• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.

• 한국환경과학회지
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• 제23권2호
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• pp.249-259
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• 2014

This paper investigates the characteristics of high $PM_{2.5}$ episodes occurred at Anmyeondo area in spring time, 2009. The monthly mean $PM_{2.5}$ concentration during April was the highest in the year and especially, high levels of $PM_{2.5}$ exceeding standard regulation level were sustained consecutively during 5 to 13 April. To analyze more detailed $PM_{2.5}$ characteristics, numerical simulations were carried out using CMAQ(Community Multi-scale Air Quality) with IPR(Integrated Process Rate) and DDM-3D(Decoupled Direct Method). $PM_{2.5}$ level was lower in daytime than that in nighttime due to vigorous vertical mixing during daytime. The chemical composition was showed that ratio of primary ion components such as sulfate($SO_4{^{2-}}$), nitrate($NO_3{^-}$) and ammonium($NH_4{^+}$) were nearly half of total amount of $PM_{2.5}$. Aerosol and transport process dominantly contributed to $PM_{2.5}$ concentration in Anmyeondo area and contribution rate of local emissions was nearly zero since Anmyeondo area has rare anthropogenic PM emission sources. DDM-3D analysis result showed that $PM_{2.5}$ in Anmyeondo area was influenced by emissions from Shanghai and Shandong region of China.

• Asian Journal of Atmospheric Environment
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• 제8권1호
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• pp.48-58
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• 2014

Energy efficiency is vital to improve energy security, environmental and social sustainability, and economic performance. Improved energy efficiency also mitigates climate change by lowering greenhouse gas (GHG) emissions. Buildings are the single largest industrial consumer of energy and are therefore key to understanding and analyzing energy consumption patterns and the opportunities for saving energy at the district level in urban environments. This study focused on two representative boroughs in the major metropolitan area of Seoul, South Korea as a case study: Gandong-gu, a typical residential district, and Jung-gu, a typical commercial district. The sources of the energy supplied to the boroughs were determined and consumption patterns in different industry sectors in Seoul used to identify current patterns of energy consumption. The study analyzed the energy consumption patterns for five different building categories and four different sectors in the building using a bottom-up energy modeling approach. Electricity and gas consumption patterns were recorded for different building categories and monthly ambient temperatures in the two boroughs. Finally, a logarithmic equation was developed to describe the correlation between commercial activity and cooling energy intensity in Jung-gu, the commercial district. Based on these results, recommendations are made regarding the current energy consumption patterns at the district level and government energy policies are suggested to reduce energy consumption and, hence, greenhouse gas emissions, in both commercial and residential buildings.

• Journal of Korean Society for Atmospheric Environment
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• 제17권E4호
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• pp.163-168
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• 2001

Estimating the emission rate of VOCs from a gasoline industry at national level can be a challenging take even though the estimation is mean-based. However, using the procedures in the US EPA AP-42 guidelines, it is possible to approximate the mean industry emission rate once enough data are available. However, this estimate can be misled in the sense that there exist many stochastic factors in the EPA\\`s estimation procedures and also throughout the marketing channels of gasoline industry. Addressing the stochasticity problem in EPA\\`s procedure is hard to tackle because the detailed data needed to execute the estimation are not usually available even from refiners. Instead, this research tries to stay focused on the second type of stochasticity issue, raised from the mean0based metrological and marketing practice data collected from the 4 major refiners. To do so emission raters from each marketing channels (8 marketing points by 3 transportation types and by storage facilities of 4 refiners) are estimated monthly, following AP-42 procedures and using Tank 4.0. Once these estimates are acquired, the distribution of VOC emission rate for each marketing channel of all 4 refiners is estimated through simulation method using @Risk. The mean-based emission rates are weighted by company quantities to estimate the emission rate from the whole gasoline industry. Simple economic implication is provided, based on the result. This study found that, on the mean-bases, about 0.66% of gasoline marketed are evaporated into air. Considering the stochasticity in the estimation, about 90% of simulation results fell into the range of 0.65 to 0.68%. For 90% chance, the estimated economic loss is $54.65 million to $57.17 million, not counting the cost caused by air quality degradation and associated health impact.

