Advertisement

Risk analysis of sudden water pollution in a plain river network system based on fuzzy-stochastic methods

  • Dayong LiEmail author
  • Liyao Shi
  • Zengchuan Dong
  • Jintao Liu
  • Wei Xu
Original Paper
  • 145 Downloads

Abstract

A comprehensive fuzzy-stochastic risk assessment method is adopted to systematically quantify the stochastic and fuzzy uncertainties related to discharge conditions, environmental standards and health assessment criteria. A sequential Monte Carlo simulation of pollutant behaviour in rivers after the accidental discharge from multiple risk sources is carried out using a risk probability analysis model of sudden water pollution. The fuzzy membership functions based on environmental water quality standards and health impact criteria are established by means of a questionnaire. Then, the simulation results and the membership functions are combined to quantify the stochastic uncertainty of the pollutant concentration and the fuzzy uncertainty of its consequences. Based on the fuzzy logic method, the general risk level is obtained by combining the risk based on the environmental water quality standard and the health risk, and the general risk scores (CS) are obtained via the interpolation method. The risk assessment method is applied to the Yixing area. The results show that (a) the ammonia nitrogen CS values are low in the upper reaches and high in the middle and lower reaches. The ammonia nitrogen CS values under sudden schemes are not less than the corresponding values under the stable scheme. (b) The dynamic concentration changes in the sub-reach affect the probability of exceeding the ammonia nitrogen standard and indirectly affect the changes in the CS values in different reaches. (c) The net variation in the ammonia nitrogen CS values exhibits a concentrated distribution of high and low scores in the upstream areas and a concentrated distribution of high scores in the downstream area. (d) For most of the sections, there is a positive linear correlation between the risk growth rate (GR) and the average duration of the accidental state, and the greater the GR value is, the more sensitive the section is to the accident risk.

Keywords

Plain river network system Sudden water pollution Risk assessment Fuzzy-stochastic method 

Notes

Acknowledgements

This research was supported by the project (41471014) sponsored by the National Natural Science Foundation, China.

