Health risk assessment of the European inhabitants exposed to contaminated ambient particulate matter by potentially toxic elements
Bibliographic record
Abstract
PM10-associated potential toxic elements (PTEs) can enter the respiratory system and cause health problems. In the current study, the health risk indices caused by PM10 inhalation by adults, children, and infants in 158 European cities between 2013 and 2019 were studied to determine if Europeans were adversely affected by carcinogenic and non-carcinogenic factors or not. The Mann–Kendall trend test examined PM10's increasing or decreasing trend. Random Forest analysis was also used to analyse meteorological factors affecting PM10 in Europe. Hazard quotient and cancer risk were estimated using PM10-associated PTEs. Our results showed a decline in continental PM10 concentrations. The correlation between PM10 concentrations and temperature (−0.40), PBLH (−0.39), and precipitation were statistically strong (−0.21). The estimated Pearson correlation coefficients showed a statistically strong positive correlation between As & Pb, As & Cd, and Cd & Pb during 2013–2019, indicating a similar origin. PTEs with hazard quotients below one, regardless of subpopulation type, posed no noncancerous risk to Europeans. The hazard quotient values positively correlated with time, possibly due to elevated PTE levels. In our study on carcinogen pollution in Europe between 2013 and 2019, we found unacceptable levels of As, Cd, Ni, and Pb among adults, children, and infants. Carcinogenic risk rates were highest for children, followed by infants, adult women, and adult men. Therefore, besides monitoring and mitigating PM concentrations, effective control of PM sources is also needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".