Four_teame

https://youtu.be/5X8xvrlsscw

https://drive.google.com/file/d/1o6YJG71WvNo1C1Wr8z1-6kLjGiZ-CGCi/view?usp=sharing

The Project Monitoring System for Physicochemical Variables in the Sea proposed to develop an intelligent buoy prototype to identify the presence of oily substances accumulated in the sea due to marine plastic debris. Figure 1 shows a descriptive image of the project.

The buoy is placed in the sea, and it has a Global Positioning System (GPS), in order to identify the location of the debris, the ocean needs to be mapped (Agarwala, 2021). It is considered to supply energy by a photovoltaic system (PV). The buoy allows capturing and taking small water samples to be analyzed and it is equipped with four sensors to monitor CO2, pH, temperature (T) and turbidity. The samples are analyzed to detect the presence of these substances and their concentrations. The monitored data are sent to the cloud storage platform with Microsoft® Azure Blob Storage. Data are tagged for further analysis with Machine Learning (ML) models using the Microsoft® Maching Learning tool to predict similarities with monitored data. Similarities are identified to distinguish marine plastic debris. These data are compared with those existing in the database of the NOAA (National Oceanic and Atmospheric Administration). The information will be used to build an open access database to be consulted in real time and a dashboard will be designed with Microsoft® Azure Dashboard to display results. The approach presented in this project addresses the challenge by proposing an experimental prototype to detect and identify plastic pollution and how it has increased through ML with Microsoft® Azure Maching Learning tool to predict similarities with the measured data. We considered that the proposal presented in this project is important because it considers characterizing marine plastic debris from physicochemical variables to detect not only floating plastic but also fractionated plastic. According to (Eriksen at al., 2013; Browne et al., 2010; Thompson et al., 2004), it is estimated that 90% of the marine plastic debris is floating and the remaining 10% are micro plastics, hence, this project contributes to solve this problem. 

Figure 1. General block diagram for the Monitoring System for Physicochemical Variables in the Sea.

For this project we decided to use the database of the NOAA (National Oceanic and Atmospheric Administration) to obtain data because (Shirah and Mitchell, 2015) it contains:

“… data from floating, scientific buoys that NOAA has been distributing in the oceans for the last 35-year represented here as white dots”

“The dataset used for the ocean buoy visualization is the Global Drifter Database from the GDP Drifter Data Assembly Center, part of the NOAA Atlantic Oceanographic & Meteorological Laboratory. The data covered the period February 1979 through September 2013”

The proposed idea is to used these data to compare and validated the obtained results with this project.

With the NASA SPACE APSS Challenge we learnt so many things about the current problems in the world and thinking that our proposal could be part of a global solution is exciting and encourages us to keep studying and working. This project inspired us because every year thousands of marine mammals and waterfowl die for consuming plastic marine debris and we decided to propose a solution to help to reduce this problem. We live in the coast so we decided to propose a project with the technology that we know. Teamwork helped us to distribute tasks so we could work against the clock. We want to thank our mentor because she motivated us every moment to move on and not give up, to keep our heads up and finish the project. Thanks to the organizers: Karla, Argenis, Saul and everybody here. Thanks to the University of Colima for bringing to us this kind of events, thanks to the NASA for allow us being part of “The Power of Ten – NASA International Space Apps Challenge”.

¡Happy hacking! 

• Agarwala, N. (2021). Managing marine environmental pollution using Artificial Intelligence. Maritime Technology and Research, 3(2), 120-136. https://doi.org/10.33175/mtr.2021.248053.
• Browne MA, Galloway TS, Thompson RC. Spatial patterns of plastic debris along Estuarine shorelines. Environ Sci Technol. 2010 May 1;44(9):3404-9. doi: 10.1021/es903784e. PMID: 20377170.
• Eriksen, M.; Mason, S.; Wilson, S.; Box, C.; Zellers, A., Edwards, W.; Farley, H.; Amato, S. (2013). Microplastic pollution in the surface waters of the Laurentian Great Lakes. Marine Pollution Bulletin, Volume 77, Issues 1–2, 177-182. https://doi.org/10.1016/j.marpolbul.2013.10.007.
• EOSDIS Ocean Data (2021, Octuber 2). Toxicological Threats of Plastic. https://earthdata.nasa.gov/learn/discipline/ocean
• Microsoft® Azure (2021, Octuber 2). https://docs.microsoft.com/es-es/azure/azure-resource-manager/templates/template-tutorial-use-template-reference?tabs=CLI.
• NOAA, Atlantic Oceanographic and Meteorological Laboratory, Six-Hourly Interpolated Metadata. (2021, Octuber 3). https://www.aoml.noaa.gov/phod/gdp/interpolated/meta/all.php
• Ocean Surface Buoys/Various/Global Drifter Database also referred to as: Ocean Drifters Data Compilation - NOAA/AOML - 2/15/1979-9/30/2013.
• Thompson R.C. (2015) Microplastics in the Marine Environment: Sources, Consequences and Solutions. In: Bergmann M., Gutow L., Klages M. (eds) Marine Anthropogenic Litter. Springer, Cham. https://doi.org/10.1007/978-3-319-16510-3_7.

Metadata:

• File dirfl_1_5000.dat contains metadata from the earliest 5,000 drifters. This file was last revised in May, 2016.
• File dirfl_5001_10000.dat contains metadata for drifters 5,001 through 10,000. This file was last revised on May, 2016.
• File dirfl_10001_15000.dat contains metadata for drifters 10,001 through 15,000. This file was last revised on May, 2016.
• File dirfl_15001_mmmyy.dat contains updated metadata through MMM of year YY. Choose the your deisired file.

#plastic pollution; #marine debris; #intelligent buoy; #physicochemical variables

This project has been submitted for consideration during the Judging process.