• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.100-119
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• 2020

During recent years, the heat environment and particulate matter (PM₁₀) have become serious environmental problems, as increases in heat waves due to rising global temperature interact with weakening atmospheric wind speeds. There exist urban heat islands and urban pollution islands with higher temperatures and air pollution concentrations than other areas. However, few studies have examined these issues together because of a lack of micro-scale data, which can be constructed from spatial data. Today, with the help of satellite images and big data collected by private telecommunication companies, detailed spatial distribution analyses are possible. Therefore, this study aimed to examine the spatial distribution patterns of urban heat islands and urban pollution islands within Busan Metropolitan City and to compare the distributions of the two phenomena. In this study, the land surface temperature of Landsat 8 satellite images, air temperature and particulate matter concentration data derived from a private automated meteorological observation system were gridded in 30m × 30m units, and spatial analysis was performed. Analysis showed that simultaneous zones of urban heat islands and urban pollution islands included some vulnerable residential areas and industrial areas. The political migration areas such as Seo-dong and Bansong-dong, representative vulnerable residential areas in Busan, were included in the co-occurring areas. The areas have a high density of buildings and poor ventilation, most of whose residents are vulnerable to heat waves and air pollution; thus, these areas must be considered first when establishing related policies. In the industrial areas included in the co-occurring areas, concrete or asphalt concrete-based impervious surfaces accounted for an absolute majority, and not only was the proportion of vegetation insufficient, there was also considerable vehicular traffic. A hot-spot analysis examining the reliability of the analysis confirmed that more than 99.96% of the regions corresponded to hot-spot areas at a 99% confidence level.

• Tuberculosis and Respiratory Diseases
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• v.60 no.4
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• pp.426-436
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• 2006

Background : Particulate matters (PM) when inhaled is known to induce pulmonary diseases including asthma and chronic bronchitis when inhaled. Despite the epidemiological proofevidence, the pathogenesis of PM-related pulmonary diseases is unclearremain poorly understood. Methods : Primary alveolar macrophages were harvested from the SPF and inflammatory rats by bronchioalveolar lavage (BAL). The cultured primary alveolar macrophages were treated with the medium only, PM only ($5{\sim}40{\mu}g/cm^2$), LPS (5ng/ml) only, and PM with LPS for 24 and 48 hours. The level of secreted nitric oxide (NO) was assayed from the cultured medium by using the Griess reaction. The cultured cells were utilized for the western blotting against the inducible nitric oxide synthase (iNOS) proteins. Immunocyto- chemical staining against the iNOS and NT-proteins were performed in cells that cultured in the $Lab-Tek^{(R)}$ chamber slide after treatments. Results : The PM that utilizein this experiments induced NO formation with iNOS expression in the cultured SPF and inflammatory rats alveolar macrophages, by itself. When the cells were co-treated with PM and LPS, there was a statistically significant synergistic effect on NO formation and iNOS expression over the LPS effect. The cells from the sham control showed minimal immunoreactivity for the NT-proteins. Significantly higher quantities of NT-proteins were detected in the PM and PM with LPS co-treated cells than from the sham control. Conclusion : Increased iNOS expression and NO formation with increased NT-proteins formation might be involved in the pathogenesis of PM-induced lung injury.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.393-405
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• 2021

With increasing anthropogenic emission sources, air pollutants are emerging as a severe environmental problem worldwide. Accordingly, the importance of landscape trees is emerging as a potential solution to reduce air pollutants, especially in urban areas. This study quantified and compared NO₂ and SO₂ reduction abilities of ten major landscape tree species and analyzed the relationship between reduction ability and physiological and morphological characteristics. The results showed NO₂ reduction per leaf area was greatest in Cornus officinalis (19.81 ± 3.84 ng cm^-2 hr^-1) and lowest in Pinus strobus (1.51 ± 0.81 ng cm^-2 hr^-1). In addition, NO₂ reduction by broadleaf species (14.72 ± 1.32 ng cm^-2 hr^-1) was 3.1-times greater than needleleaf species (4.68 ± 1.26 ng cm^-2hr^-1; P < 0.001). Further, SO₂ reduction per leaf area was greatest in Zelkova serrata (70.04 ± 7.74 ng cm^-2 hr^-1) and lowest in Pinus strobus (4.79 ± 1.02 ng cm^-2 hr^-1). Similarly, SO₂ reduction by broadleaf species (44.21 ± 5.01 ng cm^-2 hr^-1) was 3.9-times greater than needleleaf species (11.47 ± 3.03 ng cm^-2 hr^-1; P < 0.001). Correlation analysis revealed differences in NO₂ reduction was best explained by chlorophyll b content (R² = 0.671, P = 0.003) and SO₂ reduction was best described by SLA and length of margin per leaf area (R² = 0.456, P = 0.032 and R² = 0.437, P = 0.001, R² = 0.872, P < 0.001, respectively). In summary, the ability of trees to reduce air pollutants was related to photosynthesis, evapotranspiration, stomatal conductance, and leaf thickness. These findings highlight effective reduction of air pollutants by landscaping trees requires comprehensively analyzing physiological and morphological species characteristics.