Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have paradoxically declined, pointing to the confounding role of healthcare access. This study addresses that gap by constructing an 11-year (2014-2024) panel dataset across all 25 administrative districts, integrating satellite-derived vegetation indices, fire radiative power, pollutant concentrations (particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2)), carbon flux metrics and population-normalized respiratory admission rates. Two temporally validated XGBoost models were created for annual district-level respiratory rate (R^2 = 0.937) and monthly PM2.5 concentration (R^2 = 0.976) with generalization validated in 21 out of 25 districts (Mean Absolute Percentage Error (MAPE) <= 20\%). Shapley Additive Explanations (SHAP) analysis established that cumulative air quality burden is the overwhelming driver of respiratory rate variance (80.1\%), ahead of forest degradation (15.6\%) and fire activity (4.3\%). The Forest-Air-Health (FAH) Risk Index used these SHAP-derived weights to find the districts with the highest risk: Colombo (FAH = 0.802), Gampaha (0.708), and Kalutara (0.682). These findings present the inaugural evidence-based, district-level framework correlating environmental degradation with respiratory health in Sri Lanka, establishing a quantitative basis for focused public health and environmental policy.
Keywords: Machine Learning / Deep Learning | Data Science |
@inproceedings{iqbal2026environmental,
title={{Environmental Drivers of Respiratory Disease: A District Level Analysis}},
author={Iqbal, Rahim and Ahamed, Asfi and Nisfer, Izzath and Shaheed, Shazan and Ilham, Muhammadu and Athukorala, Nathali and Mendis, Madara and de Silva, Nisansa and Wickramanayake, Sandareka},
booktitle={2026 Moratuwa Engineering Research Conference (MERCon)},
pages={413-418},
doi={10.1109/MERCon71835.2026.11691342},
year={2026},
abstract={Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have paradoxically declined, pointing to the confounding role of healthcare access. This study addresses that gap by constructing an 11-year (2014-2024) panel dataset across all 25 administrative districts, integrating satellite-derived vegetation indices, fire radiative power, pollutant concentrations (particulate matter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2)), carbon flux metrics and population-normalized respiratory admission rates. Two temporally validated XGBoost models were created for annual district-level respiratory rate (R^2 = 0.937) and monthly PM2.5 concentration (R^2 = 0.976) with generalization validated in 21 out of 25 districts (Mean Absolute Percentage Error (MAPE) <= 20\%). Shapley Additive Explanations (SHAP) analysis established that cumulative air quality burden is the overwhelming driver of respiratory rate variance (80.1\%), ahead of forest degradation (15.6\%) and fire activity (4.3\%). The Forest-Air-Health (FAH) Risk Index used these SHAP-derived weights to find the districts with the highest risk: Colombo (FAH = 0.802), Gampaha (0.708), and Kalutara (0.682). These findings present the inaugural evidence-based, district-level framework correlating environmental degradation with respiratory health in Sri Lanka, establishing a quantitative basis for focused public health and environmental policy.},
misc={https://arxiv.org/pdf/2607.04416,ML,https://goo.gl/iY6aTr}
}
This dataset provides a district-level environmental and respiratory health panel for Sri Lanka from 2014 to 2024. It covers all 25 administrative districts and integrates environmental, atmospheric, vegetation, fire activity, demographic, and respiratory health indicators.
ActivePI: Nisansa de Silva
The Foundational Data Science project investigates how systematic data acquisition, integration, analysis, modelling, and visualisation can be applied to real-world problems to generate reliable and actionable knowledge.