Environmental Optimization of Aircraft Departures: Fuel Burn, Emissions, and Noise (2013)

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81 E-1. Scenario Development Methodology Because this tool is meant to provide an understanding of the environmental effects of changing airport fleet and/ or flight track utilization, setting up an analysis entails gen- erating two separate scenarios: the reference and the scenario proper. Only by generating an appropriate baseline is it pos- sible to assess the effects of any related future or alternative propositions. There are also two approaches to developing scenarios using this tool: (1) creating scenarios that look at the entire airport environment by modeling the fleet, opera- tions, and utilizations for all runways and flight tracks; and (2) developing scenarios that only cover a specific set of tracks to address a change limited to a particular runway and depar- ture procedure. The choice of approach depends on both the scenario to address and on the level of data comprehensive- ness the user is ready to enter. Analysis addressing scenarios like preferential runway usage will probably require the user to set up the tool to model the entire airport while the analy- sis of direct routing for low-noise aircraft might require only part of the information. Whatever the case, users should enter the necessary information following the order in which the input tabs are organized. The first step in creating a scenario is to review the airport layout, keeping in mind the scenario being modeled. The north parallel runway is located close to the population centers and offers a multi-turn NAP in the westerly direction, a related direct track, and a set of eight intermediate tracks between them; in addition there are also two flight tracks with west and south-west headings. On the east flow the runway provides a single-turn NAP with related direct and intermediate flight tracks which are complemented by two tracks heading east and south-east; their geometry can accommodate modeling of fanning procedures. The south- ern runway can only serve an east flow and provides tracks with north-east, east, and south-east headings. The runway is located further from the population centers and can sup- port modeling of scenarios such as preferential runway use. The next step should be to enter the scenario information. The name and description of the scenario should be filled in at this stage along with any initial notes. As the development of the scenario proceeds, the user should return to this tab to add any remarks, assumptions, and information result- ing from working through the scenario input requirements. Once the scenario is completed the date should be updated to indicate when the scenario was finalized. The technology tab is where the users can modify the avail- able fleet to better simulate their airport reality or envisioned changes. In general the two sets of aircraft, current and future, are provided so that a mix of existing and new or future air- craft can be modeled simultaneously; however, especially for an existing condition baseline, the two can be used in par- allel to extend the fleet coverage by using substitutions. As previously noted, noise adjustments for aircraft substitution can be calculated using the standard methodology that has been defined by both ICAO and ECAC. The fuel burn and emissions adjustments can be calculated by comparing the overall fuel burn and emissions over a common flight bound- ary (e.g., up to 10,000 feet altitude) to develop the different percentages. Ultimately, the user needs to decide how to best leverage the available aircraft to meet his/her modeling needs; the note field next to each of the aircraft provides a readily available space to document what has been done. Once the fleet has been defined the related operations need to be provided. The day and night number of flights for each aircraft are entered in the operations sections of the table of the operations tab. The operations are entered by-aircraft type and then split between the two sets of fleet. As the split percentages are input the overall mix can be monitored at the aircraft level, aircraft category level, and airport level using the provided summary fields. These fields can assist in devel- oping scenarios where the operations are not defined in great detail at the aircraft level and the aim is to reach a specific A P P E N D I X E Optimization Tool Scenario and Examples

