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A land use regression model (LUR model) is an algorithm often used for analyzing pollution, particularly in densely populated areas. The model is based on predictable pollution patterns to estimate concentrations in a particular area. This requires some linkage to the environmental characteristics of the area, especially characteristics that influence pollutant emission intensity and dispersion efficiency. LUR modeling is a useful approach for screening studies and can substitute for dispersion models given insufficient input data or dispersion models.

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  • A land use regression model (LUR model) is an algorithm often used for analyzing pollution, particularly in densely populated areas. The model is based on predictable pollution patterns to estimate concentrations in a particular area. This requires some linkage to the environmental characteristics of the area, especially characteristics that influence pollutant emission intensity and dispersion efficiency. LUR modeling is a useful approach for screening studies and can substitute for dispersion models given insufficient input data or dispersion models. Multiple regression equations are used to describe the relationship between sample locations and environmental variables, often relying on geographic information systems (GIS) to collect measurements. This results in an equation that can predict pollution concentrations at unmeasured locations based on data for the predictor variables in specific locations. A raster graphic image of the area is generated and intersected with area-level population data to formulate the exposure distribution. (en)
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  • A land use regression model (LUR model) is an algorithm often used for analyzing pollution, particularly in densely populated areas. The model is based on predictable pollution patterns to estimate concentrations in a particular area. This requires some linkage to the environmental characteristics of the area, especially characteristics that influence pollutant emission intensity and dispersion efficiency. LUR modeling is a useful approach for screening studies and can substitute for dispersion models given insufficient input data or dispersion models. (en)
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  • Land use regression model (en)
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