Use of atmospheric concentrations and passive samplers to assess surface-atmosphere exchange of gaseous mercury in forests
Bibliographic record
Abstract
Direct measurements of gaseous elemental mercury (GEM) exchanges over global ecosystems are challenging and require extensive and costly measurement systems. Here, we explore the use of atmospheric GEM concentration variability and passive samplers to assess underlying ecosystem GEM exchanges at two rural temperate forests in the northeastern United States. We find strong temporal alignments between atmospheric GEM concentration declines and ecosystem GEM deposition in spring at both forests, which followed patterns of CO2 and suggests that ambient air GEM concentration monitoring provides a proxy measurement to assess forest GEM sinks. In fall, we observe GEM concentration increases and reversal of GEM fluxes to emissions, but with poor temporal alignments. Diel GEM concentration variability did not correspond to diel patterns of ecosystem GEM fluxes, which is driven by boundary layer dynamics with different atmospheric mixing depths during day- and nighttime. Passive samplers (PASs) deployed to measure vertical GEM gradients across six heights throughout one of the forest canopies showed excellent agreements with active measurements in detecting seasonal concentration patterns at all deployment heights. We find frequent qualitative agreement between the direction of active and PAS derived measurements, but small concentration differences over small (1.3 and 4.9 m) gradients prevent a quantitative comparison of methods. Furthermore, time-averaged GEM concentration gradient measurements are always biased towards stable nighttime periods, while ecosystem GEM fluxes are dominated by daytime exchanges, which results in the inability of integrated measurements such as PAS to correctly quantify forest GEM exchanges. We conclude that concentration measurements both via active and passive sampling can serve as proxies to assess underlying ecosystem GEM sinks and sources, but that the use of passive samplers to quantify GEM exchange via gradient measurements is limited due their strong nighttime biases.
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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 | 0.000 |
| 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".