Articles | Volume 19, issue 7
https://doi.org/10.5194/amt-19-2575-2026
© Author(s) 2026. 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-19-2575-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2026
Research article |
| 17 Apr 2026
| 17 Apr 2026
Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry
Jose A. Perez Chavez
CORRESPONDING AUTHOR
Howard University Program for Atmospheric Science (HUPAS), Howard University, Washington, DC 20059, USA
Maria A. Zawadowicz
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
Joseph Wilkins
Howard University Program for Atmospheric Science (HUPAS), Howard University, Washington, DC 20059, USA
Department of Earth, Environment and Equity, Howard University, Washington, DC 20059, USA
Christopher Blaszczak-Boxe
Howard University Program for Atmospheric Science (HUPAS), Howard University, Washington, DC 20059, USA
Department of Earth, Environment and Equity, Howard University, Washington, DC 20059, USA
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Tamanna Subba, Michael P. Jensen, Min Deng, Scott E. Giangrande, Mark C. Harvey, Ashish Singh, Die Wang, Maria Zawadowicz, and Chongai Kuang
Atmos. Chem. Phys., 26, 2853–2879, https://doi.org/10.5194/acp-26-2853-2026, https://doi.org/10.5194/acp-26-2853-2026, 2026
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Using TRacking Aerosol Convection Interactions Experiment field campaign observations and model simulations, we studied summertime sea-breeze events in southern Texas. When sea-breeze fronts moved inland, they mixed marine and continental air, changing aerosol concentrations by up to a factor of two as far as 50 km inland. The sea breeze also reduced the number of particles that can form cloud droplets, highlighting the connection between coastal meteorology and aerosol-cloud interactions.
Jing Li, Jiaoshi Zhang, Xianda Gong, Steven Spielman, Chongai Kuang, Ashish Singh, Maria A. Zawadowicz, Lu Xu, and Jian Wang
Atmos. Chem. Phys., 25, 13975–13993, https://doi.org/10.5194/acp-25-13975-2025, https://doi.org/10.5194/acp-25-13975-2025, 2025
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Using measurements at a rural coastal site, we quantified aerosols in representative air masses and identified major sources of organics in the Houston area. Our results show cooking aerosol is likely overestimated by earlier studies. Additionally, diurnal variation of highly oxidized organics is mostly driven by air mass changes instead of photochemistry. This study highlights the impacts of emissions, atmospheric chemistry, and meteorology on aerosol properties in the coastal–rural environment.
Amie Dobracki, Ernie R. Lewis, Arthur J. Sedlacek III, Tyler Tatro, Maria A. Zawadowicz, and Paquita Zuidema
Atmos. Chem. Phys., 25, 2333–2363, https://doi.org/10.5194/acp-25-2333-2025, https://doi.org/10.5194/acp-25-2333-2025, 2025
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Biomass-burning aerosol is commonly present in the marine boundary layer over the southeast Atlantic Ocean between June and October. Our research indicates that burning conditions, aerosol transport pathways, and prolonged oxidation processes (heterogeneous and aqueous phases) determine the chemical, microphysical, and optical properties of the boundary layer aerosol. Notably, we find that the aerosol optical properties can be estimated from the chemical properties alone.
Paul J. DeMott, Jessica A. Mirrielees, Sarah Suda Petters, Daniel J. Cziczo, Markus D. Petters, Heinz G. Bingemer, Thomas C. J. Hill, Karl Froyd, Sarvesh Garimella, A. Gannet Hallar, Ezra J. T. Levin, Ian B. McCubbin, Anne E. Perring, Christopher N. Rapp, Thea Schiebel, Jann Schrod, Kaitlyn J. Suski, Daniel Weber, Martin J. Wolf, Maria Zawadowicz, Jake Zenker, Ottmar Möhler, and Sarah D. Brooks
Atmos. Meas. Tech., 18, 639–672, https://doi.org/10.5194/amt-18-639-2025, https://doi.org/10.5194/amt-18-639-2025, 2025
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The Fifth International Ice Nucleation Workshop Phase 3 (FIN-03) compared the ambient atmospheric performance of ice-nucleating particle (INP) measuring systems and explored general methods for discerning atmospheric INP compositions. Mirroring laboratory results, INP concentrations agreed within 5–10 factors. Measurements of total aerosol properties and investigations of INP compositions supported a dominant role of soil and plant organic aerosol elements as INPs during the study.
