The contribution of tipping fees to the operation, maintenance, and management of fecal sludge treatment plants: The case of Ghana
Bibliographic record
Abstract
Globally, collection of tipping fees is being promoted as a solution to sustain the operation of fecal sludge treatment plants (FSTPs). Currently, there are six large-scale FSTPs in Ghana, of which five were in operation in June 2017. In Kumasi, Sekondi-Takoradi and Tamale, fecal sludge (FS) is co-treated with landfill leachate using waste stabilization ponds (WSPs). In Tema and Accra, FS is treated using WSPs and a mechanical dewatering system coupled with an upflow anaerobic sludge blanket (UASB). The focus of this study is FSTPs and to assess how, and if, the tipping fees set by the municipalities could enable cost recovery to sustain their long-term operation. Using a questionnaire survey to interview plant managers from the public and private sectors, and directors of waste management departments, we found that the overall average operation, maintenance and management (OM&M) costs per 1000 m3 of treated waste (FS or FS + leachate) in 2017 were USD89 in Kumasi, USD150 in Tamale, USD179 in Tema, USD244 in Sekondi-Takoradi and USD1,743 in Accra. There were important disparities between FSTPs due to their scale, age, and level of treatment and monitoring. Currently, most FSTPs charge tipping fees that range between USD310 and USD530/1000 m3 of FS, averaging USD421 ± 98/1000 m3 of FS discharged at FSTPs. Our study also showed that the OM&M costs of large-scale intensive FSTPs cannot be sustained by relying solely on tipping fees. However, there could be potential to cover the routine expenditures associated with operating smaller FSTPs that relying on WSP technologies.
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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) | 0.000 | 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".