An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data
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Le résumé fourni par la source
Sparse borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. The ASI-GCN model effectively constrains pollutant concentration transfer while capturing subtle spatial variations, improving soil pollution characterization accuracy. We tested our model at a coking plant using 215 soil samples from 15 boreholes, evaluating its robustness with three pollutants of varying volatility: arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). Leave-one-out cross-validation demonstrates that the ASI-GCN_RC_G model (ASI-GCN with residual connections) achieves the highest prediction accuracy. Specifically, the R for As, BaP, and Ben are 0.728, 0.825, and 0.781, respectively, outperforming traditional models by 58.8% (vs. IDW), 45.82% (vs. OK), and 53.78% (vs. IDW). Meanwhile, their RMSE drop by 36.56% (vs. Bayesian_K), 38.02% (vs. Bayesian_K), and 35.96% (vs. IDW), further confirming the model’s superior precision. Beyond accuracy, Monte Carlo uncertainty analysis reveals that most predicted areas exhibit low uncertainty, with only a few high-pollution hotspots exhibiting relatively high uncertainty. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, and semi-volatile BaP concentrated in the silty sand layer. At the same time, volatile Ben was predominantly found in the clay and fine sand layers. By integrating spatial autocorrelation with deep graph representation, ASI-GCN redefines sparse data 3D mapping, offering a transformative tool for precise environmental governance and human health assessment.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data
- Date Crossref
- 25/06/2025
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Chinese Academy of Sciences pays non établi dans la noticeOrganisme public
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Institute of Geographic Sciences and Natural Resources Research pays non établi dans la noticeStructure de recherche
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Beijing Forestry University pays non établi dans la noticeUniversité ou école supérieure
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Jiangxi University of Science and Technology Civil and Surveying & Mapping Engineering pays non établi dans la noticeUniversité ou école supérieure
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Ministry of Ecology and Environment pays non établi dans la noticeOrganisme public
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Chinese Academy for Environmental Planning pays non établi dans la noticeOrganisme public
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Key Laboratory of Land Surface Pattern and Simulation pays non établi dans la noticeStructure de recherche
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School of Information Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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Center for Environmental Risk and Damage Assessment pays non établi dans la noticeInstitution
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Key Laboratory of Environmental Damage Identification and Restoration pays non établi dans la noticeStructure de recherche
Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research et Beijing Forestry University, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.