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.

Atmospheric pollution significantly threatens environmental sustainability and public health, particularly in rapidly developing urban areas like Haryana, India. The research introduces a holistic framework for predicting the Air Quality Index (AQI) by combining Inverse Distance Weighting (IDW) spatial interpolation with a stacked ensemble machine learning technique. Daily average levels of critical pollutants—PM ₂ . ₅ , PM ₁₀ , NO ₂ , SO ₂ , CO, O ₃ , and NH ₃ —were gathered from 2019 to 2024 at 24 CPCB monitoring stations, accumulating over 43,800 data points and LULC from Bhuvan. Statistical methods, including box plots, PCA, correlation heat maps, and VIF analysis, verified PM ₂ . ₅ and PM ₁₀ as the primary contributors to AQI, exhibiting a strong correlation (r ≈ 0.87) and seasonal fluctuations. The ensemble model, which integrates RF, SVM, ANN, GPR, and XGBoost within a linear regression meta-learner (developed in MATLAB), showcased exceptional performance with high R ² and low RMSE values, surpassing the performance of individual models. Feature importance and SHAP analysis confirmed the significant impact of particulate matter. The proposed framework enhances predictive accuracy and improves interpretability, providing actionable insights for environmental organizations. While individual machine learning models and SHAP have been used in prior AQI studies, this study’s novelty lies in their integration within a spatial forecasting pipeline combining IDW-based interpolation, stacked ensemble learning, and explainability techniques—offering spatial completeness, interpretability, and model robustness in a unified framework. Future advancements could further integrate meteorological factors and satellite-derived data to improve spatial-temporal adaptability.