Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

Abstract

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...

The relationship between spatial patterns of urban land uses and air pollutants in the Tehran metropolis, Iran

“…A negative correlation was found between spatial patterns of green spaces, including dense trees (A), scattered trees (B), bush and scrub (C), and low plants (D), and the pollutants NO 2 , SO 2 , and CO. In line with the ndings of the study by Ku (2020), the investigation of the spatial con guration of such land uses showed that the increased PD of trees led to a reduced concentration of pollutants. This can be due to increased evapotranspiration and more photosynthesis in trees than other plants (Cao et al, 2010;Lin & Xu, 2016;Zhang et al, 2009).…”

“…They showed a positive correlation between spatial trends of air pollutants with the density of built-up land in Guangzhou, China. Also, Ku (2020) have illustrated that the annual averages NO 2 is increased by increase of built-up area in Tiachung city, Taiwan. Whereas inverse relationship was observed between the increase in forest connectivity and forest area and annual average of NO 2 .…”

“…The city of Tehran is no exception to this rule and due to the increase in such pollutants of sulfur dioxide and carbon monoxide, the number of respiratory patients has increased by 60%. Thus, to achieve a deeper understanding of the effectiveness of urban air quality improvement strategies (reducing air pollution), it is nessesary to use integrated research methods such as the quanti cation of the spatial structure of urban patterns (composition and con guration) and the analysis of the correlation between air pollutants and land (Ku, 2020). Accordingly, a spatial-temporal analysis based on the spatial structure of urban patterns (composition and con guration) was conducted in this study.…”

The Complexity of Space Utilization and Environmental Pollution Control in the Main Corridor of Makassar City, South Sulawesi, Indonesia

“…Thus, it takes an active role from the government and society to deal with these problems. Furthermore, to ensure the sustainability of the urban environment, urban planning is an instrument used to formulate countermeasures for the government [104]. Furthermore, the direct and indirect effects of urban activity systems, transportation systems, and origin-destination movement patterns on environmental quality degradation and air pollution index are described in Figure 12 below.…”

Multi-Temporal Arable Land Monitoring in Arid Region of Northwest China Using a New Extraction Index

“…Early land use/cover change studies have utilized historical records and hand-drawn maps to depict arable land in arid regions [5,6]. Since the 1990s, land use/cover maps from remotely sensed images have been employed as the main data source to monitor the arable land's status and its response to external disturbance [7][8][9], because remotely sensed images can help retrieve the context, direction, and rate of arable land change in a time-and labor-saving way [10,11]. According to the spatial resolution, remotely sensed images can be categorized to coarse-, middle-and high-resolution classes.…”