Aircraft Routing Research Articles

Integrated aircraft routing and cargo routing problem for combination airlines

The combination airlines operate both passenger aircraft and freighter aircraft to meet passenger and cargo demand. At present, combination airlines employ a sequential approach to allocating their capacity for passenger and cargo demand. Nevertheless, implementing an integrated resource allocation procedure has the potential to improve overall resource allocation efficiency. In this paper, we introduce an integrated model to help combination airlines integrate their aircraft routing and cargo routing decisions to maximize the expected overall profits derived from both passenger and cargo demand. We considered the stochastic nature of passenger baggage and proposed a set of individual chance constraints to ensure the robustness of the integrated solution. We reformulate the chance constraints using piecewise linear approximation to ensure solution efficiency. In addition, we proposed a column-and-row generation based solution approach that removes the through-connection related constraints at the beginning of the solution process and then adds the columns and rows during the iterations as needed. We proved that the proposed column-and-row generation approach can obtain an optimal solution for the LP relaxation problem. The model and the solution approach were tested in a number of scenarios obtained from a major Chinese combination airline. The computational results show that the combination airline can improve their expected profits by integrating capacity allocation. The results also demonstrated that the proposed column-and-row generation solution approach can decrease the solution time of the integrated model. These findings indicate that the model and the solution method are useful and efficient tools for combination airlines when planning their aircraft and cargo routes.

An integrated approach for an aircraft routing and fuel tankering problem

In the highly competitive airline industry, it is crucial for airlines to minimise operating costs and strengthen their competitiveness. Fuel costs constitute a substantial portion of airline operating costs and are directly related to airlines' profits. In this research, we support the airlines' fuel management by presenting a model that integrates aircraft routing and fuel tankering decisions. The effectiveness of fuel tankering can be enhanced by considering the aircraft routing decisions together, leading to a significant cost reduction for airlines. The integrated model is developed as a mixed-integer linear programming model. The symmetry-breaking methods and decomposition-based heuristic algorithm are also proposed to alleviate the computational burden. A set of computational results illustrates the significant cost-saving effects of the proposed model and the heuristic algorithm.

Hybrid electric aircraft design with optimal power management

The aviation industry has continuously improved its fuel efficiency over the last decades with innovations in technology, design and operation. Further improvements are becoming more difficult as current technology reaches complete maturity. Electrification of road vehicles is predicted to completely replace combustion engines in vehicles. As the efficiency and reliability of electric batteries and motors improve, so does the case for electric propulsion systems in aircraft. Jet fuel carries significantly more energy per weight than current state of the technology batteries, making complete replacement of fuel challenging. Hybrid electric propulsion, where both fuel and batteries are used to power propulsion systems could be feasible and improve fuel efficiency. Hybrid electric powertrains use electric power to reduce the power demands from the combustion engine. Electric batteries are discharged and charged during the operation based on power requirements.This paper presents an aircraft design method that includes optimized power management in the design loop. Power management determines battery discharging and charging throughout the design mission to reduce overall fuel consumption. Power management optimization provides the necessary feedback on battery and fuel performance to design battery pack size. This method is applied to regional aircraft, which typically fly the shortest routes of a commercial airline fleet. These short routes have proportionally longer climb and descent segments. These characteristics of regional aircraft routes lead to wider variations in power during operation, where hybrid electric powertrains are the most beneficial.The proposed hybrid electric aircraft design method finds that a serial hybrid electric regional aircraft could achieve significant fuel efficiency improvements to current regional aircraft. This result is in contrast of the current literature, which requires significant improvements to battery energy density, power distribution efficiency and electric motor efficiency to achieve similar fuel performance results. The hybrid electric regional aircraft does not outperform a traditional turboprop aircraft designed with identical objectives. The turboprop aircraft design improves even greater fuel efficiency improvements over current regional turboprop aircraft. This suggests two pathways are possible; the propulsion system of the next generation of regional aircraft could still be non-electric and achieve improved fuel burn improvements considering current technology or serial hybrid-electric powertrains configurations could be adopted in an expanded time horizon if optimistic technology development is done on battery power density to match the possible improvements of the proposed turboprop aircraft.

