Noise Prediction and Mitigation for UAS and eVTOL Aircraft: A Survey

Highlights

What are the main findings?

• This survey identifies and discusses recent advances in noise prediction tools, empirical measurements, and mitigation strategies for UAS and eVTOL aircraft, integrating technical solutions with public perception studies and regulatory frameworks.
• The review provides a comparative analysis of prediction and mitigation approaches, highlighting their modeling fidelity, operational applicability, and limitations across different environmental and urban scenarios.
• A taxonomy of noise prediction and mitigation approaches is developed.

What is the implication of the main finding?

• The results emphasize that effective UAM noise management requires an integrated approach that combines accurate prediction models, mitigation strategies, regulatory adaptation, and proactive community engagement.
• These insights can guide vehicle designers, urban planners, and policymakers in creating noise-aware airspace corridors, developing certification standards, and enhancing public acceptance for UAM deployment.

Abstract

The integration of small unmanned aircraft systems (sUASs) and electric vertical takeoff and landing (eVTOL) aircraft into urban airspace presents a new challenge in managing environmental noise, which is a critical factor for the public acceptance of urban air mobility (UAM). This survey investigates the noise characteristics of UAS and eVTOL platforms, particularly multi-rotor and distributed propulsion configurations, and examines whether the operational benefits of these vehicles outweigh their acoustic footprint in dense urban environments. While eVTOLs are often perceived as quieter than conventional helicopters due to the absence of combustion engines and mechanically simpler drivetrains, their dominant noise sources are aerodynamic in nature. These include blade vortex interactions, rotor loading noise, and broadband noise, which persist regardless of whether propulsion is electric or combustion-based. Recent studies suggest that community perception of drone noise is influenced more by tonal content, frequency, and modulation patterns than by absolute sound pressure levels. This paper presents a comprehensive review of state-of-the-art noise prediction tools, empirical measurement techniques, and mitigation strategies for sUAS operating in UAM scenarios. The discussion provided in this paper assists in vehicle design, certification standards, airspace planning, and regulatory frameworks focused on minimizing noise impact in urban settings.

1. Introduction

Unmanned aircraft systems (UASs), encompassing a wide variety of designs from small multi-rotor drones to larger electric vertical takeoff and landing (eVTOL) aircraft, are increasingly being deployed in UAM applications such as last-mile delivery (LMD), emergency response, aerial logistics, and passenger transport. UAS platforms range from compact quad-rotors and hexacopters typically used for inspection and delivery tasks to more advanced lift-plus-cruise and tiltrotor eVTOL configurations designed for higher payload capacity and extended range missions [1,2,3,4,5]. Among these, LMD has seen rapid growth as a promising drone-based service for transporting goods from distribution centers to final customer locations, particularly in congested or remote areas. Major companies, including Uber [6], Grubhub [7], and DoorDash—in partnership with Wing [8]—have piloted drone-based LMD programs. Walmart has introduced trial programs in Texas, Arizona, and Florida [9], and UPS has utilized autonomous aerial delivery for time-sensitive medical supplies, including vaccines [10].

As urban drone operations continue to expand, particularly at low altitudes, they introduce new environmental and regulatory challenges, most notably, noise pollution. Although drones and eVTOLs often use electric propulsion and distributed rotor systems, their acoustic footprint remains a concern for public acceptance and urban integration. Key noise sources stem from aerodynamic interactions, blade–vortex interactions, and unsteady rotor wake phenomena, regardless of whether propulsion is electric or combustion-based. These noise emissions can adversely affect community well-being, disrupt daily routines, and lead to resistance against widespread UAS deployment in cities.

Given the increasing public and regulatory scrutiny of noise generated by drones and low-altitude UAM vehicles, there is a critical need for accurate prediction models, validated measurement techniques, and effective mitigation strategies. This article presents a comprehensive survey of the state of the art in UAS and eVTOL noise research, focusing on noise prediction tools, modeling methods, perception studies, and design considerations tailored for small UAS and eVTOL platforms that will be used in the UAM system. Recent surveys, most notably, Mane et al. (2024) [11], Lotinga et al. (2023) [12], Rascon and Martínez Carranza (2024) [13], Afari et al. (2023) [14], and Hua and Mankbadi (2024) [15], Yang C. et al. (2024) [16], and Schäffer et al. (2021) [17], have provided valuable overviews of UAS noise studies, reduction techniques, measurement methods, psychoacoustic metrics, and broadband noise control approaches. Each of these works focuses primarily on component-level noise mechanisms or control technologies for generic drone platforms and does not fully address the specific aerodynamic and acoustic characteristics of multi-rotor eVTOL vehicles in UAM context. Furthermore, prediction models, mitigation strategies, regulatory frameworks, and community acceptance are typically treated as separate topics rather than as interconnected elements of an integrated UAS and UAM noise management framework.

By contrast, our survey is unique in following key aspects that are primary objectives of this paper: (i) to examine acoustic challenges associated with drone related UAM operations, (ii) to survey current noise prediction and reduction approaches, (iii) to highlight public perception studies relevant to urban drone integration, and (iv) to evaluate implications for regulatory and airspace route planning. The survey highlights recent international initiatives and regulatory developments, offering insights that can inform both technical and policy frameworks for integrating noise-aware drones. The rest of the paper is structured as follows. Section 2 presents a literature review of existing noise prediction and mitigation approaches with the current software tools to predict and mitigate noise exposure; Section 3 presents the noise impact studies by various countries and provides public perception of UAS and drone noise studies. Section 4 provides challenges encountered during the design phase and community acceptance and makes assumption about optimized airspace routes. Section 5 highlights the regulation of general aviation noise and provides recommendations about future UAM noise regulatory considerations in the U.S. Given these challenges, the paper ends with a conclusion. Figure 1 illustrates the article’s structure following the progression from foundational NASA and academic noise prediction tools, through international noise impact studies, to design challenges, community acceptance considerations, and relevant regulatory guidelines.

2. Literature Review

This section provides a detailed review of the literature on noise assessment within UAM environments. The review process involved an initial screening of 60 latest academic papers and from industries, followed by an in-depth analysis of the most relevant and significant research articles to gain comprehensive insight into the field. We searched four major databases—Google Scholar, IEEE Xplore, ScienceDirect, and Vertical Flight Library—covering the period from January 2018 through December 2024. We used a combination of keyword strings to capture all relevant work on drone and eVTOL/UAS noise, including UAM noise, eVTOL/UAS noise prediction, eVTOL/UAS noise mitigation, and drone acoustics modeling. The search also included studies that integrate regulatory perspectives and practical guidelines into UAM and eVTOL noise research.

2.1. NASA

In this subsection, research efforts from National Aeronautics and Space Administration (NASA) regarding noise mitigation in Advanced Air Mobility (AAM) operations are presented. NASA has been actively involved in studying and addressing the acoustic challenges associated with UAM to ensure that these operations are both viable and acceptable to the public. Several NASA research initiatives have focused on understanding noise sources, propagation, and mitigation strategies for UAM vehicles. In [18], Rizzi et al. explored the preliminary noise evolution methods to analyze the acoustic impact of UAM operations on local communities. They used the FAA’s Aviation Environmental Design Tool (AEDT) and emphasized its usefulness, particularly given the lack of a noise–power–distance (NPD) database and a general performance model for UAS/eVTOL vehicles. The research team from NASA’s Langley Research Center started the conversation on noise effects and created a system that combined fixed-point flight configuration routes with user-specified noise information. Their goal was to establish a system for implementing the Aviation Environmental Design Tool (AEDT) without altering the application itself. The selection of aircraft types in the AEDT was based on several factors, including (i) the need for a common methodology that is independent of eVTOL aircraft configuration; (ii) an understanding of and approach to mitigating any unwanted behaviors; and (iii) the ability to represent as many operating states as necessary. The technique analyzed 32 DFW routes for single and multiple UAM operations using two concept air vehicles. NASA produced another document for preparing UAM noise testing towards the conclusion of the previous decade to cover additional test goals, objectives, and actions required for these tests. This document distinguishes between observational and staged studies, as well as recent and devoted research on large fixed-wing and supersonic commercial carriers [19].

In the fall of 2022 [20], NASA conducted a remotely administered psychoacoustic test as the first step of a joint investigation on human responses to eVTOL vehicle noise. This preliminary stage, called the Feasibility Test, aimed to evaluate human responses and compare them to results from an earlier in-person psychoacoustic study, which found that people reacted differently to sUAS noise versus ground vehicle noise. The objective was to create a comprehensive dataset of human reactions to UAM noise, a task too large for any single organization to undertake alone. This paper outlines the Feasibility Test’s design, including its web-based format, how audio levels were calibrated, the software development process, the selection of audio stimuli, participant recruitment strategies, and administration procedures. Test effectiveness was evaluated by comparing participants’ self-reported annoyance levels quantified using standardized subjective rating scales such as the 5-point verbal or 11-point numerical annoyance scales commonly employed in psychoacoustic studies, with those recorded in the prior in-person experiment. These annoyance metrics reflect the degree to which individuals perceive the noise as disturbing or unacceptable, offering a critical measure for understanding community responses to UAM noise. The study also explored whether providing contextual cues to respondents influenced their annoyance ratings and examined variations in responses across different geographic regions. Additionally, as noted in [21], a psychoacoustic experiment to inform UAM sound design was recently completed, and several algorithms have since been devised to determine how well UAM vehicle noise can be heard against ambient soundscapes. A technical memorandum (TM 20210017504) [22] was released to outline recommended procedures for recording and recreating background noises for UAM psychoacoustic research. To account for changing views that may arise as the number of flying vehicles grows, adaptable data analysis methods have been developed by extending current measures. The study [22] addresses a critical need in the research community for a consistent methodology to document ambient soundscape recordings based on Rizzi et al. [23].

