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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Predicting the total PAHs concentrations in sediments from selected congeners using a multiple linear relationship.

Weiwei Wang1,2,3, Huaping Xu4, Xiaolei Qu5

  • 1Department of Environmental Science, Zhejiang University, Hangzhou, 310058, China.

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Four specific polycyclic aromatic hydrocarbons (PAHs) congeners accurately predict total PAH concentrations in sediments and emissions. A new model uses these congeners and their octanol-water partition coefficients for reliable sediment PAH predictions.

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Area of Science:

  • Environmental Chemistry
  • Environmental Science
  • Organic Geochemistry

Background:

  • Polycyclic Aromatic Hydrocarbons (PAHs) are persistent organic pollutants with complex environmental behavior.
  • Accurate prediction of PAH concentrations in sediments and emissions is crucial for environmental risk assessment.
  • Existing methods for predicting total PAHs often lack precision and require extensive data.

Purpose of the Study:

  • To identify characteristic polycyclic aromatic hydrocarbons (PAHs) congeners for predicting total PAH concentrations.
  • To establish novel predictive models for total PAH concentrations (C∑PAHs) in sediments and emission factors.
  • To develop a model for predicting sediment C∑PAHs using emission data and congener properties.

Main Methods:

  • Analysis of published data spanning 30 years to establish multiple linear relationships.
  • Development of a multiple regression model linking total PAHs concentrations in sediments to four specific congeners.
  • Development of a multiple linear model for PAH emission factors based on the same four congeners.
  • Correlation analysis of the ratio of multicomponent coefficients with the octanol-water partition coefficient (logKow).

Main Results:

  • Naphthalene (Nap), acenaphthylene (Acy), phenanthrene (Phe), and benz(a)anthracene (BaA) were identified as characteristic PAH congeners.
  • A robust multiple relationship was established for predicting sediment C∑PAHs (R2 = 0.95) and emission factors (R2 = 0.99).
  • The ratio of coefficients positively correlated with logKow (R2 = 0.88), enabling a predictive model for sediment PAHs.

Conclusions:

  • The four identified congeners are reliable indicators for predicting both emission and sediment concentrations of PAHs.
  • A novel model integrating emission data and logKow of characteristic congeners accurately predicts sediment C∑PAHs.
  • The developed model demonstrates potential for accurate environmental monitoring and risk assessment of PAH contamination.