Loads and elimination of trace elements in wastewater in the Great Lakes basin
Bibliographic record
Abstract
The growing use of trace elements in industrialized societies is driving an increase in the occurrence of trace elements in anthropogenic waste streams globally. Yet, the large-scale sources of many trace elements to wastewater and their elimination during treatment remain poorly understood and potential environmental impacts on freshwater systems therefore unclear. We screened 42 wastewater treatment facilities in the North American Great Lakes basin and deployed a black-box approach to calculate representative estimates for average per-capita trace element loads and basin-scale effluent discharge rates, as well as trace element removal efficiencies across different treatment technologies. Our results show different removal of specific groups of trace elements during wastewater treatment: average removal efficiencies were 25% for alkali metals, 50% for alkaline earth metals, 74% for transition metals, and 85% for rare earth elements. Higher elimination of the majority of trace elements was generally achieved by more advanced, tertiary treatment types. Elemental loads generally followed natural abundance patterns, but anomalous loading rates were observed for various trace elements across the sampled facilities. By examining geospatial attributes of the sampled sewersheds, trends in select trace element loads were qualitatively tied to possible point sources and diffuse sources. Overall, these results illustrate the potential of wastewater surveillance to inform environmental management of emerging trace element contaminants.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".