Effects of Fe(III) and quality of humic substances on As(V) distribution in freshwater: Use of ultrafiltration and Kohonen neural network

Abstract

Humic substances (HS) are ubiquitous organic compounds able to affect mobility and availability of arsenic (As) in aquatic systems. Although it is known that associations between HS and As occur mainly via iron (Fe)-cationic bridges, the behaviour and distribution of this metalloid in HS- and Fe-rich environments is still not fully understood. In this paper, the quality of HS from different rivers in Brazil and Germany and its influence on the behaviour of As(V) under different Fe(III) concentrations were investigated. HS were extracted from four different rivers (Cascatinha, Holtemme, Selke and Warme Bode), characterised and fractionated into different molecular weight sizes (10, 5 and 1 kDa). Complexation tests were performed using an ultrafiltration system and 1 kDa membranes. All data was analysed using the Kohonen neural network (SOM - Self organising maps). All samples, except Selke, exhibited similar results of free As (<1 kDa). The results suggested that associations between HS, Fe and As were dependent on nitrogen (N)-aromatic carbon (C), amount of sulphur (S) and the molecular size of the HS. Although all HS appeared to be similar after looking at most variables analysed, the SOM could discriminate them into three different groups. Characterisation of the HS indicated that they had terrestrial material (from C plants) as precursor material. Most of the As and Fe was distributed in the fractions of higher (>10 kDa) and lower (<1 kDa) size. HS quality is an important factor to take into account when studying the behaviour of As in HS-rich environments.

Humic Acids Limit the Precipitation of Cadmium and Arsenate at the Brushite–Fluid Interface

“…As a major fraction of natural organic matters (NOM) ubiquitously present in soils and water environments, humic acid (HA) contains one or more functional groups, including phenolic, carboxyl, carbonyl and hydroxyl groups connected with aromatic or aliphatic carbons. − These functional groups can result in HA having a very strong chelation capacity with many heavy metals (metalloids) and organic pollutants. − This makes the removal or adsorption of contaminants more difficult by reactions at mineral surfaces. − Subsequently, mobilization of arsenic was enhanced, and Cd bioavailability was increased by HA, leading to a significant accumulation in aquatic biota …”

Self‐Organizing Maps for Evaluation of Biogeochemical Processes and Temporal Variations in Water Quality of Subtropical Reservoirs

“…On the other hand, maps with a large number of neurons will have samples that are too far away from each other. With both low and high number of neurons, this would have prevented the possibility to extract information (Garcia et al, 2007;Gontijo et al, 2017). For this reason, architectures from 6 × 6 to 14 × 14 neurons were trained.…”

Application of Kohonen Neural Network in Sports Cluster

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