Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model

Highlights: • Detailed PM_2.5 maps from LUR and MAIAC AOD were generated. • Spatial patterns of PM_2.5 from LUR and AOD were fully compared. • Uncertainty was larger in rural and suburban area than urban. • Integrating AOD into LUR models increased model reliability. Accurate spatial information of PM_2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM_2.5. However, the predicted PM_2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km × 1 km MAIAC AOD and observed ground PM_2.5 in Beijing, and then seasonal PM_2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM_2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM_2.5 concentration at seasonal level (R² ≥ 0.53 in model fitting and testing), which was comparable with the capability of LUR models (R² ≥ 0.81 in model fitting and testing). PM_2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM_2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing.