key: cord-0951736-3nh5j7bk
authors: Ding, Su; He, Jianhua; Liu, Dianfeng
title: Investigating the biophysical and socioeconomic determinants of China tropospheric O(3) pollution based on a multilevel analysis approach
date: 2021-01-07
journal: Environ Geochem Health
DOI: 10.1007/s10653-020-00797-8
sha: d83e640592216a5799303c40623c8fbee6949a78
doc_id: 951736
cord_uid: 3nh5j7bk

Severe tropospheric O(3) pollution has swept across China in recent years. Consequently, investigation of tropospheric O(3) concentration influencing mechanism is of significance for O(3) pollution control in China. Previous studies have rarely detected combined impacts of natural factors and anthropogenic activities behind tropospheric O(3) concentration in China at a national scale. Moreover, there is significant spatiotemporal heterogeneity of O(3) pollution distribution in China due to the temporal and regional differences of socioeconomic and natural environmental condition in the vast territory. The targeted O(3) control recommendations for different regions and seasons should be put forward in terms of the spatiotemporal heterogeneity of O(3) concentration determinants. In this context, a three-level regression model integrating multi-scale biophysical and socioeconomic variables was proposed to explore the determinants of O(3) pollution in China. The results showed that the tropospheric O(3) concentration in the eastern and southeastern regions of China was strongly affected by meteorological conditions. In contrast, tropospheric O(3) pollution concentrated in inland areas mainly depended on the emission intensity from anthropogenic sources. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10653-020-00797-8.

Tropospheric O 3 is one of the main air pollutants produced by photochemistry reaction under appropriate meteorology condition. With rapid industrial and economy development, the sharp increase in O 3 precursors emissions results in severe tropospheric O 3 episodes posing huge threat to human health and ecosystem productivity . Thousands of premature deaths are caused by high tropospheric O 3 exposure around the world each year (Cohen et al. 2017; Lelieveld et al. 2015) . Furthermore, tropospheric O 3 is also phytotoxic for vegetation (Tai et al. 2014) . Consequently, investigating the influencing mechanism of tropospheric O 3 concentration is urgent for O 3 episodes control. As a typical study area, China has experienced rapid industrialization and urbanization during the past 40 years of the reform and openup policy. As an expense, tropospheric O 3 episodes have frequently emerged in the most regions of China in recent years, which have received much attention by scholars. The investigation aiming at exploring the influencing mechanism behind O 3 episodes at China national scale is essential. Notably, the regional difference of socioeconomic development is extremely prominent in China. The environmental pressure brought from socioeconomic activities varies from regions. The spatiotemporal variation in biophysical conditions also exerts different impacts on O 3 formation and diffusion. As a consequence, pollution control measures should be available for different regions and seasons across China.

In order to understand O 3 formation and influencing mechanism, scholars have conducted a large amount of research. Much research focuses on the complex reaction mechanisms among NOx, VOCs and O 3 . Chen et al. (2019) investigates O 3 -NOx-VOC sensitivity for identifying the VOCs and NOx reduction ratio to control tropospheric O 3 pollution in Foshan City. Yang et al. (2019) analyzes the variation in the concentration of O 3 , VOCs and NOx in Wuhan City.

The exploration of VOCs impacts on photochemistry reaction, and main contributors to O 3 formation in Wuhan are also conducted. Furthermore, some scientific publications focus on O 3 sources apportionment. Li et al. (2019b) quantifies O 3 source apportionment by source regions and sectors in Yangtze River Delta (YRD) region. Shao et al. (2016) draws the conclusion that industry-related sources occupy the highest proportion of VOCs emission in YRD. Vehicular emission also contributes much to VOCs emission. Vegetation emission and liquefied petroleum gas/natural gas usage are also important VOCs sources in YRD. On the other hand, the research about O 3 concentration determinants has been carried out. Meteorological factors including temperature, relative humidity (RH), precipitation and planet boundary layer height (PBLH) are documented that they influence pollutant photochemistry and transmission directly or indirectly by many scholars (Gong et al. 2018; Hu et al. 2018; Sun et al. 2019) . As the main atmospheric oxidant, the correlation between tropospheric O 3 and secondary aerosols has gained much attention as well (Li et al. 2019a; Zhang et al. 2018) . The impacts of socioeconomic activities on tropospheric O 3 concentration also have been investigated (Huang et al. 2013; Li et al. 2019b) .

