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EUROCONTROL
EUROCONTROLExperimental Centre
Preliminary Local Air Quality Study
Ayce CelikelNora DuhanianSerge Peeters
EEC / BA / ENV / Note No. 001/2002
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Preliminary Local Air-Quality Study
EEC / BA / ENV / Note No. 001/2002
Ayce CelikelNora DuhanianSerge PeetersEnvironmental Studies Business AreaEUROCONTROL Experimental Centre
© European Organisation for the Safety of Air Navigation EUROCONTROL February 2002
This document is published by EUROCONTROL in the interest of the exchange of information. It may becopied in whole or in part providing that the copyright notice and disclaimer are included.
The information contained in this document may not be modified without prior written permission fromEUROCONTROL.
EUROCONTROL makes no warranty, either implied or express, for the information contained in thisdocument, neither does it assume any legal liability or responsibility for the accuracy, completeness or
usefulness of this information.
REPORT DOCUMENTATION PAGE
Reference:EEC / BA / ENV / Note No. 001/2002
Security Classification:Unclassified
Originator:EEC – ENVEnvironmental Studies Business Area
Originator (Corporate Author) Name/Location:EUROCONTROL Experimental CentreCentre de Bois des BordesB.P.1591222 BRETIGNY SUR ORGE CEDEXFranceTelephone: +33 1 69 88 75 00
Sponsor:EUROCONTROL EATMP
Sponsor (Contract Authority) Name/Location:EUROCONTROL AgencyRue de la Fusée, 96B –1130 BRUXELLESTelephone: +32 2 729 90 11
TITLE:Preliminary Local Air Quality Study
Authors :
Ayce Celikel,Nora Duhanian,Serge Peeters
Date
12/02
Pages
56
Figures
15
Tables
2
Appendix
5
References
3
EATMP TaskSpecification
-
Project
LAQ
Task No. Sponsor
-
Period
2001
Distribution Statement:(a) Controlled by: EUROCONTROL Project Manager(b) Special Limitations: None(c) Copy to NTIS: YES / NO
Descriptors (keywords):Local air quality –– Emission inventory– Dispersion modelling – Geographical Information Systems (GIS) – Nice Airport –EDMS - AERMOD
Abstract:In this preliminary study we aim to gain experience in local air quality issues by starting to model generic airport and tofamiliarise with the methodology. The methodology consists of two stages, namely emission inventory and dispersionmodelling. To determine the emission inventory and dispersion analysis of the generic airport pollution, FAA Emissions anddispersion Modelling System (EDMS) is used. For the simulations, Nice Airport was used as a model. Through the use ofa Geographical Information System (GIS) such as ArcView it was possible to make the entry of emission sources moreuser-friendly and improve the analysis of emission dispersion.
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Table of Contents
TABLE OF CONTENTS .......................................................................................V
EXECUTIVE SUMMARY....................................................................................VII
BACKGROUND ................................................................................................... 9Objective...................................................................................................................... ...................................9
Key Areas ....................................................................................................................... ................................9
Stakeholders ................................................................................................................... ...............................9
Technical Approach .............................................................................................................. ......................11
Emission Quantification......................................................................................................... .....................12
Simulation Day Selection...........................................................................................................................12
Pollutant Source Allocation........................................................................................................................14
Dispersion Modelling............................................................................................................ .......................15
Emission and Dispersion Modelling System ( EDMS ) ..............................................................................15
Original EDMS Dispersion Model Assumption .......................................................................................... 15
Model Capabilities .....................................................................................................................................16
Dispersion Analysis ...................................................................................................................................16
Output Options. .........................................................................................................................................16
AREAS FOR IMPROVEMENT ........................................................................... 17Database Improvement ............................................................................................................ ...................17
Methodology Improvement ......................................................................................................... ................17
Input Manipulation .....................................................................................................................................17
Paper maps ...............................................................................................................................................17
Digital maps...............................................................................................................................................19
Nice Study .................................................................................................................................................20
Analysis of EDMS dispersion results/Output Manipulation........................................................................21
RESULTS AND DISCUSSION ........................................................................... 23Assumptions made in the Study ...................................................................................................... ..........23
Simulation Scenarios ............................................................................................................ ......................23
Interpretation of Results and Concentration Maps...................................................................................2 4
Effect of Meteorological Data ....................................................................................................................24
Concentration Maps ..................................................................................................................................25
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CONCLUSIONS ................................................................................................. 27
APPENDIX A...................................................................................................... 29Data Preparation for Local Air Quality Modelling ..................................................................................... 29
Operational Data .......................................................................................................................................29
Additional Dispersion Modelling Data ........................................................................................................29
APPENDIX B...................................................................................................... 31Regulations and Standards for Airports Pollutants..................................................................................3 1
EC Principles for Air Quality Management.................................................................................................31
APPENDIX C...................................................................................................... 37Atmospheric Dispersion Models .................................................................................................... ............37
VI- References...........................................................................................................................................42
APPENDIX D...................................................................................................... 43Preliminary analysis of EDMS dispersion simulation results ..................................................................43
APPENDIX E...................................................................................................... 53ArcView Presentation for Dispersion Model Results................................................................................53
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Executive SummaryThe Eurocontrol Experimental Centre Environmental Studies Business Area (EEC/ENV) initiated this study inorder to assess the existing and potential impact of aircraft emissions on local air quality. Aircraft and airportrelated emissions have received considerable attention in recent years both national and international level(new regulations and targets). Up to now EEC/ENV activities have mainly been concentrated on globalemissions and noise studies. Due to increased awareness of air quality issues, Eurocontrol will now play anactive role in local air quality issues around airports.
While air traffic has been experiencing substantial growth, there has also been an increasing awareness aboutthe environmental impact of aviation. Air quality issues around airports are becoming more important primarilydue to the health effects of the pollutants.
In this specific preliminary study we aim to gain experience in local air quality issues by starting to modelgeneric airport and to familiarise with the methodology. The methodology consists of two stages, namelyemission inventory and dispersion modelling. Emission inventory provides the total amount of pollutantsproduced by airport related emissions sources for a specific period of time. To determine the emissioninventory of the generic airport, FAA Emissions and dispersion Modelling System (EDMS) is used. For thesimulations, Nice Airport was used as a model.
Dispersion analysis is intended to assess pollutant concentrations near the airport based on the emissioninventory, EDMS Gaussian dispersion equation and meteorological conditions. The model takes an emissionrate from a source and calculates hourly concentrations of the key pollutant for a number of receptor points.
EDMS has limited graphical display functionalities and the process to enter the spatial location of emissionsources is tedious. Through the use of a Geographical Information System (GIS) such as ArcView it waspossible to make the entry of emission sources more user-friendly and improve the analysis of emissiondispersion.
The results have not been consolidated and the study is still in progress; a work plan for Local Air QualityStudies has been initiated in cooperation with Manchester Metropolitan University (MMU) with an outline andinitial plan of co-operation for the development of a common methodology to assess the impact of aircraft andairport pollution. Thus a complete methodology starting from the quantification of the emissions and endingwith dispersion modelling will be considered. Any possible improvement during the methodology such as GISintegration, realistic flight profiles, or the use of radar data will be considered as well. For future work thestarting point will be to develop a harmonised database for the emissions inventory phase and then continuewith the dispersion phase. With a complete methodology we will be able to analyse different scenarios suchas different operational changes or improvement areas within the airport.
This study has achieved its initial goals and creates a basic understanding in the local air quality domain aboutthe effects of aircraft and airport emissions. However, this preliminary study may not have covered all thepossible scenarios and the results presented are a preliminary qualitative analysis of EDMS dispersionsimulation results.
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BackgroundWith the ongoing increase in air traffic, air quality is perceived as an important issue for airport managementand authorities. Several European airports have already made some progress such as implementingmethodologies to assess local air quality. Some of the main concerns are the influence of airport pollution onlocal air quality and people’s exposure to pollution concentrations above those defined regulations.
Environmental regulations, standards for pollution emissions and air quality are becoming increasingly morestringent. Many airports have started to monitor emission concentrations by means of emission inventories,the use of dispersion models and field observations. However, there are still some European airports wherenone or very little pollution monitoring is conducted. At this stage there is no consensus on the best dispersionmodel and each airport has its own specific concerns. Monitoring methods are often site specific and can bedifficult to implement at other locations.
It is known that airport operations can increase concentrations of nitrogen dioxide, fine particulates, carbonmonoxide and sulphur dioxide. EUROCONTROL Airport Operations Strategy’s environmental sectionconsiders the concept of environmental capacity and the development of a conceptual model for calculatingenvironmental capacity of an airport, or airport systems and, environmental assessment analysis studies.
The Eurocontrol Experimental Centre Environmental Studies Business Area (EEC/ENV) was established inresponse to member states’ environmental concerns. Up to now EEC/ENV activities have mainly beenconcentrated on global emissions and noise studies. Due to increased awareness of air quality issues,Eurocontrol will now play an active role in local air quality issues at and around airports. Co-operation withManchester Metropolitan University (MMU) has been initiated in order to develop a common methodology forlocal air quality studies.
Objective
The first objective of doing local air quality studies is to provide a methodology and tools to define the effect ofan airport on local air quality. This will be done by delivering the best practice concept, in accordance withharmonised standard European data and more realistic Landing Takeoff Cycle (LTO) models. Features suchas GIS systems will be integrated to the methodology. As a further step with the methodology, it will bepossible to investigate different scenarios; such as adding a new runway, changing operations, future growthetc.
A second objective is to gain better understanding of the contribution of ground-level airport traffic to global airtraffic emissions.
In this specific preliminary study we aim to gain experience in local air quality issues by starting to modelgeneric airport. Once we develop the methodology, we can provide services to our member states and be partof the scientific community in this area.
Key Areas
For an assessment of the impact of air traffic emissions in a local area, the first step is to define all sourcesrelated to the airport. For emissions analysis, emissions can be calculated directly from the sources by usingoperational data and emission indices, which will give total emissions. Pollutant emissions are derived fromthe fuel consumed multiplied by an emission index. For carbon dioxide, water vapour and sulphur dioxide, theindex is a constant.
