Seeking Energy System Pathways to Reduce Ozone Damage to Ecosystems through Adjoint-based Sensitivity Analysis

Wednesday, 17 December 2014: 5:45 PM
Shannon L Capps1, Robert W Pinder1, Daniel H Loughlin1, Jesse O Bash2, Matthew D. Turner3, Daven K Henze3, Peter Percell4, Shunliu Zhao5, Matthew G Russell5 and Amir Hakami5, (1)Environmental Protection Agency Research Triangle Park, Research Triangle Park, NC, United States, (2)U.S. EPA, NERL, RTP, NC, United States, (3)University of Colorado at Boulder, Boulder, CO, United States, (4)University of Houston, Houston, TX, United States, (5)Carleton University, Ottawa, ON, Canada
Tropospheric ozone (O3) affects the productivity of ecosystems in addition to degrading human health. Concentrations of this pollutant are significantly influenced by precursor gas emissions, many of which emanate from energy production and use processes. Energy system optimization models could inform policy decisions that are intended to reduce these harmful effects if the contribution of precursor gas emissions to human health and ecosystem degradation could be elucidated. Nevertheless, determining the degree to which precursor gas emissions harm ecosystems and human health is challenging because of the photochemical production of ozone and the distinct mechanisms by which ozone causes harm to different crops, tree species, and humans.

Here, the adjoint of a regional chemical transport model is employed to efficiently calculate the relative influences of ozone precursor gas emissions on ecosystem and human health degradation, which informs an energy system optimization. Specifically, for the summer of 2007 the Community Multiscale Air Quality (CMAQ) model adjoint is used to calculate the location- and sector-specific influences of precursor gas emissions on potential productivity losses for the major crops and sensitive tree species as well as human mortality attributable to chronic ozone exposure in the continental U.S. The atmospheric concentrations are evaluated with 12-km horizontal resolution with crop production and timber biomass data gridded similarly. These location-specific factors inform the energy production and use technologies selected in the MARKet ALlocation (MARKAL) model.