GeoAI-based 3D spatial distribution modeling of PAHs in industrial contaminated soils
文献类型：期刊论文
| 作者 | Zhang, Ruicong1,2; Hu, Maogui1; Ye, Guoxing1,2; Xu, Chengdong1; Wang, Jinfeng1 |
| 刊名 | ENVIRONMENTAL POLLUTION
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 390页码:127440 |
| 关键词 | Soil pollution Three-dimensional spatial interpolation GeoAI Polycyclic aromatic hydrocarbons |
| ISSN号 | 0269-7491 |
| DOI | 10.1016/j.envpol.2025.127440 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil pollution threatens human health and food security, particularly in industrial legacy sites. Accurate threedimensional distribution modeling of soil contamination is crucial for understanding pollutant migration and guiding targeted remediation. Yet, the strong heterogeneity of contaminants limits the performance of traditional methods. We proposed a GeoAI-based approach, the three-dimensional deep kriging neural network (3D-DKNN), which combines deep learning with geostatistical principles to enhance interpolation accuracy in heterogeneous environments. Applied to polycyclic aromatic hydrocarbons at a typical industrial site, 3D-DKNN was benchmarked against traditional three-dimensional ordinary kriging (3D-OK) and inverse distance weighting (IDW). Cross-validation shows that 3D-DKNN achieved the lowest RMSE and MAE, and the highest correlation coefficient. Relative to IDW, it reduced RMSE by 36 %-80 %, MAE by 40 %-58 %, and increased correlation by over 19 %. Based on risk thresholds, contamination hotspots were identified in the northern and northwestern areas, particularly manufacturing and warehouse areas, which were recognized as key risk and remediation areas. This study demonstrates the potential of GeoAI for modeling complex pollutants and improving soil risk assessment. |
| URL标识 | 查看原文 |
| WOS关键词 | POLYCYCLIC AROMATIC-HYDROCARBONS ; POLLUTION ; IMPACT |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001633275700004 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219782]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Hu, Maogui |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Ruicong,Hu, Maogui,Ye, Guoxing,et al. GeoAI-based 3D spatial distribution modeling of PAHs in industrial contaminated soils[J]. ENVIRONMENTAL POLLUTION,2026,390:127440. |
| APA | Zhang, Ruicong,Hu, Maogui,Ye, Guoxing,Xu, Chengdong,&Wang, Jinfeng.(2026).GeoAI-based 3D spatial distribution modeling of PAHs in industrial contaminated soils.ENVIRONMENTAL POLLUTION,390,127440. |
| MLA | Zhang, Ruicong,et al."GeoAI-based 3D spatial distribution modeling of PAHs in industrial contaminated soils".ENVIRONMENTAL POLLUTION 390(2026):127440. |
入库方式： OAI收割
来源：地理科学与资源研究所
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