The spatiotemporal variation and key factors of SO2 in 336 cities across China

Journal of Cleaner Production 210 (2019) 602e611

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The spatiotemporal variation and key factors of SO2 in 336 cities across China Rui Li a, Hongbo Fu a, b, c, *, Lulu Cui a, Junlin Li a, Yu Wu a, Ya Meng a, Yutao Wang a, Jianmin Chen a, b, ** a

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China c Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, PR China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 April 2018 Received in revised form 28 October 2018 Accepted 6 November 2018 Available online 9 November 2018

Sulfur dioxide (SO2) pollution has become a severe concern in China, which is closely linked to human health. Here, the officially released data of SO2 in the 336 prefecture-level cities in 2015 across the whole China were firstly collected to understand the spatiotemporal variation of the SO2 concentration. At a national scale, the SO2 concentration was highest in winter, followed by one in spring and autumn, and the lowest one in summer. The spatial econometric models, the geographical weight regression (GWR) model, and the generalized additive model (GAM) were then applied to examine the interaction of socioeconomic factors (e.g., gross domestic production (GDP)) and the meteorological indicators (e.g., precipitation) on the SO2 level in the 336 cities over China. The results suggested that the SO2 concentration was negatively associated with GDP, precipitation, wind speed (WS), and relative humidity (RH), while it showed the positive relationship with gross industrial production (GIP), population, and temperature. GDP in the Jiangsu and Zhejiang provinces presented the negative correlations with the SO2 concentration, suggesting the adaptation of industrial structure has occurred in the developed region. The positive effect of GIP on the SO2 concentration increased from West China to North China because many energy-intensive industries were concentrated on North China. The GAM analysis suggested that the combined effects of the adverse meteorological condition (e.g., RH ¼ 50e60%) and the higher GIP contributed to severe SO2 pollution. Therefore, the SO2 emission from the heavy industries especially in NCP should be reduced and many energy-intensive plants in the region should be moved to some cities with favorable diffusion condition. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Socioeconomic factors Meteorological factors Spatial econometric models China

1. Introduction In recent years, the high pollutant loading have damaged seriously air quality in China, particularly in the region such as North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (Chan and Yao, 2008; Huang et al., 2014).

* Corresponding author. Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China. ** Corresponding author. Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, PR China.l E-mail addresses: [email protected] (H. Fu), [email protected] (J. Chen). https://doi.org/10.1016/j.jclepro.2018.11.062 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

The concentration of fine particulate matter (PM) in the ambient air of China was much higher than those in many other countries (Apte et al., 2015). It was well known that SO2 was the key precursor of sulfate in the atmosphere, which was a typical component of fine particles (Liu et al., 2016). Furthermore, SO2 could play a significant role on the explosive growth of fine particles as seed aerosol, which is beneficial to the transformation from volatile organic compounds (VOCs) to secondary organic aerosol (SOA) and new particle formation (Deng et al., 2017; Liu et al., 2016). Thus, SO2 is in great favor of the formation fog-haze episodes to worsen air quality (Calkins et al., 2016). On the other hand, the frequent SO2 exposure is generally associated with cardiovascular abnormalities, nose and throat irritation, and bronchoconstriction and dyspnea (Chen et al., 2012; Song et al., 2016). Therefore, it is imperative to reveal the

R. Li et al. / Journal of Cleaner Production 210 (2019) 602e611

spatiotemporal variation of SO2 in China. To date, a growing body of studies on the levels of air pollutants have been performed in China. An et al. (2013) reported that the PM10 concentration displayed the highest value in the NCP region, whereas they exhibited the lowest one in the Tibet. Hou et al. (2010) found that the population-weighted PM10 exposure levels in Beijing decreased sharply after the 2008 Beijing Olympic Games compared with the corresponding period from 2005 to 2007. Zhang and Cao (2015) analyzed the spatial distribution of PM2.5 and demonstrated that the PM2.5 concentration was highest in NCP. Wang et al. (2014) and Chai et al. (2014) evaluated the SO2 level in China based on the data of 31 and 24 cities, respectively. Nevertheless, a small quantity of cities cannot represent the real pollution level and spatial difference. Up to date, no assessment of the SO2 exposure level was performed at a national scale over China. Apart from the spatiotemporal distribution of SO2, it is of great importance to determine the dominant factors affecting SO2 concentration at the national and regional scale due to the huge health burdens incurred from the higher SO2 exposure. A growing body of studies have investigated the pollution levels and influential factors of SO2 (Guo et al., 2014; Wang et al., 2014). Some chemical transport models such as Community Multiscale Air Quality (CMAQ) coupled with Weather Research and Forecasting (WRF) have been developed to evaluate the anthropogenic emission on the SO2 concentration in the ambient air (Voorhees et al., 2014; Wang et al., 2015). However, these models generally needed high-resolution emission inventory and hourly meteorological data (Chen et al., 2017; Sharma et al., 2017). It was difficult to obtain these data. Moreover, these chemical transport models only assess the response of air pollutants to the emission change, but they cannot quantify the relationship between economic structure and air pollution. It was well documented that the spatial distribution of SO2 emission was not always in consistent with the industrial layout (Yang et al., 2017). To fill the clear gaps of these mechanism models, many multidisciplinary researchers employed various statistical methods to recognize the key factor for the SO2 concentration. He et al. (2017) investigated the SO2 concentrations in 31 capital cities and found that meteorological conditions were primary factors affecting the SO2 concentrations. Hu et al. (2017) explored the mutual effects of meteorological elements and traffic linkages on the air quality, and showed suitable temperature and traffic restrictions were beneficial to the pollutant removal. Generally, these studies employed substantial time-series data to decipher the relevance between influential factors (e.g., socioeconomic factors and meteorological factors) and the pollution level in the ambient air and assumed that these driving forces were identical at a spatial scale. However, the spatial heterogeneity of the pollutant concentrations, as well as the spatial autocorrelation of the pollutants and driving forces cannot be ignored in the real case. In order to overcome the defects mentioned above, spatial autocorrelation model, panel data analysis, and geographical weight regression (GWR) model have been applied to elucidate the spatial correlations of pollutants and influential factors (Zhao et al., 2017; Yang et al., 2017). Up to date, the interactions of socioeconomic and natural factors were rarely examined (Yang et al., 2017; Zhang et al., 2016). It was well known that the SO2 pollution in China was inevitably affected by both of the socioeconomic and natural factors rather than single one (Yang et al., 2018). Furthermore, the combined actions of socioeconomic factors and natural factors were still unknown. The knowledge about the interactions of socioeconomic and meteorological factors can provide appropriate suggestions for government sectors to reduce the SO2 pollution. Here, the officially released data of SO2 in the 336 prefecturelevel cities in 2015 across the whole China were collected to understand the spatiotemporal distribution of the SO2 concentration

