Biochar as a novel technology for treatment of onsite domestic wastewater: A critical review
Bibliographic record
Abstract
Globally, about 2.7 billion people depend on onsite sanitation systems (OSS) (e.g., septic tanks) for their sanitation needs. Although onsite sanitation systems help in providing primary treatment for domestic wastewater, they don’t effectively remove nutrients, pathogens, and other inorganic contaminants. Previous studies have posited that the use of post treatment systems which incorporate biochar leads to improved contaminant removal efficiency. However, the mechanism through which contaminants are removed and factors potentially affecting the removal are still understudied. To fill this knowledge gaps, this review discusses factors which affect efficiency of biochar in removing contaminants found in onsite domestic wastewater, modifications applied to improve the efficiency of biochar in removing contaminants, mechanisms through which different contaminants are removed and constraints in the use of biochar for onsite wastewater treatment. It was noted that the removal of contaminants involves a combination of mechanisms which include adsorption, filtration, biodegradation, ion exchange, pore entrapment. The combination of these mechanisms is brought about by the synergy between the properties of biochar and microbes trapped in the biofilm on the surface of the biochar. Future areas of research such as the modification of biochar, use of biochar in the removal of antibiotic resistant genes (ARGs), application of wet carbonization methods and resistance of biochar to physical disintegration are also discussed. This study provides useful information that can be applied in the use of biochar for the treatment of wastewater and guide future design of treatment systems for optimized treatment performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".