Novel Methodologies for the Development of Large-Scale Airport Noise Map

Abstract

Guangzhou Baiyun International Airport (BIA) is the core airport of Guangdong–Hong Kong–Macau Greater Bay Area. This study produced the noise maps of BIA during summer and winter using a simplified calculation method for the weighted equivalent continuous perceived noise level (${\mathrm{L}}_{\mathrm{WECPN}}$). Particularly, this method used open-source flight data and short-term noise measurement to replace the traditional long-term noise measurement method. The accuracy of the developed noise map was verified by the field experimental data with an average error of 1.5 dB. The noise maps were analyzed in many aspects including the area and population under different noise levels, the spatial distribution of the aircraft noise, the distribution of noise sensitive points, and the land use condition around BIA. It was manifested that about 22.22% and 25.46% of the total population of the five administrative regions were exposed to ${\mathrm{L}}_{\mathrm{WECPN}}$ > 70 dB during summer and winter, respectively. The total area within the noise-affected area that violated the noise limit were about 18.740 km² and 18.109 km² during summer and winter, respectively.

1. Introduction

According to the latest global air passenger traffic data released by the International Air Transport Association (IATA) in 2020, in 2019 the demand of global air passenger traffic increased by 4.2% compared with 2018 [1], which shows that the global air transport industry is developing rapidly. Although the increment of airport passenger traffic demand has a positive economic spillover effect [2], which promotes the development of economy and society, it also raises some aircraft noise pollution issues. Aircraft noise has different levels of bad effects on the nervous system, mental health [3], and fetal development. Thus, the airport noise map is very important in reducing the hazards caused by aircraft noise. The traditional airport noise mapping method requires a huge amount of field noise monitoring work. Thus, the traditional method requires a lot of manpower which results in a high project cost and consequently is difficult to be implemented.

With the continuous development of computer technology, more and more researchers use numerical simulation software to construct noise maps [4,5,6]. Common large-scale environmental noise simulation software include CadnaA, SoundPlan, etc. These computer software can simulate environmental noise efficiently with low cost, thus they have been widely used all over the world. However, researchers also face some challenges when constructing airport noise maps using these software. For example, the construction of the numerical model of the airport noise map is often based on the annual flight data of the airport, which is difficult to be obtained. Researchers normally require the authorization of relevant airport departments to release the data or must pay a high cost to purchase the data. Therefore, how to obtain more accurate airport flight data is a big challenge in constructing airport noise maps. Facing this challenge, the emergence of automatic dependent surveillance broadcast (ADS-B) provides a solution to this problem. ADS-B technology enables an aircraft to broadcast its accurate position and other information (altitude, speed, etc.) in real time through its own ADS-B equipment, so people can monitor aircraft accurately [7]. In addition, the acquisition of ADS-B data is low cost because people can obtain ADS-B data (include the track, departure time, arrival time, etc.) of almost all aircrafts in an airport through the internet. Therefore, ADS-B data can provide very important flight information for the construction of an airport noise map efficiently at a much lower cost.

According to the 2020 Guangzhou traffic development report [8], in 2020 under the influence of COVID-19, despite the sudden drop in global flight volume, the annual passenger throughout of BIA reached 44 million and ranked first in the world. The third phase expansion project of BIA was officially launched in September 2020 [9]. The project will add two runways and one terminal to the airport, which is also the largest airport expansion project in the history of civil aviation in China. According to the plan of the China government, the expansion project is expected to be completed in 2025. In addition, it is predicted that the flight passenger volume of BIA will reach 120 million people in 2030 [10]. Therefore, in order to reflect the impact of aircraft noise in the real world and have better control for aircraft noise pollution, this study will use the CadnaA software to simulate the noise level around BIA based on ADS-B flight data. The outcome of the current effort can provide a low-cost airport noise mapping method and a valuable environmental protection guide for the relevant departments.

2. Methodologies

The methodologies of this study are shown in Figure 1. Firstly, the ADS-B data of the aircraft was obtained through the flight data tracking website. The information of the airport, buildings, and population were obtained through literature review and field observation. The noise model of BIA was then constructed using the CadnaA software. Finally, the noise model was validated by the field experimental data.

Figure 1. Methodologies in the present study.

2.1. Information of the Airport

BIA (ICAO: ZGGG, IATA: CAN) is a 4F civil international airport. It is located at the junction of Baiyun District and Huadu District, Guangzhou City, Guangdong Province, China. It covers an area of about 20.940 ${ \mathrm{km}}^2$. It is one of the world’s top 50 largest airports. Currently, BIA has three runways and two terminals. It is expected that BIA will become a super large airport with five runways and three terminals in 2025 [9]. The study area of this work is shown in Figure 2 which mainly covers the north and south parts of BIA (about 458 ${ \mathrm{km}}^2$). The takeoff and landing directions of runways 01, 02L, and 02R are from south to north. The takeoff and landing directions of runways 19, 20R and 20L are from north to south. The aerodrome reference point (ARP) of BIA represents the geographical location of the airport and is located at the midpoint of runway 02L/20R (23°23.6′ N 113°18.5′ E).

Figure 2. Studied area in the present study.

