Vermiculite bio-barriers for Cu and Zn remediation: an eco-friendly approach for freshwater and sediments protection

The increase in heavy metal contamination in freshwater systems causes serious environmental problems in most industrialized countries, and the effort to find ecofriendly techniques for reducing water and sediment contamination is fundamental for environmental protection. Permeable barriers made of natural clays can be used as low-cost and eco-friendly materials for adsorbing heavy metals from water solution and thus reducing the sediment contamination. This study discusses the application of permeable barriers made of vermiculite clay for heavy metals remediation at the interface between water and sediments and investigates the possibility to increase their efficiency by loading the vermiculite surface with a microbial biofilm of Pseudomonas putida, which is well known to be a heavy metal accumulator. Some batch assays were performed to verify the uptake capacity of two systems and their adsorption kinetics, and the results indicated that the vermiculite bio-barrier system had a higher removal capacity than the vermiculite barrier (?34.4 and 22.8 % for Cu and Zn, respectively). Moreover, the presence of P. putida biofilm strongly contributed to fasten the kinetics of metals adsorption onto vermiculite sheets. In open-system conditions, the presence of a vermiculite barrier at the interface between water and sediment could reduce the sediment contamination up to 20 and 23 % for Cu and Zn, respectively, highlighting the efficiency of these eco-friendly materials for environmental applications. Nevertheless, the contribution of microbial biofilm in open-system setup should be optimized, and some important considerations about biofilm attachment in a continuous-flow system have been discussed.

Assessment of diesel exhaust particulate exposure and surface characteristics in association with levels of oxidative stress biomarkers

Exposure to PM10 and PM2.5 (particulate matter with aerodynamic diameter smaller than 10 μm and 2.5 μm, respectively) is associated with a range of adverse health effects, including cancer, pulmonary and cardiovascular diseases. Surface characteristics (chemical reactivity, surface area) are considered of prime importance to understand the mechanisms which lead to harmful effects. A hypothetical mechanism to explain these adverse effects is the ability of components (organics, metal ions) adsorbed on these particles to generate Reactive Oxygen Species (ROS), and thereby to cause oxidative stress in biological systems (Donaldson et al., 2003). ROS can attack almost any cellular structure, like DNA or cellular membrane, leading to the formation of a wide variety of degradation products which can be used as a biomarker of oxidative stress. The aim of the present research project is to test whether there is a correlation between the exposure to Diesel Exhaust Particulate (DEP) and the oxidative stress status. For that purpose, a survey has been conducted in real occupational situations where workers were exposed to DEP (bus depots). Different exposure variables have been considered: - particulate number, size distribution and surface area (SMPS); - particulate mass - PM2.5 and PM4 (gravimetry); - elemental and organic carbon (coulometry); - total adsorbed heavy metals - iron, copper, manganese (atomic adsorption); - surface functional groups present on aerosols (Knudsen flow reactor). (Demirdjian et al., 2005). Several biomarkers of oxidative stress (8-hydroxy-2'-deoxyguanosine and several aldehydes) have been determined either in urine or serum of volunteers. Results obtained during the sampling campaign in several bus depots indicated that the occupational exposure to particulates in these places was rather low (40-50 μg/m3 for PM4). Size distributions indicated that particles are within the nanometric range. Surface characteristics of sampled particles varied strongly, depending on the bus depot. They were usually characterized by high carbonyl and low acidic sites content. Among the different biomarkers which have been analyzed within the framework of this study, mean levels of 8- hydroxy-2'-deoxyguanosine and several aldehydes (hexanal, heptanal, octanal, nonanal) increased during two consecutive days of exposure for non-smokers. In order to bring some insight into the relation between the particulate characteristics and the formation of ROS by-products, biomarkers levels will be discussed in relation with exposure variables.

