مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

video

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

sound

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Version

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

View:

1,044
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Download:

0
Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Cites:

Information Journal Paper

Title

Investigation of Malikan plain groundwater’ s pollution to arsenic

Pages

  151-166

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.

Cites

  • No record.

    • References

  • No record.

    • Cite

    APA: Copy

    NOROUZI, HOSSEIN, NADIRI, ATTAALLAH, & Asghari Mogaddam, Asghar. (2016). Investigation of Malikan plain groundwater’ s pollution to arsenic. IRANIAN JOURNAL OF ECOHYDROLOGY, 3(2 ), 151-166. SID. https://sid.ir/paper/254097/en

    Vancouver: Copy

    NOROUZI HOSSEIN, NADIRI ATTAALLAH, Asghari Mogaddam Asghar. Investigation of Malikan plain groundwater’ s pollution to arsenic. IRANIAN JOURNAL OF ECOHYDROLOGY[Internet]. 2016;3(2 ):151-166. Available from: https://sid.ir/paper/254097/en

    IEEE: Copy

    HOSSEIN NOROUZI, ATTAALLAH NADIRI, and Asghar Asghari Mogaddam, “Investigation of Malikan plain groundwater’ s pollution to arsenic,” IRANIAN JOURNAL OF ECOHYDROLOGY, vol. 3, no. 2 , pp. 151–166, 2016, [Online]. Available: https://sid.ir/paper/254097/en

    Related Journal Papers

    Related Seminar Papers

  • No record.

    • Related Plans

  • No record.

    • Recommended Workshops





    Move to top