82 balance instead. Operations information can be entered to represent the entire airport movements, or only a subset of a specific scenario depending on the need. Having the fleet and the operations volume developed the next step is to setup the airport’s parameters. The user should first setup the utilization by-aircraft category starting with the runways utilization. If the operations entered in the previous step were entered for the whole airport then all runways will require a percentage assigned to them, otherwise the one of interest should be set to 100% and all the other ones to zero. The flight track utilizations are entered next; only the percentages for the runways in use are required. If the user needs to further refine the utilization on a by-aircraft basis, the appropriate air- craft’s day and/or night entry fields should be activated in the utilization by-aircraft tab and the values entered. The final step in the scenario input process is to define the dispersion associated with each of the flight tracks. As with the utiliza- tions, the user should first define the dispersion distribution by-aircraft category and then refine those by-aircraft using the dispersion by-aircraft tab. Once all the data has been entered the user can verify that the scenario was modeled as desired by reviewing the com- piled operations data in both the noise and fuel burn and emissions data summary tabs. The results can be assessed in the results tab both in terms of absolute values and, if a refer- ence scenario was selected, in terms of the change. The optimization process can be approached from three angles: (1) the fleet, (2) the airport utilization, or (3) both. Approaching optimization from the fleet point of view means modifying the fleet characteristics or composition until the desired result is achieved for the defined airport utilization. This is generally more of a research approach given that the fleet composition and technology are not elements that can be necessarily affected unilaterally. Addressing optimization from the airport utilization angle, on the other hand, is in line with what an airport operator has more definite control over. Using an iterative process the user can modify where and when aircraft fly so as to limit or decrease the noise exposures at the point of interest while reducing the overall fuel burn and emissions impacts. The last approach can be used to assess a future scenario when a change in the fleet composition and technology is expected. In this scenario the change will enable implementing an alternate airport utili- zation which leverages the new fleet capabilities to improve each flight’s fuel burn and emissions performance without affecting the noise environment. E-2. Sample Analysis Scenario The following example illustrates how to approach the development of an analysis to address a specific scenario. The intent is to demonstrate how a problem should be framed in the context of the tool’s capabilities, how the data should be set up, and how the analysis should be undertaken. E-2.1. Analysis Background The airport in this scenario is a smaller medium size air- port with a fleet dominated by short and medium range air- craft, with a few heavy aircraft operations and some business jet and general aviation traffic. The airport was informed by two of the major carriers that they are planning to modern- ize their fleet, one by replacing their aging Boeing 737-300s with A319neo aircraft and the other by upgrading the avion- ics on the CRJ9 aircraft. The first airline expects that 90% of their 737 fleet operating at the airport will be replaced while the second expects a 70% penetration of the new avionics within the timeframe of interest. The airport has decided that given the improved acoustic and flight performance of the new equipment and their predominance in the airport’s daily operations, there might be an opportunity to reassess its cur- rent departure procedures and possibly diminish the airport’s environmental impacts without affecting the communities’ noise exposure. The target procedure is the Multi-turn NAP currently in effect off of one of the runways. E-2.2. Reference Scenario Setup The goal of the reference scenario is to provide the user with a baseline condition to which different scenarios can be compared. By loading the results from the baseline scenario in the reference scenario results tab of other scenarios a user can determine the environmental benefits and/or impacts resulting from the implemented changes. Additionally, the comparison can also be used to aid the analyst in applying further changes to either maximize the benefits or minimize the impacts. E-2.2.1. Scenario Information The first step for performing this analysis is to setup the baseline scenario file by copying and renaming a blank copy of the tool. The initial scenario information needs to be edited by entering the date, name and description as shown in Figure E-1. The scenario was given a descriptive name and a brief description of the contents. Any assumptions will be added to the Notes field as the development progresses. E-2.2.2. Fleet Technology Adjustments The airport in this sample problem includes a total of 30 different aircraft types, all but six in the Large and Small aircraft categories, and none requiring modeling by sub- stitution. For a baseline scenario a decision has to be made

