Land Use Regression Models Using Satellite Aerosol Optical Depth Observations and 3D Building Data from the Central Cities of Liaoning Province, China
Jiping Gong1,2, Yuanman Hu1, Miao Liu1, Rencang Bu1, Yu Chang1, Muhammad Bilal3, Chunlin Li1, Wen Wu1,2, Baihui Ren1,2
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1State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences,
Shenyang 110164, People’s Republic of China
2University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
3Department of Land Surveying and Geo-Informatics, the Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong
Submission date: 2015-10-12
Final revision date: 2016-01-04
Acceptance date: 2016-01-04
Publication date: 2016-05-25
Pol. J. Environ. Stud. 2016;25(3):1015-1026
Land use regression (LUR) modeling is a promising method for assessing the spatial variation of air pollutant concentrations. We developed an LUR model for air pollutants (SO2, NO2, and PM10) in the central cities of Liaoning Province using monitoring data collected during 2013. We evaluated whether the addition of annual satellite aerosol optical depth (AOD) observations and fi ve canyon indicators (building height, building coverage ratio, floor area ratio, building shape coefficient, and high-rise building ratio) improved the LUR models. Out-of-sample “10-fold” cross validation was used to quantify the accuracy of the model predictions. Our results showed that the gross domestic product (GDP) and the distance to the nearest industrial emissions were the common variables for the models. Annual AOD demonstrated weak correlations with air pollutant concentrations because of its instantaneity, low resolution, and limited precision; however, it was useful for improving the coeffi cient of determination (R2) of the LUR models. The full models incorporating the annual AOD data and canyon indicators showed further improvement. The improvements of R2 were 0.22, 0.19, and 0.39 for SO2, NO2, and PM10, respectively, demonstrating that the consideration of canyon indicators could still be valuable and could be used in LUR models.
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