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Inference of emission rates from multiple sources using Bayesian probability theory.

Eugene Yee1, Thomas K Flesch

  • 1Defence R&D Canada - Suffield, P.O. Box 4000 Stn Main, Medicine Hat, Alberta, CanadaT1A 8K6. eugene.yee@drdc-rddc.gc.ca

Journal of Environmental Monitoring : JEM
|May 7, 2010
PubMed
Summary

This study introduces Bayesian inference to accurately estimate atmospheric emission rates from multiple sources, overcoming limitations of traditional inversion methods and improving data reliability.

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Computational Science

Background:

  • Estimating atmospheric emission rates from multiple sources is crucial for environmental monitoring.
  • Traditional inversion techniques (e.g., regularized least-squares) are highly sensitive to measurement and model errors, often yielding unreliable results.

Purpose of the Study:

  • To reframe the problem of determining atmospheric emission rates from multiple sources as one of Bayesian inference rather than inversion.
  • To develop and validate a Bayesian probability theory-based methodology for estimating emission rates, fully incorporating measurement and model uncertainties.

Main Methods:

  • Utilized Bayesian probability theory to derive the posterior probability distribution for emission rates.
  • Accounted for both measurement errors in concentration data and model errors in the atmospheric dispersion model.
  • Validated the Bayesian inferential methodology using real-world dispersion data from a field experiment with controlled source-sensor configurations.

Main Results:

  • The Bayesian inferential approach demonstrated robust recovery of discrete emission rates.
  • Comparison with singular value decomposition inversion highlighted the advantages of the Bayesian method in handling uncertainties.
  • The methodology proved effective across various source-sensor geometries.

Conclusions:

  • Bayesian inference offers a more reliable and robust framework for estimating atmospheric emission rates from multiple sources compared to traditional inversion methods.
  • The developed Bayesian methodology successfully quantifies uncertainties, leading to more trustworthy emission rate estimations.
  • This approach enhances the accuracy of atmospheric emission monitoring and environmental modeling.