Proceedings of the Institute of Acoustics

 

 

Analysis of community departure noise exposure variation using airport noise monitor networks and operational ADS-B data

 

Ara Mahseredjian¹, Massachusetts Institute of Technology, Cambridge, USA
Jacqueline Huynh², University of California Irvine, Irvine, USA
R. John Hansman³, Massachusetts Institute of Technology, Cambridge, USA

 

ABSTRACT

 

Causes of variation in airport noise monitor network measurements due to departures remain an important source of uncertainty in the development of departure noise abatement procedures. Variation is observed to be between 10 - 15 dB at individual noise monitors for Airbus A320 and Boeing 737NG aircraft flying the same departure trajectories. In order to understand this variation, aggregate departure noise and flight procedures were examined so that factors that correlate with measured noise could be isolated. This paper identifies these factors. Operational flights at Seattle-Tacoma International Airport conducted in March and August of 2019 were examined using a framework that includes ADS-B data, a force balance kinematics model to estimate aircraft performance, and noise monitor recordings from the Port of Seattle Aircraft Noise Monitoring System. Variation in measured departure noise at six monitors within the network was examined as a function of observed data, including aircraft type, aircraft trajectory, airline, wind, temperature, and relative humidity; and inferred variables, including aircraft configuration, weight, and thrust. Altitude is shown to have the strongest effect on community noise exposure. Airline-specific departure procedures are shown to impact noise measurements. Procedures with higher thrust and higher initial climb gradients were observed to have lower measured noise. Ambient environmental conditions, including wind, temperature, and relative humidity, were found to impact noise variation.

 

1. INTRODUCTION

 

A data-driven exploration of factors that contribute to the variation in departure noise monitor measurements seen at Seattle-Tacoma International Airport (SEA) is presented. The causes of variation in airport noise monitor network measurements of departing aircraft remain a source of uncertainty which must be understood in order to improve existing noise models [1] and develop new departure noise abatement procedures. Variation in departure noise is of specific interest because it is found to be up to 15 dB when aircraft type, departure procedure, and month are held constant. Understanding the potential contributors to this variation is therefore the aim of this work.

 

The variables which may potentially contribute to this variation include those that arise as a result of flight procedures, including operator-specific practices relating to thrust, airspeed, and configuration management on departure; those that arise as a result of the environment, including ambient wind, relative humidity, and temperature; and those that are specific to individual flights, including departure weight. The Port of Seattle Noise Monitoring System was chosen for this study because of the extensive placement of monitors ranging from those at the airport boundary to those further from the airport than monitors at other airports. Noise monitor recording data for Airbus A320 and Boeing 737NG departures taken in March and August of 2019 was used for this study. March and August were chosen to determine whether variation in relative humidity between the two months would impact monitor recordings. Operational ADS-B data from SEA for the same time periods was taken from the OpenSky Network [2] and was matched with flyovers triggering noise monitor recordings. Weather data, including wind, relative humidity, and temperature, was taken from the NOAA Rapid Refresh numerical weather model [3] and was treated both as a raw variable potentially impacting monitor recordings and as a variable in modeling flight performance. The purpose of this study is to measure how noise measurements may correlate with raw data, but variables not included in surveillance data, including weight, thrust, and configuration, are modeled by necessity.

 

The impact of both raw and modeled variables on sound exposure level (SEL) recordings at various monitors throughout the SEA noise monitoring network is demonstrated. Trends that consistently appear at monitors at distances varying between 3-12 nautical miles from SEA are illustrated using data from the network. Data suggesting that specific airline operational techniques impact community noise exposure is shown, and areas for future work and model refinement are identified. While data that may explain some of the variation in the recordings is presented, they may not be the only contributors. Future model refinement or analysis using flight data recorder (FDR) data for multiple flights may help address this ambiguity. The causes of variation in departure noise may be used to improve noise models and contribute to the development of future departure noise abatement procedures.

