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      Spatial-Temporal Effects of PM 2.5 on Health Burden: Evidence from China

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          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 PM 2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM 2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM 2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM 2.5 pollution has a significant temporal lag effect on residents’ health burden. PM 2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM 2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM 2.5 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 PM 2.5 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.

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          Most cited references59

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          The Analysis of Spatial Association by Use of Distance Statistics

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            Ambient PM2.5 Reduces Global and Regional Life Expectancy

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              DO SPATIAL EFFECFS REALLY MATTER IN REGRESSION ANALYSIS?

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                25 November 2019
                December 2019
                : 16
                : 23
                : 4695
                Affiliations
                [1 ]School of Economics, Sichuan University, Chengdu 610065, China; zengming951124@ 123456163.com (M.Z.); dujiang_1@ 123456163.com (J.D.)
                [2 ]School of Public Administration, Sichuan University, Chengdu 610065, China
                [3 ]Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
                Author notes
                [* ]Correspondence: zhangwk@ 123456correo.ugr.es
                Author information
                https://orcid.org/0000-0002-5176-4460
                Article
                ijerph-16-04695
                10.3390/ijerph16234695
                6926598
                31775384
                fa29db19-0225-4d97-b6aa-84f3ffa388e5
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 October 2019
                : 22 November 2019
                Categories
                Article

                Public health
                pm2.5 exposure,health burden,spatial spillover effect,spatial durbin model (sdm)
                Public health
                pm2.5 exposure, health burden, spatial spillover effect, spatial durbin model (sdm)

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