Method for constructing an aircraft route taking into account the terrain based on the integrated use of multi-agent algorithms

Method for constructing an aircraft route taking into account the terrain based on the integrated use of multi-agent algorithms

Aircraft routing using dynamic programming and reinforcement learning: A customer-centric approach

Over the past few decades, the airline industry has evolved into a sophisticated business, with conventional objectives such as operational efficiency, effective fleet assignment, flight punctuality, and equitable crew scheduling being the primary focus. Nevertheless, given the growing accessibility to customer feedback (either through public forums or private data), it is crucial to make business decisions that cater to the customers’ requirements in addition to conventional goals. Within aircraft operations, there is a series of decisions to be made, such as flight scheduling, fleet assignment, aircraft routing, and crew scheduling. After designating a fleet type to each flight leg, airlines ascertain the sequence of flight legs, or strings, to be operated by individual aircrafts, while complying with obligatory maintenance checks. This constitutes the aircraft routing problem.We introduce a novel approach to aircraft routing problem, by infusing traditional methodologies with insightful customer feedback to create a more responsive and customer-centric model. Unlike conventional techniques of constructing routes, in our work, we construct maintenance feasible strings. Traditional aircraft routing methodologies typically concentrate on minimizing propagated delay. However, by integrating customer feedback, more effective prioritization to minimize delays for flight legs with higher customer dissatisfaction can be achieved. We propose an optimization model that strives to minimize propagated delay while prioritizing flights based on customer feedback, thus resulting in customer-aware routes. To solve the proposed optimization model, we design an iterative alternating optimization scheme where feasible strings are first constructed from the pool of available flight legs using a string generation procedure, followed by solving an integer linear optimization problem over only the feasible set of strings obtained in the first stage. To construct feasible strings of flights at each iteration, we use two different methods. The first method is based on a dynamic programming approach which is useful for mean delay information. To handle practical cases where delays are uncertain, we propose a reinforcement learning (RL) approach to construct the strings. We provide experiments of our proposed methods on synthetic and real data sets and demonstrate the effectiveness of our RL model in comparison to dynamic programming based model. Our experiments affirm that routing decisions informed by customer feedback prioritize minimizing delays for specific flight legs, resulting in routes that are inherently customer-friendly.

Repair tolerance assessment for aircraft composite structures using Bayesian updating

Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures. A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraft based on Bayesian updating is presented. The method incorporates historical damage inspection data to determine the prior distribution of damage size, which is then updated with newly collected damage size data using Bayesian theory. Monte Carlo simulation is employed to investigate the probability of failure and estimate maintenance costs, considering various factors such as the frequency and timing of damage events, damage detection, structural strength, gust loads, and maintenance expenses throughout the lifecycle of composite structures. Safety and economic factors are considered to establish a lower threshold for repairs and an upper threshold for maintenance based on the occurrence of accidental impact damage. Verification of the effectiveness and feasibility of a quantitative assessment method for repair tolerance is conducted using damage statistics data from civil aircraft routes utilizing the structural skin panels of composite outer wing. The results demonstrate that the method proposed in conjunction with extensive simulations and full utilization of field damage inspection data can effectively simulate unexpected impact damage situations that may occur during civil aircraft service and evaluate the reliability and economic feasibility of the repair of structure. The research findings hold significant theoretical and practical value for the preparation of documents for continued airworthiness of composite structures, including structural repair manuals and maintenance programs.