In [24], NASA presented a technique to forecast the noise exposure of UAM at the source, strategies to evaluate and limit the impact on the community. A thorough analysis is required to evaluate a variety of operating situations inside the flight envelope, where the vehicle is appropriately sized for the intended mission. This research identifies various aircraft configurations, including trimmed states, acknowledging that a single operating condition may correspond to multiple trimmed configurations due to redundant control systems in many UAM vehicles; some configurations inherently generate higher noise levels than others. For each such trimmed configuration, one can calculate noise contributions from components like steady and unsteady rotor noise, as well as propulsion–airframe–aeroacoustic interactions. From these calculations, three-dimensional sound radiation patterns (commonly called hemispheres) are generated. Hemispheres represent how sound emanates from a noise source (e.g., rotors or other aircraft components) in space. These hemispherical datasets serve multiple purposes: they inform noise certification analyses, feed into auralization workflows (translating numerical noise data into audible sound), support land-use planning through simulation and modeling tools, aid in developing low-noise operational procedures via flight simulators, and guide acoustic-aware flight control strategies. Figure 2 (adapted from NASA) depicts how an eVTOL aircraft’s noise reaches a ground observer, emphasizing principal noise contributors such as rotor–rotor and rotor–fuselage interactions and showing both direct sound paths and reflections off buildings and terrain.

NASA’s AAM project, in collaboration with Joby Aviation, conducted the first acoustic flight test on a full-size eVTOL aircraft [26]. This test aimed to measure the noise profile of the aircraft to ensure that it does not significantly increase ambient noise pollution, a key factor in community acceptance of AAM vehicles. In [27], NASA conducted another acoustic hover test with Moog SureFly to measure the acoustic footprints of the aircraft. They collected noise data while the Moog SureFly vehicle hovered above an array of 28 ground-level microphones. This will help to develop design tools for manufacturers that can be used to mitigate the noise impact, and this testing can be used to improve aircraft design and increase the likelihood of people’s acceptance.

In 2018, NASA established the Urban Air Mobility Noise Working Group (UNWG) [23]. The UNWG unites noise specialists from industry, academia, and governmental bodies to identify, deliberate, and resolve noise-related concerns. A white paper was issued outlining a series of overarching objectives to tackle noise-related concerns in UAM that are illustrated in Figure 3. The group assessed existing procedures, pinpointed research deficiencies, and proposed solutions to bridge these gaps to attain high-level objectives across four domains: (i) Tools and Technologies, (ii) Ground and Flight Testing, (iii) Human Response and Metrics, and (iv) Regulations and Policy. The subsequent subsections elucidate these points.

2.1.1. Tools and Technologies

The noise prediction tools are provided in the report [23]. These tools not only provide support for research and the development of technologies that reduce noise, but they also play an important role in the design of eVTOL vehicles to guarantee that they comply with certification standards. In addition, these technologies make it easier to conduct an extensive analysis of the noise consequences that UAM operations have on the surrounding population.

2.1.2. Ground and Flight Testing

The technical report [23] delves into the historical drivers behind acoustic flight testing and provides a broad overview of standard practices within aeronautics. It identifies key limitations and areas of refinement required for UAM, proposing a framework for the evolution of measurement standards tailored to the distinctive characteristics and requirements of this next generation of aircraft.

2.1.3. Human Response and Metrics

The study also provides an analysis of the human perception of UAM noise, as it is necessary to understand the human perception of UAM noise to guide vehicle design and operational strategies that promote public acceptance. By focusing on noise metrics that capture specific human responses, designers and engineers can prioritize technologies, design choices, and procedures that align with community expectations. These metrics enable the creation of predictive models that provide valuable insights for decision-making processes, helping manufacturers and regulators make informed choices to enhance the acoustic compatibility of UAM vehicles with their operating environments.

2.1.4. Regulations and Policy

The report [23] also emphasizes the regulations and policies of electric propulsion, distributed lift, and autonomous flight technologies for the development of new UAM vehicles. Additionally, it confronts substantial obstacles, such as public concerns regarding privacy, safety, pollution, and regulatory compliance.

2.2. EASA

This section provides the latest research from the European Union (EU) and its publication for UAS and eVTOL aircraft’s noise. It addresses the work performed by imAFUSA, MUSE, and SESAR.

2.2.1. Impact and Capacity Assessment Framework for U-Space Societal Acceptance (ImAFUSA)

ImAFUSA is a European initiative that studies what makes people comfortable with UAM. It aims to give city governments and other U-space partners a clear framework for rolling out UAM services in ways that benefit and are welcomed by the community [28]. The ImAFUSA has published the following research throughout its project start: In [29], Palaiologk and Arvanitidis proposed a framework for integrating UAM into sustainable urban mobility plans, identifying operational roles and stakeholder profiles relevant to smart cities. While not focused solely on acoustics, their framework underscores that noise management must be embedded into early-stage mobility planning, making it directly applicable to UAM noise regulation and community acceptance strategies. In [30], Snellen et al. provided a comprehensive review of drone and UAM noise research, covering measurement techniques, modeling approaches, and human perception studies. Their work compares laboratory and field-based findings, identifies knowledge gaps in realistic operational conditions and is an important reference for aligning prediction models with perceptual response data in eVTOL/UAS contexts. In [31], Green and Torija applied a soundscape-based “soundwalking” method to assess drone noise in real environments. By adding controlled drone events to existing acoustic scenes and collecting perceptual feedback from participants, they demonstrated how real-world context modifies perceived annoyance (i.e., subjective discomfort or irritation caused by noise) and comfort—insights valuable for noise impact assessment beyond laboratory conditions. Ramos-Romero et al. introduced a taxonomy for assessing UAS noise emissions, identifying essential acoustic and operational metadata to be reported across studies. This harmonized classification facilitates the comparison of results from different research groups and supports standardization in noise characterization for regulatory and modeling purposes [32]. In [33], Koning et al. examined the flight performance and acoustic characteristics of asymmetric propellers with uneven blade spacing. Their findings indicate that unconventional rotor designs can alter tonal and broadband noise signatures, offering potential design strategies for noise mitigation in UAM vehicles. In [34], the authors conducted psychoacoustic characterization of multirotor drones in realistic flyover maneuvers, providing metrics such as loudness, sharpness, and fluctuation strength under operational scenarios. This work bridges the gap between controlled acoustic measurements and real-world drone operations. In [35], Ramos-Romero et al. synthesized and auralized quadcopter flyover sounds for controlled psychoacoustic testing, enabling repeatable evaluation of perceptual responses to varying flight parameters. This approach supports the development of perceptually optimized operational profiles. In [36], Torija Martinez et al. analyzed how UAS noise varies with vehicle mass and flight procedure, identifying trends in acoustic and psychoacoustic metrics that can guide noise certification standards and operational best practices.

In [37], Baena et al. proposed a methodology for evaluating UAM noise and visual pollution, integrating acoustic modeling, human perception surveys, and urban context considerations. This multidisciplinary approach can inform holistic impact assessments. In [38], Ivošević et al. compared UAV noise impacts using both surveys and in situ measurements, showing how measured levels and community perceptions can diverge—underscoring the need for combining objective and subjective assessment. In [39], Torija et al. investigated how a hovering UAV affects urban soundscape perception, finding measurable shifts in perceived calmness and pleasantness. Their results emphasize that even stationary operations can influence urban acoustic comfort.

2.2.2. Measuring U-Space Social and Environmental Impact (MUSE)

MUSE, in the context of U-space, refers to “Measuring U-Space Social and Environmental Impact,” a project focused on assessing the societal and environmental effects of urban air mobility (UAM) and the associated U-space services [40].

2.2.3. Single European Sky Air Traffic Management (ATM) Research (SESAR)

SESAR, or Single European Sky Air Traffic Management (ATM) Research, is indeed the technological cornerstone of the EU’s Single European Sky policy and a crucial component of the European Commission’s Sustainable and Smart Mobility Strategy. It focuses on modernizing air traffic management in Europe through technological advancements [41].

2.3. Academia

This section provides a literature review of noise prediction and mitigation efforts from academia and a few industry reports to support this study. In [42], Yunus et al. proposed an acoustic footprint framework for propeller-driven aircraft, integrating an aerodynamic model with various aeroacoustic models. The aerodynamic model in this study is based on blade element momentum theory, whereas the aeroacoustic models use the time-domain compact dipole/monopole Ffowcs Williams and Hawkings acoustic principle. The noise model also incorporates trail edging, hemispheric database noise techniques, and the ray-tracing propagation model. The frequency-domain acoustics formulation was designed to shorten the time required for the noise footprint prediction framework to perform its computations. They used Hanson’s 1980 [43] frequency-domain acoustic formulation. The study reduced runtime by implementing and testing Hanson’s frequency-domain acoustic formulation, allowing the noise footprint prediction framework to run and offer predictions in less time, making the process more efficient. This was implemented and tested against the compact dipole/monopole Ffowcs Williams and Hawkings acoustic analogy (Williams, 1969) [44]. Various acoustic impacts were determined during the design phase and under forward flight situations. Noise footprints were determined by varying propeller layouts with an advance ratio and comparing blade counts. The results reveal that reducing the advance ratio caused a large change in source directivity for a given thrust, resulting in a fluctuation of up to 30 dBA. Keeping the advance ratio constant while raising the blade numbers from five to seven caused a 16 dBA variance due to changes in source directivity, while the maximum noise levels stay constant.

In [11], the authors examined advancements in reducing noise from UAVs, a crucial area due to the disturbances UAV noise can cause. Their work explores both passive noise reduction methods, like optimizing propeller design and using sound-absorbing materials, as well as active noise reduction methods, such as Synchro-Phaser and Active Noise Control (ANC) systems that dynamically counteract sound. The paper also discusses the sources of UAV noise, including piston engines, electric motors, propellers, and the airframe itself, as well as the health impacts of noise pollution, emphasizing the need for quieter designs to improve operational efficiency and environmental sustainability. Ultimately, the review highlights the importance of ongoing research, interdisciplinary collaboration, and regulatory compliance to address this challenge in the expanding field of UAV applications.

In [12], the authors provided review of the current understanding of noise from unconventional aircraft like drones and UAM vehicles. Their work discusses how this noise is measured and perceived, emphasizing that the unique acoustic characteristics of these vehicles can be more annoying than traditional aircraft noise, even at the same volume. The review also examines existing and proposed noise regulations, highlighting that they are often adapted from conventional aviation standards and may not fully account for these differences in perception. Finally, it identifies key research gaps, such as the need for better measurement methods for realistic flight scenarios, a deeper understanding of how personal and environmental factors influence annoyance, and the development of metrics more suited to the specific sound qualities of unconventional aircraft.

