X-ray standing-wave photoemission experiments involving multilayered samples are emerging as unique probes of the buried interfaces that are ubiquitous in current device and materials research. Such data require for their analysis a structure optimization process comparing experiment to theory that is not straightforward. In this work, we present a new computer program for optimizing the analysis of standing-wave data, called SWOPT, that automates this trial-and-error optimization process. The program includes an algorithm that has been developed for computationally expensive problems: so-called black-box simulation optimizations. It also includes a more efficient version of the Yang X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S., Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which is about an order of magnitude faster than the original version. Human interaction is not required during optimization. We tested our optimization algorithm on real and hypothetical problems and show that it finds better solutions significantly faster than a random search approach. The total optimization time ranges, depending on the sample structure, from minutes to a few hours on a modern computer, and can be up to 100x faster than a corresponding manual optimization. These speeds make the SWOPT program a valuable tool for real-time analyses of data during synchrotron experiments.

Climate change can potentially have great impacts on wintertime precipitation and stagnant conditions, which are critical for both water resources and wintertime particulate matter (PM), in California. This study utilizes the Weather Research and Forecasting model to dynamically downscale a bias-corrected coarse-resolution global climate model dataset from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to a grid size of 4 × 4 km2 over California for a present (2003–2012) and a future (2046–2055) decade. Compared to the present climate, an increase in 2-m temperature (up to 2 K) and water vapor mixing ratio (up to 1 g/kg) and a decrease in planetary boundary layer height (up to 80 m) are projected by the 2050s for the entire state of California. The number of stagnant days over the San Joaquin Valley is expected to increase by approximately 6% in the future decade, indicating potential exacerbation of the winter PM issue in this region. The wintertime precipitation is projected to increase by up to 50% in northern California and, conversely, to decrease by up to 40% in southern California during 2046–2055. The solid phase precipitation is projected to decrease over mountain ranges with lower elevations despite an overall increase in total precipitation, while it is projected to increase over the eastern side of the Sierra Nevada with elevation over 2 km.

Smoke plumes emitted from wildland-urban interface (WUI) wildfires contain toxic chemical substances that are harmful to human health, mainly due to the burning of synthetic components. Accurate measurement of these air toxics is necessary for understanding their impacts on human health. However, air pollution is typically measured using ground-based sensors, manned airplanes, or satellites, which all provide low-resolution data. Unmanned Aerial Vehicles (UAVs) have the potential to provide high-resolution spatial and temporal data due to their ability to hover in specific locations and maneuver with precise trajectories in 3-D space. This study investigates the use of an octocopter UAV, equipped with a customized air quality sensor package and a volatile organic compound (VOC) air sampler, for the purposes of collecting and analyzing air toxics data from wildfire plumes. The UAV prototype developed has been successfully tested during several prescribed fires conducted by the California Department of Forestry and Fire Protection (CAL FIRE). Data from these experiments were analyzed with emphasis on the relationship between the air toxics measured and the different types of vegetation/fuel burnt. BTEX compounds were found to be more abundant for hardwood burning compared to grassland burning, as expected.

United States Environmental Protection Agency guidance on the use of photochemical models for assessing the efficacy of an emissions control strategy for ozone requires that modeling be used in a relative sense. Consequently, testing a modeling system's ability to predict changes in ozone resulting from emission changes is critical. We evaluate model simulations for precursor species (NOx, CO, and volatile organic compounds [VOCs]), radicals (OH and HO2), a secondary pollutant (O3), and the model response of these compounds to weekend/weekday emission changes during California Nexus study in 2010. The modeling system correctly simulated the broad spatial and temporal variation of NOx and O3 in California South Coast. Although the model generally underpredicted the daytime mixing ratios of NO2 at the surface and overpredicted the NO2 column, the simulated weekend to weekday ratios are consistent with each other and match the observed ratios well. The modeling system exhibited reasonable performance in simulating the VOC compounds with fossil fuel origins but has larger bias in simulating certain species associated with noncombustion sources. The modeling system successfully captured the weekend changes of the enhancement ratios for various VOC species to CO and the relative changes of HOx, which are indicators of faster chemical processing on weekends. This work demonstrates satisfactory model performances for O3 and most relevant chemical compounds with more robust performance in simulating weekend versus weekday changes. Improved planetary boundary layer height simulations, a better understanding of OH-HO2 cycling, continued improvement of emissions, especially urban biogenic emissions and emissions of oxygenated VOCs, are important for future model improvement.