Soil contamination in outdoor shooting ranges (OSRs) is a major threat for human health, particularly when, after the end of activities, the land is used for recreational areas or agricultural production. The status of land degradation of an OSR in southern Italy was assessed using a multisensor approach. It was based on: i) proximal sensors, including electromagnetic induction (EMI) for measuring soil electrical conductivity (ECa) and magnetic susceptibility (MSa), γ-ray spectrometry for K, eU and eTh analyses and ultrasonic penetrometry detecting cone index (CI) data representative of soil\'s strength, ii) field surveys on soil thickness (ST), and iii) laboratory analyses of potentially-toxic-elements (PTEs) by portable X-ray fluorescence spectrometry and polycyclic aromatic hydrocarbons (PAHs) by gas-chromatography. Spatial variability of measurements was modelled and mapped using geostatistical methods. The most densely measured covariate (i.e., the ECa of the topsoil) was used within kriging with external drift to improve the PTEs predictions. The PTEs maps were complemented by maps of spatial uncertainty. A robust multivariate principal component analysis (rPCA) was applied to proximal sensor and laboratory data and allowed to identify associations of PAHs, lead, CI with the topsoil ECa along the first component (PC1), highlighting the correlation between land anthropogenic effects and EMI measures; while the association between the ST (estimating the depth of underground travertine hard-layers) and the bottom soil ECa and MSa along the second component (PC2) evidenced the influence of soil stratigraphy on the EMI measures. This study demonstrates that the simultaneous use of different proximal sensors associated with laboratory analysis can allow to assess and model the spatial variability of the land degradation status of an OSR, including soil compaction, organic and inorganic contamination. The correlation between EMI data with the PTEs content highlights the potential of this technique in the field of soil contamination.

The hydrogeological processes driving the hydrochemical composition of groundwater in the alpine pristine aquifer system of the Port del Comte Massif (PCM) are characterized through the multivariate statistical techniques Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM) in the framework of Compositional Data (CoDa) analysis. Also, the groundwater Natural Background Levels (NBLs) for NO3 and SO4 and Cl are evaluated, which are specially important for indicating the occurrence of groundwater contamination derived from the anthropic activities conducted in the PCM. The different hydrogeochemical facies found in the aquifer system of the PCM comprises low mineralized Ca-HCO3 water for the main Eocene karst aquifer, and Ca-SO4 and highly mineralized NaCl water types in the minor aquifers discharging from the PCM. The NBL values of SO4, Cl and NO3 obtained for the main karst aquifer are 14.33, 4.06 and 6.55 mg/L, respectively. These values are 35, 3 and 1.2 times lower than the respective official NBLs values that were determined by the water administration to be compared with in the case of conducting a pollution assessment characterization in the main karst aquifer. Official overestimation of NBLs can put important groundwater resources in the PCM at risk.

Two-way contingency tables arise in many fields, such as in medical studies, where the relation between two discrete random variables or responses is to be assessed. We propose to analyze and visualize a sample of 2 × 2 tables in the context of single-subject repeated measurements design by means of compositional data (CoDa) methods. First, we propose to visualize the tables in a quaternary diagram. Second, we show how to represent these tables by means of logratios indicating the relationship between the two variables as well as their strength and direction of dependency. Finally, we describe a technique to model those tables with a simplicial regression model. Data from a real-world study of self-prediction of migraine attack onset is used to illustrate this methodology. For each individual, the 2 × 2 table of their migraine expectation vs next day migraine occurrence is computed, generating a sample of tables. Then we visualize and interpret the prediction ability of individuals both in the simplex and in terms of logratios of components. Finally, we model the self-prediction ability with respect to demographic variables, days tracked and disease characteristics. Our application demonstrates that CoDa can be a useful tool for visualizing, modeling, and interpreting the components of 2 × 2 tables.

The specific objectives of this study were: (1) to establish background content levels of potentially harmful elements in topsoil of Quintero and Puchuncavi districts; (2) to assess the degree of topsoil pollution and identify the local sources of pollution; and (3) to assess the health risk of metal exposure to population. Data from a Chilean Environmental Ministry report were analyzed. Multivariate analysis suggests that the potentially harmful elements are split into two main groups, which can be explained by controlling geogenic (Ni, V and Zn) and anthropogenic (As, Cd, Cu and Pb) factors. The spatial distribution of As, Cd, Cu and Pb displays a spatial pattern around the Puchuncavi-Ventanas industrial park. The background values determined with MAD (median absolute deviation) method include As 31.6 mg kg-1, Cd 0.7 mg kg-1, Cu 296 mg kg-1, Ni 16.9 mg kg-1, Pb 48.6 mg kg-1, V 199 mg kg-1 and Zn 192 mg kg-1. The pollution indices reported considerable contamination to very high contamination in some soil samples. Hazard quotient values were higher for children than for adults, due to the ingestion and dermal absorption, while the cancer risk index values were in the acceptable range.

BACKGROUND: At a global level, access to safe drinking water and sanitation has been monitored by the Joint Monitoring Programme (JMP) of WHO and UNICEF. The methods employed are based on analysis of data from household surveys and linear regression modelling of these results over time. However, there is evidence of non-linearity in the JMP data. In addition, the compositional nature of these data is not taken into consideration. This article seeks to address these two previous shortcomings in order to produce more accurate estimates.
METHODS: We employed an isometric log-ratio transformation designed for compositional data. We applied linear and non-linear time regressions to both the original and the transformed data. Specifically, different modelling alternatives for non-linear trajectories were analysed, all of which are based on a generalized additive model (GAM).
CONCLUSIONS: Non-linear methods, such as GAM, may be used for modelling non-linear trajectories in the JMP data. This projection method is particularly suited for data-rich countries. Moreover, the ilr transformation of compositional data is conceptually sound and fairly simple to implement. It helps improve the performance of both linear and non-linear regression models, specifically in the occurrence of extreme data points, i.e. when coverage rates are near either 0% or 100%.