Investigation on the predictive model of mean radiant temperature in urban street canyons with panorama images
Urbanization has profoundly transformed natural landscapes into man-made environments,notably converting natural vegetation cover into impervious surfaces.This transition significantly impacts the near-surface energy balance and material exchange,leading to the formation of distinct urban climates.The design and layout of urban areas have a substantial influence on local microclimates,which in turn affects the thermal comfort of residents in those regions.The relationship between urban form and thermal comfort is complex and multifaceted.Urban street canyons,as fundamental components of urban morphology,significantly impact both indoor and outdoor microclimates,human thermal comfort,and energy consumption in buildings.Understanding this relationship is crucial for designing and planning sustainable and livable cities.Previous research has often been limited by the scale of urban data samples,typically focusing on concentrated areas such as neighborhoods and parks,with less attention paid to urban street canyons.This study aims to explore the distribution patterns of thermal comfort within urban street canyons,building on existing models for predicting Mean Radiant Temperature(MRT)and leveraging large-scale data acquisition and computational methods.The validation and optimization of the predictive model were conducted in the Xi'an area.When evaluating the effects of urban street canyon thermal environments on the quality of life for city residents,it is essential to accurately assess the microclimatic conditions within these canyons.Of particular importance is the Mean Radiant Temperature(MRT),a critical factor that significantly influences the thermal comfort of urban environments.Traditional methods,such as those employing fisheye lens photographs to calculate the Sky View Factor(SVF),are both labor-intensive and impractical for large-scale assessments of MRT's spatial and temporal distribution within urban street canyons.This paper introduces a novel approach that utilizes panoramic imaging technology to rapidly calculate MRT across extensive urban areas while incorporating the cooling effects of street-level vegetation,offering a substantial improvement over existing models.The methodology outlined in this research leverages panoramic images to derive the SVF,integrating this with the geometric characteristics and vegetation view factors of urban street canyons.This integration enables the computation of MRT at specific points within the canyons using an enhanced radiation transfer model.The model's accuracy was rigorously validated using fixed-point measurement data,ensuring its reliability for practical application.The proposed method was then applied to calculate the MRT within the street canyons of Xi'an,demonstrating the approach's effectiveness.The findings from this study indicate a high degree of accuracy in the proposed model,with the majority of relative errors falling within 20%.The Root Mean Square Error(RMSE)ranged between 2.85 and 4.66℃,showcasing the model's precision in estimating MRT.Furthermore,the model exhibited excellent agreement with measured data,accurately reflecting MRT trends over time and space,with a coefficient of determination(R2)greater than 0.74 and an Index of Agreement(IA)greater than 0.80.These metrics underscore the model's capability to reliably predict MRT variations within urban street canyons.A comparative analysis with models that only consider impermeable surfaces further highlighted the superiority of the proposed approach.Incorporating vegetation into the model led to a significant improvement in accuracy,with RMSE decreasing from 5.15℃to 3.87℃and R2 increasing from 0.72 to 0.74.This improvement confirms the model's enhanced consistency with observed data,illustrating the beneficial impact of including vegetation in urban thermal models.The innovative methodology proposed for calculating MRT using panoramic images marks a significant advancement in the field of urban microclimate assessment.By facilitating rapid and accurate evaluations of thermal conditions in street canyons on a large scale,this approach addresses the limitations of previous models and meets the needs of urban planners and environmental scientists.The inclusion of vegetation effects in the model not only contributes to a more accurate representation of urban thermal environments but also provides valuable insights for urban planning and green space management.This research demonstrates the potential for leveraging technology to enhance our understanding of urban microclimates,offering a new tool for improving thermal comfort and quality of life in urban areas.Moreover,the application of this model to the street canyons of Xi'an and the subsequent generation of an MRT distribution map for July 14,2021,at 9:00 a.m.,exemplifies the practical utility of the proposed method.This case study not only validates the model's effectiveness but also illustrates its potential to inform urban design and policy decisions aimed at mitigating the urban heat island effect and enhancing urban thermal comfort.In conclusion,the development of a panoramic image-based method for calculating the MRT in urban street canyons represents a significant contribution to the fields of urban climatology and environmental science.By offering a rapid,accurate,and scalable solution that incorporates the cooling effects of vegetation,this research paves the way for more sustainable urban planning practices.The findings underscore the importance of integrating natural elements into urban environments to improve thermal comfort,thereby enhancing the overall well-being of city dwellers.
mean radiant temperaturepanorama imagesurban street canyonsview factorsfield research
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