Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

Abstract

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.

Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

“…We compare estimated fluxes from geostatistical inversions driven by this optimized base system with fluxes estimated from geostatistical inversions driven by three broadly available models or reanalysis products: High-Resolution Rapid Refresh (HRRR), North American Regional Reanalysis (NARR), and the Global Data Assimilation System (GDAS). Additionally, seasonal variation in methane emissions has been previously observed and reported (Yadav et al, 2019) and also provides a challenge test case for our nonoptimized meteorological drivers. Los Angeles provides an opportunistic location for these tests as the large leak from the Aliso Canyon storage facility, which released an estimated 97,100 Mg over 4 months beginning in October 2015 (Conley et al, 2016), and provides what could be considered a tracer release experiment for our purposes.…”

“…In all inversions, we employ the geostatistical inversion methodology developed by Yadav et al (2019). In addition to a model linearly proportional to the distribution of emissions in the California Greenhouse Gas Emissions Measurement (CALGEM) inventory (Jeong et al, 2012;Zhao et al, 2009), we include a spatially constant model component, since we expect that the inversions using coarse meteorology may be unable to resolve the location of detected fluxes.…”

“…Note that no input singles out either the location or the time period of the Aliso Canyon natural gas leak. This is different from the Lagrange multiplier approach used in Yadav et al (2019). We constrain the methane fluxes to nonnegative values using a bounded version of limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization (Byrd et al, 1995), which is well suited to rapidly minimizing functions of many variables and thus facilitates rapid, near-real-time calculations.…”

“…In other words, this inversion makes use of no prior knowledge of the leak. Additional subtle differences between the WRF inversion case here and that of Yadav et al (2019) are that we exclude periods in which STILT transport fails using any of our meteorological products (as described above), our focused time series is slightly different, and we do not include the Canoga Park site when it comes online late in the time period. This is different from the Lagrange multiplier approach used in Yadav et al (2019).…”

“…With this meteorological underpinning, Yadav et al (2019) performs inversions in Los Angeles evaluating what can be learned with such an optimized system. In this study, rather than focusing on developing and validating an optimal transport representation, we use the Yadav et al (2019) results as a "base" case.…”