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Studies regarding the role of geochemical processes in urban environmental matrices (UEM) and their influence on respiratory bioaccessibility in humans are scarce in humid tropical regions, especially in Brazil. Contaminated UEM are potentially hazardous to humans if particles < 10 µm in diameter are inhaled and reach the tracheobronchial region. In this study, we evaluated samples collected in Brazilian UEM with a large environmental liability left by former mining industries and in a city with strong industrial expansion. UEM samples were classified into soil, sediment and mine tailings according to the characteristics of the collection sites.The respiratory bioaccessibility of potentially harmful elements (PHE) was evaluated using artificial lysosomal fluid (ALF, pH 4.5), and the BCR-sequential extraction was performed to evaluate how the respiratory bioaccessibility of the PHE was related to the solid phase partitioning. The bioaccessible fraction (BAF) ranged from 54 -98% for Cd; 21 -89% for Cu; 46 -140% for Pb, 35 -88% for Mn and; 41 -84% for Zn. The average BAF of the elements decreased in the following order: Soil: Cd> Pb> Mn> Zn> Cu; Tailing: Pb> Cd> Zn> Mn> Cu; and Sediments: Pb> Mn> Cd> Zn> Cu. BCR-fractions were useful to predict the PHE bioaccessibility (R² = 0.79 -0.98), thus suggesting that particle geochemistry and mineralogy can influence PHE behaviour in the pulmonary fluid. Therefore, this approach provides a combination of quantitative and qualitative data, which allows us to carry out a more realistic assessment of the current situation of the potentially contaminated site and possible alternatives for decision-making by the stakeholders.

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The anthropogenic input of potentially toxic elements (PTEs) from industry, agrochemicals, etc., into the environment are of great concern. Models derived from pedotransfer functions can provide estimates of the levels of PTEs based on soil attributes. Based on the importance of these models in studies in contaminated areas, we assessed the concentrations of the reactive contents of Ba, Cu, Cr, Ni, Pb, and Zn in soils cultivated with vegetable crops in the state of São Paulo, Brazil. We also evaluated the influence of chemical and physical soil attributes on their reactivity and availability. The reactive contents of PTEs represent the fraction of PTEs easily sorbed at the adsorptions sites of organic matter, iron hydroxides, or clay. This fraction can supply information about the PTE content that is more or less readily released into the soil solution. The reactive and available fraction was extracted with 0.43 M HNO and 0.01 M CaCl, respectively. The proportion of reactivity of metal pools decreased in the order of Ba>Zn > Cu > Pb > Ni > Cr. The empirical models were able to predict the relationship between the reactive fractions, the pseudototal content, and the soil attributes. The available concentrations of Cr, Cu, Ni, and Pb in the soils were lower than the limit of quantification, while 3% of the Ba content and 1% of the Zn content were available in the soil solution in relation to their pseudototal content, suggesting low mobility of these elements in the soil.

Caracterização dos solos e teores pseudototais dos elementos potencialmente tóxicos. 3.3.2 Determinação da fração reativa dos elementos potencialmente tóxicos e influência dos atributos do solo .

Lettuce (Lactuca sativa) is the main leafy vegetable produced in Brazil. Since its production is widespread all over the country, lettuce traceability and quality assurance is hampered. In this study, we propose a new method to identify the geographical origin of Brazilian lettuce. The method uses a powerful data mining technique called support vector machines (SVM) applied to elemental composition and soil properties of samples analyzed. We investigated lettuce produced in São Paulo and Pernambuco, two states in the southeastern and northeastern regions in Brazil, respectively. We investigated efficiency of the SVM model by comparing its results with those achieved by traditional linear discriminant analysis (LDA). The SVM models outperformed the LDA models in the two scenarios investigated, achieving an average of 98 % prediction accuracy to discriminate lettuce from both states. A feature evaluation formula, called F-score, was used to measure the discriminative power of the variables analyzed. The soil exchangeable cation capacity, soil contents of low crystalized Al and Zn content in lettuce samples were the most relevant components for differentiation. Our results reinforce the potential of data mining and machine learning techniques to support traceability strategies and authentication of leafy vegetables.