83 whether to take the time necessary to research the fleet actu- ally operating at the airport and develop adjustment values to adapt the model’s aircraft to better represent the actual fleet. Such level of detail might not be required when the tool is used to provide a quick assessment of a problem or to simply investigate and learn the effects of different envi- ronmental performances and facilities utilization. However, when the intent is to evaluate a more concrete situation one has to review the type of changes that are being tested in terms of fleet, operations, and flight track utilization. If the changes affect only a subset of aircraft and all other aircraft characteristics, operations, and flight track assignments will remain unchanged, then only the affected aircraft need to be adjusted. Since all other aircraft will provide the same contribution in the baseline and all scenarios, any errors in source characterization would not affect the amount of change. However, the other aircraft should be reviewed and updated as necessary if they are moved between tracks since their contribution would not be a bias that remains constant between scenarios. For this specific analysis only the 737-300 aircraft needs to be reviewed for the baseline. The main analysis will focus on rerouting the new and updated aircraft without affecting the remainder of the fleet. From the information in the Reference Data tab, we know the data in the model represents the ver- sion of the aircraft equipped with CFM56-3B-1 engines, has a maximum takeoff weight of 135,000 lb. (61,235 Kg), and a Departure average certification level of 88.4dB. The aircraft at the airport are the same model, but mount CFM56-3B-2 engines and has a MTOW of 130,000 lb. (58,967 Kg). Based on the certification data provided in the NoiseDB database webpage (see the Reference Data tab) the noise certification values for the lateral and flyover measurements positions are 89.6dB and 83.4dB respectively. So the decibel adjustment that needs to be applied to the aircraft is calculated as follows: 89 6 83 4 2 88 4 1 9 . . . . dB dB dB dB +  − = − To calculate the correction percentages for the fuel burn and emissions parameters the Takeoff data for the actual engine must be collected by searching the information provided in the ICAO Emissions Databank found at the internet address listed in the Reference Data tab. The data for the aircraft in the tool and the data reported in the databank for the CFM56- 3B-2 engines are the following: Aircraft Engine FF (kg/s) (g/kg) (g/kg) (g/kg) EI NOx EI CO EI THC Original CFM56-3B-1 0.946 17.7 0.9 0.04 Substitute CFM56-3B-2 1.056 19.4 0.9 0.036 Engine Fuel Flow NOx CO THC CFM56-3B-1 0.946 17.7 0.9 0.04 CFM56-3B-2 1.056 19.4 0.9 0.036 So the adjustment percentages for each parameter are cal- culated as follows: Fuel FlowAdj = −  × = 1 056 0 946 0 946 100 11 6 . . . . % . . . . %NOx CO Adj Adj = −  × = − 19 4 17 7 17 7 100 9 6 = −  × = = − 0 9 0 9 0 9 100 0 0 04 0 036 0 . . . % . . THCAdj . % 036 100 10   × = As expected based on the differences between the two sets of indices, the fuel flow and NOx corrections actually represent an increase, which is expressed in the tool as a negative percentage of Reduction. Figure E-2 shows the adjustment values entered in the baseline scenario Technology tab table and the informa- tion added to the Notes field that identifies the aircraft vari- ant being approximated. Note that CO2, SOx, and H2O would acquire the same adjustment value as that for Fuel Burn because their emissions are directly modeled based on fuel composition. E-2.2.3. Airport Operations When performing a whole airport analysis the operations tab should contain the annual average day (AAD) movements for Figure E-1. Baseline scenario information.

84 the airport. The reason the operations should be set accord- ing to the requirement set for noise studies is that noise is the discriminating factor on which the feasibility of a scenario is evaluated. However, in cases when only a specific noise abate- ment procedure is being addressed, the operations entered can be limited to those that are expected to fly the procedure of interest. While operations on other flight tracks and run- ways would in general affect the results near the locations of interest, their effect would not change between scenarios and, therefore, would be cancelled out when performing change comparisons. For this baseline scenario setup all the operations were entered since the data was readily available. While the sample airport facilities built in the tool might not match those of the actual airport, building a representative baseline opera- tions dataset is the most efficient approach. A fully devel- oped baseline can be edited at a later date and be adapted for use in other analyses without having to spend any addi- tional time in finding and collecting additional information. Figure E-3 shows the completed operations table with the technology mix percentages assigning all operations to the Current fleet. E-2.2.4. Runway and Flight Track Utilization How the operations are assigned to the sample airport flight tracks depends on what operations were entered, all or a subset, the goal of the analysis, and on how similar or dis- similar the actual airport is. The sample airport can be used as a full airport with operations assigned to all runways and tracks, but individual runway ends and noise abatement pro- cedures can also be used by themselves to assess the potential of new technologies and procedures even if the actual air- port’s layout is very different. In this example all runways and flight tracks were assigned percentages of utilization in the “Utilization by AC Category” tab. The data was only entered for the Current technology group because those are the only aircraft that have operations assigned to them in the operations table. The aircraft level utilization input was not required for the baseline as no one particular aircraft required special attention in the Current fleet (i.e., no data additions/changes need to be made to the “Utilization by-Aircraft” tab). Figure E-4 shows the runways and flight tracks distributions defined for this baseline sce- nario (only track information for the Current fleet shown). The figure also shows how the input validation functional- ity highlighted the runways distribution total fields for the Future fleet, which were not given any value since that fleet has no operations in this scenario. E-2.2.5. Flight Track Dispersion Utilization The flight tracks dispersion utilization controls the width of the corridor flown by the aircraft and depends on the navi- Figure E-2. Existing fleet adjustment data.

