Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China.

The accuracy in estimated fine particulate matter concentrations (PM2.5), obtained by fusing of station-based measurements and satellite-based aerosol optical depth (AOD), is often reduced without accounting for the spatial and temporal variations in PM2.5 and missing AOD observations. In this study, a city-specific linear regression model was first developed to fill in missing AOD data. A novel interpolation-based variable, PM2.5 spatial interpolator (PMSI2.5), was also introduced to account for the spatial dependence in PM2.5 across grid cells. A Bayesian hierarchical model was then developed to estimate spatiotemporal relationships between AOD and PM2.5. These methods were evaluated through a city-specific 10-fold cross-validation procedure in a case study in North China in 2014. The cross validation R(2) was 0.61 when PMSI2.5 was included and 0.48 when PMSI2.5 was excluded. The gap-filled AOD values also effectively improved predicted PM2.5 concentrations with an R(2) = 0.78. Daily ground-level PM2.5 concentration fields at a 12 km resolution were predicted with complete spatial and temporal coverage. This study also indicates that model prediction performance should be assessed by accounting for monitor clustering due to the potential misinterpretation of model accuracy in spatial prediction when validation monitors are randomly selected.