<p>Air pollution has become a major issue in the modern megalopolis because of industrial emissions and increasing urbanization. While numerous studies adopt deterministic modeling approaches for pollutant diffusion, few have addressed the joint estimation of pollutant source parameters under uncertainty. For cases where the emission rate is unknown, we model dispersion under steady-state Gaussian plume conditions, assuming short-term scenarios with quasi-stationary meteorology. We formulate a probabilistic modeling framework for pollutant emission rate estimation by treating the emission rate as a latent variable and developing the framework using the Variational Bayesian Expectation Maximization (VBEM) algorithm. Within this framework, prior knowledge is incorporated through probabilistic priors, and uncertainty is explicitly quantified via posterior distributions. To resolve the analytical intractability introduced by nonlinear terms in the physical diffusion model, a second-order Taylor series expansion is employed, yielding a closed-form approximation. The proposed framework is quantitatively evaluated against classical parameterization models, achieving a mean absolute percentage error (MAPE) of 4.2% on the Fukushima dataset and outperforming baseline approaches in both accuracy and convergence.</p>