Model Analyses of Changes in Spring Surface Ozone Concentrations over Shandong Province in the Period of 2014‒2017

Received: March 22, 2022
Revised: June 21, 2022
Accepted: July 29, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Li, J., Cai, J., Liu, H., Han, X., Xu, Y., Cai, X., Zhang, M. (2022). Model Analyses of Changes in Spring Surface Ozone Concentrations over Shandong Province in the Period of 2014‒2017. Aerosol Air Qual. Res. 22, 220139. https://doi.org/10.4209/aaqr.220139

HIGHLIGHTS

• O₃ in spring of Shandong Province increased.
• Differences of meteorology contributed more and more to changes in O₃ of spring.
• Changes in NO_x emissions were related to the variations in O₃ concentrations.

Keywords: Ozone, Meteorological conditions, Emissions effects, RAMS-CMAQ, Shandong

1 INTRODUCTION

Tropospheric ozone (O₃), a major oxidant, is mainly produced by the photochemical oxidation between the volatile organic compounds (VOCs) and nitrogen oxides (NO_x). If ozone concentration in the troposphere exceeds the natural level, it has adverse effects on human health (Canella et al., 2016), vegetation (Feng et al., 2015; Van Dingenen et al., 2009) and climate (Stevenson et al., 2013; Worden et al., 2008). According to the available monitoring data from 1950–1979 until 2000–2010 for the Northern Hemisphere, surface O₃ concentration has increased globally during the 20^th century with an increase of 1–5 ppbv per decade (Sun et al., 2019). Thus, attentions have been attracted to tropospheric O₃ worldwide (e.g., Marais et al., 2014; Monks et al., 2015; Zeng et al., 2018).

With the rapid growth of domestic economy and urbanization, a significant increase has appeared in O₃ concentration of China since the 1990s (Xing et al., 2011; Sun et al., 2019). Ma et al. (2016) showed that the maximum daily 8-hour average (MDA8) O₃ concentration increased by 1.1 ppbv per year from 2003 to 2015 at the rural site of Beijing called Shangdianzi. Sun et al. (2016) presented that there was an increase of 1.7–2.1 ppbv yr^–1 at Mt. Tai during summertime from 2003 to 2015. Wang et al. (2017b) reported that when compared to 2013, yearly average MDA8 O₃ concentrations increased by 12%, 25%, 34% and 22% in Beijing, Chengdu, Lanzhou, and Shanghai in 2015, respectively. Though a series of stringent emission control measurements have been taken in China (Li et al., 2019a; Zhao et al., 2013), O₃ pollution has become worse. According to the report of O₃ in 74 major cities from the China Ministry of Ecology and Environment, the annual averaged O₃ concentration increased from 139 µg m^–3 in 2013 to 166 µg m^–3 in 2018.

Previous studies have revealed the likely causes for the variations of tropospheric O₃ in different regions. Lou et al. (2015) presented that the variations of meteorological conditions played a more important role in the interannual variability in surface O₃ than the changes of anthropogenic emissions over eastern China from 2004 to 2012. Lu et al. (2019) clarified that the increase of O₃ in 2017 compared to 2016 had something to do with the hotter and dryer weather conditions over major Chinese city clusters. Wang et al. (2019a) presented that the sensitive regime of O₃ formation in eastern China changed from VOC control to the mixed control due to the significant reduction in NO_x emissions (25%) from 2012 to 2016 in eastern China, which resulted in more serious O₃ pollution. Wang et al. (2019b) has attributed the increase of O₃ to the decrease in PM_2.5, which led to more solar actinic flux. Li et al. (2019c) reported that the increase of O₃ in the North China Plain was probably affected by the slowing down of the aerosol sink of hydroperoxyl (HO₂) radicals due to the decrease of PM_2.5. Thus, O₃ formation is complex and varies regionally, which cannot be simply or uniformly attributed to one single factor. It is necessary to investigate the reasons for the variations of O₃ concentrations separately in each region of China to develop further action on O₃ control.

As presented above, high O₃ concentrations have been widely observed in eastern China in recent years (Lu et al., 2018; Wang et al., 2017a; Xue et al., 2014). Based on measurements from the Chinese National Environmental Monitoring Center (CNEMC), it was found that O₃ pollution became more serious in springtime and the peak of O₃ concentrations appeared earlier in Shandong Province. The observed monthly average O₃ concentrations increased by 22.2 µg m^–3 in May from 2014 to 2017. However, few studies have focused on this compared with that in the developed regions of eastern China, like Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta. To investigate the reasons of the increase of O₃ in springtime from 2014 to 2017, the roles of meteorology variations and emission changes on O₃ concentrations in May were identified based on ambient measurements and simulations with the RAMS-CMAQ (the Regional Atmospheric Modeling System coupled with the Community Multiscale Air Quality) modeling system in Shandong Province during the time period 2014–2017.

