[Source Analysis of Ambient PM_2.5 in Wuhan City Based on Random Forest Model]

Based on the online monitoring data of fine particle(PM_2.5) mass concentration, carbonaceous components, ionic constituents, and elemental components in an urban site of Wuhan from December 2019 to November 2020, the chemical characteristics of PM_2.5 were analyzed. In addition, seasonal source apportionment of PM_2.5 was conducted using the principal component analysis(PCA) method and random forest(RF) algorithm model. The results indicated that ρ(PM_2.5) was the highest in winter[(61.33±35.32) μg·m^-3] and the lowest in summer[(17.87±10.06) μg·m^-3]. Furthermore, organic carbon(OC), with a concentration of(7.27±3.51) μg·m^-3, accounted for the major proportion compared with that of elemental carbon(EC) in the carbonaceous component of PM_2.5. NO₃^-, SO₄^2-, and NH₄⁺ had the highest proportion in ionic components, with concentrations of (11.55±3.86),(7.55±1.53), and (7.34±1.99) μg·m^-3, respectively. K, Fe, and Ca were the main elements in elemental components, with concentrations of (752.80±183.98),(542.34±142.55), and (459.70±141.99) ng·m^-3, respectively. Relying on main factor extraction by PCA and quantitative analysis by RF, five emission sources were ultimately confirmed. The seasonal concentration distribution of these emission sources was as follows:coal burning and secondary sources(46%, 39%, 41%, and 52% for spring, summer, autumn, and winter, respectively) made the highest contribution to PM_2.5, followed by vehicle emission sources(22%, 28%, 27%, and 21%), industrial emission sources (14%, 18%, 17%, and 13%), dust sources (10%, 8%, 6%, and 6%), and biomass burning sources (8%, 7%, 9%, and 8%). The valuation of the RF model was evaluated using multiple indicators, including RMSE, MSE, and R². The evaluation results showed that the model for winter had the best performance (R²=0.974, RMSE=3.795 μg·m^-3, MAE=2.801 μg·m^-3), the models for spring (R²=0.936, RMSE=3.512 μg·m^-3, MAE=2.503 μg·m^-3) and autumn (R²=0.937, RMSE=4.114 μg·m^-3, MAE=3.034 μg·m^-3) performed with moderate-fitting goodness, and the summer model showed a relatively weak-fitting performance (R²=0.866, RMSE=5.665 μg·m^-3, MAE=3.889 μg·m^-3). The RF model had a satisfactory performance in PM_2.5 source apportionment and had excellent prospects in analyzing massive historical data of air pollutants.