• Journal of Korean Society of Transportation
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• v.22 no.5
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• pp.35-45
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• 2004

This paper analyzes the differences in the service quality priorities of air cargo service providers and customers. After having identified what constitutes the decisive factors with regards to air cargo service quslity, research was carried out to evaluate the differences in the service quality priorities of air cargo service providers and customers. Moreover, this paper analyzes these differences by separating air cargo service providers into Korean and international air cargo service providers. In order to attain the objectives of this research, the air cargo service sectors were divided into three general categories: supply capability(H/W), service accountability, and competitiveness of service fees and management capability(S/W). The researchers then placed 6 different criteria into each service category, for a total of 18. The evaluation of the service quality priorities of air cargo service providers and customers revealed very little difference between these two groups. With regards to the service criteria found in each service category, our research found marked differences between the service quality priorities of providers and customers in only three of the 18 criteria. However, we found marked differences in service quality priorities in 7 of the 18 criteria when the air cargo service providers were divided into Korean and international air cargo providers. While domestic air cargo service providers evaluated management's capability to handle cargo accidents and incidents as the most important priority, international air cargo service providers placed the highest priority on the cargo rate system. The results of this research imply that in order to properly evaluate the differences between the service quality priorities of air cargo service providers and customers, the air cargo service providers itself should be separated between the Korean air cargo service providers and the foreign providers because of their respective inherent perception on service quality.

• Journal of Environmental Science International
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• v.26 no.11
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• pp.1209-1222
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• 2017

On June 5, 2008, the "Act on Special Cases Concerning the Simplification of Authorization and Permission Procedures for Industrial Complexes" (Act No. 9106) was enacted. When it was implemented in August 2008, many industrial complex development projects were established, and the number of industrial complexes growth rates of 3-6% during 2003-2007 rose to around 15% in 2008. With the increase in industrial complexes, the environmental impacts of individual projects were examined, but comprehensive regional reviews of environmental impacts were not undertaken. In this study, we determined changes in air quality by applying the industrial complex development plan that completed the consultation at the end of 2010 to assess the comprehensive regional environmental impacts and presented the adequacy review plan for future industrial development plans based on the study's results. When considering these industrial complex development plans, emissions in North Jeolla and South Chungcheong Provinces and Daegu City have increased significantly. Air quality analyses showed that the 24 h mean $SO_2$ concentration in Daegu increased by more than 50% in summer compared to air quality concentrations in summer. The 24 h mean $PM_{10}$ and $NO_2$ concentrations increased by approximately 12 and 30%, respectively, in North Jeolla Province in summer. Areas exceeding the air quality standard for 1 h mean $O_3$ concentration increased by more than $3,500km^2$. Based on the above analysis, changes in air quality should be anticipated through a comprehensive evaluation of long-term development plans. Furthermore, control of air quality in accordance with the development of future industrial complexes is possible.

• Journal of Environmental Science International
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• v.27 no.8
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• pp.723-735
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• 2018

In this study, to investigate an optimal configuration method for the modeling system, we performed an optimization experiment by controlling the types of compilers and libraries, and the number of CPU cores because it was important to provide reliable model data very quickly for the national air quality forecast. We were made up the optimization experiment of twelve according to compilers (PGI and Intel), MPIs (mvapich-2.0, mvapich-2.2, and mpich-3.2) and NetCDF (NetCDF-3.6.3 and NetCDF-4.1.3) and performed wall clock time measurement for the WRF and CMAQ models based on the built computing resources. In the result of the experiment according to the compiler and library type, the performance of the WRF (30 min 30 s) and CMAQ (47 min 22 s) was best when the combination of Intel complier, mavapich-2.0, and NetCDF-3.6.3 was applied. Additionally, in a result of optimization by the number of CPU cores, the WRF model was best performed with 140 cores (five calculation servers), and the CMAQ model with 120 cores (five calculation servers). While the WRF model demonstrated obvious differences depending on the number of CPU cores rather than the types of compilers and libraries, CMAQ model demonstrated the biggest differences on the combination of compilers and libraries.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.3
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• pp.233-240
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• 2017

