Can Abies alba Needles Be Used as Bio-passive Samplers to Assess Air Quality?
Bibliographic record
Abstract
Bio-passive samplers have proved to be good alternate to assess air quality in regions where the use of active samplers is not technically feasible. Here, we tested the potential for Abies alba (silver fir) needles to be used as reliable bio-passive samplers. As these evergreen coniferous trees do not lose their needles in winter these are representative of a full year period. Needle samples were collected in 2013 from 20 different locations within the Holy Cross National Park (Świetokrzyski Park Narodowy, Holy Cross Voivodship, Poland). Both needles and the aerosols deposited on their surfaces were analyzed for their δ13C. Additionally, total carbon (TC), elemental carbon (EC) and organic carbon (OC) were determined only in surface-impacted aerosols using thermo-optical method. The overall objective of our study was to evaluate the possibility to distinguish bio-organic aerosols (crushed needles) from anthropogenically derived aerosols. The highest aerosol mass concentration (4.6 ± 2.4 mg g-1) was observed in samples with the longest exposure time, while the shortest exposure times yielded the lowest ones (1.7 ± 0.7 mg g-1). Aerosols that were enriched in 13C, indicating the impact of human activities, were located close to inhabited areas, at highly elevated points and in places situated outside the park. Our results also prove that one-year-old needles are the most reliable isotope bioindicators. We ultimately recommend that Abies alba needles can be used in future air quality monitoring programs, especially since this method is low cost and easy to implement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".