Tracing biological, human, and inorganic sources of coarse aerosols via single-particle fluorescence and optical morphology

Jönsson, Aiden; Fu, Jinglan; Freitas, Gabriel Pereira; Crawford, Ian; Dagsson-Waldhauserová, Pavla; Krejci, Radovan; Tobo, Yutaka; Yttri, Karl Espen; Zieger, Paul

doi:10.5194/egusphere-2026-59

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https://doi.org/10.5194/egusphere-2026-59

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28 Jan 2026

| 28 Jan 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Tracing biological, human, and inorganic sources of coarse aerosols via single-particle fluorescence and optical morphology

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Ian Crawford, Pavla Dagsson-Waldhauserová, Radovan Krejci, Yutaka Tobo, Karl Espen Yttri, and Paul Zieger

Abstract. Coarse-mode aerosol particles influence the environment, climate, and human health in diverse ways depending on their type. While mineral dust and sea spray aerosol (SSA) dominate this size range, rarer biological particles can have outsized impacts, for example by initiating ice formation at relatively warm temperatures. Hence, accurate, type-specific characterization of coarse-mode aerosol is essential for understanding their roles in climate and the environment. Using laboratory measurements of single-particle ultraviolet light-induced fluorescence (UV-LIF) spectroscopy and morphology, we provide a new reference dataset for coarse-mode aerosols from common sources, including pollen, dust, bacteria, and microplastics. Comparisons with previously published datasets reveal consistent source-dependent fluorescence features, but also highlights similarities between biological and non-biological particles that can bias classifications based on fluorescence alone.

We present an improved machine learning-based classification algorithm that integrates fluorescence and morphology using laboratory data for training, and evaluate its performance using observations made at Zeppelin Observatory, Svalbard. We apply domain adaptation using field data to improve the identification of combustion-sourced particles, and to better distinguish dust from SSA. The new algorithm reproduces the previously published annual bioaerosol cycle, yields higher concentrations than a fluorescence-only approach, and maintains comparable correlations with biological and combustion tracers. This open-source algorithm provides a basis for quantifying bioaerosols across diverse environments, can revise bioaerosol estimates in previously analyzed observations, and can be refined as additional characterization data become available.

Received: 14 Jan 2026 – Discussion started: 28 Jan 2026

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

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Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Ian Crawford, Pavla Dagsson-Waldhauserová, Radovan Krejci, Yutaka Tobo, Karl Espen Yttri, and Paul Zieger

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RC1:
'Comment on egusphere-2026-59', Anonymous Referee #1, 10 Mar 2026 reply
In this manuscript, the authors present a laboratory characterization study of different biological aerosol particles, dust samples, sea spray aerosol and microplastics with a single particle fluorescence spectrometer (MBS). They use this data, together with data from previously characterized particles and train a novel classification algorithm using fluorescence, but also several parameters related to the morphology of single particles. The differences and similarities between different types of particles are discussed. I especially appreciate that the algorithm is then applied to an already published data set from the Zeppelin Observatory in Svalbard and compared to the original interpretation. Additional soft labels derived from tracer measurements were used to further tune the algorithm. The code and data is/will be available online. In my opinion, the article is highly relevant and suitable for publication in ACP after some revisions.

General comments:
1) I am missing one compressed paragraph (for example towards the end of the discussion) that compares the new algorithm with Freitas’ in detail: What are the advantages? Is it superior? If not, does it need more validation in order to be? If yes, why? etc. This paragraph should clearly justify why this new algorithm is worth using.

