A study of ambient fine particles at Tianjin International Airport, China

Science of the Total Environment 556 (2016) 126–135

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

A study of ambient fine particles at Tianjin International Airport, China Jianlin Ren a, Junjie Liu a,⁎, Fei Li a, Xiaodong Cao a, Shengxiong Ren a, Bin Xu b, Yifang Zhu c a b c

Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, China College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China Environmental Health Sciences, Fielding School of Public Health, University of California Los Angeles, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The first paper that researched aircraftemitted UFPs in China. • Experimental research on the effects of wind on particle number concentration • Investigation on particle emissions during a cycle of aircraft takeoff and landing • Particle size distributions at increasing distances downwind from the runway

a r t i c l e

i n f o

Article history: Received 20 January 2016 Received in revised form 26 February 2016 Accepted 26 February 2016 Available online 12 March 2016 Editor: D. Barcelo Keywords: Airport Fine particles Aircraft emissions Number size distribution PM2.5 On-site measurements

⁎ Corresponding author. E-mail address: [email protected] (J. Liu).

http://dx.doi.org/10.1016/j.scitotenv.2016.02.186 0048-9697/© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t The total count number concentration of particles from 10 to 1000 nm, particle size distribution, and PM2.5 (aerodynamic diameter ≤ 2.5 μm) mass concentration were measured on a parking apron next to the runway at Tianjin International Airport in China. The data were collected 250, 270, 300, 350, and 400 m from the runway. Wind direction and wind speed played important roles in determining the characteristics of the atmospheric particles. An inverted U-shaped relationship was observed between the measured particle number concentration and wind speed, with an average peak concentration of 2.2 × 105 particles/cm3 at wind speeds of approximately 4–5 m/s. The atmospheric particle number concentration was affected mainly by aircraft takeoffs and landings, and the PM2.5 mass concentration was affected mainly by the relative humidity (RH) of the atmosphere. Ultrafine particles (UFPs, diameter b 100 nm), with the highest number concentration at a particle size of approximately 16 nm, dominated the measured particle size distributions. The calculated particle emission index values for aircraft takeoff and landing were nearly the same, with mean values of 7.5 × 1015 particles/(kg fuel) and 7.6 × 1015 particles/(kg fuel), respectively. The particle emission rate for one aircraft during takeoff is two orders of magnitude higher than for all gasoline-powered passenger vehicles in Tianjin combined. The particle number concentrations remained much higher than the background concentrations even beyond 400 m from the runway. © 2016 Elsevier B.V. All rights reserved.

