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For those working in the field of landslide prevention, the estimation of hazard levels and the consequent production of thematic maps are principal objectives. They are achieved through careful analytical studies of the characteristics of landslide prone areas, thus, providing useful information regarding possible future phenomena. Such maps represent a fundamental step in the drawing up of adequate measures of landslide hazard mitigation. However, for a complete estimation of landslide hazard, meant as the degree of probability that a landslide occurs in a given area, within a given space of time, detailed and uniformly distributed data regarding their incidence and causes are required. This information, while obtainable through laborious historical research, is usually partial, incomplete and uneven, and hence, unsatisfactory for zoning on a regional scale. In order to carry this out effectively, the utilization of spatial estimation of the relative levels of landslide hazard in the various areas was considered opportune. These areas were classified according to their levels of proneness to landslide activity without taking recurrence periods into account. Various techniques were developed in order to obtain upheaval numerical estimates. The method used in this study, which was applied in the area of Potenza, is based on techniques derived from artificial intelligence (Artificial Neural Network - ANN). This method requires the definition of appropriate thematic layers, which parameterize the area under study. These are recognized by means of specific analyses in a functional relationship to the event itself. The parameters adopted are: slope gradient, slope aspect, topographical index, topographical shape, elevation, land use and lithology

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The fungus Pleurotus ostreatus was investigated to assess its ability to remove diclofenac, ketoprofen, and atenolol spiked at 10 mg/L each one in hospital wastewater. The degradation test was carried out in a fluidized bed bioreactor testing both the batch and the continuous mode (hydraulic retention time in the range 1.63-3 days). In batch mode, diclofenac disappeared in less than 24 h, ketoprofen was degraded up to almost 50% in 5 days while atenolol was not removed. In continuous mode, diclofenac and ketoprofen removals were about 100% and 70% respectively; atenolol degradation was negligible during the first 20 days but it increased up to 60% after a peak of laccase production and notable biomass growth. In order to identify the enzymatic system involved, further experiments were carried out in flasks. Purified laccase completely transformed atenolol and diclofenac in less than 5 h, but not ketoprofen. In vivo experiments suggested that cytochrome P450 could be involved in diclofenac and ketoprofen degradation, while partial correlation studies confirmed the role of laccase in atenolol and diclofenac degradation. Two intermediates of diclofenac and ketoprofen were detected by nuclear magnetic resonance. Moreover P. ostreatus was able to reduce chemical oxygen demand of the hospital wastewater which is an important advantage comparing to other fungi in order to develop a wastewater treatment process. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1529-1537, 2017.

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