Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds

Guoliang Shi , Feng Wang , Zhongcheng Zhang , Gen Wang , Zhenyu Wang , Mei Li , Weiqing Liang , Jie Gao , Wei Wang , Da Chen , Yinchang Feng , Maofa Ge , Yujing Mu , Jianmin Chen , Min Shao , Zifa Wang


Received May 28, 2021,Revised , Accepted July 22, 2021, Available online January 14, 2022

Volume 34,2022,Pages 75-84

Fine particulate matter (PM2.5) and ozone (O3) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O3 and secondary organic aerosols (SOA) contributing PM2.5. Herein, we investigated 54 VOCs, O3 and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O3, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O3 was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O3 and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O3 formation; alkanes (Cn, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O3 and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O3 formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O3.

Copyright © Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights reserved.京ICP备05002858号-3