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Assessing perched aquifer vulnerability using modified DRASTIC: a case study of colliery waste in north-east England (UK)

  • Mahmoud MoustafaEmail author
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Abstract

DRASTIC is a commonly used approach for delineating areas that are vulnerable to contamination. This study examines the suitability of DRASTIC for evaluating the vulnerability of groundwater inside a colliery spoil-heap to contamination. The standard DRASTIC approach was modified according to the environmental conditions of spoil heaps that introduce contaminants to the environment. The parameter layers used in DRASTIC are depth to water, net recharge, aquifer media, soil media, topography, impact of the vadose zone and hydraulic conductivity. The values of the hydrogeological parameters used in DRASTIC were obtained from eight exploration boreholes drilled in a spoil heap in Northumberland, England (UK). The estimated DRASTIC vulnerability score was then mapped. Sensitivity analysis of the DRASTIC parameters and validation were implemented. The results indicated that, contrary to the DRASTIC assumptions, the contaminants were not introduced at ground surface, but instead were mainly initiated in the soil, vadose and aquifer zones. Consequently, these zones should receive high values of DRASTIC weights and rates. Zones characterized by low permeability and low recharge represent contaminant hot spots inside the spoil heap, and both parameters in these areas should also receive high values of weights and rates. Sensitivity analysis revealed that the soil, vadose zone, aquifer media, hydraulic conductivity and net recharge are the most controlling parameters with respect to vulnerability. The produced vulnerability map has a high degree of compatibility with the measured contaminant concentrations in the spoil. Thus, the standard DRASTIC approach should be modified before its application to mining waste spoil heaps.

Keywords

Vulnerability Colliery waste DRASTIC Contamination Geographic information systems UK 

Evaluation de la vulnérabilité d’un aquifère perché en utilisant Une version modifiée de DRASTIC: cas d’étude des terrils dans le nord-est de l’Angleterre (Royaum-Uni)

Résumé

DRASTIC est une approche couramment utilisée pour délimiter les zones qui sont vulnérables à la contamination. Cette étude examine la pertinence de DRASTIC pour évaluer la vulnérabilité à la contamination des eaux souterraines au droit d’un terril minier. L’approche normale de DRASTIC a été modifiée en fonction des conditions environnementales des terrils, à l’origine de l’introduction de contaminants dans l’environnement. Les différents paramètres, utilisés dans DRASTIC, sont la profondeur de la nappe, la recharge, le type d’aquifère, le type de sol, la topographie, l’influence de la zone non saturée et la conductivité hydraulique. Les valeurs des paramètres hydrogéologiques utilisés dans DRASTIC ont été obtenues à partir de huit sondages de reconnaissance, forés au droit des terrils de Northumberland, en Angleterre (Royaume-Uni). La note de vulnérabilité estimée avec DRASTIC a ensuite été cartographiée. L’analyse de sensibilité des paramètres de DRASTIC et leur validation ont été effectuées. Les résultats ont montré que, contrairement aux hypothèses de DRASTIC, les contaminants n’étaient pas introduits à la surface du sol, mais étaient plutôt issus du sol, et des zones non saturée et aquifère. En conséquence, ces zones devraient recevoir des valeurs élevées dans DRASTIC en termes de pondération et de niveaux. Les zones caractérisées par une conductivité hydraulique faible et une recharge faible représentent des zones de pollution accrue au sein du terril, et ces deux paramètres dans ces zones devraient aussi se voir attribuer des valeurs élevées en termes de pondération et de niveaux. L’analyse de sensibilité a révélé que le sol, la zone non saturée, le type d’aquifère, la conductivité hydraulique et la recharge sont les principaux paramètres de contrôle en ce qui concerne la vulnérabilité. La carte de vulnérabilité réalisée présente un haut degré de compatibilité avec la distribution des concentrations en contaminants dans les déblais. Ainsi, l’approche normale de DRASTIC devrait être modifiée avant d’être appliquée aux terrils de résidus miniers.

