Putting Sustainable Development into Practice? The role of local policy partnership networks

This paper examines how the concept of sustainable development is being put into practice by local policy partnership networks. We suggest that due to the highly contested nature of the concept, these networks are facing problems in trying to unravel what sustainable development actually means. Few organisations are grappling with the more fundamental implications of the concept and, as a result, policies for sustainability are being developed cheek by jowl with those of economic growth and competitiveness. Moreover, established local policy networks play a dominant role in defining a vision of sustainable development to the detriment of groups outside the policy process. Mechanisms and institutions need to be established to widen participation and debate on the issue and to draw upon lessons and ideas from organisations outside established networks, such as environmental and campaign groups, third sector organisations, community associations and socially responsible businesses. Este documento examina como el concepto de desarrollo sostenible esta siendo puesto en práctica por redes de sociedades normativas locales. Nosotros sugerimos que debido a la alta naturaleza competitiva del concepto, estas redes estan enfrentando problemas al tratar de desenredar lo que significa realmente desarrollo sostenible. Pocas organizaciones están intentando resolver las implicaciones mas fundamentals del concepto y como resultado las políticas para la sostenibilidad estan siendo desarrolladas lado a lado con esas de crecimiento económico y competitividad. Además, las redes normativas locales establecidas juegan un papel predominante en definir una visión de desarrollo sostenible para el detrimento de grupos por fuera del proceso regulador. Los mecanismos y las instituciones necesitan ser establecidos para ampliar la participación y debatir sobre el asunto y para deducir sobre lecciones e ideas tomadas de organizaciones por fuera de las redes establecidas tales como grupos ambientales y de campaña, organizaciones del tercer sector, asociaciones comunitarias y negocios socialmente responsables.     

Heavy metal removal from industrial effluents by sorption on cross-linked starch: chemical study and impact on water toxicity

Batch sorption experiments using a starch-based sorbent were carried out for the removal of heavy metals present in industrial water discharges. The influence of contact time, mass of sorbent and pollutant load was investigated. Pollutant removal was dependent on the mass of sorbent and contact time, but independent of the contaminant load. The process was uniform, rapid and efficient. Sorption reached equilibrium in 60 min irrespective of the metal considered (e.g. Zn, Pb, Cu, Ni, Fe and Cd), reducing concentrations below those permitted by law. The material also removed residual turbidity and led to a significant decrease in the residual chemical oxygen demand (COD) present in the industrial water discharge. The germination success of lettuce (Lactuca sativa) was used as a laboratory indicator of phytotoxicity. The results show that the sorption using a starch-based sorbent as non-conventional material, is a viable alternative for treating industrial wastewaters.     

Microbial and chemical markers: runoff transfer in animal manure-amended soils

Fecal contamination of water resources is evaluated by the enumeration of the fecal coliforms and Enterococci. However, the enumeration of these indicators does not allow us to differentiate between the sources of fecal contamination. Therefore, it is important to use alternative indicators of fecal contamination to identify livestock contamination in surface waters. The concentration of fecal indicators (, enteroccoci, and F-specific bacteriophages), microbiological markers (Rum-2-bac, Pig-2-bac, and ), and chemical fingerprints (sterols and stanols and other chemical compounds analyzed by 3D-fluorescence excitation-matrix spectroscopy) were determined in runoff waters generated by an artificial rainfall simulator. Three replicate plot experiments were conducted with swine slurry and cattle manure at agronomic nitrogen application rates. Low amounts of bacterial indicators (1.9-4.7%) are released in runoff water from swine-slurry-amended soils, whereas greater amounts (1.1-28.3%) of these indicators are released in runoff water from cattle-manure-amended soils. Microbial and chemical markers from animal manure were transferred to runoff water, allowing discrimination between swine and cattle fecal contamination in the environment via runoff after manure spreading. Host-specific bacterial and chemical markers were quantified for the first time in runoff waters samples after the experimental spreading of swine slurry or cattle manure.     

Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems (VMS). Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives (a true fishing set detected as a fishing set); conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. This spatially explicit information on effort, provided with error estimation, should greatly reduce misleading interpretations of catch per unit effort and thus significantly improve the adaptive management of fisheries. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.