• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.485-496
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• 2022

Modern agriculture is being transformed into smart agriculture to maximize production efficiency along with changes in the 4th industrial revolution. However, rural areas in Korea are facing challenges of aging, low fertility, and population outflow, making it difficult to transition to smart agriculture. Among ICT technologies, simulation allows users to observe or experience the results of their choices through imitation or reproduction of reality. The combination of the three-dimension (3D) model and the greenhouse simulator enable a 3D experience by virtual greenhouse for fruits and vegetable cultivation. At the same time, it is possible to visualize the greenhouse under various cultivation or climate conditions. The objective of this study is to apply the greenhouse climate management model for simulation development that can visually see the state of the greenhouse environment under various micrometeorological properties. The numerical solution with the mathematical model provided a dynamic change in the greenhouse environment for a particular greenhouse design. Light intensity, crop transpiration, heating load, ventilation rate, the optimal amount of CO₂ enrichment, and daily light integral were calculated with the simulation. The results of this study are being built so that users can be linked through a web page, and software will be designed to reflect the characteristics of cladding materials and greenhouses, cultivation types, and the condition of environmental control facilities for customized environmental control. In addition, environmental information obtained from external meteorological data, as well as recommended standards and set points for each growth stage based on experiments and research, will be provided as optimal environmental factors. This simulation can help growers, students, and researchers to understand the ICT technologies and the changes in the greenhouse microclimate according to the growing conditions.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.190-202
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• 2012

Emissions of air pollutants and greenhouse gases (GHGs) by aircraft at the Gimhae International Airport (GIA) were investigated using the Emissions and Dispersion Modeling System (EDMS) version 5.1.3. The number of Landing and Take-Off (LTO) at the GIA for aircraft B737 was dominant, accounting for more than 60% of the total LTOs. For air pollutant emissions, CO was the most dominant pollutant by aircraft, followed by $NO_x$, VOCs, $SO_x$, etc. The emissions of CO, $NO_x$, and VOCs in 2009 (and 2010) at the GIA were 974 (968), 447 (433), 118 (122) ton/yr, respectively. The emissions of GHGs such as $CO_2$, $CH_4$, and $N_2O$ in 2009 (and 2010) were 110,795 (111,114), -0.157 (-0.151), and 1,989 (1,998) ton/yr, respectively. The negative number in $CH_4$ emission represents the consumption of atmospheric $CH_4$ in the engine. In addition, the emissions of most air pollutants (except for $PM_{10}$) and GHGs were estimated to be high in Taxi-Out and Climb-Out modes.

• Atmosphere
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• v.27 no.4
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• pp.423-433
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• 2017

The spatial and temporal variations in radiative forcing (RF) and mean temperature changes of greenhouse gases (GHGs), such as $CO_2$, $CH_4$, and $N_2O$, were analyzed at urban center (Yeon-dong) and background sites (Gosan) on Jeju Island during 2010~2015, based on a modeling approach (i.e., radiative transfer model). Overall, the RFs and mean temperature changes of $CO_2$ at Yeon-dong during most years (except for 2014) were estimated to be higher than those at Gosan. This might be possibly because of its higher concentrations at Yeon-dong due to relatively large energy consumption and small photosynthesis and also the difference in radiation flux due to the different input condition (e.g., local time and geographic coordinates of solar zenith angle) in the model. The annual mean RFs and temperature changes of $CO_2$ were highest in 2015 ($2.41Wm^{-2}$ and 1.76 K) at Yeon-dong and in 2013 ($2.22Wm^{-2}$ and 1.62 K) at Gosan (except for 2010 and 2011). The maximum monthly/seasonal mean RFs and temperature changes of $CO_2$ occurred in spring (Mar. and/or Apr.) or winter (Jan. and/or Feb.) at the two sites during the study period, whereas the minimum RFs and temperature changes in summer (Jun.-Aug.). In the case of $CH_4$ and $N_2O$, their impacts on the RF and mean temperature changes were very small (an order of magnitude lower) compared to $CO_2$. The spatio-temporal differences in these RF values of GHGs might primarily depend on the atmospheric profile (e.g., ozone profile), surface albedo, local time (or solar zenith angle), as well as their mass concentrations.

• Journal of Environmental Science International
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• v.22 no.7
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• pp.823-836
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• 2013

Emissions of air pollutants and greenhouse gases (GHGs) from aircraft activities at 11 small-scale airports were investigated using the emissions and dispersion modeling system (EDMS) version 5.1.3 during the two year period of 2009~2010. The number of landing and take-off (LTO) at these airports was dominant for the aircraft type B737, accounting for more than 60% of the total LTOs. Out of the 11 small-scale airports, Gwangju (GJ, RKJJ) airport was the largest emitter of air pollutants and GHGs, whereas Yangyang (YY, RKNY) airport was the smallest emitter. The emissions of $NO_x$ and VOCs in 2010 at the 11 airports ranged from 1.9 to 83 ton/y and 0.1 to 17 ton/y, respectively. In 2010, the emissions of $CO_2$ ranged from 394 to 21,217 ton/y. The emissions of most air pollutants (except for $NO_x$ and $PM_{10}$) and GHGs were estimated to be the highest in taxi-out mode. The highest emissions of $NO_x$ and $PM_{10}$ were emitted from climb-out and approach modes, respectively. In addition, the total LTOs at the 11 small-scale airports accounted for the range of 9.3~9.9% of those at four major international airports in Korea. The total emissions of air pollutants and GHGs at the 11 airports ranged from 4.8 to 12% of those at the four major airports.

