spatial interpolation of aircraft noise and land …airport opened in 24th, may 2008 as an...

SPATIAL INTERPOLATION OFAIRCRAFT NOISE AND LAND USE

STUDY AT KEMPEGOWDAINTERNATIONAL AIRPORT

LIMITED BANGALORE USING GISAND REMOTE SENSING

Rajakumara H.N1, Jayaram A2, Arati Reddy Nilap3

Department of Civil Engineering,Kempegowda International Airport, BIAL,

Bangalore, [email protected],[email protected],

[email protected]

June 24, 2018

Abstract

This study involves Kempegowda International AirportLimited (KIAL), Bangalore as the study area for aircraftnoise intensity levels around the airport for a period ofthree months and Land Use Land Cover (LU/LC) studyfor a period of ten years. For this purpose, four noisegauges all located around the airport in four different direc-tions namely north (Bychapura), south (Kadayarappana-halli), east (Chennahalli) and west (Sadahalli). Noise in-tensities are recorded on every aircraft or noise event con-tinually, out of which only aircraft noise levels on 24-hoursequivalent are taken for the study. These noise intensitiesare the inputs for spatial interpolation over the area in the

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International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 6839-6863ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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GIS software and results are represented pictorially. Theresults show that the noise levels are within the permissi-ble range of 60 75 dB. For the LU/LC study, the satelliteimages of 1999 and 2008 are considered and the parame-ters considered are single crop, double crop, water bodiesand settlements. The results show that there is significantincrease of 9.1

Keywords: Aircraft Noise Intensity, Land Use LandCover, Spatial Interpolation, GIS, Remote Sensing.

1 INTRODUCTION

Kempegowda International Airport Limited (KIAL) is an Interna-tional Airport located in Bangalore the capital city of the Indianstate of Karnataka. Spread over 4,000 acres (1,600 ha), it is lo-cated about 40 kilometres (25 mi) north of the city near the villageof Devanahalli. It is owned and operated by Bangalore Interna-tional Airport Limited (BIAL), a publicprivate consortium. Theairport opened in 24th, May 2008 as an alternative to increasedcongestion at HAL Airport, the original primary commercial air-port serving the city. It is named after Kempegowda I, the founderof Bangalore. As of 2016, Kempegowda Airport is the third busiestairport by passenger traffic in the country, behind the airports inDelhi and Mumbai and is the 35th busiest airport in Asia. It han-dled over 22.2 million passengers in 2016 with little less than 500aircraft movements a day. The airport also handled about 314,060tonnes (346,190 short tons) of cargo.

The airport consists of a single runway and passenger terminal,which handles both domestic and international operations. A sec-ond runway and terminal are in the early stages of planning andconstruction. In addition, there is a cargo village and three cargoterminals. The airport serves as a hub for Air Asia India, Air IndiaRegional, Air Pegasus and Jet Airways.

A. Pollution Due To Airports1) Water Pollution A particularly important facet of airportoperation is the impact of the pollution caused by runoff waters.Runoff waters at an airport may contain relatively high concentra-tions of different contaminants resulting from the various aspects

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International Journal of Pure and Applied Mathematics Special Issue

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of its operation: de/anti-icing operations, washing and cleaningoperations, spills of fuel and lubricants, exhaust fumes, and weedremoval. The pollution caused by airport operations affects soil,surface waters, and groundwater. This issue is important to var-ious stakeholders, particularly those residing in communities nearairports, whose health, property values, and quality of life can beaffected by such environmental impacts.2) Air Pollution Airport is an extremely complex emission sourceof airborne pollutants that can have a significant impact on the en-vironment. Indeed, several airborne chemicals emitted during air-port activities may significantly get worse air quality and increaseexposure level of both airport workers and general population livingnearby the airports. In recent years airport traffic has increased andconsequently several studies investigated the association betweenairport-related air pollution and occurrence of adverse health ef-fects, particularly on respiratory system, in exposed workers andgeneral population resident nearby.3) Noise Pollution The noise is an unwanted sound that maycause some psychological and physical stress to the living and non-living objects exposed to it. Noise level is a measure of the energy ofsound which is expressed in units of decibels or dB. Noise thresh-old is the limit maximum noise level permitted dumped into theenvironment from the undertaking or activity so as not to causedisruption of human health and environmental comfort. Many air-ports faced noise disturbance, around an airport is caused by air-craft in the air; reverse thrust used by aircraft to slow down afterlanding; aircraft on the ground, including taxiing, engine testingand running on-board electrical generators; departing aircraft thatstray from the Preferred Noise Routes (PNRs); road traffic to andfrom the airport. In addition, operation and implementation of theairport and all its activities may cause impact on workers, commu-nities, and the environment around the airport. Noise representsone of the most significant environmental challenges associated withaircraft and airport operations. Over the years, there have beensignificant improvements based on technology evolution, effectivenoise abatement procedures and other measures. Aircraft todayare 75% less noisy than they were 30 years ago. At the same time,given the industrys growth and the presence of population agglom-erations near airports, large parts of population are still affected by

