• Journal of Korean Society for Atmospheric Environment
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• v.24 no.1
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• pp.72-82
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• 2008

Benzene is a very harmful and toxic compound known as human carcinogen by all routes of exposure. Owing to the risky feature of benzene, several countries such as Japan, UK and EU have established the ambient air quality standard and protect from that risk of it. Korea also has designated it as one of the criteria air pollutants and established the concentration limit ($5\;{\mu}g/m^3$) in the air and is going to apply the standard from 2010. Benzene is emitted from various sources such as combustion plants, production processes, waste treatment facilities and also automobiles. Mobile source is known as one of the major emission sources of benzene. In this study, we estimated the domestic emissions of benzene from mobile source and compared the results with those of advanced countries. Mobile source was divided into 2 categories, Le., on-road source and non-road source. The total emissions of benzene from mobile source were estimated as 3,106 tons/yr and 1,612 tons/yr was emitted from on-road source and 1,494 tons/yr was from non-road source. Emission ratio of benzene from on-road source showed that 80.0% was from passenger cars, 10.1% was from taxis, 7.2% was from light-duty vehicles, 2.5% was from heavy-duty vehicles and 0.2% was from buses. In the case of non-road source, the distribution showed that 66.3% was from construction machineries, 14.5% was from locomotives, 11.7% was from ships, 7.1% was from agriculture equipments and 0.5% was from aircrafts. The cold-start emissions were estimated as 942 tons/yr and this value was almost 1.5 times greater than that for hot engine emissions (608 tons/yr). In addition, the fuel-based distribution was 65.9%, 31.1% and 2.8% from gasoline, LPG and diesel vehicles, respectively. The emission ratio from mobile source occupied 65% and 30% of total benzene emissions in USA and UK, respectively. In case of Korea, the emission ratio of benzene from mobile source occupied 29% (15% from on-road source, 14% from non-road source) which showed similar value with UK.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.9-20
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• 2016

Ozone has been a problem in big cities. That is secondary air pollutant produced by nitrogen oxide and VOCs in the atmosphere. In order to solve this, the first to be the analysis of the $NO_x$ and VOCs. The main source of nitrogen oxide is the road mobile. Industrial sources in Seoul are particularly low, and mobile traffics on roads are large, so 45% of total $NO_x$ are estimated that road mobile emissions in Seoul. Thus, it is necessary to clarify the relation with the activity of road mobile source and $NO_x$ concentration. In this study, we analyzed the 4 locations with roadside automatic monitoring systems in their center. The V.K.T. calculating areas are set in circles with 50 meter spacing, 50 meter to 500 meter from their center. We assumed the total V.K.T. in the set radius affect the $NO_x$ concentration in the center. We used the hourly $NO_x$ concentrations data for the 4 observation points in July for the interference of the other sources are minimized. We used the intersection traffic survey data of all direction for construction of the V.K.T. data, the mobile activities on the roads. ArcGIS application was used for calculating the length of roads in the set radius. The V.K.T. data are multiplied by segment traffic volume and length of roads. As a result, the $NO_x$ concentration can be expressed as linear function formula for V.K.T. with high predictive power. Moreover we separated background concentration and concentrations due to road mobile source. These results can be used for forecasting the effect of traffic demand management plan.

• Journal of the Society of Disaster Information
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• v.13 no.1
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• pp.96-105
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• 2017

Global warming, mainly caused by CO2, is one of the on­going cataclysms of the human race. The nation­wide policy to reduce greenhouse gases (GHG) has been enforced, for which it is crucial to estimate reliable GHG emissions. The unit load of road­section CO2 emission (URSCE) is a prerequisite for the evaluation of GHG emissions from road mobile source, and it is mainly computed using vehicular velocity source. Unfortunately, there is real­world limitations to collect and analyse representative speed data for nation­wide road network. To tackle this problem, a method for the evaluation of URSCE, proposed in this study, is based on a disaggregated way using big GPS vehicle data. The method yields more accurate URSCE than an current aggregated data based approach and can be directly employed for nation­wide road systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.593-600
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• 2007

In this paper, we composed several road network and evaluated the network for the performance of the network with protocols. The protocols we applied were the MANET routing protocols such as AODV(Ad-hoc On-Demand Distance Vector) and DSR(Dynamic Source Routing) protocols. Generally, the AODV performs better than the DSR. However, in my ad-hoc vehicular network, the performance of the DSR is the better than the AODV when there are more vehicles in the road environment than there are the less vehicles. For the simulation, we composed 4-lane road with vehicles and simulated in the OPNET.

