The City of Los Angeles is in a unique situation to be an urban proving ground for better understanding, predicting, and mitigating the issues of air pollution for 4 million citizens.  In partnership with NASA, California State University Los Angeles (CSULA) and OpenAQ, the City embarked on an Air Quality Project with a goal of helping to mitigate the effects of air pollution through interventions that have measured results.

Predicting What We Breathe

The Predicting What We Breathe (PWWB) project has taken time-series measurements of satellite and ground data and applied machine learning to uncover patterns that may not be discernible to human analysts. Enhancing human understanding and prediction of air quality has resulted in a tool that helps inform local governments and others on appropriate measurements, analytics, predictive algorithms and mitigation strategies that are useful for dealing with air quality variability.

PWWB has built a highly accurate predictive air quality forecasting model that is being used by the City of LA and shared with cities around the world. The project has also worked with impacted communities and youth to empower them to engage on air quality.

The forecasting map can be accessed at this link. The model is open-source, so it can be tailored to other communities.  If you would like to access the predictive models or open-source code, please complete this form.  Interested cities can also email us at airquality@lacity.org.  

PEACE for EEJ

Predicting What We Breathe was initially funded by a NASA grant for Advanced Information Systems and Technology (AIST). With additional funding from NASA, including the Earth Science Applications: Equity and Environmental Justice program, the work continues through the Predictive Environmental Analytics and Community Engagement for Equity and Environmental Justice (PEACE for EEJ) project.   In this project the model has been updated to include socioeconomic datasets including education, housing burden, employment and economic status.  This allows for forward-thinking analysis of air quality at the intersection of neighborhood level data, allowing for deeper engagement with environmental justice communities.