Evaluación de la vulnerabilidad de los acuíferos colgados utilizando DRASTIC modificado: un estudio de Caso de residuos de una mina de carbón en el noreste de Inglaterra (Reino Unido)

Resumen

DRASTIC es un método comúnmente utilizado para delinear áreas que son vulnerables a la contaminación. Este estudio examina la idoneidad de DRASTIC para evaluar la vulnerabilidad de las aguas subterráneas a la contaminación en las escombreras de una mina de carbón. El método estándar de DRASTIC se modificó de acuerdo con las condiciones ambientales de las escombreras que introducen contaminantes en el ambiente. Las capas de parámetros utilizadas en DRASTIC son la profundidad del agua, la recarga neta, el medio acuífero, el suelo, la topografía, el impacto de la zona vadosa y la conductividad hidráulica. Los valores de los parámetros hidrogeológicos utilizados en DRASTIC se obtuvieron de ocho perforaciones de exploración ejecutadas en una escombrera en Northumberland, Inglaterra (Reino Unido). Se mapeó el puntaje estimado de vulnerabilidad DRASTIC. Se implementaron análisis de sensibilidad y validación de los parámetros DRASTIC. Los resultados indicaron que, a diferencia de los supuestos DRASTIC, los contaminantes no se introdujeron en la superficie del suelo, sino que se introdujeron principalmente en el suelo, la zona vadosa y el acuífero. En consecuencia, estas zonas deben recibir valores altos de pesos y tasas en el DRASTIC. Las zonas caracterizadas por una baja permeabilidad y una baja recarga representan puntos contaminantes calientes dentro de la escombrera, y ambos parámetros en estas áreas también deben recibir altos valores de pesos y tasas. El análisis de sensibilidad reveló que el suelo, la zona vadosa, el medio acuífero, la conductividad hidráulica y la recarga neta son los parámetros de mayor control con respecto a la vulnerabilidad. El mapa de vulnerabilidad producido tiene un alto grado de compatibilidad con las concentraciones de contaminantes medidas en la escombrera. Por lo tanto, el método estándar de DRASTIC debe modificarse antes de su aplicación en las escombreras de minería.

المعدلة DRASTIC تقيم مدي سهولة تعرض الخزان الجوفي بنفايات المناجم للتلوث باستخدام ط حالة دراسية شمال شرق انجلترا (المملكة المتحدة)

الملخص

طريقة DRASTIC شا ئعة الأستخدام في تحديد المناطق المعرضة للتلوث . هذة الدراسة تفحص مدي ملاءمة استخدام طريقة DRASTIC القياسية لتقيم مدي سهولة تعرض المياة الحوفية بنفايات المناجم للتلوث. طريقة DRASTIC القياسية تم تعديلها طبقا للظروف البيئية لنفايات المناجم التي تلوث للبيئة. عناصر طريقة DRASTIC التي تم استخدامها لعمل طبقات من هذة العناصر هي عمق المياة الجوفية و قيم تغذية الخزان الجوفي و التكوينات الجيولوجية للخزان الجوفي و الطبقة الغير مشبعة و التربة السطحية و طبوغرفية المنطقة و نفاذية الخزان الجوفي. قيم هذة العناصر الهيدروجيولوجية المستخدمة في الدراسة تم الحصول عليها من ثمانية أبار تم حفرها داخل نفايات المناجم في منطقة Northumberland (بالمملكة المتحدة) . قيم معامل قياس سهولة التعرض للتلوث المحسوبة من طريقة DRASTIC بعد تعديلها تم عمل منها خريطة توزيع لهذة القيم علي نفايات المناجم. وتم عمل دراسة تحليلة لتحديد تأثير العناصر المستخدمة بطريقة DRASTIC علي قيم معامل سهولة تعرض الخزان الجوفي للتلوث و كذلك تم التحقق من قيم معامل مدي سهولة التعرض للتلوث الموزعة علي نفايات المنجم. و أتضح من الدراسة أنة علي عكس طريقة DRASTIC القياسية التي تفترض ان الملوثات تدخل للخزان من سطح الارض فأن التلوث يبدء داخل المنطقة الغير مشبعة و الخزان الجوفي و التربة السطحية . لذالك هذة العناصر يجب اعطائها وزن و معدل اكبر من وزنها بالطريقة القياسية . المناطق التي تتميز بنفاذية قليلة و معدل قليل من التغذية تمثل مناطق عالية التركيز للملوثات داخل نفايات المنجم و علي ذالك هذين العنصرين يجب أعطائهما وزن و معدل كبير. كما أثبتت الدراسة ان المنطقة الغير مشبعة و الخزان الجوفي ومعامل النفاذية للخزان الجوفي ومعدل تغذية الخزان الجوفي ومنطقة التربة السطحية أهم العناصر الهيدروجيولوجية التي تتحكم في تلوث المياة الحوفية بنفايات المنجم. كما وجد أن خريطة توزيع القيم المعدلة لمعامل سهولة تعرض الخزان الحوفي للتلوث داخل نفايات المنجم متوافقة مع تركيز الملوثات التي المقاسة داخل نفايات المنجم. و بناء علي ذالك فأن طريقة DRASTIC القياسية يجب تعديلها قبل تطبيقها في تقيم مدي سهولة تعرض الخزان الجوفي بنفايات المناحم للتلوث.

