Assessment of seasonal variations of pesticides residues in water and sediments from Hadejia-Nguru wetlands, Northern Nigeria
Bibliographic record
Abstract
This study assessed seasonal variations in pesticide residues in water and sediment samples from the Hadejia-Nguru wetlands, Northern Nigeria; a critical sensitive ecosystem facing increasing anthropogenic pressures. Samples were collected monthly between March 2021 and October 2022 in three sampling stations, covering both dry and wet seasons. Pesticide extraction and cleanup were performed using the QuEChERS method, followed by analysis via GCMS. Results revealed significant increase in pesticide concentrations during the dry season (p < 0.001). Dichlorvos (peak: 231.70 µg/L), exclusively detected in the dry season, significantly exceeded WHO and USEPA toxicological thresholds. Permethrin (peak: 80.00 µg/L in water; 197.95 mg/kg in dry season sediment) exhibited year-round persistence, surpassing drinking water and sediment quality guidelines (e.g., Canadian Sediment Quality Guideline by > 1000 ×). Other pesticides, including Allethrin, Endrin, Endosulfan (banned), and Atrazine, were also predominantly found in the dry season, with Atrazine concentrations concerning for amphibian health (1.20 μg/L). Sediment samples recorded higher pesticide concentrations during the dry season, attributed to increased agricultural activity and reduced water volume, with persistent pesticides recorded across seasons, suggesting long-term accumulation and seasonal transport dynamics. By integrating these results into national action plans—such as Nigeria’s National Agricultural Resilience Framework—policy makers can prioritize wetland conservation, safeguard vulnerable communities reliant on these resources, and align with global sustainability goals (e.g., SDGs 6 and 15). The findings underscore the impact of agricultural practices on wetlands contamination and the necessity for effective monitoring and management. This study highlights the urgent need for sustainable agricultural practices and stringent regulations to mitigate pesticide contamination and safeguard the ecological integrity of the Hadejia-Nguru wetlands as well as protect vulnerable populations depending on these resources.
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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 | 0.000 |
| 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) | 0.001 | 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 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".