2.2. Climatic Characteristics of the Airport

Guangzhou is located in the subtropical monsoon climate zone. According to the wind direction data of Guangzhou over the years [11], the wind mainly blows from south or southeast during summer (March∼August). During winter (September∼February), the wind mainly blows from north or northeast. As the takeoff and landing directions of an aircraft should be against the wind, the takeoff and landing directions of aircrafts in BIA have two characteristics: (1) during summer, they mainly takeoff and land from north to south; (2) during winter, they mainly takeoff and land from south to north.

Based on the field observation, it was found that some villages (such as Shanxia Village and Jiuyi Village north of the airport) were frequently affected by aircraft noise during landing when the wind was blowing from the south. However, there were basically no aircraft flying over these villages when the wind was blowing from north. It was found that the operation modes of BIA during summer and winter are quite different, and hence their noise impact is different as well. The traditional airport noise map is generally constructed based on the annual operation data of the airport. However, BIA has the opposite wind direction during summer and winter. Therefore, its annual noise map cannot accurately represent its noise level during summer and winter. Thus, this study decided to construct the noise maps of BIA during summer and winter separately.

2.3. Categorization of Aircraft Track and Flight Procedure

In China, the aeronautical chart that used in each airport needs to be approved and managed by the Civil Aviation Administration of China (CAAC) and military [12]. All aircrafts must fly according to the tracks specified in the aeronautical chart. However, taking the aircraft takeoff track in runway 19 of BIA during summer as an example, obvious differences were found between the actual aircraft ADS-B track from the Flightradar24 website [13] and the tracks obtained from standard instrument and area navigation (RNAV) takeoff charts (see Figure 3). Flightradar24 is the world’s most advanced flight data tracking website, it can track at least 180,000 flights in real time every day. Therefore, the actual aircraft tracks of BIA were categorized based on the ADS-B flight data (about 16,000 set). The categorization of takeoff and landing tracks of aircrafts in BIA during summer are shown in Figure 4 and Figure 5, respectively. The categorization of takeoff and landing tracks of aircrafts in BIA during winter are shown in Figure A1 and Figure A2, respectively (see Appendix A). Please note that Figure 4, Figure 5, Figure A1 and Figure A2 were obtained from the Flightradar24 website after categorization.

Figure 3. Comparison between the tracks from (a) standard instrument, (b) RNAV takeoff charts [14] and (c) actual aircraft ADS-B track.

Figure 4. Categorization of takeoff tracks of aircrafts in BIA during summer. (a) Runway 19 ((i) T-19-A, (ii) T-19-B, (iii) T-19-C); (b) Runway 20R ((i) T-20R-A, (ii) T-20R-B, (iii) T-20R-C).

Figure 5. Categorization of landing tracks of aircrafts in BIA during summer. (a) Runway 19 ((i) L-19-A, (ii) L-19-B) (b) Runway 20R ((i) L-20R-A (ii), L-20R-B).

The flight procedures of BIA during summer and winter were determined based on the real aircraft tracks that were categorized previously, as shown in Table 1 and Table A1 (see Appendix A), respectively.

Table 1. Flight procedures of BIA during summer.

Type	Track	Flight Procedures
Takeoff	T-19-A	After taking off from runway 19, turn right at 15° (turning radius = 2.5 km), then fly straightly for 11.0 km, then turn right at 87° (turning radius = 4.5 km) and finally fly straightly.
	T-19-B	After taking off from runway 19, turn right at 106° (turning radius = 1.8 km), then fly straightly for 8.0 km, then turn right at 73° (turning radius = 3.0 km) and finally fly straightly.
	T-19-C	After taking off from runway 19, turn right at 15° (turning radius = 2.5 km), then fly straightly for 11.0 km, then turn right at 30° (turning radius = 4.5 km) and finally fly straightly.
	T-20R-A	After taking off from runway 20R, turn left at 15° (turning radius = 2.5 km), then fly straightly for 6.3 km, then turn right at 75° (turning radius = 3.7 km) and finally fly straightly.
	T-20R-B	After taking off from runway 20R, turn left at 15° (turning radius = 2.0 km), then fly straightly for 11.2 km, then turn right at 26° (turning radius = 2.5 km) and finally fly straightly.
	T-20R-C	After taking off from runway 20R, turn right at 15° (turning radius = 2.0 km), then fly straightly for 9.2 km, then turn right at 148° (turning radius = 4.2 km) and finally fly straightly.
Landing	L-19-A	After a 40.4 km straight-in approach, land from the end of runway 19.
	L-19-B	After a 66 km straight-in approach, turn left at 180° (turning radius = 6.5 km), then align with runway 19 and finally land from the end of runway 19 after a 40.4 km straight-in approach.
	L-20L-A	After a 40.4 km straight-in approach, land from the end of runway 20L.
	L-20L-B	After a 66 km straight-in approach, turn left at 180° (turning radius = 5.0 km), then align with runway 20L and land from the end of runway 20L after a 40.4 km straight-in approach.

