• Bingham, G. E., and Coauthors, 2009: Lidar based emissions measurement at the whole facility scale: Method and error analysis. J. Appl. Remote Sens., 3, 033510, doi:10.1117/1.3097919.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bohren, C. F., and D. R. Huffman, 1983: Absorption and Scattering of Light by Small Particles. John Wiley & Sons, 530 pp.

  • Bunton, B., and Coauthors, 2007: Monitoring and modeling of emissions from concentrated animal feeding operations: Overview of methods. Environ. Health Perspect., 115, 303307, doi:10.1289/ehp.8838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buser, M. D., 2004: Errors associated with particulate matter measurements on rural sources: Appropriate basis for regulating cotton gins. Ph.D. disseration, Texas A&M University, 348 pp.

  • Buurman, P., Th. Pape, J. A. Reijneveld, F. de Jong, and E. van Gelder, 2001: Laser-diffraction and pipette-method grain sizing of Dutch sediments: Correlations for fine fractions of marine, fluvial, and loess samples. Geol. Mijnbouw, 80, 4957.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carletti, R., M. Picci, and D. Romano, 2000: Kriging and bilinear methods for estimating spatial pattern of atmospheric pollutants. Environ. Monit. Assess., 63, 341359, doi:10.1023/A:1006293110652.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowan, I. R., 1968: Mass, heat and momentum exchange between stands of plants and their atmospheric environment. Quart. J. Roy. Meteor. Soc., 94, 523544, doi:10.1002/qj.49709440208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Faulkner, W. B., J. M. Lange, J. J. Powell, B. W. Shaw, and C. B. Parnell, 2007: Sampler placement to determine emission factors from ground level area sources. Atmos. Environ., 41, 76727678, doi:10.1016/j.atmosenv.2007.08.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hernandez, G., S. Trabue, T. Sauer, R. Pfeiffer, and J. Tyndall, 2012: Odor mitigation with tree buffers: Swine production case study. Agric. Ecosyst. Environ., 149, 154163, doi:10.1016/j.agee.2011.12.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holmén, B. A., W. E. Eichinger, and R. G. Flocchini, 1998: Application of elastic lidar to PM10 emissions from agricultural nonpoint sources. Environ. Sci. Technol., 32, 30683076, doi:10.1021/es980176p.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Husar, R. B., and S. R. Falke, 1996: The relationship between aerosol light scattering and fine mass. CAPITA Rep. 63130-4899, 28 pp.

  • Jones, H. W., L. Wang-Li, and B. Y. Boroujeni, 2012: Impact of downwind sampling location and height on inverse-Gaussian dispersion modeling: A theoretical study. Int. J. Agric. Biol. Eng., 5 (4), 3946.

    • Search Google Scholar
    • Export Citation
  • Klett, J. D., 1981: Stable analytical inversion solution for processing lidar returns. Appl. Opt., 20, 211220, doi:10.1364/AO.20.000211.

  • Klett, J. D., 1985: Lidar inversion with variable backscatter/extinction ratios. Appl. Opt., 24, 16381643, doi:10.1364/AO.24.001638.

  • Kovalev, V. A., and W. E. Eichinger, 2004: Elastic Lidar: Theory, Practice, and Analysis Methods. John Wiley & Sons, 615 pp.

  • Krichbaumer, W., and Ch. Werner, 1994: Current state-of-the-art of LIDAR inversion methods for atmospheres of arbitrary optical density. Appl. Phys., 59B, 517523, doi:10.1007/BF01082394.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lagrosas, N., H. Kuze, N. Takeuchi, S. Fukagawa, G. Bagtasa, Y. Yoshii, S. Naito, and M. Yabuki, 2005: Correlation study between suspended particulate matter and portable automated lidar data. J. Aerosol Sci., 36, 439454, doi:10.1016/j.jaerosci.2004.10.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laird, D. J., 1997: Wind tunnel testing of shelterbelt effects on dust emissions from swine production facilities. M.S. thesis, Dept. of Aerospace Engineering, The Iowa State University, 128 pp.