利用改进型的DRASTIC方法评价表层含水层的脆弱性:英国东北部煤矸石的一个研究案例

摘要

DRASTIC是描述易受污染地区的一个常用的方法。本研究检验了DRASTIC方法评估煤矸石内部地下水污染脆弱性的适宜性。根据煤矸石堆向外部环境泄漏污染物的环境条件改进了标准的DRASTIC方法。DRASTIC方法中使用的参数层为水位埋深、纯补给量、含水层介质、土壤介质、地形、包气带的影响以及水力传导率。DRASTIC方法中使用的水文地质参数值从英国若森波兰的一个煤矸石堆上八个钻探孔中获取。然后估算的DRASTIC脆弱性分数绘制成图。对DRASTIC参数进行了灵敏度分析,并进行了验证。结果表明,与DRASTIC假设相反,污染物并未泄漏至地表,而是主要进入到了土壤、包气带和含水层因此,这些地带的DRASTIC加权和等级的值应当更高。具有透水性低和补给量低特征的地带表明为煤矸石堆内部污染物的热点区域,这两个区域的参数也应当获得加权和等级的高值。灵敏度分析揭示,突然、包气带、含水层介质、水力传导率及纯补给量是脆弱性最重要的控制参数。绘制的煤矸石堆含有测出的污染物含量的脆弱性图具有很高的兼容性。因此,标准的DRASTIC方法在应用于煤矿煤矸石堆前应当改进。

Analisando a vulnerabilidade de um aquífero suspenso utilizando o DRASTIC modificado: um estudo de Caso de resíduos de mineração de carvão no nordeste da Inglaterra (Reino Unido)

Resumo

DRASTIC é um método utilizado normalmente para delinear áreas que estão vulneráveis à contaminação. Este estudo examinou a adaptação do DRASTIC para avaliar a vulnerabilidade a contaminação das águas subterrâneas dentro de uma pilha de rejeitos de uma mina de carvão. O método DRASTIC padronizado foi modificado de acordo com as condições ambientais da pilha de rejeito que introduz contaminantes no ambiente. Os parâmetros usados no DRASTIC são profundidade da água, recarga liquida, meio aquífero, tipo de solo, topografia, impacto da zona vadosa e condutividade hidráulica. Os valores dos parâmetros hidrogeológicos usados no DRASTIC foram obtidos de oito poços de monitoramento em uma pilha de rejeito na Nortúmbria, Inglaterra (Reino Unido). A pontuação de vulnerabilidade estimada pelo DRASTIC foi mapeada. Foi implementada análise de sensibilidade e validação dos parâmetros do DRASTIC. Os resultados indicaram que, diferente da suposição do DRASTIC, os contaminantes não foram introduzidos na superfície do solo, mas em vez disso foram iniciados principalmente no solo, zona vadosa e no aquífero. Consequentemente, estas zonas poderiam receber altos valores dos pesos e taxas do DRASTIC. Zonas caracterizadas com baixa permeabilidade e recarga representam pontos preferenciais de contaminação dentro da pilha de rejeito e ambos os parâmetros nestas áreas poderiam também receber altos valores de pesos e taxas. A análise de sensibilidade revelou que o solo, a zona vadosa, o meio aquífero, a condutividade hidráulica e a recarga líquida são os parâmetros mais controladores a vulnerabilidade. O mapa de vulnerabilidade produzido tem alto grau de compatibilidade com as concentrações de contaminantes no despojo. Então, o método DRASTIC padronizado deve ser modificado antes de sua aplicação em pilhas de rejeitos de mineração.

