• Journal of Korean Society of Environmental Engineers
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• v.31 no.9
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• pp.803-810
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• 2009

This study is conducted to examine spatial variations of Dissolved Organic Matter (DOM) in stream and waste waters of the different watershed areas (agricultural, residential, and industrial complex area) by using fluorescence 3D-EEMs (3 Dimensional Excitation Emission Matrix Spectroscopy). Furthermore, the research investigates the changes of DOM characterization by synchronous and 3D-EEMs during a rainfall event. The characterizations of DOM obtained by 3D-EEMs show two noticeable peaks at humic and protein-like regions. Humic-like substances (HLS) are found in rural and urban areas, and humic and protein-like substances (PLS) are shown in industrial area. According to the fluorescence peak $T_1:C_1$ ratios, it is observed that high amount of HLS was discharged from Banweol Industrial Complex (3TG). Additionally, linear relationships (Regression rate, $r^2$=0.65, $r^2$=0.66) have been shown between PLS (peak $T_1,\;B_1$) and biochemical oxygen demand (BOD), which indicates the impact of sewage. For the rainfall event (30 mm), no remarkable difference of DOM was found at rural area except increment of fluorescence intensity comparing dry period. In contrast, HLS at urban area is highly discharged within 30 minutes from the beginning of rainfall. Also, there are high influences of HLS and PLS within 20 minutes at industrial complex (4TG). Fluorescence 3D-EEMs has not only verifies a watershed of DOM origination but also monitors diffuse and point source impacts.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.869-877
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• 2008

The purpose of this study is to investigate the trends and patterns of variou kind of water uses in a household by metering in Korea. Water use components are classified by toilet, washbowl, bathing, laundry, kitchen, etc. Flow meters are installed in 146 household selected by sampling in all around Korea. The data are gathered by web-based data collection system from the year 2002 to 2006, considering pre-investigated data such as occupation, revenue, family members, housing types, age, floor area, water saving devices, education, etc. Reliable data are selected by upper fence method for each observed water use component and statistical characteristics are estimated for each residential type to determine liter per capita per day. Estimated domestic per capita day show an indoor water use with the range from $150{\ell}pcd$ to $169{\ell}pcd$ for each housing type as the order of high rise apartment, multi-house, and single house. As the order of consuming amount among water use components, it is investigated that toilet($38.5{\ell}pcd$) is the first, and the second is laundry water($30.8{\ell}pcd$), the third is kitchen($28.4{\ell}pcd$), the fourth is bathtub($24.7{\ell}pcd$), the next is washbowl($15.4{\ell}pcd$). The results are compared with water uses in U.K. and U.S. As life style has been changed into western style, pattern of water use in Korea is tend to be similar with the U.S. water use pattern. Compared with the surveying results by Bradley, on 1985. Thirty liter of total use increased with the advancement of economic level, and a little change of water use pattern can be found. Especially, toilet water take almost half part of total water use and laundry water shows lowest as 11% in surveying at the year of 1985. But, this study shows that 39 liter, 28% of toilet water, has been decreased by the spread of saving devices and campaign. It is supposed that the spread large sized laundry machine make by-hand laundry has been decreased and water use increased. Unit water amount of each end-use in household can be applied to design factor for water and wastewater facilities, and it play a role as information in establishing water demand forecasting and conservation policy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.595-601
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• 2008

The purpose of this study is to investigate the trends and patterns of various kind of water uses in a household by metering in Korea. Water use components are classified by toilet, washbowl, bathing, laundry, kitchen, miscellaneous. Flow meters are installed in 140 household selected by sampling in all around Korea. The data are gathered by web-based data collection system from the year 2002 to 2006, considering pre-investigated data such as occupation, revenue, family members, housing types, age, floor area, water saving devices, education, miscellaneous. Reliable data are selected by upper fence method for each observed water use component and statistical characteristics are estimated for each residential type to determine liter per capita per day. Estimated domestic per capita day show an indoor water use with the range from 150 lpcd to 169 lpcd for each housing type as the order of high rise apartment, multi-house, and single house. As the order of consuming amount among water use components, it is investigated that toilet (38.5 lpcd) is the first, and the second is laundry water (30.8 lpcd), the third is kitchen (28.4 lpcd), the fourth is bathtub (24.7 lpcd), the next is washbowl (15.4 lpcd). The results are compared with water uses in U.K. and U.S. As life style has been changed into western style, pattern of water use in Korea is tend to be similar with the U.S. water use pattern. Compared with the surveying results by Bradley, on 1985. Thirty liter of total use increased with the advancement of economic level, and a little change of water use pattern can be found. Especially, toilet water take almost half part of total water use and laundry water shows lowest as 11% in surveying at the year of 1985. But, this study shows that 39 liter, 28% of toilet water, has been decreased by the spread of saving devices and campaign. It is supposed that the spread large sized laundry machine make by-hand laundry has been decreased and water use increased. Unit water amount of each end-use in household can be applied to design factor for water and wastewater facilities, and it play a role as information in establishing water demand forecasting and conservation policy.