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

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。该项目的目标是利用机器学习技术，以高时空分辨率提高对美国相邻地区地面二氧化氮和臭氧浓度的估计。这些数据将在多个领域有用，包括公共卫生、环境卫生、空气质量、农业研究和环境正义/不平等。该项目将解决以下三个科学问题：(1)哪种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)模型中哪些变量/参数对估算地面NO2和臭氧值更有意义？(3)地面NO2和臭氧产品的准确度如何？pi计划使用OMI和TROPOMI的臭氧剖面数据和对流层二氧化氮垂直柱密度（TropNO2VCD）数据，以及TEMPO的综合数据，结合美国环境署（U.S. EPA）的气象数据、土地覆盖和地面测量数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.