As the literature review above shows, tropospheric O 3 pollution is a complex chemical process referring to anthropogenic and physical factors. As a consequence, the formulation of O 3 control measures should take various driving forces into consideration. Previous research focuses on O 3 formation and influencing mechanism in single scale of city or urban agglomeration to explore the biophysical and socioeconomic impacts separately, but seldom investigates the combined influences at national scale. Meanwhile, the single-level regression model is replaced with multilevel regression model in this study, since biophysical and socioeconomic influences are of significant scale effects. Meteorological conditions have monthly differences in the effects on O 3 pollution. For example, the variation in RH is abrupt in North China, with peak values of approximately 66% in July and the trough values of 45% during April (Si et al. 2018 ). In addition, Sun et al. (2019) uses sensitivity tests to demonstrate that meteorological conditions variations lead to significant changes in tropospheric O 3 . Likewise, the influence of vegetation coverage on air quality mainly relies on the vegetation biomass which has seasonal variation (Yang et al. 2015) . The spatial heterogeneity of O 3 precursors emission results from the regional differences of anthropogenic activities (Li et al. 2019a) . Besides, during the COVID-19 pandemic, industrial and vehicle emissions sharply decreased. However, severe air pollution episodes still emerged in most cities of China. It indicates that the short-term changes of socioeconomic activities have no significant influences on atmospheric environment. On the other hand, socioeconomic indicators always reflect annual activities, and the monthly and seasonal variability of biophysical influences was smoothed by statistical models integrating socioeconomic factors at annual level (Hao and Liu 2016; Liu et al. 2017; Zhan et al. 2018) . Consequently, separately explaining the monthly, seasonal and regional variations in O 3 will help distinguish the spatiotemporal heterogeneity of determinants. In other words, the multilevel regression model can be used to explore the leading driving forces of O 3 concentration variation for every season and region.

The aim of this research is to explore the comprehensive and spatiotemporal heterogeneity determinants of tropospheric O 3 pollution in China. A threelevel regression model was constructed to explore the spatiotemporal characteristics of O 3 pollution determinants, which will be helpful for developing differential pollution control measures in different regions. In the first level, monthly diurnal meteorological factors acted as the explanatory variables of monthly tropospheric O 3 pollution variation. The second level was used to explain the seasonal variation in O 3 concentration using seasonal average vegetation coverage. In the third level, socioeconomic annual impacts on O 3 concentration were uncovered in different regions. Finally, the targeted recommendations for O 3 pollution reduction in different regions and seasons could be put forwarded based on the outcomes of multilevel regression model.

The remainder of this paper is organized as follows. ''Materials and methodology'' section illustrates research data and the description of multilevel regression models. The outcomes of multilevel regression models and discussion are presented in ''Results and discussion'' and ''Limitations and future work'' sections. The final section provides the conclusions based on our results.

The data used in this research are shown in Table 1 . Data from 353 cities which cover the seven regions suggested by Fang et al. (2017) in China were collected. The data describe the conditions of atmospheric environment and socioeconomic activities in China during 2015. Three pollutants concentration monitoring data, seven meteorology factors and ten socioeconomic data were collected. Specifically, the real-time monitoring data of O 3 , PM 2.5 and NO 2 were derived from 1490 national atmospheric monitoring sites (downloaded from China National Environmental Monitoring Center http://www.cnemc.cn) ( Fig. 1) and we calculated the hourly arithmetic average of O 3 monitoring concentration in each month. Meanwhile, meteorology condition was described by MEERA-2 data. MERRA-2 reanalysis meteorological data (GMAO 2015a, b, c) were resampled into a grid at a spatial resolution of 0.1°using a bilinear algorithm further. Vegetation coverage is estimated by city area and FVC (fractional vegetation coverage) data . The seasonal vegetation coverage in each city represents the whole vegetation coverage within prefecture-level city administrative boundaries. Socioeconomic data were extracted from the China city statistical yearbook (NBSC 2016a) reflecting socioeconomic activities in prefecture-level cities in China during 2015. Finally, we extracted the meteorological factors, vegetation coverage and socioeconomic indicators at air pollution monitoring stations. Real-time monitoring data and driving forces data were spatially and temporally matched to build multilevel models. Particularly, in this research, spring consists of March, April and May; summer spans from June to August; autumn falls in the months of September, October and November; and winter starts in December and ends in February.