An interval-valued estimation method of aircraft route carbon emission: A function of aircraft seat capacity and route flight time

In this paper, we introduce a novel approach that utilizes aircraft seat capacity and its route flight time to predict carbon emissions for aircraft manufacturers during the aircraft design stage. To address data fluctuations arising from the human-aircraft-environment-management system throughout the production and operation of the future-designed aircraft, we employ the interval-valued data type, replacing the traditional point data type, and construct an interval-valued regression model. Within the parameterized method framework, our programming model incorporates additional constraints to ensure the intersection of each interval-valued sample. This addresses the challenges significant data fluctuations pose, leading to improved fitting performance. Furthermore, the model is demonstrated to satisfy Kuhn-Tucker conditions for solvability, and the obtained regressed parameters exhibit favorable small-sample properties. An empirical study using airline operating data from China validates the prediction effectiveness of the programming model. Finally, based on these predictions, we provide applications and suggestions for decision-makers aimed at reducing CO2 emissions.

Exploration of optimal control based surrogate modeling as a basis for fuel efficient 4D aircraft routing on graphs

AbstractFuel efficient coordination of aircraft operations in the Terminal Maneuvering Area of large airports can contribute to the reduction of the environmental impact of air traffic. To exploit the full potential of the air traffic system, coordinated routing in the Terminal Maneuvering Area, runway assignment and scheduling need to be optimized considering detailed models of the aircraft dynamics and performance. Due to the inhomogeneous nature and level of detail of these combined discrete and continuous decision problems, the optimization of the overall operations poses significant challenges. As part of an integrated approach to the solution of this hybrid problem, this work explores the generation of surrogate models for the fuel consumption of individual aircraft using optimal control methods to exploit the physical capability of the aircraft within an operationally permissible envelope. The surrogate models approximate the predicted fuel consumption of a given point-mass aircraft model on short generic trajectory segments. The trade-off between flight duration and fuel consumption on such segments is analyzed, focusing on the influences of initial aircraft mass, altitude, distance, mean climb angle, along-track wind velocity and linear wind shear. An extensive description of the optimal control-based data generation and surrogate modeling methodology is followed by a discussion of the effects of parameter variation. Based on an illustrative case study, the applicability of the approach is critically analyzed.

Integrated commercial and operations planning model for schedule design, aircraft rotation and crew scheduling in airlines

AbstractThe commercial and operations planning in airlines has traditionally been a hierarchical process starting with flight schedule design, followed by fleet assignment, aircraft rotation planning and finally the crew scheduling. The hierarchical planning approach has a drawback that the optimal solution for a planning phase higher in hierarchy may either be infeasible for the subsequent phase or may lead to a sub‐optimal overall solution. In this paper, we solve a profit‐maximizing integrated planning model for clean‐sheet “rotated” schedule design with flight re‐time option and crew scheduling for a low‐cost carrier (LCC) in an emerging market. While the aircraft rotation problem has been traditionally modeled in the literature as a daily routing of individual aircraft for maintenance requirement, in this work we address the requirement of planned aircraft rotations as part of schedule design for LCCs. The planned aircraft routing is important in our case to create as many via‐flights as possible due to the underserved nature of the emerging market. We solve this large‐scale integer‐programming problem using two approaches – Benders Decomposition and Lagrangian Relaxation. For Lagrangian Relaxation, we exploit the special structure of our problem and intuitive understanding behind the Lagrangian duals to develop a multiplier adjustment approach to find an improved lower bound of integrated model solution. The crew‐pairing sub‐problem is solved using column generation through multi‐label shortest path algorithm followed by branch‐and‐price for integer solution. We test our solution methodology on a flight universe of 378 unique flights for different problem sizes by varying the number of aircraft available for operations. Our computational results show that within a reasonable run time of few hours both the approaches, Benders Decomposition and Lagrangian Relaxation, are successful in finding lower bounds of the integrated model solution, which are higher than the solution of traditional hierarchical approach by 0.5%–2.5%. We find Lagrangian Relaxation methodology to usually attain an improved solution faster than the Benders Decomposition approach, particularly for large‐scale problems.