• Asian Journal of Atmospheric Environment
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• v.2 no.1
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• pp.34-46
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• 2008

Emissions inputs for use in air quality modeling of Korea were generated with the emissions inventory data from the National Institute of Environmental Research (NIER), maintained under the Clean Air Policy Support System (CAPSS) database. Source Classification Codes (SCC) in the Korea emissions inventory were adapted to use with the U.S. EPA's Sparse Matrix Operator Kernel Emissions (SMOKE) by finding the best-matching SMOKE default SCCs for the chemical speciation and temporal allocation. A set of 19 surrogate spatial allocation factors for South Korea were developed utilizing the Multi-scale Integrated Modeling System (MIMS) Spatial Allocator and Korean GIS databases. The mobile and area source emissions data, after temporal allocation, show typical sinusoidal diurnal variations with high peaks during daytime, while point source emissions show weak diurnal variations. The model-ready emissions are speciated for the carbon bond version 4 (CB-4) chemical mechanism. Volatile organic carbon (VOC) emissions from painting related industries in area source category significantly contribute to TOL (Toluene) and XYL (Xylene) emissions. ETH (Ethylene) emissions are largely contributed from point industrial incineration facilities and various mobile sources. On the other hand, a large portion of OLE (Olefin) emissions are speciated from mobile sources in addition to those contributed by the polypropylene industry in point source. It was found that FORM (Formaldehyde) is mostly emitted from petroleum industry and heavy duty diesel vehicles. Chemical speciation of PM2.5 emissions shows that PEC (primary fine elemental carbon) and POA (primary fine organic aerosol) are the most abundant species from diesel and gasoline vehicles. To reduce uncertainties in processing the Korea emission inventory due to the mapping of Korean SCCs to those of U.S., it would be practical to develop and use domestic source profiles for the top 10 SCCs for area and point sources and top 5 SCCs for on-road mobile sources when VOC emissions from the sources are more than 90% of the total.

• Particle and aerosol research
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• v.8 no.2
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• pp.47-54
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• 2012

Paved road dust emissions were investigated 14 times on 12 main roads in Seo-Cho Gu, Seoul, Korea by vehicle-based mobile sampling system(Road Dust Monitoring System) during September to December 2011. Also, fourteen heavy metals present in the dust samples were analyzed by ICP. ICP analysis showed that one of major source of the road dust would be urban construction. A large amount of silt was found, which might be originated mainly from building construction and open beds of trees. Trace element and pollution indices of heavy metals(Cd, Cu, Ni, Pb, Zn) on the roads adjacent to the commercial area had higher concentrations than those on the roads adjacent to the construction and residential areas because of traffic density and heavy traffic.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.5
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• pp.269-278
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• 2016

In order to improve air quality in the Seoul Metropolitan Area (SMA), the "Special Act on Seoul Metropolitan Air Quality Improvement" has been enforced since 2005. The strategy has resulted in some reduction of air pollution, but there has not been much research into the quantitative impact analysis of each separate preventive countermeasure. Therefore, we analyzed nitrogen oxide reduction resulting from implementation of the emission control plan for on-road mobile sources. The MM5-SMOKE-CMAQ model system was employed for air quality prediction. Reduced $NO_x$ emissions for SMA was 16,561 ton, 4.7% of reduction rate, in 2007. One countermeasure, tighter acceptable standards for manufacturing vehicles, dominated other countermeasures for effective $NO_x$ emission control. Large spatial differences in reduced emissions, those for Seoul being twice that of Incheon and Gyeonggi, showed greater $NO_x$ emission reduction impact in the heart of the metropolitan complex. The $NO_2$ concentration decreased by 0.60 ppb (2.0%), 0.18 ppb (1.5%), and 0.22 ppb (1.7%) in Seoul, Incheon, and Gyeonggi, respectively. Concentration decreases in spring and winter were larger, 1.5~2.0 times, than summer and fall. However, the $NO_2$ reduction impact did not correspond directly to local $NO_x$ emission controls in the city area because of the natural flow and dispersion, both urban and downwind.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.4
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• pp.454-465
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• 2012

