Global relevant concentration of emerging contaminants: species sensitivity distributions and ecological risks to aquatic organisms – a comprehensive data synthesis
Bibliographic record
Abstract
Emerging contaminants (ECs) are inadequately monitored or unregulated chemicals that pose growing threats to aquatic ecosystems due to their biological effects on organisms. This study provides a global overview of EC concentrations, species sensitivity distributions (SSDs), and associated ecological risks. We analysed global EC concentrations using comprehensive secondary datasets and applied multivariate statistical techniques such as One-way PERMANOVA and Non-metric Multidimensional Scaling (nMDS) to assess contaminant patterns worldwide. SSDs and Risk Quotients (RQs) were used to evaluate species-level sensitivities and ecological risk. Findings reveal considerable global variation in EC concentrations. Morocco reported the highest levels of estrogens (E1, E2), Australia in Estriol (E3), and Bangladesh in pesticides (DDT, Diazinon, Heptachlor). Canada showed peak Bisphenol A (BPA) levels, while China and Japan led in PFOS and PFOA, respectively. The Netherlands and Argentina showed elevated levels of surfactants and pharmaceuticals. PERMANOVA results (F = 1.677, p = 0.10) indicate no significant overall group differences; however, pairwise tests revealed notable distinctions, especially among estrogens, microplastics, and pesticides. nMDS analysis showed clearer clustering by region than chemical class. SSDs highlighted varying sensitivities of aquatic species based on chemical-specific NOECs (µg/L). Elevated RQs were observed in Morocco, China, Bangladesh, and Turkey, with Spain, Japan, and Singapore showing high RQs for surfactants and microplastics. These findings underscore the urgency of incorporating SSD and RQ data into regulatory frameworks to set protective concentration thresholds. Ongoing research is vital to address chronic and sub-lethal effects, supporting evidence-based policymaking and enhanced treatment and risk management strategies.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".