• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1232-1236
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• 2010

In this Paper, optimal capacity of energy storage and amount of $CO_2$ reduction in Jeju is calculated. Based on electricity demand data of Je-Ju from 2006 to 2007, the estimation electricity demand from 2009 to 2018 is performed. To calculate the amount of maximum $CO_2$ reduction and energy storage capacity in Jeju, the 4th power supply planning and IPCC guideline are used. Finally, Optimal capacity of energy storage and the amount of $CO_2$ reduction are showed.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.175-184
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• 2015

In this study GC and PAS were used to calculate $N_2O$ concentration of exhaust gas from Wood Chip combustion system. Fuel supplied to the incinerator was collected and analyzed and then the analysis result was used to calculate $N_2O$ emissions. Tier 3 and Tier 4 Method were used to calculate the $N_2O$ emissions. Plant's Specific emission factor of $N_2O$ by Tier 3 Method was 0.35 kg/TJ, while default emission factor of Wood?Wood Waste proposed by 2006 IPCC G/L was 4 kg/TJ. So the $N_2O$ emission factor of this study was 3.65 kg/TJ lower compared to the IPCC G/L. The total emissions calculated by Plant's specific emission factor was 4.22 kg during the measuring period, but by Tier 4 Method it was 7.88 kg. This difference in emissions was caused by the difference of continuous measuring and intermittent sampling. It would be necessary to apply continuous measuring to calculate emissions of $Non-CO_2$ gas whose the density distribution is relatively high. However currently, according to the target management guideline of greenhouse gas and energy, the continuous measuring method to calculate greenhouse gas emission is applied only to $CO_2$. Therefore for reliable greenhouse gas emission calculation it would be necessary to apply continuous measuring to calculate $Non-CO_2$ gas emission.

• Horticultural Science & Technology
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• v.31 no.6
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• pp.828-832
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• 2013

To report country-specific carbon and nitrogen stocks data in a pear orchard by Tier 3 approach of 2006 IPCC guidelines for national greenhouse gas inventories, an experimental pear orchard field of the Pear Research Station, National Institute of Horticultural & Herbal Science, Rural Development Administration, Naju, Korea ($35^{\circ}01^{\prime}27.70N$, $126^{\circ}44^{\prime}53.50^{\prime\prime}E$, 6 m altitude), where 15-year-old 'Niitaka' pear (Pyrus pyrifolia Nakai cv. Niitaka) trees were planted at a $5.0m{\times}3.0m$ spacing on a Tatura trellis system, was chosen to assess the total amount of carbon and nitrogen stocks stored in the trees and orchard soil profiles. At the sampling time (August 2012), three trees were uprooted, and separated into six fractions: trunk, main branches, lateral branches (including shoots), leaves, fruits, and roots. Soil samples were collected from 0 to 0.6 m depth at 0.1 m intervals at 0.5 m from the trunk. Dry mass per tree was 4.7 kg for trunk, 13.3 kg for main branches, 13.9 kg for lateral branches, 3.7 kg for leaves, 6.7 kg for fruits, and 14.1 kg for roots. Amounts of C and N per tree were respectively 2.3 and 0.02 kg for trunk, 6.4 and 0.07 kg for main branches, 6.4 and 0.09 kg for lateral branches, 6.5 and 0.07 kg for roots, 1.7 and 0.07 kg for leaves, and 3.2 and 0.03 kg for fruits. Carbon and nitrogen stocks stored between the soil surface and a depth of 60 cm were 138.29 and $13.31Mg{\cdot}ha^{-1}$, respectively, while those contained in pear trees were 17.66 and $0.23Mg{\cdot}ha^{-1}$ based on a tree density of 667 $trees{\cdot}ha^{-1}$. Overall, carbon and nitrogen stocks per hectare stored in a pear orchard were 155.95 and 13.54 Mg, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.4
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• pp.466-476
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• 2012

This study calculated monthly and sectoral (for industry, energy industry, transport, residential, commercial and public sectors) greenhouse gas (GHG) emissions of Seoul, Korea from 1999 until 2009 with following the IPCC 2006 Guideline for National Greenhouse Gas Inventories through an analysis on available monthly data of fossil fuel and electricity consumption for the period. The time series analysis showed that GHG emissions had significant cyclical pattern season by season with the highest peak in August and the lowest peak in January throughout the period. The analysis on monthly and sectoral energy consumption showed that residential, commercial and public sectors had emitted about 65% of total GHG emissions of Seoul and had consumed more energy in winter for heating. About 30% GHG of Seoul was emitted from transport sector but its monthly energy consumption showed irregular pattern and it consumed 80% petroleum (in 2009) of Seoul. Hopefully together with further study on this subject, it is expected that this study can be used as basic data for various research regarding Greenhouse gas baseline emission, energy consumption pattern and estimation for future GHG emission of Seoul.

