Protect the Fish: Predicting Ammonia Toxicity Risk in the Fraser River
Bibliographic record
Abstract
Protect the Fish: Predicting Ammonia Toxicity Risk in the Fraser RiverAbstractAmmonia is known to be acutely toxic to fish and other aquatic organisms at low concentrations. In effort to predict regulatory non-compliance due to ammonia discharged from two of Metro Vancouver’s wastewater treatment plants, regression models were developed to project final effluent ammonia concentrations at each facility over a 25-year period. These ammonia projections were compared with regulatory requirements, and the risk of non-compliance was quantified over the period under various operational scenarios, such as increased centrifuge operation and per capita flow reduction. The risk assessment concluded that some degree of noncompliance was observed in most scenarios evaluated; to target full compliance with the regulations it is estimated that a 13% reduction in effluent ammonia at LIWWTP and a 22% reduction at AIWWTP under baseline conditions is required. At both facilities, implementing side stream ammonia removal on the dewatering centrate stream would increase compliance to 100% at baseline conditions.Federal regulations are enacted in Canada to protect aquatic life from wastewater effluent with toxic ammonia concentrations. This study uses regression models to predict the final effluent ammonia toxicity risk at two facilities in Metro Vancouver over a 25-year period. The study finds that a reduction in effluent ammonia at both facilities may be required in the future to ensure compliance with federal regulations; this could be achieved with ammonia removal in the dewatering centrate stream.SpeakerGupta, SophiaPresentation time14:30:0014:50:00Session time13:30:0015:00:00SessionFrom Fish Passage Design to Ocean Discharges: Going CoastalSession locationRoom S402a - Level 4TopicIntermediate Level, Watershed Management, Water Quality, and GroundwaterTopicIntermediate Level, Watershed Management, Water Quality, and GroundwaterAuthor(s)Gupta, SophiaAuthor(s)S. Gupta 1; L. Locke 1 ; S. Gupta 1; J. Davidson 2; G. Le Penven 3; T. Gregonia 4;Author affiliation(s)AECOM 1; AECOM 1 ; AECOM 1; AECOM 2; Metro Vancouver 3; Metro Vancouver 4;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159222Volume / Issue Content sourceWEFTECCopyright2023Word count12
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".