Spatial Interpolation of Aircraft Noise and Land Use Study at Kempegowda International Airport Limited Bangalore Using Gis and Remote Sensing

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

SPATIAL INTERPOLATION OF AIRCRAFT NOISE AND LAND USE STUDY AT KEMPEGOWDA INTERNATIONAL AIRPORT LIMITED BANGALORE USING GIS AND REMOTE SENSING

Rajakumara H.N1, Jayaram A2, Arati Reddy Nilap3 Department of Civil Engineering, Kempegowda International Airport, BIAL, Bangalore, India. [email protected], [email protected], [email protected]

June 24, 2018

Abstract This study involves Kempegowda International Airport Limited (KIAL), Bangalore as the study area for aircraft noise intensity levels around the airport for a period of three months and Land Use Land Cover (LU/LC) study for a period of ten years. For this purpose, four noise gauges all located around the airport in four diﬀerent directions namely north (Bychapura), south (Kadayarappana- halli), east (Chennahalli) and west (Sadahalli). Noise intensities are recorded on every aircraft or noise event con- tinually, out of which only aircraft noise levels on 24-hours equivalent are taken for the study. These noise intensities are the inputs for spatial interpolation over the area in the

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GIS software and results are represented pictorially. The results show that the noise levels are within the permissi- ble range of 60 75 dB. For the LU/LC study, the satellite images of 1999 and 2008 are considered and the parameters considered are single crop, double crop, water bodies and settlements. The results show that there is signiﬁcant increase of 9.1 Keywords: Aircraft Noise Intensity, Land Use Land Cover, 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 Indian state of Karnataka. Spread over 4,000 acres (1,600 ha), it is located about 40 kilometres (25 mi) north of the city near the village of Devanahalli. It is owned and operated by Bangalore Interna- tional Airport Limited (BIAL), a publicprivate consortium. The airport opened in 24th, May 2008 as an alternative to increased congestion at HAL Airport, the original primary commercial airport serving the city. It is named after Kempegowda I, the founder of Bangalore. As of 2016, Kempegowda Airport is the third busiest airport by passenger traﬃc in the country, behind the airports in Delhi and Mumbai and is the 35th busiest airport in Asia. It handled over 22.2 million passengers in 2016 with little less than 500 aircraft movements a day. The airport also handled about 314,060 tonnes (346,190 short tons) of cargo. The airport consists of a single runway and passenger terminal, which handles both domestic and international operations. A second runway and terminal are in the early stages of planning and construction. In addition, there is a cargo village and three cargo terminals. The airport serves as a hub for Air Asia India, Air India Regional, Air Pegasus and Jet Airways.

A. Pollution Due To Airports 1) Water Pollution A particularly important facet of airport operation 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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of its operation: de/anti-icing operations, washing and cleaning operations, spills of fuel and lubricants, exhaust fumes, and weed removal. The pollution caused by airport operations affects soil, surface waters, and groundwater. This issue is important to various stakeholders, particularly those residing in communities near airports, whose health, property values, and quality of life can be affected by such environmental impacts. 2) Air Pollution Airport is an extremely complex emission source of airborne pollutants that can have a significant impact on the environment. Indeed, several airborne chemicals emitted during airport activities may significantly get worse air quality and increase exposure level of both airport workers and general population living nearby the airports. In recent years airport traffic has increased and consequently several studies investigated the association between airport-related air pollution and occurrence of adverse health effects, particularly on respiratory system, in exposed workers and general population resident nearby. 3) Noise Pollution The noise is an unwanted sound that may cause some psychological and physical stress to the living and non- living objects exposed to it. Noise level is a measure of the energy of sound which is expressed in units of decibels or dB. Noise thresh- old is the limit maximum noise level permitted dumped into the environment from the undertaking or activity so as not to cause disruption of human health and environmental comfort. Many airports faced noise disturbance, around an airport is caused by aircraft in the air; reverse thrust used by aircraft to slow down after landing; aircraft on the ground, including taxiing, engine testing and running on-board electrical generators; departing aircraft that stray from the Preferred Noise Routes (PNRs); road traffic to and from the airport. In addition, operation and implementation of the airport and all its activities may cause impact on workers, communities, and the environment around the airport. Noise represents one of the most significant environmental challenges associated with aircraft and airport operations. Over the years, there have been significant improvements based on technology evolution, effective noise abatement procedures and other measures. Aircraft today are 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 from urban areas. Due to increases of population, rapid urbanization and indus- trialization, airports are very close to vicinity of city. Reducing the effect 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 issue associated with airport and aircraft operators. Although, there are many other noise sources present at the airport, aircraft noise is readily identifiable and tends to stand as annoyance for many people (Airbus 2003). Some of the possible effects related to aircraft noise are annoyance, speech interference sleep interference, hearing loss, health effects (blood pressure, heart diseases, etc.), and effect on structures, effects on historical and archaeological sites, effect on domestic animals and wildlife.

