دانلود مقاله ISI انگلیسی شماره 110652
ترجمه فارسی عنوان مقاله

مدل پیش بینی آلودگی خاک سیانید در منطقه استخراج طلای صنعتی با استفاده از رگرسیون لجستیک

عنوان انگلیسی
Prediction model for cyanide soil pollution in artisanal gold mining area by using logistic regression
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
110652 2018 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : CATENA, Volume 162, March 2018, Pages 40-50

ترجمه کلمات کلیدی
مواد شیمیایی خطرناک، حوضه آبریز، آلودگی پراکنده، آلودگی خاک، ارزیابی ریسک، بورکینافاسو،
کلمات کلیدی انگلیسی
Hazardous chemicals; Catchment area; Diffuse pollution; Soil contamination; Risk assessment; Burkina Faso;
پیش نمایش مقاله
پیش نمایش مقاله  مدل پیش بینی آلودگی خاک سیانید در منطقه استخراج طلای صنعتی با استفاده از رگرسیون لجستیک

چکیده انگلیسی

It has been reported that persistent cyanide pollution occurs in artisanal small-scale gold mining (ASGM)-affected catchment areas in Burkina Faso. In the present study, the logistic regression method was employed to identify the factors that influence the spatial distribution of cyanide pollution as well as to predict the cyanide pollution map risk at catchment level. Soil samples were collected from two ASGM sites in the northern Zougnazagmiline (“North”) site and southern Galgouli (“South”) site parts of Burkina Faso, covering areas of 22 km2 and 20 km2, respectively. Free cyanide concentration in each sample was measured. It was shown that the spatial distribution of cyanide was solely controlled by the soil type in Zougnazagmiline and both the soil type and electric conductivity in Galgouli. On the other hand, the cyanidation zones within the two catchments were the places where the highest risk of cyanide pollution occurs, with probabilities of 0.8 and 1 in Zougnazagmiline and Galgouli, respectively. > 20% of the settled area in the Zougnazagmiline and 5% of that in Galgouli were exposed to cyanide pollution. Logistic regression was able to reliably predict cyanide contamination in areas affected by ASGM. The model could be useful for decision-makers to plan ASGM-site decontamination.