Theoretical Investigations on Mapping Mean Distributions of Particulate Matter, Inert, Reactive, and Secondary Pollutants from Wildfires by Unmanned Air Vehicles (UAVs)

Abstract

Evaluated Weather Research and Forecasting model inline with chemistry (WRF/Chem) simulations of the 2009 Crazy Mountain Complex wildfire in Interior Alaska served as a testbed for typical Alaska wildfire-smoke conditions. A virtual unmanned air vehicle (UAV) sampled temperatures, dewpoint temperatures, primary inert and reactive gases and particular matter of different sizes as well as secondary pollutants from the WRF/Chem results using different sampling patterns, altitudes and speeds to investigate the impact of the sampling design on obtained mean distributions. In this experimental design, the WRF/Chem data served as the “grand truth” to assess the mean distributions from sampling. During frontal passage, the obtained mean distributions were sensitive to the flight patterns, speeds and heights. For inert constituents mean distributions from sampling agreed with the “grand truth” within a factor of two at 1000 m. Mean distributions of gases involved in photochemistry differed among flight patterns except for ozone. The diurnal cycle of these gases’ concentrations led to overestimation (underestimation) of 20 h means in areas of high (low) concentrations as compared to the “grand truth.” The mean ozone distribution was sensitive to the speed of the virtual UAV. Particulate matter showed the strongest sensitivity to the flight patterns, especially during precipitation.

Share and Cite:

Mölders, N. , Butwin, M. , Madden, J. , Tran, H. , Sassen, K. and Kramm, G. (2015) Theoretical Investigations on Mapping Mean Distributions of Particulate Matter, Inert, Reactive, and Secondary Pollutants from Wildfires by Unmanned Air Vehicles (UAVs). Open Journal of Air Pollution, 4, 149-174. doi: 10.4236/ojap.2015.43014.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Elston, J., Argrow, B., Stachura, M., et al. (2014) Overview of Small Fixed-Wing Unmanned Aircraft for Meteorological Sampling. Journal of Atmospheric and Oceanic Technology, 32, 97-115.
http://dx.doi.org/10.1175/JTECH-D-13-00236.1
[2] US Census Bureau (2015) National Population Projections.
https://www.census.gov/population/projections/data/national/2014.html
[3] PaiMazumder, D. and Molders, N. (2009) Theoretical Assessment of Uncertainty in Regional Averages Due to Network Density and Design. Journal of Applied Meteorology and Climatology, 48, 1643-1666. http://dx.doi.org/10.1175/2009JAMC2022.1
[4] Shulski, M. and Wendler, G. (2007) The Climate of Alaska. University of Alaska Press, Fairbanks, 216 p.
[5] Bienek, P. (2007) Climate and Predictability of Alaska Wildfires. Master’s Thesis, Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, 95 p.
[6] Molders, N. and Kramm, G. (2007) Influence of Wildfire Induced Land-Cover Changes on Clouds and Precipitation in Interior Alaska—A Case Study. Atmospheric Research, 84, 142-168.
http://dx.doi.org/10.1016/j.atmosres.2006.06.004
[7] Molders, N. (2008) Suitability of the Weather Research and Forecasting (WRF) Model to Predict the June 2005 Fire Weather for Interior Alaska. Weather and Forecasting, 23, 953-973.
http://dx.doi.org/10.1175/2008WAF2007062.1
[8] Butwin, M.K. (2015) Theoretical Investigations on Strategies for Sampling Meteorological and Chemical Field Quantities in Smoke Plumes Using UAVs. Master’s Thesis, Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, 176 p.
[9] Mayer, S., Sandvik, A., Jonassen, M.O. and Reuder, J. (2010) Atmospheric Profiling with the UAS SUMO: A New Perspective for the Evaluation of Fine-Scale Atmospheric Models. Meteorology and Atmospheric Physics, 63, 15-26.
