Satellite-based prediction of daily SO2 exposure across China using a high-quality random forest-spatiotemporal Kriging (RF-STK) model for health risk assessment

Atmospheric Environment 208 (2019) 10–19

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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv

Satellite-based prediction of daily SO2 exposure across China using a highquality random forest-spatiotemporal Kriging (RF-STK) model for health risk assessment

T

Rui Lia, Lulu Cuia, Ya Menga, Yilong Zhaoa, Hongbo Fua,b,c,∗ 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 b Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, PR China c Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: SO2 level Spatiotemporal variation Health risk RF-STK China

China has been suffered from the severe sulfur dioxide (SO2) pollution in the past decades. The spatiotemporal estimation and health effect assessment of SO2 using two-stage machine learning models have not been performed yet. In this study, a high-quality model named random forest coupled with spatiotemporal Kriging (RFSTK) model was developed to estimate the daily SO2 concentration across the entire China from May 2014 to May 2015 based on the satellite data and geographic covariates. Compared with other statistical methods, the RF-STK model showed the better explanatory performance, with the 10-fold cross-validation R2 = 0.62 (rootmean-square error (RMSE) = 10.36 μg/m3) for daily estimations. The annually mean population-weighted SO2 concentration was estimated to be 30.49 ± 10.83 μg/m3 (mean ± standard deviation). The SO2 levels displayed the remarkably seasonal variation with the peak in winter (47.27 ± 22.64 μg/m3), followed by ones in autumn (28.41 ± 10.41 μg/m3) and spring (25.92 ± 7.95 μg/m3), and in summer (21.33 ± 6.51 μg/m3). At the national scale, only 20.31% of the population lived in the safe regions (population-weighted SO2 concentration < 20 μg/m3). The higher population-weighted SO2 concentrations were mainly concentrated on some provinces of North China Plain (NCP) (e.g., Shanxi, Hebei, Shandong), followed by the provinces of Northeast China, and the lowest one in Hainan (8.31 ± 1.38 μg/m3). The mean all-cause mortalities due to excessive SO2 exposure were estimated to be 131,957 cases, accounting for 0.009% of the whole Chinese population. Among all of the diseases, the mortalities per year were in the order of respiratory disease (RD) (11913 cases) > cardiovascular disease (CVD) (11386 cases) > chronic obstructive pulmonary disease (COPD) (8112 cases) > cerebrovascular disease (CEVD) (2188 cases). The statistical modelling of SO2 at a national scale provided the valuable data for epidemiological research and air pollution prevention.

1. Introduction In the past decades, China has been suffered from severe air pollution due to the rapid economic development and urbanization. The most prominent feature of serious air pollution is the lasting fog-haze episodes featured with high loadings of fine particles and visibility degradation (Fu and Chen, 2017; Huang et al., 2014). More serious foghaze pollution events have been observed in many city-clusters over China such as Jing-Jin-Ji (JJJ), Yangtze River Delta (YRD), and Pearl River Delta (PRD).

It was well documented that sulfur dioxide (SO2) was a key precursor of sulfate in the atmosphere, and the latter was contributed by the formation of fog-haze episodes (Lee, 2015). The transformation of ambient SO2 has been recommended to be one of the most important contributors to the particle explosive growth especially under the condition of high relative humidity (RH) via liquid-phase chemistry (Wang et al., 2016; Xie et al., 2017; Fu and Chen, 2017). Apart from the contribution to the haze formation, the ambient SO2 inflicted the direct damage to human health. Greenberg et al. (2016) confirmed a significant positive dose-response relationship between SO2 exposure and

∗ 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. E-mail address: [email protected] (H. Fu).

https://doi.org/10.1016/j.atmosenv.2019.03.029 Received 6 October 2018; Received in revised form 18 March 2019; Accepted 24 March 2019 Available online 31 March 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.

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(2016) used the chemical transport model to estimate the global NO2 concentration at a high spatiotemporal resolution and found that the population-weighted NO2 concentration tripled in East Asia during 1996–2012. Nevertheless, the health risk assessment for the SO2 exposure across the whole China has not been performed yet, which was mainly attributed to the limitations in the availability of high-resolution SO2 data. In this paper, we developed a hybrid RF-STK model incorporating SO2 column amounts, meteorological fields, and many other geographic covariates (e.g., land use types, GDP, population) to predict the daily SO2 level over conterminous China at a high spatial resolution. On the basis of the estimated SO2 concentration, we assessed the populationweighted SO2 level and the premature mortality from May 2014 to May 2015. To the best of our knowledge, this study is the first modelling work of ambient SO2 for China at a national scale using the RF-STK model. Filling the gap of statistically modelling daily SO2 level at a national scale, this study supplies the basic data for epidemiological studies and deepens the understanding of air pollution status.

