用图神经网络+物理规律，补全低密度监测区的空气污染数据。
Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data
- 构建图结构捕捉城市污染空间关系，融合物理规律约束预测。
- 在加州贫富差异区测试中，误差比基线降低9%-56%。
- 适合数据稀疏但需高精度污染建模的城市环境研究者。
本文提出GraPhy，一种基于图结构、融合物理规律的深度学习框架，用于在监测数据有限的城市区域实现高分辨率空气质量建模。细粒度污染监测对降低公众暴露风险至关重要，但社会经济弱势地区监测站点常稀疏，影响建模精度与分辨率。为此，我们设计了专为低分辨率监测数据优化的物理引导图神经网络，包含特定层结构与边特征。基于加州社会经济弱势的圣华金谷地区数据实验表明，GraPhy在均方误差（MSE）、平均绝对误差（MAE）和决定系数（R²）三项指标上均表现最优，相比多种基线模型性能提升9%-56%。且在不同空间异质性水平下均稳定优于基线，验证了模型设计的有效性。
原文摘要 · Abstract (English)
This work introduces GraPhy, a graph-based, physics-guided learning framework for high-resolution and accurate air quality modeling in urban areas with limited monitoring data. Fine-grained air quality monitoring information is essential for reducing public exposure to pollutants. However, monitoring networks are often sparse in socioeconomically disadvantaged regions, limiting the accuracy and resolution of air quality modeling. To address this, we propose a physics-guided graph neural network architecture called GraPhy with layers and edge features designed specifically for low-resolution monitoring data. Experiments using data from California's socioeconomically disadvantaged San Joaquin Valley show that GraPhy achieves the overall best performance evaluated by mean squared error (MSE), mean absolute error (MAE), and R-square value (R2), improving the performance by 9%-56% compared to various baseline models. Moreover, GraPhy consistently outperforms baselines across different spatial heterogeneity levels, demonstrating the effectiveness of our model design.
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