Literature DB >> 31557670

Socioeconomic factors and regional differences of PM2.5 health risks in China.

Zheyu Zhang1, Chaofeng Shao2, Yang Guan3, Chenyang Xue1.   

Abstract

China is a country with one of the highest concentrations of airborne particulate matter smaller than 2.5 μm (PM2.5) in the world, and it has obvious spatial-distribution characteristics. Areas of concentrated population tend to be regions with higher PM2.5 concentrations, which further aggravate the impact of PM2.5 pollution on population health. Using PM2.5-concentration and socioeconomic data for 225 cities in China in 2015, we adopted a PM2.5-health-risk-assessment method (with simplified calculation) and applied the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to analyze the effects of socioeconomic factors on PM2.5 health risks. The results showed that: (1) At the national level, the order of contribution degree of each socioeconomic factor in the PM2.5-health-risk and PM2.5-concentration model is consistent. (2) From a regional perspective, in all three regions, the industrial structure is the decisive factor affecting PM2.5 health risks, and reduction of energy intensity increases PM2.5 health risks, but the impact of the total amount of urban central heating on PM2.5 health risks is very low. In the eastern region, the increased urbanization rate and length of highways significantly increase PM2.5 health risks, but the increasing effect of the extent of built-up area is the lowest. In the central region, the increasing effects of the extent of built-up area on PM2.5 health risks are significantly greater than the decreasing effects of the urbanization rate. In the western region, economic development has the least effect on reducing PM2.5 health risks. Our research enriches PM2.5-health-risk theory and provides some theoretical support for PM2.5-health-risk diversity management in China.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Keywords:  PM(2.5) concentration; PM(2.5) health risk; STIRPAT model; Socioeconomic factors

Mesh:

Substances:

Year:  2019        PMID: 31557670     DOI: 10.1016/j.jenvman.2019.109564

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  9 in total

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  9 in total

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