Spatio-Temporal Patterns of Global Population Exposure Risk of PM2.5 from 2000–2016

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ID: 271779
2021
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Abstract
A high level of fine particulate matter (PM2.5) has become one of the greatest threats to human health. Based on multi-source remote sensing data, the pollutant population exposure model, accompanied by the Theil–Sen Median and Mann–Kendall methods, was used to analyze the spatio-temporal patterns of global population exposure risk of PM2.5 from 2000 to 2016. The population distribution patterns of high-risk exposure areas have been accurately identified; the variation trend and stability of global population exposure risk of PM2.5 have also been analyzed. According to the results, the average concentration of PM2.5 is correlated with the total population. The average concentration of PM2.5 for countries from high to low are Asia (14.7 μg/m3), Africa (8.1 μg/m3), Europe (8.03 μg/m3), South America (5.69 μg/m3), North America (4.41 μg/m3), and Oceania (1.27 μg/m3). In addition, the global average population exposure risk of PM2.5 is decreasing annually. Specifically, China, India, Southeast Asia, and other regions have higher exposure risks. Less developed mountainous regions, cold regions, deserts and tropical rainforest regions have lower exposure risks. Moreover, Oceania, North America, South America and other regions have relatively stable exposure, whereas areas with relatively unstable exposure risk of PM2.5 are mainly concentrated in Asia, India, and eastern China, followed by Southeast Asia, Europe, and Africa. Furthermore, Asia has the largest population of all the continents, followed by Africa and Europe. Countries with increased populations are mainly distributed in Africa, whereas the countries with a declining population are mainly distributed in Europe. Based on this, it is important to identify the relationship between PM2.5 concentration and population exposure risk to improve human settlements and environmental risk assessment.
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Authors Chengcheng Zhao;Jinghu Pan;Lianglin Zhang;Zhao, Chengcheng;Pan, Jinghu;Zhang, Lianglin;
Journal sustainability
Year 2021
DOI
10.3390/su13137427
URL
https://www.mdpi.com/2071-1050/13/13/7427/htm
Keywords
Air pollution pm2.5 population exposure risk remote sensing of environment

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