Assessment of Particle Pollution from Jetliners: from Smoke Visibility to

Feb 23, 2017 - *(L.D.) Phone: +41 58 765 46 08; fax: +41 58 765 69 63; e-mail: [email protected]., *(J.W.) Phone: +41 44 633 36 21; fax: +41 58 76...
0 downloads 4 Views 1MB Size
Subscriber access provided by UB + Fachbibliothek Chemie | (FU-Bibliothekssystem)

Article

Assessment of Particle Pollution from Jetliners: from Smoke Visibility to Nanoparticle Counting Lukas Durdina, Benjamin T. Brem, Ari Setyan, Frithjof Siegerist, Theo Rindlisbacher, and Jing Wang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05801 • Publication Date (Web): 23 Feb 2017 Downloaded from http://pubs.acs.org on February 28, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 23

Environmental Science & Technology

2

Assessment of Particle Pollution from Jetliners: from Smoke Visibility to Nanoparticle Counting

3 4

Lukas Durdina*,1,2, Benjamin T. Brem1,2, Ari Setyan1,2, Frithjof Siegerist3, Theo Rindlisbacher4, Jing Wang*,1,2

1

5

Affiliations:

6 7

1

Laboratory for Advanced Analytical Technologies, Empa, Dübendorf, CH-8600, Switzerland

2

Institute of Environmental Engineering (IfU), ETH Zürich, Zürich, CH-8093, Switzerland

3

SR Technics Switzerland AG, Zurich-Airport, CH-8058, Switzerland

4

Federal Office of Civil Aviation FOCA, Bern, CH-3003, Switzerland

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Abstract Aviation is a substantial and a fast growing emissions source. Besides greenhouse gases, aircraft engines emit black carbon (BC), a climate forcer and air pollutant. Aviation BC emissions have been regulated and estimated through exhaust smoke visibility (smoke number). Their impacts are poorly understood because emission inventories lack representative data. Here, we measured BC mass and number-based emissions of the most popular airliner's engines according to a new emission standard. We used a calibrated engine performance model to determine the emissions on the ground, at cruise altitude, and over entire flight missions. Compared to previous estimates, we found up to a factor of 4 less BC mass emitted from the standardized landing and take-off cycle and up to a factor of 40 less during taxiing. However, the taxi phase accounted for up to 30% of the total BC number emissions. Depending on the fuel composition and flight distance, the mass and number-based emission indices (/kg fuel burned) were 6.2 – 14.7 mg and 2.8×1014 – 8.7×1014, respectively. The BC mass emissions per passenger-km were similar to gasoline vehicles, but the number-based emissions were relatively higher, comparable to old diesel vehicles. This study provides representative data for models and will lead to more accurate assessments of environmental impacts of aviation.

33 34

Keywords: aviation, black carbon, smoke, emissions, air pollution, climate change

35

ACS Paragon Plus Environment

Environmental Science & Technology

36

INTRODUCTION

37

"The scream of jet engines rises to a crescendo on the runways of the world. Every second,

38

somewhere or other, a plane touches down, with a puff of smoke from scorched tyre rubber, or

39

rises in the air, leaving a smear of black fumes dissolving in its wake." Since David Lodge wrote

40

these lines in Small World in 19841, air travel has quadrupled and made the world even smaller2.

41

Already back then, due to growing awareness concerning the environmental impacts of aviation3

42

and prospects of supersonic transport4, measures were put in place to get rid of the jet engine

43

“scream” and “black fumes”5,6. The black smoke was not only a nuisance but a matter of safety –

44

it reduced airport visibility7. To address this issue, the International Civil Aviation Organization

45

(ICAO) has regulated engine smoke through the smoke number (SN)8.

46

SN is determined from the reflectance of a filter paper stained by a drawn exhaust

47

sample5. The filter captures particulate matter (PM) in the exhaust, predominantly consisting of

48

black carbon (BC), but is inefficient for particles < 300 nm typical for modern jet engines9. For

49

some engine types, SN is virtually undetectable at all power settings10. However, invisible

50

plumes are not particle free.

51

Several recent studies have characterized PM emissions from aircraft jet engines11–17, but

52

the data are obtained with a variety of sampling and measurement methods, which reduces their

53

potential widespread use for climate and air quality models. For such purposes, researchers have

54

estimated aviation BC emissions from combustion models18,19 and, most often, from SN20–23.

55

Besides the inherent uncertainties, these approximations neglect particle losses in the exhaust

56

sampling systems as well as fuel composition24,25. Moreover, none of the approximations

57

resolves particle number concentration, which is a metric relevant for the assessment of health

58

effects of ultrafine particles as well as for the formation of contrails and cirrus clouds26,27.

ACS Paragon Plus Environment

Page 2 of 23

Page 3 of 23

Environmental Science & Technology

59

Without up-to-date representative data, impact assessments of aircraft engine BC

60

emissions remain highly uncertain. The emission indices (EIs) used to calculate the BC emission

61

rates from the global fleet typically range from 10 to 50 mg/kg fuel burned28,29. Recent reports

62

claim that aviation BC emissions are underestimated and warm up the atmosphere as much as

63

1/3 of the aviation’s CO2 emissions with an average EI of up to 93 mg/kg fuel9,18. At airports,

64

aircraft BC emissions typically contribute to the suspended fine PM by less than one µg/m3 21,22.

65

However, even one µg of aircraft engine BC can contain billions of particles with modes as small

66

as 10 nm13,14,25,30, a characteristic that makes them a risk factor for severe lung and heart

67

diseases31–34. The PM emitted is most concentrated near runways and quickly disperses with

68

distance16,35, but is also claimed to raise ambient particle number concentrations fourfold up to

69

10 km downwind36,37.

