This layer shows potential fire locations identified on satellite imagery by the NOAA Hazard Mapping System (HMS) that are deemed to be associated with biomass burning, including wildfires, prescribed and agricultural fires. This is a blended product composed of fire detection data from GOES/ABI, the JPSS/VIIRS and EOS/MODIS sensors. A quality control procedure is performed using both machine- and analyst-based data screening, thereby discarding detections associated with industrial activity (ex., steel mills, gas flares, power plants) as well as potential false alarms caused by solar panels and other highly reflective surfaces, while also correcting for potential omission errors in the automated satellite fire products. A new daily map is typically initiated around 7-8am Eastern Time, and updated multiple times until the next morning as data becomes available. The information on fire position should be used as general guidance and for strategic planning. Tactical decisions, such as the activation of a response to fight these fires and evacuation efforts, should not be made without other information to corroborate the fire's existence and location. Users should note:The initial HMS product for the current day is created and updated by a satellite analyst roughly between 8am and 10am Eastern Time. After 10am, the analysis is fine-tuned as time permits as additional satellite data becomes available. Areas of smoke are analyzed and added to the analysis during daylight hours as visible satellite imagery becomes available. The product is finalized and "completed" for the archive the following morning - generally by around 8:00am.The fire sizes depicted in the product are primarily determined by the field of view of the satellite instrument, or the resolution of the analysis tool. They should not be used to estimate specific fire perimeters.The ability to detect fires and smoke can be compromised by many factors, including cloud cover, tree canopy, terrain, the size of the fire or smoke plume, the time of the day, etc. The satellite sensors used to detect fires are sensitive to heat sources and reflected sunlight. Analysts do their best to distinguish between fires and other heat sources or highly reflective surfaces, such as factories, mines, gas flares, solar panels, clouds, etc. but some false detects do get included in the analysis.Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

This layer shows potential fire locations identified on satellite imagery by the NOAA Hazard Mapping System (HMS) that are deemed to be associated with biomass burning, including wildfires, prescribed and agricultural fires. This is a blended product composed of fire detection data from GOES/ABI, the JPSS/VIIRS and EOS/MODIS sensors. A quality control procedure is performed using both machine- and analyst-based data screening, thereby discarding detections associated with industrial activity (ex., steel mills, gas flares, power plants) as well as potential false alarms caused by solar panels and other highly reflective surfaces, while also correcting for potential omission errors in the automated satellite fire products. A new daily map is typically initiated around 7-8am Eastern Time, and updated multiple times until the next morning as data becomes available. The information on fire position should be used as general guidance and for strategic planning. Tactical decisions, such as the activation of a response to fight these fires and evacuation efforts, should not be made without other information to corroborate the fire's existence and location. Users should note:The initial HMS product for the current day is created and updated by a satellite analyst roughly between 8am and 10am Eastern Time. After 10am, the analysis is fine-tuned as time permits as additional satellite data becomes available. Areas of smoke are analyzed and added to the analysis during daylight hours as visible satellite imagery becomes available. The product is finalized and "completed" for the archive the following morning - generally by around 8:00am.The fire sizes depicted in the product are primarily determined by the field of view of the satellite instrument, or the resolution of the analysis tool. They should not be used to estimate specific fire perimeters.The ability to detect fires and smoke can be compromised by many factors, including cloud cover, tree canopy, terrain, the size of the fire or smoke plume, the time of the day, etc. The satellite sensors used to detect fires are sensitive to heat sources and reflected sunlight. Analysts do their best to distinguish between fires and other heat sources or highly reflective surfaces, such as factories, mines, gas flares, solar panels, clouds, etc. but some false detects do get included in the analysis.Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

This layer shows the areas with observed smoke associated with fires using the latest satellite imagery from the NOAA Hazard Mapping System (HMS). The smoke analysis is based on visual classification of plumes using GOES-16 and GOES-18 ABI true-color imagery available during the sunlit part of the day. A new daily map is initiated around 7-8 Eastern Time although since the analysis generally requires multiple satellite images to help distinguish smoke from clouds and other atmospheric aerosols, the first smoke analysis for the current day is usually produced around the local noon time – until then, only fire detection points may be available. Additional smoke analysis and updates will occur throughout the day until sunset or as observation conditions permit.Smoke attributes carry the start and end times (in Universal Time Coordinated - UTC) of the satellite image sequence used to outline the smoke polygon, the corresponding satellite from which the image was derived, and the plume density. The density information is qualitatively labeled as light, medium, and heavy based on the apparent thickness (opacity) of the smoke in the satellite imagery. Those three distinct groups are meant to approximate smoke concentrations ranging between 0-10, 10-21, and 21-32 micrograms per cubic meter, respectively, although no guarantee is made about the actual smoke density measured in the atmospheric column surrounding those areas. Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

