A critical review of synthetic chemicals in surface waters of the US, the EU and China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is a wide concern that emerging organic pollutants (EOPs) in surface water could adversely affect human health and wildlife. However, the geographic distribution, exposure pattern and ecological risk of emerging organic pollutants are poorly understood at a global scale. This paper provides a comprehensive survey on the exposure level of EOPs in China, the US and the EU based on the published literature. The hazard level of three categories of EOPs, namely pharmaceuticals and personal care products (PPCPs), pesticides and industrial chemicals was further evaluated by adopting a novel Aquatic HazPi index that jointly accounts for the persistence, bioaccumulation, toxicity and bioactivity. Furthermore, a correlation analysis of land use with the surface water exposure status regarding the synthetic chemicals was conducted. According to the published data reported between 2010 and 2016, the concentration of pesticides in the US was higher than in the EU and China. The concentration of PPCPs in the EU was generally lower than in both the US and China, while the concentration of industrial chemicals in China was higher than in the EU and the US. Among the chemicals whose median concentration in surface water was >10 ng/L, the antiretroviral Efavirenz, the pesticide Fipronil, and octocrylene, an industrial chemical and cosmetic ingredient, were found with the highest aquatic HazPi value. Lastly, the spatial distribution and concentration of hazardous EOPs was shown to depend on local landscape and land usages. Our study provides the first broad overview on the geographic distribution, exposure pattern of hazardous EOPs in the three major economic entities: China, the US and the EU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it