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Int J Environ Res Public Health. 2019 Nov 4;16(21). pii: E4276. doi: 10.3390/ijerph16214276.

Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing-Tianjin-Hebei Region, China, from 2013 to 2018.

Author information

1
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. wll_0423@163.com.
2
Faculty of Geomatics, East China University of Technology, Nanchang 330013, China. xiong_ql@163.com.
3
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. wgf_0307@163.com.
4
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. atul.gautam88@gmail.com.
5
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. 2180902063@cnu.edu.cn.
6
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. 2180902092@cnu.edu.cn.
7
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. 4973@cnu.edu.cn.
8
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China. hlguan@cnu.edu.cn.

Abstract

Air pollution, including particulate matter (PM2.5) pollution, is extremely harmful to the environment as well as human health. The Beijing-Tianjin-Hebei (BTH) Region has experienced heavy PM2.5 pollution within China. In this study, a six-year time series (January 2013-December 2018) of PM2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM2.5 mass concentration in the BTH region decreased from 98.9 μg/m3 in 2013 to 64.9 μg/m3 in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM2.5 mass concentration in the BTH region from 2013-2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM2.5 pollution control decisions.

KEYWORDS:

BTH; PM2.5; air pollution; geographical detector; spatio-temporal variation

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