J. Ren et al. / Science of the Total Environment 556 (2016) 126–135

1. Introduction Airports are important sources of particulate matter (PM) in urban areas (Zhu et al., 2011; Westerdahl et al., 2008; Hu et al., 2009). Numerous epidemiological studies have shown consistent associations between exposure to airborne micro and nano particles and exacerbation of various respiratory and cardiovascular diseases (Lin et al., 2002; Jerrett et al., 2005). For example, an increased risk of hospital admissions around the Rochester and LaGuardia airports has been observed (Lin et al., 2008). In response to increasing concern regarding exposure to airportrelated particulate pollutants, many studies have been conducted to examine the characteristics of these pollutants at airports. Early studies of airport-related particulate pollutants have focused on the mass concentrations and chemical compositions of particulate pollutants (Childers et al., 2000; Woody et al., 2011; Yu et al., 2004; Unal et al., 2005; Fang et al., 2007). Recent studies have examined UFPs, which are more toxic than larger particles at the same mass concentration (Donaldson et al., 2001; Brown et al., 2000). Previous UFP studies have found that traffic-related emissions were the most significant contributors to UFP levels in urban areas (Hitchins et al., 2000; Zhu et al., 2002b). However, a study of aircraft emissions at the Santa Monica Airport showed that UFP emissions from aircraft per kg of fuel consumed were 16–100 times higher than those from light-duty vehicles and 5–8 times higher than those from heavy-duty vehicles (Hu et al., 2009). A recent study found that particle number concentrations near a regional airport were attributable mainly to aircraft activities (Klapmeyer and Marr, 2012). Data collected downwind of aircraft takeoffs at Los Angeles International Airport showed very high number concentrations of UFPs. During some monitored takeoffs, the total particle number concentration exceeded 107 particles/cm3 (Zhu et al., 2011). Hsu and colleagues found significant higher increases in UFPs at Los Angeles International airport due to landing and takeoff activity: a 2 × 106–7 × 106 particles/cm3 increase at a monitor at the end of the departure runway, and a 8 × 104–1.4 × 105 particles/cm3 increase at a site 250 m downwind from the runway (Hsu et al., 2013). Mazaheri and colleagues examined particle emissions from commercial aircraft at each stage of the takeoff and landing cycle. They found that particles were predominantly within the size range of 4–100 nm in diameter in all cases and that particles larger than 118 nm originated from background aerosols rather than the aircraft plume (Mazaheri et al., 2009). Herndon and colleagues observed two modes, one at an aerodynamic diameter of 90 nm and a second at or below the instrument cutoff (b 30 nm) (Herndon et al., 2005). In another study carried out at the Oakland International Airport, the particle size distributions from aircraft emissions were typically found to be bimodal, with a nucleation mode consisting of freshly nucleated PM and an accumulation mode consisting mostly of PM soot with some condensed volatile material (Lobo et al., 2012). The modal diameters at takeoff and idle were found to be 13.2 nm and 13.1 nm, respectively (Lobo et al., 2012). Hu and colleagues reported that even 660 m downwind of a runway, the average UFP concentrations were still an order of magnitude above background levels at the Santa Monica Airport (Hu et al., 2009). Similarly, a study carried out at the Los Angeles International Airport indicated that UFPs could persist for nearly 1 km from the end of the runway (Westerdahl et al., 2008). Based on a large number of studies conducted in developed countries, Europe and the U.S. are taking measures to improve the air quality at airports. European airports, e.g., in Switzerland, Sweden and the UK, have already applied airport emission charging schemes, and EU air quality directives may result in the introduction of more local air quality management schemes and emissions-related charging regimes in the future (EASA, 2010). To reduce all sources of airport ground emissions, especially PM, the U.S. Federal Aviation Administration (FAA) created the Voluntary Airport Low Emissions Program (VALE) in 2004 (FAA, 2007). China is an important aviation market, and nearly a quarter of

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the growth in the global aviation industry is expected to be associated with China (IATA, 2012). In view of the growing public concern regarding airport pollution, and the rapid growth of aviation travel in China, the objective of this study was to systematically evaluate PM levels, especially PM2.5 and UFPs, and analyze the physical characteristics, transport behavior and the influencing factors of particulates in the area around Tianjin Airport. 2. Experimental method 2.1. Measurement locations Tianjin International Airport is located east of Tianjin, at 39°07′ N, 117°20′ E, and has a passenger throughput of more than 7 million passengers per year. Five sampling sites were located on an abandoned parking apron without any active aircrafts or ground vehicles on the southwest side of the airport. The sites were oriented along a line perpendicular to the runway at distances of 250, 270, 300, 350, and 400 m (Fig. 1). In many prior studies, the sampling sites were located approximately in line with the runway because the runways were designed to be oriented with the direction of the prevailing wind (Hu et al., 2009; Zhu et al., 2011; Lobo et al., 2012). In this study, because of the limitations of the experimental geographical conditions, the sampling sites were located perpendicular to the runway. But in many cases, the sampling sites were also located downwind of the runway due to the variable wind direction during sampling days. It should be noticed that the distances to the runway are not typically the particle transport distances between the runway and the sites, as the wind direction was rarely perpendicular to the runway. The measurements were taken during the daytime on August 20–28, 2012, from 7:00 AM to 19:00 PM. When an aircraft took off, it started from the front of the terminal and traveled along the taxiway until it arrived at either the southern or northern end of the taxiway, after which it turned approximately 180°, accelerated and took off. When an aircraft approached, it landed on either the southern or northern end of the runway and taxied to the parking apron. During the morning of August 20 and the 12 h of daytime on August 23 and 24, aircraft took off from and landed on the northern end of the runway. During the remainder of the week, aircraft took off from and landed on the southern end of the runway. The aircraft takeoff and landing orientations were changed due to the changes of prevailing wind direction. On average, 63 aircraft took off and 67 landed on each sampling day. The flight activity number was almost the same during each test hour. 2.2. Sampling and instrumentation Two condensation particle counters (CPC 3007, TSI, Inc., St. Paul., MN) were used to measure the particle number concentration of particles ranging in size from 10 to 1000 nm, with a maximum concentration detection limit of 105 particles/cm3, and a 50% size detection threshold of 10 nm. According to the instrument specification, the accuracy of the concentration readings up to 105 particles/cm3 is ± 20%. Hämeri et al. (2002) reported that when particle concentration was up to 4 × 105 particles/cm3, the coincidence of the CPC 3007 would be serious, and measurements should be corrected. The two counters had been calibrated by the manufacturer and an inter-comparison was made between the instruments in order to ensure the comparability of the results. To measure the PM2.5 mass concentration, an optical instrument (DustTrak 8530, TSI, Inc., St. Paul., MN) (Wang et al., 2009) was used. It was calibrated by the manufacturer and was also calibrated to the local aerosols via the gravimetric method. A calibration factor of 1.1 was obtained. These instruments were set to record particle concentration data every second. An aerosol monitoring system (AGM 1500, MSP, Inc., Shoreview, MN) was used to measure particle sizes from 15 to 600 nm. Twenty-four channels were adjusted, with each channel tested for 1 s to capture the temporal variability in the particle number

