AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
A Global Modeling Source Apportionment of PM2.5: Identifying Major Sources and Quantifying Sensitivities to Policy Relevant Reductions
ERIN MCDUFFIE, Melanie Hammer, Michael Brauer, Steven Smith, Randall V. Martin, Dalhousie University, Halifax, Canada
Abstract Number: 457 Working Group: Source Apportionment
Abstract Fine particulate matter (PM2.5) is the leading environmental risk factor for human health, attributable to an estimated 5.2% of all deaths in 2017. To reduce ambient PM2.5 exposure, contributions from major emission sectors must be quantified. An increasing number of source attribution studies have utilized 3D chemical transport models due to their ability to assess contributions at continental or global scales. Much of this recent work has been conducted at regional/country scales, with emissions divided into coarse emission sectors (Transportation, Industry, Energy, etc.). While some studies have investigated global-scale contributions, these aggregate sectoral contributions are primarily quantified using a ‘zeroing-out’ method where entire sectors are removed and compared to base simulations. Removal of large, aggregate sectors can result in a nonlinear response of PM2.5 due to changes in the chemical limitations of secondary aerosol production. Such nonlinear effects are recognized, but not well quantified, leading to a potential bias in estimated sectoral contributions and associated health burden of PM2.5. There is increasing interest in investigating global PM2.5 sources at finer sectoral resolution and for evaluating policy-relevant reductions (~10-50%), thereby reducing the nonlinearities of previous methods.
This work presents global simulations from the GEOS-Chem model (v12) to quantify nonlinear emission contributions to annual PM2.5 mass. The work leverages the recently-developed Community Emissions Data System (CEDS) to investigate individual contributions from heating, cooking, and coal and biomass combustion from residential energy use, coal combustion from industrial and energy sectors, shipping, aircraft, on-road, and non-road/rail emissions from the transportation sector, as well as emissions from agricultural activities, open fires, solvents, biogenic sources, and dust. Additional simulations of policy-relevant reductions highlight the chemical sensitivities of PM2.5 under current emission scenarios and evaluate nonlinearities of previous methods, providing a framework for future studies.