• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.4
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• pp.85-92
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• 2002

To investigate the safety of the in-use vehicles emission against the tail-pipe emission regulation, in-use vehicles emission trend according to vehicle mileage should be known. But it is impossible to collect all vehicles emission data In order to know that. Therefore, it is necessary to establish a statistically meaningful inference method that can be used generally to estimate in-use vehicles emissions distribution according to the vehicle mileage with relatively less in-use vehicles emission data. To do this, a linear regression model that solved the problems of data normality and common variance of error was studied. As a way that can secure the data normality, In(emission) instead of emission itself was used as a sampled data. And a reciprocal of mileage was suggested as a factor to secure common variance of error. As an example, 36 data of FTP-75 test were handled in this study. As a result, using average value and standard deviation at each mileage which were inferred from a linear regression model, probability density distribution and cumulative distribution of emissions according to the vehicle mileage were obtained and it was possible to predict the deterioration factor through full useful life mileage and also possible to decide whether those in-use vehicles will meet the tail-pipe emission regulations or not.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• Journal of Environmental Health Sciences
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• v.30 no.3
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• pp.293-299
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• 2004

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristics of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends is towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a model of vehicle emission calculation by using real-time traffic data was studied. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It is possible that characteristics of hourly air pollutants emission rates is obtained from hourly traffic volume and speed. An emission rates model is allocated with a high resolution space by using geographic information system (GIS). Vehicle emission model was developed with a high resolution spatial, gridded and hourly emission rates.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.1
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• pp.69-77
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• 1993

Exhaust emissions are calculated as a product of the emission factor and the vehicle kilometer traveled(VKT). The emission factor is a function of several parameters such as vehicle model year, vehicle mileage, traffic conditions, etc. The representative driving cycles classified as ten different types of an average vehicle speed were selected by analyzing passenger car driving patterns in Seoul. 51 vehicles were sampled and analyzed by types of vehicles, fuels used, model years and vehicle mileages also, exhaust emissions of them were measured by chassis dynamometer. Regression equations between average vehicle speeds and exhaust emissions are made for the estimation of emission factors at different vehicle speeds. Annual emission rates of air pollutants from motor vehicles in Korea were 1116$\times10^3 ton, 149\times10^3 ton, 413\times10^3 ton and 67\times10^3$ ton for CO, HC, NOx and particulats, respetively in 1990. It was found that 56% of CO and 49% of HC were originated from passenger cars and taxis, in addition, 87% of NOx and 100% of particulates were from buses and trucks using diesel fuels.

• Journal of ILASS-Korea
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• v.21 no.4
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• pp.223-236
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• 2016

On-road source emissions are major air pollutants and have been associated with serious health effects in Seoul metropolis. Thus, it is of fundamental importance to have an accurate assessment of vehicle emissions in order to implement an effective air quality management policy. As a result, there is a need to overview vehicle emission characteristics of air pollutants. This article discusses vehicle exhaust sampling and chemical analysis, emission characteristics of air pollutants, and emission regulations from passenger cars. The vehicle exhaust sampling and chemical analysis methods were described in particulate matter and gaseous compounds. In this article, chassis dynamometer, measurement instrumentation for nano-particulate matter and carbon compounds analysis device were described. For the gasoline and diesel vehicles, the effective parameters of emissions were average vehicle speed, vehicle mileage and model year. The particle number emissions for diesel nano-particles were sensitive to the sampling conditions. Also, the particle number emissions with a diesel particle filter (DPF) largely reduced rather than those without it. This article also describes different emission characteristics of air pollutants according to biodiesel or bioethanol mixing ratio. The Korean emission standards for passenger cars were compared with those of the US and EU. Finally, the objective is to give an overview of relevant background information on emission characteristics of air pollutants from passenger cars in Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.4
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• pp.387-396
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• 2003

