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

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

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.139-141
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• 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 Fashion Business
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• v.19 no.2
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• pp.36-52
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• 2015

Nowadays, firms face the challenge of how to balance between the environmental request and business profit under the circumstances of being eco-friendly and sustainable. The purpose of this study was to investigate the effects of business environmental factors on the 4p mix of an eco-friendly textile in the textile fashion firms. This research was conducted by a questionnaire method, in which the questionnaires were distributed to the textile fashion firms. Of the returned questionnaires, one hundred of them were selected to be included in the analysis by developing descriptive statistics, factor analysis, cronbach's alpha, and regression analysis using SPSS18.0. The results of this study were as follows: There were significant effects of the firms' environmental management, organizational structure, and CEOs' environmental sensitivity in descending order of the business internal factors on the textile fashion firms' eco-friendly textile product. The factors of the firms' environmental management, organizational structure(internal factors), and legal regulation(external factor), in descending order, significantly affected the promotion of the eco-friendly textile. The factor of firms' environmental management (internal factor) significantly affected the distribution of the eco-friendly textile. The factors of CEOs' environmental sensitivity(internal factor), legal regulation(external factor), and firms' environmental management(internal factor), in descending order, significantly affected the price of the eco-friendly textile.

• Journal of Environmental Science International
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• v.17 no.4
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• pp.405-412
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• 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
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• v.23 no.1
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• pp.161-168
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• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.1
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• pp.37-47
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• 2014

In this study, field measurement was carried out for reasonable improvement of asphalt concrete manufacturing facilities' PM emissions estimation method. Through those, this study calculated PM emission factor and tried to estimate PM emissions from asphalt concrete manufacturing facilities suitable for domestic characteristics. As a result, the efficiency of the PM control device was measured as 99.9%. Using this, uncontrolled PM emission factor was calculated. PM emission factor was calculated 10.97 kg/ton at 23 asphalt concrete manufacturing facilities of 22 workplaces. The PM current emission factor of the US Environment Protection Agency (EPA) is 14.4 kg/ton, the factor calculated from this study is about 24% lower than the EPA standard.

• Journal of the Korean housing association
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• v.7 no.2
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• pp.69-77
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• 1996

Mobility in housing is a normative way to satisfy family needs for· better· living conditions. In the context of relative decisions. mobility can be understood as forced relocation or unforced relocation In making relocation. the household chooses a particular 'environmental quality profile. Therefore. the understanding of mobility and relocation in housing is needed for· the developing and planning of housing evnironment. The major findings are s follows ;1. The motives of mobility are composed of 4 factors('material porseperity’, 'convenience· safety'. environmental improvement' and 'forced relocation'). Especially, 'material prosperity' was defined the most fundamental factor· for. 2. The components of housing environment quality were composed of 3 factors related to the scale of home environment. They were 'neighborhood character factor', 'dwelling character· factor·'. And 'location character· factor'. The factor 'neighborhood character' was defined the basic factor· to choose for the housing environmental quality profile.3. It was examined that the motives of mobility and the components of home environmental quality were significantly different by the household characteristics (income. family life cycle. and tenure type).

• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.2
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• pp.26-33
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• 2007

This study was to investigate the reaction analysis of citizen on fence removal for securing green space. The results are as following; The majority of users went to green space more than one time per week to take a walk, rest and stayed there less than an hour. The places, where the fence removal was required mostly, were public institutions, parks and schools. The physical factor was the highest influence on whether the fence removal project could be expanded or not. With a slight difference from the physical factor, the environmental and emotional factor followed after. The social factor was also significant at 1 % level. In the physical aspect, the increase of garbage littering was the most negative part after fence removal.When the citizens were asked if they would participate in the fence removal project, the environmental and emotional factor and the social factor were the most influential ones on work places while the environmental and emotional factor influenced only on private houses.

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