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<p>Rapid urbanization, increased motorization, and industrialization have led to ever-increasing levels of Traffic-Related Air Pollution (TRAP), which has significant implications for public health and urban sustainability. This systematic review assesses the application of Geographic Information Systems (GIS) to model vehicle emissions and the related health impacts in urban areas. This review is based on literature published between 1990 and 2024. We screened 4,780 peer-reviewed articles and 780 met inclusion criteria. We examined the computational methods used in impact studies, including data from spatial datasets, pollutant variables, and epidemiological data. The most common methods were geo-statistical interpolation (Kriging, Geographically Weighted Regression), Land-Use Regression (LUR), and machine learning (Support Vector Regression, Neural Networks), typically with California Line Source Dispersion Model (CALINE) and Community Multiscale Air Quality Model (CMAQ). To pull multiple analytical perspectives, we purposefully combined systematic review methods with techniques of bibliometric analysis using VOS-viewer and R-software, allowing us to the research output and trends, collaborative networks and research themes. Ultimately our mixed-methods approach demonstrated important differences between developed and developing contexts regarding data availability, exposure modeling approaches and the integration of health co-benefits from active transport. Building on these findings, we introduce a GIS-based decision-support framework integrating traffic data, remote sensing, pollution modeling and health monitoring into a real-time, open-access platform to assist with evidence-based urban planning. This review, emphasizing the computational tools to create high-resolution exposure maps and better translate policy into practice, advances the field of computational urban science and provides a reproducible framework for ameliorating pollution-related health impacts in their best-case scenario rapidly urbanizing cities.</p>

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GIS-based modeling of traffic-related air pollution and public health risks: a systematic review with an emphasis on developing urban contexts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Ajay Dheekwal,
  • Kanwarpreet Singh,
  • Akriti Sharma

摘要

Rapid urbanization, increased motorization, and industrialization have led to ever-increasing levels of Traffic-Related Air Pollution (TRAP), which has significant implications for public health and urban sustainability. This systematic review assesses the application of Geographic Information Systems (GIS) to model vehicle emissions and the related health impacts in urban areas. This review is based on literature published between 1990 and 2024. We screened 4,780 peer-reviewed articles and 780 met inclusion criteria. We examined the computational methods used in impact studies, including data from spatial datasets, pollutant variables, and epidemiological data. The most common methods were geo-statistical interpolation (Kriging, Geographically Weighted Regression), Land-Use Regression (LUR), and machine learning (Support Vector Regression, Neural Networks), typically with California Line Source Dispersion Model (CALINE) and Community Multiscale Air Quality Model (CMAQ). To pull multiple analytical perspectives, we purposefully combined systematic review methods with techniques of bibliometric analysis using VOS-viewer and R-software, allowing us to the research output and trends, collaborative networks and research themes. Ultimately our mixed-methods approach demonstrated important differences between developed and developing contexts regarding data availability, exposure modeling approaches and the integration of health co-benefits from active transport. Building on these findings, we introduce a GIS-based decision-support framework integrating traffic data, remote sensing, pollution modeling and health monitoring into a real-time, open-access platform to assist with evidence-based urban planning. This review, emphasizing the computational tools to create high-resolution exposure maps and better translate policy into practice, advances the field of computational urban science and provides a reproducible framework for ameliorating pollution-related health impacts in their best-case scenario rapidly urbanizing cities.