A formal study on Machine Learning (ML) and geospatial regression methods for processing Green House Gases (GHG) and air pollutants.

Large among of aerosol and air quality (AQ) diverse data at various resolutions are collected to study air pollutants and various Machine Leaning (ML) models and methods are used for data estimations and analysis studies, but there is no systematic study on which model/algorithm is the best for which pollutants and the size and type of training datasets. This study will: (1) Conduct a formal study on AI/ML and geospatial methods for air pollutants simulation, retrieval or prediction with relevant training datasets and using various ML tools,. (2) Develop and configure a ML open-source package with default configurations for various data. Integrate this package with the AIST Air Quality ACF (AQACF) and the new JPL wildfire digital twin projects. (3) Prepare a few GHG and pollutants (methane, ozone, NO2, SO2, and PMx) Analysis Ready Datasets including uncertainties. (4) Engage high school and community college students.

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

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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