Browsing by Subject "reduced complexity model"

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    Air quality human health impact assessment: modeling and applications for environmental policy
    (2020-05) Thakrar, Sumil
    Exposure to fine particulate matter air pollution (PM2.5) is the largest environmental risk factor for death in the United States and globally. Reducing these deaths is facilitated by better understanding how specific emissions sources affect PM2.5 concentrations, but traditional methods for doing so are computationally demanding and resource intensive. In this presentation, I describe my dissertation research into air quality-related human health impacts through the development and use of reduced complexity models (RCMs) that rapidly estimate changes in PM2.5 concentrations and associated deaths. For my first chapter, I use an RCM (InMAP) to estimate the potential air quality-related human health impacts of growing switchgrass, an important bioenergy feedstock. I find that life cycle air quality-related health impacts of switchgrass production vary greatly by location and fertilizer type, and are driven primarily by ammonia emissions from fertilizer application. For my second chapter, I use InMAP and two other RCMs to estimate the air quality impacts of all domestic, human-caused emissions in the United States to identify promising targets for reducing air quality-related deaths. I find that half of the deaths are attributable to 5 human activities, all in different sectors. Promising policy decisions for reducing the deaths include targets of historical focus, such as coal-powered electricity generation, and emerging targets, such as agricultural emissions and residential solvent use. For my third chapter, I describe the development of an open source RCM (Global InMAP) for use on a global spatial domain. I generate global chemical and meteorological inputs to parametrize Global InMAP, configure its computational grid, and run InMAP on a global emissions inventory to demonstrate its use. Overall, its performance against ground observations is comparable to current global models, but at greatly reduced computational intensity. Global InMAP can be used to further inform policy decisions for reducing air quality-related deaths worldwide.