Protecting rivers by integrating supply-wastewater infrastructure planning and coordinating operational decisions
Bibliographic record
Abstract
Abstract Placing water quality in rivers at the centre of water infrastructure planning and management is an important objective. In response there has been a range of ‘whole system’ analyses. Few studies, however, consider both abstraction (water removed from rivers) and discharge (water returned) to inform the future planning of water systems. In this work we present a systems approach to analysing future water planning options where system development prioritises the water quality of the receiving river. We provide a theoretical demonstration by integrating water supply and wastewater infrastructure, and downstream river water quality, on an open-source, stylised, systems model for London, UK, at a citywide scale. We show that models which consider either supply or wastewater separately will underestimate impacts of effluent on the water quality, in some cases by amounts that would require £1 billion worth of infrastructure equivalent to mitigate. We highlight the utility of the systems approach in evaluating integrated water infrastructure planning using both socio-economic and environmental indicators. Through this approach we find unintended impacts from planning options on downstream river quality; including benefits from water demand management and rainwater harvesting, and costs from wastewater reuse. Finally, we present a novel management planning option between supply and wastewater, which we refer to as Abstraction-Effluent Dilution (AED), that is, to reduce river abstractions during high precipitation events to dilute untreated sewer spills. The AED option is found to provide up to £200 million worth of equivalent infrastructure in river quality improvements and has minimal impact on the reliability of water supply while requiring only a change in operational decision making. This proof-of-concept study highlights that seeing our water systems differently with this holistic approach could fundamentally change the way we think about future water infrastructure planning so that it works both for people and the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".