A coupled geostatistical and machine learning approach to address spatial prediction of trace metals and pollution indices in sediments of the abandoned gold mining site of Bekao, Adamawa, Cameroon.

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.

Physicochemical Characterization of Mining Waste from the Betare-Oya Gold Area (East Cameroon) and an Adsorption Test by Sabga Smectite (North-West Cameroon).

This paper, firstly, characterizes the mining waste of the Betare-Oya gold area (East Cameroon). Surface waters are sampled and characterized physically (pH, electrical conductivity, turbidity, and suspended solids) and chemically by the determination of trace metals (Cu, Zn, Ni, Mn, Fe, Cr, As, and Pb). The concentrations of heavy metals in sediment samples are determined by X-ray Fluorescence Spectrometry (XRF) analysis.