Multilevel regression model design and specification Figure 2 shows the processing of O 3 pollution influencing mechanism investigation. Before the modeling, several tests were employed to avoid spurious regression and multi-collinearity issues. The Kolmogorov-Smirnov normal distribution test was used to examine the probability density distribution of O 3 concentration. This result led to the conclusion that O 3 concentration obeyed the positive skew distribution with a peak on the left of the median. Thus, logarithmic transformation was adopted to covert O 3 concentration data into a normal distribution. Unit root and co-integration tests were performed to check the stationarity of independent variables. The results showed that all independent variables were stable. Besides, with the application of VIF (variance inflation factor) calculation, the variables whose VIF [ 10 were removed. We finally selected eight biophysical, two pollutants concentrations and ten socioeconomic variables. There was no multi-collinearity among these independent variables. Finally, a min-max normalization method was introduced to remove the unit of independent variables. First, we built a null model to describe the spatiotemporal variances in the O 3 concentration quantitatively. There are no independent variables in the null model, and it takes the following form:

Level II:

where Eqs.

(1)-(3) refer to the monthly, seasonal and annual variation concentrations of O 3 (a jk , b k and c), respectively, and the random effects (e ijk , u jk and v k ) vary among month, season and station. i, j and k represent the group code of the monthly, seasonal and annual levels, respectively. A random intercept model is called as follows: 

Compared to null model, Eqs. (4)-(9) consider meteorological (X ijk ), vegetation (W jk ) and socioeconomic (Z k ) factors at different levels as explanatory variables that are multiplied by slope coefficients A (b 2 , c 3 ), B (c 2 ) and C, respectively.

Considering the random effect of the explanatory variables, the random slope model is expressed in the following form:

Level II:

Level

where the random influences of the explanatory variables on O 3 concentration are expressed by u 2 jk ,u 3 jk and u 4 jk and v 2 k , v 3 k and v 4 k at seasonal and spatial levels, respectively.

are the fixed coefficients expressing the monthly, seasonal and annual average influences on O 3 concentration, respectively. The seasonal and station random effects of O 3 concentration are noted using u 1 jk and v 1 k , respectively (Calvin 1994; Raudenbush and Bryk 1986) . (Chan and Chan 2000; Fan et al. 2008 ). In addition, agricultural activities and wheat straw burning in October also contributed to the increase in O 3 concentrations (Fu and Chen 2017; Gong et al. 2017; Macdonald et al. 2011) .

The spatial distribution of seasonal O 3 concentration is shown in Fig. 4 . During spring, the northern area of China suffered from high O 3 concentrations. The seasonal average concentration of O 3 was higher than 85 lg m -3 at approximately 11.46% of stations. In summer, O 3 -contaminated areas further expanded. The most polluted areas covered North China, northern East China and some regions of Northwest China. Approximately 23.97% of monitoring stations had concentrations that exceeded 85 lg m -3 concentration. The concentration significantly decreased in autumn and winter. However, the O 3 levels in YRD and Pearl River Delta (PRD) were relatively higher than those in other areas, and the average concentrations in YRD and PRD were approximately 62.79 lg m -3 and 58.43 lg m -3 in autumn, respectively. In PRD, the relative dry and less windy atmospheric condition caused the increase in O 3 concentrations during autumn . In YRD, the anthropogenic heat emissions changed the local meteorological conditions to some extent, which kept photochemical reactions highly active during autumn (Li et al. 2019b ).

In this study, multilevel regression models with three levels were constructed to reveal the dominating driving forces behind O 3 episodes varying in time and region. In the multilevel regression models, the variances of intercept and slope among groups conveyed the spatiotemporal heterogeneity of tropospheric O 3 concentration and driving factor effects (Tables 2, 3 and Figs. 5, 6 and Figs. S1-S3) across China. These groups were divided by the season and station codes at each level in multilevel regression model. Moreover, the coefficient reflected the relative influences of the driving forces on O 3 pollution among groups.

The outcomes of the multilevel regression models are listed in Tables 2 and 3. The results indicated that 29.07% of the variance in O 3 concentrations originated from spatiotemporal heterogeneity, which explained approximately 49.5% of O 3 concentration variation. When ICC is larger than 5.9%, the autocorrelation of O 3 concentration cannot be ignored (Muller 1989) . As a consequence, it is necessary to explore the determinants of O 3 variation at different scales separately. While incorporating explanatory variables, the multilevel regression models could explain as much as approximately 98.2% of the variance in the O 3 concentration, and the explained variance at the monthly and seasonal levels increases by 49.39% and 98.99%, respectively. The socioeconomic indicators in Model 3 could decrease the variance by 38.74% and 36.45% at the third level compared with that in Model 1 and Model 2, respectively.

