| Issue |
E3S Web Conf.
Volume 646, 2025
Global Environmental Science Forum “Sustainable Development of Industrial Region” (GESF-2025)
|
|
|---|---|---|
| Article Number | 00020 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/e3sconf/202564600020 | |
| Published online | 28 August 2025 | |
Experimental studies of aerosol cloud parameters of human-expelled aerosol cloud
1 National Research Moscow State University of Civil Engineering, 26, Yaroslavskoe shosse, Moscow, Russia
2 Penza State Technological University, 1A/11, pr. Baydukova/Gagarina St., Penza, Russia
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The study developed an experimental methodology to characterize the volumetric velocity field and frontal geometry of exhaled air clouds containing aerosol parti-cles. The research focused on establishing the movement patterns of air expelled during human respiratory activities (speaking, sneezing, coughing) to identify potential infection zones. The experimental approach incorporated video re-cording of exhaled air dynamics under various conditions, followed by computational processing and modeling. The methodology included derivation of analytical formulas for data processing, description of experimental data analysis tech-niques, and temporal evolution analysis of results. The proposed method will allow the determination of aerosol cloud geometry, propagation velocity, and direc-tional characteristics through video analysis. Time-resolved imaging at 1/25-second intervals permits detailed study of geometric and velocity field dynamics. Computational transformation of results into vector format was achieved through specialized algorithms. Analysis revealed complex airflow patterns within mov-ing aerosol clouds, including recirculation components characteristic of turbulent flow regimes. Distinct cloud geometries were identified for different expulsion mechanisms (speech, sneeze, cough). The methodology provides foundational data for computational modeling of respiratory particle dispersion and infection risk.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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