María Aurora Armienta Hernández

Adsorption/desorption of arsenic by tropical peat: influence of organic matter, iron and aluminium

The objective of this work was to investigate the interaction of arsenic species (As(III) and As(V)) with tropical peat. Peat samples collected in Brazil were characterized using elemental analysis and 13C NMR. Adsorption experiments were performed using different concentrations of As with peat in natura and enriched with Fe or Al, at three different pH levels. Peat samples, in natura or enriched with metals, were analysed before and after adsorption processes using Fourier transform infrared spectroscopy (FTIR) spectroscopy. The adsorption kinetics was evaluated, and the data were fitted using the Langmuir and Freundlich models. The results showed that interaction between As and peat was dependent on the levels of organic matter (OM) and the metals (Fe and Al). As(III) was not adsorbed by in natura peat or Al-enriched peat, although small amounts of As(III) were adsorbed by Fe-enriched peat. Adsorption of As(V) by the different peat samples ranged from 21.3 to 52.7 μg g−1. The best fit to the results was obtained using the pseudo-second-order kinetic model, and the adsorption of As(V) could be described by the Freundlich isotherm model. The results showed that Fe-enriched peat was most effective in immobilizing As(V). FTIR analysis revealed the formation of ternary complexes involving As(V) and peat enriched with metals, suggesting that As(V) was associated with Al or Fe-OM complexes by metal bridging.

Principled Data Preprocessing: Application to Biological Aquatic Indicators of Water Pollution

In many biological studies, statistical and data mining methods are extensively used to analyze the data and discover actionable knowledge. But, bad data quality causing incorrect analysis results and wrong interpretations may induce misleading conclusions and inadequate decisions. To ensure the validity of the results, avoid bias and data misuse, it is necessary to control not only the whole analytical pipeline, but most importantly the quality of the data with appropriate data preprocessing choices. Since various preprocessing techniques and alternative strategies may lead to dramatically different outputs, it is crucial to rely on a principled and rigorous method to select the optimal set of data preprocessing steps that depends both on the input data distributional characteristics and on the inherent characteristics of the targeted statistical or data mining methods. In this paper, we propose a method that selects, given a dataset, the optimal set of preprocessing tasks to apply to the data such that the overall data preprocessing output maximizes the quality of the analytical results for various techniques of clustering, regression, and classification. We present some promising results that validate our approach on biomonitoring data preparation.