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

Fine particulate matter (PM _2.5 ) and ozone (O ₃ ) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O ₃ and secondary organic aerosols (SOA) contributing PM _2.5 . Herein, we investigated 54 VOCs, O ₃ and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O ₃ , 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 O ₃ was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O ₃ and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O ₃ formation; alkanes (C _n , 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 O ₃ 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 O ₃ 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 O ₃ .
(Copyright © 2021. Published by Elsevier B.V.)