Investigation of Malikan Plain Groundwater’s Pollution to Arsenic

Document Type : Research Article

Authors

1 Msc student of Hydrogeology, University of Tabriz, Iran

2 Assistant Professors of Hydrogeology, University of Tabriz, Iran

3 Professors of Hydrogeology, University of Tabriz, Iran

Abstract

 The presence of Heavy metals anomalies in groundwater resource and their effect on human health through both drinking water and agricultural activities is a serious worldwide. Because of with the distribution of these elements in groundwater of Malikan plain, the information does not exist, this study were performed to evaluate heavy metals, especially arsenic in groundwater and to determining the most important factors on the arsenic anomalies of plains. Therefore, 27 samples were collected from groundwater resources in September 2014, and hydrochemical analysis were carried out in hydrogeology laboratory of Tabriz university as well as some heavy metals such as iron, aluminum, manganese, arsenic and chromium were analyzed by Atomic absorption- Graphite furnace method in Water quality control laboratory in East Azerbaijan Province. In this study the random forest (RF) algorithms, as a learning method based on ensemble of decision trees, are used for the first time in this context for evaluating of arsenics vulnerability. The RF technique has advantages over other methods due to having, high prediction accuracy, non-parametric nature, ability to learn nonlinear relationships, and ability to determine the important variables in the prediction. To model induction, five categories of explanatory variables, including aquifer characteristics, heavy metals, driving forces, remote sensing and physical-chemical variables, containing 24 variables, accompany with the response variable (arsenic) were entered into the model. Based on RF model predictions, transmissivity, nitrate, hydraulic conductivity and residential areas, were identified as the most effective parameters for having arsenic anomalies. The presence of high correlation between the amounts of nitrates and arsenic implicates the same origin for these ions. Based on the purposed model, 13% of the plain area is very low 53% low, 21% moderate, 11.5% high and 1.5% very high vulnerable to the arsenic contamination.
 
 
 

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References

 
 
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Iranian journal of Ecohydrology
Volume 3, Issue 2
June 2016
Pages 151-166

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History

  • Receive Date: 22 May 2016
  • Revise Date: 05 October 2016
  • Accept Date: 09 September 2016
  • First Publish Date: 09 September 2016
  • Publish Date: 21 June 2016

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