A Multiplatform Inversion Estimation of Statewide and Regional Methane Emissions in California during 2014-2016.

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.

Comparison of continuous and filter-based carbon measurements at the Fresno supersite.

Results from six continuous and semicontinuous black carbon (BC) and elemental carbon (EC) measurement methods are compared for ambient samples collected from December 2003 through November 2004 at the Fresno Supersite in California. Instruments included a multi-angle absorption photometer (MAAP; lambda = 670 nm); a dual-wavelength (lambda = 370 and 880 nm) aethalometer; seven-color (lambda = 370, 470, 520, 590, 660, 880, and 950 nm) aethalometers; the Sunset Laboratory carbon aerosol analysis field instrument; a photoacoustic light absorption analyzer (lambda = 1047 nm); and the R&P 5400 ambient carbon particulate monitor. All of these acquired BC or EC measurements over periods of 1 min to 1 hr. Twenty-four-hour integrated filter samples were also acquired and analyzed by the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance carbon analysis protocol. Site-specific mass absorption efficiencies estimated by comparing light absorption with IMPROVE EC concentrations were 5.5 m2/g for the MAAP, 10 m2/g for the aethalometer at a wavelength of 880 nm, and 2.3 m2/g for the photoacoustic analyzer; these differed from the default efficiencies of 6.5, 16.6, and 5 m2/g, respectively. Scaling absorption by inverse wavelength did not provide equivalent light absorption coefficients among the instruments for the Fresno aerosol measurements. Ratios of light absorption at 370 nm to those at 880 nm from the aethalometer were nearly twice as high in winter as in summer. This is consistent with wintertime contributions from vehicle exhaust and from residential wood combustion, which is believed to absorb more shorter-wavelength light. To reconcile BC and EC measurements obtained by different methods, a better understanding is needed of the wavelength dependence of light-absorption and mass-absorption efficiencies and how they vary with different aerosol composition.

Preflight virtual reality training as a countermeasure for space motion sickness and disorientation.

INTRODUCTION: Research suggests that preflight training in virtual reality devices can simulate certain aspects of microgravity and may prove to be an effective countermeasure for space motion sickness (SMS) and spatial disorientation (SD). It is hypothesized that exposing subjects preflight to variable virtual orientations, similar to those encountered during spaceflight, will reduce the incidence and/or severity of SMS and SD. METHODS: Subjects were assigned to either a variable training (VT) or nonvariable training (NVT) condition to perform a simple navigation and switch activation task in a virtual space station. VT subjects performed the task starting in several different orientations, whereas NVT subjects always performed the task starting in the same orientation. On a separate day, all subjects then performed the same task in a transfer of training session starting from a novel orientation. RESULTS: When exposed to the novel test orientation, VT subjects performed the tasks more quickly (12%) and with fewer nausea symptoms (53%) than during the training session, compared with NVT subjects who performed more slowly (6%) and with more nausea symptoms (28%). Both VT and NVT conditions were effective in reducing the number of wall hits in the novel orientation (39% and 34%, respectively). DISCUSSION: These results demonstrate the effectiveness of using variable training in a virtual environment for reducing nausea and improving task performance in potentially disorienting surroundings, and suggest that such training may be developed into an effective countermeasure for SMS, SD, and associated performance decrements that occur in spaceflight.