Quantifying public health benefits of PM2.5 reduction and spatial distribution analysis in China

Science of the Total Environment 719 (2020) 137445

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Quantifying public health benefits of PM2.5 reduction and spatial distribution analysis in China Guiwen Luo 1, Lanyi Zhang 1, Xisheng Hu, Rongzu Qiu ⁎ College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Estimates health impacts and monetary value in China based on BenMAP model • The efficiency of environmental policies from 2016 to 2018 was verified. • Greater public health benefits will be obtained if the PM2.5 concentration can be controlled to 35 μg/m3. • The avoided deaths have high-value spatial clustering and exhibit a spatial positive autocorrelation. • The distribution of economic benefits shows a certain spatial aggregation effect.

a r t i c l e

i n f o

Article history: Received 15 December 2019 Received in revised form 17 February 2020 Accepted 18 February 2020 Available online 19 February 2020 Editor: Pavlos Kassomenos Keywords: PM2.5 Health benefits Spatial distribution BenMAP China

a b s t r a c t In recent years, particulate matter (PM) air pollution has become a significant and growing public health problem in China. In this study, the daily PM2.5 exposure level at a spatial resolution of 100 km2 was simulated based on the data of 1328 monitoring sites and the Voronoi Neighborhood Averaging (VNA) interpolation method. The results reveal that the daily mean PM2.5 concentration reduced from 47.82 μg/m3 (2016) to 40.87 μg/m3 (2018), a reduction of 14.53%. We first calculated the heath impacts and economic benefits of this reduction (Scenario 1) by using Environmental Benefits Mapping and Analysis Program (BenMAP). The estimated avoided premature mortalities for all-cause, cardiovascular diseases, respiratory diseases, and lung cancer were in the range of 7214 to 81,681 cases (total of 154,176 cases). The estimated economic benefits based on willingness to pay (WTP) ranged from 3.96 to 44.85 billion RMB (total of 84.66 billion RMB). Moreover, the PM2.5 concentration in the control scenario was rolled back to the Grade I standards (35 μg/m3, Scenario 2). The avoided deaths are in the range of 58,820 to 590,464 cases (total of 1,217,671 cases). The estimated monetary value of the avoided cases of all health endpoints range from 36.63 to 367.66 billion RMB based on WTP (total of 758.21 billion RMB). In addition, the spatial autocorrelation analysis reveals that the distribution of both avoided premature mortality and economic benefits exhibit a certain spatial aggregation. © 2020 Published by Elsevier B.V.

1. Background

⁎ Corresponding author. E-mail address: [email protected] (R. Qiu). 1 These authors should be considered co-first authors.

https://doi.org/10.1016/j.scitotenv.2020.137445 0048-9697/© 2020 Published by Elsevier B.V.

With the acceleration of industrialization and urbanization, particular matter (PM) air pollution has become a matter of severe concern in China. Compared with PM10, PM2.5 has a smaller aerodynamic diameter, which facilitates its attachment to toxic and hazardous substances (such

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G. Luo et al. / Science of the Total Environment 719 (2020) 137445