To distinguish between high particulate matter (HPM) and light Asian dust (LAD) events, aerosol optical properties from GOCI were investigated in Seoul from 2014 to 2016. The poor air quality case caused by fine atmospheric particulate matter (i.e., 80<$PM_{10}$<$400{\mu}g/m^3$) is clearly separated from the case of heavy Asian dust that generally shows the $PM_{10}$ concentration more than $400{\mu}g/m^3$. In this study, we have found eight cases for the poor air quality and divided them into the two events(i.e., HPM and LAD). In case of aerosol optical depth (AOD), there was no big difference between two events. However, Angstrom exponent (AE) for HPM events was greater than 1, while that for LAD events less than 1. As a result of comparing aerosol type, non-absorbing fine mode aerosols were dominant for HPM events, but coarse and absorbing coarse mode aerosols for LAD events. Therefore, AE and aerosol type from GOCI can be used to distinguish between two events effectively.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.235-253
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• 2017

This study was conducted for analyzing the contribution factors on ozone concentrations and its long term trends in each major city and province in Korea through several statistical methods such as simple linear regression, generalized linear model, KZ-filer, correlation matrix, Kringing method, and cluster analysis. The overall ozone levels in South Korea have been consistently increasing over the past 10 years. The ozone concentrations in Seoul, the biggest city in Korea, are the lowest in all areas with the highest increasing ratio for $95^{th}%$ ozone. It is thought that the active photochemical reaction could affect the higher ozone concentration increase. On the other hand, the ozone concentrations in Jeju are the highest in Korea with the highest increasing ratio for $5^{th}%$, $33^{th}%$, and $50^{th}%$ ozone. It is also thought that the weak $NO_x$ titration could be the reason of higher ozone concentrations in Jeju. In case of Jeju, transport related factors is the major factor affecting the ozone trend. Thus, it is assumed that the variation of ozone trend of Asian region affecting the ozone trend in Jeju, where domestic ozone photochemical reaction is less active than urban area. It is thought that the photochemical reaction plays the role of increasing of ozone concentrations in the urban area, even though the LRT affected on the increase of ozone concentrations in non-urban area.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.330-343
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• 2017

Climate change is an important issue, with many researches examining not only future climatic conditions, but also the interaction of climate and air quality. In this study, a new version of the emissions processing software tool - Python-based PRocessing Operator for Climate and Emission Scenarios (PROCES) - was developed to support climate and atmospheric chemistry modeling studies. PROCES was designed to cover global and regional scale modeling domains, which correspond to GEOS-Chem and CMAQ/CAMx models, respectively. This tool comprises of one main system and two units of external software. One of the external software units for this processing system was developed using the GIS commercial program, which was used to create spatial allocation profiles as an auxiliary database. The SMOKE-Asia emissions modeling system was linked to the main system as an external software, to create model-ready emissions for regional scale air quality modeling. The main system was coded in Python version 2.7, which includes several functions allowing general emissions processing steps, such as emissions interpolation, spatial allocation and chemical speciation, to create model-ready emissions and auxiliary inputs of SMOKE-Asia, as well as user-friendly functions related to emissions analysis, such as verification and visualization. Due to its flexible software architecture, PROCES can be applied to any pregridded emission data, as well as regional inventories. The application results of our new tool for global and regional (East Asia) scale modeling domain under RCP scenario for the years 1995-2006, 2015-2025, and 2040-2055 was quantitatively in good agreement with the reference data of RCPs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.203-210
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• 2019

Recently, a number of researchers have produced research and reports in order to forecast more exactly air quality such as particulate matter and odor. However, such research mainly focuses on the atmospheric diffusion models that have been used for the air quality prediction in environmental engineering area. Even though it has various merits, it has some limitation in that it uses very limited spatial attributes such as geographical attributes. Thus, we propose the new approach to forecast an air quality using a deep learning based ensemble model combining temporal and spatial predictor. The temporal predictor employs the RNN LSTM and the spatial predictor is based on the geographically weighted regression model. The ensemble model also uses the RNN LSTM that combines two models with stacking structure. The ensemble model is capable of inferring the air quality of the areas without air quality monitoring station, and even forecasting future air quality. We installed the IoT sensors measuring PM2.5, PM10, H2S, NH3, VOC at the 8 stations in Jeonju in order to gather air quality data. The numerical results showed that our new model has very exact prediction capability with comparison to the real measured data. It implies that the spatial attributes should be considered to more exact air quality prediction.

