Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India
Study Region: Mahanadi River Basin, Odisha: Study Focus: Surface water is the primary source of water supply. So, complex decision-making procedures and management techniques are required to optimize between information needs and information obtained from water quality monitoring networks. With rapi...
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2025-06-01
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| Series: | Journal of Hydrology: Regional Studies |
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| author | Abhijeet Das |
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| collection | DOAJ |
| description | Study Region: Mahanadi River Basin, Odisha: Study Focus: Surface water is the primary source of water supply. So, complex decision-making procedures and management techniques are required to optimize between information needs and information obtained from water quality monitoring networks. With rapid urbanization and population growth, the water of river Mahanadi, Odisha, is deteriorating. The study determined the water quality (WQ) index and its spatial distribution of pollutants. For its purpose, by employing innovative techniques, such as Methods Based on Removal Effects of Criteria (MEREC/Me) Water Quality Index (WQI), Multi-Criteria Decision-Making analysis namely Additive Ratio Assessment (ARAS) modeling and Machine Learning approaches entitled as Random Forest (RF) technique, the present study identifies locations, which have encountered the highest influence of cumulative factors such as discharge of sewage, lowering of water table, dilution and surface runoff, which lead to water quality variability in a water body over a monitoring period. In this investigation, water samples were collected from 16 different locations along the stretch and 21 parameters were analyzed for a period of 4 years (2020–2024), taken in monsoon period only. New hydrological insights for the Region: From the results from physicochemical parameters, most of the samples are characterized as having an alkaline nature. More than 62.5% of the examined samples is above the allowable threshold of TKN, and a high concentration of Cl- and NO3- at SN-(9), highlighted the impact of man-made factors, including fertilizers used in agriculture and industrial effluents. Additionally, geogenic contaminant F- at SN-(16), was above the WHO-permissible threshold of >0.9 mg/L. It was found that the typical cation concentrations were in the order of Fe2+ > B+ and the anions in the order of abundance were Cl- > SO42- > NO3- > F-. Also, geospatial analysis and IDW interpolation have been utilized to produce geospatial maps that accurately represent the best monitoring sites that had the greatest fluctuations in their water quality during the observation period in terms of physicochemical characteristics. Water quality at all sampling locations was expressed in terms of Me-WQI. The value of all sampling sites varied between 41 and 396, indicating excellent to unsuitable water category. The principal cause of WQ deterioration at poor/unsuitable sites is because of domestic waste water and agricultural runoff, that causes adulteration in the river’s water quality. A multi-objective decision-making tool such as ARAS, was used to rank the places according to their respective degrees of pollution. The obtained value (Ui) extracted from 20 physicochemical parameters and varied between 0.024 and 0.959. Based on its values, it was observed that at water points namely, SN-(8), (9), and (16) signified as the unhealthy site, when compared with remaining 16 locations, thus unsuitable for human consumption. The results of the spatial distribution revealed that greater values of some indicators were found in areas along the coast, indicating the presence of salinization and sea water intrusion. Again, water Quality Index by RF revealed that 18.75 % and 50 % samples belong to medium to poor water quality category, in monsoon period. However, 7 samples have a score of less than 50, indicating good water quality. Further, at sites SN-(2), (7), (8), (9), (10), (11), (12) and (16), water quality is mainly affected, illustrating the consequences of commercialization and urbanization, which includes large domestic industries in polluted regions. A comparative approach was also conducted across all the contamination indices of physicochemical parameters to determine the effectiveness of these three approaches. Therefore, the spatial distribution maps in this study highlight that Cl-, EC, SO42-, and NO3- played a central role in affecting the WQI of the river, indicating that during the monsoon season, both small manmade actions and natural causes have an impact on the chemistry of surface water. Afterwards, the results showed significant discrepancies between the indices at different sample sites. As a result, the water quality dataset made it clear that the MEREC and RF models offer a more accurate understanding of how to classify water quality in relation to the parameters under study. Moreover, the novelty of the study also appears in the determination of the sites within the same group that are comparatively more or less contaminated, by the application of ARAS utility value (Ui). The poor WQ at different places needs immediate government attention. |
| format | Article |
| id | doaj-art-b812a38d8b7f4181b3aabd01e75e797f |