as heavy metals and microorganisms). Furthermore, PM2.5 is more hazardous to human health than PM10 because it has a long residence time in the atmosphere. In recent years, fog and haze have occurred frequently in many Chinese cities. This phenomenon is closely related to the increase in the PM2.5 concentration. To improve the air quality, it is necessary to monitor the PM2.5 concentration to ensure adherence to Ambient Air Quality Standards (GB3095-2012), which was introduced in 2012. The standards define the thresholds of the daily mean PM2.5 concentration. The values according to the Grade I and Grade II standards are 35 μg/m3 and 75 μg/m3, respectively. In addition, China has implemented many environmental protection policies to reduce the PM2.5 concentration and minimize its adverse influence on human health. These include “Air Pollution Prevention and Control Action Plan” (abbreviated as Action Plan; introduced in September 2013), “Detailed Rules for the Implementation of Action Plan in Beijing–Tianjin– Hebei” (2013), and “Blue Sky Engineering.” While the government has launched a large number of policies to combat air pollution, limited research has been performed to measure the effects of these policies and to understand the relationship among human health impacts, economic benefits, and PM2.5 exposure level. This study is aimed at answering these questions. 2. Literature review 2.1. Simulation of PM2.5 exposure level In previous studies by other scholars, PM2.5 exposure levels were simulated mainly based on the data from an air quality model such as the CALPUFF model, CAMx model, and CMAQ model (Lu et al., 2019; Wang et al., 2019; Zhang et al., 2019; Zhou et al., 2003). However, they have certain disadvantages such as lower spatial resolution and longer calculation time. As of November 2018, there are 1499 air quality monitoring sites in the 367 cities of China. This density of monitoring sites enables the simulation of PM2.5 exposure level with high coverage, high precision, and high spatial resolution. 2.2. Assessment of PM2.5-related health impacts In recent years, several published epidemiological studies have verified the association between the high PM2.5 exposure level and the increasing risk of mortality and morbidity (Harrison et al., 2004; Y. Li et al., 2019; J. Li et al., 2019; Sahu et al., 2019). It is reported that the mortality risk for all cause, cardiopulmonary diseases, and lung cancer increased by 4%, 9%, and 9%, respectively, because of the increase in PM2.5 concentration (Krewski et al., 2009). Although studies on quantifying the PM2.5-related health impact have been conducted in China, these studies generally selected the regions with high population density and developed economy as the study areas, such as Beijing–Tianjin–Hebei (BTH) (Xie et al., 2016), Yangtze River Delta (YRD) (Guan et al., 2019), Pearl River Delta (PRD) (Y. Li et al., 2019; J. Li et al., 2019), and Shanghai (Voorhees et al., 2014). Furthermore, there have been few publications that have estimated the heath impact at the national scale, using the nationwide monitoring data. Moreover, previous studies focused mostly on PM2.5-realted health impact assessments (Chen et al., 2017a, 2017b, 2017c; Li et al., 2018), and the spatial distribution characteristics were neglected. However, the spatial distribution analysis of public health benefits is an important step. It is effective for formulating local environmental policies for specific regions and implementing joint control of air pollution. 2.3. Estimating economic benefits At present, the common method for estimating the economic benefits from avoiding mortality mainly include willingness to pay (WTP), human capital (HC), and cost of illness (COI). The recent air pollution cost report issued by World Bank (2016) has calculated the welfare

losses by using the WTP method. The results reveal estimated economic losses of $1,589,767 billion, amounting to 9.92% of the Gross Domestic Product (GDP). Kamal et al., 2018 estimated the total economic loss in 2016 from outdoor PM2.5 pollution to be $101.39 billion (approximately 0.91% of China's GDP). These studies revealed the substantial economic losses caused by PM2.5 pollution in China. 2.4. Summary According to the literature review, it is feasible to simulate PM2.5 exposure levels based on high-density monitoring data. Quantifying the public health benefits of PM2.5 reduction will aid in preventing the losses caused by air pollution and in the formulation of environmental policies. In this study, the daily PM2.5 exposure level at a spatial resolution of 100 km2 was simulated based on monitoring data and the VNA interpolation method. To evaluate the efficacy of environmental policies and regulations, we estimated the avoided premature mortality and economic benefits attributed to the reduction in PM2.5 concentration from 2016 to 2018 (Scenario 1) by using Benefits Mapping and Analysis Program (BenMAP). The potentially preventable premature mortality and economic benefits were also calculated assuming that the PM2.5 concentration rolls back to 35 μg/m3 based on a baseline year 2018 (Scenario 2). In addition, the spatial distribution characteristics of public health benefits were analyzed. 3. Materials and methods 3.1. Simulating daily PM2.5 exposure levels The daily mean PM2.5 concentration data in 2016 and 2018 were collected from the website of China National Environmental Monitoring Centre (CNEMC) (http://www.cnemc.cn/sssj/). According to the requirements of Ambient Air Quality Standards (GB3095-2012), we did not consider monitoring sites with valid PM2.5 concentration data of b20 h in a day or 324 days in a year. In addition, to maintain the consistency of the number of sites, the monitoring sites established after 2016 were also excluded. Based on this criterion, 1328 monitoring sites remained for the estimation. The distribution of these sites is shown in Fig. 1. Previous studies revealed that the Voronoi Neighborhood Averaging (VNA) interpolation method displays a better simulated effect compared to the inverse distance weighted and Kriging interpolation methods (Chen et al., 2004; Chen et al., 2017a, 2017b, 2017c). Therefore, the daily mean PM2.5 concentration map with a spatial resolution of 100 km2 was generated using VNA. The daily mean PM2.5 concentration at each grid cell was calculated using the VNA method, based on the nearest monitoring site by drawing polygons surrounding the center of each grid. The inverse distance squared weighted average is applied such that the closer the monitoring site is to the grid cell, the larger is the weight assigned. In this study, 31 provinces in China are included. Taiwan, Hong Kong, and Macao were excluded because of data deficiency. Two air quality control scenarios were employed. In Scenario 1, the PM2.5 monitoring data sets were used for the baseline year 2016 and the post-control year 2018. In addition, the environmental protection departments generally formulate specific goals and action plans with reference to air quality standards. Therefore, the potentially avoidable premature mortality and economic benefits were calculated, assuming that the PM2.5 concentration rolls back to 35 μg/m3 based on a baseline year 2018 (the Grade I national standards for PM2.5, GB3095-2012; Scenario 2). We anticipate that the environmental protection department will receive a quantitative understanding of the tremendous public benefits afforded by the reduction in PM2.5, so as to increase their emphasis on environmental protection and strengthen their confidence in policy implementation. Additionally, we also hope to provide a reference for

G. Luo et al. / Science of the Total Environment 719 (2020) 137445

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Fig. 1. Population density and monitoring sites in the study area.

policy makers by quantifying public benefits, for determining environmental financial investment and setting environmental goals. 3.2. Estimating human health impacts The health impacts of decreasing PM2.5 concentration, for all cause, cardiovascular diseases, respiratory diseases, and lung cancer were calculated in this study. According to the International Classification of Diseases, Revision 10 (ICD-10), the causes of death are classified into allcause (A00–R99), cardiovascular diseases (I00–I99), respiratory diseases (J00–J98), and lung cancer (C34). The health effects are calculated according to the following equations by BenMAP-CE (US EPA, 2015):
 ΔY ¼ Y0 1−e−βΔPM  Pop β¼

ð1Þ

ln ðRRÞ ΔPM

ð2Þ


 βmin ¼ β− 1:96  σ β

ð3Þ


 βmax ¼ β þ 1:96  σ β

ð4Þ

where ΔY is the variation in the health or environmental effect, Y0 is the baseline incidence rate for the health endpoint (mortality or morbidity rate), β is the exposure concentration-response coefficient, representing the percent change in a certain health impact per unit of PM2.5 concentration, ΔPM (μg/m3) is the variation in the PM2.5 concentration, Pop(person) implies the exposed population, RR is the relative risk value, which is available in epidemiological studies (Table 1; 95% confidence interval), and σβ is the standard error of β. Thirty one provinces in China are included in our study. When we could not derive the corresponding RR value of a certain province, we selected the value from the neighboring province. The BenMAP software calculates a distribution of β in each grid by using the Monte Carlo approach. The gridded population data with a 1 km2 grid for 2015 were drawn from the Geographic Information Monitoring cloud platform of China (http://www.dsac.cn), as shown in Fig. 1. The baseline incidence data for all-cause, cardiovascular diseases, respiratory diseases, and lung cancer in 2016 and 2018 were collected from the Chinese Statistical Yearbook (National Bureau of Statistics of China, 2016, 2018) and China Statistical Yearbook of Public Health (National Health Commission of China, 2016, 2018).

Table 1 Relative risk of major health endpoints and baseline incidence of the two scenarios. Health endpoints

All-cause

Cardiovascular

Respiratory

Lung cancer

RR (95% CI) (10 μg/m3)

1.009 (0.997, 1.018) 1.0036 (1.0011, 1.0061) 1.0036 (1.0012, 1.0060) 1.0064 (1.0042, 1.0086) 1.0048 (1.0038, 1.0058) 1.0019 (1.0001, 1.0004) 1.028 (1.009, 1.046) 1.0041 (1.0001, 1.0082) 1.0060 (1.0022, 1.0097) 1.0098 (1.0061, 1.0135) 1.0045 (1.0029, 1.0060) 1.0018 (1.0001, 1.0053) 1.009 (0.982, 1.040) 1.0095 (1.0016, 1.0173) 1.0051 (1.0003, 1.0098) 1.0089 (1.0038, 1.0141) 1.0073 (1.0049, 1.0097) 1.0014 (0.9977, 1.0051) 1.034 (0.9997, 1.071)

Study areas

31 cities Shanghai East China PRD Mega cities Sichuan 31 cities Shanghai East China PRD Mega cities Sichuan 31 cities Shanghai East China PRD Mega cities Sichuan 31 cities

