Abende Sayom Reynolds Yvan, Mfenjou Martin Luther, Ayiwouo Ngounouno Mouhamed, Etoundi Michele Maguy Cathya, Boroh William André, Mambou Ngueyep Luc Leroy, Meying Arsene
School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box 115, Meiganga, Cameroon.
Laboratory of Mechanics and Materials of Civil Engineering (L2MGC), CY Cergy Paris University, 5 Mail Gay Lussac, Neuville sur Oise, F-95031, Cergy-Pontoise Cedex, France.
Heliyon. 2023 Jul 20;9(8):e18511. doi: 10.1016/j.heliyon.2023.e18511. eCollection 2023 Aug.
Trace metals present in high amounts in aquatic systems are a perpetual concern. This study applied geostatistical and machine learning models namely Ordinary Kriging (OK), Ordinary Cokriging (OCK) and Artificial Neural Network (ANN) to assess the spatial variability of trace metals and pollution indices in surface sediments along the Lom River in an abandoned gold mining site at Bekao (Adamawa Cameroon). For this purpose, thirty-one (31) surface sediment samples are collected in order to determine the total concentrations of As, Cr, Cu, Fe, Mn, Ni, Pb, Sn and Zn. These trace metals are used to compute pollution indices as the sediment pollution index (SPI), the Nemerow index (NI), the modified contamination degree (mCD), and the potential ecological risk assessment (RI). OK, OCK and ANN models are compared to determine the best model performance. The best models are selected based on the values of the root mean square error (RMSE), the coefficient of determination (R), the scatter index (SI) and the BIAS. Results showed that the sequence of trace metal mean concentrations in the sediments is Fe > Mn > Cu > Ni > Sn > Cr > Zn > Pb > As. The mean concentrations of Ni, Cu, Zn and Sn are above the average shale values (ASV) and the pollution status is globally moderate to significant with a low potential ecological risk. The spatial dependency obtained with semivariogram models are moderate to weak for Mn, Fe, Ni, Pb, SPI, NI, mCD, RI As, Cr, and Sn and strong for Cu and Zn. According to cross-validation parameters, ANN model is the best method for the prediction on trace metal concentrations and pollution indices in surface sediments along the Lom River in the abandoned gold mining site of Bekao.
水生系统中大量存在的痕量金属一直是人们关注的焦点。本研究应用地统计和机器学习模型，即普通克里金法（OK）、普通协同克里金法（OCK）和人工神经网络（ANN），来评估喀麦隆阿达马瓦州贝考一个废弃金矿场洛姆河沿岸表层沉积物中痕量金属和污染指数的空间变异性。为此，采集了31个表层沉积物样本，以测定砷、铬、铜、铁、锰、镍、铅、锡和锌的总浓度。这些痕量金属用于计算污染指数，如沉积物污染指数（SPI）、内梅罗指数（NI）、修正污染程度（mCD）和潜在生态风险评估（RI）。比较OK、OCK和ANN模型以确定最佳模型性能。根据均方根误差（RMSE）、决定系数（R）、散射指数（SI）和偏差（BIAS）的值选择最佳模型。结果表明，沉积物中痕量金属平均浓度的顺序为：铁＞锰＞铜＞镍＞锡＞铬＞锌＞铅＞砷。镍、铜、锌和锡的平均浓度高于平均页岩值（ASV），污染状况总体为中度至重度，潜在生态风险较低。通过半变异函数模型获得的空间依赖性，对于锰、铁、镍、铅、SPI、NI、mCD、RI、砷、铬和锡来说为中度至弱，对于铜和锌来说为强。根据交叉验证参数，ANN模型是预测贝考废弃金矿场洛姆河沿岸表层沉积物中痕量金属浓度和污染指数的最佳方法。