In [13], the authors reviewed the literature on noise produced by Unmanned Aerial Vehicles (UAVs), commonly known as drones. Their work explores the impact of UAV noise on society, including its effects on humans and animals, using metrics like Sound Pressure Level (SPL). It also summarizes noise modeling techniques to understand how drone noise is generated and propagates and critically examines various noise mitigation techniques, such as trajectory optimization, propeller design, and automatic noise canceling, highlighting their effectiveness and limitations in addressing public acceptance of drone technology for both leisure and commercial applications. In [14], the authors reviewed various noise reduction technologies for AAM vehicles, like drones and air taxis, focusing on operations near landing areas where noise is a major concern. Their work examines both passive control methods, which involve design changes like blade shape modifications or adding ducts, and active control technologies that use systems to directly counteract noise. The paper highlights the trade-offs associated with passive methods, often impacting performance, and explores how multidisciplinary optimization can help balance these factors. Finally, it delves into ANC, including on-blade actuation and zone control, discussing their potential and the challenges in implementing them for multi-rotor AAM vehicles.

In [15], the author discussed the current state of broadband noise (BBN) in AAM vehicles (eVTOLs). The work explores the various sources of BBN, such as blade self-noise and interactions with turbulent flow, and reviews the prediction methods used to model this noise, ranging from empirical to high-fidelity numerical simulations. The article also examines different passive and active control techniques aimed at reducing BBN from AAM propellers, highlighting the trade-offs and potential of these methods. Overall, it emphasizes the importance of addressing noise challenges for the successful integration of AAM into populated areas and identifies areas for future research to improve prediction accuracy and noise control strategies.

In [45], the researchers introduced a low-fidelity methodology for approximating an aircraft’s noise footprint within a three-dimensional generic setting. Rather than using a ray-tracing propagator, they employed a Gaussian beam tracer that accounts for intricate source directivity, the three-dimensional terrain structure, and varying wind conditions. Prior work by the authors validated the accuracy of the Gaussian beam tracer. In this investigation, they focused on modeling complex source directivity within a moving medium. Noise sources were generated by a simplified tool chain and distributed over a spherical surface surrounding the aircraft, which were then propagated through an inhomogeneous, anisotropic environment. The study evaluated predicted noise footprints across different terrain configurations, source directivity patterns, and wind flow scenarios. Compared to a flat terrain baseline, the presence of building-like structures increased ground-level noise by approximately 5 dB within illuminated regions due to additional reflections. Those same structures created acoustic shadow zones behind them, reducing noise levels in those areas relative to flat ground; this shielding effect became more pronounced at higher frequencies in quieter conditions. Variations in source directivity, especially between the first and second harmonics of the blade pass frequency, can yield differences in the noise footprint of up to 40 dB. The authors also examined how wind flow alters the sound footprint, observing significant fluctuations after adjusting the lobe pattern, which further elevates noise levels. Overall, the proposed approach reduced prediction error by about 5 dB in illuminated zones and up to 35 dB within shadowed terrain regions.

In [46], Rimjha et al. estimated noise levels for future UAM commuter operations in the Northern California and Dallas–Fort Worth (DFW) regions, identifying areas where UAM noise could pose significant challenges. The study utilized the Day–Night Average Sound Level (DNL) metric at the Census Block Group level with population estimates obtained from the American Community Survey (ACS) [47]. A parametric analysis considered two scenarios: one with a 10 dBA noise reduction and another with a 15 dBA reduction compared to the Robinson R-44 helicopter. Four-dimensional flight trajectories were generated based on daily UAM demand distributions, and noise power data curves were modeled using the FAA’s AEDT tool to compare the R-44 baseline with the assumed noise reduction scenarios. The results demonstrated a substantial impact, as achieving a 15 dBA reduction instead of 10 dBA could decrease the land area with DNL values exceeding 50 dBA by 94% and reduce the highly annoyed population by 91% in Northern California. Similarly, in Dallas–Fort Worth, a 15 dBA reduction could lead to an 80% decrease in affected land area and an 85% reduction in the highly annoyed population. These findings emphasize the critical role of noise reduction strategies in mitigating UAM noise impact on communities.

In [48], Jia et al. examined the acoustic behavior of single-passenger and six-passenger UAM aircraft using a high-fidelity computational fluid dynamics (CFD) framework. Their simulations employed the Helios suite from the High-Performance Computing Modernization Program (HPCMP), specifically the CREATE-AV tool [49], which is tailored for multidisciplinary rotorcraft modeling and analysis. In parallel, the PSU-WOPWOP acoustics prediction package [50] was utilized to carry out noise simulations. The CFD setups consisted of (1) a one-passenger vehicle with an isolated rotor, (2) a one-passenger configuration including the fuselage, and (3) a six-passenger layout with an isolated rotor. The results indicated that adding the fuselage to the one-passenger rotor increased the A-weighted sound pressure level (SPL) by up to 5 dB relative to the isolated-rotor case when contrasting single-passenger versus six-passenger isolated-rotor configurations, the peak overall SPL differed by as much as 14 dB. Moreover, in an overhead flight scenario, the six-seater design emitted noise levels approximately 11 dB lower than a comparably sized conventional helicopter. These outcomes offer critical guidance for noise reduction strategies in upcoming UAM vehicle architecture. In [51], Eissfeld et al. presented a broad discussion on AAM and its prospective rollout. They argued that all citizens—regardless of whether they will actually use UAM services—should be considered stakeholders in the UAM ecosystem. The authors introduced the idea of resident participatory noise sensing (PNS), wherein local inhabitants actively measure and report ambient noise in their neighborhoods to support UAM implementation efforts. They also noted that web-based platforms and smartphone applications can streamline participation by facilitating access to live noise data, thereby aiding in updating local noise distribution maps and promoting UAM integration within smart-city initiatives.

In [52], Li et al. examined noise exposure mapping for UAM operations. A noise exposure map represents an airport area at scale, showing contours of noise levels, locations of noise-sensitive receptors, and adjacent land-use types. These maps were developed in accordance with the FAA’s 14 CFR Part 150 guidelines. The researchers designed novel airspace structures, identified optimal vertiport locations, and devised UAM flight paths based on NASA engineering simulations. To create the Dallas–Fort Worth (DFW) noise exposure map, they combined geospatial coordinates, land-use classifications, and inventories of nearby noise-sensitive facilities. Noise level estimates were generated for a six-seat electric quad-rotor prototype using NASA’s AIRNOISEUAM (Airborne Noise Model for UAM) software (Gen-1.2-NPD version) [53]. The findings from this analysis yield important information for noise compatibility planning, routing for acoustic mitigation, and vertiport siting strategies. Additionally, [54] presents several aeroacoustic metrics relevant to UAM noise forecasting. The authors used a set of essential measures to examine and comprehend predicted noise which can help guide the design process. After evaluating metrics established by aviation organizations, seven major general performance measures were selected, assessed, and addressed in the context of anticipating noise from UAMs.

In [55], Lee, S. et al. investigated the broadband noise of multi-rotor UAM’s eVTOL aircraft. They developed a multi-rotor broadband noise setting that is based on an earlier single-rotor trailing-edge noise method for prediction. In this configuration, the authors set multi-rotor coordinate transformation and introduced the amplitude modulation capabilities. These configurations predicted the UAM eVTOL broadband noise for three conceptual designs, including the vertiport conceptual design. The authors found that the noise generated by eVTOL is very significant in the high-frequency range, where the background noise levels are low. Considering the same mission profile, it has been demonstrated that VTOL designs with additional rotors tend to generate higher levels of broadband noise. The performance comparison is presented for UAM vehicles with that of conventional helicopters, and it has been found that the amplitude modulation of broadband noise from a single rotor becomes negligible beyond a distance of four rotor radii. Although multi-rotor vehicles operating at the same rotational speeds exhibit reduced amplitude modulation compared to single rotors, the increased number of rotors leads to a cumulative rise in broadband noise, particularly in the high-frequency range, which can increase overall perceived annoyance. In [3], the authors examined the noise generated by air taxi fleets while considering the potential expansion of UAM’s air taxi market. Noise levels around vertiports and along air taxi routes are influenced by fleet composition and operational patterns. The study utilized air traffic simulations with various fleet configurations to assess noise impacts. Simulations were conducted along fixed flight paths under a baseline scenario that represented the air taxi system and specific flight levels within designated air lanes. The final fleet configurations included a randomly generated mix of air taxis operating along varied flight paths. The results, analyzed using standard noise metrics and assessing variations in the number of affected residents, provide insights into strategies for mitigating community noise through future UAM traffic management.

To predict community noise exposure, researchers have employed the FAA’s AEDT framework to support modeling efforts and to evaluate the applicability, strengths, and limitations of acoustic noise modeling methodologies. Accordingly, authors in [56] present a comparative study utilizing a time-domain simulation approach with the Volpe advanced acoustics model. In that analysis, the same source characterization was used to estimate the directional sound radiation of a UAM concept vehicle by examining aeroacoustic pressure-time histories recorded at observer locations surrounding the aircraft. Beyond condensing this information into noise–power–distance data for AEDT ingestion, the pressure time histories were preserved with full spectral directivity and then transformed into one-third-octave and one-twelfth-octave band representations to serve as inputs to the advanced acoustics simulation. This investigation reports multiple metrics that quantify how variations in source directivity and propagation modeling fidelity influence predicted noise levels at receptors distributed throughout the study region.

In [57], Vascik et al. explored possible operational constraints encountered when launching or scaling UAM services. Their study concentrated on near-term UAM deployments in Los Angeles, Boston, and Dallas, chosen to reflect varied urban geographies. By employing a conceptual door-to-door operational model spanning 32 representative missions in these metropolitan areas, the authors assessed critical parameters such as flight range, passenger throughput, service market type, population overflight density, and air traffic congestion. The results identify eight principal constraints that could impede UAM expansion, with the most significant being community acceptance of aircraft noise, the availability of vertiport or landing sites, and the scalability limits of air traffic management. Complementing this, [1] examined essential operational requirements for UAM applications by reviewing the current landscape of UAM research, its potential benefits, and the challenges that lie ahead. The study assessed mission radius configurations by evaluating different operational setups while also considering consciousness and reliability criteria. Safety requirements were examined using accident rate statistics for various aircraft types to support market acceptance. Additionally, noise and exhaust emissions were analyzed for their impact on urban communities. The authors conducted their evaluation based on multiple factors, including environmental considerations, aircraft performance, certification requirements, and airworthiness standards set by the FAA and the European Union Aviation Safety Agency (EASA), all of which play a significant role in community and market acceptance.

