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

TL;DR

This paper presents GraPhy, a graph-based physics-guided neural network framework that significantly improves high-resolution urban PM2.5 air quality imputation using limited monitoring data, especially in disadvantaged regions.

Contribution

The paper introduces GraPhy, a novel physics-guided graph neural network architecture tailored for low-resolution monitoring data in urban air quality modeling.

Findings

GraPhy outperforms baseline models with 9%-56% improvement in error metrics.

It maintains high accuracy across various spatial heterogeneity levels.

The framework effectively leverages physics guidance and graph structures for improved imputation.

Abstract

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.