Spatiotemporal Estimation of PM2.5 Across Pakistan Using Machine Learning Methods

Exposure to fine particulate matter (PM2.5​) poses a significant environmental health risk, particularly in regions with limited ground-based monitoring infrastructure like Pakistan. This study presents a machine learning framework that generates hourly PM2.5​ concentration maps at a high spatial resolution. We integrate meterological features from ERA5-Land reanalysis hourly data published by the European Center for Medium-range Weather Forecasts (ECMWF) with ground-based observations from a citizen science network of low-cost sensors. 

Our approach uses model ensembling techniques with multiple tree-based gradient boosted algorithms to improve predictive accuracy of the framework. The ensemble technique captures the complex, non-linear relationships between meteorological variables and surface PM2.5​ concentrations, while improving generalizability and predictive variance.

Preliminary results from cross-validation on an independent test set indicate strong predictive performance, confirming the framework’s capability to reliably estimate pollution concentrations in areas lacking direct measurements. The framework produces spatially complete, high-resolution pollution maps, offering datasets for visualizing and analyzing particulate matter. This work provides a scalable foundation for enhanced exposure assessment, future epidemiological studies, and evidence-based policy-making to mitigate the health impacts of air pollution in data-sparse regions of Pakistan.