A Critical Review of Recent Progress in Global Water Reuse during 2019–2021 and Perspectives to Overcome Future Water Crisis
Bibliographic record
Abstract
The exacerbation of the global water crisis due to an increase in global population, industrialization, urbanization, and agricultural activities, along with global climate change and limited water resources, makes water reuse inevitable in all continents. By 2030, global water consumption may grow to ~160% of the currently available volume. This study reviews recently published articles (2019–2021) to explore global case studies of water reuse and discusses future perspectives by country based on a literature survey on water reuse. There are 17 obstacles reported worldwide regarding water reuse (e.g., the properties and low amounts of treated water, regulations, financial challenges, etc.) and 10 advantages of utilizing reused water in various fields (e.g., overcoming the global water crisis, improving the economy, benefiting the industrial sector, etc.). The concept of reusing water has been accepted by countries in almost every continent (e.g., Australia, Europe, Asia, Africa, South America, and North America); the technical findings from different countries are summarized in this study. The water reuse scenario is not restricted to countries with limited water supply and can be applied to those with sufficient water resources (e.g., Canada and Brazil have also implemented water reuse policies). Water reuse can be utilized by human beings via indirect and direct potable recycling, as well as in agriculture, textile, construction, hotel, groundwater recharge, and aquaculture industries. However, a standard guideline for the application of reclaimed water at a global scale is unavailable. Several perspectives have been suggested for the future utilization of reclaimed water worldwide as an effort to secure and ensure the sustainability of existing natural water resources. Lastly, water reuse may be considered a potential alternative for reducing the burden on water resources in the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".