[10] Skamarock, W.C., Klemp, J.B., Dudhia, J., et al. (2008) A Description of the Advanced Research WRF Version 3. NCAR/TN, 125 p.
[11] Elston, J.S., Roadman, J., Stachura, M., et al. (2011) The Tempest Unmanned Aircraft System for in Situ Observations of Tornadic Supercells: Design and VORTEX2 Flight Results. Journal of Field Robotics, 28, 461-483. http://dx.doi.org/10.1002/rob.20394
[12] Patterson, M.C.L., Mulligan, A., Douglas, J., et al. (2005) Volcano Surveillance by ACR Silver Fox. American Institute of Aeronautics and Astronautics, 1, 26-29. http://dx.doi.org/10.2514/6.2005-6954
[13] McGonigle, A.J.S., Aiuppa, A., Giudice, G., et al. (2008) Unmanned Aerial Vehicle Measurements of Volcanic Carbon Dioxide Fluxes. Geophysical Research Letters, 35, Article ID: L06303.
http://dx.doi.org/10.1029/2007gl032508
[14] EPA (2011) National Ambient Air Quality Standards (NAAQS). http://www.epa.gov/air/criteria.html
[15] Grell, G.A., Peckham, S.E., Schmitz, R., et al. (2005) Fully Coupled “Online” Chemistry within the WRF Model. Atmospheric Environment, 39, 6957-6975. http://dx.doi.org/10.1016/j.atmosenv.2005.04.027
[16] Peckham, S.E., Fast, J., Schmitz, R., et al. (2011) WRF/Chem Version 3.3 User's Guide. 96 p.
[17] Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J. and Clough, S.A. (1997) Radiative Transfer for Inhomogeneous Atmospheres: RRTM, a Validated Correlated-K Model for the Longwave. Journal of Geophysical Research, 102D, 16663-16682. http://dx.doi.org/10.1029/97JD00237
[18] Chou, M.-D. and Suarez, M.J. (1994) An Efficient Thermal Infrared Radiation Parameterization for Use in General Circulation Models. Report, Goddard Space Flight Center, Greenbelt, 85 p.
[19] Grell, G.A. and Dévényi, D. (2002) A Generalized Approach to Parameterizing Convection. Geophysical Research Letters, 29, 1693.
[20] Lin, Y.-L., Rarley, R.D. and Orville, H.D. (1983) Bulk Parameterization of the Snow Field in a Cloud Model. Journal of Applied Meteorology, 22, 1065-1092.
http://dx.doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2
[21] Barnard, J., Fast, J., Paredes-Miranda, G., Arnott, W. and Laskin, A. (2010) Technical Note: Evaluation of the WRF-Chem “Aerosol Chemical to Aerosol Optical Properties” Module Using Data from the MILAGRO Campaign. Atmospheric Chemistry and Physics, 10, 7325-7340. http://dx.doi.org/10.5194/acp-10-7325-2010
[22] Grell, G.A., Freitas, S.R., Stuefer, M. and Fast, J.D. (2011) Inclusion of Biomass Burning in WRF-Chem: Impact on Wildfires on Weather Forecasts. Atmospheric Chemistry Physics, 11, 5289-5303. http://dx.doi.org/10.5194/acp-11-5289-2011
[23] Mellor, G.L. and Yamada, T. (1982) Development of a Turbulence Closure Model for Geophysical Fluid Problems. Review of Geophysics—Space Physics, 20, 851-875.
http://dx.doi.org/10.1029/RG020i004p00851
[24] Janjic, Z.I. (1994) The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer and Turbulence Closure Schemes. Monthly Weather Review, 122, 927-945. http://dx.doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2
[25] Smirnova, T.G., Brown, J.M., Benjamin, S.G. and Kim, D. (2000) Parameterization of Cold Season Processes in the Maps Land-Surface Scheme. Journal Geophysical Research, 105D, 4077-4086. http://dx.doi.org/10.1029/1999JD901047
[26] Stockwell, W.R., Middleton, P., Chang, J.S. and Tang, X. (1990) The Second-Generation Regional Acid Deposition Model Chemical Mechanism for Regional Air Quality Modeling. Journal Geophysical Research, 95, 16343-16367. http://dx.doi.org/10.1029/JD095iD10p16343
[27] Madronich, S. (1987) Photodissociation in the Atmosphere, 1, Actinic Flux and the Effects of Ground Reflections and Clouds. Journal Geophysical Research, 92, 9740-9752.
