• Environmental Analysis Health and Toxicology
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• 제30권
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• pp.10.1-10.14
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• 2015

Recent cohort studies have relied on exposure prediction models to estimate individual-level air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finalized a list of 313 geographic variables related to air pollution sources in eight categories including traffic, demographic characteristics, land use, transportation facilities, physical geography, emissions, vegetation, and altitude. We then obtained data from different sources such as the Statistics Geographic Information Service and Korean Transport Database. After integrating all available data to a single database by matching coordinate systems and converting non-spatial data to spatial data, we computed geographic variables at 294 regulatory monitoring sites in South Korea. The data integration and variable computation were performed by using ArcGIS version 10.2 (ESRI Inc., Redlands, CA, USA). For traffic, we computed the distances to the nearest roads and the sums of road lengths within different sizes of circular buffers. In addition, we calculated the numbers of residents, households, housing buildings, companies, and employees within the buffers. The percentages of areas for different types of land use compared to total areas were calculated within the buffers. For transportation facilities and physical geography, we computed the distances to the closest public transportation depots and the boundary lines. The vegetation index and altitude were estimated at a given location by using satellite data. The summary statistics of geographic variables in Seoul across monitoring sites showed different patterns between urban background and urban roadside sites. This study provided practical knowledge on the computation process of geographic variables in South Korea, which will improve air pollution prediction models and contribute to subsequent health analyses.

• 한국대기환경학회지
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• 제27권3호
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• pp.358-366
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• 2011

The purpose of this study was to analyze the monthly and seasonal PM10 data using the Autoregressive Error (ARE) model at the southern part of the Gyeonggi-Do, Pyeongtaek monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables. The six meteorological variables are daily maximum temperature, wind speed, amount of cloud, relative humidity, rainfall, and global radiation. The four air pollution variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result shows that monthly ARE models explained about 17~49% of the PM10 concentration. However, the ARE model could be improved if we add the more explanatory variables in the model.

• 한국환경과학회지
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• 제6권6호
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• pp.551-562
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• 1997

Nine districts in Seoul were chosen randomly and a questionnaire containing 23 questions was distributed to survey the perception of noise pollution by the citizens. The results were primarily analyzed to understand the perception of Seoul citizen as a whole, and then analyzed according to 4 demographic variables -district area, age, gender and occupation -to see if there were any possible relation between nonnoise variables and noise annoyance. Actual noise level data measured by the city government were used to compare quantified noise level with the surveyed people's perception. It was found that people consider road traffic noise to be the naix source of noise pollution In Seoul and that most people have experienced annoyance in everyday life. Also it was verified that the responsibility for noise control should be on both city government and the individuals, but It was generally considered that very little effort Is actually put Into solong norse pollution from both groups. From the survey, It could be analyzed that domographic variables do affect people In the awareness of noise pollution, and that one's sensitifty and annoyance due to noise increase as one ages. From the study, It was concluded that noise pollution Is not considered currently as a hazardous problem to most Seoul citizens, however specific noise reduction policies, especially regarding road traffic noise, should be put Into practice In the near future.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.109-116
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• 2022

The energy corporation of Kosovo continuously monitors and analyzes the impact of its own activities on the environment. Regarding the environmental situation, energy corporation of Kosovo- ECK regularly informs and reports objectively to the competent state institutions, local municipal institutions and interested parties. ECK, through numerous contacts with the competent authorities, firstly with different ministers, harmonizes the positions regarding environmental issues in the direction of achieving certain environmental standards or legal requirements in order to gradually be in accordance with them, based on the real possibilities, especially the financial ones. From this point of view, the environmental issue is very sensitive, quite complex and represents one of the biggest challenges of society currently and in the future. The researched variables show a continuous increase in the need for electricity production in Kosovo and this increase in production conditions a wide range of environmental impacts both at the local, regional and global levels. The aim of the work is to reduce the emission of pollutants through the main variables without inhibiting the economic development of the country, i.e. to bring the pollution as a result of the activities of the ECK operation into compliance with the permitted environmental norms. As a result of ECK's operational activities, the following follows: Air pollution mainly as a result of emissions from TCs in the air, transport, etc. Water pollution - as a result of technological water discharges, Land degradation - as a result of surface mining activities of the entire mining area. The purpose of the paper is to research and analyze the main water variables in the area of the Kosova B power plant, which is to determine the degree of their pollution from the activities of the power plants, as well as to assess the real state of surface water quality and control the degree of pollution of these waters. Methodology of the work: The analyzes of the water samples were done in the company Institute "INKOS" JSC by simultaneous methods using different reagents.