| institution | OA Journals |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-b812a38d8b7f4181b3aabd01e75e797f2025-08-20T02:25:26ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910236610.1016/j.ejrh.2025.102366Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, IndiaAbhijeet Das0Department of Civil Engineering, C.V. Raman Global University (C.G.U), Bhubaneswar, Odisha 752054, IndiaStudy Region: Mahanadi River Basin, Odisha: Study Focus: Surface water is the primary source of water supply. So, complex decision-making procedures and management techniques are required to optimize between information needs and information obtained from water quality monitoring networks. With rapid urbanization and population growth, the water of river Mahanadi, Odisha, is deteriorating. The study determined the water quality (WQ) index and its spatial distribution of pollutants. For its purpose, by employing innovative techniques, such as Methods Based on Removal Effects of Criteria (MEREC/Me) Water Quality Index (WQI), Multi-Criteria Decision-Making analysis namely Additive Ratio Assessment (ARAS) modeling and Machine Learning approaches entitled as Random Forest (RF) technique, the present study identifies locations, which have encountered the highest influence of cumulative factors such as discharge of sewage, lowering of water table, dilution and surface runoff, which lead to water quality variability in a water body over a monitoring period. In this investigation, water samples were collected from 16 different locations along the stretch and 21 parameters were analyzed for a period of 4 years (2020–2024), taken in monsoon period only. New hydrological insights for the Region: From the results from physicochemical parameters, most of the samples are characterized as having an alkaline nature. More than 62.5% of the examined samples is above the allowable threshold of TKN, and a high concentration of Cl- and NO3- at SN-(9), highlighted the impact of man-made factors, including fertilizers used in agriculture and industrial effluents. Additionally, geogenic contaminant F- at SN-(16), was above the WHO-permissible threshold of >0.9 mg/L. It was found that the typical cation concentrations were in the order of Fe2+ > B+ and the anions in the order of abundance were Cl- > SO42- > NO3- > F-. Also, geospatial analysis and IDW interpolation have been utilized to produce geospatial maps that accurately represent the best monitoring sites that had the greatest fluctuations in their water quality during the observation period in terms of physicochemical characteristics. Water quality at all sampling locations was expressed in terms of Me-WQI. The value of all sampling sites varied between 41 and 396, indicating excellent to unsuitable water category. The principal cause of WQ deterioration at poor/unsuitable sites is because of domestic waste water and agricultural runoff, that causes adulteration in the river’s water quality. A multi-objective decision-making tool such as ARAS, was used to rank the places according to their respective degrees of pollution. The obtained value (Ui) extracted from 20 physicochemical parameters and varied between 0.024 and 0.959. Based on its values, it was observed that at water points namely, SN-(8), (9), and (16) signified as the unhealthy site, when compared with remaining 16 locations, thus unsuitable for human consumption. The results of the spatial distribution revealed that greater values of some indicators were found in areas along the coast, indicating the presence of salinization and sea water intrusion. Again, water Quality Index by RF revealed that 18.75 % and 50 % samples belong to medium to poor water quality category, in monsoon period. However, 7 samples have a score of less than 50, indicating good water quality. Further, at sites SN-(2), (7), (8), (9), (10), (11), (12) and (16), water quality is mainly affected, illustrating the consequences of commercialization and urbanization, which includes large domestic industries in polluted regions. A comparative approach was also conducted across all the contamination indices of physicochemical parameters to determine the effectiveness of these three approaches. Therefore, the spatial distribution maps in this study highlight that Cl-, EC, SO42-, and NO3- played a central role in affecting the WQI of the river, indicating that during the monsoon season, both small manmade actions and natural causes have an impact on the chemistry of surface water. Afterwards, the results showed significant discrepancies between the indices at different sample sites. As a result, the water quality dataset made it clear that the MEREC and RF models offer a more accurate understanding of how to classify water quality in relation to the parameters under study. Moreover, the novelty of the study also appears in the determination of the sites within the same group that are comparatively more or less contaminated, by the application of ARAS utility value (Ui). The poor WQ at different places needs immediate government attention.http://www.sciencedirect.com/science/article/pii/S2214581825001910Additive ratio assessmentMachine learningMethods based on removal effects of criteriaRandom ForestWater quality index |
| spellingShingle | Abhijeet Das Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India Journal of Hydrology: Regional Studies Additive ratio assessment Machine learning Methods based on removal effects of criteria Random Forest Water quality index |
| title | Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India |
| title_full | Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India |
| title_fullStr | Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India |
| title_full_unstemmed | Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India |
| title_short | Spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in Mahanadi River Basin, Odisha, India |
| title_sort | spatiotemporal evaluation and impact of superficial factors on surface water quality for drinking using innovative techniques in mahanadi river basin odisha india |
| topic | Additive ratio assessment Machine learning Methods based on removal effects of criteria Random Forest Water quality index |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825001910 |
| work_keys_str_mv | AT abhijeetdas spatiotemporalevaluationandimpactofsuperficialfactorsonsurfacewaterqualityfordrinkingusinginnovativetechniquesinmahanadiriverbasinodishaindia |