融合卫星与再分析数据，精准预测非洲颗粒物污染并标记区域可靠性。
Conformal PM2.5 Mapping Under Spatial Covariate Shift: Satellite-Reanalysis Fusion for Africa's Green Industrial Transition
- 用轻量梯度提升机结合抗泄漏空间交叉验证，融合多源数据建模。
- 模型在非洲整体表现较差（R2=0.134），东部地区实际覆盖不足65%。
- 输出区域可信度标签与优先监测评分，助力绿色工业转型决策。
非洲绿色工业化迫切需要可靠的空气质量监测基础设施。本文构建了一套基于卫星与再分析数据融合的PM2.5预测系统，训练数据来自29个非洲国家404个监测站点（OpenAQ, 2017–2022）共2,068,901条记录，采用LightGBM结合抗泄漏空间交叉验证与分位数置信区间（PICP）的置信推断方法，量化预测结果及其地理适用性边界。在五折位置分组空间交叉验证下，模型取得均方根误差（RMSE）30.83 ± 5.07 μg/m³、平均绝对误差（MAE）14.54 ± 1.66 μg/m³、决定系数（R²）0.134 ± 0.023、宏平均F1值0.336 ± 0.018。该R²显著低于随机划分基准（>0.90），反映真实地理泛化难度而非模型失效。针对90%边际覆盖率的分段置信预测显示，东非地区性能严重下降（实际PICP=65.3%，名义90%），与中等强度协变量偏移一致（湿度KS=0.2237，卫星边界层高度KS=0.2558）。研究据此提出区域可靠性等级（高/中/低/不可靠）与监测点优先级评分，指导高污染未监测人群区域的基础设施扩展，直接支持非洲绿色工业转型及可持续发展目标3.9、7.1.2、9、11.6.2和13。
原文摘要 · Abstract (English)
Africa's green industrialization imperative demands reliable infrastructure for monitoring air quality. We present a satellite-reanalysis PM2.5 fusion system trained on 2,068,901 records from 404 monitoring locations in 29 African countries (OpenAQ, 2017-2022), combining LightGBM with leakage-resistant spatial cross-validation and conformal prediction to quantify predictions and their geographic applicability limits. Under 5-fold location-grouped spatial cross-validation, LightGBM achieves RMSE = 30.83 +/- 5.07 ug/m3, MAE = 14.54 +/- 1.66 ug/m3, R2 = 0.134 +/- 0.023, and macro F1 = 0.336 +/- 0.018. This R2 is substantially below random-split benchmarks (>0.90) but reflects true geographic generalisation difficulty rather than model failure. Split conformal prediction targeting 90% marginal coverage reveals severe East Africa degradation (actual PICP = 65.3% vs. nominal 90%), consistent with medium-strength covariate shift (humidity KS = 0.2237, sat_pblh KS = 0.2558). We operationalise these findings through regional reliability flags (High/Medium/Low/Unreliable) and a monitor prioritisation score directing infrastructure expansion toward highest-burden unmonitored populations, directly supporting Africa's green industrial transition and SDGs 3.9, 7.1.2, 9, 11.6.2, and 13.
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