Evaluating the Use of Spatially Precise Diurnal Population Data in Aviation Noise Studies (2020)

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28 4 Practices and Guidelines for Using Spatiotemporal Population Data with Aviation Noise Studies 4.1 Introduction A crucial part of this research was to identify the elements of aviation noise study practices that will be affected by the introduction of spatiotemporal population data. We examined the applicability of spatiotemporal population data, limitations of the approach, and further research needs. We describe several types of noise analysis including National Environmental Policy Act (NEPA) studies, land use compatibility (Part 150), and other research studies. In section 5 we provide practical guidance for conducting a noise study using spatiotemporal population data in AEDT with examples. 4.2 Applicability of Spatiotemporal Population Data Aviation noise practitioners cover a fairly wide range of noise impact assessments. These studies range in scope from being part of a broader effort such as a NEPA analysis or a noise compatibility plan, to a one-time response to a noise complaint, to a purely academic research exercise. There is currently no regulatory requirement that is unmet without spatiotemporal population data. Instead spatiotemporal population data can help facilitate public communication by identifying times and places most likely to be affected by significant aircraft noise, new or unusual aircraft noise (such as that due to temporary runway closures), research studies, or contributing to an activity-based model approach to understanding aircraft noise impacts. Here we present a variety of use cases and indicate where spatiotemporal data may help with public communication or identification of noise-sensitive locations that vary with time. 4.3 Limitations of Current Tools and Methods Current practice typically involves examining the averaged 24-hour noise impact of operations. It does not account for movement of people at different times of the day and the noise levels they would experience while in a given location. In general, current practice recognizes that residential areas are highly sensitive to noise and that sensitivity is highest during the nighttime hours when most people are sleeping. The DNL and CNEL noise metrics describe 24-hour averaged noise with extra weight applied to operations during noise-sensitive times. Both metrics define nighttime noise as sensitive (10:00 pm through 6:59 am) and CNEL also considers evening hours (7:00 pm through 9:59 pm) as sensitive. These metrics are discussed in more detail in Section 5.3.2 Operations Weighting for Noise-Sensitive Periods. While these metrics are well understood and are tied to regulatory requirements, such as adding sound insulation to buildings, there is no commonly accepted method for interpreting noise impacts over only part of the day and the proper weightings to use for noise- sensitive times. In this study we propose two general methods for applying spatiotemporal population data and noise impacts. The first is the split day-night method in which daytime and nighttime operations are examined separately. The second method involves creating a weighted 24-hour population that combines the day and nighttime populations into one data set. This second

29 method is compatible with the well-understood DNL and CNEL metrics while accounting for population movement. The split day-night method highlights the noise levels actually experienced by people while at a given location. The day versus night population sets examined in this research only indicate the locations of people, not their level of noise sensitivity at those locations. 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 noise sensitivity. We describe the general elements of an activity-based model for aviation noise studies in the section below. Lastly the noise situation at each airport is unique. Examining noise impacts using day versus night population may not provide much additional information. If there is little difference in the location and composition of air traffic between daytime and nighttime operations the corresponding noise contours may not be substantially different. The day versus night population distribution can indicate areas of high population concentration. If these locations coincide with flight paths, the number of people exposed to aircraft noise might be higher than ordinary Census data would suggest. But location alone does not indicate the level of noise sensitivity. Areas of high daytime population may include offices, schools, or industrial areas, which may have differing levels of sensitivity to aircraft noise. 4.4 Further Research Needs The activity-based approach, such as that used for transportation planning, could be useful for understanding noise impacts at the time and place that people engage in noise-sensitive activities. Future research could potentially combine information on people’s movements through the day and their categorization by noise sensitivity. Fixed locations such as schools are known to be noise- sensitive in the daytime. Hospitals and nursing homes are noise-sensitive at all times of the day or night. Aviation noise impact on residences have often paid special attention to nighttime sleep disturbances. But social trends may bring increased attention to daytime exposure as well as more people work via telecommuting and an aging population includes larger numbers of retirees. An activity-based approach focuses on the person rather than the fixed location. Such a model would require building a database of people’s movements and their activities by time of day. For example, study participants could keep daily diaries about their locations and activities and whether those are noise-sensitive (school, work, recreation, etc.) Study participants could also potentially record when they happen to experience aircraft noise and the level of annoyance. This approach is often used to build activity-based models for transportation planning and can then be scaled up to the general population using the existing spatiotemporal data sets and other information. The model could then be used to identify noise sensitivity hotspots—times and places when people are likely to be highly sensitive to noise disturbances—and therefore areas to avoid flying to reduce overall noise impact.