References

Cao et al., 2011 Kan et al., 2007 Madaniyazi et al., 2016 Tao et al., 2012 Shang et al., 2013 Guo et al., 2018 Cao et al., 2011 Kan et al., 2007 Madaniyazi et al., 2016 Tao et al., 2012 Shang et al., 2013 Guo et al., 2018 Cao et al., 2011 Kan et al., 2007 Madaniyazi et al., 2016 Tao et al., 2012 Shang et al., 2013 Guo et al., 2018 Cao et al., 2011

Baseline incidence Scenario 1

Scenario 2

0.006047

0.006059

0.001387

0.001416

0.0006903

0.0006720

0.0005726

0.0005726

4

G. Luo et al. / Science of the Total Environment 719 (2020) 137445

3.3. Economic benefit assessments In this study, the willingness to pay (WTP) method was used to evaluate the economic benefits from avoiding premature mortality. The WTP represents the economic value that individuals are willing to pay for reducing the risk of premature death. The unit economic cost (RMB) of PM2.5-related health impact in 2010 was obtained from the China-specific valuation studies (Wang and Mullahy, 2006). The estimated unit costs of different years were converted according to the following equations: U 2016;2018 ¼ U 2010 

  Income2016;2018 e Income2010

ð5Þ

where U2016, 2018 and U2010 are the economic cost per case in 2016, 2018 and 2010, respectively, Income2016, 2018 and Income2010 are the urban per capita disposable income (UPDI) in 2016, 2018 and 2010, respectively (National Bureau of Statistics of China, 2016, 2018), and e is the income elastic coefficient, which equals 0.8 as recommended by OECD (2012). 3.4. Spatial autocorrelation To analyze the spatial autocorrelation of health benefits, Global Moran's I and Local Moran's I specified by Moran in 1948 were introduced in this study. Global Moran's I (expressed as Eq. (6)) is a preferred index for assessing a spatial pattern. It verifies whether the pattern is dispersed, clustered, or random, based on the locations and values of the feature. Local Moran's I (expressed as Eq. (7)) was used as a local indicator of spatial association (LISA). The range of Moran's Index lies between +1 and −1. Here, positive and negative Moran's I values represent positive and negative spatial autocorrelation, respectively (Kumari et al., 2019). IGlobal ¼ n

Xn Xn i¼1

j¼1


Pn Pn i¼1

ILocal ¼

S2 ¼

m¼1

ωij

 Pn

ðxi −xÞ Xn S2


 ωij ðxi −xÞ x j −x i¼1

ðxi −xÞ2

ωij ðxi −xÞ

ð6Þ

ð7Þ

j¼1

1 Xn ðxi −xÞ2 n i¼1

ð8Þ

where n is the total number of features, xi represents the variable at location i, x represents the mean value of the variable with the sample number, xj is the value at other locations (where j ≠ i), ωij is the spatial weight among features i and j (the spatial distance weight matrix was created by Geoda software), and S2 is the standard deviation. 4. Results 4.1. Estimated daily PM2.5 exposure levels The monitoring data of 1328 monitoring sites revealed that the PM2.5 daily mean concentration reduced from 47.82 μg/m3 (2016) to 40.87 μg/m3 (2018), a reduction of 14.53%. The highest and lowest PM2.5 daily mean concentration over the two years (731 days) appeared in the Hetian region and Altay region (both in Xinjiang), respectively. The monitoring data sets were used for the baseline year 2016 and post-control year 2018 (Scenario 1). The PM2.5 simulated concentration map with 100 km2 grid was developed using the VNA method. From the simulation results, the mean value of PM2.5 daily concentration reduced from 74.09 μg/m3 (2016) to 61.96 μg/m3 (2018), a reduction of 16.37%. However, the mean value over the two years exceed the present PM2.5

