• 한국산업보건학회지
• /
• 제28권2호
• /
• pp.230-239
• /
• 2018

Objective: The purpose of this study is to identify the kinds and exposure levels of health hazards in the metalworking process in relation to acute poisoning accidents caused by methanol in 2016. Methods: The number of industries, workplaces, exposed workers, regional distribution, and exposure level of health hazards in metalworking process were investigated based on employee exposure assessment database provided by KOSHA (the Korea Occupational Safety and Health Agency), which was collected from workplace hazard evaluation programs in Korea. Exposure metrics for methanol were assessed by RCR (risk characterization ratio). Results: The numbers of processes, workplaces, and exposed workers of metalworking, which include CNC (computer numerical control) were 25, 14,405, and 169,102 respectively. The numbers of samples of chemical hazards including methanol were 91,325, and it was found that workers in metalworking were exposed to 249 kinds of chemical hazards. There were 16 kinds of special controlled substances including beryllium. It is estimated that the number of workplaces involving CNC process was 2,537, and the number of exposed workers was 27,976. In CNC process, the total number of workplaces handling methanol was 36, and 298 workers were estimated to be exposed. There was no exceeded that surpassed the OEL and 49% of samples were below the limit of detection. Methanol exposure concentrations in Gyeonggido Province were statistically significantly higher than in other areas (p <0.0001). Conclusions: In the metalworking process including CNC, there is exposure to a wide variety of health hazards. There was no sample exceeding the OEL for methanol. Therefore, it is necessary to recognize the limits of the employee exposure assessment system and urgently improve measures to prevent the occurrence of events like methanol poisoning.

• Asian Journal of Atmospheric Environment
• /
• 제2권1호
• /
• pp.34-46
• /
• 2008

Emissions inputs for use in air quality modeling of Korea were generated with the emissions inventory data from the National Institute of Environmental Research (NIER), maintained under the Clean Air Policy Support System (CAPSS) database. Source Classification Codes (SCC) in the Korea emissions inventory were adapted to use with the U.S. EPA's Sparse Matrix Operator Kernel Emissions (SMOKE) by finding the best-matching SMOKE default SCCs for the chemical speciation and temporal allocation. A set of 19 surrogate spatial allocation factors for South Korea were developed utilizing the Multi-scale Integrated Modeling System (MIMS) Spatial Allocator and Korean GIS databases. The mobile and area source emissions data, after temporal allocation, show typical sinusoidal diurnal variations with high peaks during daytime, while point source emissions show weak diurnal variations. The model-ready emissions are speciated for the carbon bond version 4 (CB-4) chemical mechanism. Volatile organic carbon (VOC) emissions from painting related industries in area source category significantly contribute to TOL (Toluene) and XYL (Xylene) emissions. ETH (Ethylene) emissions are largely contributed from point industrial incineration facilities and various mobile sources. On the other hand, a large portion of OLE (Olefin) emissions are speciated from mobile sources in addition to those contributed by the polypropylene industry in point source. It was found that FORM (Formaldehyde) is mostly emitted from petroleum industry and heavy duty diesel vehicles. Chemical speciation of PM2.5 emissions shows that PEC (primary fine elemental carbon) and POA (primary fine organic aerosol) are the most abundant species from diesel and gasoline vehicles. To reduce uncertainties in processing the Korea emission inventory due to the mapping of Korean SCCs to those of U.S., it would be practical to develop and use domestic source profiles for the top 10 SCCs for area and point sources and top 5 SCCs for on-road mobile sources when VOC emissions from the sources are more than 90% of the total.

• 한국안전학회지
• /
• 제31권1호
• /
• pp.111-117
• /
• 2016

Natural disasters can cause major accidents in chemical facilities where they can lead to the release of hazardous materials which in turn can result in fires, explosions or toxic dispersion. Technological disasters triggered by natural disasters are referred to as NaTech(Natural Disaster Triggered Technological Disaster). These trends increase the probability of catastrophic future disasters and the potential for mass human exposure to hazardous materials released during disasters. In the present study, we proposed some methods for effective disaster management by conducting case study of major NaTech. First, establishing information sharing system of chemical accident for stakeholders and improving disaster manuals and standards of central and local government and co-operation support system. Second, activating information service of emergency planning and community right to know. Third, improving the integrated chemical accident database including NaTech accidents.

• 한국지하수토양환경학회지:지하수토양환경
• /
• 제24권2호
• /
• pp.8-22
• /
• 2019

Among numerous chemicals used globally, the number of emerging contaminants is increasing. Numerical modeling for contaminant fate and transport in the subsurface is critical to evaluate environmental and health risk. In general, such models require physicochemical properties of contaminants as input values, which can be found in numerous chemical databases (DB). However, there exist lack of information specific to recently emerging contaminants, which requires estimation of physicochemical properties using regression programs. The purpose of the study is to introduce the workflow for identifying physicochemical properties of potential contaminants utilizing numerous chemical DBs, which frequently lists up potential contaminants for estimating chemical behavior. In this review paper, details of several chemical DBs such as KISChem, TOXNET, etc. and regression programs including EPI $Suite^{TM}$, ChemAxon, etc. were summarized and also benefit of using such DBs were explained. Finally, a few examples were introduced to estimate predominant phase, removal ratio, partitioning, and eco-toxicities by searching or regressing physicochemical properties.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2003년도 춘계학술대회
• /
• pp.617-621
• /
• 2003

