Challenge 1 - Reduce contamination to cyclists
Challenge 1 - Reduce contamination to cyclists
Cyclist are, however, the most exposed to NOx and solid particles contamination.
How might we reduce the contamination effects on cyclists in current cities so their number could increase and effectively replace car and motorbike density.
- Solution idea: 40 min
- Business idea: 40 min
Miro: https://shorturl.at/7EaYb
Solution idea
🧠 Brainstorm
Development plan
Next week:
- Development plan (steps) to implement / to build a startup to sell or exploit the idea (40 min)
- Presentation (5 min/team) and discussion (up to 1h)
Comments
- Every presentation needs to be self contained. We all have to cover the problem
- An app is not a product. What you sell is a service and the app is a user interface
- "Iterative prototyping" sounds better than "trial and error"
Form: https://shorturl.at/HgFfB
Real solutions
AIRISE
Academic project by multidisciplinary team.
The challenge given them
- Sustainable Development Goals (SDG)11 - Make cities and human settlements inclusive, safe, resilient and sustainable
They identified that cyclists where the most affected by air pollution.
They realized that most sensors publicly available provided just average. It didn't take into account that at any point in the day, the concentration might be higher than the average.
The made a model: Caliope urban model
Starting from only 12 sensor in the city, they made a model taking into account winds, and other things getting to a scale of 10 metres.
The model development of such scale, it takes 3 months.
AEP course & Reby
They weren't allowed to work in Barcelona, just around it. They put on mobility scooters a set of sensors. They generated maps of the contamination in the city using data collected by these scooters.
Gas sensors have slow response. They needed to adjust the data. The data registered at one point, actually refers to a point behind.
Lobelia Air
It's a running company.
They use public data obtained by the 12 stations + satellites data provided by the EU. With a very few sensors they have a model of the city which is a lot simpler (less accurate) but also requires much less computational power.
They provide API to companies.