Lichens as Biomonitors of Air Quality around a Diamond Mine, Northwest Territories, Canada
Bibliographic record
Abstract
Lichens are known to be bioaccumulators of atmospheric pollutants and are abundant in the Canadian arctic. Mining in this region may negatively impact the tundra communities and these impacts may be detected by increased accumulation of heavy metals, greenhouse gas constituents, and organic compounds in lichen tissue. The effect of sampling direction and distance from a diamond mine on bioaccumulation in three lichen species, Flavocetraria nivalis, Flavocetraria cucullata, and Cladina arbuscula, was investigated. Eight sample sites were located immediately adjacent to a diamond mine, one in each cardinal and ordinal direction, and six sample sites each were located 30 and 60 km from the mine (cardinal, NE, and SE). Thirty-three major and trace elements, sulfate (SO(4)), nitrate (NO(3)), ammonium (NH(4)), polycyclic aromatic hydrocarbons (PAH), and phthalates were analyzed in lichen tissue and soil. A significant interaction occurred between distance and direction from the mine. Highest concentrations of Al, Cr, Cu, Fe, Ni, Ti, and V in lichen were at the mine site regardless of direction. Highest concentrations for all other elements were at the mine in at least two directions. Although present in lichen tissue, there was no significant difference among sites for Hg, Mn, S, and three phthalates. PAHs were below detection limits in lichen tissue. The effect of direction was dependent on element and species, although concentrations of most elements were greatest east or southeast of the mine site. At distance from the mine, direction had less of an effect on concentrations. Elevated concentrations in tissue did not negatively impact lichen or plant cover or lichen richness. This research strongly suggests selection of sample sites and species can impact results and interpretation of data from air quality monitoring programs that use lichens as biomonitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".