سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

سیفی, اکرم, ریاحی مدوار, حسین. (1399). پهنه بندی و ارزیابی عدم قطعیت ریسک آلودگی فلزهای سنگین منابع آب سطحی معدن مس با تحلیل بیزین و شبیه‌سازی گوسی متوالی. , 18(1), 165-186. doi: 10.29252/envs.18.1.165
اکرم سیفی; حسین ریاحی مدوار. "پهنه بندی و ارزیابی عدم قطعیت ریسک آلودگی فلزهای سنگین منابع آب سطحی معدن مس با تحلیل بیزین و شبیه‌سازی گوسی متوالی". , 18, 1, 1399, 165-186. doi: 10.29252/envs.18.1.165
سیفی, اکرم, ریاحی مدوار, حسین. (1399). 'پهنه بندی و ارزیابی عدم قطعیت ریسک آلودگی فلزهای سنگین منابع آب سطحی معدن مس با تحلیل بیزین و شبیه‌سازی گوسی متوالی', , 18(1), pp. 165-186. doi: 10.29252/envs.18.1.165
سیفی, اکرم, ریاحی مدوار, حسین. پهنه بندی و ارزیابی عدم قطعیت ریسک آلودگی فلزهای سنگین منابع آب سطحی معدن مس با تحلیل بیزین و شبیه‌سازی گوسی متوالی. , 1399; 18(1): 165-186. doi: 10.29252/envs.18.1.165

پهنه بندی و ارزیابی عدم قطعیت ریسک آلودگی فلزهای سنگین منابع آب سطحی معدن مس با تحلیل بیزین و شبیه‌سازی گوسی متوالی

