Trend Analysis of Total Affected Water and Total Discharged Wastewater of Nišava District (Serbia)

Nina Pavićević

doi:10.3889/oamjms.2020.4764

Authors

Nina Pavićević Megatrend University of Belgrade, Faculty of Management Zajećar (FMZ),

DOI:

https://doi.org/10.3889/oamjms.2020.4764

Keywords:

Natural resources (NR), trend analysis, statistical analysis, polynomial regression model (PRM)

Abstract

BACKGROUND: Water, as a natural resource, is the most basic substance of life that has immeasurable significance for the living world, ecosystems, and planet Earth. It is consumed by plants, animals, and humans.

AIM: We aimed to preform a trend analysis of total affected quantities of water and total discharged wastewater (TDWW) of Nišava district (Serbia).

METHODS: In this paper, a trend analysis is given of total affected quantities of water, delivered quantities of drinking water (DQDW), total discharged wastewater (TDWW), wastewater discharges to wastewater systems, and number of households connected to the water supply network of Nišava district (Serbia).

RESULTS: The values for Nišava district (Serbia) for total affected quantities of water and DQDW for the period 2006–2018 and wastewater discharges to wastewater systems for the period 2009–2018 decreased, whereas the values for Nišava district (Serbia) for TDWW for the period 2006–2018 and number of households connected to the water supply network for the period 2007–2018 increased. The paper also provides regression models for approximation DQDW (eq. 1) and TDWW (eq. 2) for Nišava district (Serbia) for the period 2006–2018.

CONCLUSION: Values for total affected quantities of water (×103 m³) for Nišava district (Serbia) for the period 2006–2018, they decreased from 41740 in 2006 to 9931 in 2018.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Plum Analytics Artifact Widget Block

References

Barbier EB. Natural Resources and Economic Development. 2nd ed. Cambridge, UK: Cambridge University Press; 2019.

Andersen AD. Towards a new approach to natural resources and development: The role of learning, innovation and linkage dynamics. Int J Technol Learn Innov Dev. 2012;5(3):291-324. https://doi.org/10.1504/ijtlid.2012.047681

Salvati L, Marco Z. Natural resource depletion and economic performance of local districts: Suggestions from a within-country analysis. J Sustain Dev World Ecol. 2008;15(6):518-23. https://doi.org/10.1080/13504500809469847

Schilling M, Chiang L. The effect of natural resources on sustainable development policy: The approach of non-sustainable externalities. Energy Policy. 2011;39(2):990-8. https://doi.org/10.1016/j.enpol.2010.11.030

George G, Schillebeeckx SJ. Managing Natural Resources: Organizational Strategy, Behaviour and Dynamics. Cheltenham, UK: Edward Elgar Publishing Ltd.; 2018. https://doi.org/10.4337/9781786435729

George G, Schillebeeckx SJ, Liak TL. The management of natural resources: An overview and research agenda. Acad Manage J. 2015;58(6):1595-613. https://doi.org/10.5465/amj.2015.4006

Nelson SL, Hudson JW, Hooker A. The use of statistical analysis to optimize pioneer natural resources’ vertical spraberry-trend portfolio. SPE Econ Manage. 2013;5(3):105-17. https://doi.org/10.2118/162927-pa.

Smith VK. Natural resource scarcity: A statistical analysis. Rev Econ Stat. 1979;61(3):423-7. https://doi.org/10.2307/1926071

Tarasyev AM, Vasilev J, Turygina VF. Statistical analysis and forecasting of extraction and use of natural resources. AIP Conf Proc. 2018;2040:050011. https://doi.org/10.1063/1.5079109.

Tarasyev AM, Vasilev JA, Turygina VF, Kravchuk SV, Strelchuk AE. Methods for predicting the production of natural resources AIP Conf Proc. 2019;2186:050010. https://doi.org/10.1063/1.5137943.

Aščić A, Imamović M. Statistical descriptions of delivered quantity of water by sources in the federation of Bosnia and Herzegovina. MATEC Web Conf 2017;126:04007. https://doi.org/10.1051/matecconf/201712604007.

Bordalo AA, Teixeira R, Wiebe WJ. A water quality index applied to an international shared river basin: The case of the Douro River. Environ Manage. 2006;38(6):910-20. https://doi.org/10.1007/s00267-004-0037-6 PMid:17039391

Egborge AB, Benka-Coker J. Water quality index: Application in the Warri River, Nigeria. Environ Pollut Ser B. 1986;12(1):27-40. https://doi.org/10.1016/0143-148x(86)90004-2.

