CLC number: X51
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-10-12
Cited: 8
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Li Chen, Shi-yong Du, Zhi-peng Bai, Shao-fei Kong, Yan You, Bin Han, Dao-wen Han, Zhi-yong Li. Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China[J]. Journal of Zhejiang University Science A, 2010, 11(11): 857-867.
@article{title="Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China",
author="Li Chen, Shi-yong Du, Zhi-peng Bai, Shao-fei Kong, Yan You, Bin Han, Dao-wen Han, Zhi-yong Li",
journal="Journal of Zhejiang University Science A",
volume="11",
number="11",
pages="857-867",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1000092"
}
%0 Journal Article
%T Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China
%A Li Chen
%A Shi-yong Du
%A Zhi-peng Bai
%A Shao-fei Kong
%A Yan You
%A Bin Han
%A Dao-wen Han
%A Zhi-yong Li
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 11
%P 857-867
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1000092
TY - JOUR
T1 - Application of land use regression for estimating concentrations of major outdoor air pollutants in Jinan, China
A1 - Li Chen
A1 - Shi-yong Du
A1 - Zhi-peng Bai
A1 - Shao-fei Kong
A1 - Yan You
A1 - Bin Han
A1 - Dao-wen Han
A1 - Zhi-yong Li
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 11
SP - 857
EP - 867
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1000092
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 (R2=0.34), the distance from an expressway (R2=0.45), and the area of residential land within a buffer of 1.5 km (R2=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 R2 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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