Journal of Korean Society for Atmospheric Environment
/
v.33
no.6
/
pp.544-553
/
2017
There is an overall guideline of the installation of air quality monitoring stations in Korea, but specified steps for the selection of monitoring sites for hazardous air pollutants(HAPs) are not provided. In this study, we proposed a systematic method for the selection of monitoring sites for HAPs using geographic information system (GIS). As a case study, the Seoul metropolitan area (Seoul, Incheon, and Gyeonggi Province) was chosen, and 15 factors including population, vehicle registration, and emission data were compiled for each grid cell ($7km{\times}7km$). The number of factors above the top 30% of individual data for each grid cell was used to select priority monitoring sites for HAPs. In addition, several background sites were added for data comparison and source identification. Three scenarios were suggested: Scenario 1 with 7 sites, Scenario 2 with 17 sites, and Scenario 3 with 30 sites. This proposal is not the final result for an intensive monitoring program, but it is an example of method development for selecting appropriate sampling sites. These results can be applied not only to HAPs monitoring in megacities but also to the national HAPs monitoring network.
Manufacturing and technology industries produce large amounts of air pollutants. Ulsan Metropolitan City, South Korea, is well-known for its large industrial complexes; in particular, the concentration of $SO_2$ here is the highest in the country. We assessed $SO_2$ monitoring sites based on conditional and joint entropy, because this is a common method for determining an optimal air monitoring network. Monthly $SO_2$ concentrations from 12 air monitoring sites were collected, and the distribution of spatial locations was determined by kriging. Mean absolute error, Root Mean Squared Error (RMSE), bias and correlation coefficients were employed to evaluate the considered algorithms. An optimal air monitoring network for Ulsan was suggested based on the improvement of RMSE.
The impact of a considerable increase in traffic volume on the emission and concentrations of air pollutants was investigated at three beaches (Haeundae (HB), Gwanganri (GB), and Songjeong (SB)) in Busan during beach opening period (BOP) in 2011. During the BOP, passenger car was the major vehicle type, followed by taxi, and van. CO was the major contributor of total air pollutant emissions followed by NOx, VOC, and $PM_{10}$. For the temporal variation of the emission of air pollutants during the BOP, it was generally the highest in the afternoon followed by the evening and morning, except for SB. For the spatial variation of their emission, it was the highest at GB followed by SB and HB. The emissions of air pollutants during the BOP were generally higher than those during the Non-BOP, except for HB. In contrast, the significant impact of the traffic volume increase on the concentrations of air pollutants at monitoring sites near the three beaches during the BOP were not found compared to the Non-BOP due to the significant distances between monitoring sites of air pollutants and monitoring sites of traffic volume at the beaches.
Park, Eun Young;Lim, Min Kyung;Yang, Wonho;Yun, E Hwa;Oh, Jin-Kyoung;Jeong, Bo Yoon;Hong, Soon Yeoul;Lee, Do-Hoon;Tamplin, Steve
Asian Pacific Journal of Cancer Prevention
/
v.14
no.12
/
pp.7725-7730
/
2013
Objective: The purpose of this study was to evaluate secondhand smoke (SHS) exposure inside selected public places to provide basic data for the development and promotion of smoke-free policies. Methods: Between March and May 2009, an SHS exposure survey was conducted. $PM_{2.5}$ levels and air nicotine concentrations were measured in hospitals (n=5), government buildings (4), restaurants (10) and entertainment venues (10) in Seoul, Republic of Korea, using a common protocol. Field researchers completed an observational questionnaire to document evidence of active smoking (the smell of cigarette smoke, presence of cigarette butts and witnessing people smoking) and administered a questionnaire regarding building characteristics and smoking policy. Results: Indoor $PM_{2.5}$ levels and air nicotine concentrations were relatively higher in monitoring sites where smoking is not prohibited by law. Entertainment venues had the highest values of $PM_{2.5}$(${\mu}g/m^3$) and air nicotine concentration(${\mu}g/m^3$), which were 7.6 and 67.9 fold higher than those of hospitals, respectively, where the values were the lowest. When evidence of active smoking was present, the mean $PM_{2.5}$ level was 104.9 ${\mu}g/m^3$, i.e., more than 4-fold the level determined by the World Health Organization for 24-hr exposure (25 ${\mu}g/m^3$). Mean indoor air nicotine concentration at monitoring sites with evidence of active smoking was 59-fold higher than at sites without this evidence (2.94 ${\mu}g/m^3$ vs. 0.05 ${\mu}g/m^3$). The results were similar at all specific monitoring sites except restaurants, where mean indoor $PM_{2.5}$ levels did not differ at sites with and without active smoking evidence and indoor air nicotine concentrations were higher in sites without evidence of smoking. Conclusion: Nicotine was detected in most of our monitoring sites, including those where smoking is prohibited by law, such as hospitals, demonstrating that enforcement and compliance with current smoke-free policies in Korea is not adequate to protect against SHS exposure.
