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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

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Front. Earth Sci.    2014, Vol. 8 Issue (2) : 242-250    https://doi.org/10.1007/s11707-013-0395-6
RESEARCH ARTICLE
A Bayesian method for comprehensive water quality evaluation of the Danjiangkou Reservoir water source area, for the middle route of the South-to-North Water Diversion Project in China
Fangbing MA1,2,3,Chunhui LI1,Xuan WANG1,2,*(),Zhifeng YANG1,2,Chengchun SUN2,Peiyu LIANG1,2
1. Key Laboratory for Water and Sediment Sciences of Ministry of Education, School of Environment, Beijing Normal University, Beijing 100875, China
2. State Key Laboratory for Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
3. Shidu Town People’s Government, Beijing 102411, China
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Abstract

The Danjiangkou Reservoir is the water source for the middle route of the South-to-North Water Diversion Project in China. Thus, its water quality status is of great concern. Five water quality indicators (dissolved oxygen, permanganate index, ammonia nitrogen, total nitrogen, and total phosphorus), were measured at three monitoring sites (the Danjiangkou Reservoir dam, the Hejiawan and the Jiangbei bridge), to investigate changing trends, and spatiotemporal characteristics of water quality in the Danjiangkou Reservoir area from January 2006 to May 2012. We then applied a Bayesian statistical method to evaluate the water quality comprehensively. The normal distribution sampling method was used to calculate likelihood, and the entropy weight method was used to determine indicator weights for variables of interest in to the study. The results indicated that concentrations of all five indicators increased during the last six years. In addition, the water quality in the reservoir was worse during the wet season (from May to October), than during the dry season (from November to April of the next year). Overall, the probability of the water’s belonging to quality category of type Ⅱ, according to environmental quality standards for surface water in China, was 27.7%–33.7%, larger than that of its belonging to the other four water quality types. The increasing concentrations of nutrients could result in eutrophication of the Danjiangkou Reservoir. This method reduced the subjectivity that is commonly associated with determining indicator weights and artificial classifications, achieving more reliable results. These results indicate that it is important for the interbasin water diversion project to implement integrated water quality management in the Danjiangkou Reservoir area.
Keywords water quality evaluation      Danjiangkou Reservoir      Bayesian method      normal distribution sampling method      entropy weight method     
Corresponding Author(s): Xuan WANG   
Issue Date: 24 June 2014
 Cite this article:   
Fangbing MA,Chunhui LI,Xuan WANG, et al. A Bayesian method for comprehensive water quality evaluation of the Danjiangkou Reservoir water source area, for the middle route of the South-to-North Water Diversion Project in China[J]. Front. Earth Sci., 2014, 8(2): 242-250.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-013-0395-6
https://academic.hep.com.cn/fesci/EN/Y2014/V8/I2/242
Fig.1  Location and sampling sites in the Danjiangkou Reservoir.
Fig.2  Procedures for comprehensive water quality evaluation with the Bayesian method.
Fig.3  Concentration changes of the indicators during 2006-2012 ((a) DO; (b) CODMn; (c) NH3-N; (d) TN; (e) TP; (f) inter annual change of five indicators at the Danjiangkou Reservoir dam). Note: Dashed lines in (a) – (e) are water quality types of the environmental quality standards for surface water in China (GB3838-2002), solid lines in (a) – (e) represent the trend change of indicator concentrations.
Water qualityDO/( mg·L-1)CODMn/( mg·L-1)NH3-N/( mg·L-1)TN/( mg·L-1)TP/( mg·L-1)
National criteriaType Ⅰ≥7.5≤2≤0.15≤0.2≤0.01
Type Ⅱ≥6≤4≤0.5≤0.5≤0.025
Type Ⅲ≥5≤6≤1≤1≤0.05
Type Ⅳ≥3≤10≤1.5≤1.5≤0.1
Type Ⅴ≥2≤15≤2≤2≤0.2
Monitoring sitesDanjiangkou Reservoir dam6.71-11.51.14-3.30.01-0.3710.909-2.550.005-0.034
Hejiawan6.62-11.81-3.560.01-0.40.904-2.320.005-0.03
Jiangbei bridge6.66-121.14-3.90.01-0.390.908-2.660.005-0.027
Tab.1  Water quality criteria and the monitoring values of the indicators at the three sites
Time scaleMonitoring sitesDOCODMnNH3-NTNTP
MonthlyDanjiangkou Reservoir dam0.136740.38745.26933E-55.16478E-84.09747E-4
Hejiawan0.229120.685537.73886E-64.29056E-90.12361
Jiangbei bridge0.222920.94731.88037E-73.56097 E-90.01822
Inter-annualDanjiangkou Reservoir dam0.066920.959260.013940.006120.06765
Hejiawan0.069170.615420.012010.011880.19165
Jiangbei bridge0.116710.480390.004060.007020.11975
Tab.2  Significance test results (p) for linear fit parameters for trends for the five indicators
Fig.4  Seasonal concentration changes of indicators at the Danjiangkou Reservoir dam.
Fig.5  Probability distribution box chart for the five water quality types.
