Integrated dataset of atmospheric bioaerosols over east Asia

Huang, Zhongwei; Zhang, Wenjin; Dong, Qing; Maki, Teruya; Wang, Yongkai; Ji, Yuanzong; Xue, Fanli; Huo, Xuefei; Lu, Da; Wang, Dongdong; Shi, Jinsen; Bi, Jianrong; Huang, Jianping

doi:10.5194/essd-2026-338

Preprints

https://doi.org/10.5194/essd-2026-338

Preprints

18 Jun 2026

| 18 Jun 2026

Status: a revised version of this preprint is currently under review for the journal ESSD.

Integrated dataset of atmospheric bioaerosols over east Asia

Zhongwei Huang, Wenjin Zhang, Qing Dong, Teruya Maki, Yongkai Wang, Yuanzong Ji, Fanli Xue, Xuefei Huo, Da Lu, Dongdong Wang, Jinsen Shi, Jianrong Bi, and Jianping Huang

Abstract. Bioaerosols are one of the main types of aerosols originating from the Earth’s biosphere and are widely found in the atmosphere. They possess both biological attributes and aerosol characteristics, thereby exerting significant influences on climate, the environment, ecosystems, and public health. However, their regional-scale distribution, influencing factors, climatic and environmental impacts remain unclear due to the scarcity of observational data. This study firstly establishes an integrated bioaerosol dataset based on a large-scale dust–bioaerosol field campaign conducted across East Asia using unified sampling and analytical methods. The dataset systematically integrates atmospheric bioaerosol number concentrations and bacterial community structure at multiple taxonomic levels across 45 sites in China, Japan, South Korea, and Mongolia. In addition, meteorological variables (e.g., air temperature, relative humidity, wind speed and direction), air quality parameters (e.g., PM₁₀ and PM_2.5), and Normalized Difference Vegetation Index (NDVI) data during the sampling period were incorporated from multiple sources. Further analysis of this integrated dataset indicates that bioaerosol number concentrations are negatively correlated with local NDVI. Moreover, there is a clear relationship between bioaerosol number concentration and air temperature, with a peak observed at approximately 10–15 °C. A pronounced diurnal variation in bioaerosol concentrations is also found, which is strongly associated with Aerosol Optical Depth (AOD) and particulate matter concentrations. In addition, substantial differences in community structure were observed across different underlying surface types, and the α-diversity indices (Shannon and Chao1 indices) were negatively correlated with NDVI. This dataset provides a robust foundation for advancing research on atmospheric bioaerosol processes, as well as their implications for climate, the environment, public health, and interdisciplinary studies. The dataset generated in this study is openly available via Zenodo (https://zenodo.org/records/19605145) (Huang et al., 2026).

Received: 01 May 2026 – Discussion started: 18 Jun 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Zhongwei Huang, Wenjin Zhang, Qing Dong, Teruya Maki, Yongkai Wang, Yuanzong Ji, Fanli Xue, Xuefei Huo, Da Lu, Dongdong Wang, Jinsen Shi, Jianrong Bi, and Jianping Huang

Status: final response (author comments only)

RC1:
'Comment on essd-2026-338', Anonymous Referee #1, 23 Jun 2026
This study has established a comprehensive dataset of East Asian atmospheric bioaerosols covering 45 sites across China, Japan, South Korea, and Mongolia based on the DuBi (Dust-Bioaerosol) large-scale field observation program. This database systematically integrates multi-source data, including biological aerosol concentrations, bacterial community structures, meteorological variables, air quality parameters, and the normalized difference vegetation index (NDVI). I believe that this dataset has significant data value and can provide clear and significant support for advancing the assessment of biological aerosol climate effects, model validation, and public health risk research. Moreover, this study’s topic fully aligns with ESSD's objective to publish original data or data collections which are of sufficient quality data. The manuscript is also well written, the figures are clear, and the conclusions are largely supported by the data. As results, I recommend minor revision after the following points are carefully considered.
My specific comments
Introduction: The authors extensively introduced the definition, health impacts, climate effects, and global standard sampling methods of biogenic aerosols. However, it failed to clearly explain why a unified dataset urgently is needed in the East Asia region? I think this is the unique feature of this study.

