| Literature DB >> 32278201 |
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.
Abstract
Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 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 PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 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 PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.Keywords: CTM; Causality model; Interaction mechanism; Meteorological condition; PM(2.5); Statistical model
Year: 2020 PMID: 32278201 DOI: 10.1016/j.envint.2020.105558
Source DB: PubMed Journal: Environ Int ISSN: 0160-4120 Impact factor: 9.621