Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning

• Published in: Environmental pollution (1987) Vol. 318; p. 120798
• Main Authors: Wang, Li, Zhao, Yuan, Shi, Jinsen, Ma, Jianmin, Liu, Xiaoyue, Han, Dongliang, Gao, Hong, Huang, Tao
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.02.2023
• Subjects: Air Pollutants - analysis; Air Pollution - analysis; air quality; algorithms; atmospheric chemistry; China; Communicable Disease Control; COVID-19; COVID-19 infection; dust storms; Ensemble machine learning; Environmental Monitoring - methods; Ground-level ozone (O3); Humans; Machine Learning; meteorology; O3 formation sensitivity; observational studies; Ozone - analysis; photochemistry; pollution; Pollution episodes; Shapley algorithm; solar radiation; Volatile Organic Compounds - analysis; China; Ensemble machine learning; Pollution episodes; O3 formation sensitivity; Ground-level ozone (O3); Shapley algorithm; O formation sensitivity; Ground-level ozone (O)

Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3–VOCs-NOx relationships applicable in different O3 pollution even...