603

over China and determine the key factors for the spatial variation. The objective of this study is (1) to investigate the spatial and seasonal variation of the SO2 levels over China; (2) to identify the dominant factor for SO2 concentration at the national and regional scale using a set of statistical methods (e.g., spatial econometric models and GWR); (3) to clarify the interaction of socioeconomic and natural factors based on the generalized additive model (GAM). The present study makes an important contribution to investigate the SO2 levels in 336 cities across the whole China for the first time. Moreover, the present study assesses the combined effects of socioeconomic and meteorological factors on the SO2 concentration using GAM. Furthermore, the key factors for the higher SO2 level have been determined. Overall, the present study sheds light upon the spatiotemporal patterns of SO2 across China and provides evidence aiming to mitigate air pollution in China. 2. Data and methods 2.1. The SO2 concentration and the influential factors in each city The real-time hourly concentrations of SO2 in the 336 cities across the whole China in 2015 were downloaded from the website of China air quality monitoring platform (http://www.aqistudy.cn/ ). The monitoring stations were evenly distributed over Mainland China and cover all of the prefecture-level cities. The data of Hongkong, Macao, and Taiwan were not included in the present study. All of the data were supplied by the national air quality monitoring sites located in each city. The monitoring sites have been designed as a mixture of urban and background sites, including most of the sites in urban area, and a few of the sites in suburban and rural areas as the background sites. The mean concentration of SO2 was calculated by averaging the concentrations at all of the monitoring sites in each city. The quality assurance of these data is summarized in supporting information in details. In order to test the effects of the influential factors including socioeconomic factors and meteorological elements on the SO2 concentration in the ambient air, the potential influential factors were selected as explanatory variables. Considering the possible linkage between the influential factors and the SO2 concentration, and the data availability at a city level, seven indicators including gross domestic production (GDP), gross industrial production (GIP), population, precipitation, temperature, wind speed (WS), and relative humidity (RH) were introduced as independent variables. The empirical researches of Environment Kuznets Curve (EKC) have demonstrated that air pollutants were closely associated with GDP (Hao and Liu, 2016; Ali et al., 2017a). Besides, the coal-fired power plants and industrial activities such as non-ferrous smelting and cement production were main source of SO2 in China (Lu et al., 2011a). Qi et al. (2017) have showed that the industry sector was the largest emission source for SO2, accounting for 72.6% of the total emission in Beijing-Tianjin-Hebei region. Therefore, GDP and GIP remained high priorities in exploring the drivers of the SO2 concentration in the atmosphere. The population was applied to the present study because high level of urbanization and industrialization could influence the air quality (Huang et al., 2014; Ali et al., 2018; Ali, 2018). Once SO2 discharged into the atmosphere, the meteorological factors played significant roles on the SO2 concentration via diffusion and turbulence. The atmospheric convection and turbulence process could be enhanced with the increase of temperature (Zhang and Cao, 2015). Atmospheric wet deposition has been proved the most effective scavenging pathway for removing the dissolved gaseous pollutants from the atmosphere (Al-Khashman, 2005). WS and RH could also play important roles on the SO2 concentration distribution through diffusion process and heterogeneous reaction (Zhang and Cao, 2015; He et al., 2017).

