Strategic routes for wastewater treatment plant upgrades to reduce micropollutants in European surface water bodies
Bibliographic record
Abstract
The European Union is determined to address the problem of contaminants of emerging concern (CEC) occurrence in natural water bodies. However, if a broad range of possible advanced treatment technological solutions exists as well as a variety of possible future conditions, then emission and consequently strategies for wastewater treatment plan (WWTP) upgrade and operation are not easy to anticipate. Here, a strategic foresight exercise was carried out to support decision-making matching anticipated future conditions toward the goal of finding suitable treatment upgrades to control CECs release in the environment. As a result, a roadmap and strategic routes were developed based on the interpretation of drivers for the EU region in the context of Shared Socioeconomic Pathways (SSPs) global change narratives. Screening and ranking of WWTP tertiary and advanced treatment technology upgrades were performed. Analysis of the resulting envisioned strategic routes allowed identifying and confronting core challenges such as the CEC removal agenda, technologies’ performance, requirements for CEC removal and water scarcity. The results also underline opportunities to shape WWTP technological upgrade planning such as: enhancing circular economy solutions in WWTP, water reclamation, and accelerated increase of the technology readiness level of hybrid and nature-based solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".