Objectives : In this study, a adjusted sewage & wastewater treatment capacity-required area estimation methodology was proposed through various data analysis for the required area of domestic sewage & wastewater treatment, the occupancy ratio of each sub facility, and the treatment processing method. In addition, the reduction rate of the required area according to the application of the adjustment methodology presented in this study was evaluated quantitatively by comparing the actual area with the expected area.
Methods : Statistical analysis was performed by collecting 654 data of more than 500 ㎥/day of treatment capacity in domestic sewage & wastewater treatment facilities. The 33 sewage & wastewater treatment facilities constructed by the public corporation sample groups were divided into functional areas and analyzed for occupancy rates by each facility.
Results and Discussion : A capacity-required area regression model was developed for the collected each samples. As a result, the distributions of the regression model were divided into two clusters according to the analysis sample differences. It was analyzed that the estimated required area using the local government sample group was estimated to be relatively higher than the public corporation sample group. In this study, a adjusted capacity-required area model based on regression model derived from public corporation sample was proposed by considering green site ratio and treatment processing methods in sewage & wastewater treatment plant. The results are as follows: ( : conversion factor for green site ratio, : conversion factor for treatment processing method).
Conclusions : Recently, the wastewater treatment facilities have been in decentralization and small scale, and the introduction of the advanced treatment method that requires a relatively small area such as MBR or SBR has been accelerating. Consequently, it could be expected that the methodology proposed in this study will save about 50 ~ 60% of the required area for wastewater treatment facilities.