Evaluation of pollutants removal efficiency to achieve successful urban river restoration.
Greater efforts to provide alternative scenarios are key to successful urban stream restoration planning. In this study, we discuss two different aspects of water quality management schemes, biodegradation and human health, which are incorporated in the restoration project of original, pristine condition of urban stream at the Gwangju (GJ) Stream, Korea. For this study, monthly monitoring of biochemical oxygen demand (BOD(5)) and fecal indicator bacteria (FIB) data were obtained from 2003 to 2008 and for 2008, respectively, and these were evaluated to explore pollutant magnitude and variation with respect to space and time window. Ideal scenarios to reduce target pollutants were determined based on their seasonal characteristics and correlations between the concentrations at a water intake and discharge point, where we suggested an increase of environmental flow and wetland as pollutants reduction drawing for BOD(5) and FIB, respectively. The scenarios were separately examined by the Qual2E model and hypothetically (but planned) constructed wetland, respectively. The results revealed that while controlling of the water quality at the intake point guaranteed the lower pollution level of BOD(5) in the GJ Stream, a wetland constructed at the discharge point may be a promising strategy to mitigate mass loads of FIB. Overall, this study suggests that a combination of the two can be plausible scenarios not only to support sustainable urban water resources management, but to enhance a quality of urban stream restoration assignment.
Cha
Sung Min
SM
Department of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 500-712, South Korea.
Ham
Young Sik
YS
Ki
Seo Jin
SJ
Lee
Seung Won
SW
Cho
Kyung Hwa
KH
Park
Yongeun
Y
Kim
Joon Ha
JH
D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D052918:Environmental Restoration and Remediation / Q000379:methods* / Q000706:statistics & numerical data; D004926:Escherichia coli; D007723:Korea; D008962:Models, Theoretical; D010100:Oxygen / Q000032:analysis; D012044:Regression Analysis; D045483:Rivers / Q000737:chemistry*; D014874:Water Pollutants, Chemical / Q000032:analysis*; D053833:Wetlands
D014874:Water Pollutants, Chemical; D010100:Oxygen