Articles | Volume 12, issue 7
https://doi.org/10.5194/amt-12-3885-2019
https://doi.org/10.5194/amt-12-3885-2019
Research article
 | 
15 Jul 2019
Research article |  | 15 Jul 2019

Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning

Kara D. Lamb

Related authors

Re-evaluating cloud chamber constraints on depositional ice growth in cirrus clouds – Part 1: Model description and sensitivity tests
Kara D. Lamb, Jerry Y. Harrington, Benjamin W. Clouser, Elisabeth J. Moyer, Laszlo Sarkozy, Volker Ebert, Ottmar Möhler, and Harald Saathoff
Atmos. Chem. Phys., 23, 6043–6064, https://doi.org/10.5194/acp-23-6043-2023,https://doi.org/10.5194/acp-23-6043-2023, 2023
Short summary
Complex refractive indices in the ultraviolet and visible spectral region for highly absorbing non-spherical biomass burning aerosol
Caroline C. Womack, Katherine M. Manfred, Nicholas L. Wagner, Gabriela Adler, Alessandro Franchin, Kara D. Lamb, Ann M. Middlebrook, Joshua P. Schwarz, Charles A. Brock, Steven S. Brown, and Rebecca A. Washenfelder
Atmos. Chem. Phys., 21, 7235–7252, https://doi.org/10.5194/acp-21-7235-2021,https://doi.org/10.5194/acp-21-7235-2021, 2021
Short summary
Understanding and improving model representation of aerosol optical properties for a Chinese haze event measured during KORUS-AQ
Pablo E. Saide, Meng Gao, Zifeng Lu, Daniel L. Goldberg, David G. Streets, Jung-Hun Woo, Andreas Beyersdorf, Chelsea A. Corr, Kenneth L. Thornhill, Bruce Anderson, Johnathan W. Hair, Amin R. Nehrir, Glenn S. Diskin, Jose L. Jimenez, Benjamin A. Nault, Pedro Campuzano-Jost, Jack Dibb, Eric Heim, Kara D. Lamb, Joshua P. Schwarz, Anne E. Perring, Jhoon Kim, Myungje Choi, Brent Holben, Gabriele Pfister, Alma Hodzic, Gregory R. Carmichael, Louisa Emmons, and James H. Crawford
Atmos. Chem. Phys., 20, 6455–6478, https://doi.org/10.5194/acp-20-6455-2020,https://doi.org/10.5194/acp-20-6455-2020, 2020
Short summary
No anomalous supersaturation in ultracold cirrus laboratory experiments
Benjamin W. Clouser, Kara D. Lamb, Laszlo C. Sarkozy, Jan Habig, Volker Ebert, Harald Saathoff, Ottmar Möhler, and Elisabeth J. Moyer
Atmos. Chem. Phys., 20, 1089–1103, https://doi.org/10.5194/acp-20-1089-2020,https://doi.org/10.5194/acp-20-1089-2020, 2020
Short summary
Secondary organic aerosol production from local emissions dominates the organic aerosol budget over Seoul, South Korea, during KORUS-AQ
Benjamin A. Nault, Pedro Campuzano-Jost, Douglas A. Day, Jason C. Schroder, Bruce Anderson, Andreas J. Beyersdorf, Donald R. Blake, William H. Brune, Yonghoon Choi, Chelsea A. Corr, Joost A. de Gouw, Jack Dibb, Joshua P. DiGangi, Glenn S. Diskin, Alan Fried, L. Gregory Huey, Michelle J. Kim, Christoph J. Knote, Kara D. Lamb, Taehyoung Lee, Taehyun Park, Sally E. Pusede, Eric Scheuer, Kenneth L. Thornhill, Jung-Hun Woo, and Jose L. Jimenez
Atmos. Chem. Phys., 18, 17769–17800, https://doi.org/10.5194/acp-18-17769-2018,https://doi.org/10.5194/acp-18-17769-2018, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: Laboratory Measurement | Topic: Data Processing and Information Retrieval
Characterization of offline analysis of particulate matter with FIGAERO-CIMS
Jing Cai, Kaspar R. Daellenbach, Cheng Wu, Yan Zheng, Feixue Zheng, Wei Du, Sophie L. Haslett, Qi Chen, Markku Kulmala, and Claudia Mohr
Atmos. Meas. Tech., 16, 1147–1165, https://doi.org/10.5194/amt-16-1147-2023,https://doi.org/10.5194/amt-16-1147-2023, 2023
Short summary
Estimation of secondary organic aerosol formation parameters for the Volatility Basis Set combining thermodenuder, isothermal dilution and yield measurements
Petro Uruci, Dontavious Sippial, Anthoula Drosatou, and Spyros Pandis
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-320,https://doi.org/10.5194/amt-2022-320, 2023
Revised manuscript accepted for AMT
Short summary
Mass spectrometry-based Aerosolomics: a new approach to resolve sources, composition, and partitioning of secondary organic aerosol
Markus Thoma, Franziska Bachmeier, Felix Leonard Gottwald, Mario Simon, and Alexander Lucas Vogel
Atmos. Meas. Tech., 15, 7137–7154, https://doi.org/10.5194/amt-15-7137-2022,https://doi.org/10.5194/amt-15-7137-2022, 2022
Short summary
A universally applicable method of calculating confidence bands for ice nucleation spectra derived from droplet freezing experiments
William D. Fahy, Cosma Rohilla Shalizi, and Ryan Christopher Sullivan
Atmos. Meas. Tech., 15, 6819–6836, https://doi.org/10.5194/amt-15-6819-2022,https://doi.org/10.5194/amt-15-6819-2022, 2022
Short summary
Thermal–optical analysis of quartz fiber filters loaded with snow samples – determination of iron based on interferences caused by mineral dust
Daniela Kau, Marion Greilinger, Bernadette Kirchsteiger, Aron Göndör, Christopher Herzig, Andreas Limbeck, Elisabeth Eitenberger, and Anne Kasper-Giebl
Atmos. Meas. Tech., 15, 5207–5217, https://doi.org/10.5194/amt-15-5207-2022,https://doi.org/10.5194/amt-15-5207-2022, 2022
Short summary

