Using water and wastewater decentralization to enhance the resilience and sustainability of cities
Bibliographic record
Abstract
The imperative to make energy and resource consumption more sustainable is prompting a critical reconsideration of all human endeavours. Within urban water management, the drive to enhance sustainability is grounded in the recognition that water services consume a substantial amount of energy and that wastewater contains valuable resources, including water, heat, organic matter and essential plant nutrients. To make urban water systems more sustainable, a paradigm shift is needed. Among the proposed strategies, source separation coupled with anaerobic co-digestion appears to be an effective means of recovering energy, water and nutrients. Here, as existing centralized infrastructure that serves tens to hundreds of thousands of people is difficult to alter and the technologies needed to realize this strategy are difficult to implement in single-family homes, we consider the scale of a city block. Using a quantitative model of unit processes that simulate energy, water and nutrient flows, we consider the technical and economic feasibility of a representative decentralized system, as well as its environmental impacts. To realize potential synergies associated with on-site use of the recovered resources, we complement the decentralized water system with vertical farming, photovoltaic energy generation and rainwater harvesting. Our analysis suggests that decentralized water systems can serve as a cornerstone of efforts to enhance resource efficiency and improve the resilience of cities. Decentralized source separation offers a cost-effective, resilient alternative to conventional methods, enhancing resource recovery and reducing environmental impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".