Article

Comparative methods for predicting cyanide pollution in artisanal small-scale gold mining catchment by using logistic regression and kriging with GIS

DOI: 10.1080/20421338.2020.1734325
Author(s): L. C. RazanamahandryInternational Institute for Water and Environmental Engineering (2iE), Department of Water and Sanitary Engineering, Laboratory of Water, Burkina Faso, P. M. DigbeuInternational Institute for Water and Environmental Engineering (2iE), Department of Water and Sanitary Engineering, Laboratory of Water, Burkina Faso, H. A. AndrianisaInternational Institute for Water and Environmental Engineering (2iE), Department of Water and Sanitary Engineering, Laboratory of Water, Burkina Faso, H. KarouiInternational Institute for Water and Environmental Engineering (2iE), Department of Water and Sanitary Engineering, Laboratory of Water, Burkina Faso, J. PodgorskiSwiss Federal Institute of Aquatic Science and Technology (EAWAG), Switzerland, E. ManikandanUNESCO-UNISA Africa Chair in Nanoscience’s/Nanotechnology Laboratories (U2AC2N), College of Graduate Studies, South Africa, M. MaazaUNESCO-UNISA Africa Chair in Nanoscience’s/Nanotechnology Laboratories (U2AC2N), College of Graduate Studies, South Africa, H. YacoubaInternational Institute for Water and Environmental Engineering (2iE), Department of Water and Sanitary Engineering, Laboratory of Water, Burkina Faso

Abstract

It has been reported that persistent cyanide pollution occurs in artisanal small-scale gold mining (ASGM) catchment areas in Burkina Faso. In the present study, logistic regression (LR) and Regression Kriging (RK) methods were applied to predict cyanide pollution hazard at the catchment level as well as to determine the most vulnerable areas to prioritize for restoration of degraded ecosystems. Soil samples were collected from two ASGM sites in Burkina Faso: the northern Zougnazagmiline site and the southern Galgouli site. Free cyanide (FCN) concentration in each sample was measured and the use of the LR and RK methods identified the relationships between pollution sources and pathways. However, RK was able to identify more areas with a high cyanide hazard than LR. Nevertheless, the two methods reveal that the cyanidation zones and catchments outlets within the two catchments are where the highest risk of cyanide pollution occurs, with probabilities of 0.8 and 1 in Zougnazagmiline and Galgouli, respectively.

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