Grade I standard (35 μg/m3). In addition, the high values (N80 μg/m3) of simulated PM2.5 concentration were distributed over East China (Shanghai, Jiangsu, Zhejiang, Anhui, and Shandong), North China (Beijing, Tianjin, Shanxi, Hebei, and Inner Mongolia), Central China (Henan and Hubei), Southwest China (Chongqing and Sichuan), Northwest China (Shaanxi, Gansu, Ningxia, and Xinjiang), and Northeast China (Heilongjiang, Jilin, and Liaoning). The number of higher value areas reduced in 2018. Furthermore, the PM2.5 concentration in North China was higher than that in South China. This is related to the landform, topography, and atmospheric diffusion conditions. As shown in Fig. 2, the large reduction (delta values N 25 μg/m3) is mainly concentrated in Northwest China (Xinjiang), Southwest China (Sichuan and Guizhou), East China (Jiangxi and Zhejiang), Central China (Henan, Hunan, and Hubei), North China (Beijing, Tianjin, Hebei, and Inner Mongolia), Northwest China (Shaanxi), and Northeast China (Liaoning). The level of PM2.5 concentration increased in Guangdong, Guangxi, Shandong, Gansu, and Qinghai. The reduction in PM2.5 concentration in the three major economic zones is ranked as follows: BTH N YRD N PRD. Overall, the areas with high reduction values presented spatial aggregation. This was embodied in the spatial structure, wherein the PM2.5 concentration decreased gradually from West China to Central China to North China. 4.2. Avoided premature mortality Table 2 presents the estimated avoided premature mortality for allcauses, cardiovascular diseases, respiratory diseases, and lung cancer, in the two scenarios. In Scenario 1, the mortality attributed to the four health endpoints by being exposed to the daily mean PM2.5 concentration were 81,681 (95% CI: (−15,714, 161,845)) cases (all cause), 45,979 (95% CI: (20,570, 60,770)) cases (cardiovascular diseases), 7214 (95% CI: (−28,716, 28,178)) cases (respiratory diseases), and 19,302 (95% CI: (−2473, 24,049)) cases (lung cancer), respectively (total of 154,176 cases). Moreover, the cases of the four health endpoints accounted for 8.40% of all deaths in the baseline year (2016). Under the more ambitious scenario, assuming that the daily mean PM2.5 concentration rolls back to 35 μg/m3 (Scenario 2), the avoided premature mortality of the four health endpoints were estimated to be 590,464 (95% CI: (−105,162, 1,230,432)) cases (all cause), 388,554 (95% CI: (149,306, 596,985)) cases (cardiovascular diseases), 58,820 (95% CI: (−171,740, 242,543)) cases (respiratory diseases), and 179,833 (95% CI: (−16,438, 329,704)) cases (lung cancer), respectively (total of 1,217,671 cases, approximately eight times the value of Scenario 1). The cases of the four health endpoints accounted for 38.99% of the baseline year (2018) in total. As shown in Fig. 3, the large numbers of avoided premature mortality are concentrated in the regions with large PM2.5 delta concentration values (Scenario 1). Specifically, the areas that prevent N1 person/ 100 km2 of premature mortality mainly include Beijing–Tianjin–Hebei, Shanxi, Inner Mongolia, Jilin, Liaoning, Henan, Hubei, Sichuan, Guizhou, and Yunnan. However, in certain regions, the increase in the PM2.5 concentration caused higher premature mortality. The areas where the number of premature mortalities increased by N1 person/100 km2 mainly include Yangtze River Delta, Pearl River Delta, Shandong, Heilongjiang, and Guangxi. Moreover, the number of premature deaths avoided varies widely among cities in certain provinces such as Hunan, Jiangxi, and Shaanxi. In addition, Global Moran's I was introduced to analyze the spatial distribution pattern of the number of premature mortalities avoided by the PM2.5 concentration variations in China from 2016 to 2018. The global spatial autocorrelation analysis revealed that the Global Moran's I value of the four health endpoints were 0.5186 (all-cause) (99% CI: P b 0.01, Z-score = 231.7786), 0.4994 (cardiovascular diseases) (99% CI: P b 0.01, Z-score = 222.9533), 0.4972 (respiratory diseases) (99% CI: P b 0.01, Z-score = 221.9239), and 0.4862 (lung cancer) (99% CI: P b 0.01, Z-score = 217.0187), respectively. These results imply that

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Fig. 2. Reduction (delta values) in PM2.5 concentration in China from 2016 to 2018.

