Soot Deposition in the Great Lakes: Implications for Semi-Volatile Hydrophobic Organic Pollutant Deposition
Bibliographic record
Abstract
Air deposition is a dominant transport mechanism for many hydrophobic organic pollutants (HOCs) to the Great Lakes. Our previous research has shown that soot exhibits large surface areas with high organic carbon contents suggesting the potential for strong HOC partitioning. As yet, however, clear data showing the link between HOCs such as PAHs to the deposition of soot into the Lakes (a proposed transport mechanism) is primarily inferential. We measured soot carbon (SC) and organic carbon (OC) in sediments collected from each of the Laurentian Great Lakes. OC and SC levels collected from locations near urban areas were higher than in sediments collected from distant locations. By far, Lake Superior had the lowest current SC flux of any lake, and Lakes Michigan and Erie had the highest. SC flux for all lakes had the following order: Superior < Huron < Ontario < Michigan < Erie, ranging 0.02-0.89 mg (m2 yr)(-1). Differences in lake size resulted in a different order for total SC loading by lake: Superior < Ontario < Huron < Erie < Michigan, ranging 2.3-420 x 10(3) tyr(-1). SC and PAH accumulation rates reported previously for Lake Michigan sediment were highly correlated; with a SC to PAH mass ratio of 10(4) (0.01%). The importance of soot as a potential sorbent for various classes of airborne HOCs was examined using a simple octanol-air partitioning model together with our previous characterization of soot particles. The results predict that both PAHs and PBDEs should have strong partitioning to soot and suggest the need to further investigate soot as a vector for PBDE transport.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".