CAFE - Space Apps Challenge

CAFE| Surface-to-Air (Quality) Mission

Project Details

Awards & Nominations

CAFE has received the following awards and nominations. Way to go!

Global Nominee

The Challenge | Surface-to-Air (Quality) Mission

Your challenge is to integrate NASA data, ground-based air quality data, and citizen science data to create an air quality surface that displays the most accurate data for a location and time. Create algorithms that select or weight the best data from several sources for a specific time and location, and display that information.

CAFE - Clean Air For Everyone

From cloud access to clean air, CAFE is the platform that combines satellite data, ground stations data with crowd-sourcing information to provide air quality forecast, assist law enforcement and raise awareness. #airquality #crowdsource #API #pollution

current and future development

While CAFE was born under 24 hours, our idea has received tremendous response from the locals. Subsequently, we are working on further developing the CAFE app in preparation for roll-out. The two directions of development are:

Enhancing Air Quality Analysis and Prediction

The app is currently using a simple diffusion model to estimate air pollution concentrations in air. While this model is proven to give accurate results under normal weather condition, it is prone to weather conditions such as rain and crosswind at higher atmosphere. To better understand how weather affects the air pollutants concentration, we turn to machine learning to give us better prediction. We are collecting comprehensive weather data, fire data and air pollutants concentration at fixed points; further plan includes engaging meteorologists and academia to contribute to the prediction models.

Engaging Local Authorities

We are in the midst of discussions with a few local authorities in Malaysia who are keen to bring this technology further. With the support of local authorities, we target to conduct a pilot run in a city in Malaysia, before rolling it out globally.

project codes and resources

The codes are written in Python 3.7. External packages involved are stated clearly in the codes.

Description is also attached in every code snippets to provide better insights of what the codes do and the techniques involved.

https://tinyurl.com/yxpuexxg

Sources

Air Pollution Data in CAFE

Nitrogen Dioxide Concentration, NASA Earth Observations:

https://neo.sci.gsfc.nasa.gov/view.php?datasetId=AURA_NO2_D&date=2019-09-01

Carbon Monoxide Concentration, NASA Earthdata:

https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aqua_NRT/AIRS2RET_NRT.006/

Sulfur Dioxide Concentration, NASA Earthdata:

https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aura_MLS_NRT/ML2SO2_NRT.004/

Real-time Air Quality Index of 800 over ground-stations, The World Air Quality Project:

http://aqicn.org/map/world/

Fire Data in CAFE

Active Fire Data from Visible Infrared Imaging Radiometer Suite (VIIRS), NASA Earthdata:

https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data

Weather Data in CAFE

Weather data and forecast using Weather API, OpenWeather:

https://openweathermap.org/api

Weather Prediction in CAFE

Simple diffusion model and its effectiveness, paper on 'A Simple Diffusion Model for Calculating Point Concentrations from Multiple Sources' by John F. Clarke:

https://www.tandfonline.com/doi/pdf/10.1080/00022470.1964.10468294