Combinatorial Benders decomposition for the operational aircraft maintenance routing problem

The operational aircraft maintenance routing problem (OAMRP) assigns the aircraft in a fleet to scheduled flights while satisfying maintenance requirements. Aviation rules enforce the aircraft to undergo maintenance before the maximum accumulated flight hour is exceeded. Due to the dynamic environment of the airline industry, aircraft routes considering operational requirements become definite a few days before their operation. Thus, disruption-prone plans entail the development of fast solution algorithms. We propose a combinatorial Benders decomposition algorithm to solve this problem. The proposed algorithm is enhanced with valid inequalities to improve convergence. In addition, we implement a branch-and-Benders-cut algorithm. A computational study is conducted to quantify the effectiveness of the derived inequalities. We also provide an extensive comparison to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm outperforms other approaches in terms of solution quality and computation time.

Sensor Network Solutions for Aircraft Route Scheduling and Parking Allocation with Localization and Synchronization

The Wireless Sensor Network (WSN) is the modernized version of the sensor networking, earlier the concerned networking system used to be wired. The wireless and modified features have been able to increase the efficiencies of the networking in routing scheduling of aircraft and allocating the parking. This new approach to the WSN system is not much different from the typical sensor networks framework that contains sensors, a communication system, and a controller. Instead of a communication system, a wireless protocol is applied within the sensor network. The smart system of parking has effectively implied due to its hugely innovative as well as in-ground sensors. This can monitor individual spaces of parking and able to relay the status of occupancy to the smart-sport gateways. Then, this can send the live status data to the platform of the smart cloud. This entire process enables the real-time parking data to be accessed and observed on multiple devices.

Aircraft fleet optimization by the simplex method

The transport system efficiency and the quality of its operation is organically connected with the improvement of air transportation technical means on the basis of modern science and technology. One of the directions of further aviation transport system development is the creation of methodological basis for optimal aircraft operation. Therefore, the increase in aircraft efficiency is one of the important tasks, as its improvement is associated with savings in material and financial resources, which affects the fleet structure and the process of passenger traffic. The article is intended to substantiate the method of optimizing the use of airline aircraft by means of mathematical apparatus. It has been found that the mathematical model structure is required to form an optimal aircraft fleet. A variant of aircraft fleet formation has been proposed taking into account the mathematical model, built on the basis of linear programming, namely, using the simplex method. It has been determined that the choice of optimal aircraft options is greatly influenced by the value of the parametric series. To obtain a reliable result, the aircraft loading must be 100%, otherwise the error grows due to coefficients at unknown target function. The proposed method of determining the optimal fleet with different types of aircraft and air traffic routes, taking into account the conditions of their usage, allows to recommend airlines to use aircraft with the highest efficiency.

A Novel Decision Support Framework for Multi-Objective Aircraft Routing Problem

A Novel Decision Support Framework for Multi-Objective Aircraft Routing Problem

Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment

Abstract. A significant share of aviation's climate impact is due to persistent contrails. Thus, avoiding the creation of contrails that exert a warming impact is a crucial step in approaching the goal of sustainable air transportation. For this purpose, a reliable forecast of when and where persistent contrails are expected to form is needed (i.e. a reliable prediction of ice supersaturation). With such a forecast at hand, it would be possible to plan aircraft routes on which the formation of persistent contrails can be avoided. One problem on the way to these forecasts is the current systematic underestimation of the frequency and degree of ice supersaturation at cruise altitudes in numerical weather prediction due to the practice of “saturation adjustment”. In this common parameterisation, the air inside cirrus clouds is assumed to be exactly at ice saturation, while measurement studies have found cirrus clouds to be quite often out of equilibrium. In this study, we propose a new ice-cloud scheme that overcomes saturation adjustment by explicitly modelling the decay of the in-cloud humidity after nucleation, thereby allowing for both in-cloud super- and subsaturation. To achieve this, we introduce the in-cloud humidity as a new prognostic variable and derive the humidity distribution in newly generated cloud parts from a stochastic box model that divides a model grid box into a large number of air parcels and treats them individually. The new scheme is then tested against a parameterisation that uses saturation adjustment, where the stochastic box model serves as a benchmark. It is shown that saturation adjustment underestimates humidity, both shortly after nucleation, when the actual cloud is still highly supersaturated, and also in aged cirrus if the temperature keeps decreasing, as the actual cloud remains in a slightly supersaturated state in this case. The new parameterisation, on the other hand, closely follows the behaviour of the stochastic box model in any considered case. The improvement in comparison with saturation adjustment is largest if slow updraughts occur in relatively clean air in models with a high spatial and temporal resolution. We conclude that our parameterisation is promising but needs further testing in more realistic frameworks.

Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment

Airlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of addressing these problems independently, potentially leading to locally optimal outcomes due to their interconnected nature. The second challenge lies in the inherent uncertainty associated with parameters like demand and non-cruise time. On the other hand, airlines can employ a strategy known as codesharing, wherein they operate shared flights, in order to minimize these challenges. In this study, we introduce a novel mathematical model designed to optimize flight planning, fleet assignment, and aircraft routing decisions concurrently, while accommodating for codesharing. This model is formulated as a three-stage non-linear mixed-integer problem, with stochastic parameters representing the demand and non-cruise time. For smaller-scale problems, optimization software can effectively solve the model. However, as the number of flights increases, conventional software becomes inadequate. Moreover, considering a wide array of scenarios for stochastic parameters leads to more robust results; however, it is not enabled because of the limitations of optimization software. In this work, we introduce two new simulation-based metaheuristic algorithms for solving large-dimensional problems, collectively called “simheuristic.” These algorithms integrate the Monte Carlo simulation technique into Simulated Annealing and Cuckoo Search. We have applied these simheuristic algorithms to various problem samples of different flight sizes and scenarios. The results demonstrate the efficacy of our proposed modeling and solution approaches in efficiently addressing flight scheduling, fleet assignment, and aircraft routing problems within acceptable timeframes.

Airline scheduling optimization: literature review and a discussion of modelling methodologies

Abstract The rapid development of civil aviation in recent decades has not only led to increasing competition among airlines, but also to the rise of irregularities, with challenges concerning the improvement of regulations and schedules under the maximization of profitability. Consequently, the airline scheduling optimization problem has received significant research interest as the foundation for an efficient deployment of airline resources and meeting market demand under complex operational requirements. In this paper, we dissect fundamental airline scheduling problems by reviewing thirteen representative mathematical models for schedule design, fleet assignment, aircraft routing, crew scheduling subproblems and their potential for integration. In contrast to existing review studies on airline scheduling problems, our main contribution lies in the introduction of state-of-the-art mathematical models with a specific focus on integration and robustness. In addition, we highlight a set of promising, yet challenging, directions for future research in this domain.

Aircraft routing clusters and their impact on airline delays

Airlines operate thousands of aircraft routings every day with a multitude of options to connect single flights integrated in a routing. Since airline delays entail significant costs for airlines and to societies, we aim to identify how far individual aircraft routings differ in on-time performance. We apply a novel approach to cluster aircraft routings and compute the routing strength to quantify the structure of aircraft routings. We then develop an econometric model to analyze the impact different routing clusters have on airline delays. As a key finding, routings with higher strengths tend to cause more delays. Our clustering of routings shows that hybrid routing structures and intense shuttling of flights between a few airports in a routing increases delays compared to routings structured star- or string-like. We also find major differences across airlines in the way routings are structured.

Evaluation of effective dose for gamma-rays of terrestrial gamma-ray flashes in aviation: spectral- and atmosphere-effects

