Site specific risk assessment of an energy-from-waste thermal treatment facility in Durham Region, Ontario, Canada. Part A: Human health risk assessment
Bibliographic record
Abstract
The regions of Durham and York in Ontario, Canada have partnered to construct an energy-from-waste thermal treatment facility as part of a long term strategy for the management of their municipal solid waste. This paper presents the results of a comprehensive human health risk assessment for this facility. This assessment was based on extensive sampling of baseline environmental conditions (e.g., collection and analysis of air, soil, water, and biota samples) as well as detailed site specific modeling to predict facility-related emissions of 87 identified contaminants of potential concern. Emissions were estimated for both the approved initial operating design capacity of the facility (140,000 tonnes per year) and for the maximum design capacity (400,000 tonnes per year). For the 140,000 tonnes per year scenario, this assessment indicated that facility-related emissions are unlikely to cause adverse health risks to local residents, farmers, or other receptors (e.g., recreational users). For the 400,000 tonnes per year scenarios, slightly elevated risks were noted with respect to inhalation (hydrogen chloride) and infant consumption of breast milk (dioxins and furans), but only during predicted 'upset conditions' (i.e. facility start-up, shutdown, and loss of air pollution control) that represent unusual and/or transient occurrences. However, current provincial regulations require that additional environmental screening would be mandatory prior to expansion of the facility beyond the initial approved capacity (140,000 tonnes per year). Therefore, the potential risks due to upset conditions for the 400,000 tonnes per year scenario should be more closely investigated if future expansion is pursued.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".