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Improving geostatistical predictions of two environmental variables using Bayesian maximum entropy in the Sungun mining site

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Abstract

In this paper, the spatial distributions and temporal changes of electrical conductivity (EC) and pH in the Sungun mining area (in the East Azarbayjan province, Iran) were assessed. These variables were measured in 2005 in three parts of the mine considered for: the mining pit, waste dump, and tailings dam. A follow-up study was devised in 2016 with a new sampling round, at almost the same locations to examine the environmental status of the study area and its changes during this time interval. First, the general statistical evaluations were conducted. After distribution assessments and spatial variability modeling, the EC and pH were predicted at unsampled locations using three geostatistical methods of kriging, Sequential Gaussian Simulation, and Bayesian Maximum Entropy (BME). BME can also efficiently take the soft information into account. Moreover, the predicted variables and their estimation variances were mapped using these methods. The hazardable zones on these maps were also noted.

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Acknowledgements

We would like to thank the Sungun copper-molybdenum mine staff for their support during the sampling stage of this study. We sincerely thank the former head of Sungun mine Research Office, Engineer Ali Esmaeilnejad, and his colleagues. We also would like to express our respect to Dr. Dimitri D’Or (currently Chief Science Officer, Ephesia Consult) for his kind permission to use a graph from his dissertation and a special thanks to Engineer Jamshid Jafari Tabrizi (a retired scientific staff of the Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran) for providing information and data from the first sampling step. Undoubtedly, we could not have reached our research aims without Engineer Mohammad Qasemi’s serious attempts who devotedly and accurately devised the sampling tools and did the sampling in the second phase (2016) during his study as a BSc student of the mining engineering at the Faculty of Mining Engineering, Sahand University of Technology in Tabriz, Iran.

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  1. Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

    Safoura Rezaei, Enayatollah Ranjineh Khojasteh & Morovvat Faridazad

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  1. Safoura Rezaei
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  2. Enayatollah Ranjineh Khojasteh
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Correspondence to Enayatollah Ranjineh Khojasteh.

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Rezaei, S., Ranjineh Khojasteh, E. & Faridazad, M. Improving geostatistical predictions of two environmental variables using Bayesian maximum entropy in the Sungun mining site. Stoch Environ Res Risk Assess 34, 1775–1794 (2020). https://doi.org/10.1007/s00477-020-01863-4

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