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Related Experiment Videos

Determination of distribution coefficients of priority polycyclic aromatic hydrocarbons using solid-phase

Doong1, Chang

  • 1Department of Nuclear Science, National Tsing Hua University, Hsinchu, Taiwan. radoong@mx.nthu.edu.tw

Analytical Chemistry
|August 22, 2000
PubMed
Summary

This study determined the SPME distribution coefficient (K(spme)) for 16 priority aromatic hydrocarbons (PAHs) using PDMS and PA fibers. Results show accurate prediction of partitioning behaviors for environmental risk assessment.

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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Separation Science

Background:

  • Accurate prediction of chemical behavior in the environment is crucial for risk assessment.
  • Distribution coefficients quantify chemical partitioning between environmental phases.
  • Solid-phase microextraction (SPME) offers a convenient method for measuring these coefficients.

Purpose of the Study:

  • To determine the SPME distribution coefficient (K(spme)) for 16 priority aromatic hydrocarbons (PAHs).
  • To evaluate the linearity of log K(spme) using partition coefficients and LeBas molar volumes.
  • To validate the K(spme) measurements using varying sample volumes and assess prediction accuracy.

Main Methods:

  • SPME using 100-microm poly(dimethylsiloxane) (PDMS) and 85-microm polyacrylate (PA) fibers.

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  • Determination of K(spme) for 16 priority PAHs.
  • Analysis of linearity with partition coefficients and LeBas molar volumes; validation with sample volume variations.

    • Main Results:

    • Log K(spme) values ranged from 3.02 to 5.69 (PDMS) and 3.37 to 5.62 (PA).
    • Higher K(spme) for low-ring PAHs observed with the PA fiber.
    • Good linear relationships (R² > 0.96) found between log K(ow) and log K(spme) for specific PAH ranges.

    Conclusions:

    • The determined log K(spme) values provide accurate estimations for analytes with log K(ow) < 6.
    • SPME is effective for predicting partitioning behaviors between fibers and water.
    • This method enhances the assessment of chemical pathways in environmental studies.