In [58], Wu H. et al. designed a large-scale experiment to evaluate the acoustic behavior of a UAM rotor blade. They conducted tests inside an anechoic chamber measuring 8.1 m in length, 6 m in width, and 5.1 m in height. The team fabricated two custom blades with a 0.5 m radius to assess both aerodynamic loading and noise emissions; blade rotational speeds varied between 600 and 800 rpm. A zig-zag turbulator was installed on these blades to disturb the boundary layer. Compared to a smooth-surface reference, the turbulator produced only a slight change in rotor noise, indicating its effect on the blade’s boundary layer development. Surface roughness applied to the blades demonstrated measurable noise reduction under turbulent boundary-layer conditions. Ultimately, the measurements revealed approximately a 3 dB decrease in high-frequency, as well as broadband noise both upstream and downstream of the rotors when operating at 1800 rpm.

In [59], Zhong, S. et al. measured the propeller acoustics and aerodynamics of the UAM at an aerodynamic facility of the Hong Kong University of Science and Technology (HKUST). The measurement analysis and high-fidelity simulations aid in forming a comprehensive database that can facilitate the growth of physics-oriented noise prediction models. They utilized a highly efficient implementation of the boundary element method for noise scattering caused by the fuselage. This method was employed to analyze the influence of the UAM layout on directivity patterns that are projected to far-field receptors, utilizing advanced Gaussian beam tracing and considering factors such as moving source effects, refraction, atmospheric attenuation with complex boundary absorption, and reflection to achieve low-noise planning. In [60], a noise impact prediction assessment was conducted using the acoustic ray tracing and Gaussian beam methods of a quad-rotor UAS flying at a fixed position in a highly dense urban environment. The feasibility of the proposed noise assessment method was verified by computational aeroacoustics.

In [61], Kim, Y. et al. proposed a deep learning-based noise propagation prediction model that aids in estimating the noise impact of UAM in urban settings. A noise hemisphere was developed by using a multi-rotor noise assessment layout that determines the noise levels. The research calculated the noise directivity in a randomly generated 3D urban area using ray tracing, considering factors like atmospheric attenuation and multiple reflections. To train the neural network, they used 45,000 2D noise maps. The findings show good accuracy with the RMSE being only 2.56 dB lower than that of the ray-tracing method while reducing the computation time by more than 1800 times. This model was tested using the UAM flying and landing scenarios at the vertiport.

In [62], Casalino et al. evaluated the noise impact of eVTOL aircraft and described the computational workflows. They developed an operational model of the eVTOL aircraft, which operates over flat terrain. Aerodynamic noise was estimated using a high-fidelity CFD solver—SIMULIA PowerFLOW, which employs a Lattice Boltzmann simulation framework analogous to a very-large-eddy approach. In addition, the Ffowcs Williams and Hawkings analogy was used to determine acoustic signals on a hemispherical surface surrounding the vehicle. This hemisphere-based acoustic modeling then drives the calculation of ground-level noise. For optimization analyses, multiple trimmed flight trajectories are evaluated to illustrate how both the vehicle’s aerodynamic trim state and its instantaneous position influence noise during overflight scenarios. Each trajectory is characterized by parameters such as rotor rotational speed, waypoint timing sequences, propulsion-unit tilt angles, and overall vehicle pitch angle. A comprehensive noise–hemisphere database is assembled by running a large number of transient flow simulations over a broad spectrum of flight conditions. The trajectory’s impact on noise is described in association with the flow physics of a few specific conditions that occur during the maneuvers, showing the significance of multiple vortex–body interactions.

In [63], Fuerkaiti et al. presented a new propagation framework based on Gaussian Beam Tracing (GBT) that integrates three essential elements of UAM noise modeling: detailed source directivity, the influence of three-dimensional terrain variations, and atmospheric conditions affecting sound propagation. The approach takes a precomputed noise sphere as input and propagates it through an inhomogeneous atmosphere while incorporating realistic 3D terrain features. Validation is performed by comparing finite-element solutions of the acoustic wave equation under diverse frequencies, terrain shapes, and meteorological conditions. These inputs are then applied to simulate the noise footprint of hovering UAM vehicles positioned over a vertiport. The results demonstrate that weather factors alone can cause noise level differences up to 35 dB in regions of terrain-induced and refractive acoustic shadowing. In [64], Gill et al. introduced an empirical broadband rotor-noise model that delivers both overall sound pressure level (OASPL) predictions and associated spectral content. Their methodology employs an extensive collection of noise measurements spanning various rotor types, including small UAS rotors (e.g., DJI and APC), midsize propellers, and large helicopter rotors such as those on the CH-53A and CH-3C. By applying a numerical scaling strategy to critical input parameters, they derived an OASPL formulation. Gene Expression Programming (GEP) was used to construct the broadband acoustic-spectrum model. The model primarily relies on fundamental rotor performance parameters, providing an empirical framework for broadband noise prediction. The results demonstrate significant accuracy and contribute to advancements in the evolving field of UAM noise analysis.

In [65], the study focuses on redesigning UAM airspace and operational procedures in metropolitan regions to minimize the potential negative impact on communities. It also emphasizes flight trajectory management to ensure compliance with allowable acoustic levels while considering terrain characteristics. The author developed a UAM airspace route framework utilizing a demographic-data-optimized map for the DFW metropolitan area, identifying routes designed for noise mitigation. Additionally, noise contours were computed based on UAM demand levels to predict noise impacts, particularly in relation to population density and noise-sensitive facilities within the affected noise footprints.

In [66], Aalmoes et al. considered the noise impact of UAS and conducted a subjective study to calculate the perception of UAM noise in two scenarios with visual appearance and without visual appearance. The simulation model was constructed in two situations: a busy urban area and a more ambient area in Amsterdam, Netherlands, by utilizing a controlled virtual reality environment using the Visual Community Noise Simulator (VCNS). The research also compared two different city environments with distinctive ambient background noise levels. The visual perception influence was checked by comparing the reaction to noise stimuli with and without visual appearance. The authors composed four hypotheses: (i) It is expected that UASs are perceived as less annoying if there is a visual presentation as well. (ii) In louder urban settings, UASs are similarly considered less annoying compared with ambient urban settings. (iii) Familiar sounds have the same annoying level as the UAS if represented with UAS visualization, (iv) UAS annoyance is related to attitude towards UASs and sound sensitivity. In [67], Can et al., an interdisciplinary group of seven, presented a review of the potential noise impact of urban mobility. They identified major mobility trends, including societal transformations and emerging transportation modalities, as having the greatest effect on global noise levels. From their collective analysis, they derived novel perspectives, such as (i) establishing integrated modeling frameworks that couple urban form, transportation dynamics, and acoustic modeling to evaluate how redevelopment initiatives influence noise, incorporating interdisciplinary studies with environmental economists and geographers to ultimately elucidate significant social implications; (ii) the creation of unified noise indicators that can handle various noise sources and specific demands such as tranquility (state of being calm) and undisturbed sleep for more accurate impact assessments; (iii) emphasizing the importance of researching the potential impacts of new transport modes to anticipate and manage future noise environments; and (iv) encouraging new governance models that involve all stakeholders in the design of urban sound environments, leveraging data from smart cities and developing advanced decision-making methods to mitigate noise.

In [68], Tan et al. evaluated the practicability of conducting low-noise UAS flights in urban micro-environments. They employed a simulation framework to predict UAS acoustic footprints during realistic flight scenarios and applied a heuristic scheme to refine trajectory planning. Their methodology incorporates a source characterization technique to generate an accurate UAS acoustic model and utilizes Gaussian Beam Tracing for outdoor noise propagation under specific authentic environmental conditions. A few case studies were conducted in these simulations in a residential community in the metropolitan region that evaluated the noise exposure from various flight paths. After validating the proposed approach, noise reduction was achieved on en-route flight paths compared to the benchmark shortest-distance path.

In [69], Li et al. examined both tonal and broadband noise effects for UAM multi-rotor blades. Earlier research on quiet helicopter rotor configurations, which varied tip speed and blade count, investigated broadband and tonal noise under specified mission profiles. The authors applied blade element theory combined with a dynamic inflow model and moment–trim analysis to compute rotor aerodynamics during edgewise forward flight. Using the PSU-WOPWOP tool, they derived loading noise and thickness noise via lifting-line distribution and dual compact thickness formulations. The forward-flight functionality was implemented in UCD-QuietFly, enabling predictions of broadband noise, trailing-edge noise, bluntness noise, and airfoil stall noise. Human annoyance metrics were quantified through psychoacoustic performance indicators such as fluctuation and roughness. The paper also reports relative differences between broadband and tonal noise across various operational configurations and rotor designs. Their findings indicate that broadband noise dominates for rotors with low tip speeds and fewer blades, whereas tonal noise prevails at higher tip speeds. From a psychoacoustic standpoint, designs featuring low tip speeds and increased blade count are considered preferable.

In [70], Tan et al. introduced a cloud-based simulator for assessing the noise impact of UAM. A propeller noise prediction model was developed that computes the sources of the noise and long-distance propagation using the GBT method. This study also covers a few recommendations, such as a user-friendly interface and platform that benefit the user in defining the operational conditions and vehicle layers. To display the simultaneous noise distributions of the flight operations, advanced interpolation methods and autonomous learning algorithms are needed to accelerate computational efficiency. To check the noise impact on receptor perception, noise metrics are essential for further analysis. Also, a virtual flight simulator can be generated, and then the noise impact during each flight under atmospheric conditions can be predicted.

In [71], Antcliff et al. provided a report outlining several assumptions for NASA’s UAM reference vehicle. That document identifies key research directions for next-generation UAM platforms, such as designing low-noise rotors optimized for edgewise flight, exploring stacked-rotor propeller arrangements, investigating deflected-slipstream concepts, evaluating ducted propellers, integrating solid oxide fuel cells (including systems fueled by liquefied natural gas), enhancing turboshaft engines, and advancing reciprocating engine technologies. Additionally, they constructed a transportation network-scale model to assess how these emerging technologies might influence overall system performance. In [72], Yuan J. et al. devised a unique approach to lessen the impact of noise on scattered ground receptors through path planning by utilizing the sound exposure proxy metric to measure the annoyance. The A* grid-based pathfinding optimal method was used to determine the best aircraft fly paths using a cost function based on local area sound footprints and background noise levels. The creation of ideal grid-based pathways, as well as the simulation of aircraft flight trajectories with noise exposure between flights, allow for the worldwide reduction of peak and total sound exposure for diverse aircraft. This simulation model can be integrated with other alternative approaches to model annoyance, ambient soundscape, and cost weighting for future operations of the UAM.

In [73], Cho, H. et al. investigated a noise-lessening landing approach for a multi-rotor UAM vehicle that uses a comprehensive multi-rotor noise assessment (CONA) framework. This study aims to identify the low-noise conditions of multi-rotor aircraft and to mitigate the ground noise outside the landing areas. The CONA framework can predict the noise for RPM-controlled multi-rotor settings. The parameterized Boddies wake model was employed with regression analysis that helps to check aerodynamic interaction and noise impacts for various landing scenarios, which are illustrated for quad-rotor UAM vehicles. The landing conditions considered are vertical descent and high- and low-noise conditions. The results indicate that low-noise conditions can avoid intense aerodynamics with a reduction in the tip Mach number, a high flight path angle, and forward speed, and these measures can help reduce the areas that are affected by noise.

In [74], Ko, J. et al. researched the impact of noise on the surrounding community from the UAM landing operations. Incorporating wind profiles by applying numerical solutions is very important to verifying UAM noise exposure in urban environments. The research focused on examining the acoustic exposure that results from flight control mechanisms, particularly different trim conditions that cause variation in wake interactions under wind influence. This study covers the various wind profiles, which include ideal steady-state atmospheric body layer (ABL) configurations and various wind guts. Noise variations were observed at low wind speeds under ideal wind scenarios for different landing operations. A nonlinear nature was observed for noise impact in the psychoacoustic domain because of various changes. The Dryden wind turbulence model was employed to make a model of the ABL profiles and wind guts that focuses on the high impact of noise and achieves optimal landing operations. The changes in acoustic signals were observed by performing the analysis and using spatial-averaged noise metrics. The sound exposure level difference between using the ABL model and a wind gust model for landing operations was minor (less than 5 dBA), and in psychoacoustic annoyance, it reached up to 40% compared to no-wind conditions.

Figure 4 presents a taxonomy of noise prediction and mitigation strategies for UAM. The hierarchy is structured around three main research objectives: (i) noise prediction approaches, (ii) mitigation techniques, and (iii) community acceptance and policy measures. Within each branch, methods are categorized based on their modeling fidelity, intervention type, or stakeholder interaction level. For example, noise prediction includes both high-fidelity CFD simulations and data-driven learning models, while mitigation strategies span from rotor design to regulatory corridor planning. Community integration is categorized by public engagement tools, regulatory frameworks, and subjective perception studies. This organization aims to reflect both the technical depth and interdisciplinary nature of the field. This diagram provides an integrated view of the diverse research efforts aimed at addressing noise challenges in UAM operations.

In [75], Ahuja V. et al. incorporated detailed acoustic prediction techniques into a surface-vorticity solver, referred to as FlightStream, to improve design capabilities for aircraft engineers and to model noise emissions from UAM vehicles. The developed solver framework includes a surface-vorticity panel method augmented with viscous boundary layer corrections, offering a rapid tool for conceptual vehicle layout and aerodynamic evaluation. Additionally, it integrates the Farassat F1A acoustic formulations into the solver, delivering intuitive functionality suited for integration within contemporary engineering software while maintaining computational and resource efficiency. The capabilities were demonstrated systematically in three use cases that consisted of a single-propeller and six-propeller Joby’s S4 eVTOL, as well as an eight-propeller Kittyhawk KH-H1 distributed electric propulsion aircraft. The results show that an enhanced flow-acoustic analysis can be achieved that can aid in the design of future aircraft. In [76], So, M. J. et al. developed noise priority routes using the AEDT tool and population data for the administrative district to minimize the number of people affected by noise, and noise analyses were performed. The potential reduction was assessed in noise exposures compared to the people affected by noise on the shortest routes. This analysis was based on the helicopter model due to the lack of eVTOL PAV developer’s data. The results found that the noise-priority route, which minimized noise exposure, was more efficient than other routes.

In [77], Czech, J. et al. estimated the noise impact of the AAM on community acceptance and adoption. The research team analyzed the significance of the noise and presented a noise framework. The authors utilized a simulation model and the latest state-of-the-art method combined with laboratory data to input the model regarding noise sources, which is part of the community integration tool of the AAM operations. The noise exposures were estimated in this framework by utilizing the trajectories of AAM, and simulations were performed in the Advanced Acoustic Model. This paper used multiple AAM cargo application use case operations in the midwestern areas of the United States. The results can help in estimating the noise impact of the proposed operations and can provide better noise impact results by having the ability to vary temporal and spatial granularity, accommodating different types of eVTOL vehicles.

In [78], Li, Y. et al. analyzed the noise generated by a hexacopter design using high-fidelity eddy simulations coupled with solvers based on the Ffowcs Williams and Hawkings acoustic analogy. The hexacopter possesses a total length of 4 m, features rotors with a diameter of 2 m, and supports a maximum takeoff mass of 500 kg. Numerical assessments were performed on both the configuration and on three simplified variations that minimize individual components. This methodology helps to isolate each component’s contribution and elucidate its aerodynamic or acoustic interdependencies. The findings indicate that rotor–rotor interactions induce significant thrust fluctuations, resulting in a pronounced increase in tonal noise across the extensive frequency spectrum. Furthermore, thrust variability was observed as a consequence of interactions between the rotors and their support arms, producing additional tonal noise at higher-order harmonics of the blade pass frequency.

In [79], Miranda, J. et al. studied the noise propagation for eVTOL aircraft by using the Volpe Transportation System Center. The noise source data were collected from two sources: quad-rotors and lift-plus-cruise aircraft. The authors conducted this noise propagation study by altering environmental elements that aid in assessing noise propagation, such as air, topography, and meteorological effects, for a specific flight trajectory in the California region. The results show that the lift-plus-cruise aircraft configuration has a larger sound footprint than the other configuration, but it has less noise exposure in the vicinity of the aircraft due to noise source characteristics. The significance of noise exposure comes from the geographical location, which raises or decreases noise exposure levels, as well as attenuation or shielding in hilly places. The temperature has an effect on sound levels away from the source, but it is less significant than the terrain. The wind gradients regarding noise propagation and sound exposure levels are also substantial, considering the omnidirectional source and wind directions.

In [80], Koehler M. et al. examined the problem associated with noise protection and its legal basis because of the major potential source of noise of the AAM operations and provided a summary of noise exposure results. The research team contributed to generating noise exposure mapping for performing simulations and assessing the impact of noise from existing noise research based on flight noise regulations and some modifications to it converging the eVTOLs. The team reviewed the Frankfurt flight noise index (FF1-2.0) due to the increased volume of air taxi noise compared to regular traffic noise. In [81], Bauer M. provided a comprehensive assessment of the noise impact on air taxi operations and contributed to the environmental challenges encountered by UAM operations. The author investigated noise reduction methods in more realistic aspects by altering flight levels and by providing multiple vehicle velocities. The results show that noise impacts can be observed not only near air taxi ports but also along en-route paths in regions that currently experience no aircraft noise.

In [82] Riley, T. et al. developed an acoustic framework analysis for assessing the noise from subscale data. Typically, the electric-driven rotors are used in eVTOL aircraft, and they exhibit strong tonal features because of their aerodynamic effects and motor noise. The research group introduced a technique that involves fitting a duct around the rotors to reduce noise and measure acoustic output by testing both open-rotor and ducted-rotor setups equipped with linear microphone arrays. Simulations generated sound data along predefined flight trajectories under realistic conditions, which were then evaluated using established noise assessment metrics. The study quantified how these modifications affect noise exposure for ground-based observers. Analyses included a comparison of Effective Perceived Noise Level (EPNL) and Sound Exposure Level (SEL) for both open-rotor and ducted-rotor configurations. Narrowband spectral results revealed that adding a duct broadens rotor tonal peaks and increased broadband noise, raising overall sound pressure levels during static tests. In contrast, the duct effectively attenuated discrete motor noise, a point source, unlike the spatially distributed aerodynamic noise source.

In [83], Vinh H. H. examined how daily population movement influences general aviation aircraft noise exposure and introduced an innovative air traffic assignment algorithm that incorporates this mobility data into noise evaluation. The primary aim was to decrease the count of individuals impacted by aviation noise through lowering sound exposure levels. This research emphasizes reducing noise consequences for specific persons instead of focusing solely on densely populated zones. The investigators developed a multi-objective optimization framework designed to support sustainable air transport operations. Within this model, trade-offs between noise annoyance and fuel efficiency were balanced by producing a Pareto set of optimal solutions. To demonstrate the practicality of their approach, the authors applied the model to operations at Belgrade Airport in Serbia. Their findings reveal a significant discrepancy between the estimated number of people annoyed when using static population distributions and the number derived from dynamic mobility patterns. Additionally, these annoyance estimates vary substantially when demographic residential locations are altered. This model achieved a sustainable reduction in noise and the number of people annoyed. In certain use cases, it acquired a gain of up to 77 percent while finding the balance between fuel consumption when compared to the reference case.

In [84], G. Xhenyu and colleagues proposed a comprehensive, fair methodology for regulating UAM traffic alongside its noise footprint within urban areas. Their framework integrates multiple noise-reduction tactics, forming a composite strategy that includes capping flight frequencies, enforcing high-altitude cruising corridors, and leveraging ambient noise masking techniques. The authors framed the challenge using a network control system perspective, constructing a multi-objective optimization formulation to orchestrate traffic flow in a multi-layer UAM network topology. The optimization aims to meet passenger demand, minimize noise, and conserve energy simultaneously. A social welfare function underpins their model, balancing efficiency and equity considerations for achieving both demand satisfaction and noise mitigation objectives under dynamic demand scenarios. This study was performed in Austin, and design trade-offs were performed by providing quantitative and visual analyses.

In [85], the authors focused on analyzing different noise prediction methods for eVTOL rotors, which are a significant concern for urban integration. The goal was to find methods that are both accurate and efficient by comparing various prediction tools based on different models, particularly for broadband noise, against experimental data. The study found that combining 2D RANS trailing edge flow parameters with the UCD-QuietFly software, specifically the updated multirotor version that includes amplitude modulation capability, produced the most accurate results. offered the best balance when benchmarked against experiments, though challenges remain in accurately predicting noise at certain frequencies. The paper details the methodologies used, including different levels of computational fidelity (2D and 3D RANS) and acoustic prediction tools like BPM and QuietFly, to assess their effectiveness.

