The remote monitoring system is made up of smart buoys that collect key parameter data.
Algae and cyanobacteria—also known as blue-green algae—are photosynthetic microorganisms that originated millions of years ago and are now present in most aquatic ecosystems. These microbes appear naturally, but the excessive discharge of nutrients from urban, agricultural and industrial developments, accompanied by an increase in temperatures during the summer season, creates the perfect breeding ground for them to grow exponentially, causing harmful outcrops, also known as Harmful Algae Blooms (HABs). These outcrops pose numerous negative effects on the quality of water— affecting its transparency, smell and taste—and generate a large variety of highly poisonous toxins for both animals and humans. When these outcrops appear in bodies of water used for hydraulic energy, as supply of water treatment plants, or for recreational purposes, it can cause a significant impact due to the degradation of ecosystems and may lead to restrictions of use.
One of the biggest challenges is to effectively monitor the rapid growth, extension and distribution of these outcrops in freshwater. Traditional manual samplings require long waiting periods—from collecting the sample and analysing it to attaining the results. In addition, carrying out daily or frequent samplings proves difficult due to the subsequent cost of the analyses. The IMDEA Water Institute has developed a solution—in collaboration with research groups from the Rey Juan Carlos University and the University of Valencia—consisting of the implementation of an autonomous system that allows to monitor this phenomenon in real time. Through the deployment of wireless sensor networks and the use of satellite images, researchers have gained remote access to water quality data from two sites: As Conchas reservoir in Galicia, and L'Albufera lagoon in Valencia.
The remote monitoring system is made up of smart buoys that collect data on key parameters—such as chlorophyll and temperature—at a specific location. Likewise, the additional use of satellite images makes possible to monitor the spatial evolution of these parameters over the entire surface of the bodies of water under study. The scientific team highlights that once the images have been analysed and sufficient data (Big Data) has been collected, advanced statistical analyses based on machine learning technology will be applied to develop predictive models. Researchers expect this project to be the starting point to design an effective warning system to allow water managers to overcome the damaging effects that this phenomenon creates in many areas around the world. Furthermore, they are currently developing a website and mobile app (for IOS and Android) that allows users to access water quality data for free, aimed at encouraging citizens to be aware of the state of the aquatic ecosystems around them.
Author: Dr. Jesús Morón López. Microbial Contamination and Cyanobacteria Research Group at IMDEA Agua Institute.
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, and the CianoAlert Project, funded by the Ministry of Economy, Industry and Competitivity and co-financed by the European Regional Development Fund (ERDF).
CIANOMOD Project with the support of: