to 2010; Li et al., 2017b), MEIC (2008 and 2014; http://meicmodel.org, last access: 6 June 2018), CAC (NO_x and CO: from 2003 to 2008 (scaled to 2010); http://www.ec.gc.ca/pdb/cac/cac_home_e.cfm, last access: 2 October 2018), and BRAVO (1999; Kuhns et al., 2003), respectively. Over China, the CO, NO_x and NMVOC emissions from MEIC for 2008 are scaled to 2003 based on the interannual variability in Regional Emission inventory in ASia (REAS-v2; Kurokawa et al., 2013), but the anthropogenic emissions for 2014 are taken directly without being scaled to 2015. According to Zheng et al. (2018), the anthropogenic NO_x and NMVOC emissions in China decreased by about 6 % and 2 % from 2014 to 2015, respectively, so here we may slightly overestimate the NO_x and NMVOC emissions. Daily biomass burning emissions are taken from the Global Fire Emission Database v4 (GFED4) (Randerson et al., 2012). Biogenic emissions in the GEOS-Chem model are calculated online from the MEGAN v2.1 scheme (Guenther et al., 2012). Natural NO_x emissions from lightning are parameterized following Price and Rind (1992) and are further constrained by the LIS/OTD satellite data (Murray et al., 2012). We obtain the vertical profile of the lightning NO_x based on Ott et al. (2010) and calculate the soil NO_x emissions online following Hudman et al. (2012).

2.2 Model simulations

Table 1 summarizes the six model scenarios we set to identify the contributions from the changes in meteorological conditions and emissions between 2003 and 2015. We refer to the scenario using the emissions described in the previous section as the standard simulation and define the standard simulations for 2003 and 2015 as 03E03M and 15E15M (2003 emissions + 2003 meteorology and 2015 emissions + 2015 meteorology). In this case, the difference between O₃ concentrations for 03E03M and 15E15M (denoted as 15E15M − 03E03M) is due to the combined effect of changes in emissions and meteorology between 2003 and 2015. Similarly, scenarios with 2003 emissions + 2015 meteorology and 2015 emissions + 2003 meteorology are defined as 03E15M and 15E03M, respectively. The contribution of the change in meteorological conditions can thus be calculated by the difference between the simulated O₃ concentrations in the 03E15M and 03E03M scenarios (03E15M − 03E03M). Similarly, the contribution of emission changes can be calculated by 15E03M − 03E03M (or 15E15M − 03E15M). The contribution of the meteorological change based on the 2015 standard simulation is given by 15E15M − 15E03M. Since the amount of O₃ formed responds nonlinearly to the NO_x and NMVOC emissions, the sum of (03E15M − 03E03M) and (15E03M − 03E03M) does not equal (15E15M − 03E03M). However, we can still compare these two scenarios to quantify the effects of meteorology and emission changes.

Table 1 Model simulation scenarios in this study.

We then investigate the effect of anthropogenic emissions (NO_x and NMVOCs) on surface O₃ concentrations based on the 2015 simulations. We replace the anthropogenic NO_x or NMVOC emissions in the 2015 standard simulation with corresponding emissions for 2003 and keep the meteorology field, biomass burning and natural emissions (NO_x from soil and lightning, biogenic VOCs (BVOCs), etc.) unchanged (03N15M and 03V15M, respectively). The contributions of anthropogenic NO_x and NMVOC emission changes can be calculated by the differences between 15E15M (the 2015 standard simulation) and 03N15M (the 2003 NO_x emission simulation) and between 15E15M and 03V15M (the 2003 NMVOC emission simulation), respectively.

3 Simulated and observed O₃ concentrations

3.1 Model evaluation

In this section, we evaluate the model's performance by comparing the simulated surface O₃ concentrations with observations from baseline sites and the network of the Chinese National Environmental Monitoring Center (http://datacenter.mee.gov.cn/aqiweb2/getAirQualityDailyEn, last access: 2 October 2018 (in English) and http://datacenter.mee.gov.cn/websjzx/queryIndex.vm, last access: 2 October 2018 (in Chinese)).