asthma. Very recently, Wang et al. (2018) quantified the health effect of SO2 exposure and estimated that a 10 μg/m3 increase in two-day average SO2 concentration led to the increments of 0.59% in mortality based on the data of 272 cities in China. In recent years, thousands of state-managed sites have been established in China to monitor the hourly concentrations of ambient SO2. However, high uncertainty emerged when the areas were located far away from the monitoring sites. Therefore, it is of great significance to accurately reflect the highresolution spatial variation of SO2 across the whole China for the health risk assessment. Up to date, many statistical methods have commonly been applied to reflect the spatiotemporal distribution of the SO2 concentration. The land use regression (LUR) model has been commonly employed to estimate the within-city concentrations of SO2 worldwide. Huang et al. (2017) employed the LUR model based on the meteorological and land use data to simulate the SO2 concentrations in the unmonitored areas of Nanjing and found that the model could explain 71% spatial variance. In the LUR model developed by Amini et al. (2014), more explanatory variables including population density, and the distance to traffic surrogates have been integrated to improve the explanation ability. Unfortunately, the LUR model encountered the severe multicollinearity problem once a large quantity of explanatory variables were included in the LUR model simultaneously (Gulliver et al., 2011). Although the LUR model generally used stepwise linear regression (SLR) or least absolute shrinkage and selection operator (LASSO) to solve the multicollinearity problem, the nonlinearity and high-order interactions of independent variables and SO2 levels were still unmanageable based on the linear model. To accurately capture the nonlinear relationships between explanatory variables and pollutant concentrations, the machine learning models (e.g., random forest (RF), support vector machine (SVM)) have been increasingly used to simulate the spatiotemporal variations of air pollutants. Many studies have demonstrated that the RF model showed the higher estimation accuracy compared with some linear models due to the strength in modelling the complex relationships between explanatory variables and response variables (Hu et al., 2017; Zhan et al., 2018). Zhan et al. (2018) employed many methods to simulate the daily NO2 exposure at a national scale and found that the R2 of the RF model (R2 = 0.61) was much higher than that of the linear regression model (R2 = 0.38). Chen et al. (2018) confirmed that the RF model (R2 = 0.83) for PM2.5 showed the better performance than those for the generalized additive model (GAM) (R2 = 0.55) and the non-linear exposure-lag-response-model (DLNM) (R2 = 0.51). Thus, the RF model was more feasible than the linear models in simulating the concentrations of air pollutants at a national scale. However, the RF model did not consider the spatial autocorrelation of air pollutants and thus increased the uncertainties of estimated pollutant levels at a large spatial scale. The spatiotemporal Kriging (STK) model can further estimate the errors based on random forest models, resulting in the improved estimation accuracy of the original model. Therefore, a hybrid model named RF-STK model was recommended to simulate the SO2 concentration at a national scale due to simultaneous considerations of space and time. To date, only a few city-level or region-level SO2-LUR models have been widely used in China, whereas the ensemble models of machine learning and geostatistical methods were used less often especially at a national scale. The accurate estimation of pollutant concentrations facilitated health risk assessment. To date, most of the studies focused on the estimation of health burden induced by particulate matter (PM). Xie et al. (2016) estimated that about 1,255,400 premature deaths were induced by the ambient PM2.5 in 2010, 42% higher than in 2000. As a step forward relative to the previous studies, Liu et al. (2017a) reported that national deaths associated with PM2.5 exposure increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. Ebenstein et al. (2017) demonstrated that a 10-μg/m3 increase of PM10 reduced life expectancy by 0.64 years. Additionally, Geddes et al.

2. Materials and methods 2.1. Ground-based SO2 measurements The hourly ground-based SO2 data from May 13, 2014 to May 12, 2015 across China were collected from the website of China air quality monitoring platform (http://www.aqistudy.cn/). This hourly SO2 data for major cities have been published on the open website since 2013. Recently, the network expanded to 1479 monitoring sites covering the 336 cities in the 31 provinces (autonomous region, municipalities) over China. All of the locations for the monitoring sites are depicted in Fig. 1 and Fig. S1. The monitoring sites in all of the cities have been designed as a mixture of urban, suburban, and background sites. These sites supplied abundant training samples to simulate the SO2 concentrations in suburban or rural areas. In each monitoring site, the automated monitoring systems were established to measure the SO2 concentration. The SO2 concentration was determined using the ultraviolet fluorescence method (TEI, Model 43i from Thermo Fisher Scientific Inc., USA). All of the SO2 analyzers were calibrated with the standard gases once a week. The data quality in each site were assured on the basis of HJ 630–2011 specifications. In addition, the reliability and validity tests were performed on the hourly data in each site to remove the missing or problematic data before calculating the mean concentration. The hourly SO2 levels were averaged by days for each site, and days with less than 20 h measurements were excluded. The monthly mean concentration of SO2 was calculated based on at least 30 daily data except in February. The daily SO2 concentration in China ranged from 1.00 to 764.00 μg/m3 with the mean ± standard deviation of 29.47 ± 25.49 μg/m3 during 2014–2015 (Fig. 1). The SO2 concentration in China showed significantly seasonal variation with the highest value in winter (44.55 ± 37.44 μg/m3), followed by autumn (28.69 ± 17.16 μg/m3) and spring (24.45 ± 14.74 μg/m3), and the lowest one in summer (20.17 ± 11.97 μg/m3). In addition, the remarkable spatial variation of SO2 was also observed in Fig. 1. The annually mean SO2 concentrations in the cities of NCP displayed the remarkably higher values than the air quality guideline (20 μg/m3) of World Health Organization (WHO). However, the annually mean SO2 concentrations in most cities of South China were significantly lower than the air quality guideline. 2.2. Satellite-retrieved SO2 column amounts The column amounts of SO2 (DU) were collected from Ozone Monitoring Instrument-SO2 (OMI-SO2) level-3 data product (0.25° × 0.25° resolution) onboard the Aura satellite. The OMI satellite showed global coverage on a daily basis, with an equatorial crossing 11

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Fig. 1. The geographical positions of the SO2 monitoring sites during 2014–2015 in China.

time during 12:00–15:00 local time. The filtering criteria of satelliteretrieved SO2 data included cloud radiance fraction < 0.3, solar zenith angles < 85°, terrain reflectivity < 30%, and 10 < cross-track positions < 50. Additionally, the cross-track pixels influenced by row anomaly were also deleted. The OMI-SO2 retrievals showed a yearly coverage rate of 57.22%, with the highest one in summer (65.46%), followed by ones in spring (61.44%) and autumn (58.82%), and the lowest one in winter (42.26%) (Table S1).

