Our strategy is to create the data the we don't have and combine it with the data that we already have to collect all the information with computational tools to discover the causes of this phenomenon.

Our Challenge

Lately algae blooms has had an unexpected increase in a short time. Its large proliferation generates both environmental and social impacts, causing problems with marine wildlife and affecting coastal populations and tourism, despite the fact that some species are toxic to humans.

The impact of algae proliferation environmentally has not yet been accurately predicted, not even explained to be possible to understand the situation.

As said by Nicola Davis, in The Guardian, in Sargasso Sea “It weighs 20m tonnes, stretches from west Africa to the Gulf of Mexico, and washes up on beaches creating a malodorous stench. Now scientists say a vast swathe of brown seaweed could be becoming an annual occurrence.

Researchers say the explosion in sargassum seaweed first materialized in 2011. But new research shows it has appeared almost every year since then, forming the largest bloom of macroalgae ever recorded. What’s more, the seaweed band – dubbed the Great Atlantic Sargassum Belt – seems to be getting bigger.”

“These phenomena are symptoms of a massive seaweed bloom scientists are calling the Great Atlantic Sargassum Belt. Researchers describe the belt and explore its causes in a study published July 4 in the journal Science.

The researchers analyzed a dataset that predates the belt's first appearance in 2011, allowing them to investigate the long-term environmental changes that set the stage for the year-to-year variations in the growth of the bloom.

They identified a tipping point around 2009 when discharge from the Amazon River brought unusually high levels of nutrients into the Atlantic Ocean. Upwelling of nutrient-rich water off the west coast of Africa in the winter of 2010 further enriched surface waters with deep-sea nutrients; that upwelling also lowered temperatures of that surface water, allowing sargassum to thrive in the summer of 2011.

A similar combination of factors led to especially large blooms in 2014, 2015 and 2017. The largest recorded bloom occurred in 2018, when the Great Atlantic Sargassum Belt grew to a mass of more than 20 million metric tons. The high levels of nutrients from the Amazon River come from deforestation and fertilizer use in the Amazon basin.” By Grant Currin july, 05 2019 Planet Eart.

Knowing that, we get the conclusion that more data is needed to get to know what’s happening, and that’s where our solution can help, getting more data from an specific location that maybe causing a bigger event, like in the sargassum sea, and with this information, we can connect the data and get better results.

Our Vision

In order to ensure our data analysis, we prototype systems capable of sensing such variables. We had in mind that we should keep it viable, and to do so, a sustainable alteration in our floater turned it into some kind of autonomous boat. As noticed by the team "biweekly data can simulate long-term trends of algal biomass reasonably well" data reliability is significantly more relevant than its frequency. This way the project became substantially more complex, and efficient. (Muttil, N.;Chau, K., 2006)

Through available literature and machine learning selection's over several studies, it was determined what the pseudo-boats have to sample. As said "a longer and wider range of training data is suggested and periodic model retraining is essential to detect the recent HABs mechanism under changing climate and watershed" the need for more sampling is in fact a concern.

The main set of selected variables where:

Temperature

sensed through a termopar, which circuit assumes a 0.25°C precision

Water PH

sensed through an electrode, with 0.2pH precision

UV light and turbidity

sensed through photodiodes and photoresistors

Nitrogen based and potassium-based compounds

as this sensing is mainly in laboratories, the discussion on it is just above

There are available ways of determining such components, but differently than expected little (actually none) information is accessible about it. The solution to avoid using humans as resources is proposed as applying spectrometry knowledge in the embedded system. As there weren't a commercial solution about, that could be used as a reference or validation to the purpose, it was developed a simple spectrometer, focused in certain specific wavelengths. The simple system is cheap, but experiments applied can confirm only its palpability. The prototype system can indeed detect presence and amount of nitrate, ammonia and urea compounds. The need for reliability leads the justification for such research.

The main idea behind the sensing proposed method it's originated from its applicability in soil (ELECTROCHEMICAL SENSORS FOR SOIL NUTRIENT DETECTION: OPPORTUNITY) the subject (in our case contaminated or suspect water source) is exposed to wavelength which reactivity in the subject compound is very expressive. In a second moment, reflection and refraction interact over those in a way that, when the light is evaluated, it's possible to determine the molecule presence and quantity.

Considering all effort, the embrace of all data happens in a MCU unit, which in case is esp32 dev board by haltec, because of its low power consumption and integrated Lora module, which is in case one great system to communicate between (tests of team proof) long distances. Although Lora is an attractive communication solution it could warn or collect other's unity's in field, long range protocol grants less probability of success than the implemented gsm model. The unviability of its use in low signal field, instigated the analysis of satellite protocols and solutions, which couldn't be granted till now due to the lack of resources and fiscal incentives in the country the study is being lead.

GPS technology is also applied to the solution, making it possible for the boat to be self-driven.

All collected data shall be cryptographed and send using roaming, to a google account, where using the google apps script tools, plains, dashboards and mainly, information would be concentrated. Using cloud computing via GAS is no more than an excuse for not to waste time in data safety, and instead in the possibilities this propose represent.

As the system is meant to cover some areas, data would be defined from all over the globe, increasing data gathering, but also creating a possible reference to the satellite sensing, in a way to correct interferences and possibly helping determine successfully more reliable information about algae.

References

Anderson, D. M. Estimated Annual Economic Impacts From Harmful Algal Blooms (HABs). United States Article, January 2000.

Cavalcante, M. A. and Haag, R. Corpo negro e determinação experimental da constante de Planck. Revista Brasileira de Ensino de Física, v. 27, n. 3, p. 343 - 348, (2005)

Costa, C. T. et al. Delatorre Phosphorus and nitrogen interaction: loss of QC identity in response to P or N limitation is antecipated in pdr23 mutant. Braz. J. Plant Physiol. vol.23 no.3 Campos dos Goytacazes 2011

Daghighi, A. Harmful Algae Bloom Prediction Model for Western Lake Erie Using Stepwise Multiple Regression and Genetic Programming. Cleveland State University, 2017.

Detection of nitrogen, phosphorus, and potassium (NPK) nutrients of soil using optical transducer. Available at https://ieeexplore.ieee.org/document/8312001

Environmentally friendly methods for controlling algae in lakes and reservoirs 28 May 2014APA citation: Environmentally friendly methods for controlling algae in lakes and reservoirs (2014, May 28). Retrieved 27 October 2019 from https://phys.org/news/2014-05-environmentally-frie...

Final Report Summary - CLEARWATERPMPC (Development of an efficient environmentally-friendly Algae Control System, based on ultrasound technology, designed for use in bigger ponds and lakes). Retrieved 27 October 2019 from https://cordis.europa.eu/project/rcn/101607/reporting/en?rcn=156461

Garland, R. M. Impact of palmitic acid coating on the water uptake and loss of ammonium sulfate particles. Atmospheric Chemistry and Physics 5(7), April 2005.

How LG Sonic Ultrasound Technology Controls Algae. Retrieved from https://www.lgsonic.com/blogs/how-ultrasound-controls-algae/

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Available at https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443

Islam, S. et al. Sensing Technology for Rapid Detection of Phosphorus in Water: A Review. Journal of Biosystems Engineering 2016; 41(2): 138-144

Lin, J. et al. ELECTROCHEMICAL SENSORS FOR SOIL NUTRIENT DETECTION: OPPORTUNITY AND CHALLENGE. Laboratory of Modern Precision Agriculture System Integration, Ministry of Education, Beijing, China, 100083

Masrie, M. Detection of nitrogen, phosphorus, and potassium (NPK) nutrients of soil using optical transducer. Conference Paper, November 2017.

Muttil, N.;Chau, K. NEURAL NETWORK AND GENETIC PROGRAMMING FOR MODELLING COASTAL ALGAL BLOOMS. International Journal of Environment and Pollution, Vol. 28, No. 3-4, 2006, pp. 223-238

Muttil, N. et al. Genetic programming for analysis and real-time prediction of coastal algal blooms. Ecological Modelling Volume 189, Issues 3–4, 10 December 2005, Pages 363-376.

Nitrate and Nitrite Detection. Retrieved from https://photonsystems.com/nitrate-and-nitrite-detection/

O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Retrieved 27 October 2019 from https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066235

Patil, S. S. et al. Detection of NPK nutrients of soil using Fiber Optic Sensor * Deepa V. International Journal of Research in Advent Technology (E-ISSN: 2321-9637) Special Issue National Conference “ACGT 2015”, 13-14 February 2015.

Pellet, J. Taming Toxic Algae Blooms. American Chemical Society, May 12, 2016

Ruopu Li et al. A review on drone-based harmful algae blooms monitoring. Environmental Monitoring and Assessment, March 2019

Sanabria, D. Algae Bloom in a Lake. University of South Florida, Undergraduate Journal of Mathematical Modeling: One + Two Volume 1, 2008 Fall

Wang, L. Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor. Saudi Journal of Biological SciencesVolume 24, Issue 3, March 2017, Pages 556-562

Water Absorption Spectrum. Retrieved from http://www1.lsbu.ac.uk/water/water_vibrational_spectrum.html

Wei, B. et al. Use of artificial neural network in the prediction of algal blooms. Water Research Volume 35, Issue 8, June 2001, Pages 2022-2028

Zachmann, M. The Best LoRa Settings for Range and Reliability. Retrieved Jun 28, 2018 from https://medium.com/home-wireless/testing-lora-radios-with-the-limesdr-mini-part-2-37fa481217ff