Wildfires and climate justice: future wildfire events predicted to disproportionally impact socioeconomically vulnerable communities in North Carolina.

Wildfire events are becoming increasingly common across many areas of the United States, including North Carolina (NC). Wildfires can cause immediate damage to properties, and wildfire smoke conditions can harm the overall health of exposed communities. It is critical to identify communities at increased risk of wildfire events, particularly in areas with that have sociodemographic disparities and low socioeconomic status (SES) that may exacerbate incurred impacts of wildfire events. This study set out to: (1) characterize the distribution of wildfire risk across NC; (2) implement integrative cluster analyses to identify regions that contain communities with increased vulnerability to the impacts of wildfire events due to sociodemographic characteristics; (3) provide summary-level statistics of populations with highest wildfire risk, highlighting SES and housing cost factors; and (4) disseminate wildfire risk information via our online web application, ENVIROSCAN. Wildfire hazard potential (WHP) indices were organized at the census tract-level, and distributions were analyzed for spatial autocorrelation via global and local Moran's tests. Sociodemographic characteristics were analyzed via k-means analysis to identify clusters with distinct SES patterns to characterize regions of similar sociodemographic/socioeconomic disparities. These SES groupings were overlayed with housing and wildfire risk profiles to establish patterns of risk across NC. Resulting geospatial analyses identified areas largely in Southeastern NC with high risk of wildfires that were significantly correlated with neighboring regions with high WHP, highlighting adjacent regions of high risk for future wildfire events. Cluster-based analysis of SES factors resulted in three groups of regions categorized through distinct SES profiling; two of these clusters (Clusters 2 and 3) contained indicators of high SES vulnerability. Cluster 2 contained a higher percentage of younger (<5 years), non-white, Hispanic and/or Latino residents; while Cluster 3 had the highest mean WHP and was characterized by a higher percentage of non-white residents, poverty, and less than a high school education. Counties of particular SES and WHP-combined vulnerability include those with majority non-white residents, tribal communities, and below poverty level households largely located in Southeastern NC. WHP values per census tract were dispersed to the public via the ENVIROSCAN application, alongside other environmentally-relevant data.

Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring.

The prevalence of wildfires continues to grow globally with exposures resulting in increased disease risk. Characterizing these health risks remains difficult due to the wide landscape of exposures that can result from different burn conditions and fuel types. This study tested the hypothesis that biomass smoke exposures from variable fuels and combustion conditions group together based on similar transcriptional response profiles, informing which wildfire-relevant exposures may be considered as a group for health risk evaluations. Mice (female CD-1) were exposed via oropharyngeal aspiration to equal mass biomass smoke condensates produced from flaming or smoldering burns of eucalyptus, peat, pine, pine needles, or red oak species. Lung transcriptomic signatures were used to calculate transcriptomic similarity scores across exposures, which informed exposure groupings. Exposures from flaming peat, flaming eucalyptus, and smoldering eucalyptus induced the greatest responses, with flaming peat grouping with the pro-inflammatory agent lipopolysaccharide. Smoldering red oak and smoldering peat induced the least transcriptomic response. Groupings paralleled pulmonary toxicity markers, though they were better substantiated by higher data dimensionality and resolution provided through -omic-based evaluation. Interestingly, groupings based on smoke chemistry signatures differed from transcriptomic/toxicity-based groupings. Wildfire-relevant exposure groupings yield insights into risk assessment strategies to ultimately protect public health.