Pollution Indices and Ecological Evaluation for Wastewater in Industrial Areas
Bibliographic record
Abstract
Wastewaters from Tenth Ramadan and El-Obour cities and El-Khadrwia drain verify their environmental effects. The study focused to study Wastewater Quality Index (WQI) and their suitability during 2018-2019. The data showed almost of chemical parameters were unacceptable for water irrigation suitability that used to derive criteria and guidelines of hazards ions interactions, FAO for irrigation and Canadian Water Quality Guidelines for aquatic organism. The results indicate that there is no effect of metals in the case of wastewater use for agricultural purposes, whereas for aquatic life, all measured metals except Fe+3, Mn+2, Pb+2, Zn+2 and Cu+2 show different degrees of contamination in wastewaters of investigated areas.The obtained results indicated organic pollution values of examined variables were higher than the recommended standards and they were major waste impacts. They supported by Organic Pollution Index (OPI) evaluation that ranged from 15 to 822 while the maximum Comprehensive Pollution Index (CPI) and OPI values were (16.1 and 822) for cheese whey wastewater at El-Obour City affects aquatic environmental live in this area and producing healthy harms. The study concluded primary treatment removed 50-60% of pollution. So, study recommended use nanoparticles with low cost to acquire positive ecological impacts and increase national goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".