Academic Journal
Spatial structure and multiscale environmental associations of arsenic in karst agricultural soils of Zhijin County, southwestern China.
| Τίτλος: | Spatial structure and multiscale environmental associations of arsenic in karst agricultural soils of Zhijin County, southwestern China. |
|---|---|
| Συγγραφείς: | Liu Z; Beijing Normal University, Beijing, China. iggeliuzhizhuo@163.com.; Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Tianjin, China. iggeliuzhizhuo@163.com., Zhang L; Tianjin Chengjian University, Tianjin, China., Ning J; Tianjin Chengjian University, Tianjin, China., Liu X; Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Tianjin, China., Liu Z; Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Tianjin, China., Zhou Y; Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Tianjin, China. |
| Πηγή: | Environmental monitoring and assessment [Environ Monit Assess] 2026 Sep 29; Vol. 198 (10). Date of Electronic Publication: 2026 Sep 29. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE |
| Imprint Name(s): | Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981- |
| Ιατρικοί όροι (MeSH): | Arsenic*/analysis , Soil Pollutants*/analysis , Soil*/chemistry , Environmental Monitoring* , Agriculture*, China |
| Περίληψη: | Soil arsenic (As) patterns in karst landscapes reflect geological, topographic, hydrological, and anthropogenic conditions, but spatial explanatory power must be distinguished from predictive contribution and causality. We analyzed agricultural topsoil from 144 sampling sites in Zhijin County, southwestern China, using descriptive statistics, Yeo-Johnson transformation, semivariograms, ordinary kriging, Geodetector, an extreme gradient boosting regressor (XGBRegressor), SHapley Additive exPlanations (SHAP), and sample-level SHAP maps. Soil As concentrations ranged from 1.79 mg kg⁻1 to values exceeding 40 mg kg⁻1, with a mean of 17.7 mg kg⁻1. The transformation reduced skewness, although normality remained rejected (p = 0.003). The exponential semivariogram provided the best fit (R2 = 0.820), with a nugget-to-sill ratio of 0.627 and a range of 37.02 km. Back-transformed kriging indicated relatively high values mainly in northern, northeastern, and central-eastern areas. Hg, soil organic carbon, distance to mining sites, elevation, distance to rivers, and lithology had nominal Geodetector p ≤ 0.003; Hg had the largest q statistic (0.4241), and the Hg-lithology interaction had the largest joint q (0.5030). Among five candidate regressors, XGBRegressor achieved the highest pooled out-of-fold R2 (0.4197) and the lowest root mean square error (6.7793 mg kg⁻1) and mean absolute error (4.5831 mg kg⁻1). SHAP ranked Hg, elevation, and lithology highest; Hg and elevation remained among the top three across all five folds. Sample-level maps showed heterogeneous signed contributions. Predictive performance was moderate, and spatial transferability remains unverified. These results support targeted verification and zoned monitoring without implying causal attribution. (© 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) |
| Competing Interests: | Declarations. Ethical approval: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests: The authors declare no competing interests. |
| References: | Adhikari, K., Mancini, M., Libohova, Z., Blackstock, J., Winzeler, E., Smith, D. R., Owens, P. R., Silva, S. H. G., & Curi, N. (2024). Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors. Science of the Total Environment, 919, Article 170972. https://doi.org/10.1016/j.scitotenv.2024.170972. (PMID: 10.1016/j.scitotenv.2024.170972) Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), 43–69. https://doi.org/10.1080/02626667909491834. (PMID: 10.1080/02626667909491834) Cao, F., Ge, Y., & Wang, J. F. (2013). Optimal discretization for geographical detectors-based risk assessment. GIScience & Remote Sensing, 50(1), 78–92. https://doi.org/10.1080/15481603.2013.778562. (PMID: 10.1080/15481603.2013.778562) Chen, H., Lundberg, S. M., & Lee, S. I. (2022). Explaining a series of models by propagating Shapley values. Nature Communications, 13(1), Article 4512. https://doi.org/10.1038/s41467-022-31384-3. (PMID: 10.1038/s41467-022-31384-3) Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785. Duan, D., Wang, P., Rao, X., Zhong, J., Xiao, M., Huang, F., & Xiao, R. (2024). Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models. Science of the Total Environment, 934, Article 173284. https://doi.org/10.1016/j.scitotenv.2024.173284. (PMID: 10.1016/j.scitotenv.2024.173284) Fouedjio, F., & Arya, E. (2024). Locally varying geostatistical machine learning for spatial prediction. Artificial Intelligence in Geosciences, 5, Article 100081. https://doi.org/10.1016/j.aiig.2024.100081. (PMID: 10.1016/j.aiig.2024.100081) Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451. (PMID: 10.1214/aos/1013203451) Goovaerts, P. (1999). Geostatistics in soil science: State-of-the-art and perspectives. Geoderma, 89(1–2), 1–45. https://doi.org/10.1016/s0016-7061(98)00078-0. (PMID: 10.1016/s0016-7061(98)00078-0) Guo, J., Yao, D., Chen, P., Chen, J., & Shi, F. (2017). Distribution, enrichment and modes of occurrence of arsenic in Chinese coals. Minerals, 7(7), Article 