The relationships between urban landscape patterns and fine particulate pollution in China: A multiscale investigation using a geographically weighted regression model

Tu, Mengzhao; Liu, Zhifeng; He, Chunyang; Fang, Zihang; Lu, Wenlu

doi:10.1016/j.jclepro.2019.117744

Cited by 51 publications

(19 citation statements)

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“…x are the values of variable x (vegetation indexes) at location i and location j, respectively. x is the average value of x, n is the total number of locations, and ij w represents the spatial weight [4,44,81,82].…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

“…A linear trend analysis of vegetation, temperature, and precipitation, and a comparison between the correlation of NDVI-temperature and NDVI-precipitation in northeastern China was carried out by Mao, et al [43]. However, the analysis methods used in most of the previous studies focused solely on a temporal scale or spatial scale, and therefore, they do not provide a comprehensive analysis to deal with variability between vegetation dynamics and envi-ronmental factors at both spatial and temporal scales simultaneously [44][45][46]. Additionally, a few efforts have been made to investigate the relationships between hydrological factors and vegetation dynamics in the ARB, with close interactions between hydrological factors and vegetation growth having been found in many studies [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

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Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

et al. 2021

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Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

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Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

et al. 2021

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“…In addition, there are spatial differences in the impacts of air pollution on regions [ 27 ]. Scholars have proved the existence of spatial heterogeneity in related research [ 28 , 29 , 30 ]. Spatial heterogeneity and less pronounced negative influences of air pollution on housing values in the southeastern area of the study region were found through a case study of Salt Lake County, Utah [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities

Biao

Yang

2020

IJERPH

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Fine particulate matter(PM2.5) pollution will affect people’s well-being and cause economic losses. It is of great value to study the impact of PM2.5 on the real estate market. While previous studies have examined the effects of PM2.5 pollution on urban housing prices, there has been little in-depth research on these effects, which are spatially heterogeneous at different conditional quantiles. To address this issue, this study employs quantile regression (QR) and geographically weighted quantile regression (GWQR) models to obtain a full account of asymmetric and spatial non-stationary effects of PM2.5 pollution on urban housing prices through 286 Chinese prefecture-level cities for 2005–2013. Considerable differences in the data distributions and spatial characteristics of PM2.5 pollution and urban housing prices are found, indicating the presence of asymmetric and spatial non-stationary effects. The quantile regression results show that the negative influences of PM2.5 pollution on urban housing prices are stronger at higher quantiles and become more pronounced with time. Furthermore, the spatial relationship between PM2.5 pollution and urban housing prices is spatial non-stationary at most quantiles for the study period. A negative correlation gradually dominates in most of the study areas. At higher quantiles, PM2.5 pollution is always negatively correlated with urban housing prices in eastern coastal areas and is stable over time. Based on these findings, we call for more targeted approaches to regional real estate development and environmental protection policies.

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“…This means that urban planning determines not only parcelling, urban mobility (vehicles and pedestrian), and housing types (single-or does a historic study of the anthropization of a wetland, in which he assesses the documented changes in traditional studies of land use or coverage change, mapping the variations of the main habitats [31]. Tu et al study the relation between urban landscape patterns and particle pollution using a regression model [32]. Cui et al propose a method to evaluate the effects of urban expansion into agricultural land using geographic data to identify multiyear patterns and improve sustainable urban growth [33].…”

Section: Introductionmentioning

confidence: 99%

Anthropization and Growth of the Electricity Grid as Variables for the Analysis of Urban Infrastructure

et al. 2020

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City growth goes together with the development of infrastructure, and the power network is one of the most relevant towards economic development. The study of urban infrastructure through the analysis of anthropization coupled with power network growth can produce a tool that supports sustainable infrastructure planning, both economic and environmental. The case study focuses on Ambato, Ecuador, in the period from 1950 to 2019, and assesses quantitatively the changes in the city layout and the evolution of its power network. The data are adjusted to a sigmoid-type objective function through a non-linear least squares problem, that is solved using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Anthropization data show how the urban area grew during the study period: at the expense of agricultural land. The forecast for new power network users by 2050 yields a result of 203,630 total users with a population density of 4850 people/km 2 . The conclusion is that this type of analysis can help city planners and decision makers further understand city and infrastructure growth dynamics and produce policies that bolster sustainable city growth.Sustainability 2020, 12, 1486 2 of 17 multi-family), but also the service networks, including the power network. However, most modern Latin American cities have experienced a fragmented urban growth, through lots and suburbs, often under speculative pressure and unplanned self-construction as new land is made available and has access to service infrastructure [5].Documenting the growth of a city can be very useful if the drawings are made with further purpose than observing formal chronological aspects of the architectural background [6]. Adding other backgrounds and variables to cross evaluate and analyse gives a better understanding of unforeseen growth tendencies and brings up otherwise hidden deficiencies of the urban service networks in the different areas of the city. This information is very valuable when establishing sustainable urban planning criteria [7,8], Herce and Miró Farellons suggest five development lines to reformulate urban models from a sustainability standpoint [9]. These include relating service infrastructure with land use in order to increase efficiency.The study of anthropization together with infrastructure growth is an essential tool to assess the state of a city and determine actions to bolster its sustainability based on economic parameters (economic accessibility, production, private sector, public services, jobs); environmental parameters (agriculture, water, air, energy, environmental management, resources, waste, noise); social parameters (quality of life, environmental education, population, identity, social inclusion, participation, safety, and health); and urban parameters (equipment, street furniture, green areas, planning and management, land, transportation) [10][11][12]. In this context, urban infrastructure includes transportation networks, fresh water and sewers networks, power network, and information network, as well as the managemen...

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The relationships between urban landscape patterns and fine particulate pollution in China: A multiscale investigation using a geographically weighted regression model

Cited by 51 publications

References 52 publications

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities

Anthropization and Growth of the Electricity Grid as Variables for the Analysis of Urban Infrastructure

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