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.
Journal of Korean Society for Atmospheric Environment
/
v.27
no.5
/
pp.545-557
/
2011
The objective of this study was to estimate the trends of each pollutant using the air pollution monitoring networks data from January 2005 to December 2008 in Daegu area. Also, the spatial characteristics of each pollutant were determined using the Pearson correlation coefficients and COD (coefficients of divergence). In this study, the trends of hourly, monthly, seasonal, and total average concentrations of each pollutant for the 10 sites were analyzed. The Ihyeon site showed highest concentration for the $SO_2$, $NO_2$, and PM10}. In the case of $O_3$, the Jisan site showed highest concentration among the other sites. Also, industrial area presented highest concentration for the $SO_2$, CO, and PM10. On the other hand, $NO_2$ showed highest in commercial area. The IDW (inverse distance weighting) method was used to estimate characteristics of spatial distribution. The results provide identify spatial distribution for each pollutant. Also, the Pearson correlation coefficients and COD values provide spatial variability among the monitoring sites. The COD of each pollutant showed very low values for all of the sites pairs. On the other hand, the Pearson correlation coefficients showed high values for all of the sites pairs. Finally, analysis of spatial variability can be used to characterize the spatial uniformity and similarity of concentrations from each pollutant.
Journal of Korean Society for Atmospheric Environment
/
v.13
no.6
/
pp.415-426
/
1997
Some arguments have been about over the representativeness of government-run air quality monitoring stations among scholars and non-governmental organizations (NGOs). However, it is not a simple problem to move monitoring stations because of continuity of data and high cost. So it is necessary to complement the monitoring data if it do not represent the ambient air quality properly. The purpose of this study was to evaluate the representativeness of some monitoring stations using passive $NO_2$ samplers and to find a complementary method from linear regression. Two stations were chosen for the evaluation: Shinlim Station was one of the most controversial stations in Seoul and Banpo Station had the best reputation. Air qualities were surveyed at seven points around each monitoring station with consideration of land use and distance. The ratios of the average $NO_2$ levels of the areas to these at the monitoring stations were 1.59 for Shinlim Station and 1.03 for Banpo Station. The differences between the average $NO_2$ levels and those at the monitoring stations were 10.75 ppb for Shilim Station and 0.34 ppb for Banpo Station. The correlation coefficients between the two levels were 0.7668 for Shinlim and 0.7662 for Banpo. The average coefficients of determination $(R^2)$ were 0.61 for Shinlim and 0.61 for Banpo. The Shinlim Station could not represent the air quality of Shinlim-Dong good because it is located in a green area at an outskirt of Shinlim-Dong. But the Banpo Station located in a central residential area of Banpo-Dong showed a fair representativeness. However, air quality turned out to be different with land use such as residential area, green area or road: the air quality data from a monitoring station located at a certain land use should not be interpreted as representing the air quality at any sites around the station. Equations to predict the average $NO_2$ levels of each area from the data from the monitoring stations were presented based on linear regression.
By using hourly $NO_2$ concentration data$(1998\~2000)$ at the Busan Metropolitan City air qualify monitoring sites, characteristics of daily mean value of $NO_2$ concentration was discussed in space and time. The correlation between $NO_2$ concentration and other relating air pollutants was analyzed by using SAS program and meteorological parameters as well. After choosing representative 4 areas, this study used hourly concentration data$(1998\~2000)$ from air quality monitoring sites on $NO_2,\;NO,\;O_3,\;CO,\;SO_2\;and\;PM_{10}$. Typical metropolitan characteristics of two peaks in a day was shown in the variation of $NO_2$ concentration of Busan city.
