MicroCokit: Microbial Community-based indicator of water quality to integrate in a modeling scenario

Microbial communities are the base of the food web pyramid, representing about 50% of the total biomass on Earth. They are responsible for the geochemical cycles and bio-removal of organic compounds and xenobiotics playing a key-role in the ecosystem function. Their capability to adapt quickly not only to the changes but also to take the advantages, makes them the drivers of the aquatic and terrestrial ecosystem and the human health. In Europe, the good quality of surface waters is established under the Water Framework Directive (WFD) based either on the chemical monitoring, or on the ecological status and pathogen detection in drinking and bathing waters. However, no indicator is foreseen which would provide a link between ecological and chemical pollutants and other anthropogenic pressures; currently water quality monitoring are either focused on ecological parameters or only chemical pollutants. The MicroCokit Project, a Marie Curie Industry-Academia Partnerships and Pathways (FP7-PEOPLE-2012-IAPP), a close collaboration of Academic groups with European Commission Joint Research Centre and leading private Enterprises, has been conceived to i) investigate and identify complex stressor indicators based on microbial communities; ii) foster the transfer of knowledge among the partners with the final goal to bring to market faster, more sensitive and robust tools as bioindicators of water quality. The tools will be developed according different kinds of bioindicators, targeting microbial community, pathogen and specific microorganisms. For this purpose the river Tiber has been chosen as a pilot case study and sampling sites were selected based on different anthropogenic pressures which they are exposed to. For each site water sample will be analyzed for both microbiological (Microarray, Metagenomic and FISH analysis) and chemical analysis (organic and inorganic compounds, including emerging pollutants). Following the validation, these data could be then integrated in a modeling system to predict, prevent and mitigate the impact of anthropogenic pressure on water management.