For 2003 and 2004, only a few nonurban sites over CEC have surface O₃ measurements available. We selected six rural/baseline sites for the model evaluation: Mt. Tai (36.25^∘ N, 117.10^∘ E; 1534 m a.s.l.), Mt. Hua (34.49^∘ N, 110.09^∘ E; 2064 m a.s.l.), Mt. Huang (30.13^∘ N, 118.15^∘ E; 1840 m a.s.l.), SDZ (40.65^∘ N, 117.12^∘ E; 293 m a.s.l.), Lin'an (30.30^∘ N, 119.73^∘ E; 139 m a.s.l.), and Cape D'Aguilar (22.22^∘ N, 114.25^∘ E; 60 m a.s.l.) (see Fig. S1 for the locations of these sites). The monthly mean O₃ concentrations at these six sites were taken from the literature (Li et al., 2007; Meng et al., 2009; Wang et al., 2009; Fan et al., 2013; Sun et al., 2016). We compare the simulated surface O₃ concentrations with the 2003 observations for Mt. Tai and Cape D'Aguilar but with the 2004 observations for the other four sites (Fig. 1a). The simulated surface O₃ in 2004 was also compared against these observations in Fig. S2.

Figure 1 (a) Comparison of observed and simulated monthly mean concentrations of surface O₃ in July 2003. (Mt. Tai: July 2003; SDZ: Shangdianzi station: July 2004; Mt. Huang: July 2004; Mt. Hua: July 2004; Lin'an: July 2004; Cape D'Aguilar (or Hok Tsui in a): July 2003). (b) Correlation between observed and modeled monthly mean MDA8 O₃ in July 2015 at 115 stations in eastern China.

Figure 1a compares the observed and simulated monthly mean O₃ concentrations at the six sites. The simulated O₃ concentrations match the observations at Mt. Tai, SDZ, and Mt. Hua well, with only minor positive biases (1–4 ppbv). In contrast, the model overestimates the O₃ concentrations at Mt. Huang, Lin'an, and Cape D'Aguilar by approximately 10 ppbv. These sites in the south sector are often rainy or cloudy during summer, so the overestimation of O₃ is likely to be due to the model's underestimation of precipitation and cloud cover (Ni et al., 2018). The overestimation at the Cape D'Aguilar coastal site of Hong Kong also reflects that the model resolution is insufficient to capture the local terrains and transport pathway (Ni et al., 2018). Similar results were obtained from the comparison between observed and simulated monthly mean O₃ concentrations at the six sites in July 2004 (see Fig. S2).

For 2015, the simulated O₃ concentrations are compared with observations by the network of the Chinese National Environmental Monitoring Center over east China (Fig. 1b). To avoid the influence of local emission and photochemical and deposition processes on small scales in urban areas, we selected one nonurban site to represent the O₃ concentrations of each city over CEC. In general, the selected nonurban sites are suburban or rural sites, which are far away from the urban and industrialized areas. For cities where no nonurban sites are available, we chose the stations that are least affected by local pollution (i.e., sites relatively far away from traffic roads, factories, power plants). As a result, 115 nonurban sites were selected to represent 115 cities in east China. For MDA8 O₃, the model results are highly correlated with the observations at most sites (R²=0.79). The model only overestimates the monthly MDA8 O₃ by approximately 2.7±5.9 ppbv over CEC.

Figure 2 Monthly mean spatial distributions of surface MDA8 O₃ in July over east China. (a) 03E03M: 2003 standard simulation; (b) 15E15M: 2015 standard simulation; (c) 03E15M: 2003 emission + 2015 meteorology; (d) 15E03M: 2015 emission + 2003 meteorology. Black contours in (a) and (b) indicate the regions with MDA8 O₃>75 ppbv. Filled circles in (b) show the observed MDA8 O₃ at 115 sites of the network of the Chinese National Environmental Monitoring Center. The red rectangle represents the central eastern China region (CEC: 103–120^∘ E, 28–40^∘ N).