A field study was conducted during the summer time of 2002 to determine compositions of volatile organic compounds (VOCs) emitted from vehicles and to develop source emission profiles that is applied to CMB model to estimate the source contribution of certain area. Source emission profile is widely used for the estimation of source contribution by the chemical mass balance model and have to be developed applicable for the target area of estimation. This study was aimed to develop source emission profile and estimation of source contribution of VOCs after application of the chemical mass balance (CMB) receptor model. After considering the emission inventory and other research results for the VOCs in Seoul, Korea, the sources like vehicle emission (tunnel), gas station (gasoline, diesel), solvent usage (painting operation, dry cleaning, graphic art), and gas fuels were selected for the major VOCs sources. Furthermore, ambient air samples were simultaneously collected from 09:00 to 11:00 for four days at eight different official air quality monitoring sites as receptors in Seoul during summer of 2001. Source samples were collected by canisters, and then about seventy volatile organic compounds were analyzed by gas chromatography with flame ionization detector (GC/FID). Based on both the developed source profiles and the database of the receptors, CMB model was intensively applied to estimate mass contribution of VOCs sources. Examining the source profile from the vehicle, the portion of alkanes of VOCs was highest, and then the portion of aromatics such toluene, m/p-xylene were followed. In case of gas fuel. they have their own components; the content of butane, propane, ethane was higher than any other component according to the fuel usage. The average of the source apportionment on VOCs for 8 sites showed that the major sources were vehicle emission and gas fuels. The vehicle emission source was revealed as having the highest contribution with an average of 49.6%, and followed by solvent with 21.3%, gas fuel with 16.1%, gasoline with 13.1%.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.3
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• pp.197-202
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• 1994

The research described in this paper is conducted to estimate the short-term concentrations of nonreactive pollutants such as CO and TSP from vehicle emissions near Kyungbu Highway. An emphasis is placed on the development of a model for a hourly traffic volume for each vehicle type, which is based on real traffic data. By using the model and the calculated emission factor due to vehicle speed for each vehicle type, the emission rate of CO and TSP for each traffic line is computed. The hourly emission rate and meteorological data are used to simulate by HIWAY-2 for the distance of 5m and 10m from the downwind edge of Kyungbu Highway located in relatively uncomplicated terrain.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.90-105
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• 2013

There are lots of variations on speed, acceleration and engine power during vehicle driving. It is well known that Green House Gas emissions by these dynamic driving properties are not precisely estimated by the average speed based emission estimation model which has been currently used in Korea. MOVES are selected as an appropriate transferable model among Micro-level emission estimation models. Based on MOVES, a novel emission estimation model can be used in Korea is developed. In this model, MOVES concept of emission estimation method and the MOVES method of estimating the Micro-level emission rate map is adopted. The results from the proposed model were compared with those from the average speed based emission model. The comparison results show the estimated base emission maps are good to be applied in Korea, but needed to be adjusted to consider the vehicle size differences between the two countries. Therefore, the factors for calibrating vehicle size difference were calculated and applied to acquired the micro-level emission maps for the Korean standard vehicle types.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.107-120
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• 2011

Traffic signal is one of the major factors that affect the amount of vehicle emissions on urban highway. The amount of vehicle emissions in urban area is highly affected by the vehicle's cruising speeds heavily influenced by the traffic signal lighting conditions. It was attempted in this study to trace the changing patterns of the vehicle emissions by collecting the emission data from a set of simulation studies and by categorizing vehicle cruising conditions into four different groups: idling, acceleration, deceleration, and running at a constant speed. Authors propose a simple emission model prepared based on Kinematic theory. The validation test results showed that the amount of the emission estimated by the proposed model was relatively satisfactory compared to the one of the existing model employing the average speed data only as the determinant.

• Journal of ILASS-Korea
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• v.22 no.2
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• pp.47-54
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• 2017

$CO_2$ emission regulation will be prescribed and main issue in automotive industry. Mostly, vehicle's fuel efficiency deeply related to $CO_2$ emission is regulated by qualified driving test cycle by using chassis dynamometer and exhaust gas analyser. But, real driving fuel consumption rate depends so much on the individual usage profile and where it is being driven: city traffic, road conditions. In this study, vehicle model of fuel consumption rate for ICEV and PHEV was developed through co-simulation with CRUISE model and Simulink based on driving control model. The simulation results of fuel consumption rate were analysed with on-road vehicle data and compared with its official level.