Spatial assessment of potential ecological risk of heavy metals in soils from informal e-waste recycling in Ghana
Bibliographic record
Abstract
The rapidly increasing annual global volume of e-waste, and of its inherently valuable fraction, has created an opportunity for individuals in Agbogbloshie, Accra, Ghana to make a living by using unconventional, uncontrolled, primitive and crude procedures to recycle and recover valuable metals from this waste. The current form of recycling procedures releases hazardous fractions, such as heavy metals, into the soil, posing a significant risk to the environment and human health. Using a handheld global positioning system, 132 soil samples based on 100 m grid intervals were collected and analysed for cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), lead (Pb) and zinc (Zn). Using geostatistical techniques and sediment quality guidelines, this research seeks to assess the potential risk these heavy metals posed to the proposed Korle Ecological Restoration Zone by informal e-waste processing site in Agbogbloshie, Accra, Ghana. Analysis of heavy metals revealed concentrations exceeded the regulatory limits of both Dutch and Canadian soil quality and guidance values, and that the ecological risk posed by the heavy metals extended beyond the main burning and dismantling sites of the informal recyclers to the school, residential, recreational, clinic, farm and worship areas. The heavy metals Cr, Cu, Pb and Zn had normal distribution, spatial variability, and spatial autocorrelation. Further analysis revealed the decreasing order of toxicity, Hg>Cd>Pb> Cu>Zn>Cr, of contributing significantly to the potential ecological risk in the study area.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".