mlsanityData to 14 Oct 2026

Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data

arXiv 2506.06917 ↗First posted 7 Jun 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 80 references that are in the map. First posted on arXiv 7 Jun 2025.

Builds on

23 of 80 references.

References · 23

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11 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysiscited by 5
  2. 2025Epi2-Net: Advancing Epidemic Dynamics Forecasting with Physics-Inspired Neural Networkscited by 0
  3. 2026NeuroDDAF: Neural Dynamic Diffusion-Advection Fields with Evidential Fusion for Air Quality Forecastingcited by 0