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aircraft noise. In earlier days, airports were located very far fromurban areas.

Due to increases of population, rapid urbanization and indus-trialization, airports are very close to vicinity of city. Reducing theeffect of aircraft noise on people and communities is one of ICAOs(International Civil Aviation Organization) main priorities. Air-port noise due to aircraft is the most contentious environmental is-sue associated with airport and aircraft operators. Although, thereare many other noise sources present at the airport, aircraft noise isreadily identifiable and tends to stand as annoyance for many peo-ple (Airbus 2003). Some of the possible effects related to aircraftnoise are annoyance, speech interference sleep interference, hearingloss, health effects (blood pressure, heart diseases, etc.), and effecton structures, effects on historical and archaeological sites, effecton domestic animals and wildlife.

B. Scope of StudyAircraft noise is the most significant cause of adverse communityreaction related to the operation and expansion of airports bothin developed and developing countries (ICAO Environment Report2007). ICAO Annex 16 determines the noise standard for subsonicaircraft. In addition it issued guidance on a new policy to addressaircraft and airport noise, referred to as the Balanced Approach. Itis ultimately the responsibility of individual countries to implementthe various elements of the Balanced Approach. The Balanced Ap-proach consists of identifying the noise problem at an airport andthen analysing the aircraft noise data by using GIS which is avail-able. In India, the Director General of Civil Aviation (DGCA)is the regulatory body for civil Aviation under Ministry of CivilAviation. DGCA has issued an Aviation Environment Circular 3of 2013 for assessment of the noise situation at all airport havingaircraft movements of 50,000 per annum, As per the circular, Kem-pegowda International Airport Limited, Bangalore (KIAL) is oneof the airports coming under the study. This Circular refers tothe guidelines for monitoring ambient noise Levels due to aircraft,issued by the Central Pollution Control Board of India. It also re-quires that noise contour maps shall be established for several noisemetrics, and that a comparison is made with the limits set in theNoise Pollution Rules of 2000, issued by the Ministry of Environ-

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ment and Forests.

C. Project AimThe aim of this Project was to produce maps of the noise from air-craft using land use and land cover that could be integrated withnoise maps produced for other noises at Kempegowda InternationalAirport Limited, Bangalore (KIAL).

D. Objectives of the StudyKIAL is one of the airports in India, which has taken an initiativeto implement the Balanced Approach as per the ICAO guidanceto reduce airport noise in BIAL airport. As a first step, BIALmanagement would like to have an overview of the current noisesituation at the airport, by identifying and monitoring the variousnoise sources including aircraft and background noise within andoutside the airport. In order to meet the requirement of DGCAscircular-3 of 2013 on Aviation Noise Management, the scope of workis divided in two parts.• Phase-1: Noise Monitoring, mapping and validation.• Phase-2: Noise modelling and mapping using the 2012 infor-mation, Land use and land cover change detection analysis usinggeospatial technologies.

2 STUDY AREA

The place under study was near Kempegowda International Air-port located 40 km to the north east of Bangalore. KempegowdaInternational Airport is an international airport serving Bangalore,the capital of Indian state of Karnataka. The study area shown inFigure 1 and 2 is spread over 4000 acres. It is near the Devanahallivillage. It is located at latitude 13◦ 12′ 25” N and longitude 77◦ 42′

15” E.The noise gauge stations are to be selected at Sadahalli and

Chennahalli both located at 10 km, Bychapur and Kadayarappana-halli are located at 3 km each towards north south directions andshown in Figure 2. The area lies between latitude and longitudeas given in Table 1. To detect the land use changes over a periodof time we required multi- temporal data. The data used for the

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study consist of land use maps of different time periods.

