Overview of mercury dry deposition, litterfall, and throughfall studies
Bibliographic record
Abstract
Abstract. The current knowledge concerning mercury dry deposition is reviewed, including dry-deposition algorithms used in chemical transport models (CTMs) and at monitoring sites and related deposition calculations, measurement methods and studies for quantifying dry deposition of gaseous oxidized mercury (GOM) and particulate bound mercury (PBM), and measurement studies of litterfall and throughfall mercury. Measured median GOM plus PBM dry deposition in Asia (10.7 µg m−2 yr−1) is almost double that in North America (6.1 µg m−2 yr−1) due to the higher anthropogenic emissions in Asia. The measured mean GOM plus PBM dry deposition in Asia (22.7 µg m−2 yr−1), however, is less than that in North America (30.8 µg m−2 yr−1). The variations between the median and mean values reflect the influences that single extreme measurements can have on the mean of a data set. Measured median litterfall and throughfall mercury are, respectively, 34.8 and 49.0 µg m−2 yr−1 in Asia, 12.8 and 16.3 µg m−2 yr−1 in Europe, and 11.9 and 7.0 µg m−2 yr−1 in North America. The corresponding measured mean litterfall and throughfall mercury are, respectively, 42.8 and 43.5 µg m−2 yr−1 in Asia, 14.2 and 19.0 µg m−2 yr−1 in Europe, and 12.9 and 9.3 µg m−2 yr−1 in North America. The much higher litterfall mercury than GOM plus PBM dry deposition suggests the important contribution of gaseous elemental mercy (GEM) to mercury dry deposition to vegetated canopies. Over all the regions, including the Amazon, dry deposition, estimated as the sum of litterfall and throughfall minus open-field wet deposition, is more dominant than wet deposition for Hg deposition. Regardless of the measurement or modelling method used, a factor of 2 or larger uncertainties in GOM plus PBM dry deposition need to be kept in mind when using these numbers for mercury impact studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".