The effect of anthropogenic emission, meteorological factors, and carbon dioxide on the surface ozone increase in China from 2008 to 2018 during the East Asia summer monsoon season
Despite the implementation of the Clean Air Action Plan by the Chinese government in 2013, the issue of increasing surface ozone (O3) concentrations remains a significant environmental concern in China. In this study, we used an improved regional climate–chemistry–ecology model (RegCM-Chem-YIBs) to investigate the impact of anthropogenic emissions, meteorological factors, and CO2 changes on summer surface O3 levels in China from 2008 to 2018. Compared to its predecessor, the model has been enhanced concerning the photolysis of O3 and the radiative impacts of CO2 and O3. The investigations showed anthropogenic emissions were the primary contributor to the O3 increase in China, responsible for 4.08–18.51 ppb in the North China Plain. However, changed meteorological conditions played a crucial role in decreasing O3 in China and may have a more significant impact than anthropogenic emissions in some regions. Changed CO2 played a critical role in the variability of O3 through radiative forcing and isoprene emissions, particularly in southern China, inducing an increase in O3 on the southeast coast of China (0.28–0.46 ppb) and a decrease in southwest and central China (−0.51 to −0.11 ppb). Our study comprehensively analyzed O3 variation across China from various perspectives and highlighted the importance of considering CO2 variations when designing long-term O3 control policies, especially in high-vegetation-coverage areas.
O3 is a strong oxidant detrimental to human health (Lu et al., 2020; Liu et al., 2018a) and vegetation growth (Monks et al., 2015; Wang et al., 2017a). Furthermore, it is a crucial active compound influencing the earth's radiative balance, with an effective radiative forcing of up to 0.47 W m−2 in 2019 (IPCC, 2021). Tropospheric O3 has garnered significant attention over the past few decades due to its crucial role in air quality and climate change (Duan et al., 2017; Li et al., 2019; Ashmore and Bell, 1991; Lu et al., 2018).
With the rapid development in China, emissions of O3 precursors have been on the rise, leading to an annual increase in O3 concentrations since the beginning of the 20th century (Liu and Wang, 2020a; Ma et al., 2016). Surface O3 pollution has become a severe air quality concern in China (Verstraeten et al., 2016; Xu et al., 2018), particularly in major urban areas such as the North China Plain (NCP), Fenwei Plain (FWP), Yangtze River Delta (YRD), Pearl River Delta (PRD), and the Sichuan Basin (SCB) (Y. Wang et al., 2020; T. Wang et al., 2017a; Yin and Ma, 2020; Shen et al., 2019; Zhao et al., 2018; X. Wang et al., 2009). Although the Chinese government initialized the Clean Air Action Plan in 2013 to control air pollution, the concentration of O3 precursors and PM2.5 has significantly decreased (Zhai et al., 2019). However, surface O3 concentrations continue to increase in major urban areas.
Recent studies have suggested that regional meteorological conditions influence surface O3 levels through various pathways (Jacob and Winner, 2009; Shen et al., 2016; Lin et al., 2008). Modeling studies have shown that O3 levels are sensitive to temperature, humidity, wind speed, mixing height, and other meteorological conditions (Pfister et al., 2014; Sanchez-Ccoyllo et al., 2006). For instance, temperature affects the chemical formation rate of O3 (Lee et al., 2014), while precipitation reduces surface O3 concentrations through wet removal (Fang et al., 2011). Additionally, the elevated planetary boundary layer (PBL) height enhances upward movement, resulting in lower surface O3 concentrations (Haman et al., 2014). Therefore, long-term modeling of surface O3 levels must consider changes in meteorological conditions.
CO2 is the primary anthropogenic radiative force of the climate system (Gauss et al., 2003; Schimel et al., 2015). CO2 can impact regional air temperature and precipitation, leading to changes in surface O3 concentrations (Lu et al., 2013; Yang et al., 2014).
