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Methane (CH4) is the primary component of natural gas and has a larger global warming potential than CO2. Some recent top-down studies based on observations showed CH4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R2 = 0.5). The emission estimates for CH4 in the posterior, 46.3 ± 9.2 Mg CH4/h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. Furthermore, we estimate that dairies contributed 5.9 ± 1.7 Mg CH4/h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH4/h in the SoCAB.
Methane mixing ratios measured at a tall-tower are compared to model predictions to estimate surface emissions of CH4 in Central California for October-December 2007 using an inverse technique. Predicted CH4 mixing ratios are calculated based on spatially resolved a priori CH4 emissions and simulated atmospheric trajectories. The atmospheric trajectories, along with surface footprints, are computed using the Weather Research and Forecast (WRF) coupled to the Stochastic Time-Inverted Lagrangian Transport (STILT) model. An uncertainty analysis is performed to provide quantitative uncertainties in estimated CH4 emissions. Three inverse model estimates of CH4 emissions are reported. First, linear regressions of modeled and measured CH4 mixing ratios obtain slopes of 0.73 ± 0.11 and 1.09 ± 0.14 using California specific and Edgar 3.2 emission maps respectively, suggesting that actual CH4 emissions were about 37 ± 21% higher than California specific inventory estimates. Second, a Bayesian 'source' analysis suggests that livestock emissions are 63 ± 22% higher than the a priori estimates. Third, a Bayesian 'region' analysis is carried out for CH4 emissions from 13 sub-regions, which shows that inventory CH4 emissions from the Central Valley are underestimated and uncertainties in CH4 emissions are reduced for sub-regions near the tower site, yielding best estimates of flux from those regions consistent with 'source' analysis results. The uncertainty reductions for regions near the tower indicate that a regional network of measurements will be necessary to provide accurate estimates of surface CH4 emissions for multiple regions.
Understanding, quantifying, and tracking atmospheric methane and emissions is essential for addressing concerns and informing decisions that affect the climate, economy, and human health and safety. Atmospheric methane is a potent greenhouse gas (GHG) that contributes to global warming. While carbon dioxide is by far the dominant cause of the rise in global average temperatures, methane also plays a significant role because it absorbs more energy per unit mass than carbon dioxide does, giving it a disproportionately large effect on global radiative forcing. In addition to contributing to climate change, methane also affects human health as a precursor to ozone pollution in the lower atmosphere. Improving Characterization of Anthropogenic Methane Emissions in the United States summarizes the current state of understanding of methane emissions sources and the measurement approaches and evaluates opportunities for methodological and inventory development improvements. This report will inform future research agendas of various U.S. agencies, including NOAA, the EPA, the DOE, NASA, the U.S. Department of Agriculture (USDA), and the National Science Foundation (NSF).
As a regulatory agency, evaluating and improving estimates of methane (CH4) emissions from the San Francisco Bay Area is an area of interest to the Bay Area Air Quality Management District (BAAQMD). Currently, regional, state, and federal agencies generally estimate methane emissions using bottom-up inventory methods that rely on a combination of activity data, emission factors, biogeochemical models and other information. Recent atmospheric top-down measurement estimates of methane emissions for the US as a whole (e.g., Miller et al., 2013) and in California (e.g., Jeong et al., 2013; Peischl et al., 2013) have shown inventories underestimate total methane emissions by ~ 50% in many areas of California, including the SF Bay Area (Fairley and Fischer, 2015). The goal of this research is to provide information to help improve methane emission estimates for the San Francisco Bay Area. The research effort builds upon our previous work that produced methane emission maps for each of the major source sectors as part of the California Greenhouse Gas Emissions Measurement (CALGEM) project (http://calgem.lbl.gov/prior_emission.html; Jeong et al., 2012; Jeong et al., 2013; Jeong et al., 2014). Working with BAAQMD, we evaluate the existing inventory in light of recently published literature and revise the CALGEM CH4 emission maps to provide better specificity for BAAQMD. We also suggest further research that will improve emission estimates. To accomplish the goals, we reviewed the current BAAQMD inventory, and compared its method with those from the state inventory from the California Air Resources Board (CARB), the CALGEM inventory, and recent published literature. We also updated activity data (e.g., livestock statistics) to reflect recent changes and to better represent spatial information. Then, we produced spatially explicit CH4 emission estimates on the 1-km modeling grid used by BAAQMD. We present the detailed activity data, methods and derived emission maps by sector. In total, we estimate the anthropogenic emissions for BAAQMD to be 116.4 Gg (1 Gg = 109 g) CH4/yr, with a likely uncertainty of ~ 50% or more (e.g., NRC, 2010; US-EPA, 2015). Including the emissions from wetland (Jeong et al., 2013), the total CH4 emission estimate for BAAQMD is 120.1 Gg CH4/yr. Table 1 summarizes the estimated CH4 emissions for 2011 by sector. The sectors were categorized following those that are used in recent regional emission quantification studies (e.g., Jeong et al., 2013; Peischl et al., 2013; Wecht et al., 2014). However, we note that this result is marginally lower than the top-down estimate of 240 ± 60 Gg CH4/yr (at 95% confidence) reported by Fairley and Fischer (2015), suggesting some combination of systematic error in the top-down estimate, underestimation of emissions from known sources, or as yet unidentified sources may be present. With respect to the relative contributions from different source sectors, the CH4 emissions from the region are dominated by urban activities. Landfill emissions represent 53% of the District's total emission followed by livestock (16%) and natural gas (15%). These three dominant sectors account for 84% of the total anthropogenic emission in BAAQMD. This suggests that mitigation efforts need to focus on these three sources. Figure 1 shows the gridded anthropogenic CH4 emissions on the BAAQMD's 1-km grid. In general, the spatial pattern of emissions follows the density of population while strong point sources are also distributed in the rural areas of the District. Detailed methods and emissions for each sector and county are described in the following sections.
The overall objective of the Research Partnership to Secure Energy for America (RPSEA)-funded research project is to develop independent estimates of methane emissions using top-down and bottom-up measurement approaches and then to compare the estimates, including consideration of uncertainty. Such approaches will be applied at two scales: basin and facility. At facility scale, multiple methods will be used to measure methane emissions of the whole facility (controlled dual tracer and single tracer releases, aircraft-based mass balance and Gaussian back-trajectory), which are considered top-down approaches. The bottom-up approach will sum emissions from identified point sources measured using appropriate source-level measurement techniques (e.g., high-flow meters). At basin scale, the top-down estimate will come from boundary layer airborne measurements upwind and downwind of the basin, using a regional mass balance model plus approaches to separate atmospheric methane emissions attributed to the oil and gas sector. The bottom-up estimate will result from statistical modeling (also known as scaling up) of measurements made at selected facilities, with gaps filled through measurements and other estimates based on other studies. The relative comparison of the bottom-up and top-down estimates made at both scales will help improve understanding of the accuracy of the tested measurement and modeling approaches. The subject of this CRADA is NREL's contribution to the overall project. This project resulted from winning a competitive solicitation no. RPSEA RFP2012UN001, proposal no. 12122-95, which is the basis for the overall project. This Joint Work Statement (JWS) details the contributions of NREL and Colorado School of Mines (CSM) in performance of the CRADA effort.