Occupational exposures to particulate matter and PM2.5-associated polycyclic aromatic hydrocarbons at the Agbogbloshie waste recycling site in Ghana
Bibliographic record
Abstract
Electronic waste (e-waste) disposal and recycling activities such as burning and smelting can emit particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and other pollutants that expose workers and nearby communities. At informal e-waste recycling facilities, both emission controls and protective measures for workers are absent. This study characterizes personal exposures (breathing zone) of PM and PAHs of e-waste workers at the large Agbogbloshie e-waste site in Accra, Ghana. We collected gravimetric and optical measurements of PM2.5 and PM10 using shift samples for workers and for an age- and gender-matched reference population. PM2.5 filters were analyzed for 26 PAHs using gas chromatography/mass spectroscopy. Among e-waste workers, PM2.5 and PM10 concentrations were 99 ± 56 and 218 ± 158 µg/m3 (median ± interquartile range, optical measurements), respectively; these levels were 78 and 57% higher, respectively, than levels measured at a fixed site that was centrally located at the waste site. In the reference community, breathing zone PM2.5 and PM10 levels were lower, 49 ± 20 and 131 ± 108 µg/m3, respectively, and the fraction of coarse PM was larger. We detected all 26 target PAHs, of which naphthalene and phenanthrene were the most abundant. PAH concentrations were weakly correlated to PM levels, but PAH abundances, representing the fraction of PAH mass to the total PM2.5 mass collected, were strongly and inversely correlated to PM levels, suggesting multiple and different sources of PAHs and PM that affected exposures. Both PM and PAH levels were elevated for workers performing burning and dismantling, and both exceeded standards or risk-based guidelines, e.g., lifetime excess cancer risks for several PAHs were in the 10−4 to 10−6 range, indicating the need to reduce emissions as well as provide respiratory protection. The study emphasizes the importance of using personal and shift samples to accurately characterize worker exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".