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Fig. 1. Sampling locations on the parking apron at Tianjin Airport: 1, 2, 3, 4 and 5 represent distances of 250 m, 270 m, 300 m, 350 m and 400 m, respectively, from the runway. The red lines represent one of the airplane taxi routes. T: terminal, P: parking apron. The wind rose displays the distribution of wind direction and speed during the course of sampling.

concentration and particle size distribution. The midpoint of the smallest channel was 16 nm. For the CPC 3007, DustTrak 8530 and AGM 1500 instruments, the aerosols were sampled into each instrument directly without any tubes to avoid particle losses in the tubes. A photo-acoustic multi-gas analyzer (INNOVA 1412, LumaSense Technologies) was used to measure the CO2 concentration. A HOBO micro weather station was used to record meteorological parameters,

including air temperature, RH, wind direction and wind speed. The weather data were averaged every minute and logged in a computerized weather station. The HOBO micro weather station, one CPC 3007 and INNOVA were fixed 250 m from the runway. Another CPC 3007, DustTrak 8530 and AGM 1500 were placed on a mobile platform, moved together, and used simultaneously at each sampling location. The measurements at different locations were conducted successively.

Table 1 Instruments used in the study. Instrument

Parameter measured

Units reported

Sample locations (distance from the runway) m 250

270

300

350

400

TSI CPC, Model 3007 TSI Dust Trak, Model 8530 MSP Aerosol Generation and Monitoring System, Model 1500 HOBO micro weather stations INNOVA

Particle count, 10–103 nm Particle mass, PM2.5 Particle size distribution Air temperature, RH, wind speed, wind direction CO2 concentration

Particles/cm3 μg/m3 Particles/cm3

A A A A A

A A A

A A A

A A A

A A A

ppm

A: application. One CPC 3007, HOBO micro weather station and INNOVA were fixed 250 m from the runway. Another CPC 3007, DustTrak 8530 and AGM 1500 were placed on a mobile platform, and used simultaneously at each sampling location.

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Fig. 2. Time series of hourly average ambient temperature and RH (a), wind speed and direction (b) during the measurement periods.

The platform was moved from sampling site 1 to site 5 in sequence, and the sampling duration for each site was at least 1 h. Due to the disturbance caused by monitoring platform movement, only the data collected in the last 50 min were used for data analysis. This sampling process was conducted twice within a day. All the instruments used during the sampling are listed in Table 1. The hourly average ambient temperature, RH, wind speed and direction during measurement periods were presented in Fig. 2. The aircraft activities, including aircraft type, start time, taxi time, takeoff time and landing time, were recorded. Based on the times of the sampling data, the experimental data could be matched with

the recorded aircraft activities. The data collected when the wind was blowing from the airport were selected to analyze the impact of aircraft activities (takeoff, landing, taxiing and idling) on the air quality. The data with no aircraft activities were used to establish the background level. The particle emission index was calculated using the CO2 concentration and particle number concentration data collected at the 250-m sampling site: EIðxÞ ¼

