HBCU-Excellence in Research: Estimate of Ground Nitrogen Dioxide (NO2) and Ozone Concentrations by Using Multiple Satellite Data and Machine Learning Techniques

Spelman College, Atlanta GA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The goal of this project is to improve estimates of ground NO2 and ozone concentrations over the contiguous U.S. at high spatial and temporal resolution using machine learning techniques. This data will be useful in multiple fields, including public health, environmental health, air quality, agricultural research and environmental justice/inequality. This project will address the following three science questions: (1) Which machine learning model(s) can best estimate the ground NO2 and ozone values in specific regions? (2) Which variables/parameters have more significance to estimate the ground NO2 and ozone values in the model? (3) How accurate are the ground NO2 and ozone products? The PIs plan to use ozone profile data and tropospheric NO2 vertical column density (TropNO2VCD) data from OMI and TROPOMI, and the synthetic data of TEMPO in conjunction of meteorological data, land cover and ground measurements from the U.S. Environmental Agency (U.S. EPA). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.