For the local air quality assessment, we need to model the airport as an entire system. First we have to definethe sources of pollution, choose appropriate models and tools and find the effected area. As a final step,atmospheric dispersion model is used to calculate the concentration-based local air quality impact at selectedairports. We are then able to compare the calculated values with the limit values.
Stakeholders
For the preliminary study only the EUROCONTROL environmental team conducted the study. However, forfuture studies stakeholders’ contribution is indispensable. Stakeholders’ involvement throughout the projectwill be:
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• EUROCONTROL, FAA, ACI, ..
• Manchester Metropolitan University
• Scientific Organisations (AERONET, ICAO Working groups, EU, ..)
• Manufactures, Airports, Airlines, Universities.
• Others.
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It is very important to have a proper methodology and necessary tools to estimate pollution caused by theoperation of an airport.
There are several models and tools to be used for local air quality purposes. As a starting point FAAEmissions and Dispersion Modelling System (EDMS) is used as an emission and dispersion tool, for a genericairport. Nice Airport was chosen as a generic airport due to its low complexity and its important location.
Technical Approach
In order to model the impact of airport operations on local air quality, the first step is to quantify the emissions.Although emissions can be quantified individually (by using any emission tool and emission indices), thisdoesn’t provide a total picture of the pollution. Therefore a local air quality model is necessary to be able tomake an impact analysis. Such analysis consists of two steps: 1) Emission Quantification 2) DispersionAnalysis
Several local air quality models are in use in Europe and worldwide. The regulations for local air qualityassessment are concentration based and only dispersion models can provide this type of results. (SeeAppendix C for dispersion models)
Based on some research on different tools and models, EDMS has initially chosen for starting work on localair quality due to its availability and specificity for airports. The model includes an emissions inventory sectionand a dispersion modelling section. The heart of the dispersion model is the Gaussian dispersion equation,which takes an emission rate from a source and calculates the concentrations of pollutants in the air at eachreceptor location.
Standard models have limited graphical display functionalities and the process for entering the spatial locationof emission sources is tedious. Through the use of a Geographical Information System (GIS) such asArcView, the EUROCONTROL approach makes it possible to capture emission sources in a user-friendly andefficient way and improves the analysis of emission dispersion results.
Step 1. Emission Quantification
Due to its specificity on airport modelling, EDMS has data not only for aircraft, auxiliary power units, andground support equipment, but also items not directly aviation specific but still in the airport area such aspower plants, fuel storage tanks, and ground access vehicles.
The first step of the study is to calculate the amount of emissions at an airport for a specific traffic simulation(peak hour/day) and the source of the pollutants. This quantitative emission result is then used for furtherdispersion analysis.
Step 2. Dispersion Analysis
During the dispersion calculation, the locations and sizes of the pollutant sources need to be provided. Onemajor area for improvement in our approach is to fully take into account the precise movements of the aircraft.Meteorological data for the simulation area is also required (dispersion parameters are usually provided insidethe model).
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Figure 1. Methodology for emission and dispersion analysis at an airport
Emission Quantification
For a complete local air quality impact assessment, the pollutant sources around the airport area must be welldefined. Therefore once the study airport is defined, the data collection for this specific airport can start. Ageneric airport (Nice) is used for the first preliminary studies.
Simulation Day Selection
As the generic airport is used for the study, there wasn’t any specific demand from the airport and thereforethe simulation day is defined by considering the maximum traffic load per month/day/hour. (See Figures 2,3.a and 3.b)
Operational profiles (hourly/daily/monthly traffic distribution ratios) are needed to calculate the trafficdistribution during the study time period. For this reason EUROCONTROL planned traffic files for Nice Airportare used to make a traffic analysis. Yearly traffic statistics for Nice Airport are extracted from the Air trafficFlow Management (ATFM) simulation tool COSAAC.
Aircraft / APU GSE /Stationary Sources /Runway / Taxiway /Maintenance / Parking Area
Emission Quantification
Emission Source input
Dispersion Analysis
Meteo input
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2000 Monthly Statistics for Nice
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Figure 3.a. Weekly traffic distribution for Nice Airport for the period 17-23/07/2000
Weekly distribution for 17-23th of July 2000 (LFMN)
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Figure 3.b.Hourly traffic distribution for Nice Airport for 20 July 2000
This set of data is analysed to find the maximum load of traffic and finally the maximum hourly profile ischosen for the study.
Pollutant Source Allocation
For an emission inventory at an airport, aircraft operations are not the only pollution source. Thus othersources should be defined as well. EDMS provides a large database (including emission factors, Time-in-modes, number of engine, etc) for different sets of aircraft-engines, GSE, APU, stationary sources and so on(for American airports). The user has also the possibility of importing new data or creating interactively apersonal database to integrate into the program. The main user input needed for a simulation is the“operational profile” or the rate of activity for all aircraft and sources. This can be done by entering yearlyactivities or activities at peak hours (see section above).
The first part of the air pollution modelling system for airports, is to define where the sources of the pollutantsare. In EDMS these sources are:
� Aircraft
� Roadways
� Airport vehicles (Shuttles, GSE systems: aircraft tractors, baggage and cargo tractors, cargo loaders,ground power units, portable air conditioners, service trucks..)
� Stationary sources (Fuel Tanks, Power plants, incinerators..)
� Training Fires
Aircraft are considered to create the biggest part of pollution. Yet when the environmental impact assessmentis made for an airport, all the sources must be considered.
In our study same tool is used both as a emission quantification and dispersion tool. For the first part of thestudy all the emission sources are quantified. EDMS is originally an FAA tool therefore when it is used forEuropean airports, certain necessary changes must be prepared.
Aircraft Engine Combination
In the EDMS system database, each aircraft has several engine combinations and for most cases a defaultengine. However the default engine may not present a current use in Europe. Therefore anotheraircraft/engine database is prepared to define most recent aircraft-engine combination for European fleets.
The European fleet with the corresponding aircraft/engine type is extracted from the JP Database1. AEuropean aircraft database is created from this source as an aircraft type and different engine combinations
1 Buchair UK Ltd
Hourly Distribution in 20 July 2000
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with their percentage of use. Aircraft types are extracted from the Central Flow Management Unit (CFMU)traffic files for Nice Airport and the most used engine is chosen from the European database. If the enginetype, which is extracted from European database, doesn’t exist in the EDMS database, the EDMS defaultvalue is chosen for the aircraft type.
Figure 4. Aircraft engine combination setting in EDMS
Dispersion Modelling
On Europe and worldwide basis there are several local air quality models in use. As it’s stated in previoussection, EDMS appeared to be the most appropriate one for our initial local air quality studies and it’s used fordispersion modelling.
Once the input data are defined and extracted we can start to use EDMS for emission quantification anddispersion modelling around the airport.
Emission and Dispersion Modelling System ( EDMS )
EDMS is the United States Environmental Protection Agency (EPA) and FAA preferred guideline model tocalculate emissions and model dispersion at airports. To quantify the amount of pollution discharged into theatmosphere, only emission coefficients and activity profiles for each source are needed. A Gaussian model isused for dispersion calculations. In addition to the emission inventory requirements, source locations, sizesand also meteorological data for the simulation area have to be specified (dispersion parameters as standardsdeviations are already provided in the model).
Original EDMS Dispersion Model Assumption
EDMS uses the AERMOD processor for dispersion calculations based on a gaussian model and the defaultoptions of the model are: (For more detailed definition see AERMOD User Guide)
� Stack-tip downwash.� Model accounts for elevated terrain effects.� Use Calms processing routine.� Use missing data processing routine.� "Upper bound" values for supersquat buildings.� No exponential decay for rural model� Emission factors based on EPA nonroad model
Look up Europeandatabase (JP)
Exist inEDMS? Y
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Use as a defaultengine
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It is possible to run an emission inventory and a dispersion simulation separately. The air-traffic activity can bedivided between arrivals and departures. It is also possible to take into account different runway/taxiwayconfigurations with respect to the wind intensity and direction. The results of the simulation are stored in anoutput file.
Model Capabilities
EDMS is a model specifically developed for assessing the air quality impacts of airport emission sources.Therefore it contains a database for aircraft engine combinations, auxiliary power units, and ground supportequipment, and stationary sources such as power plants, fuel storage tanks, etc. Therefore for emissioninventories other than aircraft this make it a good source and useful tool making analysis. However, thesedatabases were created for US airports and one of our main objectives is to update and harmonise thedatabase for European use.
Dispersion Analysis
The dispersion analysis is preformed in EDMS, incorporating AERMOD dispersion algorithms and anAERMET Wizard for processing weather data. One important part of the dispersion analysis is to getnecessary weather data in order to accurately characterise the atmosphere. (See Appendix A)
Surface data for each hour is required to determine the current wind direction, wind speed, temperature andcloud cover. In addition, twice-daily upper-air observations are required to properly determine the mixingheight. 2
For dispersion analysis more data are necessary than for emission quantification. In addition to the emissionand meteorological data, operational profiles have to be created and receptors have to be placed.
Output Options.
The basic types of printed output available with AERMOD3 are:
• Summaries of high values (highest, second highest, etc.) per receptor for each averaging period and sourcegroup combination;
• Summaries of overall maximum values (e.g., the maximum 50) for each averaging period and source groupcombination; and
• Tables of concurrent values summarised by receptor for each averaging period and source groupcombination for each day of data processed. These "raw" concentration values may also be output tounformatted (binary) files, as described below. These files are often used for special post processing of thedata. In addition to the unformatted concentration files, AERMOD provides options for two additional types offile output. One option is to generate a file of (x, y) coordinates and design values (e.g., the second highestvalues at each receptor for a particular averaging period and source group combination) that can easily beimported into many graphics plotting packages to generate contour plots of the concentration values.Separate files can be specified for all of the averaging period and source group combinations of interest to theuser.
Another output file option of the AERMOD model is to generate a file of all occurrences when a concentrationvalue equals or exceeds a user-specified threshold. Again, separate files are generated for only thosecombinations of averaging period and source group that are of interest to the user. These files include thedate on which the threshold violation occurred, the receptor location, and the concentration value.
