Browse > Article
http://dx.doi.org/10.5668/JEHS.2016.42.4.280

Chemical Oxygen Demand (COD) Model for the Assessment of Water Quality in the Han River, Korea  

Kim, Jae Hyoun (Department of Health Science, School of Natural Science, Dongduk Women's University)
Jo, Jinnam (Department of Statistics and Information Science, Dongduk Women's University)
Publication Information
Journal of Environmental Health Sciences / v.42, no.4, 2016 , pp. 280-292 More about this Journal
Abstract
Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.
Keywords
Chemical oxygen demand (COD); genetic algorithm-multiple linear regression (GA-MLR); water quality parameter (WQP);
Citations & Related Records
Times Cited By KSCI : 13  (Citation Analysis)
연도 인용수 순위
1 Han JG, Lee YK, Kim TH, Hwang EJ. Analysis of seasonal water pollution based on rainfall feature at Anyang river basin in Korea. Environ Geology. 2005; 28(4): 599-608.
2 Lee LK, Kim JH, Kim J. Monitoring the water quality of the Wangsukcheon river over a two year period. Toxicol Environ Health Sci. 2015; 7(1): 91-96.   DOI
3 Hur M, Lee I, Tak BM, Lee HJ, Yu JJ, Cheon SU, et al. Temporal shifts in cyanobacterial communities at different sites on the Nakdong River in Korea. Water Res. 2013; 47(19): 6973-6982.   DOI
4 An KG, Jeon HW, Choi JW. Spatio-temporal water quality variations at various streams of Han-River watershed and empirical models of serial impound ment reservoirs. Kor J Limnol. 2012; 45(4): 378-391.   DOI
5 Kim JK, Shin M, Jang C, Jung S, Kim B. Comparison of TOC and DOC Distribution and the Oxidation Efficiency of BOD and COD in Several Reservoirs and Rivers in the Han River System. J Kor Soc Water Qual. 2007; 23(1): 72-80.
6 Lee HW, Choi JH. Temporal Analysis of Trends in Dissolved Organic Matter in Han River Water. Environ Eng Res. 2009; 14(4): 256-260.   DOI
7 Lee KS, Bong YS, Lee D, Kim Y, Kim K. Tracing the sources of nitrate in the Han River watershed in Korea, using ${\delta}^{15}N-{NO_3}^-$ and ${\delta}^{18}O-{NO_3}^-$ values. Sci Total Environ. 2008; 395(2-3): 117-124.   DOI
8 Nguyen H, VM, Lee MH, Hu J, Schlautman MA. Variations in spectroscopic characteristics and disinfection byproduct formation potentials of dissolved organic matter for two contrasting storm events. J Hydrol. 2013; 481: 132-142.   DOI
9 Wilson F. Total organic carbon as a predictor of biological wastewater treatment efficiency and kinetic reaction rates. Water Sci Technol. 1997; 35(8): 119-126.   DOI
10 Gwaski PA, Hati SS, Ndahi NP, Ogugbuaja, VO. Modeling parameters of oxygen demand in the aquatic environment of Lake Chad for depletion estimation. ARPN J Sci Technol. 2013; 3(1): 116-123.
11 Lee J, Lee S, Yu S, Rhew D. Relationships between water quality parameters in rivers and lakes: BOD5, COD, NBOPs, and TOC. Environ Monit Assess. 2016; 188(4): 252.   DOI
12 Lee HW, Choi JH. Temporal Analysis of Trends in dissolved organic matter in Han River water. Environ Eng Res. 2009; 14(4): 256-260.   DOI
13 Lee JY, Yang JS, Kim DK, Han MY. Relationship between land use and water quality in a small watershed in South Korea. Water Sci Technol. 2010; 62(11): 2607-2615.   DOI
14 Song ES, Jeon SM, Lee EJ, Park DJ, Shin YS. Long-term trend analysis of chlorophyll a and water quality in the Yeongsan river. Kor J Limnol. 2012; 45(3): 302-313.
15 Cho HS, Kim KR, Lim GC, Bae KS, Lee MH. A Study on long-term variations of BOD and COD as indicators of organic matter pollution in the Han River. Kor J Limnol. 2012; 45(4): 474-481.   DOI
16 Jung, et al. Evaluation of water quality for the Nak-dong River watershed using multivariate analysis. Environ Technol Innovation. 2016; 5: 67-82.   DOI
17 Shin MS, Lee JY, Kim BC, Bae YJ. Long-term variations in water quality in the lower Han River. J Ecol Environ. 2011; 34(1): 31-37.   DOI
18 Venkatramanan S, Chung SY, Lee SY, Park N. (2014). Assessment of river water quality via environmentric multivariate statistical tools and water quality index: a case study of Nakdong River basin, Korea. Carpathian. J Earth Environ Sci. 2014; 9: 125-132.