On the other hand, biogenic volatile organic compounds (BVOCs) are significant O3 precursors, and isoprene is the primary species among BVOCs that vegetation emits (Zheng et al., 2009; Fiore et al., 2011). In most of China, O3 is volatile organic compound (VOC) limited in the summer, especially in industrial cities (Li et al., 2018; Wu et al., 2018). Thus, it plays a significant role in modulating O3 levels and positively correlates with O3 concentrations in major urban areas of China. It is known that CO2 can enhance vegetation's photosynthesis (Sun et al., 2013; Heald et al., 2009; Tai et al., 2013; Monson and Fall, 1989), which may directly increase isoprene emission (Rapparini et al., 2004). Based on the observation, Rosenstiel et al. (2003) found that the isoprene emissions of plants grew by about 21 % and 41 % when CO2 reached 800 and 1200 ppm, respectively. However, Wilkinson et al. (2009) indicated that different vegetation types show varying responses in isoprene emission when CO2 increases. Isoprene emission was decreased by 30 %–40 % in Populus tremuloides Michx but increased by about 100 % in Quercus rubra when CO2 concentrations were grown (Sharkey et al., 1991). High concentrations of CO2 may inhibit the emission of isoprene by reducing the activity of BVOCs synthetase or decreasing the synthesis of adenosine triphosphate (Possell et al., 2005). Guenther et al. (1991) also indicated that isoprene emissions were significantly reduced when CO2 was increased from 100 to 600 µmol mol−1. In summary, the impact of elevated CO2 on isoprene emission may be positive or negative, mainly related to the relative size of the inhibition caused by elevated CO2 and promotion by enhanced photosynthesis.
Numerous studies have concluded that anthropogenic emissions are the primary drivers of surface O3 increases in different regions or years in China. Meanwhile, the effects of meteorological parameters can be substantial (Wang et al., 2019c; Lu et al., 2019; Dang et al., 2021; Liu and Wang, 2020a). For instance, Li et al. (2020) indicated that anthropogenic emissions were the primary cause of surface O3 increase in China from 2013 to 2019. Liu and Wang (2020a) suggested that anthropogenic emissions play a dominant role in the O3 variety in China, but the effects of meteorological conditions could be more significant in some regions. Han et al. (2020) analyzed the O3 changes in summer and suggested that meteorology can explain about 43 % of that in eastern China.
Previous studies have mainly focused on the impact of anthropogenic emissions and meteorological factors on the rise of O3 levels, with limited attention given to the role of CO2 variations. However, due to the rapid socioeconomic growth in China and the subsequent surge in energy consumption, CO2 emissions and concentrations have also increased significantly, particularly in the eastern coastal region (Lv et al., 2020; Ren et al., 2014). Furthermore, given the significant impact of CO2 on O3, it is crucial to evaluate the influence of changes in CO2 concentration on the maximum daily 8 h average (MDA8) O3 concentrations at the surface. Thus, a comprehensive assessment of the impact of anthropogenic emissions, meteorological factors, and CO2 on surface O3 is imperative.
Here, we employed an advanced regional climate–chemistry–ecology model to assess the impact of anthropogenic emissions, meteorological factors, and carbon dioxide variations during the summer monsoon period (May, June, July, and August) on surface O3 levels. Our findings can facilitate the development of a comprehensive O3 improvement strategy. Section 2 describes the methods and data, and the results and discussion are given in Sect. 3; finally, the conclusions are shown in Sect. 4.
2.1 Measurement data
We compared the simulated regional meteorological factors with the European Centre for Medium-Range Weather Forecasts Interim reanalysis data (ERA-Interim) at 37 vertical levels, which included temperature, relative humidity, and wind speed (Balsamo et al., 2015; Hoffmann et al., 2019). The observed surface O3 was taken from the China National Environmental Monitoring Center (CNEMC), which had more than 1400 environmental monitoring stations in 2018 (Wang et al., 2018; Kong et al., 2021; Zheng et al., 2014). The World Data Centre for Greenhouse Gases (WDCGG) data (Liu et al., 2009; Li et al., 2017) were applied to evaluate the simulated surface CO2 concentrations. The monitoring stations of O3 and CO2 are shown in Fig. 1.
