Surface water quality evaluation, apportionment of pollution sources and aptness testing for drinking using water quality indices and multivariate modelling in Baitarani River basin, Odisha
Notice bibliographique
Résumé
Baitarani River, Odisha, faces serious deterioration due to massive human intervention. It is particularly susceptible to degradation because it receives industrial and waste water emissions from surrounding organizations and municipal bodies. The current condition of the river is deplorable, leaving behind only minimal economic and ecological values. In this Baitarani Watershed, Odisha, this study emphasizes on analysing the seasonal variation (post-monsoon) of the water quality rating of the river in terms of the Water Quality Index (WQI). Study assessed the hydro-chemical variables, collected from thirteen sampling sites, during 2021–2024; and the whole river was investigated for 15 physicochemical parameters. Again, environ-metrics techniques, such as principal component analysis (PCA), and hierarchical (H) cluster analysis (CA), were used to assess the hydro-chemical variables. In all sites, the indicator Turbidity did not meet the drinking water quality limits (< 5NTU). During the post-monsoon season, the obtained WA-WQI value scored as 21.7 to 191, signifying excellent to unsuitable water quality. In this context, the WAWQI (Weighed Arithmetic Water Quality Index) values show that almost 61.54 % sampling sites have poor to unsuitable quality of water. On the contrary, the computed CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) value of the present research, varied between 23 and 97. These values indicate that water quality ranges from excellent to very poor water quality. Spanning a spectrum, the values of Integrated Weight (I)-WQI oscillated between 14 and 97. About 23.08 % remained within the excellent-good category, suggesting low pollution. These values also indicate 76.92 % of samples renders poor water and thus, significant contamination of the research zone by elements like turbidity, EC, and TDS indicates that the water quality in these areas is below drinkable limits and requires purification before use. The method, CA grouped four zones into three clusters, i.e., relatively low-polluted, medium-polluted, and high polluted. During post-monsoon season, most of the water quality characteristics were lower owing to dilution by monsoon rainfall, while pollutants were relatively higher in at some places, which might be due to reduced river flow and concentrated pollutants. The PCA resulted into 4 components namely PC-1 (51.31 %), PC-2 (16.044 %), PC-3 (11.799 %) and PC-4 (9.04 %) and indicated that particularly PC-1 contributes parameters such as turbidity, EC, TDS, Na+, K+, Ca2+, and Mg2+, were mostly influenced by mineralization, ions dissolution, and rock weathering. Ultimately, this innovative study from both indexing techniques, concludes that out of the 13 sampling sites, around 61.54 % (WA), 76.92 % (IWQI) and 53.85 % (CCME) is observed to be the most polluted site. CA and PCA identified that natural phenomena, along with agricultural, municipal, and industrial discharges, are the major polluting sources in the river basin. Reducing sewage outflow, blocking direct stormwater discharge, and avoiding continuous solid garbage disposal by neighbouring populations are ways to improve river water quality. This study provides baseline information that would serve as an effective plan for the management of the river basin.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».