The impact of urban morphology on multiple ecological effects:Coupling relationships and collaborative optimization strategies

Urban morphology significantly affects the ecological effects of urban heat islands,ventilation,and atmospheric pollution.Here,we reveal the mechanisms linking the ecological effects of urban morphology to develop a planning approach for the collaborative optimization of multiple ecological effects.Considering Shenyang,a cold city in northern China,as the study area,a multiple regression model of morphological parameters and ecological effects was established,and the impact of morphological parameters on ecological effects was explored.The results show that the aspect ratio of the streets,building density,and vegetation coverage are sensitive to multiple ecological effects.The inflection point of the ecological effect function curve occurs when the aspect ratio of the building and building density are 0.2 and 0.3,respectively.In addition,for optimal design applications in typical areas of the city,to obtain a Pareto-optimal urban morphology,Grasshopper is used to establish a parametric platform,wherein a genetic algorithm solves the multiple regression equation set.Ultimately,five ecological effect indicators are optimized and show 8.4%,5.0%,31.6%,33.1%,and 12.5%improvement.The study effectively constructs a collaborative optimization planning and design method for multiple ecological effects.

Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters:The case of Shenyang city

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.