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The deterioration of air quality in urban areas is often closely related to urbanization, as this has led to a significant increase in energy consumption and the massive emission of air pollutants, thereby exacerbating the current state of air pollution. However, the relationship between urban development and air quality is complex, thus making it difficult to be analyzed using traditional methods. In this paper, a framework integrating spatial analysis and statistical methods (based on 170 regression models) is developed to explore the spatial and temporal relationship between urban land use patterns and air quality, aiming to provide solid information for mitigation planning. The thresholds for the influence of urban patterns are examined using different buffer zones. In addition, the differences in the effects of various types of land use pattern on air quality were also explored. The results show that there were significant differences between 1999 and 2013 with regards to the correlations between land use patterns and air pollutant concentrations. Among all land uses, forest, water and built-up areas were proved to influence concentrations the most. It is suggested that the developed framework should be applied further in the real-world mitigation planning decision-making process Sustainability 2020, 12, x FOR PEER REVIEW 3 of 16 (AWMPFD), Shannon's diversity index (SHDI), and Shannon's evenness index (SHEI) of residential 95 areas were highly correlated with acoustic noise level and PM10 concentration. Furthermore, higher 96 ED and PD of built-up areas led to higher acoustic noise levels and PM10 concentration. Detailed 97 definition of the landscape metrics mentioned above could be found in [5,16,19]. 98In order to gain a deeper understanding of the effectiveness of strategies for improving future 99 urban air quality, integrated research methods, including the quantification of urban spatial patterns 100 and structures and the correlation analysis between air quality and urban form, should be carried 101 out. In view of this, a spatiotemporal analytical framework is constructed in this study, which 102 involves first a correlation analysis between land use type indicators and air quality indicators at 103 different spatial distances, followed by multiple sets of regression analysis based on data from 104 different years. Our aim is to clearly quantify the correlation between different land use patterns and 105 air quality in cities, which can serve as a reference for the future formulation of mitigation and 106 adaptation planning strategies. This study will calculate the land use patterns within the different 107 buffer radii of air quality monitoring stations and will explore the association between urban land 108 use patterns and air quality using statistical analysis in order to serve as an important reference for 109 the formulation of adaptation strategies related to air quality, which can benefit future land use 110 planning. This proposed framework aims to provide useful information for public sec...

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Due to urbanization around the world, people living in urban areas have been suffering from a series of negative effects caused by changes in urban microclimate, especially when it comes to urban heat islands (UHIs). To mitigate UHIs, management of urban wind environments is increasingly considered as a crucial part of the process. Computational fluid dynamics (CFD) simulation of wind fields has become a prevailing method to explore the relationship between morphological factors and wind environment. However, most studies are focused on building scale and fail to reflect the effects of comprehensive planning. In addition, the combined influence of different morphological factors on wind environment is rarely discussed. Therefore, this study tries to explore the relationship between urban morphology and wind environment in a new-town area. CFD method was applied to simulate the wind field, and 11 scenarios based on criteria according to existing literature, planning regulations and local characteristics were developed. The simulation results from different scenarios show that the impact of the five selected factors on wind speeds was non-linear, and the impact varied significantly among different areas of the study region. Simulation of the differences in regional wind speeds among different planning scenarios can provide strong decision-making support.

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