Detection of Sulfur from Industrial Emissions Across a Complex Mountainous Landscape: An Isotope Approach Using Plant-Based Biomonitors in Winter
Bibliographic record
Abstract
Tree rings, tree needles, and moss can be used as biomonitors to evaluate atmospheric pollutant concentrations and deposition patterns spanning different timescales. This study compares output from air quality modeling and measurements to patterns observed using a combination of sulfur concentration and isotope composition in moss (using moss bags and controls) as biomonitors in a region of southern Alberta, Canada influenced by industrial emissions. Tree rings allow comparisons of historical to current sulfur deposition patterns. Moss, which integrates atmospheric nutrients during growth, allows for concurrent comparisons. The contrast of inorganic and organic sulfur within conifer tree needles provides a measure of pollutant uptake over their short lifespans. Sulfur uptake within biomonitors in a southern Alberta ecosystem allow assessment of the presence (in moss, needles) and effects (on conifer growth) of atmospheric sulfur deposition from industrial emissions. These data were examined relative to California Puff (CALPuff) model projections and traditional active and passive air quality sampling. Patterns in sulfur isotope abundance (δ34S) from moss bags placed throughout the eastern slopes of the southern Alberta foothills of the Rocky Mountains implicate local industry as the dominant atmospheric sulfur source over winter, with the tissues of conifers (needles and cores) and moss decreasing with distance from industrial emissions. This was consistent with apportionment calculations based on active and passive sampling, which also showed a surprising trend of sulfur deposition upwind of the industrial stack in the mountains to the west. δ34S values for pine needles and tree rings were consistent with greater sulfur stress and reductions in tree growth associated with increased industrial sulfur concentrations and deposition. We conclude that plant biomonitors are effective short-term (tree needles and moss) and long-term (tree cores) indicators of sulfur pollution in a complex, mountainous landscape.
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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) | 0.001 | 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".