• Achtemeier, G. L., 2009: On the formation and persistence of superfog in woodland smoke. Meteor. Appl., 16, 215225, https://doi.org/10.1002/met.110.

  • Akagi, S. K., R. J. Yokelson, C. Wiedinmyer, M. J. Alvarado, J. S. Reid, T. Karl, J. D. Crounse, and P. O. Wennberg, 2011: Emission factors for open and domestic biomass burning for use in atmospheric models. Atmos. Chem. Phys., 11, 40394072, https://doi.org/10.5194/acp-11-4039-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caicedo, V., R. Delgado, R. Sakai, T. Knepp, D. Williams, K. Cavender, B. Lefer, J. Szykman, 2020: An automated common algorithm for planetary boundary layer retrievals using aerosol lidars in support of the U.S. EPA Photochemical Assessment Monitoring Sites program. J. Atmos. Oceanic. Tech., 37, 18471864, https://doi.org/10.1175/JTECH-D-20-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cazorla, A., and et al. , 2017: Near-real-time processing of a ceilometer network assisted with sun-photometer data: Monitoring a dust outbreak over the Iberian Peninsula. Atmos. Chem. Phys., 17, 11 86111 876, https://doi.org/10.5194/acp-17-11861-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ciren, P., and S. Kondragunta, 2014: Dust aerosol index (DAI) algorithm for MODIS. J. Geophys. Res. Atmos., 119, 47704792, https://doi.org/10.1002/2013JD020855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eck, T. F., B. N. Holben, J. S. Reid, O. Dubovik, A. Smirnov, N. T. O’Neill, I. Slutsker, and S. Kinne, 1999: Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res., 104, 31 33331 349, https://doi.org/10.1029/1999JD900923.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Filonchyk, M., V. Hurynovich, and H. Yan, 2020: Trends in aerosol optical properties over eastern Europe based on MODIS-Aqua. Geosci. Front., 11, 21692181, https://doi.org/10.1016/j.gsf.2020.03.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gupta, P., and et al. , 2018: Impact of California fires on local and regional air quality: The role of a low-cost sensor network and satellite observations. Geohealth, 2, 172181, https://doi.org/10.1029/2018GH000136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heese, B., H. Flentje, D. Althausen, A. Ansmann, and S. Frey, 2010: Ceilometer lidar comparison: Backscatter coefficient retrieval and signal-to-noise ratio determination. Atmos. Meas. Tech., 3, 17631770, https://doi.org/10.5194/amt-3-1763-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoff, R. M., and S. A. Christopher, 2009: Remote sensing of particulate pollution from space: Have we reached the promised land? J. Air Waste Manage. Assoc., 59, 645675, https://doi.org/10.3155/1047-3289.59.6.645.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holben, B. N., and et al. , 1998: AERONET—A federated instrument network and data archive for aerosol characterization. Remote Sens. Environ., 66, 116, https://doi.org/10.1016/S0034-4257(98)00031-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holder, A. L., A. K. Mebust, L. A. Maghran, M. R. McGown, K. E. Stewart, D. M. Vallano, R. A. Elleman, and K. R. Baker, 2020: Field evaluation of low-cost particulate matter sensors for measuring wildfire smoke. Sensors, 20, 4796, https://doi.org/10.3390/s20174796.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, J., and et al. , 2016: Validation and expected error estimation of Suomi-NPP VIIRS aerosol optical thickness and Ångström exponent with AERONET. J. Geophys. Res. Atmos., 121, 71397160, https://doi.org/10.1002/2016JD024834.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, J. M., H. Liu, I. Laszlo, S. Kondragunta, L. A. Remer, J. Huang, and H.-C. Huang, 2013: Suomi-NPP VIIRS aerosol algorithms and data products. J. Geophys. Res. Atmos., 118, 12 67312 689, https://doi.org/10.1002/2013JD020449.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnston, F. H., and et al. , 2012: Estimated global mortality attributable to smoke from landscape fires. Environ. Health Perspect., 120, 695701, https://doi.org/10.1289/ehp.1104422.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kondragunta, S., I. Laszlo, H. Zhang, P. Ciren, and A. Huff, 2020: Air quality applications of ABI aerosol products from the GOES-R series. The GOES-R Series: A New Generation of Geostationary Environmental Satellites, S. J. Goodman et al., Eds., Elsevier, 203–217.