The model also captures the spatial distribution of MDA8 O₃ very well. It ranges from 40–60 ppbv in the south to 80–100 ppbv in the north of CEC (Fig. 2b), patterns similar to those reported by Lin et al. (2009) and Lou et al. (2015). Time series and diurnal variations in hourly O₃ concentrations from the model and observations at Mt. Tai in 2003 and nine representative sites in 2015 are compared in Figs. S3, S4 and S5. The nine observation sites are carefully selected to be far away from urban areas in the capital cities of nine provinces and municipalities, including Beijing, Tianjin, Ji'nan, Taiyuan, Zhengzhou, Wuhan, Chongqing, Changsha and Nanjing. The model reproduces the time series of O₃ with a normalized mean bias of 4 % at Mt. Tai. The overestimation of O₃ concentrations in the afternoon is likely to be due to the overestimated precursor emissions in the model. For the nine sites, the model captures most day-to-day variability and diurnal variations (Figs. S4 and S5). However, it produces larger biases during the night, mostly due to the titration of NO and a lower inversion layer (Yan et al., 2018a). We also compared the simulated diurnal variations in CO and NO₂ in the nine cities against the observational data (see Figs. S6 and S7). Overall, the model captures most diurnal variations in CO and NO₂. The underestimation of CO by the model may be due to the underestimation of emissions and/or the excessive OH (Yan et al., 2014; Young et al., 2013). The large bias in NO₂ may be due to the effect of local emissions. Another reason for the discrepancy between observed and modeled NO₂ is the overestimation by the measurements based on catalytic conversion of other oxidized nitrogen species to NO (Xu et al., 2013).

The observed yearly average MDA8 O₃ at SDZ station increased by about 10.9 ppbv from 2004 to 2014 (Ma et al., 2016), comparable to the simulated result, which showed an increase of about 9.5 ppbv from July 2003 to July 2015. In addition, the observed results of Sun et al. (2016) reported the MDA8 O₃ at Mt. Tai increased from 75.9±15.9 to 102.1±28.1 ppbv in July–August from 2003 to 2015, which is higher than the simulated result in this study (i.e., from 71.1±10.0 ppbv in July 2003 to 90.4±18.5 ppbv in July 2015). Nonetheless, the model captures the significant increase in surface O₃ levels over CEC between July 2003 and July 2015.

3.2 Spatial distribution and diurnal variation simulated in different model scenarios

Figure 2 shows the simulated spatial distribution of monthly mean surface MDA8 O₃ over eastern China (100 to 125^∘ E, 20 to 50^∘ N) for July 2003 and July 2015. The model simulates relatively high O₃ concentrations over the North China Plain and Sichuan Basin, where anthropogenic emissions of O₃ precursors are high. In July 2003, only a small area in CEC had an MDA8 O₃ exceeding the Level II National Ambient Air Quality Standard (75 ppbv) (Fig. 2a), but in July 2015 it had expanded to nearly half of this region. Table 2 shows the monthly mean MDA8 O₃ over CEC. The regional mean MDA8 O₃ increased from 65.5±7.9 ppbv in July 2003 to 74.4±8.7 ppbv in July 2015, showing an increase of about 8.9±3.9 ppbv in 12 years. According to the limited reports of observed long-term (>10 years) changes of O₃ concentrations, we find significant increases in summertime O₃ (1–3 ppbv yr⁻¹) in the north part (Beijing), east part (Mt. Tai) and south part (Lin'an) of CEC over the past 2 decades (Ding et al., 2008; Ma et al., 2016; Sun et al., 2016; Xu et al., 2008; Zhang et al., 2014b). Our results show that both daily mean O₃ concentration and MDA8 O₃ were significantly higher in July 2015 than in July 2003 over most areas of CEC (Fig. 3). The spatial distributions of MDA8 O₃ in July 2004 and 2014 in Fig. S8 present patterns similar to in July 2003 and 2015. The regional mean MDA8 O₃ increased from 67.8±6.2 ppbv in July 2004 to 74.8±9.8 ppbv in July 2014. In addition, the regional mean MDA8 O₃ increased from 63.4±4.9 ppbv in August 2003 to 73.8±5.0 ppbv in August 2015 (Fig. S9). These results are comparable to those derived from the comparison between July 2003 and July 2015. A detailed description is provided in the Supplement. As the MDA8 O₃ over southwestern China did not exceed the Level II National Ambient Air Quality Standard in July 2015, we do not focus our analysis on this area in the following sections.