2 http://www.aee.faa.gov/aee-100/aee-120/EDMS3 AERMOD User Guide
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Areas for ImprovementThis report demonstrates our preliminary approach to Local Air Quality Studies. Thus results and methodologywill be consolidated. Furthermore, new co-operation has started with Manchester Metropolitan University(MMU) with an outline and initial work plan for the development of a common methodology, that considersboth quantification of the emissions and dispersion modelling. Any improvement found necessary during themethodology such as database improvement, GIS integration, realistic flight profiles, or use of radar data willbe considered as well. Finally different operational changes or improvement areas within the airport will beinvestigated
Database Improvement
Database : Airport, aircraft, engine, APU, GSE default database exist for American airports. Therefore thedatabase can be customised before starting the analysis of a simulation area. A standardised dataset can beimplemented for European use.
Real aircraft profiles are not implemented, yearly or peak hour operational statistics are used in EDMS (Forexample hourly, weekly, yearly traffic load must be analysed as a portion of maximum traffic). Traffic isgenerated from yearly LTO of an aircraft considering these peak values. We cannot therefore define actualapproach and departure flight tracks (SIDS and STARS), idle take-off and APU procedures. The use of noiseanalysis tools and models such as ENHANCE can provide the realistic profiles.
Methodology Improvement
Other than database improvement another issue is to improve methodology. In general, tools and modelsused for emissions inventory and dispersion modelling are quite similar.
EDMS has limited graphical display functionalities and the process to enter the spatial location of emissionsources is tedious. Through the use of a Geographical Information System (GIS) such as ArcView it ispossible to entry of emission sources more user-friendly and improve the analysis of emission dispersion.
ArcView is a desktop application for the creation, management and integration of spatial information.ArcView™ is developed and maintained by the Environmental Systems Research Institute (ESRI).
Input Manipulation
Dispersion modelling requires the determination of the location and the extent of emission sources. In EDMSthis is done by entering manually the x and y coordinates of point, line and area sources. It is proposed todevelop an ArcView application to simplify the capture of the different emission sources.The entry of airport data can be done using maps; paper prints or in digital format.
Paper maps
Paper maps can be obtained from various aeronautical and airport authorities. These maps can be scannedand thus converted into digital raster images. Raster data can be imported into a GIS and rectified providedthe original map is to scale and has enough reference points for which coordinates are known. Rectification isthe process by which the image is converted from image coordinates to real-world coordinates. Rectificationtypically involves rotation and scaling of pixels.
A rectified map can then be used to obtain the position of emission sources such as runways, taxiways, gates,parking lots and roads. Features are defined by a single pair of x and y coordinates for points or by a series ofcoordinates for linear and surface features (vector format).
By means of customised scripts, coordinates and other relevant information are exported from the GIS toappropriately formatted DBF files that can be read directly by EDMS. The formats of these DBF files aredocumented in the EDMS Reference Manual.
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Figure 5. Paper map for Nice Airport
Figure 6. Rectified map with digitised pollution sources.
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Figure 7. Creation of DBF input files from ArcView shapefiles.
Figure 8. EDMS graphical display of sources and receptor network
Digital maps
In the case where an airport has its own computerised mapping system, emission source data in vector formatcan be transferred into a GIS in a digital format. The data capture is thus even simpler provided the airport’smapping system can export data in a standard format (e.g. Shapefile or DXF)
It should be noted that EDMS only requires a schematic representation of emission sources. It might thus benecessary to recapture emission sources in a simplified yet realistic form. For example in EDMS parking lotpolygons may not have more than twenty vertices while taxiways and roads are represented as a series ofstraight lines.
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A GIS is ideally suited to integrate data from different sources. In many countries a variety of digital mappingdata can now be obtained in both vector and raster formats. The integration of airport data with spatialinformation from other sources allows for the analysis of Local Air Quality issues in a broader context. Thelatter is essential for assessing the impact an airport might have on its surroundings.
Additional mapping data such as Topographical information can be obtained from mapping organisation suchas l’Institut Géographique National (France) or the Ordnance Survey (U.K.). Census data is particularly usefulfor Local Air Quality studies as it identifies where people live relative to an airport and the types of population.However, censuses are generally only held every ten years and population data can thus be out of date.
Nice Study
For the Nice Study the location of emission sources was obtained from a scanned ICAO Aerodrome Chart.Additional Topographic data and Census Blocks have been purchased from the Institut GéographiqueNational (IGN)
The Topographic data (BD Topo�) is made up of an exhaustive set of layers covering the following themes:
1. Road and rail networks2. Hydrography3. Buildings and miscellaneous infrastructure4. Land use and vegetation5. Altimetry (contour lines and spot heights)6. Administrative and other boundary lines7. Place names
The positional accuracy is between 1.5m and 5.0m. Buildings from the Topographic data set were used toregister and rectify the scanned map of the airport. Although, some discrepancies were observed during thisprocess these should not have any significant impact on the study.
Census Blocks are the smallest geographical units for which population figures can be obtained. At this stageof the project only block boundaries have been acquired without population figures. Attribute data will need tobe acquired separately from INSEE, the French organisation responsible for census information.
It should be noted that Topographic data and Census Blocks were not delivered in the same cartographicprojection system. However, this was not a problem as the GIS can easily deal with different projectionsystems provided all their characteristics are supplied by the data provider. Not only should the projection betaken into consideration but also the datum. For instance Airport locations are given in WGS84 geographiccoordinates (Latitude and Longitude) while data supplied by IGN is based on the Clarke 1880 ellipsoid.
Up to now dispersion simulations for Nice Airport have been based on the assumption of a flat terrain.Although EDMS does not provide the functionalities to include a terrain model this could be done indirectlythrough AERMAP the terrain pre-processor module of AEROMOD4. Although this has not yet beeninvestigated it is assumed that the altimetry data included with Topographical data could be used for thispurpose.
4 EDMS 4.0 Reference Manual p7
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Figure 9. IGN Topographic Data Figure 10. Terrain model
Figure 11. Census Blocks
Analysis of EDMS dispersion results/Output Manipulation
EDMS calculates the concentration of pollutants for user-defined receptors but has no graphical display tovisualise the concentration results and the pollutant’s dispersion. Results for each receptor at fixed timeintervals are saved in an output file. This file can then be viewed or printed by means of a text editor such asWordpad.
However, the report file can be very large and it is difficult for the user to interpret the results. ArcView allowsthe user to visualise the evolution of pollution plumes through time. The analyst can see the relationshipbetween sources and dispersion. If population and infrastructure data are available, affected households orsensitive facilities such as hospitals or schools can be identified.
The format of the EDMS output does not allow for direct importion of dispersion results into ArcView. Bymeans of customised scripts or programs, results are converted into Shapefiles (an ArcView format for spatialdata). For each receptor its coordinates and a number of attributes are stored e.g. concentration and time.Dispersion results can then be displayed by showing each receptor as a coloured square where the colour ofthe square is a function of the concentration e.g. the higher the concentration the darker the colour. Differenttimes of the day can be selected by applying a simple filter on the time attribute e.g. only show concentrationwhere time = t. Airport infrastructure and other layers of spatial information can be superimposed to visualisepollution concentrations at and around the airport.
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EDMS allows for three types of receptor configuration namely: discrete, polar and grid. The grid configurationis the most useful for visualising pollution dispersion. However, EDMS limits the number of grid points to 1500.To cover the whole study area it might be necessary to increase the grid spacing between receptors, whichresults in a lower resolution. Alternatively for a denser resolution the study area can be covered by a numberof contiguous grids.
When grid results are displayed in ArcView the dispersion layer appears very blocky (each receptor isrepresented by a square). For more esthetical concentration maps ArcView can interpolate a higher densitygrid with smooth concentration gradients.
Finally, dispersion results for each hour can be exported as images and then compiled into video clipsshowing the evolution of the pollution plumes over a period of time.
Figure 12. Dispersion results with sources Figure 13. Results with topographic data
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Results and DiscussionEDMS Gaussian-based dispersion simulations have been performed for the first preliminary study in order toobtain some familiarity with the software and test its capabilities. For the simulations Nice Airport was used asa generic airport real data sets were not used and lack of experimental data, forced us to pronounce only froma qualitative point of view on the results.
The outputs for mean concentrations of each pollutant, generated by EDMS for a dispersion problem in whichcalculations are carried out for one-hour average time during a single day (24h). A concentration of thechosen pollutant emitted by a given group of sources (or all sources) at each receptor and at each average-time is found. (Park lots, Stationary sources, Aircraft, Roadways, All)
Assumptions made in the Study
For the pollution sources around the generic airport as no real data from Nice Airport were available, someassumptions were made like Road traffic, stationary sources, taxi times, etc. To validate the study model, weneed some information and input data from the airport itself. For the meteorological data as well, we used theexample data from the US study given in the user manual.
One runway taken in the two directions has been implemented with the same number of arrivals anddepartures. Different aircraft have been associated with several gates and taxiways. There are two parkinglots with roadways all around and a single stationary source of pollution. The model was run for a 24-hourperiod with a time step of one hour. CO is the chosen pollutant and receptor points at which its concentrationsare calculated have been placed at a height of 1.8m above ground.
Simulation Scenarios
Activity profiles for pollution sources and meteorological data are given hour-by-hour in tables 1-a, 1-b and 2.An overview of the airport model with the network of receptors is shown in figure 14. For the referencesimulation, all the pollution sources (aircrafts, gates, taxiways, roadways, parking) except the stationarysource emitted together following the activity profile given in table 1-a. In addition, for any simulationperformed, the stationary source is always emitting at its maximum rate following the default activity profile(table 1-b).
Table 1-a: Activity profile for the following sources : aircraft LTO cycles (including runway,taxiway, gates), parking lots and roadways.