 

2. DEPARTURE NOISE DATA EXPLORATION AND FLIGHT PERFORMANCE MODELING METHODOLOGY USING OPERATIONAL FLIGHTS AND GROUND NOISE MEASUREMENTS

 

2.1. Identification of Variables with Potential Departure Noise Variation Impact

 

The variables investigated are organized into three categories: Observed aircraft data, environmental data, and aircraft performance parameters. Observed aircraft data includes the data that characterizes the aircraft type, position, and velocity. Environmental data characterizes the ambient wind, temperature, and relative humidity. Performance parameters include takeoff weight; landing gear, slat and flap configuration; flight path angle; and takeoff thrust. Noise data, measured in Sound Exposure Level (SEL) at discrete monitor locations and correlated with specific flights, was provided by the Port of Seattle. The noise, aircraft, and environmental data were observed, while performance parameters were modeled using observed data. The variables used in this study are listed in Table 1.

 

Table 1: Variables with Potential Noise Variation Impact

 

 

2.2. Data Sources and Seattle Noise Monitoring Network

 

Noise Data

 

Noise data from the Port of Seattle Noise Monitoring System was used to obtain flyover noise measured in SEL. Each flyover was correlated with a specific flight number by the Port of Seattle. The noise monitoring system is shown in Figure 1. The south monitors measure noise from aircraft departing to the south. The north monitors measure noise from aircraft departing to the north. The six monitors chosen for this study are highlighted green. These six were chosen because they track departures at close, medium, and far distances from the airport, and because each recorded sufficient data for both Airbus A320 and Boeing 737NGs. Close, medium-distance, and far monitors are examined because the variation at each monitor depends on its proximity to the airport.

 

 

Figure 1: Port of Seattle noise monitor network. Monitors analyzed shown in green

 

The lateral tracks of all departures to the south and north are shown in Figure 2 (a) and (b), respectively. Aircraft depart from all three runways.

 

Figure 2: Lateral tracks of Seattle departures

 

Flights were filtered so that only aircraft that flew within a 0.25 nautical mile lateral track distance of the monitor being analyzed were considered. This filtering was done as a means of holding flyover distance approximately constant.

 

Aircraft Data

 

ADS-B Data from the OpenSky Network [2] was used to obtain aircraft data including aircraft type, aircraft operator, altitude, lateral position, and groundspeed. Flight path angle was estimated using the change in altitude and lateral position at two successive ADS-B data points. There may be random fluctuations in the ADS-B data. ADS-B data was correlated with the flights that generated noise monitor recordings. Aircraft operator was used to determine whether any airline-specific operational practices impacted measured noise.

 

Environmental Data

 

Environmental data including wind, temperature, and relative humidity was obtained from the NOAA Rapid Refresh (RAP) numerical weather model [3], a grid-based model updated hourly. Weather data taken at the time closest to the aircraft flyover was used. Temperature, northward wind, and eastward wind were averaged between the surface and the aircraft altitude at the point of closest approach to the monitor. Relative humidity was averaged between the surface and 1000 ft above ground level.

 

Aircraft Performance Data

 

The Base of Aircraft Data, 4 (BADA4), a database of performance parameters from commercial aircraft manufacturers [4], was used to obtain drag data for the A320 and B737NG. Drag data was used to calculate thrust for each aircraft type.

 

2.3. Departure Flight Performance Modeling Framework

 