2.2 Model description
The RegCM-Chem-YIBs is a regional climate–chemistry–ecology model developed from the RegCM model. RegCM is a regional climate model initially developed by the International Centre for Theoretical Physics (ICTP) (Giorgi et al., 2012). Shalaby et al. (2012) integrated the Chem chemistry model into the RegCM model and incorporated the condensed version of the Carbon Bond Mechanism Z (CBM-Z) to enhance the model's capabilities. To further improve the model's performance, Yin et al. (2015) added a volatility basis set (VBS) scheme to simulate secondary organic aerosols (SOAs). Xie et al. (2020) further modified the model by incorporating CO2 as a tracer, which is subject to regulation by sources, sinks, and atmospheric transport processes. The model represents the four sources and sinks of CO2 as surface fluxes, including emissions from fossil fuels and biomass burning, air–sea CO2 exchange, and terrestrial biosphere CO2 fluxes. Additionally, the model incorporated the Yale Interactive Terrestrial Biosphere (YIBs), a land carbon cycle model that enables the simulation of ecological processes, including carbon assimilation, allocation, and autotrophic and heterotrophic respiration (Yue and Unger, 2015).
The ecological model (YIBs) was fully coupled into the regional climate–chemical model (RegCM-Chem) to reproduce the interactions between atmospheric composition and the ecosystem in the actual atmosphere (Xie et al., 2019). The meteorological factors and air components simulated by RegCM-Chem were input into the YIBs model to simulate vegetation physiological processes and calculate land surface parameters such as carbon dioxide flux, BVOCs emissions, and stomatal conductance. Conversely, the simulations of the YIBs model were fed back into the RegCM-Chem model to adjust the air qualities, temperature, humidity, circulation, and other meteorological fields. The RegCM-Chem-YIBs has been extensively applied to study surface O3, PM2.5, CO2, the summer monsoon, and the interactions between air quality and the ecosystem (Zhuang et al., 2018; Pu et al., 2017; Xu et al., 2022; Xie et al., 2018; Ma et al., 2023).
The RegCM model offers a variety of physical and chemical parameterization options. Here, the climatological chemical boundary conditions were driven by the Model for Ozone and Related Chemical Tracers (MOZART). The gas-phase chemistry employed the CBM-Z scheme (Zaveri and Peters, 1999). For the boundary layer scheme, the Holtslag PBL approach was utilized (Khayatianyazdi et al., 2021). The Grell cumulus convection scheme was employed to simulate convective processes (Grell, 1993). The CCM3 radiation scheme and CLM3.5 land surface module were used in the model (Collins et al., 2006; Giorgi and Mearns, 1999; Decker and Zeng, 2009).
2.3 Model improvements
In the previous version of the RegCM-Chem-YIBs model, radiative calculations only accounted for changes in the spatiotemporal distribution of particulate matter. To simplify the radiation calculations, the atmospheric CO2 and O3 concentrations were assumed to be constant throughout the year. However, atmospheric CO2 and O3 are subject to modulation by various sources, sinks, physical processes, and chemical processes (Ballantyne et al., 2012; Wang et al., 2019a). Additionally, rapid urbanization in China has led to an annual increase in CO2 and O3 concentrations (Guan et al., 2021; Wei et al., 2022), with elevated concentrations and growth rates primarily distributed in the eastern regions where urbanization is most prominent (Shi et al., 2016; Wang et al., 2017b). To more accurately simulate the atmospheric radiation balance and East Asian monsoon climate, it is necessary to incorporate spatiotemporal variations of CO2 and O3 concentrations into the radiation module. Therefore, we included the varying CO2 and O3 concentrations simulated by the model in the radiation module to calculate the corresponding radiative forcing.
The photolysis process was simulated using the Tropospheric Ultraviolet and Visible (TUV) model, which is commonly used to compute photolysis rates in various models (Tie et al., 2003; Shetter et al., 2002; Borg et al., 2011). The TUV model employs input parameters such as zenith angle, altitude, ozone column, SO2 column, NO2 column, aerosol optical depth (AOD), single scattering albedo (SSA), and albedo, among others, to calculate photolysis rates (Singh and Singh, 2004). However, in the TUV module of the RegCM-Chem-YIBs model, AOD and SSA were held constant. This is problematic as accurate aerosol optical parameters, such as AOD and SSA, play a crucial role in the photolysis of O3 (Lefer et al., 2003). To address this issue, we incorporated temporally and spatially varying AOD and SSA simulated by the RegCM-Chem-YIBs model into the photolysis rate calculations in the TUV module. This enabled us to accurately incorporate the extinction effect of the varying particles into the photolysis reaction, leading to more realistic simulations of air components and regional meteorology.
