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PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

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Abstract

Land use regression (LUR) models have been widely used in air pollution modeling. This regression-based 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.

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Acknowledgements

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 RLUR. The authors thank Prof. John Gulliver and Dr. David W. Morley for providing public accessible study materials about LUR modeling on the internet. The authors also thank Abley for providing reorganized traffic volume data in Auckland.

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Authors and Affiliations

  1. School of Environment, Faculty of Science, University of Auckland, Auckland, 1010, New Zealand

    Xuying Ma, Jennifer Salmond & Jay Gao

  2. National Institute of Water and Atmospheric Research, Auckland, 1010, New Zealand

    Xuying Ma & Ian Longley

Authors
  1. Xuying Ma
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  2. Ian Longley
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  3. Jennifer Salmond
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  4. Jay Gao
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Corresponding author

Correspondence to Xuying Ma.

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Highlights

• PyLUR comprises four modules for developing and applying a LUR model.

• It considers both conventional and novel potential predictor variables.

• GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction.

• Developed on Python platform, PyLUR is rather efficient in data processing.

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Ma, X., Longley, I., Salmond, J. et al. PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python. Front. Environ. Sci. Eng. 14, 44 (2020). https://doi.org/10.1007/s11783-020-1221-5

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  • DOI: https://doi.org/10.1007/s11783-020-1221-5

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