Table 2 Monthly mean (standard deviation) MDA8 O₃ over CEC based on four model simulations. ΔMDA8 O₃ represents the difference in MDA8 O₃ concentrations between the 2015 standard simulation and 2003 standard simulation: ΔMDA8 O₃=MDA8 O₃ (2015) – MDA8 O₃ (2003). MDA8 O₃>75 ppbv indicates the region of MDA8 O₃ exceeding the Level II National Ambient Air Quality Standard (75 ppbv) in July 2015.

Figure 3 Differences in monthly mean surface O₃ in July of 2003 and 2015 (2015–2003) for daily mean O₃ (a) and MDA8 O₃ (b) simulated by 2003 and 2015 standard simulations.

Figure 4 Averaged diurnal variations in surface O₃ over CEC derived from four simulation results.

The diurnal variation in O₃ over CEC illustrated in Fig. 4 shows that O₃ increases by 4.9–6.7 ppbv before dawn (02:00–07:00 LT) and by 8.5–9.0 ppbv in the afternoon (13:00–18:00 LT). The much more significant increase in O₃ in the afternoon in July 2015 is likely to be due to the stronger photochemical production, which is affected by both meteorological conditions and O₃ precursor emissions. The slight increase in nighttime O₃ reflects the residual effect of the daytime increase, despite strong nighttime titration by NO. This result is very different from the trends over the US, where summertime daytime O₃ declined over the past decades is contrasted with the nighttime growth in all seasons (Yan et al., 2018a). Considering that the nighttime O₃ is easily titrated by NO and the MDA8 O₃ is a good indicator for the overall O₃ pollution condition, we focus on the MDA8 O₃ changes over CEC between July 2003 and July 2015 instead of daily mean O₃.

4 Impacts of meteorology on surface O₃

We performed sensitivity tests to investigate the effects of meteorology and emissions on the MDA8 O₃ over CEC. The contributions of meteorological change to the change in MDA8 O₃ are defined by the 03E15M − 03E03M and 15E15M − 15E03M simulations. Here we discuss only 03E15M − 03E03M in detail, as the results of 15E15M − 15E03M are similar. The spatial distributions of O₃ precursors (NO₂ and NMVOCs) for the different simulation scenarios and their differences are shown in Figs. S10 and S11, which can better explain these results. A detailed description is given in the Supplement.

Figure 5 (a) Contributions of meteorological changes to surface MDA8 O₃, comparing 03E15M and 03E03M (2003 standard) simulations. (b) Contributions of emission changes to surface MDA8 O₃, comparing 15E03M and 03E03M (2003 standard) simulations. (c) Contributions of meteorological changes to surface MDA8 O₃, comparing 15E15M (2015 standard) and 15E03M simulations. (d) Contributions of emission changes to surface MDA8 O₃, comparing 15E15M (2015 standard) and 03E15M simulations.

The regional averaged MDA8 O₃ simulated by 03E15M is 68.7±7.1 ppbv, comparable to that simulated by 15E03M (69.6±8.9 ppbv), indicating the comparable contributions made by the changes in meteorology and in emissions. Figure 5 shows the spatial distribution of MDA8 O₃ changes among different simulation scenarios. The regional mean MDA8 O₃ of CEC is approximately 5.8±3.9 ppbv (5 %–95 % interval: −0.1–12.4 ppbv) higher in scenario 03E15M than in 03E03M (Fig. 5a) over the central part of CEC (106 to 115^∘ E, 28 to 40^∘ N). Over the eastern coastal areas (115 to 120^∘ E, 28 to 40^∘ N), however, the MDA8 O₃ in the former scenario is less than in the latter by approximately $-\mathrm{0.8}\pm \mathrm{3.5}$ ppbv (5 %–95 % interval: −6.8–3.8 ppbv), indicating great spatial variation in the influence of meteorological changes.

