Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm

• Published in: Scientific reports Vol. 12; no. 1; p. 5646
• Main Authors: Balamurugan, Vigneshkumar, Balamurugan, Vinothkumar, Chen, Jia
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 05.04.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Air Pollutants - analysis; Algorithms; Atmosphere; Chemical reactions; Cities; Emission measurements; Environmental Monitoring; Learning algorithms; Machine Learning; Meteorology; Metropolitan areas; Ozone; Ozone - analysis; Photochemical reactions; Cities
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-09619-6

Surface ozone (O[Formula: see text]) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R[Formula: see text] = 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R[Formula: see text] = 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities' (Berlin and Hamburg) measurement stations, with R[Formula: see text] ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O[Formula: see text] precursors information has little effect on the ML model's performance.