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Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM_2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire‐related smoke PM_2.5fields and variability in concentration‐response parameters from epidemiologic studies of ambient and smoke PM_2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire‐attributable PM_2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration‐response parameters derived from studies of ambient and wildfire‐specific PM_2.5are used to assess health‐related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health‐related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire‐related decision‐making.

 

Prescribed fire is the largest source of fine particulate matter emissions in the Southeastern United States, yet its air quality impacts remain highly uncertain. Here, we assess the influence of prescribed fire on observed pollutant concentrations in the region using a unique fire data set compiled from multiyear digital burn permit records. There is a significant association between prescribed fire activity and concentrations recorded at Southeastern monitoring sites, with permitted burning explaining as much as 50% variability in daily PM_2.5concentrations. This relationship varies spatially and temporally across the region and as a function of burn type. At most locations, the association between PM_2.5concentration and permitted burning is stronger than that with satellite‐derived burn area or meteorological drivers of air quality. These results highlight the value of bottom‐up data in evaluating the contribution of prescribed fire to regional air pollution and reveal a need to develop more complete burn records.

 

Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients’ hospital utilization. Methods: We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients’ primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI). Results: Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies. Conclusions: Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires. 

Background Prescribed fire is a land management tool used extensively across the United States. Owing to health and safety risks, smoke emitted by burns requires appropriate management. Smoke modelling tools are often used to mitigate air pollution impacts. However, direct comparisons of tools’ predictions are lacking. Aims We compared three tools commonly used to plan prescribed burning projects: the Simple Smoke Screening Tool, VSmoke and HYSPLIT. Methods We used each tool to model smoke dispersion from prescribed burns conducted by the North Carolina Division of Parks and Recreation over a year. We assessed similarity among the tools’ predicted smoke fields, areas of concern and potential population impacts. Key results The total smoke area predicted by the tools differs by thousands of square kilometres and, as such, spatial agreement was low. When translated into numbers of residents potentially exposed to smoke, tool estimates can vary by an order of magnitude. Conclusions Our analysis of an operational burning program suggests that the differences among the tools are significant and inconsistent. Implications While our analysis shows that improved and more consistent smoke modelling tools could better support land management, clear guidelines on how to apply their predictions are also necessary to obtain these benefits.