Is Moss Still a Reliable Biomonitor of Nitrogen and Sulfur Deposition After Decades of Emissions Reductions?
Bibliographic record
Abstract
Mosses are widely used as biomonitors of atmospheric nitrogen (N) and sulfur (S) deposition due to their broad distribution, ease of sampling, and capacity to trap and accumulate atmospheric particles. However, since 2000, S emissions have decreased by more than 80% across Europe, and N oxides by 40–50%. This study evaluated whether moss remains effective for monitoring atmospheric N and S deposition after decades of emission reductions. This assessment was conducted at 33 rural sites in Tuscany, Central Italy, a region characterized by relatively low levels of N and S deposition. The content of N and S in moss were compared with the air concentrations (gases and particles) and wet and dry deposition of N and S from the Cooperative Programme for Monitoring and Evaluation of Long-range Transmission of Air Pollutants in Europe (EMEP) model and an air pollution index derived from trace element concentrations. The average N content of moss (1.15 ± 0.42%) was an order of magnitude greater than that of S (0.11 ± 0.02%), reflecting the dominance of N deposition. Nevertheless, N and S in moss were strongly correlated (rs = 0.55), suggesting shared sources. Further, N showed a strong correlation with the dry deposition of oxidized N (rs = 0.53), while S was strongly correlated with the wet deposition of S oxides (rs = 0.53) and magnetic susceptibility (rs = 0.69). Overall, our findings confirm that mosses remain effective biomonitors of N and S deposition and can provide reliable spatial and temporal monitoring, especially as the traditional monitoring networks decline.
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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".