Atmospheric circulation patterns complicate the prediction of O₃ concentrations in a specific region (He et al., 2012). The geopotential height map in Fig. S12 shows a high-pressure system over CEC at 850 hPa in July 2015. It is well known that high-O₃-pollution events preferentially occur under high-pressure conditions (Wild et al., 2004; Zhao et al., 2009; Xu et al., 2011). This is because the relatively high geopotential height induces a stable weather condition. Neither horizontal nor vertical transport is strong, which favors the accumulation of atmospheric pollutants such as surface O₃. We found that in July 2015 the wind speeds over the southern and eastern boundaries of CEC were much lower than those in July 2003 (Fig. S13), leading to much lower O₃ flux across these two boundaries. The low O₃ over southern CEC in July 2003 was mainly due to the strong southwesterly wind, decreasing O₃ levels in this area. However, a large amount of O₃ and its precursors from the central part of CEC was transported to the eastern coastal area, which increased O₃ concentrations there (refer to Table 4: about 1343 Gg month⁻¹ of O₃ transported out across the east boundary). Conversely, in July 2015, only a small amount of O₃ (refer to Table 4: −61 Gg month⁻¹) and its precursors was transported away from the ocean by the weak southeasterly winds, which only decreased the O₃ levels in the coastal area. However, in the central part of CEC, the wind was weak, leading to accumulating O₃ pollution in this area. As a result, the O₃ concentrations increased in the central part of CEC and decreased in the eastern coastal area in July 2015 compared to July 2003. More detailed and quantitative results on O₃ transport flux will be discussed in Sect. 6.

In addition to the wind, air temperature and relative humidity are two other important meteorological parameters that can affect atmospheric O₃ concentrations. High temperatures tend to accelerate the rate of ozone-related photochemical reactions, promoting O₃ production (Ramsey et al., 2014). Cloud indirectly affects O₃ pollution by blocking solar radiation, thus affecting the emission of BVOCs and the photochemical production of O₃ (Lin et al., 2009). Figure S14 shows the simulated monthly mean spatial distributions of air temperature and relative humidity in July 2003 and July 2015. The simulated air temperatures in 2003 and 2015 were 300.6±3.2 and 300.5±3.2 K, respectively, almost at the same level. The simulated relative humidity in 2003 was 82±10 %, a little higher than in 2015 (77±12 %). The average net O₃ production over CEC simulated by 03E03M (11.7 ppbv day⁻¹) is very close to that simulated by 03E15M (11.9 ppbv day⁻¹) (Table 4), suggesting that meteorological factors in 2003 and 2015 did not greatly change O₃ photochemical reactions. Therefore, neither air temperature nor relative humidity plays an important role in explaining the difference in surface O₃ between 2003 and 2015.

We summarize the regional mean O₃ over CEC and the regions with MDA8 O₃ >75 ppbv in Table 2. To avoid the influence of uneven spatial distributions of O₃ concentration changes, we performed a gradient analysis, which selected different levels for the difference of MDA8 O₃ (ΔMDA8 O₃) between 2003 standard and 2015 standard simulation (15E15M − 03E03M). The differences in MDA8 O₃ were analyzed in four ways: regional mean, ΔMDA8 O₃≥0 ppbv, ΔMDA8 O₃≥5 ppbv and ΔMDA8 O₃≥10 ppbv. For the regional mean over CEC, the increase in MDA8 O₃ driven by meteorology is approximately 3.1±4.9 ppbv, from 65.5±7.9 ppbv (03E03M) to 68.7±7.1 ppbv (03E15M). Where ΔMDA8 O₃≥10 ppbv, mostly over the central part of CEC, the MDA8 O₃ increases by 6.7±3.4 ppbv from 64.3±9.7 ppbv to 71.0±7.4 ppbv due to the meteorological change. Thus, the meteorological conditions have a greater impact on the O₃ change when the difference between 2003 and 2015 is higher than 10 ppbv. Similar results are also found in regions with MDA8 O₃>75 ppbv, where the increase in the O₃ concentration is approximately 3.6±3.2 and 5.1±2.5 ppbv for the regional mean and for the ΔMDA8 O₃≥10 case, respectively. This indicates that surface O₃ levels are more sensitive to meteorological conditions in regions with larger O₃ increase.

5 Impact of emission changes on surface O₃

As described above, the impact of emission changes on MDA8 O₃ between 2003 and 2015 can be estimated by 15E03M − 03E03M or 15E15M − 03E15M. Here we discuss 15E03M − 03E03M in detail. Similar results were found from 15E15M − 03E15M.

