Garvin Heath

This study assesses the air quality impacts of an advanced biorefinery that produces renewable diesel blendstock (RDB) from lignocellulosic biomass via aerobic respiration (Davis et al. 2022) by estimating fine particulate matter (PM2.5) impacts from biorefinery emissions. It continues a prior analysis that used a geospatial assessment to identify source regions for biomass feedstocks and studied the impact of feedstock production emissions on air quality (Thind et al. 2022). Thind et al. (2022) identified RDB biorefineries that can use corn stover feedstocks of 2,000; 5,200; and 9,100 dry metric tons per day (DMT/day), based in Iowa, and suggested 7 unique counties can serve as hosts for a biorefinery that draws biomass feedstock from neighboring counties. Given 13 unique county-biorefinery size combinations and two waste lignin end uses at the biorefinery (lignin as a fuel for electricity generation and lignin for pellet production), the air-quality-related sustainability aspects of each of these 26 scenarios are assessed by estimating the annual average impacts of biorefinery emissions on the dispersion and formation of secondary PM2.5 in the atmosphere using a novel reduced-complexity air quality model called the Intervention Model for Air Pollution (InMAP). The 26 biorefinery design combinations help capture how a biorefinery's emissions of air pollutants and their resulting impact on local and regional air quality are influenced by the magnitude of production scale, lignin utilization strategy, and location of a proposed biorefinery. Methods developed in Thind et al. (2022) are applied to estimate the constraints on primary PM2.5 and secondary PM2.5 precursor emissions based on compliance with U.S. Environmental Protection Agency's (EPA's) annual primary National Ambient Air Quality Standard (NAAQS) of PM2.5 (i.e.12.0 micrograms per cubic meter (microgram/m3)) at downwind receptors of a biorefinery. Incremental PM2.5 concentrations caused by the emission of biorefining corn stover into RDB are assessed and compared to those of corn stover production. To illuminate which upstream supply chain stage of renewable diesel production contributes most to air quality impacts, marginal PM2.5 concentrations are compared between both stages at multiple downwind air quality monitor locations. In addition, through a hotspot analysis, we identify the primary contributing factors of emissions within the feedstock production and biorefinery stage operations. In doing so, we provide insights for improving the air pollutant emission-related sustainability of advanced lignocellulosic biofuel production.

Fossil fuel combustion for process heat in heavy industry accounts for ~15% of all United States CO₂ emissions and emits PM_2.5 and its precursors, emissions that have a disproportionate impact on minority populations. Decarbonizing process heat in the U.S. via hydrogen substitution presents an opportunity to reduce emissions of CO₂ and PM_2.5 and mitigate resulting exposure disparity. Here, we show that hydrogen substitution in steelmaking provides a large reduction in CO₂ emissions and air quality-related premature mortality, while hydrogen substitution in petroleum refining substantially benefits disadvantaged communities. When reductions in CO₂ emissions and premature mortality are monetized using standard regulatory values, we find that the sum of air pollution and climate benefits outweighs the difference in private cost associated with hydrogen substitution in steelmaking, regardless of the method of hydrogen production. The approach developed here can support evaluations of equity-focused decarbonization strategies in other industries and for specific sites.

Growth in liquefied natural gas (LNG) demand has been accompanied by debates about its greenhouse gas (GHG) emissions impact. Studies have shown that activity-based inventory accounting methods in the oil and gas sector significantly underestimates methane emissions. As a result, target-based policies and voluntary initiatives are moving towards adopting direct measurements to assess the GHG emissions intensity (EI) of LNG supply chains. Yet, most supply chain assessments of LNG do not incorporate measurement data. In this work, we develop a probabilistic, geospatial, and measurement informed life cycle assessment model to estimate the GHG EI of global LNG supply chains covering over 90% of LNG trade. We find a ~5x range in supply chain GHG EI from 8.6 g CO2e/MJ in Qatar to over 39 g CO2e/MJ in Algeria. Overall, our work suggests supply chain-weighted GHG EI of global LNG trade is underestimated by up to 31% compared to prior estimates that did not incorporate measurements. Probabilistic models of GHG EI of LNG exhibit a heavy tailed distribution, revealing the importance of low likelihood but high emitting supply chains. Incorporating direct measurements in supply chain assessments is necessary to avoid underestimation from activity-based inventories and increase confidence in target-based policies to address methane.