B. Scope of Study Aircraft noise is the most signiﬁcant cause of adverse community reaction related to the operation and expansion of airports both in developed and developing countries (ICAO Environment Report 2007). ICAO Annex 16 determines the noise standard for subsonic aircraft. In addition it issued guidance on a new policy to address aircraft and airport noise, referred to as the Balanced Approach. It is ultimately the responsibility of individual countries to implement the various elements of the Balanced Approach. The Balanced Ap- proach consists of identifying the noise problem at an airport and then 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 Civil Aviation. DGCA has issued an Aviation Environment Circular 3 of 2013 for assessment of the noise situation at all airport having aircraft movements of 50,000 per annum, As per the circular, Kem- pegowda International Airport Limited, Bangalore (KIAL) is one of the airports coming under the study. This Circular refers to the 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 noise metrics, and that a comparison is made with the limits set in the Noise Pollution Rules of 2000, issued by the Ministry of Environ-

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ment and Forests.

C. Project Aim The aim of this Project was to produce maps of the noise from aircraft using land use and land cover that could be integrated with noise maps produced for other noises at Kempegowda International Airport Limited, Bangalore (KIAL).

D. Objectives of the Study KIAL is one of the airports in India, which has taken an initiative to implement the Balanced Approach as per the ICAO guidance to reduce airport noise in BIAL airport. As a ﬁrst step, BIAL management would like to have an overview of the current noise situation at the airport, by identifying and monitoring the various noise sources including aircraft and background noise within and outside the airport. In order to meet the requirement of DGCAs circular-3 of 2013 on Aviation Noise Management, the scope of work is divided in two parts. Phase-1: Noise Monitoring, mapping and validation. • Phase-2: Noise modelling and mapping using the 2012 information,• Land use and land cover change detection analysis using geospatial technologies.

2 STUDY AREA

The place under study was near Kempegowda International Air- port located 40 km to the north east of Bangalore. Kempegowda International Airport is an international airport serving Bangalore, the capital of Indian state of Karnataka. The study area shown in Figure 1 and 2 is spread over 4000 acres. It is near the Devanahalli village. It is located at latitude 13◦ 120 25” N and longitude 77◦ 420 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 and shown in Figure 2. The area lies between latitude and longitude as given in Table 1. To detect the land use changes over a period of 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 Station Noise gauge is also called as sound recording station, sound recording meter are commonly used in noise pollution studies for the quantiﬁcation of diﬀerent kinds of noise, especially for the aircraft noise. They are a viable alternative to complex Integrated Noise and Track Monitoring (INTM) systems which rely on integration of the noise monitors with the airport’s radar and or information systems. Noise gauges also recorded temperature, wind speed; wind pressure and humidity etc. the height of noise gauge instrument is 6m. It can take many months or even years to get to the point consuming, complex and extremely expensive process. Noise gauge stations 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 Circular 3 Of 2013 Kempegowda International Airport (KIA), Bangalore, contracted the installation, operation and maintenance of a per- manent Noise Monitoring system (IBaNET) in and around KIA to nDimensional GIS Solutions (NGDS), Hyderabad and Anotech Engineering, Spain. This contract also includes the elaboration of a monthly report, summarizing the main results obtained with the system. The installed system and corresponding reports are in compliance with this CAR. The present document covers the results obtained during the month of January, February and March 2017.

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3 METHODOLOGY

A. Phase-1: 1. Aircraft noise monitoring shall be carried out with two noise units which are to be located in appropriate locations at either side of the runway. At BIAL (outside the airport boundaries) and other two inside the boundaries. Continuous monitoring shall be con- ducted for 3 months. The noise monitoring will be carried out using the latest technology instruments that can provide noise information through a web-based application. The system to be developed has been used for the noise study at CSIA Mumbai airport and is also 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 an alternative an ADS-B receiver shall be developed to obtain flight track and aircraft information from those aircraft equipped with a suitable transponder (in Mumbai a coverage of around 70% was achieved). This information will be used to obtain information needed for the noise mapping exercise (using of routes, lateral dis- persions around nominal routes, etc.) and also to correlate noise events with aircraft operations. This data will be supplemented and merged with daily flight plan and runway usage data to be provided 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 compared with the measured data with the objective to validate the mapping process and the Noise measurement modelling results and validation.

