Air quality monitoring data and meteorology data which had collected from 1995. 1. to 1999. 2. in six areas of Daegu, Manchondong, Bokhyundong, Deamyungdong, Samdukdong, Leehyundong and Nowondong, were investigated to determine the distribution and characteristic of ozone. A equation of multiple regression was suggested after time series analysis of contribution factor and meteorology factor were investigated during the day which had high concentration of ozone. The results show the following; First, 63.6% of high ozone concentration days, more than 60 ppb of ozone concentration, were in May, June and September. The percentage of each area showed that; Manchondong 14.4%, Bokhyundong 15.4%, Deamyungdong 15.6%, Samdukdong 15.6%, Leehyundong 17.3% and Nowondong 21.6%. Second, correlation coefficients of ozone, SO2, TSP, NO2 and CO showed negative relationship; the results were respectively -0.229, -0.074, -0.387, -0.190(p<0.01), and humidity were -0.677. but temperature, amount of radiation and wind speed had positive relationship; the results were respectively 0.515, 0.509, 0.400(p<0.01). Third, R2 of equation of multiple regression at each area showed that; Nowondong 45.4%, Lee hyundong 77.9%, Samdukdong 69.9%, Daemyungdong 78.8%, Manchondong 88.6%, Bokhyundong 77.6%. Including 1 hour prior ozone concentration, R2 of each area was significantly increased; Nowondong 75.2%, Leehyundong 89.3%, Samdukdong 86.4%, Daemyungdong 88.6%, Manchondong 88.6%, Bokhyundong 88.0%. Using equation of multiple regression, There were some different R2 between predicted value and observed value; Nowondong 48%, Leehyundong 77.5%, Samdukdong 58%, Daemyungdong 73.4%, Manchondong 77.7%, Bokhyundong 75.1%. R2 of model including 1 hour prior ozone concentration was higher than equation of current day; Nowondong 82.5%, Leehyundong 88.3%, Samdukdong 80.7%, Daemyungdong 82.4%, Manchondong 87.6%, Bokhyundong 88.5%.

In rural planning, the cost estimation of project is a key factor for planning. Therefore, development of reliable cost estimation method is essential. Recently, new techniques are suggested for determination of project cost using historical cost data. In this study, a multiple-regression analysis was used to determine the cost of the farm land consolidation. The results demonstrated that multiple regression analysis using historical cost data can be applicable to project cost estimation.