Meta-modeling of ADMS-Urban by dimension reduction and emulation

ADMS-Urban is a non-linear, static, urban air quality model, with high-dimensional outputs. A simulation of NO2 and PM10 concentrations every hour during a full year and over an entire city can take dozens of days of computations, which greatly limits the range of methods that can be applied to the model, especially for uncertainty quantification. This work presents a method to replace the complete model, ADMS-Urban, with a meta-model or surrogate model, i.e., a reasonably close approximation of ADMS-Urban whose computational cost is negligible. When the emissions are formulated as a function of the day and the hour, the complete-model inputs essentially contain a few scalar values, to describe the meteorological conditions, the background pollution and the target date. The complete-model outputs are first projected onto a reduced subspace. The relations between the projection coefficients and the low-dimensional inputs are then emulated by a fast statistical emulator, based on Kriging or radial basis functions (RBF). The mean error between the meta-model and ADMS-Urban is 22% with Kriging and 27% with RBF for NO2, and 14% with Kriging and 20% with RBF for PM10. The meta-model performs as well as ADMS-Urban when compared to the observations. Its computational cost is almost negligible to compute the concentrations at a given hour and date for an entire city: 50 ms with RBF and 150 ms with Kriging to simulate 1 h on one core, while the complete model requires 8 min on 16 cores.