Spatial representation of surface water monitoring and its assessment using geostatistical and non-geostatistical techniques in GIS
Abstract
Continuous monitoring of surface water is essential in terms of heavy metals investigation. Therefore, surface water quality is an environmental aspect which should be analyzed and monitored depending on its spatial distribution. The aim of this study is to provide an overview for evaluation of surface water pollution in the Mitrovica area by applying spatial distribution using Geographic Information System (GIS), geostatistical and non-geostatistical techniques. Nowadays, GIS with the geostatistics and non-geostatistics are very frequently used techniques in environmental monitoring studies. By providing the spatial distribution, there is possibility to place the pollution values in space. The surface water pollution caused by heavy metals (As, Cr, Cu, Ni, Pb, Zn and Cd) were sampled and analyzed from six monitoring stations in Sitnica river on different time series within three months countineously. The monitoring stations (samples) in Sitnica river were been distributed randomly. Pollution maps were produced using geostatistical and non-geostatistical (Spline and Kriging) approach. There were produced different pollution values in Sitnica river during the period of monitoring. Mainly the north part of Sitnica river has been poluted mostly with Heavy Metal Pollution Index (HPI) from 50 to 85 in the month of May, from 125 to 265 in the month of June and from 320 to 535 in the month of July. As well as the Metal Index (MI) from 0.60 to 2.05 in the month of May, June and July. The different statistical models were tested for geostatistical and non-geostatistical techniques in order to identify the best fitted technique for the pollution indices and the best interpolation techniques were selected on the basis of Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). These statistical tested model have shown that the best fitted interpolation technique is Kriging because of the lowest values of MSE, MAD, RMSE, MAE and MAPE. In the study were involved statistical models such as correlation and regression, for showing the relation between time series datasets and interpolated pollution indices as well. The cartographic output derived from the study were raster maps (15m spatial resolution) which represent the spatial distribution of surface water pollution as a result of monitoring process on time series. It is our believe that the present study will be used as a reference study for further environmental investigation and monitoring in Mitrovica since.
Keyword : surface water monitoring, spatial interpolation, GIS, cartographic output
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Babiker, I., Mohamed, M., Terao, H., Kato, K., & Ohta, K. (2004). Assessment of groundwater contamination by nitrate leaching from intensive vegetable cultivation using geographical information system. Environment International, 29(8), 1009-1017. https://doi.org/10.1016/S0160-4120(03)00095-3
Bonham-Carter, G. (1996). Geographic information systems for geoscientists: modeling with GIS. Computational Methods Geoscience, 13, 1-50.
Casseti, E & Semple, R. (1969). Concerning the testing of spatial diffusion hypotheses. Geographical Analysis, 1(3), 254-259. https://doi.org/10.1111/j.1538-4632.1969.tb00622.x
Dekonta. (2009). Consulting services for Environmental Assessment and Remedial Action Plan for Mitrovica Industrial Park, Kosovo. UNDP.
Fallahzadeh, R., Ghaneian, M., Miri, M. & Dashti, M. (2017). Spatial analysis and health risk assessment of heavy metals concentration in drinking water resources. Environmental Science and Pollution Research, 24, 24790-24802. https://doi.org/10.1007/s11356-017-0102-3
Ferati, F., & Ylli, K. A. (2016). Multivariate statistical analysis for the surface water quality of Trepça and Sitnica Rivers, Kosovo. Journal of International Environmental Application and Science, 11(1), 92-99.
Garnero, G., & Godone, D. (2013). Comparisons between different interpolation techniques. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-5/W3. Padua, Italy. https://doi.org/10.5194/isprsarchives-XL-5-W3-139-2013
Gharabia, A., Gharabia, S., Abushkak, Th., Wafi, H., Aish, A., Zelenakova, M., & Pilla, F. (2016). Groundwater quality evaluation using GIS based geostatistical algorithms. Journal of Geoscience and Environment Protection, 4, 89-103. https://doi.org/10.4236/gep.2016.42011
Gupta, M. & Srivastava, P. (2010). Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water International, 35, 233-245. https://doi.org/10.1080/02508061003664419
Ikechukwu, M., Ebinne, E., Idorenyin, U., & Raphael, N. (2017). Accuracy assessment and comparative analysis of IDW, spline and kriging in spatial interpolation of landform (topography): an experimental study. Journal of Geographic Information System, 9, 354-371. https://doi.org/10.4236/jgis.2017.93022
Jahanshani, R., & Zare, M. (2015). Assemsent of heavy metal pollution in groundwater of Golgohar iron ore mine area, Iran. Envrioenmental Eart Science, 74, 505-520. https://doi.org/10.1007/s12665-015-4057-8
Kumar, D., & Ahmed, S. (2003). Seasonal behaviour of spatial variability of groundwater level in a granitic aquifer in monsoon climate. Current Science, 84, 188-196.
Liu, C. W., Jang, C. S., & Liao, C. M. (2004). Evaluation of arsenic contamination potential using indicator kriging in the YunLin aquifer (Taiwan). Science of the Total Environment, 321(1-3), 173-188. https://doi.org/10.1016/j.scitotenv.2003.09.002
Maliqi, E., & Penev, P. (2018). Monitoring of vegetation change by using RS and GIS techniques in Mitrovica, Kosovo. Journal of Cartography and Geographic Information Systems, 1, 1-13. https://doi.org/10.23977/jcgis.2018.11001
Maliqi, E., Hyseni, D., & Maliqi, G. (2015). Application of GIS In the special zone of interest “Gumnishtë” – Kosovo. Micro, Macro & Mezzo Geoinformation, (4), 49-59.
Mohan, S., Nithila, P., & Reddy, S. (1996). Estimation of heavy metal in drinking water and development of heavy metal pollution index. Journal of Environmental Science and Health. Part A: Environmental Science and Engineering and Toxicology, 31(2), 283-289. https://doi.org/10.1080/10934529609376357
Simpson, G., & Wu, Y. (2014). Accuracy and effort of interpolation and sampling: Can GIS help lower field costs? ISPRS International Journal of Geo-Information, 3, 1317-1333. https://doi.org/10.3390/ijgi3041317
Singh, A., Raj, B., Tiwari, A., & Mahato, M. (2013). Evaluation of hydrogeochemical processes and groundwater quality in the Jhansi district of Bundelkhand region, India. Environmenatl Earth Science, 70(3), 1225-1247. https://doi.org/10.1007/s12665-012-2209-7
Tamasi, G., & Cini, R. (2004). Heavy metals in drinking waters from Mount Amiata (Tuscany, Italy). Possible risks from arsenic for public health in the province of Siena. Science of the Total Environment, 327(1-3), 41-51. https://doi.org/10.1016/j.scitotenv.2003.10.011
Tan, Q., & Xu, X. (2014). Comparative analysis of spatial interpolation methods: an experimental study. Sensors & Tranducers, 165(2), 155-163.
World Health Organization. (2007). Quality assurance of pharmaceuticals: a compendium of guidelines and related materials. Good manufacturing practices and inspection. World Health Organization, Geneva.
World Health Organization. (2017). Guidelines for drinking water quality: Vol. 1, recommendations (4 ed.). World Health Organization, Geneva.