• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.114-128
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• 2013

Greenhouse gas emissions have been an important issue in different countries because of their effects on global warming. The government has to organize greenhouse gas reduction measures suitable to regional characteristics by establishing annual implementation plans and comprehensive policies based on the UNFCCC. The transportation sector is one of the major contributors of air pollution; hence increasing need to estimate current and future traffic emissions precisely. Under these circumstances, a number of emission models have been developed recently. However, current methods of estimation cannot carry out effective analyses because it does not reflect vehicle movement characteristics. This study aims to present a new method for calculating road traffic emissions in Goyang city. A travel demand model is utilized to carry out GHG emission estimates according the traffic data (fleet composition, vehicle kilometers travelled, traffic intensity, road type, emission factors and speed). This study evaluates two approaches to estimate the road traffic emissions in Goyang City: Pollution-Emis and the Handbook of Emission Factors for Road Transport (HBEFA v.3.1) which is representative of the "average speed" and the "traffic situation" model types. The evaluation of results shows that the proposed emission estimation method may be a good practice if vigilant implementation of model inputs is observed.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.139-146
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• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.1
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• pp.94-104
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• 2012

The purpose of this research is to quantify the compliance of on-road emission from a light duty diesel vehicle, based on a comparison to emission regulation standard. $NO_x$, CO and THC emissions were measured using a portable device on a selected real-world driving route with a length of approximately 22 km. On-road measurements were repeated by 10 times on a same route to reflect variability in traffic conditions. A test route was divided into 22 road links with length of 1 km to analyze emission results with higher spatial resolution. The average emissions of $NO_x$, CO and THC over total travel distance, which is approximately 220 km, were quantified to be in compliance with emission regulation standards. Under higher spatial resolution, $NO_x$ concentration exceeded a standard in 92 links out of 220 links. The extended time in stop period and the stop-and-go driving cycle were identified as two important reasons for increased $NO_x$ emissions in observed cases. Heavy traffics showed higher $NO_x$ emissions than free flow. These results indicate that the real-world vehicle emissions might exceed the compliance level associate with traffic conditions. Another interesting observation of this research is that the on-road emission characteristics can be independent to the average speed of road links with higher spatial resolution. Variability in on-road emission might not be fully described by solely relying on an average speed, because variability in traffic conditions and road conditions can influence on real-world vehicle emissions.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.4
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• pp.405-415
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• 2011

The objective of this study was to compare greenhouse gas emissions from road transportation by calculation methods (Tier 1, Teir 2, and Tier 3). Tier 1 based on 2006 IPCC guidelines default emission factor and amount of fuel consumption. The Tier 2 approach is the same as Tier 1 except that country-specific carbon contents of the fuel sold in road transport are used. Tier 2 based on emission factor of guidelines for local government greenhouse gas inventories (Korea Environment Corporation), the fuel consumption per one vehicle, and the registered motor vehicles. The Tier 3 approach requires detailed, country-specific data to generate activity-based emission factors for vehicle subcategories (National Institute of Environmental Research) and may involve national models. Tier 3 calculates emissions by multiplying emission factors by vehicle activity levels (e.g., VKT) for each vehicle subcategory and possible road type. VKT was estimated by using GIS road map and traffic volume of the section. The GHG average emission rate by the Tier 1 was 728,857 $tonCO_2eq$/yr, while Tier 2 and Tier 3 were 864,757 $tonCO_2eq$/yr and 661,710 $tonCO_2eq$/yr, respectively. Tier 3 was underestimated by 10.1 and 20.7 percent for the GHG emission observed by Tier 1 and Tier 2, respectively. Based on this study, we conclude that Tier 2 is reasonable GHG emissions than Tier 1 or Tier 3. But, further study is still needed to accurate GHG emission from Tier 3 method by expanding the traffic survey area and developing the model of local road traffic.

• 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.

• International Journal of Automotive Technology
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• v.7 no.3
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• pp.269-276
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• 2006

Running tests on roads were conducted to clarify the influences of road infrastructure, traffic condition and vehicle's emission level to the amount of emission at local roadsides, and to reveal the operation patterns which can reduce the emission peaks. NOx emission peaks of two light duty freight diesel vehicles which have different emission levels were evaluated by using an on-board measurement system. Tests were carried out with various payload conditions and road conditions. As a result many NOx emission peaks were observed when the vehicles were starting or accelerating at intersections. The test vehicle which has higher emission level caused higher frequency and level of NOx emission peaks. Shifting up at lower engine speed in combination with lower acceleration brought out not only reduction of NOx emission peaks level but also of $CO_2$ mass emission.

• 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)

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.115-125
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• 2016

PURPOSES : This study proposes a methodology to collect data necessary for microlevel emission estimation, such as second-by-second speeds and road grades, and to accordingly estimate emissions. METHODS : To ease data collection for microlevel emission estimation, a vehicle equipped with speed- and location-recording instruments as well as equipment for measuring road geometry was used. As a case study, this vehicle and the proposed methodology were used on a 10-km-long highway in Yongin City, Korea. Emissions from the vehicle during driving were estimated in various microscale driving conditions. RESULTS : Differences in the estimated emission under different microscale driving conditions cannot be ignored. Compared with the estimations obtained when second-by-second data were not considered, CO and NOx emissions were more than threefold higher when considering second-by-second speed; similarly, CO and NOx emission estimations were higher by approximately 10% and 3%, respectively, when considering second-by-second road grade. CONCLUSIONS : The proposed method can estimate vehicle emissions under real-world driving conditions in such applications as road design and traffic policy assessments.

• Journal of KSNVE
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• v.6 no.6
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• pp.843-849
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• 1996

Locations and emission characteristics of noise source of motor vehicles are great important factors to control the road traffic noise in effective ways. From results of this study on emission characteristics of engine and exhaust noise, we could find that every noise emission of different kind of vehicles has smilar pattern. The main emission locations of engine noise for the front of vehicle became the space between the road surface and bottom of the body and radiator grill, and for the side of vehicle became the space between the road surface and bottom nearby the front wheel. In case of exhaust noise of passenger-car and light truck, all the highest sound intensity level located near surface of road. But it is hard to conclude the height of noise source of driving vehicles with only results of this study. So further studies are needed to check the emission characteristics of noise.