70

Such air quality and climate impacts can be better assessed through the non-volatile PM

71

(nvPM) emissions standard for commercial jet engines, the first new aviation emissions standard

72

in 40 years38. From 2020 onwards, the ICAO emissions databank10 will include nvPM number-

73

and mass-based emissions for in-production turbofan engines with rated thrust > 26.7 kN (large

74

business jets and airliners). The nvPM is almost entirely BC, which is the term we use here in the

75

context of the new regulation.

76

Here, we estimate BC mass and number emissions from a single aisle airliner modeled on

77

the Next-Generation Boeing 737 (almost 1/3 of all 100+ seater airliners in service2,39) over entire

78

flight missions. We extensively characterized particle properties and determined BC mass and

79

number EIs (EIm and EIn) for two 737NG powerplants in compliance with the new regulation

80

methodology38,40,41 as well as measured SN according to the current standard for one of the

81

engines. We first look at the correlation between SN and BC mass. We use the BC data,

ACS Paragon Plus Environment

Environmental Science & Technology

82

corrected for particle losses and fuel variability24,25, to calculate the emissions from the

83

standardized ICAO landing and take-off (LTO) cycle, which assesses emissions below 915 m

84

(3,000 ft). It defines four thrust settings (static thrust measured on an engine test bed) and time in

85

each mode that roughly represent airport operations: taxiing in and out, take-off, climb at

86

reduced thrust, and approach for landing. We used a calibrated engine performance model for

87

correcting the emissions at the ground to any condition during the flight. Finally, we use our

88

flight mission estimates to answer the question whether modern-day air travel produces more BC

89

per passenger and kilometer than cars and buses.

90

MATERIALS AND METHODS

91

Engine emission tests. The emission tests were done on two in-service well run-in engines (at

92

20% and 50% of the typical overhaul cycle, respectively) in the engine test cell of SR Technics

93

at Zurich airport, Switzerland using an nvPM measurement system compliant with the future

94

ICAO nvPM standard38. Exhaust samples were extracted at the engine exit plane using a multi-

95

orifice cruciform probe. The chosen orifice pattern provided representative samples of gaseous

96

pollutants (NOx, CO, and hydrocarbons), in agreement with the ICAO certification data (S3 in

97

the online supporting information, SI). We found a good agreement also with the certification SN

98

(S4 in the SI). Upstream of the nvPM measurement system, the sample was diluted with dry

99

synthetic air by a factor of ~10 and drawn through a 24.5 m long trace-heated line (60 °C). The

100

total length of the sample line from the probe inlet to the BC instruments was 34 m. The BC

101

mass concentration was measured with an AVL Micro Soot Sensor (MSS), and the BC number

102

was measured with an AVL Particle Counter Advanced (APC) that contains a two-stage diluter

103

and a volatile particle remover heated to 350 °C. A detailed description of the engine tests, the

ACS Paragon Plus Environment

Page 4 of 23

Page 5 of 23

Environmental Science & Technology

104

sampling and measurement system, gaseous emissions, and particle instruments performance can

105

be found in the online SI. The elements of the analysis and the corresponding sections of the SI

106

are shown in Figure 1.

Figure 1 Simplified flow chart visualizing the elements of the analysis with the corresponding sections in the online SI.

107

108

Particle loss correction. A substantial fraction of particles entering the sampling system

109

deposits on its inner walls mainly due to diffusion (long sampling lines) and thermophoresis

110

(high thermal gradients). The particles are lost due to thermophoresis mainly in the first seven

111

meters of the sampling system as the sample cools from the exhaust gas temperature (up to 600

112

°C) to the sample line temperature upstream of the diluter (160 °C). The thermophoretic loss

113

correction factor for this section of the sampling system was assumed to be independent of

114

particle size and ranged from 1.19 to 1.33 (S5.2 in the SI).

115

The diffusional losses depend on particle size and required information about the

116

sampling system penetration, the particle size distributions (PSD), and the relationship between

ACS Paragon Plus Environment

Environmental Science & Technology

117

particle size and mass (effective density). The penetration efficiencies of the sampling system

118

and instruments downstream of the probe were modeled using a software tool developed for the

119

particle loss correction in nvPM measurement systems described in the Society of Automotive

120

Engineers (SAE) Aerospace Information Report (AIR) 650442. We took into account the losses

121

due to diffusion, sample line bends, particle size cut-off as well as the internal losses in the APC

122

(diffusion, thermophoresis, and CPC counting efficiency). We estimated the number-based

123

correction factors by multiplying the modeled PSD based on Scanning Mobility Particle Sizer

124

(SMPS) measurement data by the penetration functions. The number-based correction factors

125

ranged from 2 (take-off) to 11 (idle). For the mass-based losses, we combined the PSD model

126

with particle effective density distributions25. The mass-based correction factor ranged from 1.1

127

to 1.7 (S5.3 in the SI).

128

Emission indices and fuel sensitivity. The loss-corrected BC mass and number concentrations

129

were converted to EIs. The EIs were fitted with sixth order polynomials as functions of the

130

combustor inlet temperature (T3). To calculate the LTO emissions, we used the interpolated EIs

131

and fuel flow at sea level at the T3 values corresponding to the LTO flight modes (S6.4 in the

132

SI).