Terrestrial Gamma-ray Flashes (TGFs) are intense and fast gamma-ray emissions, energy up to 100 MeV, generated within the Earth's atmosphere at altitudes below 20 km. Satellite observations indicate the TGFs are not a rare phenomenon, being produced on average at one per thunderstorm, yielding a global production rate of ≈35 TGFs/min within a latitude band of ±38°. Furthermore, the TGFs may coincide with the flight altitudes for several commercial aircraft routes, typically within the 10−14 km range, and thus cause passengers/crews unexpected exposure to large fluxes of high-energy ionizing radiation, namely for distances of the order of km.Herein, we introduce a novel approach based on a simple analytic/numeric methodology to estimate the TGFs gamma-ray emissions effective doses based on the differential fluence of photons at aircraft altitude and tabulated effective dose conversion coefficients versus the photon energy. For the first time, we examine the effective dose dependence on TGF's gamma-ray energy spectrum models found in literature and the atmosphere mass thickness crossed by the gamma-rays. Additionally, since the real irradiation geometry of the whole body in aircraft might be a combination of the several idealized geometries used in radiation protection, we considered beyond the typical anteroposterior (AP), the posteroanterior (PA), and the rotational (ROT) geometries as representative irradiation geometries in exposure to TGF gamma-ray's emissions.We calculated the effective dose versus the distance between the TGF site and the aircraft altitude (12.5 km) within the 0.1−5.0 km range and atmosphere mass thickness within the 3.3−213 g cm−2 range. Additionally, we calculated the average effective dose per fluence versus the atmosphere mass thickness crossed by the gamma-rays. At a distance of 1 km (34 g cm−2) and for the typical exponential cut-off power-law model (α = 1, EC = 7.3 MeV), the effective dose was within the 0.18−0.19 mSv range, being the respective average effective dose per fluence within the 10.8−11.6 pSv cm2 range. Moreover, for the variant of the exponential cut-off power-law model (α = 1, EC = 7.3 MeV, Emin = 1 MeV), the effective dose was within the 0.42−0.46 mSv range, being the respective average effective dose per fluence at 34 g cm−2 within the 12.7−13.6 pSv cm2 range.Generally, the data obtained agree with the scarce data found in the literature for similar conditions, evidencing the effect of the parameterization of the TGFs gamma-rays energy models, which may result in a 2- to 10-fold effective dose escalation. For the typical exponential cut-off power-law model (α = 1, EC = 7.3 MeV), the data obtained for an aircraft that passes through the vicinity of one TGF, with a distance of 1 km, points to an increase of the aircraft crew and passengers' doses of few tenths mSv over the cosmic radiation dose of few tens μSv along a typical flight of 6 h duration, representing ≈20% and ≈1% of the ICRP-2007 annual reference levels for passengers (1 mSv) and aircraft crew (20 mSv), respectively. More, with a distance of 0.5 km, the effective dose value is comparable with the annual dose for the aircraft crew due to the cosmic radiation for a typical yearly flight time of 500 h, representing ≈130% and ≈7% of the ICRP-2007 annual reference levels for passengers and aircraft crew, respectively.

From passenger itineraries to climate impact: Analyzing the implications of a new mid-range aircraft on the global air transportation system

Aircraft design usually focuses on utilizing new technologies to improve mission performance of individual aircraft and, thus, reduce cost, emissions and climate impact of the global air transportation system (ATS). However, efficiency gains in the ATS can also be achieved by improving the alignment of payload–range capabilities and aircraft–route assignment of the overall fleet. Therefore, this article analyzes the implications—from passenger itineraries to climate impact—of a new mid-range aircraft (NMRA), which addresses the current lack of modern, designated mid-range aircraft in commercial production. The analysis is based on simulating and comparing two future scenarios with similar technology levels: one with and one without a NMRA. The core of the applied novel simulation framework is highly integrated models for passenger itinerary preferences, fleet development and aircraft–route assignment. Models for passenger demand as well as aircraft trajectories, emissions and climate impact complement the simulation framework. The results show that introducing a NMRA to the ATS improves the quality of travel for passengers and the alignment of payload–range capabilities and aircraft–route assignment for the overall aircraft fleet. As a consequence, the NMRA reduces the fuel consumption, emissions and climate impact of the ATS in our future scenarios by up to 1.4% (pessimistic) and 2.1% (optimistic). We conclude that a higher fleet diversification and a better alignment of payload–range capabilities and aircraft–route assignment through introducing a NMRA can marginally improve the fuel efficiency and climate impact of the ATS.

Long-term measurement study of urban environmental low frequency noise.

Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise. We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset. We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes. We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.)VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.)LFN was minimally affected by high wind speeds at either Site. Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.