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Advancing ecological restoration: A novel 3D interpolation method for assessing ammonia-nitrogen pollution in rare earth mining areas

文献类型： 外文期刊

作者： Nie, Shengdong 1 ; Li, Hengkai 1 ; Li, Ziyang 3 ; Tao, Huan 2 ; Wang, Guanshi 1 ; Zhou, Yanbing 4 ;

作者机构： 1.Jiangxi Univ Sci & Technolog, Jiangxi Prov Key Lab Water Ecol Conservat Headwate, 1958 Ke jia Rd, Ganzhou 341000, 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.Beijing Forestry Univ, Sch Technol, Beijing 100083, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： Ammonia nitrogen; Hierarchical modeling; Geostatistics; Bayesian prediction; Three-dimensional spatial interpolation

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：7.5； 五年影响因子：7.8 ）

ISSN： 0957-4174

年卷期： 2025 年 276 卷

页码：

收录情况： SCI

摘要： The in situ leaching method for ion adsorption of rare earth ores often leads to significant soil and water pollution, of which ammonia nitrogen (AN) pollution is one of the main problems. The three-dimensional spatial distribution of AN pollution displays weak spatial autocorrelation and strong spatial heterogeneity. Traditional geostatistical methods obscure to accurately capture these characteristics due to challenges in effectively utilizing sparse drilling sample data. In this paper, we propose a three-dimensional interpolation (Hierarchical Bayesian Model with Mean Surface and Stratified Non-homogeneity, HBM-MSN) method for ammonia-nitrogen pollution distribution in mining areas based on a hierarchical Bayesian framework. The method captures local spatial structures by integrating a hierarchical Bayesian model with a stochastic Gaussian process, enhancing the robustness of semi-variogram parameter fitting in areas with sparse data. A case study involving the three-dimensional spatial distribution of AN pollution in a rare earth industrial park in China demonstrates the superiority of HBM-MSN over traditional methods. Performance was assessed using leave-one-out cross-validation with mean absolute error (MAE) and root mean square error (RMSE). The results indicated that AN distribution in the mining area exhibited significant stratified heterogeneity (q = 0.365, P < 0.01). Compared to the 3D-OK and 3D-MSN models, HBM-MSN reduced the mean absolute error (MAE) by 1.29 mg/kg and 0.13 mg/kg, and the root mean square error (RMSE) by 1.28 mg/kg and 0.73 mg/kg, respectively. This study advances the field of three-dimensional modeling of AN pollution in rare earth mining areas and provides methodological support for ecological restoration, management, and risk assessment in these regions.

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