Δx M 1  EIðCO2 Þ  air  ΔCO2 MCO2 ρair

ð1Þ

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where Δx is the incremental particle number concentration over the background (particles/cm3), ΔCO2 is the incremental CO2 concentration over the background (ppm), EI (CO2) is 3160 g CO2/(kg fuel), Mair and MCO2 are the molar masses of air (29 g/mol) and CO2 (44 g/mol), respectively, and ρair is the density of air (1.2 g/L) (Herndon et al., 2005). 3. Results and discussion 3.1. Effect of wind on the characteristics and dispersion of particles 3.1.1. Particle number concentration Zhu and colleagues found that both wind direction and wind speed play important roles in determining the characteristics of UFPs near highways (Zhu et al., 2002a,b). To analyze the effects of wind direction and speed on the particle number concentrations measured in this study, the data collected at the different sampling sites for different wind directions were shown in Fig. 3, and the particle number concentrations are plotted as a function of distance from the runway for each wind direction. The total number of observations in Fig. 3(a), (b), (c), (d) were 840, 980, 540 and 650 at 1-s average, respectively. As Fig. 3 shows, the wind direction affected particle levels in the vicinity of the airport. This finding is consistent with the findings of previous studies (Yu et al., 2004; Hsu et al., 2012; Carslaw et al., 2006). When the wind blew from the runway toward the sampling sites ((a), (b)), the particle number concentrations were higher than when the wind blew from the opposite direction ((c), (d)). These results indicated that the airport was the main contributor to the elevated UFP in the vicinity of the airport, and that the impact of the urban area nearby was limited. Fig. 3(a) and (b) shows that the aircraft activities produced particle number concentrations that were markedly higher than the background levels, and that the values decayed with distance from the runway. The particle number concentrations, shown in Fig. 3(c) and

(d), were nearly the same at every site, indicating that the urban particulate pollutants were spatially homogeneous. In the 247°–337° direction, the particle number concentrations at 250 m and 270 m were higher than those at the other three sites because these measurements were made before 9:30 AM. The relatively lower wind speed (as shown in Fig. 2.) and lower boundary layer height (Zhao et al., 2009) in the morning did not favor the diffusion of particles. Therefore, the particle number concentrations measured at the sites close to the airstrip were higher. Fig. 4 shows the total particle number concentrations measured 250 m downwind from the runway at increasing wind speeds when the wind blew from the runway toward the sampling sites. In total, 19,240 UFP number concentration scans at 1-s average for wind direction of 0°–90° were used in this figure. The inverted U-shaped curve is different from observations in a previous study near a highway (Zhu et al., 2002b), in which the curve was nearly a straight line, but similar to the relationship observed in a different near-roadway study (Zhu et al., 2002a). In the present study, the particle number concentration increased initially, reached a maximum of 2.2 × 105 particles/cm3 at a wind speed of approximately 4–5 m/s, then decreased. The nucleation, coagulation, diffusion and dilution processes have different effects on particle number concentration with respect to time. The nucleation process occurred only within a very short distance after the engine exhaust (Chan et al., 2010), so this process was not the reason for this inverted U-shaped curve. The coagulation process should also be of minor role, because of the relatively low concentration of particles in the measurement and the relatively short time scales of dispersing aerosol in the atmosphere. The diffusion and dilution processes may be the main reasons for the observed phenomenon. At extremely low wind speeds, the turbulence intensity was very small, which made it hard for the particles to be transported from the airstrip to the sampling point. At high wind speeds, the decrease in particle concentrations

Fig. 3. Particle number concentrations measured at different locations for different wind directions. (a) Wind direction: 337°–360° and 0°–67°, (b) wind direction: 67°–157°, (c) wind direction: 157°–247°, and d) wind direction: 247°–337°. The instruments were located downwind of the airstrip in (a) and (b) and upwind of the airstrip in (c) and (d). The background (Bkgd) concentration is the average concentration when there was no aircraft activity.

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Fig. 4. Total particle number concentrations measured 250 m downwind from the runway versus wind speed.

could be caused by more efficient atmospheric dilution (Zhu et al., 2002b). This may partially explain the inverted U-shaped curve observed in this study. 3.1.2. Particle size distribution Fig. 5 depicts the particle size distributions measured 250, 270, 300, 350 and 400 m downwind (a) and upwind (b) of the runway, as well as the background distribution. The data were averaged for each site on all