Figure 5b shows the contributions of emission changes to surface O₃ levels. The emission change leads to an increase in MDA8 O₃ over most areas of CEC, and it has a much smaller spatial variability than the meteorological change does (Fig. 5a). Compared to the influence of the meteorological change (03E15M − 03E03M: 3.1±4.9 ppbv), the increase in emissions leads to a higher regional mean O₃ increase (15E03M − 03E03M: 4.0±1.9 ppbv) over CEC (Table 2). The changes in NO₂ and NMVOCs also indicate the impact of emission changes is larger than that of meteorological change (Figs. S10 and S11). In contrast, for the case of ΔMDA8 O₃≥10 ppbv, the influence of emission change on O₃ (15E03M − 03E03M: 4.5±2.1 ppbv) is smaller than that of the meteorological field change (03E15M − 03E03M: 6.7±3.4 ppbv). The increases in MDA8 O₃ due to emission change are about 3.5±1.4 ppbv (5 %–95 % interval: 1.6–6.0 ppbv) and 5.6±1.8 ppbv (5 %–95 % interval: 2.2–8.4 ppbv) over the central and eastern parts of CEC, which are different from the spatial pattern caused by meteorological change. It is worth noting that in the polluted regions where MDA8 O₃>75 ppbv, the contribution of emission change increases from 5.0±1.8 ppbv for the ΔMDA8 O₃≥0 ppbv case to 5.2±1.7 ppbv for the ΔMDA8 O₃≥10 ppbv case, whilst the contribution of meteorology change increases from 3.7±3.2 to 5.0±2.5 ppbv. Even if the ΔMDA8 O₃ is greater than 10 ppbv, the O₃ increase caused by emission change is still a little higher than that caused by meteorological change, indicating the dominant effect of emissions on O₃ pollution in the highly polluted regions.

Table 3 Emissions of NO_x, CO and NMVOCs over CEC for July 2003 and July 2015, including anthropogenic emissions and biogenic emissions. Units: NO, CO and CH₂O: Gg month⁻¹; others: Gg C month⁻¹.

^a ALK4: alkanes and other nonaromatic compounds that react only with OH and have k_OH between 5×10³ and 1×10⁴ ppm⁻¹ min⁻¹. ^b PRPE: OLE1+OLE2, OLE1: alkenes (other than ethene) with k_OH<7×10⁴ ppm⁻¹ min⁻¹; OLE2: alkenes with k_OH>7×10⁴ ppm⁻¹ min⁻¹.

We summarize the emissions of NO_x, CO and NMVOCs over CEC for July 2003 and July 2015 in Table 3. The anthropogenic NO_x emissions increased from 397 Gg month⁻¹ in July 2003 to 683 Gg month⁻¹ in July 2015. The anthropogenic NMVOCs also increased significantly, with the NMVOC emissions increasing from 190 Gg C month⁻¹ in July 2003 to 365 Gg C month⁻¹ in July 2015. The spatial distributions of anthropogenic NO_x and NMVOC emissions in Figs. S15 and S16 also indicate significant increases from 2003 to 2015. Anthropogenic CO emissions increased from 4619 Gg month⁻¹ in July 2003 to 6011 Gg month⁻¹ in July 2015. The natural BVOCs, which are greatly affected by meteorological conditions, remained unchanged between 2003 and 2015. Biomass burning often occurs sequentially from south to north in CEC in the spring harvest season and lasts from late May to late June (Chen et al., 2017). In July, the biomass burning emissions generally decrease to approximately 1 % of the anthropogenic emissions (not shown). Therefore, the effect of the emission change on O₃ is primarily due to anthropogenic emissions of NO_x and NMVOCs.

Figure 6 Effects of anthropogenic NMVOCs (a) and NO_x (b) emission changes on surface MDA8 O₃ concentrations between 2003 and 2015 when other emissions and meteorological parameters are fixed at 2015 levels.

