RESUMO
Respiratory infection is the main route for the transmission of coronavirus pneumonia, and the results have shown that the urban spatial environment significantly influences the risk of infection. Based on the Wells-Riley model of respiratory infection probability, the study determined the human respiratory-related parameters and the effective influence range; extracted urban morphological parameters, assessed the ventilation effects of different spatial environments, and, combined with population flow monitoring data, constructed a method for assessing the risk of Covid-19 respiratory infection in urban-scale grid cells. In the empirical study in Shenyang city, a severe cold region, urban morphological parameters, population size, background wind speed, and individual behavior patterns were used to calculate the distribution characteristics of temporal and spatial concomitant risks in urban areas grids under different scenarios. The results showed that the correlation between the risk of respiratory infection in urban public spaces and the above variables was significant. The exposure time had the greatest degree of influence on the probability of respiratory infection risk among the variables. At the same time, the change in human body spacing beyond 1 m had a minor influence on the risk of infection. Among the urban morphological parameters, building height had the highest correlation with the risk of infection, while building density had the lowest correlation. The actual point distribution of the epidemic in Shenyang from March to April 2022 was used to verify the evaluation results. The overlap rate between medium or higher risk areas and actual cases was 78.55%. The planning strategies for epidemic prevention and control were proposed for the spatial differentiation characteristics of different risk elements. The research results can accurately classify the risk level of urban space and provide a scientific basis for the planning response of epidemic prevention and control and the safety of public activities.
RESUMO
Respiratory infection is the main route for the transmission of coronavirus pneumonia, and the results have shown that the urban spatial environment significantly influences the risk of infection. Based on the Wells-Riley model of respiratory infection probability, the study determined the human respiratory-related parameters and the effective influence range;extracted urban morphological parameters, assessed the ventilation effects of different spatial environments, and, combined with population flow monitoring data, constructed a method for assessing the risk of Covid-19 respiratory infection in urban-scale grid cells. In the empirical study in Shenyang city, a severe cold region, urban morphological parameters, population size, background wind speed, and individual behavior patterns were used to calculate the distribution characteristics of temporal and spatial concomitant risks in urban areas grids under different scenarios. The results showed that the correlation between the risk of respiratory infection in urban public spaces and the above variables was significant. The exposure time had the greatest degree of influence on the probability of respiratory infection risk among the variables. At the same time, the change in human body spacing beyond 1 m had a minor influence on the risk of infection. Among the urban morphological parameters, building height had the highest correlation with the risk of infection, while building density had the lowest correlation. The actual point distribution of the epidemic in Shenyang from March to April 2022 was used to verify the evaluation results. The overlap rate between medium or higher risk areas and actual cases was 78.55%. The planning strategies for epidemic prevention and control were proposed for the spatial differentiation characteristics of different risk elements. The research results can accurately classify the risk level of urban space and provide a scientific basis for the planning response of epidemic prevention and control and the safety of public activities.
RESUMO
The design of indoor airflow environments can significantly reduce the risk of respiratory epidemic infections indoors. Some studies have successfully developed theoretical models for calculating the effect of airflow fields on infection rates. However, up until now, studies have primarily focused on simulating and calculating the distribution of viral infection rates in current building scenarios. Due to the lack of a direct influence model for the design parameters and infection rate calculation, the present studies lack a quantitative analysis of the design parameters. This paper investigates the building openings design approach in a medium-sized kindergarten in Germany, intending to explore passive-based design solutions to improve the building's ability to prevent the virus' spread. We calculate the infection rate distribution in space by CFD combined with the Wells-Riley model. And then, use the Grasshopper platform to build an optimization model with the design parameters of building openings and infection rate values to discuss the relationship between geometric parameters and infection rate variation. The results show that the building openings' design parameters in transition spaces significantly affect the indoor infection rate under the condition that the input wind speed at the building openings is stable. We can see that optimizing building openings significantly reduces the average infection rate in space. The infection rate in the area with the largest decrease can be reduced by 18.41%. The distribution of infection rate in space is much more uniform, and the excess area is significantly reduced. This study has implications for future research and practice in designing public buildings under the influence of long-standing and cyclical outbreaks of epidemics.