The aim of the studies was to develop an alternative method which could overcome the lack of sampling to improve the efficiency of control efforts for bovine endemic fluorosis. The spatial distribution characteristics of the disease were analysed and a prediction model for the estimation of fluorosis distribution in some districts in northwest Liaoning province in China was established. The model used ordinary kriging, and was evaluated using cross-validation. Analysis showed that the distribution of the disease was spatial autocorrelation. The prediction error of the cross-validation (ME = -0.0092, PMSE = 0.627, AKSE = 0.597, and RMSP = 1.007) and comparison with the actual disease distribution indicated that the prediction map accurately distributed bovine endemic fluorosis. It is feasible to predict bovine endemic fluorosis in the area by using ordinary kriging and limited data.
11269-008-9298-9.  Wackernagel H. Principal Component Analysis for Autocorrelated Data: A Geostatistical Perspective. Technical Report N-22/98/G, Centre de Geostatistique-Ecole de Mines de Paris, 1998. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.7550&rep=rep1&type=pdf .  Savelieva E. Using ordinarykriging to model radioactive contamination data. Appl GIS. 2005;1(2):10-01-10-10. DOI: 10.2104/ag050010.  Bancheri M, Serafin F, Bottazzi M, Abera W, Formetta G, Rigon R. The design, deployment, and testing of kriging models in GEOframe with SIK
factors in health (SIMS). Stanford University, Stanford (California). Madsen, H., van Griensven, A., Hojbjerk, A.L., 2010. Model calibration and validation in model-based water management. In: Vanrolleghem, P.A. (Ed.): Modelling Aspects of Water Framework Directive Implementation. IWA Publishing, London. Raid, I., Kusnezowa, T., Seewig, J., 2013. Application of ordinarykriging for interpolation of micro-structured technical surfaces. Measurement Science & Technology, 24, 9, 095201. Sanabria, L.A., Qin, X., Li, J., Cechet, R.P., Lucas, C., 2013. Spatial interpolation of
Persistent organic pollutants (POPs) originating from agrochemical industries have become an urgent environmental problem worldwide. Ordinary kriging, as an optimal geostatistical interpolation technique, has been proved to be sufficiently robust for estimating values with finite sampled data in most of the cases. In this study, ordinary kriging interpolation integrate with 3D visualization methods is applied to characterize the monochlorobenzene contaminated soil for an agrochemical industrial site located in Jiangsu province. Based on 944 soil samples collected by Geoprobe 540MT and monitored by SGS environmental monitoring services, 3D visualization in terms of the spatial distribution of pollutants in potentially contaminated soil, the extent and severity of the pollution levels in different layers, high concentration levels and isolines of monochlorobenzene concentrations in this area are provided. From the obtained results, more information taking into account the spatial heterogeneity of soil area will be helpful for decision makers to develop and implement the soil remediation strategy in the future.
Environmental factors are among the key determinants of real estate prices. They include landscape attractiveness, land relief, exposure to sunlight and proximity to natural features. In large urban centers, traffic noise emissions significantly affect decision-making on the real estate market. Weakly developed road networks and the absence of ring roads that shift road traffic outside residential districts are a widespread problem in the cities of Central-Eastern Europe. The prevention of traffic noise pollution, one of the key environmental problems in Europe, is an important goal of European Union policy. This paper analyzes the correlations between apartment prices and traffic noise levels in Olsztyn, the capital city of the Warminsko-Mazurskie Voivodeship (province) in north-eastern Poland. A linear correlation analysis was performed, and the distribution of unit prices of apartments was mapped by ordinary kriging.
The paper presents a selection of a semivariogram model in the study of spatial variability of soil moisture in a loess agricultural catchment. Soil moisture tests were carried out in the Moszenki village, 15 km northwest of Lublin. Soil moisture measurements were performed at two dates at 104 points, located on a rectangular surface measuring 700 × 1200 m. These points were laid out in the corners of a grid of squares with sides 100 m. In addition, 6 measurements were made at a distance of less than 100 m from the nearest points. Soil moisture was measured in the soil surface (0–5 cm). ArcGis software with Geostatistical Analyst extension was used for modelling semivariograms. In both terms, five models of semivariograms were used: stable, circular, spherical, exponential and Gaussian. Kriging was used for the estimation of soil moisture values. Among the semivariogram models analyzed in this study, the largest errors in the determined values of soil moisture relative to the empirical data were observed for the exponential model, and the smallest for the Gaussian model. However, it should be emphasized that the values of the analysed errors for the individual semivariogram models were similar. Application of the ordinary kriging method for interpolation of spatial distribution of soil moisture yields good results, but it has to be kept in mind that the final shape of the spatial distribution is influenced by the choice of the semivariance function model.
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