133

Since the BC emissions strongly depend on fuel composition, we estimated the effects of

134

fuel composition variability (fuel aromatics content) using an empirical model developed on the

135

same engine as in this study24. This model uses fuel hydrogen content in mass % (m% H) as the

136

correlating variable. We extrapolated the EIm and EIn for standard fuel used in Europe (14.3 m%

137

H) to the range from 13.8 m% H to 15.0 m% H, which captures the worldwide variability in

138

commercially used jet fuel43.

ACS Paragon Plus Environment

Page 6 of 23

Page 7 of 23

Environmental Science & Technology

139

Estimation of non-LTO emissions. The emissions measured were corrected to flight conditions

140

with the correlation from Döpelheuer and Lecht44 used in previous cruise BC emission estimates

141

18,28,45

. This correlation corrects the EIm at a reference ground condition to a flight condition,

142

whereas T3ref = T3flight (Figure 1 and S6.5 in the SI). The reference EIm is corrected for the

143

combustor inlet pressure (p3), the air-fuel ratio (AFR), and adiabatic flame temperature (Tfl)

144

effects. Previous estimates of cruise BC emissions expressed Tfl as linear functions of T3 as well

145

as a function of both T3 and p318,45. We assumed the former, and thus the Tfl term is equal to one.

146

To estimate the EIn, we assumed that the geometric mean diameter (GMD) of the PSD depends

147

only on T3. This assumption is supported by emission measurements of a large turbofan engine

148

at simulated flight altitude conditions46. The data extracted from Howard et al.46 show a clear

149

trend of GMD as a function of T3 independent of the simulated flight altitude (S6.5 in the SI).

150

Hence, the ratio of EIn to EIm is a unique function of T3.

151

To determine the p3, T3, and AFR at any flight condition, we developed a detailed engine

152

performance model calibrated to sea level performance data in GasTurb 12, commercial software

153

for gas turbine simulation (S7 in the SI). We validated the model using in-flight data. Our model

154

predicted the exhaust gas temperature (EGT) within 10 K over a wide range of cruise altitudes

155

and ambient conditions and it also matched the flight recorder data for similar engine and aircraft

156

types (S7.4 in the SI). The model input requires flight Mach number, altitude, deviation from the

157

temperature in the international standard atmosphere (ISA) and engine performance limiters,

158

such as rotor speeds or fuel flow. We ran the model using climb and descent rates derived from

159

flight radar data and engine performance limiters based on the Boeing 737-800 Flight Planning

160

and Performance Manual47 and Base of Aircraft Data (BADA) tables48 (S8 in the SI).

ACS Paragon Plus Environment

Environmental Science & Technology

161

RESULTS AND DISCUSSION

162

SN – BC mass correlation. The ICAO-recommended method for estimating aircraft engine BC

163

EIs has been the first order approximation version 3.0 (FOA3) that utilizes a correlation of BC

164

mass concentration with SN23. FOA3 is based on data from measurement campaigns of aircraft

165

engine emissions8. Recently, an alternative correlation has been proposed that predicts up to a

166

factor of 3 higher BC mass concentrations than FOA39. However, our measured SN and BC

167

mass concentrations corrected for thermophoretic loss agree well with FOA3 (Figure 2).

Figure 2 Correlations of BC mass concentration with the ICAO SN. The symbols represent averaged SN and BC mass concentrations at a given engine condition. The measured BC mass concentrations were corrected for dilution and thermophoretic loss as it will be required by the nvPM regulation. The error bars represent one standard deviation.

168

The data shown in Figure 2 were collected for one engine using the multi-orifice

169

sampling probe as well as a single orifice probe in additional measurements at various thrust

170

levels, mostly above 50%, and various test point durations (S4 in the SI). All the data points are

171

well below the proposed updated correlation. Stettler et al.9 developed the correlation from SN

172

and total PM mass measurement (instead of BC) of diffusion flame soot. Diffusion flame soot

173

with GMD typical for modern jet engines (< 50 nm) even after thermal treatment may contain as

ACS Paragon Plus Environment

Page 8 of 23

Page 9 of 23

Environmental Science & Technology

174

little as 50% BC49. Thus, correlating total PM with SN, which depends on BC content

175

(“blackness”), leads to an overestimation of the BC mass.

176

An improved SN-BC mass correlation can be developed only from standardized

177

measurements of aircraft engine emissions because the PM emission characteristics change with

178

engine power. Typically, BC mass concentration and GMD increase with engine thrust (here,

179

from 10 nm at idle to 40 nm at take-off; S5.3 in the SI). SN depends on BC mass loading as well

180

as on GMD as the filter paper used is more efficient for large particles9. As engine manufacturers

181

will report their nvPM certification data together with SN (as long as the old SN regulation stays

182

in force), ICAO plans to update the FOA338. The updated SN-BC mass correlation will be

183

necessary for older out of production engines that are excluded from the nvPM emissions

184

certification scheme but will remain in service for decades to come. For the SN range

185

investigated here, the updated correlation will likely be close to FOA3.

186

Emission indices and the LTO cycle. The BC emission characteristics strongly depended on

187

engine operating conditions (Figure 3a, b). The EIn peaked at minimum idle (lowest combustion

188

efficiency), but EIm was extremely low due to the small particle size (GMD at the exit plane ~12

189

nm). The EIs were the lowest between 15 – 20% thrust (highest AFR; S7.4 in the SI), and from

190

there on steeply increased with thrust (decreasing AFR). EIm peaked at maximum thrust, whereas

191

EIn reached the maximum at ~65%. EIn decreased with further thrust increase likely due to

192

particle coagulation within the engine. Such emission characteristics are typical for Rich-

193

Quench-Lean (RQL) combustors, representative of most today’s in-service jet engines

194

worldwide.

