Analysis of wintertime O3 variability using a random forest model and high-frequency observations in Zhangjiakou—an area with background pollution level of the North China Plain

Liu, Huazhen; Liu, Junfeng; Liu, Ying; Ouyang, Bin; Xiang, Songlin; Yi, Kan; Tao, Shu

Analysis of wintertime O3 variability using a random forest model and high-frequency observations in Zhangjiakou—an area with background pollution level of the North China Plain

2020

The short-term health effects of ozone (O₃) have highlighted the need for high-temporal-resolution O₃ observations to accurately assess human exposure to O₃. Here, we performed 20-s resolution observations of O₃ precursors and meteorological factors to train a random forest model capable of accurately predicting O₃ concentrations. Our model performed well with an average validated R² of 0.997. Unlike in typical linear model frameworks, variable dependencies are not clearly modelled by random forest model. Thus, we conducted additional studies to provide insight into the photochemical and atmospheric dynamic processes driving variations in O₃ concentrations. At nitrogen oxides (NOₓ) concentrations of 10–20 ppb, all the other O₃ precursors were in states that increased the production of O₃. Over a short timescale, nitrogen dioxide (NO₂) can almost track each high-frequency variation in O₃. Meteorological factors play a more important role than O₃ precursors do in predicting O₃ concentrations at a high temporal resolution; however, individual meteorological factors are not sufficient to track every high-frequency change in O₃. Nevertheless, the sharp variations in O₃ related to flow dynamics are often accompanied by steep temperature changes. Our results suggest that high-temporal-resolution observations, both ground-based and vertical profiles, are necessary for the accurate assessment of human exposure to O₃ and the success and accountability of the emission control strategies for improving air quality.

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