用数据揭示斯里兰卡空气污染与呼吸疾病关系，发现医疗可及性是关键影响因素。
Environmental Drivers of Respiratory Disease: A District Level Analysis
- 构建11年25个区级面板数据，融合卫星与污染物监测
- 空气污染累积负担贡献80.1%的呼吸病率差异，远超森林退化
- 首次提出区级环境健康风险指数，助力精准公共卫生决策
斯里兰卡过去十年经历森林持续退化和大气污染上升，但区级呼吸系统住院率却呈下降趋势，暗示医疗可及性可能起混杂作用。本研究构建了2014-2024年覆盖全部25个行政区的面板数据集，整合遥感植被指数、火辐射功率、污染物浓度（PM2.5、NO2、SO2）、碳通量指标及人口归一化的呼吸系统住院率。采用两个经时间验证的XGBoost模型，分别预测年度区级呼吸病率（R²=0.937）和月度PM2.5浓度（R²=0.976），在21/25个区实现泛化验证（平均绝对百分比误差MAPE≤20%）。SHAP分析表明，累积空气质量负担是呼吸病率变异的主导因素（贡献80.1%），高于森林退化（15.6%）和火灾活动（4.3%）。基于SHAP权重构建的森林-空气-健康（FAH）风险指数识别出高风险区：科伦坡（FAH=0.802）、甘帕哈（0.708）、卡尔图拉（0.682）。该研究首次提供斯里兰卡层面环境退化与呼吸健康关联的证据，建立量化政策制定基础。
原文摘要 · Abstract (English)
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.
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