A global assessment of the temporal and spatial variability of national dilution factors
Bibliographic record
Abstract
Of the many factors which influence the exposure of the freshwater aquatic environment to contaminating chemicals none has a more dramatic effect than dilution. Both where and when a chemical enters surface water will make an enormous difference to its impact on wildlife. This is of particular importance for "down-the-drain” chemicals as these substances are discharged to freshwaters via sewer systems after consumer use. However, too often, this dilution capacity is fixed to a “generic” value. Although the spatial variability of dilution factors is often acknowledged, temporal variability is often unaccounted for which may potentially lead to underestimating the environmental risk. To address the magnitude of these dilution differences across the world, estimates of dilution factors were developed globally at a 0.5° resolution using gridded data. Thus, the focus here is on the numbers and location of the human population and the river water available to dilute their waste. The river flows estimates are calculated at both annual and monthly resolution based on readily available annual and monthly runoff estimates. The domestic waste water effluent is derived from combining gridded population and national per capita domestic water use estimates. For each grid cell both annual and monthly dilution factors were generated. This approach allowed the quantification of temporal and spatial variability of dilution factors not only at a catchment level but also at a national level, by means of statistical measures such as median and percentiles. This method revealed the dramatic differences in available dilution of chemicals both within and between countries, for example Canada has on average 4-orders of magnitude more dilution available than Tunisia, and Finland 3-orders of magnitude more than Spain. Over the course of a year, national dilution could vary between 10 and a 1000-fold depending on the country. The work presented here is a significant step forward in terms of understanding the \nimpact of river flow temporal variability on dilution factors at a national and global scale. The proposed methodology has great potential for scientists and decision \nmakers across the globe, as it provides the means to improve screening-level chemical risk assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 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 teacher head, 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".