Raw FASTQ data representing wildland fire smoke plume microbiome. This dataset is associated with the following publication: Kobziar, L., D. Vuono, R. Moore, B. Christner, T. Dean, D. Betancourt, A. Watts, J. Aurell, and B. Gullett. Wildland Fire Smoke Alters the Composition, Diversity, and Potential Atmospheric Function of Microbial Life in the Aerobiome. International Society for Microbial Ecology. Nature Publishing Group, London, UK, na, (2022).

Raw FASTQ data representing wildland fire smoke plume microbiome. This dataset is associated with the following publication: Kobziar, L., D. Vuono, R. Moore, B. Christner, T. Dean, D. Betancourt, A. Watts, J. Aurell, and B. Gullett. Wildland Fire Smoke Alters the Composition, Diversity, and Potential Atmospheric Function of Microbial Life in the Aerobiome. International Society for Microbial Ecology. Nature Publishing Group, London, UK, na, (2022).

High time resolution (10 s) chamber study burn emission measurements and commercial laboratory fuel analysis reports. This dataset is associated with the following publication: Urbanski, S., R. Long, H. Halliday, A. Habel, E. Lincoln, and M. Landis. Fuel layer specific pollutant emission factors for fire prone forest ecosystems of the western U.S. and Canada. Atmospheric Environment: X. Elsevier B.V., Amsterdam, NETHERLANDS, 0000, (2022).

This data set includes the emission factors and emission estimates that are used to generate the figures and tables in the manuscript. This dataset is associated with the following publication: Holder, A., A. Ahmed, J. Vukovich, and V. Rao. Hazardous air pollutants emission estimates from wildfires in the wildland urban interface. PNAS Nexus. Oxford University Press, OXFORD, UK, 2(6): pgad186, (2023).

This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.

These data were compiled so that annual wildfire could be modelled across the sagebrush region in the western United States. Our goal was to understand how wildfire probability relates to climate and fuel conditions across the entire sagebrush region. To do this we developed a statistical model that represents the relationship between annual wildfire probability and a small number of climate and fuel variables. Specifically, created predictions of wildfire probability using a biologically plausible logistic regression model that related wildfire probability to mean temperature, annual precipitation, the proportion summer precipitation (PSP), and aboveground biomass of annual herbaceous plants and perennial herbaceous plants. The biomass variables were used as proxies for fine fuel availability. These data represent annual fire occurrence in 1 km pixels (i.e. did a given pixel burn that year), predicted wildfire probability, as well as the three year running average (i.e. average across the current and previous two years) of climate and vegetation variables. These data were collected across the sagebrush region (the extent of the study area is provided by the cell_number_ids.tif file). The climate and vegetation data were compiled using a existing gridded dataset (Daymet) of daily precipitation and temperature, and vegetation data were summaries of annual estimates of aboveground biomass of annual and perennial herbaceous plants from the Rangeland Analysis Platform (https://rangelands.app/). These data can be used to understand spatial and temporal variability in wildfire occurrence and modelled wildfire probability between 1988 and 2019 and how that variability relates to spatial and temporal variability in climate and vegetation.

The data includes PM2.5 emission factors from prescribed forest fires at Fishlake National Forest, Utah, USA. Portions of this dataset are inaccessible because: The non-generated data was not commissioned by EPA. They can be accessed through the following means: By contacting the creator lkobziar@uidaho.edu. Format: Data were created by Leda Kobizar and is publicly available upon request. This dataset is associated with the following publication: Kobziar, L., P. Lampman, A. Tohidi, A. Kochanski, A. Cervantes, A. Hudak, R. McCarley, B. Gullett, J. Aurell, R. Moore, D. Vuono, B. Christner, A. Watts, J. Cronan, and R. Ottmar. Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58: 2413-2422, (2024).

Data contains various indicators of vulnerability to wildfire smoke exposure and health effects. This dataset is associated with the following publication: Jung, J., J.L. Wilkins, C.L. Schollaert, Y.J. Masudae, J.C. Flunkerd, R.E. Connolly, S.M. D’Evelynd, E. Bonilliab, A. Rappold, R.D. Haugo, M.E. Marlierf, and J.T. Spector. Advancing the Community Health Vulnerability Index for Wildland Fire Smoke Exposure. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 906: 167834, (2024).

This dataset was generated to support the differential expression analysis, transcriptomic similarity scoring analysis, exposure chemistry profiles, mouse pulmonary toxicity measures and associated figures contained within the manuscript. This dataset is associated with the following publication: Koval, L., C. Carberry, Y.H. Kim, E. McDermott, H. Hartwell, I. Jaspers, M. Gilmour, and J. Rager. Wildfire Variable Toxicity: Identifying Biomass Smoke Exposure Groupings through Transcriptomic Similarity Scoring. International Journal of Environmental Science and Technology. Springer, Heidelburg, GERMANY, 56(23): 17131-17142, (2022).

These are GC-MS data for producing the levoglucosan plot, black carbon data from an aethalometer, time series data of wind speed and normalized PM concentrations. As well as certain particle number and CO concentration data deltas. This dataset is associated with the following publication: Kimbrough , S., M. Hays , B. Preston, D. Vallero , and G. Hagler. Episodic Impacts from California Wildfires Identified in Las Vegas Near-Road Air Quality Monitoring. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 50(1): 18-24, (2016).