30 4.5 Types of Aviation Noise Studies 4.5.1 NEPA Studies Noise impact studies typically come under the requirements of NEPA as implemented in FAA Order 1050.1F, Environmental Impacts: Policies and Procedures, and its associated Desk Reference. According to NEPA, major federal actions that have the potential to affect the environment are subject to environmental review. NEPA and Order 1050.1F provide for three levels of review: 1) Categorical Exclusion (CATEX), 2) Environmental Assessment (EA), and 3) Environmental Impact Statement (EIS). Regardless of the level of review required, there are several impact categories that must be assessed (see 1050.1F, Chapter 4, and the 1050.1F Desk Reference). 4.5.2 Environmental Justice Analysis The other impact category that deserves consideration for the use of diurnal population data is Environmental Justice (see 1050.1F, Chapter 4, and the 1050.1F Desk Reference). Environmental justice analysis considers the potential of federal actions to cause disproportionate and adverse effects on low-income or minority populations and ensures that no low-income or minority population bears a disproportionate burden of effects resulting from federal actions. Typical Environmental Justice (EJ) analyses utilize census population centroids combined with demographic information from the ACS to identify communities with concentrations of low- income and minority populations. Standard noise impact assessments are then carried out to evaluate whether low-income and minority populations are impacted at higher numbers or at greater levels than the rest of the population. DNL is the standard metric, though other supplemental noise metrics might be used to evaluate the nature of the impact. Diurnal population data has the potential to be of significance to EJ analyses. However, it would require that the demographics of the daytime population distributions also be determined and identified. Since ACS only describes the demographics of the residential or nighttime populations, these data would require a source other than the ACS. One promising source of such data could be the LEHD LODES data that describes the origin–destination (OD) data for individual census blocks. 4.5.3 Land Use Compatibility Planning (“Part 150” Studies) The noise compatibility of land use is determined by comparing the aircraft DNL values at a location to the values in the land use compatibility guidelines in 14 CFR part 150, Noise Compatibility Planning, Appendix A, Table 1. These studies include existing and forecasted noise contour mapping and population impact counts utilizing U.S. Census tract data and existing land use and zoning features to develop Noise Exposure Maps (NEMs). They might also include supplemental noise analysis such as sleep disturbance, speech interference, Time Above noise metrics, socio-demographic, and child learning impacts. While Part 150 studies do not mandate the use of population centroids, aviation noise practitioners will often include population centroids to be able to provide estimates of the number of people residing within the 65 dB, 70 dB, and 75 dB DNL contours. This is usually done to support the development of a NEM for current and proposed conditions and to identify adverse impacts early in the process. This process might be able to utilize diurnal population data instead of static

31 population data to better understand the impacts. However, policy and regulatory decisions would still be undertaken on the number of people residing within the contours of interest. 4.5.4 Other Analyses/Studies In addition to the noise analyses that are conducted in support of NEPA reviews and Part 150 studies, there are others that are carried out for specific planned actions, or to augment a larger study. A few examples of actions, activities, or areas for which other noise analyses might be carried out include: • Preferential runway usage • Preferential route usage • Noise complaint investigation • Sensitive areas (e.g., hospitals, schools, day cares, nursing homes) • Sleep disturbance, speech interference, childhood learning • Areas where quiet is an attribute (parks, historic properties, refuges) Metrics include DNL, which is the standard, and supplemental metrics such as Equivalent Sound Level (Leq and LAeq [A-weighted]), Sound Exposure Level (SEL), Maximum Sound Level (LMAX), Number Above (NA), and Time Above (TA). Generally, these supplemental metrics are used in an effort to clarify the potential noise impacts to the public. After reviewing numerous reports on various types of noise analyses and metrics, and after interviewing a variety of aviation noise practitioners and airport noise offices, it has become evident that these other noise analyses and supplemental metrics rarely, if ever, use population centroid data. Standard analyses typically utilize a combination of noise contours, individual grid point modeling, and/or onsite measurements. Interviewed respondents were unanimous in stating that the existing census population centroid data set has not been of much value in carrying out these other noise studies. However, some suggestions for the possible use of diurnal population data to support these noise analyses were provided by respondents. These included the following possibilities: • In support of analyses where quiet is an attribute—an estimate of daytime population at parks would be very useful to analysts investigating annoyance from park visitors. • Estimates of highly transient prison populations would be helpful for inclusion in the EJ process. • Being able to quantify and forecast the numbers of people presumed to be at home during the day could become very valuable to land use compatibility planning. This is particularly relevant as the population of retired persons (e.g., Baby Boomers) increases and the number of people working from their homes increases. • Being able to assess the nighttime uses of spaces in commercial land use areas such as theaters or convention centers for inclusion in sensitive areas. • With the increase in Performance-Based Navigation (PBN) there exist possibilities to design daytime traffic flows to pass over depopulated residential areas or avoid populated residential areas.

