Research on evaluation of airport environment capacity

Abstract

Based on the concept of environmental carrying capacity and airport capacity, in this paper we propose the concept of airport environment capacity (AEC). We calculated the maximum pollutant concentration in an airport by constructing a pollutant evaluation model, established the relationship between the pollutant concentration and the sorties of aircraft taking off and landing, and provided a method of determining the AEC. Using Shanghai Pudong International Airport as an example, we evaluated AEC according to the proposed evaluation method and process. The result showed that the evaluation method was correct and effective.

Keywords

Environment Capacity Emission Airport concentration distribution

1 Introduction

In recent years, with rapid economic and social development in China, the number of aircraft and air traffic flow have increased rapidly, and flight delays are increasingly getting worse. Airports themselves are fast becoming the major bottleneck restricting the development of air transportation. In order to use airport resources safely and rationally, it is necessary to conduct a comprehensive and accurate assessment of airport service capacity. Limited by assessment means and technologies, current evaluation methods are based on factors such as the physical structure of the airport and the surrounding airspace, the regulatory and operating environment, and the workload of air traffic controllers. The capacity value obtained from evaluation is used as an important indicator of airport service capacity. With the increase in environmental awareness and the need for sustainable development of air traffic, there is a growing concern regarding the environmental impact of air traffic. It has therefore become necessary to take environmental carrying capacity into consideration when assessing airport capacity to achieve a “win-win” situation in terms of transportation development and environmental protection.

The concept of ETC (environmental traffic capacity) was first proposed by Buchanan [1] in 1963. ETC refers to the largest development scale that a traffic environment can withstand without any damage to human survival, ecological environment, and use of resources in a certain time period and a region with certain traffic structures [2, 3]. ETC is a component of environmental capacity, and can be divided into traffic environmental resource-carrying capacity and traffic environmental pollution-carrying capacity. The resource-carrying capacity of an airport transportation environment is reflected in the usable airspace unit, equipment and facilities, air traffic controllers, and other integrated service capabilities; that is, the airport capacity. The pollution-carrying capacity of an airport transportation environment is reflected in the affordability of the airport surroundings to pollutant emissions, noise, and other environmental impacts, but does not yet have a clear definition. When the air traffic of an airport increases to a certain degree, its environmental resource-carrying capacity and environmental pollution-carrying capacity are challenged. In order to ensure that the airport surroundings are not compromised, the environmental air traffic capacity of the airport must be defined to put a limit on the development scale of the airport, and an evaluation method of environmental air traffic capacity based on the environmental affordability of the airport surroundings must be established to guide the sustainable development of theairport.

For airports, the bottleneck to early air traffic growth is usually in traffic environmental resources. Therefore, in the study on airport environmental resource-carrying capacity, scholars have carried out much research and have achieved fruitful results since the beginning of the last century. In 1948, Bowen and Pearcey evaluated runway capacity using the Poisson distribution of flight arrival flow [4]. In 1982, Janic and Tosic extended the runway capacity model to terminal regions and established the terminal capacity model [5]. In 1996, Lee proposed an airport capacity model based on delay [6]. And in 2011, Branlo proposed an airport capacity assessment method based on time utilization [7]. Human factors in air traffic at an airport is mainly limited by the workload of ATCs (air traffic controllers). In 1963, Arad and others described the relationship between the workload of ATCs, airspace capacity, and airspace design [8]. In 1993, Tofukuji proposed the sector capacity assessment method based on the workload of ATCs [9]. In 2002, Majumdar analyzed factors affecting the workload of controllers, concluded that traffic complexity is the major factor, and proposed the capacity assessment model based on complexity [10]. In 2013, Welch et al., by considering weather effects, proposed the capacity estimation method based on the workload of micro-ATCs [11]. By considering the complexity of the airport system and randomness of the influencing factors at the same time, evaluation software such as SIMMOD, TAAM, and AirTOp extensively employ rapid simulation technology to obtain more accurate and digitized analysis results [12].

