A land use regression for predicting NO2 and PM10 concentrations indi erent seasons in Tianjin region, China
Li Chen , Zhipeng Bai , Shaofei Kong , Bin Han , Yan You , Xiao Ding , Shiyong Du , Aixia Liu
DOI:
Received October 30, 2009,Revised March 25, 2010, Accepted , Available online
Volume 22,2010,Pages 1364-1373
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Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 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 NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area;
and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and
PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season),
0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the
central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating
season), and 0.096 (PM10, 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. R2 values increased 0.09 (heating
season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2,
LUR model performed more e ectively in heating season than non-heating season in the study area and gave a better result for NO2
compared with PM10.
