Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities

The high concentration of fine particulate matter (PM2.5) has always been a key indicator affecting Chinese environmental quality and a core factor restricting sustainable development. This article refers to the implementation of Air Pollution Prevention and Control Action Plan, analyzes the spatiot...

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Autores principales:	Qian Liu, Zheyu Zhang, Chaofeng Shao, Run Zhao, Yang Guan, Chen Chen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	PM2.5 health risks Temporal variation type Driving factors STIRRAT model Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/2bbef26c0b764765bf47e3266a68a2c6
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spelling	oai:doaj.org-article:2bbef26c0b764765bf47e3266a68a2c62021-12-01T04:56:12ZSpatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities1470-160X10.1016/j.ecolind.2021.107937https://doaj.org/article/2bbef26c0b764765bf47e3266a68a2c62021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21006026https://doaj.org/toc/1470-160XThe high concentration of fine particulate matter (PM2.5) has always been a key indicator affecting Chinese environmental quality and a core factor restricting sustainable development. This article refers to the implementation of Air Pollution Prevention and Control Action Plan, analyzes the spatiotemporal variation characteristics of PM2.5 health risks in 247 cities in China from 2015 to 2017, and divides the cities into four types by the interannual variation differences in health risks: cities with PM2.5 health risks continuously decreased (hereinafter referred to as I type cities), cities with PM2.5 health risks continuously increased (Ⅱ type cities), cities with unknown changes of PM2.5 health risks (IIItype cities), and cities with irregular changes of PM2.5 health risks (Ⅳ type cities). The STIRPAT model and cross-sectional regression method are used to explore the influence and difference in socioeconomic drivers on PM2.5 health risks in types of cities. The results show that (1) Chinese PM2.5 health risks decreased by 3% from 2015 to 2017. According to the change characteristics of health risks, the cities are divided into four types of cities. And the distribution of the four types of cities is regional. (2) From the perspective of drivers, all the cities with PM2.5 health risks are vulnerable to the impact of human activities in built-up areas. I type cities have high per capita GDP and low energy consumption per unit GDP, indicating that the economic development of these cities has low dependence on energy. Ⅱ type cities are easily affected by urbanization rate. The highway mileage has the greatest impact on III type cities. In Ⅳ type cities, the proportion of secondary industry is the smallest, but it is easily affected by the proportion of secondary industry. This study provides theoretical support for the fine control of PM2.5 pollution in each city.Qian LiuZheyu ZhangChaofeng ShaoRun ZhaoYang GuanChen ChenElsevierarticlePM2.5 health risksTemporal variation typeDriving factorsSTIRRAT modelEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107937- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	PM2.5 health risks Temporal variation type Driving factors STIRRAT model Ecology QH540-549.5
spellingShingle	PM2.5 health risks Temporal variation type Driving factors STIRRAT model Ecology QH540-549.5 Qian Liu Zheyu Zhang Chaofeng Shao Run Zhao Yang Guan Chen Chen Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities
description	The high concentration of fine particulate matter (PM2.5) has always been a key indicator affecting Chinese environmental quality and a core factor restricting sustainable development. This article refers to the implementation of Air Pollution Prevention and Control Action Plan, analyzes the spatiotemporal variation characteristics of PM2.5 health risks in 247 cities in China from 2015 to 2017, and divides the cities into four types by the interannual variation differences in health risks: cities with PM2.5 health risks continuously decreased (hereinafter referred to as I type cities), cities with PM2.5 health risks continuously increased (Ⅱ type cities), cities with unknown changes of PM2.5 health risks (IIItype cities), and cities with irregular changes of PM2.5 health risks (Ⅳ type cities). The STIRPAT model and cross-sectional regression method are used to explore the influence and difference in socioeconomic drivers on PM2.5 health risks in types of cities. The results show that (1) Chinese PM2.5 health risks decreased by 3% from 2015 to 2017. According to the change characteristics of health risks, the cities are divided into four types of cities. And the distribution of the four types of cities is regional. (2) From the perspective of drivers, all the cities with PM2.5 health risks are vulnerable to the impact of human activities in built-up areas. I type cities have high per capita GDP and low energy consumption per unit GDP, indicating that the economic development of these cities has low dependence on energy. Ⅱ type cities are easily affected by urbanization rate. The highway mileage has the greatest impact on III type cities. In Ⅳ type cities, the proportion of secondary industry is the smallest, but it is easily affected by the proportion of secondary industry. This study provides theoretical support for the fine control of PM2.5 pollution in each city.
format	article
author	Qian Liu Zheyu Zhang Chaofeng Shao Run Zhao Yang Guan Chen Chen
author_facet	Qian Liu Zheyu Zhang Chaofeng Shao Run Zhao Yang Guan Chen Chen
author_sort	Qian Liu
title	Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities
title_short	Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities
title_full	Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities
title_fullStr	Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities
title_full_unstemmed	Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities
title_sort	spatio-temporal variation and driving factors analysis of pm2.5 health risks in chinese cities
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2bbef26c0b764765bf47e3266a68a2c6
work_keys_str_mv	AT qianliu spatiotemporalvariationanddrivingfactorsanalysisofpm25healthrisksinchinesecities AT zheyuzhang spatiotemporalvariationanddrivingfactorsanalysisofpm25healthrisksinchinesecities AT chaofengshao spatiotemporalvariationanddrivingfactorsanalysisofpm25healthrisksinchinesecities AT runzhao spatiotemporalvariationanddrivingfactorsanalysisofpm25healthrisksinchinesecities AT yangguan spatiotemporalvariationanddrivingfactorsanalysisofpm25healthrisksinchinesecities AT chenchen spatiotemporalvariationanddrivingfactorsanalysisofpm25healthrisksinchinesecities
_version_	1718405651707723776

Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities

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