Jerome D. Fast, Adam C. Varble, Fan Mei, Mikhail Pekour, Jason Tomlinson, Alla Zelenyuk, Art J. Sedlacek III, Maria Zawadowicz, and Louisa Emmons
Atmos. Chem. Phys., 24, 13477–13502, https://doi.org/10.5194/acp-24-13477-2024, https://doi.org/10.5194/acp-24-13477-2024, 2024
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Aerosol property measurements recently collected on the ground and by a research aircraft in central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign exhibit large spatial and temporal variability. These measurements coupled with coincident meteorological information provide a valuable data set needed to evaluate and improve model predictions of aerosols in a traditionally data-sparse region of South America.
Xiaoli Shen, David M. Bell, Hugh Coe, Naruki Hiranuma, Fabian Mahrt, Nicholas A. Marsden, Claudia Mohr, Daniel M. Murphy, Harald Saathoff, Johannes Schneider, Jacqueline Wilson, Maria A. Zawadowicz, Alla Zelenyuk, Paul J. DeMott, Ottmar Möhler, and Daniel J. Cziczo
Atmos. Chem. Phys., 24, 10869–10891, https://doi.org/10.5194/acp-24-10869-2024, https://doi.org/10.5194/acp-24-10869-2024, 2024
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Single-particle mass spectrometry (SPMS) is commonly used to measure the chemical composition and mixing state of aerosol particles. Intercomparison of SPMS instruments was conducted. All instruments reported similar size ranges and common spectral features. The instrument-specific detection efficiency was found to be more dependent on particle size than type. All differentiated secondary organic aerosol, soot, and soil dust but had difficulties differentiating among minerals and dusts.
Christopher R. Niedek, Fan Mei, Maria A. Zawadowicz, Zihua Zhu, Beat Schmid, and Qi Zhang
Atmos. Meas. Tech., 16, 955–968, https://doi.org/10.5194/amt-16-955-2023, https://doi.org/10.5194/amt-16-955-2023, 2023
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This novel micronebulization aerosol mass spectrometry (MS) technique requires a low sample volume (10 μL) and can quantify nanogram levels of organic and inorganic particulate matter (PM) components when used with 34SO4. This technique was successfully applied to PM samples collected from uncrewed atmospheric measurement platforms and provided chemical information that agrees well with real-time data from a co-located aerosol chemical speciation monitor and offline data from secondary ion MS.
François Burgay, Rafael Pedro Fernández, Delia Segato, Clara Turetta, Christopher S. Blaszczak-Boxe, Rachael H. Rhodes, Claudio Scarchilli, Virginia Ciardini, Carlo Barbante, Alfonso Saiz-Lopez, and Andrea Spolaor
The Cryosphere, 17, 391–405, https://doi.org/10.5194/tc-17-391-2023, https://doi.org/10.5194/tc-17-391-2023, 2023
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The paper presents the first ice-core record of bromine (Br) in the Antarctic plateau. By the observation of the ice core and the application of atmospheric chemical models, we investigate the behaviour of bromine after its deposition into the snowpack, with interest in the effect of UV radiation change connected to the formation of the ozone hole, the role of volcanic deposition, and the possible use of Br to reconstruct past sea ice changes from ice core collect in the inner Antarctic plateau.
Fabian Mahrt, Carolin Rösch, Kunfeng Gao, Christopher H. Dreimol, Maria A. Zawadowicz, and Zamin A. Kanji
Atmos. Chem. Phys., 23, 1285–1308, https://doi.org/10.5194/acp-23-1285-2023, https://doi.org/10.5194/acp-23-1285-2023, 2023
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Major aerosol types emitted by biomass burning include soot, ash, and charcoal particles. Here, we investigated the ice nucleation activity of 400 nm size-selected particles of two different pyrolyis-derived charcoal types in the mixed phase and cirrus cloud regime. We find that ice nucleation is constrained to cirrus cloud conditions, takes place via pore condensation and freezing, and is largely governed by the particle porosity and mineral content.
