Potentially Toxic Elements andPersistent Organic Pollutionsin Hair:A Case Study of GreaterManchester
Bibliographic record
Abstract
This study aimed to compare metal and Persistent Organic Pollutant (POP) concentrations in human hair clippings between metropolitan boroughs in Greater Manchester with different death rates. The official statistics for death rates for the years 2014-2017 and life expectancy data were used to identify nine boroughs in Greater Manchester for the study. Five of these are located predominantly in the northern and western parts of the city and reports the highest death rates. These boroughs were Bolton, Bury, Rochdale, Oldham and Wigan. Four other boroughs mainly in the central and southern parts of the city with low annual death rates were also identified for use in the work, namely Manchester, Trafford, Stockport and Salford. In each borough, two male and two female hairdresser shops were chosen for the sample collection, and hair of each type were combined to form in a composite hair sample through the application of the quarter and coning technique. Different hair washing methods were evaluated to determine their effect on the metal analyses of each hair sample, and the results were then compared with the published literature. For the remainder of the work reported in this study, a simple wash (deionised water wash) was selected as the sample preparation method. The metal contents were extracted by means of microwave digestion and analysed by ICP-OES. QUeChERS combined with a dispersive solid phase extraction method, was used to extract the POPs (i.e. polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs)) and Gas Chromatography-Mass Spectroscopy was employed for the analysis. The focus was primarily on the contents of Cd, Pb, Ni, Cu, Zn and Mn analyses as far as the metallic elements were concerned, due to their toxic and carcinogenic behaviour in the human body. Various typical PCBs and PAHs were analysed in the POPs determinations. It was found that Pb and Zn were the two metallic elements with the highest concentrations in both the male and female hair samples in each borough from which hair samples were collected. For the PCB analyses, PCB 118 was present in much higher concentrations than the rest in both the female and male hair samples, and was also present to a much larger extent in the female hair samples than in the male samples. The three PCBs, apart from PCB118, had similar concentrations compared to each other in both the male and female hair samples. During the PAHs measurements, the analysis revealed that the PAH contents are dominated by these three compounds, i.e. acenaphthene, anthracene and naphthalene. In the female hair samples high values of acenaphthene were found. In the male hair samples, acenaphthene was also found in higher concentrations than any of the other PAHs, while naphthalene occurred in higher concentrations in the male hair samples than the rest of the PAHs, but was generally lower in concentration than acenaphthene. There was no discernible correlation between the death rates and/or life expectancy of the population in the various boroughs of Greater Manchester and the metallic element concentration profiles and POPs contents of the male and female hair samples.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".