• Korean Parent-Child Health Journal
• /
• v.17 no.1
• /
• pp.37-45
• /
• 2014

Purpose: This study aimed to review measurements of environmental health behavior and assess the construct validity of Environmental Health Engagement Profile (EHEP) through confirmatory factor analysis. Methods: The literature review was performed for selection of measurements. Confirmatory factor analysis with AMOS 19.0 was used for validation of EHEP. Results: The model fitness was not appropriate in the one-factor model; $x^2=91.11$ (df=5, p<.001), Comparative Fit Index (CFI)=8.19, Non Normed Fit Index (NNFI)=6.39, and Root Mean Square Error of Approximation (RMSEA)=0.20. The model fitness was appropriate in the two-factor model; $x^2=3.19$ (df=1, p=.074), CFI=9.95, NNFI= 9.71, RMSEA=0.07. A modification of scale was found to be the most suitable for use in the investigation of environmental health behavior. Conclusion: This study confirms that a two-factor model underlies the concept of environmental health behavior. The review of measurements can help nurses and researchers to assess the environmental health behaviors.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2013.05a
• /
• pp.139-141
• /
• 2013

Construction industry has been participated in the effort for the reduction of environmental pollution such as introduction of green building certification, enactment of environment related regulation. However these efforts are focused on the design and maintenance phases of entire life cycle, construction phase that can occur intensive environmental impact in a short period is insufficient. Therefore this study aim to derive environmental management factors in construction phase and assess them using reliability analysis and factor analysis. As a results, the 20 factors was classified into 4 superordinate such as 'plan and supervision', 'environmental factor management', 'licensing management', 'surrounding environment management'.Based on result of this study, further study should be developed the checklist for effective environmental management in construction phase.

• Journal of Environmental Science International
• /
• v.17 no.4
• /
• pp.405-412
• /
• 2008

Statistical analysis technique was applied to operating parameters and removal efficiency data sets obtained from advanced wastewater treatment plant during 1 year. Through factor analysis three factors derived varimax rotation were selected each plant. Three components explained 96%, 87% of the total variance of the process, respectively. The components on $A_2O$ Plant were identified in the following order : 1) Shortening the SRT during high-flow period, 2) Keeping biomass high on winter 3) factor was related to DO. On DNR plant, we defined them as follows: factor 1, Prolonged the SRT during high-flow period; factor 2 was related to sludge return; factor 3, Influent BOD during low-DO period. This technique was believed to assist operators in identifying priorities to improve operation efficiency.

• Journal of Korean Society on Water Environment
• /
• v.23 no.1
• /
• pp.161-168
• /
• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3764-3774
• /
• 2024

In accordance with Regulatory Guide 1.207, Rev.1, fatigue assessments must be conducted considering the influence of primary coolant environment in nuclear reactors. Environmental fatigue, resulting from corrosion in the primary coolant, is evaluated in air fatigue life assessments through the application of an environmental fatigue correction factor. This environmental fatigue correction factor depends on sulfur content, operating temperature, dissolved oxygen, and strain rate. It remains constant for sulfur content, operating temperature, and dissolved oxygen, while strain rate introduces potential errors based on the analysis method. The current fatigue evaluation procedure for air, following ASME B&PV Code Sec.III, NB-3200, employs elastic analysis with a simplified elastic-plastic correction factor(Ke). However, Ke factor is considered excessively conservative, prompting less conservative alternatives proposed by JSME, RCC-M, ASME Code Case N-779. This study applied both ASME Ke and JSME Ke for fatigue evaluations considering environmental effects. Additionally, fatigue assessments accounting for elastic-plastic effects were conducted using Neuber and Glinka methods, compared with actual experiments. The analysis systematically examined changes in fatigue life and the environmental fatigue correction factor due to plastic effects in environmental fatigue evaluations.

