Articles | Volume 14, issue 12
Geosci. Model Dev., 14, 7411–7424, 2021
https://doi.org/10.5194/gmd-14-7411-2021
Geosci. Model Dev., 14, 7411–7424, 2021
https://doi.org/10.5194/gmd-14-7411-2021
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.

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Cited articles

Adams, M. D. and Kanaroglou, P. S.: Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models, J. Environ. Manag., 168, 133–141, https://doi.org/10.1016/j.jenvman.2015.12.012, 2016. a, b, c
Araki, S., Shima, M., and Yamamoto, K.: Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan, Sci. Total Environ., 634, 1269–1277, https://doi.org/10.1016/j.scitotenv.2018.03.324, 2018. a
Auvinen, M., Boi, S., Hellsten, A., Tanhuanpää, T., and Järvi, L.: Study of realistic urban boundary layer turbulence with high-resolution large-eddy simulation, Atmosphere, 11, 201, https://doi.org/10.3390/atmos11020201, 2020. a
Benoit, K.: Linear regression models with logarithmic transformations, London School of Economics, London, 22, 23–36, 2011. a
Britter, R. E. and Hanna, S. R.: Flow and dispersion in urban areas, Ann. Rev. Fluid Mech., 35, 469–496, https://doi.org/10.1146/annurev.fluid.35.101101.161147, 2003. a
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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.