195

The EIm and the BC mass emissions calculated for the LTO cycle were mostly

196

overpredicted by methods using SN correlations and combustion models (Figure 3a, c). With

ACS Paragon Plus Environment

Environmental Science & Technology

197

respect to the results with nominal fuel (14.3 m% H), they overpredicted the BC mass emissions

198

at taxi by up to a factor of 40. The FOA38 overestimated the low thrust emissions by up to a

199

factor of 12 mainly due to the large uncertainties in the certification SN data (the maximum SN

200

measurement error is estimated to be ± 3 SN8). Almost 97% of the total BC mass was emitted

201

during take-off and climb. Thus, any under- or overprediction for these phases has the biggest

202

impact on the estimated total LTO BC emissions. The formation-oxidation (FOX) method18

203

overestimated the total BC mass by up to a factor of 4. On the other hand, the improved FOX

204

(ImFOX) method50 as well as the ICAO-recommended FOA3 underpredicted the BC mass at

205

high thrust by up to 40%, which is a good estimate considering the uncertainties (we estimated

206

38% uncertainty for the EIm; S6.1 in the SI). The approximative methods can be potentially

207

improved as standardized BC mass emissions data become available but will remain applicable

208

only to engines that emit maximum BC mass at take-off (FOX and ImFOX) and have

209

measurable SN (SN correlations).

ACS Paragon Plus Environment

Page 10 of 23

Page 11 of 23

Environmental Science & Technology

210 211 212 213

Figure 3 Emission indices of BC mass (a) and BC number (b), and emissions from the LTO cycle per aircraft (2 engines) in terms of BC mass (c) and BC number (d). The error bars represent the estimated EI uncertainty of 38% and 32% in terms of mass and number, respectively (S6.1 in the SI).

214

In contrast to the BC mass emissions, the BC number emissions at taxi constituted up to

215

30% of the total LTO emissions, depending on the fuel composition (Figure 3d). The fuel

216

composition affects the BC emissions at low power the most (S6.3 in the SI). For example, the

217

cumulative taxi and approach emissions with the high aromatics fuel (13.8 m% H) used in North

218

America43 and low aromatics fuel (15.0 m% H), typical for a fuel blend of Jet A-1 with synthetic

219

jet fuel43, differed by a factor of ~6. The fuel composition negligibly affected the total BC mass

220

emitted from the LTO cycle. However, the total BC number emissions can be readily drastically

ACS Paragon Plus Environment

Environmental Science & Technology

221

reduced using fuel blends due to the high contribution of the taxi and approach modes, which

222

would, in turn, improve the airport air quality.

223

An accurate assessment of the airport air quality is demanding because airport operations

224

differ from the regulatory LTO cycle used for engine technology comparison both in terms of

225

time and engine power used. Except in an emergency, pilots utilize less thrust than the engines

226

are capable of producing. Operators use derated engines (electronically reduced rated thrust) and

227

flexibly reduce take-off thrust as much as conditions permit to extend the engine service life and

228

optimize the overall cost. Since the BC mass emissions of the engine type investigated here peak

229

at maximum thrust, reduced take-off thrust diminishes the total BC mass emissions. For

230

example, a Boeing 737-800 with engines derated by 15% would produce 35% less BC mass per

231

standard LTO cycle. However, the total BC number emissions calculated for the derated engine

232

would be 25% higher, because the taxi condition (7% of the derated take-off thrust) corresponds

233

to a lower power setting at which the EIn is 60% higher (Figure 3b, S6.4 of the SI). In practice,

234

the taxi or idle thrust is lower than 7%. Compared to the LTO taxi setting, the fuel flow of an on-

235

wing engine of a Boeing 737NG at idle with nominal bleed air extraction (cabin air conditioning)

236

is ~10% lower.51 Since the EIn peaks at minimum idle, the actual taxi EIn may be up to an order

237

of magnitude higher than at the LTO taxi setting. Thus, the regulatory LTO cycle may

238

underestimate the actual BC mass and number emissions from busy airports with long taxi times.

239 240

Cruise. The EIs at cruise were strongly affected by ambient conditions and fuel composition. For

241

the nominal case (ISA; 35,000 ft altitude; Mach 0.8; 14.3 m% H), we determined EIm of 11

242

mg/kg fuel and EIn of 6.8×1014/kg fuel (Figure 4). When an aircraft flying with a constant thrust

243

and Mach number encounters warmer air, the engines need to run at a higher rotational speed to

ACS Paragon Plus Environment

Page 12 of 23

Page 13 of 23

Environmental Science & Technology

244

compensate for the loss of thrust due to decreasing air density, and T3 and BC emissions

245

increase. Cold air has the opposite effect (S8 in the SI). The cruise EIs varied less due to ambient

246

temperature (±10 K range) than due to fuel composition. The EIs varied with fuel composition

247

by up to 50%, highlighting a possible reduction of aviation BC emissions and their climate

248

impacts at cruise using low-aromatics fuels. These predictions are in line with recent airborne

249

studies (ACCESS I and II campaigns) investigating effects of fuel composition on BC emissions

250

using a similar engine type52.

Figure 4 Comparison of cruise emission indices with chase plane studies and estimates obtained by predictive methods.