2.4. Model description and evaluation The RF-STK model was considered as the random forest model combined with the spatiotemporal Kriging model. The OMI-SO2 satellite data and other auxiliary variables (e.g., meteorological factors, land use types) were incorporated into the model. The RF-STK model is a two-way model that the simulation errors simulated by random forest model were then estimated by spatiotemporal Kriging model. The predictions of random forest model coupled with spatiotemporal Kriging model were summed as the final results of RF-STK model. The detailed equation was as follows:

2.3. Meteorological data, land use types, and other explanatory variables The daily meteorological data in the 839 weather stations during 2014–2015 were obtained from China meteorological bureau. All of the meteorological data included precipitation (Prec) (Unit: mm), air temperature (T) (°C), wind speed (WS) (m/s), sunshine duration (tsun) (h), air pressure (P) (kPa), and RH (%) (Table S2). The elevations were collected from the Shuttle Radar Topography Mission (30 m resolution). The data of GDP, population density, and net primary production (NPP) with 1 km × 1 km resolution were obtained from the China Resource and Environmental Science Data Center (http://www.resdc.cn/). The GDP and population density in 2015 were used to estimate the SO2 concentration across China because these data were open access once in five years. The monthly NPP during 2014–2015 was incorporated into the model. In addition, the potential pollution point source, transportation source, and land use data have been obtained from Land Resource Bureau. The pollution point source data consist of restaurant, railway station, airport, and gas station. The transportation source data included shipping, provincial road, railway, county road, bus rapid transit (BRT), metro line, and highway. The land use data comprise of green space, urban land, forest land, agricultural land, and industrial land (30 m resolution) (Table S2). In addition, the day of year (DOY) was also incorporated into the model. All of the input data obtained were resampled to 0.25° × 0.25° grids using proper spatial operations for the SO2 estimation. The daily meteorological data for each cell were interpolated from the weather stations using co-kriging with elevation. The point number, road length, and land use area for each cell was summed within the cell through spatial clipping. The data of elevation, population densities, GDP, and NPP were resampled to the 0.25° grids using area-weighted averages. All of the explanatory variables were joined to an integrated table for model development on the basis of the spatiotemporal information.

Z (s, t ) = R (s, t ) + K (s, t )

(1)

where Z(s,t) represents the estimated SO2 concentration at the location s and time t; R(s,t) denotes the SO2 concentration simulated by the random forest model; K(s,t) is the estimated errors by spatiotemporal Kriging model. The random forest model developed in the present study was composed of 500 regression trees. All of the trees were built on the bootstrapped samples extracted randomly from the training dataset that contained the observation values of SO2 and many explanatory variables. The number of trees was determined on the basis of the fact that the improvement in the simulation performance was negligible after 500 trees, but the computing cost increased rapidly with the increase for the number of trees. In the random forest model, about 2/3 of the data for the original training set were applied to plant each tree and the data in each tree is not exactly the same those in other trees. Tree in the random forest model can be treated as the classifier, and the random forest model is consisted of N decision trees. In the process of node splitting, not all attributes of random forest participate in the calculation of attribute index. Therefore, the significant correlations of decision trees can be avoided. The predictions of the random forest model were estimated through averaging the estimations of all of the individual trees. The backward variable selection was performed on the random forest model to achieve the higher performance. The detailed algorithm was described previously (Zhan et al., 2018). In the spatiotemporal Kriging model, the out-of-bag (OOB) errors were interpolated instead of the fitting residuals of the random forest model because the fitting residuals were generally much lower than the expected estimation errors. The OOB error for the training sample xi denoted the mean estimation error of the trees grown on the bootstrap 12

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samples without xi. The RF-STK model made predictions for all of the grid cells and days by summing the results of the final random forest model and the OOB errors interpolated by the spatiotemporal Kriging. The R package gstat and scikit-learn python package were applied to perform the RF-STK model. The estimation accuracy of the RF-STK model was evaluated using the 10-fold cross validations based on the observation data in the monitoring sites. All of the observation data were randomly divided into 10 similarly sized groups. In each of the 10 rounds, nine groups were applied to train a RF-STK model and then to simulate the SO2 concentration in the remaining group. After ten rounds, every SO2 observation value showed a corresponding paired estimation. Many statistical indicators including determination coefficient (R2), root mean square error (RMSE), and relative prediction error (RPE) were employed to test the performance of the RF-STK model.