Impacts of fire smoke plumes on regional air quality, 2006–2013 data. Shape files of smoke plumes that define the geographic extent of smoke are from the NOAA Hazard Mapping System (HMS), and O3, total PM2.5, and PM2.5 constituent measurements for 2006–2013 are from the U.S. Environmental Protection Agency’s (EPA) Air Quality System database. This dataset is associated with the following publication: Larsen, A., B. Reich, M. Ruminski, and A. Rappold. Impacts of wildfire smoke plumes on regional air quality, 2006-2013. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 28(4): 319-327, (2018).

Seasonal-mean concentrations of particulate matter with diameters smaller than 2.5 μm (PM2.5) have been decreasing across the United States (US) for several decades, with large reductions in spring and summer in the eastern US. In contrast, summertime-mean PM2.5 in the western US has not significantly decreased. Wildfires, a large source of summertime PM2.5 in the western US, have been increasing in frequency and burned area in recent decades. Increases in extreme PM2.5 events attributable to wildland fires have been observed in wildfire-prone regions, but it is unclear how these increases impact trends in seasonal-mean PM2.5. Using two distinct methods, (1) interpolated surface observations combined with satellite-based smoke plume estimates and (2) the GEOS-Chem chemical transport model (CTM), we identify recent trends (2006–2016) in summer smoke, nonsmoke, and total PM2.5 across the US. We observe significant decreases in nonsmoke influenced PM2.5 in the west..., , , # Spatially interpolated non-smoke and smoke PM2.5 concentrations for the US from 2006-2023

https://doi.org/10.5061/dryad.k0p2ngfhv

Description of the data and file structure

We estimated daily smoke and non-smoke PM2.5 across the contiguous US (CONUS) for 2006-2023 using Environmental Protection Agency (EPA) ground monitors and NOAA Hazard Mapping System (HMS) smoke polygons.

The daily PM2.5 data from EPA ground monitors are interpolated to ~15 km resolution to create a total PM2.5 estimate. The HMS smoke polygons are then used to identify locations where there is likely smoke somewhere in the atmospheric column. The seasonal mean or median background is calculated using pixels within the season where an HMS (dense, medium, or light) smoke polygon is not located. The seasonal background can then be subtracted from the total PM2.5 to estimate smoke PM2.5.

In recent years, an active fire season has resulted in some regions having HMS...,

The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in Nature Communications. Data are the final processessed and merged datasets sourced from the following publicly available data products:

• National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (https://nsrdb.nrel.gov/).
  • Bulk download in July 2023 via AWS: https://registry.opendata.aws/nrel-pds-nsrdb/
  • Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth
• National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product.
  • Access: https://www.ospo.noaa.gov/Products/land/hms.html#maps
  • Variables: smoke plume locations
• National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006).
  • Access: https://lpdaac.usgs.gov/products/mcd19a2v006/
  • Variables: aerosol optical depth and cloud mask
• NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1)
  • Access: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp
  • Variables: cloud optical depth

A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al.

Associated code scripts are located in the linked code repository.