Time slice Activity profile (%) Time slice Activity profile (%)
1h 0 13h 85
2h 0 14h 65
3h 0 15h 95
4h 0 16h 85
5h 20 17h 70
6h 50 18h 80
7h 65 19h 55
8h 70 20h 65
9h 60 21h 40
10h 65 22h 15
11h 90 23h 5
12h 100 24h 10
Table 1-b: Default activity profile. The single stationary source is always emitting in accordance with thisprofile
24
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% % % % % % % %
Y = 0m
X=
0m
N
E
S
W
Taxiway
Parking
RoadwaysGates
Runway
Stat. Source
+
ObservationPoint
Queue
Queue
Time slice Activity profile (%)
1h-24h 100
Six different simulations were performed, the first one (Run_1) being the reference. The second run (Run_2)differs from the first one in the fact that meteorological conditions for time-slice 12 are the same as for the 6th.For the third run (Run_3), all sources are emitting at a maximum rate for all time-slices (the default activityprofile shown in table 1-b is used). In the fourth run (Run_4), all sources are emitting in accordance with thedefault activity profile and all wind directions are forced to 122°. In Run_6, all sources are emitting followingthe default activity profile and all meteorological input parameters, except wind direction, are forced to be thesame as the 4h ones (in other words, stable atmospheric conditions are imposed during the entire run). Thelast run (Run_5) is similar to the reference one except that receptors are placed at a height of 10m instead of1.8m.
Figure 14: Overview of the airport with homogeneous network of receptors (little points).
Interpretation of Results and Concentration Maps
EDMS dispersion results can be post-processed with the help of other software such as ArcView and Excel.For the interpretation of the results horizontal maps with concentrations at each receptor point for a giventime-slice and, concentration curves versus time at a given receptor point are used. The concentration curveat a given receptor point may be useful to compare simulation results to the experimental (or field observation)ones. However for this initial study experimental data is not used for such comparisons.
Effect of Meteorological Data
Concentrations of the pollutants are examined for specific receptor points. Meteorological input for dispersionsimulations play an important role. The atmospheric stability that is found meteorological parameters affects
25
the dispersion behaviour of the pollutants. Two effects are investigated for this analysis, “thermal andmechanical turbulence” and, “wind direction”. (See Appendix D)
For unstable atmospheric conditions, dispersion is more favourable due to the vertical mixing in whichpollutants of each source are transferred to the upper layers.
The wind direction also has an effect on dispersion. For receptor points, wind direction determines how thepollutant plume will move ahead. For the same receptor point with a different wind direction and identicalemission rate the concentration exposition can be different.
Concentration Maps
As discussed in the previous section ArcView is used to show results graphically and to create concentrationmaps. (A series of dispersion maps are shown in Appendix E)
Figure 15. Example of ArcView concentration map for Nice Airport
26
CCoonncclluussiioonnss
27
ConclusionsThis study has achieved its goals and created a basic understanding in the local air quality domain aboutaircraft and airport emissions contribution. However, this preliminary study may not have covered all thepossible scenarios and the results presented are a preliminary qualitative analysis of EDMS dispersionsimulation results. The study has been partly based on Nice Airport with some assumptions made for themodelling of the airport. Part of the difficulty is that EDMS is US model and the data consist of Americanairports/aircraft/engines/GSE… Some assumptions made therefore, may be inappropriate for Europeanairports, even though a real effort has been made to use European aircraft types and to implement realisticemission rate profiles especially for the LTO cycles.
The results have not been consolidated and the study is still in progress; a work plan for Local Air QualityStudies has been initiated in cooperation with Manchester Metropolitan University (MMU) with an outline andinitial plan of co-operation in the development of a common methodology to assess the impact of aircraft andairport pollution. Thus a complete methodology starting from the quantification of the emissions and endingwith dispersion modelling will be considered. Any possible improvement during the methodology such as GISintegration, realistic flight profiles, or the use of radar data will be considered as well. For future work thestarting point will be to develop a harmonised database for the emissions inventory phase and then continuewith the dispersion phase. With a complete methodology we will be able to analyse different scenarios suchas different operational changes or improvement areas within the airport.
The initial study has shown that simulation results are in good agreement, from a qualitative point of view, withthe input data provided. It has also shown the possibilities for the use of GIS for both the input manipulationand the post-processing of the results.
Overall, this report provides an estimation of how dispersion analysis can be performed for an airport. Alsohighlighted are the need for improvements and the areas requiring further attention for a better understandingof the concepts involved.
28
AAppppeennddiixx AA
29
Appendix A
Data Preparation for Local Air Quality Modelling
Operational Data
For a chosen scenario for creation of emission inventory:
� Aircraft/Traffic
Hourly/Yearly LTO movements/day, statistical distribution/hourly/daily/yearly (depending on the scenario time)
� Taxi data, including:
Runway, queuing, idle time etc.Taxiway with co-ordinates and heightTaxiway-Aircraft allocation
If the other sources are included
� List of all stationary sources, GSE, APU
Co-ordinates, fuel/emission rates and operational times
From CFMUStatistical analysis can be made with CFMU All_Fligth Data. COSAAC tool is used to extract Nice traffic data.(See figures 1, 2 and 3)
For a chosen scenario for a dispersion modeling
Additional Dispersion Modelling Data
Airport PlanGates (co-ordinates)
Airport Taxiways (co-ordinates/ taxi time)
Runways (co-ordinates, ends, time in queue)
Configurations (runway to taxiway, wind conditions (angle range, minimum speed)
List of type and fuel consumption of stationary sources (incinerator, power plant, fuel tank, degreaser, surfacecoating…)
Roadway/peak or yearly traffic with speed and vehicle round trip distance
Any source that can cause pollution /airport specific or non-specific
Meteorological DataAnnual average temperature
Meteorological surface observations
National development strategy for airport and the regulation applied to the airport (Local, regional or possiblyinternational) can be useful for the future scenarios.
30
Meteorological DataRequirements for EDMS model we need:
Annual average temperature
Meteorological surface observations (Like albedo, bowen ratio, roughness length wind speed, direction,clouding cover, ambient temperature) Lat/Lon, X/Y/Z
AERMET module provides the creation of meteorological input files for AERMOD dispersion processor. Thesemeteorological data are extracted from an upper air database and also a surface conditions database. Theygive the software access to wind speed and direction, temperature, cloud cover, sun radiation, etc in order toestimate for instance air stability and mixing height. AERMET can read these meteorological data fromdifferent files formats.
Surface weather data: (Originally in SCRAM surface data format, but as a options: TD-3280 (Variable or fixedlength blocks), SAMSON, or CD-144 can be chosen)Upper air data: (Originally TD-6201 Fixed Length Blocks data format but can also be variable length)From the above data requirements summary list is prepared for the data preparation process. (Table A1)
TTaabbllee AA11.. IInnppuutt DDaattaa ffoorr EEDDMMSS❶ Daily Traffic Data (For each a/c)a)AircaftType (1)
b)EngineType (1)
c)T.O.W(1)
d)ArrivalTime (1)
e)Dep.Time (1)
f)Terminal/Gate no
g)Total Taxitime
h)GSE/APUassign. (2)
i)TaxiwayAssign.
j)RunwayAssign.
❷ Airside and Ground access vehiclesRoadway ~ Parking traffic / Ground Vehiclesa)Roadwayscoordinat.
b)Vehiclefleet
c)Vehicletraffic/number/time.
d)Trip distance e)AverageSpeed
❸ Stationary Sources (Fuel Tanks, Power plant, Incinerator….)Dimension Material
Used(fuel /Coal..) kl
Emission rate Operationalprofile
EmissionIndice (2)
TrainingFires(fuel typeand mass used)
❹ Meteorological Data
Annual averagetemperature
Annual surface weatherdata (SCRAM format)
Annual Upper Air Data(TD-6201 Fixed LengthBlocksdata format)
(1)Can be found from CFMU / COSAAC system(2)EDMS database contains generic values
AAppppeennddiixx BB
31
Appendix B
Regulations and Standards for Airports Pollutants
For the emissions, there are international (and sometimes local) limits and guidelines. Some countries havetheir own air quality regulations. Percentile values are generally limit values, which should not be exceeded,and limit values are generally the values, which in long term can be harmful to human health. EC (EuropeanCommission) and WHO (World Health Organisation) have limit values and recommend guidelines for differentemissions.
The regulatory function consists of four primary tasks:
� setting standards for ambient air quality and for emissions to air from specified sources,� the issuing of licences or permits for certain activities which cause emissions to air,� monitoring and inspecting activities to ensure that licence or permit conditions are being adhered
to, and� taking enforcement action in cases of non-compliance.
EC Principles for Air Quality Management 5
The objectives of this Directive shall be to:
� establish limit values and, as appropriate, alert thresholds for concentrations of emissions,� assess concentrations of emissions,� obtain adequate information on concentrations of emissions, and� maintain ambient-air quality where it is good and improve it in other cases with respect to emissions
For ambient air quality standards (limit values and guide values):Effects-based approach. Ambient air quality standards (limit values and guide values) for pollutants are setaccording to their scientifically observed or estimated effects on human health and/or on the environment andare not based on the technological or economic feasibility of achieving them.Universality. The same standards apply in general throughout the EU. There are however provisions forspecial zones (e.g. for nature protection).
Legislation Considered in the Air Sector:
Ambient air quality assessment and management:· The Air Quality Framework Directive, 96/62/EC.
Ambient air quality standards (limit values and guidelines):· The Directive on Sulphur Dioxide, Nitrogen Dioxide and Oxides of Nitrogen, Particulate Matter and Lead inAmbient Air, 99/30/EC. This Directive will repeal the following directives:
· The Sulphur Dioxide Air Pollution Directive, 80/779/EEC;
· The Lead in Air Pollution Directive, 82/884/EEC; and
· The Nitrogen Dioxide Air Pollution Directive, 85/203/EEC.
The first of the daughter directives under the Air Quality Framework Directive, the Sulphur Dioxide, NitrogenDioxide and Oxides of Nitrogen, Particulate Matter and Lead in Ambient Air Directive (99/30/EC) is now inforce. It will repeal most of the provisions of the three directives regulating the levels of sulphur dioxide, leadand nitrogen dioxide in air by July 2001, and the rest of the provisions of the directives by January 2005 (forthe directives on sulphur dioxide and lead) and January 2010 (for the directive on nitrogen dioxide).
National governments are ultimately responsible for achieving and maintaining compliance with EC policiesand legislation on air quality issues. Typically the primary responsibility for achieving and maintainingcompliance is delegated to a single national institution e.g. a Ministry, Department or government Agency withresponsibility for the environment.
