Climate change and pollution of aquatic ecosystems due to the impact of urban, agricultural and industrial development, have led to the increase the frequency of harmful blooms of algae (Harmful Algal Blooms, HABs) and cyanobacteria (blue-green algae) in many inland waterbodies around the world. HABs cause numerous adverse effects on the water quality, affecting its transparency, odour and taste, and producing highly toxic compounds for animals and humans. Therefore, its monitoring and prediction is key to implementing possible solutions.
One of the current challenges is to achieve the effective monitoring of the rapid growth, extension and distribution of these blooms in surface water. The solution developed by the IMDEA Agua Institute, in collaboration with research groups from the Rey Juan Carlos University and the University of Valencia, involves the implementation of an autonomous system that allows to track their occurrence in real time. The researchers have obtained remote access to the water quality data through a web platform, after deploying wireless sensor networks on buoys and acquiring weekly satellite images. This system is currently being tested at two different environments: the As Conchas reservoir, in Galicia, and the shallow L'Albufera lagoon in Valencia. At least a one-year data cycle will be necessary to accomplish reliable statistical models that allow to predict HABs in such environments.
The remote monitoring system is made up of two types of smart buoys: some equipped with plug-and-play solutions, which facilitate the quantification of essential variables such as chlorophylls; and other custom-made buoys, which allow increasing the number of sampling points at lower cost. While the deployed buoys collect specific data in a specific location, the use of satellite images allows monitoring the spatial evolution of these parameters over the entire area under study.
The scientific team emphasizes that several satellite images have already been analysed satisfactorily and once obtained enough data series, correlations will be established between the acquired images and the data collected by the buoys. Besides, to validate the obtained outcomes, a comparison with the data collected by water management agencies using conventional techniques will be also carried out. Future steps will be focused on the development of statistical models based on the data gathered.
The researchers hope this project to be the starting point for designing effective prediction models, which will allow water management agencies to overcome the devastating effects that HABs generates in multiple aquatic environments.
This work has been carried out within the framework of the CianoMOD project, supported by Biodiversity Foundation of the Ministry for the Ecological Transition and the demographic challenge.
With the support of: