Abstract
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.
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Azevedo L, Pereira MJ, Ribeiro MC, et al. (2020). Geostatistical COVID-19 infection risk maps for Portugal. International Journal of Health Geographics, 19: 25.
Bañuelos-Ruedas F, Angeles-Camacho C, Rios-Marcuello S (2010). Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights. Renewable and Sustainable Energy Reviews, 14: 2383–2391.
Borak JS, Jasinski MF, Crago RD (2005). Time series vegetation aerodynamic roughness fields estimated from modis observations. Agricultural and Forest Meteorology, 135: 252–268.
Buonanno G, Stabile L, Morawska L (2020). Estimation of airborne viral emission: Quanta emission rate of SARS-CoV-2 for infection risk assessment. Environment International, 141: 105794.
Chen SC, Liao CM (2008). Modelling control measures to reduce the impact of pandemic influenza among schoolchildren. Epidemiology and Infection, 136: 1035–1045.
Chen Y, Yang K, Zhou D, et al. (2010). Improving the Noah land surface model in arid regions with an appropriate parameterization of the thermal roughness length. Journal of Hydrometeorology, 11: 995–1006.
Dai H, Zhao B (2020). Association of the infection probability of COVID-19 with ventilation rates in confined spaces. Building Simulation, 13: 1321–1327.
Dlamini WM, Dlamini SN, Mabaso SD, et al. (2020). Spatial risk assessment of an emerging pandemic under data scarcity: A case of COVID-19 in Eswatini. Applied Geography, 125: 102358.
Duan J, Yang B, Zhou L, et al. (2020). Planning improves city’s immunity: A written conversation on COVID-19 breakout. City Planning Review, 44(02): 115–136. (in Chinese)
Furuya H (2007). Risk of transmission of airborne infection during train commute based on mathematical model. Environmental Health and Preventive Medicine, 12: 78–83.
Gong S, Mo H (2021). Geographic analysis of the COVID-19 epidemic in Hunan Province in 2020. Tropical Geography, 41(04): 708–722. (in Chinese)
Gralton J, Tovey E, McLaws ML, et al. (2011). The role of particle size in aerosolised pathogen transmission: a review. Journal of Infection, 62: 1–13.
Grimmond CSB (1998). Aerodynamic roughness of urban areas derived from wind observations. Boundary-Layer Meteorology, 89: 1–24.
Grimmond CSB, Oke TR (1999). Aerodynamic properties of urban areas derived from analysis of surface form. Journal of Applied Meteorology, 38: 1262–1292.
Gualtieri G, Secci S (2011). Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy. Renewable Energy, 36: 1081–1094.
Jacobs J (1964). The Death and Life of Great American Cities. Harmondsworth, UK: Penguin Books.
Jiang P, Fu X, Fan YV, et al. (2021). Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. Journal of Cleaner Production, 279: 123673.
Kamel Boulos MN, Geraghty EM (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. International Journal of Health Geographics, 19: 8.
Kang JY, Michels A, Lyu F, et al. (2020). Rapidly measuring spatial accessibility of COVID-19 healthcare resources: a case study of Illinois, USA. International Journal of Health Geographics, 19: 36.
Lan T, Yu M, Xu Z, et al. (2018). Temporal and spatial variation characteristics of catering facilities based on POI data: A case study within 5th ring road in Beijing. Procedia Computer Science, 131: 1260–1268.
Li J, Li J, Yuan Y, et al. (2019). Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi’an, Shaanxi, China. Cities, 86: 62–70.
Li S, Chen Y, Shi T, et al. (2020). Risk assessment of respiratory exposure in urban public space for air epidemic prevention. City Planning Review, 44(08): 21–32. (in Chinese)
Liu L, Wei J, Li Y, et al. (2017). Evaporation and dispersion of respiratory droplets from coughing. Indoor Air, 27: 179–190.
Liu Y, Xu Y, Zhang F, et al. (2019). Research and application of urban surface ventilation potential: Cases of Beijing and Guangzhou. Planners, 35(10): 32–40. (in Chinese)
Liu J (2020). Community epidemic prevention planning and govemance system against COVID-19 epidemic. Planners, 36(6): 86–89. (in Chinese)
Mehta V (2020). The new proxemics: COVID-19, social distancing, and sociable space. Journal of Urban Design, 25: 669–674.
Mei F, Zhang N, Xi Y, et al. (2018). The aerodynamic roughness length over rough surfaces derived from whole wind velocity profiles with the log law and its spatial variations. Journal of Desert Research, 38(03): 445–454. (in Chinese)
Noakes CJ, Beggs CB, Sleigh PA, et al. (2006). Modelling the transmission of airborne infections in enclosed spaces. Epidemiology and Infection, 134: 1082–1091.
Noakes CJ, Sleigh PA (2008). Applying the Wells-Riley equation to the risk of airborne infection in hospital environments: The importance of stochastic and proximity effects. In: Proceedings of the 11th International Conference on Indoor Air Quality and Climate (Indoor Air 2008), Copenhagen, Denmark.
Oke TR (1987). Boundary Layer Climates. London: Routledge.
Peixoto PS, Marcondes D, Peixoto C, et al. (2020). Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil. PLoS One, 15: e0235732.
Qian H, Li Y, Nielsen PV, et al. (2009). Spatial distribution of infection risk of SARS transmission in a hospital ward. Building and Environment, 44: 1651–1658.
Qian H, Zheng X, Zhang X (2012). Prediction of risk of airborne transmitted diseases. Journal of Southeast University (Natural Science Edition), 42(03): 468–472. (in Chinese)
Riley EC, Murphy G, Riley RL (1978). Airborne spread of measles in a suburban elementary school. American Journal of Epidemiology, 107: 421–432.
Shafaghi AH, Rokhsar Talabazar F, Koșar A, et al. (2020). On the effect of the respiratory droplet generation condition on COVID-19 transmission. Fluids, 5: 113.
Sun C, Zhai Z (2020). The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustainable Cities and Society, 62: 102390.
Tibbalds F (1992). Making People-Friendly Towns: Improving the Public Environment in Towns and Cities. Harlow, UK: Longman.
Urrego J, Andrews JR, Yeckel CW, et al. (2015). The impact of ventilation and early diagnosis on tuberculosis transmission in Brazilian prisons. The American Journal of Tropical Medicine and Hygiene, 93: 739–746.
Villafruela JM, Olmedo I, Ruiz de Adana M, et al. (2013). CFD analysis of the human exhalation flow using different boundary conditions and ventilation strategies. Building and Environment, 62: 191–200.
Wang Z, Cheng C, Yang Y, et al. (2018). Research on urban ventilation channel planning strategy which based on multivariate date analysis: Take Beijing sub center as an example. Urban Development Studies, 25(01): 87–96. (in Chinese)
Wang J, Li G, Wang J, et al. (2020). Spatiotemporal evolution and risk profiling of the COVID-19 epidemic in Shaanxi Province. Tropical Geography, 40(3), 432–445. (in Chinese)
Wells WF (1995). Airborne Contagion and Air Hygiene: An Ecological Study of Droplet Infection. Cambridge, MA, USA: Harvard university Press.
WHO (2020). Coronavirus Disease 2019 (COVID-19) Situation Report-30. Available at https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200219-sitrep-30-covid-19.pdf?sfvrsn=3346b04f_2.
Wong MS, Nichol JE, To PH, et al. (2010). A simple method for designation of urban ventilation corridors and its application to urban heat island analysis. Building and Environment, 45: 1880–1889.
Wu Z, Li D (2011). Principles of Urban Planning, 4th Edn. Beijing: China Architecture & Building Press. (in Chinese)
Wu Z, Ye Z (2016). Research on urban spatial structure based on Baidu heat map: A case study on the central city of Shanghai. City Planning Review, 40(04): 33–40. (in Chinese)
Xie X, Li Y, Chwang ATY, et al. (2007). How far droplets can move in indoor environments—Revisiting the Wells evaporation-falling curve. Indoor Air, 17: 211–225.
Xu C, Nielsen PV, Liu L, et al. (2017). Human exhalation characterization with the aid of schlieren imaging technique. Building and Environment, 112: 190–199.
Xu C, Wei X, Liu L, et al. (2020). Effects of personalized ventilation interventions on airborne infection risk and transmission between occupants. Building and Environment, 180: 107008.
Yang J, Shi B, Shi Y, et al. (2020). Construction of a multi-scale spatial epidemic prevention system in high-density cities. City Planning Review, 44(03): 17–24. (in Chinese)
Zachreson C, Mitchell L, Lydeamore MJ, et al. (2021). Risk mapping for COVID-19 outbreaks in Australia using mobility data. Journal of the Royal Society Interface, 18: 20200657.
Zhang H, Zhu S, Wang M, et al. (2015). Sky view factor estimation based on 3D urban building data and its application in urban heat island—Illustrated by the case of Adelaide. Remote Sensing Technology and Application, 30(5): 899–907. (in Chinese)
Zhang Y, Feng G, Bi Y, et al. (2019). Distribution of droplet aerosols generated by mouth coughing and nose breathing in an air-conditioned room. Sustainable Cities and Society, 51: 101721.
Zhang Y, Wang X, Bi Q (2020). Travel-infected susceptibility based on transmission mechanism of COVID-19. Transport Research, 6(1): 73–80. (in Chinese)
Zhu S, Srebric J, Spengler JD, et al. (2012). An advanced numerical model for the assessment of airborne transmission of influenza in bus microenvironments. Building and Environment, 47: 67–75.
Acknowledgements
This project was financially supported by the General Program of National Natural Science Foundation of China (No. 51978421). The authors express their sincere gratitude to all members of the research team for their invaluable contributions.
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Li, S., Li, Z., Dong, Y. et al. Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters: The case of Shenyang city. Build. Simul. 16, 683–699 (2023). https://doi.org/10.1007/s12273-022-0918-8
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DOI: https://doi.org/10.1007/s12273-022-0918-8