An Investigation of Local and Regionally Significant Non-Agricultural Sources of Ammonia in Toronto and the Great Lakes Region
Bibliographic record
Abstract
Atmospheric ammonia is a significant environmental pollutant that contributes both to human mortality and morbidity and can also negatively influence ecosystem health. While the globally dominant source of ammonia pollution is agriculture, other sources may be influential on local and regional scales. High time resolution NH3(g) measurements were conducted in Toronto from March 2021 – March 2024 to evaluate the role of combustion sources, urban greenspace and lacustrine emissions in the atmospheric loading of NH3(g) in the urban atmosphere.During three intensive field campaigns (FROST, THE CIX, SWAPIT), other atmospheric measurements were available to support the interpretation of the variability in NH3(g). These observations suggest an important role for combustion sources, such as vehicle traffic, and suggest possible influence from natural gas combustion during winter months and wildfire-affected air during the spring and summer of 2023. An isotherm model for ammonia adsorption to soil was developed to evaluate the potential for urban greenspaces to represent an important source of ammonia. Using this approach, the soil emission potential was estimated across various soil types across Toronto, and combined with the ammonia monitoring record to identify periods when ammonia emissions from warming soils during spring months could plausibly contribute to atmospheric concentrations. Data compiled from a long-term Great Lakes water quality dataset was used to parameterize an ammonia emission potential for lacustrine surfaces in the Great Lakes region. This parameterization was incorporated into a research version of the GEM-MACH chemical transport model in collaboration with ECCC, and used to evaluate the potential for the Great Lakes to influence the regional balance of ammonia. The model predicted that in July - September, NH3(g) concentrations in urban areas along the lake shore could increase by up to 10 - 20% on days affected by wind from over the lake.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".