Integration of Mosses (Funaria hygrometrica) and Lichens (Xanthoria parietina) as Native Bioindicators of Atmospheric Pollution by Trace Metal Elements in Mediterranean Forest Plantations
Bibliographic record
Abstract
Atmospheric emissions of industrial-origin trace metals are a major environmental problem that negatively affects air quality and the functioning of forest ecosystems. Traditional air quality monitoring methods require investments in equipment and infrastructure. Indeed, it is difficult to measure most of these pollutants because their concentrations usually occur at very low levels. However, this study explores an ecological approach for low-cost air quality biomonitoring that is based on native biological indicators in the context of the Mediterranean basin. This study aims (i) to evaluate the lichen species composition, diversity, and distribution across three distinct forest sites; (ii) investigate the relationship between lichen species richness and proximity to the pollution source; and (iii) evaluate heavy metal bioaccumulation using a moss species (Funaria hygrometrica) and a lichen species (Xanthoria parietina) as bioindicators of atmospheric pollution. High concentrations of toxic metals were observed along the transect and closer to the pollutant source with marked interspecies variability. X. parietina exhibited high bioaccumulation potential for most toxic metals (Fe, Zn, Pb, Cr, Cu, and Ni) compared to F. hygrometrica with concentrations varying across the three sites, reaching maximum dry-mass values of 6289 µg/g for Fe at the first site and 226 µg/g for Zn at Site 3. Our results suggest that X. parietina can be used as a potential bioindicator for long-term spatial biomonitoring of air quality by determining atmospheric toxic metals concentrations.
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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) | 0.000 | 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 | 0.001 |
| 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".