Water reuse through managed aquifer recharge (MAR): assessment of regulations/guidelines and case studies
Bibliographic record
Abstract
Managed aquifer recharge (MAR) with reclaimed water is an important water reuse application. As an intentional way of recharging water into aquifers, MAR can be used to address water shortages and contribute to sustainable water resources management practices. The establishment of a MAR system depends on the source of recharge water, the selection of a recharge method and site, the type of water treatment system, and the ultimate purpose of recovered water, and these components are closely related and integrated. However, at present, detailed regulations or guidelines that specifically guide MAR with reclaimed water are unavailable in most countries. The complexity of MAR systems and the lack of a sophisticated regulatory framework increase the difficulties of MAR implementation. This review provides an introduction to MAR with reclaimed water and a comparison of current worldwide water reuse regulations or guidelines, including a proposed approach for MAR implementation. An analysis of selected MAR with reclaimed water case studies was also done within the context of this proposed approach. This paper recommends the development of specific regulatory or design criteria, including a complete quantitative risk assessment framework for the evaluation and operation of MAR systems.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".