Advancing the community health vulnerability index for wildland fire smoke exposure

Jihoon Jung; Joseph L Wilkins; Claire L Schollaert; Yuta J Masuda; John C Flunker; Rachel E Connolly; Savannah M D'Evelyn; Eimy Bonillia; Ana G Rappold; Ryan D Haugo; Miriam E Marlier; June T Spector

doi:10.1016/j.scitotenv.2023.167834

Advancing the community health vulnerability index for wildland fire smoke exposure

Sci Total Environ. 2024 Jan 1:906:167834. doi: 10.1016/j.scitotenv.2023.167834. Epub 2023 Oct 14.

Authors

Affiliations

¹ Department of City and Regional Planning, University of North Carolina, Chapel Hill, NC, USA. Electronic address: climategeo@gmail.com.
² Interdisciplinary Studies Department, Howard University, Washington, DC, USA; School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA.
³ Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA, USA.
⁴ Partnerships and Programs, Vulcan LLC, Seattle, WA, USA.
⁵ Department of Environmental Health Sciences, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, CA, USA.
⁶ Interdisciplinary Studies Department, Howard University, Washington, DC, USA.
⁷ United States Environmental Protection Agency, Office of Research and Development, Durham, NC, USA.
⁸ The Nature Conservancy, Portland, OR, USA.

PMID: 37839481
DOI: 10.1016/j.scitotenv.2023.167834

Abstract

Wildland fire smoke risks are not uniformly distributed across people and places, and the most vulnerable communities are often disproportionately impacted. This study develops a county level community health vulnerability index (CHVI) for the Contiguous United States (CONUS) using three major vulnerability components: adaptive capacity, sensitivity, and exposure at the national and regional level. We first calculated sensitivity and adaptive capacity sub-indices using nine sensitivity and twenty adaptive capacity variables. These sub-indices were then combined with an exposure sub-index, which is based on the Community Multiscale Air Quality data (2008-2018), to develop CHVI. Finally, we conducted several analyses with the derived indices to: 1) explore associations between the level of fine particulate matter from wildland fires (fire-PM_2.5) and the sub-indices/CHVI; 2) measure the impact of fire-PM_2.5 on the increase in the annual number of days with 12-35 μg/m³ (moderate) and >35 μg/m³ (at or above unhealthy for sensitive groups) based on the US EPA Air Quality Index categories, and 3) calculate population size in different deciles of the sub-indices/CHVI. This study has three main findings. First, we showed that the counties with higher daily fire-PM_2.5 concentration tend to have lower adaptive capacity and higher sensitivity and vulnerability. Relatedly, the counties at high risk tended to experience a greater increase in the annual number of days with 12-35 μg/m³ and >35 μg/m³ than their counterparts. Second, we found that 16.1, 12.0, and 17.6 million people out of 332 million in CONUS reside in the counties in the lowest adaptive capacity decile, highest sensitivity decile, and highest vulnerability decile, respectively. Third, we identified that the US Northwest, California, and Southern regions tended to have higher vulnerability than others. Accurately identifying a community's vulnerability to wildfire smoke can help individuals, researchers, and policymakers better understand, prepare for, and respond to future wildland fire events.

Keywords: Adaptive capacity; Assessment; PM(2.5); Sensitivity; Vulnerability; Wildland fire.

MeSH terms

Air Pollutants / adverse effects
Environmental Exposure* / adverse effects
Humans
Particulate Matter / adverse effects
Public Health
Smoke* / adverse effects
United States
Wildfires*

Substances

Air Pollutants
Particulate Matter
Smoke