Paul A. Barrett, Steven J. Abel, Hugh Coe, Ian Crawford, Amie Dobracki, James Haywood, Steve Howell, Anthony Jones, Justin Langridge, Greg M. McFarquhar, Graeme J. Nott, Hannah Price, Jens Redemann, Yohei Shinozuka, Kate Szpek, Jonathan W. Taylor, Robert Wood, Huihui Wu, Paquita Zuidema, Stéphane Bauguitte, Ryan Bennett, Keith Bower, Hong Chen, Sabrina Cochrane, Michael Cotterell, Nicholas Davies, David Delene, Connor Flynn, Andrew Freedman, Steffen Freitag, Siddhant Gupta, David Noone, Timothy B. Onasch, James Podolske, Michael R. Poellot, Sebastian Schmidt, Stephen Springston, Arthur J. Sedlacek III, Jamie Trembath, Alan Vance, Maria A. Zawadowicz, and Jianhao Zhang
Atmos. Meas. Tech., 15, 6329–6371, https://doi.org/10.5194/amt-15-6329-2022, https://doi.org/10.5194/amt-15-6329-2022, 2022
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To better understand weather and climate, it is vital to go into the field and collect observations. Often measurements take place in isolation, but here we compared data from two aircraft and one ground-based site. This was done in order to understand how well measurements made on one platform compared to those made on another. Whilst this is easy to do in a controlled laboratory setting, it is more challenging in the real world, and so these comparisons are as valuable as they are rare.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Ka Ming Fung, Colette L. Heald, Jesse H. Kroll, Siyuan Wang, Duseong S. Jo, Andrew Gettelman, Zheng Lu, Xiaohong Liu, Rahul A. Zaveri, Eric C. Apel, Donald R. Blake, Jose-Luis Jimenez, Pedro Campuzano-Jost, Patrick R. Veres, Timothy S. Bates, John E. Shilling, and Maria Zawadowicz
Atmos. Chem. Phys., 22, 1549–1573, https://doi.org/10.5194/acp-22-1549-2022, https://doi.org/10.5194/acp-22-1549-2022, 2022
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Understanding the natural aerosol burden in the preindustrial era is crucial for us to assess how atmospheric aerosols affect the Earth's radiative budgets. Our study explores how a detailed description of dimethyl sulfide (DMS) oxidation (implemented in the Community Atmospheric Model version 6 with chemistry, CAM6-chem) could help us better estimate the present-day and preindustrial concentrations of sulfate and other relevant chemicals, as well as the resulting aerosol radiative impacts.
Yang Wang, Guangjie Zheng, Michael P. Jensen, Daniel A. Knopf, Alexander Laskin, Alyssa A. Matthews, David Mechem, Fan Mei, Ryan Moffet, Arthur J. Sedlacek, John E. Shilling, Stephen Springston, Amy Sullivan, Jason Tomlinson, Daniel Veghte, Rodney Weber, Robert Wood, Maria A. Zawadowicz, and Jian Wang
Atmos. Chem. Phys., 21, 11079–11098, https://doi.org/10.5194/acp-21-11079-2021, https://doi.org/10.5194/acp-21-11079-2021, 2021
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This paper reports the vertical profiles of trace gas and aerosol properties over the eastern North Atlantic, a region of persistent but diverse subtropical marine boundary layer (MBL) clouds. We examined the key processes that drive the cloud condensation nuclei (CCN) population and how it varies with season and synoptic conditions. This study helps improve the model representation of the aerosol processes in the remote MBL, reducing the simulated aerosol indirect effects.
Maria A. Zawadowicz, Kaitlyn Suski, Jiumeng Liu, Mikhail Pekour, Jerome Fast, Fan Mei, Arthur J. Sedlacek, Stephen Springston, Yang Wang, Rahul A. Zaveri, Robert Wood, Jian Wang, and John E. Shilling
Atmos. Chem. Phys., 21, 7983–8002, https://doi.org/10.5194/acp-21-7983-2021, https://doi.org/10.5194/acp-21-7983-2021, 2021
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This paper describes the results of a recent field campaign in the eastern North Atlantic, where two mass spectrometers were deployed aboard a research aircraft to measure the chemistry of aerosols and trace gases. Very clean conditions were found, dominated by local sulfate-rich acidic aerosol and very aged organics. Evidence of
long-range transport of aerosols from the continents was also identified.
