• Analytical Science and Technology
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• v.26 no.4
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• pp.268-275
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• 2013

Many kinds of liquid spray products are used in livelihood activities these days. Spray products can be distinguished by the target to be sprayed (like into the air or on human skin (body)). Because human can be exposed to volatile organic compounds (VOC) emitted from spray products, some considerations on safety or hazard of spray products should be needed. In this study, emission characteristics of VOCs were investigated against 10 types of liquid spray products (6 skin spray and 4 air spray products). The concentrations of benzene and toluene were determined by gas chromatography/mass spectrometry (GC/MS) equipped with a thermal desorber (TD). Their average concentrations from 6 skin spray products exhibited$ 5.64{\pm}1.95$ ($mean{\pm}S.D$) and $8.52{\pm}2.89$ ppb(w), respectively. In contrast, those of 4 air spray samples had $7.30{\pm}1.31$ and $7.19{\pm}1.78$ ppb(w), respectively. If liquid contents in spray samples are completely vaporized in one cubic meter (1 m3) after spraying for 10 seconds, their mean concentrations of skin spray products are $31.7{\pm}8.80$ (benzene) and $50.5{\pm}17.1{\mu}g/Sm^3$ (toluene). In contrast, those of air spray products are $24.0{\pm}4.30$ (benzene) and $23.6{\pm}5.83{\mu}g/Sm^3$ (toluene). The estimated concentration levels of benzene from two types of products (31.7 and $24.0{\mu}/Sm^3$) exceeded the Korean atmospheric environmental guideline ($5{\mu}g/Sm^3$). The results of this study thus suggest that some measures should be made to reduce or suppress the contents of VOC in spray products.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.163-174
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• 2020

Particulate matter levels are rapidly increasing daily, and this can affect human health. Therefore, air pollutant emissions from sea vessels require management. This study evaluates the status of air pollutants, focusing on air pollutant emissions from the vessels of the Korea Coast Guard (KCG), and proposes national management measures to reduce emissions. According to a report recently released (2018) by the National Institute of Environmental Research (NIER), emissions from vessels constituted 6.4 % of the total domestic emissions, including 13.1 % NO_x, 10.9 % SO_x, and 9.6 % particulate matter (PM10/PM2.5). Among the rates of pollutant emission from vessels, the emission rates of domestic and overseas cargo vessels were the highest (50.6 %); the ratio of fishing boats was 42.6 %. With respect to jurisdictional sea area, 44.1 % of the emissions are from the south sea, including the Busan and Ulsan ports, and 24.8 % of the emissions are from the west sea, including the Gwangyang and Yeosu ports. The KCG inspects boarding lines to manage emission conditions and regulate air pollutant emissions, but it takes time and effort to operate various discharge devices and measure fuel oil standards. In addition, owing to busy ship schedules, inspection documents are limited in terms of management. Therefore, to reduce the air pollutant emissions of such vessels, regulations will be strengthened to check for air pollutants, and a monitoring system based on actual field data using KCG patrol ships will be established, for each sea area, to manage the emissions of such vessels. Furthermore, there is a need for technological development and institutional support for the introduction of environmentally friendly vessels.

• Archives of design research
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• v.19 no.4 s.66
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• pp.133-144
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• 2006

According to the latest report by the Customer Protection Board, those who moved into newly constructed buildings are complaining about unidentified pains, asking for more careful selection of constructive materials for prevention of such potential problems. It is internationally recognized today that ecological materials can serve a significant factor for users' health, environmental protection and better industrial competitiveness. This study examined eco-design aspects of each interior material through web site search, in order to help customers learn about and capitalize on eco materials in a proper manner. As a result, 1. It turned out that the domestic industry are giving an impetus to releasing new eco items focusing on lower VOCs emission or addition of functional components as part of the marketing strategy. However, it is recommended that company understand significance of life cycle, and produce eco-concept materials. 2. The reliable standard for choosing the domestic material is EL, HB, GR marks. It is desirable to enhance recycling technologies and expand the sustainable consumption. customer class, since many recycled items are not developed. 3. The sourcing is a vulnerable part in terms of the concept of being environment-friendly material. Therefore, many manufacturers should design the easy knock-down products and produce the good items using recycled materials instead of new raw materials. Also solutions for making the energy from burning material should be studied. 4. The guidebook or manual with correct information about eco-materials is required to promote production and consumption with sustainable concept. 5. Many manufacturers are emphasizing ecological materials for customers, but some of them intended to disrupt customers' proper selection by promoting even unverified items to be environment-friendly.

