Articles | Volume 14, issue 12
Geosci. Model Dev., 14, 7411–7424, 2021
Geosci. Model Dev., 14, 7411–7424, 2021

Model description paper 02 Dec 2021

Model description paper | 02 Dec 2021

Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Moritz Lange et al.

Data sets

Input data for article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations" Sasu Mikael Karttunen and Mona Liisa Vilhelmiina Kurppa

Assessing pollutant ventilation in a city-boulevard using large-eddy simulation M. Kurppa, A. Helssten, M. Auvinen, and L. Järvi

Input and output files and datasets for a LES case study of city-boulevard ventilation S. Karttunen and M. Kurppa

Model code and software

Datasets of Air Pollutants on Boulevard Type Streets and Software to Replicate Large-Eddy Simulations of Air Pollutant Concentrations Along Boulevard-Type Streets Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki

Short summary
This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.