19 Lim, et al. Evaluation of pollutant characteristics in Yeongsan River using multivariate analysis. Kor J Ecol Environ. 2012; 45(4): 368-377.   DOI
20 Khan TA. Groundwater quality evaluation using multivariate methods, in parts of Ganga Sot subbasin, Ganga basin, India. J Water Resource Prot. 2015; 7: 769-780.   DOI
21 Kim YY, Lee SJ. Evaluation of water quality for the Han River tributaries using multivariate analysis. J KSSE. 2011; 33(7): 501-510.
22 Berlemann A. Using a water quality index to determine and compare creek water quality. Am Water Works Assoc. 2013; 105(6): E291-E298.   DOI
23 Park J, Moon M, Lee H, Kim K. A study on characteristics of water quality using multivariate analysis in Sumjin River basin. J Kor Soc Water Environ. 2014; 30(2): 119-127.   DOI
24 Hammer O, Harper, DAT, Ryan, PD. PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica. 2001; 4(1): 9pp.
25 Sun R, Wang ZZ, Chen L, Wang W. Assessment of surface water quality at large watershed scale: Land-use, anthropogenic, and administrative impacts. J Am Water Res Assoc. 2013; 49: 741-752.   DOI
26 An KG. Determination of a limiting nutrient regulating algal biomass using in situ experiments of nutrient enrichment bioassay (NEB) and empirical relations of nutrients and chlorophyll-a. J Environ Biol. 2003; 24(3): 229-239.
27 Kim JH. Assessment through statistical methods of water quality parameters (WQPs) in the Han River in Korea. Kor J Environ Health. Sci. 2015; 41(2): 90-101.   DOI
28 Yuan F, Quellos JA, Fan C. Controls of Phophorus loading and transport in the Cuyahoga River of northeastern Ohio, USA. Appl Geochem. 2013; 38: 59-69.   DOI
29 Chapman D. 1992. Water quality assessment: a guide of the use of biota, sediments and water in environmental monitoring. University Press, Cambridge, 1992; pp: 585.
30 Kim JY, An KG. Integrated ecological river health assessments, based on water chemistry, physical habitat quality and biological integrity. Water. 2015; 7: 6378-6403.   DOI
31 Ebise S, Inoue T. Change in C:N:P ratios during passage of water areas from rivers to a lake. Water Res. 1991; 25(1): 95-100.   DOI
32 Pall E, Niculae M, Kiss T, Sandru CD, Spinu M. Human impact on the microbiological water quality of the rivers. J Med Microbiol. 2013; 62(Pt 11): 1635-1640.   DOI
33 Frenzel SA, Couvillion CS. Fecal-indicator bacteria in streams along a gradient of residential development. JAWRA. 2002; 38: 265-273.
34 Kim JY, Lee H, Lee JE, Chung MS, Ko GP. Identification of human and animal fecal contamination after rainfall in the Han River, Korea. Microbes Environ. 2013; 28(2): 187-194.   DOI
35 Kim YY, Lee SJ. Evaluation of water quality for the Han River tributaries using multivariate analysis. J KSSE. 2011; 33(7): 501-510.
36 Jeong YH, Kim HS, Yang JS. Statistical Analyses of Long-Term Water Quality Variation in the Geumgang-Reservoir: Focused on the TP load by migrating birds excrement. J Kor Soc Mar Environ Engineer. 2010; 13(4): 223-233.
37 WHO. 2008. Recommendations. Guidelines for Drinking-Water Quality. 3rd ed., Vol. 1. Geneva: World Health Organization.
38 Aziz A, Tebbutt THY. Significance of COD, BOD and TOC correlations in kinetic models of biological oxidation. Water Res. 1980; 14(4): 319-324.   DOI
39 Tango T, Takahashi K. A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters. Stat Med. 2012; 31(30): 4207-4218.   DOI
40 Yeon YJ, Kim DH, Lee JL. Water quality modeling for integrated management of urban stream networks. IJESD. 2016; 7(12): 928-932.   DOI