Search results

When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination.

The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growing public concern regarding Hg high toxicity. Here we present the exemplary case study of an abandoned Hg mine located in the Somiedo Natural Reserve (Spain). Until its closure in the 1970s, this mine operated under no environmental regulations, its tailings dumped in two spoil heaps, one of them located uphill and the other in the surroundings of the village of Caunedo. This study attempts to outline the degree to which soil and other environmental compartments have been affected by the two heaps. To this end, we used a novel combination of multivariate statistical, geostatistical and machine-learning The techniques used included principal component and clustering analysis, Bayesian networks, indicator kriging, and sequential Gaussian simulations. Our results revealed high concentrations of Hg and, secondarily, As in soil but not in water or sediments. The innovative methodology abovementioned allowed us to identify natural and anthropogenic associations between 25 elements and to conclude that soil pollution was attributable mainly to natural weathering of the uphill heap. Moreover, the probability of surpassing the threshold limits and the local backgrounds was found to 31 be high in a large extension of the area. The methodology used herein demonstrated to be effective for addressing complex pollution scenarios and therefore they are applicable to similar cases.

The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growing public concern regarding Hg high toxicity. Here we present the exemplary case study of an abandoned Hg mine located in the Somiedo Natural Reserve (Spain). Until its closure in the 1970s, this mine operated under no environmental regulations, its tailings dumped in two spoil heaps, one of them located uphill and the other in the surroundings of the village of Caunedo. This study attempts to outline the degree to which soil and other environmental compartments have been affected by the two heaps. To this end, we used a novel combination of multivariate statistical, geostatistical and machine-learning methodologies. The techniques used included principal component and clustering analysis, Bayesian networks, indicator kriging, and sequential Gaussian simulations. Our results revealed high concentrations of Hg and, secondarily, As in soil but not in water or sediments. The innovative methodology abovementioned allowed us to identify natural and anthropogenic associations between 25 elements and to conclude that soil pollution was attributable mainly to natural weathering of the uphill heap. Moreover, the probability of surpassing the threshold limits and the local backgrounds was found to be high in a large extension of the area. The methodology used herein demonstrated to be effective for addressing complex pollution scenarios and therefore they are applicable to similar cases.

Soil screening levels (SSLs) are reference threshold values required by environmental laws, established based on soil geochemical background data from often-extensive sampling areas. Such areas are often inappropriate for interpreting the true risk of pollution in small areas, since they overlook local factors (e.g., geology, industry, and traffic), which are unfeasible to encompass in large-scale samplings. To solve this issue, the calculation of local SSLs is proposed herein, performed on amajor scale closer to the area of interest. To exemplify this proposal, a soil sampling campaign was performed in the Municipality of Langreo, one of the most industrialized areas in the Principality of Asturias (northwestern Spain). Sampling allowed the measurement of local soil screening levels for several inorganic contaminants. Afterwards, a soil pollution index was calculated, referred to both regional and local thresholds, to assess the degree of contamination. Both pollution indicators were subjected to a methodology based on a Bayesian network analysis, followed by a stochastic sequential Gaussian simulation approach. The methodologies used showed differences in the identification of potentially polluted areas depending on the soil screening levels (regional or local) used. It was concluded that, in urban/industrial cores, local soil screening levels facilitate the identification of polluted areas and also reduce the uncertainty associated with sampling density and diffuse contamination. Thus, the use of local levels circumvents false-positive areas that would be classified as polluted were regional soil screening levels to be used.

A probabilistic Structural Equation Model (SEM) based on a Bayesian network construction is introduced to perform effective safety assessments for technicians and managers working on-site. Using novel AI software, the introduced methodology aims to show how to deal with complex scenarios in blasting operations, where typologically different variables are involved. Sequential Bayesian networks, learned from the data, were developed while variables were grouped into different clusters, representing related risks. From each cluster, a latent variable is induced giving rise to a final Bayesian network where cause and effect relationships maximize the prediction of the accident type. This hierarchical structure allows to evaluate different operational strategies, as well as analyze using information theory the weight of the different risk groups. The results obtained unveil hidden patterns in the occurrence of accidents due to flyrock phenomena regarding the explosive employed or the work characteristics. The integration of latent class clustering in the process proves to be an effective safeguard to categorize the variable of interest outside of personal cognitive biases. Finally, the model design and the software applied to show a flexible workflow, where workers at different corporate levels can feel engaged to try their beliefs to design safety interventions.

Workers around the world spend nearly a quarter of their time at work Occupational health is gaining great importance due to the profound impact on people long term health. The health status of the primary sector workforce is a great unknown for medical geography where health maps and spatial patterns have not been able to explain years of changing disease rates. This article proposes a new approach based on a solid characterization of the health status, which is the target node of an information theory-based Bayesian network machine-learnt from 13,000 medical examinations undertook to rural workers in Spain between 2012 and 2016. From the main health risks identified, a supervised binary logistic regression is used to produce a classification of adverse medical conditions giving rise to not healthy workers. Finally, Area-to-Point Poisson kriging is computed to provide a spatial analysis representing the incidence rate and spatial patterns of the main adverse medical conditions over the Spanish territory. The study illustrates how to overcome the challenges of working with discrete occupational data. Conceptually, high cholesterol and high glucose can be pinpointed with accuracy as the two main health risks for the working population in the primary sector.

Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3. Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.

“This is a post-peer-review, pre-copyedit version of an article published in Environmental Geochemistry and Health. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10653-019-00347-x”.