Introduction :

Algal blooms, the modern time mystery that remains unsolved nowadays was the ninth challenge proposed by NASA this year. The task mainly required defining factors causing their occurrence in some water bodies and not others, besides of figuring out a solution that can predicttheir fast growth, in order to prevent harm to aquatic and human life.

During this short presentation we will try to give insights related to these given axes, and to propose a solution that can help predicting, thus controlling algae blooms.

We have to understand that the main challenge was to collect and analyze different data sources, in order to extract information related to the several factors affecting the proliferation of algae.

Problem

Algae can be defined asa autotrophic components of the plankton community and a key part of oceans, seas and freshwater basin ecosystems

Phytoplankton can also be the harbingers of death or disease. Certain species of phytoplankton produce powerful biotoxins, making them responsible for so-called “red tides,” or harmful algal blooms. These toxic blooms can kill marine life and people who eat contaminated seafood.

Phytoplankton cause mass mortality in other ways. In the aftermath of a massive bloom, dead phytoplankton sink to the ocean or lake floor. The bacteria that decompose the phytoplankton deplete the oxygen in the water, suffocating animal life; the result is a dead zone

Certain conditions can cause a rapid, out-of-control algal growth causing “blooms” that can alter the quantity of light and the levels of oxygen in water, causing harm to marine life. Some algae can be particularly harmful to humans, producing toxins that cause rashes, breathing problems, and liver damage.

Finality :

The idea is mainly extracting from the resources clean data that will help us in creating patterns, thus, predicting algae behaviors, and saving eventually marine ecosystem and human lives.

Tools and method “APROACH”

A.Analysing data base : ressources :

• Nasa Pace “ in the future “
• Earth observation
• Water samples Analyzing

B. Create a comprehensive list of keys and factors

Factors

• human Activity
• Seasonal change
• Temperature
• Salinity
• Acidity
• CO2
• human Activity
• Seasonal change
• Temperature
• Salinity
• Acidity
• CO2

C. Form a clean data

Options

• Building a visualization tool for all bloom factors.
• Use available and existing tools to create layers and interplays between factors in a specific area.

D. Identify a pattern

• Extracting results from the previous step

E. Make a prediction

• Starting with an algorithm that can predict blooms behavior

Potential Team :

• Data scientists
• Developers
• Ecologists
• Oceanographers

Efficiency :

Defining factors and gathering them in one platform/Software make studies easier for scientists and helps them be more effective in less time, in addition of creating in parallel, patterns that can explain the mystery of blooms in different areas. If we can anticipate their occurrence we will be able to control their movement, and prevent dead zones.

Link : slides https://drive.google.com/file/d/1zLmaRftxfpBeyMOT-...

Resources :

https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/

https://coastalscience.noaa.gov/products/

https://www.epa.gov/water-research/cyanobacteria-a...

https://coastalscience.noaa.gov/data_reports-explo...

https://oceancolor.gsfc.nasa.gov/

https://earthdata.nasa.gov/eosdis/daacs/obdaac

https://sentinels.copernicus.eu/web/sentinel/thematic-areas/atmospheric-monitoring

https://phys.org/news/2019-03-nitrogen-dioxide-pol...

https://earth.esa.int/web/guest/earth-topics/oceans-and-coasts

https://earthobservatory.nasa.gov/

http://www.esa.int/Applications/Observing_the_Eart...

https://pace.gsfc.nasa.gov/