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Portuguese freshwater reservoirs are important socio-economic resources, namely for recreational use. National legislation concerning bathing waters does not include mandatory levels or guidelines for cyanobacteria and cyanotoxins. This is an issue of concern since cyanotoxin-based evidence is insufficient to change the law, and the collection of scientific evidence has been hampered by the lack of regulatory levels for cyanotoxins in bathing waters. In this work, we evaluate the profile of cyanobacteria and microcystins (MC) in eight freshwater reservoirs from the center of Portugal, used for bathing/recreation, in order to determine the risk levels concerning toxic cyanobacteria occurrence. Three of the reservoirs did not pose a risk of MC contamination. However, two reservoirs presented a high risk in 7% of the samples according to the World Health Organization (WHO) guidelines for MC in bathing waters (above 20 µg/L). In the remaining three reservoirs, the risk concerning microcystins occurrence was low. However, they exhibited recurrent blooms and persistent contamination with MC up to 4 µg/L. Thus, the risk of exposure to MC and potential acute and/or chronic health outcomes should not be disregarded in these reservoirs. These results contribute to characterize the cyanobacterial blooms profile and to map the risk of toxic cyanobacteria and microcystins occurrence in Portuguese inland waters. [ABSTRACT FROM AUTHOR]

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO 2 , mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process. [ABSTRACT FROM AUTHOR]