Multiscale analysis of heavy metal contents in Spanish agricultural topsoils

This study characterized and mapped the spatial variability patterns of seven topsoil heavy metals (Cr, Ni, Pb, Cu, Zn, Hg and Cd) within the Ebro river basin (9.3 million ha) by Multivariate Factorial Kriging. The variograms and cross-variograms of heavy metal concentrations showed the presence of multiscale variation that was modeled using three variogram models with ranges of 20km (short-range), 100km (medium-range) and 225km (long-range). Our results indicate that the heavy metal concentration is influenced by bedrock composition and dynamics at all the spatial scales, while human activities have a notorious effect only at the short- and medium-range scale of variation. Sources of Cu, Pb and Zn (and secondary Cd) are associated with agricultural practices (at the short-range scale of variation), whereas Hg variation at the short- and medium-range scale of variation is related to atmospheric deposition.     

Analysis of land suitability for the siting of inter-municipal landfills in the Cuitzeo Lake Basin, Mexico

This paper presents three spatial decision-support models (Boolean logic, binary evidence and overlapping index of multiple class maps) to perform a land suitability analysis for sanitary landfill siting. The study was carried out in the basin of Lake Cuitzeo, Mexico, with the objective of locating areas that comply with environmental regulations and with the inter-municipality criterion, i.e., that are accessible by at least two municipalities. Biophysical and socio-economic data were processed in a Geographic Information System (GIS). The three models differ in their complexity and restrictiveness. The Boolean logic model is easier to apply and more restrictive than the other two, because it is based on the assessment of single attributes. On the other hand, the binary data and overlapping index methods are relatively more complex because they require attribute weighting. The results showed that 23 of the 28 municipalities included in the basin have at least one area that was classified as highly suitable. The most suitable areas covered from 63.8 to 204.5 km(2) (from 1.5% to 5%), and they are not distributed homogeneously, but clustered around four main sites. The larger and most suitable of these sites is located in the central part of the basin, and it can be accessed by five of the most densely populated municipalities. The proposed approach represents a low-cost alternative to support a common spatial decision-making process in developing countries.     

Coupled solar photo-Fenton and biological treatment for the degradation of diuron and linuron herbicides at pilot scale

A coupled solar photo-Fenton (chemical) and biological treatment has been used to remove biorecalcitrant diuron (42 mg l(-1)) and linuron (75 mg l(-1)) herbicides from water at pilot plant scale. The chemical process has been carried out in a 82 l solar pilot plant made up by four compound parabolic collector units, and it was followed by a biological treatment performed in a 40 l sequencing batch reactor. Two Fe(II) doses (2 and 5 mg l(-1)) and sequential additions of H2O2 (20 mg l(-1)) have been used to chemically degrade the initially polluted effluent. Next, biodegradability at different oxidation states has been assessed by means of BOD/COD ratio. A reagent dose of Fe=5 mg l(-1) and H2O2=100 mg l(-1) has been required to obtain a biodegradable effluent after 100 min of irradiation time. Finally, the organic content of the photo-treated solution has been completely assimilated by a biomass consortium in the sequencing batch reactor using a total suspended solids concentration of 0.2 g l(-1) and a hydraulic retention time of 24h. Comparison between the data obtained at pilot plant scale (specially the one corresponding to the chemical step) and previously published data from a similar system performing at laboratory scale, has been carried out.     

Use of electrochemical technology to increase the quality of the effluents of bio-oxidation processes. A case studied

In this work, it has been studied the use of conductive-diamond electrochemical oxidation (CDEO) as a refining technology to assure the quality of the effluents of door manufacturing processes (DMP). To do this, the raw effluents of these factories have been treated by a combination of physicochemical, biological and CDEO treatments. CDEO was found to be a feasible alternative to the refinement of a wooden DMP waste. It can successfully decrease the organic load of the effluents of the biological oxidation with low energy requirements. In addition, in case of incidents in the biological process, CDEO can treat successfully the effluents of the coagulation process. The effluents of the biological treatment have also been treated by CDEO in order to check the possible use of electrochemical technology to increase the biodegradability of the effluents and their possible recycle to the biological treatment. Unfortunately, electrochemical technology was found to be not adequate to increase the biodegradability of the effluents of a biological treatment. The hard oxidation conditions generated during CDEO do not lead to the accumulation of intermediates but to the almost direct formation of carbon dioxide. Lowering the current density or changing the electrodes can not enhance the biodegradability of the effluents of an electrochemical cell.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Managers as drivers of CSR in state-owned enterprises

