Trace Element Occurrence in Wastewater and Effects on Surface Water Quality in the Laurentian Great Lakes and the Grand River, Ontario
Bibliographic record
Abstract
Wastewater treatment plays an integral role in maintaining surface water quality in industrialized societies around the world. Wastewater effluent and sewage sludge can both be important vectors of contaminants that are not fully eliminated during treatment, and thus understanding effluent and sludge composition is key to preventing deleterious environmental impacts to receiving environments. Trace elements are one important class of contaminants in wastewater, as their increased use in industrialized societies is reflected in their growing occurrence in anthropogenic waste streams globally. Yet, the potential large-scale sources of trace elements to wastewater and their behavior during wastewater treatment remain poorly understood and potential environmental impacts on receiving environments therefore unclear. Because only a handful of specific parameters in wastewater and sludge are regulated in Canada, not including many trace elements, monitoring of their occurrence and potential impacts is crucial. In this thesis, >40 wastewater treatment facilities in the North American Great Lakes basin were screened for major and trace elements and a black-box approach was deployed 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 wastewater treatment technologies. In addition, I report concentrations of major and trace elements in >30 riverine and effluent samples collected in the Grand River catchment, Ontario, in an attempt to assess imprints of effluent discharge on riverine trace element loads. The findings of this thesis demonstrate the effectiveness of wastewater surveillance for a quantitative exploration of anthropogenic versus geogenic trace element emissions and highlight its value to further the understanding of the current state of contaminants in wastewater and sewage sludge and their behavior during the wastewater treatment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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".