Cited articles
Anderson, B. J., Musicant, D. R., Ritz, A. M., Ault, A., Gross, D., Yuen, M., and Gälli, M.: User-Friendly Clustering for Atmospheric Data Analysis, Carleton College Computer Science Technical Report, Department of Computer Science at Brown University, 9 pp., https://cs.brown.edu/people/aritz/files/tech2005a.pdf (last access: 20 March 2026), 2005.
Andreae, M. O. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth Sci. Rev., 89, 13–41, 2008.
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo, K. M., and Silva-Dias, M. A. F.: Smoking rain clouds over the Amazon, Science, 303, 1337–1342, 2004.
Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., Burovski, E., Chauhan, G., Chourdia, A., Constable, W., Desmaison, A., DeVito, Z., Ellison, E., Feng, W., Gong, J., Gschwind, M., Hirsh, B., Huang, S., Kalambarkar, K., Kirsch, L., Lazos, M., Lezcano, M., Liang, Y., Liang, J., Lu, Y., Luk, C. K., Maher, B., Pan, Y., Puhrsch, C., Reso, M., Saroufim, M., Siraichi, M. Y., Suk, H., Zhang, S., Suo, M., Tillet, P., Zhao, X., Wang, E., Zhou, K., Zou, R., Wang, X., Mathews, A., Wen, W., Chanan, G., Wu, P., and Chintala, S.: PyTorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation, in: ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, La Jolla, CA, USA, 27 April–1 May 2024, Association for Computing Machinery (ACM), New York, NY, United States, 2, 929–947, https://doi.org/10.1145/3620665.3640366, 2024.
Arndt, J., Sciare, J., Mallet, M., Roberts, G. C., Marchand, N., Sartelet, K., Sellegri, K., Dulac, F., Healy, R. M., and Wenger, J. C.: Sources and mixing state of summertime background aerosol in the north-western Mediterranean basin, Atmos. Chem. Phys., 17, 6975–7001, https://doi.org/10.5194/acp-17-6975-2017, 2017.
Atkinson, J. D., Murray, B. J., Woodhouse, M. T., Whale, T. F., Baustian, K. J., Carslaw, K. S., Dobbie, S., O'Sullivan, D., and Malkin, T. L.: The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds, Nature, 498, 355–358, 2013.
Atwood, S. A., Kreidenweis, S. M., DeMott, P. J., Petters, M. D., Cornwell, G. C., Martin, A. C., and Moore, K. A.: Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model, Atmos. Chem. Phys., 19, 6931–6947, https://doi.org/10.5194/acp-19-6931-2019, 2019.
Ault, A. P. and Axson, J. L.: Atmospheric aerosol chemistry: Spectroscopic and microscopic advances, Anal. Chem., 89, 430–452, 2017.
Axson, J. L., May, N. W., Colón-Bernal, I. D., Pratt, K. A., and Ault, A. P.: Lake spray aerosol: A chemical signature from individual ambient particles, Environ. Sci. Technol., 50, 9835–9845, 2016.
Beck, A. G., Muhoberac, M., Randolph, C. E., Beveridge, C. H., Wijewardhane, P. R., Kenttämaa, H. I., and Chopra, G.: Recent developments in machine learning for mass spectrometry, ACS Meas. Sci. Au., 4, 233–246, 2024.
Bellouin, N., Boucher, O., Haywood, J., and Reddy, M. S.: Global estimate of aerosol direct radiative forcing from satellite measurements, Nature, 438, 1138–1141, 2005.
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment: Black carbon in the climate system, J. Geophys. Res., 118, 5380–5552, 2013.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols, in: Climate Change 2013 – The Physical Science Basis, vol. 5, edited by: Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 571–658, https://doi.org/10.1017/cbo9781107415324.016, 2014.
Casuccio, G. S., Janocko, P. B., Lee, R. J., Kelly, J. F., Dattner, S. L., and Mgebroff, J. S.: The use of computer controlled scanning electron microscopy in environmental studies, J. Air Pollut. Control Assoc., 33, 937–943, 1983.