A comparative summary of the reviewed noise prediction approaches is provided in

Table 1. Comparative analysis of noise prediction approaches for UAS/eVTOL.

Reference	Prediction Approach	Modeling Fidelity	Application Stage	Key Strengths	Limitations
Yunus  et al. (2022) [42]	Acoustic footprint model integrating Blade Element Momentum Theory with Ffowcs Williams–Hawkings (FW-H) acoustic analogy	Mid-fidelity (hybrid time/freq domain)	Concept design	Reduces runtime; adaptable for prop layout studies	Limited atmospheric effects modeling
Yunus  et al. (2023)  [45]	Gaussian beam tracer with terrain/wind effects	Mid-fidelity propagation	Concept design/ops planning	Captures terrain-induced shadowing; good speed–accuracy trade-off	Requires source directivity input
Li  et al. (2021) [52]	AIRNOISUAM for vertiport siting and exposure mapping	Mid-fidelity empirical	Ops planning	Direct link to population exposure mapping	Limited to modeled vehicle configs
Lee  et al. (2021) [55]	Multi-rotor broadband noise prediction	Semi-empirical	Concept design	Captures modulation differences in rotor configs	High-frequency noise underrepresented
Wu  et al. (2023)  [58]	Anechoic chamber rotor testing	Experimental	Component testing	High control over test variables	Lab conditions may differ from field ops
Kim  et al. (2024) [61]	AI-based noise propagation using ray-tracing dataset	Data-driven	Ops planning/real-time prediction	Extremely fast; low RMSE	Requires large training dataset
Casalino  et al. (2019) [62]	Lattice Boltzmann computational fluid dynamics + FW–H analogy	High-fidelity	Detailed design	Accurate near-field and far-field coupling	Computationally expensive
Fuerkaiti  et al. (2022) [63]	Gaussian Beam Tracing with terrain/atmosphere	Mid-fidelity	Ops planning	Handles complex terrain/wind scenarios	Assumes precomputed noise sphere
Gill  et al. (2024)  [64]	Empirical broadband rotor noise model	Empirical	Preliminary design	Simple inputs, scalable across rotor types	Limited to rotor noise only
Hok Kwan Ng (2022)  [65]	Demographic-data-based route prediction	Operational model	Ops planning	Integrates population data with acoustic contours	Static model; not reactive to live ops
Ahuja  et al. (2022) [75]	FlightStream vortex panel + FW–H	Mid-fidelity	Concept design	Rapid design iteration with acoustic feedback	Lower fidelity than full CFD
Miranda  et al. (2023) [79]	Environmental effects on noise propagation	Semi-empirical	Ops planning	Highlights terrain & meteorology impacts	Limited aircraft configurations
Li  et al. (2022) [69]	High-fidelity rotor interaction CFD + FW–H	High-fidelity	Detailed design	Isolates noise from each component	Very high computational cost
Hohuu  et al. (2019) [83]	Mobility-aware noise mapping	Operational model	Ops planning	Links population movement to exposure	Requires dynamic mobility datasets
Gao  et al. (2024) [84]	Multi-layer urban air mobility network optimization with noise constraints	Operational model	Airspace mgmt	Balances noise, demand, equity	Complex to implement

. This table highlights the core methodologies, application domains, and associated strengths and weaknesses of each technique.

Table 2. Comparative analysis of noise mitigation approaches for UAS/eVTOL.

Reference	Mitigation Approach	(Active, Passive Operational)	Application Stage	Key Strengths	Limitations
Mane  et al. (2024) [11]	Optimized propeller design, ANC systems, sound-absorbing materials	Passive and Active	Design/Retrofit	Reduces tonal and broadband noise, adaptable	Trade-off with efficiency; added complexity
Lotinga  et al. (2023) [12]	Regulatory adaptation, measurement refinements	Regulatory or Operational	Policy	Addresses perception and compliance gaps	Slow to implement; depends on governance
Rascon  et al. (2024) [13]	Trajectory optimization, propeller redesign, ANC	Operational, Passive or Active	Flight ops and design	Directly addresses source and path	Requires accurate noise mapping
Li  et al. (2021) [52]	Vertiport siting and optimized routing	Operational	Planning	Minimizes exposure to sensitive areas	Limited flexibility in urban layouts
Lee  et al. (2021) [55]	Increased rotor count for reduced amplitude modulation	Passive	Design	Reduces modulation effects	Higher broadband noise; structural complexity
Wu  et al. (2023) [58]	Blade surface treatments (zig-zag turbulator)	Passive	Design	3 dB reduction in broadband noise	Limited effect in some flow regimes
Kim  et al. (2024) [61]	AI-based noise-optimized routing	Operational	Flight ops	Real-time adaptation to conditions	Requires large data and model reliability
Hok Kwan Ng (2022)  [65]	Demographic noise-aware route design	Operational	Planning	Targets population density reduction	Static design; less adaptive to live ops
Ahuja  et al. (2022) [75]	Rotor geometry optimization via FlightStream	Passive	Concept design	Rapid trade-off analysis	Lower fidelity than CFD
Yuan  et al. (2024) [72]	The A (A-star) algorithm grid-based pathfinding minimizing noise exposure	Operational	Flight ops	Minimizes total and peak exposure	Path deviation may increase flight time
Cho  et al. (2023) [73]	Low-noise landing profiles (CONA framework)	Operational	Flight ops	Reduces landing zone noise footprint	Limited to multi-rotor ops
Ko  et al. (2024) [74]	Wind-profile-aware landing ops	Operational	Flight ops	Accounts for wind gust impacts	Requires weather integration
So  et al. (2020) [76]	Noise-priority route design	Operational	Planning	Effective vs. shortest-path routes	Needs accurate demand and noise data
Riley  et al. (2021) [82]	Ducted rotors for motor noise attenuation	Passive	Design	Reduces discrete tonal motor noise	Increases broadband noise from aerodynamics
Hohuu  et al. (2019) [83]	Mobility-based dynamic flight allocation	Operational	Airspace mgmt	Reduces affected population by up to 77%	Requires dynamic population data
Gao  et al. (2024)  [84]	Multi-objective network optimization	Op./Reg.	Airspace mgmt	Balances noise, demand, and equity	Complex to implement

presents a comparison of key noise mitigation strategies for UAS/eVTOL operations. It consolidates mitigation efforts from various studies, outlining their implementation context and relative effectiveness.

2.4. Software Tools

As the demand for drones and eVTOL operations increases, various software tools have been developed to assess and mitigate noise impacts. These tools are essential for simulating noise emissions, evaluating community noise exposure, and optimizing aircraft designs to comply with regulatory standards. For example, NASA’s OVERset grid FLOW solver (OVERFLOW) [86] is used for high-fidelity CFD-based noise simulation of rotorcraft, while the FAA’s AEDT tool supports integrated noise modeling for aviation scenarios [18,56], including fixed-wing and rotorcraft operations. Pennsylvania State University—Wake Optics Prediction With-Out Prediction Of Pressure (PSU-WOPWOP) [48] has been utilized in academic studies for predicting rotor noise using the Ffowcs Williams and Hawkings (FW-H) analogy. Tools like SIMULIA PowerFLOW [62] use Lattice Boltzmann Methods (LBMs) for detailed noise simulations in urban environments.

Table 3. Noise prediction tools for general aviation aircraft, UAS, and eVTOL aircraft.

Tool	Description	Key Features	References
FAA’s Aviation Environmental Design Tool (AEDT)	Tool used for modeling and assessing the environmental impacts of aviation operations, including noise, fuel burn, air quality, and emissions.	Comprehensive environmental analysis supports various scenarios; used for regulatory assessments.	[18,56]
NASA’s OVERFLOW, source code	Tool for fluid flows (air), plus pressures, forces, moments, and power requirements.	High-fidelity noise prediction, integrates with NASA’s aerodynamic tools, useful for detailed aerodynamic analysis.	[86]
AIRNOISEUAM	Novel software tool introduced by NASA for assessing noise exposures of UAM operations.	Fast-time computing, GUI interface, modular design, validated with R66 helicopter and six-passenger quad-rotor.	[53]
SIMULIA PowerFLOW	CFD-based tool for predicting aerodynamic noise and analyzing complex flow problems.	High-resolution simulations, accurate noise prediction, used in various aerodynamic applications.	[62]
PSU-WOPWOP	Analyzing rotorcraft noise.	High-fidelity acoustic analysis, widely used in rotorcraft industry, integrates with various aerodynamic data sources.	[48]
CREATE AV’S Helios	High-fidelity multidisciplinary computational analysis platform for rotorcraft aeromechanics applications.	Aerodynamics solutions using a dual-mesh paradigm (unstructured meshes + Cartesian meshes).	[49]
Visual Community Noise Simulator (VCNS)	Designed for visualizing and predicting the community noise impacts of aviation operations.	User-friendly interface, visual simulations of noise impacts, useful for community noise assessment.	[66]
Surface-Vorticity Solver or FlightStream	Surface-vorticity-based CFD tool used for aerodynamics and noise prediction.	Efficient aerodynamic simulations, accurate noise predictions, suitable for rapid analysis of complex geometries.	[75]
UCD-QuietFly	Physics-based method to predict multi-rotor eVTOL broadband noise for UAM.	Predicting broadband noise from multi-rotor vehicles.	[87]
ANOPP2	Tool for next-generation Aircraft Noise Prediction.	Integrates acoustic approaches for aircraft noise component prediction, propulsion system installation effects, and far-field sound propagation.	[88]
NASA Auralization Framework Advanced Plugin Libraries (NAF-APL)	Tool for creating audible sound files from numerical data to assess noise impact of air vehicles.	APL for UAM, integration for human subject testing, with perception-influenced design.	[88]

summarizes these and other tools, highlighting their core functionalities. These tools support noise propagation modeling, community exposure assessment, airframe and propulsion system acoustic evaluation, and route planning optimization for noise-sensitive urban environments. Detailed discussion and references for each tool can be found in the corresponding rows of the table.

3. Noise Impact Studies

This section presents research on UAS and eVTOL noise studies and community acceptance conducted by various countries, including Germany, South Korea, Spain, Turkey, and studies from the EASA.

