An Improved LUR-based Spatial Distribution Simulation for the Large Area PM_2.5 Concentration

There exists the shortage of traditional land use regression (LUR) model in losing information of predictor variables when simulating the air pollutant concentration. An improved model which combined principal component regression (PCR) and stepwise multiple line regression (SMLR)-LUR (PCA+SMLR) was developed to simulate the spatial distribution of PM_2.5 in large area. Firstly, the correlation analysis was conducted to screen out effective predictor variables. Secondly, principal component analysis (PCA) was employed to transform effective predictor variables to principle components. Finally, all principal components were used to conduct SMLR to simulate the spatial distribution of PM_2.5. Meanwhile, the reliability of the improved model was tested in Beijing-Tianjin-Hebei urban agglomeration. Experimental results of three models (PCR, SMLR and PCA+SMLR) were compared and analyzed. The results indicated that the PCA+SMLR model has an adjusted R² of 0.883 by improving the contribution of the predictor variables. Besides, it is better than the traditional mo-del for accuracy index and the mapping results. Therefore, it can be concluded that the PCA+SMLR is a promising PM_2.5 modeling method and could be very use-ful for air pollution mapping.