Browsing by Author "Boente, Carlos"
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- Assessment of metal and metalloid contamination in soils trough compositional data: the old Mortórios uranium mine area, central PortugalPublication . Neiva, A.M.R.; Albuquerque, M.T.D.; Antunes, I.M.H.R.; Carvalho, Paula; Santos, A.C.T.; Boente, Carlos; Cunha, Pedro Proença; Henriques, S.B.A.; Pato, R.L.Soils from the old Mortórios uranium mine area were studied to look for contamination, as they are close to two villages, up to 3 km away, and used for agriculture. They are mainly contaminated in U and As and constitute an ecological threat. This study attempts to outline the degree to which soils have been affected by the old mining activities through the computation of significant hot clusters, Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the analyzed elements represent the soil's entirety. However, in geochemical studies these elements are just a fraction of the total soil composition. Thus, considering compositional data is pivotal. The spatial characterization, considering raw and compositional data together, allowed a broad discussion about not only the concentrations' spatial distribution, but also a better understanding on the possibility of trends of "relative enrichment" and, furthermore an insight in U and As fate. The highest proportions (compositional data) on U (up to 33%), As (up to 35%) and Th (up to 13%) are reached in the south-southeast segment. However, the highest concentrations (raw data) occur in north and northwest of the studied area, pointing out to a "relative enrichment" toward the south-southeast zone. The Mondego Sul area is mainly contaminated in U and As, but also in Co, Cu, Pb and Sb. The Mortórios area is less contaminated than the Mondego Sul area.
- Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soilsPublication . Boente, Carlos; Albuquerque, M.T.D.; Fernández-Braña, A.; Gerassis, Saki; Sierra, C.; Gallego, J.R.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.
- Compositional baseline assessments to address soil pollution : an application in Langreo, SpainPublication . Boente, Carlos; Albuquerque, M.T.D.; Gallego, J.R.; Pawlowsky-Glahn, Vera; Egozcue, Juan JoséPotentially 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.
- A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reservePublication . Boente, Carlos; Albuquerque, M.T.D.; Gerassis, Saki; Rodríguez-Valdés, E.; Gallego, J.R.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.
- A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reservePublication . Boente, Carlos; Albuquerque, M.T.D.; Gerassis, Saki; Rodríguez-Valdés, E.; Gallego, J.R.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.
- Local versus regional soil screening levels to identify potentially polluted areasPublication . Boente, Carlos; Gerassis, Saki; Albuquerque, M.T.D.; Taboada, Javier; Gallego, J.R.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.
- Mapping occupational health risk factors in the primary sector: a novel supervised machine learning and area-to-point poisson kriging approachPublication . Gerassis, Saki; Boente, Carlos; Albuquerque, M.T.D.; Ribeiro, M.M.A.; Abad, A.; Taboada, JavierWorkers 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.
- Nova metodologia para a construção de Índices Composicionais de Poluição em solos: um estudo de caso em Langreo, EspanhaPublication . Albuquerque, M.T.D.; Boente, Carlos; Gallego, J.R.; Pawlowsky-Glahn, Vera; Egozcue, Juan JoséO complexo comportamento geoquímico de Elementos Potencialmente Tóxicos (EPT) em solos pode afetar severamente os ecossistemas e a saúde humana. No entanto, é necessário compreender primeiro o que se entende por um solo “limpo” para depois poder avaliar um solo “poluído “e correspondente grau de severidade associado. Tendo em conta que a composição química de um solo é composicional, os atributos variam juntos, é, neste trabalho, introduzida uma nova metodologia para a construção de Indicadores Composicionais com base no formalismo da Análise de Dados Composicionais (CoDa). Na identificação de um equilíbrio composicional é necessário definir os elementos poluentes e os não poluentes para depois construir um Indicador de Poluição Composicional (IPC). Apresenta-se como estudo de caso a região contaminada de Langreo, noroeste de Espanha, onde num conjunto de 150 amostras de solo georreferenciadas foram analisados, em cada amostra, 37 elementos químicos. Calcularam-se depois, três IPCs, com base no formalismo CoDa e no conhecimento pericial. Num último passo, foram construídos mapas de distribuição espacial e de probabilidade, de exceder um dado limite, para os três novos indicadores de poluição, através de um processo estocástico de simulação sequencial – Simulação Sequencial Gaussiana (SSG). Os padrões espaciais obtidos e correspondente variabilidade associada, permitiu uma melhor compreensão dos processos associados à distribuição e ao enriquecimento relativo em EPTs.
- Understanding complex blasting operations: a structural equation model combining Bayesian networks and latent class clusteringPublication . Gerassis, Saki; Albuquerque, M.T.D.; García, J.F.; Boente, Carlos; Giráldez, E.; Taboada, Javier; Martín, JoséA 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.
- Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-makingPublication . Pazo, María; Boente, Carlos; Albuquerque, M.T.D.; Gerassis, Saki; Roque, Natália; Taboada, JavierThe health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including, ordinary kriging, and clustering, was used to generate national distribution maps for several factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers.