Bayesian inference and wind field statistical modeling applied to multiple source estimation.

Area of Science:

• Atmospheric science
• Environmental modeling
• Computational physics

Background:

• Identifying atmospheric pollutant sources is crucial for environmental protection and public health.
• Existing methods often struggle with multiple sources and data uncertainties.

Purpose of the Study:

• To develop and validate a novel methodology for identifying multiple atmospheric pollutant sources.
• To enhance the accuracy and robustness of source apportionment techniques.

Main Methods:

• A data-driven atmospheric dispersion model integrated with Bayesian inference and uncertainty quantification.
• Utilized a multivariate dynamic linear model (DLM) for realistic wind field estimation.
• Employed an adjoint transient advection-diffusion partial differential equation with finite element formulation.
• Applied Metropolis in Gibbs Monte Carlo Markov chain (MCMC) for parameter estimation, initialized with particle swarm optimization.

Main Results:

• The methodology successfully identifies multiple pollutant sources by combining dispersion modeling and Bayesian inference.
• The approach effectively handles uncertainty in concentration data and wind field estimations.
• The proposed method demonstrates superior performance compared to existing inversion techniques.

Conclusions:

• The integrated methodology offers a powerful tool for accurate atmospheric pollutant source identification.
• This approach advances the field of environmental source apportionment through improved modeling and inference.
• The findings have significant implications for air quality management and pollution control strategies.