5 http://europa.eu.int/comm/environment/enlarg/handbook/air.pdf
32
The role of regional and local government in the context of air quality management is important for tworeasons. Most countries have a tiered administrative structure in which certain powers are devolved to theregional (county, department, Länder) or local level of government (local planning authority or municipality).This decentralisation is stronger in federal countries, but exists elsewhere, and usually includes at least someair quality management functions, for example those relating to road traffic and domestic heating boilers.Consequently, the implementation of central government functions would not in itself be sufficient toimplement EC requirements on air quality. Certainly, some air quality issues are most easily and efficientlydetected and resolved at local level.
To ensure a uniform approach, national technical standards must be adopted. These should comply with therequirements of EC directives regulating emissions. Standards need to take account of best practice in thecountry and elsewhere, and of economic constraints on the operators of emission sources.
Transport, will, as in previous years, be a growth area in the year ahead, with increasing congestion on a largescale having the potential to pose problems for compliance of the CO2 Treaty. European legislation can andwill provide and impetus for change. But for real change to occur there must be stronger ties within MemberStates, between their regional and local planners, transport operators and environmental departments toachieve "Sustainable mobility" 6
6 Europa, European Commission web site, DGXI Publication; Guide to Approximation of EU EnvironmentalLegislation, Part 2, Air Quality.
33
EC(Policies andLegislation)
MinistrySet Policy/standards
National Policy andstandards
Planning
StandardsAuthority
Consultants monitoring Reports on air quality
RegulatoryAuthority
Public/PrivateSectors
set tech.standards
Techn.guidance notes
Imple-mentation
Compliance
Permitting Permits/ Limits
Figure 1. Process Flow Chart 7
The directive does not itself specify any air quality thresholds. These are to be set out in “daughter” directives.The first of which is now in force - Directive 99/30/EC relating to limit values for sulphur dioxide, nitrogendioxide and oxides of nitrogen, particulate matter and lead in ambient air. The thresholds will take the form ofa number of different values whose purposes are defined in the directive:A “limit value”;A “target value”;An “alert threshold”; andA “margin of tolerance”.
7 Handbook on the implementation of EC environmental legislation. Overview air quality
34
TTaabbllee 11.. EECC aanndd WWHHOO AAiirr QQuuaalliittyy SSttaannddaarrddss aanndd GGuuiiddeelliinneess
For Nice Airport study, French standards and regulations should be considered (Table 2)
Table 2. (NOx) Normes Dioxyde d'azoteEnµg/m 3
Pas detemps Tolérance Type Origine
400 1 heure(1 hour)
/ Seuil d’alerte(Alert threshold)
Directive CEE 99
200 1 heure / Recommandation(Recommendation)
OMS pour l’Europe96
200 1 heure
18 heures par anApplication au 1er janvier2010(18 hours per year.Application start from01/01/2010)
Valeur limite(Limit value) Directive CEE 99
135 1 heure 17 jours par an(17 days per year)
Valeur guide(Guide value)
PRQA PACA
40 1 heure 50% de l’année (4 380heures) Valeur guide PRQA PACA
40 1 année(1 year)
Application au 1er janvier2010 Valeur limite Directive CEE 99
40 1 année / Recommandation OMS pour l’Europe96
30 1 année Application au 19 juillet 2001Valeur limite pour laVégétation
Directive CEE 99
30 1 année / Recommandation pourla végétation
OMS pour l’Europe96
T a b le 1 . A i r Q u a l i t y L i m i t V a l u e s a n d G u i d e l i n e s f o r N O 2 ( µµµµ g m - 3 )O r g a n is a t io n 5 0 P e r c e n t i le 9 8 P e r c e n t i le 2 4 - H o u r
A v e r a g e1 - H o u rA v e r a g e
E CD i r e c t iv eL im i t V a l u e 2 0 0G u id e V a l u e 5 0 1 3 5W H Og u id e l i n e
1 5 0 4 0 0
T a b le 2 . A i r Q u a l i t y S t a n d a r d s a n d G u i d e l i n e s f o r S u lp h u r D i o x i d e (µµµµ gm - 3 )
O r g a n is a t io n A n n u a lA v e r a g e s
9 8P e r c e n t i le
2 4 - H o u rA v e r a g e
1 - H o u rA v e r a g e
1 0 - M inA v e r a g e
E CD i r e c t iv eL im i tV a l u e
8 0 (≥≥≥≥ 3 4 )1 2 0 (≤≤≤≤ 3 4 )
2 5 0 (≥≥≥≥1 2 8 )3 5 0 (≤≤≤≤1 2 8 )
G u id eV a l u e
4 0 - 6 0 1 0 0 - 1 5 0
W H O 3 5 0 5 0 0
T a b le 3 . W H O H u m a n H e a l t h G u id e l i n e s f o r C a r b o n M o n o x i d e a n dT o l u e n e ( m g m - 3 ) < L o n g h u r s t 9 2 >
O r g a n is a t io n 2 4 h o u re x p o s u r eg u id e l i n e
8 - H o u re x p o s u r eg u i d e l in e
1 - H o u re x p o s u r eg u id e l in e
1 5 - M ine x p o s u r eg u id e l in e
C O 1 0 3 0 1 0 0T o l u e n e 7 .5
35
Once we have the concentration of the pollutants we can show the air quality values by classifying them.(See Table3 and 4)
Calculation of the quality of the air with the defined indices (See Table 3 & 4)
TTaabbllee 33.. AATTMMOO IInnddiicceess
10 Tres Mauvais (Very bad)9 Mauvais (Bad)8 Mauvais7 Mediocre (Poor)6 Mediocre5 Moyen (Medium)4 Bon (Good)3 Bon2 Tres bon (Very good)1 Tres bon
TTaabbllee 44.. CCoonnffiigguurraattiioonn ddeess iinnddiicceess
36
AAppppeennddiixx CC
37
Appendix C
Atmospheric Dispersion Models
The atmospheric dispersion theories’ objective is to provide an analytical description versus space and time ofthe concentration field of a substance emitted in the atmosphere. The heart of this kind of problem is to givean accurate description of turbulence, which plays a great part in transport phenomena.
Two different approaches are usually employed: the Eulerian approach in which the fluid motions are tracedwith respect to a fixed frame of reference and the Lagrangian approach (statistical description) in which theframe of reference is moving at the mean fluid velocity.
The Gaussian plume model, which is the most widely employed in dispersion studies, is inspired from theLagrangian and the Eulerian approaches.
I- The Eulerian Approach
This is the classical way to study fluid motions. First, fundamental equations defining the fluid velocity,pressure, temperature and concentration are written. By the way, a set of four equations (Navier-Stokes,continuity, energy, mass transport) with the addition of adequate boundary and initial conditions, definecompletely the studied system. In the atmosphere, the air can be assimilated to an ideal gas thus the idealgas law has also to be satisfied. Finally, all these equations being coupled, they have to be solvedsimultaneously.
However, for dilute chemical substances in the fluid (order of magnitude of about few ppm and low mixingcoefficient) we can assume that the mass transport equation can be solved separately from the set of thethree other equations. In this case, heat production or absorption by chemical reactions does not affect thefluid temperature much and thus, the fluid motion. This is usually the case in the troposphere where even theabsorption, reflection and refraction phenomena for radiation do not interfere with the fluid motion.Consequently, we can focus on the resolution of the mass transport equation alone.
In order to take turbulence phenomena into account in the mass transport equation, the classical methodconsists in splitting up the fluid velocity and the species concentrations into a mean and a fluctuating term, thisis the perturbation method.
The resulting equation is difficult to solve because of the turbulent transport term, inuc ′′ . Indeed, one of the
most important parts in turbulent flow studies is to describe these terms with respect to the mean physicalquantities. The resulting approximated relations are called closure relations and are at the origin of the mainerrors generated by the Eulerian approach.
The most widely used closure relation derives from the Prandtl mixing-length theory or the K-theory :
j
nijin x
cKuc
∂∂
−=′′
ijK is the turbulent diffusion coefficient tensor. iu is the fluid velocity component along the ith axis and nc ,
the concentration of the nth species.
Finally, the Eulerian fundamental mass transport equation becomes :
),(),,,( 1 trSccRx
cK
xx
cu
t
cnnn
i
nii
ii
ni
n �
�� ++���
����
�
∂∂
∂∂=
∂∂
+∂
∂
Let’s recall that iu is the mean fluid velocity component along the ith axis, nc , the mean concentration of
the species ‘n’. ),,,( 1 �� nn ccR is the source term which expresses the production or disappearance
38
of the species ‘n’ by chemical reaction. ),( trSn
�
is the source term which expresses the addition or
subtraction of the chemical substance ‘n’ at location ),,( zyxr�
and time t .
II- The Lagrangian Approach
This time, the frame of reference is moving at the mean fluid velocity v�
. From a fixed point in this frame, onlyturbulent velocity fluctuations are visible. The principle of this approach is to follow the random motion of thefluid particles. The probability to have a given number of particles in a given location is the probability to havea given concentration at that location. After having evaluated the probability density function of speciesconcentration, a statistical definition is used to describe the mean concentration field.
In the Eulerian approach, a perturbation equation had to be established and solved to find the meanconcentration field. In the Lagrangian approach, a statistical relation giving directly the mean concentrationfield will be established (no perturbation equation to be solved).The fundamental Lagrangian relation is :
� � � �� � �+∞
∞−
+∞
∞−
′′′′′′+=t
t
ooooo
o
tdrdtrStrtrQrdtrctrtrQtrc 33 ),(),/,(),(),/,(),(�������
),/,( trtrQ ′′��
is the probability density function that a fluid particle located at r ′� , at the initial time t ′ , could
be located at r�
, at time t .
The first term in the right hand side of this equation describes the concentration due to the behaviour of the
fluid particles already present in the system at the initial time ot . The second term describes the concentration
due to a source of particles emitting between the initial and the observation times.
III- Conclusion on the Eulerian and Lagrangian approaches
The aim of dispersion studies is to calculate the mean concentration versus time and space of a givenspecies. In the Eulerian approach, the mean species concentrations are calculated by resolving thefundamental Eulerian equation. In this approach, the major problem is to establish an appropriate closure
relation (an expression for inuc ′′ ). In the Lagrangian approach, the mean species concentrations are
calculated using a statistical formula (the fundamental Lagrangian relation). In this approach, the majorproblem is to establish an accurate transition probability density function, ),/,( trtrQ ′′��
.