B. Phase-2: 1. Conduct ﬁeld survey for characteristics and land use and generate the base maps of the greater area of BIAL. 2. Obtain satellite image from NRSC and generate the land use pattern of the study area 3. 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 the web-based solutions c. Description of the measurements performed. d. Description of the greater area, characteristics and land use special emphasis to residential areas, population data, existence of sensitivereceptors (e.g. schools, hospitals etc.) e. Analysis of measurement results (Noise and Air traﬃc data). Whereas noise data will necessarily be limited to the measurement positions, air traﬃc data (especially tracks) will extend beyond the airport boundaries. Noise maps presented will include standard international noise indices like Leq-24 hr. f. Validation of noise maps with monitoring data.

Figure 5: Methodology Flowchart of Both Phases

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

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the following thresholds were applied, depending on the period of the 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 considered in this study are provided for each of the measurement positions, based on the noise recordings acquired at each test point and grouped as per the relevant regulations. Daily values for these noise metrics are provided. The following ﬁgures provide a map of the typical trajectories detected with the IBaTrack system for the West and East conﬁguration respectively. Trajectories map of reporting period is shown in Figure 6 and 7. One of the remarkable characteristics is the wide spread (dispersion) around the nominal routes, caused by the wide variation of the point where the turn is initiated (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 and March in all stations viz. BLR-1, BLR-2, BLR-3 BLR-4 as shown in the Bar Graphs 1 to 12 below.

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D. Land Use and Land Cover Land use and land cover (LULC) change is a major issue of global environment change. The land use/land cover pattern of any region is an outcome of natural and socioeconomic factors and their utilization by man in time and space. Land is becoming a scarce resource due to immense agricultural and demographic pressure. Hence, information on land use / land cover and possibilities for their optimal use is essential for the selection, planning and implementation of land use schemes to meet the increasing demands for basic human needs and welfare. This information also assists in monitoring the dynamics of land use resulting out of changing demands of increasing population. Land use and land cover change has become a central component in current strategies for managing natural 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, texture pattern and association are used to prepare land use map of airport area with the help of GIS software. Structure features i.e. build- ings, roads, railway are main representation information extracted from satellite imagery directly. In classification process, Supervised Classification method in GRASS was performed based on a set of user-defined classes, by creating the appropriate user-defined polygon. The methodology of ex- tracting Land uses / Land cover from satellite image is such that, in supervised classification process, User-Defined Polygon function re- duces the chance of underestimating class variance since it involved a high degree of user control. Training points were repeatedly selected from the whole study area by drawing a polygon around training sites of interests. Land use / Land cover classes of these training points were extracted with respect to general knowledge obtained from topographic maps and field visits. The supervised classification was performed using the maximum likelihood algorithm. To evaluate the accuracy of the classified image, Accuracy As- sessment tool in ERDAS was used. The reference class values were compared with the classified class in error matrix. Then overall accuracy 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 in hectares of the resulting land use/land cover types for each study year and subsequently comparing the results. The comparison of the land use land cover statistics assisted in identifying the percentage change, trend and rate of change between 1999 and 2008. In achieving this, the first task was to develop a table showing the area in hectares and the percentage change for each year (1999 and 2008) measured against each land use land cover type. Percentage change to determine the trend of change can then be calculated by dividing observed change by sum of changes multiplied by 100. In obtaining annual rate of change, the percentage change is divided by 100 and multiplied by the number of study year 1999 2008. E. Interpretation Tools Visiting every location in a study area to measure the height, magnitude, or concentration of a phenomenon is usually difficult or expensive. Instead, you can measure the phenomenon at strategically disperse sample locations, and predicted values can be assigned to all other locations. Input points can be either randomly or regularly spaced or based on a sampling scheme. Surface interpolation functions create a continuous (or prediction) surface from sampled point values. The continuous surface representation of a raster dataset represents height, concentration, or magnitude for example, elevation, pollution, or noise. Surface interpolation functions make predictions from sample measurements for all locations in a raster dataset whether or not a measurement has been taken at the location. There is a variety of ways to derive a prediction 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 example, 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 are referred to as deterministic interpolation methods because they as- sign values to locations based on the surrounding measured values and on specified mathematical formulaes that determine the smoothness of the resulting surface. A second family of interpolation 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 of producing a prediction surface, but they also provide some measure of the certainty or accuracy of the prediction The interpolation tools are generally divided into deterministic and geo-statistical methods. IDW, Spline, and Trend are deterministic, while Kriging is a geo-statistical method. Topo to Raster and Topo to Raster by File use an interpolation method designed for creating continuous surfaces from contour lines, and the methods also 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 influence that diminishes with distance. It weights the points closer to the processing cell greater than those further away. A specified number of points or all points within a specified radius can be used to determine the output value of each location. Use of this method assumes the variable being mapped decreases in influence with distance from its sampled location. The Inverse Distance Weighting (IDW) algorithm effectively is a moving average interpolator that is usually applied to highly variable data. For certain data types it is possible to return to the collection site and record a new value that is statistically different from the original reading but within the general trend for the area. Examples of this type of data include soil chemistry results, environmental monitoring data, and consumer behaviour observations. It is not desirable to honour local high/low values but rather to look at a moving average of nearby data points and estimate the local trends. Inverse distance weighting models work on the premise that observations further away should have their contributions diminished according to how far away they are. The simplest model involves dividing each of the observations by the distance it is from the target point raised to a power :

