Spatial Relationships between Urban Structures and Air Pollution in Korea

Jung, Meen Chel; Park, Jae-Woo; Kim, Sunghwan

doi:10.3390/su11020476

Cited by 17 publications

(12 citation statements)

References 66 publications

(97 reference statements)

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“…Previous studies analyzed using the ratio of secondary industry, the area and ratio of industrial area, and the ratio of manufacturing workers, similar to air pollution emission facilities. The results of this study were similar to those of Jung et al [18]. On the other hand, Cho•Choi [31] and Cárdenas Rodríguez et al [8] did not show a significant relationship between NO 2 concentration and emission facility density.…”

Section: Discussionsupporting

confidence: 89%

“…Moreover, the number of registered vehicles per capita is an index reflecting not only the quantitative increase of vehicles but also the economic aspect of a region. When it is connected to the economic power improvement of a region or an individual household, it also indirectly supported Jung et al [18], who showed that gross regional domestic product(GRDP) and the NO 2 concentration were positively related.…”

Section: Discussionsupporting

confidence: 53%

“…Few studies have examined comprehensive relationships using multiple urban structural variables in terms of land use. Most studies have used area of each land use (residential, commercial, industrial, and transportation) and area ratios [8,10,13,[18][19][20]. Additionally, since most of these studies were conducted in North American cities, China, and Europe, it is difficult to apply the results directly to South Korean cities.…”

Section: Related Literaturementioning

confidence: 99%

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The Impacts of Urban Air Pollution Emission Density on Air Pollutant Concentration Based on a Panel Model

Baek

Ban

2020

Sustainability

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In terms of urban planning, the impact of urbanization and high density on the environment is a major issue. This study intended to analyze the effect of spatial density characteristics of urban air pollution sources on urban air pollution concentration using a panel model. As the total population density, the number of cars registered per capita, and the total emission facility density increased, together with a closer distance to a thermal power plant, the nitrogen dioxide(NO2) concentration increased. Net population density was also found to have the greatest impact on the structure and density of emission sources of ozone(O3) followed by the number of cars registered per person and the total emission facility density. It was confirmed that particular matter(PM10) concentrations are strongly influenced in positive directions by the spatial density characteristics of emission sources that show significant differences between regions.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 53%

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

The Impacts of Urban Air Pollution Emission Density on Air Pollutant Concentration Based on a Panel Model

Baek

Ban

2020

Sustainability

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“…The Bayesian model can synthesize information from different sources and improve the reliability of inferred conclusions [71,72]. Therefore, when judging the category of a pixel whose difference between the maximum probability value and the second-maximum probability value is small, the spatial structure information of the pixels can be further introduced to improve the reliability of the judgment by using the Bayesian model.…”

Section: Introductionmentioning

confidence: 99%

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

Zhang

Han

Li³

et al. 2019

Remote Sensing

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When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models (CNN-Bayesian model) for improved extraction accuracy. In this model, a feature extractor generates a feature vector for each pixel, an encoder transforms the feature vector of each pixel into a category-code vector, and a two-level classifier uses the difference between elements of category-probability vectors as the confidence value to perform per-pixel classifications. The first level is used to determine the category of a pixel with high confidence, and the second level is an improved Bayesian model used to determine the category of low-confidence pixels. The CNN-Bayesian model was trained and tested on Gaofen 2 satellite images. Compared to existing models, our approach produced an improvement in overall accuracy, the overall accuracy of SegNet, DeepLab, VGG-Ex, and CNN-Bayesian was 0.791, 0.852, 0.892, and 0.946, respectively. Thus, this approach can produce superior results when winter wheat spatial distribution is extracted from satellite imagery. 2 of 21 over the past few decades at regional or global scales [6][7][8]. As extraction of crop spatial distribution mainly relies on pixel-based image classification, correctly determining pixel features for accurate classification is the basis for this approach [9][10][11][12].The spectral characteristics of low-and middle-resolution remote sensing images are usually stable. Vegetation indexes are generally used as pixel features in studies using data from sources including the Moderate Resolution Imaging Spectroradiometer (MODIS) [6,[13][14][15][16], Enhanced Thematic Mapper/Thematic Mapper [13,17], and Systeme Probatoire d' Observation de la Terre [7,10]. These indices include the normalized difference vegetation index (NDVI) [5,6,[13][14][15], relationship analysis of NDVI [8], and enhanced vegetation index (EVI) [3,18], which are extracted from band values. Common classification methods include decision trees [5,11,13], linear regression [6], statistics [7], filtration [13], time-series analysis [14,15], the iterative self-organizing data analysis technique (ISODATA) [16], and the Mahalanobis distance [17]. Texture features can better describe the spatial structure of pixels, the Gray-Level Co-Occurrence Matrix is a commonly used texture feature [19], and Gabor [20] and wavelet transforms [19,21] are often used to extract texture features. Moreover, object-based image analysis technology is also widely used in pre-pixel classification [22,23]. Such methods can successfully extract the spatial distribution of winter wheat and other crops, but limitations in spatial resolution restrict the applicability of the results.The spatial resolution and precision of crop extraction can be significantly improved by using high-resolution imagery [8,24,25]. However,...

