Meteorological Influences on Spatiotemporal Variation of PM 2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing–Tianjin–Hebei Region, China

Suxian Wang, Jiangbo Gao, Linghui Guo, Xiaojun Nie and Xiangming Xiao
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Suxian Wang: College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Jiangbo Gao: Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Rd., Beijing 100101, China
Linghui Guo: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Xiaojun Nie: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Xiangming Xiao: Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA

IJERPH, 2022, vol. 19, issue 3, 1-18

Abstract: Understanding the spatiotemporal characteristics of PM 2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM 2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM 2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m −3 ) in 2019. PM 2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM 2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM 2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM 2.5 pollution.

Keywords: PM 2.5 concentration; spatiotemporal patterns; meteorological factors; “2 + 26” cities; Beijing–Tianjin–Hebei region (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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