Assessment of Pesticide Residues in Vegetables Commonly Consumed in The Democratic Republic of Congo (DRC): Inadequate Agricultural Practices and Potential Impacts for Public Health
Bibliographic record
Abstract
Pesticides residues in food pose a serious risk to children and adults consuming pesticide-contaminated food. The aim of present study was to assess pesticide residues in vegetables in the Kinshasa and Lubumbashi cities of the Democratic Republic of Congo (DRC). The levels of three pesticide residues were determined by gas chromatography coupled with electron capture detector (ECD) or mass spectrometer-time of flight detector (GC–ECD or GC-MS-TOF) in 96 samples of four vegetables amaranth, spinach, sorrel and sweet potato-leaves purchased from wholesale markets. The Dichlorodiphenyltrichloroethane with its metabolites (3-DDTs), endosulfan and malathion residues were found in 100% of the vegetable samples from Lubumbashi and in 62.5% to 87.5% of all vegetable samples from Kinshasa. Risks were mainly associated with the residues of DDTs pesticides in vegetables. The HQ and HI estimations revealed a serious potential risk to consumers, children particularly. Due to multiple pesticide residues exceeding the MRLs for single residue levels, the consumers are exposed to pesticides, heavily in Lubumbashi. Due to increasing trend in pesticide use, continuous monitoring of pesticide residues in vegetables and other food is recommended in order to develop the base line data on which pesticide regulations could be enhanced in DRC.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".