3. Results and discussion

and predictor variables was not always in line with the actual situation. It was widely recognized that the aerosol particles and gaseous pollutants displayed the nonlinear relationship with the explanatory variables rather than linear correlation (Chen et al., 2018; Zhan et al., 2018), and thus the prediction performance of MLR was relatively worse than the nonlinear model. As a temporal extension of ordinary Kriging method, the STK model showed the superior accuracy in spatial interpolation due to the simultaneous consideration of space and time, but STK increased the model uncertainty when scarce monitoring sites were distributed at a large spatial scale (Wu et al., 2018). Mirroring with other statistical models, the RF model was user-friendly because it did not need to predefine the complex (linear/nonlinear) relationships between SO2 and explanatory variables. In addition, it can take full advantage of the strength of each explanatory variable and it is robust to avoid the occurrence of overfitting (Breiman, 2001). Moreover, the RF approach used in the present study showed the higher predictive ability compared with many other models (e.g., MLR, support vector machine) (Chen et al., 2018). Another strength of the RF approach was that it provided the variable importance value of each explanatory factor. The variable importance measures could pinpoint which variables were the most important in the reduction of estimation errors. Despite the possession of great prediction advantage, the RF model did not consider the spatiotemporal correlation of the SO2 level across China, and thus the R2 of the RF model (R2 = 0.59) was slightly lower than that of the RF-STK model (R2 = 0.62). Compared with the firststage model, the better performance of RF-STK suggested that the estimation errors exhibited the remarkably spatial correlation. In the present study, we adopted a backward elimination strategy developed by Diaz-Uriarte and Alvarez de Andres (2006) and then achieved the best estimation accuracy of the RF-STK model, discarding the variables with the lowest importance value. The 10-fold cross-validation results indicated that the final model captured the higher explanatory performance on daily (R2 = 0.62, Slope = 0.70, RMSE = 10.36 μg/m3) (Fig. 2a), seasonally (R2 = 0.66, Slope = 0.79, RMSE = 5.65 μg/m3) (Fig. 2b), and yearly (R2 = 0.67, Slope = 0.85, RMSE = 3.24 μg/m3) bases (Fig. 2c). Mirroring previous studies, we found that the prediction accuracy varied with space and time. For instance, the model accuracy was the best in summer, followed by ones in spring and autumn, and the worst one in winter in terms of the cross validation R2 (Table S6). It was assumed that increase of uncertain pollution sources (e.g., bulk coal combustion in the rural regions) could decreased the estimation accuracy. Besides, the chemical transformation from SO2 to sulfate was frequently observed during the serious foghaze episodes of winter. However, the chemical mechanism was not considered in the RF-STK model, thereby leading to the lower R2 value in winter (Zhao et al., 2017). The model performance also showed the remarkably spatial variation with the relatively higher R2 values in NCP (R2 = 0.65) and Southeast China (R2 = 0.63) and the lower ones in Northwest China (R2 = 0.54) and Southwest China (R2 = 0.56) (Table S6). It was assumed that the fewer stations available in West China probably decreased the cross validation R2 values in these regions.

3.1. Predictive performance

3.2. Variable importance

All of the meteorological factors (e.g., WS, Prec, T, P, RH, and tsun), land use types (e.g., urban land, industrial land), GDP, elevation, and SO2 column concentration were applied to simulate the spatiotemporal variability of SO2. In the present study, five statistical models including multiple linear regression (MLR), STK, and MLR-STK, RF, RF-STK were used to simulate the daily SO2 concentration. MLR, STK, and MLR-STK explained 36% (RMSE = 15.42 μg/m3, RPE = 50.14%), 56% (RMSE = 11.18 μg/m3, RPE = 41.52%), and 57% (RMSE = 10.99 μg/ m3, RPE = 40.96%) of SO2 variability (Table S5), respectively. Daily RF and RF-STK model displayed much higher CV R2 and lower RMSE than those for MLR, STK, and MLR-STK methods. Although MLR was usually simple and convenient, the predefined linear relationship between SO2

The results of the variable importance are depicted in Fig. 3. In the final RF-STK model, the meteorological factors collectively occupied 57.43% of the overall relative importance (Fig. 3). Among all of the explanatory variables, WS, Prec, and T ranked the top three most important variables, with the relative important values of 13.15, 13.06, and 9.61, respectively. In addition, some other meteorological parameters including P, RH, and tsun also played the important roles on the SO2 concentration in the ambient air (Fig. 3). It was well documented that the SO2 concentration was closely associated with the meteorological condition (Xie et al., 2015; Liu et al., 2017b). As the most important meteorological factors, WS and Prec exhibited the adverse effects on SO2 because the advection was a key process for the SO2

2.5. The estimation method of the population-weighted SO2 exposure concentration The daily SO2 predictions in all of the grids were estimated using the RF-STK model. The population-weighted SO2 exposure concentration during the period in each grid was calculated based on the estimated SO2 concentration and the population in the corresponding grid. The calculation equation of the population-weighted SO2 concentration was as follows:

PWEL =

∑ (Pi × Ci)/ ∑ Pi

(2)

where i is the grid number, Pi represents the population inside the grid, and Ci represents the SO2 concentration inside the grid. 2.6. The mortality estimation equation The mortality was assumed as the probabilistic events subjected to the Poisson distribution in statistics. The avoided premature mortality attributable to the SO2 exposure was estimated using the following formula:

N = y0 (1 − 1/exp[β × (C − C0 )]) × Pop

(3)

where N (Unit: case) is the avoided premature mortality due to the SO2 exposure; y0 (%) represents the baseline mortality of a specific disease for a given population (Table S3); β denotes the exposure-response coefficient (Table S4); C is the SO2 concentration in the ambient air (μg/m3); C0 represents a threshold level with no health risks (20 μg/ m3); Pop (case) denotes the exposed population in each grid. It was calculated that the avoided premature mortalities for all-cause disease, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), cerebrovascular disease (CEVD), and respiratory disease (RD) due to the SO2 exposure.

13

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Fig. 2. The performance of the random forest-spatiotemporal Kriging (RF-STK) model in predicting the (a) daily, (b) seasonal, and (c) annual SO2 concentrations in China.

