Abstract
In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM2.5. We first examined the inversion modeling performance by comparing the model predictions with PM2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m3, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM2.5 concentrations and the corresponding monthly emission fluxes.
Highlights
PM2.5 refers to atmospheric particulates with aerodynamic diameters less than 2.5 μm in ambient air and is one of the six major atmospheric pollutants [1]
Atmospheric PM2.5 particulates account for a small proportion of the particles in the earth’s atmosphere, they have an important impact on air quality, air visibility, the atmosphere radiation balance and precipitation [1,2]
This paper focuses on evaluating the performance of the Bayesian inversion method in improving air quality forecasting using the FLEXPART-WRF modeling system at high resolutions over the domain of Xuzhou, China
Summary
PM2.5 refers to atmospheric particulates with aerodynamic diameters less than 2.5 μm in ambient air and is one of the six major atmospheric pollutants [1]. Atmospheric PM2.5 particulates account for a small proportion of the particles in the earth’s atmosphere, they have an important impact on air quality, air visibility, the atmosphere radiation balance and precipitation [1,2]. Many cities have experienced severe haze pollution with exceedingly high PM2.5 levels [3,4,5]. The government has taken active measures to improve air quality. The implementation of these measures requires pollutant emission inventory and prediction of PM2.5 concentrations
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