References

  1. Bao LJ, Maruya KA, Snyder SA, Zeng EY (2012) China’s water pollution by persistent organic pollutants. Environ Pollut 163:100–108.  https://doi.org/10.1016/j.envpol.2011.12.022 CrossRefGoogle Scholar
  2. Chen SJJ, Hwang CL, Beckmann MJ, Krelle W (1992) Fuzzy multiple attribute decision making: methods and applications. Springer, BerlinCrossRefGoogle Scholar
  3. Cheng SK (2000) Development of a fuzzy multi-criteria decision support system for municipal solid waste management. M.Sc.thesis, University of Regina, ReginaGoogle Scholar
  4. Deng YU, Ni FQ, Yao ZG (2012) The Monte Carlo-based uncertainty health risk assessment associated with rural drinking water quality. J Water Resour Prot 4(9):772–778.  https://doi.org/10.4236/jwarp.2012.49088 CrossRefGoogle Scholar
  5. Dura G, Kambourova V, Simeonova F (2006) Management of intentional and accidental water pollution. Nato security through science C: environmental security. Springer, BerlinCrossRefGoogle Scholar
  6. Gómez AG, Ondiviela B, Puente A, Juanes JA (2015) Environmental risk assessment of water quality in harbor areas: a new methodology applied to European ports. J Environ Manag 155:77–88.  https://doi.org/10.1016/j.jenvman.2015.01.042 CrossRefGoogle Scholar
  7. Grifoll M, Jordà G, Borja Á, Espino M (2010) A new risk assessment method for water quality degradation in harbour domains, using hydrodynamic models. Mar Pollut Bull 60(1):69–78.  https://doi.org/10.1016/j.marpolbul.2009.08.030 CrossRefGoogle Scholar
  8. Grifoll M, Jordà G, Espino M, Romo J, García-Sotillo M (2011) A management system for accidental water pollution risk in a harbour: the Barcelona case study. J Mar Syst 88(1):60–73.  https://doi.org/10.1016/j.jmarsys.2011.02.014 CrossRefGoogle Scholar
  9. Grifoll M, Campo AD, Espino M, Mader J, González M, Borja Á (2013) Water renewal and risk assessment of water pollution in semi-enclosed domains: application to Bilbao Harbour (Bay of Biscay). J Mar Syst 109–110(1):S241–S251.  https://doi.org/10.1016/j.jmarsys.2011.07.010 CrossRefGoogle Scholar
  10. Hu GH, Xia J (2001) Grey-stochastic risk method for risk analysis. J Hydraul Eng 32(4):1–6 (in Chinese) Google Scholar
  11. Jin JL, Wu KY, Li RZ (2008) Coupling method of stochastic simulation with triangular fuzzy numbers for water environment risk assessment. J Hydraul Eng 39(11):1257–1261 (in Chinese) Google Scholar
  12. Kontos YN, Katsifarakis KL (2016) Optimal management of a theoretical coastal aquifer with combined pollution and salinization problems, using genetic energy algorithms. Energy 136:32–44.  https://doi.org/10.1016/j.energy.2016.10.035 CrossRefGoogle Scholar
  13. Li LW, Zhou S (2008) Method for risk analysis of water quality based on fuzzy probabilistic theory. J Hydraul Eng 39(11):1257–1261 (in Chinese) Google Scholar
  14. Li JB, Huang GH, Zeng GM, Maqsood I, Huang YF (2007) An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination. J Environ Manag 82(2):173–188.  https://doi.org/10.1016/j.jenvman.2005.12.018 CrossRefGoogle Scholar
  15. Liang SD, Jia HF, Xu CQ, Xu T, Melching C (2016) A Bayesian approach for evaluation of the effect of water quality model parameter uncertainty on TMDLs: a case study of Miyun Reservoir. Sci Total Environ 560–561:44–54.  https://doi.org/10.1016/j.scitotenv.2016.04.001 CrossRefGoogle Scholar
  16. Liu JH, Hu J, Chu JY, Gao XR (2012) Multiscale risk assessment of sudden water pollution of rivers in areas lacking of information. J Tsinghua Univ 8(6):830–835 (in Chinese) Google Scholar
  17. Lu MF, Chen F (2009) Monitoring and early warning technology of sudden water pollution incident in Taihu Lake basin. In: The academic annual meeting of Chinese Society for Environmental Sciences, pp 532–536 (in Chinese)Google Scholar
  18. Lyubimova T, Lepikhin A, Parshakova Y, Tiunov A (2016) The risk of river pollution due to washout from contaminated floodplain water bodies during periods of high magnitude floods. J Hydrol 534:579–589.  https://doi.org/10.1016/j.jhydrol.2016.01.030 CrossRefGoogle Scholar
  19. Navoni JA, Pietri DD, Olmos V, Gimenez C, Mitre GB, Titto ED, Villaamil Lepori EC (2014) Human health risk assessment with spatial analysis: study of a population chronically exposed to arsenic through drinking water from Argentina. Sci Total Environ 499:166–174.  https://doi.org/10.1016/j.scitotenv.2014.08.058 CrossRefGoogle Scholar
