Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Ziyue Chen; Danlu Chen; Chuanfeng Zhao; Mei-Po Kwan; Jun Cai; Yan Zhuang; Bo Zhao; Xiaoyan Wang; Bin Chen; Jing Yang; Ruiyuan Li; Bin He; Bingbo Gao; Kaicun Wang; Bing Xu

doi:10.1016/j.envint.2020.105558

Influence of meteorological conditions on PM_2.5 concentrations across China: A review of methodology and mechanism

Environ Int. 2020 Jun:139:105558. doi: 10.1016/j.envint.2020.105558. Epub 2020 Apr 8.

Authors

Ziyue Chen¹, Danlu Chen², Chuanfeng Zhao¹, Mei-Po Kwan³, Jun Cai⁴, Yan Zhuang², Bo Zhao⁵, Xiaoyan Wang⁶, Bin Chen⁷, Jing Yang⁸, Ruiyuan Li², Bin He¹, Bingbo Gao⁹, Kaicun Wang¹⁰, Bing Xu¹¹

Affiliations

¹ State Key Laboratory of Remote Sensing Science, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China; Joint Center for Global Change Studies, Beijing 100875, China.
² State Key Laboratory of Remote Sensing Science, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China.
³ Department of Geography and Resource Management, and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China; Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, the Netherlands.
⁴ Department of Earth System Science, Tsinghua University, Beijing 100084, China.
⁵ Department of Geography, University of Washington, Seattle, Washington 98195, USA.
⁶ State Key Laboratory of Remote Sensing Science, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China; Institute of Atmospheric Science, Fudan University, Shanghai 200433, China.
⁷ Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA.
⁸ State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Faculty of Geographical Science, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China.
⁹ China College of Land Science and Technology, China Agriculture University, Tsinghua East Road, Haidian District, Beijing 100083, China.
¹⁰ State Key Laboratory of Remote Sensing Science, College of Global and Earth System Sciences, Beijing Normal University, 19 Xinjiekou Street, Haidian, Beijing 100875, China; Joint Center for Global Change Studies, Beijing 100875, China. Electronic address: kcwang@bnu.edu.cn.
¹¹ Department of Earth System Science, Tsinghua University, Beijing 100084, China. Electronic address: bingxu@tsinghua.edu.cn.

PMID: 32278201
DOI: 10.1016/j.envint.2020.105558

Abstract

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

Keywords: CTM; Causality model; Interaction mechanism; Meteorological condition; PM(2.5); Statistical model.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
China
Environmental Monitoring
Meteorology
Particulate Matter / analysis
Seasons

Substances

Air Pollutants
Particulate Matter