Evaluation of environmental quality of mediterranean coastal lagoons using persistent organic pollutants and metals in thick-lipped grey mullet

The evaluation of past and present anthropogenic impacts affecting the ecological quality status of transitional ecosystems is crucial from the perspective of protecting them from further deterioration, and to evaluate remediation and restoration measures. Contamination patterns of thick-lipped grey mullet from two Mediterranean coastal lagoons within a protected area in Italy were assessed and compared in order to evaluate their overall quality status and to collect information that can provide useful feedback on management choices aimed at enhancing environmental quality and biodiversity conservation. The quality status of the two lagoons was evaluated by an environmental assessment methodology based on indicators of direct and indirect human pressures, while a broad range of analyses were carried out to determine the presence and concentration of persistent organic pollutants (POPs) and metals in fish muscle tissue. A good quality status resulted for both lagoons, and an overall limited anthropogenic impact in the surrounding area. This could account for POPs and metal contamination levels found in mullet, although limited, and relating to their patterns. The overlap of results achieved with the two evaluation approaches can provide support for management choices in Mediterranean lagoon environments, especially for those committed to the protection and conservation of biodiversity

Multi-criteria approach for the environmental impact assessment of inland aquaculture

Trout farming, that represents the most important sector for aquaculture inland production in Italy, can cause negative effects on aquatic ecosystems. Recently, in the framework of Water Frame Directive 2000/60/EC and national law DL 152/2006, concerning the sustainable uses of water resources, multi-criteria approaches have been suggested to evaluate the impact of fish farming on aquatic ecosystems. In this study trout farms of central Italy were selected to investigate the effects of their effluents, on receiving water bodies using a multi-criteria approach based on physicochemical parameters, microbiological and macrobenthonic indicators, detected in sampling stations located upstream/downstream the trout farm. Moreover, antibiotic susceptibility against antibiotics allowed and/or forbidden by current law (D.lgs 193/56/06) was tested on E. coli strains. The results indicate variations of chemical parameters and biological indicators from upstream to downstream sites in some of the investigated farms. Antibiotic resistance of E. coli strains suggested a large use of tetracycline and a possible past use of chloramphenicol. This study represents a first contribute to the knowledge of fish farm impacts on aquatic systems in Central Italy

LAKE SHOREZONE FUNCTIONALITY INDEX (SFI) A tool for the definition of ecological quality as indicated by Directive 2000/60/CE

www.appa.provincia.tn.it/binary/pat_appa/pubblicazioni/IFP_Manual_english_ver2.1310115028.pd

LAKE SHOREZONE FUNCTIONALITY INDEX (SFI) A tool for the definition of ecological quality as indicated by Directive 2000/60/CE

www.appa.provincia.tn.it/binary/pat_appa/pubblicazioni/IFP_Manual_english_ver2.1310115028.pd

Predicting the spread of COVID-19 in Italy using machine learning: Do socio-economic factors matter?

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June. In the first estimate, the results highlight the dominance of factors related to the intensity and interactions of economic activities. In the second, the relevance of these variables is highly reduced, suggesting mitigation of the pandemic following the lockdown of the economy. Finally, by considering cases at onset of the “second wave”, we confirm that the territorial distribution of the epidemic is associated with economic factors