Our cruise BC estimates are in the range of EIs found during airborne studies of various

251 252

types of passenger jets in the 1990s28 (Figure 4). However, those EIm and EIn were calculated

253

from measured particle size distributions. Also, the measurements were done at lower altitude,

254

speed, and weight to accommodate the chase plane.Therefore, the BC emissions were likely

255

lower than at our nominal cruise condition. For example, for the flight conditions of the 737-300

256

in Figure 4 (26,000 ft and Ma 0.53) we obtained 30% lower fuel burn and EIm of just 3 mg/kg

257

fuel.

ACS Paragon Plus Environment

Environmental Science & Technology

258

The cruise EIm estimates compared to those calculated by previously published methods.

259

The EIm estimates obtained with the methods of Peck et al.45, Stettler et al. (FOX)18, and

260

Abrahamson et al. (ImFOX)50 for the fuel flow corresponding to cruise at 35,000 ft and Mach 0.8

261

were within 50% of our data. However, the method of Peck et al. and FOX predict cruise BC

262

emissions by correcting their ground-based estimates, which we have shown to overestimate our

263

measurements. Using our measured BC mass concentrations for their respective ground

264

reference conditions decreased their cruise estimates by up to a factor of 10. ImFOX, on the

265

other hand, is calibrated to cruise BC measurements of the predecessor of the engine tested here.

266

The ImFOX method also uses fuel hydrogen content for scaling the BC mass concentration. It

267

predicts a range of EIm overlapping with ours but is limited only to ground and cruise, whereas

268

our model can predict the engine performance parameters at any flight condition.

269

Flight Mission. Since the Boeing 737 aircraft is used worldwide for a broad range of flight

270

missions lasting from under one hour to up to eight hours, the emissions from LTO, climb to

271

cruise altitude, and descent may represent a major fraction of the total emissions depending on

272

the flight time (Figure 5). For a one hour flight, over 70% of the BC mass and number emissions

273

came from the climb phase and over 25% dispersed under 3,000 ft altitude (LTO). The descent

274

phase with engines at flight idle contributed less than 0.5% and 3% in terms of BC mass and

275

number, respectively.With increasing flight time, the cruise BC mass and number emissions

276

reached 50% of the total emissions after four and two hours, respectively. Thus, the climb can

277

much contribute to the total BC emissions of short to medium-haul flights.

ACS Paragon Plus Environment

Page 14 of 23

Page 15 of 23

Environmental Science & Technology

Figure 5 Ratio of the BC mass (a) and number (b) emissions per flight phase and the total emissions as a function of flight time (nominal fuel composition with 14.3 m% H). The flight time on the x-axis is the airborne time above 3,000 ft altitude.

278

The mission EIm (time-weighted arithmetic means of all flight phases; S8.4 in the SI)

279

increased or decreased depending on the cruise EIm for a given fuel composition. The EIn, on the

280

other hand, increased with flight time for all fuel composition cases (Table S13 in the SI). For

281

the nominal fuel, the EIn increased from 5.15×1014/kg for a one hour flight to 6.53×1014/kg for a

282

seven-hour flight. For the same interval, the mission EIm moderately decreased from 11.94

283

mg/kg to 11.20 mg/kg. These estimates are a factor of 2 to 3 lower than the mission EIs used for

284

calculating global fleet emissions (38 mg/kg for the 1992 fleet28 and 25 mg/kg for the 2000

285

fleet29). Given the high number of in-service Boeing 737NG and other aircraft with similar

286

engines (Airbus A320 family), our results are representative of the current fleet of single-aisle

287

airliners (65% of all passenger aircraft2).

288

Emissions per passenger-distance. The BC emissions per passenger-km of great circle distance

289

decreased with flight time regardless of fuel composition (Figure 6). For the nominal fuel, the

290

normalized BC mass and number emissions decreased with flight distance by up to a factor of 4

291

and 2, respectively. The drop was most significant in the first three hours of flight, and any

ACS Paragon Plus Environment

Environmental Science & Technology

292

longer flight only moderately decreased the emission rates. The range of per passenger-km

293

emission rates increased when we factored in the fuel effects. A short flight using 13.8 m% H

294

fuel produced up to a factor of 9 more BC mass and a factor of 5 more BC number than a long

295

flight using fuel with 15.0 m% H.

Page 16 of 23

Figure 6 BC mass and number emissions per passenger (assuming 130 people on board of a 737-800 at 80% occupancy) and distance.

296

We took the ranges of BC emission rates per passenger-km and compared them with road

297

vehicles (Figure 7). We assumed two car passengers and 30 bus passengers and used published

298

data for various engine technologies53,54. We note that the BC data for vehicles shown in Figure

299

7 were obtained with methods different from the nvPM standard methodology. The BC mass was

300

determined by correcting the filter-based total PM mass using the reported upper estimate of the

301

BC content for each vehicle type. The BC number concentration was measured with a particle

302

size cut-off of 23 nm instead of 10 nm used for the aircraft engines. Regarding BC mass, the

303

aircraft engine emissions were in the middle of the range reported for the various vehicle engine

304

technologies (Figure 7a). For a short flight and 13.8 m% H, the BC mass emissions were

305

comparable to a gasoline direct injected (GDI) car, which is characterized by relatively high

306

particle emissions, and decreased with flight distance and increasing fuel hydrogen content to a

ACS Paragon Plus Environment

Page 17 of 23

Environmental Science & Technology

307

level of a common port fuel injected (PFI) gasoline car. However, the BC number emissions

308

were as high as a diesel car without a particulate filter (DPF) (Figure 7b). This assessment shows

309

that the mass-based BC emissions per passenger-km of the engine type investigated are relatively

310

low, whereas the number-based BC emissions are considerable. This finding is in line with the

311

general emission characteristics of modern gas turbine engines that produce particles with small

312

GMD and thus low BC mass emissions, but relatively high number-based emissions. At the same

313

time, we note that although these findings are relevant for a large fraction of the current fleet,

314

they would not apply to other engine types, e.g. with lean-burn and staged combustors.