114. https://doi.org/10.3390/min7070114. (PMID: 10.3390/min7070114) Hu, H., Zhou, W., Liu, X., Guo, G., He, Y., Zhu, L., Chen, D., & Miao, R. (2025). Machine learning combined with geodetector to predict the spatial distribution of soil heavy metals in mining areas. Science of the Total Environment, 959, Article 178281. https://doi.org/10.1016/j.scitotenv.2024.178281. (PMID: 10.1016/j.scitotenv.2024.178281) Jena, R. K., Bandyopadhyay, S., Pradhan, U. K., Moharana, P. C., Kumar, N., Sharma, G. K., Roy, P. D., Ghosh, D., Ray, P., Padua, S., Ramachandran, S., Das, B., Singh, S. K., Ray, S. K., Alsuhaibani, A. M., Gaber, A., & Hossain, A. (2022). Geospatial modelling for delineation of crop management zones using local terrain attributes and soil properties. Remote Sensing, 14(9), Article 2101. https://doi.org/10.3390/rs14092101. (PMID: 10.3390/rs14092101) Kopecký, M., Macek, M., & Wild, J. (2021). Topographic wetness index calculation guidelines based on measured soil moisture and plant species composition. Science of the Total Environment, 757, Article 143785. https://doi.org/10.1016/j.scitotenv.2020.143785. (PMID: 10.1016/j.scitotenv.2020.143785) Li, Z. (2024). Geoshapley: A game theory approach to measuring spatial effects in machine learning models. Annals of the American Association of Geographers, 114(7), 1365–1385. https://doi.org/10.1080/24694452.2024.2350982. (PMID: 10.1080/24694452.2024.2350982) Liu, J., Zheng, B., Aposhian, H. V., Zhou, Y., Chen, M. L., Zhang, A., & Waalkes, M. P. (2002). Chronic arsenic poisoning from burning high-arsenic-containing coal in Guizhou, China. Environmental Health Perspectives, 110(2), 119–122. https://doi.org/10.1289/ehp.02110119. (PMID: 10.1289/ehp.02110119) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9. (PMID: 10.1038/s42256-019-0138-9) Lundberg, S., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arxiv.1705.07874. Qin, Y., Zhang, F., Xue, S., Ma, T., & Yu, L. (2022). Heavy metal pollution and source contributions in agricultural soils developed from karst landform in southwestern China. Toxics, 10(10), 568. https://doi.org/10.3390/toxics10100568. (PMID: 10.3390/toxics10100568) Song, Y., Wang, J., Ge, Y., & Xu, C. (2020). An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GIScience & Remote Sensing, 57(5), 593–610. https://doi.org/10.1080/15481603.2020.1760434. (PMID: 10.1080/15481603.2020.1760434) Sørensen, R., Zinko, U., & Seibert, J. (2006). On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10(1), 101–112. https://doi.org/10.5194/hess-10-101-2006. (PMID: 10.5194/hess-10-101-2006) Szatmári, G., Pásztor, L., Takács, K., Mészáros, J., Benő, A., & Laborczi, A. (2024). Space-time modelling of soil organic carbon stock change at multiple scales: Case study from Hungary. Geoderma, 451, Article 117067. https://doi.org/10.1016/j.geoderma.2024.117067. (PMID: 10.1016/j.geoderma.2024.117067) Vingiani, S., Buttafuoco, G., Fagnano, M., Guarino, A., Perreca, C., & Albanese, S. (2024). A multisensor approach coupled with multivariate statistics and geostatistics for assessing the status of land degradation: The case of soils contaminated in a former outdoor shooting range. Science of the Total Environment, 933, Article 172398. https://doi.org/10.1016/j.scitotenv.2024.172398. (PMID: 10.1016/j.scitotenv.2024.172398) Wang, J. F., & Xu, C. D. (2017). Geodetector: Principle and prospective [in Chinese]. Acta Geographica Sinica, 72(1), 116–134. https://doi.org/10.11821/dlxb201701010. Wani, O. A., Sharma, V., Kumar, S. S., Malik, A. R., Pandey, A., Devi, K., Kumar, V., Gairola, A., Yadav, D., Valente, D., Petrosillo, I., & Babu, S. (2024). Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems. Ecological Indicators, 166, Article 112540. https://doi.org/10.1016/j.ecolind.2024.112540. (PMID: 10.1016/j.ecolind.2024.112540) World Health Organization. (2022). Arsenic. https://www.who.int/news-room/fact-sheets/detail/arsenic. Yan, Y., & Yang, Y. (2025). Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models. Journal of Hazardous Materials, 482, Article 136578. https://doi.org/10.1016/j.jhazmat.2024.136578. (PMID: 10.1016/j.jhazmat.2024.136578) Ye, M., Zhu, L., Li, X., Ke, Y., Huang, Y., Chen, B., Yu, H., Li, H., & Feng, H. (2023). Estimation of the soil arsenic concentration using a geographically weighted XGBoost model based on hyperspectral data. Science of the Total Environment, 858, Article 159798. https://doi.org/10.1016/j.scitotenv.2022.159798. (PMID: 10.1016/j.scitotenv.2022.159798) Zhan, J., Li, X., Christie, P., & Wu, L. (2021). A review of soil potentially toxic element contamination in typical karst regions in southwest China. Current Opinion in Environmental Science & Health, 23, Article 100284. https://doi.org/10.1016/j.coesh.2021.100284. (PMID: 10.1016/j.coesh.2021.100284) |
| Contributed Indexing: | Keywords: Environmental monitoring; Geodetector; Ordinary kriging; Potentially toxic elements; SHAP; XGBRegressor |
| Substance Nomenclature: | N712M78A8G (Arsenic) 0 (Soil Pollutants) 0 (Soil) |
| Entry Date(s): | Date Created: 20260929 Date Completed: 20260929 Latest Revision: 20261008 |
| Update Code: | 20261009 |
| DOI: | 10.1007/s10661-026-15960-4 |
| PMID: | 42809119 |
| Βάση Δεδομένων: | MEDLINE |
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