The COVID-19 pandemic offered an unprecedented opportunity to look at changes in the Earth system in response to reduced human activity. Your challenge is to develop tools to better understand changes in the interconnected Earth system as seen through the EO Dashboard.
The team G-FORCE from India aims to predict water level increase/floods through displacement of sea level due to suspended matter, algae sedimentation on plastic and sewage disposal in the San Francisco Bay area. We have used total suspended matter, algae chlorophyll A data provided by NASA’s EO-Dashboard and external maps to locate the exact position of the suspended matter. Our solution aims at plotting sediment mountains formed in the bay due to waste accumulation over time. We have merged data with different timeline images using pixel merge and value optimization techniques, thus providing a time pyramid for suspended matter and predictability model for water level rise/floods.
The project takes highly accredited data such as Night-lights data, Total Suspended Matter
(TSM) data, Images of the bay and external maps (TO FIND EXACT POSITIONS OF THE SUSPENDED MATTER) and Sewage data from the internet and the EO-Dashboard to predict water level rises caused by microplastics and sewage; these unwanted materials eventually get dumped into the bay and together can eventually form mountains out of waste which plays a huge role in causing floods in coastal and city areas. With the water level rising and mountains of accumulated waste forming, our earth is in danger of either being hugely polluted by this mass accumulation underwater or of being almost fully submerged underwater by the year 2100.
Suspended Matter -
Chlorophyll Algae
Sewage dump by the city near the coast ( this is correlated to the population of the city)
Impact on Coastal Cities | Increase by 0.957 inches in San Francisco Bay area annually
We are a group of 5 students Ayaan Shankta, Aarav Seksaria, Vikram Karra, Krish Unadkat, Aditya Borele, all in the age of 11 to 13 years old from India
Our first try of using data actually went flat and then we reworked with another set of data to come up with a great solution and really enjoyed and had fun with number crunching and coding
We are tech enthusiasts and have really brainstormed to come up with a great solution that can really make a difference to the society
There has been a massive change in the environment in the past few years. Huge issues such as climate change, coastal flooding etc. I think what really inspired us to take up this cause was the need to take action on the steadily deteriorating water quality of our planet. We had shortlisted certain target areas using NASA nightlight data where there was a good scope of researching on the topic; i.e. those areas which have had long- lasting issues which have ultimately culminated in either huge levels of sea rise or mass accumulation of waste underneath the water. The Nightlight data, with its light intensity tool, helped us to specifically pinpoint areas where there was a lot of human- environment reaction taking place( denoted through varied levels of light intensity), i.e. areas with scope to analyze and areas where lots of beneficial data would be available. Initially we targeted the city of Tokyo, more specifically, regions surrounding the Tama River. On the sea level rise front, we had collected highly authentic and credible data from the Tokyo metropolitan government’s bureau of sewerage on how much sewage( mostly in the form of wastewater) is dumped into the Tama River on an annual basis. This data which we obtained in the form of a csv file would help us towards our final aim which was to predict how much the water in the Tokyo Bay( which is where the Tama River eventually flows into) would rise upon the addition of sewage to it. Now, on the mass waste accumulation front, we utilized a lot of data from the EO- Dashboard such as total suspended matter maps, charts etc. Our final aim on this front was to calculate, using image processing and algorithms though python, how much of suspended matter floating around in the Tokyo Bay actually sinks to the bottom forming a mountain of mass accumulation. However, midway into our project in Tokyo, we hit a snag. We noticed that in the total suspended matter map, suspended matter was defined as the amount of algae floating around in water. This was not what we hoped to obtain under the category of suspended matter and we had no option but to shift our target city.
Obviously, we had to now shift to a place which not only had maps of total suspended matter as solid waste but which also had good night light data. After some research we narrowed down to San Francisco and in San Francisco, the water body which we were focusing on would be the San Francisco Bay. On the sea level rise front, we decided to specifically focus on the San Mateo region in the San Francisco Bay Area. We started researching to find specific data pertaining to the above said region. After a lot of researching without any avail we finally got the breakthrough. Pacific institute had specifically pinpointed how much sewage is discharged on an annual basis into the San Francisco Bay. According to them 20 million gallons of sewage wastewater was discharged on a daily basis. This was our first variable done. Now, we had to calculate the volume of the water which after calculations we got as 1946208000000 gallons. We then added the volume of sewage discharged and the volume of water and got the total volume to be 21946208000000 gallons. To calculate the sea level rise we divided the volume by the surface area which was 4100 km squared and obtained 5352733658 gallons./ km squared. Upon converting that to inches we finally were able to calculate how much does the sea level rise on a daily basis and that number when rounded to 2 decimal places is 0.02 inches. We then multiplied that by 365 and got 0.957 inches. So, our final output is that on an annual basis the water of the San Francisco Bay in the San Mateo Region rises by 0.957 inches. Now, on the mass accumulation front, after researching, we were able to conclude that 5,346,834,497,000 pieces of plastic sink to the bottom on an annual basis in the San Francisco Bay.
We have used accurate data from NASA which gives us Total Suspended Matter Data that is able to give us data about the concentration of plastics and algae in the SF Bay. We have used external data from research papers that gives us accurate data about the values of plastic being dumped into the San Francisco Bay. We have used this data which allows us to clearly create 3D mountains of plastics that raises the water level therefore increasing level. The NASA EO Dashboard data is giving us accurate numerical values therefore increasing the accuracy of our entire project. We have also utilized Nightlight data from the EO- Dashboard which helps us pinpoint locations where there are high levels of man-environment interaction taking place by denoting these areas with varied levels of light intensity. WE have also used external data from Pacific institute of research which gave us sewage discharge points in the San Francisco Bay Area. NASA has given us highly credible data as well as accurate mapping using indicators thus helping us find the most plausible solutions to the problem.
To show the data on EO Dashboard we have converted our images to matplotlib array format and share such data using share key link (API) to EO Dashboard.
Entire solution and code is written in python EDC 0.24.5, we have used libraries numpy, OpenCV, matplotlib those libraries used for converting csv data to image, structuring data to matrix form and showing results in graphical form respectively.
References:
#socialImpact, #waterquality
This project has been submitted for consideration during the Judging process.