This layer shows potential fire locations identified on satellite imagery by the NOAA Hazard Mapping System (HMS) that are deemed to be associated with biomass burning, including wildfires, prescribed and agricultural fires. This is a blended product composed of fire detection data from GOES/ABI, the JPSS/VIIRS and EOS/MODIS sensors. A quality control procedure is performed using both machine- and analyst-based data screening, thereby discarding detections associated with industrial activity (ex., steel mills, gas flares, power plants) as well as potential false alarms caused by solar panels and other highly reflective surfaces, while also correcting for potential omission errors in the automated satellite fire products. A new daily map is typically initiated around 7-8am Eastern Time, and updated multiple times until the next morning as data becomes available. The information on fire position should be used as general guidance and for strategic planning. Tactical decisions, such as the activation of a response to fight these fires and evacuation efforts, should not be made without other information to corroborate the fire's existence and location. Users should note:The initial HMS product for the current day is created and updated by a satellite analyst roughly between 8am and 10am Eastern Time. After 10am, the analysis is fine-tuned as time permits as additional satellite data becomes available. Areas of smoke are analyzed and added to the analysis during daylight hours as visible satellite imagery becomes available. The product is finalized and "completed" for the archive the following morning - generally by around 8:00am.The fire sizes depicted in the product are primarily determined by the field of view of the satellite instrument, or the resolution of the analysis tool. They should not be used to estimate specific fire perimeters.The ability to detect fires and smoke can be compromised by many factors, including cloud cover, tree canopy, terrain, the size of the fire or smoke plume, the time of the day, etc. The satellite sensors used to detect fires are sensitive to heat sources and reflected sunlight. Analysts do their best to distinguish between fires and other heat sources or highly reflective surfaces, such as factories, mines, gas flares, solar panels, clouds, etc. but some false detects do get included in the analysis.Email your questions to the HMS fire team at: ssdfireteam@noaa.gov

Two specific fires from 2011 are tracked for local to regional scale contribution to ozone (O3) and fine particulate matter (PM2.5) using a freely available regulatory modeling system that includes the BlueSky wildland fire emissions tool, Spare Matrix Operator Kernel Emissions (SMOKE) model, Weather and Research Forecasting (WRF) meteorological model, and Community Multiscale Air Quality (CMAQ) photochemical grid model. The modeling system was applied to track the contribution from a wildfire (Wallow) and prescribed fire (Flint Hills) using both source sensitivity and source apportionment approaches. The model estimated fire contribution to primary and secondary pollutants are comparable using source sensitivity (brute-force zero out) and source apportionment (Integrated Source Apportionment Method) approaches. Model estimated O3 enhancement relative to CO is similar to values reported in literature indicating the modeling system captures the range of O3 inhibition possible near fires and O3 production both near the fire and downwind. O3 and peroxyacetyl nitrate (PAN) are formed in the fire plume and transported downwind along with highly reactive VOC species such as formaldehyde and acetaldehyde that are both emitted by the fire and rapidly produced in the fire plume by VOC oxidation reactions. PAN and aldehydes contribute to continued downwind O3 production. The transport and thermal decomposition of PAN to nitrogen oxides (NOX) enables O3 production in areas limited by NOX availability and the photolysis of aldehydes to produce free radicals (HOX) causes increased O3 production in NOX rich areas. The modeling system tends to overestimate hourly surface O3 at routine rural monitors in close proximity to the fires when the model predicts elevated fire impacts on O3 and Hazard Mapping System (HMS) data indicates possible fire impact. A sensitivity simulation in which solar radiation and photolysis rates were more aggressively attenuated by aerosol in the plume reduced model O3 but does not eliminate this bias. A comparison of model predicted daily average speciated PM2.5 at surface rural routine network sites when the model predicts fire impacts from either of these fires shows a tendency toward overestimation of PM2.5 organic aerosol in close proximity to these fires. The standard version of the CMAQ treats primarily emitted organic aerosol as non-volatile. An alternative approach for treating organic aerosol as semi-volatile resulted in lower PM2.5 organic aerosol from these fires but does not eliminate the bias. Future work should focus on modeling specific fire events that are well characterized in terms of size, emissions, and have extensive measurements taken near the fire and downwind to better constrain model representation of important physical and chemical processes (e.g. aerosol photolysis attenuation and organic aerosol treatment) related to wild and prescribed fires. This dataset is associated with the following publication: Baker, K., M. Woody, G. Tonnesen, B. Hutzell, H. Pye, M. Beaver, G. Pouliot, and T. Pierce. Contribution of regional-scale fire events to ozone and PM2.5 air quality estimated by photochemical modeling approaches. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 140: 539–554, (2016).

