Isotopic Signature and Impact of Car Catalysts on the Anthropogenic Osmium Budget
Bibliographic record
Abstract
Higher osmium concentrations and lower 187Os/188Os ratios in sediments from urban areas have been linked to anthropogenic osmium sources. Automobile catalytic converters that use platinum group metals (PGM) are a potential source for this Os pollution. We present the first direct Os concentrations and isotopic measurements of catalytic converters for major automobile brands to test the assumption that car catalysts release Os with a distinct signature in the environment. The analysis of four new catalytic converters yields similar low 187Os/188Os ratios (0.1-0.2), suggesting a similar source for the PGM. The Os concentrations measured are in the ppt range (6-228 ppt). From our results, the osmium contribution of the car catalysts to the environment through attrition (wearing and grinding down of the catalyst by friction) is predicted to be low, <0.2 pg Os/m2/year in highly urbanized environment. We show that Os loss from catalysts as volatile OsO4 is important at car catalyst operating temperatures. Moreover, we estimate that car catalysts may be responsible for up to approximately 120 pg Os/m2 deposited per year in urban areas and that part of it may be exported to sedimentary sinks. Car catalytic converters are thus an important anthropogenic osmium source in densely populated areas. The NIST car catalyst standard (SRM-2557, made from recycled used catalysts) yields higher concentrations (up to 721 ppt Os) and a more radiogenic isotopic composition (approximately 0.38), perhaps indicative of Os contamination during its preparation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".