Mountaintop Removal Coal Mining Contaminates Snowpack across a Broad Region
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mountaintop removal coal mining is a source of downstream pollution. Here, we show that mountaintop removal coal mining also pollutes ecosystems downwind. We sampled regional snowpack near the end of winter along a transect of sites located 3–60 km downwind of coal mining in the Elk River valley of British Columbia, Canada. Vast quantities of polycyclic aromatic compounds (PACs), a toxic class of organic contaminants, are emitted and transported atmospherically far from emission sources. Summed PAC (ΣPAC) snowpack concentrations ranged from 29–94,866 ng/L. Snowpack ΣPAC loads, which account for variable snowpack depth, ranged from <10 μg/m 2 at sites >50 km southeast of the mines to >1000 μg/m 2 at sites in the Elk River valley near mining operations, with one site >15,000 μg/m 2 . Outside of the Elk River valley, snowpack ΣPAC loads exhibited a clear spatial pattern decreasing away from the mines. The compositional fingerprint of this PAC pollution matches closely with Elk River valley coal. Beyond our study region, modeling results suggest a depositional footprint extending across western Canada and the northwestern United States. These findings carry important implications for receiving ecosystems and for communities located close to mountaintop removal coal mines exposed to air pollution elevated in PACs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".