River pollution by priority chemical substances under the Water Framework Directive: A provisional pan-European assessment
Bibliographic record
Abstract
In this paper, we build a preliminary inventory of dissolved phase water emissions of 36 of the 45 chemical priority substances under the European Union's Water Framework Directive. For point sources, we consider the European Pollutant Release and Transfer Register (E-PRTR) containing reported emissions from major industrial facilities. We consider all other sources as diffuse, and we estimate European average chemical emission factors from available measurements of dissolved phase concentrations, assuming simple emission patterns such as population and agricultural land. The emission inventory enables modelling concentrations, which have been compared with independent measurements. Due to the way they are estimated, they cannot withstand a point-by-point comparison. However, predicted concentrations exhibit a frequency distribution and order of magnitude compatible with observations, and match a fair proportion of independently reported exceedances of environmental quality standards for many of the substances studied. While apparently a preliminary picture based on crude simplifications, our representation suggests that simple drivers such as population and agriculture are useful to describe chemical pollution at European scale. From our preliminary inventory, E-PRTR industrial point emissions seem to account for a relatively small share of total emissions. Consequently, apart from specific measures such as upgrades to urban wastewater treatment plants in certain high impact areas, the management of priority substances may require a more strategic approach to emission control, addressing chemical use across sectors and the management of out-phased, legacy chemicals. At the same time, we advocate that improving emission inventories requires monitoring data reflecting the variability of emission patterns across Europe, as presently available monitoring data do not enable a catchment-specific estimation of emissions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".