2.4. Basic Information of the Noise Model

(1) Noise model: Integrated Noise Model (INM) 7.0 in airport noise module (FLG) of the CadnaA software was used to simulate the airport noise level in the present study;

(2) Reference time: the periods from 7:00 to 19:00, 19:00 to 22:00, and 22:00 to 7:00 were defined as “day”, “evening”, and “night”, respectively [15];

(3) Meteorological data: The data of temperature and relative humidity were obtained from the climate report of Guangzhou Climate and Agrometeorological Center [16] and Guangzhou Meteorological Bureau [17], respectively. The average temperature and average relative humidity during summer are 27 °C and 84%, respectively. The average temperature and average humidity during winter are 20 °C and 73%, respectively;

(4) Topographic data: The altitude of BIA is 15.2 m. The terrains around BIA are relatively flat and based on the recommendation of relevant standard [18], this study only considered the altitude difference between each field measurement point and the airport, and did not consider the altitude differences of all terrains around the airport. The altitude difference between each measurement point and the airport was obtained based on the altitude data of Google Earth Pro;

(5) Grid size and height of the receiver point: The preliminary simulation results of this study remained consistent when the size of the grid was changed from 10 m × 10 m to 50 m × 50 m. Therefore, the grid size was set to 50 m × 50 m in order to save the computation time [19]. According to the requirements of China’s national standard [15], the microphone needs to be placed at 1.2 m from the ground surface. Hence, the height of the receiver point was set to 1.2 m from the ground surface in the present study [20,21,22,23,24].

2.5. Outline Map of Airport and Building Simplification

The outline map of BIA was obtained from OpenStreetMap (an open-source map). However, this outline map was not complete as most of the buildings, vegetation zones and roads were missing as shown in Figure 6a. Therefore, these missing data were further supplemented into the outline map using the latest satellite images in 2021 from Google Earth Pro and the map data provided by Amap as shown in Figure 6b.

Figure 6. (a) Incomplete airport area outline map; (b) complete airport area outline map.

Based on the field observation and satellite images, it was found that the buildings around the airport are aggregated with very high density as shown in Figure 7a. Thus, in order to save the computation cost [25,26,27], this study merged the adjacent buildings in the same community or street into one building as shown in Figure 7b.

Figure 7. (a) Buildings before merge; (b) buildings after merge.

2.6. Information of the Runway and Airport Flight Data

The runway information of BIA is shown in Table 2. The daily ADS-B flight data of aircrafts in BIA including the actual takeoff and landing time, model, and other information were obtained and summarized as shown in Table 3.

Table 2. Runway information of BIA (all runways are fabricated of concrete).

Runway	Direction (°)	Length (km) × Width (km)
01	16	3.6 × 0.045
19	196	3.6 × 0.045
02L	16	3.8 × 0.06
20R	196	3.8 × 0.06
02R	16	3.8 × 0.06
20L	196	3.8 × 0.06

Table 3. Flight data of aircraft 737-800 in BIA (not all aircraft flight data are shown for brevity).

Runway	Track	Time	Number
19	L-19-A	Day, Evening, Night	61, 15, 25
20L	L-20L-A	Day, Evening, Night	46, 12, 28

Parameters of the aircrafts in BIA such as the engine and takeoff or landing weight were obtained from the aircraft noise and performance (ANP) database [28]. For the aircraft that cannot be found in the ANP database, the most appropriate replacement aircraft was selected based on the two aircraft replacement tables provided by the Eurocontrol Experimental Centre (EEC) [29,30].

2.7. Simulated Aircraft Track

The simulated aircraft tracks of BIA in the noise model were generated based on the parameters provided by the flight procedures determined in the earlier section. These parameters include straight-line distance, turning angle, turning radius, etc. Due to the influence of various environmental factors, the aircraft might not fly completely according to the simulated track in noise model as shown in Figure 8a, so the actual track during operation might more or less deviate from the simulated track. In the noise model, the corridor width describes the degree of deviation as shown in Figure 8b. In the present study, the width and number of corridors (15) of the noise model were selected based on the guidelines from “Technical Guidelines for Noise Impact Assessment” [31] and Liu [32], respectively.

Figure 8. (a) Simulated track in noise model; (b) corridor width; (c) number of corridors.

2.8. Height and Sound Absorption Coefficient of Building

From the field observation, it was noticed that some of the rural and urban areas around BIA have the same building height. This agrees with the findings of other researchers that the five street and town administrative regions around BIA can be divided into 36 small areas with basically the same building height (see Figure 9) [27]. In the present study, the number of floors in each small area were determined by using Tencent Street View Map [33]. With the estimated floor height of 3 m around the BIA area, the heights of the buildings can be calculated as shown in Table A2 in Appendix A (building height = number of floors × floor height) [25,34]. Specifically, some residential areas with prominent building height (see yellow areas in Figure 9) were investigated separately in order to obtain more accurate building height. These building heights are indicated in Table A3 in Appendix A. Based on the field observation, most of the buildings around BIA are composed by masonry walls with balconies. Thus, the sound absorption coefficients of the buildings in the noise model were set to 0.4 [34].

Figure 9. Thirty-six small areas with basically the same building height.

2.9. Census Data

In the present study, the total population (876,915 [35,36]) of the five administrative regions around the airport was allocated to each building according to the geometric size of the building (floor area and building height) [34].

