Multiparameter bioaerosol spectrometer (MBS) laboratory characterization of coarse-mode particles — raw data

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Paul Zieger, Pavla Dagsson-Waldhauserová, Yutaka Tobo

This dataset contains single-particle fluorescence spectra and optical scattering properties of aerosols from known biological and non-biological sources measured using a Multiparameter Bioaerosol Spectrometer (MBS). The data are provided as timestamped tables including raw fluorescence, scattering, and derived classification variables for individual particles.

This dataset is used to develop and validate methods for identifying and classifying bioaerosols, particularly distinguishing biological particles from non-biological fluorescent particles, and to support the development of a supervised machine learning classification algorithm that will enable comparison between laboratory-characterized particles and unknown ambient aerosols.

Measurements were conducted in controlled laboratory experiments at Stockholm University’s aerosol laboratory in 2021–2022 using wet and dry aerosol generation methods. The dataset represents single-particle observations of pollen, bacteria, dust, microplastics, and cellulose. The MBS measures optical particle diameter, the fluorescence emission spectrum over the ~300-650 nm wavelength range when excited with 280 nm light, and two linear chords of optical scattering signals from diffraction patterns for each particle.

AtmosphereAerosolsAerosolsBioaerosolsPollenBacteriaDustFluorescence spectroscopyOptical scatteringMorphology

Name

Version

Citation

References

Jönsson A, Fu J, Freitas GP, Crawford I, Dagsson-Waldhauserová P, Krejci R, Tobo Y, Yttri KE, Zieger P (2026a) Tracing biological, human, and inorganic sources of coarse aerosols via single-particle fluorescence and optical morphology. EGUsphere [Manuscript in review]. https://doi.org/10.5194/egusphere-2026-59

Jönsson A, Fu J, Pereira Freitas G, Zieger P, Crawford I, Dagsson-Waldhauserová P, Tobo Y (2026b) Multiparameter bioaerosol spectrometer (MBS) laboratory characterization of coarse-mode particles — processed data. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/jonsson-2026-aerosol-mbs-1

Ruske S, Topping DO, Foot VE, Kaye PH, Stanley WR, Crawford I, Morse AP, Gallagher MW (2017) Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer. Atmospheric Measurement Techniques 10:695–708. https://doi.org/10.5194/amt-10-695-2017

Data description

Data are provided in 37 comma-separated variable (csv) files.

There are also two files with data documentation.

Total size: 1.9 GiB

data

Files

Raw Multiparameter bioaerosol spectrometer (MBS) output files in csv format, as produced by the MBS and its software (see Ruske et al., 2017 for a characterization of the instrument), including unadjusted fluorescence emission spectra, optical scattering patterns, and bulk properties of scattering patterns.

Fluorescence is excited by 280 nm light. Optical scattering signals are detected in two linear complementary metal oxide semiconductor (CMOS) arrays of 512 pixels each, placed to the left (L) and right (R) of the scattering beam centerline.

Rows in csv files represent individual measurements of either single particles or of force triggered (FT) detections for background measurements.

Each row (except those with FT = 1, which are background detections of the optical stage without particles) corresponds to a measurement of a single particle, and all statistics derived from the MBS's optical components.

The MBS output files have the prefix MBS_raw and include a date (YYYYMMDD) and timestamp (HHMMSS) for the first measurement (e.g. MBS_raw_20220314_142440.csv, with the first measurement taking place on 2022-03-14 at 14:24:40 UTC).

The MBS creates a new output file automatically after a certain amount of time or when the current file reaches a size of ~69 MB. There are a total of 37 raw files that may all be loaded at once (e.g., with Pandas as a multifile dataframe).

The correct time blocks corresponding to each particle source type are given in the table below. Times outside of these blocks are not valid as they may include measurements taken during interruptions (e.g. plumbing exchanges). Two blank tests with empty nebulizer setups were made to monitor background concentrations from the experimental setup without samples.

