A machine learning model library with long prediction time and high accuracy was established based on meteorological and environmental data,early observation and later numerical weather forecast data,ground and high-altitude forecast factors to improve the prediction accuracy of PM2.5 heavy pollution.Tak-ing heavy PM2.5 pollution forecast in Chang-Zhu-Tan urban agglomeration as an example,using data preprocess-ing,feature engineering,algorithm optimization and hyperparameter tuning and other technologies,this model li-brary could predict the concentration and grade of PM2.5,and warn heavy PM2.5 pollution within 4 days.Inter-pretability of the model was studied to enhance its transparency.Ex ante interpretability analysis showed that PM2.5 concentration prediction model had three ex ante characteristics:preceding factors were more important than late factors,environmental factors were more important than meteorological factors,and ground factors were more important than high-altitude factors.Post interpretability analysis showed that the heavy pollution weather process on January 18,2022 in Changde was influenced by upstream transmission and local pollution ac-cumulation,in which transmission played a larger role.
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