2.10. Simplified Calculation Method of L_WECPN

Based on the guidelines from the “Technical Guidelines for Noise Impact Assessment” [31] and “Standard of Aircraft Noise for Environment Around Airport” [37] and recommendation from the International Civil Aviation Organization (ICAO), the airport noise in the current study was evaluated by ${\mathrm{L}}_{\mathrm{WECPN}}$ (in dB). ${\mathrm{L}}_{\mathrm{WECPN}}$ requires 24 h noise measurement as shown in Equation (1) [15]:

$${\mathrm{L}}_{\mathrm{WECPN}}={\overline{\mathrm{L}}}_{\mathrm{EPN}} +10\lg \left({\mathrm{N}}_1{+3\mathrm{N}}_2{+10\mathrm{N}}_3\right)- 39.4,$$

(1)

where ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ is the energy average of the effective perceived noise level; ${\mathrm{N}}_1$, ${\mathrm{N}}_2,$ and ${\mathrm{N}}_3$ are the number of aircrafts during day (7:00∼19:00), evening (19:00∼22:00), and night (22:00∼7:00), respectively. ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ is defined as [15]:

$${\overline{\mathrm{L}}}_{\mathrm{EPN}}=10\lg \left[\frac{1}{\mathrm{N}}\left({\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{N}}{10}^{{\mathrm{L}}_{\mathrm{EPNi}}/10}\right)\right],$$

(2)

where ${\mathrm{L}}_{\mathrm{EPNi}}$ is the ith flight event of ${\mathrm{L}}_{\mathrm{EPN}}$ (effective perceived noise level) and N is all day flight event numbers at the monitoring point.

In order to simplify the calculation of ${\mathrm{L}}_{\mathrm{WECPN}}$, for the part of “10lg(${\mathrm{N}}_1$ + 3${\mathrm{N}}_2$ + 10${\mathrm{N}}_3$)”, ADS-B flight data were used to obtain the number of aircrafts passed through the measurement point in three periods of the day, which means whole day noise measurement was no longer needed. For the part of “${\overline{\mathrm{L}}}_{\mathrm{EPN}}$”, it can be observed from Equation (2) that as long as the proportion of the aircraft models during the measurement period is close to the proportion of the aircraft models throughout the day, the ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ obtained from the short-term noise measurement is close to the ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ obtained from the whole day noise measurement.

Short-Term Measurement Time

In order to make the results of short-term noise measurement closer to the results of whole day noise measurement, the proportion of aircraft types during takeoff and landing at different time periods and different runways of BIA on 30 March 2021 was analyzed as shown in Figure 10 (ADS-B flight data). During the period from 12:00 to 22:00, the proportions of aircraft types during takeoff and landing at each runway of the airport are close to those of the whole day. Thus, the short-term noise measurement period which covered 12:00 to 22:00 was selected in the present study in order to shorten the noise measurement time while maintaining the accuracy of ${\mathrm{L}}_{\mathrm{WECPN}}$ at the same time.

Figure 10. Proportion of aircraft types during taking off and landing at different runways of BIA (main aircraft types). (a) Takeoff from runway 19, (b) takeoff from runway 20R, (c) landing from runway 19, (d) landing from runway 20L.

2.11. Small Area Validation Experiment

A 24 h field noise measurement was carried out on 24 April 2021 in order to validate the accuracy of the short-term measurement time method. The location and information of the measurement point (S4) are shown in Figure 11b and Table 4, respectively. S4 is located in the flat area below the aircraft track where every aircraft passed through the measurement point can be observed clearly as shown in Figure 12. The microphone (Hengsheng electronics, model: HS6228) and its calibrator (Hengsheng electronics, model: HS6020) used for aircraft noise measurement should meet the requirements of national standards GB/T 3785.1 [38] and GB/T 15173 [39], respectively. The microphone was fixed at 1.2 m away from the ground surface with a tripod. The distance between the microphone and the surrounding reflectors was greater than 1 m [15]. The aircraft noise was measured when there was no rain, snow, or lightning, and wind speed was lower than 10 m/s [40]. The wind direction was also consistent throughout the day. The A-weighted maximum sound pressure level (${\mathrm{L}}_{\mathrm{Amax}}$) of each flight event exceeded the ambient background noise level by at least 20 dBA [15]. Two ${\mathrm{L}}_{\mathrm{WECPN}}$ were obtained using Equation (1) based on the 24 h and 10 h (12:00 to 22:00) noise data on the 24th of April. It was shown that the variation between them was only about 1.3 dB, which suggests that the simplified calculation method of ${\mathrm{L}}_{\mathrm{WECPN}}$ based on the short-term measurement time of 10 h was feasible.

Figure 11. Location of the measurement points on different date of 2021; (a) 15 March, (b) 30 March (S4, S5), and 24 April (S4).

Table 4. Information of the measurement points for small area field experiment (${\mathrm{D}}_1$ is the distance between the measurement point and the end of the runway. ${\mathrm{D}}_2$ is the altitude difference between the measurement point and runway).

Point	Coordinate	${\mathbf{D}}_1 \left(\mathbf{km}\right)$	${\mathbf{D}}_2 \left(\mathbf{m}\right)$
S1	23°20′57.58″ N 113°18′19.16″ E	2.8	−4
S2	23°19′55.40″ N 113°18′18.81″ E	4.8	−4
S3	23°19′6.97″ N 113°18′27.09″ E	6.3	−6
S4	23°24′40.26″ N 113°19′8.39″ E	1.0	0
S5	23°25′32.08″ N 113°19′20.62″ E	2.6	3

Figure 12. Measurement of aircraft noise.