Fixed effects uncovered the average influences of the independent variables on O 3 concentration. All the coefficients, except that of LPGC, were statistically significant at 1% confidence level. In Model 3, LPGC significantly impacted O 3 pollution at 5% confidence level. For monthly explanatory variables, T, PBLH and MW had positive correlations with O 3 pollution. RH, TP, ZW and AP affected O 3 concentration negatively. Additionally, the concentrations of both PM 2.5 and NO 2 in the air had negative relationships with O 3 concentration. VC was associated with O 3 pollution inversely in Model 2, but showed positive correlation in Model 3. Model 3 explained more variance at the third level than that of Model 2. Therefore, we regarded that the performance of Model 3 was concentration are attributed to dominant process. Thus, the impacts of meteorological indicators on O 3 concentration show significant spatiotemporal heterogeneity (Fig. S2) . Moreover, the O 3 formation closely related to NOx concentration. The strong oxidation capacity of O 3 leads to complex chemistry reaction with other air pollutants and further deteriorates air quality. As a consequence, we also investigated the relationship among NO 2 , PM 2.5 and O 3 concentration (Fig. S3) . In summary, negative relationships between NO 2 and O 3 concentration were found in the most regions of China over the whole year. However, the concentration of NO 2 in northern Northeast China and Anhui Province, correlated with O 3 pollution positively during spring and autumn. O 3 affects PM 2.5 formation positively under high O 3 level condition. Conversely, the decrease in PM 2.5 results in O 3 concentration climbing under high PM 2.5 level condition. Figure 5 shows the spatiotemporal distribution of vegetation coverage influences on O 3 pollution. Overall, vegetation coverage had positive impacts on the concentration of O 3 . BVOCs emitted from vegetation are one of the important photochemistry 

where v 0j represents the variance of jth layer in the null model. v ij is the variance of the jth layer in the ith model (Bai et al. 2017) . Furthermore, the impacts of vegetation coverage contributed to the spatial heterogeneity, with negligible seasonal variance at national level. Vegetation positive influence was more prominent in Northwest China and some regions in Southwest China. Since the strong solar radiation in western China is the breeding ground for the conversion of BVOCs to O 3 , BVOCs are also more reactive than anthropogenic VOCs (Li et al. 2019c; Wei et al. 2007 ). Furthermore, Yli-Pelkonen et al. (2017) found that urban vegetation contributed less to the decrease in the O 3 concentration. One of the reasons is that thick canopies can reduce air flow and obstruct pollutant diffusion, which results in O 3 accumulation. The spatial pattern differences of socioeconomic influences were also more significant than those of seasons. Overall, the correlation of the most socioeconomic indicators and O 3 concentration was positive in Northwest China and Southwest China (Fig. 6) . The transport and industrial emissions, economic development and energy consumption positively affected the concentration of tropospheric O 3 in western China. In early twenty-first century, ''Energy Golden Triangle'' and the largest coal-to-liquids industry were constructed in Northwest China under national strategies of ''Grand Western Development'' and ''West-East energy transmission programmes'' (Liang et al. 2019 ). These heavy industries caused a large amount of pollutants emissions. Besides, they contributed much to the economy development in Northwest China. Therefore, economy increase impacts O 3 pollution positively in western China. Conversely, the O 3 concentration in Northeast China, North China and some stations in Central China, Shanghai City and the coastal South China had strong and negative correlations with TGRP, since the adoption of new technologies and increased investment in environmental governance were beneficial for improving the atmospheric environment with continuous economic development, especially in Northeast China and North China where the secondary industry was dominant (Liu et al. 2019a) . Moreover, the cities with better economic development have more investment in pollution control (Zhao et al. 2019 ). For energy consumption impacts, GC and LPGC represented negative impacts on O 3 concentration in Central China and Northeast China. However, the influences were opposite in about 15.77% and 24.69% of stations mainly distributing in Fujian and Guangdong provinces. The hydrocarbon (HC) species originating from liquefied petroleum (LPG) combustion are in favor of O 3 formation (Blake and Rowland 1995) . As a consequence, the usage of LPG exerts positive correlation with tropospheric O 3 . Conversely, because of the advantage of less air pollutant emissions, gas and LPG are regarded as environment friendly fuel in Central China and Northeast China where fossil fuels are the dominant source of energy (Liu et al. 2019b; NBSC 2016b) . It is exhibited that LPG and gas consumption are related to O 3 concentration inversely in these regions. The BN influences displayed significantly different characteristics. Developed public transport was conducive to mitigating the O 3 pollution in some regions of Central China and costal area of South China.