References

  1. Appelo CAJ, Postma D (1993) Geochemistry, groundwater and pollution. Balkema, Rotterdam, The NetherlandsGoogle Scholar
  2. Akbari GH, Rahimi M (2011) Sensitivity analysis of water at higher risk subjected to soil contaminations. Comput Methods Civ Eng 2(1):83–94Google Scholar
  3. Albinet M, Margat J (1970) Cartographie dela vulnerability pollution des nappes d’eau souterraine [Mapping of groundwater vulnerability to contamination]. Bull BRGM 2(4):13–22Google Scholar
  4. Aller L, Bennet T, Lehr JH, Petty RJ (1987) DRASTIC: a standardized system for evaluating groundwater pollution potential using hydro geologic settings. USEPA document no. EPA/600/2–85-018, USEPA, Washington, DCGoogle Scholar
  5. Almsari MN (2008) Assessment of intrinsic vulnerability to contamination for Gaza coastal aquifer. Palestine J Environ Manag 88:577–593Google Scholar
  6. Al-Zabet T (2002) Evaluation of aquifer vulnerability to contamination potential using the DRASTIC method. Environ Geol 43:203–208Google Scholar
  7. Amos PW (1999) A permeable reactive barrier for treatment of acidic mine drainage: site investigation and design. MSC Thesis, University of Newcastle Upon Tyne, UKGoogle Scholar
  8. Atanacković N, Dragišić V, Živanović V, Štrbački J, Ninković S (2016) Mining meets water, conflicts and solutions. Proceedings IMWA, IMWA, Wendelstein, GermanyGoogle Scholar
  9. Babiker IS, Mohamed MAA, Hiyama T, Kato K (2005) A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu prefecture, central Japan. Sci Total Environ 345(1–3):127–140Google Scholar
  10. Baldridge E (2004) Developing spatially interpolated surfaces and estimating uncertainty. Original research article, USEPA, Office of Air Quality Planning and Standards, Washington, DC, pp 49–73Google Scholar
  11. Banks D, Younger PL, Arnesen R-T, Iversen ER, Banks SB (1997) Mine-water chemistry: the good, the bad and the ugly. Environ Geol 32(3):157–174Google Scholar
  12. Bazimenyera JDD, Zhonghua T (2008) A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Hangzhou-Jiaxing-Huzhou plain. China Res J Appl Sci 3:550–559Google Scholar
  13. Bhuiyan MA, Islam MA, Dampare SB, Parvez L, Suzuki S (2010) Evaluation of hazardous metal pollution in irrigation and drinking water systems in the vicinity of a coal mine area of northwestern Bangladesh. J Hazard Mater 179(1):1065–1077Google Scholar
  14. Bojo’rquez-Tapia LA, Cruz-Bello GM, Luna-Gonza’lez L, Jua’rez L, Ortiz-Pe’rez MA (2009) V-DRASTIC: using visualization to engage policymakers in groundwater vulnerability assessment. J Hydrol 373:242–255Google Scholar
  15. Bonham-Carter GF (1996) Geographic information systems for geoscientists. Modeling with GIS. Elsevier, AmsterdamGoogle Scholar
  16. Bukowski TB, Augustyniak I (2006) Using the DRASTIC system to assess the vulnerability of ground water to pollution in mined areas of the upper Silesian Coal Basin. Mine Water Environ 25:15–22Google Scholar
  17. Daugherty AJ (1998) Monitoring and improvement of a reed-bed receiving acidic leachate at Shillbottle, Northumberland. MSC Thesis, University of Newcastle upon Tyne, UKGoogle Scholar
  18. Doherty J (2003) Groundwater model calibration using pilot points and regularization. Ground Water 41(2):170–177Google Scholar
  19. Edet A (2014) An aquifer vulnerability assessment of the Benin formation aquifer, Calabar, southeastern Nigeria, using DRASTIC and GIS approach. Environ Earth Sci 71:1747–1765Google Scholar