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“…Macpherson et al [27] used a Bayesian network model to analyze wetland protection decision making and management; Richards [28] applied this model to regional adaptation adjustment and early warning under climate change; while Stritih [5] used Bayesian networks to quantify uncertainties in ecosystem services assessment. Jung et al [29] used Bayesian spatial regression models to analyze the relationship between urban structure and air pollution in Korea; Wu et al [30] applied the Bayesian network and other methods to assess the fire risk at underground subway stations; Pérez-Sánchez et al [31] used the Bayesian model to evaluate the hydrological model of water resources in the Spanish peninsula. In addition to these studies, Bayesian networks have also been widely used in fishing net construction, water purification, landscape protection and development, and food security [15,25,[32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

How to Optimize Ecosystem Services Based on a Bayesian Model: A Case Study of Jinghe River Basin

Tang

Zhou

et al. 2019

Sustainability

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Based on a Bayesian Network Model (BBN), we established an ecological service network system of the Jinghe River Basin in 2015. Our method consisted of using the distributed eco-hydrological model (Soil and Water Assessment Tool (SWAT) model) to simulate water yield, the Carnegie-Ames-Stanford Approach (CASA) model to estimate Net Primary Productivity (NPP), the Universal Soil Loss Equation (USLE) model to calculate soil erosion and the Crop Productivity (CP) model to simulate agricultural productivity to quantify the four ecosystem services. Based on the network established, the key variable subset and the visual optimal state subset, which we visualized, were analyzed and used to provide spatial optimization suggestions for the four kinds of ecosystem services studied. Our results indicate that water yield, concentrated in the middle and lower reaches of the mountain and river areas, is increasing in the Jinghe River Basin. NPP is continuously increasing and is distributed in the middle and lower reaches of the mountain areas on both sides of the river. Agricultural productivity also shows an upward trend, with areas of high productivity concentrated in the southern downstream mountain areas. On the contrary, the amount of soil erosion is declining, and the high erosion value is also declining, mainly in the upper reaches of the basin (in the Loess Hilly Area). Additionally, we found that a synergistic relationship exists between water yield, NPP and agricultural productivity, which can increase vegetation cover, leading to enhanced agricultural productivity. However, water yield can be reduced as required in order to balance the tradeoff between water yield and soil erosion. Clear regional differences exist in ecosystem services in the river basin. In the future, the two wings of the middle and lower reaches of the river basin will be the main areas of optimization, and it is likely that an optimal ecosystem services pattern can be reached.

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Spatial Relationships between Urban Structures and Air Pollution in Korea

Cited by 17 publications

References 66 publications

The Impacts of Urban Air Pollution Emission Density on Air Pollutant Concentration Based on a Panel Model

The Impacts of Urban Air Pollution Emission Density on Air Pollutant Concentration Based on a Panel Model

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

How to Optimize Ecosystem Services Based on a Bayesian Model: A Case Study of Jinghe River Basin

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