Wu Qinhuizi, Li Tao, Zhang Shifu, Fu Jianbo, Seyler Barnabas C, Zhou Zihang, Deng Xunfei, Wang Bin, Zhan Yu
Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
Chengdu Academy of Environmental Sciences, Chengdu, Sichuan, 610072, China.
Atmos Environ (1994). 2022 Jun 1;278:119083. doi: 10.1016/j.atmosenv.2022.119083. Epub 2022 Mar 25.
Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China. We obtained the hourly data of NO concentrations and meteorological conditions for 337 cities in China from January 1, 2019, to December 31, 2020. Three random-forest based meteorological normalization methods were compared, including (1) the method that only resamples meteorological variables, (2) the method that resamples meteorological and temporal variables, and (3) the method that does not need resampling, denoted as Resample-M, Resample-M&T, and Resample-None, respectively. The comparison results show that Resample-M&T considerably underestimated the emission reduction of NOx during the lockdowns, Resample-None generates widely fluctuating estimates that blur the emission recovery trend during work resumption, and Resample-M clearly delineates the emission changes over the entire period. Based on the Resample-M results, the maximum emission reduction occurred during January to February 2020, for most cities, with an average decrease of 19.1 ± 9.4% compared to 2019. During April of 2020 when work resumption initiated to the end of 2020, the emissions rapidly bounced back for most cities, with an average increase of 12.6 ± 15.8% relative to those during the strict lockdowns. Consequently, we recommend using Resample-M for meteorological normalization, and the normalized NO concentration dynamics for each city provide important implications for future emission reduction.
气象归一化是指消除气象因素对空气污染物浓度的影响，以评估排放变化。目前存在多种气象归一化方法，其结果并不一致。本研究旨在确定用于表征中国新冠疫情期间氮氧化物排放时空变化的气象归一化的先进方法。我们获取了2019年1月1日至2020年12月31日中国337个城市的每小时一氧化氮浓度和气象条件数据。比较了三种基于随机森林的气象归一化方法，包括：（1）仅对气象变量进行重采样的方法，（2）对气象和时间变量进行重采样的方法，以及（3）无需重采样的方法，分别记为Resample-M、Resample-M&T和Resample-None。比较结果表明，Resample-M&T大大低估了封锁期间氮氧化物的减排量，Resample-None产生的估计值波动较大，模糊了复工期间的排放恢复趋势，而Resample-M清楚地描绘了整个时期的排放变化。基于Resample-M的结果，大多数城市的最大减排量出现在2020年1月至2月，与2019年相比平均下降了19.1±9.4%。在2020年4月复工开始至2020年底期间，大多数城市的排放量迅速反弹，相对于严格封锁期间平均增加了12.6±15.8%。因此，我们建议使用Resample-M进行气象归一化，每个城市的归一化一氧化氮浓度动态对未来的减排具有重要意义。
