Worldwide estimation of river concentrations of any chemical originating from sewage-treatment plants using dilution factors
Bibliographic record
Abstract
Abstract Dilution factors are a critical component in estimating concentrations of so-called “down-the-drain” chemicals (e.g., pharmaceuticals) in rivers. The present study estimated the temporal and spatial variability of dilution factors around the world using geographically referenced data sets at 0.5° × 0.5° resolution. Domestic wastewater effluents were derived from national per capita domestic water use estimates and gridded population. Monthly and annual river flows were estimated by accumulating runoff estimates using topographically derived flow directions. National statistics, including the median and interquartile range, were generated to quantify dilution factors. Spatial variability of the dilution factor was found to be considerable; for example, there are 4 orders of magnitude in annual median dilution factor between Canada and Morocco. Temporal variability within a country can also be substantial; in India, there are up to 9 orders of magnitude between median monthly dilution factors. These national statistics provide a global picture of the temporal and spatial variability of dilution factors and, hence, of the potential exposure to down-the-drain chemicals. The present methodology has potential for a wide international community (including decision makers and pharmaceutical companies) to assess relative exposure to down-the-drain chemicals released by human pollution in rivers and, thus, target areas of potentially high risk. Environ Toxicol Chem 2014;33:447–452. © 2013 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial, and no modifications or adaptations are made.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".