Groundwater vulnerability to pollution assessment : an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model
dc.contributor.author | Chukwuma, Emmanuel Chibundo | |
dc.contributor.author | Okonkwo, Chris Chris | |
dc.contributor.author | Afolabi, Oluwasola Olakunle Daniel | |
dc.contributor.author | Pham, Quoc Bao | |
dc.contributor.author | Anizoba, Daniel Chinazom | |
dc.contributor.author | Okpala, Chikwunonso Divine | |
dc.date.accessioned | 2024-05-28T11:39:43Z | |
dc.date.available | 2024-05-28T11:39:43Z | |
dc.date.issued | 2023-04 | |
dc.description | DATA AVAILABILITY : In addition to the supplementary material, all other data and materials are available upon request. | en_US |
dc.description.abstract | This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multicriteria decision-making (MCDM) model integrating Interval Rough Numbers (IRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) was used to investigate the interrelationships between critical hydrogeologic factors (and determine their relative weights) via a novel vulnerability index based on the DRASTIC model. The flexibility of GIS in handling spatial data was employed to delineate thematic map layers of the hydrogeologic factors and to improve the DRASTIC model. The hybrid MCDM model results show that net recharge (a key hydrogeologic factor) had the highest priority with a weight of 0.1986. In contrast, the topography factor had the least priority, with a weight of 0.0497. A case study validated the hybrid model using Anambra State, Nigeria. The resultant vulnerability map shows that 12.98% of the study area falls into a very high vulnerability class, 31.90% falls into a high vulnerability, 23.52% falls into the average vulnerability, 21.75% falls into a low vulnerability, and 9.85% falls into very low vulnerability classes, respectively. In addition, nitrate concentration was used to evaluate the degree of groundwater pollution. Based on observed nitrate concentration, the modified DRASTIC model was validated and compared to the traditional DRASTIC model; interestingly, the spatial model of the modified DRASTIC model performed better. This study is thus critical for environmental monitoring and implementing appropriate management interventions to protect groundwater resources against indiscriminate sources of pollution. | en_US |
dc.description.department | Future Africa | en_US |
dc.description.librarian | am2024 | en_US |
dc.description.sdg | SDG-06:Clean water and sanitation | en_US |
dc.description.uri | https://www.springer.com/journal/11356 | en_US |
dc.identifier.citation | Chukwuma, E.C., Okonkwo, C.C., Afolabi, O.O.D. et al. 2023, 'Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model', Environmental Science and Pollution Research, vol. 30, pp. 49856-49874. https://DOI.org/10.1007/s11356-023-25447-1. | en_US |
dc.identifier.issn | 0944-1344 (print) | |
dc.identifier.issn | 1614-7499 (online) | |
dc.identifier.other | 10.1007/s11356-023-25447-1 | |
dc.identifier.uri | http://hdl.handle.net/2263/96271 | |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License. | en_US |
dc.subject | Groundwater pollution | en_US |
dc.subject | Decision-making model | en_US |
dc.subject | Drastic model | en_US |
dc.subject | Environmental monitoring | en_US |
dc.subject | SDG-06: Clean water and sanitation | en_US |
dc.subject | Multicriteria decision-making (MCDM) | en_US |
dc.subject | Analytical network process (ANP) | en_US |
dc.subject | Interval rough numbers (IRN) | en_US |
dc.subject | Decision making trial and evaluation laboratory (DEMATEL) | en_US |
dc.subject | Geographic information system (GIS) | en_US |
dc.title | Groundwater vulnerability to pollution assessment : an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model | en_US |
dc.type | Article | en_US |
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