An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data
文献类型：期刊论文
| 作者 | Tao, Huan2; Li, Ziyang1; Nie, Shengdong5; Li, Hengkai5; Zhao, Dan3,4 |
| 刊名 | LAND
 |
| 出版日期 | 2025-06-25 |
| 卷号 | 14期号:7页码:1348 |
| 关键词 | soil pollution graph neural network sparse samples 3D spatial interpolation contaminated site |
| DOI | 10.3390/land14071348 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| WOS关键词 | POLYCYCLIC AROMATIC-HYDROCARBONS |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001535927500001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215653]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Li, Ziyang; Zhao, Dan |
| 作者单位 | 1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 3.Chinese Acad Environm Planning, Ctr Environm Risk & Damage Assessment, Beijing 100012, Peoples R China 4.Minist Ecol & Environm, Key Lab Environm Damage Identificat & Restorat, Beijing 100041, Peoples R China; 5.Jiangxi Univ Sci & Technol, Civil & Surveying & Mapping Engn, Ganzhou 341000, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Tao, Huan,Li, Ziyang,Nie, Shengdong,et al. An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data[J]. LAND,2025,14(7):1348. |
| APA | Tao, Huan,Li, Ziyang,Nie, Shengdong,Li, Hengkai,&Zhao, Dan.(2025).An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data.LAND,14(7),1348. |
| MLA | Tao, Huan,et al."An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data".LAND 14.7(2025):1348. |
入库方式： OAI收割
来源：地理科学与资源研究所
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