Nature-based solutions coupled with advanced technologies: An opportunity for decentralized water reuse in cities
Bibliographic record
Abstract
Decentralized water reuse in cities is a prominent alternative to mainstream top-down models for urban water treatment, which are based on centralized, linear dynamics of resource management. In this sense, Nature-based Solutions (“green” technologies) coupled with advanced technologies (“grey” technologies) constitute a promising approach for fomenting onsite water treatment and reuse in cities, while also providing multiple co-benefits. This article puts forward a conceptual advancement by providing a better understanding of coupled “green-grey”/“grey-green” technologies (CGGT). To do this, we critically discuss the main reasons for pairing these technologies instead of using them separately, as well as their treatment performance and constraints regarding data reporting issues. Moreover, the article discloses the most common treatment configurations, water quality parameters being evaluated, potential reuse schemes, costs, and energy requirements. A systematic selection and analysis of scientific articles was carried out to this end. Of 395 pre-selected articles, only 17 addressed coupled (green-grey/grey-green) technologies in the treatment of urban wastewaters for further reuse or safe discharge onsite. Despite the relatively low number of articles, 80% were published in the past five years, showing the increased interest in this novel topic. The selected articles were analysed and here we present the resulting comprehensive Excel database (343 datasets) containing detailed information about the design, operation, and performance of such systems. Green-grey technologies were found to be predominant, the configuration constructed wetlands followed by advanced oxidation process and electrochemical process being the most studied. Grey technologies are normally applied at a second stage to remove pathogens in compliance with reuse standards (normally when green technologies alone cannot deliver the standards). Meanwhile, green technologies are commonly used at a second stage to break down slowly biodegradable substances that have not been completely removed by grey technologies (normally as a polishing step following grey technology). The design parameters for combining these technologies have not yet been fully optimized, since they were mainly designed as sole technologies and forcibly put together as a coupled treatment. Hence, further studies should focus on variables and parameters influencing the functioning of coupled technologies as a whole. Finally, due to the novelty and relevance of the topic, transparency and consistency in data reporting is essential to support the optimization and competitiveness of coupled green-grey/grey-green technologies against existing decentralized/centralized approaches.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".