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Therefore, these meteorological factors were applied to the present models. Although some other indicators such as air pressure, sun duration, vehicle population, and the number of industrial enterprises were also considered to be introduced to our study, these variables were not selected due to some restrictive conditions. For instance, air pressure, sun duration, and the number of industrial enterprises displayed high collinearity with other variables, which leaded to the failure of models. Besides, the data of vehicle population and the gross production of detailed industrial sector at a city level were not available. Consequently, the data of GDP, GIP, population, temperature, precipitation, WS, and RH at a city level were collected from the statistical yearbook of each province, city and municipality in Mainland China.





bðui ; vi Þ ¼ X TW ðui ; vi ÞX 1 X T Wðui ; vi ÞY

(2)

where bðui ; vi Þ denoted the local regression coefficient at city i; X was the matrix of the socioeconomic factors; Y was the vector of the SO2 concentration in all of the cities; and W(ui,vi) was the spatial weight matrix reflected the spatial autocorrelation of the neighboring cities. The spatial weight matrix was calculated using the exponential distance decay form:

 .  Wðui ; vi Þ ¼ exp  d2 ðui ; vi Þ b2

(3)

where dðui ; vi Þ was the distance between i and j, and b denoted the kernel bandwidth.

2.2. Grey correlation analysis and spatial econometric models The grey correlation analysis was firstly applied to determine the predominant factor regulating the SO2 concentration in China. The detailed algorithm was summarized in supporting information. However, the grey correlation analysis could not be utilized to investigate the spatial correlation of the SO2 concentration and the socioeconomic factors. Thus, the spatial econometric models were further performed to investigate the spatial relevance between the SO2 concentration and the socioeconomic factors. The spatial autocorrelation was evaluated based on Moran's I statistics. The statistic mainly relied on the spatial weight matrices, which reflected the spatial relationships of the neighboring cities (Anselin and Bera, 1998). The detailed formula of Moran's I statistics was as follows:

n

PP

wij ðyi  yÞ

i jsi

I PPP wij ðyi  yÞ2

(1)

2.4. The GAM algorithm The spatial econometric model and the GWR model cannot explain the meteorological and socioeconomic factors and their interactive impact on the SO2 concentration in the ambient air. Thus, GAM was employed to evaluate the combined effects of the influential factors on the SO2 concentration. The detailed algorithm of the GAM model was as follows (Hastie and Tibshirani, 1990):

  gðmÞ ¼ a þ f1 ðX1 Þ þ f2 ðX2 Þ þ / þ fp Xp

(4)

where m ¼ EðY=X1 ; X2 ; X3 …Xp Þ ; g(m) was the contiguous function. fp was treated as the smooth function explaining the dependent variable. Xp represented the independent variables. In GAM, the distribution of response variable was the Gauss-Markov distribution. GAM was programmed in R. The mgcv and DAAG R package were used for the higher computing performance of GAM.

i jsi i

where wij was the value of each prefecture-level city in the spatial weight matrix W, and y denoted the SO2 concentration. The Moran's I denoted a positive spatial autocorrelation when it varied between 0 and 1. However, the Moran's I represented a negative autocorrelation when it ranged from 1 to 0. No spatial relevance was observed when the Moran's I equaled to zero. The p value was regarded as an indicator to determine the reliability of Moran's I statistics. The SO2 concentration displayed significantly spatial correlation when the p value was less than 0.05. The spatial econometric models were recognized as the extensions of ordinary regression models through incorporating the spatial relevance of variables. Two spatial econometric models comprised of the spatial lag model (SLM) and the spatial error model (SEM), both of which were applied to investigate the spatial relationships of the SO2 concentration and socioeconomic factors. The detailed formula was summarized in supporting information. In the present study, SLM, SEM, and the Moran's I statistics, as well as the corresponding tests, were estimated using a GeoDa software.

2.3. Geographical weight regression (GWR) model SLM and SEM could not address the spatial heterogeneity of the relationship between the SO2 concentration and the socioeconomic indicators. Thus, the GWR model was used to produce the coefficient of determination (R2) and the local regression coefficients for each city of the study areas, which were then mapped to show the spatial variability. The regression coefficients could be calculated based on the following formula:

3. Results and discussion 3.1. The spatiotemporal variation of SO2 in China 3.1.1. The temporal variation of SO2 in China The annual mean concentration of SO2 in China was 33.97 ± 18.49 mg/m3 (Fig. 1a) at a national scale. The SO2 concentration displayed a remarkably seasonal variation in China (Fig. 1bee). The SO2 concentration was highest in winter (mean ± standard deviation: 54.00 ± 34.35 mg/m3) (Fig. 1e), followed by ones in spring (32.12 ± 17.65 mg/m3) (Fig. 1b) and autumn (29.10 ± 16.62 mg/m3) (Fig. 1d), and the lowest one in summer (20.66 ± 11.92 mg/m3) (Fig. 1c) over four seasons. At a month scale, the mean concentration of SO2 exhibited the higher values in January (62.42 ± 38.77 mg/m3), December (54.06 ± 35.27 mg/m3), February (45.53 ± 33.48 mg/m3), and March (40.92 ± 23.37 mg/m3), while it displayed relatively lower ones from June to September (June: 21.40 ± 12.52 mg/m3, July: 19.64 ± 11.45 mg/m3, August: 20.94 ± 13.25 mg/m3, September: 21.14 ± 12.12 mg/m3). It was well documented that fossil fuel burning was the primary source of SO2 in the ambient air (Wang et al., 2016a). The fossil fuel combustion for heating was inclined to accumulate SO2 in winter (Lu et al., 2011b; Zhang et al., 2009). In addition, the stagnant meteorological conditions characterized by slow WS and shallow mixing layers appeared more frequently in winter, which could trap the pollutants and promote the SO2 accumulation (Tai et al., 2010). In contrast, the great solar radiation, the strong turbulent eddies and the precipitation scavenging diluted greatly the pollutants released at the surface, thereby generating the lower SO2 concentration in summer (Antony Chen et al., 2001).

