Visual and Statistical Comparisons of Surface Modeling Techniques for Point-based Environmental Data

The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy)

In relation to 3D bathymetric modelling, this article aims to analyze the performance of Kriging approaches in dependence of the location and density of the measured depth points. The experiments were carried out on a multi-beam sonar (MBS) dataset that includes 240,000 soundings covering a sea-bottom area near Giglio Island (Italy). Seven subsets were derived in random way from the initial regular MBS dataset, selecting an increasing number of points uniformly spaced. Seven models were generated for both Ordinary Kriging and Universal Kriging. Each model was submitted to leave-one-out cross-validation to define the exactness of the predictive values and compared with the initial grid to better evaluate the accuracy in dependence of the point number and dissemination. To investigate this relationship, a new index called MVI (Morphological Variation Index) was introduced as a measurement of the level of variation of seabed morphology. The results validate the efficiency of the Kriging methods and remark the influence of the dataset distribution on the 3D model, highlighting MVI as a useful index to represent the seabed variation as a unique value. Finally, in no rugged areas using 1 point every 1000 m2, the RMSE of the differences between measured and interpolated values falls below 1 m, while a further increment of soundings is required in the presence of a high level of variation of seabed morphology.

Predicting dioxin-like PCBs soil contamination levels using milk of grazing animal as indicator

Dioxin-like PCBs (dl-PCBs) are ubiquitous persistent organic pollutants of recognized negative effects on human health. Assessing highly polluted areas should be an important public health issue. This study proposes to use the milk of grazing animals as a bioindicator of dl-PCB contamination in the environment. The hypothesis is that milk concentration of dl-PCBs are related to soil concentrations of these compounds, and that soils are generally reflective of a larger environmental issue of dl-PCB contamination. In this study, we evaluate the possibility of predicting soil concentrations using milk of sheep, cows and buffalos in a spatial model. For this purpose, samples of soil and milk collected in Campania (Italy) were introduced in a GIS platform to perform geostatistical analysis for building a simple predictive model. The ordinary least squares regressions (OLS) showed a statistically significant correlation (p<0.05) between soil and milk contamination. However, this relationship was spatially variable. Thus, a geographically weighted regression (GWR) was performed, obtaining R(2) values of 0.91, 0.77 and 0.66 for sheep's, buffalo's and cow's milk respectively. Assessed the mathematical relationships between the variables, new data was introduced to evaluate the performance of the model. Predictions of soil contamination with dl-PCBs using sheep's, cow's and buffalo's milk showed a mean error of 23%, 25% and 36% respectively. According to these results the sheep's milk can be considered the best bioindicator of dl-PCBs contamination among the three species. The results of this project evidence the potentialities of the proposed approach to assess bioindicator performance in a spatial predictive model.