However, the studies of airport environmental pollution-carrying capacity started late, and still outstanding is a calculation of airport emissions and a study of the environmental protection model. Since 1995, the ICAO (International Civil Aviation Organization) has constantly updated the Aircraft Emission Databank according to the data provided by engine manufacturers [13]. In 2008, Shuqing et al. calculated LTO (landing and take-off) cycle gas pollutant emissions at China’s 123 airports using EDB data, and obtained the relationship between the number of LTO cycles and each pollutant’s gas emission index by fitting [14]. In 2010, Zhiqiang et al. derived the calculation method of aircraft performance parameters to replace EDB data, and provided the emission index correction model for various pollutants [15]. In 2011, Kurniawan et al. performed a comparative analysis of the advantages and disadvantages of each method for calculating emissions in LTOs, and determined the uncertainty of the calculation method [16]. In 2009, David and Gregg, in accordance with fuel consumption characteristics of engines at low altitude and low speed, established the TSFC model by improving the terminal fuel consumption model, which can improve the fuel consumption calculation accuracy in the arrival and departure phase below 10,000 feet [17]. In 2011, the analysis of Dumont et al. showed that airplanes approaching with small thrust/small resistance under 10,000 feet can save fuel and carbon emissions by 30–40 % [18]. In 2013, Bruno et al. demonstrated that the use of RNP (Required Navigation Performance) procedures can significantly reduce fuel consumption and emissions at SDU airports [19].

In summary, airport environmental resource-carrying capacity is clearly defined, has mature evaluation technology, and can obtain the specific quantitative index taken from the sorties of airplane LTOs. The definition of airport environmental pollution carrying capacity, however, is not yet clear, owing to a lack of an evaluation method, understanding of the process of pollutant emission concentration, and other factors.

In order to accurately assess the impact of air traffic on airport environments, and to further evaluate airport ETC, based on the related concepts of ground transportation, we proposed, for the first time to our knowledge, a definition of airport environment capacity (AEC), and used it to construct an evaluation model of pollutant concentration by combining the Gaussian mode with aircraft operating characteristics around the airport. We then analyzed the relationships between different air traffic and pollutant concentrations, and finally selected Shanghai Pudong International Airport as an example with which to calculate the AEC under typical weather conditions, and verified the validity of the evaluation method.

2 Determination of AEC

2.1 Definition

Aircraft operations near an airport include taxiing-out, takeoff, landing, taxiing-in, and partial climb and descent. In order to regulate airport assessment of air pollutants, ICAO uses the LTO cycle to unilaterally calculate aircraft pollutant emissions. The spread of aircraft pollutant emissions in the atmosphere can affect the local air quality. With a small amount of air traffic at an airport, the concentration of pollutant emissions is within the range of the environment’s self-purification ability. With the increase in airport flight traffic, the concentration of polluted air increases as well. Owing to air quality standards, the sorties of aircraft LTOs at an airport are limited [20]. Thus, airport environment capacity (AEC) is defined as the maximum amount of traffic that can be reached at the airport in a unit time period without causing environmental degradation. In other words, the AEC is within a certain time interval, with a given airport’s structure and operating rules, and meeting ambient air quality standards, and includes the maximum sorties of aircraft LTOs that an airport can accommodate (Fig. 1).

Fig.1

Determination of airport environment capacity.

The maximum pollutant concentration (MPC) at an airport is the maximum pollutant concentration emitted by the aircraft activities at the airport within a certain time period, which can be expressed as: $MPC [Δ t] = \underset{z \in [z_{min}, z_{max}]}{\underset{y \in [y_{min}, y_{max}]}{max_{x \in [x_{min}, x_{max}]}}} [PC (x, y, z, Δ t)] .$ (1)

2.2 Pollutant concentration assessment model

The aircraft moves in the atmosphere according to the scheduled track, and the pollutants are emitted and spread into the atmosphere along the track as well. At different locations, the concentration of pollutants is also different. Thus, in addition to atmospheric conditions, the concentration of pollutants at the assessment point is also closely related to the distance between the assessment point and the aircraft, the pollutant emission index, and the amospheric diffusion pattern.

For the assessment of airport pollutant concentration, some scholars have conducted preliminary studies. In 2012, Lobo et al. analyzed the particulate-matter (PM) emissions of a large number of aircraft models in the 100–300-m downwind direction of the airport, and devised a PM emission calculation model and distribution of flight LTO cycle [21]. In 2013, Huiling et al. developed a line source diffusion model based on the integration of the Gaussian point source diffusion formula. After amending the parameter of the line source diffusion model, they established the emission concentration model during the aircraft takeoff and landing cycle [22]. Currently, Gaussian [23] and Lagrange models [24], among others, are used to describe atmospheric diffusion. The Gaussian model is a semi-empirical diffusion model, and the concentration of pollutant gas diffusion follows a normal distribution, so it is widely used in the research field of various scales. In the ICAO’s Doc 9889 files, the Gaussian model is the recommended model for airport pollutant assessment [25]. In this paper, we also chose the Gaussian model to evaluate the distribution of the pollutant concentration of air traffic at airports.