http://dx.doi.org/10.1029/JD092iD08p09740
[28] Wesely, M.L. (1989) Parameterization of Surface Resistances to Gaseous Dry Deposition in Regional-Scale Numerical Models. Atmospheric Environment, 23, 1293-1304.
http://dx.doi.org/10.1016/0004-6981(89)90153-4
[29] Molders, N., Tran, H.N.Q., Quinn, P., et al. (2011) Assessment of WRF/Chem to Capture Sub-Arctic Boundary Layer Characteristics during Low Solar Irradiation Using Radiosonde, Sodar, and Station Data. Atmospheric Pollution Research, 2, 283-299. http://dx.doi.org/10.5094/APR.2011.035
[30] Ackermann, I.J., Hass, H., Memmesheimer, M., Ziegenbein, C. and Ebel, A. (1995) The Parametrization of the Sulfate-Nitrate-Ammonia Aerosol System in the Long-Range Transport Model EURAD. Meteorology and Atmospheric Physics, 57, 101-114. http://dx.doi.org/10.1007/BF01044156
[31] Ackermann, I.J., Hass, H., Memmesheimer, M., et al. (1998) Modal Aerosol Dynamics Model for Europe: Development and First Applications. Atmospheric Environment, 32, 2981-2299.
http://dx.doi.org/10.1016/S1352-2310(98)00006-5
[32] Kramm, G., Beheng, K.-D. and Müller, H. (1992) Vertical Transport of Polydispersed Aerosol Particles in the Atmospheric Surface Layer. In: Schwartz, S.E. and Slinn, W.G.N., Eds., Precipitation Scavenging and Atmosphere-Surface Exchange Processes, Vol. 2, Hemisphere Publishing Company, Washington/Philadelphia/London, 1125-1141.
[33] Schell, B., Ackermann, I.J., Hass, H., Binkowski, F.S. and Ebel, A. (2001) Modeling the Formation of Secondary Organic Aerosol within a Comprehensive Air Quality Model System. Journal of Geophysical Research, 106, 28275-28293. http://dx.doi.org/10.1029/2001JD000384
[34] Freitas, S.R., Longo, K.M., Alonso, M.F., et al. (2011) Prep-Chem-SRC—1.0: A Preprocessor of Trace Gas and Aerosol Emission Fields for Regional and Global Atmospheric Chemistry Models. Geoscientific Model Development, 4, 419-433. http://dx.doi.org/10.5194/gmd-4-419-2011
[35] NASA EOSDIS (2012) Near Real-Time Data.
http://lance-modis.eosdis.nasa.gov/imagery/subsets/?area=global
[36] Olson, J., Watts, S. and Allison, L.J. (2000) Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database (Revised November 2000). Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge.
[37] van Aardenne, J.A., Dentener, F., Olivier, J.G.J. and Peters, J.A.H.W. (2005) The EDGAR 3.2 Fast Track 2000 Dataset. (32FT2000)
[38] Molders, N. (2010) Alaska Emission Model (AkEM)—Version 1.01 Description. Internal Report, Fairbanks, 16 p.
[39] Guenther, A., Hewitt, C., Erickson, D., et al. (1994) A Global Model of Natural Volatile Organic Compound Emissions. Journal Geophysical Research, 100D, 8873-8892.
