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Mapping near-field pollutant concentration is essential to track accidentaltoxic plume dispersion in urban areas. By solving a large part of theturbulence spectrum, large-eddy simulations (LES) have the potential toaccurately represent pollutant concentration spatial variability. Finding a wayto synthesize this large amount of information to improve the accuracy oflower-fidelity operational models (e.g. providing better turbulence closureterms) is particularly appealing. This is a challenge in multi-query contexts,where LES become prohibitively costly to deploy to understand how plume flowand tracer dispersion change with various atmospheric and source parameters. Toovercome this issue, we propose a non-intrusive reduced-order model combiningproper orthogonal decomposition (POD) and Gaussian process regression (GPR) topredict LES field statistics of interest associated with tracer concentrations.GPR hyperpararameters are optimized component-by-component through a maximum aposteriori (MAP) procedure informed by POD. We provide a detailed analysis ofthe reducedorder model performance on a two-dimensional case studycorresponding to a turbulent atmospheric boundary-layer flow over asurface-mounted obstacle. We show that near-source concentrationheterogeneities upstream of the obstacle require a large number of POD modes tobe well captured. We also show that the component-by-component optimizationallows to capture the range of spatial scales in the POD modes, especially theshorter concentration patterns in the high-order modes. The reduced-order modelpredictions remain acceptable if the learning database is made of at leastfifty to hundred LES snapshot providing a first estimation of the requiredbudget to move towards more realistic atmospheric dispersion applications.

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion