| Literature DB >> 35524066 |
Savannah M D'Evelyn1, Jihoon Jung2, Ernesto Alvarado3, Jill Baumgartner4, Pete Caligiuri5, R Keala Hagmann3,6, Sarah B Henderson7, Paul F Hessburg3,8, Sean Hopkins9, Edward J Kasner2, Meg A Krawchuk10, Jennifer E Krenz2, Jamie M Lydersen11, Miriam E Marlier12, Yuta J Masuda5, Kerry Metlen5, Gillian Mittelstaedt13, Susan J Prichard3, Claire L Schollaert2, Edward B Smith5, Jens T Stevens14, Christopher W Tessum15, Carolyn Reeb-Whitaker16, Joseph L Wilkins3,17, Nicholas H Wolff5, Leah M Wood18, Ryan D Haugo5, June T Spector2.
Abstract
PURPOSE OF REVIEW: Increasing wildfire size and severity across the western United States has created an environmental and social crisis that must be approached from a transdisciplinary perspective. Climate change and more than a century of fire exclusion and wildfire suppression have led to contemporary wildfires with more severe environmental impacts and human smoke exposure. Wildfires increase smoke exposure for broad swaths of the US population, though outdoor workers and socially disadvantaged groups with limited adaptive capacity can be disproportionally exposed. Exposure to wildfire smoke is associated with a range of health impacts in children and adults, including exacerbation of existing respiratory diseases such as asthma and chronic obstructive pulmonary disease, worse birth outcomes, and cardiovascular events. Seasonally dry forests in Washington, Oregon, and California can benefit from ecological restoration as a way to adapt forests to climate change and reduce smoke impacts on affected communities. RECENTEntities:
Keywords: Air quality; Collaborative partnerships; Ecological restoration; Environmental justice; Exposure; Interdisciplinary; Prescribed burning; Public health; Smoke; Wildland fire
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Year: 2022 PMID: 35524066 PMCID: PMC9076366 DOI: 10.1007/s40572-022-00355-7
Source DB: PubMed Journal: Curr Environ Health Rep ISSN: 2196-5412
Fig. 1Historical weather and wildland vegetation in the western US in May-October. A Time series of the annual mean fire weather index and B the annual maximum temperatures from 1980 to 2019 within seasonally dry coniferous forests. The fire weather index is a measure of potential fire intensity based on temperature, relative humidity, wind speed, and 24-h precipitation. Only the wildfire season from May through October was plotted. C Fire regime map based on vegetation cover and natural fire regimes in 2019. Data downloaded from climatologylab.org/gridmet for weather data and landfire.gov/ for fire regime and vegetation cover data. Methodology described in Supplemental Text 1
Populations living in and near the wildland urban interface (WUI) in the western US. This table displays populations and housing units located in the WUI within and adjacent to seasonally dry coniferous forests (LANDFIRE fire regime groups 1 and 3) in Washington, Oregon, and California. The 1 km buffer is intended as a conservative estimate of proximity to prescribed fire and local smoke events. WUI was defined and calculated based on block level housing units and populations (2010 dataset), and WUI data were obtained from the Silvis Lab at University of Wisconsin (2017 dataset) [58]. For the purposes of this table, WUI intermix, the area where structures and wildland vegetation directly intermingle, and interface, the area where structures are adjacent to the wildland vegetation, were combined into the “WUI” [55]
| In WUI overlapping seasonally dry forests | In WUI + 1 km buffer | |||
|---|---|---|---|---|
| Population | Housing unit | Population | Housing unit | |
| 43,536 (0.6) | 22,457 (0.8) | 644,947 (9.6) | 307,344 (10.7) | |
| 64,767 (1.7) | 29,334 (1.8) | 609,964 (15.9) | 282,124 (16.9) | |
| 103,494 (0.3) | 56,362 (0.4) | 1,230,627 (3.3) | 649,875 (4.8) | |
Fig. 2Populations vulnerable to the health risks of smoke exposure. The Community Health Vulnerability Index (CHVI) is designed to capture community vulnerability to wildfire smoke based on factors known to increase the risk of health effects from airborne pollutants (15 parameters described in Supplemental Text 1). Percentiles by county are adapted from Rappold et al. [40]. Higher percentiles indicate more vulnerable areas