Elezović N, Ilić-Komatina D, Dervišević I, Ketin S, Dašić P. Analysis of SWQI index of the river Ibar (Serbia). Fresenius Environ Bull. 2018;27(4):2505-9.

Selvam S, Manimaran G, Sivasubramanian P, Balasubramanian N, Seshunarayana T. GIS-based evaluation of water quality index of groundwater resources around Tuticorin coastal city, South India. Environ Earth Sci. 2014;71(6):2847-67. https://doi.org/10.1007/s12665-013-2662-y

Von der Ohe CP, Prüß A, Schäfer RB, Liess M, De Deckere E, Brack W. Water quality indices across Europe-a comparison of the good ecological status of five river basins. J Environ Monit. 2007;9(9):970-8. https://doi.org/10.1039/b704699p PMid:17726558

Ferreira NC, Bonetti C, Seiffert WQ. Hydrological and water quality indices as management tools in marine shrimp culture. Aquaculture. 2011;318(3-4):425-33. https://doi.org/10.1016/j.aquaculture.2011.05.045

Boyacioglu H. Utilization of the water quality index method as a classification tool. Environ Monit Assess. 2010;167(1-4):115-24. https://doi.org/10.1007/s10661-009-1035-1 PMid:19543993

Kannel PR, Lee S, Lee YS, Kanel SR, Khan SP. Application of water quality indices and dissolved oxygen as indicators for river water classification and urban impact assessment. Environ Monit Assess. 2007;132(1-3):93-110. https://doi.org/10.1007/s10661-006-9505-1 PMid:17279460

Gupta AK, Gupta SK, Patil RS. A comparison of water quality indices for coastal water. J Environ Sci Health Part A Toxic Hazard Subst Environ Eng. 2003;38(11):2711-25. https://doi.org/10.1081/ese-120024458 PMid:14533934

Kaurish FW, Younos T. Developing a standardized water quality index for evaluating surface water quality. J Am Water Resour Assoc. 2007;43(2):533-45. https://doi.org/10.1111/j.1752-1688.2007.00042.x

Rene ER, Saidutta MB. Prediction of water quality indices by regression analysis and artificial neural networks. Int J Environ Res. 2008;2(2):183-8.

Kovačević M. Municipalities in the Serbia, 2006. Belgrade, Serbia: Republican Bureau of Statistics of Serbia; 2007.

Milojić A. Municipalities in the Serbia, 2010. Belgrade, Serbia: Republican Bureau of Statistics of Serbia; 2010.

Milojić A. Municipalities and Regions in the Republic of Serbia, 2014. Belgrade, Serbia: Republican Bureau of Statistics of Serbia; 2014.

Gavrilović D. Municipalities and Regions in the Republic of Serbia, 2016. Belgrade, Serbia: Republican Bureau of Statistics of Serbia; 2016.

Gavrilović D. Municipalities and Regions in the Republic of Serbia, 2019. Belgrade, Serbia: Republican Bureau of Statistics of Serbia; 2019.

Dašić P. Application of polynomial regression models for approximation of time series. J Econ Manage Based New Technol. 2012;1(2):81-160.

Dašić P, Dašić J, Antanasković D, Pavićević N. Statistical analysis and modeling of global innovation index (GII) of Serbia. In: Lecture Notes in Networks and Systems. Berlin, Germany: Springer; 2020;128:515-21. https://doi.org/10.1007/978-3-030-46817-0_59.

Tošović R, Dašić P, Ristović I. Sustainable use of metallic mineral resources of Serbia from an environmental perspective. Environ Eng Manage J. 2016;15(9):2075-84. https://doi.org/10.30638/eemj.2016.224

Turmanidze R, Dašić P, Popxadze, G. Statistical analysis of e-government development index (EGDI) of Georgia. In: Lecture Notes in Networks and Systems. Berlin, Germany: Springer; 2020;128:930-8. https://doi.org/10.1007/978-3-030-46817-0_105.

Cowan G. Statistical Data Analysis. New York, USA: Oxford University Press; 1998.

Schmuller J. Statistical Analysis with Excel for Dummies. 4th ed. Hoboken, New Jersey, USA: John Wiley & Sons Inc.; 2016.

Winston W. Microsoft Excel Data Analysis and Business Modeling. 5th ed. Redmond, Washington, USA: Microsoft Press; 2016.