including SO2 column concentration, DOY, terrain, land use types, and socioeconomic indicators significantly contributed to the SO2 accumulation in the ambient air. In the present study, the spatial correlation of SO2 column amount and the measured SO2 concentration was not prominent (R2 = 0.35), and the daily correlation was much weaker (R2 = 0.20). It was widely recognized that the use of vertically scaled retrievals achieved worse explanatory performance than the application of tropospheric satellite retrievals, which might be attributed to the inadequate knowledge about the environmental processes for chemical transport models (Zhan et al., 2018). Therefore, the relative importance value of SO2 column concentration for the surface SO2 estimation was significantly lower than that of tropospheric NO2 column for the prediction of NO2 (Zhan et al., 2018). Furthermore, some geographic covariates including meteorological condition and land use types should be applied to improve the model. As a dummy variable, the relative importance value of DOY reached 7.37, suggesting that the explanatory variables and SO2 concentrations exhibited markedly temporal correlation. The relative importance value of elevation was 9.16, reflecting the well-established correlation between elevation and the SO2 level. Zhang et al. (2015) also found that the SO2 concentration in the ambient air was strongly dependent on the geographical factors especially the elevation. Compared with the meteorological conditions and terrain, the relative importance values of land use types and socioeconomic factors to the SO2 concentrations were slightly lower. Although more than 20 land use/socioeconomic variables were employed to the model, most of them were excluded from the model through the variable selection process. Industrial land, urban land, and GDP were the remaining geographical variables of the RF-STK model. It was well known that the combustion of fossil fuel should be the important sources of SO2, which was mainly located on the industrial land surrounding the urban areas (Huang et al., 2017; Fu and Chen, 2017). Especially, the coal combustion for heating in winter was frequently observed in the urban

Fig. 3. Variable importance plot for the RF-STK model estimating the daily SO2 concentrations in China.

removal (Zhao et al., 2016). Additionally, T was also closely linked to the pollutant concentration through modulating atmospheric turbulence and chemical reactions (e.g., heterogeneous reactions from SO2 to sulfate) (Li et al., 2015a), which has been observed in many countries such as the United States and China (Tai et al., 2010; He et al., 2017). In general, the low T tended to be linked with the high P, low tsun, and thin mixing boundary layer, leading to the air stagnation and the condensed SO2 on the ground surface (Li et al., 2013, 2017). Besides, Zhao et al. (2017) verified that water promoted SO2 adsorption and sulfate formation on the surface of soot when RH ranged from 60% to 70%. Apart from the effects of meteorological conditions, other variables 14

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Table 1 The annually and seasonally average population-weighted daily SO2 concentrations (μg/m3) in different provinces (autonomous region, municipality) over China. Province

Spring

Summer

Autumn

Winter

Annual

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei Henan Heilongjiang Hubei Hunan Jilin Jiangsu Jiangxi Liaoning Inner Mongolia Ningxia Qinghai Shandong Shanxi Shaanxi Shanghai Sichuan Tianjin Tibet Xinjiang Yunnan Zhejiang Nation

25.55 ± 3.66 27.51 ± 4.94 19.97 ± 2.28 15.82 ± 4.01 21.64 ± 4.01 14.19 ± 3.01 17.13 ± 2.98 20.83 ± 2.27 9.25 ± 1.94 41.43 ± 7.04 30.91 ± 5.35 24.00 ± 3.77 25.13 ± 3.49 24.45 ± 3.22 22.48 ± 2.87 27.75 ± 4.06 23.24 ± 2.77 32.78 ± 3.97 29.25 ± 5.70 26.07 ± 5.10 14.77 ± 3.50 40.46 ± 8.75 39.92 ± 6.39 24.06 ± 4.81 24.56 ± 3.11 21.65 ± 3.36 34.08 ± 4.39 11.39 ± 5.22 45.39 ± 8.13 17.39 ± 2.49 20.53 ± 3.54 25.92 ± 7.95

21.25 ± 4.08 18.04 ± 3.00 17.48 ± 2.20 13.46 ± 2.88 17.39 ± 3.96 14.32 ± 3.88 15.89 ± 2.89 15.55 ± 2.90 8.80 ± 1.33 32.37 ± 7.11 28.56 ± 4.68 13.31 ± 1.65 23.17 ± 3.58 20.41 ± 2.53 13.73 ± 2.49 21.24 ± 4.56 19.91 ± 1.55 20.71 ± 3.64 24.15 ± 6.89 21.38 ± 3.60 18.83 ± 5.38 33.72 ± 9.63 31.49 ± 6.26 18.22 ± 3.59 18.83 ± 2.46 20.81 ± 4.64 27.80 ± 4.78 13.08 ± 3.50 15.51 ± 2.58 18.97 ± 4.77 16.54 ± 2.89 21.33 ± 6.51

26.60 ± 5.15 30.64 ± 5.92 18.60 ± 2.18 16.75 ± 3.87 18.98 ± 5.66 17.98 ± 4.95 19.81 ± 4.61 19.34 ± 2.43 9.40 ± 1.59 42.27 ± 8.08 39.30 ± 7.08 32.85 ± 6.42 26.28 ± 4.66 26.86 ± 3.89 29.56 ± 6.00 27.56 ± 5.42 25.26 ± 2.40 36.72 ± 6.57 40.48 ± 7.46 33.75 ± 10.05 18.88 ± 6.33 43.94 ± 9.58 50.07 ± 12.35 23.05 ± 7.48 24.00 ± 3.43 20.75 ± 3.40 34.65 ± 4.52 12.06 ± 2.65 15.55 ± 3.82 20.18 ± 2.55 21.20 ± 3.86 28.41 ± 10.41

38.46 ± 8.60 61.10 ± 11.13 25.97 ± 2.99 21.23 ± 5.43 46.71 ± 13.26 20.28 ± 4.74 23.67 ± 4.88 33.15 ± 3.47 10.20 ± 1.53 89.51 ± 17.59 57.63 ± 16.10 75.72 ± 16.30 35.23 ± 5.63 34.25 ± 3.50 61.71 ± 13.42 39.98 ± 11.41 32.99 ± 3.56 78.27 ± 11.84 80.04 ± 16.89 69.96 ± 17.52 27.99 ± 9.06 75.34 ± 13.97 103.45 ± 20.07 49.71 ± 18.46 32.33 ± 1.94 28.80 ± 6.11 77.06 ± 7.49 13.80 ± 10.48 85.60 ± 16.21 22.30 ± 3.85 28.45 ± 3.73 47.27 ± 22.64

