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

Nitrous oxide (N_2O) 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, N_2O is the third largest contributor to the state's 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 N_2O emissions. Utilizing continuous N_2O 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 N_2O 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 N_2O could be an important component in meeting California's emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO_2 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

Atmospheric observation-based estimation of fossil fuel CO_2 emissions from regions of central and southern California

Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (^(14)CO_2) measurements and atmospheric inverse modeling to estimate fossil fuel CO_2 (ffCO_2) emissions for 2009–2012 from a site in central California, and for June 2013–May 2014 from two sites in southern California. A priori predicted ffCO_2 mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO_2 prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO_2 emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013–May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO_2 emissions from California as a whole

Atmospheric observation-based estimation of fossil fuel CO_2 emissions from regions of central and southern California

Combustion of fossil fuel is the dominant source of greenhouse gas emissions to the atmosphere in California. Here, we describe radiocarbon (^(14)CO_2) measurements and atmospheric inverse modeling to estimate fossil fuel CO_2 (ffCO_2) emissions for 2009–2012 from a site in central California, and for June 2013–May 2014 from two sites in southern California. A priori predicted ffCO_2 mixing ratios are computed based on regional atmospheric transport model (WRF-STILT) footprints and an hourly ffCO_2 prior emission map (Vulcan 2.2). Regional inversions using observations from the central California site suggest that emissions from the San Francisco Bay Area (SFBA) are higher in winter and lower in summer. Taking all years together, the average of a total of fifteen 3-month inversions from 2009 to 2012 suggests ffCO_2 emissions from SFBA were within 6 ± 35% of the a priori estimate for that region, where posterior emission uncertainties are reported as 95% confidence intervals. Results for four 3-month inversions using measurements in Los Angeles South Coast Air Basin (SoCAB) during June 2013–May 2014 suggest that emissions in SoCAB are within 13 ± 28% of the a priori estimate for that region, with marginal detection of any seasonality. While emissions from the SFBA and SoCAB urban regions (containing ~50% of prior emissions from California) are constrained by the observations, emissions from the remaining regions are less constrained, suggesting that additional observations will be valuable to more accurately estimate total ffCO_2 emissions from California as a whole

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 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)

Direct Measurements and Kinetic Studies of Reaction Intermediates in the Ozonolysis of Alkenes Using Cavity Ring-Down Spectroscopy

As our understanding of the different physical and chemical aspects of tropospheric processes increases, complex reaction mechanisms are developed and tested in local, regional, and global atmospheric models. These complex mechanisms give rise to nonlinear dynamical processes that depend not only on the additive effects of the reactions that comprise them. In addition to studying kinetics of elementary reactions and unimolecular processes, it is necessary to study mechanisms as a whole. This work utilizes a flow reactor and cavity ring-down spectroscopy (CRDS) to study the mechanism of ozonolysis of various alkenes in real time. The use of a flow reactor as a cavity for CRDS measurements allows the simulation of concentration profiles of analytes at different reactor segments by modelling the plug-flow behaviour as a series of continuously-stirred tank reactors (CSTRs). Experimental measurements are used to validate ozonolysis mechanisms used for these simulations. Informed by kinetic modelling, direct measurements of formaldehyde oxide (CH2OO) produced in situ from ozonolysis of ethene and direct measurements of vinoxy radicals (∙CH2CHO) from ozonolysis of 2-butenes are carried out under various reaction conditions. New insights on the mechanisms of ozonolysis of ethene and 2-butenes are obtained by comparing measurements of these reaction intermediates and formaldehyde with simulations from mechanisms containing current kinetic information of elementary reactions, pointing out to the importance of existing and new reaction pathways. Yields of the fraction of stabilized carbonyl oxides produced from ozonolysis of several alkenes are also measured at low pressures, and nascent yields are determined by extrapolation to the zero-pressure limit, providing benchmarks for theoretical and master equation calculations

Low-Pressure and Nascent Yields of Thermalized Criegee Intermediate in Ozonolysis of Ethene

The yields of thermalized formaldehyde oxide (CH2OO, the simplest Criegee intermediate) produced from ozonolysis of ethene at low pressures were measured indirectly using cavity ringdown spectroscopy (CRDS) and chemical titration with an excess amount of sulfur dioxide (SO2). The method of monitoring the consumption of SO2 as a scavenger allows better characterization of the CH2OO at low pressure and short residence time. The yield of thermalized CH2OO from ethene ozonolysis was found to decrease with decreasing pressure. The nascent yield of thermalized CH2OO was determined to be 20.1 ± 2.5% by extrapolation of the 7–19 Torr measurements to the zero-pressure limit. Kinetic models enable better evaluation and understanding of the different measurement methods of thermalized Criegee intermediates. The information on the low-pressure yields from this work serves as a benchmark for theoretical calculations and facilitates a better understanding of the alkene ozonolysis reaction mechanisms

Reconciliation of asynchronous satellite-based NO2 and XCO2 enhancements with mesoscale modeling over two urban landscapes

International audienceFossil fuel carbon dioxide (CO 2ff), the main driver of global warming and climate change, is often co-emitted with nitrogen oxides (NO x) and precursors to ground-level ozone from anthropogenic sources like power plants or vehicles. In urban and suburban areas, satellite-based NO 2 can be used as a proxy to track the emissions of CO 2ff. Because of NO 2 's shorter lifetime, urban NO 2 plumes are more distinguishable from backgrounds and more sensitive to variations in emissions. However, the combination of these two gases is limited by the asynchrony among NO 2 and CO 2 monitoring satellites. We used CO 2ff simulated by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model to reconcile the tropospheric NO 2 vertical column density (VCD) from Tropospheric Monitoring Instrument (TROPOMI) and column-averaged dry-air mole fractions of carbon dioxide enhancements (ΔXCO 2) from Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Maps (SAMs) over a multicity area, Washington D.C.-Baltimore (DC-Balt), and a basin city, Mexico City. NO 2 /CO 2ff ratios over DC-Balt are smaller than Mexico City, indicative of stricter emission restrictions, a more combustionefficient vehicle fleet, and higher combustion efficiency due to lower altitude in DC-Balt. For single-track cases, the spatial correlations between NO 2 and ΔXCO 2 over Mexico City are stronger than DC-Balt because the NO 2 and CO 2 are mostly trapped in the valley of Mexico City, while DC-Balt is severely affected by distant sources (i. e., US East Coast cities). Using multi-track averaging, spatial correlation coefficients increase with the number of days used for averaging. The correlations reached a maximum when averaging >12 continuous images for DC-Balt and >10 continuous images for Mexico City. This finding indicates that multi-track averaging using modeled CO 2ff as a proxy is helpful to filter the noise in single-track images, to cancel the interference from distant sources, and to magnify correlations between NO 2 and CO 2ff. Mexico City showed stronger spatial correlations but weaker temporal correlations than DC-Balt due to biomass burning hot spots and large transport errors caused by the trapping effects of the surrounding mountains. Tracking the 20-day moving average of CO 2ff emissions using TROPOMI NO 2 seems technically feasible, considering the relationship between correlation coefficients and the number of available satellite images

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 (\u3c ~ 0.5 m/s), direction (\u3c ~ 15°), and boundary layer height (\u3c ~ 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