Protective effect of divalent cations against aluminum toxicity in soybean

A large proportion of soybean fields in Brazil are currently cultivated in the Cerrado region, where the area planted with this crop is growing considerably every year. Soybean cultivation in acid soils is also increasing worldwide. Since the levels of toxic aluminum (Al) in these acid soils is usually high it is important to understand how cations can reduce Al rhizotoxicity in soybean. In the present study we evaluated the ameliorative effect of nine divalent cations (Ca, Mg, Mn, Sr, Sn, Cu, Zn, Co and Ba) in solution culture on Al rhizotoxicity in soybean. The growth benefit of Ca and Mg to plants in an acid Inceptisol was also evaluated. In this experiment soil exchangeable Ca:Mg ratios were adjusted to reach 10 and 60 % base saturation, controlled by different amounts of CaCl2 or MgCl2 (at proportions from 100:0 up to 0:100), without altering the soil pH level. The low (10 %) and adequate (60 %) base saturation were used to examine how plant roots respond to Al at distinct (Ca + Mg)/Al ratios, as if they were growing in soils with distinct acidity levels. Negative and positive control treatments consisted of absence (under native soil or undisturbed conditions) or presence of lime (CaCO3) to reach 10 and 60 % base saturation, respectively. It was observed that in the absence of Aluminum, Cu, Zn, Co and Sn were toxic even at a low concentration (25 µmol L-1), while the effect of Mn, Ba, Sr and Mg was positive or absent on soybean root elongation when used in concentrations up to 100 µmol L-1. At a level of 10 µmol L-1 Al, root growth was only reverted to the level of control plants by the Mg treatment. Higher Tin doses led to a small alleviation of Al rhizotoxicity, while the other cations reduced root growth or had no effect. This is an indication that the Mg effect is ion-specific and not associated to an electrostatic protection mechanism only, since all ions were divalent and used at low concentrations. An increased exchangeable Ca:Mg ratio (at constant soil pH) in the acid soil almost doubled the soybean shoot and root dry matter even though treatments did not modify soil pH and exchangeable Al3+. This indicates a more efficient alleviation of Al toxicity by Mg2+ than by Ca2+. The reason for the positive response to Mg2+ was not the supply of a deficient nutrient because CaCO3 increased soybean growth by increasing soil pH without inducing Mg2+ deficiency. Both in hydroponics and acid soil, the reduction in Al toxicity was accompanied by a lower Al accumulation in plant tissue, suggesting a competitive cation absorption and/or exclusion of Al from plant tissue stimulated by an Mg-induced physiological mechanism.

Background and Reference Values of Metals in Soils from Paraíba State, Brazil

ABSTRACT Soil contamination by heavy metals threatens ecosystems and human health. Environmental monitoring bodies need reference values for these contaminants to assess the impacts of anthropogenic activities on soil contamination. Quality reference values (QRVs) reflect the natural concentrations of heavy metals in soils without anthropic interference and must be regionally established. The aim of this study was to determine the natural concentrations and quality reference values for the metals Ag, Ba, Cd, Co, Cu, Cr, Mo, Ni, Pb, Sb and Zn in soils of Paraíba state, Brazil. Soil samples were collected from 94 locations across the state in areas of native vegetation or with minimal anthropic interference. The quality reference values (QRVs) were (mg kg-1): Ag (<0.53), Ba (117.41), Cd (0.08), Co (13.14), Cu (20.82), Cr (48.35), Mo (0.43), Ni (14.44), Sb (0.61), Pb (14.62) and Zn (33.65). Principal component analysis grouped the metals Cd, Cr, Cu, Ni, Pb and Sb (PC1); Ag (PC2); and Ba, Co, Fe, Mn and Zn (PC3). These values were made official by Paraíba state through Normativa Resolution 3602/2014.

