We report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO₂ exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground‐based observations of radiocarbon and total CO₂, together with column‐mean CO₂ observations from NASA's Orbiting Carbon Observatory (OCO‐2). The work includes an initial examination of statistical uncertainties in prior models for CO₂ exchange, in radiocarbon‐based fossil fuel CO₂ measurements, in OCO‐2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find that flask measurements of radiocarbon and total CO₂ at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO‐2 observations yields posterior uncertainties in monthly‐mean fossil fuel emissions of ~5–10%, levels likely useful for policy relevant evaluation of bottom‐up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO₂ exchange of ~6%–12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5 ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.

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.

Studying airflow characteristics in the human respiratory system is important for understanding the properties of the aerosol transport and deposition. This study first constructed a human whole-lung airway model (WLAM) with random features extending from the mouth to the 13th generation bronchus. The large eddy simulation method is utilized to investigate the features of both the averaged airflow fields and flow unsteadiness under inspiratory flow rates of 15 and 30 l/min, respectively. The results reveal the following novel findings: (1) secondary flow strength is not monotonically changing throughout the respiratory tract, depending on the local flow state and geometry, which allows for strong secondary flow motion even in the G13 bronchial airway. (2) In the upper airway, the peak distribution of turbulent kinetic energy (TKE) is due to in-plane secondary flow shear and longitudinal flow shear; local kinetic energy plays a vital role in the persistence of unsteadiness throughout the bronchial tree, which allows instability to still exist even in the bronchial airway with low Reynolds number. (3) TI is more dependent on the local flow state and geometric structure than TKE. These findings indicate that secondary flow and airflow unsteadiness in the thin bronchial airway are far stronger than previously reported and cannot be neglected. Therefore, further studies should investigate airflow unsteadiness in the thin bronchial airway using a suitable WLAM instead of a segmented bronchial airway model.