[40] Simpson, D., Guenther, A., Hewitt, C.N. and Steinbrecher, R. (1995) Biogenic Emissions in Europe 1. Estimates and Uncertainties. Journal Geophysical Research, 100D, 22875-22890.
http://dx.doi.org/10.1029/95JD02368
[41] Department of Commerce (2000) NCEP FNL Operational Model Global Tropospheric Analyses, Continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. http://rda.ucar.edu/datasets/ds083.2/
[42] Sassen, K. (1991) The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment. Bulletin of the American Meteorologlogical Society, 72, 1848-1866.
http://dx.doi.org/10.1175/1520-0477(1991)072<1848:TPLTFC>2.0.CO;2
[43] Mishchenko, M.I. and Sassen, K. (1998) Depolarization of Lidar Returns by Small Ice Crystals: An Application to Contrails. Geophysical Research Letters, 25, 309-312.
http://dx.doi.org/10.1029/97GL03764
[44] Sassen, K. (2000) Lidar Backscatter Depolarization Technique for Cloud and Aerosol Research. In: Mishchenko, M.I., Hovenier, J.W. and Travis, L.D., Eds., Light Scattering by Nonspherical Particles, Academic Press, San Diego, 393-416. http://dx.doi.org/10.1016/B978-012498660-2/50041-0
[45] Sassen, K. (2005) Dusty Ice Clouds over Alaska. Nature, 434, 456. http://dx.doi.org/10.1038/434456a
[46] Madden, J.M. (2014) Using WRF/Chem, In-Situ Observations, and CALIPSO Data to Simulate Smoke Plume Signatures on High-Latitude Pixels. Master’s Thesis, Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks, 106 p.
[47] Madden, J.M., Molders, N. and Sassen, K. (2015) Assessment of WRF/Chem Simulated Vertical Distributions of Particulate Matter from the 2009 Minto Flats South Wildfire in Interior Alaska by CALIPSO Total Backscatter and Depolarization Measurements. Open Journal of Air Pollution, 4, 119-138. http://dx.doi.org/10.4236/ojap.2015.43012
[48] Sassen, K. (2008) Boreal Tree Pollen Sensed by Polarization Lidar: Depolarizing Biogenic Chaff. Geophysical Research Letters, 35, Article ID: L18810. http://dx.doi.org/10.1029/2008gl035085
[49] Martins, J.V., Hobbs, P.V., Weiss, R.E. and Artaxo, P. (1998) Sphericity and Morphology of Smoke Particles from Biomass Burning in Brazil. Journal of Geophysical Research—Atmosphere, 103, 32051-32057. http://dx.doi.org/10.1029/98JD01153
[50] Murayama, T., Müller, D., Wada, K., et al. (2004) Characterization of Asian Dust and Siberian Smoke with Multi-Wavelength Raman Lidar over Tokyo, Japan in Spring 2003. Geophysical Research Letters, 31, Article ID: L23103. http://dx.doi.org/10.1029/2004GL021105
[51] Sassen, K. (2008) Identifying Atmospheric Aerosols with Polarization Lidar. In: Kim, Y.J. and Platt, U., Eds., Advanced Environmental Modeling, Springer Science + Business Media Inc., New York, 136-142. http://dx.doi.org/10.1007/978-1-4020-6364-0_10
[52] Wandinger, U., Müller, D., Bockmann, C., et al. (2002) Optical and Microphysical Characterization of Biomass-Burning and Industrial-Pollution Aerosols from Multiwavelength Lidar and Aircraft Measurements. Journal of Geophysical Research, 107, 8125-8145. http://dx.doi.org/10.1029/2000JD000202
[53] Lee, C.H., Kim, J.H., Park, C.B., et al. (2004) Continuous Measurements of Smoke of Russian Forest Fire by 532/1064 nm Mie Scattering Lidar at Suwon, Korea. In: Proceedings of the 22nd International Laser Radar Conference, European Space Agency, Paris, 535-538.
