Seongeun Jeong

On the Sources of Methane to the Los Angeles Atmosphere

We use historical and new atmospheric trace gas observations to refine the estimated source of methane (CH(4)) emitted into Californias South Coast Air Basin (the larger Los Angeles metropolitan region). Referenced to the California Air Resources Board (CARB) CO emissions inventory, total CH(4) emissions are 0.44 ± 0.15 Tg each year. To investigate the possible contribution of fossil fuel emissions, we use ambient air observations of methane (CH(4)), ethane (C(2)H(6)), and carbon monoxide (CO), together with measured C(2)H(6) to CH(4) enhancement ratios in the Los Angeles natural gas supply. The observed atmospheric C(2)H(6) to CH(4) ratio during the ARCTAS (2008) and CalNex (2010) aircraft campaigns is similar to the ratio of these gases in the natural gas supplied to the basin during both these campaigns. Thus, at the upper limit (assuming that the only major source of atmospheric C(2)H(6) is fugitive emissions from the natural gas infrastructure) these data are consistent with the attribution of most (0.39 ± 0.15 Tg yr(-1)) of the excess CH(4) in the basin to uncombusted losses from the natural gas system (approximately 2.5-6% of natural gas delivered to basin customers). However, there are other sources of C(2)H(6) in the region. In particular, emissions of C(2)H(6) (and CH(4)) from natural gas seeps as well as those associated with petroleum production, both of which are poorly known, will reduce the inferred contribution of the natural gas infrastructure to the total CH(4) emissions, potentially significantly. This study highlights both the value and challenges associated with the use of ethane as a tracer for fugitive emissions from the natural gas production and distribution system.

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

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 CARBs inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of Californias CH_4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32).

Simulating Estimation of California Fossil Fuel and Biosphere Carbon Dioxide Exchanges Combining In-situ Tower and Satellite Column Observations:

Author(s): Fischer, Marc L.; Parazoo, Nicholas; Brophy, Kieran; Cui, Xinguang; Jeong, Seongeun; Liu, Junjie; Kelling, Ralph; Taylor, Thomas E.; Gurney, Kevin; Oda, Tomohiro; Graven, Heather | Abstract: We report simulation experiments estimating the uncertainties in California regional fossil fuel 36 and biosphere CO2 exchanges that might be obtained using an atmospheric inverse modeling 37 system driven by the combination of ground-based observations of radiocarbon and total CO2, 38 together with column-mean CO2 observations from NASA’s Orbiting Carbon Observatory 39 (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for 40 CO2 exchange, in radiocarbon-based fossil fuel CO2 measurements, in OCO-2 measurements, 41 and in a regional atmospheric transport modeling system. Using these nominal assumptions for 42 measurement and model uncertainties, we find that flask measurements of radiocarbon and total 43 CO2 at 10 towers can be used to distinguish between different fossil fuel emissions data products 44 for major urban regions of California. We then show that the combination of flask and OCO-2 45 observations yield posterior uncertainties in monthly-mean fossil fuel emissions of ~ 5-10%, 46 levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. 47 Similarly, we find that inversions yield uncertainties in monthly biosphere CO2 exchange of ~ 48 6%-12%, depending on season, providing useful information on net carbon uptake in 49 California’s forests and agricultural lands. Finally, initial sensitivity analysis suggests that 50 obtaining the above results requires control of systematic biases below approximately 0.5 ppm, 51 placing requirements on accuracy of the atmospheric measurements, background subtraction, and 52 atmospheric transport modeling.

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF-STILT (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< ~ 0.5 m/s), direction (< ~ 15°), and boundary layer height (< ~ 200 m) were small. However, random errors were large (~1.5–3.0 m/s for wind speed, ~ 40–60° for wind direction, and ~ 300–500 m for boundary layer height). Regression analysis of predicted and measured CO yielded near-unity slopes (i.e., within 1.0 ± 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (~30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF-STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.

Investigating seasonal methane emissions in Northern California using airborne measurements and inverse modeling

Seasonal methane (CH4) emissions in northern California are evaluated during this study using airborne measurement data and inverse model simulations. This research applies Alpha Jet Atmospheric eXperiment (AJAX) measurements obtained during January – February 2013, July 2014, and October – November 2014 over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV) in order to constrain seasonal CH4 emissions in northern California. The California Greenhouse Gas Emissions Measurement (CALGEM) a priori emission inventory was applied in conjunction with the Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model and inverse modeling techniques to optimize CH4 emissions. Comparing model-predicted CH4 mixing ratios with airborne measurements, we find substantial underestimates suggesting that CH4 emissions were likely larger than the year 2008 a priori CALGEM emission inventory in northern California. Using AJAX measurements to optimize a priori emissions resulted in CH4 flux estimates from the SFBA/SJV of 1.77 ± 0.41, 0.83 ± 0.31, and 1.06 ± 0.39 Tg yr-1 when using winter, summer, and fall flight data, respectively. Averaging seasonal a posteriori emission estimates (weighted by posterior uncertainties) results in SFBA/SJV annual CH4 emissions of 1.28 ± 0.38 Tg yr-1. A posteriori uncertainties are reduced more effectively in the SFBA/SJV region compared to state-wide values indicating that the airborne measurements are most sensitive to emissions in this region. A posteriori estimates during this study suggest that dairy livestock was the source with the largest increase relative to the a priori CALGEM emission inventory during all seasons.