27.64 ± 5.27 35.83 ± 6.76 20.24 ± 2.18 16.27 ± 4.16 28.24 ± 5.83 16.78 ± 4.36 19.99 ± 4.10 21.14 ± 1.87 8.31 ± 1.38 50.07 ± 9.92 41.50 ± 7.65 32.20 ± 4.80 27.76 ± 4.51 25.67 ± 3.22 32.33 ± 4.98 28.99 ± 6.56 26.02 ± 2.26 42.59 ± 5.15 38.35 ± 8.35 37.83 ± 9.52 30.50 ± 5.80 48.36 ± 10.42 53.85 ± 11.63 30.08 ± 7.83 24.94 ± 2.61 20.23 ± 4.07 45.85 ± 5.63 15.03 ± 4.28 24.38 ± 6.25 22.25 ± 3.24 21.22 ± 3.53 30.49 ± 10.83

industries and populations tended to result in the SO2 emission hotspot (Li et al., 2017, 2019). Moreover, Yang et al. verified that the spatial pattern of the SO2 level was closely related to some meteorological conditions such as Prec and RH. The drier meteorological condition was not favorable to the SO2 removal. Nevertheless, Hainan province exhibited the lowest SO2 concentration due to the relatively less power plants and industrial points and higher rainfall amount and WS (Li et al., 2015b). The seasonal variations of the population-weighted SO2 concentrations were estimated to be 25.92 ± 7.95, 21.33 ± 6.51, 28.41 ± 10.41, and 47.27 ± 22.64 μg/m3 (Fig. 5) for spring, summer, autumn, and winter, respectively. The seasonality, that was, considerably higher SO2 in winter than ones in summer, was consistent across all of the provinces over China (Fig. 5a–d). It was associated with the meteorological conditions and energy consumption. It was widely acknowledged that the stagnant weather characterized with shallow mixing layers, weak solar radiation, and slow wind speed emerged more frequently in winter, which was not beneficial to the SO2 advection and convection (Zhao et al., 2018a,b), as suggested by the high importance value of meteorological factors and DOY in the RF-STK model. Besides, increased coal consumption for domestic heating brought about the higher SO2 concentrations in winter. Liu et al. (2015) demonstrated that the SO2 emission from coal-fired boilers have increased by 23.95%/year during 1990–2010, which could exacerbate the seasonal variability of the SO2 level. Moreover, the less OH and H2O were also responsible for the higher SO2 level in winter because these species were inclined to the transformation from SO2 to sulfate (Gen et al., 2019). Additionally, the SO2 concentrations in different seasons exhibited distinct spatial variability. Shanxi and Hebei provinces showed the higher SO2 levels all the year, while Xinjiang and Inner Mongolia autonomous regions, and Liaoning province only exhibited the higher SO2 concentrations in winter. It was well known that many power plants, mining enterprise, and iron and steel industries were

areas of many northern cities (Huang et al., 2014). Amini et al. (2014) also reported that the SO2 concentration in Tehran was closely related to the commercial land use area, which was the most developed region for the whole city. Thus, the higher SO2 concentration generally focused on the industrial and urban areas with high GDP. It was interesting to note that the explanatory variables such as road length and transportation point removed from the final model were more or less related to the SO2 concentration. Nevertheless, they were excluded from the final model because their introduced noise was higher than the gained information.

3.3. Spatiotemporal variation of SO2 The annually mean population-weighted SO2 concentration was estimated to be 30.49 ± 10.83 μg/m3 across China during 2014–2015 (Table 1). The overall estimated SO2 concentration in China exceeded the air quality guideline (20 μg/m3). The standard deviation of 10.83 μg/m3 reflected the remarkably spatial variability of the SO2 concentration. At a spatial scale, the SO2 concentration was estimated to be the highest one in Shanxi province (53.85 ± 11.63 μg/m3), followed by Hebei province (50.07 ± 9.92 μg/m3), Shandong province (48.36 ± 10.42 μg/m3), Tianjin municipality (45.85 ± 5.63 μg/m3), Liaoning province (42.59 ± 5.15 μg/m3), and the lowest one in Hainan province (8.31 ± 1.38 μg/m3) (Fig. 4 and Table 1). The higher SO2 level was observed on some provinces of NCP because many coal-fired power plants, steel factories, and cement industries were mainly concentrated on the region (Hua et al., 2016). Although more strict control technologies such as cyclones, electrostatic precipitator (ESP), wet scrubber, and fabric filters (FF) have been installed on the industrial equipment, the SO2 emission in NCP was still higher than other regions of China (Li et al., 2015a). In addition, the provinces with the higher population-weighted SO2 levels were in good agreement with the highly populated areas. It was generally believed that the dense 15

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Fig. 4. The spatial distribution of predicted SO2 concentration in China.

events reduced the SO2 concentration in the spring of Xinjiang and Inner Mongolia autonomous region (He et al., 2017).

densely distributed on Hebei and Shanxi provinces (Tian et al., 2011). Therefore, the SO2 emissions from coal-fired power plants in both of the provinces were significantly higher than those of other provinces all year round (Li et al., 2019). However, the higher SO2 levels in the winter of other provinces (autonomous region) in North China (e.g., Xinjiang, Inner Mongolia, and Liaoning) was mainly contributed by the massive combustion of fossil fuels for residential heating (Lu et al., 2013). However, the domestic heating was generally not performed in the other three seasons. Furthermore, the local meteorological conditions in spring and summer were not favorable to the SO2 accumulation in these provinces. For instance, the dense precipitation in summer decreased the SO2 level of Liaoning province and the frequent dust

