| Based on the online monitoring data of fine particle(PM2.5) 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 PM2.5 were analyzed. In addition, seasonal source apportionment of PM2.5 was conducted using the principal component analysis(PCA) method and random forest(RF) algorithm model. The results indicated that ρ(PM2.5) 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 PM2.5. NO3-, SO42-, 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 PM2.5, 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 PM2.5 source apportionment and had excellent prospects in analyzing massive historical data of air pollutants. |
