Articles | Volume 21, issue 13
https://doi.org/10.5194/acp-21-10081-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-21-10081-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jul 2021
Research article |
| 06 Jul 2021
| 06 Jul 2021
Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach
Liine Heikkinen
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
now at: Department of Environmental Science and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Mikko Äijälä
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Kaspar R. Daellenbach
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
now at: Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland
Gang Chen
Laboratory of Atmospheric Chemistry, Paul Scherrer Institute,
Villigen, Switzerland
Olga Garmash
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Diego Aliaga
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Frans Graeffe
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Meri Räty
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Krista Luoma
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Pasi Aalto
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Markku Kulmala
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Tuukka Petäjä
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Douglas Worsnop
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
Aerodyne Research Inc., Billerica, MA, USA
Institute for Atmospheric and Earth System Research/Physics, Faculty
of Science, University of Helsinki, Helsinki, 00014, Finland
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Cited
17 citations as recorded by crossref.
- Cloud response to co-condensation of water and organic vapors over the boreal forest L. Heikkinen et al. 10.5194/acp-24-5117-2024
- Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm F. Yang & G. Sun 10.1155/2022/3684176
- Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets P. Pande et al. 10.1021/acsearthspacechem.1c00344
- Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. 10.1016/j.scitotenv.2023.163189
- Molecular Characterization of Organosulfate-Dominated Aerosols over Agricultural Fields from the Southern Great Plains by High-Resolution Mass Spectrometry G. Vandergrift et al. 10.1021/acsearthspacechem.2c00043
- Real-Time Source Apportionment of Organic Aerosols in Three European Cities G. Chen et al. 10.1021/acs.est.2c02509
- A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization A. Rusanen et al. 10.5194/amt-17-1251-2024
- European aerosol phenomenology − 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets G. Chen et al. 10.1016/j.envint.2022.107325
- Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison M. Via et al. 10.5194/amt-15-5479-2022
- A 1-year aerosol chemical speciation monitor (ACSM) source analysis of organic aerosol particle contributions from anthropogenic sources after long-range transport at the TROPOS research station Melpitz S. Atabakhsh et al. 10.5194/acp-23-6963-2023
- On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest G. Ciarelli et al. 10.5194/gmd-17-545-2024
- Application of fuzzy c-means clustering for analysis of chemical ionization mass spectra: insights into the gas phase chemistry of NO3-initiated oxidation of isoprene R. Wu et al. 10.5194/amt-17-1811-2024
- Enhanced Aerosol Source Identification by Utilizing High Molecular Weight Signals in Aerosol Mass Spectra Y. Zhang et al. 10.1021/acsestair.3c00102
- Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site L. Yang et al. 10.1021/acs.est.1c07005
- Detecting and Characterizing Particulate Organic Nitrates with an Aerodyne Long-ToF Aerosol Mass Spectrometer F. Graeffe et al. 10.1021/acsearthspacechem.2c00314
- Comprehensive the seasonal characterization of atmospheric submicron particles at urban sites in the North China Plain P. Xu et al. 10.1016/j.atmosres.2024.107388
- The effect of clouds and precipitation on the aerosol concentrations and composition in a boreal forest environment S. Isokääntä et al. 10.5194/acp-22-11823-2022
17 citations as recorded by crossref.
- Cloud response to co-condensation of water and organic vapors over the boreal forest L. Heikkinen et al. 10.5194/acp-24-5117-2024
- Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm F. Yang & G. Sun 10.1155/2022/3684176
- Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets P. Pande et al. 10.1021/acsearthspacechem.1c00344
- Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. 10.1016/j.scitotenv.2023.163189
- Molecular Characterization of Organosulfate-Dominated Aerosols over Agricultural Fields from the Southern Great Plains by High-Resolution Mass Spectrometry G. Vandergrift et al. 10.1021/acsearthspacechem.2c00043
- Real-Time Source Apportionment of Organic Aerosols in Three European Cities G. Chen et al. 10.1021/acs.est.2c02509
- A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization A. Rusanen et al. 10.5194/amt-17-1251-2024
- European aerosol phenomenology − 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets G. Chen et al. 10.1016/j.envint.2022.107325
- Rolling vs. seasonal PMF: real-world multi-site and synthetic dataset comparison M. Via et al. 10.5194/amt-15-5479-2022
- A 1-year aerosol chemical speciation monitor (ACSM) source analysis of organic aerosol particle contributions from anthropogenic sources after long-range transport at the TROPOS research station Melpitz S. Atabakhsh et al. 10.5194/acp-23-6963-2023
- On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest G. Ciarelli et al. 10.5194/gmd-17-545-2024
- Application of fuzzy c-means clustering for analysis of chemical ionization mass spectra: insights into the gas phase chemistry of NO3-initiated oxidation of isoprene R. Wu et al. 10.5194/amt-17-1811-2024
- Enhanced Aerosol Source Identification by Utilizing High Molecular Weight Signals in Aerosol Mass Spectra Y. Zhang et al. 10.1021/acsestair.3c00102
- Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site L. Yang et al. 10.1021/acs.est.1c07005
- Detecting and Characterizing Particulate Organic Nitrates with an Aerodyne Long-ToF Aerosol Mass Spectrometer F. Graeffe et al. 10.1021/acsearthspacechem.2c00314
- Comprehensive the seasonal characterization of atmospheric submicron particles at urban sites in the North China Plain P. Xu et al. 10.1016/j.atmosres.2024.107388
- The effect of clouds and precipitation on the aerosol concentrations and composition in a boreal forest environment S. Isokääntä et al. 10.5194/acp-22-11823-2022
Latest update: 08 May 2024
Short summary
In many locations worldwide aerosol particles have been shown to be made up of organic aerosol (OA). The boreal forest is a region where aerosol particles possess a high OA mass fraction. Here, we studied OA composition using the longest time series of OA composition ever obtained from a boreal environment. For this purpose, we tested a new analysis framework and discovered that most of the OA was highly oxidized, with strong seasonal behaviour reflecting different sources in summer and winter.
In many locations worldwide aerosol particles have been shown to be made up of organic aerosol...
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