• Journal of Climate Change Research
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• v.7 no.4
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• pp.443-450
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• 2016

The major greenhouse gases (GHGs) in agricultural sector are methane ($CH_4$), nitrous oxide ($N_2O$), carbon dioxide ($CO_2$). GHGs emissions are estimated by pertinent source category in a guideline book from Intergovernmental Panel on Climate Change (IPCC) such as methane from rice paddy, nitrous oxide from agricultural soil and crop residue burning. The methods for estimation GHGs emissions in agricultural sector are based on 1996 and 2006 IPCC guideline, 2000 and 2003 Good Practice Guidance. In general, GHG emissions were calculated by multiplying the activity data by emission factor. The total GHGs emission is $10,863Gg\;CO_2-eq$. from crop cultivation in agricultural sector in 2013. The emission is divided by the ratio of greenhouse gases that methane and nitrous oxide are 64% and 34%, respectively. Each gas emission according to the source categories is $7,000Gg\;CO_2-eq$. from rice paddy field, $3,897Gg\;CO_2-eq$. from agricultural soil, and $21Gg\;CO_2-eq$. from field burning, respectively. The GHGs emission in agricultural sector had been gradually decreased from 1990 to 2013 because of the reduction of cultivation. In order to compare with indirect emissions from agricultural soil, each emission was calculated using IPCC default factors (D) and country specific emission factors (CS). Nitrous oxide emission by CS applied in indirect emission, as nitrogen leaching and run off, was lower about 50% than that by D.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• KIEAE Journal
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• v.10 no.4
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• pp.101-109
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• 2010

As global warming progresses, nations around the world are trying to reduce emission of $CO_2$ that accounts for the greatest portion of greenhouse gases. To reduce $CO_2$ emission, it is first necessary to estimate $CO_2$ emission of each industry. Government authorities estimate basic unit of $CO_2$ emission from re-bar that is one of the key materials of construction industry with LCA technique (Life Cycle Assessment). However, basic unit of $CO_2$ emission varies from organization to organization. The Ministry of Land, Transport and Maritime Affairs (2004) publishes it 3.48($TCO_2/ton$) and 0.30($TCO_2/ton$) with input-output analysis while the Korea Environmental Industry & Technology Institute (2008) defines it as 0.34($TCO_2/ton$) with process analysis, which indicates ambiguity in application of basic unit of $CO_2$emission. Based on the analysis of conventional methods used for estimating the $CO_2$ emission, therefore, this research suggests existing problems on the methods and focuses on proposing an strategy to effectively estimate the basic unit of $CO_2$ emission according to the energy consumption limited to the re-bar production in steel mill in order to overcome the problems. The result of this research is expected to be helpful in calculating and reducing $CO_2$ emission.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1087-1094
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• 2013

This study aims at enhancing the objective estimation of social cost of air quality due to mobile emission. More specifically, it examines the difference between the daily oriented and hourly oriented estimation results of social air quality cost and draws implications from the comparative analysis. The result indicates that the social cost of air quality differs up to approximately 24 times depending on the analysis time period. Moneywise, the difference between daily and hourly assignments amounts to the average of 653.5 billion won whereas only 1% of error occurred in the estimation result based on peak and nonpeak based hourly assignment. This study reaffirms the need for time-based travel demand management for emission reduction, and confirms the feasibility of emission estimation by travel demand forecasting method over the conventional method employed by the CAPSS.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.6
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• pp.117-124
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• 2002

For the correct estimation of air pollutant emission from automobiles which is the largest contributor of metropolitan area air pollution, the total pollutant emission from automobiles should be estimated accurately. Nationwide emissions from automobiles, such as CO, HC and NOx, are calculated by using emission factor and total VMT(vehicle miles traveled). The emission factors were derived from the emissions data on chassis dynamometer with test modes which represent the real driving patterns. In the present study, test modes to derive the emission factors for light duty vehicles are developed by using the real driving pattern data for the urban, suburban and express way.

• Journal of Mechanical Science and Technology
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• v.17 no.3
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• pp.391-399
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• 2003

The thermo-acoustic emission (AE) technique has been applied for nondestructive characterization of composite laminates subjected to cryogenic cooling. Thermo-AE events during heating and cooling cycles showed a Kaiser effect. An analysis of the thermo-AE behavior obtained during the 1st heating period suggested a method for determining the stress-free temperature of the composite laminates. Three different thermo-AE types classified by a short-time Fourier transform of AE signals enabled to offer a nondestructive estimation of the cryogenic damages of the composites, in that the different thermo-AE types corresponded to secondary microfracturing in the matrix contacting between crack surfaces and some abrasive contact between broken fiber ends during thermal load cycles.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.240-246
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• 2000

An in-process estimation of the ground surface roughness is a bottle-neck and an essential field in conventional grinding operation. We defined the dimensionless average roughness factor (D.A.R.F) that exhibits a roughness characteristics of ground surface. The D.A.R.F was composed easily of the absolute average and the standard deviation values which were the analytic parameters of the acoustic emission (AE) signal generated during the machining process. The theoretical equation between the surface roughness and the D.A.R.F has been derived from the linear regressive analysis and verified its availability through the experimentation on the surface grinding machine.

• Journal of Urban Science
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• v.9 no.1
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• pp.69-73
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• 2020

Sudokwon landfill("Sudokwon" means regions of Seoul, Kyunggi and Incheon metropolitan cities in Korea), the world's largest sanitary landfill, has been systematically managing statistics on the incoming and dumping wastes and satisfactorily controlling pollutants including leachate and LFG. According to our long time experience of LFG field monitoring, the emission of GHG from landfill estimated by the IPCC Guideline showed much difference with our results. C&D waste has high concentration of sulfate compared to other wastes. Increased C&D waste of dumping waste had changed the COD/sulfate ratio in the landfill, which caused the increase of H₂S gas and the decrease of CH₄ gas. But the IPCC estimation method does not consider the effects of sulfate. In addition to that, the oxidation factor of the cover soil is set to the default values of 0.1 but the measured values by the field monitoring, are showing much higher than that, especially in the closed landfill.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.335-339
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• 2016

The purpose of this study is to analyze effects of greenhouse gas reduction in organic agriculture. To accomplish the objective of the study, a field survey was conducted. Based on the field survey results, LCA method was used to estimate the greenhouse gas emission. The farmer survey and LCA estimation data were provided by The Foundation of Agricultural Technology Commercialization and Transfer. The GHG estimation results showed that GHG emission of organic farming is less by 10.6~89.3% when compared with the conventional farming. In addition, the economic value of greenhouse gas reduction in organic farming amounts to 1,097 million won. Based on major findings, in response to national greenhouse gas reduction target, it is needed to expand organic farming, supporting organic farmers' income.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.30-36
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• 2005

This study is to investigate a damage estimation of single edge notched tensile specimens as a function of acoustic emission(AE) according to the uni-directionally oriented carbon fiber/epoxy composites, CFRP In fiber reinforced composite materials, AE signals due to several types of failure mechanisms are typically observed. These are due to fiber breakage, fiber pull-out matrix cracking, delamination, and splitting or fiber bundle breaking. And these are usually discriminated on the basis of amplitude distribution, event counts, and energy related parameters. In this case, AE signals were analyzed and classified 3 regions by AE event counts, energy and amplitude for corresponding applied load. Bath-tub curve shows 3 distinct periods during the lifetime of a single-edge-notch(SEN) specimen. The characterization of AE generated from CFRP during SEN tensile test is becoming an useful tool f3r the prediction of damage failure or/and failure mode analysis.