Table 1: DESCRIPTION OF LATITUDE AND LONGITUDE

Figure 1: Study Area

Figure 2: Noise Gauge Stations

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A. Noise Gauge StationNoise gauge is also called as sound recording station, sound record-ing meter are commonly used in noise pollution studies for thequantification of different kinds of noise, especially for the aircraftnoise. They are a viable alternative to complex Integrated Noiseand Track Monitoring (INTM) systems which rely on integration ofthe noise monitors with the airport’s radar and or information sys-tems. Noise gauges also recorded temperature, wind speed; windpressure and humidity etc. the height of noise gauge instrumentis 6m. It can take many months or even years to get to the pointconsuming, complex and extremely expensive process. Noise gaugestations are located at 4 points, near Sadahalli, Bychapura, Kada-yarappanahalli and Chennahalli as shown in Figure 3 and 4.

Figure 3: Location of NMS at Sadahalli

Figure 4: Location of NMS at Chennahalli

In compliance with the DGCA Aviation Environment Circular3 Of 2013 Kempegowda International Airport (KIA), Bangalore,contracted the installation, operation and maintenance of a per-manent Noise Monitoring system (IBaNET) in and around KIAto nDimensional GIS Solutions (NGDS), Hyderabad and AnotechEngineering, Spain. This contract also includes the elaborationof a monthly report, summarizing the main results obtained withthe system. The installed system and corresponding reports are incompliance with this CAR. The present document covers the re-sults obtained during the month of January, February and March2017.

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3 METHODOLOGY

A. Phase-1:1. Aircraft noise monitoring shall be carried out with two noiseunits which are to be located in appropriate locations at either sideof the runway. At BIAL (outside the airport boundaries) and othertwo inside the boundaries. Continuous monitoring shall be con-ducted for 3 months. The noise monitoring will be carried out usingthe latest technology instruments that can provide noise informa-tion through a web-based application. The system to be developedhas been used for the noise study at CSIA Mumbai airport and isalso being used at several EU airports.2. Air traffic Monitoring shall be carried out simultaneously. Dur-ing the trial no link to the radar tracks will be established. As analternative an ADS-B receiver shall be developed to obtain flighttrack and aircraft information from those aircraft equipped witha suitable transponder (in Mumbai a coverage of around 70% wasachieved). This information will be used to obtain informationneeded for the noise mapping exercise (using of routes, lateral dis-persions around nominal routes, etc.) and also to correlate noiseevents with aircraft operations. This data will be supplementedand merged with daily flight plan and runway usage data to beprovided by BIAL.3. Generate noise maps using Integrated Noise Modelling (INM)software for the period covering the noise measurement campaign,based on the actual traffic data. These noise maps will be com-pared with the measured data with the objective to validate themapping process and the Noise measurement modelling results andvalidation.

B. Phase-2:1. Conduct field survey for characteristics and land use and gener-ate the base maps of the greater area of BIAL.2. Obtain satellite image from NRSC and generate the land usepattern of the study area3. Pre-process all input including air traffic, airport operational,land use for noise modelling, covering sensitive receptors.

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Final Report shall include the following:a. Description of project, location, scope etc.b. Description of the noise monitoring system deployed and theweb-based solutionsc. Description of the measurements performed.d. Description of the greater area, characteristics and land usespecial emphasis to residential areas, population data, existence ofsensitivereceptors (e.g. schools, hospitals etc.)e. Analysis of measurement results (Noise and Air traffic data).Whereas noise data will necessarily be limited to the measurementpositions, air traffic data (especially tracks) will extend beyond theairport boundaries. Noise maps presented will include standard in-ternational noise indices like Leq-24 hr.f. Validation of noise maps with monitoring data.

Figure 5: Methodology Flowchart of Both Phases

C. Noise DataA total of 28659 valid aircraft noise events has been detected duringthe reporting period. Data for each individual noise event at eachmeasurement station is provided. On request of BIAL an additionallist with only the noisiest events has been included. For this list

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the following thresholds were applied, depending on the period ofthe day that the event occurred:1. LAeq event > 70 dB for the day period (06:00-22:00)2. LAeq event > 75 dB for the night period (22:00-06:00)In the following tables the average values of the noise metrics con-sidered in this study are provided for each of the measurementpositions, based on the noise recordings acquired at each test pointand grouped as per the relevant regulations. Daily values for thesenoise metrics are provided. The following figures provide a mapof the typical trajectories detected with the IBaTrack system forthe West and East configuration respectively. Trajectories map ofreporting period is shown in Figure 6 and 7. One of the remarkablecharacteristics is the wide spread (dispersion) around the nominalroutes, caused by the wide variation of the point where the turn isinitiated (which depends on aircraft altitude).

