Laboratory Comparison of Oil Removal by Four Drain Inlet Inserts
Bibliographic record
Abstract
Vehicular motor oil from leaking vehicles, illegal disposal, and accidental spills is often carried to receiving waters via stormwater flows. Storm drain inlet inserts have been developed to remove oil from stormwater. Unfortunately, knowledge of their effectiveness in removing oil and grease under common field conditions is lacking. In this study, oil capture and retention performance was examined for four drain inlet inserts — the DrainPacTM, FloGard+PlusTM, Hydro-CartridgeTM, and Ultra-UrbanTM inserts. To approximate the illegaldisposal or accidental spill scenario, a spike dose of 4 liters of used motor oil was applied to each insert. The inserts were subsequently flushed with oil-free water to determine retention during runoff events. The percent of oil retained by the inserts after flushing ranged from 5 to 55 percent. To approximate the stormwater runoff scenario, used motor oil was continuously dosed at around 15 mg/L. Each insert received a cumulative volume of nearly 454,000 liters (120,000 gallons), which simulated an annual loading. The loading was broken up into 20 applications to simulate individual storms. Flow rates for individual events were either 57, 95, or 132 L/min (15, 25, and 35 gpm) to simulate different rainfall intensities. Paired composite samples of influent and effluent were collected for each insert test. While removal was consistent throughout the test for individual inserts, removal efficiencies among the inserts varied considerably, ranging from negligible to about 60 percent. When sediment was added to the synthetic runoff, inserts that provided settling showed better oil removal than those without settling. Even so, the best removal efficiency observed when sediment was present was only about 40%, though this result is based on only two experimental runs.
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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) | 0.002 | 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 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".