Heavy metal accumulation by intestinal helminths of vertebrates

The relevancy of parasites as potential indicators of environmental quality has been increasing over the last years, mostly due to the variety of ways in which they respond to anthropogenic pollution. The use of fish parasites as bioindicators of heavy metal pollution in aquatic ecosystems has been widely studied. However, little information concerning terrestrial habitats is presently available. In fact, in the last two decades several studies have been performed worldwide in different habitats and/or conditions (theoretically both in polluted and unpolluted terrestrialecosystems, but mainly in aquatic ecosystems) in order to investigate heavy metal pollution using parasitological models. Different groups of vertebrates (mainly fish, mammals and birds) and several parasitological models have been tested involving acanthocephalans mostly, but also cestodes and nematodes. It is not the aim of this chapter to do a complete revision of the availabledata concerning this subject. Instead, we emphasize some general aspects and compile a mini-review of the work performed in this field by our research group. The results obtained until now allow confirming several parasitic models as promising bioindicator systems to evaluate environmental cadmium and mainly lead pollution in terrestrial non-urban habitats, as it was already demonstrated for aquatic ecosystems. The present knowledge also allows confirming that parasites can reveal environmental impact. Environmental parasitology is an interdisciplinary field, which needs simultaneous expertise from toxicology, environmental chemistry and parasitology. Furthermore, environmental parasitology should be taken into account in order to increase the efficiency of environmental monitoring programs.

Heavy metal accumulation by intestinal helminths of vertebrates

The relevancy of parasites as potential indicators of environmental quality has been increasing over the last years, mostly due to the variety of ways in which they respond to anthropogenic pollution. The use of fish parasites as bioindicators of heavy metal pollution in aquatic ecosystems has been widely studied. However, little information concerning terrestrial habitats is presently available. In fact, in the last two decades several studies have been performed worldwide in different habitats and/or conditions (theoretically both in polluted and unpolluted terrestrialecosystems, but mainly in aquatic ecosystems) in order to investigate heavy metal pollution using parasitological models. Different groups of vertebrates (mainly fish, mammals and birds) and several parasitological models have been tested involving acanthocephalans mostly, but also cestodes and nematodes. It is not the aim of this chapter to do a complete revision of the availabledata concerning this subject. Instead, we emphasize some general aspects and compile a mini-review of the work performed in this field by our research group. The results obtained until now allow confirming several parasitic models as promising bioindicator systems to evaluate environmental cadmium and mainly lead pollution in terrestrial non-urban habitats, as it was already demonstrated for aquatic ecosystems. The present knowledge also allows confirming that parasites can reveal environmental impact. Environmental parasitology is an interdisciplinary field, which needs simultaneous expertise from toxicology, environmental chemistry and parasitology. Furthermore, environmental parasitology should be taken into account in order to increase the efficiency of environmental monitoring programs.

Assessment of diesel exhaust particulate exposure and surface characteristics in association with levels of oxidative stress biomarkers

Exposure to PM10 and PM2.5 (particulate matter with aerodynamic diameter smaller than 10 μm and 2.5 μm, respectively) is associated with a range of adverse health effects, including cancer, pulmonary and cardiovascular diseases. Surface characteristics (chemical reactivity, surface area) are considered of prime importance to understand the mechanisms which lead to harmful effects. A hypothetical mechanism to explain these adverse effects is the ability of components (organics, metal ions) adsorbed on these particles to generate Reactive Oxygen Species (ROS), and thereby to cause oxidative stress in biological systems (Donaldson et al., 2003). ROS can attack almost any cellular structure, like DNA or cellular membrane, leading to the formation of a wide variety of degradation products which can be used as a biomarker of oxidative stress. The aim of the present research project is to test whether there is a correlation between the exposure to Diesel Exhaust Particulate (DEP) and the oxidative stress status. For that purpose, a survey has been conducted in real occupational situations where workers were exposed to DEP (bus depots). Different exposure variables have been considered: - particulate number, size distribution and surface area (SMPS); - particulate mass - PM2.5 and PM4 (gravimetry); - elemental and organic carbon (coulometry); - total adsorbed heavy metals - iron, copper, manganese (atomic adsorption); - surface functional groups present on aerosols (Knudsen flow reactor). Several biomarkers of oxidative stress (8-hydroxy-2'-deoxyguanosine and several aldehydes) have been determined either in urine or serum of volunteers. Results obtained during the sampling campaign in several bus depots indicated that the occupational exposure to particulates in these places was rather low (40-50 μg/m3 for PM4). Bimodal size distributions were generally observed (5 μm and <1 μm). Surface characteristics of PM4 varied strongly, depending on the bus depot. They were usually characterized by high carbonyl and low acidic sites content. Among the different biomarkers which have been analyzed within the framework of this study, mean urinary levels of 8-hydroxy-2'-deoxyguanosine increased significantly (p<0.05) during two consecutive days of exposure for non-smoker workers. On the other hand, no statistically significant differences were observed for serum levels of hexanal, nonanal and 4- hydroxy-nonenal (p>0.05). Biomarkers levels will be compared to exposure variables to gain a better understanding of the relation between the particulate characteristics and the formation of ROS by-products. This project is financed by the Swiss State Secretariat for Education and Research. It is conducted within the framework of the COST Action 633 "Particulate Matter - Properties Related to Health Effects".

