Balancing environmental quality standards and infrastructure upgrade costs for the reduction of microcontaminant loads in rivers
Bibliographic record
Abstract
Investments for upgrading wastewater treatment plants (WWTPs) with tertiary treatment to reduce microcontaminant loads in surface waters at a catchment scale can be daunting. These investments are highly sensitive to the selection of environmental quality standards (EQSs) for the target microcontaminants. Our hypothesis is that there is a balance between EQS selection and investment that needs to be considered in decision-making. We used a customized microcontaminant fate and transport model coupled to an optimization algorithm to validate this hypothesis in the Llobregat river basin and for the pharmaceutical compound diclofenac. The algorithm optimizes the number of WWTPs in this catchment requiring an upgrade to minimize the total amount of diclofenac that exceeds the EQS in every river section and the total cost. We simulated and optimized 40 scenarios representing a combination of 4 potential EQSs (10, 30, 50 and 100 ng L −1 ), 5 levels of uncertainty bounds in the predictions of river concentrations and 2 hydrological scenarios (average flows, flows annually exceeding 30% of the days; and environmental flows, flows annually exceeding 99% of the days). The results showthat there is a nonlinear relationship between the EQS and the required investment. The investment increases by 100% from an EQS of 100 ng L −1 to 10 ng L −1 , significantly increasing (by 60%) from 30 to 10 ng L −1 . Thus, establishing an EQS of 30 ng L −1 would balance environmental protection and costs. The selection of the hydrological conditions also plays a key role in the upgrade analysis because the costs for environmental flows are 50% higher than for average flows. Finally, we highlight that the investment in research would allow the reduction of uncertainties, hence allowing more qualified decisions to be made and a reduction in the WWTP upgrade costs (up to 4 €·household −1 ·year −1 ).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".