The purpose of this paper is to analyze piping failure trend of safety pipings In domestic nuclear power plants. First, database for the piping failure was constructed with 105 data fields. The database includes plant population data, event data, and service history data. 7 kinds of piping failures in domestic NPPs were investigated. Among the 7 cases, detailed root causes were investigated for 3 cases. The first one is pipe wall thinning in main feedwater pipings of Westinghouse 3 loop type plants. The root cause of the wall thinning was flow accelerated corrosion near welding area. The next one is leak event in chemical and volume control system(CVCS) due to vibration. Some cracks occurred in socket welding area. The events showed that the integrity or socket weld is very vulnerable to vibration. The last one is also a leak event in primary sampling line in Korean standard reactor due to thermal fatigue. Although the structural integrity was not maintained by the events, there was no effect on nuclear safety in the above 3 piping failure eases.

• Journal of the Korean Wood Science and Technology
• /
• 제20권4호
• /
• pp.43-48
• /
• 1992

A database system for retrieving the information on wood collections was developed using the D-BASE III Plus program for personal computers. Basic information of biological, physical, chemical and technological properties was added to the data which used to be employed in simple card systems, such as identification number, species name and collection site. All information was fast and reliably retrievable. This paper also discussed the results of a survey on wood collections housed at domestic colleges and research institutes.

• 공업화학전망
• /
• 제22권4호
• /
• pp.13-19
• /
• 2019

최근 가공할만한 성능의 슈퍼컴퓨터에 머신 러닝 기법을 연동한 인공 지능형 소재 정보학이 과학 기술 및 산업계에 새로운 연구개발 패러다임으로 급속히 확산되고 있다. 본 기고문에서는 이 기법의 성공에 핵심적 요소인 정확한 데이터베이스 구축을 위해 제일원리 전산을 적용하는 것과 이를 기반으로 소재를 구성하는 원소 간 인공 신경망 포텐셜을 만드는 방법을 소개하고자 한다. 이 연구 방법론은 나노 스케일 신소재 개발에 적용할 경우, 양자역학 수준의 정밀도로 순수 제일원리 전산 대비 100배 이상의 빠른 결과를 도출할 가능성이 있음을 예시한다. 이는 향후 다양한 산업계에 막대한 파급효과를 가져올 것으로 예상된다.

• 한국환경보건학회지
• /
• 제48권4호
• /
• pp.206-215
• /
• 2022

Background: Fragrance substances in consumer products can cause adverse health effects such as contact allergy. In South Korea, consumer chemical products must list 26 known fragrance allergens on product labels when they contain more than 0.01%. Fragrance substances are mostly used in combination, so co-exposure can occur via use of a consumer chemical product. Co-exposure to fragrance allergens may show a synergistic effect on the human body. Objectives: The aims of the study were to analyze the characteristics of fragrance allergens in consumer chemical products available on public websites and to identify the co-occurrence patterns of fragrance allergens. Methods: The chemicals in 1,443 ingredient disclosures for consumer chemical products were collected through the Ecolife database. The 26 labelled fragrance allergens were identified by category of consumer chemical product. The co-occurrence patterns of the 26 labelled fragrance allergens were analyzed by frequent pattern mining. The unlabelled fragrance allergens presented by European Union Scientific Committee on Consumer Safety were also identified. Results: Consumer chemical products contained an average of 5.3±4.2 substances among the 26 labelled fragrance allergens. More than 85% of air fresheners, deodorizing agents, and fabric softeners contained at least one of the 26 labelled fragrance allergens. The most frequently contained fragrance allergens were limonene (50.5%), linalool (49.9%), hexyl cinnamal (34.0%), and citronellol (28.3%). 16.7% of consumer chemical products showed a co-occurrence of limonene, linalool, hexyl cinnamal, and citronellol. Thirty-eight unlabelled fragrance allergens were found in the consumer chemical products, with hexamethylindanopyran (25.2%) being the most frequently contained substance. Conclusions: The characteristics and co-occurrence patterns of 26 labelled fragrance allergens would be useful information for the management of co-exposure to fragrance allergens in consumer chemical products. It is necessary for attention to be paid to unlabelled fragrance allergens.

• 한국환경보건학회지
• /
• 제50권2호
• /
• pp.113-124
• /
• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• Bulletin of the Korean Chemical Society
• /
• 제31권2호
• /
• pp.379-383
• /
• 2010

Metabolites of colorectal cancer tissues from 12 patients were analyzed and compared with those of the normal tissues by two-dimensional NMR spectroscopy. NMR data were analyzed with the help of the metabolome database and the statistics software. Cancerous tissues showed significantly altered metabolic profiles as compared to the normal tissues. Among such metabolites, the concentrations of taurine, glutamate, choline were notably increased in the cancerous tissues of most patients, and those of glucose, malate, and glycerol were decreased. Changes in individual metabolites varied significantly from patient to patient, but the combination of such changes could be used to distinguish cancerous tissues from normal ones, which could be done by PCA analysis. The traditional chemometric analysis was also performed using AMIX software. By comparing those two results, the analysis via $^1H-^{13}C$ HSQC spectra proved to be more robust and effective in assessing and classifying global metabolic profiles of the colorectal tissues.