To separate the effect of anthropogenic emissions from the effect of natural emission on O₃ variability, we conducted two further simulations, 03N15M and 03V15M (see Sect. 2.2). Figure 6 shows the spatial distribution of the MDA8 O₃ differences between the 2015 standard simulation and these two simulations. Anthropogenic NMVOCs (Fig. 6a) have a great impact on MDA8 O₃ over the eastern part of CEC, increasing MDA8 O₃ by approximately 2.5±0.8 ppbv (5 %–95 % interval: 1.1–3.7 ppbv). The emissions of NMVOCs increased greatly over the eastern part of CEC (see Fig. S16). The change in MDA8 O₃ due to anthropogenic NMVOCs varies from −0.5 to 5.1 ppbv over different subregions of CEC, with a regional mean of 1.4±1.1 ppbv. The effect of anthropogenic NO_x (Fig. 6b), in comparison, is more complicated. From 2003 to 2015, MDA8 O₃ declined in some cities such as Tianjin, Ji'nan, Taiyuan and Nanjing in the eastern part of CEC, but increased in the central and western parts (regional mean: 2.8±0.9 ppbv, 5 %–95 % interval: 1.4–4.1 ppbv). The change in MDA8 O₃ due to anthropogenic NO_x varies from −3.1 to 6.7 ppbv, with a regional mean of 2.5±1.1 ppbv over CEC (5 %–95 % interval: −0.2–3.3 ppbv). The reduction of O₃ in the urban area is likely to be due to the abundant NO_x from industrial and traffic sources. Beijing shows a slight decrease in NO_x emissions, leading to a slight change in O₃ levels. In most rural areas of CEC, O₃ formation tends to be limited by the concentrations of NO_x (the so-called NO_x-limited regime). Thus, O₃ is increased significantly as we increase the anthropogenic emissions of NO_x. A VOC-limited regime in a few urban areas and a NO_x-limited or transition regime in regional rural areas of CEC have been reported in some observational and model simulation studies (Wang et al., 2017, and references therein). The change in BVOC emissions only leads to a small change in MDA8 O₃ over CEC, resulting in an increase in the O₃ level of only 0.3 ppbv (not shown), mostly due to the change in meteorological conditions. Therefore, if the meteorological conditions are fixed as the 2015 conditions, the increase in anthropogenic NMVOCs is the most important factor responsible for the O₃ increase over the eastern part of CEC, whereas NO_x emissions tend to increase MDA8 O₃ over central and western parts but decrease it in a few urban areas over eastern parts of CEC.

6 Budget analyses

Ozone concentrations are determined by chemical and dynamic processes including transport, chemical production and loss, and deposition. In this section, we discuss the effects of these processes on the surface O₃ over CEC.

Table 4 Horizontal and vertical flux (Gg month⁻¹), photochemical production and loss (Gg month⁻¹; the numbers in the parentheses are in ppbv day⁻¹), and dry deposition (Gg month⁻¹) of O₃ over CEC from the surface to 850 hPa based on four types of simulations. For horizontal flux, positive values indicate eastward or northward transport. For vertical fluxes, positive values indicate upward transport. “Total” refers to the sum of horizontal and vertical transport. Net photochemical O₃ production is the difference between production and loss of O₃.

Table 4 documents the horizontal and vertical mass fluxes of O₃ over CEC at four boundaries (north, east, south and west). The flux at each boundary was calculated from the surface to 850 hPa. In July 2003, the air flows into CEC through the south boundary, and then out across the other three boundaries. In contrast, the air masses flow into this area across the east boundary in July 2015, and then out across the left three boundaries. The larger O₃ flux from each boundary in July 2003 is due to stronger winds. Compared to the 03E03M simulation (−897 Gg month⁻¹; negative value means export of O₃ from this region), 03E15M shows a much lower O₃ flux (−401 Gg month⁻¹), indicating that weather conditions in 2015 play a more important role in pollutant accumulation, which is consistent with our analysis in Sect. 4. The larger O₃ flux in 15E03M (−1100 Gg month⁻¹) in comparison to the 03E03M simulation, however, is mostly due to the increased precursor emissions in 2015.

