Urban green space planning based on computational fluid dynamics model and landscape ecology principle: A case study of Liaoyang City, Northeast China

摘要

As a result of environmental degradation, urban green space has become a key issue for urban sustainable development. This paper takes Liaoyang City in Northeast China as an example to develop green space planning using the computational fluid dynamics (CFD) model, landscape ecological principles and Geographical Information System (GIS). Based on the influencing factors of topography, building density and orientation, Shou Mountain, Longding Mountain and the Taizi River were selected as the urban ventilation paths to promote wind and oxygen circulation. Oxygen concentration around the green spaces gradually decreased with wind speed increase and wind direction change. There were obvious negative correlation relationships between the oxygen dispersion concentration and urban layout factors such as the building plot ratio and building density. Comparison with the field measurements found that there was significant correlation relationship between simulated oxygen concentration and field measurements (R 2 = 0.6415, p 0.001), moreover, simulation precision was higher than 92%, which indicated CFD model was effective for urban oxygen concentration simulation. Only less than 10% areas in Liaoyang City proper needed more green space urgently to improve oxygen concentration, mainly concentrated in Baitai and west Wensheng districts. Based on landscape ecology principle, green space planning at different spatial scales were proposed to create a green space network system for Liaoyang City, including features such as green wedges, green belts and parks. Totally, about 2012 ha of green space need to be constructed as oxygen sources and ventilation paths. Compared with the current green space pattern, proposed green space planning could improve oxygen concentration obviously. The CFD model and research results in this paper could provide an effective way and theory support for sustainable development of urban green space.