avoided premature mortality exhibits strong high-value clustering and spatial positive autocorrelation. 4.3. Assessment of economic benefits Based on BenMAP-CE, the values of economic benefits from avoiding premature mortality in conjunction with 95% CI were estimated. As presented in Table 3, from 2016 to 2018 (Scenario 1), the monetary values of the avoided cases for all-cause mortality, cardiovascular mortality, respiratory mortality, and lung cancer were estimated to be 44.85 billion RMB, 25.25 billion RMB, 3.96 billion RMB, and 10.60 billion RMB. These accounted for 0.061%, 0.034%, 0.005%, and 0.014% of China's GDP in 2016 (74,010 billion RMB). The fractions of cardiovascular disease, respiratory disease, and lung cancer attributable to all-cause were 56.28%, 8.82%, and 23.63%, respectively. In 2018, the economic benefits because of avoiding premature mortality by the decrease in the PM2.5 concentration (Scenario 2: roll back to 35 μg/m3) were estimated to be 367.66 billion RMB, 241.94 billion RMB, 36.63 billion RMB, and 111.98 billion RMB, respectively. These accounted for 0.408%, 0.269%, 0.041%, and 0.124% of China's GDP in 2018 (90,030 billion RMB). The fractions of cardiovascular diseases, respiratory diseases, and lung cancer attributable to all-cause were 65.80%, 9.96%, and 30.45%, respectively. A comparison of the results of the two scenarios revealed that the higher economic effects were produced when the PM2.5 concentrations were decreased to 35 μg/m3. Furthermore, the economic cost avoided in Scenario 2 was approximately 8.96 times that in Scenario 1. As shown in Fig. 4, the spatial distribution of the economic benefits of health impacts was consistent with the avoided premature mortality. From 2016 to 2018 (Scenario 1), the economic benefits (values of health effects N 1000 RMB/100 km2) obtained by preventing mortality by decreasing the PM2.5 concentration reduced from West China to Central China to North China. The highest values were located in the regions

with high population density. The difference in economic benefits is related to the pollution level, exposed population, and GDP of each region. It also reflects the effectiveness of the emission reduction measures in the region. The spatial agglomeration of the health impact values from 2016 to 2018 was analyzed by ArcGIS and Geoda software. First, we calculated the Global Moran's I value for all-cause mortality, cardiovascular mortality, respiratory mortality, and lung cancer mortality. These were 0.2038 (all-cause) (99% CI: P b 0.01, Z-score = 713.3001), 0.2005 (cardiovascular) (99% CI: P b 0.01, Z-score = 700.7999), 0.2000 (respiratory diseases) (99% CI: P b 0.01, Z-score = 699.2970), and 0.1980 (lung cancer) (99% CI: P b 0.01, Z-score = 692.1999), respectively. Moreover, the economic benefits of each health endpoint presented a strong spatial clustering character. Next, the Global Moran's I of the total health economic benefits were calculated, as shown in Fig. 5(a). The standardized value of health effects in the first quadrant (High–High) and third quadrant (Low–Low) exhibit positive spatial autocorrelation, whereas the standardized value of health effects in the second quadrant (High– Low) and fourth quadrant (High–Low) appear as spatial negative correlation. As Fig. 5(a) displays, the scatter points were distributed mainly in the first quadrant (High–High, positive correlation) (18.52% of total points) and third quadrant (Low–Low, positive correlation) (74.16% of total points). The regions with high health economic benefits were surrounded by other high-value regions. This implies that the regions with higher health economic benefits exert higher impact on the neighboring regions. Similarly, the regions with low health economic benefits were surrounded by other low-value regions, indicating that the regions with lower health economic benefits exert a spatial agglomeration effect. Furthermore, the Local Moran's I index was adopted to perform clustering analysis and generate LISA map (P ≤ 0.05). As shown in Fig. 5(b), the spatial distributions of the clustering health economic benefits results from 2016 to 2018 (Scenario 1) were classified into four types:

Table 2 Estimated avoided premature mortality in the two scenarios. Health endpoint

All-cause Cardiovascular Respiratory Lung cancer

Scenario 1

Scenario 2

Mean value (person)

Percent of baseline (%)

Standard deviation

Mean value (person)

Percent of baseline (%)

Standard deviation

81,681 45,979 7214 19,302

1.05 2.98 0.96 3.41

1.59 0.60 0.50 0.49

590,464 388,554 58,820 179,833

4.78 13.79 4.42 16.00

3.44 1.15 1.06 0.89

6

G. Luo et al. / Science of the Total Environment 719 (2020) 137445

Fig. 3. Spatial distribution of avoided premature mortality in Scenario 1.

