Meteorological Parameters and Gaseous Pollutant Concentrations as Predictors of Ground-level PM_2.5 Concentrations in the Beijing-Tianjin-Hebei Region, China

摘要

Ground-level PM_(2.5) concentrations—especially those during episodes of heavy pollution—are severely underestimated by mixed-effects models that ignore the effects of primary pollutant emissions and secondary pollutant conversion. In this work, meteorological parameters and NO_(2), SO_(2), CO, and O_(3) concentrations are introduced as predictors to a mixed-effects model to improve the estimated concentration of PM_(2.5), which is based on the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The Beijing-Tianjin-Hebei (JingJinJi) region is used as the study area. The model provides an overall cross-validation (CV) R~(2) of 0.84 and root-mean-square prediction error (RMSE) of 33.91 μg m~(–3). The CV R~(2) and RMSE of the proposed model are higher by 0.11 and lower by 9.16 μg m~(–3), respectively, than those of a model lacking gaseous pollutants as predictors. The R~(2) and RMSE of the proposed model increases and decreases by 0.14 and 13.37 μg m~(–3), respectively, when PM_(2.5) concentrations exceed 75 μg m~(–3). The high values predicted for the PM_(2.5) concentration indicate a drastic improvement in the estimation, and the spatial distribution generated by the model for periods of heavy pollution is highly consistent with that inferred from monitoring data. Thus, the proposed model can be used to generate highly accurate maps of the PM_(2.5) distribution for long-term and short-term exposure studies and to correctly classify exposure in heavily polluted areas.