 
2 DATA AND METHODS

 
2.1 Observational Data

We collected the measured surface O₃, NO₂ and NO_x concentrations at 14 monitoring sites (Fig. 1) in Shandong Province from January 2014 to December 2017 from the CNEMC. The corresponding meteorological factors (including temperature [T], relative humidity [RH], wind speed [WS] and wind direction [WD]) were from the Meteorological Information Comprehensive Analysis and Process System (MICAPS). The measured data of cloud fraction (CF) was obtained from the MODerate Resolution Imaging Spectroradiometer (MODIS) through https://ladsweb.modaps.​eosdis.nasa.gov/search/ (last accessed 19 April, 2021).

Fig. 1. Model domain and geographical location of the 14 monitoring sites in Shandong Province. BZ, Binzhou; DY, Dongying; HZ, Heze; JN, Jinan; LC, Liaocheng; LW, Laiwu; LY, Linyi; QD, Qingdao; RZ, Rizhao; TA, Tai’an; WF, Weifang; WH, Weihai; ZB, Zibo; ZZ, Zaozhuang.

 
2.2 Model Description

The version of CMAQv4.7.1 coupled with the gas-phase photochemical mechanism SAPRC99 (1999 Statewide Air Pollutant Research Center) (Carter, 2000) was used to simulate and trace the evolution of the concentrations of pollutants. Different inventories were combined to make the emission sources. The anthropogenic emissions over China were from the Multi-resolution Emission Inventory for China (MEIC) of 2014 (0.25° × 0.25°) (www.meicmodel.org, last accessed 17 November 2019), while the emissions of surrounding countries were from the MIX inventory (Li et al., 2017). The biogenic emissions provided by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al., 2012) were derived from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad3.sedoo.fr, last accessed 8 October, 2021), namely MEGAN-MACC Biogenic emission inventory (0.5° × 0.5°, the year of 2010). The open biomass burning emissions of 2014 were collected from the Global Fire Emissions Database, Version 4 (Randerson et al., 2015). The monthly soil emissions of NO_x were derived from the Regional Emission inventory in ASia, Version 2.1 (0.25° × 0.25°, the year of 2008) (Kurokawa et al., 2013), while the monthly lightning NO_x emissions were obtained from the Global Emissions Inventory Activity (1° × 1°, the year of 2000) (Benkovitz et al., 1996). According to Han et al. (2004) and Gong et al. (2003), the online calculation of dust and sea salt emissions were added in the model. The output of RAMS provided the meteorological inputs to drive CMAQ. The initial and lateral boundary conditions used for the RAMS were from the National Center for Environmental Prediction reanalysis datasets.

Fig. 1 presents the two nested domains used in our simulations. The outer domain (D01), covering most of East Asia, was divided into 105 × 86 grid cells with a horizontal resolution of 64 km. The center of D01 was located at (35°N, 110°E). The nested domain (D02) was a 16 km × 16 km horizontal resolution domain that covered the North China Plain (1504 km × 1440 km) with the center located at (40°N, 116°E).

 
2.3 Sensitivity Experiments

Fig. 2 shows the observed seasonal variations of O₃ concentrations in Shandong Province from 2014 to 2017. As shown in Fig. 2, the period of maximum of O₃ concentration (90.1–91.7 µg m^–3) lasted from May to August in 2014, which was broader than that in 2017 (May and June, 112.3–117.8 µg m^–3). Unlike the continuous increase of O₃ in early summer (June) between years, there was a significant and sudden increase of O₃ in the late spring (May) of 2017. It indicated that more serious ozone pollution appeared in springtime over Shandong Province. Thus, we decided to identify the roles of meteorology variations and emission changes on O₃ concentrations in May based on ambient measurements and simulations with the RAMS-CMAQ modeling system in Shandong Province during the time period 2014–2017.

 Fig. 2. Observed seasonal variations of O₃ concentrations in Shandong Province from 2014 to 2017.

Four sensitivity experiments (Table 1) were designed to analyze the effects of emissions and meteorological conditions on pollutant concentrations over Shandong Province in May during the time period 2014–2017 with the RAMS-CMAQ modeling system.