3.4. The population exposure and mortality risk The exposure intensity and duration of SO2 across the entire China are summarized in Table 2. 20 μg/m3 was considered as the critical value of SO2 level. The nonattainment regions represented the areas with the SO2 concentration higher than 20 μg/m3. At the national scale, 79.69% of the Chinese people were estimated to live in the nonattainment regions (20 μg/m3). At a regional scale, nearly all of the people in Beijing, Shanghai, and Tianjin municipalities, and Hebei,

Fig. 5. The seasonal variation of the simulated SO2 concentrations during 2014–2015 (a: spring; b: summer; c: autumn; d: winter). 16

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85.08 ± 4.49). The severe SO2 pollution observed in the winter of Northeast China was mainly contributed by the dense coal combustion for domestic heating (Zhao et al., 2018a,b). However, the lower SO2 exposure in the summer of Northeast China resulted from the abundant rainfall and appropriate diffusion condition. Han et al. (2015) verified that the precipitation in Northeast China featured the large temporal inhomogeneity with the dense rainfall in summer, which could reduce the ambient SO2 level. Thus, the energy-intensive industries should be replaced by the industries with high energy consumption unless they take effective measures to meet national emission standards. Table 3 presents the avoided premature mortalities for all-cause disease, COPD, CVD, CEVD, and RD. The mean all-cause mortalities due to excessive SO2 exposure were estimated to be 131,957 (95%CI: (35421–227,223)) cases per year based on the long-term health function, accounting for 0.009% of the whole Chinese population. The allcause mortalities exhibited notably seasonal difference with the highest mortalities in winter (95%CI: (20,698–131,768)), followed by autumn (95%CI: (6900–46,676)), spring (95%CI: (5195–33,708)), and the lowest one in summer (95%CI: (2627–17,071)), which was in good agreement with the ambient SO2 concentration. Among all of the diseases, the mean RD mortalities incurred from SO2 reached 11,913 (95%CI: (4911–18,806)) cases per year, followed by CVD (mean: 11,386 (95%CI: (6432–16300)) and COPD (mean: 8112 (95%CI: (3310–12,871)) (Table 3). The mean mortalities for CEVD (mean: 2188 (95%CI: (1459–2917)) was the lowest ones compared with other three diseases. The attributable fractions of COPD, CVD, CEVD, and RD to allcause mortalities were 6.15%, 8.63%, 1.66%, and 9.03%, respectively. The mean mortalities for all-cause, COPD, CVD, CEVD, and RD due to the SO2 exposure were significantly lower than those caused by the PM2.5 exposure (Song et al., 2016). Furthermore, the affected areas due to PM2.5 and SO2 presented marked difference. PM2.5 imposed negative health effect on the population over China, while the adverse effect of SO2 was mainly concentrated on some provinces of NCP (e.g., Shandong, Hebei, Beijing, and Tianjin). The spatial distributions of all-cause, COPD, CVD, CEVD, and RD mortalities are illustrated in Fig. 7 and Fig. S2. At the spatial scale, the all-cause, COPD, CVD, CEVD, and RD mortalities ranged from 0 to 900 cases, from 0 to 60 cases, from 0 to 50 cases, from 0 to 15 cases, and from 0 to 80 cases per grid element, respectively (Fig. 7). The all-cause mortalities and the mortalities of four diseases showed the similar spatial distribution, which was also in good agreement with the variation of SO2 concentration and the Heihe-Tengchong Line (the line reflecting the spatial distribution of population in China) over China. It was assumed that the urbanization resulted in the concentrated energy use, thereby leading to the severe air pollution with potentially major effects on human health (Chen et al., 2017). In contrast to NCP, other regions exhibited the lower mortalities though some of them such as Kashi (Xinjiang autonomous region) showed relatively higher SO2 concentration. It was supposed that only 4.51 million lived in Kashi and the less population led to the lower mortalities despite the higher SO2 level.

Table 2 The population ratios (%) exposed to the SO2 exceeding air quality guideline (20 μg/m3) in 31 provinces (autonomous region, municipality). Province

Spring

Summer

Autumn

Winter

Annually mean ratio

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei Henan Heilongjiang Hubei Hunan Jilin Jiangsu Jiangxi Liaoning Inner Mongolia Ningxia Qinghai Shandong Shanxi Shaanxi Shanghai Sichuan Tianjin Tibet Xinjiang Yunnan Zhejiang Nation

95.42 100 40.76 13.72 80.23 0.42 25.48 48.48 0 100 100 77.72 93.13 88.79 76.97 100 91.45 100 89.95 94.45 89.16 100 100 78.44 100 45.83 100 0.13 43.82 48.47 65.29 73.04

46.25 18.69 5.55 0.22 41.50 4.49 13.15 6.63 0 94.46 97.33 2.69 87.20 52.02 2.43 51.46 52.45 62.35 60.24 61.07 93.23 95.23 95.48 28.41 29.77 8.17 98.65 18.96 6.71 52.80 1.27 45.48

97.96 100 9.84 17.52 56.26 37.83 52.84 23.98 0 100 100 99.99 94.03 92.07 99.96 99.71 98.96 100 100 77.36 93.79 100 100 71.49 93.16 13.09 100 5.85 50.54 79.37 71.66 75.60

100 100 100 45.77 99.97 59.55 91.17 100 0 100 100 100 100 100 100 100 100 100 100 100 99.76 100 100 100 100 89.98 100 25.43 97.16 96.34 98.86 93.81

