Cyanobacteria harmful algal blooms (Cyano-HAB) is an increasingly pressing environmental problem affecting inland water bodies and aquatic ecosystems because of their ability to produce bioactive secondary metabolites (cyanotoxins) that have detrimental effects on mammalian health and the ecocystem.
Toxic-cyanobacteria have recently become more prevalent and persistent due to eutrophication of surface waters, rising of CO2 in the atmosphere, and global warming. Their presence in drinking water reservoirs pose a severe public health. Based on the above, there is an evident need for a system able to detect diverse groups of cyanotoxins and provide an early warning for the prevention of cyanobacteria blooming from public and private waterbody administrators. The CyanoBox project aims to develop an innovative, remote system that can perform in-situ continuous monitoring of the quality of cyanobacterial contaminated water in urban, rural, and isolated sites. It will provide an autonomous, affordable, and reliable monitoring tool for cyano-HABs so that loca communities can obtain an early warning on the toxicity of a developing bloom and take all necessary actions to mitigate its effects with limited resources and funding. CyanoBox combines development of novel cyanotoxin biosensors and assays, mechanical/electrical hardware and software development, system integration, and field validation. A combined strategy of methods, processes and actions will define the success of the project and deliver the desired results. It is anticipated that such a system will sustain and even improve the quality of surface water resources and safeguard public health and the wellness of the aquatic ecosystem. Its autonomous feature will provide remote, continuous water monitoring, as an alternative to the traditional discrete (and in many cases costly) monitoring activities that lack the ability to track short-term changes in water quality and toxicity.
The project is co-funded by the Republic of Cyprus and European Regional Development Fund (ERDF) via Research and Innovation Foundation’s project number ENTERPRISES/0618/157