3.1. UAS Noise Studies by Germany

In [89], Schuchardt et al. presented a comprehensive study commissioned by the German Environment Agency, conducted at the German Aerospace Center (DLR), on the environmental noise impacts of UAS operations, including air taxis and multi-rotor aircraft with a gross takeoff weight of up to 25 kg. The study assessed the UAS market, noise emissions, public acceptance, and regulatory implications. It highlighted that only limited noise evaluation methods exist for multicopter aircraft, that psychoacoustic research is mostly restricted to laboratory settings, and that the distinct tonality of UAS noise easily separable from other urban or natural sounds can potentially be mitigated through design improvements. The findings also underscore the need for action concerning the societal and environmental risks associated with urban air mobility [90]. They aimed to develop a smartphone app with three features: (i) UAM flight track data visualization; (ii) (objective) UAM noise measurements; and (iii) (subjective) UAM noise assessments. The testing of this app was performed at the DLR’s facility in Cochstedt City. The app can provide an opportunity to distribute the noise as fairly as possible among the residents of the surrounding community and provide an opportunity for acquiring flight routes and profiles.

The 2018 study titled “Acceptance of Civil UAS in Germany” employed computer-assisted telephone interviews (CATI) to assess public sentiment [2]. Infas GmbH, an applied social sciences institute in Bonn, Germany, carried out the survey, interviewing 832 individuals, with each call averaging 18 min. Respondents were selected via a random digital dial procedure that included both landline and mobile numbers, ensuring a sample representative of Germany’s demographic composition. This approach yielded a detailed snapshot of German attitudes towards civil UAS deployment. Seven primary concerns emerged: crime and misuse (89%), privacy infringement (84%), liability and insurance issues (76%), potential damages and injuries (75%), traffic safety (74%), animal welfare (71%), and noise pollution (52%).

In [91], researchers conducted a virtual reality experiment to examine how flight altitude, visual clutter, and UAS noise influence UAM acceptance. Forty-seven participants evaluated scenarios depicting an air taxi landing, with survey results indicating that flight height and environmental visual density significantly impacted acceptability, whereas UAS noise did not. Within the air taxi context, participants reported reduced concerns regarding noise and privacy. In [92], Eissfeld et al. reported findings from a nationwide study on civil UAS social acceptance, focusing specifically on how targeted information campaigns might improve public perceptions. Their conclusions underscore the importance of well-structured awareness initiatives and community engagement to integrate UASs into future urban sound environments.

In [93], the authors explored the role of gender on UAS acceptance. They performed a telephone survey in Germany with 832 participants. The Chi-square Automatic Interaction Detection (CHAID) analysis found that noise concerns were the most significant factor impacting female respondents’ sentiments about civil UASs, whilst male participants were more concerned with damages and injuries. The poll also discovered that males were generally less concerned about civil UASs than females, notably about noise, and investigated whether these gender variations might be explained by confounding variables like UAS experience.

3.2. K-UAM Studies by South Korea

In [1], the authors examined factors such as efficiency, reliability, stability, noise, performance, and certification technology standards for operating UAM systems in South Korea. The study also analyzed the requirements to ensure successful implementation. The South Korean government confirmed and announced the “Korean Urban Air Mobility (K-UAM) Roadmap” on June 4, 2020, aiming to commence commercial UAM services by 2025, during the 2nd Innovation Growth Strategy Meeting [1]. This survey provided units for evaluating aircraft noise, which vary by country. In South Korea, weighted equivalent continuous perceived noise level (WECPNL) is used. Globally, however, advanced aviation countries like the United States and European regions have adopted the day–evening–night average sound level (Lden) system. The WECPNL takes into account factors such as the number of flights, noise levels, duration, and the time of occurrence of the noise. On the other hand, the Lden method averages noise levels over the day, evening, and night, applying different weightings to each period to reflect their relative impact on human perception and disturbance.

3.3. UAS Noise Studies in Spain

In [94], the Observatorio Jurídico Aeroespacial published a proposal for the regulatory needs of UAM in Spain. This proposal addresses specific features of UAM noise and future issues, as well as certain noise measuring recommendations for UASs weighing less than 600 kg and operating in the low- and medium-risk categories. This introduces standardized methods for measuring noise to ensure consistent and comparable data across regions and vehicle types. The proposal used a report from the EASA that illustrates, “Noise is the second main concern expressed; the study indicates that the level of annoyance varies with the familiarity of the sound with familiar city sounds at the same decibel levels being better accepted; it also confirms that the distance, duration, and repetition of the sound impact its acceptance [5].” It is suggested that noise remains an urgent issue that could hinder AAM’s development. Therefore, there is a strong emphasis on both technological advancements and regulatory measures to limit noise emissions.

3.4. UAS Noise Studies in Turkey

In [95], a survey of 518 people from Ankara and Istanbul, the most populated provinces in Turkey, was conducted online to examine its society’s views of UAM. The results find that the public sees the UAM system as helpful, especially in emergencies with general acceptance for its use. This study highlighted six challenges of UAM, which are airspace management, security, safety, public acceptance, noise, and integration with existing land transportation networks. The study found noise to be the fifth highest factor affecting UAM public acceptance. The survey asked participants to respond on a 5-point Likert scale of strongly agree, agree, neutral, disagree, and strongly disagree with each prompt, as well as their average scores and standard deviations. The future noise impact of UAM was evaluated and received a mean score of 3.517 with a standard deviation of 1.0364. This indicates that participants have concerns about the increased noise levels associated with this mode of transportation. The high standard deviation suggests varying perceptions and sensitivities regarding this issue. It was also illustrated that mitigating the noise challenges can minimize environmental impact concerns and facilitate smoother transitions within this multimodal transportation system.

3.5. EASA Studies

In [5], the EASA, in collaboration with McKinsey & Company [96], conducted one of the most comprehensive societal acceptance studies for UAM in Europe between November 2020 and April 2021. The survey involved 3690 participants from six locations—Barcelona, Budapest, Hamburg, Milan, Paris, and the cross-border Öresund region—selected as potential early UAM markets. The methodology combined a large-scale quantitative survey, over 40 qualitative interviews with stakeholders, and a noise perception test with 20 residents. The results revealed ten key findings. EU citizens generally expressed a positive initial attitude toward UAM, particularly for community-benefiting use cases such as medical transport or connecting remote areas. Expected benefits included faster travel, reduced emissions, and improved connectivity. However, concerns emerged around safety, noise, and environmental impact noise being the second most cited issue. Participants reported greater acceptance for familiar urban sounds at equivalent decibel levels and emphasized that distance, duration, and repetition strongly influenced annoyance.

4. Challenges

The chapter provides challenges that need to be considered, reviewed, and then discussed to develop optimum solutions for noise prediction, reduction, and planning mechanisms. This includes three stages: design and operations, the challenge of community acceptance, and the optimized airspace routes. A separate section is also presented for regulatory recommendations and their challenges.

4.1. Design and Operations

The concept vehicles for air taxi operations were presented in [4], which serve as the basis for designing the eVTOL vehicle and guiding NASA for research activities enabling aircraft development support for emerging AAM aviation markets. This research presents a comprehensive conceptual design assessment that incorporates the payload design-space parameters, including occupant capacity (passengers and pilots), aircraft classification, mission ranges, and propulsion system options. Three distinct vehicle configurations were conceptualized: (1) a single-seat quad-rotor with electric propulsion capable of carrying a 250 lb payload over a 50 nm range; (2) a six-seat, side-by-side rotorcraft with hybrid propulsion that supports a 1200 lb payload and a 200 nm mission length; and (3) a fifteen-seat aircraft with turboelectric propulsion, accommodating a 3000 lb payload for a 400 nm range. In [4], the authors highlighted multiple noise-related design challenges, such as rotor–rotor interactions—specifically the aft rotors operating within the wake of the forward rotors, and overlapping side-by-side rotor wakes on the retreating sides, which can exacerbate blade–vortex interaction noise. To address these issues, the study proposes optimizing blade geometry and rotor spacing to reduce blade–vortex interactions and mitigate high-speed impulsive noise generation. Several source noise reduction technologies were presented in [23],showing significant potential for minimizing noise emission to UAM aircraft in design space. The noise metrics for rotorcraft can be established, and using low-tip-speed rotors might not suffice to meet these new demands. The active control of rotor noise has shown significant reductions in noise levels, ranging from 6 to 12 dB reduction through analysis, wind tunnel tests, and flight tests. Following are some key points which need to be considered during the design phase of UAS/eVTOL aircraft:

• Rotor Spacing: Adjusting horizontal, vertical, and axial distances between rotors to minimize aerodynamic interactions. Irregular rotor spacing (e.g., vertical stacking) remains largely unexplored.
• Blade Spacing: Typically even spacing of rotor blades. The fan-in-fin concept uses irregular spacing. Limited research on irregular blade spacing offers potential noise reduction.
• Blade Length: Conventional rotors have uniform blade lengths. Exploring different lengths for opposing blade sets could reduce noise, though it poses computational and design challenges.
• Rotor Phasing: Adjusting azimuthal positions of rotors to reduce noise through decreased radiation efficiency or directional superposition. This requires precise control and applies primarily to rotors operating at the same speed.
• Active Control: While active control has been extensively studied for conventional rotorcraft, UAM vehicles face challenges due to high operational frequencies. Small vehicles may use RPM control, whereas larger ones might require swashplates or individual blade control devices. Current on-blade controls lack sufficient authority.
• Exterior Liners: Using external liners or porous materials on the fuselage can mitigate noise from rotor–fuselage interactions, which are driven by unsteady loading.
• Electric Motor Noise: Isolating vibrations and reducing the acoustic radiation efficiency of electric motors is critical in UAM vehicles.

