Using Passive Air Samplers to Quantify Vertical Gaseous Elemental Mercury Concentration Gradients Within a Forest and Above Soil
Bibliographic record
Abstract
Abstract Gaseous elemental mercury (GEM), the primary form of mercury (Hg) in the atmosphere, can undergo bidirectional exchange with the Earth's surface, with uptake in growing vegetation constituting the dominant atmospheric deposition process. Whereas direction and magnitude of this exchange can be determined using micrometeorological and chamber techniques, these approaches are complex, costly and labor‐intensive, and often only yield fluxes over relatively short time‐scales at few sites. We demonstrate that an inexpensive and easy‐to‐use passive air sampler can identify and quantify vertical GEM concentration gradients on scales ranging from centimeters to tens of meters over long time periods (up to 1.5 years) and with coarse temporal resolution (monthly to seasonally). Samplers were deployed above clean and contaminated soil and through a deciduous forest canopy at two sites in Southern Ontario. Significant and seasonally variable gradients of GEM concentrations, both increasing and decreasing with height above ground, were observed and can be explained through the influence of factors such as soil contamination, canopy growth, temperature, solar irradiance, and snow cover. At a minimum, the sampler can identify the GEM flux direction, but, when combined with the Modified Bowen Ratio method, can also be used to estimate the evaporative GEM flux from mercury‐contaminated soil. We further demonstrate the application of the approach to contaminated site assessment, using a field in Brescia, Italy, known to contain elevated levels of Hg in soil. By allowing for cost‐effective measurements at multiple sites and over extended time periods, passive sampling complements existing techniques for studying atmosphere‐surface exchange of GEM.
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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 | 0.000 |
| 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) | 0.000 | 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".