  20. El-Naqa A, Hammouri N, Kuisi M (2006) GIS-based evaluation of groundwater vulnerability in the Russeifa area, Jordan. Rev Mexi Cienc Geol 23:277–287Google Scholar
  21. Ettazarini S (2006) Groundwater pollution risk mapping for the Eocene aquifer of the Oum Er-Rabia Basin. Morocco Environ Geol 51(3):341–347Google Scholar
  22. Evangelou VP, Zhang YL (1995) A review: pyrite oxidation mechanisms and acid mine drainage prevention. Crit Rev Environ Sci Technol 25(2):141–199Google Scholar
  23. Evans BM, Myers WL (1990) A GIS-based approach to evaluating regional groundwater pollution potential with DRASTIC. J Soil Water Conserv 45:242–245Google Scholar
  24. Ewusi A, Ahenkorah I, Kuma JSY (2017) Groundwater vulnerability assessment of the Tarkwa mining area using SINTACS approach and GIS. Ghana Min J 17(1):18–30Google Scholar
  25. Farjad B, Mohd B, Shafri HZ, Mohamed TA, Pirasteh S, Wijesekara N (2012) Groundwater intrinsic vulnerability and risk mapping. Proc ICE-water Manag 165(8):441–450Google Scholar
  26. François A, David S, Théophile L, Blaise K, Olivier B, Miguel L, Ernest A, Marc Y (2016) Mapping of the vulnerability to pollution of aquifers in a mining area: Afema gold mine case (south-eastern Cote d’Ivoire). ISSR J 17(2):682–697Google Scholar
  27. Ghazavi R, Ebrahimi Z (2015) Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models. Int J Environ Sci Technol 12(2015):2909–2918.  https://doi.org/10.1007/s13762-015-0813-2 Google Scholar
  28. Ghosh A, Tiwari A, Das S (2015) A GIS based DRASTIC model for assessing groundwater vulnerability of Katri watershed, Dhanbad, India. https://doi.org/10.1007/s40808-015-0009-2
  29. Gogu RC, Dassargues A (2000) Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environ Geol 39:549–559Google Scholar
  30. Gundogdu KS, Guney I (2007) Spatial analyses of groundwater level using universal kriging. J Earth Syst Sci 116:49–55Google Scholar
  31. Hasiniaina F, Zhou J, Guoyi L (2010) Regional assessment of groundwater vulnerability in Tamtsag basin Mongolia using drastic model. J Am Sci 6:65–78Google Scholar
  32. Houan H, Wang J, Teng Y (2012) Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: a case study in Jilin city of Northeast China. Sci Total Environ 440:14–23Google Scholar
  33. Kabera T, Zhaohui L (2008) A GIS based DRASTIC model for assessing groundwater in shallow aquifer in Yuncheng Basin, Shanxi, China. Res J Appl Sci 3:195–205Google Scholar
  34. Karan SK, Samadder SR, Singh V (2018) Groundwater vulnerability assessment in degraded coal mining areas using the AHP-modified DRASTIC model. Land Degrad Dev 29(8):2351–2365Google Scholar
  35. Kumar V (2007) Optimal contour mapping of groundwater levels using universal kriging: a case study. Hydrol Sci J 52:1038–1050Google Scholar
  36. Lodwick WA, Monson W, Svoboda L (1990) Attribute error and sensitivity analysis of map operations in geographical information systems: suitability analysis. Int J Geogr Inf Syst 4(4):413–428Google Scholar
  37. Lodwick WA, Monson W, Svoboda L (1994) Attribute error and sensitivity analysis of map operations in geographical information systems: suitability analysis. Int J Geogr Inf Syst 4(4):413–428Google Scholar
  38. Margat (1968) Vulnérabilité des nappes d’eau souterraines á la pollution [Groundwater vulnerability to pollution]. Bases de la cartographie. BRGM 68, SLG198 HYD, BRGM, Orléans, FranceGoogle Scholar
