• 한국대기환경학회지
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• 제33권6호
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• pp.544-553
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• 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.

• 한국환경과학회지
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• 제26권9호
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• pp.1073-1080
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• 2017

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.

• 한국환경과학회지
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• 제21권9호
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• pp.1149-1162
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• 2012

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.

• Asian Pacific Journal of Cancer Prevention
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• 제14권12호
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• pp.7725-7730
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• 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.

• 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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• 제27권5호
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• pp.545-557
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• 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.

• 한국대기환경학회지
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• 제13권6호
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• pp.415-426
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• 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.

• 한국환경과학회지
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• 제14권11호
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• pp.1035-1047
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• 2005

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.

• 한국환경과학회지
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• 제14권11호
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• pp.1049-1056
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• 2005

[ $NO_2$ ] 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.

• 한국환경독성학회:학술대회논문집
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• 한국환경독성학회 2003년도 춘계학술대회
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• pp.148-148
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• 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.