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  • Title: Elucidating contributions of volatile organic compounds to ozone formation using random forest during COVID-19 pandemic: A case study in China.
    Author: Lyu Y, Gao Y, Pang X, Sun S, Luo P, Cai D, Qin K, Wu Z, Wang B.
    Journal: Environ Pollut; 2024 Apr 01; 346():123532. PubMed ID: 38365075.
    Abstract:
    Ozone has been reported to increase despite nitrogen oxides reductions during the COVID-19 pandemic, and ozone formation needs to be revisited using volatile organic compounds (VOCs), which are rarely measured during the pandemic. Here, a total of 98 VOCs species were monitored in an economy-active city in China from January 2021 to August 2022 to assess contributions to ozone formation during the pandemic. Total VOCs concentrations were 35.55 ± 21.47 ppb during the entire period, among which alkanes account for the largest fraction (13.78 ppb, 38.0%), followed by aromatics (6.16 ppb, 16.8%) and oxygenated VOCs (OVOCs, 5.69 ppb, 15.7%). Most VOCs groups (e.g., alkenes, OVOCs) and individual species (e.g., isoprene, methyl vinyl ketone) display obvious seasonal and diurnal variations, which are related to their sources and reactivities. No weekend effects of VOCs suggest limited influences from traffic emissions during pandemic. Aromatics and alkenes are the major contributors (39% and 33%) to ozone formation potential, largely driven by o/m/p-xylene (21%), ethylene (15%), toluene (9%). Secondary organic aerosol formation potential is dominated by toluene (>50%) despite its low proportion (5%). Further inclusion of VOCs and meteorology in the Random Forest model shows good ozone prediction performance (R2 = 0.77-0.86, RMSE = 11.95-19.91 μg/m3, MAE = 8.89-14.58 μg/m3). VOCs and NO2 contribute >50% of total importance with the largest difference in importance ratio of VOCs/NO2 in the summer and winter, implying ozone formation regime may vary. No seasonal variations in importance of meteorology are observed, while importance of other variables (e.g., PM2.5) is highest in the summer. This work identifies critical VOCs groups and species for ozone formation during the pandemic, and demonstrates the feasibility of machine learning algorithms in elucidation of ozone formation mechanisms.
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