Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5687-2018
© 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/amt-11-5687-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Oct 2018
Research article |
| 18 Oct 2018
| 18 Oct 2018
A machine learning approach to aerosol classification for single-particle mass spectrometry
Costa D. Christopoulos
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Sarvesh Garimella
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: ACME AtronOmatic, LLC, Portland, OR, USA
Maria A. Zawadowicz
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Ottmar Möhler
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Daniel J. Cziczo
CORRESPONDING AUTHOR
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Viewed
Total article views: 3,928 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Jan 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,403 | 1,438 | 87 | 3,928 | 87 | 109 |
- HTML: 2,403
- PDF: 1,438
- XML: 87
- Total: 3,928
- BibTeX: 87
- EndNote: 109
Total article views: 2,923 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Oct 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,898 | 952 | 73 | 2,923 | 83 | 92 |
- HTML: 1,898
- PDF: 952
- XML: 73
- Total: 2,923
- BibTeX: 83
- EndNote: 92
Total article views: 1,005 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Jan 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 505 | 486 | 14 | 1,005 | 4 | 17 |
- HTML: 505
- PDF: 486
- XML: 14
- Total: 1,005
- BibTeX: 4
- EndNote: 17
Viewed (geographical distribution)
Total article views: 3,928 (including HTML, PDF, and XML)
Thereof 3,725 with geography defined
and 203 with unknown origin.
Total article views: 2,923 (including HTML, PDF, and XML)
Thereof 2,820 with geography defined
and 103 with unknown origin.
Total article views: 1,005 (including HTML, PDF, and XML)
Thereof 905 with geography defined
and 100 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
22 citations as recorded by crossref.
- Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning X. Wang et al. 10.1021/acsestair.4c00076
- Current State of Atmospheric Aerosol Thermodynamics and Mass Transfer Modeling: A Review K. Semeniuk & A. Dastoor 10.3390/atmos11020156
- Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets P. Pande et al. 10.1021/acsearthspacechem.1c00344
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- 1D-CNN Network Based Real-Time Aerosol Particle Classification With Single-Particle Mass Spectrometry G. Wang et al. 10.1109/LSENS.2023.3315554
- Atomic spectrometry update – a review of advances in environmental analysis J. Bacon et al. 10.1039/C9JA90060H
- Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning K. Patel et al. 10.1080/02786826.2023.2193237
- Composition and source based aerosol classification using machine learning algorithms S. Annapurna et al. 10.1016/j.asr.2023.09.068
- Mineralogy and mixing state of north African mineral dust by online single-particle mass spectrometry N. Marsden et al. 10.5194/acp-19-2259-2019
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- Machine learning: our future spotlight into single-particle ICP-ToF-MS analysis T. Holbrook et al. 10.1039/D1JA00213A
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning Y. Xu et al. 10.1016/j.scitotenv.2024.172822
- Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification X. Gong et al. 10.5194/acp-22-5175-2022
- Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier J. Rausch et al. 10.1016/j.scitotenv.2021.149832
- Machine learning approaches for automatic classification of single-particle mass spectrometry data G. Wang et al. 10.5194/amt-17-299-2024
- Relationships Between Supermicrometer Sea Salt Aerosol and Marine Boundary Layer Conditions: Insights From Repeated Identical Flight Patterns J. Schlosser et al. 10.1029/2019JD032346
- 基于1D-CNN的生物气溶胶衰减全反射傅里叶变换红外光谱识别 汪. Wang Yang et al. 10.3788/AOS231963
- Understanding atmospheric aerosol particles with improved particle identification and quantification by single-particle mass spectrometry X. Shen et al. 10.5194/amt-12-2219-2019
- Differences in mass concentration and elemental composition of leaf surface particulate matter: Plant species and particle size ranges S. Zhou et al. 10.1016/j.psep.2023.05.040
- A high-speed particle phase discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber F. Mahrt et al. 10.5194/amt-12-3183-2019
- Data‐Driven Compound Identification in Atmospheric Mass Spectrometry H. Sandström et al. 10.1002/advs.202306235
22 citations as recorded by crossref.
- Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning X. Wang et al. 10.1021/acsestair.4c00076
- Current State of Atmospheric Aerosol Thermodynamics and Mass Transfer Modeling: A Review K. Semeniuk & A. Dastoor 10.3390/atmos11020156
- Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets P. Pande et al. 10.1021/acsearthspacechem.1c00344
- Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning K. Lamb 10.5194/amt-12-3885-2019
- 1D-CNN Network Based Real-Time Aerosol Particle Classification With Single-Particle Mass Spectrometry G. Wang et al. 10.1109/LSENS.2023.3315554
- Atomic spectrometry update – a review of advances in environmental analysis J. Bacon et al. 10.1039/C9JA90060H
- Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning K. Patel et al. 10.1080/02786826.2023.2193237
- Composition and source based aerosol classification using machine learning algorithms S. Annapurna et al. 10.1016/j.asr.2023.09.068
- Mineralogy and mixing state of north African mineral dust by online single-particle mass spectrometry N. Marsden et al. 10.5194/acp-19-2259-2019
- Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
- Machine learning: our future spotlight into single-particle ICP-ToF-MS analysis T. Holbrook et al. 10.1039/D1JA00213A
- Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement A. Rahman et al. 10.1021/acsestengg.4c00262
- Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning Y. Xu et al. 10.1016/j.scitotenv.2024.172822
- Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification X. Gong et al. 10.5194/acp-22-5175-2022
- Automated identification and quantification of tire wear particles (TWP) in airborne dust: SEM/EDX single particle analysis coupled to a machine learning classifier J. Rausch et al. 10.1016/j.scitotenv.2021.149832
- Machine learning approaches for automatic classification of single-particle mass spectrometry data G. Wang et al. 10.5194/amt-17-299-2024
- Relationships Between Supermicrometer Sea Salt Aerosol and Marine Boundary Layer Conditions: Insights From Repeated Identical Flight Patterns J. Schlosser et al. 10.1029/2019JD032346
- 基于1D-CNN的生物气溶胶衰减全反射傅里叶变换红外光谱识别 汪. Wang Yang et al. 10.3788/AOS231963
- Understanding atmospheric aerosol particles with improved particle identification and quantification by single-particle mass spectrometry X. Shen et al. 10.5194/amt-12-2219-2019
- Differences in mass concentration and elemental composition of leaf surface particulate matter: Plant species and particle size ranges S. Zhou et al. 10.1016/j.psep.2023.05.040
- A high-speed particle phase discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber F. Mahrt et al. 10.5194/amt-12-3183-2019
- Data‐Driven Compound Identification in Atmospheric Mass Spectrometry H. Sandström et al. 10.1002/advs.202306235
Latest update: 12 Nov 2024
Short summary
Compositional analysis of atmospheric and laboratory aerosols is often conducted with mass spectrometry. In this study, machine learning is used to automatically differentiate particles on the basis of chemistry and size. The ability of the machine learning algorithm was then tested on a data set for which the particles were not initially known to judge its ability.
Compositional analysis of atmospheric and laboratory aerosols is often conducted with mass...
