## Inspiration

While we were thinking about the sustainability track, we realized that one of the biggest challenges faced by humanity is carbon emissions, global warming and climate change.
According to Dr.Fatih Birol, IEA Executive Director -
*"Global carbon emissions are set to jump by 1.5 billion tonnes this year. This is a dire warning that the economic recovery from the Covid crisis is currently anything but sustainable for our climate."*
With this concern in mind we decided to work on a model which could possibly be a small compact carbon capturing system to reduce the carbon footprint around the world.

## What it does

The system is designed to capture CO2 directly from the atmosphere using microalgae as our biofilter.

## How we built it

Our plan was to first develop a design that could house the microalgae. We designed a chamber in Fusion 360 which we later 3D printed to house the microalgae. The air from the surroundings is directed into the algal chamber using an aquarium aerator. The pumped in air moves into the algal chamber through an air stone bubble diffuser which allows the air to break into smaller bubbles. These smaller air bubbles make the CO2 sequestration easier by giving the microalgae more time to act upon it. We have made a spiral design inside the chamber so that the bubbles travel upward through the chamber in a spiral fashion, giving the microalgae even more time to act upon it. This continuous process in due course would lead to capturing of CO2 and production of oxygen.

## Challenges we ran into

3D printing the parts of the chamber within the specified time.
Getting our hands on enough micro algae to fill up the entire system in its optimal growth period (log phase) for the best results.
Making the chamber leak proof.

## Accomplishments that we're proud of

The hardware design that we were able to design and build over the stipulated time.
Develop the system which could actually bring down CO2 levels by utilizing the unique side of microalgae.

## What we learned

We came across a lot of research papers implicating the best use of microalgae in its role to capture CO2.
Time management: Learnt to design and develop a system from scratch in a short period.

## What's next for Aria

We plan to conduct more research using microalgae and enhance the design of the existing system we built so that we could increase the carbon capture efficiency of the system.
Keeping in mind the deteriorating indoor air quality, we also plan to integrate it with the inorganic air filters so that it could help in improving the overall indoor air quality.
We also plan to conduct research on finding out how much area does one unit of Aria covers
## Inspiration

Mood tracking apps are known to help many individuals dealing with mental health struggles understand and learn about their mood changes, improve their mood, and manage their mental illnesses (AMIA Annu Symp Proc. 2017; 2017: 495–504). However, many people have trouble fitting the task of charting mood into their daily routine -- recording daily mood seems unimportant, many individuals struggling with their mental health have low motivation to accomplish this kind of mundane task, it's a small task that's easy to push off indefinitely, and it serves as a daily reminder of their difficulties or disabilities.

## What it does

Pilot encourages individuals to fill out their mood tracker, and it helps them do so in an efficient, natural, and engaging way. And while many suffering from low mood find themselves stuck in self-propagating cycles of negative thinking and isolation, Pilot strives to boost mood more effectively than current trackers by actively listening and responding, and by introducing people to unique places and cultures around the world!

Pilot serves as a browser landing page, and each day a unique picture of a mystery location encourages users to engage with the app in order to identify where the image was taken. Users are prompted to discuss the events of the day out loud, though they can enter text if they wish -- this style of information is more efficient and natural than the process required by most mood trackers, and encourages comprehensiveness and candor. Pilot then responds to the individual's sentiment and prompts them to reflect on their current mood further with a probing question. Once the user has responded to these questions, Pilot recommends how the user may be feeling on three different scales, mood, anxiety, and cynicism, allowing the user to adjust as they see fit. Once this step is complete, the destination is revealed and the user can explore the location they were matched to for that day. Pilot also provides users with a clean environment with which to view their mood and mental health history.

## How I built it

We used Watson for sentiment analysis, Rev for speech-to-text, and Amadeus to identify sights and Mapbox and Wikipedia to display information about locations.

## Accomplishments that I'm proud of

We're extremely proud of all the features we were able to pack into Pilot in 24 hours.
## Inspiration

It all started when I said it's impossible to have access to satellite data.
I started my search and found plenty of open sources, either from space agency data or other online tools.
First, I identified a disaster to work on, which is algae blooms.
I chose it because of its riskiness as they are often instigated by pollution and changing temperatures and can kill a variety of marine and freshwater life through eutrophication.

## What it does

Algal blooms affect coastal communities. I came up with a tracking mechanism to measure the presence of HAB. The use of RS products by a variety of available imagery NASA satellite data and other open sites and geospatial information would help address our challenge effectively. I noticed that chlorophyll is one major variable in algal blooms in the area; using (OLCI) system, I could capture signatures of biogeochemical that affect algal bloom growth. We have developed an algorithm using Ai to analyze and according to the presence of causing factors to generate early warning signals for detected hazards. Finally, I used a visual interface ''GUI" to understand the impacts of the phenomenon.

## How we built it

Usually, these blooms give a distinct coloration visible in imagery, such as the red tide, although the coloration does vary depending on the type of bloom.

Given the importance of knowing how these blooms affect aquatic life, remote sensing techniques using a variety of available imagery have been developed. Variation in chlorophyll is one major variable in algal blooms in the area; using the Sentinel-3 Ocean and Land Color Instrument (OLCI) system, this instrument has been designed to capture signatures of biogeochemical that affect algal bloom growth.
My idea can be divided into two parts:
The first part is the gathering of information using geographic information systems (GIS) - sensing from the distance, and also collecting sufficient information about the disaster, its causes, and everything about it.
The second part is developing an algorithm using artificial intelligence to analyze and generate early warning signals for detected hazards.

* GIS
The GIS will collect, save, retrieve, process, analyze, and display spatial data and information. It will produce maps, extract information, using software that conducts data management and analysis using DBMS software and designs, and displays data using AutoCAD.
* RS Remote Sensing

I will collect information about the phenomenon and this process is done using satellites, which occurs through an interaction between electromagnetic energy (the light source) and the phenomena of the surface of the earth to be photographed, and then it is necessary to use special imaging systems that can record the reflected energy.
I found a prior solution depending on the integrated ocean observing system, as satellites, buoys on the surface, and sensors on the ocean floor are collecting data on ocean color and currents, if algae blooms were predicted, scientists track them to estimate where they are travelling.
But to detect the disaster before happening, I decided to have a look at past images from satellites regards:
The fossil fuel factors, agricultural land, marine population and then collect statistics regards nitrogen and phosphorus presence in air, Lakes and Rivers, Coasts and Bay, Groundwater and Drinking Water.
Those factors play a crucial role in nutrient pollution which in turn cause algae blooms.
The further data we get, the most accurate we reach.
I processed the data by: Imagary Processing
These data will be put in an algorithm in which several conditions will be tested and according to statistics it can estimate the predicted timing depending on the time it took to happen at past in presence of those factors.
Till now, I have achieved the part of image processing, data collection from the satellites.
Now, I am working on the exact algorithm and trying to experiment it to show how accurate it could be.

## Challenges we ran into

Finding NASA open resources to find the differences in colors of algae blooms.

## Accomplishments that we're proud of

Working solely on imagery processing and collecting data from satellites.

## What we learned

I learned how to use AI algorithms to process and detect hazards.

## What's next for Earthling

Developing a more complicated algorithm to predict the hazard more quickly and more efficiently