Spatial assessment of soil contamination by heavy metals from informal electronic waste recycling in Agbogbloshie, Ghana
Bibliographic record
Abstract
Objectives This study examined the spatial distribution and the extent of soil contamination by heavy metals resulting from primitive, unconventional informal electronic waste recycling in the Agbogbloshie e-waste processing site (AEPS) in Ghana.Methods A total of 132 samples were collected at 100 m intervals, with a handheld global position system used in taking the location data of the soil sample points.Observing all procedural and quality assurance measures, the samples were analyzed for barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), using X-ray fluorescence.Using environmental risk indices of contamination factor and degree of contamination (Cdeg), we analyzed the individual contribution of each heavy metal contamination and the overall Cdeg.We further used geostatistical techniques of spatial autocorrelation and variability to examine spatial distribution and extent of heavy metal contamination. ResultsResults from soil analysis showed that heavy metal concentrations were significantly higher than the Canadian Environmental Protection Agency and Dutch environmental standards.In an increasing order, Pb > Cd > Hg > Cu > Zn > Cr > Co > Ba > Ni contributed significantly to the overall Cdeg.Contamination was highest in the main working areas of burning and dismantling sites, indicating the influence of recycling activities.Geostatistical analysis also revealed that heavy metal contamination spreads beyond the main working areas to residential, recreational, farming, and commercial areas.Conclusions Our results show that the studied heavy metals are ubiquitous within AEPS and the significantly high concentration of these metals reflect the contamination factor and Cdeg, indicating soil contamination in AEPS with the nine heavy metals studied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".