Modeling Optimal Pathways to a Triple Win in Air Quality, Climate, and Equity

Abstract

“Triple win” strategies in air quality are those that benefit health, climate, and equity. Traditional air pollution modeling estimates these outcomes based on simulated changes in emissions. Here, we develop an inverse modeling approach to identify optimal “triple wins” for the contiguous United States. We use receptor-oriented Bayesian optimization to derive spatially-explicit emission-reductions that meet national standards for fine particulate matter (PM2.5) concentrations while (1) reducing CO2 emissions and (3) reducing disparities in exposure to PM2.5. We compare our optimal solutions against a solution space of over a million runs derived from conventional “forward engineering” approaches. Our approach demonstrates important advantages over conventional methods. It is modular across space, sector, and priority set, and provides a data-driven and scalable framework for explicitly identifying multivariable solutions for air, climate, health, and equity.