Detection of Aircraft Emissions Using Long-Path Differential Optical Absorption Spectroscopy at Hefei **nqiao International Airport

Abstract

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei **nqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO₂ average concentrations of approximately 25 ppbV and SO₂ average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO₂ and SO₂ concentrations in the airport area were very low, and the maximum hourly average NO₂ and SO₂ concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO₂ and SO₂ emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO₂ and SO₂ aircraft emissions during routine airport operations without interfering with the normal operation of the airport.

1. Introduction

With the rapid growth of the aviation industry, the number of commercial aircraft and aircraft movements, and the amount of passenger throughput at airports are constantly increasing. Around 2500 airports worldwide processed more than 4 billion passengers in 2018, and the growing volume of commercial air traffic has raised concerns about the impact of aircraft emissions on local and regional air quality near airports. Aircraft emit a variety of emissions during take-off, climb out, cruise, approach, and taxi, including nitrogen oxides, carbon oxides, sulfur oxides, hydrocarbons, and particulate matter, all of which can have a detrimental effect on human health and air quality [1]. For example, nitrogen oxides (NO_x = NO + NO₂) are primarily emitted in urban environments by fossil fuel combustion, biomass combustion, and soil emissions. NO contributes significantly to atmospheric chemistry by rapidly reacting with ambient ozone or radicals to form NO₂ on a minute scale, and NO₂ is a key molecule in the formation of O₃, acid deposition, and secondary particulate pollution, with extensive effects on human health [2,3,4,5]. Sulfur dioxide (SO₂) is emitted into the atmosphere by both human and natural sources. It plays a critical role in the aerosol system as a sulfate precursor and has an indirect effect on acid deposition, and exposure to SO₂ is associated with an increased risk of mortality and morbidity [6,7]. Although the COVID-19 pandemic has reduced air traffic, air traffic is expected to rebound in the coming years and continue to grow [8]. Therefore, aircraft emissions of NO_x, SO₂, and other substances will have an effect on atmospheric chemical processes, aerosol microphysical properties, and human health. Airport emissions have received increased attention in recent years.

Numerous studies have focused on the ground-level effects of aircraft emissions because these emissions contribute significantly to air pollution near airports and residential areas [9,10,11,12,13,14,15]. Currently, methods for assessing airport pollution emissions depend primarily on the International Civil Aviation Organization’s aircraft emission database and benchmark emission model [16,17,18,19]. The engine manufacturer provides emission indices (EIs) for carbon monoxide (CO), nitrogen oxides (NO and NO₂), unburned hydrocarbons (UHC), and particulate matter (PM) for engines operating at four different thrust levels (idle, approach, cruise, and take-off) in this database. These emission indices are based on well-defined measurement procedures and conditions during aircraft engine certification. Additionally, during the certification process, the International Civil Aviation Organization (ICAO) obtains emission indices from a very limited number of newly manufactured engines. However, the emission behavior of an engine installed on an aircraft may differ from that of an engine operating on a testbed due to the fact that real-world operating conditions vary. Furthermore, deviations from the certificated emission indices may occur as a result of the impact of factors, such as the aircraft’s life expectancy, the engine type (specific modifications, such as different combustion chambers) installed on the aircraft, and meteorological conditions (temperature, humidity, and pressure of ambient air, which can be different for certification conditions) [10,20]. According to a study by Carslaw et al. (2008), NOx concentrations can vary by up to 41% between aircraft with the same airframe and engine type [9]. Turgut and Rosen discovered significant differences in the emissions of certain pollutants for aircraft with varying characteristics during a study conducted at eight major busy airports [21].

Comparing these ICAO emissions indices to actual measurements is indeed critical for evaluating the accuracy of airport air quality models. Masiol and Harrison summarized the most meaningful studies on the characterization of aircraft emissions in both tests and real operations [22]. Clearly, normal aircraft operations must be maintained throughout the measurements, and passenger safety must be ensured in all circumstances. For this reason, any approach to an aircraft is strictly prohibited. As a result, determining the true emission characteristics of aircraft and other sources of pollution under actual operating conditions is difficult, and accurately simulating and forecasting airport air quality is also difficult. To overcome the limitations of the ICAO database, several measurement campaigns at various airports have been conducted. We discovered that several studies have been published on aircraft emissions during real operations. These studies used non-invasive instruments or mobile laboratories located downwind of active runways. For instance, Popp et al. (1999) measured the NO/CO₂ emission ratios of commercial aircraft in use at Heathrow Airport using the open path infrared and ultraviolet sensors developed for measuring on-road motor vehicle emissions [23]. Schäfer et al. (2003) measured idling aircraft exhaust at major European airports using non-intrusive spectroscopic methods, such as Fourier transform infrared spectrometry (FT-IR) and differential optical absorption spectroscopy (DOAS); parallel open paths ranging in length from 80 m to 150 m were installed directly behind the aircraft. The researchers discovered that the emission indices for NOx determined in their work were lower than those in the ICAO’s emission database and that there was a high degree of variation in the emission indices across aircraft families and engines with the same engine type [10]. Herndon et al. (2004) used a dual tunable infrared laser differential absorption spectroscopy instrument to measure NO and NO₂ emissions from 30 individual aircraft during taxiing and take-off, and each of the taxiway plumes was lower than the ICAO certification value [11]. Schürmann et al. (2007) collected the data on NO, NO₂, CO, and CO₂ emissions from idling aircraft using open path devices, and their findings revealed discrepancies with the emission indices published in the ICAO database [24]. Carslaw et al. (2008) investigated the NOx emissions at London Heathrow and discovered statistically significant differences in the emissions from identical engine types installed on identical aircraft frames. Additionally, they noted that the values of the EIs (emission indices) might have been significantly impacted by a lack of knowledge regarding certain critical aircraft operational factors, such as the aircraft’s weight and thrust setting at take-off [9]. **. (c) Only data for conditions with low wind speeds (<4 m/s) were retained. (d) If the aircraft departed from the runway’s southern end, we were unable to obtain valid aircraft emission data.

In this experiment, we obtained approximately 140 groups of NO₂ and SO₂ peak heights for the aircraft emissions in total, covering a wide variety of common aircraft types, including the Boeing 737–800, Boeing 737–900, Airbus A319, Airbus A320–214, Airbus A320–232, Airbus A321, Embraer E190LR, Modern Ark 60 (MA 60), and Beechcraft King Air 350.

Table 1. Results for the Boeing 737–800 aircraft.

Flight Number	NO2 Peak Height Taxi Out (ppbV)	NO2 Peak Height Take Off (ppbV)	SO2 Peak Height Taxi Out (ppbV)	SO2 Peak Height Take Off (ppbV)	Age of Aircraft (Year)
ZH9710	6.8	13.9	0.74	1.05	1.5
HU7441	8.7	22.2	0.74	2.06	19.5
CA1256	8.7	16.4	0.92	1.46	11.3
CA1256	9.1	22.0	0.81	2.12	11.3
Y87595	9.3	22.2	0.46	1.20	2.1
KN5878	7.7	15.1	2.99	5.65	9.0
HU7692	10.6	19.5	0.72	1.30	7.6
HU7692	9.0	16.6	0.69	1.09	2.3
SC8879	10.6	17.0	0.74	1.09	5.1
SC8879	7.6	16.0	0.55	1.01	3.9
SC8879	10.3	15.8	0.53	0.68	4.5
HU7468	7.9	16.3	1.08	2.23	5.0
HU7468	7.5	14.4	0.63	1.23	1.1
HU7468	6.4	14.8	0.67	1.03	4.9
HU7468	8.0	24.9	1.42	1.67	1.1
HU7441	9.7	20.7	0.61	1.61	14.3
Y87595	10.0	16.0	0.57	1.02	2.1
Y87595	8.9	18.7	0.48	0.91	2.1
Y87595	10.4	16.8	0.58	1.11	7.3
Y87595	7.3	15.4	0.41	1.17	7.3
ZH9710	10.6	16.6	0.61	0.95	4.3
HU7209	8.6	18.7	0.53	1.21	14.3
KN5878	7.8	21.9	2.51	5.29	5.3
DZ6343	9.6	15.9	0.53	1.04	0.8
DR6592	10.2	17.6	0.55	1.16	2.1
CZ3665	9.1	10.6	1.00	0.79	1.4
ZH9719	11.1	17.0	0.51	0.74	6.0
CA8933	7.0	16.4	0.47	0.98	1.4
HU7209	12.1	16.3	0.86	1.20	5.0
HU7209	8.1	12.0	0.81	1.04	3.8
ZH9710	7.2	13.3	0.94	1.16	6.2
HU7441	9.1	11.7	0.75	1.06	5.3
CA1256	12.8	14.4	1.19	1.37	6.5
SC8796	8.4	16.6	0.57	0.83	7.8
SC8796	13.0	15.2	0.95	1.26	11.3
SC8735	11.4	19.2	0.84	1.58	6.3
SC8796	8.3	19.6	0.80	1.29	6.0
ZH9710	6.3	20.8	0.56	1.24	1.5
SC8879	6.6	12.1	0.60	0.97	3.3
8L9862	7.1	18.7	0.57	1.13	3.1
CA1256	8.6	18.3	0.65	1.67	2.9
HU7441	12.5	18.1	0.90	1.31	1.1
HU7473	9.8	11.7	0.93	1.29	4.1
SC8879	10.4	16.1	0.27	1.17	5.1
HU7692	9.9	13.9	0.55	0.95	1.6
HU7572	9.5	12.1	0.56	0.87	4.3
HU7209	8.9	13.8	0.42	0.97	3.3

summarizes the results for the Boeing 737–800 aircraft, which had the largest amount of observed data of any aircraft type in this experiment. The observation experiment established that the LP-DOAS system was capable of capturing both NO₂ and SO₂ emissions from aircraft operating in real conditions. The aircraft’s take-off resulted in a maximum peak height of approximately 25 ppbV for NO₂ and approximately 8 ppbV for SO₂ in the measured area average concentrations. Owing to the airport’s open space, the pollution concentrations decreased rapidly, and the overall pollution level in the airport area was relatively low.

