Big data and Artificial Intelligence to Control Algal Blooms

Climate change and pollution lie at the source of most algal blooms, making it a problem that is not easy nor quick to solve


Toxic algal blooms are a problem that is globally increasing due to nutrients pollution and climate change. Although the use of chemicals may provide temporary relief to the problem, it does not offer a solution. Now an alternative method for chemical algae control is available. Based on the acquisition of big data, artificial intelligence and ultrasound, this novel method can control algal blooms in large water surfaces without disrupting the ecosystem.

This article is written by written Lisa Brand, CTO of LG Sonic

 

Toxic blooms of algae are increasing globally in our waterways, causing a variety of health-related issues and environmental degradation. Climate change and pollution lie at the source of most algal blooms, making it a problem that is not easy nor quick to solve. Nitrogen and Phosphorous form the main food source for algae, which enters our waterways through pollution from industrial or urban sources. These nutrients may build-up in the sediments of our lakes and waterways, providing food for algae for years to come, even when nutrient inflow to the lake has already stopped.

Blue-green algae, also known as cyanobacteria, may cause illness in humans and kill animals because they can produce a variety of toxins and release them in the water. In the production of drinking water, algae growth may hinder the treatment process by clogging-up filters, increased THM formation and increased use of water treatment chemicals. In addition, taste and odor molecules produced by blue-green algae give a foul taste and odor to the water which is difficult to remove from the final product.

For the last 100 years, the main practice of water treatment plant operators to remove algae from their reservoirs has been the addition of chemicals. Chemicals that have been dosed in our waterways may include copper sulfate or other algaecides as well as metals (Fe, Cu, Ag, Al, Ca). Although these methods are considered as fast working and economical, their impact on the ecological balance of a waterbody cannot be disregarded. Effects of chemical algal control may include toxicity, lysis of the algal cells and non-target response, which will lead to a degradation of the overall quality of the water.

Based on a European Research project, LG Sonic has developed a method to selectively control algae in freshwater lakes and reservoirs called the MPC-Buoy (Monitor, Predict, Control). In-situ monitoring equipment is used to detect and determine algae species, and predict blooms based on water quality parameters. The MPC-Buoy controls algae by using relatively low power, an ultrasonic signal that is emitted over the surface of the reservoir. The ultrasound of one MPC-Buoy system can cover a surface area of approximately 50 acres. Algae rely on their buoyancy to float close enough to the water surface to take up sunlight. This allows them to outcompete other organisms in the water such as plants, that are also depending on sunlight intake. The ultrasound used by the MPC-Buoy systems fixes the algal cells in a deeper layer of the water column, preventing them to take up sunlight at the surface.

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Why do we need to use data to control algae?

Many different algal types can bloom in a water body. Some of these algae are plant cells, while others are bacteria. Algae may vary in size, shape and cell characteristics. Typically, the type of algae that blooms depends on water temperature and sun hours. As a result, algal types may change several times during a season. In addition to that, algae are also highly adaptive. Some algal types can resist extreme temperatures or pH values and cope with fluctuations in these conditions better than other organisms such as plants. Because of algae’s adaptability, algae can easily develop resistance to algaecides and other treatment methods, including ultrasound.

LG Sonic has been researching the effect of ultrasound on specific algal types, different types of water bodies and variations in water quality since 2005. In collaboration with different European universities, they have created a database defining optimal ultrasonic parameters for different algal species and water quality characteristics. By continuously updating these ultrasonic parameters, the system prevents algae from becoming resistant to ultrasound.

The MPC-Buoy automatically monitors, analyzes and predicts algal presence in the waterbody. For the collection of water quality data, near real-time sensors are used to measure pH, Chlorophyll a, Phycocyanin, Turbidity, Dissolved Oxygen, and optionally nitrogen and phosphorous. Based on this information, the MPC-Buoy can determine the presence of different algal species and forecast algal blooms.

Data Sources

By using different sources of data and combining those in a unique algorithm, LG Sonic can forecast algal blooms 3 to 10 days in advance. This allows for effective mitigation strategies through the MPC-Buoy systems. The algorithm can be fed by different data sources, such as hydrological characteristics of the water body and meteorological data sources. The hydrological data consists of streams of data generated by in-situ water quality measurement and remote sensing satellite imagery.

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The use of remote sensing has an unparalleled advantage when forecasting algal blooms and specifically scum layers because remote sensing is the only data-source that provides a spatial overview of water quality parameters over the entire lake. For this forecast, mostly data from Sentinel-2 and Landsat satellites are used due to the resolution. Sentinel-2 and Landsat satellites can provide a resolution between 10 and 30 meters and provide images every 5-8 days. Other satellites such as MODIS and Sentinel-3 show images on a more frequent basis, but the resolution is generally lower, which makes them unsuitable for smaller lakes and reservoirs.

For the collection of in-situ water quality data, near real-time sensors are used to measure pH, Chlorophyll a, Phycocyanin, Turbidity, Dissolved oxygen, Nitrogen, and Phosphate. Nutrients, temperature, and oxygen can be measured at different depths in the reservoir to identify lake stratification and nutrient release from the sediments. Meteorological data is provided through different data sources. Sun irradiance is measured through an irradiance sensor installed on the water quality monitoring buoys on the lake while wind speed and rainfall are gathered through the closest weather station.

Effective algae control

The MPC-Buoy controls algae by using relatively low power, an ultrasonic signal that is emitted over the surface of the reservoir. The ultrasound of one MPC-Buoy system can cover a surface area of approximately 50 acres. Algae rely on their buoyancy to float close enough to the water surface to take up sunlight. This allows them to outcompete other organisms in the water such as plants, that are also depending on sunlight intake. The ultrasound used by the MPC-Buoy systems fixes the algal cells in a deeper layer of the water column, preventing them to take up sunlight at the surface. Currently, LG Sonic is running project in Dubai in collaboration with Dubai Municipality to treat algal blooms in Al Quadra lakes. 60 days after the installation of systems, blue-green algae concentration was reduced by 73%.

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Big data and Artificial Intelligence to Control Algal Blooms

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