Li Qiulun, Zhu Qingyang, Xu Muwu, Zhao Yu, Narayan K M Venkat, Liu Yang
Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
School of The Environment, Nanjing University, Nanjing 210023, China.
Remote Sens (Basel). 2021 Apr;13(7). doi: 10.3390/rs13071351. Epub 2021 Apr 1.
China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM concentrations in China. Our study period consists of a reference semester (1 November 2018-30 April 2019) and a pandemic semester (1 November 2019-30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R (RMSE) of 0.79 (17.55 μg/m) and the pandemic semester model obtained a 10-fold cross-validated R (RMSE) of 0.83 (13.48 μg/m) for daily PM predictions. Our prediction results showed high PM concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM levels were lowered by 4.8 μg/m during the pandemic semester compared to the reference semester and PM levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).
2020年1月新冠疫情爆发后，中国立即在全国范围内实施了严格的封锁措施。随着中国逐渐摆脱新冠疫情对国家经济和工业活动的影响，中国成为了评估新冠疫情对区域空气质量影响的大规模自然实验场。然而，细颗粒物（PM）浓度的地面测量无法提供全面的空间覆盖，尤其是在郊区和农村地区。在本研究中，我们开发了一种机器学习方法，以卫星气溶胶遥感数据、气象场和土地利用参数作为主要预测变量，来估计中国时空分辨的每日PM浓度。我们的研究期包括一个参考学期（2018年11月1日至2019年4月30日）和一个疫情学期（2019年11月1日至2020年4月30日），每个学期有六个月用于建模。然后将每个时期分为子时期1（11月和12月）、子时期2（1月和2月）和子时期3（3月和4月）。参考学期模型在每日PM预测中获得的10倍交叉验证R（均方根误差）为0.79（17.55μg/m），疫情学期模型获得的10倍交叉验证R（均方根误差）为0.83（13.48μg/m）。我们的预测结果显示，在参考学期期间，华北平原、长江三角洲、四川盆地和新疆自治区的PM浓度较高。与参考学期相比，疫情学期的PM水平降低了4.8μg/m，其中子时期2的PM水平下降最多，下降了18%。东南部地区受新冠疫情影响最大，子时期2的PM水平下降了31%，其次是长江三角洲北部（29%）和珠江三角洲（24%）。
