Water quality indicators of the Nima Creek, and potential for sustainable urban agriculture in Ghana
Bibliographic record
Abstract
Urban and peri-urban agriculture, a widely accepted practice of food cultivation in urban centers contributes to positive environment. It has the benefits of job creation, increased access to healthy and affordable food, and important means of improving community health. Use of wastewater or disturbed surface water for urban agriculture is a common practice in the developing world due to lack of adequate infrastructure and widespread poverty. This study assessed the urban water quality parameters of the Nima Creek, a major water resource for peri-urban and urban farming in Southeastern Accra. Water sampled from six locations along the NE – SW stretch of the creek were evaluated for physicochemical parameters including pH, conductivity, Total Suspended Solids (TSS), Total Dissolved Solids (TDS), cations, zinc, iron, oil and grease, Biological Oxygen demand (BOD), Chemical Oxygen demand (COD). Results show high enrichment of nutrients, ammonia (NH3), nitrate (NO3), and phosphate (PO4) as well as elevated levels of BOD, COD, and grease at sites receiving solid and liquid wastes. The physicochemical parameters such as conductivity, TSS, and TDS exhibited periods of elevated values that were congruent with seasonal rainfall patterns within the catchment area. Sodium concentration ranged from 32 to 297 mg/L. Nitrate levels generally ranged from 1.5 to 7.13 mg/L. The cation concentrations showed broad temporal and spatial variation characteristic of disturbed surface freshwater. Principal component analysis of the data discriminated four distinct components accounting for 63.6% of the total variance. The component plots constrained three major classes explaining the physical quality of the water and two other groups defining nutrient and alkalinity levels in the water.
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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 | 0.000 |
| 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".