Operational ADS-B and weather data were used to model flight performance. Aircraft position, groundspeed, and altitude were included in ADS-B data, while ambient wind, temperature and relative humidity were included in the weather data. Wind data was used to convert aircraft groundspeed to true airspeed. True airspeed was converted to indicated airspeed using atmospheric pressure and density estimates based on the temperature data. Aircraft weight was modeled as a function of true airspeed and altitude 10 nautical miles from the runway, which correlated with FDR data as explained in [5]. This method was chosen because it allowed departure weight to be modeled using only surveillance and weather data. Once weight was estimated, thrust was modeled. The thrust modeling required assumptions about aircraft landing gear, slat, and flap configuration to be established. Landing gear retraction was assumed to occur 0.25 nautical miles after liftoff. Flaps and slats were assumed to be extended from the takeoff roll up until 10 knots below the maximum flap extension speed. Airbus A320s were assumed to take off with a slats and flaps extended to CONF2, and Boeing 737NGs were assumed to take off with slats and flaps extended to Flaps 5. Flap and slat retraction thus occurred at 190 KIAS and 200 KIAS for the A320 and 737NG, respectively. Once configuration assumptions were defined, thrust was calculated. A force-balance kinematics model was used to estimate thrust using performance characteristics including the drag as a function of configuration setting. These drag characteristics were obtained from BADA4. The aircraft was treated as a point mass for simplicity in the performance modeling framework. Further details of the aircraft performance framework are detailed in [1]. The flight performance modeling framework is summarized in Figure 3.

 

Figure 3: Flight Profile Modeling Framework

 

3. ANALYSIS OF VARIATION IN AIRCRAFT NOISE MEASUREMENTS USING SEATTLE ADS-B AND NOISE MONITOR MEASUREMENT DATA

 

3.1. Boeing 737NG Noise Trends at South Monitors

 

The noise impact of each variable for the B737NG at the south close monitor is given in Figure 4. Altitude, thrust per engine, true airspeed, and flight path angle were taken at the point of closest approach to the monitor. Environmental data was averaged as described in Section 2.2. Results at the close, mid, and far monitors are generally consistent. Plots are color-coded by airline so that the noise impact of airline-specific operating procedures can be seen. The correlation coefficient between SEL and each variable, as well as the slope of the linear regression between SEL and each variable are included.

 

Figure 4: Trends for the Boeing 737NG at the south close monitor

 

As shown in Figure 4 (a), there is strong correlation between noise and altitude at the point of closest approach to the monitor, with lower noise at higher altitude. This trend is consistent with spherical spreading and attenuation losses.

 

In Figure 4 (b), a strong correlation between noise and takeoff weight is shown. This trend is not expected given that heavier aircraft typically climb more slowly and with more thrust than light aircraft. This trend is only observed at the close monitor and could be impacted by airline-specific operational practices, such as policies regarding de-rated thrust, in the early phases of the climb.

 

Figure 4 (c) shows strong correlation between noise and thrust per engine. This trend is also unexpected. This indicates that the reduction in noise that arises with the increased altitude is greater than the increase in thrust associated with higher takeoff thrust.

 

As shown in Figure 4 (d), noise increases with true airspeed. This trend is consistent with more aggressive climb rates at lower airspeeds, or with increased airframe noise at higher airspeed.

 

Noise is shown to increase with flight path angle in Figure 4 (e). This trend is expected given that higher flight path angles correlate with higher altitude.

 

In Figure 4 (f), noise is shown to decrease with temperature. This trend is not expected since increased temperature is known to reduce noise attenuation. This trend may be a result of airline specific thrust corrections based on temperature.

 

Noise is shown to decrease with northward wind in Figure 4 (g). For departures to the south, positive northward wind is a headwind. The trend is consistent with improved climb performance with headwinds.

 

Figure 4 (h) shows that noise increases with eastward wind. As shown in Figure 1, the south close monitor is east of the airport, so wind blowing towards the east may cause noise from airplanes to advect towards the monitor. The impact of advection on measured noise depends on the relative locations of the noise monitor and the aircraft.

 

As shown in Figure 4 (i), noise increases with relative humidity. This trend is consistent with the findings in [6], which demonstrates lower noise attenuation for increased relative humidity values above 20 percent.