85 gation technology installed. The application’s three settings provide a range of dispersions from no dispersion to wide dispersion. The “None” setting assumes a perfect navigation with no dispersion; the “Standard” represents the dispersion observed during regular operations, and the “SID” (for Stan- dard Instrument Departure) dispersion estimates what can be expected with RNAV implementation. One of the changes expected by this airport is for the CRJ9 aircraft operating there to receive an avionics upgrade. In this scenario, the basic assumption is that all of the aircraft in the Heavy category and most of those in the Large category are already equipped with RNAV naviga- tion equipment while the remaining categories, Small and Propeller, are not. Of the Large aircraft, only the CRJ9s, the 727s, DC9s, and the MD80s do not have the more pre- cise navigation technology. In order to model this baseline condition both the dispersion by category and by-aircraft tabs have to be used. As shown in Figure E-5, the basic cat- egory wide assumptions are set in the Dispersion by AC Cat- egory tab by assigning all operations to the SID dispersion for the Heavy and Large aircraft and to Standard dispersion for the Small and Propeller (only one runway shown). The exception Figure E-3. Airport baseline operations.

86 for the CRJ9, 727, DC9, and MD80 aircraft is instead estab- lished by modifying the information in the Dispersion by- Aircraft tab. In this tab the ad-hoc information for these aircraft is first made active by switching the Current tech- nology Enable toggle to the on position and then by set- ting the dispersion utilization percentages for all tracks to the Standard dispersion. Figure E-6 exemplifies the entered settings for the CRJ9-ER and DC95HW aircraft (only one runway shown). Identical settings also need to be made for 727EM2, MD82, and MD83 aircraft. E-2.2.6. Scenario Review and Completion Having entered all the input information for the baseline sce- nario the user can review the information in terms of numbers of operations assigned to each of the flight tracks. The two data summaries, for noise (“Noise Data Summary” tab) and fuel burn/emissions (“FB & Emissions Data Summary” tab), enable the user to see both the actual operations assigned as well as the number of operations as affected by the technology input given to the model. Depending on those parameters, and the number Figure E-4. Runway and flight track utilization by-aircraft group (only track information for the current fleet shown).

87 of night operations for noise, the total number of operations actually modeled will be different compared to those initially entered by the user in the operations tab. For this scenario a quick review of the operations totals for the noise computations shows that • About 132 actual operations (day + night) are flown using standard dispersion and about 246 actual operations (day + night) using SID dispersion; • The noise adjustment for the 737-300 aircraft causes the operations assigned to SID dispersion to be decreased by approximately 30 operations; and • Accounting for the night-time penalty results in a total of approximately 277 operations flown using the standard dispersion and 409 using the SID dispersion. A review of the fuel burn and emissions data shows that the emissions adjustment parameters entered in the technology Figure E-5. Flight track dispersion by-aircraft category (only one runway shown).

88 tab caused the operations used to model the different param- eters to change as follows: • Modeling operations for Fuel Burn increased by approxi- mately ten operations compared to the original operations for SID dispersion; • Operations to model NOx increased by eight operations; and • The THC modeling related operations decreased by eight operations. Having verified that the modeled operations for noise and emissions changed as expected, the last task is to record all information on the assumptions implemented into the Notes field of the scenario information tab, Figure E-7, update the date, and save the scenario making sure that the file has been Figure E-6. Flight track dispersion by-aircraft type.

