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

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Paul Zieger, Ian Crawford, 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 processed 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 using wet and dry aerosol generation methods. The dataset represents single-particle observations of pollen, bacteria, dust, microplastics, cellulose, sea spray aerosol, and fungal spores. 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

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Name

jonsson-2026-aerosol-mbs

Version

Citation

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Paul Zieger, Ian Crawford, Pavla Dagsson-Waldhauserová, Yutaka Tobo (2026) 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

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, Dagsson-Waldhauserová P, Tobo Y (2026b) Multiparameter bioaerosol spectrometer (MBS) laboratory characterization of coarse-mode particles — raw data. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/jonsson-2026-aerosol-mbs-raw-1

Freitas GP, Stolle C, Kaye PH, Stanley W, Herlemann DPR, Salter ME, Zieger P (2022) Emission of primary bioaerosol particles from Baltic seawater. Environmental Science: Atmospheres 2:1170–1182. https://doi.org/10.1039/d2ea00047d

Crawford I, Topping D, Gallagher M, Forde E, Lloyd JR, Foot V, Stopford C, Kaye P (2020) Detection of Airborne Biological Particles in Indoor Air Using a Real-Time Advanced Morphological Parameter UV-LIF Spectrometer and Gradient Boosting Ensemble Decision Tree Classifiers. Atmosphere 11:1039. https://doi.org/10.3390/atmos11101039

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

Karlsson L, Baccarini A, Duplessis P, Baumgardner D, Brooks IM, Chang RYW, Dada L, Dällenbach KR, Heikkinen L, Krejci R, Leaitch WR, Leck C, Partridge DG, Salter ME, Wernli H, Wheeler MJ, Schmale J, Zieger P (2022) Physical and Chemical Properties of Cloud Droplet Residuals and Aerosol Particles During the Arctic Ocean 2018 Expedition. Journal of Geophysical Research: Atmospheres 127. https://doi.org/10.1029/2021jd036383

Beck I, Moallemi A, Heutte B, Pernov JB, Bergner N, Rolo M, Quéléver LLJ, Laurila T, Boyer M, Jokinen T, Angot H, Hoppe CJM, Müller O, Creamean J, Frey MM, Freitas G, Zinke J, Salter, M, Zieger P, Mirrielees JA, Kempf HE, Ault AP, Pratt KA, Gysel-Beer M, Henning S, Tatzelt C, Schmale J (2024) Characteristics and sources of fluorescent aerosols in the central Arctic Ocean. Elem Sci Anth 12. https://doi.org/10.1525/elementa.2023.00125

Crawford I, University of Manchester (2025) ACS 2019 & 2021 MBS-M fungal spore aerosol training data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15374773

Data description

Data are provided in 38 comma-separated variable (csv) files.

There is also a python script used for preprocessing.

Total size: 4.2 GiB

`data`

Preprocessed files in .csv format. This includes background-adjusted and normalized fluorescence emission spectra, subsetted optical scattering pattern properties (as calculated by the MBS software), fluorescence-based classifications according to the decision tree method of Freitas et al. (2022). For background adjustment, the mean background signals from the latest force trigger measurement series (FT= 1) is subtracted from subsequent particle measurements.

Rows in .csv files represent individual measurements of single particles or (in raw data only, i.e. Jönsson et al. 2026b) of force triggered (FT) detections for background measurements.

Variables

Variables include

Time Timestamp of detection (in UTC timezone)
XE1_1 to XE1_8 Background-adjusted 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 (%)

In addition to the metrics above, our preprocessing routine yields the following variables:

XE1_1_norm to XE1_8_norm Fluorescence intensities normalized by the maximum detected fluorescence intensity for each particle
FL Summed fluorescence from all channels (au)
FL_ratio Fluorescence ratio, calculated with the sum of the first three channels divided by the sum of the last five channels
saturated Flag for saturation in any one detected channel
no_of_sat Number of saturated channels
group Decision tree-based grouping according to the methods of Freitas et al. (2022). The decision is based on the number of standard deviations (γ) above mean background fluorescence intensities in any channel, where possible groups are:
- CP Non-fluorescent (< 3γ fluorescence) coarse-mode particles
- FP Weakly fluorescent (3-9γ fluorescence in any channel) particles
- HFP Highly fluorescent (> 9γ fluorescence in any channel) particles
- PBAP Highly fluorescent (> 9γ fluorescence in any channel) particles with their peak in the B channel
label Decision tree-based classification labels (label) denoting the channels with high (> 9γ) fluorescence (e.g., A, AB, ABC ...)
count Particle counting flag

