Incremental sampling methodology for petroleum hydrocarbon contaminated soils: volume estimates and remediation strategies
Bibliographic record
Abstract
Current environmental assessments for petroleum hydrocarbon (PHC) contaminated sites are dependent on discrete soil sampling to estimate the degree and extent of contamination, leading to unreliable and non-reproducible results. Incremental sampling methodology (ISM) involves collecting and combining samples within a targeted area and holds promise for being a cost-effective, representative, and reproducible sampling strategy for contaminated site characterization. We hypothesized that traditional Phase II Environmental Site Assessments (ESA) discrete and ISM sampling protocols were not mutually exclusive, and the two approaches can be used to formulate a responsible land management strategy. Results gathered through ISM were compared to those from Phase II ESA for two PHC contaminated sites in Canada. Both methods indicated the sites were impacted with PHC beyond Saskatchewan Tier I guidance, however, the delineation of the PHC plume differed by as much as 75% for the heavier hydrocarbons. The Phase II ESA methods had higher incidences of false positive results and an overestimation of contamination at depth. A laboratory experiment confirmed that ISM does not “dilute” the samples as to cause underestimation, whereby the hydrocarbon concentrations for a single combined sample was equivalent to the mean of 30 discrete samples. Based on our results, sites should undergo risk assessment based on the estimates of the Phase II ESA results using vapor phase logs to estimate contaminant extent. If exposure pathways cannot be eliminated through the risk assessment process, remediation planning based on the ISM results is justified given the demonstrated cost-effectiveness, representativeness, and reproducibility.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".