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. 2019 Nov 25;16(23):4695.
doi: 10.3390/ijerph16234695.

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

Ming Zeng  1 , Jiang Du  1 , Weike Zhang  2   3
Affiliations

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

Ming Zeng et al. Int J Environ Res Public Health. .

Abstract

By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM2.5 pollution has a significant temporal lag effect on residents' health burden. PM2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

Keywords: PM2.5 exposure; health burden; spatial Durbin model (SDM); spatial spillover effect.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The spatial distribution of the core variables in 2008 and 2017.
Figure 2
Figure 2
Local Moran’s I scatter plot in 2008 and 2017. Note: Numbers 1 to 29 represent Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang, respectively.
Figure 3
Figure 3
Hot spot analysis results in 2008 and 2017.
See this image and copyright information in PMC

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