High-Level Project Summary
We developed a POC data visualization which indicates the debris spreading from China Seashores into the ocean and the effect of 25% of cleansing. We estimated the size of the debris created by China by using scientific outputs which is 4.2M tons of plastic per year. The size of the cleaned debris by the volunteers is calculated 210 tons of plastic which was 0.003% of the total waste.In the APEC region marine litter cost USD 622M per year in the tourism sector (Mcllgorm 2009) and by 25% of cleansing could save 156M USD. This study proves that constantly moving marine debris becomes a worldwide problem which needs a shared afford for prevention and cleaning.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Water pollution is a serious situation for the environment, marine life and for the world economy. Artificial Intelligence/Machine Learning techniques could be used to monitor, detect, and quantify plastic pollution and increase our understanding for this purpose.
Each year 8M tons of plastic litter are leaking into marine ecosystems. 90 percent of plastic that pollutes our oceans comes from 10 rivers, 6 of which are in China.
There are Citizen Scientists’ efforts such as debristracker.org by Morgan Stanley in order to clean the debris to solve the problem. They clean roughly 210 tons of plastic debris which is 0.003% of total debris each year.
https://debristracker.org/data
Blowed study indicates that in the APEC region marine litter cost USD 622 million per year in the tourism sector (Mcllgorm 2009) and by 25% of cleansing could save 156M USD.
Using NOAA’s (National Oceanic and Atmospheric Administration) datasets from 1998 to 2021 we tracked debris drifts from China seashores into the ocean and nearby countries. We also calculated 25% decreased debris effect and plotted on the map.
We completed this proof of concept study. This study proves that the marine debris is constantly moving and becomes a worldwide problem. Because of that it needs a shared afford for prevention and cleaning.
It could be extended for every countries’ shores and drifts of debris in order to show international effects of pollution. New international laws could be arranged between governments based on the severity of the effects. Citizen Scientists’ efforts could be sponsored by these governments and other international/local organizations.
We used buoy datasets from NOAA’s official web-site. Buoy movements also show the debris movements in the ocean. We used google maps to find the coordinates of China’s seashores. We filtered buoys which located in this area and tracked them. We used AWS SageMaker Studio and Python to clean and visualize the data. We used Matplotlip, Pandas and Geopandas libraries for the visualization. Detailed explanation could be found in the related area.
Space Agency Data
We saw the Garbage Patch Visualization Experiment study and it inspired us to use the National Oceanic and Atmospheric Administration database.
We downloaded buoy datasets from NOAA's web-site which consists of 5 files and nearly 8GB of data.
We used AWS SageMaker Studio and Python Engine in order to parse and use the data for our study. The parsed dataset have 13 columns.
We filtered out the data and only get Buoy ID, time (month and year), longitude and latitude information.
We used google maps to get the China's seashore coordinates. We thought that the buoys which was shipped or visited the seashore should be tracked. 1.589.574 unique buoy ID was captured.
lat_min=18.067
lat_max=39.558
long_min=108.116
long_max=124.108
Then we spotted the outliers in the coordinates and clean the data. We also get time between 1998 and 2021.
We scatter plot the data points on the map to show the debris spread from China's seashores into the ocean.
Then we randomly filtered out the data points by 25% in order to show the cleaning affect and get the blow diagram which shows the reduce of the spread.
Hackathon Journey
Thank you for the organization. Our awareness of environmental issues have been increased by this event. We have learned so much about marine life and marine pollution. We realized that we could be a part of the solution no matter what type of support we could afford, picking and recycling the litter or creating a visualization data of spread.
We also thank to our local mentors for their support.
References
Tags
#python #AWS #DataVisiulization #NOAA #AI #ML #MarineDebris
Global Judging
This project has been submitted for consideration during the Judging process.