摘要
利用机器学习模型控制气象因素影响,定量分析了疫情期间污染源减排对咸阳空气质量的影响.结果表明,与未发生疫情情景相比,疫情期间咸阳PM_(2.5)、PM10、SO_(2)、NO2和CO浓度分别下降19.3%、26.0%、13.4%、60.1%和9.1%,NO2降幅最大,SO_(2)和CO降幅较小,O_(3)浓度不降反而上升50.9%.在一次排放和二次生成前体物都下降的情况下,PM_(2.5)降幅低于预期,O_(3)浓度不降反升,反映出PM_(2.5)和O_(3)治理的复杂性,暗示了剩余污染源对咸阳空气质量影响较大,而停产限产政策(与疫情影响类似)对咸阳空气质量改善有限,未来应重点关注散煤和生物质燃烧、热力生产和供应、原油加工及石油制品制造等剩余污染源的治理.
Using a Machine Learning Model(MLM)to decouple meteorological parameters,this paper quantified true impacts of emission reduction by pollution sources resulting from COVID-19 on air quality in Xianyang.Compared with the non-epidemic scenario,the results showed that concentrations of PM_(2.5),PM10,SO_(2),NO2,and CO in Xianyang had significantly decreased by 19.3%,26.0%,13.4%,60.1%and 9.1%,respectively,with NO2 decreasing the most,SO_(2) and CO decreasing slightly,and O_(3) increased by 50.9%conversely.Under the condition that both primary emission and precursors of secondary particulate matter decreased,the concentration of PM_(2.5) dropped lower than expected,and O_(3) increased though,showing the complexity of PM_(2.5) and O_(3) control,in the meanwhile implying that the impact of operating pollution sources during the epidemic on air quality was greater than malfunctioned sources,and official regulations to restrict and suspend production in factories(similar to the impact of the pandemic)had limited improvement on air quality.In the future,emphases should be put on the treatment of operating pollution sources during the pandemic such as scattered coal and biomass combustion,heat production and supply,and crude oil processing and petroleum product manufacturing.
作者
代兴良
宋国君
姜晓群
余景娟
方丹阳
DAI Xing-liang;SONG Guo-jun;JIANG Xiao-qun;YU Jing-juan;FANG Dan-yang(School of Environment&Natural Resources,Renming University of China,Beijing 100872,China)
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2021年第7期3106-3114,共9页
China Environmental Science
基金
中国人民大学2021年度中央高校建设世界一流大学(学科)和特色发展引导专项资金。
关键词
污染源减排
空气质量
新冠肺炎疫情
随机森林模型
emission reduction of pollution sources
air quality
COVID-19 pandemic
random forest model