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Abstract 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 (PM2.5) 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 PM2.5fields and variability in concentration‐response parameters from epidemiologic studies of ambient and smoke PM2.5. 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 PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration‐response parameters derived from studies of ambient and wildfire‐specific PM2.5are 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.
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Dynamics of PM2.5 and network activity during extreme pollution events
Abstract In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM2.5) data. The established spatial correlation networks reveal significant PM2.5anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.
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- Award ID(s):
- 2125326
- PAR ID:
- 10525635
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Climate and Atmospheric Science
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2397-3722
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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