Concentration-weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada
Bibliographic record
Abstract
Abstract. Regional and local sources contributing to gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) at an urban coastal site in Dartmouth, Nova Scotia, Canada were investigated using the Concentration-Weighted Trajectory model (CWT) and Conditional Probability Function. From 2010–2011, GEM, GOM, and PBM concentrations were 1.67 ± 1.01 ng m−3, 2.07 ± 3.35 pg m−3, and 2.32 ± 3.09 pg m−3, respectively. Seasonal variability was observed, with statistically higher GEM and PBM concentrations in winter and spring and higher GOM in spring. In the CWT, concentrations are the weighting factors for the trajectory residence time in modeled grid cells, which results in the identification of source areas based on the CWT values in the grid cells. Potential source areas were identified in regions with known industrial Hg sources particularly in the fall season, but also in regions without these sources (e.g. Atlantic Ocean, northern Ontario and Quebec). CWTs for GOM and PBM that were associated with ≥ 5 kg industrial Hg emissions from 2010–2011 were statistically larger than those with zero Hg emissions, despite a lack of strong correlations. A large proportion of elevated CWTs (85–97%) was in regions with zero industrial Hg sources indicating the potential role of non-point sources, natural emissions, and residential-scale combustion. Analysis of wind data suggests that a commercial harbor and vehicular traffic were potential local sources. Evaluating modeled source areas against Hg emissions inventories was not an ideal method for assessing the CWT model accuracy because of insufficient data on Hg emissions at more precise locations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".