Line 106-110: A custom-designed bioaerosol sampler was employed here. I’m wondering whether the collection efficiency of this sampler has been verified before? What are the comparison results with commonly used international standard samplers (such as the Andersen sampler)?

In Table 1, the Land Surface labels for several sites are marked as "-" (such as Dunhuang, Kanazawa). Is it a case of missing data or unclassified data? Please fill in the land use types for all sites (such as desert, farmland, forest, etc.).

Also in the Sampling Duration column of Table 1, the stations of Namie and Yongin are marked as "-". What is the exact duration of the sampling?

On Line 235-239, I wonder if the "mid-April concentration peak" might correspond to a specific biological event (such as pollen outbreak, agricultural activities)? Has the author provided any other relevant data (such as pollen counts) for support or made an appropriate speculation about the possible causes?

This study mainly showed the diurnal variation characteristics. However, the sampling period is from 2011 to 2021, covering different seasons. So if you have those data, It’s better that the authors analyze the variation patterns of concentration and community structure on a seasonal scale. This will be of great value for understanding the annual cycle characteristics of biogenic aerosols.

One interesting finding of this study is that the concentration of biogenic aerosols and the α diversity index are significantly negatively correlated with NDVI. However, the present result comes mainly from descriptive correlation analysis, lacking quantitative interpretation of the underlying mechanisms. It is suggested that further mechanisms research should be carried out in future work. At least, this deficiency should be mentioned in the conclusion.

In conclusions, the author believes that this database can serve as a data foundation for climate models. Could you provide some examples to illustrate its potential application scenarios?
Citation: https://doi.org/10.5194/essd-2026-338-RC1
- AC1: 'Reply on RC1', Jianping Huang, 01 Aug 2026
  
  We thank Referee 1 for their careful review and constructive comments. Please find our responses to these comments in the supplement.
  
  Citation: https://doi.org/10.5194/essd-2026-338-AC1
RC2:
'Comment on essd-2026-338', Anonymous Referee #2, 16 Jul 2026
The manuscript “Integrated dataset of atmospheric bioaerosols over east Asia” presents a dataset of atmospheric bioaerosols collected across multiple observation sites in East Asia using fluorescence-based measurements combined with microbial community analysis. The dataset has the potential to provide valuable information on the spatial distribution and characteristics of bioaerosols and may serve as a useful resource for future studies on atmospheric bioaerosols and their environmental impacts. Overall, the manuscript is generally well organized, and the dataset appears to be of potential interest to the atmospheric science community. However, I have several concerns regarding the definition of the measured bioaerosols, the scientific motivation, the presentation of the dataset, and the interpretation of some results. The manuscript requires revision before it can be considered for publication.
The motivation for conducting a dust–bioaerosol field campaign should be explained more clearly. The Introduction emphasizes the role of dust in bioaerosol transport, but meteorological conditions (e.g., wind) likely play an equally or even more important role. More background should be provided on why dust is specifically emphasized. In addition, there is an inconsistency: The Introduction states that bioaerosols are enhanced by dust-related transport, whereas Lines 194–195 state that the data were "analysed under non-special weather conditions (e.g., dust events)." This statement seems contradictory and should be clarified.

The definition of bioaerosol throughout the manuscript is unclear and should be better clarified. In general, bioaerosols, often referred to as primary biological aerosol particles (PBAPs), include bacteria, fungal spores, pollen, plant debris, and viruses. However, the fluorescence analysis in this study appears to recognize only cellular particles. Viruses therefore appear to be excluded from the measurements. Furthermore, the reported significant negative correlation between bioaerosol concentration and NDVI raises additional questions. Since NDVI is positively related to vegetation cover, one might expect higher concentrations of plant-derived particles (e.g., pollen or plant fragments) in regions with higher NDVI. The manuscript also switches to the term microbial (e.g., Line 199). Since microbial bioaerosols generally refer only to microorganisms (e.g., bacteria and fungi) and exclude pollen and plant fragments, the distinction between bioaerosol and microbial bioaerosol should be clearly defined and used consistently. Does this imply that the "bioaerosols" measured in this study do not include plant-derived particles? If so, this should be explicitly stated and the use of the term "bioaerosol" throughout the manuscript may not be appropriate.

Overall, the study appears to focus on microbial particles rather than the broader category of PBAPs. This distinction is important because PBAPs commonly include bacteria, fungal spores, pollen, algae, plant fragments, and viruses, many of which (especially fungal spores, pollen, plant fragments, and ice-nucleating bacteria such as Pseudomonas syringae) are important CCN or INP. The scope of this dataset should therefore be clearly defined.
Why were these observation sites selected? Most sites appear to be located in or near dust source regions. Are these regions expected to have sufficiently high bioaerosol concentrations? If the primary objective is to characterize bioaerosols, why were regions with abundant vegetation (e.g., southwestern China or the forested regions of northeastern China) not included? The current site selection seems to emphasize dust source regions rather than regions with potentially strong biological aerosol emissions.

The descriptions of mineral particles appear to be inconsistent. Line 137 states that mineral particles appear white, whereas Line 140 states that yellow-green particles represent mineral dust enriched with water-soluble salts or organic materials. Please clarify the relationship between these two particle categories and explain why mineral particles are represented by different colors.

Figures need to be improved:

Please add the units of bioaerosol concentration to all figures where concentrations are presented.

Please label all figure panels with identifiers such as (a), (b), and (c).

Could the authors provide representative examples corresponding to each of the four particle colors shown in Figure 2? The distinction among the different particle categories in Figures 2e–g is currently not sufficiently clear.

For the top panel of Figure 6, please clarify what the color scale of bioaerosol concentration represents.

As this is a Scientific Data paper, I would expect a more comprehensive description of the dataset itself before presenting downstream analyses. For example, it would be useful to first present the basic characteristics of bioaerosol concentrations and particle-type compositions at each observation site. For example, Section 3.3 and Figure 7 appear to analyze bacterial community composition using all samples combined. Would it be more informative to report the bacterial community composition separately for different sites, considering the large geographical differences among them?
Citation: https://doi.org/10.5194/essd-2026-338-RC2
- AC2: 'Reply on RC2', Jianping Huang, 01 Aug 2026
  
  We thank Referee 2 for their careful review and constructive comments. Please find our responses to these comments in the supplement.
  
  Citation: https://doi.org/10.5194/essd-2026-338-AC2

Zhongwei Huang, Wenjin Zhang, Qing Dong, Teruya Maki, Yongkai Wang, Yuanzong Ji, Fanli Xue, Xuefei Huo, Da Lu, Dongdong Wang, Jinsen Shi, Jianrong Bi, and Jianping Huang

Data sets

Integrated dataset of atmospheric bioaerosols over east Asia Zhongwei Huang, Wenjin Zhang, Qing Dong, Teruya Maki, Yongkai Wang, Yuanzong Ji, Fanli Xue, Xuefei Huo, Da Lu, Dongdong Wang, Jinsen Shi, Jianrong Bi, and Jianping Huang https://doi.org/10.5281/zenodo.19605145

Zhongwei Huang, Wenjin Zhang, Qing Dong, Teruya Maki, Yongkai Wang, Yuanzong Ji, Fanli Xue, Xuefei Huo, Da Lu, Dongdong Wang, Jinsen Shi, Jianrong Bi, and Jianping Huang

Viewed

Total article views: 107 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
69	33	5	107	2	5

HTML: 69
PDF: 33
XML: 5
Total: 107
BibTeX: 2
EndNote: 5

Views and downloads (calculated since 18 Jun 2026)

Month	HTML	PDF	XML	Total
Jul 2026	64	33	5	102
Aug 2026	5	0	5

Cumulative views and downloads (calculated since 18 Jun 2026)

Month	HTML	PDF	XML	Total
Jul 2026	64	33	5	102
Aug 2026	5	0	5

Viewed (geographical distribution)

Total article views: 86 (including HTML, PDF, and XML) Thereof 86 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 02 Aug 2026

Short summary

Bioaerosols exert significant influences on climate, the environment, ecosystems, and public health. This study establishes an integrated bioaerosol dataset across East Asia using unified sampling and analytical methods. It was found that bioaerosol concentrations are negatively correlated with local Normalized Difference Vegetation Index (NDVI), and their relationship with atmospheric temperature is nonlinear. The α-diversity indices were negatively correlated with NDVI.


Total:	0
HTML:	0
PDF:	0
XML:	0