Cool City Design: Integrating Real-Time Urban Canyon Assessment into the Design Process for Chinese and Australian Cities

Many cities are undergoing rapid urbanisation and intensification with the unintended consequence of creating dense urban fabric with deep ‘urban canyons’. Urban densification can trap longwave radiation impacting on local atmospheric conditions, contributing to the phenomena known as the Urban Heat Island (UHI). As global temperatures are predicted to increase, there is a critical need to better understand urban form and heat retention in cities and integrate analysis tools into the design decision making process to design cooler cities. This paper describes the application and validation of a novel three-dimensional urban canyon modelling approach calculating Sky View Factor (SVF), one important indicator used in the prediction of UHI. Our modified daylighting system based approach within a design modelling environment allows iterative design decision making informed by SVF on an urban design scale. This approach is tested on urban fabric samples from cities in both Australia and China. The new approach extends the applicability in the design process of existing methods by providing ‘real-time’ SVF feedback for complex three-dimensional urban scenarios. The modelling approach enables city designers to mix intuitive compositional design modelling with dynamic canyon feedback. The approach allows a greater understanding of existing and proposed urban forms and identifying potential canyon problem areas, improved decision making and design advocacy, and can potentially have an impact on cities’ temperature

A Simplified Heat Wave Warning System

Abstract A Simplified Heat Wave Warning System Arya Nakhaie Ashtiani Extreme heat is a natural hazard that could rapidly increase in frequency, duration, and magnitude in the 21st century. During the summer, the combined effect of urban heat island (UHI), climate change and global warming increases ambient air temperature. This leads to a rise in indoor environment temperature, reduction of thermal comfort, increase of cooling demand, and heat related morbidity and mortality especially among vulnerable people such as the elderly and those who are living in buildings without mechanical ventilation systems. Cities are developing tools to predict the indoor air temperature during extreme heat waves in order to be able to provide emergency plans if necessary. To do so, it is required to find a relationship between the indoor and outdoor conditions. Hence there is an urgent need to develop a reliable method for indoor air temperature prediction by taking into consideration not only the outdoor conditions but also the socio-economic aspects of the neighborhood. The objective of this study is to develop a warning system to predict the indoor air thermal condition during heat wave events in buildings without mechanical ventilation systems. In order to develop a regional heat warning system, two different methods were proposed and tested for an indoor air temperature forecasting application with respect to neighborhood parameters. The first method was based on regression and the second one was based on the Artificial Neural Network (ANN) model. The inputs and outputs to the proposed models were the field measurement data which has been collected on Montreal Island during the summer of 2010 (Park et al, 2010). To find the most practical approach, both proposed models were compared with respect to their accuracy and the required resources. A comparison of the proposed regression and ANN models was conducted by two different levels of simulation. The ANN model showed better accuracy in predicting the indoor dry-bulb temperature, but it was more complicated to apply