{"ID":5937751,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-08T16:57:24.029979796Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04416","arxiv_id":"2607.04416","title":"Environmental Drivers of Respiratory Disease: A District Level Analysis","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) \u003c= 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.","short_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 a...","url_abs":"https://arxiv.org/abs/2607.04416","url_pdf":"https://arxiv.org/pdf/2607.04416v1","authors":"[\"Rahim Iqbal\",\"Asfi Ahamed\",\"Izzath Nisfer\",\"Shazan Shaheed\",\"Muhammadu Ilham\",\"Nathali Athukorala\",\"Madara Mendis\",\"Nisansa de Silva\",\"Sandareka Wickramanayake\"]","published":"2026-07-05T17:09:52Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}