Analysis of heavy metals and pathogen levels in vegetables cultivated using selected water bodies in urban areas of the Greater Accra Metropolis of Ghana
Bibliographic record
Abstract
Open-surfaced water sources have been used to irrigate vegetable farms in cities. Open-surface water often contains unmonitored concentrations of health-threatening contaminants that pose health risks, especially when used to produce vegetables for human consumption. However, information on levels of heavy metals and faecal coliform bacteria in such vegetables in selected sites, especially in Greater Accra Metropolitan Area (GAMA) of Ghana is rare. This study examines the levels of heavy metals and faecal coliform in two vegetables-lettuce and bell pepper - that were cultivated using open-surface wastewater from drains and constructed reservoirs at different locations of the GAMA. Using concurrent mixed methods, questionnaires were administered to 67 vegetable farmers, followed by the collection of vegetable samples from three urban farm sites, Haatso and Dzorwulu and Weija irrigation scheme site (WISS) for laboratory analysis. The concentrations of Lead (Pb), Mercury (Hg) and Cadmium (Cd) were determined using atomic absorption spectroscopy after microwave digestion of the vegetables while total faecal coliform was quantified using MacConkey-Endo broth method. The results from all three sites showed that the concentrations of Cd (=0.001 μg/mg) and Pb (=0.005 μg/mg) in lettuce were within the World Health Organization's (WHO) permissible levels. However, the Hg (≥0.309 μg/mg) and faecal coliform (>5 count/100 ml) in the vegetables from all three sites exceeded the WHO permissible limits. Therefore, consumers of vegetables from such urban farms are exposed to health risks associated with Hg and faecal coliforms. There is the need to intensify education on the health risks of consuming vegetables produced from open-surface water sources from the observed sites. The enforcement of existing phytosanitary standards to enhance food safety and the quality of urban vegetables is also recommended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".