Soil Contamination and Bioaccumulation of Heavy Metals by a Tropical Earthworm Species (<i>Alma nilotica</i>) at Informal E-Waste Recycling Sites in Douala, Cameroon
Bibliographic record
Abstract
Soil contamination at electronic waste (e-waste) recycling sites is pervasive, though many locations have yet to be studied. While such contamination can present risks to soil organisms, little is known on the risks to native species. The objective of the present study was to assess soil contamination by heavy metals at e-waste recycling sites, and the potential of Alma nilotica, a native earthworm species, to bioaccumulate these metals. Soil samples collected from eight informal e-waste recycling sites and two non-e-waste sites in Douala, Cameroon, were analyzed for metal content. Metal concentrations in earthworm juveniles exposed to the soils for 21 days followed by a 14-day post-exposure period were measured weekly. Mean soil metal concentrations at e-waste sites ranked as Cu > Pb > Zn > Hg > Ni > As > Cd > Co > Cr. Based on contamination factors, soil contamination ranged from "moderate" (Cr), through "considerable" (Co and Cd), to "very high" for the rest of the metals. Based on the modified degree of contamination and risk index, all e-waste sites had "ultra-high" contamination with Ni, Pb, and Zn posing very high ecological risks and Bonaberi being the most contaminated site. There was a positive correlation between soil metal concentrations and metal accumulation (retention) by eathworms, but Hg and Co had the highest bioaccumulation factors (BAFs) despite having low soil concentrations. These results document that e-waste sites in Douala are contaminated with metals and that native earthworm species can bioaccumulate the studied metals at levels that could account for the toxic effects earlier recorded. With e-waste recycling growing worldwide, there is a need for more data, especially from understudied locations. Environ Toxicol Chem 2022;41:356-368. © 2021 SETAC.
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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 | 0.000 |
| 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) | 0.000 | 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".