Abstract
Guwahati, the lone city on the bank of the entire midstream of the Brahmaputra River, is facing acute civic problem due to severe depletion of water quality of its natural water bodies. This work is an attempt towards water quality assessment of a relatively small tributary of the Brahmaputra called the Bharalu River flowing through the city that has been transformed today into a city drainage channel. By analyzing the key physical, chemical and biological parameters for samples drawn from different locations, an assessment of the dissolved load and pollution levels at different segments in the river was made. Locations where the contaminants exceeded the permissible limits during different seasons were identified by examining spatial and temporal variations. A GIS developed for the watershed with four layers of data was used for evaluating the influence of catchment land use characteristics. BOD, DO and total phosphorus were found to be the sensitive parameters that adversely affected the water quality of Bharalu. Relationship among different parameters revealed that the causes and sources of water quality degradation in the study area were due to catchments input, anthropogenic activities and poor waste management. Elevated levels of total phosphorus, BOD and depleted DO level in the downstream were used to develop an ANN model by taking total phosphorus and BOD as inputs and dissolved oxygen as output, which indicated that an ANN based predictive tool can be utilized for monitoring water quality in the future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
American Society of Civil Engineers Task Committee on Application of Artificial Neural Networks in Hydrology (2000a). Artificial neural networks in hydrology, I preliminary concepts. Journal of Hydrologic Engineering, ASCE, 5(2), 115–123.
American Society of Civil Engineers Task Committee on Application of Artificial Neural Networks in Hydrology (2000b). Artificial neural networks in hydrology, II: Hydrologic applications. Journal of Hydrologic Engineering, ASCE, 5(2), 124–137.
APHA (1992). Standard methods for examination of water and wastewater 18th Edition; Prepared and published jointly by: American Public Health Association (APHA), American Water Works Association (AWWA), Water Pollution Control Federation (WPCF), New York.
Barica, J. (1974). Extreme fluctuations in water quality of eutrophic fish kill lakes: Effect of sediment mixing. Water Research, 8(11), 881–888.
Bellos, D., & Sawidis, T. (2005). Chemical pollution monitoring of the river Pinios (Thessalia – Greece). Journal of Environmental Management, 76, 282–292.
Brainwood, M. A., Burgin, S., & Maheshwari, B. (2004). Temporal variations in water quality of farm dams: Impacts of land use and water sources. Agricultural Water Management, 70, 151–175.
Capkin, E., Altinoc, I., & Karahan, S. (2006). Water quality and fish size affect toxicity of endosulfan, an organochlorine pesticide, to rainbow trout. Chemosphere, 64, 1793–1800.
de Vlaming, V., Di Giorgio, C., Fong, S., Deanovic, L. A, de la Paz Carpio-Obeso, M. D., Miller, J. L. et al. (2004). Irrigation runoff insecticide pollution of rivers in the Imperial Valley, California, USA. Environmental Pollution, 132, 213–229.
Donnelly, T. H., Barnes, C. J., Wasson, R. J., Murray, A. S., & Short, D. L. (1998). Catchment phosphorus sources and algal blooms – An interpretative review. Technical Report 18/98-CSIRO Land and Water Canberra, ACT 2601. Retrieved October 5, 2004 from http://www.clw.csiro.au/publication/technical98/tr18-98.pdf.
Farley, R. H. (2004). Calcium and alkalinity. Reefkeeping – an online magazine for aquarist. Retrieved February 8, 2005 from http://www.reefkeeping.com/issues/2002–04.
Goswami, D. C. (1985). Brahmaputra River, Assam, India: Physiography, basin denudation, and channel aggradation. Water Resources Research, 21(7), 959–978.
Hairstone, J. E., & Stribling, L. (1995). Typical contaminants and problems. Agriculture and natural resources. Retrieved October 5, 2004 from http://www.aces.edu/pubs/docs/A/ANR-0790/WQ2.3.6.pdf.