In the LTO cycle, the movement of aircraft includes five stages: takeoff, landing, climb, approach, and taxiing. The status of aircraft engines at each stage is different, and the volume of pollutant emission per unit time is also different, but must be calculated by combining the operating characteristics of the aircraft to establish the line source and to surface the source diffusion model based on the conventional point source Gaussian diffusion model. The aircraft flies along flight routes during takeoff, landing, climb, and approach, and the moving trajectory can be viewed as a line source consisting of a series of continuous point sources. The pollutant concentration generated by this line source for any evaluation point is the sum of the pollutant concentrations generated by the successive point sources on the line source for that evaluation point, equivalent to the numerical integration of the pollutant concentration at this evaluation point generated by all the continuous point sources along the direction of the line source. When using the Gaussian diffusion model to calculate the gas pollutant concentration generated by the line pollution source, we need to know the horizontal distance and downwind distance between the evaluation point and the pollution source. Thus, the line source pollution source diffusion coordinate system can be established by using the line source center as the origin, the aircraft flight direction as the Y axis, the vertical direction of the line source as the X axis, and the line source height as the Z axis, as shown in Fig. 2.

Fig.2

Coordinate system of line source pollution source diffusion.

For any evaluation point (x, y, z) and any wind direction V_W, the pollutant concentration PC_k (x, y, z) generated by the line pollution source k formed by aircraft flying along the flight route can be expressed as $\begin{matrix} {PC}_{k} (x, y, z) = \frac{{SS}_{k}}{\sqrt{2 π} V_{W} σ_{y, k} σ_{z, k}} \\ \times {\begin{matrix} exp [- \frac{{(z - H_{k})}^{2}}{2 \cdot σ_{z, k}^{2}}] + \\ exp [- \frac{{(z + H_{k})}^{2}}{2 \cdot σ_{z, k}^{2}}] \end{matrix}} \cdot \frac{1}{\sqrt{2 π}} \\ \times \int_{- l_{k}}^{l_{k}} exp (- \frac{{(y_{k})}^{2}}{2 \cdot σ_{y, k}^{2}}) dy \end{matrix}$ (2) ${\begin{matrix} x_{k} = (y_{p, k} - y) cos φ_{k} + x sin φ_{k}, \\ y_{k} = (y - y_{p, k}) sin φ_{k} + x cos φ_{k}, \end{matrix}$ (3) ${SS}_{k} = \sum_{i \in F_{Δ t}^{k}} \frac{{EI}_{i, k} \cdot {FF}_{i, k}}{V_{i, k} \cdot Δ t}$ (4) $H_{k} = H_{min, k} + l_{k} \cdot | G_{k} | + y_{k} \cdot G_{k},$ (5) where y_p,k is the distance between any point on the line source pollution source k and the origin; φ_k is the angle between wind direction V_W and k; σ_y,k,σ_z,k is the diffusion coefficient [1], which can be obtained by x_k; SS_k is the emission intensity of the pollution source k, in units of g/(m s), kɛtakeoff, landing, climb, descent£¬ $F_{Δ t}^{k}$ is the collection of aircraft within time Δt of pollution source k; l_k is half the length of k; EI_i,k is the emission index of aircraft i from pollution source k; FF_i,k is the fuel flow rate of aircraft i from pollution sourcek; V_i,k is the flight speed of aircraft i from pollution source k; Δt is the evaluation time slice; H_min,k is the lowest point of the line source pollution source; G_k is the gradient of the line source pollution source, which is positive for climb and negative for descent; and x ∈ [x_min, x_max], y ∈ [y_min, y_max], z ∈ [z_min, z_max].[x_min, x_max] is the evaluation range of the x axis, [y_min, y_max] is the evaluation range of the y axis, and [z_min, z_max] is the evaluation range of the z axis.