فصلنامه علوم محیطی
مقاله 12، دوره 18، شماره 1، فروردین 1399، صفحه 165-186    اصل مقاله (1.55 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.29252/envs.18.1.165
نویسندگان
اکرم سیفی* ؛ حسین ریاحی مدوار
گروه علوم و مهتدسی آب، دانشکده کشاورزی، دانشگاه ولیعصر(عج) رفسنجان، رفسنجان، ایران
چکیده
سابقه و هدف:
فعالیت ­های صنعتی و کشاورزی و در نتیجه عنصرهای سنگین و سمی حاصل از آن­ها به ­شدت کیفیت آب­ ها، سلامت عمومی و محیط زیست را تهدید می­ کنند. بنابراین تعیین مناطق تحت تأثیر عناصر سنگین و بررسی ریسک آلودگی و عدم قطعیت ­های مکانی آب ­های سطحی به­ عنوان موضوعی مهم و حساس مطرح است که کمتر به آن پرداخته شده است. هدف اصلی تحقیق حاضر، ترکیب روش ­های شبکه ­های بیزین و تکنیک­ های شبیه ­سازی متوالی گوسی به منظور ارزیابی خطر آلودگی فلزهای سنگین و عنصرهای سمی در آب­ های سطحی منطقه مس سرچشمه است.
مواد و روش ­ها:
در این مقاله، از 924 نمونه آب مربوط به 82 نقطه از سه منطقه متفاوت شامل زه ­آب ­های سطحی رودخانه شور، سدهای رسوبگیر و سایت اصلی معدن کاری منطقه مس سرچشمه و 9 عنصر سنگین استفاده و نقشه­ های پهنه ­بندی عدم قطعیت ریسک تهیه شد. اطلاعات براساس استاندارد سازمان حفاظت محیط زیست در دو کلاس خطر کم و زیاد طبقه ­بندی شدند. از تحلیل بیزین و الگوریتم ­های یادگیری بیزین برای تحلیل و بررسی ویژگی ­های همبستگی عناصر سنگین و استخراج وزن­ های بیزین استفاده شد. براساس ساختار به ­دست آمده از شبکه بیزین، عناصر کلیدی آلودگی منطقه مورد مطالعه انتخاب شدند. برای این ۳ عنصر، احتمال شرطی به هر نقطه اختصاص داده شد و سنجه ریسک بیزین (BRI) به ­عنوان نرخ خطی وزن ­دهی کلاس ­های ریسک، محاسبه شد. در نهایت مدل­ سازی زمین ­آماری و روش شبیه­ سازی متوالی گوسی (SGS) برای تولید نقشه­ های ریسک آلودگی بر مبنای سنجه BRI و نقشه انحراف استاندارد سنجه ریسک بیزین در آنالیز عدم قطعیت ریسک در شبیه­ سازی متوالی گوسی به­ کار برده شد.
نتایج و بحث:
براساس نتایج تحلیل بیزین سه عنصر روی، آهن و مولیبدن به­ عنوان ویژگی­ های اساسی و کلیدی در تعیین و پیش­ بینی ریسک آلودگی زون­ های مورد مطالعه تشخیص داده شدند که متعلق به ساختار اصلی شبکه بیزین با الگوریتم درختی تعیین حداکثر وزن (MWST) بودند. نتایج نشان دادند که بیشترین ریسک آلودگی در منطقه ­های سایت اصلی معدن کاری و در سد رسوب­گیر وجود دارد. براساس نتایج حاصله از مؤلفه­ های BRIZn، BRIMo و BRIFe، قسمت­هایی از مناطق جنوبی و شمالی واقع در زون شماره 1 (سایت اصلی معدن­ کاری) و بیشتر نقاط زون شماره 3 (سد رسوب­گیر) شامل مناطق غربی، مرکزی و جنوبی، ریسک زیاد آلودگی دارند که باید تمهیدهای لازم برای رفع مشکل آلودگی منبع­ های آب در این مناطق اندیشیده شود. نتایج در زون شماره 2 (زه­آب جاری در رودخانه شور) ریسک آلودگی کمی را نشان دادند. بنابر نتایج، به ­طور متوسط به­ ترتیب 19 و 22 درصد مساحت زون­ ها در کلاس­ های ریسک خطر آلودگی زیاد و کم قرار گرفتند. نقشه پهنه­ های حاصل از ریسک و غلظت فلزهای سنگین نشان ­دهنده انحراف معیار زیاد و تغییرات وسیع در محدوده مجتمع مس و سد رسوب­گیر و بیانگر عدم قطعیت مکانی زیاد ریسک توزیع آلودگی فلزهای سنگین در منبع­ های آب سطحی حوزه مورد مطالعه است. نتایج تحلیل عدم قطعیت، انتقال فلزها از محل مجتمع مس و تجمع آن­ها در سد را نشان می ­دهد و نیاز به پایش و تصفیه فلزهای سنگین از زه ­آب تولیدی شرکت مس و لزوم دست­یابی بهتر فلزهای جانبی از زه­آب مس را ایجاب می ­نماید.
نتیجه­ گیری:
بنابر نتایج، آلودگی عنصرهای سنگین و سمی در منبع­ های آب منطقه مس سرچشمه و جریان­ های پایین دست آن بالاست که سبب نفوذ آلاینده­ ها به منبع­ های آب زیرزمینی دشت رفسنجان می­ شود. این وضعیت نشان دهنده فقدان تصفیه مناسب فلزهای سنگین در فرآیندهای مجتمع مس سرچشمه است.
کلیدواژه‌ها
زمین آمار؛ سامانه اطلاعات جغرافیایی؛ شبیه سازی گوسین متوالی؛ عدم قطعیت مکانی؛ کلاس ریسک؛ معدن مس سرچشمه؛ نیم تغییرنما
عنوان مقاله [English]
Zoning and uncertainty analysis of heavy metal pollution risk in surface water resources of copper mine by Bayesian analysis and sequential Gaussian simulation
نویسندگان [English]
Akram Seifi؛ Hossien Riahi
Water Science and Engineering Department, Faculty of Agricultural, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
چکیده [English]
Introduction:
Industrial and agricultural activities resulting in the production of toxic heavy metals may endanger water quality, public health, and the environment. Therefore, the determination of areas that are affected by heavy metals and spatial uncertainty of pollution risks are considered as an important and sensitive issue, which are less studied. The main aim of this study was to combine Bayesian network analysis with Sequential Gaussian Simulations (SGS) to evaluate the pollution risk of heavy metal and toxic elements in the surface water of Sarcheshmeh copper mine.
Material and methods:
In this study, a dataset of 924 water samples from 82 locations from three different zones including the surface water of Shour River, tailing dam, and also the main mining site of Sharcheshmeh copper complex and nine heavy metals were used. The information was classified into two risk classes of low and high according to the standard of the Department of Environment of Iran. A Bayesian analysis and learning algorithm were applied to investigate the characterization of heavy metal correlations and Bayesian weights extraction. Based on the obtained Bayesian network structure, important metals were chosen as key pollution parameters. For these metals, the conditional probability was dedicated to every observed point and then the Bayesian Risk Index (BRI) was calculated as a linear rating of the weighted risk classes. Finally, the geostatistical modeling and SGS were applied for generating pollution risk and standard deviation maps of BRI were used as an uncertainty measure of SGS based on BRI elements.
Results and discussion:
Based on the results of Bayesian analysis, three elements of Zn, Mo, and Fe were identified as the most important parameters of pollution risk in the studied zones, which were derived by the MWST Bayesian network. The highest risk existed in the main mining zone and sedimentation dam. The results of BRIzn, BRIMo, and BRIFe declared that areas in north and south of zone 1 and all of zone 2 had high pollution risk, which requires appropriate treatment operations. The results also showed that the high-risk cluster was mainly located within the main mining and tailing dam zones. Also, 19% and 22% of zones’ area was classified as high and low risk of water pollution, respectively. Zoning maps of risk and heavy metals showed that there are high standard deviation and great variation in copper complex and distilling dam. The results of the uncertainty risk assessment showed high concentrations of heavy metals in the surface water arose from the transportation of heavy metal from copper mine to distilling dam, which requires treatment operation on the output water of the factory. 
Conclusion:
Based on the results, the pollution of heavy metal and toxic elements in water resources near Sarcheshmeh copper mine and downstream water resources was high and this will increase the pollution risk of Rafsanjan aquifer. These indicate the inadequate treatment of heavy metals in Sarcheshmeh copper mine water.
کلیدواژه‌ها [English]
Geostatistics, Geographical information system, Sequential Gaussian simulations, Spatial uncertainty, Risk class, Sarcheshmeh copper mine, Variogram
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