  39. Moustafa M, Parkin G, Younger P (2005) Modeling spoil heap heterogeneity and its impact on PRB performance. In: Fifth international conference on calibration and reliability in groundwater modeling from uncertainty to decision-making, The Hague, The Netherlands, April 2005Google Scholar
  40. Moustafa M (2006) Characterization and modeling of the performance of a novel hybrid passive treatment system for acidic mine drainage. PhD Thesis, University of Newcastle upon Tyne, UKGoogle Scholar
  41. Nadiri AA, Gharekhani M, Khatibi R, Moghaddam AA (2017a) Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environ Sci Pollut Res 24(9):8562–8577Google Scholar
  42. Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Moghaddam AA (2017b) Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Sci Total Environ 574:691–706Google Scholar
  43. Nadiri AA, Gharekhani M, Khtibi R (2018) Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning. Water Resour Manag 9(2018):1–18Google Scholar
  44. Napolitano PA, Fabbri AG (1996) Single-parameter sensitivity analysis for aquifer vulnerability assessment using DRASTIC and SINTACS. In: Kovar K, Nachtnebel HP (eds) Application of geographic information systems in hydrology and water resources management. IAHS Press, IAHS Publ. 235, Wallingford, pp 559–566. Available at: http://www.iahs.info/redbooks/235.htm
  45. Naqa A, Hammouri N, Kuisi M (2006) GIS-based evaluation of groundwater vulnerability in the Russeifa area, Jordan. Rev Mexi Cienc Geol 23(3):277–287Google Scholar
  46. Neves O, Matias MJ (2008) Assessment of groundwater quality and contamination problems ascribed to abandoned uranium mine (Cunha Baixa region, central Portugal). Environ Geol 53(8):1799–1810Google Scholar
  47. Niknam R, Mohammadi K, Majid VJ (2007) Groundwater vulnerability evaluation of Tehran–Karaj aquifer using DRASTIC method and fuzzy logic. Water Resour Res 3(2):39–47Google Scholar
  48. Olıas M, Nieto JM, Sarmiento AM, Cerón JC, Cánovas CRM (2004) Seasonal water quality variations in a river affected by acid mine drainage: the Odiel River (south west Spain). Sci Total Environ 333(1):267–281Google Scholar
  49. Pacheco FAL, Sanches Fernandes LF (2012) The multivariate structure of DRASTIC model. J Hydrol.  https://doi.org/10.1016/jhydrol.2012.11.20
  50. Piscopo G (2001) Groundwater vulnerability map, explanatory notes. Castlereagh Catchment, NSW Department of Land and Water Conservation, AustraliaGoogle Scholar
  51. Rahman A (2008) A GIS based model for assessing groundwater vulnerability in shallow aquifer in Algarh. India Appl Geogr 28(1):32–53Google Scholar
  52. Rezaei F, Safavi HR, Ahmadi A (2013) Groundwater vulnerability assessment using fuzzy logic: a case study in the zayandehrood aquifers, Iran. Environ Manag 51(1):267–277Google Scholar
  53. Rosen L (1994) A study of the DRASTIC methodology with emphasis on Swedish conditions. Ground Water 32(2):278–285Google Scholar
  54. Saidi S, Bouri S, Ben Dhia H (2011) Groundwater vulnerability and risk mapping of the Hajeb-jelma aquifer (Central Tunisia) using a GIS-based DRASTIC model. Environ Earth Sci 59:1579–1588Google Scholar
  55. Saatsaz M, Sulaiman WN (2011) GIS DRASTIC model for groundwater vulnerability estimation of Astaneh–Kouchesfahan plain, northern Iran. Int J Water 6(1/2):250–254Google Scholar