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applicable dates. The data are for various durations at the sites, thus, the quantity of data from each site is different. The number of observations for sites 250 m, 270 m, 300 m, 350 m, 400 m and background in Fig. 5(a) was 460, 280, 305, 470, 280 and 330 using 24-s scans, respectively. The number of observations for these sites in Fig. 5(b) was 420, 350, 370, 510, 255 and 430 using 24-s scans, respectively. As Fig. 5 shows, throughout the measured particle size range, the particle number concentrations downwind of the runway were higher than those upwind. The particle size distributions changed with increasing distance. The size distributions had two modes, similar to those observed in previous studies (Herndon et al., 2005; Lobo et al., 2012). Due to the limit of the AGM 1500 aerosol monitoring system used in this study, the midpoint of the smallest channel was selected at 16 nm. The first peak occurred in the nucleation range at a diameter of 16 nm or smaller diameter. The second peak occurred at 25–50 nm. Fig. 5(a) showed that the aircraft takeoff and landing activities produced particle number concentrations that were markedly higher than the background levels, and the values decayed with distance from the runway. The dominant mode was approximately 16 nm or smaller with a number concentration of approximately 4.0 × 105 particles/cm3 at the nearest sampling site. The dominant mode remained at 16 nm or smaller for the second sampling site, with the number concentration dropped to 2.2 × 105 particles/cm3. The number concentration continued to decrease at other sampling sites. The decrease in particle number concentration of the 16 nm size was probably due to atmospheric dilution and several atmospheric aerosol particle loss mechanisms that favor small particles, such as diffusion to surfaces and evaporation (Zhu et al., 2002a). However, the particle number concentrations in the size range around 25–50 nm changed unregularly. This pattern maybe have been caused by the uncertain factors in the study, such as the wind speed, direction and aircraft activity, in consideration of its relative low number concentration. The particle number concentrations, shown in Fig. 5(b) were nearly the same at every site, indicating that the urban particulate pollutants were mixed to homogeneous levels.

3.2. Effect of temperature and RH on the characteristics and dispersion of particles

Fig. 5. Particle number concentrations measured at different locations for different wind directions. (a) Wind direction: 337°–360° and 0°—157°, (b) wind direction: 157°–337°. The instruments were located downwind of the airstrip in (a) and upwind of the airstrip in (b). The background concentration is the average concentration when there was no aircraft activity. The numbers in the figure represent the dominant modes.

In total, 2360 observations in 60-s scans were used to study the effect of temperature on the characteristics of particle. Additionally, 2470 observations in 60-s scans were used to study the effects of RH on the characteristics of particle. Significance tests were conducted using SPSS for Windows version 22 (SPSS Inc.), and the statistical analyses showed that the relationships between temperature and RH and the characteristics of particle were all significant (all p b 0.01). As Fig. 6(a) shows, the background of the particle number concentration and PM2.5 concentration decreased slightly with increasing temperature. This is similar to the results obtained at other studies (Turalıoğlu et al., 2005; Ilten and Selici, 2008). Lower temperatures always occurred in the morning during the test periods, as shown in Fig. 2. The boundary layer usually begins to form after sunrise, a process accompanied by increases in temperature and wind speed, and it grows higher around noontime and into the afternoon (Zhao et al., 2009). A higher boundary layer provides a larger volume for the dilution of pollutants, resulting in a negative correlation between the temperature and PM2.5 concentration in daytime to some extent. A strong relationship between the PM2.5 concentration and RH was observed in Fig. 6b. This relationship is similar to those found at other studies (Hien et al., 2002; Ilten and Selici, 2008). The high RH increased particle mass concentration by water absorption (Maybee et al., 2005). Additionally, higher RH also occurred in the morning during the test periods, as shown in Fig. 2. The change in boundary layer height was also a reason for the positive correlation between RH and PM2.5 concentration to some extent. While the particle number concentration decreased slightly with increasing RH.

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aircraft takeoffs and landings mainly affected the particle number concentration. This was caused by the fact that aircraft-emitted particles are mainly UFPs (as shown in Fig. 5), which have limited effect on PM2.5 mass concentration. All available particle concentration data were collected and plotted as a function of temperature and RH. 3.3.2. Particle emissions during typical aircraft takeoff and landing To investigate the contribution of aircraft takeoff and landing to particle emissions, a typical B737-800 aircraft was monitored at the 250-m sampling site. The B737-800 is the most commonly used aircraft at Tianjin Airport. Fig. 8 shows the total particle number concentrations measured during a complete cycle of takeoff and landing. During takeoff, as the aircraft taxied and passed upwind of the instruments, the total particle number concentration gradually increased from approximately 5 × 10 4 particles/cm 3 to approximately 7 × 10 4 particles/cm 3 . After traveling to the end of the taxiway, the aircraft turned 180° then accelerated. At that time, the total particle number concentration rose dramatically to approximately 2 × 105 particles/cm3, which was four times greater than the background level. The aircraft then flew northward away from the sampling locations. During landing, after the aircraft approached and landed on the runway, the total particle number concentration increased sharply to 1.6 × 105 particles/cm3, which was three times greater than the background level.

