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

查看全文 作　　者：Li [1]Chen;Yuming [1]Wang;Peiwu [1]Li;Yaqin [2,3]Ji;Shaofei [2,3]Kong;Zhiyong [2,3]Li;Zhipeng [4]Bai

机构地区：[1]College of Urban and Environmental Science,Tianjin normal University,Tianjin 300387,China;[2]College of Environmental Science and Engineering,Nankai University,Tianjin 300071,China;[3]State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution and Control,Tianjin 300071,China;[4]Chinese Research Academy of Environmental Sciences,Beijing 100012,China

出　　处：《Journal of Environmental Sciences》索引2012年第24卷第7期,共8页

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) 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 PM10 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 SO2, NO2 and PM10 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 km). 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. 关键词：模型数据工业用地点源污染回归模型土地使用土地利用类型多元线性回归方程环境空气污染

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

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