• Journal of Korean Society on Water Environment
• /
• v.28 no.1
• /
• pp.84-93
• /
• 2012

The study was conducted to analyze the spatio-temporal changes in water quality of the major 36 sampling stations of Nakdong River, depending on each station, season using the 17 water quality variables from 2000 to 2010. The result was verified to interpret the characteristics of water quality variables in a more accurate manners. According to the Principal component analysis (PCA) and Exploratory factor analysis (EFA) results; the results of these analyses were identified 4 factors, Factor 1 (nutrients) included the concentrations of T-N, T-P, $NO_{3}-N$, $PO_{4}-P$, DTN, DTP for sampling station and season, Factor 2 (organic pollutants) included the concentrations of BOD, COD, Chl-a, Factor 3 (microbes) included the concentrations of F.Coli, T.Coli, and Factor 4 (others) included the concentrations of pH, DO. The results of a Cluster analysis indicated that Geumhogang 6 was the most contaminated site, while tributaries and most of the down stream sites of Nakdong River were mainly affected by each nutrients (Factor 1) and organic pollutants (Factor 2). The verification consequence of Confirmatory factor analysis (CFA) from Exploratory factor analysis (EFA) result can be summarized as follows: we could find additional relations between variables besides the structure from EFA, which we obtained through the second-order final modeling adopted in CFA. Nutrients had the biggest impact on water pollution for each sampling station and season. In particular, It was analyzed that P-series pollutant should be controlled during spring and winter and N-series pollutant should be controlled during summer and fall.

• Journal of Environmental Science International
• /
• v.5 no.1
• /
• pp.15-23
• /
• 1996

During the last two decades, many industrial complexes for heavy and chemical industries have been established along the Korean coastline, thereby increasing the pollution materials burden on the coastal environment of seawater. Masan Bay is one of the most polluted coastal areas in Korea and the main soures of pollutants are domestic and industrial wastewater from Masan, Changwon. This study was aimed to evaluate relationships among the physicochemical parameters in the bottom water of Masan bay and to examine environmental factors affecting to pollutions of seawater by factor analysis. 'rife factor loading, 1 is showed higher increasing inclination after 1989 year in station 1. The variance of pollutant materials is showed 43.7% in which the coastal inflow water is indicated external loadings(factor 1 : NO3--N, TN, factor 4 : SiO2-Si) corresponded to domestic sewage, industrial wastewater, and earth-sands in the bottom water of Masan bay And the internal loadings(factor 2 : SS, salinity, factor 3 . W.T., DO) are explained 33.8%'corresponded the phenomena of sedimentary layer and oxygen concentration. Therefore, The external loadings are explained by the higher factor pollutantal variance in Masan bay.

• Journal of Environmental Health Sciences
• /
• v.48 no.4
• /
• pp.236-243
• /
• 2022

Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.

• Journal of environmental and Sanitary engineering
• /
• v.18 no.4
• /
• pp.6-14
• /
• 2003

A spring water quality was depend on the aquifer soil status. However, water quality was rapidly contaminated by artificial affects. In the contaminate components, the heavy metals were significantly important because the heavy metals influence the plants and the animals. But, it is difficult to find out how the heavy metal can affect in the water quality. According to the group analysis and the factor analysis, water quality management was advanced. The experimental area was divided into three region and six factor. The six factor could not define the overall water quality, however this method were one of the useful methods.

• Journal of Environmental Science International
• /
• v.14 no.2
• /
• pp.251-258
• /
• 2005

Statistical analysis between operating parameters and effluent quality on advanced wastewater treatment plant was performed. Through factor analysis four factors derived varimax rotation were selected each plant. Four components explained $80\%,\;82\%$ of the total variance of the process, respectively. The components on MLE plant were identified in the following order: 1) HRT increase and BOD load decrease by influent decrease, 2) Biomass, 3) SVI increase by internal return increase, 4) Microbial diversity by SRT increase. On $A_2O$ plant, we defined them as follows: factor 1, high MLSS by return rate increase, HRT increase by influent decrease; factor 2, biomass; factor 3, BOD of influent; factor 4 was relate to DO.