4.2. Community Acceptance

Noise pollution is a major issue, as the distinctive sounds of eVTOL can be more noticeable and disruptive than other urban noises, leading to public dissatisfaction and challenges in the successful implementation of AAM and achieving public acceptance. The limited availability of noise evaluation methods for multicopter aircraft in the domain of AAM and the fact that psychological investigations are mostly restricted to lab conditions complicate the understanding and mitigation of noise impact in real-world settings, except for one test conducted by NASA [97]. In different regions, varying standards for measuring and evaluating noise, such as WECPNL in South Korea and Lden in Europe and the US, add complexity to creating consistent and universally accepted noise regulations. The public concerns about noise are twisted together with other issues like privacy and safety, influencing the overall acceptance of UAM. Addressing noise challenges requires a multi-sided approach, including advancements in eVTOL aircraft design to reduce noise emissions, comprehensive noise measurement standards, and effective public communication and engagement tools. To this end [98], EASA published a noise standard level document for measuring the noise of eVTOL aircraft with its certification process. To make sure that environmental protection is taken into account when designing these new aircraft, EASA released the Environmental Protection Technical Specifications (EPTS) document. The EASA [5] findings underscore that, while the public perceives clear benefits, acceptance hinges on mitigating key risks, especially noise, through technology, regulation, and community outreach. This aligns with the broader literature on UAS and eVTOL noise perception, reinforcing the need for standardized noise metrics, context-sensitive mitigation strategies, and early stakeholder involvement in UAM deployment. The FAA AC 150/5000-9B [99] presents a few considerations that can be employed for community acceptance, such as introducing the community outreach process. Furthermore, public acceptance can be improved by incorporating community feedback into route planning decisions. Studies have emphasized the role of transparent communication, participatory sensing, and visual noise simulators to increase awareness and trust in UAS and UAM operations [66,89].

4.3. Optimized Airsapace Routes

The design of airspace routes for UASs and drones involves carefully planning flight paths to minimize noise impact while maximizing operational efficiency, particularly over residential areas, workplaces, and schools. Altitude optimization methods can help reduce noise exposure in sensitive areas by avoiding low-altitude flights over neighborhoods and promoting the use of quieter aircraft technologies. Routes may need to be adjusted, such as incorporating higher-altitude flight paths, to further mitigate noise. A standardized approach to route design is essential, as it can support regulatory bodies in establishing guidelines for noise-aware flight corridors.

Several studies have explored algorithmic approaches to airspace design, including the use of A* search [72], convex optimization frameworks [84], and demographic-data-informed routing [65]. These methods aim to compute low-noise flight trajectories by integrating noise contours, land-use data, and noise exposure metrics such as SEL and DNL. Recent works by Gao et al. [84] also introduced a multi-layer airspace structure and optimized traffic flow through vertically separated corridors to reduce noise annoyance on communities. The noise impact assumptions are a critical consideration in the early stages of the implementation in urban environments, particularly in densely populated areas. Involving the local community in the planning process and addressing their concerns about noise will be essential. Transparent communication about noise levels, flight paths, and mitigation strategies can help build public acceptance and trust. Assuming that multiple eVTOLs from different manufacturers, i.e., Joby [97] and/or Moog [27], flying together will create more annoying noise effects. This is due to the cumulative noise and the logarithmic nature of sound measurement, where the combined noise level is not simply the arithmetic sum of the individual levels but depends on the logarithmic relationship between them [100]. Therefore, accurate modeling and simulation, flight path design, and relative regulations of noise levels for multiple eVTOLs flying together are essential for predicting and mitigating noise impacts in urban environments. In summary, the development of optimized, noise-aware routes for UAS requires an interdisciplinary framework combining acoustic modeling, optimization techniques, airspace regulation, and human factors, ensuring a scalable and acceptable integration of UAM into urban environments.

4.4. Challenges Associated with Different Environmental Scenarios

Noise propagation and perception for UAS/eVTOL operations are highly sensitive to environmental conditions. Variations in terrain, such as dense urban canyons, open rural fields, and coastal regions, influence acoustic reflections, diffractions, and shielding effects, leading to spatially non-uniform noise footprints [60,63]. Meteorological factors, including wind profiles, atmospheric temperature gradients, humidity, and turbulence, can alter both the amplitude and directionality of sound propagation, sometimes amplifying noise in downwind areas or creating shadow zones upwind [74,79]. Seasonal and diurnal changes further impact background noise levels, affecting human perception and annoyance thresholds [66,67]. Additionally, environmental soundscapes differ across operational contexts: industrial zones may mask certain frequency ranges, while quiet residential or natural areas heighten sensitivity to even low-level eVTOL noise [65,66]. These variabilities present significant challenges for accurate noise modeling, regulatory compliance, and mitigation strategy design, necessitating scenario-specific prediction frameworks and adaptive operational planning [70,72].

5. Regulatory Recommendations

This section outlines the regulatory guidelines and recommendations for noise reduction efforts in general aviation that can be applied to the UAM system, which employs UAS and eVTOL aircraft. In [99], the FAA provides Advisory Circulars (AC) and guidelines for sound insulation programs (SIPs) near airports that involve measures such as installing noise-reducing windows, doors, and insulation in buildings to minimize the impact of aircraft noise on residents. These guidelines assist in the development and implementation of SIPs. The AC is intended to be used by the FAA, airport operators, airport sponsors, and airport consultants or contractors. It aims to assist in the development and management of sound SIPs designed to mitigate noise impacts on structures exposed to aircraft noise around airports. Since the Aviation Safety and Noise Abatement Act (ASNA) in 1979 [101] and the regulations for Noise Compatibility Programs (14 CFR Part 150) in 1984, the FAA has been instrumental in promoting airport noise compatibility planning. The FAA supports airports in reducing noise impacts through financial assistance for SIPs, including soundproofing residences and public buildings, acquiring surrounding properties, and funding noise mitigation research. The FAA has also presented the Aviation Noise Complaint and Inquiry Response System (ANCIR) [102], where citizens in the vicinity of airports, local businesses, and airport workers can submit aviation noise complaints. The goal of this web-based application was to demonstrate how the FAA may efficiently respond to noise concerns in a clear and repeatable manner that is responsive to the public while making optimal use of FAA resources.

Table 4. Summary of key noise-related regulations and guidelines for UAM/eVTOL operations.

Rules & Regulations	Key Noise Provisions	Relevance to UAS/eVTOL Noise	Challenges
Aviation Safety and Noise Abatement Act (1979) [101,104]	Establishes noise measurement system and compatible land-use planning; requires noise impact maps for airports.	UAM vertiports will require similar noise mapping in urban areas; residential proximity necessitates stricter standards.	Defining DNL metrics for distributed vertiports; measuring urban noise contours.
14 CFR Part 150 [105]	Establishes Noise Compatibility Programs (NCPs); procedures for noise-exposure map submission/review; stakeholder collaboration.	Apply NCPs to vertiports and drone corridors; scale mapping for multi-rotor and small UAS; identify noise-sensitive urban zones.	Adapting airport-scale maps to street-level detail; coordinating multiple vertiport operators.
AC 150/5000-9B (Sound Insulation Program) [99,106]	Advisory for SIP development; funding eligibility and implementation guidance.	Extend SIPs to buildings near vertiports; define envelope standards for drone-noise insulation.	Retrofitting urban structures is costly; coordination with building codes.
ANCIR (Noise Complaint System) [102]	Standardized logging and response to aviation noise complaints.	Integrate UAS/eVTOL noise into ANCIR; link with participatory noise-sensing (PNS) apps.	Training operators to triage drone vs aircraft complaints.
FAA Order 5100.38 (AIP Handbook) [107]	Defines grant eligibility for noise-abatement under AIP; justification criteria for funding.	Include vertiport and drone-corridor mitigation in AIP; specify UAS noise-control criteria.	Competing priorities for airport vs vertiport grants.
NEPA (1969) [108]	Requires Environmental Impact Assessments for federal actions; must evaluate noise impacts.	Mandate noise-impact studies for new vertiports/UAM routes; embed low-altitude noise modeling in NEPA docs.	New vertiports trigger full environmental impact statement time and cost.
63 FR 1640 (1998 Final Policy) [109]	Limits mitigation eligibility based on DNL contour publication date.	Revise cutoff rules to cover communities near new vertiports; allow mitigation irrespective of construction date.	Balancing equity for existing vs new communities.

summarizes the key existing noise-related regulations and guidelines, highlighting the proposed adaptations needed to support UAS/eVTOL operations. The International Organization for Standardization (ISO) published a document for noise measurement in the UAS [103]. This document significantly contributes by providing a standardized approach to characterizing UAS noise across various operational conditions. It ensures comprehensive noise characterization by detailing procedures for measuring noise during critical flight phases such as hovering, takeoff, landing, and cruising. The document emphasizes the methods for capturing sound pressure signals and facilitating subsequent data analysis, such as extracting tonal noise components through narrowband noise spectra, and provides common noise metrics and computational procedures. It can serve as a valuable reference for manufacturers and regulatory bodies in assessing UAS noise, thereby promoting better noise management and mitigation strategies in the UAS for UAM. The manufacturers can use this ISO standard document to conduct the three types of test facilities for measuring noise levels, such as anechoic chambers, anechoic wind tunnels, and outdoor environments.

6. Conclusions and Future Scope

This paper surveyed noise prediction and mitigation methods relevant for UASs and eVTOL aircraft and reviewed the latest developments in the field, including—but not limited to—academic, industry, and government references. It examined software tools developed for drone and general aviation aircraft. These tools are instrumental in predicting acoustic emissions, supporting design optimization, and enabling effective noise simulations. Our survey paper reviewed international noise perception studies conducted in countries such as Germany, South Korea, Spain, EASA studies, and Turkey. These studies offer valuable insights into how communities respond to drone and low-altitude UAM operations, reinforcing the importance of stakeholder engagement and early communication to address acoustic concerns. The survey also outlined technical challenges associated with drone-based eVTOL design, including noise from distributed propulsion systems, operational routing limitations, and public perception barriers. Additionally, the relevance of existing FAA guidelines and the need for evolving regulatory frameworks were discussed in the context of UAS integration into urban airspace. Overall, this work underscores the importance of interdisciplinary research, community involvement, and policymaking to ensure the environmentally responsible and socially acceptable integration of UAM systems into modern transportation networks.

Future research should explore advanced noise prediction methods that integrate high-fidelity CFD with machine learning models for real-time noise estimation in dynamic urban environments [61,64]. Further work is needed on adaptive noise mitigation strategies, such as intelligent rotor control systems, ANC in distributed propulsion, and geometry-optimized propeller designs that respond to changing environmental conditions [11,14]. Additionally, environmental scenario modeling incorporating meteorology, urban morphology, and ambient noise masking can improve prediction accuracy and help develop scenario-specific operational guidelines [63,74]. Future studies could also investigate human-centric metrics, psychoacoustic assessments, and soundscape integration techniques to enhance public acceptance [66,72]. Lastly, developing standardized international frameworks for UAS and UAM noise certification, interoperable community noise monitoring networks, and participatory noise mapping platforms [51,67] will be critical to achieving sustainable and socially acceptable integration of UASs/eVTOLs into global airspace systems.