Articles | Volume 18, issue 17
https://doi.org/10.5194/acp-18-12699-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-12699-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Sep 2018
Research article |
| 03 Sep 2018
| 03 Sep 2018
Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach
Martha A. Zaidan
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, 00560 Helsinki, Finland
Aalto Science Institute, School of Science, Aalto University, 00076 Espoo, Finland
Department of Applied Physics, Aalto University, 00076 Espoo, Finland
Ville Haapasilta
Department of Applied Physics, Aalto University, 00076 Espoo, Finland
Rishi Relan
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Pauli Paasonen
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, 00560 Helsinki, Finland
Veli-Matti Kerminen
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, 00560 Helsinki, Finland
Heikki Junninen
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, 00560 Helsinki, Finland
Institute of Physics, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
Markku Kulmala
Institute for Atmospheric and Earth System Research/Physics, Helsinki University, 00560 Helsinki, Finland
Aerosol and Haze Laboratory, Beijing University of Chemical Technology, 100096 Beijing, China
Adam S. Foster
Department of Applied Physics, Aalto University, 00076 Espoo, Finland
WPI Nano Life Science Institute (WPI-NanoLSI), Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
Graduate School Materials Science in Mainz, Staudinger Weg 9, 55128 Mainz, Germany
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Cited
16 citations as recorded by crossref.
- Input-Adaptive Proxy for Black Carbon as a Virtual Sensor P. Fung et al. 10.3390/s20010182
- Towards a Living Lab for Enhanced Thermal Comfort and Air Quality: Analyses of Standard Occupancy, Weather Extremes, and COVID-19 Pandemic G. Ulpiani et al. 10.3389/fenvs.2021.725974
- Unwanted Indoor Air Quality Effects from Using Ultraviolet C Lamps for Disinfection F. Graeffe et al. 10.1021/acs.estlett.2c00807
- Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective H. Lappalainen et al. 10.5194/acp-22-4413-2022
- The impact of the atmospheric turbulence-development tendency on new particle formation: a common finding on three continents H. Wu et al. 10.1093/nsr/nwaa157
- Rainfall pattern analysis in 24 East Asian megacities using a complex network K. Kim et al. 10.5194/hess-26-4823-2022
- Local synergies and antagonisms between meteorological factors and air pollution: A 15-year comprehensive study in the Sydney region G. Ulpiani et al. 10.1016/j.scitotenv.2021.147783
- Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies M. Zaidan et al. 10.3390/app9204475
- Decennial time trends and diurnal patterns of particle number concentrations in a central European city between 2008 and 2018 S. Mikkonen et al. 10.5194/acp-20-12247-2020
- Exploring Non-Linear Dependencies in Atmospheric Data with Mutual Information P. Laarne et al. 10.3390/atmos13071046
- Solar Signature in Climate Indices C. Mares et al. 10.3390/atmos13111898
- Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models M. Zaidan et al. 10.3390/app9224976
- Vertical transport of ultrafine particles and turbulence evolution impact on new particle formation at the surface & Canton Tower H. Wu et al. 10.1016/j.atmosres.2024.107290
- Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis M. Maciejewska et al. 10.3390/s23177630
- ennemi: Non-linear correlation detection with mutual information P. Laarne et al. 10.1016/j.softx.2021.100686
- Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters M. Zaidan et al. 10.3390/s20102876
16 citations as recorded by crossref.
- Input-Adaptive Proxy for Black Carbon as a Virtual Sensor P. Fung et al. 10.3390/s20010182
- Towards a Living Lab for Enhanced Thermal Comfort and Air Quality: Analyses of Standard Occupancy, Weather Extremes, and COVID-19 Pandemic G. Ulpiani et al. 10.3389/fenvs.2021.725974
- Unwanted Indoor Air Quality Effects from Using Ultraviolet C Lamps for Disinfection F. Graeffe et al. 10.1021/acs.estlett.2c00807
- Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective H. Lappalainen et al. 10.5194/acp-22-4413-2022
- The impact of the atmospheric turbulence-development tendency on new particle formation: a common finding on three continents H. Wu et al. 10.1093/nsr/nwaa157
- Rainfall pattern analysis in 24 East Asian megacities using a complex network K. Kim et al. 10.5194/hess-26-4823-2022
- Local synergies and antagonisms between meteorological factors and air pollution: A 15-year comprehensive study in the Sydney region G. Ulpiani et al. 10.1016/j.scitotenv.2021.147783
- Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies M. Zaidan et al. 10.3390/app9204475
- Decennial time trends and diurnal patterns of particle number concentrations in a central European city between 2008 and 2018 S. Mikkonen et al. 10.5194/acp-20-12247-2020
- Exploring Non-Linear Dependencies in Atmospheric Data with Mutual Information P. Laarne et al. 10.3390/atmos13071046
- Solar Signature in Climate Indices C. Mares et al. 10.3390/atmos13111898
- Bayesian Proxy Modelling for Estimating Black Carbon Concentrations using White-Box and Black-Box Models M. Zaidan et al. 10.3390/app9224976
- Vertical transport of ultrafine particles and turbulence evolution impact on new particle formation at the surface & Canton Tower H. Wu et al. 10.1016/j.atmosres.2024.107290
- Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis M. Maciejewska et al. 10.3390/s23177630
- ennemi: Non-linear correlation detection with mutual information P. Laarne et al. 10.1016/j.softx.2021.100686
- Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters M. Zaidan et al. 10.3390/s20102876
Latest update: 20 Nov 2024
Short summary
This article promotes the use of the mutual information method for finding any non-linear associations among atmospheric variables. We demonstrate that the same results from previous studies are obtained by this method, which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.
This article promotes the use of the mutual information method for finding any non-linear...
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