Predicting What We Breathe: Using Machine Learning to Understand Air Quality

Every 7 seconds, someone dies from the effects of air pollution. Air pollution is responsible for 4.5 million deaths and 107.2 million disability-adjusted-life-years globally. With the percentage of the global population living in urban areas projected to increase from 54% in 2015 to 68% in 2050 and in the U.S. up to 89%, the prevention of a significant increase in air pollution-related loss of life requires comprehensive mitigation strategies, as well as forecast systems, to limit and reduce the exposure to harmful urban air. While some megacities like Los Angeles operate an extensive network of ground-based monitoring stations for ozone, particulate matter (PM) 2.5, and other pollutants, air quality (AQ) in many cities around the globe are poorly characterized at ground level. The main source of information on atmospheric environmental conditions in those cities is space-based monitoring of a limited set of AQ indicators. We propose the development of advanced Machine Learning (ML)-based algorithms and models that links ground-based in situ and space-based remote sensing observations of major AQ components, with the aim to (a) classify patterns in urban air quality, (b) enable the deduction and forecast of air pollution events related to PM2.5 and ozone from space-based observations, and ultimately (c) identify similarities in AQ regimes between megacities around the globe for improved air pollution mitigation strategies. Furthermore, this proposal will help us understand the correlation between air pollution and health conditions all over the City of Los Angeles, and predict individuals' health risks related to air pollution based on air quality measurements. Using the City of Los Angeles as a test case, this proposal work will focus on elements (a) and (b), with the extension to element (c) envisaged for follow-on studies. The objective of this proposal is to increase the accessibility and use of space data by using machine learning to help cities predict air quality in ways that can be acted upon to improve human health outcomes and provide better data to individuals and cities. Secondarily, the goal is to provide these tools and algorithms to future Earth science missions (e.g., MAIA) to provide rapid ground truth, combine multiple data sources, and support more rapid use of mission data. This proposal will focus on maturing the technologies involved in: --Developing machine learning algorithms for predictive models for air quality based on PM2.5 and other air pollutants --Build a big data analytics algorithm for integrating ground and space data --Provide predictive models for health risk using deep learning and machine learning --Build an open source PM2.5 stack for integrating ground and space data --Create a model for cities with shared attributes to understand predictions and effective interventions

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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