1 ChaY K, StowC A, ReckhowK H, DeMarchiC, JohengenT H (2010). Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model. Water Res, 44(10): 3270-3282
doi: 10.1016/j.watres.2010.03.008 pmid: 20382406
2 ChenS Z, WangX J, ZhaoX J (2008). An attribute recognition model based on entropy weight for evaluating the quality of groundwater sources. Journal of China University of Mining and Technology, 18(1): 72-75
doi: 10.1016/S1006-1266(08)60016-4
3 ChibS (1996). Calculating posterior distributions and modal estimates in Markov mixture models. J Econom, 75(1): 79-97
doi: 10.1016/0304-4076(95)01770-4
4 DebelsP, FigueroaR, UrrutiaR, BarraR, NiellX (2005). Evaluation of water quality in the Chillán River (Central Chile) using physicochemical parameters and a modified water quality index. Environ Monit Assess, 110(1-3): 301-322
doi: 10.1007/s10661-005-8064-1 pmid: 16308794
5 IpW C, HuB Q, WongH, XiaJ (2009). Applications of grey relational method to river environment quality evaluation in China. J Hydrol (Amst), 379(3-4): 284-290
doi: 10.1016/j.jhydrol.2009.10.013
6 JensenF V (1996). An Introduction to Bayesian Networks (Vol. 210). London: UCL press
7 JiangB Q, WangW S, WenX C (2007). An improved BP neural networks model on water quality evaluation. Computer Systems Applications, 9: 46-50
8 JinJ L, HuangH M, WeiY M (2004). Comprehensive evaluation model for water quality based on combined weights. Journal of Hydroelectric Engineering, 23(3): 13-19
9 JunK S, ChungE S, SungJ Y, LeeK S (2011). Development of spatial water resources vulnerability index considering climate change impacts. Sci Total Environ, 409(24): 5228-5242
doi: 10.1016/j.scitotenv.2011.08.027 pmid: 21940039
10 LiS Y, ChengX L, XuZ F, HanH Y, ZhangQ F(2009a). Spatial and temporal patterns of the water quality in the Danjiangkou Reservoir, China. Hydrol Sci J, 54(1): 124-134
doi: 10.1623/hysj.54.1.124
11 LiS Y, GuS, LiuW Z, HanH Y, ZhangQ F (2008). Water quality in relation to the land use and land cover in the Upper Han River basin, China. Catena, 75(2): 216-222
doi: 10.1016/j.catena.2008.06.005
12 LiS Y, LiuW Z, GuS, ChengX L, XuZ F, ZhangQ F (2009b). Spatio-temporal dynamics of nutrients in the upper Han River basin, China. J Hazard Mater, 162(2-3): 1340-1346
doi: 10.1016/j.jhazmat.2008.06.059 pmid: 18675508
13 LiaoJ, WangJ Y, DingJ (2009). Water quality assessment of main rivers in Sichuan based on improved Bayes model. Journal of Sichuan Normal University (Natural Science), 32(4): 518-521
14 LiuL, ZhouJ Z, AnX L, ZhangY C, YangL (2010). Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China. Expert Syst Appl, 37(3): 2517-2521
doi: 10.1016/j.eswa.2009.08.004
15 NiS H, BaiY H (2000). Application of BP neural network model in groundwater quality evaluation. Systems Engineering Theory and Practice, 20(8): 124-127
16 ParinetB, LhoteA, LegubeB (2004). Principal component analysis: an appropriate tool for water quality evaluation and management-application to a tropical lake system. Ecol Modell, 178(3-4): 295-311
doi: 10.1016/j.ecolmodel.2004.03.007
17 QianS S, ReckhowK H (2007). Combining model results and monitoring data for water quality assessment. Environ Sci Technol, 41(14): 5008-5013
doi: 10.1021/es062420f pmid: 17711216
18 QianS S, SchulmanA, KoplosJ, KotrosA, KellarP (2004). A hierarchical modeling approach for estimating national distributions of chemicals in public drinking water systems. Environ Sci Technol, 38(4): 1176-1182
doi: 10.1021/es020686q pmid: 14998034
19 SchoenM E, SmallM J, VanbriesenJ M (2010). Bayesian model for flow-class dependent distributions of fecal-indicator bacterial concentration in surface waters. Water Res, 44(3): 1006-1016
doi: 10.1016/j.watres.2009.10.016 pmid: 19931885
20 UnnikrishnanN K (2010). Bayesian analysis for outliers in survey sampling. Comput Stat Data Anal, 54(8): 1962-1974
doi: 10.1016/j.csda.2010.02.021
21 WuH Y, ChenK L, ChenZ H, ChenQ H, QiuY P, WuJ C, ZhangJ F (2012). Evaluation for the ecological quality status of coastal waters in East China Sea using fuzzy integrated assessment method. Mar Pollut Bull, 64(3): 546-555
doi: 10.1016/j.marpolbul.2011.12.022 pmid: 22245438
22 WuR, QianS S, HaoF H, ChengH G, ZhuD S, ZhangJ Y (2011). Modeling contaminant concentration distributions in China’s centralized source waters. Environ Sci Technol, 45(14): 6041-6048
doi: 10.1021/es1038563 pmid: 21692445
23 XingS L, ZhangZ F, TianR, YangL P (2011). Evaluation of underground water quality in QingShuihe District, Inner Mongolia Autonomous Region. Procedia Environmental Sciences, 11(Part C): 1434-1440
doi: 10.1016/j.proenv.2011.12.215
24 YinK H, YuanH R, RuanY, LiZ Y (2001). Variation and correlation of environmental parameters in the water of Danjiangkou Reservoir. Resources and Environment in the Yangtze Basin, 10(1): 81-87
25 ZhangQ F, XuZ F, ShenZ H, LiS Y, WangS S (2009). The Han River watershed management initiative for the South-to-North Water Transfer project (Middle Route) of China. Environ Monit Assess, 148(1-4): 369-377
doi: 10.1007/s10661-008-0167-z pmid: 18306047
26 ZhaoY, NanJ, CuiF Y, GuoL (2007). Water quality forecast through application of BP neural network at Yuqiao reservoir. Journal of Zhejiang University SCIENCE A, 8(9): 1482-1487
doi: 10.1631/jzus.2007.A1482
27 ZouZ H, YunY, SunJ N (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environ Sci (China), 18(5): 1020-1023
doi: 10.1016/S1001-0742(06)60032-6 pmid: 17278765
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