Identifying and evaluating urban mercury emission sources through passive sampler-based mapping of atmospheric concentrations
Bibliographic record
Abstract
Beyond emissions from coal-fired power generation, urban sources of mercury (Hg) to the atmosphere, especially minor fugitive sources, are relatively poorly characterized. To identify urban sources of fugitive Hg emissions, passive air samplers (PASs) were deployed for periods of 4–6 weeks in the summer of 2016 at 145 sites across the Greater Toronto Area (GTA). PASs were also deployed along transects of increasing distance from five sites listed as Hg sources in the National Pollution Release Inventory (NPRI) within or near the GTA. Mean gaseous Hg concentrations in downtown Toronto (1.77 ± 0.28 ng m ^−3 ) are slightly, but significantly elevated relative to other parts of the GTA (1.42 ± 0.20 ng m ^−3 ). Similarly, concentrations at sites close to waste/recycling (1.61 ± 0.22 ng m ^−3 ) and hospitals/dental facilities (1.63 ± 0.21 ng m ^−3 ) are significantly higher than at sites presumably distant from potential sources (1.37 ± 0.20 ng m ^−3 ). Gaseous Hg concentrations are elevated near four of five NPRI source sites, but not near a wastewater treatment plant. Measured or predicted concentrations (using extrapolated transect relationships) close to known Hg sources do not correlate with reported NPRI emissions. For example, the Hg disposal company Aevitas Inc. has the lowest reported NPRI emissions (0.11 kg yr ^−1 ) among the five sources, but measured (12.3 ng m ^−3 ) and predicted (60.0 ng m ^−3 ) concentrations outside the facility are the highest. The PAS’s ability to precisely and accurately discriminate small differences in gaseous Hg concentration (<0.2 ng m ^−3 ) at and near global background concentrations enables the mapping of the spatial concentration variability and the identification of fugitive Hg emission sources.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".