Articles | Volume 18, issue 13
https://doi.org/10.5194/acp-18-9597-2018
© 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-18-9597-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jul 2018
Research article |
| 09 Jul 2018
| 09 Jul 2018
Identification of new particle formation events with deep learning
Jorma Joutsensaari
CORRESPONDING AUTHOR
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Matthew Ozon
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Tuomo Nieminen
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Santtu Mikkonen
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Timo Lähivaara
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Stefano Decesari
Institute of Atmospheric Sciences and Climate of the Italian National
Research Council, Bologna, Italy
M. Cristina Facchini
Institute of Atmospheric Sciences and Climate of the Italian National
Research Council, Bologna, Italy
Ari Laaksonen
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Climate research Unit, Finnish Meteorological Institute, Helsinki,
Finland
Kari E. J. Lehtinen
Department of Applied Physics, University of Eastern Finland, P.O. Box
1627, 70211 Kuopio, Finland
Atmospheric Research Centre of Eastern Finland, Finnish Meteorological
Institute, Kuopio, Finland
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Total article views: 3,313 (including HTML, PDF, and XML)
Thereof 3,312 with geography defined
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Total article views: 1,165 (including HTML, PDF, and XML)
Thereof 1,140 with geography defined
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Cited
17 citations as recorded by crossref.
- Atmospheric new particle formation identifier using longitudinal global particle number size distribution data S. Kecorius et al. 10.1038/s41597-024-04079-1
- Infrequent new particle formation in a coastal Mediterranean city during the summer A. Aktypis et al. 10.1016/j.atmosenv.2023.119732
- Influence of transportation exhaust and acid rain on the strength of concrete bridges and possible solutions to reduce harmful effects R. Abdel Hafez et al. 10.1016/j.jobe.2024.110933
- Non-exhaust traffic emissions: Sources, characterization, and mitigation measures A. Piscitello et al. 10.1016/j.scitotenv.2020.144440
- Seasonal significance of new particle formation impacts on cloud condensation nuclei at a mountaintop location N. Hirshorn et al. 10.5194/acp-22-15909-2022
- Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations C. Liang et al. 10.1016/j.scitotenv.2020.140923
- Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data M. Petters 10.5194/amt-14-7909-2021
- Tailpipe and Nontailpipe Emission Factors and Source Contributions of PM10 on Major Freeways in the Los Angeles Basin V. Jalali Farahani et al. 10.1021/acs.est.1c06954
- Estimation of groundwater storage from seismic data using deep learning T. Lähivaara et al. 10.1111/1365-2478.12831
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- New particle formation event detection with convolutional neural networks X. Zhang et al. 10.1016/j.atmosenv.2024.120487
- Measurement report: Contribution of atmospheric new particle formation to ultrafine particle concentration, cloud condensation nuclei, and radiative forcing – results from 5-year observations in central Europe J. Sun et al. 10.5194/acp-24-10667-2024
- Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China R. Feng et al. 10.1016/j.envpol.2019.05.101
- New particle formation event detection with Mask R-CNN P. Su et al. 10.5194/acp-22-1293-2022
- Wildfire plume ageing in the Photochemical Large Aerosol Chamber (PHOTO-LAC) H. Czech et al. 10.1039/D3EM00280B
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al. 10.5194/gmd-16-5251-2023
- Nanoparticle ranking analysis: determining new particle formation (NPF) event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles D. Aliaga et al. 10.5194/ar-1-81-2023
17 citations as recorded by crossref.
- Atmospheric new particle formation identifier using longitudinal global particle number size distribution data S. Kecorius et al. 10.1038/s41597-024-04079-1
- Infrequent new particle formation in a coastal Mediterranean city during the summer A. Aktypis et al. 10.1016/j.atmosenv.2023.119732
- Influence of transportation exhaust and acid rain on the strength of concrete bridges and possible solutions to reduce harmful effects R. Abdel Hafez et al. 10.1016/j.jobe.2024.110933
- Non-exhaust traffic emissions: Sources, characterization, and mitigation measures A. Piscitello et al. 10.1016/j.scitotenv.2020.144440
- Seasonal significance of new particle formation impacts on cloud condensation nuclei at a mountaintop location N. Hirshorn et al. 10.5194/acp-22-15909-2022
- Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations C. Liang et al. 10.1016/j.scitotenv.2020.140923
- Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data M. Petters 10.5194/amt-14-7909-2021
- Tailpipe and Nontailpipe Emission Factors and Source Contributions of PM10 on Major Freeways in the Los Angeles Basin V. Jalali Farahani et al. 10.1021/acs.est.1c06954
- Estimation of groundwater storage from seismic data using deep learning T. Lähivaara et al. 10.1111/1365-2478.12831
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- New particle formation event detection with convolutional neural networks X. Zhang et al. 10.1016/j.atmosenv.2024.120487
- Measurement report: Contribution of atmospheric new particle formation to ultrafine particle concentration, cloud condensation nuclei, and radiative forcing – results from 5-year observations in central Europe J. Sun et al. 10.5194/acp-24-10667-2024
- Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China R. Feng et al. 10.1016/j.envpol.2019.05.101
- New particle formation event detection with Mask R-CNN P. Su et al. 10.5194/acp-22-1293-2022
- Wildfire plume ageing in the Photochemical Large Aerosol Chamber (PHOTO-LAC) H. Czech et al. 10.1039/D3EM00280B
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al. 10.5194/gmd-16-5251-2023
- Nanoparticle ranking analysis: determining new particle formation (NPF) event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles D. Aliaga et al. 10.5194/ar-1-81-2023
Discussed (preprint)
Latest update: 23 Nov 2024
Short summary
New particle formation (NPF) in the atmosphere is globally an important source of aerosol particles. NPF events are typically identified and analyzed manually by researchers from particle size distribution data day by day, which is time consuming and might be inconsistent. We have developed an automatic analysis method based on deep learning for NPF event identification. The developed method can be easily utilized to analyze any long-term datasets more accurately and consistently.
New particle formation (NPF) in the atmosphere is globally an important source of aerosol...
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