• Journal of Korean Society of Rural Planning
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• v.19 no.4
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• pp.125-135
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• 2013

Analysis and evaluation of uncertainty is adopting the advanced methodology among the methods for greenhouse gas emission assessment that was defined in GPS2000 (Good practice guideline 2000) and GPG-LULUCF (GPG Land Use, Land-Use Change and Forestry). In 2006 IPCC guideline, two approaches are suggested to explain the uncertainty for each section with a national net emission and a prediction value on uncertainty as follows; 1) Spread sheet calculation based on the error propagation algorithm that was simplified with some assumptions, and 2) Monte carlo simulation that can be utilized in general purposes. There are few researches on the agricultural field including greenhouse gas emission that is generated from livestock and cultivation lands due to lack of information for statistic data, emission coefficient, and complicated emission formula. The main objective of this study is to suggest an evaluation method for the uncertainty of greenhouse gas emission in agricultural field by means of intercomparison of the prediction value on uncertainties which were estimated by spread sheet calculation and monte carlo simulation. A statistic analysis for probability density function for uncertainty of emission rate was carried out by targeting livestock intestinal fermentation, excrements treatment, and direct/indirect emission from agricultural lands and rice cultivation. It was suggested to minimize uncertainty by means of extraction of emission coefficient according to each targeting section.

• Journal of Climate Change Research
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• v.3 no.3
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• pp.211-224
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• 2012

Technology for energy recovery from waste can reduce the greenhouse gas emissions. So recently, there are several companies using RDF, RPF, WCF instead of using only coal fuel and it's part of the fuel on the increase. In this study, we developed Wood chip fuel $CO_2$ emission factor through fuel analysis. It's moisture content is 23%, received net calorific value is 2,845 kcal/kg, and received basis carbon is 34%. The result of emission factor is $105ton\;CO_2/TJ$, it's 5.9% lower than 2006 IPCC guideline default factor $112ton\;CO_2/TJ$. The gross GHG(Greenhouse gases) emissions of plant A is $178,767ton\;CO_2 eq./yr$, and Net GHG emissions is $40,359ton\;CO_2 eq./yr$. Therefore, the reduction of GHG emissions is $138,408ton\;CO_2/yr$ through using WCF, and I accounts for 77% of all GHG emissions.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.657-665
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• 2010

Carbon dioxide ($CO_2$) is known to be a major greenhouse gas partially emitted from waste combustion facilities. According to the greenhouse gas emission inventory in Korea, the quantity of the gas emitted from waste sector in 2005 represents approximately 2.5 percent of all domestic greenhouse gas emission. Currently, the emission rate of greenhouse gas from the waste sector is relatively constant partly because of both the reduced waste disposal in landfills and the increased amounts of waste materials for recycling. However, the greenhouse gas emission rate in waste sectors is anticipated to continually increase, mainly due to increased incineration of solid waste. The objective of this study was to analyze the property of Municipal Solid Waste (MSW) and estimate $CO_2$ emissions from domestic MSW incineration facilities. The $CO_2$ emission rates obtained from the facilities were surveyed, along with other two methods, including Tier 2a based on 2006 IPCC Guideline default emission factor and Tier 3 based on facility specific value. The $CO_2$ emission rates were calculated by using $CO_2$ concentrations and gas flows measured from the stacks. Other parameters such as waste composition, dry matter content, carbon content, oxidation coefficient of waste were included for the calculation. The $CO_2$ average emission rate by the Tier 2a was 34,545 ton/y, while Tier 3 was 31,066 ton/y. Based on this study, we conclude that Tier 2a was overestimated by 11.2 percent for the $CO_2$ emission observed by Tier 3. Further study is still needed to determine accurate $CO_2$ emission rates from municipal solid waste incineration facilities and other various combustion facilities by obtaining country-specific emission factor, rather than relying on IPCC default emission factor.

