Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7411-2021
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, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki

Related authors

Global fields of daily accumulation-mode particle number concentrations using in situ observations, reanalysis data and machine learning
Aino Ovaska, Elio Rauth, Daniel Holmberg, Paulo Artaxo, John Backman, Benjamin Bergmans, Don Collins, Marco Aurélio Franco, Shahzad Gani, Roy M. Harrison, Rakes K. Hooda, Tareq Hussein, Antti-Pekka Hyvärinen, Kerneels Jaars, Adam Kristensson, Markku Kulmala, Lauri Laakso, Ari Laaksonen, Nikolaos Mihalopoulos, Colin O'Dowd, Jakub Ondracek, Tuukka Petäjä, Kristina Plauškaitė, Mira Pöhlker, Ximeng Qi, Peter Tunved, Ville Vakkari, Alfred Wiedensohler, Kai Puolamäki, Tuomo Nieminen, Veli-Matti Kerminen, Victoria A. Sinclair, and Pauli Paasonen
Aerosol Research Discuss., https://doi.org/10.5194/ar-2025-18,https://doi.org/10.5194/ar-2025-18, 2025
Preprint under review for AR
Short summary
Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025,https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Carbon sequestration in different urban vegetation types in Southern Finland
Laura Thölix, Leif Backman, Minttu Havu, Esko Karvinen, Jesse Soininen, Justine Trémeau, Olli Nevalainen, Joyson Ahongshangbam, Leena Järvi, and Liisa Kulmala
Biogeosciences, 22, 725–749, https://doi.org/10.5194/bg-22-725-2025,https://doi.org/10.5194/bg-22-725-2025, 2025
Short summary
PALM-SLUrb v24.04: A single-layer urban canopy model for the PALM model system – Model description and first evaluation
Sasu Karttunen, Matthias Sühring, Ewan O'Connor, and Leena Järvi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-235,https://doi.org/10.5194/gmd-2024-235, 2024
Revised manuscript accepted for GMD
Short summary
Soil respiration across a variety of tree-covered urban green spaces in Helsinki, Finland
Esko Karvinen, Leif Backman, Leena Järvi, and Liisa Kulmala
SOIL, 10, 381–406, https://doi.org/10.5194/soil-10-381-2024,https://doi.org/10.5194/soil-10-381-2024, 2024
Short summary

Related subject area

Numerical methods
Stabilized two-phase material point method for hydromechanical coupling problems in solid–fluid porous media
Xiong Tang, Wei Liu, Siming He, Lei Zhu, Michel Jaboyedoff, Huanhuan Zhang, Yuqing Sun, and Zenan Huo
Geosci. Model Dev., 18, 4743–4758, https://doi.org/10.5194/gmd-18-4743-2025,https://doi.org/10.5194/gmd-18-4743-2025, 2025
Short summary
asQ: parallel-in-time finite element simulations using ParaDiag for geoscientific models and beyond
Joshua Hope-Collins, Abdalaziz Hamdan, Werner Bauer, Lawrence Mitchell, and Colin Cotter
Geosci. Model Dev., 18, 4535–4569, https://doi.org/10.5194/gmd-18-4535-2025,https://doi.org/10.5194/gmd-18-4535-2025, 2025
Short summary
Optimized step size control within the Rosenbrock solvers for stiff chemical ordinary differential equation systems in KPP version 2.2.3_rs4
Raphael Dreger, Timo Kirfel, Andrea Pozzer, Simon Rosanka, Rolf Sander, and Domenico Taraborrelli
Geosci. Model Dev., 18, 4273–4291, https://doi.org/10.5194/gmd-18-4273-2025,https://doi.org/10.5194/gmd-18-4273-2025, 2025
Short summary
Potential-based thermodynamics with consistent conservative cascade transport for implicit large eddy simulation: PTerodaC3TILES version 1.0
John Thuburn
Geosci. Model Dev., 18, 3331–3357, https://doi.org/10.5194/gmd-18-3331-2025,https://doi.org/10.5194/gmd-18-3331-2025, 2025
Short summary
Positive matrix factorization of large real-time atmospheric mass spectrometry datasets using error-weighted randomized hierarchical alternating least squares
Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
Geosci. Model Dev., 18, 2891–2919, https://doi.org/10.5194/gmd-18-2891-2025,https://doi.org/10.5194/gmd-18-2891-2025, 2025
Short summary

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
Download
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.
Share