• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.2
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• pp.73-82
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• 2009

The traffic volume of Seoul is extremely high in comparison to other major cities in Korea, and the result has been harmful physical and mental exposure to pollution by Seoulites on a regular basis. The street air pollution is more important than the others, because the air pollution generated by street traffic directly impacts the health of nearby pedestrians. This problem requires urgent attention and resolution. Among the factors creating the air pollution originating from the street, is the configuration of streets, which have emerged as the most significant because it is related to air and pollutant dispersion. Therefore, this study was conducted under the assumption that street form affects the air quality. Study sites were classified by street characteristics, and air quality was analyzed in each class. Then the OSPM (Operational Street Pollution Model) was employed to simulate the relationship between street configuration and air quality of streets within the old city center and new city center in Seoul. After that this study analyzed the correlation between air pollution and the spatial configuration of urban streets (ex. street width, building height, building density, etc.) to determine their contributions to air pollution. The outcome of this study is as follows : First, the result that was derived from the correlation analysis between street configuration and air quality hewed that the air pollution of the street is influenced by the average height of building, width of the roads as well as traffic volume. On the roadside, the concentration level of $NO_2$ is mainly affected by the average height of building and the deviation of building height along the street and CO is affected by street width. The outcome of this study can be used as a basis for more sound urban design policies, and the promotion of desirable street environments for pedestrians.

• Journal of Environmental Health Sciences
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• v.34 no.5
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• pp.361-368
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• 2008

Objective to assess whether exposure to air pollutants is associated with postneonatal infant death, using a timeseries methodology, between 1999 and 2003 in Seoul, Korea.. Methods We investigated the short-term effects of air pollution for 548,725 live births during the study period. The daily count of postneonatal infant deaths from all causes and from SIDS (sudden infant death syndrome) by birth order was analyzed by a Generalized Additive Poisson model, with controlling for the effects of seasonal trends, air temperature, relative humidity, barometric pressure, and day of the week as covariates. Results During the study period, we observed 699 deaths from all causes and 47 deaths from SIDS. We did not find any significant associations between daily mortality and ambient levels of air pollutants except for CO and $NO_2$. The estimated relative risk of postneonatal infant death from all causes was 1.17 (95% CI=1.04-1.32) and 1.16 (95% CI=1.03-1.29) by IQR (interquartile range) for CO and $NO_2$ respectively. Also, we observed no clear trend of the mortality effects of air pollution by birth orders. Conclusion In conclusion, our findings suggest that air pollution, in general, influenced adversely postneonatal infant death from all-cause and SIDS although it was not statistically significant. This study may support that the rationale.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.542-553
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• 2018

Since many previous studies reported the health effect of air pollution and indicated traffic as a major pollution source, significant policy efforts have been made to control traffic to reduce air pollution. However, there have been few studies that evaluated such policy implementation. In Seoul, Korea, the exclusive median bus lane system was implemented in 2004, and the metropolitan government applied air pollution reduction policies such as conversion of diesel buses to compressed natural gas buses and installation of emission control devices. This paper aimed to investigate the impact of the exclusive median bus lane system on air pollution reduction. Using hourly concentrations of particulate matter ($PM_{10}$) and nitrogen dioxide ($NO_2$) measured at 131 regulatory monitoring sites in Seoul and Gyeonggi-do for 2001-2014, we calculated annual and daily average concentrations at each site. We assessed the impact of the policy using differences-in-differences analysis by annual and daily average models after adjusting for geographic and/or meteorological variables. This method divides population into treatment and control groups with and without policy application, and compares the difference between the two time periods before and after the policy implementation in the treatment group with the difference in the control group. We classified all monitoring sites into treatment and control groups using two definitions: 1) Seoul vs. Gyeonggi-do; 2) within vs. outside 300 meters from the median bus lane. Pre- and post-policy periods were defined as 2001-2005 and 2006-2014, and 2004 and 2014 in the annual and daily models, respectively. The decrease in $PM_{10}$ concentrations between the two periods across monitoring sites in the treatment group was larger by $1.73-5.88{\mu}g/m^3$ than in the control group. $NO_2$ also showed the decrease without statistical significance. Our findings suggest that an efficient public transport policy combined with pollution abatement policies can contribute to reduction in air pollution.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.328-337
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• 2021

Air pollution, which is a class 1 carcinogen, such as asbestos and benzene, is the cause of various diseases. The spread of ultra-air pollution is one of the important causes of the spread of the corona virus. This paper analyzes and predicts fine dust and ultra-air pollution from 2015 to 2019 based on weather data such as average temperature, precipitation, and average wind speed in Seoul and atmospheric environment data such as SO2, NO2, and O3. Linear regression, SVM, and ensemble models among machine learning models were compared and analyzed to predict fine dust by grasping and analyzing the status of air pollution and ultra-air pollution by season and month. In addition, important features(attributes) that affect the generation of fine dust and ultra-air pollution are identified. The highest ultra-air pollution was found in March, and the lowest ultra-air pollution was observed from August to September. In the case of meteorological data, the data that has the most influence on ultra-air pollution is average temperature, and in the case of meteorological data and atmospheric environment data, NO2 has the greatest effect on ultra-air pollution generation.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.521-528
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• 2013

Recently the increasing numbers of cars and traffic jamming makes air pollution condition more severely. Especially high-density population area, in most big cities like Bejing and Seoul, can lead to lung illness and other diseases. In this study to decrease this kind of air pollution condition, a method of air pollution information transmission is proposed. For the transmission of air pollution and traffic control data, international standard technology, the TPEG is applied.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.169-169
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• 2003