Fig. 6. The UFPs number concentration and PM2.5 mass concentrations versus temperature and RH.

3.3.3. Particle emission index In total, 91,950 groups of 1-s total particle number concentration peaks and CO2 concentration peaks measured at the 250-m sampling site were used to calculate the particle emission index (EIpn). The particle number concentration was averaged by the time interval of the CO2 concentration, and the averaged particle number and CO2 concentration

3.3. Effect of flight activities on the characteristics and dispersion of particles 3.3.1. Particle number concentration and mass concentration On August 27, the data collected at the 250-m sampling site showed that the wind direction ranged from 20° to 60°. Fig. 7 shows concurrently measured particle number and PM2.5 concentrations. The comparison between particle number and PM2.5 concentrations is based on 1500 1-s scans. During takeoffs and landings, the particle number concentration reached more than 2 × 105 particle/cm3, indicating that aircraft activities greatly affected the particle number concentrations near Tianjin Airport. Each significant peak corresponded to an aircraft takeoff or landing activity. However, the mass concentration of PM2.5 also fluctuated to some extent, but its value was relatively stable overall. Thus,

Fig. 7. The particle number and PM2.5 concentrations 250 m from the runway on August 27.

Fig. 8. Particle number concentrations for a typical cycle of (a) takeoff and (b) landing.

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Fig. 9. Calculated EIpn for aircraft takeoff and landing compared with measured total particle number concentrations 250 m downwind of the runway.

subtracted the background levels. Eq. (1) was then used to calculate EIpn for each peak. The EIpn and the particle number concentration were plotted in Fig. 9. The calculated EIpn values for aircraft takeoff and landing were nearly the same, with mean values of 7.5 × 1015 particles/(kg fuel) and 7.6 × 1015 particles/(kg fuel), respectively. The values are comparable with results reported for Logan International Airport in Boston (8.8 × 1015 particles/(kg fuel)) (Herndon et al., 2005) and the Atlanta International Airport (3.2 × 1015 particles/(kg fuel)) (Herndon et al., 2008) and are much higher than those reported for light-duty vehicles (4.6 × 10 10 particles/(kg fuel)) (Kirchstetter et al., 1999). The mean aircraft fuel consumption rates during takeoff and landing are 1.09 kg/s and 0.31 kg/s, respectively (EASA, 2010). The rates of particle emissions from aircraft during takeoff and landing are 8.1 × 1015 particles/s and 2.3 × 1015 particles/s, respectively. The value during takeoff is higher than that during landing, which is consistent with the results of the present study. The fuel consumption rate for gasoline-powered motor vehicles varies with vehicle speed, with values of approximately 0.2 × 10 − 3 –1.0 × 10 − 3 kg/s. At a vehicle speed of 30 km/h, the fuel consumption rate is approximately 0.6 × 10 − 3 kg/s (Tong et al., 2000). The corresponding particle emission rate for gasoline-powered motor vehicles is 2.8 × 107 particles/s. The number of gasoline-powered passenger vehicles in Tianjin was approximately 2 million in 2011 (Tianjin Statistical Information Net, 2012). The particle emission rate for one aircraft during takeoff is two orders of magnitude higher than for all gasoline-powered passenger vehicles in Tianjin combined.

Fig. 10. Particle number concentration decay models for takeoff and landing. The background concentration is the average concentration measured when there was no aircraft activity.

