Drinking water security is a life safety issue as an adequate supply of safe water is essential for economic, social and sanitary reasons. Damage to any element of a water system, as well as corruption of resource quality, may have significant effects on the population it serves and on all other dependent resources and activities. As well as an analysis of the reliability of water distribution systems in ordinary conditions, it is also crucial to assess system vulnerability in the event of natural disasters and of malicious or accidental anthropogenic acts. The present work summarizes the initial results of research activities that are underway with the intention of developing a vulnerability assessment methodology for drinking water infrastructures subject to hazardous events. The main aim of the work was therefore to provide decision makers with an effective operational tool which could support them mainly to increase risk awareness and preparedness and, possibly, to ease emergency management. The proposed tool is based on Bayesian Belief Networks (BBN), a probabilistic methodology which has demonstrated outstanding potential to integrate a range of sources of knowledge, a great flexibility and the ability to handle in a mathematically sound way uncertainty due to data scarcity and/or limited knowledge of the system to be managed. The tool was implemented to analyze the vulnerability of two of the most important water supply systems in the Apulia region (southern Italy) which have been damaged in the past by natural hazards. As well as being useful for testing and improving the predictive capabilities of the methodology and for possibly modifying its structure and features, the case studies have also helped to underline its strengths and weaknesses. Particularly, the experiences carried out demonstrated how the use of BBN was consistent with the lack of data reliability, quality and accessibility which are typical of complex infrastructures, such as the water distribution networks. The potential applications and future developments of the proposed tool have been also discussed accordingly.

This paper reports the results obtained using the sequencing batch biofilter granular reactor (SBBGR) for the treatment of the wastewater from a dyeing and finishing factory. The treatment of such a wastewater is challenging as it usually contains considerable amounts of different recalcitrant, toxic and inhibitory pollutants, which results in low biodegradability and in the need for numerous treatment steps. Different operational conditions were tested in order to assess SBBGR performance as a function of the applied organic and hydraulic load and to verify its suitability for on-site dyeing wastewater treatment at each factory. The reported measurements demonstrate how this innovative biological technology exhibits various promising features for this purpose, as good treatment efficiencies can be achieved even at high organic load values (2.4-2.6 kg(COD) m(-3) d(-1)) and with hydraulic retention times lower than one day. Furthermore, the treatment is characterized by a sludge production as low as 0.1 kg of dry sludge per kg of COD removed. Therefore, SBBGR has proved to be an effective pre-treatment for dyeing textile wastewater before discharge into municipal sewer system, as it produces a suitable effluent using just only one biological step with high hydraulic and organic loadings and low sludge production. (C) 2012 Elsevier Ltd. All rights reserved.

Textile effluents are among the most difficult industrial wastewaters to treat because of their compositional variability and of the presence of numerous different chemicals intentionally designed to resist degradation. Though biological technologies offer a cheaper and more environmental friendly alternative for the treatment of textile effluents, an additional step to remove recalcitrant compounds is still needed. Integrated biological and chemical treatment is a rather new approach that allows improving treatment performance and stability without increasing too much treatment costs. Ozone integration in a sequencing batch biofilter granular reactor was tested at laboratory scale for treating a printing wastewater characterized by high concentrations of surfactants and nitrogen. The process was optimized in terms of applied organic load and ozone dose. The results have shown that the process assures the possibility to comply with the limits for direct discharge for all investigated parameters by operating at an organic load value lower than 1.5 kg(COD)/m(3) d and with an ozone dose of 135 mg/l. A synergetic biological and chemical oxidation activity was observed with a ratio between ozone dose and COD removed lower than 0.75. Finally, the process was characterized by a sludge production as low as 0.17 kg(TSS)/kg(CODremoved) due to the high biomass concentration in the biological system used. (c) 2012 Elsevier B.V. All rights reserved.

Perforated plates are widely used in pipeline systems either to reduce flow nonuniformities or to attenuate the onset and the development of cavitation. This experimental work aims at investigating the dependence of the pressure losses through sharp-edged perforated plates with respect to the geometrical and flow key parameters. The data, collected in two large experimental campaigns carried out on different pilot plants, are reported and discussed. Several plates with different geometrical characteristics were tested. More precisely, perforated plates whose equivalent diameter ratio varies between 0.20 and 0.72; relative hole thickness between 0.20 and 1.44; and number of holes between 3 and 52. Experimental data from literature are also considered in order to ensure the reliability of the parametric investigation. The dependence of the pressure loss coefficient upon the Reynolds number, the equivalent diameter ratio, the relative thickness, and the number and disposition of the holes is studied. A comparison to different empirical equations, as available by the technical literature, and to the standard ISO 5167-2 single-hole orifice is also provided. (C) 2012 Elsevier Ltd. All rights reserved.

This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based on its geological and hydrological characteristics, as well as on the number of recent and remote flooding events already occurred, this area seems to be naturally prone to flooding. The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet?s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC) and Multi-Layer Perceptron (MLP) neural network, the Artificial Neural Networks (ANN) approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and cost-effective means to map and analyse land cover changes over time that can be used as input in land management and policy decision-making.

Continuous monitoring of river basins has become a significant requirement of our times. Due to increasing water scarcity and unprecedented flood calamities, assessing existing water resources and gathering timely information on water increase are nowadays essential to develop suitable strategies in water resources management. Hydrological models are being studied to increase hydrological process understanding and to support decision making in this field. River basin management models typically operate on wide territories and, given the complexity of most river basins, they are based on semi-empirical lumped parameterizations of hydrological processes. To overcome the uncertainties inherent in such models and achieve acceptable model performance, calibration techniques are indispensable. Remote sensing and satellite-based data with high temporal resolution have the potential to fill such critical information gaps. With its nine spectral bands and very high resolutions (spectral and radiometric) WorldView-2 satellite sensor (WV-2) can provide new insights in the on-going debate comparing object-oriented and spectral-based classifications for the highest accuracy. This paper proposes an efficient object-based method for land cover mapping from Worldview-2 imagery in order to assess its potentiality in acquiring detailed basic information on an ephemeral river area (Lama di Castellaneta, Taranto, Italy), to support further studies in the field of hydrological processes modeling. The approach suggested was evaluated by estimating classification accuracy.