Cyanobacterial blooms occur worldwide and pose a serious threat to human health and natural environment. Cyanobacterial blooms monitoring is important for environmental agencies, water authorities, human and animal health organizations, since cyanobacteria cause a range of issues related with water quality and treatment problems. The main reason, why cyanobacterial blooms are hazardous to human and animal health is that about 60-80 of 300 blooms forming species of phytoplankton can produce toxins. There are various health issues associated with more than 60 identified toxins of cyanobacteria.
Blooms of non-toxic cyanobacteria may cause problems for a water body’s ecosystem. It is well known that the cyanobacterial surface scums can be very thick and cause oxygen depletion in the affected area. Also, extensive blooms of cyanobacteria can cause reduction of light penetration to the bottom, which will decrease densities of submerged aquatic vegetation. The effects are also economical: incidences of dying blooms washing upon beaches during the peak of the summer holiday season has frequently resulted in economic losses.
The management of surface water bodies requires appropriate monitoring. For the water quality, it is not so straight forward to set up an appropriate monitoring. There is a wide range of parameters which can be measured, and the variation over space and time needs to be considered when choosing measurement locations and frequencies, also the costs of equipment and method need to justify the returns.
Water quality is determined by a range of different properties such as physical-chemical parameters like nutrients and chemical compounds, physical properties like temperature and turbidity, and biological parameters like the presence and abundance of plants and microorganisms.
To be able to carry out efficient water management, users, such as water managers, require information over varying periods of time and varying measuring intervals. To gain the most insights in (ecological) processes taking place, the best is to combine data over a longer period (e.g. months or years to understand the historical situation) with spatial data (e.g. many sampling locations, or maps obtained from satellites) to understand the distribution of components. Furthermore, because water systems can be very dynamic, it is very important to also take high-frequency data into account to understand the dynamics, and to use the high frequency information as a basis for models to forecast future developments and trends.
Water Insight can already deliver high-frequency and spatial monitoring data. Their WISPstation instrument collects spectral data that can be translated into parameters such as: cyanobacteria pigments (as proxy for cyanobacteria biomass), Chlorophyll-a (as proxy for total biomass) and an index for the presence/absence of scum layers. To complete the set of monitoring services, the objective of INNO-CYANO was to develop an innovative, data-driven statistical model to provide fast forecasts of cyanobacteria blooms, especially scums.
