Influence of Matrix Composition on the Bioaccessibility of Copper, Zinc, and Nickel in Urban Residential Dust and Soil
Bibliographic record
Abstract
This study examines factors affecting oral bioaccessibility of metals in household dust, in particular metal speciation, organic carbon content, and particle size, with the goal of addressing risk assessment information requirements. Investigation of copper (Cu) and zinc (Zn) speciation in two size fractions of dust (< 36 μ m and 80–150 μ m) using synchrotron X-ray absorption spectroscopy (XAS) indicates that the two metals are bound to different components of the dust: Cu is predominately associated with the organic phase of the dust, while Zn is predominately associated with the mineral fraction. Total and bioaccessible Cu, nickel (Ni), and Zn were determined (on dry weight basis) in the < 150 μ m size fraction of a set of archived indoor dust samples (n = 63) and corresponding garden soil samples (n = 66) from the City of Ottawa, Canada. The median bioaccessible Cu content is 66 μ g g−1 in dust compared to 5 μ g g−1 in soil; the median bioaccessible Ni content is 16 μ g g−1 in dust compared to 2 μ g g−1 in soil; and the median bioaccessible Zn content is 410 μ g g−1 in dust compared to 18 μ g g−1 in soil. For the same data set, the median total Cu content is 152 μ g g−1 in dust compared to 17 μ g g−1 in soil; the median total Ni content is 41 μ g g−1 in dust compared to 13 μ g g−1 in soil; and the median total Zn content is 626 μ g g−1 in dust compared to 84 μ g g−1 in soil. Organic carbon is elevated in indoor dust (median 28%) compared to soil (median 5%), and is a key factor controlling metal partitioning and therefore bioaccessibility. The results show that house dust and soil have distinct geochemical signatures and should not be treated as identical media in exposure and risk assessments. Separate measurements of the indoor and outdoor environment are essential to improve the accuracy of residential risk assessments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".