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Fig. 1. The spatial distribution of the annual mean SO2 concentration in China (Unit: mg/m3).

3.1.2. Spatial distribution of SO2 in China The spatial distribution of the SO2 concentration in the ambient air of Mainland China is illustrated in Fig. 1a. The mean concentration of SO2 displayed higher values in some cities of NCP (e.g., Zibo (90.30 mg/m3), followed by ones at some cities in Loess Plateau (e.g., Shuozhou (86.77 mg/m3), Jinzhong (82.43 mg/m3)) and YRD (Shaoxing (29.60 mg/m3), Lianyungang (28.99 mg/m3)), and they showed the lowest one in Sanya (0.67 mg/m3). In the cities of NCP and Loess Plateau, the coal combustion for heating played a vital role on the SO2 emissions because these regions were main coal producers and/or consumers (Wang et al., 2002). More coal-based industries including coal-fired power plants, iron and steel manufacturing were concentrated on these regions (Kong et al., 2011). Although electrostatic precipitators (ESP) for the PM removal and fabric filters (FFs) have been widely employed in steelmaking, iron-making and cement production process, the SO2 concentrations in these cities were still higher than those in other cities. Compared with the cities in NCP and Loess Plateau, relatively lower SO2 concentrations were observed on the regions of YRD and PRD due to less coal-fired power plants, non-ferrous industries, and iron and steel manufacturing (Govindaraju and Tang, 2013). In addition, biomass burning also played a significant role on the accumulation of SO2 in the ambient air (Wang et al., 2002). The periodic open burning of crop residues in summer could also increase the SO2 concentration in North China and Northeast China (Li et al., 2007). In the present study, the ratios of the SO2 concentration in summer and the SO2 mean concentration (summer/ mean ratio) showed the highest value in Daxinganling (0.77), followed by some cities in Xinjiang province (ie., Baotou (0.72) and NCP (e.g., Zhengzhou (0.69)), and they exhibited the lowest ones in Sanya (0.05). Tian et al. (2011) estimated that nearly 20% of SO2 was released from livestock manure burning due to high SO2 emission factor, which were widespread distributed in Northwest China. The higher SO2 concentration and summer/mean ratios in Baotou

(47.56 mg/m3, 0.72) corresponded to the SO2 emission inventory made by Tian et al. (2011). The SO2 concentration showed similar spatial distribution in four seasons as a whole, while the spatial variability of the SO2 concentration was not coincident in four seasons. In spring (Fig. 1b), autumn (Fig. 1d), and winter (Fig. 1e), the SO2 concentration displayed significantly spatial variability across the whole China. Among all of the 336 cities, the highest values were concentrated on Xuchang (133.42 mg/m3, 110.27 mg/m3, and 152.31 mg/m3), followed by one in Zibo (126.07 mg/m3, 105.03 mg/m3, and 149.07 mg/ m3), and the lowest one in Sanya (1.50 mg/m3, 2.07 mg/m3, and 3.10 mg/m3). However, the SO2 concentration did not exhibit remarkably spatial variability in summer (Fig. 1c). The SO2 concentration increased slightly from south to north and displayed relatively higher value in some cities of Shandong province (e.g., Zibo (57.80 mg/m3). 3.2. Driving forces of the SO2 concentration over Mainland China 3.2.1. Identification of the dominant driving forces for the SO2 concentration Grey correlation analysis was an important method to determine the dominant economic and meteorological factor affecting the SO2 concentration in the atmosphere. Three socioeconomic indicators including GDP, GIP, and population in each city were selected to assess their effects on the SO2 concentration. GIP (0.85) showed the highest grey correlation coefficient with SO2, followed by population (0.84), and GDP (0.81) (Table 1), suggesting that urban SO2 concentration was strongly affected by the secondary industry. Given the cities in NCP relied on the energy-intensive industries (e.g., non-ferrous smelting, power plants), the characteristics of economic development in these cities were manifested with extensive production modes (e.g., high fossil fuel consumption, high pollutant emission). The spatial distribution of the SO2

Table 1 The grey correlation coefficient of the SO2 concentration and the influential factors. Grey correlation coefficient

GDP

GIP

Population

Precipitation

Temperature

WS

RH

SO2

0.81

0.85

0.84

0.66

0.69

0.73

0.69

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concentration was in good agreement with the industrial layout in China. Although the SO2 emission has shown gradual decline in recent years due to the co-benefit removal effects of the PM and SO2 control devices (Tian et al., 2014), more advanced technologies and integrated management measures were still in great need to be implemented in the industrial production. For example, structural transformation of the economy and reduction of the reliance on secondary industry especially heavy industries should be speeded up in the near future. In addition, the energy consumption structure should also be improved to use cleaner energy such as natural gas and wind energy instead of fossil fuel (Hao and Liu, 2016). Apart from the effects of secondary industry, the population also played a significant role on the SO2 concentration in the ambient air. In the present study, most of the cities with severe SO2 pollution were located on the eastern of China, where generally showed the more population (e.g., Xingtai (7.89 million), Zibo (4.70 million)). It was assumed that the human activities such as the residential heating and cooking could increase the SO2 emission (Qi et al., 2017). GDP displayed a significant relationship with the SO2 concentration, but the correlation coefficient was lower than those for GIP and population (Table 1). In addition, four meteorological factors including precipitation, temperature, WS, and RH were applied to assess their effects on the SO2 concentration. WS (0.73) was considered as a key factor

