• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.1
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• pp.89-96
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• 2020

Adverse impact of Particulate Matters(PM10, PM2.5; PMs) significantly affects daily lives. Major countermeasures for reducing concentration of PMs were focused on emission source without considering spatial difference of PMs concentration. Thus, this study analyzed spatial·temporal distribution of PMs with observation data as well as potential contributing factors on PMs concentration. The annual average concentration of PMs have been decreased while the particulate matter warnings and alerts were significantly increased in 2018. The average concentration of PMs in spring and winter was higher than the other seasons. Also, the spatial distribution of PMs were also showed seasonality while concentration of PMs were higher in Seoul-metropolitan areas in all seasons. Climate variables, emission source, spatial structure and potential PM sinks were selected major factors which could affects on ambient concentrations of PMs. This paper suggest that countermeasures for mitigating PM concentration should consider characteristics of area. Climatic variables(temperature, pressure, wind speed etc.) affects concentrations of PMs. The effects of spatial structure of cities(terrain, ventilation corridor) and biological sinks(green infrastructure, urban forests) on concentration of PMs should be analyzed in further studies. Also, seasonality of PMs concentration should be considered for establishing effective countermeasures to reduce ambient PMs concentration.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.327-354
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• 2019

In this paper, we conducted panel data analysis considering spatial correlation between regions, which were not considered in previous papers in analyzing the causes of fine dust concentration in Korea. Many existing researchers implicitly assume the independence of the effects of incomes and other explanatory variables of adjoining countries(or regions). Using panel data on fine dust concentration, this paper has established that existing EKC can be established even when considering the spatial correlation of the region, and when these effects are not taken into account, it can be underestimated or overestimated on the effects and causes of fine dust concentration.

• Journal of Environmental Science International
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• v.31 no.12
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• pp.999-1012
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• 2022

To investigate the reason for the spatial difference in PM_2.5 (Particulate Matter, < 2.5 ㎛) concentration despite a similar synoptic pattern, a synoptic analysis was performed. The data used for this study were the daily average PM_2.5 concentration and meteorological data observed from 2016 to 2020 in Busan and Seoul metropolitan areas. Synoptic pressure patterns associated with high PM_2.5 concentration episodes (greater than 35 ㎍/m³) were analyzed using K-means cluster analysis, based on the 900 hPa geopotential height of NCEP (National Centers for Environmental Prediction) FNL (Final analysis) data. The analysis identified three sub-groups related to high concentrations occurring only in Busan and Seoul metropolitan areas. Although the synoptic patterns of high PM_2.5 concentration episodes that occur independently in Busan and Seoul metropolitan areas were similar, there was a difference in the intensity of pressure gradient and its direction, which tends to be an important factor determining the movement time of pollutants. The spatial difference in PM_2.5 concentration in the Korean Peninsula is due to the difference and direction of the atmospheric pressure gradient that develops from southwest to northeast direction.

• Spatial Information Research
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• v.21 no.5
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• pp.15-22
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• 2013

The purpose of this work is to investigate correlation between $CO_2$ concentration and NDVI (Normalized Difference Vegetation Index) in North East Asia. Geographically weighted regression techniques were used to evaluate the spatial relationships between GOSAT (Greenhouse Observing SATellite) $CO_2$ measurement and MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index. The results reveals that $CO_2$ concentration to be negatively associated with NDVI. The analysis of Global Morans' I index and Anselin Local Morasn's I showed spatial autocorrelation between the overall spatial pattern of $CO_2$ and NDVI. Ultimately, there were clustered patterns in both data sets. The results show that carbon dioxide concentration shows non-random distribution patterns in relation to NDVI clusters, which proves that intense development activities such as deforestation are influencing carbon dioxide emission across the area of analysis. However, as the concentration of carbon dioxide varies depending on a variety of factors such as artificial sources, plant respiration, and the absorption and discharge of the ocean, follow-up studies are required to evaluate the correlations among more related variables.

• Journal of Environmental Science International
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• v.19 no.6
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• pp.761-769
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• 2010

Temporal trends of ozone concentration in Jinju were investigated by using observation data from 3 air quality monitoring stations for the period of 2004~2008. In addition, spatial comparisons of ozone concentration at Jeoguri, upwind and downwind directions of Jinju were investigated between May and September 2009. Annual mean exhibited increasing trends +1.7ppb/yr throughout the study period. In the case of diurnal variation, the lowest ozone concentration was shown from 7 am to 8 am and the highest around 4 pm. The ozone concentrations of Jeoguri station of the south coast were higher than Jinju. In particular, the upwind direction of Jinju had relatively hight ozone concentration

• The Journal of Asian Finance, Economics and Business
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• v.4 no.2
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• pp.37-44
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• 2017

