Cancer and Non-Cancer Risks of Exposure to Some Potentially Toxic Metals (PTMs) In Indoor Settled Dust from Selected Offices
Bibliographic record
Abstract
Dust particulate has been reported to harbour a significant level of potentially toxic metals (PTMs), and the risk of human exposure to it has been a major concern. The study aimed to investigate the health risk assessment of exposure to PTMs in indoor dust from selected offices in Abeokuta, Ogun State, Nigeria. Twenty-four (24) composite settled dust samples were strategically collected from offices into a sample bag, and then transported to the laboratory for analysis. A 20 mL Aqua regia was used to digest one gram of the prepared sample, then PTM analyzed was done using AOAC Standards. Pollution intensity was evaluated using contamination factors (CF) and enrichment factors (EF). The findings showed that Cu (0.01–5.36 mg/kg), Zn (0.16-48.3 mg/kg), Cd (0.03–0.73 mg/kg), Mn (0.67–50.9 mg/kg), and Pb (3.04–88.3 mg/kg) were present, although within the UK, Canada, and Dutch guidelines. Pollution indexing revealed that the settled dust is severely enriched with Zn (6.58) and Pb (7.79), and moderately contaminated with Cd (1.33). Furthermore, the average daily dose (ADD) revealed Zn as the most dosed PTM in the settled dust. Non-cancer and cancer risk assessments showed that the occupants are not at risk of a significant cancer and non-cancer effect. In conclusion, the study showed that the indoor settled dust poses no human health risk during the investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".