2.4 Emissions and Experiment settings
Anthropogenic emissions from 2008 to 2018 were obtained from the Multi-resolution Emission Inventory for China (MEIC), which has been compiled and maintained by Tsinghua University since 2010 (Zheng et al., 2018; Wang et al., 2014). CO2 emissions and boundary conditions were derived from the NOAA CarbonTracker CT2019 dataset (Jacobs et al., 2021). The initial meteorological boundary data, such as temperature, relative humidity, and wind, are derived from the ERA-Interim reanalysis dataset with a horizontal resolution of 0.125∘, a temporal resolution of 6 h, and 37 vertical levels (Liu et al., 2018b). The weekly mean sea surface temperature dataset was obtained from the National Ocean and Atmosphere Administration (NOAA) (Reynolds et al., 2002).
The simulation domain was illustrated in Fig. 1, with the target region centered at 36∘ N and 107∘ E and with a grid resolution of 60 km by 60 km. The model used 18 vertical levels, ranging from the surface to 50 hPa.
In the Base experiment, we incorporated interannual variations in anthropogenic emissions, meteorological fields, and CO2 emissions. Meteorological conditions (CO2 emissions) were kept constant at 2008 levels over 10 years, referred to as the SIMMET=2008 (SIM) experiment.
The changes in O3 concentrations relative to 2008 between 2009 and 2018 were determined by comparing simulations of different years with 2008 in the Base experiment (Eq. 1). The impact of changed meteorological conditions on O3 concentrations relative to 2008 was assessed by comparing results between SIMMET=2008 and the Base experiment in the same year (Eq. 2). The contribution of changed CO2 emissions was similarly estimated (Eq. 3). Finally, the influence of anthropogenic emissions was calculated by excluding the impact of meteorological factors and CO2 from the changes in O3 concentrations (Eq. 4). Table 1 shows the results of the numerical experiments.
ΔOi represents the changes in O3 concentrations in the year i relative to 2008. Basei represents the O3 concentrations in the Base experiment in the year i. ΔMi represents the changes in O3 concentrations in the year i due to meteorological factors variations. SIM represents the O3 concentrations in the SIMMET=2008 experiment in the year i. ΔCi represents the changes in O3 concentrations in the year i due to CO2 variations. SIM represents the O3 concentrations in the SIM experiment in the year i. ΔEi represents the changes in O3 concentrations in the year i due to anthropogenic emissions variations.
In this work, both meteorological and CO2 boundary conditions were kept consistent in base and sensitivity studies. We did not consider the impact of boundary conditions on O3 due to the following reasons. First, in general, the regional model was coupled with the global model to get a more realistic influence from the boundary. However, for long-term climate–chemistry modeling, such a coupling means a large computing resource. Second, the boundary conditions were derived from global models (Liu et al., 2017; Ban et al., 2014) and have to be prescribed in numerical experiments. Finally, fixed boundary conditions were widely used in some O3 studies in China (Liu and Wang, 2020a, b; Wang et al., 2019b). Moreover, regional emissions are the primary source of surface O3 in China, with contributions accounting for 80 % from May to August (Lu et al., 2019). Therefore, the impact of fixed boundary conditions can be ignored in the current stage.
3.1 Model evaluation
The ability of RegCM to reproduce East Asian climate and air quality has been widely evaluated in recent years. Previous studies have demonstrated that RegCM was capable of the essential characteristics and interannual variations of air components and meteorological fields in East Asia (Xu et al., 2023; Ma et al., 2023; Zhuang et al., 2018). Given that the monitoring of near-surface O3 levels by CNEMC was initiated only in late 2013, the monitoring sites in 2013 and 2014 were limited, and the monitoring period was disjointed. As a result, in this study, we compared the simulated meteorological fields, O3, and CO2 levels with observations only from 2015 to 2018.
Figures S1–S4 demonstrated that the RegCM-Chem-YIBs model effectively captured the spatial distribution and magnitude of temperature, humidity, and wind over East Asia at 500, 850, and 1000 hPa between 2015 and 2018. However, due to the complex terrain's influence on the lower atmosphere, most models show better results at higher levels (Zhuang et al., 2018; Anwar et al., 2019; Xie et al., 2019). Thus, the simulations at 500 hPa were more consistent with the reanalysis data. At 1000 hPa, the simulated wind speed was slightly higher than the reanalysis data in eastern China. This difference may be due to common deficiencies in meteorological models, such as insufficient horizontal resolution, initial and boundary conditions, and physical parameterizations (Cassola and Burlando, 2012; Accadia et al., 2007), particularly in areas with low wind speeds (Carvalho et al., 2012).