• 한국환경과학회지
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• 제24권6호
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• pp.793-806
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• 2015

The purpose of this study is to find out the air flow patterns affecting the PM10 concentration in Busan and the potential sources within each trajectory pattern. The synoptic air flow trajectories are classified into four clusters by HYSPLIT model and the potential sources of PM10 are estimated by PSCF model for each cluster from 2008 to 2012. The potential source locations of PM10 are compared with the distribution of PM10 anthropogenic emissions in east Asia developed in 2006 for the NASA INTEX-B mission. The annual mean concentrations of PM10 in Busan decreased from $51ug/m^3$ in 2008 to $43ug/m^3$ in 2012. The monthly mean concentrations of PM10 were high during a spring season, March to May and low during a summer season, August and September. The cluster2 composed of the air trajectories from the eastern China to Busan through the west sea showed the highest frequency, 44 %. The cluster1 composed of the air trajectories from the inner Mongolia region to Busan through the northeast area of China showed the second high frequency, 26 %. The cluster3 and 4 were composed of the trajectories originated in the southeast sea and the east sea of Busan respectively and showed low frequencies. The concentrations of in each cluster were $47ug/m^3$ in cluster1, $56ug/m^3$ in cluster2, $42ug/m^3$ in cluster3 and $37ug/m^3$ in cluster4. From these results, it was proved that the cluster1 and 2 composed of the trajectories originated in the east and northeast area of China were the causes of high PM10 concentrations in Busan. The results of PSCF and CWT model showed that the potential sources of the high PM10 concentrations were the areas of the around Mongolia and the eastern China having high emissions of PM10 from Beijing, Hebei to Shanghai through Shandong, Jiangsu.

• 한국환경과학회지
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• 제28권2호
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• pp.169-182
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• 2019

Temporal and spatial variations in surface ozone concentrations in Busan were investigated by using observation data from urban air quality sites during 2001-2016. The annual ozone concentrations showed a significant increasing trend of $+0.40ppb\;yr^{-1}$ in this period, with a more rapid increase of $+0.81ppb\;yr-1$ since 2010. For the monthly analysis, the increase in ozone concentration was the greatest in August ($+0.68ppb\;yr-1$). These ozone trends were due mainly to rising temperature ($+0.05^{\circ}C\;yr^{-1}$) and weak decreasing precipitation ($-6.42mm\;yr^{-1}$). However, the extreme weather events (heat wave, localized heavy rain, etc.) lead to an increase in short-term variability of ozone since 2010. The relatively low ozone concentrations in the downtown area were caused by high NOx emissions from mobile sources. The increases in ozone concentrations were observed at most of the air quality monitoring sites due to the reductions in anthropogenic emissions of NOx during 2001-2015. However, in the southern coastal area, lower rates of increase in ozone concentrations were observed by $-0.10{\sim}0.25ppb\;yr^{-1}$ due to the significant NOx emitted by ships in the Busan port and Busan new port.

• 한국환경과학회지
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• 제22권1호
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• pp.91-98
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• 2013

In order to study the seasonal patterns and possible origins of air concentrations of volatile organic compounds(VOC), measurements were taken with GC-MS at 3 sampling sites in Jinju for 12 months from Mar. 2010 to Feb. 2011. Atmospheric VOC are sampled on tubes containing solid adsorbents(Tenax TA) with a time resolution of 2hrs. Composition and concentration of VOC are analysed with a GC system equipped with thermal desorption apparatus(ATD). The most abundant compound appeared to be Toluene, Ethylbenzene and m,p-Xylene. The mean concentrations of Benzene were 0.20 ppb at GN site, 0.18 ppb at DA site, and 0.25 ppb at SP site, respectively. VOC concentration showed a strong seasonal variation, with higher concentrations during the spring and lower concentrations during the summer. The results showed that monthly fluctuations in measured VOC concentrations depended on variations in the strength of sources, as well as on photochemical activity and meteorological conditions. In Jinju, the total VOC emissions for 2009 were estimated to be 4,407 ton/year by Clean Air Policy Support System(CAPSS). It is shown that solvent use 57.5%(2,534 ton/yr), waste treatment and disposal 23.3%(1,025 ton/yr), and mobil source-road traffic 12.2%(537 ton/yr) are the most significant anthropogenic source.

