Articles | Volume 17, issue 12
Atmos. Chem. Phys., 17, 7509–7528, 2017
Atmos. Chem. Phys., 17, 7509–7528, 2017

Research article 21 Jun 2017

Research article | 21 Jun 2017

Methane emissions from dairies in the Los Angeles Basin

Camille Viatte1, Thomas Lauvaux2, Jacob K. Hedelius1, Harrison Parker3, Jia Chen4,a, Taylor Jones4, Jonathan E. Franklin4, Aijun J. Deng2, Brian Gaudet2, Kristal Verhulst5, Riley Duren5, Debra Wunch1,b, Coleen Roehl1, Manvendra K. Dubey3, Steve Wofsy4, and Paul O. Wennberg1 Camille Viatte et al.
  • 1Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
  • 2Department of Meteorology, Pennsylvania State University, University Park, PA, USA
  • 3Earth System Observations, Los Alamos National Laboratory, Los Alamos, NM, USA
  • 4School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
  • 5Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
  • anow at: Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany
  • bnow at: Department of Physics, University of Toronto, Toronto, ON, Canada

Abstract. We estimate the amount of methane (CH4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic 13∕12CH4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES).

The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution.

Our top-down CH4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s−1 are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH4 emissions of 2.2 to 3.5 ppt s−1 at Chino.

Short summary
This study estimates methane emissions at local scale in dairy farms using four new mobile ground-based remote sensing spectrometers (EM27/SUN) and isotopic in situ measurements. Our top-down estimates are in the low end of previous studies. Inverse modeling from a comprehensive high-resolution model simulations (WRF-LES) is used to assess the geographical distribution of the emissions. Both the model and the measurements indicate a mixture of anthropogenic and biogenic emissions.
Final-revised paper