We used the changes in the observed pollutant concentrations between different time spaces as the total changes (Eq. (1)). We kept the emissions of the CMAQ simulations unchanged between years and only changed the meteorological inputs. Thus, the changes in the predicted pollutant concentrations were due to differences in the meteorological conditions (Eq. (2)). The changes caused by different emissions were obtained by excluding the changes due to meteorological conditions from the total changes (Eq. (3)). The scheme was proved to be reasonable in Wang et al. (2019b).

where i represents the pollutant; j and k represent the year (j > k); ∆O_i,j is the total changes in the observed concentration of pollutant i from year k to j; Obs_i,j and Obs_i,k represent the observed concentration of pollutant i in year j and k, respectively; ∆M_i,j is the change in the simulated concentration of pollutant i in year j due to changes in the meteorological conditions compared to year k; Sim_i,j and Sim_i,k represent the modeled concentration of pollutant i in year j and k, respectively; and ∆E_i,j is the difference in the simulated concentration of pollutant i between the year j and k due to the changes in emissions.

The contribution of the meteorological conditions and the changes in emissions cannot be completely differentiated as a result of the complex atmospheric processes and therefore our results were only approximations. This analysis was used to give an overview of Shandong Province.

2.4 Model Evaluation

To evaluate the reliability of the modeled results, we compared the modeled and observed meteorological factors (including temperature, relative humidity, wind direction and wind speed) in May of each year and the hourly concentrations of pollutants (including O₃, NO₂ and NO_x) in May 2014 derived from 14 monitoring sites over Shandong (Tables 2 and 3).

Table 2 shows statistical results of the meteorological parameters, where: N presents the total number of samples; C_sim and C_obs are the averaged results of simulations and observations, respectively; MB is the mean bias; RMSE and GE are the root-mean-square and gross error, respectively; R is the correlation coefficient between the measured and simulated values; P_22.5° and P_45° represent the percentages of compared points at which the absolute biases between the modeled and measured wind directions are within 22.5° and 45°, respectively.

As shown in Table 2, the model reproduced the variation and magnitude trend of the temperature and relative humidity quite well according to the statistical results. For T, though the absolute GE values were a little higher than the benchmark (2.0), the absolute MB values were within the benchmark (0.5) suggested by Emery et al. (2001) and the values of R were no less than 0.8. Besides, the RMSE of T were comparable to that in Gao et al. (2016), also suggesting a reasonable simulation of T. While for RH, MB and R values were no more than 2.5% and no less than 0.7, respectively. Compared to Wang et al. (2019b), RMSE and GE values for RH in this study were even smaller. All these indicated the good reproduction of the relative humidity by the model. For WS, both the values of GE and MB met the benchmarks (GE ≤ 2.0 and |MB| ≤ 0.5), suggesting a better simulation than Feng et al. (2016). The values of RMSE for WS were also within the N is the total number of samples; C_sim and C_obs are the average values of modeled and observed results, respectively; MB is the mean bias; GE is the gross error; RMSE is the root-mean-square error; R is the correlation coefficient between the observed and simulated results; P_22.5° and P_45° represent the proportions of compared results that the absolute biases between the simulated and measured wind directions are within 22.5° and 45°, respectively. benchmark (≤ 2.0) for a good performance. For WD, P_22.5° and P_45° were larger than 40% and 60%, which was comparable to Li et al. (2019b), indicating the Beasonable simulation of wind directions. Thus, the model was performed well to provide a reasonable meteorological field.

The evaluated results for the modeled O₃, NO₂ and NO_x concentrations are shown in Table 3. MB values of O₃, NO₂ and NO_x in May of 2014 were small (5.9, –1.8 and –4.5 µg m^–3) with R values equaling 0.5, 0.6 and 0.6, respectively. Besides, the normalized mean bias (NMB) and the normalized mean error (NME) values of O₃ were comparable to those reported by Wang et al. (2019a), while NMB and NME values of NO₂ were smaller than the results of Wang et al. (2019a). Thus, the simulated pollutant concentrations were reliable.

 
3 RESULTS AND DISCUSSION

According to previous studies (e.g., Atkinson, 2000; Meleux et al., 2007), temperature, relative humidity, wind speeds and cloud fraction played important roles in O₃ formation. Thus, we first examined the relationships between the observed 90th percentile of hourly O₃ concentration (O₃-h_90) and the measured temperature, relative humidity, wind field and cloud fraction in May at the 14 monitoring sites during the time period 2014–2017. Fig. 3 shows the variations of observed O₃-h_90, temperature, relative humidity and CF, while Fig. 4 shows the statistical results for the observed wind direction and wind speed.

