Abstract
River water is a major resource of drinking water on earth. Management of river water is highly needed for surviving. Yamuna is the main river of India, and monthly variation of water quality of river Yamuna, using statistical methods have been compared at different sites for each water parameters. Regression, correlation coefficient, autoregressive integrated moving average (ARIMA), box-Jenkins, residual autocorrelation function (ACF), residual partial autocorrelation function (PACF), lag, fractal, Hurst exponent, and predictability index have been estimated to analyze trend and prediction of water quality. Predictive model is useful at 95 % confidence limits and all water parameters reveal platykurtic curve. Brownian motion (true random walk) behavior exists at different sites for BOD, AMM, and total Kjeldahl nitrogen (TKN). Quality of Yamuna River water at Hathnikund is good, declines at Nizamuddin, Mazawali, Agra D/S, and regains good quality again at Juhikha. For all sites, almost all parameters except potential of hydrogen (pH), water temperature (WT) crosses the prescribed limits of World Health Organization (WHO)/United States Environmental Protection Agency (EPA).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
29 August 2020
As corresponding author, while going through higher research in this area, it is found that the formula for Hurst exponent given in equation (13) on page no. 401 is wrongly written.
References
Akoto O, Adiyiah J (2007) Chemical analysis of drinking water from some communities in the Brong Ahafo region. Int J Environ Sci Technol 4(2):211–214
Alam MJB, Islam MR, Muyen Z, Mamun M, Islam S (2007) Water quality parameters along rivers. Int J Environ Sci Technol 4(1):159–167
Amiri BJ, Nakane K (2009) Modeling the linkage between river water quality and landscape metrics in the Chugoku district of Japan. Water Resour Manag 23:931–956
APHA (1995) Standard methods for examination of water and waste water American Public Health Association. Washington D.C.19th Edn
Bhardwaj R, Parmar KS (2013a) Water quality index and fractal dimension analysis of water parameters. Int J Environ Sci Technol 10(1):151–164
Bhardwaj R, Parmar KS (2013b) Wavelet and statistical analysis of river water quality parameters. App Math Comput 219(20):10172–10182
Bhardwaj R, Parmar KS (2014) Water quality management using statistical analysis and time-series prediction model. Appl Water Sci. doi:10.1007/s13201-014-0159-9
Boskidis I, Gikas GD, Sylaios GK, Tsihrintzis VA (2012) Hydrologic and water quality modeling of lower Nestos river basin. Water Resour Manag 26:3023–3051
Box GEP, Jenkins GM, Reinsel GC (2008) Time series analysis: forecasting and control, 4th edn. John Wiely & Sons; Inc, U.K
Calvo IP, Estrada JCG, Savic D (2012) Heuristic modelling of the water resources management in the Guadalquivir river basin, southern Spain. Water Resour Manag 26:185–209
Chenini I, Khemiri S (2009) Evaluation of ground water quality using multiple linear regression and structural equation modeling. Int J Environ Sci Technol 6(3):509–519
CPCB (2006) Water Quality Status of Yamuna River (1999–2005): Central Pollution Control Board, Ministry of Environment & Forests, Assessment and Development of River Basin Series: ADSORBS/41/2006-07
DeLurgio SA (1998) Forecasting Principles and Applications; 1st Edition. Irwin McGraw-Hill Publishers
Diodato N, Guerriero L, Fiorillo F, Esposito L, Revellino P, Grelle G, Guadagno FM (2014) Predicting monthly spring discharges using a simple statistical model. Water Resour Manage 28:969–978
Dutta D, Wilson K, Welsh YD, Nicholls D, Kim S, Cetin L (2013) A new river system modelling tool for sustainable operational management of water resources. J Environ Manag 121:13–28
Fang H, Wang X, Lou L, Zhou Z, Wu J (2010) Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Res 44(5):1562–1572
Hermans C, Erickson J, Noordewier T, Sheldon A, Kline M (2007) Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont. J Environ Manag 84:534–546
Kahya E, Kalayci S (2004) Trend analysis of streamflow in Turkey. J Hydrol 289:128–144
McCleary R and Hay RA (1980) Applied Time Series Analysis for the Social Sciences,Beverly Hills, Sage
Mousavi M, Kiani S, Lotfi S, Naeemi N, Honarmand M (2008) Transient and spatial modeling and simulation of polybrominated diphenyl ethers reaction and transport in air, water and soil. Int J Environ Sci Technol 5(3):323–330
Movahed M, Hermanisc E (2008) Fractal analysis of river flow fluctuations. Physica A 387(4):915–932
Panepinto D, Genon G (2010) Modeling of Po River water quality in Torino (Italy). Water Resour Manag 24:2937–2958
Park J, Park C (2009) Robust estimation of the Hurst parameter and selection of an onset scaling. Stat Sinica 19(4):1531–1555
Parmar KS, Chugh P, Minhas P, Sahota HS (2009) Alarming pollution levels in rivers of Punjab. Indian J Env Prot 29(11):953–959
Rangarajan G (1997) A climate predictability index and its applications. Geophys Res Lett 24(10):1239–1242
Rangarajan G, Ding M (2000) Integrated approach to the assessment of long range correlation in time series data. Phys Rev E 61(5):4991–5001
Rangarajan G, Sant DA (2004) Fractal dimensional analysis of Indian climatic dynamics. Chaos Solitons Fractals 19(2):285–291
Shukla JB, Misra AK, Chandra P (2008) Mathematical modeling and analysis of the depletion of dissolved oxygen in eutrophied water bodies affected by organic pollutant. Non-linear Anal: Real World Appl 9(5):1851–1865
Singh KP, Malik A, Mohan D, Sinha S (2004) Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study. Water Res 38(18):3980–3992
Su S, Li D, Zhang Q, Xiao R, Huang F, Wu J (2011) Temporal trend and source apportionment of water pollution in different functional zones of Qiantang River. China Water Res 45(4):1781–1795
Toprak ZF (2009) Flow discharge modeling in open canals using a new fuzzy modeling technique (SMRGT). CLEAN-Soil Air Water 37(9):742–752
Toprak ZF, Eris E, Agiralioglu N, Cigizoglu HK, Yilmaz L, Aksoy H, Coskun G, Andic G, Alganci U (2009) Modeling monthly mean flow in a poorly gauged basin by fuzzy logic, CLEAN-Soil, Air. Water 37(7):555–564
Vassilis Z, Antonopoulos M, Mitsiou AK (2001) Statistical and trend analysis of water quality and quantity data for the Strymon River in Greece. Hydrology Earth Syst Sci 5(4):679–691
Weng YC, Chang NB, Lee TY (2008) Nonlinear time series analysis of ground-level ozone dynamics in Southern Taiwan. J Environ Manag 87:405–414
WHO (1971) International standards for drinking water. World Health Organization, Geneva
Yarar A (2014) A hybrid wavelet and neuro-fuzzy model for forecasting the monthly streamflow data. Water Resour Manag 28:553–565
Acknowledgments
Authors are thankful to University Grant Commission (UGC) (F. 41-803/2012 (SR)), Government of India for financial support; Central Pollution Control Board (CPCB), Government of India for providing the research data; and Guru Gobind Singh Indraprastha University, New Delhi (India) for providing research facilities. First author is also thankful to Sant Baba Bhag Singh Institute of Engineering and Technology for providing study leave to pursue research degree.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Michael Matthies
Rights and permissions
About this article
Cite this article
Parmar, K.S., Bhardwaj, R. Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management. Environ Sci Pollut Res 22, 397–414 (2015). https://doi.org/10.1007/s11356-014-3346-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-014-3346-1