The entire scene structure can be regarded as a planar network. When the diffusion model of the gas pollutant emission by the aircraft taxiing in the scene is the typical surface diffusion model ^[26], the surface diffusion model integrates the numerical value of the width of the emission surface based on the line source. In the calculation of Gaussian surface source diffusion, it can be transformed into a virtual point source Gaussian diffusion model. Assuming that the virtual pollution source is a point source, the pollution source is in the wind direction of the source geometric center, and the width of the pollutant generated in the direction of the surface source centerline is the surface width, a_k = 4.3σ_{y
₀}, as shown in Fig. 3.

Fig.3

Coordinate system of virtual point source.

The pollutant concentration PC_k (x, y, z) of any evaluation point (x, y, z) can be expressed as: $\begin{matrix} {PC}_{k} (x, y, z, Δ t) = \frac{{SS}_{k}}{π V_{W} (σ_{y, k} + a_{k} / 4.3) σ_{z, k}} \\ \cdot exp [- \frac{y^{2}}{2 \cdot (σ_{y, k} + a_{k} / 4.3)}] \end{matrix}$ (6) ${SS}_{k} = \sum_{i \in F_{Δ t}^{k}} \frac{{EI}_{i, k} \cdot {FF}_{i, k} \cdot t_{i}}{Δ t}$ (7) where σ_y,k, σ_z,k are the horizontal and vertical diffusion coefficients at x_k + x_k,0, respectively [13]; σ_{y
_k,0} is the horizontal diffusion coefficient of initial diffusion at x_k,0; the scene taxiing process is regarded as the surface source pollution source; a_k is the length of the surface source pollution source k; SS_k is the emission intensity of the virtual pollution point source; EI is the gas emission index; FF is the fuel flow rate; and t_i is the taxiing time of aircraft i within Δt.

The diffusion of gas pollutants is calculated by regarding each flight stage as an independent source of pollution. The gas pollutant concentration PC (x, y, z) of any evaluation point (x, y, z) near the airport is the superposition of the polluted gas concentration formed by each pollution source, which can be expressed as: $PC (x, y, z) = \sum_{k} {PC}_{k} (x, y, z)$ (8) where PC_k (x, y, z) is the emission concentration of any evaluation point (x, y, z) at flight stage k, and kɛtakeoff, landing, climb, descent.

2.3 Evaluation method

According to the pollutant concentration assessment model presented in Sec. 2.2, the concentration distribution of pollutants in the airport can be calculated. In actual operation, the concentration of pollutants throughout the airport can fluctuate with wind direction, wind speed, runway-use direction, aircraft models, etc. It is a time variable, and is thus not conducive to guide the daily operation of the airport. Therefore, it is necessary to analyze the historical data of the meteorological conditions and flight plans of the airport. According to the characteristics of the statistical analysis, the typical airport operation scene is constructed, and then the evaluation results are representative andoperable.

According to the definition of environmental air traffic capacity at an airport, the amount of air traffic that is restricted by ambient air quality standards is denoted the airport environment capacity (AEC). At present, most of the aircraft air pollutant emissions do not meet the ambient air quality standards, indicating that the existing flight volume is not saturated. Drawing on the assessment method of airport capacity, the evaluation of AEC is also carried out by increasing the amount of flights in accordance with certain laws to calculate the pollutant emission concentrations under different amounts of flights, to establish the relationship between the amount of flights and airport pollutant emissions, and to finally obtain the AEC corresponding to the ambient air quality standards. This includes, but is not limited to, the following five steps:

Step 1: Build the coordinate system. The pollutant concentration evaluation coordinate system is established according to the airport’s existing and planned runway structure, taxiway layout, and arrival and departure route structure. The runway center is the origin, the runway direction is the Y axis (the outermost runway is selected in a multi-runway airport), the X axis points to the main area of the airport, perpendicular to the Y axis, and the Z axis is the height. The evaluation range is set according to the airport range.

Step 2: Calculate the pollutant emission concentration. First analyze the airport pollutant emissions according to the air traffic characteristics at the airport, establish a collection of pollutant sources, calculate the airport atmospheric diffusion parameters according to the typical airport weather conditions, and calculate the emission intensity of the pollution sources from the type of pollution source, traffic flow characteristics, and aircraft characteristics. Next, the PC_k (x, y, z, Δt) formed by the kth pollution source at any location in time Δt is calculated using the pollutant concentration assessment model. By superimposing the pollutant concentrations formed by all pollution sources, the pollutant concentration distribution characteristic PC (x, y, z, Δt) is obtained. Finally, the maximum concentration within the scope of the assessment is the MPC.