 Fig. 3. Observed (a) 90^th percentile of hourly O₃ concentrations (O₃-h_90), the monthly mean (b) temperature (T) and relative humidity (RH) and (c) the cloud fraction (CF) in May at the 14 observation sites in Shandong Province during the time period 2014–2017. The different color columns in (a) represent different years, which is also used to indicate that in (b) and (c).

 Fig. 4. Statistical results for wind direction and wind speed measured in May at the 14 observation sites in Shandong Province during the time period 2014–2017.

Fig. 3(a) shows that O₃-h_90 decreased at most of the sites (except Binzhou, Liaocheng, Tai′an, Heze and Linyi) in May 2015 compared with 2014. Fig. 3(b) shows that the temperature decreased and the relative humidity either increased or remained stable in May 2015 compared with 2014. According to the previous studies (e.g., Atkinson, 2000; Hu et al., 2008; Zhang et al., 2015), lower T and higher RH were unfavorable for ozone production. The rate of ozone formation was repressed in May 2015 compared with 2014 due to the increase in CF, which can reduce the intensity of surface illumination (Fig. 3(c)). We therefore concluded that the temperature, relative humidity and CF in May 2015 reduced the formation of ozone compared with 2014 over Shandong.

O₃-h_90 in May 2016 decreased at most sites compared with 2015 (except Dongying, Laiwu, Tai’an, Zibo, Qingdao and Weihai) (Fig. 3(a)). A significant increase in the CF (Fig. 3(c)) led to a decrease in O₃ at all sites in May 2016 compared with 2015. Different from the CF, the effects from the changes in temperature and relative humidity were not consistent at all sites. However, it was easy to find that both the two factors had positive effects on the formation of O₃ at most sites in May 2016 compared with 2015 (Fig. 3(b)) due to the decrease of relative humidity and slight increase of temperature.

O₃-h_90 in May 2017 (Fig. 3(a)) significantly increased at most sites compared with 2016 (except Weihai and Zibo). Cloud fraction can affect the surface temperature and photochemical production of surface ozone due to its impacts on the amount of insolation (Meleux et al., 2007; Lee et al., 2015); thus the decreased cloud fraction (Fig. 3(c)) at most sites (except Weihai) contributed to the increase of O₃ production. Besides, the decrease in the relative humidity (Fig. 3(b)) at most sites in May 2017 compared with 2016 was also conducive to the formation of O₃.

As shown in Fig. 4, the observed distribution of winds in May was similar between the years with the southerly winds as the main winds. The continuous reduction in the mean wind speed (i.e., 2.7, 2.4, 2.3, and 2.3 m s^–1) of May from 2014 to 2017 can result in the less air mass of O₃ taken away from Shandong Province. Thus, the changes in wind field were conducive to the accumulation of O₃ in May during the time period 2014–2017.

In conclusion, the variations of meteorological parameters had different effects on the formation of O₃ in May between years. It was easy to identify the individual effect of the changes in each meteorological factor on O₃ production at each site, but the combined effects from all the meteorological conditions could not be determined. It was necessary to use the modeling system to evaluate the integrated effects.

Thus, secondly, as described in Section 2.3, four sensitivity experiments (Table 1) were performed with RAMS-CMAQ modeling system to analyze the effects of emissions and meteorological conditions on pollutant concentrations over Shandong Province in May during the time period 2014–2017.

Fig. 5 shows the changes in the 90^th percentile of the hourly O₃ concentrations (∆O_ozone) in May between each year and the year before resulting from changes in meteorological conditions (∆M_ozone) and emissions (∆E_ozone) during the time period 2014–2017.

Fig. 5. Changes of 90th percentile of hourly O₃ concentrations (∆O_ozone) due to changes in meteorological conditions (∆M_ozone) and emissions (∆E_ozone) in (a) May 2015 compared with May 2014, (b) May 2016 compared with May 2015, and (c) May 2017 compared with May 2016.

