Predicting PM2.5 along road paths using deep neural networks and spatio-temporal models

Abstract

Outdoor air pollution remains a major public-health challenge in rapidly urbanizing regions such as Uganda, where road tra!c and industrial activity generate elevated levels of fine particulate matter PM2.5. Road users—drivers, pedestrians, cyclists, and nearby communities—face heightened exposure risks, especially along congested and industrial corridors. However, the sparse and uneven distribution of sensors along these road networks makes it di!cult to understand how pollution varies along frequently used routes. The purpose of this study was therefore to develop a high-resolution approach for predicting near-road PM. in areas with limited sensor coverage by combining deep learning and geostatistical modelling. Analysis of AirQo sensor data from the Jinja (5 stations) and Kibuli (4 stations) industrial air clouds, covering April 2023 to April 2024, revealed clear di”erences in pollution levels, with Jinja consistently recording higher PM2.5 concentrations and more frequent Unhealthy–Hazardous episodes than Kibuli. To model these temporal and spatial dynamics, this study employed a Long Short-Term Memory (LSTM) neural network to predict hourly PM2.5 at sensor locations, achieving low errors across devices. The LSTM outputs were then integrated with several spatial interpolation techniques; Ordinary Kriging, Regression Kriging, Classification Kriging, Universal Kriging, and Radial Basis Function, evaluated using k-fold cross-validation across multiple variogram models. Results showed that Universal Kriging with the Exponential variogram achieved the best performance (RMSE = 1.88), outperforming all other variogram–model combinations. The final road-level maps highlighted severe pollution hotspots along industrial corridors in Jinja and mixed but elevated concentrations in Kibuli. Overall, the study demonstrates the e”ectiveness of combining deep learning and geostatistical modelling for high-resolution near-road PM2.5 prediction, providing actionable insights for targeted air-quality management in Ugandan urban environments.