Metal concentrations in used engine oils: Relevance to site assessments of soils
Bibliographic record
Abstract
Used engine oils contain metals, which upon entering soils may pose risks to human health or the environment. In this study, previously published concentrations of 23 metals in 213 used engine oil samples from the early 1970s to the mid-1990s are statistically evaluated. Neat (100%) used engine oils were found to contain relatively high concentrations of lead, calcium, and zinc, attributable to piston blow-by of leaded gasoline, calcium salt detergent additives, and zinc-bearing anti-corrosion/anti-oxidation additives, respectively. Wear metal concentrations were lower. The lead concentration in used engine oils in the U.S. declined between the 1970s and early 1990s, potentially providing a basis to constrain the “age” of used engine oil(s) in soils. The concentrations of 23 metals in used engine oils were compared to soil risk benchmarks in 15 representative jurisdictions in the U.S., Canada, Australia, and Europe. The maximum concentrations in neat (100%) used engine oil of eight metals – Be, Co, Fe, Mn, Ni, Se, Ag, and Ti – were lower than their collective minimum benchmarks in soils for the jurisdictions surveyed, indicating their concentrations in soils could not be reasonably expected to exceed any soil benchmarks. Nine metals (As, Ba, Cd, Cr, Cu, Pb, Sn, V and Zn), but particularly arsenic, cadmium, lead, tin, and zinc, were identified as potential contaminants of concern (PCOC) for soils from locations impacted with used engine oils, owing to their higher median concentrations (i.e., 2.5, 1.4, 1038, 5.0, and 922 mg/kg in oil, respectively) relative to most soil benchmarks. Site-specific benchmarks and metal concentrations at reasonable oil in soil concentrations require consideration when developing the suite of PCOC metal analytes for conducting site assessments of soils impacted by used engine oil.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".