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Heavy metal ions are potent inhibitors of protein folding.

Environmental and occupational exposure to heavy metals such as cadmium, mercury and lead results in severe health hazards including prenatal and developmental defects. The deleterious effects of heavy metal ions have hitherto been attributed to their interactions with specific, particularly susceptible native proteins. Here, we report an as yet undescribed mode of heavy metal toxicity. Cd2+, Hg2+ and Pb2+ proved to inhibit very efficiently the spontaneous refolding of chemically denatured proteins by forming high-affinity multidentate complexes with thiol and other functional groups (IC(50) in the nanomolar range). With similar efficacy, the heavy metal ions inhibited the chaperone-assisted refolding of chemically denatured and heat-denatured proteins. Thus, the toxic effects of heavy metal ions may result as well from their interaction with the more readily accessible functional groups of proteins in nascent and other non-native form. The toxic scope of heavy metals seems to be substantially larger than assumed so far.

Metals and Selenium as bioindicators of geographic and trophic segregation in Giant Petrels Macronectes spp.

We analysed concentrations of cadmium, lead, mercury and selenium in blood from males and females of the 2 sibling species of giant petrels, the northern Macronectes halli and the southern M. giganteus, breeding sympatrically at Bird Island (South Georgia, Antarctica). Blood samples were collected in 1998 during the incubation period, from 5 November to 10 December. Between species, cadmium and lead concentrations were significantly higher for northern than for southern giant petrels, which probably resulted from northern giant petrels wintering in more polluted areas (mainly on the Patagonian Shelf and Falkland Islands) compared to southern giant petrels (wintering mainly around South Georgia and the South Sandwich Islands). Between sexes, cadmium concentrations were significantly higher for females than for males in both species, corresponding to the more pelagic habits of females compared to the more scavenging habits of males. Lead and cadmium concentrations in circulating blood decreased significantly over the incubation period, suggesting that when breeding at Bird Island, exposure to the source of pollution had ended, and these metals had been cleared from the blood and excreted, or rapidly transferred to other tissues. Association of lead and cadmium with a common source of pollution was further corroborated by a significant positive correlation between the levels of the 2 elements found. Mercury levels were similar between the species, but showed an opposite trend between sexes, with males showing higher levels than females in northern giant petrels, and the opposite was true in southern giant petrels, with no changes throughout incubation. Selenium levels were similar between sexes, but significantly greater for northern than for southern giant petrels. Moreover, there was a significant increase in the selenium levels over the incubation period in northern giant petrels. Age of adult birds did not affect metal concentrations. Coefficients of variation of metal levels were consistently lower for northern than for southern giant petrels, particularly for mercury, suggesting that the former species is more dietary specialised than the latter. Contaminant analyses, when combined with accurate information on seabird movements, obtained through geolocation or satellite tracking, help us to understand geographic variation of pollution in the marine environment.

