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Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Related Experiment Video

Updated: May 20, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model.

Li Zhao1, Jian Chen2, Jiaqi Wen3

  • 1Guangdong Institute for Drug Control, Guangzhou 510006, China; School of Environment, South China Normal University, Guangzhou 510006, China.

Environment International
|May 6, 2025
PubMed
Summary

This study maps per- and polyfluoroalkyl substances (PFAS) in European surface waters, revealing widespread contamination and ecological risks. A key finding is that proximity to PFAS sources significantly impacts pollution levels.

Keywords:
Affected populationEcosystem safetyInterpretable machine learningPFAS contaminationTipping point

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Area of Science:

  • Environmental Chemistry
  • Ecotoxicology
  • Data Science

Background:

  • Per- and polyfluoroalkyl substances (PFAS), or "forever chemicals", are widespread in surface waters, posing risks to human health and ecosystems.
  • Current monitoring struggles to capture the full extent of PFAS contamination in European surface waters.
  • Understanding spatial PFAS distribution is crucial for effective risk assessment and management.

Purpose of the Study:

  • To develop machine-learning models for mapping PFAS concentrations and ecological risks in European surface waters.
  • To identify areas with high PFAS levels and ecological concern across 44 countries.
  • To determine key factors influencing PFAS pollution and establish risk-related distance thresholds.

Main Methods:

  • Development and application of two machine-learning models.
  • Generation of high-resolution (2-km) spatial maps of PFAS levels and risks.
  • Analysis of factors influencing PFAS concentrations, including distance to point sources.

Main Results:

  • Estimated nearly 8,000 individuals exposed to surface waters exceeding the 100 ng/L European Drinking Water guideline.
  • Identified high-risk areas primarily in Germany, the Netherlands, Portugal, Spain, and Finland.
  • Quantified distance to PFAS point sources as a critical factor (14%-19%) and determined an elevated hazard threshold within 4.1-4.9 km.

Conclusions:

  • Machine learning provides unprecedented spatial insights into European surface water PFAS contamination.
  • Proximity to industrial sources is a major driver of PFAS pollution.
  • Findings offer a critical distance threshold to guide regulatory actions for PFAS mitigation.