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1 1 Summary This study examines the potential role of spatiotemporal population data in aviation noise studies. Aviation noise analysis has traditionally focused on modeling the noise from an average day of operations. We investigate the potential of moving from this static approach to identifying high-aircraft-noise areas to a dynamic method of assessing aircraft noise experienced by people where they are as they move about the day and night. Aviation-related noise is one of the most environmentally significant effects of commercial aviation. Knowing where people are at different times of the day potentially enables designers to design airspace routes that minimize the environmental impact to a shifting population on the ground. In addition, increased accuracy can potentially inform regulatory oversight and regulatory processes. For example, one outcome of Part 150 noise studies has been the apportioning of federal dollars toward sound insulation of residences exposed to high levels of aircraft noise. Typical noise impact studies are based on counting residential housing units to estimate population or by using population distributions from U.S. Census data. Currently, the population data does not include the variation of population density over time as people change locations over the course of their day. We explore the current and emerging practices of using spatiotemporal data across a range of applications in section 2. These data sets have predominantly been developed to assist transportation planning, emergency response, and marketing. The current state of such practices is described, along with a summary of the current literature. Section 3 evaluates each of the data sets for their availability, quality, and applicability for use with aviation noise studies, and we present a final score for each. We also assessed data compatibility, which we define as the ease or difficulty in adapting the data sets for use in Aviation Environmental Design Tool (AEDT) or with noise contours and GIS software. A promising data source we evaluated was LandScan USA produced by the Oak Ridge National Laboratory (ORNL). This high-resolution data set describes the distribution of the population across the United States in both the daytime and the nighttime. This data set is available only to U.S. government customers, but there are other alternatives. Environmental Systems Research Institute (ESRI) publishes a nationwide data set for daytime population, and the Longitudinal Employer- Household Dynamics (LEHD) OriginâDestination Employment Statistics (LODES) data set produced by the U.S. Census shows origin and destination location for the working population. In addition, a number of businesses specialize in collecting location data from mobile phones. These data tend to be the costliest and face limitations due to privacy concerns. We also describe the limitations of using spatiotemporal data, the most important being that the data sets only show the location of people and not whether they are engaged in noise-sensitive activities. A fuller picture of noise impact by location and time of day may be achieved by developing an activity-based model, which combines data on location and level of noise sensitivity. We outline the basic elements of such a model under future research needs in section 4. The noise situation at each airport is unique. Examining noise impacts using day versus night
2 population may not provide much additional information if flight tracks generally avoid the highest concentrations of daytime population. The daytime population sets generally show movement of people into urban centers. These locations can be identified as areas to possibly avoid flying over. Residential areas have traditionally been considered noise-sensitive areas, but the current practice is less established for gauging the sensitivity of urban downtowns in the daytime. Land use and other elements of an activity-based model are necessary to understanding noise sensitivity. Using AEDT, the Federal Aviation Administrationâs (FAAâs) main environmental analysis tool, we model two demonstration cases in section 5 showing the differences in noise impacts from daytime and nighttime operations. We compare the estimates of people exposed to aircraft noise and provide practical guidance for users to create their own analysis using spatiotemporal data. We also include practical guidance for noise practitioners on setting up their spatiotemporal noise studies. This guidance includes separating operations into day and night flight sets, applying day and night averaged noise metrics, handing event weightings during noise-sensitive times, and creating a synthetic 24-hour averaged population set from day and night data that is more directly comparable with traditional 24-hour noise metrics such as DNL (Day-Night Level) and CNEL (Community Noise Equivalent Level). We also include the relevant policy and regulatory guidance for noise studies. While no policy or regulatory requirement is unmet without spatiotemporal population data, the data sets allow noise practitioners to develop a more dynamic understanding of how aircraft noise affects people and to help facilitate communication of noise impacts.