affecting the SO2 concentration due to high grey correlation coefficient with the SO2 concentration (Table 2), followed by temperature (0.69), RH (0.69), and precipitation (0.66). High WS was conducive to the atmospheric transport and the pollutant diffusion, thereby weakening the SO2 accumulation (Csavina et al., 2014). Compared with other meteorological factors, the precipitation presented relatively lower correlation with the SO2 concentration because sulfate formed via the photochemical reaction of SO2 were mainly concentrated on the fine particles, which were difficult to be scavenged by precipitation compared with the coarse fractions (Pu et al., 2017). 3.2.2. Spatial correlation of the SO2 concentration and the driving forces On the basis of the spatial autocorrelation test, the Moran's I of the SO2 concentration reached 0.53 (Fig. 2a), and the p value was lower than 1%, indicating the significantly spatial autocorrelation of the SO2 concentration at a city level. In the Moran scatterplot, the standardized concentration of SO2 and spatial lagged values of SO2 showed a remarkable spatial correlation. The spatial autocorrelation was categorized into four types through four quadrants: highhigh (upper right) and low-low (lower left) for the positive spatial autocorrelation; high-low (lower right) and low-high (upper left) for the negative spatial autocorrelation. As shown in Fig. 2b, most of

Table 2 The OLS analysis for the SO2 concentration and the influential factors. Variable

Coefficient of regression

Standard deviation

t value

P value

Constant GDP GIP Population Precipitation Temperature WS RH LM lag Robust LM lag LM error Robust LM error

11.81 0.0045 0.0017 0.0317 2.95 0.68 2.40 0.02 e e e e

3.19 0.0009 0.0005 0.0048 0.42 0.20 0.90 0.07 e e e e

3.69 5.10 3.68 6.55 2.01 3.32 2.65 2.28 162.78 43.18 119.59 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.97

Fig. 2. The Moran's I scatterplots for the SO2 concentration in the 336 cities in China.

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the cities were concentrated on the upper right and the lower left quadrants. Some cities in the NCP region such as Baoding and Handan displayed the high-high cluster, suggesting that they could import or export SO2 from their neighboring cities. Therefore, the serious SO2 emission control technologies and the joint prevention of air pollution should be implemented in all of the cities of the region. In contrast, some cities in Southwest China and Southeast China such as Guilin, and Shangrao were surrounded by many cities with lower SO2 levels (Fig. 1). Several cities in the Inner Mongolia such as Erdos were inclined to import the pollutant from neighboring cities. The ordinary least square (OLS), SLM, and SEM analysis were utilized to investigate the correlation between various variables and the SO2 concentration to determine the appropriate approach for the estimation. The results of three methods are illustrated in Table 2. GDP, GIP, and population displayed the significant relevance with the SO2 concentration based on the OLS analysis (R2 ¼ 0.44, p < 0.01). The SO2 concentration elevated with the increase of GIP, population, and temperature, while it reduced with the increase of GDP, precipitation, WS, and RH. Given the spatial autocorrelation of the SO2 concentration, the OLS model only revealed the relevance between the influential factors and the SO2 concentration to some extent. Therefore, LM (Lagrange Multiplier) and robust LM tests were employed to determine the best method to decipher accurately the relationships between the driving forces and the SO2 concentration at the spatial scale. LM and robust LM for SLM both displayed the remarkable correlations, whereas the robust LM for SEM exhibited an insignificant relevance (p > 0.05) (Table 2). Thus, SLM was selected to elucidate the relationship between the influential factors and the SO2 concentration at the spatial scale. From the socioeconomic perspective, the SLM results suggested that the SO2 concentration displayed a negative correlation with GDP at a national scale (Table 3), whereas it showed the significantly positive relevance with GIP and population. It was assumed that the development of secondary industry coupled with the rapid growth of energy consumption played the important roles on the SO2 emission in China (Streets and Waldhoff, 2000). Hao and Liu (2016) also found that more than 70% of energy was consumed in the secondary industry in China. Furthermore, coal was the main energy of the secondary industry in China and coal combustion contributed to the SO2 level greatly in the atmosphere (Hua et al., 2016). Especially, high consumption of fossil fuels for heating in winter probably contributed to the higher SO2 accumulation especially in NCP and Loess Plateau (Xu et al., 2014, 2015). In the present study, some cities such as Shuozhou (SO2 concentration: 86.77 mg/m3), Jinzhong (82.43 mg/m3), Taiyuan (77.62 mg/m3) were located on the important coal-producing area of China, which fully verified the contribution of coal combustion to SO2 pollution. Besides, the cities in NCP such as Zibo and Xingtai showed the higher SO2 concentration because they possessed a large number of thermal power plants (Tian et al., 2014). Therefore, the advanced

Table 3 The LM lag results for the SO2 concentration and the influential factors. Variable

