Measurement of gaseous mercury emissions from natural sources
Bibliographic record
Abstract
To better estimate natural source emissions of gaseous mercury, air-surface emission rates (fluxes) were measured from contrasting geological settings (natural sources) across Canada and the United States using micrometeorological (micromet) and chamber techniques. Micrometeorological measurements were made using a flux gradient approach. Chamber measurements were made using Teflon ®-lined plexiglass, quartz glass and poly-carbonate chambers. Fluxes were measured from substrates (soils) containing high concentrations (>100 [mu]g g-1) of mercury in the substrate (Pinchi Lake, Clyde Forks), moderately high (1 to 10 [mu]g g-1) substrate mercury concentrations (Klages, Steamboat Springs), as well as from background (<200 [mu]g g-1) sites (Kakabeka Falls, Wilson Farm site). Fluxes of gaseous mercury were found to correlate strongly with total substrate mercury concentration, on a log-log basis, for both the chamber and micromet methods. Relationships between total substrate mercury concentration ([mu]g g-1) and gaseous mercury flux (ng m-2 h-1) were found to be: log Flux m icromet=0.60* log H g substrate+1.7 log Flux c hamber=0.35* log Hg substrate+1.5 for the micromet and chamber methods, respectively. Fluxes measured using the flux gradient technique were approximately twice the magnitude as those measured using chamber techniques. Differences between the two relationships were attributed to the limitations of the methods. Factors influencing mercury emissions including environmental conditions and chamber operating parameters were examined. Increases in temperature, both soil and air, net radiation and wind speed, and decreases in absolute humidity, were found to correlate with increased mercury emissions. When all environmental parameters were examined simultaneously, chamber air temperature was found to have the greatest effect on mercury flux. Varying chamber hydraulic retention time appeared to affect flux measurements but the effects were not statistically significant. It was suggested that blank corrections for chamber measurements were unnecessary under steady-state conditions. Spectral properties of chamber materials were also examined. Relationships developed through this research may be useful to regulators and modelers of mercury emissions and may be combined with geochemical data to scale up natural source mercury emissions for Canada. They may also be used to put anthropogenic emissions estimates into perspective.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".