|Title||Spatio-temporally resolved methane fluxes from the Los Angeles megacity|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Yadav V., Duren R., Mueller K., Verhulst K.R, Nehrkorn T., Kim J., Weiss RF, Keeling R., Sander S., Fischer M.L, Newman S., Falk M., Kuwayama T., Hopkins F., Rafiq T., Whetstone J., Miller C.|
|Type of Article||Article|
|Keywords||basin; CalNex; carbon-dioxide; co2 fluxes; emissions; gas; geostatistical approach; Meteorology & Atmospheric Sciences; transport|
We combine sustained observations from a network of atmospheric monitoring stations with inverse modeling to uniquely obtain spatiotemporal (3-km, 4-day) estimates of methane emissions from the Los Angeles megacity and the broader South Coast Air Basin for 2015-2016. Our inversions use customized and validated high-fidelity meteorological output from Weather Research Forecasting and Stochastic Time-Inverted Lagrangian model for South Coast Air Basin and innovatively employ a model resolution matrix-based metric to disentangle the spatiotemporal information content of observations as manifested through estimated fluxes. We partially track and constrain fluxes from the Aliso Canyon natural gas leak and detect closure of the Puente Hills landfill, with no prior information. Our annually aggregated fluxes and their uncertainty excluding the Aliso Canyon leak period lie within the uncertainty bounds of the fluxes reported by the previous studies. Spatially, major sources of CH4 emissions in the basin were correlated with CH4-emitting infrastructure. Temporally, our findings show large seasonal variations in CH4 fluxes with significantly higher fluxes in winter in comparison to summer months, which is consistent with natural gas demand and anticorrelated with air temperature. Overall, this is the first study that utilizes inversions to detect both enhancement (Aliso Canyon leak) and reduction (Puente Hills) in CH4 fluxes due to the unintended events and policy decisions and thereby demonstrates the utility of inverse modeling for identifying variations in fluxes at fine spatiotemporal resolution.