89 renamed to avoid overwriting the original application blank template. E-3. Future Scenario Setup The future scenario for this example is the alternative condition in which the previously described fleet changes are implemented. The scenario development comprises two steps: (1) the implementation of the actual fleet changes, both in terms of source characteristics and performance, and (2) the revision of the airport procedure and flight track uti- lization to optimize the system’s environmental performance. Since the future scenario is a variation of the baseline, the initial work performed can be directly leveraged by using it as the starting point for the new scenario. The first step is therefore to create a copy of the baseline scenario file with a new name that reflects what the new scenario will represent. E-3.1. Scenario Information After opening the new file the first step is to update the information contained in the scenario information tab to reflect the new scenario intent. As shown in Figure E-8, the descrip- tion field in this case also includes a note regarding the original source of the study. This information can be helpful in tracing the genesis of the data contained. Alternatively, this background information can be maintained by not deleting the informa- tion entered in the notes field during the development of the Figure E-7. Completed baseline scenario information.

90 source file and then adding notes on the changes applied to create the new analysis. E-3.2. Fleet Technology Adjustments This example’s future scenario prescribes that the A319neo aircraft will be introduced in the airport’s fleet. Since this air- craft does not appear in the set included within the applica- tion, it needs to be modeled using a replacement adjusted to reflect the new aircraft characteristics. Actual certification noise and emissions data is not available for the new aircraft, so the adjustments have to be based on the information that is available from the manufacturer. The new aircraft should be modeled using the A319 entry within the tool’s future fleet aircraft set. Using the alternative set allows controlling its uti- lization and parameters separately without influencing the way the current fleet is setup and operated within the model. The Airbus website does not provide specific information on the performance of the A319neo—the link points to a document that mentions the aircraft type, but provides more specific information for the A320 version. For the purposes of this example, the assumption is made that the A320 data is applicable to A319neo. Based on Airbus, the A319neo aircraft will be able to achieve the following: • 15dB below Chapter 4 limit, • 50% less NOx emissions compared to the CAEP/6 limit, and • 15% reduction in fuel burn. Figure E-8. Future scenario information.

91 modeled as 100% proportional to fuel burn. For CO and THC emissions, care must be taken as they are not modeled proportional to fuel burn. Without any further data, the user may choose not to model these emissions or simply apply the fuel burn adjustment as a rough, first-order approximation to obtain some “ball-park” numbers. For example purposes, the fuel burn adjustment will be applied to CO and THC emis- sions. Figure E-9 shows the updated Future fleet entry for the A319 along with the comment that explains what the adjust- ments are meant to replicate. E-3.3. Airport Operations The future scenario for this example calls for 90% of the 737-300 operations to be moved to the new A319 aircraft modeled in the future fleet. For example, the 737-300 day operations decreases from 76.67 to 7.67 (69 difference) while the A319 day operations increases from 14.19 to 83.19 (69 increase). Since the A319 aircraft already has operations assigned to it in the baseline scenario, the fleet assignment percentages have to be calculated so as to preserve the appro- priate fleet assignments. The percentage split between the Current and Future fleets for the A319 are therefore com- puted by determining the percentage the two sets of origi- nal day and night operations represent of the new totals. For example, 69 new A319 aircraft (A319neo) out of the 83.19 total A319 aircraft represent approximately 83%. The noise adjustment has to be performed based on the departure certification information for the A319 in the tool as well as the departure Chapter 4 noise limit. The Chapter 4 cumulative limit for a departure operation of an aircraft of its weight is 186.5 dB; the aircraft in the tool has an average depar- ture certification level of 88.1, which means that the cumulative departure level was 176.2 dB (the average value times 2). Based on this information, the original A319 already achieves 10.3 dB below the Chapter 4 margin, so the A319neo will require an additional reduction of 4.7 dB. To determine NOx emissions reduction necessary to model the new aircraft using the one existing in the database, the information for the existing aircraft relative to the CAEP/6 standard needs to be assessed. A review of the information in the engine emissions databank shows that the original engine was slightly over the CAEP/6 limit for NOx (101.1% of the limit). Based on this information, to reflect the 50% below CAEP/6 limit figure provided by Airbus will require the adjustment factor to be 51.1%, the predicted reduction plus the overage the original aircraft’s engines exhibited. Finally, the fuel burn adjustment needs to be applied to both the fuel burn for the future aircraft as well as to all the remaining pollutants. Since the manufacturer did not provide any information for other pollutants beyond NOx, applying the fuel burn adjustment to the other pollutants needs to be carefully considered. In the case of CO2, H2O, and SOx, the adjustment should be applied because these emissions are Figure E-9. Future fleet adjustment data.

