Air pollution is one of the greatest environmental threats to human health. It can result in heart and chronic respiratory illness, cancer, and premature death. Currently, no single satellite instrument provides ready-to-use, high resolution information on surface-level air pollutants. This gap in information means that millions of people cannot take daily action to protect their health.
The goal of this competition was to use remote sensing data and other geospatial data sources to develop models for estimating daily levels of two different pollutants: particulate matter 2.5 (PM2.5) and nitrogen dioxide (NO2). Successful models can provide critical data to help the public take action to reduce their exposure to air pollution.
This repository contains code from winning competitors in the two tracks for the NASA Airathon: Predict Air Quality DrivenData challenge. The code can be found in the subdirectories for the respective tracks:
Winning code for other DrivenData competitions is available in the competition-winners repository.