Cargando…
Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model
This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria decision-making (MCDM) model integrating Interval Rough Numbers (IRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) was us...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104943/ https://www.ncbi.nlm.nih.gov/pubmed/36781674 http://dx.doi.org/10.1007/s11356-023-25447-1 |
_version_ | 1785026142662557696 |
---|---|
author | Chukwuma, Emmanuel Chibundo Okonkwo, Chris Chukwuma Afolabi, Oluwasola Olakunle Daniel Pham, Quoc Bao Anizoba, Daniel Chinazom Okpala, Chikwunonso Divine |
author_facet | Chukwuma, Emmanuel Chibundo Okonkwo, Chris Chukwuma Afolabi, Oluwasola Olakunle Daniel Pham, Quoc Bao Anizoba, Daniel Chinazom Okpala, Chikwunonso Divine |
author_sort | Chukwuma, Emmanuel Chibundo |
collection | PubMed |
description | This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-023-25447-1. |
format | Online Article Text |
id | pubmed-10104943 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101049432023-04-16 Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model Chukwuma, Emmanuel Chibundo Okonkwo, Chris Chukwuma Afolabi, Oluwasola Olakunle Daniel Pham, Quoc Bao Anizoba, Daniel Chinazom Okpala, Chikwunonso Divine Environ Sci Pollut Res Int Research Article This study evaluated the susceptibility to groundwater pollution using a modified DRASTIC model. A novel hybrid multi-criteria 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-023-25447-1. Springer Berlin Heidelberg 2023-02-14 2023 /pmc/articles/PMC10104943/ /pubmed/36781674 http://dx.doi.org/10.1007/s11356-023-25447-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Chukwuma, Emmanuel Chibundo Okonkwo, Chris Chukwuma Afolabi, Oluwasola Olakunle Daniel Pham, Quoc Bao Anizoba, Daniel Chinazom Okpala, Chikwunonso Divine Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model |
title | Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model |
title_full | Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model |
title_fullStr | Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model |
title_full_unstemmed | Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model |
title_short | Groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated IRN-DEMATEL-ANP decision model |
title_sort | groundwater vulnerability to pollution assessment: an application of geospatial techniques and integrated irn-dematel-anp decision model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104943/ https://www.ncbi.nlm.nih.gov/pubmed/36781674 http://dx.doi.org/10.1007/s11356-023-25447-1 |
work_keys_str_mv | AT chukwumaemmanuelchibundo groundwatervulnerabilitytopollutionassessmentanapplicationofgeospatialtechniquesandintegratedirndematelanpdecisionmodel AT okonkwochrischukwuma groundwatervulnerabilitytopollutionassessmentanapplicationofgeospatialtechniquesandintegratedirndematelanpdecisionmodel AT afolabioluwasolaolakunledaniel groundwatervulnerabilitytopollutionassessmentanapplicationofgeospatialtechniquesandintegratedirndematelanpdecisionmodel AT phamquocbao groundwatervulnerabilitytopollutionassessmentanapplicationofgeospatialtechniquesandintegratedirndematelanpdecisionmodel AT anizobadanielchinazom groundwatervulnerabilitytopollutionassessmentanapplicationofgeospatialtechniquesandintegratedirndematelanpdecisionmodel AT okpalachikwunonsodivine groundwatervulnerabilitytopollutionassessmentanapplicationofgeospatialtechniquesandintegratedirndematelanpdecisionmodel |