PM2.5 Forecast Correction Based on Multi-Machine Learning

LIU Chao; GONG Yu; ZHANG Bihui; KE Huabing

doi:10.16032/j.issn.1004-4965.2024.065

Abstract：Fine particulate matter(PM2.5)pollution in the atmosphere profoundly affects human health,atmospheric visibility,and climate change;therefore,accurate forecasting of PM2.5 concentration is essential.This study developed PM2.5 forecast correction models for the Beijing-Tianjin-Hebei region using the China Meteorological Administration Unified Atmospheric Chemistry Environment for Haze V3.0(CUACE-Haze 3.0)model and various machine learning methods,including random forest(RF),light gradient boosting machine(LightGBM),and extreme gradient boosting machine(XGBoost).The forecast performance and differences among these machine learning models were then compared and analyzed.The results show that the mean error(ME)and mean absolute error(MAE)for the three machine learning algorithms were significantly lower than those of the CUACE model.The variation ranges of ME and MAE under different forecast time intervals were smaller,indicating better stability of the PM2.5 forecasts obtained based on machine learning algorithms.Furthermore,among the three machine learning algorithms,RF exhibited the best forecast performance,with ME and MAE of-3.0 μg m-3 and 23.6 μg m-3,respectively.The improvement in MAE for RF was the most prominent,reaching 11.5%,and the proportion of stations with positive correction was 97.7% in this region,significantly better than those of LightGBM and XGBoost.Additionally,during the verification and evaluation of forecast for the haze from March 9 to 12,2024,RF achieved the highest threat score(TS),with TS scores of 0.43,0.19,and 0.03 for light pollution,moderate pollution,and severe pollution or above,respectively.This demonstrates that the forecast performance of the RF algorithm is superior,and the research results provide valuable references for operational forecasting.