Management actions to mitigate the occurrence of pharmaceuticals in river networks in a global change context
Bibliographic record
Abstract
Human consumption of pharmaceuticals leads to high concentrations of pharmaceuticals in wastewater, which is usually not or insufficiently collected and treated before release into freshwater ecosystems. There, pharmaceuticals may pose a threat to aquatic biota. Unfortunately, occurrence data of pharmaceuticals in freshwaters at the global scale is scarce and unevenly distributed, thus preventing the identification of hotspots, the prediction of the impact of Global Change (particularly streamflow and population changes) on their occurrence, and the design of appropriate mitigation actions. Here, we use diclofenac (DCL) as a typical pharmaceutical contaminant, and a global model of DCL chemical fate based on wastewater sanitation, population density and hydrology to estimate current concentrations in the river network, the impact of future changes in runoff and population, and potential mitigation actions in line with the Sustainable Development Goals. Our model is calibrated against measurements available in the literature. We estimate that 2.74 ± 0.63% of global river network length has DCL concentrations exceeding the proposed EU Watch list limit (100 ng L-1). Furthermore, many rivers downstream from highly populated areas show values beyond 1000 ng L-1, particularly those associated to megacities in Asia lacking sufficient wastewater treatment. This situation will worsen with Global Change, as streamflow changes and human population growth will increase the proportion of the river network above 100 ng L-1 up to 3.10 ± 0.72%. Given this background, we assessed feasible source and end-of-pipe mitigation actions, including per capita consumption reduction through eco-directed sustainable prescribing (EDSP), the implementation of the United Nations Sustainable Development Goal (SDG) 6 of halving the proportion of population without access to safely managed sanitation services, and improvement of wastewater treatment plants up to the Swiss standards. Among the considered end-of-pipe mitigation actions, implementation of SDG 6 was the most effective, reducing the proportion of the river network above 100 ng L-1 down to 2.95 ± 0.68%. However, EDSP brought this proportion down to 2.80 ± 0.64%. Overall, our findings indicate that the sole implementation of technological improvements will be insufficient to prevent the expected increase in pharmaceuticals concentration, and that technological solution need to be combined with source mitigation actions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".