Environ Monit Assess. 2026 Jan 21;198(2):155. doi: 10.1007/s10661-026-14999-7.
ABSTRACT
This article aims to predict the concentration of air pollutants at any unmonitored location based on sparse monitoring points in the monitoring area, thereby achieving the goal of fine-grained air pollution mapping. To learn the spatial distribution characteristics of air pollutants from sparse monitoring data, this article proposes a novel Graph Neural Network (GNN) model called Graph Convolutional Neural Networks on K Neighbors (KN-GCN). Additionally, a data augmentation method is employed to enhance the sparse monitoring data and prevent overfitting of the KN-GCN model during the training process. Moreover, since the ground truth concentration value is unavailable at unmonitored locations, the accuracy of the prediction cannot be measured. Therefore, a training strategy is designed to reflect the unmeasurable accuracy on the metrics of the KN-GCN model. To evaluate the proposed method, a Computational Fluid Dynamics (CFD) simulation experiment and a public dataset experiment are conducted. The results reveal that the proposed method outperforms the baseline methods by an average of 65% and 17.8% in the CFD experiment and public dataset experiment, respectively.
PMID:41563526 | DOI:10.1007/s10661-026-14999-7
