Projecting wildfire emissions over the south-eastern United States to mid-century
Uma Shankar A D E , Jeffrey P. Prestemon B , Donald McKenzie C , Kevin Talgo A , Aijun Xiu A , Mohammad Omary A , Bok Haeng Baek A , Dongmei Yang A and William Vizuete D
+ Author Affiliations
- Author Affiliations
A Center for Environmental Modeling for Policy Development, University of North Carolina Institute for the Environment, Campus Box 1105, Suite 490, Europa Center, 100 Europa Drive, Chapel Hill, NC 27517, USA.
B USDA Forest Service, Southern Research Station, PO Box 12254, Research Triangle Park, NC 27709, USA.
C Pacific Wildland Fire Sciences Lab, USDA Forest Service, 400 N. 34th Street, Suite 201, Seattle, WA 98103, USA.
D Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Campus Box 7431, Chapel Hill, NC 27599, USA.
E Corresponding author. Email: shankaruma00@gmail.com
International Journal of Wildland Fire 27(5) 313-328 https://doi.org/10.1071/WF17116
Submitted: 8 August 2017 Accepted: 18 March 2018 Published: 8 May 2018
Journal Compilation © IAWF 2018 Open Access CC BY-NC-ND
Abstract
Wildfires can impair human health because of the toxicity of emitted pollutants, and threaten communities, structures and the integrity of ecosystems sensitive to disturbance. Climate and socioeconomic factors (e.g. population and income growth) are known regional drivers of wildfires. Reflecting changes in these factors in wildfire emissions estimates is thus a critical need in air quality and health risk assessments in the south-eastern United States. We developed such a methodology leveraging published statistical models of annual area burned (AAB) over the US Southeast for 2011–2060, based on county-level socioeconomic and climate projections, to estimate daily wildfire emissions in selected historical and future years. Projected AABs were 7 to 150% lower on average than the historical mean AABs for 1992–2010; projected wildfire fine-particulate emissions were 13 to 62% lower than those based on historical AABs, with a temporal variability driven by the climate system. The greatest differences were in areas of large wildfire impacts from socioeconomic factors, suggesting that historically based (static) wildfire inventories cannot properly represent future air quality responses to changes in these factors. The results also underscore the need to correct biases in the dynamical downscaling of wildfire climate drivers to project the health risks of wildfire emissions more reliably.
Additional keywords: climate and socioeconomic change, emissions projections, wildfires.
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