(1)

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(2)

4 RESULTS AND DISCUSSIONS

A. Overview The study of Aircraft noise at Kempegowda International Airport Limited 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 determination of noise level data produced by aircrafts and Land use and Land cover.

B. Spatial Interpolation of Aircraft Noise Levels Using GIS System 1) Ground Truth Points The ground truth points on four noise gauge stations were recorded and are shown in Figure 8.

Figure 8: Screenshot of GUI Showing Ground Truth Points

2) Map of Noise Gauge Stations The map of noise gauge stations is shown in Figure 9. 3) Spatial interpolation of January-2017 In the month of January 2017, the noise intensity shown in Fig- ure 10 indicates that the noise levels vary in the range 55.7dB to 57.74dB. The west region to the airport being lowest and east region being 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-2017 In the month of February 2017, the noise intensity shown in Fig- ure 11 indicates that the noise levels vary in the range 56.2dB to 58.32dB. The west region to the airport is lowest and north, north- east region is highest whereas southern region lies in the range of 56.59dB to 57.01dB. 5) Spatial interpolation of March-2017 In the month of March 2017, the noise intensity shown in Figure 12 indicates that the noise levels vary in the range 56.2dB to 56.50dB. The north south region are highest whereas the noise intensity level gradually 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 March 2017

C. Land Use and Land Cover 1) Satellite Image 1999 The Figure 13 shows the 1999 satellite image of Bangalore Interna- tional Airport. 2) Satellite Image 2008 The Figure 14 shows the 2008 satellite image of Bangalore Interna- tional Airport. 3) Land Use and Land Cover of Study Area 1999 Land use and land cover of study area 1999 is shown in Figure 15 4) Land Use and Land Cover of Study Area 2008 Land 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 study The results are tabulated in Table II below.

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

1. As of 1999 single crop was 29.08 Sq Km and decreased to 20.46 Sq Km in 2008 that is 10% decrease. 2. As of 1999 water bodies was 3.06 Sq Km and decreased to 2.27 Sq 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 55.04 Sq Km in 2008 that is 1.8% increase. 4. As of 1999 settlements was 0.88 Sq Km and increased to 8.74 Sq Km in 2008 that is 9.1% increase.

D. Land Use Land Cover Distribution 1) Accuracy Assessment In order to assess the classification accuracy, 200 points are gen- erated randomly throughout each image using the Add Random Point utility in ERDAS A class value is then entered for each of these points. These class values are taken as the reference points, to make a comparison with the class values of the classified images. The overall accuracy and KAPPA statistics are used to assess classification accuracy based on error matrix. Overall accuracy is computed by dividing the total correct value (i.e. sum of the major diagonal) by the total number of pixels in the error matrix. Ac- curacy assessment is performed for 1999 2008 LU/LC maps. An overall accuracy of 78% for 1999 & 82.50% for 2008 are obtained.

2) Change Detection Analysis The most commonly used Change Detection methods are, i) Image overlay, ii) Classification comparisons of land cover statistics or calculate the area in hectares of the resulting Land use/Land cover types 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 Difference Vegetation Index (NDVI) (Duadze, 2004). The method used in this project is classification comparison of land cover statistics. The comparison of the Land use/Land cover statistics, assisted in identifying the increase and decrease in area under different classes between 1999 and 2008 is shown in Table II. The change in area can be interpreted with reference to time of acquisition of image which shows a seasonal variation. The final land use and land cover maps derived are shown in Fig. 15 and 16. It is evident from area statistics of supervised classification that there is an increase in area of about 155.00 Ha in double crop in 2008 image. Single crop has decreased in area of 862.00 Ha. Water bodies have seen decrease in area 79.00 Ha. An area of about 786.00 Ha, 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 from the maps is presented in the Table 2.

5 CONCLUSION

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

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