2) One main concern lies in the definitions, interpretations and discussions regarding pollen (3.1: and 480 – 487):
Regarding the definition of SPP (Sub-pollen particles): There is no uniform definition of SPP and people use the term loosely. The term originally comes from the medical community and refers only to the solid starch granules from the inside of the pollen grains (Bacsi et al., 2006; Schleh et al., 2010; Sénéchal et al., 2015; Burkart et al., 2021). These solid starch granules are usually between 0.5 and 2.5 µm in size (e.g. Burkart et al., 2021 for birch; Suphioglu et al., 1992; Schleh et al., 2010 for grasses).
Beside soluble cytoplasmic content, these starch granules are the particles that get expelled during rupture at high RH or when immersed in water. However, at least for birch, this only works for freshly collected pollen grains, and not for purchased ones, even if you put them in water for a longer period (Burkart et al., 2021). Since your measured size distributions of the pollen samples (Fig. 1) are mostly above 2 µm, and you used purchased (not fresh) pollen, I think it is unlikely that the majority of the measured particles are starch granules. This should be clarified.
Some authors also use the term SPP to describe aerosolized and dried aqueous extracts of pollen (Gute and Abbatt, 2020; Gute et al., 2020; Mikhailov et al., 2019; Steiner et al., 2015), but these are typically in the range <500 nm. Since your measured size distribution does not fit any of those two definitions, I would suggest simply calling them pollen fragments, as done, for example by Hughes et al., 2020, or to clearly define what you are talking about. In general, I would switch out “pollen” with “pollen fragments” whenever you discuss your results, since you are not actually measuring pollen grains. This could be confusing to readers.
I would also suggest examining one pollen sample under a microscope to see what these fragments > 2 µm look like. It should be easy to identify if these are starch granules (smooth surface, round to ellipsoid in shape, compare with Fig. 2-4 in Burkart et al., 2021), or really fragments of the pollen exine. You could include microscopy images in the Supplement.
This should also be considered in your discussion in 480-487.

3) 425-445:
The discussion about pollen (fragments) needs to be even more critical. I highly doubt that 60-80 % of bioaerosols in the arctic are pollen grains, let alone pollen fragments which aerobiologists do not observe in higher concentrations than intact pollen grains in their Hirst traps (they would be colored pink after treatment with fuchsin, as the pollen grains are, and therefore easy to spot). Some fragments, however, were observed by Hughes et al., 2020 (see Fig. S5 in their Supplement).
You already discuss possible reasons for misclassification. However, this could be better structured. Therefore, I would start with a statement that such high pollen (fragment) concentrations are highly unlikely, and support this with a comparison to earlier studies that you already mention throughout the paragraph (SEM pictures and pollen counts). You state that earlier studies found low concentrations of pollen grains. What is the approximate concentration Johansen and Hafsten found, and how does this compare to your pollen (fragments) concentration?
I would then continue to explain the possible reasons why the particles classified as pollen (fragments) here are too high. You have already discussed several things. For instance, misclassification of combustion particles and fungal spores. However, the correlation of pollen and fungal spores with mannitol and arabitol are not similar, but pollen correlation is significantly higher. This should be clearly emphasized.
Parts of this paragraph already read more as a discussion than results, and lines 514-527 in the discussion section partly repeat the earlier discussion but also introduces new ideas, such how the validation could be improved (for instance through comparison with pollen and fungal spore counts). I recommend merging those parts and put all in the discussion.

4) The last thing that is unclear for me is the following: You used soft labels in order to tune the algorithm. Since prior to tuning, the results seemed unreasonable, do you expect that you need these labels and therefore tracer measurements in every future campaign, or was this sufficient as a one-time adjustment to get meaningful classes for future measurements? Please comment on this.

Specific comments:
The Supplement could be better structured according to their appearance in the main text.

Titel: Wouldn’t “anthropogenic” be a better word than “human”?

6: I suggest to add the instrument here.

30 – 31: Can the concentration range you give for bacteria also be found in Huang et al., 2021? If yes, make that clear, if not, find a reference supporting this range.

33 & 35: Since “online aerosol mass spectrometry” includes “Single-particle mass spectrometry”, I suggest rephrasing the former, or explain both under the parent phrase “online aerosol mass spectrometry”.

34: change “only one size range” to “only a narrow size range”.

60: While it may be true, that microplastics contain PAHs, they are not produced during production. The main reason for their fluorescence is either because they contain aromatic structures that are expected to fluoresce (e.g. PET, PS) (Gratzl et al., 2024), the production of trace amounts of α-β-unsaturated carbonyls during synthesis or storage (e.g. PE, PP) (Grabmayer et al., 2014; Htun and Klein, 2010), or due to additives (Morgana et al., 2024). I also suggest including Beres et al., 2024 in the references, which also used autofluorescence to detect airborne microplastics.

89: Add the names of the channels (A - H). I think it would also make it easier for the readers if the central wavelengths were written here.

95: Is the background only measured with one single light pulse?

124: How was it ground into particles?

125-132: How old were the pollen samples? How were they stored?

