PFAS—emerging constituents of concern for mine closure studies
Bibliographic record
Abstract
A screening level site investigation of per and/or polyfluoroalkyl substances (PFAS) was conducted at six legacy mining sites in North America; the sites have been inactive from 10 to 92 years before present. Soil and water samples were collected based on the location of historical infrastructure footprints, current monitoring networks (upstream and downstream of mine facilities), hydrogeological conditions, and flow gradients. Representative samples of groundwater, leach draindown solutions, and pit lake water were collected. Soil media samples included a vertical characterization of tailings storage facilities at locations near maintenance shops, processing plants, and pregnant leach solution ponds. The samples were sent to a third-party testing lab and analysed for 36 PFAS compounds included under Method 537 (modified). The initial screening was successfully conducted, indicating the presence of nine PFAS compounds at five of the six legacy sites. The sample results were compared with five reference guidelines–U.S. EPA proposed MCLs, U.S. EPA Regional Screening Levels, Hawaii Department of Health, Health Canada, and Heads of EPAs Australia and New Zealand. The results of this screening study will be used to advance the PFAS research at these mining sites related to mine closure planning and assessing the value of expanding the groundwater monitoring and/or establishing a continuous monitoring program at select locations. The voluntary monitoring provides the business with baseline data to inform mine closure issues and to prepare for potential future regulation of PFAS. PFAS comprises more than 9000 chemicals (NIOSH 2022) and are characterised by a long carbon-fluorine backbone. They possess excellent thermal stability and chemical resistance, making them desirable for various industry applications. PFAS have been manufactured since the 1930s. However, the health risks related to PFAS only gained attention in the early 2000s. Due to their bio-accumulative nature, they persist in the food chain and assimilate into living organisms causing damage to the immune system, kidney, and liver and high risks of cancer, thyroid, and obesity. PFAS have widespread applications in multiple industries, including electrical, printing, metal, laundry, textile, aerospace, and automotive. They are also widely used in consumer products like food packaging, non-stick cookware, waterproof carpets, cleaning reagents, and aqueous film-forming foam. In the mining industry, they are used as surfactants in cyanide and sulfuric acid leach solutions to enhance the recovery of gold and copper and as mist suppression agents in electroplating processes. PFAS assessment at both operational and legacy sites in North America was completed in a voluntary, exploration phase driven by corporate desire to identify and reduce potential future risks. There are currently no regulatory drivers for enforcement of PFAS standards at mining facilities. Assessments of PFAS at legacy sites is challenging given the typical age of information about specific chemical products and quantities historically used in the processing circuit.
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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) | 0.000 | 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".