Using elemental analyses and multivariate statistics to identify the off-site dispersion from informal e-waste processing
Bibliographic record
Abstract
Electronic waste (e-waste) is informally processed and recycled in Agbogbloshie in Accra (Ghana), which may be the largest such site in West Africa. This industry can lead to significant environmental contamination. In this study, surface dust samples were collected at a range of sites within Accra to establish the offsite consequences of such activities. Fifty-one samples were collected and analysed for 69 elements by ICP-mass spectrometry after nitric acid digestion. The data indicated a significant enrichment in metals associated with solder and copper wire at the site itself and a downwind dispersion of this source material to a distance of approximately 2.0 km. Chlorine and bromine were also elevated at this site as residues from polyvinyl chloride combustion and flame retardants respectively. The elemental composition indicated that only low technology electrical equipment was being treated this way. Multivariate statistical analyses by principal components analysis and polytopic vector analysis identified three sources contributing to the system; (i) burn site residue dispersing within 2 km from the source site, (ii) marine matter on the beaches alone and (iii) the baseline soil conditions of the city of Accra. Risk ratios and hazard quotients developed from the measured concentrations indicated that copper was providing the greatest risk to inhabitants in most cases although nickel, vanadium, chromium and zinc also contributed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".