Effects of Electronic and Electrical Waste–Contaminated Soils on Growth and Reproduction of Earthworm (<i>Alma nilotica</i>)
Bibliographic record
Abstract
Informal recycling of electronic waste (e-waste) contaminates local environments with metals and other organic compounds. The adverse effects on native earthworm populations are poorly understood. The objective of the present study was to determine metal concentrations in soils from e-waste activity sites in Douala (Cameroon) and assess the effects of these soils on the growth and reproduction of the local earthworm, Alma nilotica. Concentrations of nine metals (arsenic [As], cadmium [Cd], cobalt [Co], chromium [Cr], copper [Cu], mercury [Hg], nickel [Ni], lead [Pb], and zinc [Zn]) were measured in soil samples collected from eight e-waste activity and two non-e-waste sites. Earthworms were then exposed to these soils in the laboratory following test guidelines of the Organisation for Economic Co-operation and Development. Metal concentrations in the e-waste-contaminated soils were significantly higher than in the non-e-waste soils. The e-waste soils were found to have a different soil metal profile (Cu > Pb > Zn > Cr > Ni > Co > As > Cd > Hg) from that of the non-e-waste soils (Zn > Cr > Cu > Pb > Ni > As > Cd > Co > Hg). Earthworm growth and reproduction were significantly inhibited in organisms exposed to soils from e-waste sites. Reproduction was particularly affected, with a mean of 8 ± 5.6 offspring/10 worms in the e-waste-exposed worm groups compared with 90.5 ± 0.7 in non-e-waste soil worms. Notably, earthworm growth recovered during depuration in clean soil, indicating the possibility of remediation activities. The results demonstrate that soils at e-waste sites can affect the health of resident worm populations, which may be more sensitive than temperate species. They also highlight the potential of a bioassay-based approach in monitoring risks at e-waste sites. Environ Toxicol Chem 2022;41:287-297. © 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".