92 The future CRJ9 in this example does not receive modifica- tions that affect its environmental performance at the source level, but instead it will be modified to fly more precise trajec- tories as compared to the original aircraft. This upgrade can be simply modeled within the tool by changing the percent- age assignment for the dispersion utilizations while leaving all operations assigned to the Current aircraft fleet. However, as a modeling preference and to make this scenario setup more easily understandable, the operations for the upgraded aircraft are assigned to the Future fleet even if the future air- craft environmental parameters have not been modified as in the case of the A319neo (i.e., the results will make no differ- ence whether the CRJ9 operations are assigned to the Current or Future fleet). Assuming a 30/70 split, the Current fleet is reassigned only 30% of the operations while the Future fleet is given the remaining 70%. Figure E-10 presents the results of all of the aforementioned changes to the operations. E-3.4. Runway and Flight Track Utilization In the future scenario that represents the do-nothing con- dition, no changes are necessary to the runways and flight Figure E-10. Future scenario operations.

93 track utilization tables, both by-aircraft category and by- aircraft type, for the Current fleet. However, since no infor- mation was previously entered for the Future fleet, both the runway and flight tracks information for that fleet need to be updated. In the do-nothing future scenario, these utiliza- tion values can be set to match those for the Current fleet. The values will need to be updated at a later stage during the optimization process to determine what benefits can be achieved by assigning the new aircraft to use more efficient procedures. Figure E-11 shows the initial runways and flight track utilizations for the Future fleet (only track informa- tion for the Future fleet shown). Figure E-11. Runway and flight track utilization by-aircraft group (only track information for the Future fleet shown).

94 E-3.5. Flight Track Dispersion Utilization Since the updated CRJ9 operations were assigned to the Future fleet in the operations tab and the A319neo also pos- sess the same capability, the dispersion settings for the large aircraft category in the future fleet also need to be set to use the SID dispersion for all tracks. Figure E-12 shows the dis- persion utilization by-aircraft category for the Future fleet (only one runway shown). No changes are required to the dispersion utilization by-aircraft table. E-3.6. Operations Review After having input the entire new scenario information, a review of the noise and emissions modeled operations and a comparison to the related actual operations should be con- ducted to ensure that the scenario reflects the changes that were intended. As expected, the number of both the noise and emissions modeled operations for the new A319neo aircraft reflect the reduction expected based on the adjustment values provided in the technology tab. The operations for the CRJ9 Figure E-12. Flight track dispersion by-aircraft category (only one runway shown).

95 also confirm that in the new scenario the aircraft is now using the RNAV dispersion tracks in the percentage intended. E-3.7. Loading the Reference Scenario The final preparatory step in the setup of the future sce- nario is the loading of the data for the reference (baseline) scenario. The reference scenario data is loaded from a pre- viously developed file (i.e., the previously developed base- line scenario file). Figure E-13 shows the information that appears in the reference scenario results tab after the sample base information/data has been loaded (baseline scenario results only partially shown). The scenario information section allows identifying the file from which the data was retrieved, which is important since the information in this sheet is not updated if changes are made to the external file. E-3.8. Initial Results In the future scenario of this example, two fleet changes have been introduced: the arrival of a new aircraft and the upgrade of the navigation technology for another. Both changes affect the noise footprint of the aircraft and their fuel burn and emissions levels. The new Airbus A319neo has the largest effect as compared to the baseline condition as it affords significant reductions for all parameters. The new avionics on ERJ9, however, also afford a reduction as the nar- rower dispersion affects both the location of the aircraft in flight as well as the distance traveled. The narrower disper- sion reduces the reach of its noise effects and also causes the aircraft to fly a different distance, which directly relates to the amount of fuel burned and pollutants produced. A review of the noise values in the “Results” tab shows how the simple introduction of these two changes has posi- tively affected the noise exposures around the sample airport. The SEL noise levels show reductions that range from 0.1 to 0.6 dB while the points of maximum noise change show a 0.1 dB reduction for the two POI points, a 0.6 dB reduction at the City1 point, and a 0.9 dB reduction at the City2 point. Additionally, the emissions results summary tab shows that even without changing the way the fleet utilizes the facili- ties, the airport will experience improvements to its environ- mental footprint. As shown in Figure E-14, the “Emissions Results Summary” tab shows that the fleet changes returned an overall 5.4% reduction in fuel burn, 9.2% decrease in NOx, 11.4% reduction in CO, and 1% decrease in THC. As pre- viously indicated, these results for CO and THC are “ball- park” estimates as only the fuel burn adjustment was applied to them (i.e., no pollutant-specific adjustments reflecting engine characteristics). E-3.9. Multi-turn NAP Optimization The final step in the analysis is to assess what can be achieved by allowing the new aircraft to fly more direct flight paths for the Multi-turn NAP procedure. The analysis has to be performed by reassigning the operations for the aircraft of interest to progressively more direct trajectories until the noise change results in an unacceptable increase over the populated areas of the City2 location point, which is the closest point. To Figure E-13. Loaded baseline scenario data in the baseline scenario tab.