Cited articles

Adachi, K., Moteki, N., Kondo, Y., and Igarashi, Y.: Mixing states of light-absorbing particles measured using a transmission electron microscope and a single-particle soot photometer in Tokyo, Japan, J. Geophys. Res.-Atmos., 121, 9153–9164, 2016. a, b, c, d
Baumgardner, D., Popovicheva, O., Allan, J., Bernardoni, V., Cao, J., Cavalli, F., Cozic, J., Diapouli, E., Eleftheriadis, K., Genberg, P. J., Gonzalez, C., Gysel, M., John, A., Kirchstetter, T. W., Kuhlbusch, T. A. J., Laborde, M., Lack, D., Müller, T., Niessner, R., Petzold, A., Piazzalunga, A., Putaud, J. P., Schwarz, J., Sheridan, P., Subramanian, R., Swietlicki, E., Valli, G., Vecchi, R., and Viana, M.: Soot reference materials for instrument calibration and intercomparisons: a workshop summary with recommendations, Atmos. Meas. Tech., 5, 1869–1887, https://doi.org/10.5194/amt-5-1869-2012, 2012. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a, b
Christopoulos, C. D., Garimella, S., Zawadowicz, M. A., Möhler, O., and Cziczo, D. J.: A machine learning approach to aerosol classification for single-particle mass spectrometry, Atmos. Meas. Tech., 11, 5687–5699, https://doi.org/10.5194/amt-11-5687-2018, 2018. a, b
Dahlkötter, F., Gysel, M., Sauer, D., Minikin, A., Baumann, R., Seifert, P., Ansmann, A., Fromm, M., Voigt, C., and Weinzierl, B.: The Pagami Creek smoke plume after long-range transport to the upper troposphere over Europe – aerosol properties and black carbon mixing state, Atmos. Chem. Phys., 14, 6111–6137, https://doi.org/10.5194/acp-14-6111-2014, 2014. a
Download
Short summary
Recent atmospheric observations have indicated emissions of iron-oxide-containing aerosols from anthropogenic sources could be 8x higher than previous estimates, leading models to underestimate their climate impact. Previous studies have shown the single particle soot photometer (SP2) can quantify the atmospheric abundance of these aerosols. Here, I explore a machine learning approach to improve SP2 detection, significantly reducing misclassifications of other aerosols as iron oxide aerosols.