Coefficient of regression Standard deviation Z value P value

Constant GDP GIP Population Precipitation Temperature WS RH

1.91 0.002 0.0006 0.02 1.40 0.44 0.49 0.03 0.64

r

2.59 0.0007 0.0004 0.0036 0.33 0.16 0.70 0.06 0.04

0.74 3.44 1.79 5.81 3.25 3.32 3.46 3.35 14.61

0.46 0.05 0.00 0.00 0.01 0.00 0.00 0.00 0.00

607

and high-efficient desulfurization technology should be broadly utilized for the large-scale coal-fired plants as soon as possible (Ali et al., 2017b). The relationship between population and the SO2 concentration in the ambient air was usually difficult to identify and varied at the geographical space. Yang et al. (2017) investigated the spatial relationship of population and the SO2 concentration based on the data in the 30 provincial capital cities and found that the correlation failed to pass the test of significance. However, the coefficients of population turned out to be positive based on the data collected from the 336 cities, suggesting that the increase of population tended to enhance the SO2 concentration. It was well documented that the dramatic expansion of metropolitan and the large-scale human activities could bring about more urban atmospheric pollution (Xu et al., 2015; Hao and Liu, 2016). The GDP effect on the SO2 concentration was relatively weaker compared with other factors, although GDP showed a negative relevance with SO2 in the SLM model To date, the relationship of GDP and air pollutants was not unclear yet, which varied with the city significantly (Gorica et al., 2015). Hao and Liu (2016) emphasized that the inverted-U relationship between the pollutant concentration and GDP may be quite robust. However, Xu et al. (2015) found that rapid economic development contributed to the linear increase of the pollutant concentration in China. At a city level, the present result was similar to that reported by Hao and Liu (2016) because a remarkable inverted-U of SO2 and GDP was observed. A great deal of industries especially energy-intensive ones could promote the GDP increase at the early stage of economic development, resulting in the elevation of SO2 (Zhao et al., 2017). However, the energyintensive industries could be replaced by high-technology industries with the economic development, thereby leading to the decreased SO2 emission. China has been suffered from the seriously unbalanced development and the gap of eastern-western region has been increasing since 1980s (Weng et al., 2018). In some cities of West China such as Kashi and Ali, the SO2 concentration showed a slight increase with the GDP (p ¼ 0.11), suggesting that they remained the extensive development pattern. On the contrary, the SO2 concentration exhibited the negative correlation with GDP in the cities of East China such as Zibo and Xingtai (p < 0.05) because GDP in East China has become more reliance on the development of service industry rather than energy-intensive one, which could much less release SO2 (Yang et al., 2017). At a national scale, the SO2 concentration displayed a negative relationship with GDP, suggesting that the economic development in China has begun to transform from the energy-intensive pattern to the low-pollution one as a whole. From the meteorological perspective, the SLM results suggested that the SO2 concentration displayed the negative correlation with precipitation, WS, and RH, while it showed a positive relevance with temperature (Table 3). It was well documented that precipitation and WS played significant roles on the SO2 concentration by wash-out and diffusion process (Pu et al., 2017; He et al., 2017). RH could influence the SO2 concentration in the ambient air through affecting the photochemical pathway and aqueous phase chemistry of SO2 (Wang et al., 2016b; Cheng et al., 2016). Yang et al. (2016) have demonstrated that sufficient RH could enhance the heterogeneous transformation from SO2 to sulfates, and thus decrease the precursor concentration. Although the recent studies confirmed that high air temperature could strengthen the pollutant diffusion, global warming could weaken WS by the transformation of air pressure, thereby deteriorating the air quality (Chang et al., 2016; Ali, 2018). The GWR model was applied to explore the relationship between the SO2 level and the socio-economic factors in depth. The mean value of the local regression coefficients and the R2 value included in the GWR model are summarized in Fig. 3a. The high R2

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Fig. 3. The local B coefficients of the socioeconomic and meteorological factors on the SO2 concentration.

value implied that the GWR model was more suitable to being used in the present study. Compared with the grey correlation analysis and the SLM model, the GWR model not only demonstrated their results, but also displayed remarkable geographic variation in the relationships of the SO2 concentration and the influential factors. These maps provided insights into the spatial relationship variance between the SO2 level and the influential factors through the local B coefficients comparison. GDP displayed a negative correlation with the SO2 concentration in most of regions (Fig. 3b), while it showed the opposite result in some cities of Tibet and Xinjiang autonomous region such as Hetian (local B coefficient: 0.006) and Ali (0.007). Both of Tibet and Xinjiang autonomous region were less-developed regions in China, which could keep at the early stage of the inverted-U EKC (Ali et al., 2017a). In contrast, GDP in some other regions, especially some cities of East China such as Shanghai (0.0032) and Nanjing (0.0034) presented the negative correlation with the SO2 concentration, suggesting that these developed cities have begun to adapt their industrial structure and aimed to achieve the ecological development. However, the positive GIP effect on the SO2 concentration increased from West China to East China (Fig. 3c), because large iron and steel industries, power plants, and non-ferrous smelting plants were concentrated on Northeast China and NCP, which could be the main sources of SO2 (De Gouw et al., 2014). Although there were also massive industries and transportation in Sichuan Basin and Chang-Zhu-Tan urban agglomeration, the amount of industrial waste gas (e.g., SO2) and coal combustion emission in these regions were still much less than those in NCP. In addition, Saikawa et al. (2017) estimated that 52e61% of total SO2 emission were emitted from the power sector in China. NCP possessed much more power plants compared with the other regions in China (Li et al., 2017). In order to alleviate the severe SO2 cluster in NCP, the industrial structure adaption not only required the reduction of heavy industries, but also demanded the regional cooperation of the whole region. High energy-consuming industries should not be centered on a single region to reduce the environmental pressure in the heavy pollution region. Apart from the effects of GDP and GIP, the population exerted an important role on the SO2 concentration in the ambient air. The population showed less influence on Tibet autonomous region due to the lowintensity industrial activities (Fig. 3d). The population effect on the