 

Significant differences between noise recordings produced by different airlines are seen in the monitor data. Airline 6 (depicted as the orange triangles) had the lowest average noise measurements in the data observed. Airline 7 (shown as the black stars) had the highest average noise measurements in the data observed. Both Airline 6 and 7 depart with similar takeoff weight but with different operational procedures. Airline 6 appears to use an initial climb procedure with high thrust, high climb angle, and low airspeed, whereas Airline 7 appears to operate with lower takeoff thrust, resulting in lower climb gradients and lower altitudes over the noise monitors.

 

The trends at the south close monitor are also seen at the south mid and south far monitors, as shown in Figure 5 for the south mid monitor and Figure 6 for the south far monitor. However, at these monitors, noise increases with departure weight as expected. This may be because thrust de-rating occurs during the early phases of climb, so the impact of de-rates seen at the south close monitor are no longer observed at the south mid and far monitors.

 

Noise measurements for the B737NG at the north monitors are consistent with the results at the south monitors, with the exception that noise measurements increase with the northward wind for northbound departures. This is likely because tailwinds decrease climb performance. Results for northbound B737NG departures are given in Appendix A.

 

Figure 5: Trends for the Boeing 737NG at the south mid monitor

 

Figure 6: Trends for the Boeing 737NG at the south far monitor

 

3.2. Airbus A320 Noise Trends at South Monitors

 

The noise impact of each variable for the A320 at the south close monitor is given in Figure 7. Altitude, thrust per engine, true airspeed, and flight path angle were taken at the point of closest approach to the monitor. Environmental data was averaged as described in Section 2.2. Results at the close, mid, and far monitors are generally consistent. Results for the A320 are generally consistent with results for the B737NG. Plots are color-coded by airline so that the noise impact of airline specific operating procedures can be seen. The correlation coefficient between SEL and each variable, as well as the slope of the linear regression between SEL and each variable are included.

 

Figure 7: Trends for the Airbus A320 at the south close monitor

 

As shown in Figure 7 (a), there is strong correlation between noise and altitude at the point of closest approach to the monitor, with lower noise at higher altitude. This trend is consistent with spherical spreading and attenuation losses.

 

Figure 7 (b) shows that noise increases with takeoff weight. This trend is expected given that heavier aircraft are expected to climb more slowly and with more thrust than light aircraft.

 

Noise is shown to increase with thrust per engine in Figure 7 (c). This trend is expected because noise is known to increase with thrust when all other potential factors are held constant.

 

As shown in Figure 7 (d), noise does not vary significantly with true airspeed. This trend is not expected. Aircraft with more aggressive climb rates fly at lower airspeeds.

 

Figure 7 (e) shows that noise increases with flight path angle. This trend not is expected because higher flight path angles correlate with higher altitude.

 

Noise decreases with temperature, as shown in Figure 7 (f). This trend is not expected since increased temperature is known to reduce noise attenuation. This trend may be a result of airline specific thrust corrections based on temperature.

 

Noise decreases with northward wind, as shown in Figure 7 (h). For departures to the south, positive northward wind is a headwind. Thus, the negative correlation is expected because headwinds improve climb performance.

 

Figure 7 (h) shows that noise increases with eastward wind. As shown in Figure 1, the south close monitor is east of the airport, so wind blowing towards the east may cause noise from airplanes to advect towards the monitor. The impact of advection on measured noise depends on the relative locations of the noise monitor and the aircraft.

 

As shown in Figure 7 (i), noise increases with relative humidity. This trend is consistent with the findings in [6], which demonstrates lower noise attenuation for increased relative humidity values above 20 percent.

 

Airlines are not as clearly segregated for Airbus A320s as they were for Boeing 737NGs. However, the aircraft with the highest observed noise measurements are operated by Airline 1 (shown as a purple circle), Airline 4 (depicted as a red diamond) and Airline 9 (drawn as a pink triangle). Airline 1, Airline 4, and Airline 9 operate A320s powered by both CFM and IAE engines, so an engine-specific noise impact is unlikely.

 

The trends at the south close monitor are also evident at the south mid and south far monitors, as shown in Figure 8 for the south mid monitor and Figure 9 for the south far monitor. However, noise is shown to decrease with thrust and flight path angle at these monitors. This trend is consistent with the finding that climbing to higher altitude lowers community noise exposure.