Figures S5 and S6 demonstrated that the model accurately reproduced the observed increase in surface CO2 and O3 from 2015 to 2018, with high correlation coefficients ranging from 0.39 to 0.74 (Table 2). The model effectively captured the high concentrations of O3 in major urban areas such as the NCP, the YRD, the PRD, the SCB, and the FWP, while also successfully reproducing the gradient in CO2 concentrations between eastern and western China. However, the model slightly underpredicted MDA8 O3 concentrations (−4.02 to −3.21 ppb) and overestimated CO2 levels (3.32–7.07 ppm). These discrepancies are mainly attributed to uncertainties in the emissions inventory (Hong et al., 2017). Overall, the simulated meteorological factors and surface CO2 and O3 concentrations were deemed acceptable.
OBS: observation; SIM: simulation; MB: bias; RMSE: root mean square error; R: correlation coefficient. MDA8 O3: the maximum daily 8 h average O3.
3.2 Ozone variation from 2008 to 2018
Figure S7 illustrates the mean seasonal MDA8 O3 concentrations in East Asia during the summer monsoon period from 2008 to 2018. High O3 concentrations appeared in eastern China, which can be attributed to increased emissions, high temperatures, high humidities, and intense radiation in the region (Gao et al., 2020; Mousavinezhad et al., 2021; Wei et al., 2022). Surface O3 increased annually in most of China between 2008 and 2018, with megacity clusters experiencing a more significant increase.
We conducted a regional analysis of surface O3 increases in five target regions: the NCP, the YRD, the PRD, the SCB, and the FWP. In 2018, the surface MDA8 O3 concentrations averaged 74 ppb in the NCP region, while the other areas had lower concentrations (ranging from 42 to 67 ppb in the FWP, YRD, PRD, and SCB). The lower surface O3 levels in the SCB and FWP regions were attributed to lower anthropogenic emissions. The YRD and PRD regions were more affected by meteorological factors, with the East Asian summer monsoon bringing in cleaner air and precipitation from the sea, leading to lower air pollution concentrations (He et al., 2012). The spatial distribution and increasing trend of surface MDA8 O3 concentrations in China were consistent with previous studies (Li et al., 2020; Shen et al., 2022).
Figure 2 and Table 3 illustrate the changes in surface MDA8 O3 concentrations from 2009 to 2018 relative to 2008. The surface MDA8 O3 concentrations in China increased drastically over the past decade, particularly in 2017 and 2018 (6.79–32.03 ppb). We divided the period from 2009 to 2018 into two phases based on the Clean Air Action Plan implemented in 2013: the pre-governance period (PreG, 2009–2013) and the post-governance period (PostG, 2014–2018). The surface MDA8 O3 concentration increased significantly in NCP (18.42 ppb), followed by SCB (11.21 ppb), FWP (10.9 ppb), and the YRD (10.07 ppb), while it increased slightly in PRD (4.94 ppb) in PostG relative to 2008. Our results were consistent with previous studies by Lu et al. (2020), Ma et al. (2016), and Mousavinezhad et al. (2021).
3.3 The effect of meteorology in the 2008–2018 ozone increase
Overall, the meteorological variations from 2008 to 2018 were unfavorable for the O3 increase during the EASM (East Asia summer monsoon) period, as illustrated in Fig. 3.
Based on Fig. 3 and Table 4, it is evident that meteorological conditions had a significant impact on surface MDA8 O3 in the NCP and FWP regions during the PostG period (−0.09 to −0.04 ppb) compared to the PreG period (−1.41 to −0.88 ppb). In the SCB region, the impact of meteorological fields was relatively weak (−0.41–0.71 ppb), attributed to the basin topography and stable atmospheric conditions. However, in the eastern and southeastern coastal areas of China, due to the significant influence of the EASM, the impact of meteorological conditions may be more critical than that of anthropogenic emissions. For instance, in the YRD and PRD regions, meteorological conditions significantly changed O3 levels (−1.29–1.3 ppb) compared to anthropogenic emissions (0.81–0.87 ppb) in 2013, indicating the significant influence of meteorological conditions on surface O3.
