|Title||A multiplatform inversion estimation of statewide and regional methane emissions in California during 2014-2016|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Cui Y.Y, Vijayan A., Falk M., Hsu Y.K, Yin D.Z, Chen X.M, Zhao Z., Avise J., Chen Y.J, Verhulst K., Duren R., Yadav V., Miller C., Weiss R., Keeling R., Kim J., Iraci L.T, Tanaka T., Johnson M.S, Kort E.A, Bianco L., Fischer M.L, Stroud K., Herner J., Croes B.|
|Type of Article||Article|
|Keywords||ch4; co2; Engineering; Environmental Sciences & Ecology; gosat; land-surface model; los-angeles; natural-gas; south coast; system; transport|
California methane (CH4) emissions are quantified for three years from two tower networks and one aircraft campaign. We used backward trajectory simulations and a mesoscale Bayesian inverse model, Mbring ratios (VI ' initialized by three inventories, to achieve the emission quantification. Results show total statewide CH4 emissions of 2.05 +/- 0.26 (at 95% confidence) Tg/yr, which is 1.14 to 1.47 times greater than the anthropogenic emission estimates by California Air Resource Board (GARB). Some of differences could be biogenic emissions, superemitter point sources, and other episodic emissions which may not be completely included in the CARB inventory. San Joaquin Valley (SJV) has the largest CH4 emissions (0.94 +/- 0.18 Tg/yr), followed by the South Coast Air Basin, the Sacramento Valley, and the San Francisco Bay Area at 0.39 +/- 0.18, 0.21 +/- 0.04, and 0.16 +/- 0.05 Tg/yr, respectively. The dairy and oil/gas production sources in the SJV contribute 0.44 +/- 0.36 and 0.22 +/- 0.23 Tg CH4/yr, respectively. This study has important policy implications for regulatory programs, as it provides a thorough multiyear evaluation of the emissions inventory using independent atmospheric measurements and investigates the utility of a complementary multiplatform approach in understanding the spatial and temporal patterns of CH4 emissions in the state and identifies opportunities for the expansion and applications of the monitoring network.