  20. Nkuiya B, Costello C (2016) Pollution control under a possible future shift in environmental preferences. J Econ Behav Organ 132(B):193–205.  https://doi.org/10.1016/j.jebo.2016.05.021 CrossRefGoogle Scholar
  21. Palaniappan M, Gleick PH, Allen L, Cohen MJ, Christiansmith J, Smith C (2012) Water quality. The World’s Water: the biennial report on freshwater resources, vol 7. Island Press, Washington DC, pp 45–72Google Scholar
  22. Persson K, Destouni G (2009) Propagation of water pollution uncertainty and risk from the subsurface to the surface water system of a catchment. J Hydrol 377(3–4):434–444.  https://doi.org/10.1016/j.jhydrol.2009.09.001 CrossRefGoogle Scholar
  23. Quan WM, Yan LJ (2012) Effects of agricultural non-point source pollution on eutrophication of water body and its control measure. Acta Ecol Sin 22(3):291–299 (in Chinese) Google Scholar
  24. Rose M, Fernandes A, Mortimer D, Baskaran C (2015) Contamination of fish in UK fresh water systems: risk assessment for human consumption. Chemosphere 122(2):183–189.  https://doi.org/10.1016/j.chemosphere.2014.11.046 CrossRefGoogle Scholar
  25. Saha N, Rahman MS, Ahmed MB, Zhou JL, Ngo HH, Guo W (2017) Industrial metal pollution in water and probabilistic assessment of human health risk. J Environ Manag 185:70–78.  https://doi.org/10.1016/j.jenvman.2016.10.023 CrossRefGoogle Scholar
  26. Shah MT, Ara J, Muhammad S, Khan S, Tariq S (2012) Health risk assessment via surface water and sub-surface water consumption in the mafic and ultramafic terrain, Mohmand agency, northern Pakistan. J Geochem Explor 118:60–67.  https://doi.org/10.1016/j.gexplo.2012.04.008 CrossRefGoogle Scholar
  27. Singare PU (2016) Distribution and risk assessment of suspected endocrine-disrupting pesticides in creek water of Mumbai, India. Mar Pollut Bull 102(1):72–83.  https://doi.org/10.1016/j.marpolbul.2015.11.055 CrossRefGoogle Scholar
  28. Sun W, Feng L, Sun DZ, Zhang LQ (2016) The current situation of the water quality and its assessment on river system, Yixing. Adv Environ Prot 4:60–66.  https://doi.org/10.12677/AEP.2014.46B009 CrossRefGoogle Scholar
  29. Torres L, Yadav OP, Khan E (2017) Holistic risk assessment of surface water contamination due to Pb-210 in oil produced water from the Bakken Shale. Chemosphere 169:627–635.  https://doi.org/10.1016/j.chemosphere.2016.11.125 CrossRefGoogle Scholar
  30. US EPA (1992) Guidelines for exposure assessment, EPA/600/Z-92/001. US EPA, Risk Assessment Forum, Washington, DCGoogle Scholar
  31. US EPA (2003) Integrated risk information system: xylene (CASRN 1330-20-7). http://www.epa.gov/iris/subst/0270.htm. Accessed Mar 2018
  32. Wang LP, Zhou XW, Li XQ (2008) Fuzzy-stochastic model for pollution risk assessment of drinking water sources. J Tsinghua Univ (Sci Technol) 48(9):1449–1452 (in Chinese) Google Scholar
  33. Wawrzynczak A, Kopka P, Borysiewicz M (2013) Sequential Monte Carlo in bayesian assessment of contaminant source localization based on the sensors concentration measurements. In: International conference on parallel processing and applied mathematics, pp 407–417Google Scholar
  34. Yan SW, Yu H, Zhang LL, Xu J, Wang ZP (2011) Water quantity and pollutant fluxes of inflow and outflow rivers of Taihu Lake. J Lake Sci 23(6):855–862 (in Chinese) CrossRefGoogle Scholar
  35. Zeng XK, Wang D, Wu JC (2012) Sensitivity analysis of the probability distribution of groundwater level series based on information entropy. Stoch Environ Res Risk Assess 26(3):345–356.  https://doi.org/10.1007/s00477-012-0556-2 CrossRefGoogle Scholar
  36. Zhang QQ, Xu YP, Zhang XJ (2012) Uncertainty analysis of water quality modeling and risk-based decision-making based on DRAM. J Zhejiang Univ (Eng Sci) 46(12):2231–2236 (in Chinese) Google Scholar
  37. Zhu HN, Yuan XZ, Liang J, Zeng GM, Jiang HW (2011) An integrated model for assessing the risk of water environmental pollution based on fuzziness. China Environ Sci 31(3):516–521 (in Chinese) Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Hydrology and Water ResourceHohai UniversityNanjingChina

Personalised recommendations