Our findings are consistent with previous studies. Liu and Wang (2020a) reported a decrease in O3 in Shanghai from 2013 to 2017 due to changes in meteorological conditions. Chen et al. (2019) and Liu and Wang (2020a) also suggested that changed meteorological conditions had a negative impact on O3 formation in the NCP and FWP regions and that the influence of meteorology on surface-level O3 decreased in PostG. In addition, Cheng et al. (2019) found that the effects of meteorological conditions on long-term O3 variations were less than 3 %, which is similar to our study.
As we know, the formation of surface O3 is promoted by rising temperatures (Steiner et al., 2010). However, increased surface temperatures can also intensify turbulence within the planetary boundary layer (PBL), increasing PBL height (Guo et al., 2016). This increase in PBL height, coupled with the enhanced upward motion, can transport near-surface pollutants to the upper atmosphere, reducing their concentration in the lower atmosphere (Gao et al., 2016). Additionally, the upward motion can also facilitate cloud formation and precipitation, resulting in a reduction of near-surface atmospheric pollutants via precipitation washout (Yoo et al., 2014).
We have improved the accuracy of O3 photodissociation rate calculations by including varying AOD and SSA in the TUV module, as described in Sect. 2.3.2. As a result, the increase in cloud cover reduced the shortwave radiation flux and photochemical formation rates of near-surface O3, leading to decreased formation. Thus, the increase in near-surface temperature is often accompanied by an elevation in PBL height, enhanced cloud cover, precipitation, and reduced shortwave radiation. Moreover, higher wind speeds can enhance the dispersion of O3 (Gorai et al., 2015).
The variations of MDA8 O3, precipitation, clouds, shortwave flux (SWF), wind speed, temperature, and PBL height are presented individually in Fig. 4. The increase in SWF can accelerate O3 formation through photochemistry (Jiang et al., 2012; Lelieveld and Crutzen, 1990). Therefore, the increased cloud fraction reduced surface O3 by decreasing shortwave radiation, especially in NCP, FWP, YRD, and SCB in the PreG period (−10.63 to −1.6 W m−2). Furthermore, the enhanced precipitation in these regions (0.37–1.81 mm d−1) reduced surface O3 levels significantly. The significant increase in wind speed (0.17–0.26 m s−1) also contributed to the reduction of surface O3 in the NCP region (Table 4).
Another crucial factor is the elevated surface temperature (0–5 K), which intensified upward motion and raised the planetary boundary layer (PBL) height (0–500 m) across much of East Asia. Consequently, the increased temperature and PBL height could disperse surface-level O3, thereby reducing its concentration.
3.4 The effect of CO2 in the 2008–2018 ozone increase
The surface O3 in southern China, which includes the YRD, PRD, and SCB regions, was characterized by high precipitation, temperatures, and vegetation cover and was significantly impacted by CO2 (Fig. 5). Figure 6e and f demonstrate a marked rise in CO2 levels across East Asia, particularly in eastern China, which was attributable to extensive human activity.
CO2 is a significant driver of climate change and alterations in biogenic emissions. As shown in Fig. 6b and c, the impact of CO2 on O3 levels varies across locations, with a positive effect of 0.28–0.46 ppb along the southeastern coast of China but a negative influence of −0.51 to −0.11 ppb in southwest and central China. CO2 affects O3 concentration by influencing both precipitation and isoprene emissions. In western and central China, CO2 primarily affects O3 concentration through its impact on precipitation (Table 5). Elevated CO2 concentrations lead to increased precipitation (0.06–0.64 mm d−1) in the FWP and SCB regions, resulting in a decrease in surface O3 (up to −0.51 ppb). In eastern and southern coastal China, where vegetation is abundant, CO2 has a greater impact on isoprene emissions. In the YRD region, decreased isoprene (−0.58 to −0.32 µg m−3) and increased precipitation (0.09–0.13 mm d−1) reduced MDA8 O3 levels (0.09–0.14 ppb). In PRD, increased isoprene levels (0.31–0.92 µg m−3) and decreased precipitation (−1.02 to −0.33 mm d−1) led to the enhancement of MDA8 O3 (0.28–0.46 ppb).