This research is devoted to the development of methods general and standard methodological approaches and approbation those for the evaluation of economic potential and level of concentration of the regions of Kazakhstan. This paper presents the results of development of the authors on the selection and justification of the methodological approaches for quantitative evaluation of the economic potential (the degree of territorial differentiation of the profile) and concentration of regions. In this study, we used scientific methods: method of analysis the main trends of economic development, and method of evaluation of concentration of the region. Based on the analysis of foreign techniques developed and tested methodical approaches to the assessment of the economic potential (index and coefficient methods). Proposed methodological approaches to the assessment profile of the territory and developed a system of indicators, which includes an aggregated index of spatial concentration, which accurately reflects the concentration of production in the region. This study shows the results of the analysis of the potential regional disparities and trends of economic development of Kazakhstan. By using, the proposed methodology shows the possibility of their use; we calculated the indicators of integrated assessment of the economic potential and indicators of spatial concentration.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.64-77
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• 2021

The purpose of this study is to examine the spatial distribution of highly educated human capital and to identify key factors affecting their spatial distribution. We analyzed the spatial concentration and inequality using Gini's coefficient and exploratory spatial data analysis and identified the economic and amenity factors to affect the spatial concentration of highly educated human capital using spatial regression model. The findings show that the spatial pattern of highly educated human capital is concentrated, imbalanced, and clustered in Capital region and part of Chungcheong and Gangwon region. The spatial concentration were more affected by economic factor than by amenity factors. This study provides some implication on the regional economic strategies to attract the human capital.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.61-69
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• 2014

In this study spatial analysis of PM10 was performed to Particulate Materials(PM) less than $10{\mu}m$ in diameter in Seoul city. Because PM10 are responsible for the increasing mortality rate of lung cancer and cardiovascular diseases, spatial distribution of PM10 are special interest in air pollution of Seoul. In this study, spatial analysis of Particulate Materials were monitored by monthly averaged PM10 concentration of 2010, 2011. The monthly spatial patterns of PM10 showed the west area of Seoul(Youngdungpo) higher PM10 concentration than northern part of Seoul in early spring and winter seasons. In the comparison of PM10 concentration distribution patterns in 2010 and 2011, the PM10 concentration of 2011 at Gangnam and Songpa-gu were more increased than yearly averaged patterns of 2010. The distribution patterns of PM10 in Seoul city showed the high concentration PM10 of several areas with Youngdungpo-gu, Gangnam-gu and Cheongnyangni. Therefore we need to establish PM10 management strategy for these area.

• Journal of Korea Trade
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• v.25 no.5
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• pp.93-109
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• 2021

Purpose - The logistics industry is often featured by its location relevance and industrial concentration. Industrial concentration is conducive to the effective transmission of information by reducing transaction costs and improving transaction efficiency, thus promoting the development of trade. The main purpose of this paper is to measure the spatial total factor productivity and location quotient of the logistics industry in Korea, and to study the impact of the logistics industry efficiency and agglomeration effect on import and export trade in Korea. Design/methodology - First, used the spatial stochastic frontier method to measure the spatial total factor productivity of the logistics industry in Korea, this serves as the efficiency index of the logistics industry in various regions of Korea. Second, calculated the location quotient (LQ) of the logistics industry to measure the industry's concentration degree. Third, employed a spatial econometric model to analyze the impacts of factors such as the efficiency and concentration levels of the logistics industry on import and export trade in Korea. Findings - This study's main findings can be summarized as follows: this study found that the overall efficiency of the logistics industry in Korea needs to be improved, even though it showed an upward trend in all regions of the country; Moreover, the agglomeration level of Korea's logistics industry needs to be improved; Finally, the positive spatial correlation and industrial agglomeration effect of Korea's logistics industry had a positive impact on the country's import and export trade. Originality/value - This study is innovative in terms of research perspective and methods. Most of the previous studies have measured the development level of the logistics industry using the logistics performance index (LPI), Fewer studies have assessed through the spatial total factor productivity and location quotient of the logistics industry in Korea to measure the efficiency index of the logistics industry in various regions of Korea and concentration degree, as well as there was almost no study on the impact of logistics industry efficiency and agglomeration effect on import and export trade in Korea. This study addresses this limitation by analyzing the impacts of the efficiency and agglomeration effect of the logistics industry on import and export trade in Korea.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.347-351
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• 2015

Sea ice is a key parameter for understanding the climate change in cryosphere. In this study, we investigated the correlation with the factors that influenced change of the sea ice extent. We used the Sea Ice Concentration (SIC) from Ocean and Sea Ice Satellite Application Facility (OSI-SAF), and surface albedo provided by The Satellite Application Facility on Climate Monitoring (CM SAF). We converted the same temporal and spatial resolution of the data and detected the sea ice using SIC data. We performed the relationship analysis between SIC and sea ice albedo. As a result, we found they have a strong positive correlation. We performed the linear regression between SIC and sea ice albedo, and found they have high-level coefficient of determination. It shows using either SIC or sea ice albedo is possible to estimate the sea ice products.