Step 3: Traffic flow increment. If the pollutant emission concentration is less than the ambient air quality standards, the air traffic flow must be cloned to obtain the limit. According to the airport’s traffic flow distribution characteristics, aircraft model distribution characteristics, and the development of airport scale, a flight increment is made to the existing flight plans to generate the new traffic flow that has the common distribution characteristics of the existing traffic flow. This is in line with the development of airport traffic flow.

Step 4: Determine the relationship between sorties of LTOs at the airport and the MPC. The pollutant emission concentration is calculated for the increased traffic flow data, and the correlation between sorties of LTOs at the airport and the MPC under different flows is quantified.

Step 5: Determine the AEC value. The AEC value is determined according to the air quality standards and the correlation between the sorting and pollutant emission concentrations.

A detailed flowchart of AEC assessment is shown in Fig. 4.

Fig.4

Evaluation process of airport environment capacity.

3 Case study

In this paper, we choose Shanghai Pudong International Airport to evaluate the environmental air traffic capacity according to the process of AEC assessment.

3.1 Evaluation coordinate system

The datum longitude and latitude of Shanghai Pudong International Airport benchmarks are N31°08^′39^″, E121°47^′33^″. There are four runways at the airport, two runways for takeoff (34L, 35R) and two runways for landing (34R, 35L).

We established the PC evaluation coordinate system. The origin is the midpoint of runway 34R at the outermost edge of the wind, the X axis is along the direction of the wind perpendicular to the runway, the Y axis is along the runway direction to the North, and the Z axis is along the direction of the height. According to the airport layout, the evaluation range is set as 0 ≤ x ≤ 10000, -30000 ≤ y ≤ 24000, as shown in Fig. 5.

Fig.5

Coordinate of concentration evaluation and evaluation range at Shanghai Pudong International Airport.

3.2 Typical meteorological scenarios

According to the meteorological data from August 22, 2014 to August 21, 2015 at Shanghai Pudong International Airport obtained from the U. S. National Oceanic and Atmospheric Administration (NOAA), the annual wind speed at a height of 10 m is calculated as 4.4 m/s, and the rose wind map is shown in Fig. 6.

Fig.6

Rose wind map of Shanghai Pudong International Airport.

According to the rose wind map, the pollution coefficient of each wind direction is calculated. The pollution coefficient [27] is the ratio of the wind direction frequency to the average wind speed. The greater the pollution coefficient, the greater the influence of the pollutant emission in this wind direction on the airport. Table 1 shows the wind-direction-related data of the first five pollution coefficients. As can be seen in the figure, the pollution coefficient is 0.031 in the Southeast (SE) (135°) direction, which is the greatest pollution coefficient in all wind directions. The pollutant emission in this direction has the greatest impact on the airport environment. In addition, according to the meteorological data and atmospheric stability method [28], the annual distribution of various types of atmospheric stability is obtained. At Shanghai Pudong International Airport, the annual daytime (08 : 00–19 : 59) atmosphere is dominated by C stability, with a ratio of 41%. The nighttime (00 : 00–07 : 59, 20 : 00–23 : 59) atmosphere is dominated by D stability.

Table 1

Pollution parameters at Shanghai Pudong International Airport

Wind	Wind	Average wind	Contamination
direction	frequently	speed (m/s)	factor
SE	11.8%	3.8	0.031002
ESE	9.0%	4.1	0.022051
N	9.9%	5	0.019726
SSE	5.5%	3	0.018265
NNE	8.5%	4.9	0.017333

3.3 Traffic flow data

Taking August 21, 2015 as an example, we analyzed the flight traffic flow data at Shanghai Pudong International Airport. The average traffic flow at the airport is 1348 sorties, and the arrival and departure ratio is 50% :50%. The time flow distribution and model ratio of each runway are shown in Figs. 7 and 8. As can be seen from the figure, the peak period of the day is 08 : 00–22 : 59, of which 07 : 00–08 : 59 is the peak of departures.

Fig.7

Time flow distribution at Shanghai Pudong International Airport.

Fig.8

Model ratio of each runway at Shanghai Pudong International Airport.