[ $NO_2$ ] concentration characteristics of Busan metropolitan city was analysed by statistical method using hourly $NO_2$ concentration data$(1998\~2000)$ collected from air quality monitoring sites of the metropolitan city. 4 representative regions were selected among air quality monitoring sites of Ministry of environment. Concentration data of $NO_2$, 5 air pollutants, and data collected at AWS was used. Both Stepwise Multiple Regression model and ARIMA model for prediction of $NO_2$ concentrations were adopted, and then their results were compared with observed concentration. While ARIMA model was useful for the prediction of daily variation of the concentration, it was not satisfactory for the prediction of both rapid variation and seasonal variation of the concentration. Multiple Regression model was better estimated than ARIMA model for prediction of $NO_2$ concentration.
Park, Kyunghee;Daeil Kang;Junheon Youn;Lee, Choong;Sunghwan Jeon;Jingyun Na
Proceedings of the Korea Society of Environmental Toocicology Conference
/
2003.05a
/
pp.148-148
/
2003
This study is to investigate the environmental levels and trend of dioxins, which was the 3$\^$rd/ year of environmental monitoring research for endocrine disrupting chemicals since 1999. Total 282 samples were analyzed from 115 sites including 26 sites of airs, 43 sites of waters, 11 sites of sediments and 35 sites of soil, which were the same as those of investigated sites in 2000. Sampling period was from June 2001 to June 2002. Target chemicals were seventeen species of 2,3,7,8-chlorine-substituted PCDD and PCDF congeners and were analyzed by the standard methods, established by National Institute Environmental Research (NIER). The average concentration of dioxins in air decreased from 0.324 pg-TEQ/N㎥ in 2000 to 0.287 pg-TEQ/N㎥ in 2001, and those in water and soil were 0.073pg-TEQ/L and 1.703pg-TEQ/dry g, respectively, which was the less values detected in 2000. In sediment, however, the value was 0.086pg-TEQ/dry g, which was the increase from the value of the year 2000. The concentration range of dioxins in air for 26 sites in 17 regions detected were 0.013∼l.664pg-TEQ/N㎥, 4 sites from those were exceeded the Air Quality Standards of Dioxin in Japan (0.6 pg-TEQ/N㎥). The tolerable daily intake of dioxins was calculated at the highest level (1.664) in air, with referring the soil and food data from Japan, was calculated to be 2.85pg-TEQ/kg/day, which was below the level of 4 pg-TEQ/kg/day suggested in KFDA(Korea). While the average concentration of dioxins in 15 big cities was 0.190 pg-TEQ/N㎥, that in 8 medium/small cities constituting an industrial complex was 0.558 pg-TEQ/N㎥. In water, the concentration range detected were 0∼0.946pg-TEQ/L and the trend of the average concentrations shows an increase from those of 1999 but decreased from those of 2000, any sites however were not exceeded the Water Quality Standards of Dioxin in Japan (1 pg- TEQ/L). In soil. the detected range were 0∼43.333 pg-TEQ/dry g and the average concentration decreased, compared with the results of 2000. According to the monitoring results by land utilization, the detected range were 0∼43.333pg-TEQ/dry g in farmland, 0.017∼0.601 pg-TEQ/dry g in the industrial area, 0.005∼0.049pg-TEQ/dry g in the park and 0.008∼1.825 pg-TEQ/dry g in the rest. In sediment, the detected range increased from 0∼0.244 pg-TEQ/dry g to 0∼0.537 pg-TEQ/dry g, based on the results of 2000. For the proper control of dioxins, continuous monitoring needs to be performed and in addition, the dioxin inventory should be prepared for major sources through the dioxin emission survey. These results would provide sound and solid basis for proper decision making of dioxins management like establishment of environmental quality standards in Korea.
본 웹사이트에 게시된 이메일 주소가 전자우편 수집 프로그램이나
그 밖의 기술적 장치를 이용하여 무단으로 수집되는 것을 거부하며,
이를 위반시 정보통신망법에 의해 형사 처벌됨을 유념하시기 바랍니다.
[게시일 2004년 10월 1일]
이용약관
제 1 장 총칙
제 1 조 (목적)
이 이용약관은 KoreaScience 홈페이지(이하 “당 사이트”)에서 제공하는 인터넷 서비스(이하 '서비스')의 가입조건 및 이용에 관한 제반 사항과 기타 필요한 사항을 구체적으로 규정함을 목적으로 합니다.
제 2 조 (용어의 정의)
① "이용자"라 함은 당 사이트에 접속하여 이 약관에 따라 당 사이트가 제공하는 서비스를 받는 회원 및 비회원을
말합니다.
