Numerical simulations of contaminant dispersion after a gas leakage incident at a chemical plant can provide valuable insights for both emergency response and preparedness. High-fidelity simulation approaches combine incompressible Navier-Stokes (INS) equations with advection-diffusion processes to model wind and concentration field. However, their computational cost increases rapidly for complex geometries and extended domains like urban environments. This renders them unfeasible in time-critical or multi-query "what-if" scenarios, making model order reduction (MOR) techniques particularly valuable for enabling fast, accurate predictions. This study describes a MOR-based, application-driven workflow for uncertainty-aware contaminant concentration prediction, demonstrated for a two-dimensional benchmark system. Starting with the model selection, relevant comparison criteria and trade-offs are discussed for well-established proper orthogonal decomposition based (non-)intrusive methods, applied to the computationally more demanding parametric INS problem. They include accuracy, computational efficiency, data requirements, and extrapolation capability. Based on these insights, a non-intrusive parametric reduced-order model is constructed that enables accelerated Monte Carlo simulations for estimating the influence of potential wind measurement uncertainties on the spatio-temporal concentration field. This enables extracting valuable information for local consequence analyses and evacuation planning. Simulation results are furthermore interactively visualized in a dashboard and can serve as a building block within broader decision-support systems.

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