A Multiplatform Inversion Estimation of Statewide and Regional Methane Emissions in California during 2014–2016
Citations Over TimeTop 21% of 2019 papers
Abstract
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, 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 (CARB). 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.
Related Papers
- → Urban Air Pollution and Greenness in Relation to Public Health(2023)82 cited
- → The Relationship Between the Actual Level of Air Pollution and Residents’ Concern about Air Pollution: Evidence from Shanghai, China(2019)68 cited
- → Air quality indices: A review of methods to interpret air quality status(2020)43 cited
- → REGULATION OF AIR QUALITY AND AIR QUALITY MODELING(2013)1 cited
- H15-310: Regional air quality management assessment by using CHIMERE air quality model(2013)