Modeling air pollution: Informing policies to address a global environmental challenge
Air pollution is a leading cause of global mortality: according to the World Health Organization, 90% of people worldwide breathe polluted air, and outdoor air pollution causes over 4 million deaths annually. Different models can simulate various aspects of the air pollution problem by quantifying pollutant emissions from different sectors and their socio-economic drivers, tracing the chemistry and transport of atmospheric processes, attributing pollutant concentrations to specific sources, and quantifying the health and economic burdens of pollutant exposure. To inform efforts to mitigate air pollution, however, we need to trace the entire pathway by which policies to address emissions translate into societal benefits. Doing this requires connecting models from different academic fields, and which exist in different modeling languages, with different temporal and spatial scales, and with different core scientific assumptions. In this talk, I summarize work from my research group evaluating the impacts of air pollution policies by connecting and integrating models across this conceptual chain. Examples provided include assessing the air pollution and related health impacts of proposed policies to address climate change in the U.S. and China, and quantifying the domestic and international benefits of mercury reduction policies in China, India, and the U.S. Technical challenges of linking models include accounting for issues of temporal and spatial scale, complexity, and boundaries. Effectively informing decision-making, however, also requires that decision-makers see models as credible, salient, and legitimate. Thus, I also describe ways in which we have engaged with stakeholders and decision-makers, and examine how these efforts have influenced the impact of this research on policy.