College of Food, Ag & Nat Res Sci
Air pollution kills an estimated three million people per year, which is more than AIDS and malaria combined. Determining and evaluating strategies for reducing these deaths is complex, as air quality-induced human health impacts depend on spatio-temporal variables including meteorological conditions, atmospheric concentrations of certain species, and population density. This has traditionally required chemical transport models (CTMs); however, running CTMs is time-consuming, requires expertise, and is computationally intensive. As a result, assessing the air quality-induced human health impacts of policies and interventions has been limited by the resources available to run CTMs for different scenarios. InMAP, a reduced-form CTM, was created to solve this problem, by using simplified modeling assumptions to increase speed and reduce computational intensity, largely without compromising predictive accuracy. So far, it has only been run using North America as its spatial domain, although the majority of the burden of disease for air pollution is elsewhere. The researchers are now running InMAP with a global spatial domain, using output from GEOS-Chem, which is a full-scale chemical transport model. This has the potential to greatly expand the facility of evaluating the impacts of policy decisions worldwide.