2.12. Correction of the Airport Noise Model

Another two field noise measurements were carried out in 2021 based on the short-term measurement time (12:00 to 22:00) in order to correct the airport noise model. The correction process is shown in Figure 13. S1, S2, and S3 were located under the takeoff track of runway 20R as shown in Figure 11a. S4 and S5 were located under the landing track of runway 20L as shown in Figure 11b. The information of all measurement points is shown in Table 4. The measurement results (${\mathrm{L}}_{\mathrm{WECPNm}}$) were compared with the simulation results of the same date and same location. Finally, the runway, track, corridor width, etc., of the noise model were corrected according to the differences from the comparison. The corrected simulation results (${\mathrm{L}}_{\mathrm{WECPNc}}$) are shown in Table 5. According to “Calculations and Predictions of Airplane Noise Around Airports” (MH/T 5105-2007) [41], the error between ${\mathrm{L}}_{\mathrm{WECPNc}}$ and ${\mathrm{L}}_{\mathrm{WECPNm}}$ should not be greater than 3 dB. As can be seen in Table 5, only the error in S3 is greater than 3 dB. This is because some agricultural machines worked at S3 on March 15th and ended up with high background noise level. As a result, the measured value was much larger than the simulated value.

Figure 13. Correction process of the airport noise model.

Table 5. The error between ${\mathrm{L}}_{\mathrm{WECPNm}}$ and ${\mathrm{L}}_{\mathrm{WECPNc}}$. Error = |${\mathrm{L}}_{\mathrm{WECPNm}}$ − ${\mathrm{L}}_{\mathrm{WECPNc}}$ |.

Date	Point	${\mathbf{L}}_{\mathbf{WECPNm}}\left(\mathbf{dB}\right)$	${\mathbf{L}}_{\mathbf{WECPNc}}\left(\mathbf{dB}\right)$	Error (dB)
15 March	S1	78.3	76.0	2.3
	S2	75.3	72.8	2.5
	S3	74.5	70.9	3.6
30 March	S4	89.6	91.4	1.8
	S5	83.5	84.5	1.0

2.13. Construction of BIA Noise Map

The takeoff and landing flight numbers of BIA during summer are shown in Table 6. The data of the months that were greatly affected by COVID-19 (June and August [42,43]) were excluded in the present study. Hence, the average daily takeoff and landing flight numbers (1196 sorties) in March, April, May, and July were applied to represent the operation level of BIA during summer. In particular, the summer noise map was constructed based on the flight data from 25 to 31 March 2021 since the flight data of this week (1210 sorties) was close to the operation level (1196 flights) of BIA during summer with the difference of 1.2% [18,40]. The takeoff and landing flight number of BIA during winter of 2020 is shown in Table A4 in the Appendix A. The winter noise map of BIA was constructed using the flight data from 14 to 20 December 2020 (1212 sorties per day).

Table 6. Takeoff and landing flight numbers of BIA during summer of 2021 [44].

Month	Monthly Number	Average Daily Number
March	39,777	1283
April	39,175	1306
May	36,457	1176
June	12,456	415
July	31,653	1021
August	24,474	789

2.14. Large Area Validation Experiment

The construction processes of the summer and winter noise maps are the same. Therefore, only the large area field experiment during summer was conducted to verify the accuracy of noise map. Six measurement points around the airport were selected as shown in Figure 14. The information of the measurement points is shown in Table 7. Three days of noise measurements were conducted at each point from 12:00 to 22:00 when the operation of the airport was normal (no large-scale flight cancellation). The weather condition and measurement method are same with those that described in Section 2.11.

Figure 14. Location of the measurement points for large area validation field experiment.

Table 7. Information of the measurement points for large area validation field experiment.

Date	Point	Coordinate	${\mathbf{D}}_{1 }\left(\mathbf{km}\right)$	${\mathbf{D}}_2 \left(\mathbf{m}\right)$
9–11 May	L1	23°22′18.44″ N 113°16′55.74″ E	0.6	0
	L2	23°22′03.04″ N 113°16′53.63″ E	1.0	−1
21–23 May	L3	23°20′57.58″ N 113°18′19.16″ E	2.8	−4
	L4	23°19′55.40″ N 113°18′18.81″ E	4.8	−4
4–6 September	L5	23°24′50.11″ N 113°19′11.69″ E	1.3	0
	L6	23°25′32.08″ N 113°19′20.62″ E	2.6	+3

3. Results and Discussion

3.1. Noise Map of BIA

The noise maps of BIA during summer and winter are shown in Figure 15. Only areas with ${\mathrm{L}}_{\mathrm{WECPN}}$ > 70 dB are shown in both maps according to the guideline from “Technical Guidelines for Noise Impact Assessment” [31]. These areas are referred to as noise-affected areas and their boundaries are shown in Figure 16. It was found that during summer the farthest noise-affected areas in the east, west, south, and north directions are Botang Industrial Zone, Fuhe Yayuan Community, Guangzhou Zhongguan Village Information Innovation Demonstration Base, and Shidong Reservoir, respectively, where Shidong Reservoir (14.82 km) is farthest from ARP. During winter the farthest noise-affected areas in the east, west, south, and north directions are Fuli Jingang Community, Civil Aviation of China (Guangzhou regional control center), Ganlong Building, and Guangzhou International Airport R and F Integrated Logistics Park, respectively, where Ganlong Building is farthest from ARP (14.96 km).

