A new organic-rich soil reference material certified for its EDTA- and acetic acid- extractable contents of Cd, Cr, Cu, Ni, Pb and Zn, following collaboratively tested and harmonised procedures

There is an increasing requirement for assessment of the bioavailable metal fraction and the mobility of trace elements in soils upon disposal. One of the approaches is the use of leaching procedures, but the results obtained are operationally defined; therefore, their significance is highly dependent on the extraction protocol performed. So, for this type of study, there is a need for reference materials that allow the quality of measurements to be controlled. This paper describes the steps involved in the certification of an organic-rich soil reference material, BCR-700, for the EDTA- and acetic acid-extractable contents of some trace elements, following collaboratively tested and harmonised extraction procedures. Details are given for the preparation of the soil, homogeneity and stability testing, analytical procedures and the statistical selection of data to be included in the certification.     

Adsorption mechanism of chromium(III) using biosorbents of Jatropha curcas L.

Environmental Science and Pollution Research - The removal of Cr3+ from water solutions by biosorbents from the rind, endosperm, and endosperm + episperm of the Jatropha curcas was evaluated....     

Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Commonly used methods to predict streamflow at ungauged watersheds implicitly predict streamflow magnitude and temporal sequence concurrently. An alternative approach that has not been fully explored is the conceptualization of streamflow as a composite of two separable components of magnitude and sequence, where each component is estimated separately and then combined. Magnitude is modeled using the flow duration curve (FDC), whereas sequence is modeled by transferring streamflow sequence of gauged watershed(s). This study tests the applicability of the approach on watersheds ranging in size from about 25‐7,226 km² in Southeastern Coastal Plain (U.S.) with substantial surface storage of wetlands. A 19‐point regionalized FDC is developed to estimate streamflow magnitude using the three most selected variables (drainage area, hydrologic soil index, and maximum 24‐h precipitation with a recurrence interval of 100 years) by a greedy‐heuristic search process. The results of validation on four watersheds (Trent River, North Carolina: 02092500; Satilla River, Georgia: 02226500; Black River, South Carolina: 02136000; and Coosawhatchie River, South Carolina: 02176500) yielded Nash‐Sutcliffe efficiency values of 0.86‐0.98 for the predicted magnitude and 0.09‐0.84 for the predicted daily streamflow over a simulation period of 1960‐2010. The prediction accuracy of the method on two headwater watersheds at Santee Experimental Forest in coastal South Carolina was weak, but comparable to simulations by MIKE‐SHE.     

The recovery of schools from earthquake effects: Lessons from Mexico City

Much work has been done to prepare schools and institutions for the immediate effects of earthquakes and other disasters. This paper looks at the preparation needed for medium to long term recovery from the physical and psychological effects of earthquakes. Colegio Madrid, a school of 3,000 students that suffered major damage in the September 1985 Mexico City earthquake serves as the focus of this study. The experiences and actions of faculty, staff, students and parents at this school provide lessons for not only schools, but all institutions and communities which may face the prospect of reconstructing themselves following a disaster.     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

A measure of sustainability of Brazilian agribusiness using directional distance functions and data envelopment analysis

The social and environmental impacts caused by the modernization of Brazilian agribusiness have evoked a growing interest in the search of balance between social-economics development and the adequate use of natural resources, driving the country to sustainable development. Therefore, the main aim of this article is to estimate a set of indicators of relative efficiency in the Brazilian agricultural sector, which satisfy the concept of Pareto optimality, potentiates simultaneously both economic, ecological dimensions and social functions. To reach this purpose, the method of directional distance functions and data envelopment analysis was applied. These indicators confirm, in different ways, the hypothesis that it is possible to perform consistent productive strategies with the maximization of social welfare, despite the apparent antagonism among these three dimensions. In addition, it is shown that efficient Brazilian states tend to combine the three dimensions in different ways. Hence, it can be concluded that several equilibrium taken sustainable can be achieved through different actions on poverty and environmental impact reduction without necessarily generating productive inefficiencies. This result can be considered of prominent importance for sustainable development in Brazil and can also serve as a reference in the definition of goals of the plan ‘Brazil without Misery’ and international commitments to reduce Greenhouse Gas – GHG – in Brazil, especially for the 17 inefficient Brazilian states.     

乐安江沉积物酸可挥发硫化物含量及溶解氧对重金属释放特性的影响

本研究进行了乐安江沉积物不同溶解氧浓度条件下的重金属释放实验和沉积物样品酸可挥发硫化物（ＡＶＳ）及同时可提取金属浓度（ＳＥＭ）的测定。结果表明,乐安江沉积物中ＡＶＳ含量较低,沉积物中重金属在存在再释放的可能性,铁锰氧化和可能是乐安江沉积结合重金属的主要成分。