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Analyzing source apportioned methane in northern California during...

Johnson, M. S., E. Yates, L. Iraci, M. Loewenstein, J. Tadic, K. J. Wecht, S. Jeong, and M. L. Fischer (2014), Analyzing source apportioned methane in northern California during Discover-AQ-CA using airborne measurements and model simulations, Atmos. Environ., 99, 248-256, doi:10.1016/j.atmosenv.2014.09.068.

This study analyzes source apportioned methane (CH4) emissions and atmospheric mixing ratios in northern California during the Discover-AQ-CA field campaign using airborne measurement data and model simulations. Source apportioned CH4 emissions from the Emissions Database for Global Atmospheric Research (EDGAR) version 4.2 were applied in the 3-D chemical transport model GEOS-Chem and analyzed using airborne measurements taken as part of the Alpha Jet Atmospheric eXperiment over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV). During the time period of the Discover-AQ-CA field campaign EDGAR inventory CH4 emissions were ~5.30 Gg day-1 (Gg = 1.0 × 109 g) (equating to ~1.90 × 103 Gg yr-1) for all of California. According to EDGAR, the SFBA and northern SJV region contributes ~30% of total CH4 emissions from California. Source apportionment analysis during this study shows that CH4 mixing ratios over this area of northern California are largely influenced by global emissions from wetlands and local/global emissions from gas and oil production and distribution, waste treatment processes, and livestock management. Model simulations, using EDGAR emissions, suggest that the model under-estimates CH4 mixing ratios in northern California (average normalized mean bias (NMB) = -5.2% and linear regression slope = 0.20). The largest negative biases in the model were calculated on days when large amounts of CH4 were measured over local emission sources and atmospheric CH4 mixing ratios reached values >2.5 parts per million. Sensitivity emission studies conducted during this research suggest that local emissions of CH4 from livestock management processes are likely the primary source of the negative model bias. These results indicate that a variety, and larger quantity, of measurement data needs to be obtained and additional research is necessary to better quantify source apportioned CH4 emissions in California.

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