Atmospheric Measurement and Inverse Modeling to Improve Greenhouse Gas Emission Estimates.

摘要

California has committed to an ambitious plan to reduce statewide greenhouse gas (GHG) emissions to 1990 levels by 2020 through Assembly Bill 32, which requires accurate accounting of emissions for effective mitigation planning and verification of future emission reductions. Atmospheric GHG measurements from networks of towers can be combined with existing knowledge of emissions in a statistical inverse model - weighing existing knowledge with the new observations - to more accurately quantify GHG emissions. This study estimates top-down emission estimates of major anthropogenic GHGs including fossil fuel CO2 (ffCO2), methane (CH4) and nitrous oxide (N2O) within California with a Bayesian inverse modeling framework, using atmospheric observations from an expanded GHG measurement network across California over multiple years. The study indicates that the top-down estimates for statewide CH4 and N2O emissions are higher than the current ARB inventory (1.0 - 1.6 times for CH4, and 1.3 - 2.3 times for N2O), while the top-down ffCO2 emissions estimates are within approximately 10% of the statewide inventory. This study combined with other studies suggests that the livestock sector is the major contributor to the Statewide CH4 emissions, while agricultural activities are likely a significant source of anthropogenic N2O emissions in California, in agreement with CARB’s GHG inventory.