Estimating the Bowen Ratio for Application in Air Quality Models by Integrating a Simplified Analytical Expression with Measurement Data

K. M. Lin Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan

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J. Y. Juang Department of Geography, National Taiwan University, Taipei, Taiwan

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Y.-W. Shiu Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan

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L. F. W. Chang Graduate Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan

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Abstract

In air quality models, daytime sensible and latent heat fluxes are important factors that influence atmospheric stability. These heat fluxes originate from heat that is generated from solar radiation and is then released from the earth’s surface. Different climates and surface conditions may lead to varying heat flux distributions. Because latent heat flux is influenced by both solar radiation and plant evapotranspiration, it is often difficult to estimate. The objective of this study was to apply thermodynamic concepts to determine an equation that could be used to estimate the Bowen ratio in the absence of latent and sensible heat fluxes. This study showed that, using two meteorological parameters (i.e., absolute temperature and relative humidity), the Bowen ratio for the climate in Taiwan could be obtained and then used to estimate sensible and latent heat fluxes in a series of equations. Furthermore, the approach’s applicability was determined by testing the sensitivities of parameters used in the Bowen ratio equation. A comparison of results determined through the Priestly–Taylor and Penman–Monteith methods with meteorological data for Yilan and Chiayi counties, Taiwan, for the 2006 summer and winter is performed. The results of this study showed that, among the simulated latent heat fluxes in the two study areas, the values estimated using the Penman–Monteith method were the largest, followed by those estimated using the Priestly–Taylor method. Values estimated using the Bowen ratio method were the smallest. Predictions generated by the proposed Bowen ratio equation correlated with those generated by the other models; however, the values estimated with the Priestly–Taylor method were closest to the simulated values.

Corresponding author address: Jehn-Yih Juang, Dept. of Geography, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan. E-mail: jjuang@ntu.edu.tw

Abstract

In air quality models, daytime sensible and latent heat fluxes are important factors that influence atmospheric stability. These heat fluxes originate from heat that is generated from solar radiation and is then released from the earth’s surface. Different climates and surface conditions may lead to varying heat flux distributions. Because latent heat flux is influenced by both solar radiation and plant evapotranspiration, it is often difficult to estimate. The objective of this study was to apply thermodynamic concepts to determine an equation that could be used to estimate the Bowen ratio in the absence of latent and sensible heat fluxes. This study showed that, using two meteorological parameters (i.e., absolute temperature and relative humidity), the Bowen ratio for the climate in Taiwan could be obtained and then used to estimate sensible and latent heat fluxes in a series of equations. Furthermore, the approach’s applicability was determined by testing the sensitivities of parameters used in the Bowen ratio equation. A comparison of results determined through the Priestly–Taylor and Penman–Monteith methods with meteorological data for Yilan and Chiayi counties, Taiwan, for the 2006 summer and winter is performed. The results of this study showed that, among the simulated latent heat fluxes in the two study areas, the values estimated using the Penman–Monteith method were the largest, followed by those estimated using the Priestly–Taylor method. Values estimated using the Bowen ratio method were the smallest. Predictions generated by the proposed Bowen ratio equation correlated with those generated by the other models; however, the values estimated with the Priestly–Taylor method were closest to the simulated values.

Corresponding author address: Jehn-Yih Juang, Dept. of Geography, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan. E-mail: jjuang@ntu.edu.tw
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  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, 174 pp.

  • Behrendt, A., S. Pal, V. Wulfmeyer, A. M. Valdebenito, and G. Lammel, 2011: A novel approach for the characterisation of transport and optical properties of aerosol particles near sources. Part I: Measurement of particle backscatter coefficient maps with a scanning UV lidar. Atmos. Environ., 45, 27952802, doi:10.1016/j.atmosenv.2011.02.061.

    • Search Google Scholar
    • Export Citation
  • Bowen, I. S., 1926: The ratio of heat losses by conduction and by evaporation from any water surface. Phys. Rev., 27, 779787, doi:10.1103/PhysRev.27.779.

    • Search Google Scholar
    • Export Citation
  • Byun, D. W., and J. K. S. Ching, 1999: Science algorithms of the EPA Models-3 community multi-scale air quality (CMAQ) modeling system. EPA/600/R-99/030, U.S. Environmental Protection Agency, 770 pp.

  • Chen, D., D. A. Stow, L. Tucker, and S. Daeschner, 2001: Detecting and enumerating new building structures utilizing very-high resolution image data and image processing. Geocarto Int., 16, 7184, doi:10.1080/10106040108542185.

    • Search Google Scholar
    • Export Citation
  • Cheng, F. Y., and D. W. Byun, 2008: Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston metropolitan area, Part I: Meteorological simulation results. Atmos. Environ., 42, 77957811, doi:10.1016/j.atmosenv.2008.04.055.

    • Search Google Scholar
    • Export Citation
  • Cheng, F. Y., S. T. Kim, and D. W. Byun, 2008: Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston metropolitan area, Part II: Air quality simulation results. Atmos. Environ., 42, 48534869, doi:10.1016/j.atmosenv.2008.02.059.

    • Search Google Scholar
    • Export Citation
  • Cook, D. R., 2007: Energy balance Bowen ratio (EBBR) handbook. Department of Energy Tech. Rep. DOE/SC-ARM-TR-037, 26 pp.

  • Dugas, W. A., M. L. Heuer, and H. S. Mayeux, 1999: Carbon dioxide fluxes over Bermuda grass, native prairie, and sorghum. Agric. For. Meteor., 93, 121139, doi:10.1016/S0168-1923(98)00118-X.

    • Search Google Scholar
    • Export Citation
  • ENVIRON, 2015: CAMx v6.20 user’s guide. [Available online at http://www.camx.com/files/camxusersguide_v6-20.pdf.]

