Predicting ambient PM_2.5 concentrations via time series models in Anhui Province, China

Due to rapid expansion in the global economy and industrialization, PM_2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM_2.5 levels. In this paper, ambient PM_2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM_2.5 concentrations, with cross-validation coefficients of determination R², RMSE, and MAE values of 0.83, 10.39 µg/m³, and 6.83 µg/m³, respectively. PFM achieved the average results (R² = 0.71, RMSE = 13.90 µg/m³, and MAE = 9.05 µg/m³), while the predicted results by ARIMA are comparatively poorer (R² = 0.64, RMSE = 15.85 µg/m³, and MAE = 10.59 µg/m³) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM_2.5 and can be applied to other regions for new findings.