In some years, the impact of changed CO2 can be as significant as or even surpass that of anthropogenic emissions and meteorology (Fig. 10). For example, in 2013, CO2 caused an increase of 0.95 ppb in MDA8 O3 in the YRD region, which exceeded that of anthropogenic emissions (0.87 ppb). Similarly, in the PRD region in 2012, the effect of CO2, anthropogenic emissions, and meteorology was 1.41, 1.77, and 1.95 ppb, respectively. Even in the NCP in 2010, the impact of CO2 (0.75 ppb) was comparable to that of anthropogenic emissions (1.5 ppb). In summary, CO2 has a significant impact on surface O3 concentrations by influencing radiation and isoprene emissions, with more prominent effects in regions with abundant vegetation.
3.5 The effect of anthropogenic emission in the 2008–2018 ozone increase
Finally, we calculated the anthropogenic emissions' effect on the 2008–2018 O3 increase. Figure S8 and Table S1 illustrate that the levels of PM2.5, PM10, SO2, CO, and OC emissions remained consistently high during the PreG period. However, a linear decrease in emissions was observed after the implementation of the Clean Air Action Plan in 2013. Prior to 2013, the emission of VOCs increased steadily but subsequently stabilized. Similarly, the emission of nitrogen oxides (NOx) exhibited an upward trend before 2013, but since then, the emissions have shown a linear decrease, with each subsequent year exhibiting lower levels of NOx emissions. In comparison to other species, the emissions of ammonia (NH3) remained relatively stable from 2008 to 2018. Our analysis results of the emissions of different species align with those of Zheng et al. (2018), who computed the changes of each species in the MEIC inventory from 2010 to 2017.
Figure 7 illustrates that anthropogenic emissions have caused a notable increase in surface O3 levels across most of China, particularly in megacity clusters. The impact of anthropogenic emissions on O3 concentration ranged from 2.33 to 18.51 ppb in the five target regions.
Figure 8 and Table 6 illustrate that the changes in surface O3 caused by anthropogenic emissions are similar in magnitude and spatial distribution to the changes in the Base experiment. This suggests that anthropogenic emissions were the dominant factor driving the increase in surface O3 in China from 2008 to 2018. Notably, a high-impact center of anthropogenic emissions was simulated in northern China, with the NCP region experiencing the most significant increase in surface O3 (4.08–18.51 ppb), followed by the FWP, YRD, and SCB regions (4.10–11.5 ppb). In the PRD region, anthropogenic emissions led to a slight enhancement of O3 by 2.33–5.74 ppb.
The role of anthropogenic emissions increased linearly from 2008 to 2018, despite the implementation of the Clean Air Action Plan in 2013 (Table 6). For example, anthropogenic emissions significantly increased surface MDA8 O3 in the NCP region (4.08 ppb in PreG and 18.51 ppb in PostG). Similarly, FWP experienced increases of 5.15 and 11.5 ppb in the PreG and PostG periods, respectively. In the SCB region, the surface MDA8 O3 was mainly affected by variations in anthropogenic emissions due to the high levels of anthropogenic emissions in the complex basin topography. In the YRD and PRD regions, anthropogenic emissions resulted in changes to O3 of 2.33–11.17 ppb.
The reasons for this characteristic are multiple. Before 2013, the continuous increase in VOCs and NOx emissions (Fig. S8b, c) facilitated the rise of O3 levels. Following the implementation of the Clean Air Action Plan in 2013, the emissions of VOCs and NOx were regulated. However, with the decrease in PM2.5 levels, direct radiation increased, and scattered radiation decreased (Fig. 9), thereby promoting the photochemical formation of O3 (Bian et al., 2007). In addition, the reduced NO emission weakened the titration effect (Fig. S8b), thus increasing surface O3 (Li et al., 2022).
Our results are consistent with previous studies by Wang et al. (2019b) and Liu and Wang (2020b), which also showed the dominant and almost linear role of anthropogenic emissions in the increase in O3 from 2013 to 2015 in four major Chinese cities (Beijing, Shanghai, Guangzhou, and Chengdu).
3.6 Attribution analysis of ozone changes in 2008–2018
Finally, we presented an attribution diagram depicting the changes in O3 concentration from 2008 to 2018. The total variation in O3 concentration can be attributed to the combined effects of meteorological changes, changes in CO2 concentration, and anthropogenic emissions (Fig. 10).