Chen, Y., Liu, H., Huang, R.-J., Yang, F., Tian, M., Yao, X., Shen, Z., Yan, L., and Cao, J.: Atmospheric processing of loess particles in a polluted urban area of northwestern China, J. Geophys. Res., 124, 7919–7929, 2019.
Christopoulos, C.: A Machine Learning Approach to Aerosol Classification for Single Particle Mass Spectrometry, V1, Harvard Dataverse [data set], https://doi.org/10.7910/DVN/J1FZYU, 2018.
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.
Cortes, C. and Vapnik, V.: Support-vector networks, Mach. Learn., 20, 273–297, 1995.
Cziczo, D. J., Murphy, D. M., Hudson, P. K., and Thomson, D. S.: Single particle measurements of the chemical composition of cirrus ice residue during Crystal-face: Chemical composition of ice residue, J. Geophys. Res., 109, https://doi.org/10.1029/2003JD004032, 2004.
Cziczo, D. J., Thomson, D. S., Thompson, T. L., DeMott, P. J., and Murphy, D. M.: Particle analysis by laser mass spectrometry (PALMS) studies of ice nuclei and other low number density particles, Int. J. Mass Spectrom., 258, 21–29, 2006.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting global atmospheric ice nuclei distributions and their impacts on climate, P. Natl. Acad. Sci. USA, 107, 11217–11222, 2010.
DeMott, P. J., Hill, T. C. J., McCluskey, C. S., Prather, K. A., Collins, D. B., Sullivan, R. C., Ruppel, M. J., Mason, R. H., Irish, V. E., Lee, T., Hwang, C. Y., Rhee, T. S., Snider, J. R., McMeeking, G. R., Dhaniyala, S., Lewis, E. R., Wentzell, J. J. B., Abbatt, J., Lee, C., Sultana, C. M., Ault, A. P., Axson, J. L., Diaz Martinez, M., Venero, I., Santos-Figueroa, G., Stokes, M. D., Deane, G. B., Mayol-Bracero, O. L., Grassian, V. H., Bertram, T. H., Bertram, A. K., Moffett, B. F., and Franc, G. D.: Sea spray aerosol as a unique source of ice nucleating particles, P. Natl. Acad. Sci. USA, 113, 5797–5803, 2016.
Farmer, D. K., Cappa, C. D., and Kreidenweis, S. M.: Atmospheric processes and their controlling influence on cloud condensation nuclei activity, Chem. Rev., 115, 4199–4217, 2015.
Freutel, F., Drewnick, F., Schneider, J., Klimach, T., and Borrmann, S.: Quantitative single-particle analysis with the Aerodyne aerosol mass spectrometer: development of a new classification algorithm and its application to field data, Atmos. Meas. Tech., 6, 3131–3145, https://doi.org/10.5194/amt-6-3131-2013, 2013.
Froyd, K. D., Murphy, D. M., Brock, C. A., Campuzano-Jost, P., Dibb, J. E., Jimenez, J.-L., Kupc, A., Middlebrook, A. M., Schill, G. P., Thornhill, K. L., Williamson, C. J., Wilson, J. C., and Ziemba, L. D.: A new method to quantify mineral dust and other aerosol species from aircraft platforms using single-particle mass spectrometry, Atmos. Meas. Tech., 12, 6209–6239, https://doi.org/10.5194/amt-12-6209-2019, 2019.
Fuller, R., Landrigan, P. J., Balakrishnan, K., Bathan, G., Bose-O'Reilly, S., Brauer, M., Caravanos, J., Chiles, T., Cohen, A., Corra, L., Cropper, M., Ferraro, G., Hanna, J., Hanrahan, D., Hu, H., Hunter, D., Janata, G., Kupka, R., Lanphear, B., Lichtveld, M., Martin, K., Mustapha, A., Sanchez-Triana, E., Sandilya, K., Schaefli, L., Shaw, J., Seddon, J., Suk, W., Téllez-Rojo, M. M., and Yan, C.: Pollution and health: a progress update, Lancet Planet. Health, 6, e535–e547, 2022.
Gard, E., Mayer, J. E., Morrical, B. D., Dienes, T., Fergenson, D. P., and Prather, K. A.: Real-time analysis of individual atmospheric aerosol particles: Design and performance of a portable ATOFMS, Anal. Chem., 69, 4083–4091, 1997.
