Importance of ozone precursor’s measurements in modelling urban surface ozone variability using machine learning model

Abstract

Surface ozone (O3) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors (NOX and VOC). Exploring the potential of machine learning (ML) in modeling surface ozone has received little attention, particularly when it comes to the inclusion of limited available ozone precursors information in the ML model. The ML model with past O3 , meteorology (relative humidity, temperature, boundary layer height, wind direction), season type and in-situ NO information explains 87 % (R2 = 0.87) of the ozone variability over Munich, a German metropolitan area. The ML model trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R2 = 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 R2 ranges from 0.72 to 0.84, giving confidence to use the ML model trained for one location to other locations with sparse ozone measurements. In all cases, including coarse CAMS model O3 simulations in the ML model slightly improves the ML model’s performance in predicting surface ozone.