 

Noise measurements for the A320 at the north monitors are consistent with the results at the south monitors, with the exception that noise measurements increase with the northward wind for northbound departures. This is likely because tailwinds decrease climb performance, increasing noise exposure on the ground. Results for northbound A320 departures are given in Appendix B.

 

Figure 8: Trends for the Airbus A320 at the south mid monitor

 

Figure 9: Trends for the Airbus A320 at the south far monitor

 

4. CONCLUSION

 

Variation in departure noise can be attributed to operator-specific climb procedures, aircraft weight, and environmental factors. Altitude is shown to have the strongest effect on community noise exposure. Airline-specific procedures with higher thrust and higher initial climb gradients were observed to have lower noise exposure. This finding may help inform the development of new noise abatement departure procedures. Future validation studies may examine the impact of specific airline standard operating procedures on aircraft noise. Data from flight data recorders can also be used to obtain precise configuration and weight data.

 

Environmental factors including ambient wind and relative humidity are shown to have impacts on climb performance (headwind), advection of noise (crosswind), and attenuation of noise (relative humidity).

 

ACKNOWLEDGEMENTS

 

This work was sponsored by the Federal Aviation Administration (FAA) under ASCENT Center of Excellence Project 44. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government. The authors would like to acknowledge the support of Chris Dorbian, Joseph DiPardo, and Bill He of the FAA Office of Environment and Energy as well as Thomas Fagerstrom and Stan Shepherd the Port of Seattle.

 

REFERENCES

 

1. Ara Mahseredjian, Jacqueline Thomas, and R. John Hansman. Advanced Procedure Noise Model Validation using Airport Noise Monitor Networks. In 50th International Congress and Exposition on Noise Control Engineering, Virtual Event, 2021.
2. M. Schäfer, M. Strohmeier, V. Lenders, I. Martinovic, and M. Wilhelm. Bringing up opensky: A large-scale ads-b sensor network for research. Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN), pages 83–94, April 2014.
3. Stanley G. Benjamin, Stephen S. Weygandt, John M. Brown, Ming Hu, Curtis R. Alexander, Tatiana G. Smirnova, Joseph B. Olson, Eric P. James, David C. Dowell, Georg A. Grell, Haidao Lin, Steven E. Peckham, Tracy Lorraine Smith, William R. Moninger, Jaymes S. Kenyon, and Geoffrey S. Manikin. A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh. Monthly Weather Review, 144(4):1669–1694, 2016.
4. A. Nuic. User Manual for the Base of Aircraft Data (BADA) Revision 3.12. Technical Report 12/11/22-58, Eurocontrol, 2013.
5. Sandro Salgueiro, Jacqueline L Huynh, and R. John Hansman. Aircraft Takeoff and Landing Weight Estimation from Surveillance Data. In AIAA Science and Technology Forum, San Diego, CA, 2022.
6. Cyril M. Harris. Absorption of sound in air versus humidity and temperature. The Journal of the Acoustical Society of America, 40(1), 1966.

 

APPENDIX A: BOEING 737NG NOISE TRENDS AT NORTH MONITORS

 

North Close Monitor

 

Figure 10: Trends for the Boeing 737NG at the north close monitor

 

North Mid Monitor

 

Figure 11: Trends for the Boeing 737NG at the north mid monitor

 

Figure 12: Trends for the Boeing 737NG at the north far monitor

 

APPENDIX B: AIRBUS A320 NOISE TRENDS AT NORTH MONITORS

 

 

Figure 13: Trends for the Airbus A320 at the north close monitor

 

 

Figure 14: Trends for the Airbus A320 at the north mid monitor

 

Figure 15: Trends for the Airbus A320 at the north far monitor

 

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¹ aramahs@mit.edu

² huynhlj@uci.edu

³ rjhans@mit.edu