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.

中文翻译：

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PyLUR：高效的土地利用回归软件，使用Python中的GDAL / OGR库对空气污染物的空间分布进行建模

摘要

土地利用回归（LUR）模型已被广泛用于空气污染建模中。这种基于回归的方法通过考虑周围环境浓度与从周围环境中选择的几个预测变量之间的关系来估算未采样兴趣点处的环境污染物浓度。尽管从概念上讲很简单，但是要成功实施，则需要该领域的详细知识，GIS方面的专业知识，统计信息和编程技能，这使得这种建模方法对于新手用户而言相对较难。在此贡献中，我们介绍了一个名为PyLUR的LUR建模和污染映射软件。它使用基于Python平台的GDAL / OGR库，可以构建LUR模型并有效地生成污染物浓度图。该自行开发的软件包含四个模块：潜在的预测变量生成模块，回归建模模块，模型验证模块以及预测和映射模块。在模型准确性，处理效率和软件稳定性方面，将新开发的PyLUR的性能与称为RLUR的现有LUR建模软件（具有在R语言平台上实现的相似功能）进行了比较。结果表明，在检查的Bradford和Auckland案例研究中，PyLUR的建模效果优于RLUR。此外，PyLUR在数据处理方面效率更高，并且能够处理详细的GIS输入数据。在模型准确性，处理效率和软件稳定性方面，将新开发的PyLUR的性能与称为RLUR的现有LUR建模软件（具有在R语言平台上实现的相似功能）进行了比较。结果表明，在检查的Bradford和Auckland案例研究中，PyLUR的建模效果优于RLUR。此外，PyLUR在数据处理方面效率更高，并且能够处理详细的GIS输入数据。在模型准确性，处理效率和软件稳定性方面，将新开发的PyLUR的性能与称为RLUR的现有LUR建模软件（在R语言平台上实现的功能相似）进行了比较。结果表明，在检查的Bradford和Auckland案例研究中，PyLUR的建模效果优于RLUR。此外，PyLUR在数据处理方面效率更高，并且能够处理详细的GIS输入数据。