Wildland fire event polygons for 2004-2017 reconciled in SmartFire 2 for the EPA Air Quality Times Series (EQUATES) modeling project (https://doi.org/10.1016/j.dib.2023.109022). These event polygons represent a combination of properties from a collection of remotely sensed and ground-based fire activity datasets. The primary underlying fire activity datasets for the fire event polygons are the Hazard Mapping System (HMS) remote sense fire product (https://www.ospo.noaa.gov/Products/land/hms.html), SIT-ICS/209 Incident Reports (https://www.wildfire.gov/application/sit209), GeoMAC Fire Event polygons (https://data-nifc.opendata.arcgis.com/datasets/nifc::historic-perimeters-combined-2000-2018-geomac/about), and the Monitoring Trends in Burn Severity (MTBS) burn scar event perimeters (https://www.mtbs.gov/direct-download). This dataset includes events identified as over wildland and does not contain biomass burning events over agricultural areas, such as crop residue field burns. Additionally, certain grass fires, such as the annual prescribed fires in the Flint Hills region, have been removed for inclusion in a separate processing stream. Some minor updates have been made to the dataset since the publishing of the EQUATES emission inventories including removal of known errors related to issues in the underlying activity. This dataset is associated with the following publication: Beidler, J., G. Pouliot, and K. Foley. 2004-2017 Geospatial Dataset of Wild and Prescribed Fire Activity Over the Conterminous United States. Data in Brief. Elsevier B.V., Amsterdam, NETHERLANDS, 56: 110856, (2024).

Wildfire smoke is frequently present over the U.S. during the agricultural growing season and will likely increase with climate change. Studies of smoke impacts have largely focused on air quality and human health; however, understanding smoke’s impact on photosynthetically active radiation (PAR) is essential for predicting how smoke affects plant growth. We compare surface shortwave irradiance and diffuse fraction (DF) on smoke-impacted and smoke-free days from 2006-2020 using data from multifilter rotating shadowband radiometers at ten U.S. Department of Agriculture (USDA) UV-B Monitoring and Research Program stations and smoke plume locations from operational satellite products. On average, 20% of growing season days are smoke-impacted, but smoke prevalence increases over time (r = 0.60, p < 0.05). Smoke presence peaks in the mid- to late growing season (i.e., July, August), particularly over the northern Rocky Mountains, Great Plains, and Midwest. We find an increase in the distribution of PAR DF on smoke-impacted days, with larger increases at lower cloud fractions. On clear-sky days, daily average PAR DF increases by 10 percentage points when smoke is present. Spectral analysis of clear-sky days shows smoke increases DF (average: +45%) and decreases total irradiance (average: -6%) across all six wavelengths measured from 368-870 nm. Optical depth measurements from ground and satellite observations both indicate that spectral DF increases and total spectral irradiance decreases with increasing smoke plume optical depth. Our analysis provides a foundation for understanding smoke’s impact on PAR, which carries implications for agricultural crop productivity under a changing climate. Methods This dataset contains information on surface-level photosynthetically active radiation, smoke plume location, aerosol optical depth, and cloud fraction from four publicly available sources:

U.S. Department of Agriculture's UV-B Monitoring and Research Program (UVMRP) National Oceanic and Atmospheric Administration/National Enviromental Satellite, Data, and Information Service's Hazard Mapping System (HMS) Smoke Product National Aeronautics and Space Administration's Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth Product (MCD19A2) National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere L3 Daily Product (MOD08_D3, MYD08_D3)

The dataset covers 10 UVMRP stations located across the contiguous U.S.:

Davis, California Pullman, Washington Pawnee, Nunn, Colorado Poplar, Montana Fargo, North Dakota Billings, Oklahoma Grand Rapids, Minnesota Bondville, Illinois Starkville, Mississippi Geneva, New York

These sites were selected to provide broad spatial coverage of the regions analyzed in the Brey et al. (2018) smoke climatology, capture much of the smoke variability across the U.S., align with agricultural areas, and reduce the impact of metropolitan air pollution. The UVMRP staff were instrumental in providing the underlying UVMRP data and advise on working with the data. Extensive cleaning was conducted to remove data anomalies, quality control issues, and high solar zenith angles (> 75 degrees). Additional processing of underlying records created additional factors, such as average diffuse fraction, used for analysis. We also averaged values to a daily resolution. A detailed description of the site selection, data cleaning, and data processing methods used to produce this final merged dataset are available in the article by Corwin et al. entitled "Smoke-driven changes in photosynthetically active radiation during the U.S. agricultural growing season."

Fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. We hypothesized that predictive models for personal chemical exposure would be significantly improved by expanding beyond stationary PM2.5 AQI data or satellite HMS data to also include environmental and behavioral information. In Eugene, Oregon, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a seven-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons (PAHs). Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.