Table 4 also shows the chemical production and loss of O₃ over CEC from the surface to 850 hPa. The net photochemical production of O₃ in July 2015 (2158 Gg month⁻¹ or 15.5 ppbv day⁻¹) is higher than that in July 2003 (1629 Gg month⁻¹ or 11.7 ppbv day⁻¹). By comparing the 03E03M simulation with the 03E15M simulation, we find that the weather conditions in 2015 do not promote excessive net O₃ production (03E15M: 1657 Gg month⁻¹ or 11.9 ppbv day⁻¹), almost the same level as 03E03M simulation. In comparison, due to more O₃ precursor emissions in 2015, the net O₃ production by 15E03M (2166 Gg month⁻¹ or 15.6 ppbv day⁻¹) is much higher than the 03E03M simulation. The net photochemical O₃ production in this study is similar to the result of Li et al. (2007), who reported a net production of 10–32 ppbv day⁻¹ at three mountain sites over CEC in 2004. Deposition (mainly dry deposition) is another factor that affects O₃ concentrations. The 03E15M simulation shows an increase in O₃ dry deposition by only 10 Gg month⁻¹, compared to the 03E03M simulation (156 Gg month⁻¹). Thus dry deposition is less affected by changes in weather conditions.

As shown in Table 4, the O₃ budget analysis indicates CEC is a strong photochemical source region in both 2003 and 2015. The photochemically produced O₃ is mostly exported by transport and to a lesser extent removed by dry deposition. In July 2003, about half of the net photochemically formed O₃ in the CEC region was removed by transport (897 out of 1629 Gg month⁻¹). In comparison, only one-fourth of the net photochemically produced O₃ (502 out of 2158 Gg month⁻¹) was transported out of CEC in July 2015. Comparing the results of the 2003 and 2015 standard simulations (15E15M − 03E03M), we find less O₃ export from CEC in 2015 than in 2003, which means about 395 Gg month⁻¹ (2015–2003) of O₃ was accumulated in this region. In addition, net O₃ production increased by 529 Gg month⁻¹ and O₃ dry deposition only increased by 24 Gg month⁻¹ from the 2003 standard simulation to 2015 standard simulation. As a result, the increase in O₃ concentrations from July 2003 to July 2015 should be due to the enhanced photochemical production (mainly due to the increased emissions) and the weakened export (due to the meteorological conditions).

7 Conclusions

In this study, we used the global GEOS-Chem model and its Asian nested model to simulate surface O₃ over central eastern China between July 2003 and July 2015. We found that the regional averaged concentration of MDA8 O₃ increased from 65.5±7.9 ppbv in 2003 to 74.4±8.7 ppbv in 2015. The increase in the regional average MDA8 O₃ due to emission changes (4.0±1.9 ppbv) is higher than that caused by meteorological changes (3.1±4.9 ppbv) compared with the 2003 standard simulation. The effects of meteorological changes have a larger spatial variability than those of emission changes. The increase in anthropogenic NMVOC emissions increased O₃ over the eastern part of CEC, whereas the increased anthropogenic NO_x emissions dominated the increase in O₃ over the central and western parts of CEC but decreased O₃ levels in a few urban areas over eastern CEC. The O₃ formation over most areas is in a NO_x-limited or transition regime, whereas a few urban areas tend to be in a VOC-limited regime. The increase in surface O₃ is mainly via photochemical production and transport processes. The meteorological conditions (mostly due to wind patterns) in July 2015 tended to accumulate pollution and reduced O₃ export over the central part of CEC and thus enhanced O₃ levels there. Air temperature and relative humidity do not promote the O₃ production in July 2015. The increased net O₃ photochemical production is mostly due to increased precursor emissions.

Our results have implications for the formulation of effective control strategies for O₃ air pollution in CEC. Although the simulated average effect of emission changes is larger than the effect of meteorological changes, the regions with larger O₃ increases (e.g., ΔMDA8 O₃≥10 ppbv) show a higher sensitivity to meteorology than to emission changes. The results imply that assessment of the effectiveness of regional and urban O₃ control strategies needs to be placed in the context of meteorology. The O₃ transport flux analysis further suggests that large-scale regional transport is an important contributor to the surface O₃ increases from 2003 to 2015. Transport issues in local O₃ control strategies should go beyond transport from neighboring areas (e.g., cities) and account for the long-distance transport (e.g., across provinces).