Advertisement

Frontiers of Earth Science

, Volume 8, Issue 2, pp 242–250 | Cite as

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 Ma
  • Chunhui Li
  • Xuan WangEmail author
  • Zhifeng Yang
  • Chengchun Sun
  • Peiyu Liang
Research Article

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cai Y P, Huang G H, Tan Q, Chen B (2011). Identification of optimal strategies for improving eco-resilience to floods in ecologically vulnerable regions of a wetland. Ecol Model, 222(2): 360–369CrossRefGoogle Scholar
  2. Cha Y K, Stow C A, Reckhow K H, DeMarchi C, Johengen T H (2010). Phosphorus load estimation in the Saginaw River, MI using a Bayesian hierarchical/multilevel model. Water Res, 44(10): 3270–3282CrossRefGoogle Scholar
  3. Chen S Z, Wang X J, Zhao X 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): 72–75CrossRefGoogle Scholar
  4. Chib S (1996). Calculating posterior distributions and modal estimates in Markov mixture models. J Econom, 75(1): 79–97CrossRefGoogle Scholar
  5. Debels P, Figueroa R, Urrutia R, Barra R, Niell X (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): 301–322CrossRefGoogle Scholar
  6. Ip W C, Hu B Q, Wong H, Xia J (2009). Applications of grey relational method to river environment quality evaluation in China. J Hydrol (Amst), 379(3–4): 284–290CrossRefGoogle Scholar
  7. Jensen F V (1996). An Introduction to Bayesian Networks (Vol. 210). London: UCL PressGoogle Scholar
  8. Jiang B Q, Wang W S, Wen X C (2007). An improved BP neural networks model on water quality evaluation. Computer Systems Applications, 9: 46–50 (in Chinese)Google Scholar
  9. Jin J L, Huang H M, Wei Y M (2004). Comprehensive evaluation model for water quality based on combined weights. Journal of Hydroelectric Engineering, 23(3): 13–19 (in Chinese)Google Scholar
  10. Jun K S, Chung E S, Sung J Y, Lee K S (2011). Development of spatial water resources vulnerability index considering climate change impacts. Sci Total Environ, 409(24): 5228–5242CrossRefGoogle Scholar
  11. Li S Y, Cheng X L, Xu Z F, Han H Y, Zhang Q F (2009a). Spatial and temporal patterns of the water quality in the Danjiangkou Reservoir, China. Hydrol Sci J, 54(1): 124–134CrossRefGoogle Scholar
  12. Li S Y, Gu S, Liu W Z, Han H Y, Zhang Q F (2008). Water quality in relation to the land use and land cover in the Upper Han River Basin, China. Catena, 75(2): 216–222CrossRefGoogle Scholar
  13. Li S Y, Liu W Z, Gu S, Cheng X L, Xu Z F, Zhang Q F (2009b). Spatiotemporal dynamics of nutrients in the upper Han River basin, China. J Hazard Mater, 162(2–3): 1340–1346CrossRefGoogle Scholar
  14. Liao J, Wang J Y, Ding J (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 (in Chinese)CrossRefGoogle Scholar
  15. Liu L, Zhou J Z, An X L, Zhang Y C, Yang L (2010). Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China. Expert Syst Appl, 37(3): 2517–2521CrossRefGoogle Scholar
  16. Ni S H, Bai Y H (2000). Application of BP neural network model in groundwater quality evaluation. Systems Engineering Theory and Practice, 20(8): 124–127 (in Chinese)Google Scholar
  17. Parinet B, Lhote A, Legube B (2004). Principal component analysis: an appropriate tool for water quality evaluation and management-application to a tropical lake system. Ecol Model, 178(3–4): 295–311CrossRefGoogle Scholar
  18. Qian S S, Reckhow K H (2007). Combining model results and monitoring data for water quality assessment. Environ Sci Technol, 41(14): 5008–5013CrossRefGoogle Scholar
  19. Qian S S, Schulman A, Koplos J, Kotros A, Kellar P (2004). A hierarchical modeling approach for estimating national distributions of chemicals in public drinking water systems. Environ Sci Technol, 38(4): 1176–1182CrossRefGoogle Scholar
  20. Schoen M E, Small M J, Vanbriesen J M (2010). Bayesian model for flow-class dependent distributions of fecal-indicator bacterial concentration in surface waters. Water Res, 44(3): 1006–1016CrossRefGoogle Scholar
  21. Tan Q, Huang G H, Cai Y P (2011). Radial interval chance-constained programming for agricultural non-proint source water pollution control under unceitainty. Agricultural Water Management, 98(10): 1595–1606CrossRefGoogle Scholar
  22. Unnikrishnan N K (2010). Bayesian analysis for outliers in survey sampling. Comput Stat Data Anal, 54(8): 1962–1974CrossRefGoogle Scholar
  23. Wu H Y, Chen K L, Chen Z H, Chen Q H, Qiu Y P, Wu J C, Zhang J 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): 546–555CrossRefGoogle Scholar
  24. Wu R, Qian S S, Hao F H, Cheng H G, Zhu D S, Zhang J Y (2011). Modeling contaminant concentration distributions in China’s centralized source waters. Environ Sci Technol, 45(14): 6041–6048CrossRefGoogle Scholar
  25. Xing S L, Zhang Z F, Tian R, Yang L P (2011). Evaluation of underground water quality in Qingshuihe District, Inner Mongolia Autonomous Region. Procedia Environmental Sciences, 11(Part C): 1434–1440Google Scholar
  26. Yin K H, Yuan H R, Ruan Y, Li Z 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 (in Chinese)Google Scholar
  27. Zhang Q F, Xu Z F, Shen Z H, Li S Y, Wang S 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): 369–377CrossRefGoogle Scholar
  28. Zhao Y, Nan J, Cui F Y, Guo L (2007). Water quality forecast through application of BP neural network at Yuqiao reservoir. Journal of Zhejiang University SCIENCE A, 8(9): 1482–1487CrossRefGoogle Scholar
  29. Zou Z H, Yun Y, Sun J 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): 1020–1023CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fangbing Ma
    • 1
    • 2
    • 3
  • Chunhui Li
    • 1
  • Xuan Wang
    • 1
    • 2
    Email author
  • Zhifeng Yang
    • 1
    • 2
  • Chengchun Sun
    • 2
  • Peiyu Liang
    • 1
    • 2
  1. 1.Key Laboratory for Water and Sediment Sciences of Ministry of Education, School of EnvironmentBeijing Normal UniversityBeijingChina
  2. 2.State Key Laboratory for Water Environment Simulation, School of EnvironmentBeijing Normal UniversityBeijingChina
  3. 3.Shidu Town People’s GovernmentBeijingChina

Personalised recommendations