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.