Comparison of active measurements, lichen biomonitoring, and passive sampling for atmospheric mercury monitoring
Bibliographic record
Abstract
The number of atmospheric mercury (Hg) monitoring stations is growing globally. However, there are still many regions and locations where Hg monitoring is limited or non-existent. Expansion of the atmospheric Hg monitoring network could be facilitated by the use of cost-effective monitoring methods. As such, biomonitoring and passive monitoring offer a unique alternative to well-established monitoring by active measurements, since they do not require a power supply and require minimal workload to operate. The use of biomonitoring (lichens and mosses) and passive air samplers (PASs) (various designs with synthetic materials) has been reported in the literature, and comparisons with active measurement methods have also been made. However, these studies compared either biomonitoring or PASs (not both) to only one type of active measurement. In our work, we used transplanted (7 sampling sites) and in situ lichens (8 sampling sites) for biomonitoring, two PASs from different producers (3 sampling sites), and two different active measurement types (continuous and discontinuous active measurements, 1 and 8 sampling sites, respectively) to evaluate their effectiveness as monitoring methods. In the 9-month sampling campaign, 3 sampling locations with different characteristics (unpolluted, vicinity of a cement plant, and vicinity of a former Hg mine) were used. The results obtained with lichens and PASs clearly distinguished between sampling locations with different Hg concentrations; using both PASs and lichens together increased the confidence of our observations. The present work shows that biomonitoring and passive sampling can be effectively used to identify areas with elevated atmospheric Hg concentrations. The same can be said for discontinuous active measurements; however, the discrepancy between atmospheric Hg concentrations derived from PASs and discontinuous active measurements should be further investigated in the future.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".