The relationship between site history and human health risks: Lessons from 13 years of contaminated land risk assessments in Victoria, Australia
Bibliographic record
Abstract
Abstract Site history is widely recognized as one of the key determinants of the nature and scale of contamination that is likely to be present at a location. While there is vast anecdotal and empirical evidence on the relationship between site history and risk of exposure to pollution, few attempts have been made to systematically collate data across sites to rigorously identify such trends. This study presents an analysis of human health risk metrics extracted from all 1732 contaminated land environmental audits published in Victoria, Australia between 2006 and 2018. The contaminating activities found to be most likely associated with elevated risks to human health were service stations, fuel depots, dry cleaners, gasworks, mechanical parts manufacturers, imported fill and unknown offsite sources generating regional groundwater pollution (usually polluted with trichloroethene). The ten chemical contaminants most frequently assessed in human health risk assessment reports included (in order): benzene, C>10–16 petroleum hydrocarbons, naphthalene, C6–10 petroleum hydrocarbons, xylenes, trichloroethene, toluene, ethylbenzene, benzo(a)pyrene, and tetrachloroethene. The findings of this report include recommendations to develop regulatory guideline values in Australia for trimethylbenzenes, heavy fraction petroleum hydrocarbons (C>16), and trans‐1,2‐dicholoethene, and to strengthen the evidence base informing risks associated with light fraction petroleum hydrocarbons (C6–16) and trichloroethene. To the author's knowledge, this study is the first to extract risk metrics from environmental audit reports and provides a strong evidence base to help regulators, academics and commercial practitioners rank and prioritize sites based on their site history and associated risks.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".