As shown in Fig. 5(a), the variations of O₃ in May due to changes in meteorological conditions and emissions between 2015 and 2014 were –35.9–35.2 µg m^–3 and –43.4–92.7 µg m^–3, respectively. The changes in meteorological conditions in May 2015 could have caused the increase in O₃ (∆M_ozone was positive) at most sites (11.8–35.2 µg m^–3) (except Qingdao, Weihai and Rizhao). Our analyses of the results from Figs. 3 and 4 suggested that, except the effects of wind field, the changes of temperature, relative humidity and cloud fraction had negative effects on O₃ formation in May 2015 compared to 2014. Therefore, it can be concluded that the changes of wind field have dominated the effects of changes in meteorological conditions in May 2015. Furthermore, the wind field transport had the greatest effects on ∆M_ozone (positive) in northwestern, central and most parts of southern Shandong Province, whereas the meteorological factors affecting the photochemical generation of O₃ (e.g., temperature and CF) had the greatest effect on ∆M_ozone (negative) over Byland and a small part of southern Shandong Province in May 2015.

Combined with the results for ∆O_ozone, it showed that changes in emissions dominated the variations in total O₃ concentrations in May 2015 over most regions of Shandong. For example, ∆O_ozone was positive and ∆M_ozone was negative at Dongying and therefore the final changes in the concentration of O₃ in May 2015 were dominated by the effects of emissions. As another example, both ∆O_ozone and ∆M_ozone were positive at Liaocheng, but ∆M_ozone contributed < 30% of ∆O_ozone. Thus, the dominant effects were still from the changes in emissions. There were 10 such sites and most of the sites were in northwestern and central Shandong. While O₃ variations at other four sites (28.6%) were dominated by the changes of meteorological conditions.

In Fig. 5(b), ∆M_ozone and ∆E_ozone in May between 2016 and 2015 varied from –34.1 to 43.2 µg m^–3 and from –47.6 to 123.3 µg m^–3, respectively. By contrast with 2015, the variations in meteorological conditions in May 2016 resulted in a decrease in O₃ (–34.1 to –4.6 µg m^–3) at most sites (except Qingdao, Weihai and Rizhao). Similarly, according to the analyzed results from Figs. 3 and 4, except the cloud fraction, the changes of temperature, relative humidity and wind field were conducive to the increase of O₃ at most sites over Shandong. We therefore concluded that the cloud fraction played a dominant role in the effects of changes in meteorological conditions on O₃ concentration through its impacts on the photochemical generation of O₃, especially the regions over northwestern, central and most parts of southern Shandong. Combined with the results for ∆O_ozone, this showed that there were eight sites (57.1%) at which the variations in the concentration of O₃ were dominated by the changes in emissions. Compared with May 2015, the effects of meteorological conditions on the concentration of O₃ increased over Shandong Province in 2016 (6 sites, 42.9%).

Fig. 5(c) showed that ∆M_ozone and ∆E_ozone in May between 2017 and 2016 varied from –20.4 to 43.1 µg m^–3 and from –35.7 to 28.6 µg m^–3, respectively. The differences in meteorological conditions in May 2017 compared with May 2016 resulted in an increase in O₃ at most sites (7.6–43.1 µg m^–3) (except Weihai and Rizhao). Combined with the results for ∆O_ozone, it was clear that the differences of meteorological conditions contributed more to the changes of O₃ concentration than that of emissions at most sites (11 sites, 78.6%) in May 2017. ∆M_ozone was positive at 10 of the 11 sites. According to the analyzed results from Figs. 3 and 4, except the relative humidity, the changes of temperature and cloud fraction had positive effects on O₃ formations at nearly all the sites. Since there were few differences in the effects of wind field transport in May between 2016 and 2017, it can be concluded that the meteorological parameters affecting the photochemical generation of O₃ dominated the increases in O₃ concentrations over most regions of Shandong Province in May 2017.

The effects of changes in meteorological conditions have become more and more important in the variation of O₃ concentrations from May 2014 to May 2017, especially the factors that affected the photochemical generation of O₃.

NO_x is one of the photochemical precursors of O₃. Previous studies (e.g., Wang et al., 2019a) presented that changes in the formation of O₃ were closely related to variations in NO_x emissions over eastern China, thus we further investigated the effects of meteorological conditions and emissions on NO_x concentrations, respectively.

Fig. 6 shows the changes in the monthly mean NO_x concentrations (∆O_NOx) in May between two adjacent years resulting from changes in the meteorological conditions (∆M_NOx) and emissions (∆E_NOx) during the time period 2014–2017.

Fig. 6. Changes in the monthly mean NO_x concentrations (∆O_NOx) in (a) May 2015 compared with May 2014, (b) May 2016 compared with May 2015, and (c) May 2017 compared with May 2016 as a result of changes in the meteorological conditions (∆M_NOx) and emissions (∆E_NOx).