In Korea, there is no CRA studies and has not well known CRA and not well established their methodologies. Therefore, objectives of this study is to establish the framework of CRA consisting of health risk, economic risk and perceived risk and the detail methodologies of three main component of estimating and comparing those risks for on the three environmental problems of air pollution, indoor air pollution and drinking water contamination which being subjective to the eight sub-problems of hazardous ai. pollutants (HAPs), regulated pollutants (representative as PM10) and Dioxins (PCDDS/ PCDFs) in air pollution, and indoor ai. pollutants (IAPs) and Radon in indoor air pollution, and drinking water pollutants (DWPs), disinfection-by- products(DBPs) and radionuclides in drinking water contamination in Seoul, Korea. And then, their problems set priorities by individual and integrated risk. As a results, ranking of health risk were the following order of indoor air pollution, air pollution and then drinking water contamination, in three environmental problems and of radon, PM10, IAPs, HAPs, DWPs, Dioxins, DBPs, and then radionuclides in eight sub-problems. And that of economic risk were the same order. In the contrary, ranking of perceived risk were the following order of air pollution, drinking water contamination, and then indoor air pollution, and of HAPs, Dioxins, radionuclides, PM10, DWPs, IAPs, Radon and then DBPs.

• Journal of Environmental Science International
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• v.19 no.5
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• pp.591-605
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• 2010

This study were to simulate major criteria air pollutants and estimate regional source-receptor relationship using air quality prediction model (TAPM ; The Air Pollution Model) in the Seoul Metropolitan area. Source-receptor relationship was estimated by contribution of each region to other regions and region itself through dividing the Seoul metropolitan area into five regions. According to administrative boundary, region I and region II were Seoul and Incheon in order. Gyeonggi was divided into three regions by directions like southern(region III), northern(IV) and eastern(V) area. Gridded emissions ($1km{\times}1km$) by Clean Air Pollicy Support System (CAPSS) of National Institute of Environmental Research (NIER) was prepared for TAPM simulation. The operational weather prediction system, Regional Data Assimilation and Prediction System (RDAPS) operated by the Korean Meteorology Administration (KMA) was used for the regional weather forecasting with 30km grid resolution. Modeling period was 5 continuous days for each season with non-precipitation. The results showed that region I was the most air-polluted area and it was 3~4 times more polluted region than other regions for $NO_2$, $SO_2$ and PM10. Contributions of $SO_2$ $NO_2$ and PM10 to region I, II and III were more than 50 percent for their own sources. However region IV and V were mostly affected by sources of region I, II and III. When emissions of all regions were assumed to reduce 10 and 20 percent separately, air pollution of each region was reduced linearly and the contributions of reduction scenario were similar to those of base case. As input emissions were reduced according to different ratio - region I 40 percent, region II and III 20 percent, region IV and V 10 percent, air pollutions of region I and III were decreased remarkably. The contributions to region I, II, III were also reduced for their own sources. However, region I, II and III affected more regions IV and V. Shortly, graded reduction of emission could be more effective to control air pollution in emission imbalanced area.

• Journal of Preventive Medicine and Public Health
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• v.36 no.1
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• pp.47-53
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• 2003

Objectives : I used a case-crossover design to investigate the association between air pollution, and hospital admissions for asthmatic children under the age of 15 years in Seoul, Korea Methods : I estimated the changes in the levels of hospitalization risk from theinterquartile (IQR) increase in each pollutant concentrations, using conditional logistic regression analyses, with controls for weather information. Results : Using bidirectional control sampling, the results from a conditional logistic regression model, with controls for weather conditions, showed the estimated relative risk of hospitalization for asthma among children to be 1.04 (95% confidence interval (CI), 1.01-1.08) for particulate matter with an aerodynamic diameter less than or equal to 10m (IQR=40.4ug/m3); 1.05 (95% CI, 1.00- 1.09) for nitrogen dioxide (IQR=14.6ppb): 1.02 (95% CI,0.97-1.06) for sulfur dioxide (IQR=4.4ppb): 1.03 (95% CI, 0.99-1.08) for ozone (IQR=21.7ppb): and 1.03 (95% CI, 0.99-1.08) for carbon monoxide f10R=1.0ppm). Conclusions : This empirical analysis indicates the bidirectional control sampling methods, by design, would successfully control the confounding factors due to the long-term time trends of air pollution. These findings also support the hypothesis that air pollution at levels below the current ambient air quality standards of Korea is harmful to sensitive subjects, such as asthmatic children.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.53-63
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• 1998

The purpose of this study is to investigate the current status of automatic measuring systems for air pollution emissions in Seoul and to suggest an improvement method using GIS and GPS. In Korea, there have been very few critical researches and managements for mobile and area sources regarding moving subjects such as automobiles. In order to control or to make a plan for reducing air pollutions, air pollution emission data based on tim and location, emission inventory systems and emission models should be implemented. Using digital maps and MS Visual Basic, we developed a visualized interface for air pollution emission data from automatic emission measurement systems in Seoul.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.222-232
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• 2021

The power output of solar panels highly depends on environmental situations like weather and air pollution. Due to bad weather or air pollution, it is difficult for solar panels to operate at their full potential. Knowing the power output of solar panels in advance helps set up the solar panels correctly and work their possible potential. This paper presents an approach to predict the power output of solar panels based on weather and air pollution features using machine learning methods. We create machine learning models with three kinds set of features, such as weather, air pollution, and weather and air pollution. Our datasets are collected from the area of Seoul, South Korea, between 2017 and 2019. The experimental results show that the weather and air pollution features can be efficient factors to predict the power output of solar panels.