[54] Hu, Y.M., Vaughan, M., Liu, Z., et al. (2007) The Depolarization—Attenuated Backscatter Relation: CALIPSO Lidar Measurements vs. Theory. Optical Express, 15, 5327-5332.
http://dx.doi.org/10.1364/OE.15.005327
[55] Chang, J.C. and Hanna, S.R. (2004) Air Quality Model Performance Evaluation. Meteorology and Atmospheric Physics, 87, 167-196. http://dx.doi.org/10.1007/s00703-003-0070-7
[56] EPA (2007) Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. 262 p.
http://www3.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf
[57] Appel, K., Roselle, S., Gilliam, R. and Pleim, J. (2010) Sensitivity of the Community Multiscale Air Quality (CMAQ) Model v4.7 Results for the Eastern United States to MM5 and WRF Meteorological Drivers. Geoscience Model Development, 3, 169-188. http://dx.doi.org/10.5194/gmd-3-169-2010
[58] Hines, K.M. and Bromwich, D.H. (2008) Development and Testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet Meteorology. Monthly Weather Review, 136, 1971-1989. http://dx.doi.org/10.1175/2007MWR2112.1
[59] Hines, K.M., Bromwich, D.H., Bai, L.-S., Barlage, M. and Slater, A.G. (2011) Development and Testing of Polar WRF. Part III. Arctic Land. Journal of Climate, 24, 26-48. http://dx.doi.org/10.1175/2010JCLI3460.1
[60] Avissar, R. and Pielke, R.A. (1989) A Parameterization of Heterogeneous Land Surface for Atmospheric Numerical Models and Its Impact on Regional Meteorology. Monthly Weather Review, 117, 2113-2136. http://dx.doi.org/10.1175/1520-0493(1989)117<2113:APOHLS>2.0.CO;2
[61] Molders, N. and Raabe, A. (1996) Numerical Investigations on the Influence of Subgrid-Scale Surface Heterogeneity on Evapotranspiration and Cloud Processes. Journal of Applied Meteorology, 35, 782-795. http://dx.doi.org/10.1175/1520-0450(1996)035<0782:NIOTIO>2.0.CO;2
[62] Molders, N., Tran, H.N.Q., Cahill, C.F., Leelasakultum, K. and Tran, T.T. (2012) Assessment of WRF/Chem PM2.5 Forecasts Using Mobile and Fixed Location Data from the Fairbanks, Alaska Winter 2008/09 Field Campaign. Air Pollution Research, 3, 180-191. http://dx.doi.org/10.5094/apr.2012.018
[63] Pirhalla, M.A., Gende, S. and Molders, N. (2014) Fate of Particulate Matter from Cruise-Ship Emissions in Glacier Bay during the 2008 Tourist Season. Journal of Environmental Protection, 4, 1235-1254. http://dx.doi.org/10.4236/jep.2014.512118
[64] von Storch, H. and Zwiers, F.W. (1999) Statistical Analysis in Climate Research. Cambridge University Press, Cambridge, 484 p.
[65] Molders, N. and Kramm, G. (2014) Lectures in Meteorology. Heidelberg, Springer, 591 p.
[66] Seinfeld, J.H. and Pandis, S.N. (1997) Atmospheric Chemistry and Physics, from Air Pollution to Climate Change. John Wiley & Sons, New York, 1326 p.
[67] Hanna, S. and Chang, J. (2012) Acceptance Criteria for Urban Dispersion Model Evaluation. Meteorology and Atmospheric Physics, 116, 133-146. http://dx.doi.org/10.1007/s00703-011-0177-1
[68] Freitas, S.R., Longo, K.M., Silva Dias, M.A.F. and Artaxo, P. (1996) Numerical Modeling of Air Mass Trajectories from the Biomass Burning Areas of the Amazon Basin. Annals of the Brazilian Academy of Sciences, 68, 193-206.
[69] Pope, I.C.A., Dockery, D.W. and Schwartz, J. (1995) Review of Epidemiological Evidence of Health Effects of Particulate Air Pollution. Inhalation Toxicology, 7, 1-18.
http://dx.doi.org/10.3109/08958379509014267

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.