Effects of calcium hydroxide and calcium chloride addition to bentonite in iron ore pelletization.

Pyrite ash is created as waste from the roasting of pyrite ores during the production of sulphuric acid. These processes generate great amounts of pyrite ash waste that is generally land filled. This creates serious environmental pollution due to the release of acids and toxic substances. Pyrite ash waste can be utilized in the iron production industry as a blast furnace feed to process this waste and prevent environmental pollution. The essential parameters affecting the pelletization process of pyrite ash were studied using bentonite as a binder. Experiments were then carried out using bentonite and a mixture of bentonite with calcium hydroxide and calcium chloride in order to make the bentonite more effective. The metallurgical properties of pyrite ash, bentonite, calcium hydroxide, calcium chloride, a mixture of these and sintered pellets were studied using X-ray analysis. The crushing strength tests were carried out to investigate the strength of pyrite ash waste pellets. The results of these analyses showed that pyrite ash can be agglomerated to pellets and used in the iron production industry as a blast furnace feed. The crushing strength of the pellets containing calcium hydroxide and calcium chloride in addition to bentonite was better than the strength of pellets prepared using only bentonite binder.     

Working backwards to move forwards: Graph-based connectivity metrics for reserve network selection

In this paper we apply graph theory in a reserve selection exercise to explore the tradeoffs between maintaining connectivity and minimizing the total area of a protected area network. Rather than focus on a single organism, we used a multi-species approach and looked at the tradeoff curves for organisms with varying dispersal abilities. We first generated the tradeoff curves using a graph-based metric to determine the importance of individual patches for maintaining connectivity. We then performed an analogous set of analyses using patch size as a surrogate measure of importance.     

Assessing protected area effectiveness using surrounding (buffer) areas environmentally similar to the target area

Many studies are based on the assumption that an area and its surrounding (buffer) area present similar environmental conditions and can be compared. For example, in order to assess the effectiveness of a protected area, the land use/cover changes are compared inside the park with its surroundings. However, the heterogeneity in spatial variables can bias this assessment: we have shown that most of the protected areas in Mexico present significant environmental differences between their interior and their surroundings. Therefore, a comparison that aims at assessing the effectiveness of conservation strategies, must be cautioned. In this paper, a simple method which allows the generation of a buffer area that presents similar conditions with respect to a set of environmental variables is presented. The method was used in order to assess the effectiveness of the Calakmul Biosphere Reserve, a protected area located in the south-eastern part of Mexico. The annual rate of deforestation inside the protected area, the standard buffer area (based upon distance from the protected area only) and the similar buffer area (taking into account distance along with some environmental variables) were 0.3, 1.3 and 0.6%, respectively. These results showed that the protected area was effective in preventing land clearing, but that the comparison with the standard buffer area gave an over-optimistic vision of its effectiveness.     

One-year monitoring survey of organic compounds (PAHs, PCBs, TBT), heavy metals and biomarkers in blue mussels from the Arcachon Bay, France

Marine mussels Mytilus sp. were transplanted on a monthly basis in cages over one year to oyster farms and harbours in the Arcachon Bay (France) in order to assess the water quality of the bay. Contaminant levels (organotin compounds, trace metals, PCBs and PAHs) were measured in tissues of transplanted mussels and mussels from a reference station, along with physiological parameters of the mussels (condition indexes, lipid content and dry weight). Four biomarkers (AChE: acetylcholinesterase activity, GST: gluthathione S-transferase activity, CAT: catalase activity and TBARS: thiobarbituric acid-reactive substance content) were also monitored. The remote stations monitored (oyster parks) exhibited no accumulation pattern of pollutants. Their respective concentrations therefore constitute a background level of the contamination in the bay ([TBT]= 30 ng Sn g(-1) dw, [SigmaHAPs]= 100 ng g(-1) dw, [SigmaPCBs]= 35 ng g(-1) dw). The elevated chemical contamination of the largest harbour of the bay, the Arcachon harbour, can be interpreted in terms of persistence of organotin compounds ([SigmaOTs]= 1500-2000 ng Sn g(-1) dw) and PAHs ([SigmaHAPs]= 4500-5000 ng g(-1) dw) in sediments and, to a lesser extent, of direct inputs of copper ([Cu]= 20 microg g(-1) dw in harbours versus 7 in oyster parks) and petrogenic PAHs ([methylphenanthrenes]= 1600 ng g(-1) dw in the dockyard versus 170 at the gas stations), related to the use of copper-based antifouling paints and to dockyard activity, respectively. However, the Arcachon Bay presents a low contamination level by PCBs and metals, including harbour stations. Furthermore, higher levels of other PAHs (particularly alkyl PAHs such as methylphenanthrenes/1600 ng g(-1) dw) not included in the 16 PAHs from the EPA priority list (usually studied in biomonitoring programmes/1500 ng g(-1) dw) in the Arcachon harbour underline the need to integrate these compounds in biomonitoring of highly PAH-polluted areas such as harbours in order to avoid misinterpretation of the biological responses observed. Biomarker responses were not able to discriminate the different chemical contamination levels recorded in the Arcachon Bay and rather reflected changes in environmental factors. Furthermore, the strong intraspecies variability of biological responses could be due to genetic differences of mussels from the Arcachon Bay. It is the first time that such an integrated monitoring is performed in the Arcachon Bay, also taking into account seasonal variations of chemical contents and biomarkers levels in mussel tissues.     

A modular approach to Integrated Assessment modeling

In this paper, we present a new approach to model coupling that probably forms the methodological basis of a new generation of Integrated Assessment models. This approach respects the knowledge and expertise that is embodied in existing models and encourages their gradual evolution. Modularity is the guiding principle. Our approach is distinguished by the way modules are coupled which is based on an interplay of a job control module, a numerical coupling module, and a couple of stand-alone functional modules. The numerical coupling module - the core component - serves to treat the feedbacks between the functional modules. A first implemented example that couples an economic and a climate module by means of a two-phase meta-optimization is presented here. The algorithm and mathematical structure behind are discussed as well as important features such as convergence behavior and reliability.     

Optimizing the removal of small fish passage barriers

Removing small artificial barriers that hinder upstream migrations of fish is a major problem in riparian habitat restoration. Because of budgetary limitations, it is necessary to prioritize barrier removal and repair decisions. These have usually been based on scoring and ranking procedures, which, although simple to use, can be very inefficient in terms of increasing the amount of accessible instream habitat. We develop a novel decision-making approach, based on integer programming techniques, which optimizes repair and removal decisions. Results show based on real datasets of barrier culverts located in Washington State that scoring and ranking is over 25% below the optimum on average and a full 100% below in the worst case, producing no net habitat gain whatsoever. This is compared to a dynamic programming method that was able to find optimal solutions in less than a second, even for problems with up to several hundred variables, and a heuristic method, which found solutions with less than a 1% average optimality gap in even less time.