Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach

Sun, Jie; Li, Y. P.; Gao, Pan; Suo, C.; Xia, Beicheng

doi:10.1016/j.envres.2018.06.009

Cited by 35 publications

(10 citation statements)

References 22 publications

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“…We selected the following study sites: Qifeng Park (113°45′50″-113°46′26″ E, 23°00′50″-23°01′04″ N) in the urban areas, the Tongsha Ecological park (113°45′54″-113°46′45″ E, 22°57′45″-23°58′25″ N) in the suburban areas about 6 km from the city center, and the Dalingshan Forest Park (113°45′56″-113°46′46″ E, 22°52′28″-22°53′09″ N) in the exurban areas about 16 km from the city center. The population densities of the three sites decreased from urban to exurban areas [33]. Qifeng Park is located in the center of Dongguan with a high degree of disturbance.…”

Section: Study Sitementioning

confidence: 93%

“…The soil type is red and the soil texture is sandy and loam. The evergreen broad-leaved zonal forest is well preserved in the south of Dongguan [33]. Therefore, we selected the research sites from the city center to the south, with an almost consistent longitude.…”

Section: Study Sitementioning

confidence: 99%

“…Nine independent plots with identical size (20 × 20 m) were randomly designated within each site for vegetation census and soil sampling. The space between each two plots was more than 20 m. In each plot, we recorded all trees We selected the following study sites: Qifeng Park (113 • The population densities of the three sites decreased from urban to exurban areas [33]. Qifeng Park is located in the center of Dongguan with a high degree of disturbance.…”

Section: Experimental Design Plant Census and Soil Samplingmentioning

confidence: 99%

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The Composition and Diversity of Soil Bacterial and Fungal Communities Along an Urban-To-Rural Gradient in South China

Tan

Kan

et al. 2019

Forests

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Soil microbes are of great significance to driving the biogeochemical cycles and are affected by multiple factors, including urbanization. However, the response of soil microbes to urbanization remains unclear. Therefore, we designed an urban-to-rural gradient experiment to investigate the response of soil microbial composition and diversity to urbanization. Here, we used a high-throughput sequencing method to analyze the biotic and abiotic effects on soil microbial composition and diversity along the urban-to-rural gradient. Our results showed that soil bacterial diversity was the highest in urban areas, followed by suburban areas, and was the lowest in exurbs; however, fungal diversity did not vary significantly among the three areas. Plant traits, i.e., tree richness, shrub richness, the number of tree stems, diameter at breast height of trees, and soil properties, i.e., pH, soil organic carbon, soil exchangeable calcium and magnesium, and soil water content, were only significantly influenced bacterial diversity, but not fungal diversity. The effect of trees and shrubs was higher than that of herbs on microbial composition. Soil organic carbon, pH, soil available nitrogen, soil exchangeable calcium, and magnesium were the major soil factors influencing the soil bacterial and fungal composition. Soil properties had a greater influence on bacterial than on fungal composition at genus level, while plant traits contributed more to fungal than to bacterial composition at genus level. Our study suggests that the urban-to-rural gradient affect the composition and diversity of bacterial community as well as the fungal composition, but not the fungal diversity.

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Section: Study Sitementioning

confidence: 93%

Section: Study Sitementioning

confidence: 99%

Section: Experimental Design Plant Census and Soil Samplingmentioning

confidence: 99%

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The Composition and Diversity of Soil Bacterial and Fungal Communities Along an Urban-To-Rural Gradient in South China

Tan

Kan

et al. 2019

Forests

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“…The GS areas dramatically decreased in Dongguan from 1994 to 2015. As Sun et al (2018) note, human activities, especially urban construction, have adverse impacts on green space. In addition, a large amount of agricultural land was converted to urban land, as Chen et al (2014) noted.…”

Section: Implications For Sustainable Urban Developmentmentioning

confidence: 99%

Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development

Zhang

Estoque

Murayama

et al. 2021

Environ Monit Assess

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Land use/cover change is the main driving force of urban expansion which influences humanenvironment interactions. Generally, the formation of urban heat islands (UHIs) can be referred to as a negative "by-product" of urbanization. In the context of rapid urbanization, the present paper aims to capture the landscape changes and three patterns of urban expansion (i.e., infill, extension, and leapfrog), and provide a better understanding of the formation of the surface urban heat island (SUHI) in Dongguan, China, during the past 20+ years.

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“…where n is the number of variables and Xi and Yi are the values of the ith variable at points X and Y, respectively [23]. SCA includes several steps: (1) Screening out the major predictors; (2) generating cluster trees through a series of cutting and merging actions; (3) validating the generated cluster trees and selecting the most suitable one; and (4) comparing with other statistical techniques for illustrating the applicability of SCA [24].…”

Section: Backward Trajectory Using Noaa Hypslit and Group Clusteringmentioning

confidence: 99%

Origin and Background Estimation of Sulfur Dioxide in Ulaanbaatar, 2017

Prikaz

Fang

Dash³

et al. 2018

Environments

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Particulate matter studies have been conducted regularly in the capital city of Mongolia. In contrast, studies related to the source and general estimation of levels of sulfur dioxide (SO 2 ) over whole years are lacking. To explore the yearly trend in SO 2 , whole-year data of air pollutants were obtained from the Air Pollution Reducing Department. The results showed that the annual average concentration of SO 2 was 32.43 µg/m 3 at the Amgalan official monitoring station in 2017, which changed from 53 µg/m 3 in 2016, representing a reduction of around 40%. The back-trajectory model and the National Oceanic and Atmospheric Administration (NOAA)'s hybrid single particle Lagrangian integrated trajectory model (HYPSLIT) were used to determine the source of SO 2 . A total of 8760 backward trajectories were divided into eight groups. The results showed that 78.8% of the total trajectories in Ulaanbaatar came from an area inside Mongolia. The results showed that pollutants enter Ulaanbaatar mainly from the northwest and north during the winter season. There are industrial cities, such as Darkhan and Sukhbaatar, in North Mongolia. Air pollutants created in the industrial area traveled into Ulaanbaatar during the winter season.

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Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach

Cited by 35 publications

References 22 publications

The Composition and Diversity of Soil Bacterial and Fungal Communities Along an Urban-To-Rural Gradient in South China

The Composition and Diversity of Soil Bacterial and Fungal Communities Along an Urban-To-Rural Gradient in South China

Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development

Origin and Background Estimation of Sulfur Dioxide in Ulaanbaatar, 2017

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