Figure 7 Comparison of the BC mass (a) and number (b) emissions per passenger-km. Vehicle emissions data were taken from Giechaskiel et al. and Hallquist et al. and normalized per two car passengers and 30 bus passengers. The abbreviations for engine technology are: D – diesel without any emission control, DPF – diesel with a particle filter, PFI – gasoline port fuel injection, and GDI – gasoline direct injection. The solid vertical lines represent values for 3-hour flights and the nominal fuel composition.

315

Implications. In the near future, estimates of aviation BC emissions can be predicted more

316

accurately using standardized BC measurement data. We have shown first such estimates of BC

317

mass and number emissions for all flight phases of the most widely used airliner determined

318

from certification quality data. We have provided updated emission indices for cruise as well as

319

for the flight mission and looked at the sensitivities to fuel composition and ambient temperature.

ACS Paragon Plus Environment

Environmental Science & Technology

320

For the representative cruise at ISA conditions with nominal fuel, the determined mission EIm is

321

half the estimate used for the BC emission rates of the global fleet in the year 200029 and merely

322

13% of the average EIm reported by the FOX method for the 2005 fleet18. Also, the LTO

323

emissions determined here strongly contrast with recent studies that claim FOA3 to significantly

324

underestimate BC mass emissions from aircraft turbine engines9,18. We found a good agreement

325

with the SN – BC mass correlation used in the FOA3. However, our data also suggest FOA3

326

may overestimate the mass-based emissions from taxiing aircraft by up to an order of magnitude.

327

Since airports face regulatory limits on ambient PM mass concentration, they need better

328

emission inventories and tools for implementing the best practices with respect to airport air

329

quality. The question remains whether the BC mass concentration is a useful metric for that

330

because it does not capture the considerable number of soot nanoparticles an aircraft turbine

331

engine can emit at the detection limit of the current BC mass instruments while producing no

332

visible smoke.

333

ASSOCIATED CONTENT

334

Supporting Information. Engine emission tests, description of the exhaust sampling and

335

measurement system, cross-check of gaseous emissions with ICAO certification data, smoke

336

number analysis, particle loss correction, calculation of the non-volatile PM emission indices,

337

engine performance model, flight mission calculation.

338

AUTHOR INFORMATION

339

Corresponding Authors

340 341 342

*

Phone: +41 58 765 46 08. Fax: +41 58 765 69 63. E-mail: [email protected]

*

Phone: +41 44 633 36 21. Fax: +41 58 765 69 63. E-mail: [email protected]

Notes

ACS Paragon Plus Environment

Page 18 of 23

Page 19 of 23

Environmental Science & Technology

343

The authors declare no competing financial interest.

344

ACKNOWLEDGEMENTS

345

Funding was provided by the Swiss Federal Office of Civil Aviation (FOCA) through the

346

projects “Particulate Matter and Gas Phase Emission Measurement of Aircraft Engine Exhaust”

347

and “EMPAIREX – EMissions of Particulate and gaseous pollutants in AIRcraft engine

348

EXhaust”. The support by the Swiss National Science Foundation through the project

349

206021_157663 “A traversing probe for mapping the particulate and gaseous emissions at the

350

aircraft engine exhaust plane” is acknowledged. Additional funding for the engine lease was

351

provided by the US Federal Aviation Administration, the European Aviation Safety Agency, and

352

Transport Canada. We further acknowledge the following organizations and individuals for

353

technical support, instrumentation, and analysis: SR Technics Switzerland AG, Andrea Fischer at

354

Empa, Stefan Fischer – SF Aviation, Cardiff University Gas Turbine Research Centre, GE

355

Aviation, and Snecma.

356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375

References (1) Lodge, D. Small world: An academic romance; Secker & Warburg: London, 1984. (2) Airbus. Global Market Forecast 2016-2036; http://www.airbus.com/company/market/forecast/ (accessed Jul 1, 2016). (3) Kuhn, P. M. Airborne Observations of Contrail Effects on the Thermal Radiation Budget. J. Atmos. Sci. 1970, 27 (6), 937–942; DOI 10.1175/1520-0469(1970)0272.0.CO;2. (4) Johnston, H. Reduction of stratospheric ozone by nitrogen oxide catalysts from supersonic transport exhaust. Science 1971, 173 (3996), 517–522. (5) ICAO. Environmental Protection: Volume II Aircraft Engine Emissions, 3rd ed.; Annex 16 to the Convention on International Civil Aviation; ICAO: Montréal, Quebec, 2008. (6) ICAO. Environmental Protection: Volume I Aircraft Noise, 6th ed.; ICAO: Montréal, Québec, 2011. (7) George, R. E.; Nevitt, J. S.; Verssen, J. A. Jet Aircraft Operations: Impact on the Air Environment. J. Air Pollut. Control Assoc. 1972, 22 (7), 507–515; DOI 10.1080/00022470.1972.10469667. (8) Wayson, R. L.; Fleming, G. G.; Iovinelli, R. Methodology to Estimate Particulate Matter Emissions from Certified Commercial Aircraft Engines. J.Air Waste Manage. Assoc. 2009, 59 (1), 91–100; DOI 10.3155/10473289.59.1.91. (9) Stettler, M. E. J.; Swanson, J. J.; Barrett, S. R. H.; Boies, A. M. Updated Correlation Between Aircraft Smoke Number and Black Carbon Concentration. Aerosol Sci. Technol. 2013, 47 (11), 1205–1214; DOI 10.1080/02786826.2013.829908. (10) ICAO. ICAO Aircraft Engine Emissions Databank; http://easa.europa.eu/node/15672 (accessed Oct 1, 2016).