• 한국정보통신학회논문지
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• 제25권1호
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• pp.8-19
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• 2021

최근 산업화에 따른 대기오염 문제는 국가와 국민 모두에 큰 관심을 끌고 있다. 국내의 광역(廣域) 대기오염 정보는 국가적으로 공공 데이터를 통해 국민에게 제공하고 있으나, 환경변수가 다른 지역적인 대기오염 정보는 매우 부족한 상황이다. 따라서 본 연구에서는 지역적인 대기오염 현상을 보다 정확하게 분석하고 예측할 수 있는 지역 환경변수 기반의 대기오염 분석 및 예측 시스템을 설계하고 구현한다. 특히 제안한 시스템은 지역적으로 측정된 환경 데이터와 공공 빅데이터를 기반으로 지역의 대기정보를 정확하게 분석하여 제공하고, 인공지능 알고리즘을 통해 미래의 지역 대기정보를 예측하여 제시한다. 나아가 제안된 시스템을 통해 지역적인 대기오염의 발생 원인을 정확하게 파악하여 지역의 대기오염을 예방할 수 있을 것으로 기대할 수 있다.

• 한국대기환경학회지
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• 제24권5호
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Ocean and Polar Research
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• 제26권1호
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• pp.11-22
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• 2004

Benthic pollution assessment based on macrobenthic community structure with environmental variables was carried out at twelve stations during two periods on a presumed pollution gradient in Gamak Bay. Univariate and multivariate methods were applied to investigate structural changes in the benthic communities. A clear gradient of pollution effects on the macrobenthic community was observed from the interior to the exterior of the bay. The community on the northwestern basin was severely disturbed due to a low level of hydrodynamics and a large amount of pollutant input from nearby cities. Exterior regions on the southern basin appeared to have the best benthic environmental characteristics among all stations according to most methods of analysis. Central ridge regions and two stations around the islets in the mouth of the bay exhibited intermediate levels of perturbation when compared to the more disturbed interior and undisturbed exterior regions. Pollution effects on the communities were attenuated at the southern area of the central ridge during spring compared to those of summer, where aquacultural farming was densely distributed. The environmental variables primarily correlated to the macrobenthic community structure were total organic carbon (C), organochlorine pesticides (OCPs), and tributyltins (TBTs), contents found on the surface sediment, as anthropogenic variables indicating organic materials.

• 한국농촌생활과학회지
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• 제8권1호
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• pp.57-72
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• 1997

The purpose of this study was to investigate some influencing variables related to the management behavior on environmental preservation of housewives in Pusan. The subject of this study were 411 housewives and interviewed with a questionnaire. The data was analyzed by SPSS/PC＋ Program for the frequency, mean, standard deviation, Cronbach's$\alpha$, t-test, F-test, Duncan's multiple range test and regression. The major result of this study were as follows : 1. The level of Environmental Management Knowledge was relatively high. The perceived level of environmental pollution was relatively high. The score of perception on water pollution was the highest. The level of management behavior on environmental preservation was relativity low. The score of management behavior on food pollution was the highest. 2. Frequencies of management behavior on environmental preservation differ according to age, education, relegion, and mass - media. 3. The influenced level of perception on air pollution was the highest among the other variables.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1153-1164
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• 2008

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.

• Journal of the Korean Data and Information Science Society
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• 제21권6호
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• pp.1117-1124
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• 2010

미세먼지 농도는 국가의 중요한 환경 척도 중의 하나이다. 본 연구에서는 경기도 남부에 위치한 수원시 2003년-2009년 미세먼지 농도를 주위에서 쉽게 구할 수 있는 대기자료와 기상자료를 이용하여 자기회귀오차모형으로 월별로 분석하였다. 미세먼지 농도 분석을 위한 대기자료는 이산화황, 이산화질소, 일산화탄소, 오존 등을 사용했고, 기상자료로는 일 최고온도, 풍속, 상대습도, 강수량, 일사량, 운량을 사용하였다. 분석 결과, 자기회귀오차모형으로 월별 미세먼지 농도를 13%-49% 정도 설명할 수 있다.