Gong, X., Wex, H., Müller, T., Henning, S., Voigtländer, J., Wiedensohler, A., and Stratmann, F.: Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification, Atmos. Chem. Phys., 22, 5175–5194, https://doi.org/10.5194/acp-22-5175-2022, 2022.
Gross, D. S., Robert Atlas, Rzeszotarski, J., Turetsky, E., Christensen, J., Benzaid, S., Olson, J., Smith, T., Steinberg, L., and Sulman, J.: Environmental chemistry through intelligent atmospheric data analysis, Environ. Modell. Softw., 25, 760–769, 2010.
Hinz, K.-P., Kaufmann, R., and Spengler, B.: Laser-induced mass analysis of single particles in the airborne state, Anal. Chem., 66, 2071–2076, 1994.
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., and Krämer, M.: Overview of ice nucleating particles, Meteorol. Monogr., 58, 1.1–1.33, 2017.
Kim, D. S., Hopke, P. K., Massart, D. L., Kaufman, L., and Casuccio, G. S.: Multivariate analysis of CCSEM auto emission data, Sci. Total Environ., 59, 141–155, 1987.
Lamb, K. D.: Classification of iron oxide aerosols by a single particle soot photometer using supervised machine learning, Atmos. Meas. Tech., 12, 3885–3906, https://doi.org/10.5194/amt-12-3885-2019, 2019.
Li, L., Huang, Z., Dong, J., Li, M., Gao, W., Nian, H., Fu, Z., Zhang, G., Bi, X., Cheng, P., and Zhou, Z.: Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles, Int. J. Mass Spectrom., 303, 118–124, 2011.
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J.: On the variance of the adaptive learning rate and beyond, arXiv [preprint], https://doi.org/10.48550/arXiv.1908.03265, 2019.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005.
Loshchilov, I. and Hutter, F.: Decoupled weight decay regularization, arXiv [preprint], https://doi.org/10.48550/arXiv.1711.05101, 2017.
McKeown, P. J., Johnston, M. V., and Murphy, D. M.: On-line single-particle analysis by laser desorption mass spectrometry, Anal. Chem., 63, 2069–2073, 1991.
McNamara, S. M., Kolesar, K. R., Wang, S., Kirpes, R. M., May, N. W., Gunsch, M. J., Cook, R. D., Fuentes, J. D., Hornbrook, R. S., Apel, E. C., China, S., Laskin, A., and Pratt, K. A.: Observation of road salt aerosol driving inland wintertime atmospheric chlorine chemistry, ACS Cent. Sci., 6, 684–694, 2020.
Moffet, R. C. and Prather, K. A.: Extending ATOFMS measurements to include refractive index and density, Anal. Chem., 77, 6535–6541, 2005.
Moffet, R. C. and Prather, K. A.: In-situ measurements of the mixing state and optical properties of soot with implications for radiative forcing estimates, P. Natl. Acad. Sci. USA, 106, 11872–11877, 2009.
Murphy, D. M.: The design of single particle laser mass spectrometers, Mass Spectrom. Rev., 26, 150–165, 2007.
Murphy, D. M. and Thomson, D. S.: Chemical composition of single aerosol particles at Idaho Hill: Negative ion measurements, J. Geophys. Res., 102, 6353–6368, 1997.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forcing, in: Climate Change 2013 – The Physical Science Basis, edited by: Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 659–740, https://doi.org/10.1017/cbo9781107415324.018, 2014.
Noble, C. A. and Prather, K. A.: Real-time measurement of correlated size and composition profiles of individual atmospheric aerosol particles, Environ. Sci. Technol., 30, 2667–2680, 1996.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Phares, D. J., Rhoads, K. P., Wexler, A. S., Kane, D. B., and Johnston, M. V.: Application of the ART-2a algorithm to laser ablation aerosol mass spectrometry of particle standards, Anal. Chem., 73, 2338–2344, 2001.
Phares, D. J., Rhoads, K. P., Johnston, M. V., and Wexler, A. S.: Size-resolved ultrafine particle composition analysis 2, Houston, J. Geophys. Res., 108, https://doi.org/10.1029/2001JD001212, 2003.
