Data

The goal of this work is to increase the accessibility and use of space science for a broad community in the City of Los Angeles and then to other megacities. By applying machine learning to satellite and ground data, this work will immediately help to inform other cities on appropriate measurements, analytics, predictive algorithms, and mitigation strategies that are useful for dealing with air quality variability. 

Satellite and Ground-based observations of PM2.5, nitrogen oxides (NOx), volatile organic compounds (VOCs), pressure, temperature, humidity, and wind speed from a wide range of data sources will be used to train the machine learning algorithms.

The Project will use Data from NASA, OpenAQ, City of LA as well as other open data sources. Here's a preliminary list:

• NASA Missions & Measurement Data - including MODIS, Sentinel

• NASA Worldview - Precipitation Measurement Missions

• OpenAQ

• Data.gov

• South Coast AQMD

• AirNow

• PurpleAir

• City of LA Data