Eco-friendly and sustainability assessment of technologies for nutrient recovery from human urine—a review
Bibliographic record
Abstract
Nitrogen (N), phosphorus (P), and potassium (K) represent the primary components of commercial NPK fertilizer and are primarily derived from finite resources through complex and expensive processes. To ensure global food security, the development of sustainable and eco-friendly procedures for fertilizer production has gained attention. Humans generally excrete urine containing 11 g of N/L, 0.3 g of P/L of P and 1.5 g of K/L, which benefit plant growth. The recovery of these essential plant nutrients from human urine has become the focal point of increasing research endeavors. Despite the potential advantages of nutrient recovery from urine, this process is complicated, and the economic implications are substantial. Furthermore, human urine may harbor undesirable contaminants, such as pathogens, pharmaceutical residues, hormones, and elevated salt levels, which could be disseminated into the environment through agriculture. This study appraised various emerging technologies for nutrient recovery from human urine, considering their challenges, environmental impact, economic viability, and the overall sustainability of the processes. This review elucidated that most nutrient recovery technologies demonstrated elevated efficiency in nutrient recovery. Nevertheless, a recurrent oversight involves neglecting the potential transfer of contaminants and pathogens into environmental matrices. The complexity of these processes and their economic feasibility vary, with some proving intricate and economically unviable. Given that no singular technology fully mitigates these challenges, integrating two or more technologies appears imperative to address drawbacks and enhance overall system 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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".