② "회원"이라 함은 서비스를 이용하기 위하여 당 사이트에 개인정보를 제공하여 아이디(ID)와 비밀번호를 부여
받은 자를 말합니다.
③ "회원 아이디(ID)"라 함은 회원의 식별 및 서비스 이용을 위하여 자신이 선정한 문자 및 숫자의 조합을
말합니다.
④ "비밀번호(패스워드)"라 함은 회원이 자신의 비밀보호를 위하여 선정한 문자 및 숫자의 조합을 말합니다.
제 3 조 (이용약관의 효력 및 변경)
① 이 약관은 당 사이트에 게시하거나 기타의 방법으로 회원에게 공지함으로써 효력이 발생합니다.
② 당 사이트는 이 약관을 개정할 경우에 적용일자 및 개정사유를 명시하여 현행 약관과 함께 당 사이트의
초기화면에 그 적용일자 7일 이전부터 적용일자 전일까지 공지합니다. 다만, 회원에게 불리하게 약관내용을
변경하는 경우에는 최소한 30일 이상의 사전 유예기간을 두고 공지합니다. 이 경우 당 사이트는 개정 전
내용과 개정 후 내용을 명확하게 비교하여 이용자가 알기 쉽도록 표시합니다.
제 4 조(약관 외 준칙)
① 이 약관은 당 사이트가 제공하는 서비스에 관한 이용안내와 함께 적용됩니다.
② 이 약관에 명시되지 아니한 사항은 관계법령의 규정이 적용됩니다.
제 2 장 이용계약의 체결
제 5 조 (이용계약의 성립 등)
① 이용계약은 이용고객이 당 사이트가 정한 약관에 「동의합니다」를 선택하고, 당 사이트가 정한
온라인신청양식을 작성하여 서비스 이용을 신청한 후, 당 사이트가 이를 승낙함으로써 성립합니다.
② 제1항의 승낙은 당 사이트가 제공하는 과학기술정보검색, 맞춤정보, 서지정보 등 다른 서비스의 이용승낙을
포함합니다.
제 6 조 (회원가입)
서비스를 이용하고자 하는 고객은 당 사이트에서 정한 회원가입양식에 개인정보를 기재하여 가입을 하여야 합니다.
제 7 조 (개인정보의 보호 및 사용)
당 사이트는 관계법령이 정하는 바에 따라 회원 등록정보를 포함한 회원의 개인정보를 보호하기 위해 노력합니다. 회원 개인정보의 보호 및 사용에 대해서는 관련법령 및 당 사이트의 개인정보 보호정책이 적용됩니다.
제 8 조 (이용 신청의 승낙과 제한)
① 당 사이트는 제6조의 규정에 의한 이용신청고객에 대하여 서비스 이용을 승낙합니다.
② 당 사이트는 아래사항에 해당하는 경우에 대해서 승낙하지 아니 합니다.
- 이용계약 신청서의 내용을 허위로 기재한 경우
- 기타 규정한 제반사항을 위반하며 신청하는 경우
제 9 조 (회원 ID 부여 및 변경 등)
① 당 사이트는 이용고객에 대하여 약관에 정하는 바에 따라 자신이 선정한 회원 ID를 부여합니다.
② 회원 ID는 원칙적으로 변경이 불가하며 부득이한 사유로 인하여 변경 하고자 하는 경우에는 해당 ID를
해지하고 재가입해야 합니다.
③ 기타 회원 개인정보 관리 및 변경 등에 관한 사항은 서비스별 안내에 정하는 바에 의합니다.
제 3 장 계약 당사자의 의무
제 10 조 (KISTI의 의무)
① 당 사이트는 이용고객이 희망한 서비스 제공 개시일에 특별한 사정이 없는 한 서비스를 이용할 수 있도록
하여야 합니다.
② 당 사이트는 개인정보 보호를 위해 보안시스템을 구축하며 개인정보 보호정책을 공시하고 준수합니다.
③ 당 사이트는 회원으로부터 제기되는 의견이나 불만이 정당하다고 객관적으로 인정될 경우에는 적절한 절차를
거쳐 즉시 처리하여야 합니다. 다만, 즉시 처리가 곤란한 경우는 회원에게 그 사유와 처리일정을 통보하여야
합니다.
