  The problem of locating an odor source in turbulent environments is central
to key applications such as environmental monitoring and disaster response. We
address this challenge by designing an algorithm based on Bayesian inference,
which uses odor measurements from an ensemble of static sensors to estimate the
source position through a stochastic model of the environment. Given the
practical impossibility of achieving a fully consistent turbulent model and
guaranteeing convergence to the correct solution, we propose a method to rank
'many wrong models' and to blend their predictions. We evaluate our weighted
Bayesian update algorithm by its ability to estimate the source location with
predefined accuracy and/or within a specified time frame, and compare it to
standard Monte Carlo sampling methods. To demonstrate the robustness and
potential applications of both approaches under realistic environmental
conditions, we use high-quality direct numerical simulations of the
Navier-Stokes equations to mimic the transport of odors in the atmospheric
boundary layer. Despite minimal prior information about the source and
environmental conditions, our proposed approach consistently proves to be more
accurate, reliable, and robust than Monte Carlo methods, thus showing promise
as a new tool for addressing the odor source localization problem in real-world
scenarios.
