Ten years of elemental atmospheric metal fallout and Pb isotopic composition monitoring using lichens in northeastern France
Bibliographic record
Abstract
We report on the chemical and Pb isotopic compositions of epiphytic lichens collected from small tree branches in the urban area of the city of Metz (NE France). Lichens were collected in five different years between 2001 and 2009. The data are first compared year to year in order to document any temporal change and trend in metal atmospheric fallout. The area studied was then subdivided into different zones on the basis of land use (urban, suburban, rural and industrial) in order to determine potential spatial gradients. The median concentrations and enrichment factors (EF, normalized to Al) of Pb and other metals (Cu, Zn, Cd, Ni, Cr, Hg, Fe) in lichens from the urban, suburban, and rural zones show no systematic variation between 2001 and 2008. However, the metal EFs show spatial variation and are generally highest in the urban area and lowest in the rural area. Lichens within the industrial zone (collected in 2009), which is dominated by steel industries, are richest in Al, Fe, Cr, Pb, and Zn. Although the Al concentration is high in these lichens, the EFs for the cited metals are several times higher than those measured in lichens from the other three zones. No significant differences were noted for Hg, Cd, Cu and or Ni. Pb isotopic compositions measured in lichens may be highly variable from year to year and from zone to zone. The variation is primarily interpreted to result from mixing between: (i) Pb added to gasoline (and recycled through re-emission of road dust in the atmosphere); (ii) regional industrial Pb from long-range transportation and/or mixed with urban Pb; and (iii) local industrial Pb. The median isotopic compositions of individual zones are distinct, suggesting variable mixing of these three sources. The annual variations show that 2001 was most affected by gasoline Pb, whereas 2003 and 2006 were more affected by the local steel industry.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".