96 facilitate the process, a new duplicate window (by selecting “New Window” under the “View” ribbon) is created for the results tab and the view of the original window is switched to show the Utilization by-aircraft tab. Placing the two win- dows side-by-side (by selecting “Arrange All” in the “View” ribbon and picking the “Vertical” option in the dialog box) allows the user to make changes to the scenario and observe the results at the same time without having to continuously toggle between the two different tabs. The first change consists of reassigning the A319neo and upgraded ERJ9 operations to the more direct flight tracks by updating the Large aircraft Future fleet utilization by-aircraft percentages for the N09 tracks. Since both aircraft were mod- eled as Future fleet this change moves both aircraft to new tracks. By iteratively selecting the more direct flight track and monitoring the noise changes the flights in this example can be moved all the way to the direct flight track (N09DIR) with- out affecting the level at the City2 location point. This result means that the decrease in noise level of the new aircraft plus the effects of the narrower dispersion for the ERJ9 provide enough reduction to possibly allow the whole fleet operat- ing on the Multi-turn NAP to be moved to a more efficient trajectory. The reassignment can be approached in two ways: (1) by moving the two aircraft and the remainder of the fleet independently, or (2) by moving them all as a whole. To explore the first scenario the flight track utilization by category in the Current fleet for all aircraft categories has to be changed by progressively reassigning the utilization per- centages to the more direct flight paths. A few iterations of this process reveal that the N0903 flight track is the most efficient track all Current fleet operations can be assigned to without negatively affecting the noise levels at the City2 point. The noise level improvement changes from 0.9 dB for the unmodified scenario to 0.2 dB once the new utilization is implemented. At the same time, the total fuel burn reduction increases almost another 2% while NOx emissions reduction increases by about 1.5%. To review the second scenario the operations of the two aircraft in the Future fleet need to be first reassigned to the same flight track as the remainder. Since the previous sce- nario showed that flight track N0903 was a feasible option, the first track to attempt would be N0904 based on the assumption that the noise reduction afforded by moving the two aircraft in the new fleet farther away from the City2 point will provide enough surplus to accommodate the Cur- rent fleet as well. The resulting noise change level, however, reveals that moving the two aircraft does not provide enough and moving the whole fleet to flight track N0904 still results in an increase in noise level at the point of interest. This result means that in this situation, it is best to have the Future fleet flying the direct track and the other aircraft the N0903 flight path, which maximizes the fuel burn and emissions saving without adversely affecting the noise exposures. In this example, the analysis treated the Future and Cur- rent fleets as single blocks and all changes were performed at that level. The results, however, highlight the possibility that changes to the way other aircraft fly could be implemented to further improve the environmental performance of the multi-turn NAP procedure off of runway 09. The maximum reductions can potentially be achieved by an analysis that assesses the benefits resulting from allowing different aircraft to fly different flight tracks. Figure E-14. Fuel burn and emissions reductions resulting from the introduction of fleet changes.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation