Land use regression(LUR)models have been widely used in air pollution modeling.This regressionbased approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relations...Land use regression(LUR)models have been widely used in air pollution modeling.This regressionbased approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment.Although conceptually quite simple,its successful implementation requires detailed knowledge of the area,expertise in GIS,statistics,and programming skills,which makes this modeling approach relatively inaccessible to novice users.In this contribution,we present a LUR modeling and pollution-mapping software named PyLUR.It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently.This self-developed software comprises four modules:a potential predictor variable generation module,a regression modeling module,a model validation module,and a prediction and mapping module.The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR(with similar functions implemented on R language platform)in terms of model accuracy,processing efficiency and software stability.The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined.Furthermore,PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.展开更多
基金This research did not receive any specific grant from funding agencies in the public,commercial,or not-for-profit sectors.The development of PyLUR was inspired by the open source software RLURThe authors thank Prof.John Gulliver and Dr.David W.Morley for providing public accessible study materials about LUR modeling on the internetThe authors also thank Abley for providing reorganized traffic volume data in Auckland.
文摘Land use regression(LUR)models have been widely used in air pollution modeling.This regressionbased approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment.Although conceptually quite simple,its successful implementation requires detailed knowledge of the area,expertise in GIS,statistics,and programming skills,which makes this modeling approach relatively inaccessible to novice users.In this contribution,we present a LUR modeling and pollution-mapping software named PyLUR.It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently.This self-developed software comprises four modules:a potential predictor variable generation module,a regression modeling module,a model validation module,and a prediction and mapping module.The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR(with similar functions implemented on R language platform)in terms of model accuracy,processing efficiency and software stability.The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined.Furthermore,PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.