Estimating methane emissions from biological and fossil-fuel sources in the San Francisco Bay Area

Author(s): Jeong, S; Cui, X; Blake, DR; Miller, B; Montzka, SA; Andrews, A; Guha, A; Martien, P; Bambha, RP; LaFranchi, B; Michelsen, HA; Clements, CB; Glaize, P; Fischer, ML | Abstract: ©2016. American Geophysical Union. All Rights Reserved. We present the first sector-specific analysis of methane (CH4) emissions from the San Francisco Bay Area (SFBA) using CH4 and volatile organic compound (VOC) measurements from six sites during September – December 2015. We apply a hierarchical Bayesian inversion to separate the biological from fossil-fuel (natural gas and petroleum) sources using the measurements of CH4 and selected VOCs, a source-specific 1 km CH4 emission model, and an atmospheric transport model. We estimate that SFBA CH4 emissions are 166–289 Gg CH4/yr (at 95% confidence), 1.3–2.3 times higher than a recent inventory with much of the underestimation from landfill. Including the VOCs, 82 ± 27% of total posterior median CH4 emissions are biological and 17 ± 3% fossil fuel, where landfill and natural gas dominate the biological and fossil-fuel CH4 of prior emissions, respectively.

Top‐down estimate of methane emissions in California using a mesoscale inverse modeling technique: The San Joaquin Valley

We quantify methane (CH4) emissions in Californias San Joaquin Valley (SJV) by using 4 days of aircraft measurements from a field campaign during May–June 2010 together with a Bayesian inversion method and a mass balance approach. For the inversion estimates, we use the FLEXible PARTicle dispersion model (FLEXPART) to establish the source-receptor relationship between sampled atmospheric concentrations and surface fluxes. Our prior CH4 emission estimates are from the California Greenhouse Gas Emissions Measurements (CALGEM) inventory. We use three meteorological configurations to drive FLEXPART and subsequently construct three inversions to analyze the final optimized estimates and their uncertainty (one standard deviation). We conduct May and June inversions independently and derive similar total CH4 emission estimates for the SJV: 135 ± 28 Mg/h in May and 135 ± 19 Mg/h in June. The inversion result is 1.7 times higher than the prior estimate from CALGEM. We also use an independent mass balance approach to estimate CH4 emissions in the northern SJV for one flight when meteorological conditions allowed. The mass balance estimate provides a confirmation of our inversion results, and these two independent estimates of the total CH4 emissions in the SJV are consistent with previous studies. In this study, we provide optimized CH4 emissions estimates at 0.1° horizontal resolution. Using independent spatial information on major CH4 sources, we estimate that livestock contribute 75–77% and oil/gas production contributes 15–18% of the total CH4 emissions in the SJV. Livestock explain most of the discrepancies between the prior and the optimized emissions from our inversion.

Elliptic Cylinder Airborne Sampling and Geostatistical Mass Balance Approach for Quantifying Local Greenhouse Gas Emissions

In this study, we explore observational, experimental, methodological, and practical aspects of the flux quantification of greenhouse gases from local point sources by using in situ airborne observations, and suggest a series of conceptual changes to improve flux estimates. We address the major sources of uncertainty reported in previous studies by modifying (1) the shape of the typical flight path, (2) the modeling of covariance and anisotropy, and (3) the type of interpolation tools used. We show that a cylindrical flight profile offers considerable advantages compared to traditional profiles collected as curtains, although this new approach brings with it the need for a more comprehensive subsequent analysis. The proposed flight pattern design does not require prior knowledge of wind direction and allows for the derivation of an ad hoc empirical correction factor to partially alleviate errors resulting from interpolation and measurement inaccuracies. The modified approach is applied to a use-case for quantifying CH4 emission from an oil field south of San Ardo, CA, and compared to a bottom-up CH4 emission estimate.

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

Author(s): Jeong, S; Newman, S; Zhang, J; Andrews, AE; Bianco, L; Dlugokencky, E; Bagley, J; Cui, X; Priest, C; Campos-Pineda, M; Fischer, ML | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Nitrous oxide (N2O) is a potent long-lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N2O is the third largest contributor to the states anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top-down inverse modeling. Here we present the first annual (2013–2014) statewide top-down estimates of anthropogenic N2O emissions. Utilizing continuous N2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N2O could be an important component in meeting Californias emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability.

An Estimate of Natural Gas Methane Emissions from California Homes

We estimate postmeter methane (CH4) emissions from Californias residential natural gas (NG) system using measurements and analysis from a sample of homes and appliances. Quiescent whole-house emissions (i.e., pipe leaks and pilot lights) were measured using a mass balance method in 75 California homes, while CH4 to CO2 emission ratios were measured for steady operation of individual combustion appliances and, separately, for transient operation of three tankless water heaters. Measured quiescent whole-house emissions are typically <1 g CH4/day, though they exhibit long-tailed gamma distributions containing values >10 g CH4/day. Most operating appliances yield undetectable CH4 to CO2 enhancements in steady operation (<0.01% of gas consumed), though storage water heaters and stovetops exhibit long-tailed gamma distributions containing high values (∼1-3% of gas consumed), and transients are observed for the tankless heaters. Extrapolating results to the state-level using Bayesian Markov chain Monte Carlo sampling combined with California housing statistics and gas use information suggests quiescent house leakage of 23.4 (13.7-45.6, at 95% confidence) Gg CH4, with pilot lights contributing ∼30%. Emissions from steady operation of appliances and their pilots are 13.3 (6.6-37.1) Gg CH4/yr, an order of magnitude larger than current inventory estimates, with transients likely increasing appliance emissions further. Together, emissions from residential NG are 35.7 (21.7-64.0) Gg CH4/yr, equivalent to ∼15% of Californias NG CH4 emissions, suggesting leak repair, improvement of combustion appliances, and adoption of nonfossil energy heating sources can help California meet its 2050 climate goals.