Urban environmental geochemistry in metal mining districts: street dusts as a screening tool
Bibliographic record
Abstract
Human exposure to toxic metals in urban environments located in mining districts is a source of concern for countries like Chile, where higher backgrounds may be found, and soil quality regulations are absent.This work compares metal concentrations in 185 street dusts samples collected in urban and peri-urban areas in three cities impacted by mining operations in northern Chile, as described in [1] and [2].The mean concentrations (in mg/kg) of Cu, Pb, and Zn were: Cu:527±600, Pb:47±48 and Zn:220±118 for Chañaral; Cu:690±1,400, Pb:55±58 and Zn:257±329 for Copiapó; and Cu:635±244, Pb<22 and Zn: 146±120 for Andacollo.Arsenic had <15% quantification frequency in each city.While the mean concentrations of Cu, Pb, and Zn had similar values, Copiapó showed higher extreme values, associated to many uncontrolled tailings in the city, which originate from several operations processing minerals from different sources.For Cu, As, Pb and Zn, 100, 7, 5, and 25% of samples, respectively, were above the Canadian guideline of residential/parkland soils, which was used as a reference.Statistical differences in street dusts concentrations (p<0.01) were significant for: cooper and zinc between Andacollo/Chañaral and Andacollo/Copiapó (but not Chañaral/Copiapó); and manganese for all city pairs.The concentrations of metals in street dusts are not regulated, but they represent a rapid and simple screening tool for comparison and hotspot detection.Street dusts also offer a cost-effective proxy of the superficial metal pool relevant for human exposure.Improving our knowledge of metal geochemistry and spatial distribution in mining cities is needed for the development of public policies protecting public health in urban environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".