环境科学
稀释
天气研究与预报模式
2019年冠状病毒病(COVID-19)
过程(计算)
气溶胶
大气科学
平流
过程分析
气象学
化学
气候学
环境化学
工艺工程
地理
物理
地质学
计算机科学
工程类
热力学
病理
操作系统
传染病(医学专业)
疾病
医学
作者
Lei Chen,Hong Liao,Ke Li,Jia Zhu,Ziyu Long,Xu Yue,Yang Yang,Meigen Zhang
标识
DOI:10.1021/acs.estlett.3c00490
摘要
By using an improved process-level quantification method implemented in the WRF-Chem model, we provide a quantitative analysis on contribution of each physical/chemical process to PM2.5 change from before to during the COVID-19 lockdown and further identify a dominant process responsible for inverse PM2.5 changes over the southern and northern North China Plain (NCP). From before to during the lockdown period, the PM2.5 concentration over the southern NCP decreased by 61.0 μg m–3; a weakened aerosol chemistry production process mainly resulting from emission mitigation of precursors was identified to be the leading process for the PM2.5 decrease. However, the northern NCP suffered from an unexpected PM2.5 increase of 10.0 μg m–3, which was primarily attributed to a weakened advection dilution process induced by decreased wind speed. The improved process analysis method, superior to the traditional one, can be applied to any two periods rather than two instantaneous time points, and therefore it exerts a new contribution to understand the pollution evolution mechanism from a process-level quantitative perspective.
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