Once the needed relations established, except for very simple cases for which analytical solutions areavailable (which occurs very seldom in real industrial cases), mean concentration calculations are performedby numerical simulation. The finite-difference method is then usually employed. The advantages anddisadvantages of numerical simulations based on Eulerian and Lagrangian approaches are summarisedbelow.
1- The Eulerian numerical model
_ Fluid velocities described in the fundamental Eulerian equation are the same as those directlymeasured by anemometers.
_ The advection term in this equation (second term on the left hand side) leads to an artificial diffusionphenomenon because of the finite differentiation.
_ An accurate numerical description of the system requires grid dimensions much smaller than the plumedimensions. This condition is very difficult to satisfy near point sources.
_ The fundamental Eulerian equation can be used when characteristic lengths and times for the meanconcentration (in order to observe variations in the mean concentration) are much greater than theturbulence characteristic lengths and times. This condition is not satisfied near strong isolated sources.
39
2- The Lagrangian numerical model
_ No need to handle complicated perturbation equations whose resolution involves importantapproximations (such as in the K-theory).
_ In atmospheric dispersion, if chemical reactions are neglected, ),/,( trtrQ ′′��
is dependent on
meteorological conditions only. Otherwise, ),/,( trtrQ ′′��
is also dependent on the nature of the
species.
_ It is difficult to take chemical reactions into account. In its basic formulation, chemical reactions have notbeen implemented in the fundamental Lagrangian relation.
_ It is not easy to convert measured meteorological data (directly useable in the Eulerian model) in orderto be used in the Lagrangian model.
IV- The Gaussian model
According to the Eulerian and Lagrangian approaches, it can be shown that for idealised cases (mainlystationary and homogeneous turbulence), the mean concentration of a chemical substance emitted in theatmosphere from a point source (no matter whether instantaneous or continuous) has a Gaussian probabilitydensity function.
Here, the Gaussian model refers to the stationary mean concentration formula of a plume. The plume is thecloud formed by the chemical species rejected in the atmosphere from a continuous elevated point source.
When turbulence is stationary and homogeneous, in the fundamental Lagrangian relation, the transitionprobability density function has a Gaussian form and does not depend on r
�
, r ′� , t and t ′ but on the
differences rr ′− ��
and tt ′− : ),(),/,( ttrrQtrtrQ ′−′−=′′ ����
.
When the mean wind is parallel to the x coordinate axis, we can then choose the following form for thetransition probability density function :
[ ]
��� ���� ������� ������ ��
��
termvertical
zz
Z
termhorizontal
yyttuxx
YX
ZYX eetrtrQ2
2
2
2
2
2
2
)(
2
)(
2
)(
2
1
2
1),/,( σσσ
σπσσπ
′−−′−−
′−−′−−
×=′′
where, Xσ , Yσ and Zσ (standard deviations of the distances along the x, y and z coordinate axes, travelled
by the fluid particle between the instants t ′ and t ) are functions of )( tt ′− .
This equation is valid when there are no limits in space for coordinates x, y and z. Now, in real cases, limits doexist, at least, for the vertical coordinate z because of the ground (z=0) and usually a particular atmosphericboundary layer (for instance, the mixing height z=H at which a temperature inversion begins). In order to takethese vertical boundary conditions into account, in the relation just above, the vertical term is modified :
��
�
�
��
�
�+=′′
′+−′−−
2
2
2
2
2
)(
2
)(
2
1),/,( ZZ
zzzz
Z
eetztzQ σσ
σπwhen the ground is not permeable to the species (total
reflection).
��
�
�
��
�
�−=′′
′+−′−−
2
2
2
2
2
)(
2
)(
2
1),/,( ZZ
zzzz
Z
eetztzQ σσ
σπwhen the ground is completely permeable to the
species (total absorption).
The final solution for the mean plume concentration has been inspired by the Lagrangian approach for acontinuous point source :
40
( )� ��
�
�
��
�
�+
′′′
′= ′
+−′
−−′
−′
′−−
∞→
tt
hz
t
hz
t
y
t
tux
ZYXt
stationary
LagrangianZZYX eee
ttt
tdqzyxc
0
)(2
)(
)(2
)(
)(2)(2
)(
3
2
2
2
2
2
2
2
2
)()()(2lim),,( σσσσ
σσσπThe source is now located at hz =′ and we have total reflection on the ground without an upper limit in theatmosphere. The integration of this equation with the slender plume approximation (molecular diffusion isneglected in the mean wind direction) leads to :
��
�
�
��
�
�+=
+−
−−−
2
2
2
2
2
2
2
)(
2
)(
2
2),,( ZZY
hzhzy
ZYplume
eeeu
qzyxc σσσ
σσπ
where+∞<≤ z0 and total reflection at the ground (z=0).
the mean velocity is parallel to the x coordinate axis : iuv�
� = .
the source is a continuous point source of intensity q, located at (0,0,h).the turbulence is stationary and homogeneous.the slender plume approximation is applied.
Xσ , Yσ and Zσ are functions ofu
x.
By the way, for total absorption at the ground (z=0) :
��
�
�
��
�
�−=
+−
−−−
2
2
2
2
2
2
2
)(
2
)(
2
2),,( ZZY
hzhzy
ZYplume
eeeu
qzyxc σσσ
σσπ
It can also be shown that the Eulerian solution for a continuous point source also has a Gaussian form.Consequently, it is possible to obtain these same solutions by resolving the fundamental Eulerian equation. Bythis method, it is also possible to take other kinds of boundary conditions into account (such as partialpermeability).
For instance, when the reflection is total at z=0 and z=H and the turbulent transport is neglected in the meanfluid velocity direction (slender plume approximation), the solution of the fundamental Eulerian equation isas follows :
��
�
�
��
�
���
���
���
���
�+=��
���
�−∞+
=
−
�2
1
2
22
2
2
coscos2
1
2
2),,(
z
y H
n
n
y
yplume
eeH
hn
H
zn
uH
qzyxc
σπσ ππ
σπwhere
Hz <≤0 and total reflection at the z=0 and z=H.
the mean velocity is parallel to the x coordinate axis : iuv�
� = .
the source is a continuous point source of intensity q, located at (0,0,h).the slender plume approximation is applied.
Yσ and Zσ are functions ofu
x.
Until now, we have established different theoretical descriptions for the Gaussian plume formula in onlyidealised situations (as is very often the case when it is question of providing analytical solutions). Theadaptation of these idealised formulae to real industrial situations consists in adjusting the so-called dispersion
parameters which are the standard deviations, Yσ and Zσ .
These parameters can be measured on the field, also semi-empirical formulations adapted to different casesdo exist. The final adjustment of these dispersion parameters is assured by the fitting of the Gaussian formulato experimental concentration points.
41
In particular, the dispersion parameters depend on the fluid characteristic parameters extracted from theboundary layer theories :
*u : friction velocity (m/s).
L : Monin-Obukhov length (m).f : Coriolis parameter (rad/s).
iz : the mixing length (m).
*w : convective velocity scale (m/s).
oz : roughness (m).
h : source height (m).κ : Von Karman constant.
Because most of the measurements are made close to the ground level (for practical reasons), Yσ is better
known than Zσ . Furthermore, for sources emitting close to the ground, the vertical terms in the Gaussian
plume formula are not fully valid. For all these reasons, it is most difficult to fit the vertical terms in theGaussian analytical formula with the experimental measurement points.
Finally, the fitting of these dispersion parameters permits the atmosphere stability state to be taken intoaccount and also to compensate the fact that, in reality, turbulence is seldom stationary and homogeneous.
V- Conclusion on the Gaussian model
In the Eulerian approach, the perturbed mass transport equation had to be solved in order to determine themean concentrations field. In the Lagrangian approach, the classical mass transport equation was solved anda statistical relation used to establish the mean concentrations field.
With the Gaussian model, there is not any equation to be solved. A type solution to the mass transportequation is directly used to calculate the mean concentrations. This solution has been inspired by the Eulerianand the Lagrangian approaches and adapted to the actual industrial cases.
The adaptation to reality is provided both analytically, by making simplifying assumptions and mainlyempirically, by doing measurements on the field and fitting the analytical solutions to the experimental ones.
In the Gaussian plume formula, the parameters which can be adjusted according to the measurements, are
the standard deviations (variances) Yσ and Zσ . This is why, the latter are called the dispersion parameters.
Their values take the atmosphere stability into account and the fact that turbulence is not stationary andhomogeneous.
For each case, several analytical or empirical methods do exist for evaluating Yσ and Zσ . Providing a more
and more accurate description of Yσ and Zσ is an important research axis for dispersion studies.
Because a good deal of simplifying assumptions are used, the Gaussian model is less precise than theLagrangian and the Eulerian approaches but, at the same time, it is very easy to use and offers a goodcompromise between accuracy and computational speed.
42
VI- References
[Bro] Brown R. A. (1991) : Fluid Mechanics of the Atmosphere
[Diu] Diu B. (1996) : Physique Statistique
[Gar] Garratt J. R. (1992) : The Atmospheric Boundary Layer
[Han] Hanna S. R. and al. (1982) : Handbook on Atmospheric Diffusion
[Jac] Jacobson M. Z. (1999) : Fundamentals of Atmospheric Modeling
[Pan] Panofsky H. A. and Dutton J. A. (1984) : Atmospheric Turbulence
[Pas] Pasquill F. (1974) : Atmospheric Diffusion
[Sei] Seinfeld J. H. (1986) : Atmospheric Chemistry and Physics of Air Pollution
[Stu] Stull R. B. (1988) : An Introduction to Boundary Layer Meteorology
[Zan] Zannetti P. (1990) : Air Pollution Modeling
AAPPPPEENNDDIIXX DD
43
APPENDIX D
Preliminary analysis of EDMS dispersion simulation results
EDMS Gaussian-based dispersion simulations have been performed on a first airport sample in order to getused to the software and test its capabilities. Although not actually representative of it, the model has beeninspired by the Nice Airport. The lack of experimental data forced us to pronounce only from a qualitative pointof view on the results.
