Exposure risks of pedestrians during walking along the street

Huiting Guo; Chenhong Jin; Shan Hou; Ke Pan

doi:10.1117/12.2639011

17 May 2022 Exposure risks of pedestrians during walking along the street

Huiting Guo, Chenhong Jin, Shan Hou, Ke Pan

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 1225904 (2022) https://doi.org/10.1117/12.2639011
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

The air quality near urban street is deteriorating due to more and more ultrafine particles emitted by vehicles. Therefore, the evaluation of exposure risks for pedestrians walking along the street is of significance. However, the pedestrians are usually ignored or regarded as motionless in previous investigations. These kinds of consumptions are different from the real scenario and tend to lead large errors. In order to mimic the real exposure risks of walking pedestrians, the dynamic mesh method combined with drift flux model was used to deal with human walking process and the dispersion of ultrafine particles. These results show that the moving pedestrians accelerate the air velocity nearby and the disturbance effect increases with the number of people. Besides, two vortices are generated along the pedestrians’ line. For the cases with lower wind speed, the more people there are, the higher the exposure concentration is. Yet this is not the case for scenarios with higher wind speed. Finally, the walking pedestrians have larger excess lung cancer risks and the people at the front and back of the line are more easily to expose to higher concentrations.

Citation Download Citation

Huiting Guo, Chenhong Jin, Shan Hou, and Ke Pan "Exposure risks of pedestrians during walking along the street", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 1225904 (17 May 2022); https://doi.org/10.1117/12.2639011

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KEYWORDS

Particles

Lung cancer

Atmospheric particles

Cancer

Mathematical modeling

Tumor growth modeling

Lead

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