Figure 15. Noise maps of BIA during (a) summer and (b) winter (not all aircraft tracks are shown for brevity).

Figure 16. Noise-affected areas surrounding BIA.

Throughout the year, the farthest noise-affected areas in the east, west, south, and north are Botang Industrial Zone (summer), Fuhe Yayuan Community (summer), Ganlong Building (winter), and Shidong Reservoir (summer), respectively, where their distances from ARP are 2.48 km, 3.86 km, 14.96 km, and 14.82 km, respectively, as shown in Figure 16. It can be concluded that the aircraft noise during summer has a greater impact in the east and west directions, while there is almost no difference between summer and winter in the south and north directions.

3.2. Comparison between Simulated and Measured Noise Data

The errors between the simulated noise data (${\mathrm{L}}_{\mathrm{WECPNs}}$) and ${\mathrm{L}}_{\mathrm{WECPNm}}$ of BIA during summer is shown in Table 8. The results show that all errors are less than 3 dB and the average error is 1.5 dB, which meets the requirement of China’s national standard [41]. The error of this study is small compared with other similar studies where their average errors were about 1.6∼3 dB [20,45]. For the large and small area field experiments, the largest errors are found at L4 (2.6 dB) and S3 (2.8 dB), respectively. In fact, L4 (4.8 km) and S3 (6.3 km) are the furthest points from the runway in the large and small area field experiment, respectively. Therefore, it can be concluded that the accuracy of the noise model is higher in the area closer to the airport where Sari et al. [19] also reached the same conclusion.

Table 8. Comparison between ${\mathrm{L}}_{\mathrm{WECPNs}}$ and ${\mathrm{L}}_{\mathrm{WECPNm}}$ of BIA during summer. Error = |${\mathrm{L}}_{\mathrm{WECPNm}}$ −${ \mathrm{L}}_{\mathrm{WECPNs}}$ |.

Point	${\mathbf{L}}_{\mathbf{WECPNm}}\left(\mathbf{dB}\right)$	${\mathbf{L}}_{\mathbf{WECPNs}}\left(\mathbf{dB}\right)$	Error (dB)
L1	84.8	83.6	1.2
L2	82.7	82.1	0.6
L3	78.5	77.0	1.5
L4	76.4	73.8	2.6
L5	87.4	88.7	1.3
L6	83.0	84.8	1.8
S1	78.3	76.4	1.9
S2	75.3	73.5	1.8
S3	74.5	71.8	2.7
S4	89.6	91.6	2.0
S5	83.5	84.8	1.3
S4 (24 h)	91.4	91.5	0.1

In addition, it was found that the correlation between the measured and the simulated results is high where the determination coefficient is as high as 0.976 as shown in Figure 17. Therefore, the accuracy of the simulation results of this study is ensured.

Figure 17. Correlation between the measured and simulated results.

3.3. Relationship between the Number of Aircraft and ${\overline{L}}_{EPN}$

The ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ of the aircrafts (with an interval of five sorties) passing through S5 from 12:00 to 22:00 were computed as shown in Figure 18. The results show that when the number of aircraft exceeds 100, the variation in ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ is very small (0.04%). In order to further verify this finding, the same analysis was conducted on the data of 24 h field experiment (S4, 330 aircrafts). A similar finding also concluded that the variation in ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ is less than 0.41% as shown in Figure 19. Hence, it can be concluded that when the number of aircraft is greater than 100, the ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ is almost consistent.

Figure 18. Relationship between the number of aircraft and ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ (12:00∼22:00).

Figure 19. Relationship between the number of aircraft and ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ (24 h).

3.4. Population and Area under Different Noise Levels

The population and area of BIA under different noise levels during summer and winter are shown in Table 9. The results show that 194,842 and 223,261 people are exposed to ${\mathrm{L}}_{\mathrm{WECPN}}$ > 70 dB during summer and winter, respectively, accounting for 22.22% and 25.46% of the total population of the five administrative regions around the airport (876,915). It should be noted that about 155 and 436 people are exposed to ${\mathrm{L}}_{\mathrm{WECPN}}$ > 90 dB during summer and winter, respectively. For summer, these people live in Shanxia Village in the north of BIA (near the north of runway 02R/20L). For winter, these people mainly live in Star Village in the south of BIA (close to the south of runway 02R/20L). These two villages are located under the landing track and are the residential areas that suffered with the most serious aircraft noise pollution. It can be seen from Table 9 that totally 83.885 ${\mathrm{km}}^2$ and 78.386 ${\mathrm{km}}^2$ of the area around BIA are exposed to ${\mathrm{L}}_{\mathrm{WECPN}}$ >70 dB during summer and winter, respectively, accounting for 18.30% and 17.10% of the total area of the five administrative regions around the airport (458.280 ${\mathrm{km}}^2$). In addition, 2.960 ${\mathrm{km}}^2$ and 2.594 ${\mathrm{km}}^2$ of the area around BIA are exposed to ${\mathrm{L}}_{\mathrm{WECPN}}$ > 90 dB during summer and winter, respectively. These areas are mainly located around the airport runway.

Table 9. Population and area under different noise levels around BIA.

Season	${\mathbf{L}}_{\mathbf{WECPN}}\left(\mathbf{dB}\right)$	Population	$\mathbf{Area} \left({\mathbf{km}}^2\right)$
Summer	>70	194,842	83.885
	>75	75,514	38.813
	>80	13,414	16.925
	>85	1831	7.165
	>90	155	2.960
Winter	>70	223,261	78.386
	>75	76,473	36.249
	>80	22,260	15.790
	>85	4033	6.536
	>90	436	2.594

The noise-affected area during winter is smaller compared with summer, but the noise-exposed population is higher during winter compared with summer. This is because the population density in the south of the airport is higher. For example, the administrative region with the highest population density is located in the south direction of the airport (the population density = 7292 people/${\mathrm{km}}^2$), while the population density in the north of the airport is generally lower as shown in Table A5 in Appendix A.

In addition, the proportions of population and area under different noise levels during summer and winter are shown in Figure 20 and Figure 21, respectively. The results indicate that the population and area of the noise-affected area are mainly distributed within the range of 70 dB < ${\mathrm{L}}_{\mathrm{WECPN}}$ < 75 dB. In addition, the area proportions under different noise levels during summer and winter are similar.

Figure 20. Proportion of the population and area under different noise levels during summer (only consider the noise-affected area).

Figure 21. Proportion of the population and area under different noise levels during winter (only consider the noise-affected area).

3.5. Noise Exposure Level of Noise Sensitive Point

The location of noise sensitive points (village, hospital and schools) within the noise-affected area is determined based on the “Technical Guideline for Environmental Impact Assessment (Constructional Project of Civil Airport)” [18] using Amap as shown in Figure 22. The number of noise sensitive points under different noise levels is shown in Table 10, Figure 23 and Figure 24. The aircraft noise limits for village, school, and hospital are determined: ${\mathrm{L}}_{\mathrm{WECPN}\left(\mathrm{l}\right)}$= 70 dB, ${\mathrm{L}}_{\mathrm{A}\left(\mathrm{l}\right)}$ = 90 dBA (see Table A6 in Appendix A for more details) based on the classification of “Code for Classification of Urban Land Use and Planning Standards of Development Land” [46] on noise sensitive land and guidelines from China’s national standards [37,40]. Table 10 shows that during summer, ${\mathrm{L}}_{\mathrm{WECPN}}$ of 74 noise sensitive points exceed the noise limit including 31 villages, 20 schools, and 23 hospitals, where Shanxia Village Health Station has the highest ${\mathrm{L}}_{\mathrm{WECPN}}$ (84.4 dB). During winter, about 86 noise sensitive points exceed the noise limit including 35 villages, 32 schools, and 19 hospitals, where Guangzhou 73rd Secondary School has the highest ${\mathrm{L}}_{\mathrm{WECPN}}$ (84.5 dB). Moreover, it can be seen from Figure 23 and Figure 24 that the noise sensitive points are mainly distributed within the range of 70 dB < ${\mathrm{L}}_{\mathrm{WECPN}}$ <75 dB during both winter and summer, while none of their ${\mathrm{L}}_{\mathrm{WECPN}}$ are higher than 85 dB.

Figure 22. Distribution of noise sensitive points around BIA.

Table 10. Number of noise sensitive points under different noise levels.

Season	${\mathbf{L}}_{\mathbf{WECPN}}\left(\mathbf{dB}\right)$	Village	School	Hospital
Summer	>70	31	20	23
	>75	11	6	9
	>80	2	2	4
	>85	0	0	0
Winter	>70	35	32	19
	>75	10	13	7
	>80	3	3	1
	>85	0	0	0

Figure 23. Number of sensitive points under different noise levels during summer.

Figure 24. Number of sensitive points under different noise levels during winter.

The noise sensitive points are indicated in Figure 22. During summer, ${\mathrm{L}}_{\mathrm{Amax}}$ of 16 villages, 13 hospitals, and 15 schools exceed the noise limit where Shanxia Village Health Station has the highest ${\mathrm{L}}_{\mathrm{Amax}}$ (103.5 dBA). During winter, ${\mathrm{L}}_{\mathrm{Amax}}$ of 13 villages, 10 hospitals, and 14 schools exceed the noise limit where Guangzhou 73rd Secondary School has the highest ${\mathrm{L}}_{\mathrm{Amax}}$ (104.1 dBA). In summary, the ${\mathrm{L}}_{\mathrm{WECPN}}$ and ${\mathrm{L}}_{\mathrm{Amax}}$ of 42 and 22 sensitive points exceed the noise limit throughout the year, respectively, where seven of them exceed both limits of ${\mathrm{L}}_{\mathrm{WECPN}}$ and ${\mathrm{L}}_{\mathrm{Amax}}$. They are Gaozeng Village, Shanxia Village, Mingxing Village Health Station, Jiuhu Village Health Station, Huadong Health Center, Guangzhou 73rd Secondary School, and Jiuyi Primary School. These noise sensitive points are seriously disturbed by aircraft noise throughout the year.

3.6. Noise Exposure Level of Noise Sensitive Functional Areas

The land functional areas within the noise-affected area are divided into agricultural area, residential area, education and research area, construction area, medical service area, trade area, warehousing area, administrative area, industry area, BIA, lake, and river by using satellite images and Amap as shown in Figure 25. According to Table A6 in Appendix A, the residential area, education and research area, medical service area, trade area, warehousing area, administrative area, and industrial area are defined as noise sensitive functional areas (with noise limit) while others are defined as insensitive functional areas (without noise limit) as shown in Table 11.

