Characterizing and sourcing metal air contamination coupling concentrations and lead isotopes from moss biomonitoring in urban cemeteries
Bibliographic record
Abstract
Abstract Populations are constantly exposed to airborne metals, in particular in urban areas. Despite their proven links to health issues, their origin and fate are still subject to debate. Bioindicators, by taking up and cumulating atmospheric metals over time, have been widely used to proxy environmental quality over large areas, at various time scales. Using the example of the Paris region, we investigated the potential for the Grimmia pulvinata moss species to both characterize air metal contamination and to identify its main sources. To this end, we coupled metal/metalloid (Al, As, Cd, Cr, Cu, Fe, Ni, Pb, Sb, Sr, V and Zn) concentrations and Pb isotope ratios from samples collected in cemeteries in the city and its suburbs. Metal enrichment factors ranged between 2 and 10 for As, Cr, Fe, Ni, Sr, V, between 50 and 100 for Cu, Pb and Zn and > 100 for Cd and Sb, indicating a dominant anthropogenic origin. Principal component analysis showed that 3 principal components explained 89% of the metal variations: (i) European atmospheric background, (ii) regional urban sources, and (iii) resuspension of regional soils. This was corroborated by Pb isotope ratios, whose variations were modelled by a ternary mixing that considered the same 3 emission sources. Using a MixSIAR isotope model, we reveal that the European atmospheric background contributes slightly (< ~ 5%) and that within 20 km of the city center bioindicators are mostly impacted by urban sources (contributions: 50–80%). Samples collected > 20 km show almost equal contributions of the endmembers representing urban activities and agricultural soil resuspension.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".