• 대한원격탐사학회지
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• 제24권1호
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• 환경영향평가
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• 제21권2호
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• pp.255-264
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• 2012

The present study attempts to understand the emission pattern of greenhouse gases in people's daily life through the estimation and analysis of the amount and characteristics of the greenhouse gases. Based on the survey of 1,000 people throughout the nation, monthly emission of greenhouse gases per-capita was estimated from their use of fuels, electricity, water, and personal and public transportation means in addition to their waste generation. In the case of personal car drivers, greenhouse gas emission was the greatest from their cars, followed by the emission from electricity, fuels, and public transportation. Emission from water consumption and waste generation was relatively low. Fuel consumption varied depending on the number of household members, their housing type, and the size of their living spaces. Results showed that single-person households emitted the largest amount of per-capita greenhouse gas while greenhouse gas emission from electricity was inversely proportional to the number of persons in a given household.

• 한국대기환경학회지
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• 제34권1호
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• pp.16-37
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• 2018

In this study, difference in chemical composition of $PM_{2.5}$ observed between the year 2013 and 2015 at six air quality intensive monitoring stations (Bangryenogdo (BR), Seoul (SL), Daejeon (DJ), Gwangju (GJ), Ulsan (US), and Jeju (JJ)) was investigated and the possible factors causing their difference were also discussed. $PM_{2.5}$, organic and elemental carbon (OC and EC), and water-soluble ionic species concentrations were observed on a hourly basis in the six stations. The difference in chemical composition by regions was examined based on emissions of gaseous criteria pollutants (CO, $SO_2$, and $NO_2$), meteorological parameters (wind speed, temperature, and relative humidity), and origins and transport pathways of air masses. For the years 2013 and 2014, annual average $PM_{2.5}$ was in the order of SL ($${\sim_=}DJ$$)>GJ>BR>US>JJ, but the highest concentration in 2015 was found at DJ, following by GJ ($${\sim_=}SJ$$)>BR>US>JJ. Similar patterns were found in $SO{_4}^{2-}$, $NO_3{^-}$, and $NH_4{^+}$. Lower $PM_{2.5}$ at SL than at DJ and GJ was resulted from low concentrations of secondary ionic species. Annual average concentrations of OC and EC by regions had no big difference among the years, but their patterns were distinct from the $PM_{2.5}$, $SO{_4}^{2-}$, $NO_3{^-}$, and $NH_4{^+}$ concentrations by regions. 4-day air mass backward trajectory calculations indicated that in the event of daily average $PM_{2.5}$ exceeding the monthly average values, >70% of the air masses reaching the all stations were coming from northeastern Chinese polluted regions, indicating the long-range transportation (LTP) was an important contributor to $PM_{2.5}$ and its chemical composition at the stations. Lower concentrations of secondary ionic species and $PM_{2.5}$ at SL in 2015 than those at DJ and GJ sites were due to the decrease in impact by LTP from polluted Chinese regions, rather than the difference in local emissions of criteria gas pollutants ($SO_2$, $NO_2$, and $NH_3$) among the SL, DJ, and GJ sites. The difference in annual average $SO{_4}^{2-}$ by regions was resulted from combination of the difference in local $SO_2$ emissions and chemical conversion of $SO_2$ to $SO{_4}^{2-}$, and LTP from China. However, the $SO{_4}^{2-}$ at the sites were more influenced by LTP than the formation by chemical transformation of locally emitted $SO_2$. The $NO_3{^-}$ increase was closely associated with the increase in local emissions of nitrogen oxides at four urban sites except for the BR and JJ, as well as the LTP with a small contribution. Among the meterological parameters (wind speed, temperature, and relative humidity), the ambient temperature was most important factor to control the variation of $PM_{2.5}$ and its major chemical components concentrations. In other words, as the average temperature increases, the $PM_{2.5}$, OC, EC, and $NO_3{^-}$ concentrations showed a decreasing tendency, especially with a prominent feature in $NO_3{^-}$. Results from a case study that examined the $PM_{2.5}$ and its major chemical data observed between February 19 and March 2, 2014 at the all stations suggest that ambient $SO{_4}^{2-}$ and $NO_3{^-}$ concentrations are not necessarily proportional to the concentrations of their precursor emissions because the rates at which they form and their gas/particle partitioning may be controlled by factors (e.g., long range transportation) other than the concentration of the precursor gases.