Abstract for the 2019 HEI Annual Conference
Quantifying Societal Health Benefits of Transportation Emission Reductions in the United States and Canada
Amir Hakami1, Armistead Russell2, Alan Krupnick3, Howard Chang4
1Carleton University, Ottawa, ON, Canada; 2Georgia Institute of Technology, Atlanta, GA, USA; 3Resources for the Future, Washington, DC, USA; 4Emory University, Atlanta, GA, USA
Background. One measure of the efficacy of an air pollution control option is the Marginal Benefit (MB) associated with the option, or the monetized societal benefits of reducing one metric tonne of emissions through its implementation. We use adjoint modeling, a recent approach for quantifying source-receptor relationships, and integrate air quality modeling, epidemiology, and economic valuation to estimate location-specific MBs.
Objectives. We aim to (a) create a database of location-specific MBs for transportation and other select sectors in Canada and the U.S., (b) quantify the uncertainty in estimated MBs, (c) examine the sensitivity to various assumptions made in estimation of MBs, (d) develop an associated database of co-benefits from combustion-based CO2 reductions, and (e) present per-vehicle transportation MBs for various vehicle types and vintages.
Methods. We use the adjoint version of the U.S. EPA’s Community Multiscale Air Quality (CMAQ) model to estimate MBs at 12 km resolution for various locations and times in Canada and the contiguous U.S. The adjoint approach allows for the estimation of influences from individual sources on nationwide health outcomes. Our analysis will primarily focus on health impacts associated with chronic exposure to PM2.5 and O3, as well as exposure to NO2 in Canada. We will examine the robustness of MB estimates with respect to various assumptions such as the choice of health and valuation metrics, simulated episodes, model resolution, shape of concentration-response functions, and the emission inventory level. We will combine estimated MBs with sector-specific CO2 emission profiles to develop a related database of co-benefits.
Past results. Our past results suggest that there is great spatial (and temporal) variability in MBs for various emitted species, particularly at higher spatial resolutions. As expected, this spatial variability closely follows that of population distribution for primary pollutants (e.g., primary PM2.5) but is more nuanced for precursor emissions (i.e., SO2, NOx, and NH3). Our past findings also indicate that co-benefits of CO2 reductions from transportation and electricity generation can be significantly larger than the social cost of carbon for many source locations in the U.S. and Canada.
Poster by Hakami et al., 2019 Annual Conference