Over the past decade, the PI has established himself as a leader in using atmospheric science and modeling to drive health impact and quantitative policy analyses, connecting air pollution, climate…

This is a grant awarded by the National Aeronautics and Space Administration (NASA) to the University of North Carolina at Chapel Hill (UNC-CH), doing business as the Office of Sponsored Research, for a project titled "Using Atmospheric Science to Inform Decision-Making on Air Quality and Health."

The project has two main tasks: 1) Estimating global surface ozone concentrations through a statistical fusion of global surface observations and global multi-model ensembles, to improve the Global Burden of Disease (GBD) team's assessment of the global health burden of ambient ozone; and 2) Modeling the global air quality and health co-benefits of greenhouse gas (GHG) reductions pledged under the 2015 Paris Agreement, to inform decisions by the U.S. Environmental Protection Agency and other government agencies worldwide. The grant has a ceiling value of $746,719.31 and a period of performance ending on August 10, 2022. This award is not designated as a set-aside contract.

Over the past decade, the PI has established himself as a leader in using atmospheric science and modeling to drive health impact and quantitative policy analyses, connecting air pollution, climate change, and energy with human health and policy. He was the first to use a global atmospheric model for a health impact analysis, and his research group was the first to use a single global model and later an ensemble of global models to estimate the global burden of disease due to outdoor air pollution. His abilities to conduct interdisciplinary studies that address key policy questions are exemplified by studies of the costs and health benefits of methane mitigation as a tool for ozone air quality management, and of the CO-benefits of global greenhouse gas mitigation for global air quality and health. As a member of H-aqast, the PI can also offer a strong understanding of atmospheric science, air pollution health effects, and climate change, leadership and communication of science for informing decision-making, and abilities to work in atmospheric modeling on global, regional, and local scales, human health impact assessment, economics and quantitative policy analysis, and energy-economic modeling. Here we aim to make atmospheric science meaningful for government decision-making through two tasks. First, we will estimate global surface ozone concentrations through a statistical fusion of global surface observations and global multi-model ensembles. The combined global ozone dataset will be then provided to the global burden of disease (GBD) team for their use in evaluating the global health burden of ambient ozone. Since previous GBD exercises estimated ozone concentrations using a single model and no observations, we expect our work to significantly improve forthcoming GBD estimates, leading to a more accurate understanding of ozone exposure and its health effects globally. We will use an unprecedented global database of ozone observations currently being compiled for the tropospheric ozone assessment report (TOAR), and multiple global model simulations for the htap-2, accmip, and aerchemmip multi-model intercomparisons. Three methods of statistical data fusion will be used in succession based on their complexity, using observations to correct for model biases. This project uses NASA satellite products for model evaluation, directly by our study or by the modeling teams. The target audience will be the GBD team, and by strengthening the GBD assessment, we will reach governments and decision-makers globally. Second, we will model the global air quality and health CO-benefits of GHG reductions currently pledged under the 2015 paris agreement to address climate change. Our previous CO-benefits research has revealed that for global GHG reductions, a significant fraction of the CO-benefits realized in the US may result from foreign emissions controls. Here we will use simulations of a reference scenario and of the paris agreement commitments, as modeled previously in the global energy-economics model gcam, to give global air pollutant emissions. A global atmospheric model will simulate concentrations of ozone and PM2.5 in 2030 (the current paris target period), and in 2050 and 2100 for scenarios that continue paris goals with the same level and increased levels of commitment. Global mortality impacts of reduced air pollution will be assessed using the PIS methods. We will further conduct simulations that separate CO-benefits due to emissions from the US VS. Foreign nations. This project will use NASA meteorological reanalyses as input, and NASA satellite products to evaluate the simulation for the base year (2008-2012). This project will aim to inform decisions by the US EPA and other government agencies, as well as air quality and health managers worldwide.