• Spatial Information Research
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• v.23 no.1
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• pp.1-8
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• 2015

The purpose of this study is to develope BIM Template according to major building material for efficiently and quantitatively evaluating greenhouse gas emission at the design stage. Template users consider various environmental impacts without connecting simulation tools for analyzing environmental impact and Template users who have no prior knowledge can Life Cycle Assessment by using The green template. For this study, Database which was reflected in template was constructed considering environmental performance. and 6 kinds of environmental impact categories and PPS standard construction codes were analyzed by major building material derived from literature. Based on this analyzed data, The major Material Family according to the main building material was developed. When users conduct modeling by utilizing Family established, evaluating result can be confirmed in the Revit BIM Modeling program by using the schedule function of the Revit. Users through the modeling, the decision-making environment performance possible. In addition, we propose to create a guideline for the steps required to build an additional established family.

• Spatial Information Research
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• v.22 no.1
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• pp.9-17
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• 2014

This study was intended to reliably predict the traffic green house gas emission in Daegu with the use of spatial statistical technique and calculate the traffic green house gas emission of each administrative district on the basis of the accurately predicted emission. First, with the use of the traffic actually surveyed at a traffic observation point, and traffic green house gas emission was calculated. Secondly, on the basis of the calculation, and with the use of Universal Kriging technique, this researcher set a suitable variogram modeling to accurately and reliably predict the green house gas emission at non-observation point suitable through spatial correlation, and then performed cross validation to prove the validity of the proper variogram modeling and Kriging technique. Thirdly, with the use of the validated kriging technique, traffic green gas emission was visualized, and its distribution features were analyzed to predict and calculate the traffic green house gas emission of each administrative district. As a result, regarding the traffic green house gas emission of each administration, it was found that Bukgu had the highest green house gas emission of $291,878,020kgCO_2eq/yr$.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.150-159
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• 2014

Since the release of mid-term domestic GHG goals until 2020, in 2009, some various GHG reduction policies have been proposed. There are two types of modeling approaches for identifying options required to meet greenhouse gas (GHG) abatement targets and assessing their economic impacts: top-down and bottom-up models. Examples of the bottom-up optimization models include MARKAL, MESSAGE, LEAP, and AIM, all of which are developed based on linear programming (LP) with a few differences in user interface and database utilization. In this paper, we suggest a simplified LP formulation and how can build it through step-by-step procedures.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.79-91
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• 2020

Accurate assessment of greenhouse gas emissions is a cornerstone of every climate change response study, and reliable assessment of greenhouse gas emission data is being used as a practical basis for the entire climate change prediction and modeling studies. Essential, fundamental technologies for estimating greenhouse gas emissions include an on-site monitoring technology, an evaluation methodology of uncertainty in emission factors, and a verification technology for reductions. The closed chamber method is being commonly used to measure gas fluxes between soil-vegetation and atmosphere. This method has the advantages of being simple, easily available and economical. This study presented the technical bases of the closed chamber method for measuring methane fluxes from a rice paddy. The methane fluxes from rice paddies occupy the largest portion of a single source of greenhouse gas in the agricultural field. We reviewed the international and the domestic studies on automated chamber monitoring systems that have been developed from manually operated chambers. Based on this review, we discussed scientific concerns on chamber methods with a particular focus on quality control for improving measurement reliability of field data.

• Journal of Bio-Environment Control
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• v.29 no.3
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• pp.277-284
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• 2020

To increase the utilization of the intelligent methodology of smart farm management, estimation modeling techniques are required to assess prior examination of crops and environment changes in realtime. A mandatory environmental factor such as CO₂ is challenging to establish a reliable estimation model in time domain accounted for indoor agricultural facilities where various correlated variables are highly coupled. Thus, this study was conducted to develop an artificial neural network for reducing time complexity by using environmental information distributed in adjacent areas from a time perspective as input and output variables as CO₂. The environmental factors in the smart farm were continuously measured using measuring devices that integrated sensors through experiments. Modeling 1 predicted by the mean data of the experiment period and modeling 2 predicted by the day-to-day data were constructed to predict the correlation of CO₂. Modeling 2 predicted by the previous day's data learning performed better than Modeling 1 predicted by the 60-day average value. Until 30 days, most of them showed a coefficient of determination between 0.70 and 0.88, and Model 2 was about 0.05 higher. However, after 30 days, the modeling coefficients of both models showed low values below 0.50. According to the modeling approach, comparing and analyzing the values of the determinants showed that data from adjacent time zones were relatively high performance at points requiring prediction rather than a fixed neural network model.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.91-104
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• 2017

In this study, we discuss the modeling of domestic transport sector using GCAM(Global Change Assessment Model). The GCAM is one of integrated assessment models widely used in internationally modeling community, and applied for the evaluation of IPCC 5th Report. Nevertheless, it is noted that there are a considerable number of problems in its application to domestic transport sector. First, the base year information of GCAM for detailed transportation service demand is found not consistent with national statistics. Second, the transportation sector simulation results do not properly reflect the past trends of service demand. Thus, the base year service demand is carefully matched with the detailed national statistics. In addition, the existing models were checked and modified so that the simulation results of service demand can accurately reflect past trends of national statistics. As a result, it is reported in detail how the current GCAM simulation results are corrected and how the trend of past transportation sector service demands is properly reflected. This study is expected to be useful as a basic tool for future scenario analysis for transportation policy, technology evaluation and greenhouse gas reduction measures.