One runway taken in the two directions has been implemented with arrivals and departures in equal quantity.Different aircrafts have been associated with several gates and taxiways. There are two parking lots withroadways all around and a single stationary source of pollution. The model is run for 24 hours with a time stepof one hour. CO is the chosen pollutant and receptor points at which its concentrations are calculated havebeen placed at a height of 1.8m above ground.
Activity profiles for pollution sources and meteorological data are given hour-by-hour in tables D1-a, D1-b andD2. An overview of the airport model with network of receptors is given in figure D1. For the simulation ofreference, all the pollution sources (aircrafts, gates, taxiways, roadways, parking) except the stationary sourceare emitting together following the activity profile given in table D1-a. Besides, for any simulation performed,the stationary source is always emitting at its maximum rate following de default activity profile (table D1-b).
Six different simulations have been performed, the first one (Run_1) being the reference. The second run(Run_2) differs from the first one by the fact that meteorological conditions for the time-slice 12 are the sameas the 6th. For the third run (Run_3), all sources are emitting at a maximum rate for all time-slices (the defaultactivity profile shown in table D1-b is used). In the fourth run (Run_4), all sources are emitting in accordancewith the default activity profile and all wind directions are forced to 122°. In Run_6, all sources are emittingfollowing the default activity profile and all meteorological input parameters, except the wind direction, areforced to be the same as the 4h ones (in other words, stable atmospheric conditions are imposed during theentire run). The last run (Run_5) is similar to the reference one except that receptors are placed at a height of10m instead of 1m80.
EDMS puts the calculation results in output files that can be post-processed with the help of other software asArcView and Excel. Thus, we have at our disposal two kinds of representation to interpret results. Horizontalmaps with concentrations at each receptor point for a given time-slice and concentration curves versus time ata given receptor point. The last kind of presentation is very useful to compare simulation results to theexperimental ones (in the case we have any).
The results shown and discussed below are these of the concentrations generated by the additional effect ofall pollution sources. The contribution brought to the total pollution by each individual source or group ofsources is not investigated in the present study.
44
Table D1-a: Activity profile for the following sources : aircraft LTO cycles (includingrunway, taxiway, gates), parking lots and roadways.
Time slice Activity profile (%) Time slice Activity profile (%)1h 0 13h 852h 0 14h 653h 0 15h 954h 0 16h 855h 20 17h 706h 50 18h 807h 65 19h 558h 70 20h 659h 60 21h 40
10h 65 22h 1511h 90 23h 512h 100 24h 10
Table D1-b: Default activity profile. The single stationary source is always emitting inaccordance with this profile.
Time slice Activity profile (%) Time slice Activity profile (%)1h 100 13h 1002h 100 14h 1003h 100 15h 1004h 100 16h 1005h 100 17h 1006h 100 18h 1007h 100 19h 1008h 100 20h 1009h 100 21h 100
10h 100 22h 10011h 100 23h 10012h 100 24h 100
Figure D1: Overview of the airport with homogeneous network of receptors (small dots).
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Y = 0m
X=
0m
N
E
S
W
Taxiway
Parking
RoadwaysGates
Runway
Stat. Source
+
ObservationPoint
Queue
Queue
45
1- Meteorological input data
Table D2: Meteorological parameters typical of a summer day (July) in northern latitudes (about 40-45°N). The‘Stability level’ column has been added for the purposes of this report.
Timeslice(h)
H(W/m2)
u*(m/s)
w*(m/s)
dθ/dz(°/m)
z_CBL (m)
z_MBL (m) L (m)
Stabilitylevel
z0
(m)
Bo-wenratio Albedo
W_speed(m/s)
W_dir.(°)
z_anemo (m)
T_ref(K)
z_Τ(m)
1 -31.7 0.434 -9 -9 -999 657 232.8 Stable 0.1 1 1 4.6 158 6.1 297 22 -22.6 0.278 -9 -9 -999 352 85.7 Stable 0.1 1 1 3.1 117 6.1 296.4 23 -24.3 0.275 -9 -9 -999 332 77.6 Stable 0.1 1 1 3.1 153 6.1 296.4 24 -12.3 0.169 -9 -9 -999 164 35.2 Stable 0.1 1 1 2.1 122 6.1 296.4 2
5 -999 -9 -9 -9 -999 -999-99999 -99999 0.1 1 0.5 0 0 6.1 297 2
6 -9.6 0.18 -9 -9 -999 176 54.7 Stable 0.1 1 1 2.1 104 6.1 298.1 27 -9.6 0.18 -9 -9 -999 176 54.9 Stable 0.1 1 0.86 2.1 103 6.1 299.3 28 8.2 0.307 0.221 0.005 48 391 -318.1 Unstable 0.1 1 0.6 3.1 103 6.1 300.4 29 20.5 0.454 0.554 0.005 300 703 -411.2 Unstable 0.1 1 0.53 4.6 115 6.1 300.9 210 82.2 0.513 1.044 0.007 501 845 -148.5 Unstable 0.1 1 0.51 5.1 109 6.1 303.8 211 74.4 0.662 1.063 0.005 583 1238 -352.3 Unstable 0.1 1 0.5 6.7 111 6.1 303.8 212 113.1 0.808 1.317 0.005 730 1668 -421.8 Unstable 0.1 1 0.5 8.2 139 6.1 304.9 213 86 0.806 1.245 0.005 812 1664 -549.8 Unstable 0.1 1 0.5 8.2 118 6.1 304.3 214 114.2 0.866 1.432 0.005 929 1848 -513 Unstable 0.1 1 0.5 8.8 95 6.1 304.3 215 75.6 1.007 1.259 0.005 954 2314 -998 Unstable 0.1 1 0.5 10.3 95 6.1 302 216 32.2 1.004 0.952 0.005 967 2315 -998 Unstable 0.1 1 0.5 10.3 95 6.1 300.9 217 46.5 0.86 1.083 0.005 987 1868 -998 Unstable 0.1 1 0.51 8.8 76 6.1 300.9 218 13 0.751 0.709 0.005 993 1517 -998 Unstable 0.1 1 0.52 7.7 81 6.1 300.4 219 2 0.749 0.377 0.005 993 1493 -998 Unstable 0.1 1 0.58 7.7 68 6.1 300.4 220 -36.9 0.694 -9 -9 -999 1336 820.2 Neutral 0.1 1 0.78 7.2 89 6.1 300.4 221 -34.3 0.645 -9 -9 -999 1196 707 Neutral 0.1 1 1 6.7 106 6.1 299.9 222 -39.5 0.743 -9 -9 -999 1471 938.7 Neutral 0.1 1 1 7.7 105 6.1 299.9 223 -36.9 0.694 -9 -9 -999 1335 818.8 Neutral 0.1 1 1 7.2 108 6.1 299.9 224 -36.9 0.694 -9 -9 -999 1331 818.8 Neutral 0.1 1 1 7.2 107 6.1 299.9 2
In table D2, above, the ‘Stability level’ column has been added for the purposes of this report only in order tospecify the state of the atmosphere in accordance with the Monin-Obukhov length (L). From the boundarylayer theories, it is well known that the atmosphere is stable for L>0, unstable for L<0 and neutral forL>>z_observation. We can assume this last condition satisfied when L is two or three orders of magnitudehigher than z_observation (between 2m to 6.1m in this example).
Once the convective stability level is established with the Monin-Obukhov length, it is possible to estimate themechanical instability with the help of the wind velocity. The higher is the wind speed, the stronger is themechanical turbulence intensity. This can be easily checked with the mechanical boundary layer height(z_MBL) and the friction velocity (u*). u* is characteristic of the turbulent momentum vertical transpor in thesurface layer. With the increase of wind speed, during stable, unstable and neutral conditions, we can see thatz_MBL and u* are also increasing.
We can also notice the tendency of sensible heat flux (H) and temperature (T_ref) to increase rapidly between5-6h and 14h, to decrease slowly between 15h and 19h and to stabilise between 20h and 4h. The presentmeteorological data are typical of a summer day (July) in northern latitudes (about 40-45°) when the sun risesat about 5am and sets after 20h. Sun radiation (heating the ground) usually reaches its maximum intensity atabout 14h. This matches well with the convective boundary layer height values (z_CBL) which, duringunstable meteorological conditions, increase rapidly until 14h then slow down reaching their maximum (ataround 990m) between 15h and 19h.
46
2- Thermal and mechanical turbulence
In unstable meteorological conditions, the vertical mixing of the atmosphere is provided first by free convection(thermal origin) and second by mechanical turbulence (wind plus relief). Pollutants emitted at each source aretransferred to the upper layers of the atmosphere, which leads to concentrations dilution in the air. In otherwords, an unstable atmosphere is very favourable for dispersion. Conversely, high concentrations aregenerated for stable atmospheric conditions (unfavourable for dispersion).
Curve D1 : For Run_4, CO concentrations versus time at a receptor located at point (-40m, 0, 1.8m). Defaultactivity profile for all sources and wind direction equal to 122° during all run.
These atmospheric stability effects on concentrations field are well displayed in Run_4 where, at any time-slice, all sources are emitting at their maximum rate and wind directions are constant (see curve D1 above).
In curve D1, demarcations between stable (1h to 7h), unstable (8h to 19h) and neutral atmospheric conditionsare perfectly visible and confirm the Monin-Obukhov length (L) predictions. The point (x=-40 ; y=0 ; z=1.8) islocated near the coordinates origin and has been chosen because concentrations are significant there (withhigh values) and this receptor point is not too close to a pollution source (figure D1). In the present exampleand also further in the report, we wont consider simulation results for hour 5 because of the lake of sufficientmeteorological data at that time-slice (see table D2).
As the model does not take chemical reactions into account, the choice of CO as a pollutant is not of anyimportance for the present study. Our aim is to check the accordance between input data (mainly meteorologyand sources’ activity profiles) and output results on a qualitative point of view only.
