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Emergency planning and hazard assessment of Department of Energy (DOE) facilities require consideration of potential exposures to mixtures of chemicals released to the atmosphere. Exposure to chemical mixtures may lead to additive, synergistic, or antagonistic health effects. In the past, the consequences of exposures to each chemical have been analyzed separately. This approach may not adequately protect the health of persons exposed to mixtures. This article presents default recommendations for use in emergency management and safety analysis within the DOE complex where potential exists for releases of mixtures of chemicals. These recommendations were developed by the DOE Subcommittee on Consequence Assessment and Protective Actions (SCAPA). It is recommended that hazard indices (e.g., HIi = Ci/Limiti, where Ci is the concentration of chemical "i") be calculated for each chemical, and unless sufficient toxicological knowledge is available to indicate otherwise, that they be summed, that is, sigma i(n) = 1HIi = HI1 + HI2 + ... + HIn. A sum of 1.0 or less means the limits have not been exceeded. To facilitate application of these recommendations for analysis of exposures to specific mixtures, chemicals are classified according to their toxic consequences. This is done using health code numbers describing toxic effects by target organ for each chemical. This methodology has been applied to several potential releases of chemicals to compare the resulting hazard indices of a chemical mixture with those obtained when each chemical is treated independently. The methodology used and results obtained from analysis of one mixture are presented in this article. This article also demonstrates how health code numbers can be used to sum hazard indices only for those chemicals that have the same toxic consequence.

Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine-learning algorithms are trained on the ensemble data and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 10 3 ), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200 m. The start time is determined to within 5 min out of 2 h, and the duration to within 50 min out of 4 h. Over a range of release amounts of 10 to 1000 kg, the estimated amount exceeds the actual amount of 146 kg by only 32 kg. The inversion also estimates probabilities of different WRF configurations. To best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.

The accuracy of boundary-layer wind profiles occurring during nocturnal low-level jet (LLJ) events, and their sensitivities to variations of user-specifiable model configuration parameters within the Weather Research and Forecasting model, was investigated. Simulations were compared against data from a wind-profiling lidar, deployed to the Northern Great Plains during the U.S. Department of Energy-supported Weather Forecast Improvement Project. Two periods during the autumn of 2011 featuring LLJs of similar magnitudes and durations occurring during several consecutive nights were selected for analysis. Simulated wind speed and direction at 80 and 180 m above the surface, the former a typical wind turbine hub height, bulk vertical gradients between 40 and 120 m, a typical rotor span, and the maximum wind speeds occurring at 80 and 180 m, and their times of occurrence, were compared with the observations. Sensitivities of these parameters to the horizontal and vertical grid spacing, planetary boundary layer and land surface model physics options, and atmospheric forcing dataset, were assessed using ensembles encompassing changes of each of these configuration parameters. Each simulation captured the diurnal cycle of wind speed and stratification, producing LLJs during each overnight period; however, large discrepancies in relation to the observations were frequently observed, with each ensemble producing a wide range of distributions, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error and bias values computed over the LLJ cycle (late evening through the following morning) revealed that, while some configurations performed better or worse in different aspects and at different times, none exhibited definitively superior performance. The considerable root mean square error and bias values, even among the 'best' performing simulations, underscore the need for improved simulation capabilities for the prediction of near-surface winds during LLJ conditions.

The Atmospheric Release Advisory Capability (ARAC) at Lawrence Livermore National Laboratory is a centralized federal project for assessing atmospheric releases of hazardous materials in real time. Since ARAC began making assessments in 1974, the project has responded to over 60 domestic and international incidents. ARAC can model radiological accidents in the United States within 30 to 90 min, using its operationally robust, three-dimensional atmospheric transport and dispersion models, extensive geophysi-cal and dose-factor databases, meteorological data acquisition systems, and experienced staff. Although it was originally conceived and developed as an emergency response and assessment service for providing dose-assessment calculations after nuclear accidents, it has proven to be an extremely adaptable system, capable of being modified to respond also to nonradiological hazardous releases. In 1991, ARAC responded to three major events: the oil fires in Kuwait, the eruption of Mt. Pinatubo in the Philippines, and an herbicide spill into the upper Sacramento River in California. Modeling the atmospheric effects of these events added significantly to the range of problems that ARAC can address and demonstrated that the system can be adapted to assess and respond to concurrent, multiple, unrelated events at different locations. Bulletin of the American Meteorological Society FIG. 13. The upper Sacramento River and Lake Shasta region affected by the metam sodium spill.