用稀疏传感器+卫星数据，精准预测城市空气质量时空变化。
Spatiotemporal Air Quality Mapping in Urban Areas Using Sparse Sensor Data, Satellite Imagery, Meteorological Factors, and Spatial Features
- 结合图神经网络与多源数据建模时空依赖关系。
- 在拉合尔案例中实现高分辨率空气质量地图生成。
- 适合城市环境管理与公共健康研究者参考。
监测空气污染对保护人类健康至关重要。传统方法如地面传感器和卫星遥感受限于部署成本高、传感器覆盖稀疏及环境干扰。本文提出一种利用稀疏传感器数据、卫星影像及多种时空因素的高分辨率空气质量指数（AQI）映射框架。通过图神经网络（GNNs），基于空间与时间依赖性估算未监测点的AQI值。框架融合气象数据、道路网络、兴趣点（PoIs）、人口密度与城市绿地等环境特征，提升预测精度。以巴基斯坦拉合尔为例，采用多分辨率数据，在精细时空尺度上生成空气质量指数图。
原文摘要 · Abstract (English)
Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face limitations due to high deployment costs, sparse sensor coverage, and environmental interferences. To address these challenges, this paper proposes a framework for high-resolution spatiotemporal Air Quality Index (AQI) mapping using sparse sensor data, satellite imagery, and various spatiotemporal factors. By leveraging Graph Neural Networks (GNNs), we estimate AQI values at unmonitored locations based on both spatial and temporal dependencies. The framework incorporates a wide range of environmental features, including meteorological data, road networks, points of interest (PoIs), population density, and urban green spaces, which enhance prediction accuracy. We illustrate the use of our approach through a case study in Lahore, Pakistan, where multi-resolution data is used to generate the air quality index map at a fine spatiotemporal scale.
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