A land use regression model incorporating data on industrial point source pollution

作     者：Li Chen Yuming Wang Peiwu Li Yaqin Ji Shaofei Kong Zhiyong Li Zhipeng Bai 

作者机构：College of Urban and Environmental ScienceTianjin normal UniversityTianjin 300387China College of Environmental Science and EngineeringNankai UniversityTianjin 300071China State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution and ControlTianjin 300071China Chinese Research Academy of Environmental SciencesBeijing 100012China 

出 版 物：《Journal of Environmental Sciences》 (环境科学学报（英文版）)

年 卷 期：2012年第24卷第7期

页      面：1251-1258页

核心收录：

学科分类：08[工学] 0815[工学-水利工程] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Special Environmental Research Funds for Public Welfare (No. 200909005) the National Natural Science Foundation of China (No.20677030) the Doctor Funds of Tianjin Normal University (No. 52XB1110) 

主　　题：land use regression air pollution Tianjin point source GIS 

摘      要：Advancing the understanding of the spatial aspects of air pollution in the city regional environment is an area where improved methods can be of great benefit to exposure assessment and policy support. We created land use regression （LUR） models for SO2, NO2 and PMI0 for Tianjin, China. Traffic volumes, road networks, land use data, population density, meteorological conditions, physical conditions and satellite-derived greenness, brightness and wetness were used for predicting SOa, NO2 and PMt0 concentrations. We incorporated data on industrial point sources to improve LUR model performance. In order to consider the impact of different sources, we calculated the PSIndex, LSIndex and area of different land use types （agricultural land, industrial land, commercial land, residential land, green space and water area） within different buffer radii （1 to 20 kin）. This method makes up for the lack of consideration of source impact based on the LUR model. Remote sensing-derived variables were significantly correlated with gaseous pollutant concentrations such as SO2 and NO2. R2 values of the multiple linear regression equations for SO2, NO2 and PM10 were 0.78, 0.89 and 0.84, respectively, and the RMSE values were 0.32, 0.18 and 0.21, respectively. Model predictions at validation monitoring sites went well with predictions generally within 15% of measured values. Compared to the relationship between dependent variables and simple variables （such as traffic variables or meteorological condition variables）, the relationship between dependent variables and integrated variables was more consistent with a linear relationship. Such integration has a discernable influence on both the overall model prediction and health effects assessment on the spatial distribution of air pollution in the city region.