제 11 조 (회원의 의무)
① 이용자는 회원가입 신청 또는 회원정보 변경 시 실명으로 모든 사항을 사실에 근거하여 작성하여야 하며,
허위 또는 타인의 정보를 등록할 경우 일체의 권리를 주장할 수 없습니다.
② 당 사이트가 관계법령 및 개인정보 보호정책에 의거하여 그 책임을 지는 경우를 제외하고 회원에게 부여된
ID의 비밀번호 관리소홀, 부정사용에 의하여 발생하는 모든 결과에 대한 책임은 회원에게 있습니다.
③ 회원은 당 사이트 및 제 3자의 지적 재산권을 침해해서는 안 됩니다.
제 4 장 서비스의 이용
제 12 조 (서비스 이용 시간)
① 서비스 이용은 당 사이트의 업무상 또는 기술상 특별한 지장이 없는 한 연중무휴, 1일 24시간 운영을
원칙으로 합니다. 단, 당 사이트는 시스템 정기점검, 증설 및 교체를 위해 당 사이트가 정한 날이나 시간에
서비스를 일시 중단할 수 있으며, 예정되어 있는 작업으로 인한 서비스 일시중단은 당 사이트 홈페이지를
통해 사전에 공지합니다.
② 당 사이트는 서비스를 특정범위로 분할하여 각 범위별로 이용가능시간을 별도로 지정할 수 있습니다. 다만
이 경우 그 내용을 공지합니다.
제 13 조 (홈페이지 저작권)
① NDSL에서 제공하는 모든 저작물의 저작권은 원저작자에게 있으며, KISTI는 복제/배포/전송권을 확보하고
있습니다.
② NDSL에서 제공하는 콘텐츠를 상업적 및 기타 영리목적으로 복제/배포/전송할 경우 사전에 KISTI의 허락을
받아야 합니다.
③ NDSL에서 제공하는 콘텐츠를 보도, 비평, 교육, 연구 등을 위하여 정당한 범위 안에서 공정한 관행에
합치되게 인용할 수 있습니다.
④ NDSL에서 제공하는 콘텐츠를 무단 복제, 전송, 배포 기타 저작권법에 위반되는 방법으로 이용할 경우
저작권법 제136조에 따라 5년 이하의 징역 또는 5천만 원 이하의 벌금에 처해질 수 있습니다.
제 14 조 (유료서비스)
① 당 사이트 및 협력기관이 정한 유료서비스(원문복사 등)는 별도로 정해진 바에 따르며, 변경사항은 시행 전에
당 사이트 홈페이지를 통하여 회원에게 공지합니다.
② 유료서비스를 이용하려는 회원은 정해진 요금체계에 따라 요금을 납부해야 합니다.
제 5 장 계약 해지 및 이용 제한
제 15 조 (계약 해지)
회원이 이용계약을 해지하고자 하는 때에는 [가입해지] 메뉴를 이용해 직접 해지해야 합니다.
제 16 조 (서비스 이용제한)
① 당 사이트는 회원이 서비스 이용내용에 있어서 본 약관 제 11조 내용을 위반하거나, 다음 각 호에 해당하는
경우 서비스 이용을 제한할 수 있습니다.
- 2년 이상 서비스를 이용한 적이 없는 경우
- 기타 정상적인 서비스 운영에 방해가 될 경우
② 상기 이용제한 규정에 따라 서비스를 이용하는 회원에게 서비스 이용에 대하여 별도 공지 없이 서비스 이용의
일시정지, 이용계약 해지 할 수 있습니다.
제 17 조 (전자우편주소 수집 금지)
회원은 전자우편주소 추출기 등을 이용하여 전자우편주소를 수집 또는 제3자에게 제공할 수 없습니다.
제 6 장 손해배상 및 기타사항
제 18 조 (손해배상)
당 사이트는 무료로 제공되는 서비스와 관련하여 회원에게 어떠한 손해가 발생하더라도 당 사이트가 고의 또는 과실로 인한 손해발생을 제외하고는 이에 대하여 책임을 부담하지 아니합니다.
제 19 조 (관할 법원)
서비스 이용으로 발생한 분쟁에 대해 소송이 제기되는 경우 민사 소송법상의 관할 법원에 제기합니다.
[부 칙]
1. (시행일) 이 약관은 2016년 9월 5일부터 적용되며, 종전 약관은 본 약관으로 대체되며, 개정된 약관의 적용일 이전 가입자도 개정된 약관의 적용을 받습니다.