Conversely, the positive influences of BN were observed in Northwest China, some regions of North China and Southwest China. On the one hand, the usage of buses can reduce the emissions of taxis and private cars to some extent. Moreover, some buses have used new energy in China in recent years. This kind of public transport is more environmentally friendly than conventional diesel buses (Zhang et al. 2014) . In contrast, public transport contributes less to the decrease in private car and taxi emissions in sparsely populated cities in Northwest China, North China and Southwest China. The emissions from buses also exert positive impacts on air pollution.

There are also a few of limitations in this research. First, the multilevel model only explains correlation between tropospheric O 3 and driving forces. The causal relationship cannot be revealed by regression model. Consequently, the statistical method, such as Granger test, will be integrated into the multilevel model in our future work. Moreover, there are always nonlinear relationship between O 3 concentration and driving forces. However, the multilevel linear model is competent for reflecting linear relationship. Besides Granger test, nonlinear statistical method will be also combined with multilevel model in our future research. On the other hand, we only focus on the determinants of O 3 concentration at monitoring station in this research dispersedly. The remote effects resulting from atmosphere transport are always ignored by statistical model. Thus, the temporally and spatially heterogeneous response of O 3 pollution to anthropogenic activities and meteorology changes will be explored based on space-time continuous tropospheric O 3 concentration data in the future.

As previous scientific publications, controlling the O 3 precursors emitted from industrial production, solvent usage, vehicle exhaust, energy usage and vegetation emissions contributes much to O 3 reductions (Shao et al. 2016) . Meanwhile, meteorology conditions also exhibit important impacts on tropospheric O 3 formation. Consequently, we took the spatiotemporal heterogeneity of biophysical and socioeconomic influences into consideration to highlight the regions where atmospheric environment was easily contaminated by O 3 . The rule depicted that the regions would be marked as two-level potential O 3 -contaminated areas. The Level I potential O 3 -contaminated areas were characterized as covering the suitable atmospheric environment where at least three meteorological factors play positive roles in O 3 formation simultaneously. Level II-contaminated areas were under high risk of being O 3 -contaminated which were influenced by at least three positive meteorological and five socioeconomic factors simultaneously (Fig. 7) . The results indicated that the atmospheric environment in Fig. 7 The potentially O 3 -contaminated areas (at least three meteorological factors had positive impacts in the Level I area. At least three meteorological and five socioeconomic factors exerted positive influences in the Level II area. Besides, ad reflect the conditions in spring, summer, autumn and winter, respectively) East China and South China was always suitable for O 3 formation over the whole year. Temperature, PBLH and zonal wind were the major factors influencing O 3 concentration positively and should be regarded as key observations. Moreover, the meteorological conditions in Central China during spring, autumn and winter and the atmospheric environment in Northeast China during spring, summer and winter were advantageous for O 3 formation and accumulation. In these regions, both the local emission and O 3 precursors transmission should be closely focused. The tropospheric O 3 episodes emerging in Northwest China and Southwest China were much influenced by socioeconomic activities over the whole year. During spring and summer, transport and industrial emissions in South China also worked as positive anthropogenic indicators. Moreover, the concentrations of NO 2 and PM 2.5 should be closely considered. LPG and bus usage are necessary to be controlled in the cities of South China during summer as well. As has been stated, these proposed measures comprise a powerful method for mitigating O 3 pollution in each region and season.

Tropospheric O 3 concentration was characterized by significant spatiotemporal heterogeneity with serious O 3 episodes over Southwest China, Northwest China, North China and northern East China from spring to autumn. This phenomenon results from the pronounced spatial and temporal heterogeneity of the driving forces at different levels. However, previous research using statistical methods seldom considers multi-scale effects of biophysical and socioeconomic influences on O 3 level. In this study, we employed a three-level regression model to investigate the dominant driving forces in each region varying seasons. The monthly, seasonal and regional variations in O 3 concentration were explained by meteorological, vegetation and socioeconomic factors at three levels in the multilevel regression models, respectively.

Meteorological factors including temperature, planet layer boundary layer height and zonal wind had more significant impacts on O 3 concentration in coastal regions including South China and East China than that in other regions. In inland areas that are characterized as ecologically fragile including Northwest China and western North China, anthropogenic sources significantly played positive roles in O 3 concentration. The determinants of tropospheric O 3 episodes exhibited significant seasonal heterogeneity in the cities which were located in the middle transition zone between the two regions mentioned above. In summary, the tropospheric O 3 concentration covering the regions near coastal area is strongly influenced by meteorology condition. In contrast, tropospheric O 3 level over inland regions mainly depends on the emission intensity from anthropogenic sources.

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