97.45 100 55.95 13.14 95.59 11.84 43.65 64.55 0 100 100 99.92 96.18 93.00 100 100 99.35 100 100 100 94.28 100 100 88.85 100 49.40 100 7.23 74.52 76.31 71.30 79.69

Henan, Jilin, Jiangsu, Liaoning, Inner Mongolia, Ningxia, Shandong, and Shanxi provinces (autonomous region) suffered from the excessive SO2 exposure (20 μg/m3). In contrast, Hainan province showed excellent air quality with no excessive SO2 exposure. Except Fujian (13.14%), Guangdong (11.84%), Guangxi (43.65%), Hainan (0%), Sichuan (49.40%), and Tibet (7.23%), other provinces (autonomous region, municipality) possessed more than 50% of the population suffering from the excessive SO2 exposure. The exposure intensity also exhibited remarkably seasonal variation with the highest exposure ratios in winter and the lowest ones in summer across the whole China. Except Tibet autonomous region (25.43%), Fujian (45.77%), Guangdong (59.55%), and Hainan (0%) provinces, nearly all of the people in other provinces were faced of the severe SO2 exposure (96–100%) in winter. The population in most of the provinces presented the relatively lower SO2 exposure in summer, while more than 95% of the population in NCP (Tianjin (98.65%), Henan (97.33%), Shandong (95.23%)) still experienced the severe SO2 pollution even in summer. The average nonattainment days over China ranged from 0 day in Hainan to 315.12 days in Shanxi, with the mean values of 164.65 days (Table S7). Apart from Fujian, Guangdong, Hainan, and Tibet provinces (autonomous region), nearly all of the people in other provinces experienced the severe SO2 pollution for more than 100 days (Fig. 6). Especially, the nonattainment days in Shanxi and Shandong reached 315 and 305 days, respectively. Besides, the nonattainment days in China displayed significantly temporal difference with more in winter (62.63 ± 27.11 days), followed by autumn (40.38 ± 29.88 days), spring (34.20 ± 25.99 days), and the fewest days in summer (27.44 ± 28.51 days). Hainan and Tibet showed relatively lower SO2 temporal variation because the SO2 level in these areas were significantly lower than those in other provinces of China. It was interesting to note that Heilongjiang and Jilin provinces displayed the distinct SO2 nonattainment days in summer (Heilongjiang: 1.19 ± 1.68, Jilin: 3.72 ± 10.21) and winter (Heilongjiang: 79.11 ± 15.16, Jilin:

4. Conclusion A high-quality statistical model named RF-STK with high accuracy was applied to estimate the daily SO2 concentration across China during 2014–2015. Taking advantage of SO2 data in 1479 monitoring sites across China, the developed RF-STK model showed the better predictive performance than other statistical models and chemical transport models (CTMs), with the 10-fold cross-validation R2 = 0.62 (RMSE = 10.36 μg/m3) for daily predictions. Among all of the explanatory variables, WS, Prec, and T were the most important variables, with the relative important values of 13.15, 13.06, and 9.61, respectively. The predicted SO2 concentration over China reached 30.49 ± 10.83 μg/m3. The estimated SO2 levels were in the order of winter (47.27 ± 22.64 μg/m3) > autumn (28.41 ± 10.41 μg/ 17

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Fig. 6. The spatial variations of the nonattainment days (> 20 μg/m3). Table 3 The avoided premature mortalities due to the excessive SO2 exposure in China. Health endpoints

Spring mortality (case)

Summer mortality (case)

Autumn mortality (case)

Winter mortality (case)

Average mortality (case)

All-cause COPD CVD CEVD RD

19465 (5195, 33708) 1195 (264, 2419) 1681 (946, 2414) 321 (214, 427) 1746 (720, 2756)

9851 (2627, 17071) 605 (134, 1225) 850 (478, 1222) 162 (108, 216) 883 (364, 1394)

25825 (6900, 44676) 1586 (351, 3208) 2229 (1256, 3201) 426 (284, 568) 2319 (956, 3660)

76814 (20698, 131768) 4726 (2563, 6020) 6626 (3752, 9462) 1279 (853, 1705) 6965 (2871, 10995)

131957 (35421, 227223) 8112 (3310, 12871) 11386 (6432, 16300) 2188 (1459, 2917) 11913 (4911, 18806)

m3) > spring (25.92 ± 7.95 μg/m3) > summer (21.33 ± 6.51 μg/ m3). At the national scale, 79.69% of the population lived in the nonattainment regions (population-weighted SO2 concentration > 20 μg/ m3). The higher population-weighted SO2 concentrations were mainly concentrated on some provinces of NCP (e.g., Shanxi, Hebei, Shandong), followed by the provinces of Northeast China, and the lowest one in Hainan. The mean all-cause mortalities due to excessive SO2 exposure were estimated to be 131,957 cases, accounting for 0.009% of the whole Chinese population. Among all of the diseases, the mortalities per year were in the order of RD (11913 cases) > CVD (11386 cases) > COPD (8112 cases) > CEVD (2188 cases).

The contribution of the present study is to estimate the spatiotemporal variations of SO2 across the whole China using a newly developed RF-STK model and assessed potential health risks due to the SO2 exposure for the first time. Considering the important role and severe pollution status of SO2 in China, many efforts should be made to control the SO2 pollution especially in the highly populated regions such as NCP. It should be noted that some limits existed in our study. First of all, we only used five statistical models and compared their performances. In the future work, more statistical models and CTMs should be performed to determine the optimal one. In addition, the explanatory variables included in the statistical model were still

Fig. 7. The spatial variation of all-cause mortalities due to the excessive SO2 exposure across the entire China. 18

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relatively scarce. More explanatory variables such as the detailed industrial points should be integrated to further improve the model.

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