The wastewater treatment plant operation estimation through the use of the water quality index – the case study of WWTP-Montana, Bulgaria
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) are designed to treat the used water by improving its quality, so it is no longer harmful to the environment when discharged. The long-term mandatory monitoring of wastewater produces large amounts of data. It can be converted to a unitless number – the Water Quality Index (WQI) and used to assess the wastewater quality by checking compliance with the set regulations. It has gained increasing popularity among decision-makers, wastewater professionals, and environmental agencies. Operation assessment of the WWTP-Montana for a period of 12 years (2011-2022) was performed using the Canadian Council of Ministers Water Quality Index (CCME WQI) calculation for the raw water (influent) and the treated water (effluent) of the WWTP and applying time series analysis of CCME WQI and water quality indicators – chemical oxygen demand (COD), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) in the influent. For the entire period, the calculated CCME WQI for the treated waters shows the perfect score of 100 (excellent water quality). The calculated CCME WQI at the inlet, on the other hand, classifies the raw water’s quality as “poor” (64% of the CCME WQI values), “marginal” (32%) and “fair” (4%). The time series analysis reveal that higher water quality of the inlet wastewater is detected in summer (August) due to the lower concentrations of the five mandatory physicochemical indicators.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".