Pope III, C. A. and Dockery, D. W.: Health effects of fine particulate air pollution: lines that connect, J. Air Waste Manag. Assoc., 56, 709–742, 2006.
Prather, K. A., Nordmeyer, T., and Salt, K.: Real-time characterization of individual aerosol particles using time-of-flight mass spectrometry, Anal. Chem., 66, 1403–1407, 1994.
Prather, K. A., Hatch, C. D., and Grassian, V. H.: Analysis of atmospheric aerosols, Annu. Rev. Anal. Chem., 1, 485–514, 2008.
Pratt, K. A., Mayer, J. E., Holecek, J. C., Moffet, R. C., Sanchez, R. O., Rebotier, T. P., Furutani, H., Gonin, M., Fuhrer, K., Su, Y., Guazzotti, S., and Prather, K. A.: Development and characterization of an aircraft aerosol time-of-flight mass spectrometer, Anal. Chem., 81, 1792–1800, 2009.
Qin, X., Pratt, K. A., Shields, L. G., Toner, S. M., and Prather, K. A.: Seasonal comparisons of single-particle chemical mixing state in Riverside, CA, Atmos. Environ., 59, 587–596, 2012.
Ravishankara, A. R., Rudich, Y., and Wuebbles, D. J.: Physical chemistry of climate metrics, Chem. Rev., 115, 3682–3703, 2015.
Riemer, N., Ault, A. P., West, M., Craig, R. L., and Curtis, J. H.: Aerosol mixing state: Measurements, modeling, and impacts, Rev. Geophys., 57, 187–249, 2019.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics, 3rd edn., Wiley-Blackwell, Hoboken, NJ, 1152 pp., https://doi.org/10.1029/2018rg000615, 2016.
Shen, X., Saathoff, H., Huang, W., Mohr, C., Ramisetty, R., and Leisner, T.: Understanding atmospheric aerosol particles with improved particle identification and quantification by single-particle mass spectrometry, Atmos. Meas. Tech., 12, 2219–2240, https://doi.org/10.5194/amt-12-2219-2019, 2019.
Shen, X., Bell, D. M., Coe, H., Hiranuma, N., Mahrt, F., Marsden, N. A., Mohr, C., Murphy, D. M., Saathoff, H., Schneider, J., Wilson, J., Zawadowicz, M. A., Zelenyuk, A., DeMott, P. J., Möhler, O., and Cziczo, D. J.: Measurement report: The Fifth International Workshop on Ice Nucleation phase 1 (FIN-01): intercomparison of single-particle mass spectrometers, Atmos. Chem. Phys., 24, 10869–10891, https://doi.org/10.5194/acp-24-10869-2024, 2024.
Silva, P. J., Liu, D.-Y., Noble, C. A., and Prather, K. A.: Size and chemical characterization of individual particles resulting from biomass burning of local southern California species, Environ. Sci. Technol., 33, 3068–3076, 1999.
Song, X.-H., Hopke, P. K., Fergenson, D. P., and Prather, K. A.: Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A, Anal. Chem., 71, 860–865, 1999.
Su, Y., Sipin, M. F., Furutani, H., and Prather, K. A.: Development and characterization of an aerosol time-of-flight mass spectrometer with increased detection efficiency, Anal. Chem., 76, 712–719, 2004.
Tarvainen, A. and Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, Neural Inf. Process. Syst., 30, 1195–1204, 2017.
Usher, C. R., Michel, A. E., and Grassian, V. H.: Reactions on mineral dust, Chem. Rev., 103, 4883–4940, 2003.
Wang, G., Ruser, H., Schade, J., Passig, J., Adam, T., Dollinger, G., and Zimmermann, R.: 1D-CNN network based real-time aerosol particle classification with single-particle mass spectrometry, IEEE Sens. Lett., 7, 1–4, 2023.
Wang, G., Ruser, H., Schade, J., Passig, J., Zimmermann, R., Dollinger, G., and Adam, T.: A fuzzy convolutional neural network for the classification of aerosol particle mass spectral patterns generated by single-particle mass spectrometry, in: 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 30 June–5 July 2024, 1–8, https://doi.org/10.1109/ijcnn60899.2024.10650883, 2024a.