The method of linear regression was used to analyze the measured NO₂ peak height, SO₂ peak height and age of the aircraft. Figure 7 illustrates the linear dependence of the measured NO₂ and SO₂ peak heights on the aircraft’s age for 737–800 aircraft, which was fitted within the 0.95 prediction interval (the red areas). Interestingly, we discovered that the aging Boeing 737–800 aircraft exhibit a weak positive correlation with the NO₂ and SO₂ peak heights in this measurement. However, owing to the limited number of observations and the lack of information about the aircraft’s engine type, this discrepancy could be explained by the fact that the 737–800 family of aircraft used a variety of engine types or by other uncertainties, such as aircraft maintenance and environmental conditions. Additionally, we attempted to analyze other types of aircraft, but strangely, we did not discover this phenomenon. In the study of Zaporozhets and Synylo, the relationship between emissions indices of NO_x and the engine age exhibited a similar phenomenon [20]. It was unsurprising that the engine age was a significant factor affecting emission formation, since the conditions of the combustion chamber, cooling systems, and required cooling air were not identical to those of a new engine [21]. Additionally, the modeled estimate of the aging effects on the NOx emission ratio was in the range of -1% to 4% (Solutions, B.A. Back Fleet Database. 2001). In the future, long-term observations can be conducted to elucidate the relationship between aircraft emissions and aircraft ages in greater detail.

Figure 8 summarizes the NO₂ and SO₂ peak heights of the aircraft emissions data for various aircraft types. The figure shows that there was no significant variance in emissions between the different types of jet aircraft and that the small propeller aircraft emitted significantly fewer pollutants than turbojet aircraft. However, the small propeller aircraft had a lower passenger capacity than conventional jets.

Finally, we attempted to discuss the relationships between the observations results and the meteorological conditions. We first examined the relationship between aircraft emissions and wind speed or direction for each of the same aircraft types, but we found no significant correlations. Naturally, there were some differences between aircraft with the same model (engine type, age, operation, etc.). As a result, we selected the same aircraft from the observation period’s results to compare emissions under various meteorological conditions. Because we only had limited information, such as the flight number and age, we assumed that if two aircraft on different dates during the observation period both had the same model and age, they were likely to be identical. Certainly, sharing a flight number and age did not guarantee that the aircraft were identical, and the aircraft could have been purchased concurrently by the airline. While various certain critical aircraft operational factors, such as the aircraft’s weight and thrust setting at take-off, varied for identical aircraft on different flight dates, they might still be used to analyse the relationship between aircraft emissions and meteorological conditions. Several ‘identical’ aircraft were chosen from the entire measurement data set, and the aircraft emissions data for different dates are shown in

Table 2. Identical aircraft emissions data with different meteorological conditions.

Flight Number	Aircraft Type	Aircraft’s Age	Measurement Time	Wind Speed	Wind Direction	Time Interval	NO2 Peak Height
SC8879	737–800	5.1 years	16 Oct 2019 16:15	2 m/s	330°	192 s	16.1 ppbV
			21 Oct 2019 15:53	3 m/s	100°	184 s	17.0 ppbV
Y87595	737–800	7.3 years	23 Oct 2019 15:47	1 m/s	90°	196 s	15.4 ppbV
			21 Oct 2019 16:06	2 m/s	100°	152 s	16.8 ppbV
Y87595	737–800	2.1 years	19 Oct 2019 15:53	2 m/s	140°	180 s	16.0 ppbV
			20 Oct 2019 15:50	3 m/s	150°	172 s	18.7 ppbV
8L9862	320–251N	1.3 years	23 Oct 2019 11:25	1 m/s	0°	192 s	20.1 ppbV
			17 Oct 2019 14:22	2 m/s	330°	160 s	22.6 ppbV

, along with the wind direction and wind speed at the airport. It could be found that the time interval between a rapid increase in pollutant concentration and complete reduction to a stable background value was shorter for the identical aircraft at higher wind speeds due to the pollutant’s faster dispersion at higher wind speeds. Additionally, we discovered that the NO₂ peak heights measured at higher wind speeds were slightly higher, which was likely due to the pollutants spread over a larger area of the measured light path at higher wind speeds, resulting in higher mean LP-DOAS measurements. However, analyzing the relationships between the results of the observations and the wind direction was difficult due to the limited amount of data available. This can be explored in greater detail in future work.