RESUMEN
Based on the offline sampling data of volatile organic compounds (VOCs) and the simultaneous online measurements of conventional gaseous air pollutants and meteorological parameters in urban Huanggang, the volume fractions and component characteristics of VOCs were analyzed. The sources and ozone (O3) formation sensitivity of VOCs during severe ozone pollution episodes were analyzed using the positive matrix factorization (PMF) model and the photochemical box model coupled with master chemical mechanism (PBM-MCM), respectively. The results revealed that the average volume fractions of total volatile organic compounds were (21.57±3.13)×10-9, with higher volume fractions in winter and spring compared to those in summer and autumn. Among these, alkanes (49.9%) and alkenes (16.4%) accounted for the highest proportion. The PMF analysis results showed that fuel combustion (27.8%), vehicle emission (19.9%), solvent use (15.7%), industrial halogenated hydrocarbon emission (12.1%), chemical enterprise emission (10.5%), natural sources (7.8%), and diesel vehicle emission (6.2%) were the main sources of VOC emissions. Anthropogenic VOCs emitted by solvent use, fuel combustion, and chemical enterprises contributed significantly (60.9% in total) to generating O3, which indicates that these three types of anthropogenic sources should be controlled first when it comes to preventing and controlling ozone pollution. Further, the relative incremental reactivity (RIR) and empirical kinetic method approach (EKMA) revealed that O3 formation was in a VOCs-limited regime during the observation period in Huanggang, China. Furthermore, O3 formation was more sensitive to m-xylene, p-xylene, ethylene, 1-butene, and toluene; therefore, reducing these VOCs should be prioritized.
RESUMEN
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.