  • Lewandowski, P. A., 2009: Advances in lidar applications. Ph.D. disseration, The University of Iowa, 101 pp.

  • Lewandowski, P. A., W. E. Eichinger, H. Holder, J. Prueger, J. Wang, and L. I. Kleinman, 2010: Vertical distribution of aerosols in the vicinity of Mexico City during MILAGRO-2006 campaign. Atmos. Chem. Phys., 10, 10171030, doi:10.5194/acp-10-1017-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Q., and Coauthors, 2013: The national air emissions monitoring study’s southeast layer site: Part II. Particulate matter. Trans. ASABE, 56, 11731184, doi:10.13031/trans.56.9571.

    • Search Google Scholar
    • Export Citation
  • Lin, X.-J., S. Barrington, J. Nicell, D. Chointière, and A. Vézina, 2006: Influence of windbreaks on livestock odour dispersion plume in the field. Agric. Ecosyst. Environ., 116, 263272, doi:10.1016/j.agee.2006.02.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malone, G., G. VanWicklen, S. Collier, and D. Hansen, 2006: Efficacy of vegetative environmental buffers to capture emissions from tunnel ventilated poultry houses. Proc. Workshop on Agricultural Air Quality, Potomac, MD, North Carolina State University, 875878. [Available online at http://www.umad.de/infos/woaaq2006/Posters-M.pdf.]

  • Monin, A. D., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163187.

    • Search Google Scholar
    • Export Citation
  • Murphy, D. M., D. J. Cziczo, P. K. Hudson, M. E. Schein, and D. S. Thomson, 2004: Particle density inferred from simultaneous optical and aerodynamic diameters sorted by composition. J. Aerosol Sci., 35, 135139, doi:10.1016/S0021-8502(03)00386-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Research Council, 2003: Air Emissions from Animal Feeding Operations: Current Knowledge, Future Needs. National Academies Press, 286 pp., doi:10.17226/10586.

    • Search Google Scholar
    • Export Citation
  • Parker, D. B., G. W. Malone, and W. D. Walter, 2011: Vegetative environmental buffers and exhaust fan deflectors for reducing downwind odor and VOCs from tunnel-ventilated swine barns. Trans. ASABE, 55, 227240, doi:10.13031/2013.41250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prueger, J. H., W. E. Eichinger, L. E. Hipps, J. L. Hatfield, and D. I. Cooper, 2008: Air-flow distortion and turbulence statistics near an animal facility. Atmos. Environ., 42, 33013314, doi:10.1016/j.atmosenv.2007.08.048.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanhill, G., 1969: A simple instrument for the field measurement of turbulent diffusion flux. J. Appl. Meteor., 8, 509513, doi:10.1175/1520-0450(1969)008<0509:ASIFTF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thernelius, S. M., 1997: Wind tunnel testing of odor transportation from swine production facilities. M.S. thesis, Dept. of Aerospace Engineering, Iowa State University, 121 pp.

  • Tyndall, J., and J. Colletti, 2007: Mitigating swine odor with strategically designed shelterbelt systems: A review. Agrofor. Syst., 69, 4565, doi:10.1007/s10457-006-9017-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., and J. Walter, 1987: Particle density correction for the aerodynamic particle sizer. Aerosol Sci. Technol., 6, 191198, doi:10.1080/02786828708959132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang-Li, L., 2013: Techniques for characterization of particulate matter emitted from animal feeding operations. Evaluating Veterinary Pharmaceutical Behavior in the Environment, G. P. Cobb and P. N. Smith, Eds., ACS Symposium Series, Vol. 1126, Amer. Chem. Soc., 15–39.

  • Wang-Li, L., Z. Chao, M. Buser, D. Whitelock, C. B. Parnell, and Y. Zhang, 2013: Techniques for measuring particle size distribution of particulate matter emitted from animal feeding operations. Atmos. Environ., 66, 2532, doi:10.1016/j.atmosenv.2012.08.051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wanjura, J. D., C. B. Parnell Jr., B. W. Shaw, and R. E. Lacey, 2005: Design and evaluation of a low-volume total suspended particulate sampler. Trans. ASAE, 48, 15471552, doi:10.13031/2013.19186.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willis, W. B., and Coauthors, 2016: LiDAR data: Bill Eichinger; A LiDAR method to estimate emission rates from an animal production facility. IIHR, The University of Iowa, accessed 31 March 2016. [Available online at http://www.iihr.uiowa.edu/research/lidar-data-bill-eichinger/.]