The primary driver of the O3 concentration variation from 2008 to 2018 was the changes in anthropogenic emissions, particularly in regions with high emissions, such as the NCP and FWP. Although the Clean Air Action Plan was implemented in 2013, it did not reduce the contribution of anthropogenic emissions to the O3 increase. Even in the PostG period, with the development of urbanization and industrialization, the impact of changed anthropogenic emissions on O3 has gradually become more prominent than changed meteorology and CO2. The contribution of changed meteorology to O3 was generally negative in the five regions, with a more significant impact in the YRD and PRD regions. This may be attributed to their proximity to the ocean and susceptibility to the summer monsoon influence. Changes in CO2 concentration affected O3 concentration by altering radiation and isoprene emissions, with a more significant impact in the YRD and PRD regions where vegetation was abundant. In some years, it even surpassed the effects of anthropogenic emissions. Therefore, we suggest that the influence of CO2 concentration changes on O3 concentration should be considered in regions with high vegetation coverage.
In this study. First, we improved the RegCM-Chem-YIBs model regarding the photolysis of O3 and the radiation effect of CO2 and O3. Second, we assessed the impacts of anthropogenic emissions, meteorological factors, and CO2 on the surface O3 increase in China during the EASM season from 2008 to 2018.
In the NCP and FWP regions. The increased surface O3 was primarily attributed to the changes in anthropogenic emissions (4.08–5.15 ppb in PreG and 11.5–18.51 ppb in PostG). Furthermore, the impact of anthropogenic emissions has increased linearly, despite the Clean Air Action Plan being implemented in 2013. In contrast, the effects of meteorological factors and CO2 on O3 were weak during the study period.
In the YRD and PRD regions. Ignoring the principal contributions of anthropogenic emissions, CO2 significantly impacted the O3 variations (−0.14–0.46 ppb). The varied CO2 led to surface MDA8 O3 changes of −0.09 to −0.14 ppb in the YRD and 0.28–0.46 ppb in the PRD by modulating the isoprene emissions and precipitation. On the other hand, the meteorological conditions played a more significant role in surface O3 than in the NCP, FWP, and SCB regions, resulting in a decrease in MDA8 O3 from 2008 to 2018 (−4.42–3.25 ppb).
In the SCB region. The increase in surface O3 from 2008 to 2018 was primarily driven by variations in anthropogenic emissions. The effect of meteorological conditions was weak due to the high level of emissions and basin topography. However, the changes in CO2 significantly impacted surface O3 levels and were unfavorable to O3 formation during the study period (−3.0 ppb in PreG and PostG).
In conclusion, anthropogenic emissions dominated the O3 increase in China from 2008 to 2018, and the effects of meteorological conditions on surface O3 could be more significant in some regions. Furthermore, we emphasize the significance of CO2 emissions, particularly in southern China, as a critical contributor to O3 variations. Therefore, it is vital to consider CO2 variability in future predictions of O3 concentrations. Such consideration would be helpful for designing long-term O3 control policies.
ERA-Interim data are available at https://apps.ecmwf.int/datasets/data/interim-full-daily/ (Dee et al., 2011). MEICv1.3 data are available at http://meicmodel.org/?page_id=560 (MEIC Team, 2012). CarbonTracker data are available at https://gml.noaa.gov/aftp/products/carbontracker/co2/CT2019/ (Peters et al., 2007). OISST data are available at https://downloads.psl.noaa.gov/Datasets/noaa.oisst.v2/ (Marullo et al., 2007). WDCGG CO2 data are available at https://gaw.kishou.go.jp/search/gas_species/co2/latest/ (Diao et al., 2017). CNEMC data are available at http://www.cnemc.cn/ (Zhai et al., 2019) but only in Chinese.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-6525-2023-supplement.
DM: performed experiments; TW: designed the overall research; HW, YQ, JiL, JaL, and SL: reviewed and edited the manuscript; BZ, ML, and MX: contributed to the development of the RegCM-Chem-YIBs model.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We would like to acknowledge the anthropogenic emissions inventory support from Tsinghua University and the observed data from the China National Environmental Monitoring Center. We also gratefully acknowledge a wide range of other institutional partners.
This work was supported by the National Natural Science Foundation of China (grant nos. 42077192 and 41621005), the National Key Basic Research and Development Program of China (grant nos. 2020YFA0607802 and 2019YFC0214603), the Creative talent exchange program for foreign experts in the Belt and Road countries, and the Emory University–Nanjing University Collaborative Research Grant.
This paper was edited by Frank Dentener and reviewed by two anonymous referees.
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