DEVELOPMENT OF WATER QUALITY MATRIX THROUGH SURROGATE MODELING

Authors

  • Laura Kusari University of Prishtina

DOI:

https://doi.org/10.6092/issn.2281-4485/7735

Keywords:

Water quality, monitoring matrix, river pollution, linear regression, surrogate relationships.

Abstract

This paper presents the outcome of a research project that was focused on the monitoring of surface water quality through the development of a correlation matrix. The matrix was developed for six main water quality parameters by the use surrogate relations. The grab sampling was performed at selected sites and the same samples were used in the laboratory for the preparation of subsamples. Those subsamples were tested for Turbidity, Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), Total Organic Carbon (TOC) and Nitrates. Data were analyzed by statistical analyses, using linear regression and the outcome was used for the development of a correlation matrix of main water quality parameters.

The analyses revealed that in this study site, TSS has high positive correlation with BOD, COD and NO3 as well as with turbidity. The highest positive correlation was noticed between turbidity and BOD, NO3, TSS andCOD. On the other hand, only Total Organic Carbon (TOC) was negatively (inversely) correlated with the studied parameters. The correlation matrix developed will help in determining the water quality status by using few parameters.

References

BERTRAND-KRAJEWSKI, J. L., (2007). Stormwater Pollutant Loads Modelling: Epistemological Aspects and Case Studies on the Influence of Field Data Sets Calibration and Verification. Water Science and Technology, 55(4): 1-17.

BERTRAND-KRAJEWSKI, J. L., BRIAT, P., & SCRIVENER, O., (1993). Sewer Sediment Production and Transport Modelling: A Literature Review. Journal of Hydraulic Research, 31(4), 435-460.

BOANO, F., REVELLI, R., & RIDOLFI, L., (2005). Source Identification in River Pollution Problems: A Geostatical Approach. Water Resources Research, 41(7).

GOONETLLIKE, A., EGODAWWATTA, P., & KITCHEN, B., (2009). Evaluation of Pollutant Bulid up and Wash off from Selected Land UUses at the Port of Brisbane, Australia. Marine Pollution Bulletin, 58, 213-221.

HAN, Y., LAU, S., KAYANIAN, M., & STENSTORM, M. (2006). Characteristics of Highway Stormwater Runoff. Water Environment Research, 78(12), 2377-2388.

HELSEL, D., & HIRSCH, R. (2002). Statistical Methods in Water Resources- Analyses and Interpretations. U.S. Geological Survey, Tecniques of Water Resources Investigations. Retrieved from http://pubs.usgs.gov/twri/twri4a3/

KAYHANIAN, M., SUVERKROPP, C., RUBY, A., & TSAY, K. (2007). Characterisation and Prediction of Highway Runoff onstituent Event Mean Concentration. Journal of Environment Management, 85, 279-295.

KUSARI, L., (2017). Regression Model as a Tool to Predict Concentrations of Total Suspended Solids in Rivers. EQA- International Journal of Environmental Quality, 23, 35-42.

LINDBLOM, E., AHLMAN, S., & MIKKELSEN, P. (2007). Uncertainty in Model - based Prediction of Copper Loads in Stormwater Runoff. Water Science and Tecnology, 56(6), 11-1.

MINGUTANA, N. S., EGODAWATTA, P., KOKOT, S., & GOONETILLEKE, A. (2010). Determination of a Set of Surrogate Parameters to Asses Urbanstormwater Quality. Science of the Total Environment, 408(24), 6251-6259.

RAZAVI, S., TOLSON, B., BURN, D., (2012).Review of Surrogate Modelling in Water Resources. Water Resources Research, 48 (7)

SETTLE, S., GOONETILLIKE, A., & AYOKO, G. (2007). Determination of Surrogate Indicators for Phosphorus and Solids in Urban Stormwater. Application of Multivariete Data Analyses Techniques. Water, Air and Soil Pollution, 182(1-4), 149-161.

SING, K., MALIK, A., MOHAN, D., & SINBA, S. (2004). Multivariete Statistical Techniques or the Evaluation of Spatial and Temporal Variations in Water Quality of Gomi River - case study. Water Resources, 38, 3980-3992.

THOMSON, N., MCBEAN, E., SNODGRASS, W., & MONSTRENKO, I. (1997). Highway Stormwater Quality: Development of Surrogate Parameter Relationship. Water, Air and Soil Pollution, 94, 307-347.

WAGENER, T., & GUPTA, H. (2005). Model Identification for Hydrological Forecasting under Uncertainty. Stochastic Environmental Research and Risk Assessment, 19, 378-387.

WANG, X., LU, Y., HAN, J., HE, GZ, & WANG, T. (2007). Identification of Anthropogenic Influences on Water Quality of Rivers in Taihu Watershed. Journal of Environmental Science, 19, 475-481.

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Published

2018-03-05

How to Cite

Kusari, L. (2018). DEVELOPMENT OF WATER QUALITY MATRIX THROUGH SURROGATE MODELING. EQA - International Journal of Environmental Quality, 28, 25–34. https://doi.org/10.6092/issn.2281-4485/7735

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