Figure 25. Land functional area map within the noise-affected areas during (a) summer and (b) winter. For example, “E” represents the location of the ${\mathrm{L}}_{\mathrm{WECPN}\left(\max \right)}$ of educational and research area.

Table 11. Classification and proportion of the noise sensitivity level for land functional areas within the noise-affected areas.

Functional Area	Noise Limit	Sensitivity	Summer (%)	Winter (%)
Residential area	${\mathrm{L}}_{\mathrm{WECPN}\left(\mathrm{l}\right)}$= 70 dB	Sensitive	19.92	20.95
Education and research area
Medical service area
Trade area	${\mathrm{L}}_{\mathrm{WECPN}\left(\mathrm{l}\right)}$= 75 dB	Quite sensitive	2.81	2.84
Administrative area
Warehousing area	${\mathrm{L}}_{\mathrm{WECPN}\left(\mathrm{l}\right)}$ = 80 dB	Less sensitive	4.67	11.31
Industry area
Agricultural area	-	Insensitive	72.60	64.90
Construction area
BIA
River
Lake

The area and proportion of all sensitive functional areas are computed as shown in Table 12. The area proportion of all sensitive functional areas during summer and winter are compared as shown in Figure 26. It can be seen from Table 11 and Table 12 that the areas that are “sensitive” to noise are the main component of the sensitive functional area (19.92% and 20.95% during summer and winter, respectively). It can be observed from Figure 26 that the area proportion of the residential area is the highest during both summer and winter (19.43% and 20.11%, respectively). This means that residential areas are most affected by aircraft noise around BIA. In addition, the location and value of the maximum ${\mathrm{L}}_{\mathrm{WECPN}}$ (${\mathrm{L}}_{\mathrm{WECPN}\left(\max \right)}$) of all sensitive functional areas are shown in Figure 25 and Table 12, respectively.

Table 12. Noise exposure level of the sensitive functional areas within the noise-affected areas.

		Residential	Education and Research	Trade	Warehousing	Medical Service	Administrative	Industry
Summer	$\mathrm{Area} ({\mathrm{km}}^2$)	16.302	0.398	0.516	2.437	0.016	1.833	1.477
	Area proportion (%)	19.43	0.47	0.62	2.91	0.02	2.19	1.76
	$\mathrm{Violation} \mathrm{area} ({\mathrm{km}}^2$)	16.302	0.398	0.058	0.072	0.016	1.833	0.063
	Violation rate (%)	100	100	11.24	2.95	100	100	4.27
	${\mathrm{L}}_{\mathrm{WECPN}\left(\max \right)}$(dB)	90.4	83.0	76.7	87.2	84.4	87.5	83.0
Winter	$\mathrm{Area} ({\mathrm{km}}^2$)	15.762	0.602	0.266	3.001	0.055	1.962	5.867
	Area proportion (%)	20.11	0.77	0.34	3.83	0.07	2.50	7.48
	$\mathrm{Violation} \mathrm{area} ({\mathrm{km}}^2$)	15.762	0.602	0.138	0.183	0.055	1.093	0.276
	Violation rate (%)	100	100	51.88	6.10	100	55.71	4.70
	${\mathrm{L}}_{\mathrm{WECPN}\left(\max \right)}$(dB)	91.8	85.0	80.9	82.2	83.4	86.9	84.1

Figure 26. Area proportion of all sensitive functional areas during summer and winter (only consider the noise-affected area).

It can be seen from Table 12 that during winter, the violation rates of the residential area, education and research area, and medical service area are as high as 100%. During summer, in addition to these areas, the violation rate of the administrative area also reaches 100%. Finally, the total areas within the noise-affected area that violate the noise limit are 18.742${ \mathrm{km}}^2$ (22.34%) and 18.109${ \mathrm{km}}^2$ (23.10%) during summer and winter, respectively. Therefore, it can be concluded that the compliance rate of aircraft noise within the noise-affected area of BIA is not satisfactory. The noise compliance map of all sensitive functional areas during summer and winter is shown in Figure 27. Functional areas that are “insensitive” to noise such as the agricultural area, etc., are assumed to comply with the noise limit since there is no noise limit for these areas (see Table 11).

Figure 27. Aircraft noise level compliance map of BIA during (a) summer and (b) winter.

4. Conclusions

Unlike other airport noise map studies, the present study produced noise maps of BIA during summer and winter separately since the operation modes of BIA during summer and winter were very different. The building and building height simplification methods were used to shorten the modelling time. ADS-B flight data were used to obtain the number of aircrafts passing through the measurement point whereby a whole day noise measurement was no longer necessary. It was also proved that as long as the proportion of the aircraft models during the measurement period were close to the proportion of the aircraft models throughout the day, the ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ obtained from the short-term noise measurement can be close to the ${\overline{\mathrm{L}}}_{\mathrm{EPN}}$ obtained from the whole day noise measurement. Accuracy of the present methodologies were verified by field experimental data with the average error of 1.5 dB. The multifaceted analysis of aircraft noise in current study reveals the aircraft noise pollution level around BIA accurately, which provides a valuable reference for the relevant departments. Finally, for future work, this study will design a series of practical noise reduction schemes according to the aircraft noise pollution level and working characteristics of BIA.