Metal contamination of natural surface soils from long-range atmospheric transport: Existing and missing knowledge
Bibliographic record
Abstract
This review focuses on the long-range atmospheric transport of metals to organic-rich surface soils (mostly 50–90% organic matter) in the temperate, coniferous, and boreal zones of North America and Europe. From various air-pollution related measurements (air, precipitation, moss, peat cores) Pb and Zn are known to be transported long distances in the air in large amounts. Arsenic, Cd, Hg, Sb, and Se are also typical representatives of long-range transported air masses, and there is evidence that Ag, Bi, In, Mo, Tl, and W belong to this group of elements. Through the use of “environmental archives” such as ice and peat cores it has become evident that long-range transport of pollutants and associated contamination of natural surfaces is not just a recent phenomenon. There is compelling evidence for widespread enrichment of surface soil horizons in Pb from long-range transport, and many studies support enrichment of Zn. Mercury is also generally elevated by anthropogenic emissions over natural levels in organic-rich surface soils, whereas results for Cd are less conclusive. There is evidence that As, Se, Ag, Mo, In, Sb, W, Tl, and Bi all are subject to some enrichment in organic-rich surface soils from long-range atmospheric transport, but studies are still few for most of these elements. With the exception of Pb, little is known about residence times of the elements in the organic-rich surface horizon, and more research is needed on this topic. Further studies are desirable on the temporal and spatial trends in supply of the above elements, which are poorly known in large parts of the northern temperate zone.Key words: natural soils, metals, long-range atmospheric transport, organic matter, lead, zinc, cadmium, mercury.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 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".