Recovery of plant nutrients from human excreta and domestic wastewater for reuse in agriculture: a systematic map and evidence platform
Bibliographic record
Abstract
BACKGROUND: Achieving a more circular and efficient use of nutrients found in human excreta and domestic (municipal) wastewater is an integral part of mitigating aquatic nutrient pollution and nutrient insecurity. A synthesis of research trends readily available to various stakeholders is much needed. This systematic map collates and summarizes scientific research on technologies that facilitate the recovery and reuse of plant nutrients and organic matter found in human excreta and domestic wastewater. We present evidence in a way that can be navigated easily. We hope this work will help with the uptake and upscaling of new and innovative circular solutions for the recovery and reuse of nutrients. METHODS: The systematic map consists of an extension of two previous related syntheses. Searches were performed in Scopus and Web of Science in English. Records were screened on title and abstract, including consistency checking. Coding and meta-data extraction included bibliographic information, as well as recovery pathways. The evidence from the systematic map is embedded in an online evidence platform that, in an interactive manner, allows stakeholders to visualize and explore the systematic map findings, including knowledge gaps and clusters. RESULTS: The evidence base includes a total of 10 950 articles describing 11 489 recovery pathways. Most of the evidence base is about recovery technologies (41.9%) and the reuse of recovered products in agriculture (53.4%). A small proportion of the evidence base focuses on the characteristics of recovered products (4.0%) and user acceptance and perceptions of nutrient recovery and reuse (0.7%). CONCLUSIONS: Most studies we mapped focused on nutrient recovery from 'conventional' systems, that is, from centralized sewer and wastewater treatment systems that produce biosolids and a treated effluent. While we also found substantial research on nutrient recovery from source-separated urine, and to some extent also on nutrient recovery from source-separated excreta (notably blackwater), the body of research on nutrient recovery from source-separated feces was relatively small. Another knowledge gap is the relative lack of research on the recovery of potassium. More research is also needed on user acceptance of different recovery technologies and recovered products.
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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 | 0.054 | 0.195 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.098 | 0.057 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".