IDENTIFICATION OF THE ORIGINS OF ELEVATED ATMOSPHERIC MERCURY EPISODES USING A LAGRANGIAN MODELLING SYSTEM
Bibliographic record
Abstract
Abstract: We report the application of a receptor-oriented transport model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model, to the interpretation of hourly total gaseous mercury (TGM) concentrations at three monitoring sites in Southern Ontario during four episodes of high TGM. STILT is a Lagrangian modelling system (Lin, J.C. et al. 2003) that simulates the transport of ensembles of air parcels backward in time from an observation point to upstream locations where surface inputs of target species occurred. A complete inventory of anthropogenic and natural mercury sources were used to compute the emissions. The study was initiated by simulating the mercury concentrations in a North American domain using CMAQ-Hg, a regional Eulerian chemical transport model (CTM). The STILT model was applied to several short episodes (usually lasting for 1-4 days) in which the TGM measurements at four air quality measurement stations in Southern Ontario significantly exceeded the predictions of the CTM. The STILT analysis compared the origins of air parcels arriving during the elevated TGM episodes with those of air parcels arriving at proximal times when the measurements and the CTM predictions were both low. The results consist of the STILT–predicted hourly concentrations at the measurement site as well as the surface footprint where the mercury responsible for the episode was emitted. The temporal STILT prediction is in better agreement with the measured time series than that of CMAQ-Hg. We believe this is partly due to the superior ability of STILT to capture near-field influences and partly due to the spatial averaging inherent in Eulerian modelling. Also, the predicted footprint locations were reasonable, coinciding with known locations of large mercury sources during the high episodes and with cleaner areas otherwise. 1.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".