Bioleaching of cadmium and nickel from synthetic sediments by Acidithiobacillus ferrooxidans

The microbial leaching process was evaluated for the treatment of synthetic sediments contaminated with cadmium and nickel sulfides. A series of batch leaching experiments was conducted to compare metal solubilization in sediment inoculated with Acidithiobacillus ferrooxidans -inoculated sediments to that in sterile control sediment. The rate and extent of metal solubilization were significantly higher in A. ferrooxidans -inoculated reactors than in acidified sterile reactors. The efficiency of cadmium (Cd) solubilization (80) in the bioleaching process was higher than that of nickel (Ni) solubilization (60). The performance of leaching reactors containing only culture supernatants was comparable to that of A. ferrooxidans -inoculated reactors, indicating that indirect non-contact leaching by the products of microbial metabolism is the predominant mechanism for metal solubilization rather than direct microbial sulfide oxidation. Moreover, the similar (60–75%) extents of Cd²⁺ leaching with A. ferrooxidans , cell-free filtrate, and Fe³⁺ suggest that abiotic oxidation of CdS by Fe³⁺ controls the overall leaching rate, and the role of A.␣ferrooxidans is most likely not to oxidize CdS mineral directly but to regenerate Fe³⁺ as an oxidant.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

Short-term influence of phosphate and nitrate on heavy metal accumulation by red alga Acrosorium uncinatum

Heavy metal accumulation (Cu, Zn, Ni, and Pb) in common marine macroalga, Acrosorium uncinatum under nutrient (phosphate and nitrate) enriched (experiment 1) and starved (experiment 2) conditions over a short exposure period (12 h) were examined in this study. Control was maintained in seawater contained nutrient solution without addition of metals and in seawater alone for experiment 1 and 2, respectively. Among the four metals studied, the accumulation of Zn, Ni, and Pb was considerably lower than Cu. The accumulation factor for all metals varies greatly in different nutrient concentrations, but it increases as the exposure of metal concentration decreases in both the experiments. The results of the present findings established that this macroalga is an accumulator of metals Cu, Zn, Ni, and Pb and have the potential to accumulate these metals even in a short time exposure period (12 h). Even though metal accumulation by A. uncinatum largely depends on the available concentration in the medium, nutrients like phosphate and nitrate can affect the accumulation significantly.     

Desiccation of contaminant barrier materials amended with aqueous carboxymethyl cellulose solution

The stabilization potential of negatively charged sodium carboxymethyl cellulose (CMC) solution was assessed through investigation of its retention on clays under environmental conditions that promote soil desiccation. Sodium montmorillonite and kaolinite, commonly used in clayey soils, were mixed with aqueous CMC solutions in concentrations ranging from 0 to 10 g/L. These samples were dried in a specially-designed desiccation chamber which was operated at a temperature of 25°C and relative humidity of 30%. The results show an inverse proportionality between liquid loss and CMC concentration. Liquid loss from clayey soil follows the first-order reaction with a rate constant in the range of 4.6–6.7 mg/h. CMC half-lives on sodium montmorillonite during desiccation ranged from 103 to 181 h for an aqueous concentration range of 0.5–10 g/L compared to 108 h for distilled water. For kaolinite, more liquid was retained at 10 g/L CMC concentration than at other concentrations, but liquid retention was generally insignificant. These conclusions are valid for a desiccation duration of 890 h, a time that is reasonably simulative of the duration of exposures of bare ground surfaces to weather elements. The experimental results are explained in terms of the role of CMC molecular interactions with clay minerals in controlling fluid flow to desiccating clay surfaces.     

Creating integrated business and environmental value within the context of China’s circular economy and ecological modernization

This paper investigates the challenges and opportunities of how firms and organizations can and will be able to strike a better balance between economic growth and environmental stewardship in the context of China’s emerging ‘circular economy’ policy paradigm and based on ecological modernization theoretic approaches.Based on three company case studies in the information technology and electronic industries in China, we identify and demonstrate how a blended business and environmental value can be created from adopting a sustainable supply chain management approach. The adoption of a sustainable supply chain management approach is rapidly becoming a key business challenge and opportunity in China and other large emerging economies around the world, where our greatest environmental management challenges currently reside and will continue to exist for many years to come. The value creation framework proposed in research focuses on evaluating three case study companies who appear in various stages of an electronic industry supply chain. Value creation within a supply chain can provide the impetus for organizations to adopt circular economy, sustainable supply chain practices, for competitive reasons.In addition, we describe how a value proposition can be evaluated at two levels of analysis, a more specific micro-level and a more general meso-level of analysis. The four major business value dimensions include cost reduction, revenue generation, resiliency, and legitimacy and image.The initial findings are that a variety of opportunities exist for electronic firms in emerging and developing countries, while results from this study provide an important scholarly foundation to develop and refine sustainable supply chain management practices in emerging and developing economies.     

AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems

AQUATOX combines aquatic ecosystem, chemical fate, and ecotoxicological constructs to obtain a truly integrative fate and effects model. It is a general, mechanistic ecological risk assessment model intended to be used to evaluate past, present, and future direct and indirect effects from various stressors including nutrients, organic wastes, sediments, toxic organic chemicals, flow, and temperature in aquatic ecosystems. The model has a very flexible structure and provides multiple analytical tools useful for evaluating ecological effects, including uncertainty analysis, nominal range sensitivity analysis, comparison of perturbed and control simulations, and graphing and tabulation of predicted concentrations, rates, and photosynthetic limitations. It can represent a full aquatic food web, including multiple genera and guilds of periphyton, phytoplankton, submersed aquatic vegetation, invertebrates, and fish and associated organic toxicants. It can model up to 20 organic chemicals simultaneously. (It does not model metals.) Modeled processes for organic toxicants include chemodynamics of neutral and ionized organic chemicals, bioaccumulation as a function of sorption and bioenergetics, biotransformation to daughter products, and sublethal and lethal toxicity. It has an extensive library of default biotic, chemical, and toxicological parameters and incorporates the ICE regression equations for estimating toxicity in numerous organisms. The model has been implemented for streams, small rivers, ponds, lakes, reservoirs, and estuaries. It is an integral part of the BASINS system with linkage to the watershed models HSPF and SWAT.