Figure 6: Trajectories Map of Reporting Period West

Figure 7: Trajectories Map of Reporting Period East

Noise data are collected for the month of January, February andMarch in all stations viz. BLR-1, BLR-2, BLR-3 BLR-4 as shownin the Bar Graphs 1 to 12 below.

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D. Land Use and Land CoverLand use and land cover (LULC) change is a major issue of globalenvironment change. The land use/land cover pattern of any re-gion is an outcome of natural and socioeconomic factors and theirutilization by man in time and space. Land is becoming a scarceresource due to immense agricultural and demographic pressure.Hence, information on land use / land cover and possibilities fortheir optimal use is essential for the selection, planning and im-plementation of land use schemes to meet the increasing demandsfor basic human needs and welfare. This information also assistsin monitoring the dynamics of land use resulting out of changingdemands of increasing population. Land use and land cover changehas become a central component in current strategies for managingnatural resources and monitoring environmental changes.

The study is based on both primary and secondary data sources.General methodology adopted to carry out this research work. In-terpretation keys like size (small, medium, big), shape, tone, texturepattern and association are used to prepare land use map of airportarea with the help of GIS software. Structure features i.e. build-ings, roads, railway are main representation information extractedfrom satellite imagery directly.

In classification process, Supervised Classification method inGRASS was performed based on a set of user-defined classes, by cre-ating the appropriate user-defined polygon. The methodology of ex-tracting Land uses / Land cover from satellite image is such that, insupervised classification process, User-Defined Polygon function re-duces the chance of underestimating class variance since it involveda high degree of user control. Training points were repeatedly se-lected from the whole study area by drawing a polygon aroundtraining sites of interests. Land use / Land cover classes of thesetraining points were extracted with respect to general knowledgeobtained from topographic maps and field visits. The supervisedclassification was performed using the maximum likelihood algo-rithm.

To evaluate the accuracy of the classified image, Accuracy As-sessment tool in ERDAS was used. The reference class values werecompared with the classified class in error matrix. Then overallaccuracy and kappa values were computed by using users accuracy(a measure of commission error) and producers accuracy (a mea-

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sure of omission error) of each class. Calculation of the Area inhectares of the resulting land use/land cover types for each studyyear and subsequently comparing the results. The comparison ofthe land use land cover statistics assisted in identifying the per-centage change, trend and rate of change between 1999 and 2008.In achieving this, the first task was to develop a table showing thearea in hectares and the percentage change for each year (1999 and2008) measured against each land use land cover type. Percentagechange to determine the trend of change can then be calculated bydividing observed change by sum of changes multiplied by 100. Inobtaining annual rate of change, the percentage change is dividedby 100 and multiplied by the number of study year 1999 2008.E. Interpretation ToolsVisiting every location in a study area to measure the height, mag-nitude, or concentration of a phenomenon is usually difficult or ex-pensive. Instead, you can measure the phenomenon at strategicallydisperse sample locations, and predicted values can be assigned toall other locations. Input points can be either randomly or regularlyspaced or based on a sampling scheme.

Surface interpolation functions create a continuous (or predic-tion) surface from sampled point values. The continuous surfacerepresentation of a raster dataset represents height, concentration,or magnitude for example, elevation, pollution, or noise. Surface in-terpolation functions make predictions from sample measurementsfor all locations in a raster dataset whether or not a measurementhas been taken at the location. There is a variety of ways to derive aprediction for each location; each method is referred to as a model.With each model, there are different assumptions made of the data,and certain models are more applicable for specific data- for exam-ple, one model may account for local variation better than another.Each model produces predictions using different calculations.

The Inverse Distance Weighted (IDW) and Spline methods arereferred to as deterministic interpolation methods because they as-sign values to locations based on the surrounding measured val-ues and on specified mathematical formulaes that determine thesmoothness of the resulting surface. A second family of interpola-tion methods consists of geo-statistical methods, such as kriging,that are based on statistical models that include autocorrelation(the statistical relationship among the measured points). Because

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of this, not only do geo-statistical techniques have the capability ofproducing a prediction surface, but they also provide some measureof the certainty or accuracy of the prediction

The interpolation tools are generally divided into deterministicand geo-statistical methods. IDW, Spline, and Trend are determin-istic, while Kriging is a geo-statistical method. Topo to Raster andTopo to Raster by File use an interpolation method designed forcreating continuous surfaces from contour lines, and the methodsalso contain properties favourable for creating surfaces for hydro-logic analysis.