• Journal of Preventive Medicine and Public Health
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• v.42 no.2
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• pp.89-95
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• 2009

Objectives : To assess the protective effects of wearing protective devices among the residents and volunteers who participated in the cleanup of the Hebei Spirit oil spill. Methods : A total of 288 residents and 724 volunteers were surveyed about symptoms, whether they were wearing protective devices and potential confounding variables. The questionnaires were administered from the second to the sixth week following the accident. Spot urine samples were collected and analyzed for metabolites of 4 volatile organic compounds(VOCs), 2 polycyclic aromatic hydrocarbons(PAHs), and 6 heavy metals. The association between the wearing of protective devices and various symptoms was assessed using a multiple logistic regression adjusted for confounding variables. A multiple generalized linear regression model adjusted for the covariates was used to test for a difference in least-square mean concentration of urinary biomarkers between residents who wore protective devices and those who did not. Results : Thirty nine to 98% of the residents and 62-98% of volunteers wore protective devices. Levels of fatigue and fever were higher among residents not wearing masks than among those who did wear masks(odds ratio 4.5; 95% confidence interval 1.23-19.86). Urinary mercury levels were found to be significantly higher among residents not wearing work clothes or boots(p<0.05). Conclusions : Because the survey was not performed during the initial high-exposure period, no significant difference was found in metabolite levels between people who wore protective devices and those who did not, except for mercury, whose biological half-life is more than 6 weeks.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.5
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• pp.577-583
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• 2007

To overcome certain disadvantages of past typical control techniques for toxic contaminants emitted from various industrial processes, the current study was conducted to establish a thermal catalytic system using mesh-type transition-metal platinum(Pt)/stainless steel(SS) catalyst and to evaluate catalytic thermal destruction of five chlorinated hydrocarbons[chlorobenzene(CHB), chloroform(CHF), perchloroethylene (PCE), 1,1,1-trichloroethane(TCEthane), trichloroethylene(TCE)]. In addition, this study evaluated the catalyst poison effect on the catalytic thermal destruction. Three operating parameters tested for the thermal catalyst system included the inlet concentrations, the incineration temperature, and the residence time in the catalyst system. The thermal decomposition efficiency decreased from the highest value of 100% to the lowest value of almost 0%(CHB) as the input concentration increased, depending upon the type of chlorinated compounds. The destruction efficiencies of the four target compounds, except for TCEthane, increased upto almost 100% as the reaction temperature increased, whereas the destruction efficiency for TCEthane did not significantly vary. For the target compounds except for TCEthane, the catalytic destruction efficiencies increased up to 30% to 97% as the residence time increased from 10 sec to 60 sec, but the increase of destruction efficiency for TCEthane stopped at the residence time of 30 sec, suggesting that long residence times are not always proper for thermal destruction of VOCs, when considering the destruction efficiency and operation costs of thermal catalytic system together. Conclusively, the current findings suggest that when applying the transition-metal catalyst for the better destruction of chlorinated hydrocarbons, VOC type should be considered, along with their inlet concentrations, and reaction temperature and residence time in catalytic system. Meanwhile, the addition of high methyl sulfide(1.8 ppm) caused a drop of 0 to 50% in the removal efficiencies of the target compounds, whereas the addition of low methyl sulfide (0.1 ppm), which is lower than the concentrations of sulfur compounds measured in typical industrial emissions, did not cause.

• Analytical Science and Technology
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• v.22 no.6
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• pp.488-497
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• 2009

In this study, analytical methodology for several organic fatty acids (OFA: propionic acid (PA), butyric acid (BA), isovaleric acid (IA), and valeric acid (VA)) designated as new offensive odorants in Korea (as of year 2010) was investigated along with some odorous VOCs (styrene, toluene, xylene, methyl ethyl ketone, methyl isobutyl ketone, butyl acetate, and isobutyl alcohol). For this purpose, working standards (WS) containing all of these 13 compounds were loaded into adsorption tube filled with Tenax TA, and analyzed by gas chromatography (GC) system thermal desorber interfaced with. The analytical sensitivities of organic fatty acids expressed in terms of detection limit (both in absolute mass (ng) and concentration (ppb)) were lower by 1.5-2 times than other compounds (PA: 0.24 ng (0.16 ppb), BA: 0.19 ng (0.11 ppb), IA: 0.15 ng (0.07 ppb), and VA: 0.28 ng (0.13 ppb)). The precision of BA, IA, and VA, if assessed in terms of relative standard error (RSE), maintained above 5%, while the precison of other compounds were below 5%. The reproducibility of analysis improved with the aid of internal standard calibration (PA: $1.1{\pm}0.4%$, BA: $10{\pm}0.46$, IA; $12{\pm}0.3%$, VA: $4{\pm}0.1%$), respectively. The results of this study showed that organic fatty acid can be analyzed using adsorption tube and thermal desorber in a more reliable way to replace alkali absorption method introduced in the odor prevention law of the Korea Ministry of Environment (KMOE).

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.