Simultaneous targeted and non-targeted analysis of plastic-related contaminants in e-waste impacted soil in Agbogbloshie, Ghana
Bibliographic record
Abstract
An LC-MS based analytical method was developed and validated for the simultaneous targeted analysis and suspect screening of plastic-related contaminants in e-waste impacted soils. Satisfactory recoveries (97 ± 13 %) were achieved using ultrasound-assisted extraction for 14/15 of the targeted analytes (7 bisphenols and 8 plasticizers) in a range of agricultural and non-agricultural soils. The method was applied to 53 soil samples collected in May 2015 in the region of Agbogbloshie (Ghana) at e-waste facilities (incl. Dump, trade, and burn sites), neighboring non-agricultural (incl. upstream, downstream, and community) and agricultural fields, and at two control agricultural sites away from e-waste recycling facilities. Bisphenol A (BPA) and bis(2-ethylhexyl) phthalate (DEHP) were the two dominant contaminants in e-waste soil (with concentrations up to 48.7 and 184 μg g−1, respectively), especially at the trade site, where e-waste was sorted and dismantled. The non-targeted workflow was successfully applied to identify additional plastic-related contaminants previously unreported in e-waste impacted soils, including bis(2-propylheptyl) phthalate, diisononyl phthalate, trioctyl trimellitate, 4-dodecylbenzenesulfonic acid, perfluorooctanesulfonic acid, perfluorobutanesulfonic acid, diphenyl phosphate, and triethylene glycol monobutyl ether. The agricultural soils surrounding the e-waste sites were also contaminated by plastic-related chemicals (especially DEHP), highlighting the impact of e-waste activities on the surrounding agricultural system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".