32 • Preferential runway use programs might benefit from an accurate knowledge of populations in residential areas by increasing the usage of specific Standard Instrument Departure (SID) routes to take advantage of depopulated residential areas. • If daytime population data can include both the locations and counts of persons at schools, there is the potential to impact the design and implementation of noise abatement procedures. • The updated annoyance (Schultz 1978) curve is based on surveys conducted by the FAA. Would diurnal movement of people have any bearing on the development of this curve? 4.5.5 Research Studies The most promising uses of diurnal population data are potentially in the field of aviation noise research. These can include the assessment of localized noise impacts from the inevitable increase in the use of Unmanned Aircraft Systems (UAS) or a larger scenario evaluating the environmental impacts of National Airspace System (NAS)-level NextGen initiatives. Similar to noise modeling for NEPA and Part 150 processes, typical research efforts examine the noise impact differences between two or more scenarios. Here, we provide a brief overview of sample research projects that we have conducted that have assessed noise at population centroids. As these are research efforts, there is greater leeway to explore the use of diurnal population data instead of static population data and present the findings in new and innovative ways. 4.5.5.1 NextGen NAS-Wide Environmental Assessment The FAA’s Joint Planning and Development Office (JPDO) conducted NAS-wide studies for the Interagency Portfolio and System Analysis (IPSA) to model the benefits of NextGen capabilities such as new technology or high-density operations. The studies were focused on the benefits attained by modeling the 56 airports that formed the Future Air Capacity Task (FACT). As part of the analysis, environmental benefits were quantified by running each scenario through environmental modeling tools. Noise impacts were quantified by computing DNL noise exposure at U.S. Census population centroids and aggregating the total numbers of people at different noise levels to report on the population exposed to 65 dB DNL or greater (see Figure 9). We adapted data from this study for the demonstration of spatiotemporal population data in section 5.6. Figure 9: NextGen 2013 JPDO/IPSA Noise Analysis

33 4.5.5.2 Environmental Assessment of Air Traffic Flow Management Simulations A variety of Air Traffic Flow Management (ATFM) simulation tools exist to provide fast-time evaluation of design concepts. Commonly simulated are metrics such as delay, capacity, sector occupancy, fix loading, weather avoidance, etc. Examples of such simulation environments are tools such as the National Aeronautics and Space Administration’s (NASA’s) Airspace Concept Evaluation System (ACES), the FAA’s System Wide Analysis Capability (SWAC), or MITRE’s Total Airport and Airspace Model (TAAM). As the assessment of environmental impacts has grown in importance, processes have been developed to extract the output from these environments and process them with environmental modeling tools such as the FAA’s AEDT to quantify the noise impacts. The most common analytical method employed is to compute DNL noise exposure at U.S. Census population centroids and aggregate the total numbers of people at different noise levels. 4.5.5.3 Other Past Research A variety of other research efforts could benefit from greater fidelity of population data in the temporal dimension. Examples of past aviation noise projects involving population data include: • The Denver airport airspace optimization efforts that sought to optimize traffic flows by measuring their cumulative noise impact on population centroids in the airport vicinity. • The Dynamic Noise Avoidance Planner (DNAP), a decision tool that sought to assess tactical route sequencing options by measuring their instantaneous time-dependent noise impact to populations on the ground. • The Technology Portfolio Assessment and Decision Support Tool (TPADS) developed to support NASA’s Environmentally Responsible Aviation (ERA). This research tool utilized precomputed population noise maps based on static population distributions.