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Using model-based screening to help discover unknown environmental contaminants.

Michael S McLachlan1, Amelie Kierkegaard, Michael Radke

  • 1Department of Applied Environmental Science (ITM), Stockholm University , Stockholm SE-106 91, Sweden.

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|May 30, 2014
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Summary
This summary is machine-generated.

A new model effectively identifies environmental contaminants by predicting chemical concentrations. This approach discovered three previously unknown organosilicon contaminants in air, sewage sludge, and sediment samples.

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Area of Science:

  • Environmental Chemistry
  • Chemical Risk Assessment
  • Analytical Chemistry

Background:

  • Thousands of chemicals lack environmental analysis.
  • Prioritization methods are needed to identify environmental contaminants.
  • Organosilicon chemicals are widely used but under-analyzed.

Purpose of the Study:

  • To develop and apply a model for prioritizing chemicals for environmental analysis.
  • To identify novel organosilicon environmental contaminants.
  • To validate model predictions through target analysis.

Main Methods:

  • Utilized a high-throughput model for chemical emissions, fate, and bioaccumulation.
  • Screened 215 organosilicon chemicals based on production statistics.
  • Developed trace analytical methods for target compound detection in environmental matrices.

Main Results:

  • Model successfully prioritized known and identified three novel organosilicon contaminants.
  • Phenyl-tris(trimethylsiloxy)silane detected in air and sediment.
  • Two novel cyclosiloxanes found in sediment samples.
  • All three prioritized compounds confirmed as environmental contaminants.

Conclusions:

  • Predictive modeling is a valuable tool for prioritizing chemicals for environmental assessment.
  • The study identified previously unrecognized organosilicon environmental contaminants.
  • This approach enhances the discovery of emerging environmental pollutants.