Compositional baseline assessments to address soil pollution: An application in Langreo, Spain.

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.

Assessment of metal and metalloid contamination in soils trough compositional data: the old Mortórios uranium mine area, central Portugal.

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.

A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reserve.

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.

Spatial environmental risk evaluation of potential toxic elements in stream sediments.

Potential toxic elements (PTE), in stream sediments, were used as contamination indicators for the definition of high-/low-grade spatial clusters in the Monfortinho area (Central Portugal). A set of 271 stream sediment samples was used for spatial modelling and further definition of rings of enrichment-high and low rings. A three-step multivariate statistical and geostatistical approach was used: (1) principal components analysis for PTE's association evaluation and dimensionality reduction; (2) ordinary kriging as an unbiased interpolator for content inference and construction of a continuous representation of the considered attributes, at any arbitrary spatial location; (3) G clustering algorithm for the definition of high and low significance clusters. A moderate contamination in stream sediments is observed for almost all the considered PTE and a very high contamination for Ba, Cr and B. High contamination clusters are observed for Fe, Ni, Ba, Cu, B, Zn, V-northwest and southeast clusters-and for Cr-north and southwest clusters. The contamination degree index varies from moderate to high, which is mainly associated with the old mineralizations. The high computed rings often overlap the areas of abandoned Ba-Zn mineralization, as well as the sedimentary gold concentrations, along the Erges River banks. Tin and Cd spatial distribution may be related to former cassiterite exploitations in the survey area. Chromium is possibly connected with the schists. The definition of clusters with a PTE spatial enrichment will allow for the identification of contamination activities and therefore, the definition of adequate monitoring and mitigation actions.

Combining raw and compositional data to determine the spatial patterns of Potentially Toxic Elements in soils.

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 (80km2), 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 analyzed make 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. Based on the obtained findings it was possible to conclude that the Langreo area is deeply affected by its industrial and mining legacy. City center is highly enriched in Pb and Hg and As shows enrichment in a northwesterly direction.

Potential toxic elements in stream sediments, soils and waters in an abandoned radium mine (central Portugal).

The Alto da Várzea radium mine (AV) exploited ore and U-bearing minerals, such as autunite and torbernite. The mine was exploited underground from 1911 to 1922, closed in 1946 without restoration, and actually a commercial area is deployed. Stream sediments, soils and water samples were collected between 2008 and 2009. Stream sediments are mainly contaminated in As, Th, U and W, which is related to the AV radium mine. The PTEs, As, Co, Cr, Sr, Th, U, W, Zn, and electrical conductivity reached the highest values in soils collected inside the mine influence. Soils are contaminated with As and U and must not be used for any purpose. Most waters have pH values ranging from 4.3 to 6.8 and are poorly mineralized (EC = 41-186 µS/cm; TDS = 33-172 mg/L). Groundwater contains the highest Cu, Cr and Pb contents. Arsenic occurs predominantly as H2(AsO4)- and H(AsO4)2-. Waters are saturated in goethite, haematite and some of them also in lepidocrocite and ferrihydrite, which adsorbs As (V). Lead is divalent in waters collected during the warm season, being mobile in these waters. Thorium occurs mainly as Th(OH)3(CO3)-, Th(OH)2(CO3) and Th(OH)2(CO3) 22- , which increase water Th contents. Uranium occurs predominantly as UO2CO3, but CaUO2(CO3) 32- and CaUO2(CO3)3 also occur, decreasing its mobility in water. The waters are contaminated in NO2-, Mn, Cu, As, Pb and U and must not be used for human consumption and in agricultural activities. The water contamination is mainly associated with the old radium mine and human activities. A restoration of the mining area with PTE monitoring is necessary to avoid a public hazard.

Developing a new Bayesian Risk Index for risk evaluation of soil contamination.

Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.

Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal).

Mining and mineral-processing activities can modify the environment in a variety of ways. Sulfide mineralization is notorious for producing waters with high metal contents. Arsenic is commonly associated with sulfide mineralization and is considered to be toxic in the environment at low levels. The studied abandoned mining area is located in central Portugal and the resulting tailings and rejected materials were deposited and exposed to the air and water for the last 50 years. Sixteen water sample-points were collected. One of these was collected outside the mining influence, with the aim of obtaining a reference background. The risk assessment, concerning the proximity to abandoned mineralized deposits, needs the evaluation of intrinsic and specific vulnerabilities aiming the quantification of the anthropogenic activities. In this study, two indicator variables were constructed. The first one (I(1)), a specific vulnerability, considers the arsenic water supply standard value (0.05 mg/L), and the probability of it being exceeded is dependent on the geologic and hydrological characteristics of the studied area and also on the anthropogenic activities. The second one (I(2)), an intrinsic vulnerability, considers arsenic background limit as cut-off value, and depends only on the geologic and hydro-geological characteristics of the studied area. At Segura, the arsenic water content found during December 2006 (1.190 mg/L) was higher than the arsenic water content detected in October 2006 (0.636 mg/L) which could be associated to the arsenic released from Fe oxy-hydroxide. At Segura abandoned mining area, the iso-probability maps of October 2006 and December 2006, show strong anomalies associated with the water drainage from abandoned mining activities. Near the village, the probability of exceeding the arsenic background value is high but lower than the probability of exceeding the arsenic water supply value. The arsenic anomalies indicate a high probability for water arsenic contamination and those waters should not be used for human consumption.