3.4 Aircraft pollutant emissions factor

Emissions of polluting gases are related to gas emission index (EI), fuel flow (FF), and emission time. The emission volume of each polluting gas can be expressed as: $E_{m} = {EI}_{m} \cdot FF \cdot t,$ (9) where E_m is the emission volume of the polluting gasm, and EI_m is the emission index of the polluting gas m. The standard fuel flow rate and emission index can be obtained by the discrete data regression provided by the Engine Emissions Database (EDB version 20) from the ICAO. Fig. 9 shows the double-logarithmic regression curve of fuel flow rate and emission index of NOx, HC, and CO calculated using the BFFM2 (Boeing Fuel Flow Model2) ^[29] method for the CFM56-7B26 engine.FF is the fuel flow rate and t is the emission time.

Fig.9

Logarithmic regression relationship between emission index and fuel flow rate (CFM56-7B26 engine).

3.5 PC distribution calculation

According to the selection of typical scenes, the wind direction (SE) of the maximum influence is used to calculate the PC distribution of Pudong International Airport. This wind direction forms a 135° angle with the Y coordinate of the evaluation coordinate system. Combined with the air traffic flow characteristics of August 21, 2015, the MPC of each pollutant at various time periods is calculated. The results are shown in Table 2.

Table 2
MPC value (μg/m³) in each time period for each pollutant

Time period NOx CO HC SO₂ TSP

00 : 00–00 : 59 60.7 0.9 0.2 1.9 0.6

01 : 00–01 : 59 25.6 0.5 0.1 0.8 0.2

02 : 00–02 : 59 41.6 0.7 0.1 1.3 0.4

03 : 00–03 : 59 41.8 1.1 0.1 1.3 0.4

04 : 00–04 : 59 38.6 1.1 0.1 1.2 0.4

05 : 00–05 : 59 25.7 0.6 0.1 0.8 0.2

06 : 00–06 : 59 48.0 0.8 0.1 1.5 0.4

07 : 00–07 : 59 80.7 1.2 0.2 2.5 0.7

08 : 00–08 : 59 90.1 1.4 0.3 2.8 0.8

09 : 00–09 : 59 69.5 1.2 0.2 2.2 0.6

10 : 00–10 : 59 62.0 1.0 0.2 1.9 0.6

11 : 00–11 : 59 80.8 1.3 0.2 2.5 0.8

12 : 00–12 : 59 82.7 1.5 0.3 2.6 0.8

13 : 00–13 : 59 67.7 1.3 0.2 2.1 0.6

14 : 00–14 : 59 52.7 1.1 0.2 1.7 0.5

15 : 00–15 : 59 60.2 1.1 0.2 1.9 0.6

16 : 00–16 : 59 71.4 1.3 0.2 2.2 0.7

17 : 00–17 : 59 71.5 1.3 0.2 2.2 0.7

18 : 00–18 : 59 60.3 1.4 0.2 1.9 0.6

19 : 00–19 : 59 56.5 1.3 0.2 1.8 0.5

20 : 00–20 : 59 105.7 1.9 0.3 3.3 1.0

21 : 00–21 : 59 124.8 2.0 0.4 3.9 1.2

22 : 00–22 : 59 57.6 0.9 0.2 1.8 0.5

23 : 00–23 : 59 48.1 1.0 0.1 1.5 0.4

Time period	NOx	CO	HC	SO₂	TSP
00 : 00–00 : 59	60.7	0.9	0.2	1.9	0.6
01 : 00–01 : 59	25.6	0.5	0.1	0.8	0.2
02 : 00–02 : 59	41.6	0.7	0.1	1.3	0.4
03 : 00–03 : 59	41.8	1.1	0.1	1.3	0.4
04 : 00–04 : 59	38.6	1.1	0.1	1.2	0.4
05 : 00–05 : 59	25.7	0.6	0.1	0.8	0.2
06 : 00–06 : 59	48.0	0.8	0.1	1.5	0.4
07 : 00–07 : 59	80.7	1.2	0.2	2.5	0.7
08 : 00–08 : 59	90.1	1.4	0.3	2.8	0.8
09 : 00–09 : 59	69.5	1.2	0.2	2.2	0.6
10 : 00–10 : 59	62.0	1.0	0.2	1.9	0.6
11 : 00–11 : 59	80.8	1.3	0.2	2.5	0.8
12 : 00–12 : 59	82.7	1.5	0.3	2.6	0.8
13 : 00–13 : 59	67.7	1.3	0.2	2.1	0.6
14 : 00–14 : 59	52.7	1.1	0.2	1.7	0.5
15 : 00–15 : 59	60.2	1.1	0.2	1.9	0.6
16 : 00–16 : 59	71.4	1.3	0.2	2.2	0.7
17 : 00–17 : 59	71.5	1.3	0.2	2.2	0.7
18 : 00–18 : 59	60.3	1.4	0.2	1.9	0.6
19 : 00–19 : 59	56.5	1.3	0.2	1.8	0.5
20 : 00–20 : 59	105.7	1.9	0.3	3.3	1.0
21 : 00–21 : 59	124.8	2.0	0.4	3.9	1.2
22 : 00–22 : 59	57.6	0.9	0.2	1.8	0.5
23 : 00–23 : 59	48.1	1.0	0.1	1.5	0.4