Inverse Distance Weighting (IDW): The Inverse Distance Weight-ing interpolator assumes that each input point has a local influencethat diminishes with distance. It weights the points closer to theprocessing cell greater than those further away. A specified numberof points or all points within a specified radius can be used to deter-mine the output value of each location. Use of this method assumesthe variable being mapped decreases in influence with distance fromits sampled location.

The Inverse Distance Weighting (IDW) algorithm effectively is amoving average interpolator that is usually applied to highly vari-able data. For certain data types it is possible to return to thecollection site and record a new value that is statistically differentfrom the original reading but within the general trend for the area.Examples of this type of data include soil chemistry results, envi-ronmental monitoring data, and consumer behaviour observations.It is not desirable to honour local high/low values but rather tolook at a moving average of nearby data points and estimate thelocal trends.

Inverse distance weighting models work on the premise that ob-servations further away should have their contributions diminishedaccording to how far away they are. The simplest model involvesdividing each of the observations by the distance it is from thetarget point raised to a power :

(1)

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(2)

4 RESULTS AND DISCUSSIONS

A. OverviewThe study of Aircraft noise at Kempegowda International AirportLimited Bangalore was carried out using GIS and Remote Sensing.The study was carried out at various locations (Sadahalli, Bycha-pura, Kadayarappanahalli and Chennahalli) for the determinationof noise level data produced by aircrafts and Land use and Landcover.

B. Spatial Interpolation of Aircraft Noise Levels Using GIS System1) Ground Truth PointsThe ground truth points on four noise gauge stations were recordedand are shown in Figure 8.

Figure 8: Screenshot of GUI Showing Ground Truth Points

2) Map of Noise Gauge StationsThe map of noise gauge stations is shown in Figure 9.3) Spatial interpolation of January-2017In the month of January 2017, the noise intensity shown in Fig-ure 10 indicates that the noise levels vary in the range 55.7dB to57.74dB. The west region to the airport being lowest and east regionbeing highest.

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Figure 9: Noise Gauge Stations

Figure 10: Noise Level Spatial Interpolation for the Month of Jan-uary 2017

4) Spatial interpolation of February-2017In the month of February 2017, the noise intensity shown in Fig-ure 11 indicates that the noise levels vary in the range 56.2dB to58.32dB. The west region to the airport is lowest and north, north-east region is highest whereas southern region lies in the range of56.59dB to 57.01dB.5) Spatial interpolation of March-2017In the month of March 2017, the noise intensity shown in Figure 12indicates that the noise levels vary in the range 56.2dB to 56.50dB.The north south region are highest whereas the noise intensity levelgradually decreases in either horizontal direction.

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Figure 11: Noise Level Spatial Interpolation for the Month of Febru-ary 2017

Figure 12: Noise Level Spatial Interpolation for the Month of March2017

C. Land Use and Land Cover1) Satellite Image 1999The Figure 13 shows the 1999 satellite image of Bangalore Interna-tional Airport.2) Satellite Image 2008The Figure 14 shows the 2008 satellite image of Bangalore Interna-tional Airport.3) Land Use and Land Cover of Study Area 1999Land use and land cover of study area 1999 is shown in Figure 154) Land Use and Land Cover of Study Area 2008Land use and land cover of study area 2008 is shown in Fig. 16.

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Figure 13: Satellite Image 1999

Figure 14: Satellite Image 2008

Figure 15: LU/LC of Study Area 1999

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Figure 16: LU/LC of Study Area 2008

5) Results of LU/LC studyThe results are tabulated in Table II below.

Table 2: COMPARISON OF LU/LC FOR 1999 TO 2008 FORTHE STUDY AREA

1. As of 1999 single crop was 29.08 Sq Km and decreased to 20.46Sq Km in 2008 that is 10% decrease.2. As of 1999 water bodies was 3.06 Sq Km and decreased to 2.27Sq Km in 2008 that is 0.9% decrease.

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3. As of 1999 double crop was 53.49 Sq Km and increased to 55.04Sq Km in 2008 that is 1.8% increase.4. As of 1999 settlements was 0.88 Sq Km and increased to 8.74 SqKm in 2008 that is 9.1% increase.