  • Hanel, G., 1976: The properties of atmospheric aerosol particles as functions of relative humidity at thermodynamic equilibrium with surrounding moist air. Advances in Geophysics, Vol. 19, Academic Press, 73–188.

    • Search Google Scholar
    • Export Citation
  • Hargreaves, G. H., and Z. A. Samani, 1982: Estimating potential evapotranspiration. J. Irrig. Drain Engr., 108, 223230.

  • Holtslag, A. A. M., and A. P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes from routine weather data. J. Climate Appl. Meteor., 22, 517529, doi:10.1175/1520-0450(1983)022<0517:ASSFDE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kosugi, Y., S. Takanashi, H. Tanaka, S. Ohkubo, M. Tani, M. Yano, and T. Katayama, 2007: Evapotranspiration over a Japanese cypress forest. I. Eddy covariance fluxes and surface conductance characteristics for 3 years. J. Hydrol., 337, 269283, doi:10.1016/j.jhydrol.2007.01.039.

    • Search Google Scholar
    • Export Citation
  • Lee, S. M., S. C. Yoon, and D. W. Byun, 2004: The effect of mass inconsistency of the meteorological field generated by a common meteorological model on air quality modeling. Atmos. Environ., 38, 29172926, doi:10.1016/j.atmosenv.2004.02.008.

    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1981: Evaporation and surface temperature. Quart. J. Roy. Meteor. Soc., 107, 127, doi:10.1002/qj.49710745102.

  • Ortega-Farias, S. O., R. H. Cuenca, and M. Ek, 1996: Daytime variation of sensible heat flux estimated by the bulk aerodynamic method over a grass canopy. Agric. For. Meteor., 81, 131143, doi:10.1016/0168-1923(95)02278-3.

    • Search Google Scholar
    • Export Citation
  • Paine, R. J., 1987: User’s guide to the CTDM Meteorological Preprocessor (METPRO) program. EPA-600/8-88-004, U.S. Environmental Protection Agency, 160 pp. [NTIS BP-88-162102]

  • Pal, S., M. Haeffelin, and E. Batchvarova, 2013: Exploring a geophysical process-based attribution technique for the determination of the atmospheric boundary layer depth using aerosol lidar and near-surface meteorological measurements. J. Geophys. Res. Atmos., 118, 92779295, doi:10.1002/jgrd.50710.

    • Search Google Scholar
    • Export Citation
  • Pal, S., M. Lopez, M. Schmidt, M. Ramonet, F. Gibert, I. Xueref-Remy, and P. Ciais, 2015: Investigation of the atmospheric boundary layer depth variability and its impact on the 222Rn concentration at a rural site in France. J. Geophys. Res. Atmos., 120, 623643, doi:10.1002/2014JD022322.

    • Search Google Scholar
    • Export Citation
  • Priestley, C. H. B., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 8192, doi:10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Prueger, J. H., T. J. Sauer, and J. L. Hatfield, 1998: Turbulence flux estimates of sensible and latent heat near shelterbelts during low wind conditions. Trans. ASAE, 41, 16431650, doi:10.13031/2013.17339.

    • Search Google Scholar
    • Export Citation
  • Quintanar, A. I., R. Mahmood, J. Loughrin, and N. C. Lovanh, 2008: A coupled MM5-NOAH land surface model-based assessment of sensitivity of planetary boundary layer variables to anomalous soil moisture conditions. Phys. Geogr., 29, 5478, doi:10.2747/0272-3646.29.1.54.

    • Search Google Scholar
    • Export Citation
  • Scire, J. S., D. G. Strimaitis, and R. J. Yamartino, 2000: A User’s Guide for the CALPUFF Dispersion Model. Earth Tech, Inc., 521 pp. [Available online at http://www.src.com/calpuff/download/CALPUFF_UsersGuide.pdf.]

    • Search Google Scholar
    • Export Citation
  • Seinfeld, J. H., 1986: Atmospheric Chemistry and Physics of Air Pollution. Wiley Interscience, 738 pp.

  • Unland, H. E., P. R. Houser, W. J. Shuttleworth, and Z. L. Yang, 1996: Surface flux measurement and modelling at a semi-arid Sonoran Desert site. Agric. For. Meteor., 82, 119153, doi:10.1016/0168-1923(96)02330-1.

    • Search Google Scholar
    • Export Citation
  • USEPA, 2004: User’s guide for the AERMOD Meteorological Preprocessor (AERMET). EPA-454/B-03-002. U.S. Environmental Protection Agency. [Available online at http://www3.epa.gov/scram001/7thconf/aermod/aermetugb.pdf.]

  • USEPA, 2013: AERSURFACE user’s guide. EPA-454/B-08-001, U.S. Environmental Protection Agency, 25 pp. [Available online at http://www3.epa.gov/scram001/7thconf/aermod/aersurface_userguide.pdf.]

  • Valdebenito, A. M., S. Pal, A. Behrendt, V. Wulfmeyer, and G. Lammel, 2011: A novel approach for the characterization of transport and optical properties of aerosol particles near sources—Part II: Microphysics–chemistry-transport model development and application. Atmos. Environ., 45, 29812990, doi:10.1016/j.atmosenv.2010.09.004.

    • Search Google Scholar
    • Export Citation
  • Zoras, S., A. G. Triantafyllou, and D. Deligiorgi, 2006: Atmospheric stability and PM10 concentrations at far distance from elevated point sources in complex terrain: Worst-case episode study. J. Environ. Manage., 80, 295302, doi:10.1016/j.jenvman.2005.09.010.

    • Search Google Scholar
    • Export Citation
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