  • Zirschky, J., 1985: Geostatistics for environmental monitoring and survey design. Environ. Int., 11, 515524, doi:10.1016/0160-4120(85)90187-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 289 142 0
PDF Downloads 218 106 0

Lidar Method to Estimate Emission Rates from Extended Sources

View More View Less
  • a IIHR—Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa
  • | b National Laboratory for Agriculture and the Environment, Agricultural Research Service, U.S. Department of Agriculture, Ames, Iowa
  • | c Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland
  • | d Department of Animal and Food Sciences, University of Delaware, Newark, Delaware
  • | e Biosystems and Agricultural Engineering Department, Oklahoma State University, Stillwater, Oklahoma
  • | f Cotton Production and Processing Research, Agricultural Research Service, U.S. Department of Agriculture, Lubbock, Texas
  • | g Department of Civil and Environmental Engineering, University of Maryland, College Park, College Park, Maryland
Restricted access

Abstract

Pollutant emissions to the atmosphere commonly derive from nonpoint sources that are extended in space. Such sources may contain area, volume, line, or a combination of emission types. Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. Point measurement arrays often lack in spatial and temporal resolution and accuracy. In recent years, lidar has supplemented point measurements in agricultural research by sampling spatial ensembles nearly instantaneously. Here, a methodology using backscatter data from an elastic scanning lidar is presented to estimate emission rates from extended sources. To demonstrate the approach, a known amount of particulate matter was released upwind of a vegetative environmental buffer, a barrier designed to intercept emissions from animal production facilities. The emission rate was estimated downwind of the buffer, and the buffer capture efficiency (percentage of particles captured) was calculated. Efficiencies ranged from 21% to 74% and agree with the ranges previously published. A comprehensive uncertainty analysis of the lidar methodology was performed, revealing an uncertainty of 20% in the emission rate estimate; suggestions for significantly reducing this uncertainty in future studies are made. The methodology introduced here is demonstrated by estimating the efficiency of a vegetative buffer, but it can also be applied to any extended emission source for which point samples are inadequate, such as roads, animal feedlots, and cotton gin operations. It can also be applied to any pollutant for which a lidar system is configured, such as particulate matter, carbon dioxide, and ammonia.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: William B. Willis, willbranwill@gmail.com

Abstract

Pollutant emissions to the atmosphere commonly derive from nonpoint sources that are extended in space. Such sources may contain area, volume, line, or a combination of emission types. Currently, point measurements, often combined with models, are the primary means by which atmospheric emission rates are estimated from extended sources. Point measurement arrays often lack in spatial and temporal resolution and accuracy. In recent years, lidar has supplemented point measurements in agricultural research by sampling spatial ensembles nearly instantaneously. Here, a methodology using backscatter data from an elastic scanning lidar is presented to estimate emission rates from extended sources. To demonstrate the approach, a known amount of particulate matter was released upwind of a vegetative environmental buffer, a barrier designed to intercept emissions from animal production facilities. The emission rate was estimated downwind of the buffer, and the buffer capture efficiency (percentage of particles captured) was calculated. Efficiencies ranged from 21% to 74% and agree with the ranges previously published. A comprehensive uncertainty analysis of the lidar methodology was performed, revealing an uncertainty of 20% in the emission rate estimate; suggestions for significantly reducing this uncertainty in future studies are made. The methodology introduced here is demonstrated by estimating the efficiency of a vegetative buffer, but it can also be applied to any extended emission source for which point samples are inadequate, such as roads, animal feedlots, and cotton gin operations. It can also be applied to any pollutant for which a lidar system is configured, such as particulate matter, carbon dioxide, and ammonia.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: William B. Willis, willbranwill@gmail.com
Save