  56. Sakala E, Fourie F, Gomo M, Coetzee H, Magadaza L (2016) Specific groundwater vulnerability mapping: case study of acid mine drainage in the Witbank coal field South Africa. Sixth IASTED International Conference, Gaborone, Botswana, September 2016Google Scholar
  57. Shirazi SM, Imran HM, Akib S (2012) GIS-based DRASTIC method for groundwater vulnerability assessment: a review. J Risk Res 15:991–1011Google Scholar
  58. Shrestha S, Semkuyu DJ, Pandey VP (2016) Assessment of groundwater vulnerability and risk to pollution in Kathmandu Valley, Nepal. Sci Total Environ 15(556):23–35.  https://doi.org/10.1016/j.scitotenv.2016.03.021 Google Scholar
  59. Singer PC, Stumm W (1970) Acid mine drainage: the rate limiting step. Science 167:1121–1123Google Scholar
  60. Singh AK, Mahato M, Neogi B, Singh KK (2010) Quality assessment of mine water in the Raniganj coalfield area, India. Mine Water Environ 29:248–262Google Scholar
  61. Singh AK, Mahato MK, Neogi B, Mondal GC, Singh TB (2011) Hydrogeochemistry, elemental flux, and quality assessment of mine water in the Pootkee-Balihari mining area, Jharia coalfield, India. Mine Water Environ 30(3):197–207Google Scholar
  62. SNIFFER (Scotland and Northern Ireland Forum for Environmental Research) (2004) Development of a groundwater vulnerability screening methodology for the water framework directive. Project report code WFD 28, September 2004, SNIFFER, EdinburghGoogle Scholar
  63. Tesoriero AJ, Inkpen EL, Voss F(1998) Assessing ground-water vulnerability using logistic regression. In: Proceedings for the Resource Water Assessment and Protection 98 conference, Dallas, TX, February 1998, pp 157–165Google Scholar
  64. Tiwary RK (2001) Environmental impact of coal mining on water regime and its management. Water Air Soil Pollut 132:185–199Google Scholar
  65. Tiwari K, Singh K, Maio M (2016) Evaluation of aquifer vulnerability in a coal mining of India by using GIS-based DRASTIC model. J Arabian of Geosciences.  https://doi.org/10.1007/s12517-016-2456-0
  66. Umar R, Ahmed I, Alam F (2009) Mapping groundwater vulnerable zones using modified DRASTIC approach of an alluvial aquifer in parts of central Ganga Plain, Western Uttar Pradesh. J Geol Soc India 73:193–201Google Scholar
  67. Wen X, Wu J, Si J (2008) A GIS-based DRASTIC model for assessing shallow groundwater vulnerability in the Zhangye Basin, northwestern China. Environ Geol 57(6):1435–1442Google Scholar
  68. Wood SC, Younger PL, Robins NS (1999) Long-term changes in the quality of polluted mine water discharges from abandoned underground coal workings in Scotland. Q J Eng Geol 32:69–79Google Scholar
  69. Worrall F, Besien T (2005) The vulnerability of groundwater to pesticide contamination estimated directly from observations of presence or absence in wells. J Hydrol 303:92–107Google Scholar
  70. Younger PL (1997) The longevity of mine water pollution: a basis for decision-making. Sci Total Environ 194/195:457–466Google Scholar
  71. Younger PL, Moustafa M (2005) Remediation of acidic colliery spoil leachate in a hybrid passive treatment system comprising a permeable reactive barrier, ponds and a reedbed (Shilbottle, Northumberland, UK). Edited book, Permeable Reactive Barriers. IAHS, London, V.298, P117–122Google Scholar
  72. Younger PL, Banwart SA, Hedin RS (2002) Mine water: hydrology, pollution, remediation. Kluwer, Dordrecht, The Netherlands, 464 ppGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Suez UniversitySuezEgypt

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