CarbonRoad has received the following awards and nominations. Way to go!
We developed an app to prevent people from tripping over our carbon footprints. The app informs the best routes and locations that avoid areas with high concentration of carbon dioxide.
The app works by crossing data from NASA, CSA and SIRENE, transforming them into easy-to-interpret maps for diagnosis.
This information is especially important for people with respiratory problems, who will be able to avoid heavily polluted areas, thus avoiding the worsening of their health condition.
It will be possible to indicate which regions of the city are the properties located in areas with better air quality.
With this information in hand, city halls will be able to think of alternatives for CO2 capture and control in the most affected areas, such as the elaboration of urban projects and improvements in public transportation.
Our team also proposes an architectural solution, based on bioplastic and microalgae, which will be implemented in buildings and will capture Co2, while producing valuable biomass.
Therefore, through our diagnostic app, together with the solution involving microalgae, we hope to achieve a future where people have clean and safe air to survive.
What inspires us is the opportunity to contribute with SDG 3, SDG 11 and SDG 13, providing a clean and safe air to survive. Our approach was to make data access and interpretation of carbon levels in the atmosphere possible and accessible. To develop this project we used the expo.io, the framework React Native and the program language TypeScript to develop the app. In the phase of development, we had some problems by trying to convert some datas of OCO-2 XCO2 Observations (NASA). The archives were in the .netCDF format and we had to pass to the .csv (or .json) to use it. Because of that, we couldn’t apply it on the deck.gl and be able to integrate the interactive map, with datas of the concentration levels of CO2, on the app.
We use specially carbon emission space data. Our goal is to identify mainly the CO2 levels over roads, streets, avenues. We did it by using the OCO-2 XCO2 Observations (NASA), that help us to identify places with a high CO2 content when applied to kepler.gl (is a powerful open source geospatial analysis tool for large-scale data sets) and can be integrated into the application using the deck. gl (is a WebGL-powered framework for visual exploratory data analysis of large datasets.)