Evaluation and downstream effects of household and industrial effluents discharge on some physicochemical parameters and surface Water Quality Index of River Mahanadi, Odisha, India
Why this work is in the frame
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Bibliographic record
Abstract
Mahanadi River (MR) System, Odisha, is under a great deal of stress due to the rapidly growing population, water pollution, and climate change, all of which raise the needs for home, agriculture, and industry. During the current study, water samples were gathered for the evaluation of 20 physicochemical determinants, obtained from 16 sampling locations, for a period of 2020–2024. Considering the findings of the current investigation, the water quality were applied in order to evaluate the water quality (WQ) for effective management by using Weighted Arithmetic (WA), Canadian Council of Ministers of the Environment (CCME), Nemerow’s Pollution Index (NPI), Overall Index of Pollution (OIP), Nitrate Pollution Index (Ni-PI), Trophic State Index (TSI), Synthetic Pollution Index (SPI), Eutrophication Index (EI), Comprehensive Pollution Index (CPI), Organic Pollution Index (OPI), and Sea water Mixing Index (SMI), respectively. According to the first six indices, it is seen that around 81.25% (WA), 31.25% (CCME), 87.50% (SPI), 81.25% (NPI), 81.25% (OIP) and 81.25% (CPI) seems to be of good quality in terms of drinking and rest portion in each case is seen to be poor for drinking purposes. Further, method of assessment by EI, contributes around 81.25% that indicates water having zero eutrophication zone. Also, OPI obtained around 13 samples, which is depicted as good water quality. In considering the SMI for its evaluation, it is seen that samples of around 18.75%, are found to be unfit and comes under the category of imprint of sea water. To conclude from these eleven indexing techniques, the WQI revealed that, aside from nine locations, the water quality in this watershed was normal, but the TKN and TC content was in dreadful condition. To ascertain how WQ is distributed in this river water, multivariate approaches like Correlation Theory (CT), Cluster Analysis (CA), Principal Component Analysis (PCA) and Partial Least Square Regression methods were incorporated on the robust subset WQ indicators. However, CT suggested that most parameters were found to have a strong correlation, that helps in deciding the key indicators for assessing the water variation. In the CA model, the dataset is differentiated into top three pollution sources, depending on comparable water quality attributes. Thus, it aids in determining an appropriate source resolution for every parameter. PCA results accounted for almost 94% of the variance altogether and identified the primary pollution sources. These include urban districts, fertilizer, industry, and weather. The R2 value of 0.78–0.99 in calibration and 0.79–0.97 in validation model, indicated by PLSR method, which indicates that the water parameters as well as indexing WQ methods explains around more than 90% of the variability. Hence, this PLSR technique may be trustworthy when it comes to choosing important water quality indicators that gave the final assessment’s WQ data. Prior to estimating the indicators’ weight values by integrated weighted (I) approach, which suggests I-WQI value decipher about 43.75% of water, that contributes safe water while, 56.25% falls in polluted category. By putting this integrated weight, the study ranked the recommended indicators according to their relative significance, with the help of Compromise Programming (CP) procedure. After obtaining the results, first rank goes to SN-(9), followed by SN-(8) as 2nd and ultimately, 3rd rank projects to SN-(16), indicating most contaminated site. So, this strategy was judged to be better than the current ones. Finally, nine sites from the study area are found polluted and completely unfit for drinking because of organic, industrial pollution, fertilizer application, vehicle exhaust emission and urban activities. However, a stringent policy should be adapted for water management practices, in order to maintain and improve the quality. The basic highlights of this research are:
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it