This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for the contents or use thereof. ACKNOWLEDGMENTS This study was conducted with the help and cooperation of the FAA Academy. We would like to thank Mr. Richard W. Pollard (Manager, En Route Training Branch), Mr. Robert E. Davis (Manager, En Route Radar Associate Section), and the Instructional Air Traffic Control Specialists of the Academy who participated as subject matter experts and contributed their time and expertise in performing this analysis. Special thanks are due to the Quality Assurance Specialists at Atlanta Air Route Traffic Control Center for their assistance in reviewing the SA requirements analysis.

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Situation awareness is a fundamental requirement for effective air traffic control forming the basis for controller decision making and performance. To develop a better understanding of the role of situation awareness in air traffic control, an analysis was performed to determine the specific situation awareness requirements for air traffic control. This was conducted as a goal-direct task analysis in which the major goals, subgoals, decisions and associated situation awareness requirements for En Route Air Traffic Control (ATC) were delineated based on elicitation from eight experienced Air Traffic Control Specialists. This effort was supported by available task analyses and video-tapes of simulated air traffic control tasks. A determination of the major situation awareness requirements for En Route ATC was developed from this analysis, providing a foundation for future system development which seeks to enhance controller situation awareness and provides a basis for the development of situation awareness measures for air traffic control.

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Underprediction of peak ambient pollution by air quality models hinders development of effective strategies to protect health and welfare. The U.S. Environmental Protection Agency's community multiscale air quality (CMAQ) model routinely underpredicts peak ozone and fine particulate matter (PM2.5) concentrations. Temporal misallocation of electricity sector emissions contributes to this modeling deficiency. Hourly emissions are created for CMAQ by use of temporal profiles applied to annual emission totals unless a source is matched to a continuous emissions monitor (CEM) in the National Emissions Inventory (NEI). More than 53% of CEMs in the Pennsylvania-New Jersey-Maryland (PJM) electricity market and 45% nationally are unmatched in the 2008 NEI. For July 2006, a United States heat wave with high electricity demand, peak electric sector emissions, and elevated ambient PM2.5 mass, we match hourly emissions for 267 CEM/NEI pairs in PJM (approximately 49% and 12% of unmatched CEMs in PJM and nationwide) using state permits, electricity dispatch modeling and CEMs. Hourly emissions for individual facilities can differ up to 154% during the simulation when measurement data is used rather than default temporalization values. Maximum CMAQ PM2.5 mass, sulfate, and elemental carbon predictions increase up to 83%, 103%, and 310%, at the surface and 51%, 75%, and 38% aloft (800 mb), respectively.