Files

File name	Description	Reference
`alder_preprocessed.csv`	Alder pollen	Jönsson et al. (in review)
`ash_preprocessed.csv`	Ash pollen	Jönsson et al. (in review)
`birch_preprocessed.csv`	Birch pollen	Jönsson et al. (in review)
`hazel_preprocessed.csv`	Hazel pollen	Jönsson et al. (in review)
`juniper_preprocessed.csv`	Juniper pollen	Jönsson et al. (in review)
`pine_preprocessed.csv`	Pine pollen	Jönsson et al. (in review)
`willow_preprocessed.csv`	Willow pollen	Jönsson et al. (in review)
`wetalder_preprocessed.csv`	Alder pollen (wet nebulized)	Jönsson et al. (in review)
`wetash_preprocessed.csv`	Ash pollen (wet nebulized)	Jönsson et al. (in review)
`wetbirch_preprocessed.csv`	Birch pollen (wet nebulized)	Jönsson et al. (in review)
`wethazel_preprocessed.csv`	Hazel pollen (wet nebulized)	Jönsson et al. (in review)
`wetjuniper_preprocessed.csv`	Juniper pollen (wet nebulized)	Jönsson et al. (in review)
`wetpine_preprocessed.csv`	Pine pollen (wet nebulized)	Jönsson et al. (in review)
`wetwillow_preprocessed.csv`	Willow pollen (wet nebulized)	Jönsson et al. (in review)
`bacteria60BSN_preprocessed.csv`	Bacteria culture (60B SN)	Jönsson et al. (in review)
`bacteriaB9B6_preprocessed.csv`	Bacteria culture (B9, B6)	Jönsson et al. (in review)
`bacteriaB10B6_preprocessed.csv`	Bacteria culture (B10, B6)	Jönsson et al. (in review)
`cellulose_preprocessed.csv`	Shredded pure cellulose	Jönsson et al. (in review)
`PE_preprocessed.csv`	Shredded polyethylene (PE)	Jönsson et al. (in review)
`PE_UV_preprocessed.csv`	Shredded PE exposed to UV	Jönsson et al. (in review)
`dyngjusandur_dust_preprocessed.csv`	Dyngjusandur dust	Jönsson et al. (in review)
`myrdalssandur_dust_preprocessed.csv`	Mýrdalssandur dust	Jönsson et al. (in review)
`sakurajima_dust_preprocessed.csv`	Sakurajima dust	Jönsson et al. (in review)
`svalbard_dust_preprocessed.csv`	Svalbard dust	Jönsson et al. (in review)
`kaolin_clay_preprocessed.csv`	Kaolinite clay dust	Jönsson et al. (in review)
`AO18_pollution_preprocessed.csv`	Ship plume (Arctic Ocean 2018)	Karlsson et al. (2020)
`AoM_pollution_preprocessed.csv`	Ship plume (ARTofMELT 2023)
`AO18_bacteria_preprocessed.csv`	Picocyanobacteria culture	Beck et al. (2024)
`AO18_filtrated_seawater_preprocessed.csv`	Filtered seawater	Beck et al. (2024)
`AO18_sea_salt_preprocessed.csv`	Artifical sea salt solution	Beck et al. (2024)
`AO18_PSL_preprocessed.csv`	Polystyrene latex spheres (PSLs)	Beck et al. (2024)
`aternaria_preprocessed.csv`	Alternaria alternaria spores	Crawford et al. (2020), Crawford et al. (2025)
`cladosporium_preprocessed.csv`	Cladosporium herbarum spores	Crawford et al. (2020), Crawford et al. (2025)

`code`

MBS_source_experiments_preprocess.py Python script used for preprocessing raw MBS data

Calculates statistics, concatenates output to a single dataframe, and removes the full optical scattering profiles and force trigger data points.

This was applied to raw data from our experiments available in Jönsson et al. (2026b), and to raw data from Crawford et al. (2025), Karlsson et al. (2020), and Beck et al. (2024), by executing the code for each raw data source in respective directories.

Comments

This dataset includes processed 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), as well as data from these experiments and those of Crawford et al. (2020), Ruske et al. (2017), Beck et al. (2024), and Karlsson et al. (2020).

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 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.

We also compared the properties of particles measured in these experiments 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 was used to develop a classification algorithm with supervised machine learning components, also presented in detail in Jönsson et al. (2026a).

Along with raw MBS output data from our characterization experiments, this dataset includes data from these previously published experiments/observations in preprocessed form, as used in the training of our classification algorithm. The Python script used for applying the preprocessing routine to raw data is included.

Related raw data

The raw data from these experiments are published in a companion data set by Jönsson et al. (2026b).

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 characterized 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. For complete sample descriptions of these sources, please see Jönsson et al. (in submission).

Processed fungal spore data are from samples characterized and described in Crawford et al. (2020). Further pollen samples and E. coli samples were characterized in Ruske et al. (2017); we are thankful to David Topping for providing these data. Sea spray aerosol simulation and polystyrene latex sphere measurement data were characterized in Beck et al. (2024). Ship exhaust plume observations carried out in the Arctic Ocean 2018 research cruise were obtained via the pollution mask derived in Karlsson et al. (2020).

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

Dataset language

English

DOI

10.17043/jonsson-2026-aerosol-mbs-1

Time

2026-02-20T12:53:19.119+00:00