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Open AccessArticle

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

by 1,2,3, 4, 1, 2,3 and 5,6,*
1
Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
4
School of Geography Science, Nanjing Normal University, Nanjing 210023, China
5
School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
6
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(9), 2058; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092058
Received: 20 August 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
(This article belongs to the Section Environmental Health)
Analyzing the association between fine particulate matter (PM2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m3 in mainland China, and the highest population exposure to PM2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM2.5 was positively related to GDP. An apparent upward trend of population exposure to PM2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM2.5 increases by 1.05 person/km2 at the 80th percentile, and 1.88 person/km2 at the 95th percentile, respectively. View Full-Text
Keywords: spatial heterogeneity; population exposure; economic effects; quantitative analysis spatial heterogeneity; population exposure; economic effects; quantitative analysis
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MDPI and ACS Style

Yao, L.; Huang, C.; Jing, W.; Yue, X.; Xu, Y. Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China. Int. J. Environ. Res. Public Health 2018, 15, 2058. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092058

AMA Style

Yao L, Huang C, Jing W, Yue X, Xu Y. Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China. International Journal of Environmental Research and Public Health. 2018; 15(9):2058. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092058

Chicago/Turabian Style

Yao, Ling; Huang, Changchun; Jing, Wenlong; Yue, Xiafang; Xu, Yuyue. 2018. "Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China" Int. J. Environ. Res. Public Health 15, no. 9: 2058. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph15092058

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