• Journal of Environmental Science International
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• v.24 no.3
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• pp.303-316
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• 2015

The relationship between urban spatial structures and GHG-AP integrated emissions was investigated by statistically analyzing those from 25 administrative districts of Seoul. Urban spatial structures, of which data were obtained from Seoul statistics yearbook, were classified into five categories of city development, residence, environment, traffic and economy. They were further classified into 10 components of local area, population, number of households, residential area, forest area, park area, registered vehicles, road area, number of businesses and total local taxes. GHG-AP integrated emissions were estimated based on IPCC(intergovernmental panel on climate change) 2006 guidelines, guideline for government greenhouse inventories, EPA AP-42(compilation of air pollutant emission factors) and preliminary studies. The result of statistical analysis indicated that GHG-AP integrated emissions were significantly correlated with urban spatial structures. The correlation analysis results showed that registered vehicles for GHG (r=0.803, p<0.01), forest area for AP (r=0.996, p<0.01), and park area for AP (r=0.889, p<0.01) were highly significant. From the factor analysis, three groups such as city and traffic categories, economy category and environment category were identified to be the governing factors controlling GHG-AP emissions. The multiple regression analysis also represented that the most influencing factors on GHG-AP emissions were categories of traffic and environment. 25 administrative districts of Seoul were clustered into six groups, of which each has similar characteristics of urban spatial structures and GHG-AP integrated emissions.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.3
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• pp.451-458
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• 2012

This study was conducted to investigate the characteristic factors which have been influenced on nitrous oxide ($N_2O$) emissions related to the environment change of nitrogen application level, rainfall and temperature during the soybean cultivation at black volcanic ash soil from 2010 to 2011. During the soybean cultivation, the more amount of nitrogen fertilizer applied, $N_2O$ emissions amounts were released much. $N_2O$ emissions with the cultivation time were released much at the first and middle of cultivation with heavy rainfall, but it was released very low until the end of cultivation and drought season. $N_2O$ emissions mainly were influenced by the rainfall and soil water content. The correlation ($r$) with $N_2O$ emissions, soil water, soil temperature and soil EC in 2010 were very significant at $0.4591^{**}$, $0.6312^{**}$ and $0.3691^{**}$ respectively. In 2011, soil water was very significant at $0.4821^{**}$, but soil temperature and soil EC were not significant at 0.1646 and 0.1543 respectively. Also, $NO_3$-N and soil nitrogen ($NO_3-N+NO_4-N$) were very significant at $0.6902^{**}$ and $0.6277^*$ respectively, but $NO_4$-N was not significant at 0.1775. During the soybean cultivation, the average emissions factor of 2 years released by the nitrogen fertilizer application was presumed to be 0.0202 ($N_2O$-N kg $N^{-1}\;kg^{-1}$). This factor was higher about 2.8 and 2 times than the Japan's (0.0073 $N_2O$-N kg $N^{-1}\;kg^{-1}$) value and 2006 IPCC guideline default value (0.0100 $N_2O$-N kg $N^{-1}\;kg^{-1}$) respectively.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.67-74
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• 2013

To know the emission amount of greenhouse gases from bedding materials of cowshed floor, the emission rates of methane ($CH_4$) and nitrous oxide ($N_2O$) gases from a simulated cowshed floor (SCF) with sawdust that manure loading rate into the bedding material could be accurately controlled were assessed in this study. The manure loading rates of Korean beef and Holstein dairy cattle into the SCF of $0.258m^2$ surface area with 10 to 15 cm height sawdust were $1.586kg/m^2/d$ and $3.588kg/m^2/d$, respectively, and those were calculated on the basis of "Standard model for sustainable livestock" and "Data for excretion amount of manure from livestock". All experiments were done in triplicates in three different seasons (May to July, Sep. to Nov., and Feb. to Apr.) using 12 SCFs. The effects of bedding material thickness on $CH_4$ and $N_2O$ emission from SCFs for both Korean beef cattle and Holstein dairy cattle were not statistically significant (p<0.05). Emission amount of $CH_4$ and $N_2O$ per square meter of SCF for Holstein dairy cattle was 7.5 and 1.2 times higher than that of Korean beef cattle, respectively. The yearly $CH_4$ amount per head was 17.7 times higher in Holstein dairy cattle, obtaining 130.4 g/head/year from SCF for Holstein dairy cattle and 7.4 g/head/year from SCF for Korean beef cattle, and $N_2O$ was also 3.8 times higher in Holstein dairy cattle (3,267 g/head/year in Korean beef cattle and 14,719 g/head/year in Holstein dairy cattle). However, the $N_2O$-N per loaded nitrogen into SCF was higher in Korean beef cattle, having 0.2148 and 0.1632 kg $N_2O$-N/kg N in Korean beef cattle and Holstein dairy cattle, respectively, and those values were 3.07 and 2.33 times higher than that of Intergovernmental Panel on Climate Change (IPCC) 2006 guideline (GL) (0.07 kg $N_2O$-N/kg N).