Variables

Variables include

• Time Timestamp of detection (in UTC timezone)
• XE1_1 to XE1_8 Raw fluorescence detector intensities for channel 1-8/A-H in detector arbitrary units (au)
• TOF Time of flight in stage (μs)
• Size Optical diameter (μm)
• Total Total number of particles sensed at timestamp
• Measured Total number of particles measured for fluorescence and optical scattering at timestamp
• FT Force trigger flag for detector background signals
• AsymLR% Left-right mirror asymmetry across corresponding pixels (%)
• AsymLR%inv Left-right mirror asymmetry across inverted pixels, with one array reversed top-to-bottom (%)
• SumL, SumR Sum of scattering signal intensities in L and R arrays (au)
• PeakL, PeakR Peak scattering signal intensities in L and R arrays (au)
• MeanL, MeanR Mean scattering signal intensities in L and R arrays (au)
• PeakMeanL, PeakMeanR Peak-to-mean ratios of scattering signal intensities in L and R arrays
• VarianceL, VarianceR Variance in scattering signal intensities in L and R arrays (au)
• PeakWidthL, PeakWidthR Peak width at half height in L and R arrays (pixels)
• PeakCountL, PeakCountR Number of peaks over threshold in L and R arrays
• KurtosisL, KurtosisR Kurtosis of scattering signal treated as a distribution in L and R arrays
• SkewL, SkewR Skew of scattering signal treated as a distribution in L and R arrays
• MirrorL, MirrorR Mirror symmetry across middle pixel in L and R arrays (%)
• CA1_0 to CA1_511, CA2_0 to CA2_511 Optical scattering detector intensities for the L and R arrays, respectively (au)

Additional raw output variable descriptors, including raw scattering signal pixel intensities, are described in the file documents/MBS_output_appendix.pdf.

documents

Data documentation including the following files:

• MBS_output_appendix.pdf Describing MSB output variables
• Labbook_MBS_2022_JF.txt Logbook for the laboratory experiments

Comments

This dataset includes raw single particle Multiparameter Bioaerosol Spectrometer (MBS; University of Hertfordshire, UK) output data from laboratory characterization experiments performed by Jinglan Fu, Gabriel Freitas, and Paul Zieger and analyzed by Aiden Jönsson at the Department of Environmental Science (Stockholm University).

Method overview

We characterized single-particle fluorescence spectra and optical scattering properties of particles from known sources in controlled settings at the Department of Environmental Science (Stockholm University)'s aerosol laboratory using a Multiparameter Bioaerosol Spectrometer (MBS; University of Hertfordshire, UK) in 2021-2022.

The MBS measures optical particle diameter, the fluorescence emission spectrum over the ~300-650 nm wavelength range when excited with 280 nm light, and two linear chords of optical scattering signals from diffraction patterns for each particle. The source samples include pollen, bacteria, dust, microplastics, and cellulose. These particles were nebulized using wet (Topas GmbH, Germany, model ATM228) and dry (vibration by speaker at a stable frequency) aerosol generation methods and dried before measuring.

The samples, procedures, and results of these characterization experiments are described in Jönsson et al. (2026a). These data provide aerosol fluorescence and optical morphology properties on the single-particle level in tables of each timestamped particle's data, and can be used to compare against ambient aerosol properties of unknown origin for identifying their potential sources.

In Jönsson et al. (2026a), the properties of particles measured in these experiments are comapred with the characterization data of Crawford et al. (2020) (fungal spores), Ruske et al. (2017) (pollen, bacteria, and dust), and Beck et al. (2024) (sea spray aerosol and polystyrene latex spheres), along with observations of ship exhaust plumes from Karlsson et al. (2020). This combined dataset is used to develop a classification algorithm with supervised machine learning components.

Related processed data

A corresponding dataset, published separately in Jönsson et al. (2026b), includes data from these previously published experiments/observations as well as our source experiments in the preprocessed form used to train component models for our classification algorithm.

Source sample acknowledgements

Pollen characterized in our experiments were collected in the Czech Republic and analyzed by Pharmallerga CZ S.r.o, and provided by Zbynek Drab. Bacteria culture samples were provided by Julika Zinke and the Baltic Sea Center. Dust samples characterized here include volcanic sand collected by Pavla Dagsson-Waldhauserová in Myrdalssandur and Dyngjusandur, Iceland, glacial outwash sediment collected in Svalbard by Yutaka Tobo, kaolinite clay provided by Birgitta Liewenborg, and volcanic ash collected by Ingrid Zieger. Cellulose and PE samples were provided by Elena Gorokhova.

We are thankful for the support of the University of Hertfordshire, particularly from Paul Kaye and Warren Stanley, in the collaboration, development, and maintenance of the MBS.

Project

European Union's Horizon Europe Programme (Grant Agreement No. 101137639, CleanCloud) and the Swedish Research Council (grant no. 2018-05045)

Publisher

Bolin Centre Database

License

Open Data Commons Attribution License (ODC-By) v1.0

First name

Paul

Last name or organisation

Zieger

Email address

paul.zieger@aces.su.se

Address

Department of Environmental Science; Stockholm University

Postal code

SE-106 91

City

Stockholm

Country

Sweden

GCMD science keyword

Earth science > Atmosphere > Aerosols

GCMD location

Geographic Region > Global

DOI

Time