Microscale pollutant dispersion is a critical aspect of air quality assessment with significant environmental and public health impacts, especially in urban environments. To accurately assess these impacts, there is a growing consensus for the use of high-fidelity models, such as large-eddy simulations (LES), which can explicitly account for the complex and multi-scale interactions between the atmospheric boundary layer (ABL) and the urban environment. However, LES are very expensive and remain subject to uncertainties, particularly due to the lack of knowledge of large-scale atmospheric forcing and variability. In emergency situations, where we need to predict the location of pollutant peaks, it is essential to reduce forecast time and to estimate and quantify uncertainties in order to quickly cover different dispersion scenarios.

In this work, we design a reduced-cost data assimilation based on an ensemble Kalman filter (EnKF) that combines in situ concentration measurements with LES model predictions to reduce uncertainty in large-scale atmospheric forcing parameters. To reduce the computational cost, a surrogate model based on proper orthogonal decomposition (POD) combined with Gaussian process regression (GPR) is trained in an offline stage using a large dataset of LES predictions and replaces the LES model in the EnKF prediction step. In addition, we develop a bootstrap approach to quantify the unreducible uncertainty in LES predictions and measurements due to the internal variability of the atmospheric boundary layer. This uncertainty is taken into account in the data assimilation framework to make it more robust and realistic.

The assimilation of measurements from the MUST field-scale experiment provides a proof-of-concept of the system’s ability to reduce meteorological parametric uncertainties, correct model boundary condition biases, and thereby improve LES pollutant concentration field predictions. The use of the POD-GPR surrogate model reduces the cost of a 500-member EnKF cycle to a few tens of seconds.

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