Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event.
Megan M JohnsonFernando Garcia-MenendezPublished in: GeoHealth (2022)
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 2.5 fields 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 2.5 are 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.
Keyphrases
- particulate matter
- air pollution
- public health
- human health
- healthcare
- lung function
- risk assessment
- mental health
- health information
- decision making
- health promotion
- climate change
- heavy metals
- chronic obstructive pulmonary disease
- machine learning
- type diabetes
- drinking water
- coronary artery disease
- adipose tissue
- cystic fibrosis
- cardiovascular events
- artificial intelligence
- life cycle