Corporate social responsibility (CSR) is increasingly required not only of private sector companies, but also of state-owned enterprises (SOEs), whose main function is to provide services to society and not principally to obtain financial reward. The important role played by managers in implementing CSR has been demonstrated by prior research. The objective of this paper is to examine how managers of SOEs perceive the concept of CSR, the reasons for their involvement and how it is integrated into everyday business practices. In addition, we examine whether managers' attitudes towards CSR policies are influenced by their personal profiles and by the sector in which the SOEs work. We find that, although these managers are aware of the importance of the CSR principles underlying their activities and understand the meaning, scope and dimensions of CSR, there remains an evident need to enhance the application of CSR policies among the strategies of SOEs. The manager profile and the sector in which the SOEs work both have a strong influence on their behaviour regarding CSR issues.     

Microbial biomass governs enzyme activity decay during aging of worm-worked substrates through vermicomposting

Vermicomposting is the biooxidation and stabilization of organic matter involving the joint action of earthworms and microorganisms, thereby turning wastes into a valuable soil amendment called vermicompost. Studies have focused on the changes in the type of substrates available before and after vermicomposting, but little is known on how these changes take place, especially those changes related with maturation of vermicompost. This study investigated the effects of aging on the microbiological properties of fresh vermicompost produced from pig slurry by analyzing the substrate after the earthworms had left it. We incubated 16-wk-old vermicompost and sampled it after 15, 30, 45, and 60 d analyzing microbial biomass and activity (assessed as microbial biomass N and basal respiration respectively) and four enzymatic activities (beta-glucosidase, cellulase, protease, and alkaline phosphatase). Aging of vermicompost resulted in decreases of microbial biomass and activity. Three of the four enzymes analyzed also showed decrease. An initial increase followed by a rapid decrease in alkaline phosphatase was also recorded. High and significant correlations between microbial biomass and beta-glucosidase (r = 0.62, P < 0.001), cellulase (r = 0.56, P < 0.01), and protease (r = 0.82, P < 0.001) were found. Results suggest that there may be two steps involved in the aging dynamics of vermicompost with regards to extracellular enzyme activity; the first step was characterized by a decrease in microbial populations, which resulted in a reduction in the synthesis of new enzymes. The second step was the degradation of the pool of remaining enzymes. This dynamic does not seem to be affected by earthworms because similar decaying patterns of microbial biomass and activity were found in substrate where earthworms were present.     

Waste materials for activated carbon preparation and its use in aqueous-phase treatment: a review

Commercial activated carbon is a preferred adsorbent for the removal of micropollutants from the aqueous phase; however, its widespread use is restricted due to high associated costs. To decrease treatment costs, attempts have been made to find inexpensive alternative activated carbon (AC) precursors, such as waste materials. Some reviews report the use of waste materials for the preparation of AC; however, these studies are restricted to either type of wastes, preparation procedures, or specific aqueous-phase applications. The present work reviews and evaluates literature dedicated both to the preparation of AC by recycling different types of waste materials and also to its application in various aqueous-phase treatments. It is clear that conventional (from agriculture and wood industry) and non-conventional (from municipal and industrial activities) wastes can be used to prepare AC, that can be applied in various aqueous treatment processes, namely to remove organic pollutants, dyes, volatile organic compounds, and heavy metals. Moreover, high surface areas can be obtained using either physical or chemical activation; however, combined treatments might enhance the surface properties of the adsorbent, therefore increasing its adsorption capacity. It is evident from the revision made that AC prepared from both conventional and non-conventional wastes might effectively compete with the commercial ones. This happens mostly when the activation procedures are optimized considering both the raw material used to produce the carbons and the contaminants to be removed.