    • Crossref
    • Export Citation
  • Koplitz, S. N., C. G. Nolte, G. A. Pouliot, J. M. Vukovich, and J. Beidler, 2018: Influence of uncertainties in burned area estimates on modeled wildland fire PM2.5 and ozone pollution in the contiguous US. Atmos. Environ., 191, 328339, https://doi.org/10.1016/j.atmosenv.2018.08.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, K. R., Y. Yin, V. Sivakumar, N. Kang, X. Xu, Y. Diao, A. J. Adesina, and R. R. Reddy, 2015: Aerosol climatology and discrimination of aerosol types retrieved from MODIS, MISR and OMI over Durban (29.88°S, 31.02°E), South Africa. Atmos. Environ., 117, 918, https://doi.org/10.1016/j.atmosenv.2015.06.058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larkin, N. K., S. M. Raffuse, S.-M. Huang, N. Pavlovic, P. Lahm, and V. Rao, 2020: The Comprehensive Fire Information Reconciled Emissions (CFIRE) inventory: Wildland fire emissions developed for the 2011 and 2014 U.S. National Emissions Inventory. J. Air Waste Manage. Assoc., 70, 11651185, https://doi.org/10.1080/10962247.2020.1802365.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lassman, W. B., R. W. Ford, G. Gan, S. Pfister, E. V. Magzamen, J. R. Fischer, and J. R. Pierce, 2017: Spatial and temporal estimates of population exposure to wildfire smoke during the Washington State 2012 wildfire season using blended model, satellite, and in situ data. Geohealth, 1, 106121, https://doi.org/10.1002/2017GH000049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laszlo, I., and H. Liu, 2017: EPS aerosol optical depth (AOD): Version 3.0.4. NOAA NESDIS Center for Satellite Applications and Research Algorithm Theoretical Basis Doc., 77 pp., https://www.star.nesdis.noaa.gov/smcd/spb/aq/AerosolWatch/docs/JPSS_VIIRS_EPS_AOD_ATBD_V3.0.4_20170106.pdf.

  • Laszlo, I., P. Ciren, H. Liu, S. Kondragunta, J. D. Tarpley, and M. D. Goldberg, 2008: Remote sensing of aerosol and radiation from geostationary satellites. Adv. Space Res., 41, 18821893, https://doi.org/10.1016/j.asr.2007.06.047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levy, R. C., L. A. Remer, S. Mattoo, E. F. Vermote, and Y. J. Kaufman, 2007: Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res., 112, D13211, https://doi.org/10.1029/2006JD007811.

    • Search Google Scholar
    • Export Citation
  • Levy, R. C., L. A. Remer, R. G. Kleidman, S. Mattoo, C. Ichoku, R. A. Kahn, and T. F. Eck, 2010: Global evaluation of the collection 5 MODIS dark-target aerosol products over land. Atmos. Chem. Phys., 10, 10 39910 420, https://doi.org/10.5194/acp-10-10399-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levy, R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, and N. C. Hsu, 2013: The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech., 6, 29893034, https://doi.org/10.5194/amt-6-2989-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., L. A. Remer, J. Huang, H.-C. Huang, S. Kondragunta, I. Laszlo, M. Oo, and J. M. Jackson, 2014: Preliminary evaluation of S-NPP VIIRS aerosol optical thickness. J. Geophys. Res., 119, 39423962, https://doi.org/10.1002/2013JD020360.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petrenko, M., C. Ichoku, and G. Leptoukh, 2012: Multi-sensor Aerosol Products Sampling System (MAPSS). Atmos. Meas. Tech., 5, 913926, https://doi.org/10.5194/amt-5-913-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappold, A. G., and et al. , 2011: Peat bog wildfire smoke exposure in rural North Carolina is associated with cardiopulmonary emergency department visits assessed through syndromic surveillance. Environ. Health Perspect., 119, 14151420, https://doi.org/10.1289/ehp.1003206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Samenow, J., 2020: Controlled burn near Quantico causes smoke conditions across Washington region. Washington Post, 8 March 2020, https://www.washingtonpost.com/weather/2020/03/08/controlled-burn-near-quantico-causes-smoky-conditions-across-washington-region/.

  • Sapkota, A., and et al. , 2005: Impact of the 2002 Canadian forest fires on particulate matter air quality in Baltimore City. Environ. Sci. Technol., 39, 2432, https://doi.org/10.1021/es035311z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmit, T. J., P. Griffith, M. M. Gunshor, J. M. Daniels, S. J. Goodman, and W. J. Lebair, 2017: A closer look at the ABI on the GOES-R series. Bull. Amer. Meteor. Soc., 98, 681698, https://doi.org/10.1175/BAMS-D-15-00230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanaway, J. D., and et al. , 2018: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease study 2017. Lancet, 392, 19231994, https://doi.org/10.1016/S0140-6736(18)32225-6.

    • Search Google Scholar
    • Export Citation
  • U.S. Government, 2017: Network design criteria for ambient air quality monitoring. U.S. Code of Federal Regulations 40, Part 58, Appendix D, 295–310, https://www.govinfo.gov/app/details/CFR-2019-title40-vol6/CFR-2019-title40-vol6-part58-appD.