• Advances in environmental research
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• v.6 no.2
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• pp.83-94
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• 2017

Air quality planning and management requires accurate and consistent records of the air quality parameters. Limited number of monitoring stations and inconsistent measurements of the air quality parameters is a very serious problem in many parts of India. It becomes difficult for the authorities to plan proactive measures with such a limited data. Estimation models can be developed using soft computing techniques considering the physics behind pollution dispersion as they can work very well with limited data. They are more realistic and can present the complete picture about the air quality. In the present case study spatio-temporal models using Linear Genetic Programming (LGP) have been developed for estimation of air quality parameters. The air quality data from four monitoring stations of an Indian city has been used and LGP models have been developed to estimate pollutant concentration of the fifth station. Three types of models are developed. In the first type, models are developed considering only the pollutant concentrations at the neighboring stations without considering the effect of distance between the stations as well the significance of the prevailing wind direction. Second type of models are distance based models based on the hypothesis that there will be atmospheric interactions between the two stations under consideration and the effect increases with decrease in the distance between the two. In third type the effect of the prevailing wind direction is also considered in choosing the input stations in wind and distance based models. Models are evaluated using Band Error and it was observed that majority of the errors are in +/-1 band.

• Journal of Environmental Health Sciences
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• v.43 no.4
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• pp.267-272
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• 2017

Objectives: The purposes of the study were to analyze the temporal variation of carbon dioxide ($CO_2$) and particulate matter (PM) in daycare centers and evaluate the appropriateness of the official test method of one-time measurement. Methods: Indoor air quality in 46 daycare centers in the Seoul Metropolitan Area was measured as specified in the official test method of Indoor Air Quality Management law. In addition, indoor air quality in the 46 daycare centers was measured over 37 days using a real-time monitor (AirGuard K). Results: The daily means of $CO_2$ and PM in the 46 daycare centers were $1042.74{\pm}134.45ppm$ and $67.60{\pm}18.25{\mu}g/m^3$, respectively. Indoor air quality in the daycare centers showed significant temporal fluctuation. Measurements for single days were significantly different from the 37-day average exposure. Relative error of short term exposure decreased with an increase in the number of sampling days. The noncompliance rate for $CO_2$ using the official testing method was 2.17%, and none exceeded the $PM_{10}$ standard of $100{\mu}g/m^3$. With monitoring over 37 days, the daily noncompliance rate for $CO_2$ was 50.4% and the daily noncompliance rate for PM was 13.8%. Conclusions: When the official test method evaluates the indoor air at daycare centers one day per year, the results may not represent actual indoor air quality over a longer period of time. Real-time monitoring devices could be an alternative for managing indoor air quality.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.4
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• pp.343-360
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• 1998

In this study, comprehensive air quality monitoring was undertaken with a variety of objectives to collect data on the levels of various indoor and ambient air quality parameters in the urban areas of Seoul and Taegu. The sampling sites were comprised of six offices, six residences and six restaurants in each city. The ambient air adjacent to the indoor sites was also simultaneously sampled for the same constituents. The sampling was conducted in two phases: summer of 1994 and winter of 1994/95. A range of air quality parameters were measured simultaneously, which include RSP, CO, COB, NOB, a range of VOC, airborne microbials, temperature, and relative humidity. The indoor and ambient levels of the pollutants measured in this study varied widely between the three types of environments studied. Comparison of median values for the three groups revealed that restaurants had higher indoor levels of most pollutants than homes or offices. There was also a clear pattern of the indoor levels of target pollutants being higher than those outdoors, particularly in restaurants. Concentrations of CO and most of the VOC were found to be significantly higher in the commercial districts, indicating the influence of vehicle exhaust emissions. A very wide range of VOC levels was documented in this study. Although median indoorloutdoor ratios indicated a generally increasing level of VOC indoors when compared to those outdoors, no statistically significant differences were found between indoor and outdoor VOC levels in homes and offices, implying the importance of ambient air quality in determining the quality of indoor air for homes and offices in urban areas. In addition, there was a general pattern of increasing concentrations from summer to winter, and similarly from outdoor to indoor air for nearly all target compounds. The seasonal differences in median levels were very clearly seen for fuel combustion related pollutants such as RSP, CO and VOC, this being attributed to the effects of increased fuel consumption during the cold season and to meteorological factors.