Estimating methane emissions in California's urban and rural regions using multitower observations

Abstract

We present an analysis of methane (CH_4) emissions using atmospheric observations from 13 sites in California during June 2013 to May 2014. A hierarchical Bayesian inversion method is used to estimate CH_4 emissions for spatial regions (0.3° pixels for major regions) by comparing measured CH_4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on seasonally varying California-specific CH_4 prior emission models. The transport model is assessed using a combination of meteorological and carbon monoxide (CO) measurements coupled with the gridded California Air Resources Board (CARB) CO emission inventory. The hierarchical Bayesian inversion suggests that state annual anthropogenic CH_4 emissions are 2.42 ± 0.49 Tg CH_4/yr (at 95% confidence), higher (1.2–1.8 times) than the current CARB inventory (1.64 Tg CH_4/yr in 2013). It should be noted that undiagnosed sources of errors or uncaptured errors in the model-measurement mismatch covariance may increase these uncertainty bounds beyond that indicated here. The CH_4 emissions from the Central Valley and urban regions (San Francisco Bay and South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, respectively. This study suggests that the livestock sector is likely the major contributor to the state total CH_4 emissions, in agreement with CARB's inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of California's CH_4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32).