Abstract
Abstract Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO 2 and PM 10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO 2 in heating season ( R 2 = 0.74), and non-heating season ( R 2 = 0.61) in the whole study area; and PM 10 in heating season ( R 2 = 0.72), and non-heating season ( R 2 = 0.49). Maps of spatial concentration distribution for NO 2 and PM 10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO 2, heating season), 0.035 (NO 2, non-heating season), 0.068 (PM 10, heating season), and 0.092 (PM 10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO 2, heating season), 0.043 (NO 2, non-heating season), 0.087 (PM 10, heating season), and 0.096 (PM 10, non-heating season). These intercept values might imply an area's background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R 2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO 2, and 0.08 (heating season) and 0.04 (non-heating season) for PM 10. In terms of R 2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NO 2 compared with PM 10.
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