Quantitative assessment of upstream source influences on total gaseous mercury observations in Ontario, Canada
Bibliographic record
Abstract
Abstract. Hourly total gaseous mercury (TGM) concentrations at three monitoring sites (receptors) in Ontario were predicted for four selected periods at different seasons in 2002 using the Stochastic Time-Inverted Lagrangian Transport (STILT) model, which transports Lagrangian air parcels backward in time from the receptors to provide linkages to the source region in the upwind area. The STILT model was modified to deal with Hg deposition and high stack Hg emissions. The model-predicted Hg concentrations were compared with observations at three monitoring sites. Estimates of transport errors (uncertainties in simulated concentrations due to errors in wind fields) are also provided that suggest such errors can reach approximately 10% of simulated concentrations. Results from a CMAQ chemical transport model (CTM) simulation in which the same emission and meteorology inputs were used are also reported. The comparisons show that STILT-predicted Hg concentrations usually agree better with observations than CMAQ except for a subset of cases that are subject to biases in the coarsely resolved boundary conditions. In these comparisons STILT captures high frequency concentration variations better than the Eulerian CTM, likely due to its ability to account for the sub-grid scale position of the receptor site and to minimize numerical diffusion. Thus it is particularly valuable for the interpretation of plumes (short-term concentration variations) that require the use of finer mesh sizes or controls on numerical diffusion in Eulerian models. We report quantitative assessments of the relative importance of different upstream sources for the selected episodes, based on emission fluxes and STILT footprints. The STILT simulations indicate that natural sources (which include re-emission from historical anthropogenic activities) contribute much more than current-day anthropogenic emissions to the Hg concentrations observed at the three sites.
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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 | 0.001 |
| 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".