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	Determination of major drive of ozone formation and improvement of O3 prediction in typical North China Plain based on interpretable random forest model
	Yao, Liyin1,2; Han, Yan2; Qi, Xin2; Huang, Dasheng2; Che, Hanxiong2; Long, Xin2; Du, Yang2; Meng, Lingshuo2; Yao, Xiaojiang2; Zhang, Liuyi1
	2024-07-15
摘要	O3 pollution in China has become prominent in recent years, and it has become one of the most challenging issues in air pollution control. We used data on atmospheric pollutants and meteorology from 2019 to 2021 to build an interpretable random forest (RF) model, applying this model to predict O3 concentration in 2022 in five cities in the Southwest North China Plain. The model was also used to identify and explain the influence of various factors on O3 formation. The correlation coefficient R2 between the predicted O3 concentration and observed O3 concentration was 0.82, the MAE was 15.15 mu g/m3, and the RMSE was 20.29 mu g/m3, indicating that the model can effectively predict O3 concentration in the studying area. The results of correlation analysis, feature importance, and the driving factor analysis from SHapley Additive exPlanations (SHAP) model indicated that temperature (T), NO2, and relative humidity (RH) are the top three features affecting O3 prediction, while the weights of wind speed and wind direction were relatively low. Thus, O3 in the southwestern North China Plain may mainly come from the formation of local photochemical activities. The dominant factors behind O3 also varied in different seasons. In spring and autumn, O3 pollution is more likely to occur under high NO2 concentration and high-temperature conditions, while in summer, it is more likely to occur under hightemperature and precipitation-free weather. In winter, NO2 is the dominant factor in O3 formation. Finally, the interpretable RF model is used to predict future O3 concentration based on features provided by Community Multiscale Air Quality (CMAQ) and Weather Research & Forecast (WRF) model, and the simulation performance of CMAQ on O3 concentration is enhanced to a certain extent, improving the prediction of future O3 pollution situations and guiding pollution control.
关键词	O3 Prediction Interpretable random forest SHAP CMAQ
DOI	10.1016/j.scitotenv.2024.173193
发表期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
卷号	934页码:12
通讯作者	Zhang, Liuyi(zhangliuyi@sanxiau.edu.cn) ; Chen, Yang(chenyang@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:001264194800001
语种	英语