High–High (positive spatial autocorrelation, accounted for 47.18%), Low–Low (positive spatial autocorrelation, accounted for 47.23%), Low–High (negative spatial autocorrelation, accounted for 3.73%), and High–Low (H–L, negative spatial autocorrelation, accounted for 1.86%). The regions with High–High positive spatial autocorrelation were found mainly in Jilin, Liaoning, Beijing–Tianjin–Hebei, Shanxi, Shaanxi, Henan, western Anhui, northern Hubei, Hunan, Sichuan, Zhejiang, and Jiangxi. The regions with Low–Low positive spatial correlation appeared mainly in Heilongjiang, Shandong, Jiangsu, Shanghai, eastern Anhui, southern Hubei, Pearl River Delta, Guangxi, Hainan, Gansu, Qinghai, and Xinjiang. These results reveal that the health economic benefits obtained by avoiding mortality by decreasing the PM2.5 concentration exhibits an apparent bipolar spatial agglomeration phenomenon. Moreover, the higher value areas interact with each other and gradually spread out to affect the surrounding lower value areas. 5. Discussion In this study, PM2.5 exposure levels were simulated based on the monitoring data of 1328 monitoring sites from 2016 to 2018, as described previously. The reduction in the PM2.5 daily mean concentration attained 14.53%. This result reveals that the implementation of environmental protection policy has achieved primary benefits. Since the introduction of “Air Pollution Prevention and Control Action Plan” in September 2013, the local government has responded positively, and a series of environmental protection policies have been formulated, such as “Regulations on Prevention and Control of Air Pollution” in Beijing (2014) and “13th Five-Year Plan for Environmental Protection” in Anhui (2017). These measures are likely to result in future reduction in the PM 2.5 concentration. As shown in Fig. 2, the regional differences in PM2.5 reduction are closely related to the implementation of policies, meteorological conditions, industrial activities, and vehicle emissions. The meteorological conditions Table 3 Estimated health effects in the two scenarios (WTP, billion RMB). Health endpoint

All-cause Cardiovascular Respiratory Lung cancer

Scenario 1

Scenario 2

2.5th

50th

97.5th

2.5th

50th

97.5th

−129.15 −41.56 −53.80 −46.44

44.85 25.25 3.96 10.60

209.41 86.24 53.52 58.28

−65.49 92.97 −106.94 −10.24

367.66 241.94 36.63 111.98

766.15 371.72 151.02 205.29

exert an important impact on the diffusion process of PM2.5. For example, Fujian, Jiangxi, and Guangxi have higher precipitation and annual average wind speed owing to the subtropical monsoon climate. These are conducive to the removal and diffusion of PM2.5. Because the air quality in these areas was maintained at a good level from 2016 to 2018, the PM2.5 reduction is relatively marginal. Furthermore, industrial emissions and vehicle emissions also contributed to PM2.5 pollution. Considering the three major economic zones as an example, BTH has a high urbanization rate and vehicle population. However, stringent environmental protection policies on industrial emissions and vehicle exhaust have been implemented in BTH, and the proportion of secondary industry (including the manufacturing industry, mining, construction, etc.) has declined continuously. Therefore, the emission reduction effect is significant. Both YRD and PRD have higher vehicle ownership. However, the proportion of secondary industry in YRD is higher than that in PRD. In addition, the PM2.5 pollution in PRD is more straightforwardly diffused owing to the subtropical monsoon climate and topography. Therefore, the pollution level in YRD is more severe than that in PRD, and the emission reduction effects afforded by policies are more apparent in YRD. The World Health Organization used the global burden of disease (GBD) model to estimate a median-population-weighted PM2.5 level of 54 μg/m3 for China, with a range of 37 to 80 μg/m3. They determined that over 1,032,833 deaths (uncertainty interval: 869,033 to 1,212,034) were caused by exposure to PM2.5 (World Health Organization, 2016). In addition, the World Bank estimated that 1,625,164 deaths were caused by PM2.5 pollution (mean annual ambient PM 2.5 = 54.36 μg/m3 ) in 2013, and the total welfare losses were estimated to be $1,589,767 billion ($2011) (9.92% of GDP) (World Bank, 2016). In our study, the total avoided premature mortalities from 2016 to 2018 because of the environmental protection policy implementation (Scenario 1) were estimated to be 154,176 cases. The estimated economic benefits were 84.65 billion RMB, (0.11% of the GDP in 2016). Moreover, the total avoided premature mortality was estimated to be 1,217,671 cases. The total economic benefits obtained by preventing mortality were estimated to be 758.21 billion RMB. Furthermore, the gain from rolling back the PM2.5 concentration to 35 μg/m3 is projected to be 0.84% of China's GDP in 2018 (Scenario 2). Comparisons of these studies are challenging because the differences in location, time, health endpoints, health function, and the PM2.5 concentration threshold may affect the estimates. In this study, all-cause, cardiovascular diseases, respiratory diseases, and lung cancer were selected as the health

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Fig. 4. Spatial distribution of economic benefits in Scenario 1.

endpoints. The impacts are underestimated compared with the results of WHO and World Bank owing to the number of health endpoints. Moreover, we used the WTP method to calculate the health economic benefit, which may yield higher values than those obtained using HC (Chen et al., 2017a, 2017b, 2017c).

