Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China |
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Authors: | Chen Li Shi-yong Du Zhi-peng Bai Kong Shao-fei You Yan Han Bin Han Dao-wen and Zhi-yong Li |
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Institution: | (1) Department of Geography, Laboratory for Applied Geomatics and GIS Science (LAGGISS), University of Ottawa, Simard Hall, 60 University Pvt., Room 047, Ottawa, Ontario, K1N 6N5, Canada; |
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Abstract: | SO2, NO2, and PM10 are the major outdoor air pollutants in China, and most of the cities in China have regulatory monitoring sites for these
three air pollutants. In this study, we developed a land use regression (LUR) model using regulatory monitoring data to predict
the spatial distribution of air pollutant concentrations in Jinan, China. Traffic, land use and census data, and meteorological
and physical conditions were included as candidate independent variables, and were tabulated for buffers of varying radii.
SO2, NO2, and PM10 concentrations were most highly correlated with the area of industrial land within a buffer of 0.5 km (R
2=0.34), the distance from an expressway (R
2=0.45), and the area of residential land within a buffer of 1.5 km (R
2=0.25), respectively. Three multiple linear regression (MLR) equations were established based on the most significant variables
(p<0.05) for SO2, NO2, and PM10, and R
2 values obtained were 0.617, 0.640, and 0.600, respectively. An LUR model can be applied to an area with complex terrain.
The buffer radii of independent variables for SO2, NO2, and PM10 were chosen to be 0.5, 2, and 1.5 km, respectively based on univariate models. Intercepts of MLR equations can reflect the
background concentrations in a certain area, but in this study the intercept values seemed to be higher than background concentrations.
Most of the cities in China have a network of regulatory monitoring sites. However, the number of sites has been limited by
the level of financial support available. The results of this study could be helpful in promoting the application of LUR models
for monitoring pollutants in Chinese cities. |
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