Parametrització d’un procés basat en marro de cafè pel tractament d’aigües residuals que contenen Cr(VI)

Aquest projecte forma part d’un estudi sobre l’eliminació de metalls pesants utilitzant com aadsorbents residus d’indústries agroalimentàries, que des de fa uns quants anys està duent aterme el grup de Metalls i Medi Ambient (MiMA) del Departament d’Enginyeria Química,Agrària i Tecnologia Agroalimentària de la Universitat de Girona. L’objecte del treball és parametritzar un procés basat en marro de cafè que inclouetapes de reducció/adsorció i precipitació per al tractament d’aigües residuals quecontenen Cr(VI) i altres metalls. La parametrització permetrà conèixer en quinmoment s’ha assolit la reducció total del Cr(VI) en la primera etapa del tractament perpoder passar a una segona etapa d’eliminació de la resta dels metalls presents en solució

Adsorção de íons Cu2+ em latossolo vermelho-amarelo húmico

In environmental studies it is necessary to know the adsorption behavior of metals by soils, since the unfavorable effects of heavy metals and even the micronutrients at high concentrations in the environment are related to these adsorbents' ability to immobilize them. A sample of a humic yellow red oxisol from Araponga region in the State of Minas Gerais, Brazil, was used to verify the adsorption behavior of Cu2+ ions in this substrate. The mathematical model described by Langmuir's adsorption equation in its linearized form was applied and the values of the maximum capacity b and those of the constant related to the bonding energy a were obtained. Aliquots of copper nitrate solutions containing several concentrations of this metal were added to soil samples, the pH being predetermined for developing the adsorption experiments. The chemical and physical characterization of soil sample were performed by determining the organic carbon, nitrogen and phosphorus concentrations, cation exchange capacity (CEC), pH, concentration of metals (Al, Fe, K, Mg, Ca, Zn, Cu, Ni, Cr, Co, Pb, and Cd), granulometric analysis and X-ray diffraction. Langmuir isotherms presented two distinct adsorption regions at both pH 4 and pH 5, showing that the adsorptive phenomenon occurs in two distinct stages. The adsorption sites for the lower part presented greater bonding energy and low adsorption capacity compared with the adsorption sites of the part of the curve corresponding to higher Cu concentrations in the equilibrating solution.

Remoção de metais pesados de efluentes aquosos pela zeólita natural escolecita - influência da temperatura e do pH na adsorção em sistemas monoelementares

Cation exchange capabilities of a Brazilian natural zeolite, identified as scolecite, were evaluated for application in wastewater control. We investigated the process of sorption of chromium(III), nickel(II), cadmium(II) and manganese(II) in synthetic aqueous effluents, including adsorption isotherms of single-metal solutions. The natural zeolite showed the ability to take up the tested heavy metals in the order Cr(III) > Cd(II) > Ni(II) > Mn(II), and this could be related to the valence and the hydration radius of the metal cations. The influence of temperature (25, 40 and 60 ºC) and initial pH value (from 4 to 6) was also evaluated. It was found that the adsorption increased substantially when the temperature was raised to 60 ºC and that maximum adsorption capacity was observed at pH 6. These results demonstrate that scolecite can be used for removal of heavy metals from aqueous effluents, under optimized conditions.

Uptake of Hg2+ from aqueous solutions by microporous titano- and zircono-silicates

Being mercury one of the most toxic heavy metals present in the environment, it is of major concern to develop cleanup technologies to remove it from wastewater and recover mercury polluted ecosystems. In this context, we study the potential of some microporous titanosilicates and zirconosilicates for taking up Hg2+ from aqueous solutions. These materials have unique chemical and physical properties, and here we are able to confirm that they readily remove Hg2+ from aqueous solutions. Moreover, the presence of the competitive Mg2+ and Na+, which are some of the dominant cations in natural waters, does not reduce the uptake capacity of some of these materials. Thus, several inorganic materials reported here may have important environmental applications, efficiently removing Hg2+ from aqueous solutions.

Remoção de metais de solução aquosa usando bagaço de caju

The metal ions removal on cashew bagasse, a low-cost material, has been studied by batch adsorption. The parameters chemical treatment, particle size, biosorbent concentration, and initial pH were studied. In this study the maximum ions removal was obtained on the cashew bagasse treated with 0.1 mol/L NaOH/3 h, at optimum particle size (20-59 mesh), biosorbent concentration (50 g/L) and initial solution pH 5. The kinetic study indicated that the adsorption metal follows pseudo-second order model for a multielementary system and equilibrium time was achieved in 60 min for all metal ions.