融合车载与固定传感器数据，实现500米精度的高分辨率污染地图。
Integrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning
- 用机器学习建立固定与移动数据的映射关系。
- 生成500米分辨率、5分钟更新的污染图，偏差仅+4.35%。
- 适合城市环境治理与健康风险评估人员参考。
以低成本构建高分辨率空气污染地图对可持续城市发展和公众健康风险评估至关重要。传统固定站点监测存在空间覆盖不足，而车载低成本传感器数据则不稳定。本研究整合了320辆出租车搭载的移动传感器与52个固定监测站的PM2.5数据，通过机器学习方法建立两者浓度间的映射关系。最终生成的地图达到500米空间分辨率和5分钟时间分辨率，与固定监测数据偏差仅为+4.35%，显著优于原始移动数据的-31.77%。融合地图既保留了移动数据的精细空间变化特征，又呈现了更接近固定站的稳定时间变异性（固定：1.12±0.73%，移动：3.15±2.44%，融合：1.01±0.65%）。结果表明大规模车载低成本传感网络可用于高分辨率空气质量制图，支持精准的城市环境治理与健康风险防控。
原文摘要 · Abstract (English)
Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors exhibit significant data instability. This study integrates PM2.5 data from 320 taxi-mounted mobile low-cost sensors and 52 fixed monitoring stations to address these limitations. By employing the machine learning methods, an appropriate mapping relationship was established between fixed and mobile monitoring concentration. The resulting pollution maps achieved 500-meter spatial and 5-minute temporal resolutions, showing close alignment with fixed monitoring data (+4.35% bias) but significant deviation from raw mobile data (-31.77%). The fused map exhibits the fine-scale spatial variability also observed in the mobile pollution map, while showing the stable temporal variability closer to that of the fixed pollution map (fixed: 1.12 plus or minus 0.73%, mobile: 3.15 plus or minus 2.44%, mapped: 1.01 plus or minus 0.65%). These findings demonstrate the potential of large-scale mobile low-cost sensor networks for high-resolution air quality mapping, supporting targeted urban environmental governance and health risk mitigation.
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