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Meteorological Parameters and Gaseous Pollutant Concentrations as Predictors of Ground-level PM2.5 Concentrations in the Beijing-Tianjin-Hebei Region, China

Category: Air Pollution Modeling

Accepted Manuscripts
DOI: 10.4209/aaqr.2018.12.0449
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Xinpeng Wang , Wenbin Sun, Zhen Wang, Yahui Wang, Hongkang Ren

  • College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Highlights

  • Feasibility of gaseous pollutant as predictors for PM2.5 estimation was confirmed.
  • The underestimation of high PM2.5 concentrations can be improved.
  • We can provide highly accurate maps of PM2.5 distribution.

Abstract

Ground-level PM2.5 concentrations are severely underestimated by mixed-effects model that ignore the effects of primary pollutant emissions and secondary pollutant conversion. The model, in particular, underestimates the ground-level PM2.5 concentrations associated with periods of heavy pollution. In this work, meteorological parameters and NO2, SO2, CO, and O3 concentrations are introduced as predictors into a mixed-effects model to improve the estimation of PM2.5 concentration based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The Beijing–Tianjin–Hebei (JingJinJi) Region is taken as the study area. The model provides an overall cross-validation (CV) R2 of 0.84 and root-mean-square prediction error (RMSE) of 33.91 µg m−3. The CV R2 and RMSE of the proposed model are higher by 0.11and lower by 9.16 µg m−3, respectively, than those of the model that lacks gaseous pollutants as predictors. The R2 and RMSE of the model increases and decreases by 0.14 and 13.37 µg m−3, respectively, when PM2.5 concentrations exceed the secondary standards set by the Ministry of Environment Protection of China (PM2.5 > 75 µg m−3). High PM2.5 concentrations are associated with drastic improvements in the underestimation of PM2.5 concentrations. The spatial distribution of PM2.5 during periods of heavy pollution predicted by the proposed model is highly consistent with that inferred from monitoring data. Thus, the proposed model can be used to generate highly accurate maps of PM2.5 distribution for long-term and short-term PM2.5 exposure studies and can help reduce the misclassification of PM2.5 exposure in heavily polluted areas.

Keywords

PM2.5 Aerosol optical depth Gaseous pollutant Heavy pollution Mixed-effects model


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