3- Wind direction
The wind direction effects on pollution dispersion can be evaluated comparing Run_4 to Run_3. In that lastrun, all sources are also emitting at a maximum rate for all time-slice and more, wind directions are varying(see curve D2, below). The pollution plums are brought into alignment with the wind direction. Thus, if thepoint (-40 ; 0) is placed downwind compared to important pollution sources, then concentrations will also beimportant at that location. Otherwise, even if pollution sources are emitting at a maximum rate, when the winddirects pollution plumes far from the point (-40 ; 0), concentrations will be low at that location. This is typicallywhat occurs at time-slice 12, which explains the very low concentration values at that time in curves D2, D3,D5 and D6, in spite of the fact that 12h is a peak hour.
CO concentration versus time at the receptor of coordinates(x=-40m ; y =0m)
0
500
1000
1500
2000
2500
0 5 10 15 20
t in (h)
Cin
( µµ µµg/
m3 )
Stable Unstable Neutral
47
CO concentration versus time at receptor of coordinates(x=-40m ; y =0m)
0
2000
4000
6000
8000
10000
12000
14000
16000
0 5 10 15 20
t in (h)
Cin
( µµ µµg/
m3 )
Curve D2 : For Run_3, CO concentrations versus time at a receptor located at (-40m, 0, 1.8m). Default activityprofile for all sources.
Let’s notice that in Run_3, above, wind direction effects are mixed to the atmosphere stability ones. To have abetter understanding of the wind direction influences, a simulation where the atmosphere stability level isconstant is run. Thus, in Run_6, with the default activity profile (table D1-b) applied to all sources, stableatmospheric conditions at each time slice have been chosen. All meteorological conditions are identical to the4h ones except for the wind direction which is varying as usual.
CO concentration versus time at receptor of coordinates(x=-40m ; y =0m)
05000
100001500020000250003000035000400004500050000
0 5 10 15 20
t in (h)
Cin
(g/
m3 )
Curve D3 : For Run_6, CO concentrations versus time at a receptor located at (-40m, 0, 1.8m). Default activityprofile for all sources and identical (stable) meteorological conditions during all run.
In curve D3 above, we can see that to avoid pollution at the observation point (-40 ; 0 ; 1.8) wind direction isthe most unfavorable at time-slice 17-18 (75° to 80°) and most favorable at time-slice 1, 3 and 12 (140° to160°). Indeed, when the wind blows from a direction between 140° and 160°, the point (-40 ; 0 ; 1.8) is notsituated downwind powerful pollution sources (just the middle of the runway which, with the presentmeteorological conditions, can not really affect pollution level at 1.8m above ground and few taxiways). On the
48
other hand, when the wind blows from a direction between 75° to 80°, the observation point is situateddownwind gates and taxiways including one where aircrafts are queuing before takeoff. Unfortunately, theinfluence on total pollution of each source taken individually has not been investigated in the present study(mainly by lake of time). Otherwise, this could have shown that ‘queues’ are one of the most importantpollution sources on the airport.
4- Source activity profiles
From now, let’s focus on the global pollution computed on the entire numerical area. From stabilityconsiderations only and according what have been already said in paragraphs above, during the 24-hourreference simulation, higher concentrations are most likely to be observed for the time-slice 4 because theatmosphere is then stable and the wind speed the lowest (2.1m/s). By a similar reasoning, insofar as theatmosphere is unstable and the wind speed the highest (10.3m/s), lower concentrations are likely to beobserved for time-slices 15 to 16.
Now, on the first simulation results (Run_1), highest concentrations are observed for 6-7h instead of 4h andthe lowest ones between 1h and 5h instead of 15-16h (maps D1-a, D1-b and D2-a, D2-b).
These differences can be explained by the non-uniformity of the sources’ emission profiles (table D1-a).Between 1 to 4h, only the stationary source is emitting. However, this is not visible on the maps, above,because of the choice of concentration scale which is too coarse to display very low concentrations. Between11-20h, all sources are emitting at more than 50% but the atmosphere is unstable which is very favourable for
Stable atmosphere and low wind (2.1m/s).Activity profile : 0% ; wind direction : 122°
MapD1-a: Results ofRun_1 (of reference) at 4h.
Unstable atmosphere and strong wind (10.3m/s).Activity profile: 95%, wind direction : 95°
MapD1-b: Results ofRun_1 (of reference) at15h.
MapD2-a: Results ofRun_1 (of reference) at 6h.
Stable atmosphere and low wind (2.1m/s).Activity profile: 50% ; wind direction : 104°
Stable atmosphere and low wind (3.1m/s).Activity profile: 0% ; wind direction : 117°
MapD2-b: Results ofRun_1 (of reference) at 2h.
49
dispersion (for low concentrations). When the atmosphere is stable (between 1-7h), thus favourable for highconcentrations, the maximum emission rate occurs for 6-7h (about 65% to 70%). Thus, no matter the stabilitystate of the atmosphere, lowest concentrations occur when most of the pollution sources are not emitting.Besides, highest concentrations occur when sources are emitting at a high rate in a stable atmosphere.
To check these assumptions, a simulation with homogeneously emitting sources during all run, have beenperformed (Run_3 and Run_4). In maps D3-a and D3-b above, we can see that, in the case of uniformlyemitting sources, the maximum pollution is observed at 4h (stable atmosphere and low wind) and theminimum for 15h (unstable atmosphere and strong wind).
Also, by a similar reasoning, in the reference simulation (Run_1), according to the sources’ activity profile(table D1-a), we could have expected to see the highest concentrations to occur at peak-hour (12h) instead ofat the time-slice 6 (maps D4-a and D2-a).
This is explained again by the meteorological conditions (see table D2), which are stable with a low windspeed (2.1m/s) at 6h and unstable with a higher wind speed (8.2m/s) at 12h. When we impose the samemeteorological conditions at these two time-slices (Run_2, maps D2-a and D4-b), we notice thatconcentrations are about two times higher at 12h than at 6h. This is more visible in curve D4, below, whereconcentrations versus time have been plotted at a given observation point (-40 ; 0 ; 1.8).
MapD4-a: Results ofRun_1 (reference) at 12h.
Unstable atmosphere and strong wind (8.2m/s).Activity profile: 100% , wind direction : 139°
MapD4-b: Results of Run_2at 12h.
Stable atmosphere and low wind (2.1m/s).Activity profile: 100% ; wind direction : 104°
MapD3-a: Results ofRun_4 at 4h.
Stable atmosphere and low wind (2.1m/s).Activity profile: 100% ; wind direction : 122°
MapD3-b: Results ofRun_4 at 15h.
Unstable atmosphere and strong wind (10.3m/s).Activity profile: 100% , wind direction : 122°
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Curve D4 : For Run_2, CO concentrations versus time at a receptor located at (-40m, 0, 1.8m). The secondpeak is due to the fact that we have forced 12h meteorological conditions to be the same as the 6h ones.
In this curve, the second peak is caused by the fact that identical meteorological input parameters are appliedto time-slices 6 and 12. We can read about 6mg/m3 for 6h and about 12mg/m3 at 12h. This is in very goodagreement with the fact that emission rates are of 50% at 6h and 100% at 12h.
5- Observation height
Simulation results for Run_5 (receptors placed at 10m above ground instead of 1m80) seem similar to thereference ones as far as the form of the pollution plumes is concerned (for instance, maps D5-a and D5-bshow simulations results for 7h).
We can notice that the pollution plumes at 10m above ground are a bit more horizontally displaced comparedto the locations of the sources in the downwind direction than the pollution plumes at 1.8m. This seems to bethe case at any time-slice and not only at 7h. Indeed, the plumes are bent over by the effect of wind. Atdifferent altitudes, when taking a horizontal cut through the plumes, the later can then appear displaced withrespect to the sources.
We can also compare concentration values at point (-40, 0, 1.8) calculated in Run_1 (reference simulation) tothe ones at point (-40, 0, 10) calculated in Run_5 (see curves D5 and D6, below).
MapD5-b: Results ofRun_5 at 7h.Level : 10m
Stable atmosphere and low wind (2,1m/s).Activity profile: 65% , wind direction : 103°
MapD5-a: Results ofRun_1 (of reference) at 7h.Level : 1m80
Stable atmosphere and low wind (2,1m/s).Activity profile: 65% , wind direction : 103°
CO concentration versus time at receptor of coordinates(x=-40m ; y =0m)
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Curve D5: For Run_5, CO concentrations versus time at a receptor located at (-40m, 0, 10m).
Curve D6: For Run_1 (reference), CO concentrations versus time at a receptor located at (-40m, 0, 1m80).
The variation of these two curves, representing concentrations versus time at two points vertically alignedduring an identical run, is very similar. Only concentration values seem different. At 10m, concentrations arelower. This can be explained by the fact that point (-40 ; 0 ; 10) is more distant from the plume centreline thanthe point (-40 ; 0 ; 1.8). To check these assumptions and draw out any more conclusions, we need to look atseveral other points located in different places and at different levels. This study is still in progress.
6- Conclusion
Although we do not pretend to have covered all possible questions, we have presented here a preliminaryqualitative analysis of EDMS dispersion simulation results. The model has been partly inspired by Nice airport,and missing data were substituted by available ones (for instance, our meteorological data have been
CO concentration versus time at receptor of coordinates(x=-40m ; y =0m)
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CO concentration versus time at receptor of coordinates(x=-40m ; y =0m)
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extracted from the EDMS tutorials and do not correspond to Nice weather conditions). We have made a realeffort to use aircraft types actively employed in Europe and to implement realistic emission rate profilesespecially for the LTO cycles.
From a qualitative point of view, at first sight, it appears that EDMS simulation results are in good agreementwith the input data. We have investigated the effects of atmospheric stability level (including free convectionand mechanical turbulence), of wind direction, of sources’ emission rates and receptor height.
This study is still in progress. We have to set up a more realistic and complete input database. We need toexplore the effect of each individual source or group of sources on global pollution. Display of results can beimproved mainly in order to reveal low as well as high concentration values. It would be interesting to comparethe present results to other simulation results computed with different software (Gaussian, Eulerian andLagrangian). In the future, we expect also to have concentration readings on the field in order to complete thisstudy with a quantitative approach and check the validity of the model.
AAppppeennddiixx EE
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Appendix E
ArcView Presentation for Dispersion Model Results
(Same from hour 1 untill hour 5)
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