SO2 concentration in East China (e.g., Xingtai (0.0018)) was relatively lower compared to the northwestern of China (e.g., Altay (0.07)) (Fig. 3d) because the industrial activities were the main deriver for the SO2 pollution in East China (Tian et al., 2011). The precipitation influence on the SO2 level exhibited a gradual increase trend from Southeast China to Northwest China (Fig. 3e). It was supposed that sulfate in the coarse particles were easier to be scavenged via wet deposition (Pu et al., 2017). The temperature effect on the SO2 concentration increased from West China to East China except Kashi (2.31) (Fig. 3f). WS showed the remarkable effect on the SO2 concentration especially in the coastal cities of South China such as Guangzhou (1.80), Shenzhen (1.95), and Haikou (2.14) (Fig. 3g) because WS in these cities were generally higher than those of the inland cities (Jiang et al., 2010). The RH effect on the SO2 concentration both showed the higher value in the coastal cities of YRD such as Shanghai (0.40) and Zhenjiang (0.39) (Fig. 3h). As a hydrophilic aerosol particle, sulfate generally showed strong hygroscopic property, and the particle radius could be doubled by coating with water vapor under high RH (Liu et al., 2011). 3.2.3. The joint effects of socioeconomic factors and meteorological factors on the SO2 level Table 4 presents the hypothesis test result of GAM between the

Table 4 The GAM model hypothesis test for SO2 concentration and the explanatory variables.

GDP-precip GIP-precip Population-precip GDP-T GIP-T Population-T GDP-WS GIP-WS Population-WS GDP-RH GIP-RH Population-RH

Estimated degree of freedom

F

P

1.13 10.11 25.37 2.72 22.69 19.10 2.92 18.95 19.91 2.81 20.20 16.63

9.90 2.56 5.11 1.06 3.46 3.24 1.18 2.87 1.83 1.53 3.42 4.59

<0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

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SO2 concentration and the influential factors. All of the cross terms were significantly higher than 1.00 except GDP-precipitation (Table 4), suggesting that most of the cross terms showed the nonlinear relationship with the SO2 concentration. The p value of all the cross terms were significantly lower than 0.01, indicating the remarkable relevance between the influential factors and the SO2 concentration. In Fig. 4a, the SO2 concentration did not exhibit notable variation with GDP and precipitation increases. As shown in Fig. 4b, the SO2 concentration increased with T and reached the

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higher value when T was in the range of 10e25  C and GDP was kept constant. It was supposed that atmospheric turbulence and convection were expected to strengthen along with the T increase (Yang et al., 2017). However, the SO2 concentration showed a gradual decrease especially when T was higher than 25  C. As depicted in Fig. 4c, the SO2 level increased rapidly with the WS increase and arrived at the maximum value when WS reached 3 m/ s. However, the SO2 level decreased rapidly when WS was higher than 3 m/s. The SO2 concentration significantly elevated with the

Fig. 4. Three-dimensional effect graph of the driving factors on the variation of SO2 concentration.

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RH increase firstly and then decreased gradually (Fig. 4d). The higher SO2 concentration occurred at the lower GDP (5000e10000  108 yuan) and medium RH (50e60%). As shown in Fig. 4eeh, the SO2 concentration showed gradual decrease with the precipitation increase when GIP was constant. It was reported that the wet deposition generally removed gaseous pollutants efficiently from the atmosphere (Xu et al., 2014). In addition, the SO2 concentration reached the higher value when WS was around 3 m/s and RH was in the range of 50e60% coupled with the higher GIP (around 25000  108 yuan), respectively. The result was in good agreement with the GDP-meteorological factors. The SO2 concentration peaked at the medium population (500e1500  104), the lower daily precipitation (<2 mm), appropriate temperature (10e20  C), WS (around 3 m/s), and RH (50e60%) (Fig. 4i-l). Based on the results of GAM and the SO2 level in the 336 cities, it was found that some medium cities such as Xingtai (68.94 mg/m3) and Tangshan (53.17 mg/m3) rather than the megacities (Beijing: 12.82 mg/m3) suffered from the most severe SO2 pollution in China because the interaction of socioeconomic and natural factors reinforced each other in these cities. Some studies observed that large-scale industrial transfer from the megacities to the mediumsized cities occurred in NCP (Zhang et al., 2016). For instance, many high energy-consuming industries in Beijing have been migrated to some industrial cities in Hebei and Henan provinces. However, the handy migration was not in favor of the alleviation of SO2 pollution because NCP generally showed the medium RH (50e60%) and low rainfall amount. The GAM result indicated the adverse meteorological condition combined with the higher energy-intensive industries aggravated the air pollution in these cities. In light of the environmental protection, the energy-intensive industries should be moved to some cities with high WS, precipitation, and RH instead of be concentrated on the cities with medium RH and low rainfall amount. 4. Conclusions and policy implications The daily SO2 data in 2015 were collected from the 336 cities to investigate the SO2 level and evaluate the health effect in China. Meanwhile, the relationships between the influential factors (e.g., socioeconomic factors and meteorological indexes) and the SO2 concentration were examined. The annual mean SO2 concentration across the whole China was 33.97 ± 18.49 mg/m3, 1.7 times of the Grade I standard (20 mg/m3). The SO2 concentration displayed a negative correlation with GDP, whereas it showed the significantly positive relevances with GIP and population. The negative relationship between SO2 and GDP suggested that most of the cities in East China have begun to transform from the energy-intensive development pattern to the one with low-pollution, while some cities in West China might take more time to update industrial structure. Additionally, the strong effects of GIP on the SO2 concentration still showed the higher values in East China especially in NCP, suggesting that China should speed up the upgrade of economic structure and reduce the reliance on secondary industry especially the energy-intensive one. Meanwhile, stringent emission limits should be implemented and all of the newly built and in-use plants must be equipped with the advanced flue gas desulfurization (FGD). Apart from the impacts of socioeconomic factors, the adverse effects of the meteorological factors should not be ignored. The precipitation, WS, and RH reduce the SO2 concentration, but T could increase the SO2 level through weakening WS. At the regional scale, the effect of GDP on the SO2 level decrease from West China to East China, whereas the GIP effect showed the opposite trend. The population played an important role on the SO2 concentration in NCP. The precipitation and T effects were mainly concentrated on Northwest China and NCP,

respectively. However, the most significant effect of WS and RH on the SO2 level focused on some coastal cities of East China. The GAM result suggested that the interaction of adverse meteorological condition (e.g., RH ¼ 50e60%) and the higher GIP contributed to severe SO2 pollution. Thus, the energy-intensive industries should not be centered on the regions with medium RH and low precipitation such as NCP to reduce the environmental pressure and avoid the explosive growth of the SO2 concentration. The higher WS and precipitation in the cities of Guangxi and Hainan province were inclined to the advection and dispersion of SO2. It was proposed that some energy intensive industries (e.g., power plants, non-ferrous smelting industries) could be moved to these regions. The high Moran's I index suggested that SO2 showed high spatial autocorrelation in NCP, suggesting that the impact of spatial spillover on the environmental quality of neighboring regions were widespread. Moreover, appropriate meteorological condition could promote the diffusion of SO2 to other regions. Thus, the advanced control technologies of the SO2 emission should be extended to all of regions in China, and regionally joint prevention of air pollution were imperative. The contribution of this study is to seek out the dominant driving force and assess the joint effects of socioeconomic and meteorological factors regulating the SO2 concentration, especially at the regional scale, and raise some useful suggestions for government sectors about the industrial layout in light of the environmental effect. It should be noted that some limits existed in our study. The explanatory variables included in the statistical model were relatively scarce. More detailed socioeconomic factors such as gross production of each sector should be incorporated in the future research when more data are accessible. Acknowledgements This work was supported by National Key R&D Program of China (2016YFC0202700), National Natural Science Foundation of China (Nos. 91744205, 21777025, 21577022, 21177026), International cooperation project of Shanghai municipal government (15520711200), and Marie Skłodowska-Curie Actions (690958MARSU-RISE-2015). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2018.11.062. References Al-Khashman, O.A., 2005. Study of chemical composition in wet atmospheric precipitation in Eshidiya area, Jordan. Atmos. Enviorn. 39, 6175e6183. Ali, G., Ashraf, A., Bashir, M.K., Cui, S.S., 2017a. Exploring environmental Kuznets curve (EKC) in relation to green revolution: a case study of Pakistan. Environ. Sci. Pol. 77, 166e171. Ali, G., Pumijumnong, N., Cui, S.H., 2017b. Decarbonization action plans using hybrid modeling for a low-carbon society: the case of Bangkok Metropolitan Area. J. Clean. Prod. 168, 940e951. Ali, G., Pumijumnong, N., Cui, S.H., 2018. Valuation and validation of carbon sources and sinks through land cover/use change analysis: the case of Bangkok metropolitan area. Land Use Pol. 70, 471e478. Ali, G., 2018. Climate change and associated spatial heterogeneity of Pakistan: empirical evidence using multidisciplinary approach. Sci. Total Environ. 634, 95e108. An, X., Hou, Q., Li, N., Zhai, S., 2013. Assessment of human exposure level to PM10 in China. Atmos. Environ. 70, 376e386. Anselin, L., Bera, A., 1998. Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah, A., Giles, D.E. (Eds.), Handbook of Applied Economic Statistics. Marcel Dekker, New York, pp. 237e289. Antony Chen, L.W., Doddridge, B.G., Dickerson, R.R., Chow, J.C., Muller, P.K., Quinn, J., Bulter, W.A., 2001. Seasonal variations in elemental carbon aerosol, carbon monoxide and sulfur dioxide: implications for sources. Geophys. Res. Lett. 28, 1711e1714.

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