• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.11a
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• pp.136-138
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• 2000

The vehicle emissions of primary air pollutants are described by the emission factor (EF), defined as the emitted mass (g) of a compound per distance (km) and vehicle. The EF can be determined by exhaust measurements from single vehicles in dynamometric tests. However, the EF of a large number of vehicles has to be measured to obtain the representative results for actual road traffic emissions. Road traffic emissions can also be determined by exhaust measurements of driving vehicles or in tunnel measurements. (omitted)

• Environmental Analysis Health and Toxicology
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• v.30
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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.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.5
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• pp.528-535
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• 2013

Air pollution in large cities is reduced through the environmental health policies, but due to increased population and automobile, some pollutants are still a problem. These air pollutants are known to cause asthma and respiratory diseases. According to an OECD report, the number of premature deaths will increase. Hazardous air pollutants should be managed through a systematic monitoring, risk assessment, and many studies are in progress. In order to manage hazardous air pollutants, transformation of policy for the protection of human health is required. management policy through the calculation of the excess number of deaths that occur from hazardous air pollutants for the public health is necessary. Korea has put a lot of efforts for air quality, but health risk assessment should be more considered.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.6
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• pp.575-582
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• 2016

Seoul city has high population density as well as high traffic congestion, which are vulnerable to exposure of environmental pollutions caused by car traffic. However, recent studies are only on local regions about road traffic and air pollution or health effect of road traffic on residents. Thus, comprehensive study data are needed in terms of overall Seoul regions. In this study utilized the nitrogen dioxide concentration through the national air pollution monitoring network data, 2012 to 2013. It also divided regions into high and low exposure districts via the Origin destination data developed by the Korea transport institute to quantify and evaluate the effect of transport policies and analyzed a correlation of asthma symptoms with high and low exposure districts through raw data of community health survey from the Korea centers for disease control and prevention. Based on the collected data, the pearson's correlation analysis was conducted between air pollution substance concentration and high exposure district and multiple logistic regression analysis was conducted to determine the effect of traffic environment and factors on asthma symptoms of residents. Accordingly, the following results were derived. First, the high exposure district was higher concentrations of nitrogen dioxide ($NO_2$) as per time compared to those of the low exposure district (p<0.01). Second, analysis on correlation between average daily environmental concentration in the air pollution monitoring network and road traffic showed that nitrogen dioxide had a significant positive correlation (p<0.01) with car traffic and total traffic as well as with truck traffic (p<0.05) statistically. Third, an adjusted odds ratio about asthma doctor's diagnosis in the high and low exposure districts was analyzed through the logistic regression analysis. With regard to an adjusted model 2 (adjusted gender, age, health behavior characteristics, and demographic characteristics) odds ratio of asthma doctor's diagnosis in the high exposure district was 1.624 (95% CI: 1.269~2.077) compared to that of the low exposure district, which was significant statistically (p<0.001).

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.05a
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• pp.161-162
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• 2004

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.11a
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• pp.401-402
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• 2004

• Proceedings of the Korea Air Pollution Research Association Conference
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• 1999.04a
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• pp.257-258
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• 1999

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.10a
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• pp.117-120
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• 2006

• Environmental Engineering Research
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• v.19 no.4
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• pp.331-338
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• 2014

Descriptive statistics methods were used to study the spatiotemporal variations and sources of ambient particulate matter ($PM_{10}$) in Luzhou, China. The analyzed datasets were collected from four national air quality monitoring stations: Jiushi (S1), Xiaoshi (S2), Zhongshan (S3), Lantian (S4) over the period of 2003-2012. This city was subjected serious $PM_{10}$ pollution, and the long-term annual average $PM_{10}$ concentrations varied from 76 to $136{\mu}g/m^3$. The maximum concentration was more than 3-fold of the annual average ($40{\mu}g/m^3$) issued by EPA-China for the ambient air quality. General temporal pattern was characterized by high concentrations in winter and low concentrations in summer, and general spatial gradient was in the reduction order of S2 > S4 > S3 > S1, which were both due to different particulate contributors and special meteorological conditions. The source apportionment indicated that vehicular emissions, road dusts, coal burning and chemical dusts were the major contributors of the identified $PM_{10}$ pollution, and the vehicular emissions and the road wear re-suspended particles dominated the heavy $PM_{10}$ pollution in recent years. Two other potential sources, agricultural and celebration activities could decrease the air quality in a short term. Finally, some corresponding suggestions and measures were provided to improve the air quality.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.11
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• pp.1035-1043
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• 2004

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The feedforward prediction algorithm and the cascade control algorithm were developed to regulate the CO level in a tunnel. The feedforward prediction algorithm composed of the traffic estimation algorithm and the CO density prediction algorithm, and the cascade control algorithm composed of the jet fan control algorithm and the air velocity setpoint algorithm. The verification of control algorithms was carried out by dynamic models developed from the actual tunnel data. The simulation results showed that control algorithms developed for this study were effective for the control of the tunnel ventilation system.