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

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。 该项目的目标是使用机器学习技术以高空间和时间分辨率改善对美国本土地面NO2和臭氧浓度的估计。这些数据将在公共卫生、环境卫生、空气质量、农业研究和环境正义/不平等等多个领域发挥作用。该项目将解决以下三个科学问题：（1）哪些机器学习模型可以最好地估计特定区域的地面NO2和臭氧值？(2)在模式中，哪些变量/参数对估算地面NO2和臭氧值更有意义？(3)地面NO2和臭氧产品的准确性如何？PI计划使用臭氧廓线数据和对流层NO2垂直柱密度OMI和TROPOMI的（TropNO 2 VCD）数据，以及克里思的综合数据与气象数据，美国环境署的土地覆盖和地面测量（美国环保署）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.