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3.3.4. The particle number concentration decay Fig. 10 presents the particle number concentrations measured during takeoff and landing as a function of downwind distance from the runway with particle diameters from 10 to 103 nm. Wind data from the 0°–90° directions and wind speeds from 2.5–7 m/s were used. The linear decay relationships shown in the figure are different from those observed in studies near highways (Zhu et al., 2002a,b), where the total particle number concentrations decayed exponentially. This difference is most likely caused by the limitation of the measurements. In this study, we did not measure the particle number concentrations close enough to the runway. As described in other literatures (Zhu et al., 2002a,b), the exponential decay law of the particle number concentration with the downwind distance only occurred in the range quite close to the source. Then the linear decay relationships presented with the increasing distance away from the source, which was consistent to our findings. At the farthest sampling site, located 400 m from the runway, the particle number concentrations were still much higher than the background levels. An impacted area of elevated UFP concentrations was observed to extend beyond 660 m downwind at SMO (Hu et al., 2009). Zhu and colleagues found that the concentration of 15-nm particles 600 m downwind of the end of the runway was higher than the background values at LAX (Zhu et al., 2011). Westerdahl and colleagues monitored the UFPs 900 m downwind of the runway at LAX (Westerdahl et al., 2008). However, the sampling sites in these studies were all located in line with the runway, and the aircraft plume during takeoff was emitted forcefully in the direction of the prevailing wind. Thus, the particles could be transported a greater distance.

Fig. 11. Particle size distributions measured at different locations downwind of the airstrip during aircraft (a) takeoff and (b) landing. The background concentration is the average concentration measured when there was no aircraft activity.

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3.3.5. Particle size distribution Fig. 11 depicts the particle size distributions which measured 250, 270, 300, 350 and 400 m downwind from the runway, as well as the background distribution. The data were averaged for each site on all applicable dates. The number of observations for the sites was 250 m, 270 m, 300 m, 350 m, 400 m and those in the background in Fig. 11(a) were 460, 310, 305, 540, 350 and 430 in 24-s scans, respectively. The number of observations for these sites in Fig. 11(b) was 480, 280, 350, 470, 280 and 330 in 24-s scans, respectively. As Fig. 11 shows, throughout the measured particle size range, the particle number concentrations during aircraft takeoff were higher than those during landing. This was possibly caused by the fact that the sampling locations were closer to the acceleration starting point for the majority of takeoffs, while for landings, the aircraft may still have been aloft when passing the sampling locations. The particle size distributions changed with increasing distance. At the 250-m sampling site, the concentration increased sharply, especially for smaller-diameter particles, compared with the background levels. For both takeoff and landing, the modal diameter was 16 nm or smaller. Modal diameters of 14 nm and 11 nm have been previously reported at LAX (Zhu et al., 2011) and SMO (Hu et al., 2009), respectively. In the present study, when aircraft took off, the particle number concentration increased by a factor of more than sixty, from 6 × 103 particles/cm3 to 4 × 10 5 particles/cm 3 , and when aircraft landed, the particle number concentration increased by a factor of more than thirty, to 2.1 × 10 5 particles/cm 3. The diameters for which the particle number concentrations increased sharply were all b60 nm. Therefore, we conclude that aircraft-emitted particles are mainly UFPs. With increasing distance, the concentrations of small particles decreased; however, the concentrations of large particles increased. The particle number concentrations measured at the 400-m sampling site show that the concentrations of particles with diameters N40 nm decreased nearly to the background value but that particles smaller than 40 nm were still present at much higher concentrations than the background levels. These results indicate that the air quality around the airport is mainly affected by the UFPs emitted during takeoff and landing. 4. Conclusion In this study, both wind direction and speed were found to affect the particle number concentrations near Tianjin Airport. An inverted U-shaped relationship was observed between the measured particle number concentration and wind speed, with an average peak concentration of 2.2 × 105 particles/cm3 at wind speeds of approximately 4–5 m/s. Aircraft activities were found to cause particle number concentrations to increase sharply near the airport, while the mass concentration of PM2.5 was affected mainly by the RH of the atmosphere. The measured aircraft-emitted particles were mainly b60 nm in diameter. The results of this study demonstrate that emissions due to aircraft takeoff and landing are the most significant contributors to UFPs near the studied airport. The calculated EIpn values for aircraft takeoff and landing were nearly the same, with mean values of 7.5 × 1015 particles/(kg fuel) and 7.6 × 1015 particles/(kg fuel), respectively. A linear model was found to describe the decay of the particle concentrations downwind of the runway well. The particle number concentrations remained much higher than the background concentrations even beyond 400 m from the runway. Acknowledgments This research project was sponsored financially by the National Basic Research Program of China (The 973 Program, Grant No. 2012CB720100). We would like to thank Dr. Chao-Hsin Lin

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