• 생태와환경
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• 제40권2호
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• pp.285-293
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• 2007

본 연구는 대청호에서 영양염과 엽록소-${\alpha}$의 구간별 특성과 이에 따른 영양단계와의 관계를 살펴보았으며, 유수대, 추이대, 정수대의 세 구간에 걸쳐 환경부에서 제공하는 1993년에서 2002년까지의 수질 및 영양상태 자료를 비교 분석하였다. 총인(TP)과 투명도(SD)및 엽록소-${\alpha}$(CHL)는 호소대에서 정수대로 갈수록 감소하는 양상을 보였으나, 총질소의 경우는 종적구배를 보이지 않았다. 대청호에서의 총인과 엽록소-${\alpha}$는 여름철 장마기(7, 8월)에 가장 큰 계절적 변이를 보였으며, 총질소의 농도와 총질소 : 총인 비율은 각각 평균 1.67 mg $L^{-1}$, 88로 나타나 대청호의 제한요인은 총인으로 사료되었다. 엽록소-${\alpha}$와 총인, 투명도를 기반으로 한 영양상태지수는 구간별, 계절별로 구분되어 비교하였다. TSI(TP)는 장마기에 유수대는 평균 62로 나타나 과영양상태를 보인 반면, 대조적으로 정수대는 평균 40으로 거의 빈영양 상태를 가까운 수치를 보였으며, TSI(CHL)는 장마기에 추이대에서 가장 높은 값을 나타냈다. TSI 편차분석에 따르면 TSI(CHL)-TSI (TP)와 TSI (CHL)-TSI (SD)의 약 65% 가량이 0보다 낮은 수치를 보였으며, 또한 최저 -42를 보여, 이는 인공호에서의 조류성장에 무기물에 의한 탁수가 영향을 끼치는 것으로 판단되었다. 엽록소-${\alpha}$와 투명도의 상관분석 결과 추이대에서의 상관도(p<0.001, r=-0.47)가 다른 두 지점(p<0.001, r=-0.40)에 비하여 높게 나타났으며, 한편 총인과 엽록소-${\alpha}$의 경우 정수대에서 상관도가 가장 높게 나타난 반면, 총인과 조류생산량은 가장 낮은 수치를 보였다. 결론적으로 인공호 상 하 류간의 형태적 종적구배가 뚜렷한 경우 각 구역별 영양염류 농도구배 및 광투과도 구배는 1차 생산력에 직접적으로 영향을 주어 뚜렷하게 다른 양상을 보였다.

• Korean Journal of Health Education and Promotion
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• 제2권1호
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• pp.31-61
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• 2000

Introduction. Despite the fact that half of premature deaths are caused by unhealthy lifestyles such as smoking tobacco, sedentary lifestyle, alcohol and drug abuse and poor nutrition, there are no theoretical models which accurately explain these health promotion related behaviors. This study tests a new model of health behavior called the Model of Health Promotion Behavior. This model draws on elements and frameworks suggested by the Health Belief Model, Social Cognitive Theory, the Theory of Planned Action and the Health Promotion Model. This model is intended as a general model of behavior but this first test of the model uses amount of exercise as the outcome behavior. Design. This study utilized a cross sectional mail-out, mail-back survey design to determine the elements within the model that best explained intentions to exercise and those that best explained amount of exercise. A follow-up questionnaire was mailed to all respondents to the first questionnaire about 10 months after the initial survey. A pretest was conducted to refine the questionnaire and a pilot study to test the protocols and assumptions used to calculate the required sample size. Sample. The sample was drawn from 2000 eligible participants at two blue collar (utility company and part of a hospital) and two white collar (bank and pharmaceutical) companies located in Southeastern Michigan. Both white collar site had employee fitness centers and all four sites offered health promotion programs. In the first survey, 982 responses were received (49.1％) after two mailings to non-respondents and one additional mailing to secure answers to missing data, with 845 usable cases for the analyzing current intentions and 918 usable cases for the explaining of amount of current exercise analysis. In the follow-up survey, questionnaires were mailed to the 982 employees who responded to the initial survey. After one follow-up mailing to non-respondents, and one mailing to secure answers to missing data, 697 (71.0％) responses were received, with 627 (63.8％) usable cases to predict intentions and 673 (68.5％) usable cases to predict amount of exercise. Measures. The questionnaire in the initial survey had 15 scales and 134 items; these scales measured each of the variables in the model. Thirteen of the scales were drawn from the literature, all had Cronbach's alpha scores above .74 and all but three had scores above .80. The questionnaire in the second mailing had only 10 items, and measured only outcome variables. Analysis. The analysis included calculation of scale scores, Cronbach's alpha, zero order correlations, and factor analysis, ordinary least square analysis, hierarchical tests of interaction terms and path analysis, and comparisons of results based on a random split of the data and splits based on gender and employer site. The power of the regression analysis was .99 at the .01 significance level for the model as a whole. Results. Self efficacy and Non-Health Benefits emerged as the most powerful predictors of Intentions to exercise, together explaining approximately 19％ of the variance in future Intentions. Intentions, and the interaction of Intentions with Barriers, with Support of Friends, and with Self Efficacy were the most consistent predictors of amount of future exercise, together explaining 38％ of the variance. With the inclusion of Prior Exercise History the model explained 52％ of the variance in amount of exercise 10 months later. There were very few differences in the variables that emerged as important predictors of intentions or exercise in the different employer sites or between males and females. Discussion. This new model is viable in predicting intentions to exercise and amount of exercise, both in absolute terms and when compared to existing models.