Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 822, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2022.153418
Keywords
Air quality modeling; Air quality planning; Fine particulate matter; PM2.5 health impacts; Characterization factors; Life cycle impact assessment (LCIA); Biomass production
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This paper introduces a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health. The framework includes a pollutant's characterization factor and three new metrics, which can be applied to various pollutants, energy source sectors, source types, and spatial modeling domains. A case study of fine particulate emissions is presented to demonstrate the applicability of the framework.
In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based cm human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM2.5) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM2.5 formation in the atmosphere using a novel reducedcomplexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM(2.5 )CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.
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