6. Conclusion In this study, the daily PM2.5 exposure levels of China at a 100 km2 resolution from 2016 to 2018 were simulated based on monitoring data obtained from the national air pollution monitoring network. The daily mean PM2.5 concentration decreased from 47.82 μg/m3 (2016) to 40.87 μg/m3 (2018). From 2016 to 2018 (Scenario 1), the avoided premature mortality because of exposure to PM2.5 were estimated to be 154,176 cases, and the estimated economic benefits amount to 84.66 billion RMB. Furthermore, deaths avoided by decreasing the PM2.5 concentration to 35 μg/m3 (Scenario 2) were estimated to be 1,217,671 cases, and the economic benefits were estimated to be 758.21 billion RMB based on the WTP method. There are several limitations in our study. First, the daily PM2.5 exposure levels were simulated based on the monitoring data of 1328 monitoring sites in China. However, the spatial location of the monitoring sites is not distributed uniformly. While the distribution of monitoring sites in central and eastern China is dense, the number of monitoring sites in western China is relatively small, and the spatial distribution is scattered. This may affect the simulation accuracy of the PM2.5 concentration in western China. Second, we only estimated the avoided premature mortality and health economic benefits for all-cause, cardiovascular diseases, respiratory diseases, and lung cancer, which do not estimate the health and economic benefits of other PM2.5 associated health endpoints. In addition, the exposure-response coefficient of the major health endpoints should be selected based on the corresponding results in different regions. However, owing to the limited data, we used the results of the study covering only 31 cities in China. This may result in uncertainty in the estimation process. Third, the population data used in the model were collected from the registered urban population in the census statistics. Since rural residents still account for a certain proportion of China's population, the spatial distribution data of population used in this study cannot completely represent the real situation, and the estimated values of the health and economic

benefits may be low. Fourth, PM2.5 pollution exposure exerts different health effects across genders and ages. However, it is challenging to accurately obtain data on the gender composition and age distribution in different regions. Therefore, the gender and age of the population were not considered when we calculated the avoided premature mortality. Finally, the value of the Grade I standard of Chinese Ambient Air Quality Standards (35 μg/m3) that was selected in Scenario 2 is higher than the WHO air quality guideline (25 μg/m3) (World Health Organization, 2016), and the health effects and health economic benefits may have been underestimated. Health and environmental benefit–cost analyses can aid in designing polices for reducing air pollution. Based on the results of this study, we recommend the implementation of more environmental protection strategies to reduce the impacts on human health and prevent further economic loss. Specifically, the PM2.5 pollution control strategies are as follows: (1) The acquisition of monitoring data is the basis of air pollution prevention and control. Therefore, it is necessary to increase the distribution density of air quality monitoring sites. (2) To reduce vehicle emissions, it is necessary to control the number of motor vehicles, improve fuel quality, implement the China VI emission standard at the earliest, accelerate elimination of vehicles with yellow license plates, promote the use of vehicles that are partially or completely powered by electricity, and develop public transportation. (3) To optimize the energy structure, the use of clean energy (wind, solar, biomass, etc.) should be promoted. In addition, the industrial structure should shift from high to low energy consumption. (4) Referring to the success of air quality assurance work for major events such as Beijing Olympics (2008) and Shanghai World Expo (2010), establishing the regional joint prevention mechanism is an effective approach to improve regional air quality.

This study also introduces an efficient method for identifying the health impact and economic benefits, and verifying the effectiveness of air quality improvement plans. In the future, more accurate monitoring data can be used as input to completely understand the public health benefits in different cities of China. Future research should also consider the age and gender of the population in estimating the cost of disease.

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G. Luo et al. / Science of the Total Environment 719 (2020) 137445

Fig. 5. Global Moran's I for health economic benefits (a) and LISA cluster map for health economic benefits (b) from 2016 to 2018.

Credit authorship contribution statement

References

Guiwen Luo: Conceptualization, Methodology, Writing - original draft. Lanyi Zhang: Writing - review & editing. Xisheng Hu: Writing review & editing. Rongzu Qiu: Writing - review & editing.

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