D. Land Use Land Cover Distribution1) Accuracy AssessmentIn order to assess the classification accuracy, 200 points are gen-erated randomly throughout each image using the Add RandomPoint utility in ERDAS A class value is then entered for each ofthese points. These class values are taken as the reference points,to make a comparison with the class values of the classified im-ages. The overall accuracy and KAPPA statistics are used to assessclassification accuracy based on error matrix. Overall accuracy iscomputed by dividing the total correct value (i.e. sum of the majordiagonal) by the total number of pixels in the error matrix. Ac-curacy assessment is performed for 1999 2008 LU/LC maps. Anoverall accuracy of 78% for 1999 & 82.50% for 2008 are obtained.

2) Change Detection AnalysisThe most commonly used Change Detection methods are, i) Imageoverlay, ii) Classification comparisons of land cover statistics orcalculate the area in hectares of the resulting Land use/Land covertypes for each study years and subsequently comparing the results,iii) Change vector analysis, iv) Principal component analysis, v)Image rationing and vi) The differencing of Normalized DifferenceVegetation Index (NDVI) (Duadze, 2004).

The method used in this project is classification comparison ofland cover statistics. The comparison of the Land use/Land coverstatistics, assisted in identifying the increase and decrease in areaunder different classes between 1999 and 2008 is shown in TableII. The change in area can be interpreted with reference to time ofacquisition of image which shows a seasonal variation. The finalland use and land cover maps derived are shown in Fig. 15 and 16.It is evident from area statistics of supervised classification thatthere is an increase in area of about 155.00 Ha in double crop in2008 image. Single crop has decreased in area of 862.00 Ha. Waterbodies have seen decrease in area 79.00 Ha. An area of about 786.00Ha, increase in area in settlements has been observed. The static

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land use land cover distribution for each study year as derived fromthe maps is presented in the Table 2.

5 CONCLUSION

1. The noise pollution affects both health and behaviour of floraand fauna, unwanted sound noise can cause psychological damagessuch as hyper tension, high stress levels tinnitus, hearing loss, sleepdisturbances, and other harmful diseases.2. The aircraft noise level should be in the range of 60 to75 dB, thestudy concludes that the noise levels are well below in the range of60 dB.3. We can also suggest methods of reducing noise levels by usingnoise maps since the areas having maximum noise levels are iden-tified by these maps.4. Areas of annoyance that remain to be investigated include therelationship between single event and annoyance.5. Choosing low noise emission aircraft using low noise take-off andlanding can reduce the noise in domestic areas.6. In comparing the 1999 to 2008 land use and land cover maps, themost visible change is the pattern and distribution of single cropagriculture.7. The other parameters like water bodies and double crop havebeen affected by negligible values.8. Settlements have increased to 9.1% significantly.

References

[1] Ernesto et. al. (2006) The impact of aircraft noise on commu-nities in the vicinity of the Ninoy Aquino International AirportPhilippines engineering journal; Vol. 27 No. 2:7184.

[2] Issarayangyun, T.Black, J. Black, D. Samuels, S, (2005), Air-craft Noise and Methods for The Study of Community Healthand Well-Being, Journal of The Eastern Asia Society for Trans-portation Studies, Vol.6, p.32933308.

[3] Keerthana et.al. (2013) An Analysis of noise pollution inTirupur city, Sch. J. Eng. Tech; 1(3):154-168, ISSN 2321-435X.

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[4] Lathasree et. al. (2007) Modeling, Simulation, and Analysis ofHAL Bangalore International Airport. Defence Science Jour-nal, 57(6):865-876.

[5] Mortez et. al. (2015) The Study of Noise Pollution Caused byBirjand Airport on the Surrounding Residents. Indian Journalof Science and Technology. Vol. 8(11), 0974-564

[6] Stephen (2015) Noise Effects on Health in the Context of AirPollution Exposure Int. J. Environ. Res. Public Health, 12,12735-12760; ISSN 1660-4601.

[7] Zhao et. al. (2003) GIS in airport noise management: A casestudy at Split in Croatia. Research gate Journal.

[8] Zia, (2013) Using meta-regression to determine Noise Depre-ciation Indices for Asian airports. Asian Geographer: a ge-ographical journal on Asia and the Pacific Rim, 30 (2). 127141.

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spatial interpolation of aircraft noise and land …airport opened in 24th, may 2008 as an...

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