As can be seen from the data in Table 1, the MPC of NOx gas generated by the same traffic flow in the same period is approximately 30–300 times larger than that of the other polluting gases because the NOx emission index is the largest in each flight phase of the LTO cycle, resulting in the largest generated NOx gas concentration. Thus, the concentration of NOx in all pollutants emitted by air traffic will become the first bottleneck in the environmental quality of the airport. To obtain the MPC value of NOx at the airport, the average PC distribution of the NOx in each time period of the day must be calculated. The calculation results are shown in Fig. 10.

Fig.10

PC distribution of NOx in each time period.

As can be seen from the PC distribution diagram, within the evaluation range, the PC value is larger in the vicinity of the runway. The farther one is from the runway in the downwind direction, the smaller the PC value, and the decrease in PC value is dispersed by the runway. In addition, the MPC value of NOx at 21 : 00–21 : 59 is 124.8μg/m3, which is the largest hourly concentration of the entire day. This is because this period has reached the nighttime period, and the atmospheric stability is D. Compared to the atmospheric stability C in the daytime, the emission gases are not easily spread. Furthermore, because this period is still the busiest hour for the airport, the concentration of pollutants during this period is the largest over the entire day.

In order to further analyze the PC’s transverse distribution of NOx, the PC value along the X-axis direction at Y = 1800 and the location of the four runways are given in Fig. 11.

Fig.11

Y = 1800, PC distribution along X-axis direction (each time period).

The largest PC value is located at runway 35L, and the other PC values in the X-axis direction are smaller. The MPC value is the largest in the time period 21 : 00–21 : 59. Owing to the fact that the greatest impact of aircraft on the concentration of pollutants is in the takeoff stage, the concentration of pollutants in the early peak hours (07 : 00–08 : 59) is quite large. Comparing time periods TA (11 : 00–11 : 59) and TB (12 : 00–12 : 59), although the number of sorties in TB is significantly increased compare to that in TA, the corresponding MPC values are almost same due to the similar departure sorties, as shown in Fig. 12.

Fig.12

Ratio of arrival and departure aircrafts and MPC (NOx) values in each time period.

3.6 Relationship between MPC and flight flow

The flight data are compressed. When the flight flow is increased, the maximum hourly pollutant concentration (MHPC) and the maximum daily pollutant concentration (MDPC) will vary; the specific values are shown in Table 3.

Table 3
NOx concentration for different flight flow values

Daily flight MPDC Hourly flow MHPC

volume (sortie) (μg/m³) (sortie) (μg/m³)

1342 63.5 88 124.8

1500 70.7 97.9 138.8

1700 80.1 111 157.3

1900 89.5 124 175.8

2100 99 137.1 194.4

2500 117.8 163.2 231.3

3000 141.3 195.8 277.6

Daily flight	MPDC	Hourly flow	MHPC
1342	63.5	88	124.8
1500	70.7	97.9	138.8
1700	80.1	111	157.3
1900	89.5	124	175.8
2100	99	137.1	194.4
2500	117.8	163.2	231.3
3000	141.3	195.8	277.6

The data in Table 1 were plotted as a curve in Fig. 12, where it can be seen that the MHPC and MDPC of NOx are approximately linear with the number of flight sorties. According to the ambient air quality standard (GB 3095-2012) [30], the 24-h average concentration limit of NOx is 100μg/m³ and its hourly average limit is 250μm/m3. As a result, the daily AEC (DAEC) of Pudong International Airport is 2121 sorties, and the hourly AEC (HAEC) is 176 sorties.

Using the same flight flow data and SIMMOD software to simulate the airport terminal area, we obtained a theoretical capacity of 104 sorties/h for Pudong International Airport in four-runway operation. The actual operating capacity is 90 sorties/h, which is also the airport capacity (AC) limited by the airport environmental resource capacity.

