Abstract
Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality. Traditional control strategies typically reduce emissions for specific air pollutants and sectors to maintain pollutant concentrations below standards. Here we directly set national PM2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, in scenarios to 2050, to identify endogenously the control actions, sectors, and locations that most cost-effectively reduce PM2.5 mortality. We show that substantial health benefits can be cost-effectively achieved by electrifying sources with high primary PM2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. More stringent PM2.5 reduction targets expedite the phaseout of high emission intensity sources, leading to larger declines in major pollutant emissions, but very limited co-benefits in reducing CO2 emissions. Control strategies limiting health damages achieve the greatest emission reductions in the East North Central and Middle Atlantic states.
Highlights
Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality
We define a reference scenario (BASE REF) that includes updated baseline assumptions about technology trajectories harmonized with the 2018 Energy Information Administration (EIA) Annual Energy Outlook (AEO)[23], as well as an alternative reference scenario (HR REF) with much higher shares of wind and solar generation (e.g., 48% versus 25% in 2050), intended to reflect a continuation of recent, rapid growth in renewables
Current US air pollution control is driven by compliance with air pollutant concentration standards and achieved through emission reductions from individual sources
Summary
Exposure to fine particulate matter (PM2.5) from fuel combustion significantly contributes to global and US mortality. Air quality management studies[10,11,12,13,14,15,16,17] have explored the development of multi-pollutant control strategies using an optimization framework While these studies in general confirm that integrated planning across multiple pollutants is more cost-effective than individual mitigation actions, their focus generally has been on a relatively small spatial scale[12,13,15,16] or small number of pollutants[14] over a short period[10,12,13,17], due to the complexity of their optimization algorithms required to address exposure-outcome nonlinearties. This finding holds under a scenario with a more aggressive transition toward renewable energy
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