scholarly journals Community Vulnerability to Health Impacts of Wildland Fire Smoke Exposure

2017 ◽  
Vol 51 (12) ◽  
pp. 6674-6682 ◽  
Author(s):  
Ana G. Rappold ◽  
Jeanette Reyes ◽  
George Pouliot ◽  
Wayne E. Cascio ◽  
David Diaz-Sanchez
Keyword(s):  
Wildland Fire ◽  
Smoke Exposure ◽  
Health Impacts ◽  
Community Vulnerability ◽  
Fire Smoke

scholarly journals Measuring acute pulmonary responses to occupational wildland fire smoke exposure using exhaled breath condensate

2019 ◽  
Vol 75 (2) ◽  
pp. 65-69 ◽  
Author(s):  
Chieh-Ming Wu ◽  
Anna Adetona ◽  
Chi (Chuck) Song ◽  
Luke Naeher ◽  
Olorunfemi Adetona
Keyword(s):  
Wildland Fire ◽  
Exhaled Breath Condensate ◽  
Smoke Exposure ◽  
Exhaled Breath ◽  
Fire Smoke ◽  
Breath Condensate

scholarly journals Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 308 ◽  
Author(s):  
Patricia D. Koman ◽  
Michael Billmire ◽  
Kirk R. Baker ◽  
Ricardo de Majo ◽  
Frank J. Anderson ◽  
...  
Keyword(s):  
Air Quality ◽  
Wildland Fire ◽  
Average Concentration ◽  
Atmospheric Modeling ◽  
Smoke Exposure ◽  
Geographic Scale ◽  
Fire Smoke ◽  
Model Predictions ◽  
Health Studies ◽  
The Impact

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007–2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California’s population lived in a county that experienced at least one episode of high smoke exposure (“smokewave”) from 2007–2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (−2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.

scholarly journals Status Update: Is smoke on your mind? Using social media to determine smoke exposure

2017 ◽  
Author(s):  
Bonne Ford ◽  
Moira Burke ◽  
William Lassman ◽  
Gabriele Pfister ◽  
Jeffrey R. Pierce
Keyword(s):  
Social Media ◽  
Particulate Matter ◽  
Model Simulation ◽  
Wildland Fire ◽  
Population Level ◽  
Smoke Exposure ◽  
Washington State ◽  
Satellite Observations ◽  
Hazard Mapping ◽  
Fire Smoke

Abstract. Exposure to wildland-fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland-fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for determining these smoke-event properties (ground-based measurements, satellite observations, and chemical-transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using social-media posts regarding smoke, haze, and air quality from Facebook to determine population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook data to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), surface particulate-matter measurements, and model (WRF-Chem) simulated surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well-correlated (R2 generally above 0.5) with these other methods in smoke-impacted regions. Removing days with considerable cloud coverage further improves correlations of Facebook data to traditional exposure datasets, which implies that the population is less aware of smoke on cloudy days relative to sunny days. The Facebook dataset is better correlated with surface measurements of PM2.5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation are. We also present an example case for Washington state in 2015, where we combine this Facebook dataset with MODIS observations and WRF-Chem simulated PM2.5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate-matter measurements.

scholarly journals Special Issue Air Quality and Smoke Management

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1361
Author(s):  
Scott L. Goodrick
Keyword(s):  
Air Quality ◽  
Human Health ◽  
Wildland Fire ◽  
Health Impacts ◽  
Special Issue ◽  
Smoke Management ◽  
Future Studies ◽  
Fire Smoke ◽  
Human Health Impacts

The Atmosphere Special Issue “Special Issue Air Quality and Smoke Management” explores our ability to simulate wildland fire smoke events and the potential to link such modeling to future studies of human health impacts [...]

Symptoms and Behaviors Related to Wildland Fire Smoke Exposure: Data from the EPA Smoke Sense Citizen Science Smartphone Application

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Steven Prince ◽  
Linda Wei ◽  
Anne Corrigan ◽  
Kristen Rappazzo ◽  
Christina Baghdikian ◽  
...  
Keyword(s):  
Citizen Science ◽  
Wildland Fire ◽  
Smartphone Application ◽  
Smoke Exposure ◽  
Exposure Data ◽  
Fire Smoke ◽  
And Behaviors

scholarly journals Status update: is smoke on your mind? Using social media to assess smoke exposure

2017 ◽  
Vol 17 (12) ◽  
pp. 7541-7554 ◽  
Author(s):  
Bonne Ford ◽  
Moira Burke ◽  
William Lassman ◽  
Gabriele Pfister ◽  
Jeffrey R. Pierce
Keyword(s):  
Social Media ◽  
Particulate Matter ◽  
Model Simulation ◽  
Wildland Fire ◽  
Population Level ◽  
Smoke Exposure ◽  
Washington State ◽  
Satellite Observations ◽  
Hazard Mapping ◽  
Fire Smoke

Abstract. Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (ground-based measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM2. 5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM2. 5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate matter measurements.

scholarly journals The health impacts and economic value of wildland fire episodes in the U.S.: 2008–2012

2018 ◽  
Vol 610-611 ◽  
pp. 802-809 ◽  
Author(s):  
Neal Fann ◽  
Breanna Alman ◽  
Richard A. Broome ◽  
Geoffrey G. Morgan ◽  
Fay H. Johnston ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Economic Value ◽  
Health Impacts ◽  
The U.S

scholarly journals Supplementary material to "A low-cost PM<sub>2.5</sub> monitor for wildland fire smoke"

2017 ◽  
Author(s):  
Scott Kelleher ◽  
Casey Quinn ◽  
Daniel Miller-Lionberg ◽  
John Volckens
Keyword(s):  
Wildland Fire ◽  
Low Cost ◽  
Fire Smoke ◽  
Supplementary Material
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