ACS Paragon Plus Environment

Environmental Science & Technology

376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429

(11) Beyersdorf, A. J.; Timko, M. T.; Ziemba, L. D.; Bulzan, D.; Corporan, E.; Herndon, S. C.; Howard, R.; MiakeLye, R.; Thornhill, K. L.; Winstead, E.; Wey, C.; Yu, Z.; Anderson, B. E. Reductions in aircraft particulate emissions due to the use of Fischer–Tropsch fuels. Atmos. Chem. Phys. 2014, 14 (1), 11–23; DOI 10.5194/acp-1411-2014. (12) Kinsey, J. S.; Hays, M. D.; Dong, Y.; Williams, D. C.; Logan, R. Chemical characterization of the fine particle emissions from commercial aircraft engines during the Aircraft Particle Emissions eXperiment (APEX) 1 to 3. Environ. Sci. Technol. 2011, 45 (8), 3415–3421; DOI 10.1021/es103880d. (13) Kinsey, J. S.; Dong, Y.; Williams, D. C.; Logan, R. Physical characterization of the fine particle emissions from commercial aircraft engines during the Aircraft Particle Emissions eXperiment (APEX) 1–3. Atmos. Environ. 2010, 44 (17), 2147–2156; DOI 10.1016/j.atmosenv.2010.02.010. (14) Lobo, P.; Durdina, L.; Smallwood, G. J.; Rindlisbacher, T.; Siegerist, F.; Black, E. A.; Yu, Z.; Mensah, A. A.; Hagen, D. E.; Miake-Lye, R. C.; Thomson, K. A.; Brem, B. T.; Corbin, J. C.; Abegglen, M.; Sierau, B.; Whitefield, P. D.; Wang, J. Measurement of Aircraft Engine Non-Volatile PM Emissions: Results of the Aviation-Particle Regulatory Instrumentation Demonstration Experiment (A-PRIDE) 4 Campaign. Aerosol Sci. Technol. 2015, 49 (7), 472–484; DOI 10.1080/02786826.2015.1047012. (15) Onasch, T. B.; Jayne, J. T.; Herndon, S.; Worsnop, D. R.; Miake-Lye, R. C.; Mortimer, I. P.; Anderson, B. E. Chemical Properties of Aircraft Engine Particulate Exhaust Emissions. J. Propul. Power 2009, 25 (5), 1121–1137; DOI 10.2514/1.36371. (16) Mazaheri, M.; Johnson, G. R.; Morawska, L. An inventory of particle and gaseous emissions from large aircraft thrust engine operations at an airport. Atmos. Environ. 2011, 45 (20), 3500–3507; DOI 10.1016/j.atmosenv.2010.12.012. (17) Lobo, P.; Hagen, D. E.; Whitefield, P. D. Comparison of PM emissions from a commercial jet engine burning conventional, biomass, and Fischer-Tropsch fuels. Environ. Sci. Technol. 2011, 45 (24), 10744–10749; DOI 10.1021/es201902e. (18) Stettler, M. E. J.; Boies, A. M.; Petzold, A.; Barrett, S. R. H. Global civil aviation black carbon emissions. Environ. Sci. Technol. 2013, 47 (18), 10397–10404; DOI 10.1021/es401356v. (19) Yim, S. H. L.; Lee, G. L.; Lee, I. H.; Allroggen, F.; Ashok, A.; Caiazzo, F.; Eastham, S. D.; Malina, R.; Barrett, S. R. H. Global, regional and local health impacts of civil aviation emissions. Environ. Res. Lett. 2015, 10 (3), 34001; DOI 10.1088/1748-9326/10/3/034001. (20) Unal, A.; Hu, Y.; Chang, M. E.; Talat Odman, M.; Russell, A. G. Airport related emissions and impacts on air quality: Application to the Atlanta International Airport. Atmos. Environ. 2005, 39 (32), 5787–5798; DOI 10.1016/j.atmosenv.2005.05.051. (21) Zhu, Y.; Fanning, E.; Yu, R. C.; Zhang, Q.; Froines, J. R. Aircraft emissions and local air quality impacts from takeoff activities at a large International Airport. Atmos. Environ. 2011, 45 (36), 6526–6533; DOI 10.1016/j.atmosenv.2011.08.062. (22) Rissman, J.; Arunachalam, S.; Woody, M.; West, J. J.; BenDor, T.; Binkowski, F. S. A plume-in-grid approach to characterize air quality impacts of aircraft emissions at the Hartsfield–Jackson Atlanta International Airport. Atmos. Chem. Phys. 2013, 13 (18), 9285–9302; DOI 10.5194/acp-13-9285-2013. (23) ICAO. Airport air quality manual, 1st ed.; Doc 9889; International Civil Aviation Organization: Montréal, 2011-. (24) Brem, B. T.; Durdina, L.; Siegerist, F.; Beyerle, P.; Bruderer, K.; Rindlisbacher, T.; Rocci-Denis, S.; Andac, M. G.; Zelina, J.; Penanhoat, O.; Wang, J. Effects of Fuel Aromatic Content on Nonvolatile Particulate Emissions of an In-Production Aircraft Gas Turbine. Environ. Sci. Technol. 2015, 49 (22), 13149–13157; DOI 10.1021/acs.est.5b04167. (25) Durdina, L.; Brem, B. T.; Abegglen, M.; Lobo, P.; Rindlisbacher, T.; Thomson, K. A.; Smallwood, G. J.; Hagen, D. E.; Sierau, B.; Wang, J. Determination of PM mass emissions from an aircraft turbine engine using particle effective density. Atmos. Environ. 2014, 99, 500–507; DOI 10.1016/j.atmosenv.2014.10.018. (26) Kärcher, B.; Möhler, O.; DeMott, P. J.; Pechtl, S.; Yu, F. Insights into the role of soot aerosols in cirrus cloud formation. Atmos. Chem. Phys. Discuss. 2007, 7 (3), 7843–7905; DOI 10.5194/acpd-7-7843-2007. (27) Kärcher, B.; Yu, F. Role of aircraft soot emissions in contrail formation. Geophys. Res. Lett. 2009, 36 (1); DOI 10.1029/2008GL036649. (28) Petzold, A.; Döpelheuer, A.; Brock, C. A.; Schröder, F. In situ observations and model calculations of black carbon emission by aircraft at cruise altitude. J. Geophys. Res. 1999, 104 (D18), 22171–22181; DOI 10.1029/1999JD900460.

