A multivariate non-parametric approach for estimating probability of exceeding the local natural background level of arsenic in the aquifers of Calabria region (Southern Italy)

The concept of natural background level (NBL) aims at distinguishing the natural and anthropogenic contributions to concentrations of specific contaminants, as groundwater management and protection tools. This is usually defined as a unique value at a regional scale, even when the hydrogeological and geochemical features of a certain territory are far from homogeneous. The concentration of target contaminants is affected by multiple hydrogeochemical processes. This is the case of arsenic in the Calabria region, where concentrations are definitely variable in groundwater. To overcome the limitation of a traditional approach and to include the intrinsic hydrogeological and geochemical heterogeneity into the definition of the natural contribution to As content in groundwater, an integrated probabilistic approach to the NBL assessment combining aquifer-based preselection criteria and multivariate non-parametric geostatistics was proposed. In detail, different NBL values were selected, based on the aquifer type and/or hydrogeochemical features. Then, these aquifer-based NBL values of arsenic were used in the Probability Kriging method to map the probability of exceedance and to provide contamination risk management tools. This multivariate geostatistical approach that takes advantage of the physico-chemical variables used in the aquifer-based NBL values definition allowed mapping the probability of exceedance of As in a physically-based way. The hydrogeochemical diversity of the study area and all the processes affecting As concentrations in the aquifers have been considered too. As a result, the obtained map was characterized by a short-range and long-range variability due to local hydrogeochemical anomalies and water-rock interaction and/or atmospheric precipitation. By this approach, the NBL exceedance probability maps proved to be less "noisy", because the local hydrogeochemical conditions were filtered, and more capable of pointing out anthropogenic inputs or very anomalous natural contributions, which need to be investigated more in detail and properly managed.

Modeling 3D soil lithotypes variability through geostatistical data fusion of CPT parameters

The Cone Penetration Test (CPT) measures enable to recognize vertical lithological sequence at each investigated point. From the tip resistance q_c and sleeve resistance f_s profiles, the Soil Behavior Type index I_SBT has been calculated, in order to identify the lithotypes alongside depth. The present study focuses on the combination of different variables to provide a lithological and mechanical subsoil characterization. The main objectives of the paper are: (1) to model the 3D spatial variability structure of the soil lithotypes and mechanical properties using q_c and f_s profiles; 2) to evaluate the uncertainties of the estimates for designing purposes. 182 CPTs were collected in a 900 km² area (corresponding to a subsoil volume of about 12 km³) located in the study site in the Bologna province (Italy). The study area is made up of fine-grained soils, silt and clay mixtures that are intercalated at different depths by sandy and gravelly soils. These variations of each soil fraction affect the engineering properties of these alluvial deposits. For 3D modeling, two geostatistical methods, Ordinary Kriging (stationary method), and Intrinsic Random Function theory (non-stationary method) have been used. The results show that the non-stationary method allows to obtain more reliable q_c and f_s predicted values. The final stochastic mechanical and lithological model enables engineers and geologists to detect the emerging of fan and paleochannels bodies where mean resistance values can abruptly change in terms of bearing capacity, liquefaction potential and static and dynamic settlement occurrence.

Site-specific management of salt affected soils: A case study from Egypt

In Egypt all agricultural practices are generally applied uniformly without taking spatial variability into consideration, which is not efficient and may be more expensive than site-specific management approach. This is based on accurate assessment of within-field variation and on field delineation into homogeneous zones to be submitted to differential management. Multivariate geostatistics allows to assess and model the spatial variation of a set of soil attributes influencing management. The objective of this paper was to propose an approach for determining spatially variable rate application (VRA) of leaching water, to control soil salinity, and of fertilizer to improve productivity while reducing environmental impact. The research was conducted in an experimental 3.1-ha field in Egypt and the following soil attributes were measured: electrical conductivity (ECe), available nitrogen (N), available phosphorus (P), available potassium (K) and organic matter content (OM). Ordinary cokriging was applied to produce thematic maps of soil attributes and the appropriateness of the linear model of coregionalization was evaluated with cross-validation. Spatial maps of the five soil variables were classified into three isofrequencies classes and the mean values were calculated for each class. These values were then compared with critical reference values to assess the local soil requirements for reducing soil salinity and/or improving soil fertility. The results showed that the estimations of soil attributes were unbiased and accurate. Only for ECe and available nitrogen site-specific management would be preferable because it would reduce the agricultural costs for both soil reclamation (saving water used to leach salts) and improvement of soil N fertility in comparison with the traditional uniform methods. The proposed approach, though producing encouraging results, would require improvements in the determination of the threshold values used to plan salt leaching and soil fertilization.

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325-1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R(2) (LL R(2)) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.

Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing

Soil survey is generally time-consuming, labor-intensive, and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (EC a ) recorded with electromagnetic induction (EMI) sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk EC a survey, has been applied in an agricultural field in Apulia region (southeastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries, and preliminary observations. Three optimization criteria were used. the first criterion (minimization of mean of the shortest distances, MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (minimization of weighted mean of the shortest distances, MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid EC a data as weighting function; and the third criterion (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time, and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The use of bulk EC a gradient as an exhaustive variable, known at any node of an interpolation grid, has allowed the optimization of the sampling scheme, distinguishing among areas with different priority levels.

[The apexcardiogram in acute myocardial infarct]

The relation between intracardial haemodynamics and apicocardiogram (ACG) parameters is explained. A wave (amplitude and duration), A/H ratio, true and total TCI, total systole, total expulsion, RIV, RFW, TE/TCI (total) and TE/TCI (true) findings in 22 patients with acute myocardial infarct are presented. Attention is also given to clinical and radiological signs of cardiac insufficiency and the infarct site. Constant and significant increases in the A wave, A/H ratio and RIV, together with a decrease in total expulsion, were noted, particularly in cases with clinical evident insufficiency. In the pre-expulsive stage, ACG Data could not be taken as a reliable index of myocardial contractility in cases where insufficiency was not manifest. It is felt, therefore, that ACG may be of assistance in the evaluation of changes in myocardial performance, even where clinical and radiological signs of decompensation are absent.