Pejović and 5 co-authors reported in 2017 that prevailing climatic conditions and local topography can be classified as the most influential environmental factors that affect the spatial dispersion of pollutants emanating from industrial sources. In this study, the combined effects of these factors were considered with respect to terrain exposure in order to explain the complex spatial trend of Arsenic (As) concentration that was atmospherically-deposited from one of the largest Copper Mining and Smelting Complexes in Europe, Bor in Serbia [see Google search results (here)]
Several exposure parameters were created and employed as spatial covariates within the so-called “Spline-Then-Krige” approach for producing maps of As
concentration at three standard soil depth layers (0–5 cm, 5–15 cm and 15–30 cm). The exposure parameters were created to quantify two different aspects of terrain exposure: Geometrical (Proximity) and Topographical exposure. Regression analysis confirmed the presence of a significant statistical association between the As data and all exposure parameters.
The trend model showed good overall accuracy explaining 52% of the variance in As data for the surface soil layer, 49% for the middle layer and 35% for the deepest layer. Relative importance analysis revealed the importance of considering a more general model that includes interactions between exposure parameters. The kriging interpolation improved, to some extent, the regression accuracy for all three layers with R2 values ranging from 55% for the surface layer to the 36% for the deepest soil layer.
The prediction maps show that As contamination levels are well above allowable Serbian agricultural concentration limits (As < 25 mg/kg) for approximately 78% of the mapping area, thereby indicating that long term smelting activity leaves significant consequences on soil even on deeper unexposed layers.
The authors reviewed a method for geostatistical mapping of atmospherically-deposited pollutants from a known source by considering terrain exposure. The methodology applied is based on the so-called “Spline-Then-Krige” approach, which enables the production of a suite of maps for different soil depths. The exposure parameters were created to explain two different aspects of terrain exposure: Geometrical (Proximity) and Topographical exposure. Based on the obtained results, the main conclusion can be drawn as follows:
1. The equal-area spline depth function provides a reliable estimate of continuous vertical distribution of As data.
2. Stepwise regression analysis confirmed the utility of the predictive capability for all of the designed exposure parameters as well as for the two interactions: ES:DEM and DEM:CD. This confirms the hypothesis that there is an association between spatial spreading of Arsenic from the copper smelter in Bor and terrain exposure parameters.
3. The trend model showed good overall accuracy for all soil layers. The highest accuracy was obtained for the surface soil layer, where the model explained 52% of data variation. The trend model explained 49% of variations for the second layer, and for the third layer 35% of data variations.
4. The relative importance analysis showed that the trend models at each depth are highly controlled by the CD and DEM. Significant influences of interaction effects between ES and DEM as well as between DEM and CD at each depth indicate the importance of considering a more general model that includes interactions between exposure parameters.
5. The residual spatial dependence showed significant differences in structure between surface and other soil layers, indicating different effects of trend removal.
6. The kriging interpolation improved, to some extent, the regression accuracy for all three layers with R2 ranging from 0.36 for the deepest layer to the 0.55 for the surface soil layer.
7. The relatively high RMSE values that follows the prediction on each soil layer indicates that a great portion of Arsenic data variation remains unexplained by the trend model, which implies that other variables in addition to the wind-driven process affects the Arsenic spatial distribution. However, in a situation when the wind indeed has an important role on spatial distribution of soil pollutants, integration of topographic exposure parameters could be useful for prediction, even on a deeper soil layers.
8. The obtained results were consistent with those reported by Goovaerts et al., 2008, suggesting that such an approach could be a promising alternative for complex air dispersion models. The direct comparison of these two approaches was not possible in this study due to missing data necessary for dispersion model characterization but it will certainly be the focus of a future study.
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