In the current airspace structure and control capacity, AEC is greater than AC, indicating that the current airport operation bottleneck is the airport environmental resources. By adjusting the airspace structure and operational rules, the efficiency of airspace resources can be improved to increase the AC value. When the airport operating conditions and traffic flow reach a certain level, AEC will become the bottleneck of airport operations.

Fig.13

Relationship between MHPC and MDPC of NOx with number of sorties.

In 2009–2014, the annual growth rate of flight flow at Pudong International Airport was 3–7 %. Fig. 14 shows the change in flight volume at different growth rates and the relationship between limitations of airport conditions and the corresponding sorties. When the MDPC reaches 100μg/m³, the daily traffic volume is 2121 sorties. With four runways in operation and an assumed 7% flight flow growth rate, the flight flow of Pudong International Airport will exceed the AEC in 2023. If the flight flow growth rate is 3%, flight flow will exceed the AEC in 2035.

Fig.14

Airport environment capacity at different growth rate.

4 Conclusion

In this paper, we discussed, for the first time to our knowledge, the definition and evaluation method of AEC based on environmental quality standards. By establishing airport environmental factors and combining diffusion models, the relationship between airport sorties and air quality was obtained to determine AEC. In addition, the bottleneck period of airport development was predicted according to the airport development trend.

The determination method of AEC in this paper provides the following improvements and applications for followup studies:

AEC can be obtained, with the air pollutant concentration as a limiting factor. The air pollutant concentration is closely related to the meteorological conditions, including wind speed, wind direction, and atmospheric stability. The typical meteorological scene was used for the research presented in this paper. This meteorological scene was determined by the size of the pollution factor. The scene used the pollutant concentration formed by the wind parameters, which is not the largest in all wind directions, as shown in Fig. 15, but due to its high frequency and small average wind speed, it has the greatest impact on airport pollution concentration for the airport studied (Pudong International Airport). Thus, it was considered the typical weather scene of the airport.

To refine the AEC method, one or more typical meteorological scenes can be obtained via data processing of the large amount of historical meteorological data. Thereby, the scene can be more representative of the airport weather conditions.

The assessment method presented in this paper was used to evaluate an existing operational airport. Using the LTO sorties at the existing airport, an increment was made according to the existing traffic flow distribution characteristics, including the aircraft sorting characteristics and aircraft model ratio, to obtain the AEC. The assessment of new or rebuilt airports should add to the analysis of flow demand so that the assessment method can be more suitable to an airport’s social, economic, and environmental needs.

The determination of AEC can provide a theoretical basis for the development of flight schedules. For example, due to the constraint of nighttime pollutant concentration and the concentrations of all daytime periods below the environmental limit, the airport flow can be increased by reasonable arrangements of flight schedules.

Fig.15

NOx concentration values in different wind directions.

Figure 16 shows the distribution of hourly sorties and the concentration of pollutants throughout the day when the MHPC reached 250μg/m3 at night with the increment at the airport studied. The time period with the largest MHPC is not the peak hour of the day’s flow. With the hourly average limits as the standard, except for the time 21 : 00–21 : 59, the traffic flow of other hours can continue to increase, as shown in Fig. 17.

Fig.16

Relationship between pollutant concentration and number of sorties after increase in number of flights.

Fig.17

Increasable sorties under the constraint of hourly environment.

As can be seen from the increasable number of sorties under hourly environmental limits depicted in Fig. 17, with the consideration of hourly environmental limits, the increments in the busy daytime hour (07 : 00–19 : 59) are 30–50 sorties, relatively 40% more than the increments of 20–35 sorties in other periods. Thus, under the current operating conditions of the airport studied, the incrementing of flight schedules should be reflected in the time period 07 : 00–19 : 59 from the environmental point of view.

However, in addition to hourly environmental limits, there are other time dimension limits. If only the daily environmental limit of 100μg/m³ is considered, the daily ETC can reach 2121 sorties, but the maximum hourly sorties can only reach 138.5 sorties/h. Therefore, with comprehensive consideration of all limitations, the hourly ETC should be 138.5 sorties/h at the airport studied.

Footnotes

Acknowledgment

This research was partially supported by the National Natural Science Foundation of China (Grant Nos. 61671237) and the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20160798).

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