  • Weber, S. A., T. Z. Insaf, E. S. Hall, T. O. Talbot, and A. K. Huff, 2016: Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City metropolitan area using hierarchical Bayesian model estimates. Environ. Res., 151, 399409, https://doi.org/10.1016/j.envres.2016.07.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiedinmyer, C., B. Quayle, C. Geron, A. Belote, D. McKenzie, X. Y. Zhang, S. O’Neill, and K. K. Wynne, 2006: Estimating emissions from fires in North America for air quality modeling. Atmos. Environ., 40, 34193432, https://doi.org/10.1016/j.atmosenv.2006.02.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., and S. Kondragunta, 2021: Daily and hourly surface PM2.5 estimation from satellite AOD. Earth Space Sci., 8, e2020EA001599, https://doi.org/10.1029/2020EA001599.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., S. Kondragunta, I. Laszlo, and M. Zhou, 2020: Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm. Atmos. Meas. Tech., 13, 59555975, https://doi.org/10.5194/amt-13-5955-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Tracking Smoke from a Prescribed Fire and Its Impacts on Local Air Quality Using Temporally Resolved GOES-16 ABI Aerosol Optical Depth (AOD)

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  • 1 a I. M. Systems Group, College Park, Maryland
  • | 2 b NOAA/NESDIS, College Park, Maryland
  • | 3 c Joint Center of Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland
  • | 4 d NOAA Center for Earth System Sciences and Remote Sensing Science and Technologies, University of Maryland, Baltimore County, Baltimore, Maryland
  • | 5 e NASA GSFC, Greenbelt, Maryland
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Abstract

Aerosol optical depth (AOD) retrieved from the GOES-16 Advanced Baseline Imager (ABI) was used to track a smoke plume from a prescribed fire in northeastern Virginia on 8 March 2020. Weather and atmospheric conditions created a favorable environment to transport the plume through the Washington, D.C., and Baltimore, Maryland, metro areas in the afternoon and concentrate smoke near the surface, degrading air quality for several hours. ABI AOD with 5-min temporal resolution and 2-km spatial resolution definitively identified the timing and geographic extent of the plume during daylight hours. Comparison to AERONET AOD indicates that ABI AOD captured the relative change in AOD due to passage of the smoke, with a mean absolute error of 0.047. Ground-based measurements of fine particulate matter (PM2.5) confirm deteriorations in air quality coincident with the progression of the smoke. Ceilometer aerosol backscatter profiles verify plume transport timing and indicate that smoke aerosols were well mixed in a shallow boundary layer. This event illustrates the advantages of using multiple datasets to analyze the impacts of aerosols on ambient air quality. Given the quickly evolving nature of the event over several hours, ABI AOD provided information for the public and decision-makers that was not available from any other source, including polar-orbiting satellite sensors. This study suggests that PM2.5 concentrations estimated from ABI AOD can be used to fill in the gaps in nationwide regulatory PM2.5 monitor networks and may be a valuable addition to EPA’s PM2.5 NowCast of current air quality conditions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 1 June 2021 to include a reprocessed version of the original Fig. 7 that improved its resolution.

Corresponding author: Amy K. Huff, amy.huff@noaa.gov

Abstract

Aerosol optical depth (AOD) retrieved from the GOES-16 Advanced Baseline Imager (ABI) was used to track a smoke plume from a prescribed fire in northeastern Virginia on 8 March 2020. Weather and atmospheric conditions created a favorable environment to transport the plume through the Washington, D.C., and Baltimore, Maryland, metro areas in the afternoon and concentrate smoke near the surface, degrading air quality for several hours. ABI AOD with 5-min temporal resolution and 2-km spatial resolution definitively identified the timing and geographic extent of the plume during daylight hours. Comparison to AERONET AOD indicates that ABI AOD captured the relative change in AOD due to passage of the smoke, with a mean absolute error of 0.047. Ground-based measurements of fine particulate matter (PM2.5) confirm deteriorations in air quality coincident with the progression of the smoke. Ceilometer aerosol backscatter profiles verify plume transport timing and indicate that smoke aerosols were well mixed in a shallow boundary layer. This event illustrates the advantages of using multiple datasets to analyze the impacts of aerosols on ambient air quality. Given the quickly evolving nature of the event over several hours, ABI AOD provided information for the public and decision-makers that was not available from any other source, including polar-orbiting satellite sensors. This study suggests that PM2.5 concentrations estimated from ABI AOD can be used to fill in the gaps in nationwide regulatory PM2.5 monitor networks and may be a valuable addition to EPA’s PM2.5 NowCast of current air quality conditions.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 1 June 2021 to include a reprocessed version of the original Fig. 7 that improved its resolution.

Corresponding author: Amy K. Huff, amy.huff@noaa.gov
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