137: How was the PE ground? How was the UV-aging exactly achieved? What light source / what wavelength was chosen for the aging? How long was the exposure?

140-145: Are these commercial dust samples or did you collect them?

156: Were background measurements taken between the samples?

Table 1: Include the size of the PSL.

174: here you call them objectives, but later on you refer to task 1, task 2, … Maybe stick to task.

215: eBC data: Since there is a minutely resolution available, why did the author only compare 24 h data, and not for example 1 h data?

216: You should include for what aerosol classes the tracers are for in Table S3 and give some examples here in the main text.

264: You could add the central emission wavelength of the B channel here.

265: The authors may correct me if I’m wrong, but I did not find evidence that bacteria fluorescence peaks in this channel based on the two references provided. While I agree that tryptophan, the main fluorophore responsible for bacterial fluorescence peaks in the B channel, whole cells are not discussed in these publications. I suggest to reference Kinahan et al., 2019; Savage et al., 2017 and/or Hernandez et al., 2016). They do not use the exact same excitation/emission wavelength as the MBS but measure actual cells.

293: What is the approximate size range of the combustion particles?

296: regarding discrimination: Studies found a high correlation of eBC concentrations with a WIBS channel using a different excitation wavelength than the MBS (Gratzl et al., 2025; Gao et al., 2024; Beck et al., 2024).

301: Why was 3 µm used?

310: The explanation of what PSL particles are should come in the methods, not here.

310-328. The dust and SSA results should also be compared to the other particle types and not just to each other (e.g. low to zero HFAP contribution).

4: A color legend for the different classes would be nice.

340: Why was 2.5 µm chosen as the threshold?

341: The terms “Pollution model” and “pollution-likelyhood” should appear already in Section 2.5. Same for “dust model” at line 348.

343, 348: What is the confidence threshold?

396: Since you reference to the Supplement figure here that includes “tuned” and “untuned” data, you should introduce these two terms already earlier in the section.

398: Refer to Fig. 7A after “biological aerosol influence”.

7a: fPBAP (this study) is the tuned one, right? This should be in the legend.

404: Which tracers were used as soft lables?

407: Why 20%?

411: Interferents in HFPs are not directly visible in Fig. 7A. You could include the interferents concentration in Fig 7A if it does not become too messy. Alternatively, explain again that interferents are HFPs minus fPBAPs.

412: There is no “pollution class” in Fig. 7e. Do you mean combustion? Be consistent! What does “lead” mean in lead eBC?

413: I would add “on fPBAP” after “reduces the influence of combustion particles”.

415: For what type is it validated?

424: State the correlation coefficients here.

446: Name the SSA and dust tracers.

Technical comments:
24: < - 20 °C (the minus sign is missing)

161: “of” missing: inlet OF the MBS.

220: “and the” is written twice.

240: you just refer to the SI here. Which part?

326: Citation should be in brackets.

364: Missing full stop after “classes”.

405: Where in the SI?

461: Where in the SI?

465: should be “repeated”.

Supplement:
Page 8, Fig. S3: It would make more sense to add the starting wavelength of each channel instead of random 100 nm steps on the lower x-axis. Add the excitation wavelength in the caption.

S4: a and b are switched!

Table S3: You should include what these substances are tracers for in the table.

S5: One bar is always orange instead of red.

References:

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Citation: https://doi.org/10.5194/egusphere-2026-59-RC1

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Ian Crawford, Pavla Dagsson-Waldhauserová, Radovan Krejci, Yutaka Tobo, Karl Espen Yttri, and Paul Zieger

Supplement

https://doi.org/10.5194/egusphere-2026-59-supplement

Model code and software

TRUFFLE: Trained Recognition of Unique Fluorescence- & Form-based Labels for Environmental aerosols Aiden Jönsson https://github.com/aidenrobert/truffle/

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Ian Crawford, Pavla Dagsson-Waldhauserová, Radovan Krejci, Yutaka Tobo, Karl Espen Yttri, and Paul Zieger

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Short summary

Coarse-mode aerosols, like dust and bioaerosols, play important roles in environmental and climate processes. We measured fluorescence and morphological properties of key coarse-mode particle types, compared them with previous characterizations, and trained machine learning models with these data to classify unknown particles. This algorithm improves bioaerosol identification and successfully reproduces the annual bioaerosol cycle previously identified in a year of observations from Svalbard.


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