Holmer, M., & Storkholm, P. (2001). Sulphate reduction and sulphur cycling in lake sediments: A review. Freshwater Biology, 46, 431–451.
Izonfuo, L. W. A., & Bariweni, A. P. (2001). The effect of urban runoff water and human activities on some physico chemical parameters of the Epic Creek in the Niger Delta. Journal of Applied Science and Environmental Management, 5(1), 47–55.
Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environmental Modeling and Software, 15(1), 101–123.
Marnette, E. C., Hordijk, C., Breemen, N. V., & Coppenberg, T. E. (1992). Sulphate reduction in S-oxidation in Moorland Pool sediment. Biogeochemistry, 17, 123–143.
Martin, J. C., Hoggart, C., & Matissa, A. (1998). Improvement priorities for sewage treatment in Latvian small and medium sized towns. Water Science and Technology, 37(8), 137–144.
Neal, C., Harrow, M., & Williams, R. J. (1998). Dissolved carbon dioxide and oxygen in river Thames: Spring–summer 1997. The Science of the Total Environment, 210/211, 205–217.
Ohlrogge (2004). UW extention community needs assessment survey. Retrieved November 5, 2005 from http://www.uwex.edu/ces/cty/iowa/cnred/documents/waterqualityflyer.pdf.
Olajire, A. A., & Imeokparia, F. E. (2001). Water quality assessment of Osun River: Studies on inorganic nutrients. Environmental Monitoring and Assessment, 69, 17–28.
Parr, L. B., & Mason, C. F. (2003). Long-term trends in water quality and their impact on macroinvertebrate assemblages in eutrophic lowland rivers. Water Research, 37, 2969–2971.
Parr, L. B., & Mason, C. F. (2004). Causes of low oxygen in a lowland, regulated eutrophic river in Eastern England. Science of the Total Environment, 321, 273–286.
Rounds, S. A. (2002). Development of a neural network model for dissolved oxygen in the Tualatin River, Oregon. In proceedings of the second federal inter agency hydrologic modeling conference, Las Vegas, Nevada, July 29– August 1.Subcommittee on hydrology of the interagency advisory committee on water information. Retrieved October 14, 2005; from http://smig.usgs.gov/SMIG/features_0902/tualatin_ann.html.
Rutherford, J. C., Wilcock, R. J., & Hickey, C. W. (1991). Deoxygenation in a mobile bed river: I. Field studies. Water Research, 25(12), 1487–1497.
Sharma, J. N. (2005). Fluvial Process and Morphology of the Brahmaputra River in Assam. Geomorphology, 70, 226–256.
Silva, A. M. M., & Sacomani, L. B. (2001). Using chemical and Physical Parameters to define the quality of Pardo River water. Water Research, 35(6), 1609–1616.
Suen, J. P., & Eheart, J. W. (2003). Evaluation of neural networks for modeling nitrate concentrations in rivers. Journal of Water Resources Planning and Management, ASCE, 129(6), 505–510.
Tsiouris, S. E., Mamolos, A. P., Kalburtji, K. L., & Alifrangis, D. (2002). The quality of runoff water collected from a wheat field margin in Greece. Agriculture, Ecosystem and Environment, 89, 117–125.
USEPA (2004). Edition of the drinking water standards and health advisories. EPA/822-R-04–005, Washington, District of Columbia: US Environmental Protection Agency, 2004.
Wang, M., & Hjelmfelt, A. T. (1998). DEM based overland flow routing model. ASCE Journal of Hydrologic Engineering, 3(1), 1–8.
World Health Organization (WHO) (1997). Guidelines for drinking water quality (vol. 1) recommendations. Geneva: World Health Organization.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Girija, T.R., Mahanta, C. & Chandramouli, V. Water Quality Assessment of an Untreated Effluent Impacted Urban Stream: The Bharalu Tributary of the Brahmaputra River, India. Environ Monit Assess 130, 221–236 (2007). https://doi.org/10.1007/s10661-006-9391-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10661-006-9391-6