As shown in Fig. 6(a), the concentration of NO_x decreased (–33.9 to –0.7 µg m^–3) at most sites in May 2015 compared with May 2014, which was dominated by the effects of changes in NO_x emissions over most regions of Shandong (|∆E_NOx| > |∆M_NOx| at 10 sites). Combined with the analysis from Fig. 5(a), ∆E_ozone dominated the changes in total O₃ concentrations in May 2015. There were seven sites where both ∆E_ozone and ∆E_NOx contributed more to corresponding changes in the concentration of pollutants (e.g., Dongying, Tai’an and Qingdao). By comparing ∆E_ozone and ∆E_NOx at the seven sites, the variation tendency was same at five sites between ∆E_ozone and ∆E_NOx, suggesting that the variations in O₃ were closely related to the changes in NO_x emissions in May 2015 compared to 2014.

As presented in Fig. 6(b), the changes in NO_x emissions still dominated the variations in NO_x concentrations at most sites (11 sites) in May 2016 compared to 2015. Combined with the results shown in Fig. 5(b), ∆E_ozone also dominated the variations in O₃ formation in May 2016 compared with 2015. There were seven sites where both ∆E_ozone and ∆E_NOx contributed more to corresponding changes in the concentration of pollutants (e.g., Dongying, Zibo and Rizhao). By comparing ∆E_ozone and ∆E_NOx at the seven sites, the variation tendency was same at six sites between ∆E_ozone and ∆E_NOx, suggesting that the variations in O₃ were still closely related to the changes in NO_x emissions in May 2016 compared to 2015.

In Fig. 6(c), NO_x concentrations decreased at most of the sites (except Heze) in May 2017 compared with 2016, which was still dominated by the changes in NO_x emissions. However, combined with the results shown in Fig. 5(c), the variations in meteorological conditions that affected the photochemical generation of O₃ dominated the changes of O₃ concentrations. Therefore, the variations in NO_x emissions may have relatively small effects on the changes in O₃ concentrations.

In conclusion, the changes in NO_x emissions had a close relationship with the variations in O₃ concentrations when the changes in emissions dominated the variations of O₃ concentrations in Shandong.

 
4 CONCLUSIONS

The changes of surface O₃ concentrations due to meteorological conditions and emissions in the late springtime (May) of Shandong during the time period 2014–2017 were discussed based on ambient measurements and simulations with the RAMS-CMAQ modeling system.

Based on the measurements, an increase was observed in O₃ pollution over Shandong during the time period 2014–2017. The peak period of O₃ pollution gradually changed from a broad (May–August) in 2014 to a narrow (May and June) range in 2017, while the observed monthly mean O₃ concentrations during the peak period changed from 90.1–91.7 µg m^–3 in 2014 to 112.3–117.8 µg m^–3 in 2017. There was a significant and unexpected increase of O₃ in May 2017 compared to 2016, indicated that more serious ozone pollution appeared in the late springtime over Shandong Province. Further to analyze the effects of each meteorological factor on O₃ concentrations in May between years, it was found that the changes in wind field were conducive to the accumulation of O₃, while the effects of other meteorological factors on O₃ concentrations were different at the same site between years.

In order to identify the effects of the changes in O₃ concentrations due to meteorological conditions and emissions in May during the time period 2014–2017, four sensitivity simulations were designed and performed with RAMS-CMAQ. The simulation showed that the effects of changes in meteorological conditions became more and more important in the variations of O₃ concentrations in May during the time period 2014–2017, especially the factors that affected the photochemical generation of O₃. For instance, the percentage of the sites where O₃ variations were dominated by the changes of the meteorological conditions increased from 28.6% to 78.6% over the region in May between years. The effects of meteorological parameters on O₃ concentrations were different and were positive (11.8–35.2 µg m^–3), negative (–34.1 to –4.6 µg m^–3) and positive (7.6–43.1 µg m^–3) at most sites in May between years. By analyzing the effects of the changes in NO_x concentrations due to meteorological conditions and emissions, it was found that the changes in NO_x emissions had a close relationship with the variations in O₃ concentrations when the changes of O₃ were dominated by the emission variations in Shandong.

 
ACKNOWLEDGMENTS

This study was founded by the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (Grant XDA19040204), the National Natural Science Foundation of China (41830109), the China Postdoctoral Science Foundation and the Key Research and Development Plan of Ningxia Hui Autonomous Region (Grants 2019BFG02025).