Application of quantitative mineralogy to determine sources of airborne particles at a European copper smelter
Bibliographic record
Abstract
Copper processing operations, such as smelters and refineries, can produce airborne particles that may impact the health of workers. At these operations, worker exposure to chemicals are regularly monitored to ensure that regulatory compliance with occupational exposure limit values (OELVs) are maintained. Determining the type of airborne particles present is important for characterizing the composition of dust exposures and better understanding the relationship between worker exposure and health. Routine methods of analysis (e.g., chemical assay) are unable to differentiate between phases containing the same elements and may result in ambiguity. A novel approach of a combination of Quantitative Evaluation of Materials by Scanning Electron Microscope (QEMSCAN) and chemical characterization was used here to evaluate airborne and settled dust collected at key locations throughout a copper smelter in Europe. The copper (Cu) phases present in the airborne dust are indicative of the activities performed at specific locations. In the batch preparation area where Cu concentrate is received, significant amounts of Cu were carried in sulfidic minerals (chalcocite, chalcopyrite/bornite, >40%), whereas near the anode and electric furnace, the majority of Cu in dust was carried in metallic and oxidic phases (60-70%). Particle size analysis of the settled dust indicates that the sulfidic and oxidic Cu minerals are more likely to become airborne over metallic Cu. Furthermore, overall Cu concentrations decreased with particle size where metallic and oxidic Cu dominate, which suggests that differences in the proportion of Cu forms present in the dust will impact how much Cu ends up in the respirable fraction. These results highlight the need to understand the characterization of Cu in dust in order to set better OELVs.
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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 | 0.002 |
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".