The Korean government decided to reduce 30% of GHG (greenhouse gas) emissions BAU in 2020. Since many efforts to reduce emissions are urgently needed in Korea, the central administrative organization urges local governments to establish their own reduction schemes. Among many GHG emission categories, the emission from mobile source in Gyeonggi Province accounted for 25.3% of total emissions in 2007 and further the emission from road transport sector occupied the most dominant portion in this transportation category. The objective of this study was to compare 3 types of GHG emissions from road transport sector in 31 local cities/counties of Gyeonggi Province, which have been estimated by Tier 1, Tier 2, and Tier 3 methodologies. As results, the GHG emission rates by the Tier 1 and Tier 2 were $19,991kt-CO_2\;Eq/yr$ and $18,511kt-CO_2\;Eq/yr$, respectively. On the other hand, the emission rate by Tier 3 excluding a branch road emission portion was $18,051kt-CO_2\;Eq/yr$. In addition, the total emission rate including all the main and branch road portions in Gyeonggi Province was $24,152kt-CO_2\;Eq/yr$, which was estimated by a new Tier 3 methodology. Based on this study, we could conclude that Tier 3 is a reasonable methodology than Tier 1 or Tier 2. However, more accurate and less uncertain methodology must be developed by expanding traffic survey areas and adopting a suitable model for traffic volumes.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.3
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• pp.233-248
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• 2012

The national emission from energy sector accounted for 84.7% of all domestic emissions in 2007. Of the energy-use emissions, the emission from mobile source as one of key categories accounted for 19.4% and further the road transport emission occupied the most dominant portion in the category. The road transport emissions can be estimated on the basis of either the fuel consumed (Tier 1) or the distance travelled by the vehicle types and road types (higher Tiers). The latter approach must be suitable for simultaneously estimating $CO_2$, $CH_4$, and $N_2O$ emissions in local administrative districts. The objective of this study was to estimate 31 municipal GHG emissions from road transportation in Gyeonggi Province, Korea. In 2008, the municipalities were consisted of 2,014 towns expressed as Dong and Ri, the smallest administrative district unit. Since mobile sources are moving across other city and province borders, the emission estimated by fuel sold is in fact impossible to ensure consistency between neighbouring cities and provinces. On the other hand, the emission estimated by distance travelled is also impossible to acquire key activity data such as traffic volume, vehicle type and model, and road type in small towns. To solve the problem, we applied a hierarchical cluster analysis to separate town-by-town road patterns (clusters) based on a priori activity information including traffic volume, population, area, and branch road length obtained from small 151 towns. After identifying 10 road patterns, a rule building expert system was developed by visual basic application (VBA) to assort various unknown road patterns into one of 10 known patterns. The expert system was self-verified with original reference information and then objects in each homogeneous pattern were used to regress traffic volume based on the variables of population, area, and branch road length. The program was then applied to assign all the unknown towns into a known pattern and to automatically estimate traffic volumes by regression equations for each town. Further VKT (vehicle kilometer travelled) for each vehicle type in each town was calculated to be mapped by GIS (geological information system) and road transport emission on the corresponding road section was estimated by multiplying emission factors for each vehicle type. Finally all emissions from local branch roads in Gyeonggi Province could be estimated by summing up emissions from 1,902 towns where road information was registered. As a result of the study, the GHG average emission rate by the branch road transport was 6,101 kilotons of $CO_2$ equivalent per year (kt-$CO_2$ Eq/yr) and the total emissions from both main and branch roads was 24,152 kt-$CO_2$ Eq/yr in Gyeonggi Province. The ratio of branch roads emission to the total was 0.28 in 2008.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2338-2356
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• 2019

Vehicular ad-hoc network (VANET) is the name of technology, which uses 'mobile internet' to facilitate communication between vehicles. The aim is to ensure road safety and achieve secure communication. Therefore, the reliability of this type of networks is a serious concern. The reliability of VANET is dependent upon proper communication between vehicles within a given amount of time. Therefore a new formula is introduced, the terms of the new formula correspond 1 by 1 to a class special ST route (SRORT). The new formula terms are much lesser than the Inclusion-Exclusion principle. An algorithm for the Source-to-Terminal reliability was presented, the algorithm produced Source-to-Terminal reliability or computed a Source-to-Terminal reliability expression by calculating a class of special networks of the given network. Since the architecture of this class of networks which need to be computed was comparatively trivial, the performance of the new algorithm was superior to the Inclusion-Exclusion principle. Also, we introduce a mobility metric called universal speed factor (USF) which is the extension of the existing speed factor, that suppose same speed of all vehicles at every time. The USF describes an exact relation between the relative speed of consecutive vehicles and the headway distance. The connectivity of vehicles in different mobile situations is analyzed using USF i.e., slow mobility connectivity, static connectivity, and high mobility connectivity. It is observed that $p_c$ probability of connectivity is directly proportional to the mean speed ${\mu}_{\nu}$ till specified threshold ${\mu}_{\tau}$, and decreases after ${\mu}_{\tau}$. Finally, the congested network is connected strongly as compared to the sparse network as shown in the simulation results.