Wang, G., Ruser, H., Schade, J., Passig, J., Zimmermann, R., Dollinger, G., and Adam, T.: CNN-based aerosol particle classification using 2D representations of single-particle mass spectrometer data, in: 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Osaka, Japan, 19–22 February 2024, 1–6, https://doi.org/10.1109/icaiic60209.2024.10463253, 2024b.
Wang, G., Ruser, H., Schade, J., Passig, J., Adam, T., Dollinger, G., and Zimmermann, R.: Machine learning approaches for automatic classification of single-particle mass spectrometry data, Atmos. Meas. Tech., 17, 299–313, https://doi.org/10.5194/amt-17-299-2024, 2024c.
Wang, G., Ruser, H., Schade, J., Passig, J., Zimmermann, R., Dollinger, G., and Adam, T.: Rapid classification of aerosol particle mass spectra using data augmentation and deep learning, in: 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, Singapore, 25–27 June 2024, 1167–1172, https://doi.org/10.1109/cai59869.2024.00208, 2024d.
Wang, S., He, B., Yuan, M., Su, F., Yin, S., Yan, Q., Jiang, N., Zhang, R., and Tang, X.: Characterization of individual particles and meteorological conditions during the cold season in Zhengzhou using a single particle aerosol mass spectrometer, Atmos. Res., 219, 13–23, 2019.
Wolf, M. J., Zhang, Y., Zawadowicz, M. A., Goodell, M., Froyd, K., Freney, E., Sellegri, K., Rösch, M., Cui, T., Winter, M., Lacher, L., Axisa, D., DeMott, P. J., Levin, E. J. T., Gute, E., Abbatt, J., Koss, A., Kroll, J. H., Surratt, J. D., and Cziczo, D. J.: A biogenic secondary organic aerosol source of cirrus ice nucleating particles, Nat. Commun., 11, 4834, https://doi.org/10.1038/s41467-020-18424-6, 2020.
Xu, J., Wang, H., Li, X., Li, Y., Wen, J., Zhang, J., Shi, X., Li, M., Wang, W., Shi, G., and Feng, Y.: Refined source apportionment of coal combustion sources by using single particle mass spectrometry, Sci. Total Environ., 627, 633–646, 2018.
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods, in: Proceedings of the 33rd annual meeting on Association for Computational Linguistics, Cambridge, Massachusetts, 26–30 June 1995, https://doi.org/10.3115/981658.981684, 1995.
Zawadowicz, M. A., Froyd, K. D., Murphy, D. M., and Cziczo, D. J.: Improved identification of primary biological aerosol particles using single-particle mass spectrometry, Atmos. Chem. Phys., 17, 7193–7212, https://doi.org/10.5194/acp-17-7193-2017, 2017.
Zawadowicz, M. A., Lance, S., Jayne, J. T., Croteau, P., Worsnop, D. R., Mahrt, F., Leisner, T., and Cziczo, D. J.: Quantifying and improving the optical performance of the laser ablation aerosol particle time of flight mass spectrometer (LAAPToF) instrument, Aerosol Sci. Tech., 54, 761–771, 2020.
Zelenyuk, A., Imre, D., Nam, E. J., Han, Y., and Mueller, K.: ClusterSculptor: Software for expert-steered classification of single particle mass spectra, Int. J. Mass Spectrom., 275, 1–10, 2008.
Zhang, G., Han, B., Bi, X., Dai, S., Huang, W., Chen, D., Wang, X., Sheng, G., Fu, J., and Zhou, Z.: Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry, Atmos. Res., 153, 286–295, 2015.
Zhou, Z.-H.: Semi-supervised learning, in: Machine Learning, Springer Singapore, Singapore, 315–341, https://doi.org/10.1007/978-981-15-1967-3_13, 2021.
Short summary
In this study, we leverage the power of machine learning to develop classifiers using a comprehensive dataset of SPMS spectra. These classifiers enable automatic differentiation of aerosol particles based on their chemistry and size, facilitating more accurate and efficient aerosol classification. Our results show increased accuracy when including unlabeled data in a semi-supervised framework.
In this study, we leverage the power of machine learning to develop classifiers using a...