ACS Paragon Plus Environment

Page 20 of 23

Page 21 of 23

430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484

Environmental Science & Technology

(29) Lee, D. S.; Pitari, G.; Grewe, V.; Gierens, K.; Penner, J. E.; Petzold, A.; Prather, M. J.; Schumann, U.; Bais, A.; Berntsen, T. Transport impacts on atmosphere and climate: Aviation. Atmos. Environ. 2010, 44 (37), 4678–4734; DOI 10.1016/j.atmosenv.2009.06.005. (30) Lobo, P.; Hagen, D. E.; Whitefield, P. D.; Raper, D. PM emissions measurements of in-service commercial aircraft engines during the Delta-Atlanta Hartsfield Study. Atmos. Environ. 2015, 104, 237–245; DOI 10.1016/j.atmosenv.2015.01.020. (31) Geiser, M.; Kreyling, W. G. Deposition and biokinetics of inhaled nanoparticles. Part. Fibre Toxicol. 2010, 7, 2; DOI 10.1186/1743-8977-7-2. (32) Block, M. L.; Calderón-Garcidueñas, L. Air pollution: Mechanisms of neuroinflammation and CNS disease. Trends Neurosci. 2009, 32 (9), 506–516; DOI 10.1016/j.tins.2009.05.009. (33) Health Effects Institute. Understanding the health effects of ambient ultrafine particles; HEI perspectives 3; Health Effects Institute: Boston, Massachusetts, 2013. (34) Peters, A.; Veronesi, B.; Calderón-Garcidueñas, L.; Gehr, P.; Chen, L. C.; Geiser, M.; Reed, W.; RothenRutishauser, B.; Schürch, S.; Schulz, H. Translocation and potential neurological effects of fine and ultrafine particles a critical update. Part. Fibre Toxicol. 2006, 3, 13; DOI 10.1186/1743-8977-3-13. (35) Hsu, H.-H.; Adamkiewicz, G.; Houseman, E. A.; Zarubiak, D.; Spengler, J. D.; Levy, J. I. Contributions of aircraft arrivals and departures to ultrafine particle counts near Los Angeles International Airport. Sci. Total Environ. 2013, 347–355; DOI 10.1016/j.scitotenv.2012.12.010. (36) Hudda, N.; Gould, T.; Hartin, K.; Larson, T. V.; Fruin, S. A. Emissions from an international airport increase particle number concentrations 4-fold at 10 km downwind. Environ. Sci. Technol. 2014, 48 (12), 6628–6635; DOI 10.1021/es5001566. (37) Keuken, M. P.; Moerman, M.; Zandveld, P.; Henzing, J. S.; Hoek, G. Total and size-resolved particle number and black carbon concentrations in urban areas near Schiphol airport (the Netherlands). Atmos. Environ. 2015, 104, 132–142; DOI 10.1016/j.atmosenv.2015.01.015. (38) Rindlisbacher, T.; Jacob, S. D. New particulate matter standard for aircraft gas turbine engines. ICAO Environmental Report 2016; pp 85–88. (39) The Boeing Company. 737 Model Summary Through August 2016. http://active.boeing.com/commercial/orders/displaystandardreport.cfm?cboCurrentModel=737&optReportType=All Models&cboAllModel=737&ViewReportF=View+Report (accessed Sept 1, 2016). (40) Miake-Lye, R. C.; Brem, B. T. From smoke to nanoparticles: international measurement campaigns for the establishment of a new nvPM regulation. ICAO Environmental Report 2016; pp 89–92. (41) AIR 6241 Procedure for the Continuous Sampling and Measurement of Non-Volatile Particle Emissions from Aircraft Turbine Engines; SAE International, 2013. (42) AIR6504 Procedure for the Calculation of Sampling and Measurement System Penetration Functions and System Loss Correction Factors; SAE International, 2016. (43) Hadaller, O. J.; Johnson, J. M. World fuel sampling program. CRC Reports 2006 (647). (44) Döpelheuer, A.; Lecht, M. Influence of engine performance on emission characteristics. RTO Meeting Proceedings 14; RTO/NATO: Hull, Canada, 1999. (45) Peck, J.; Oluwole, O. O.; Wong, H.-W.; Miake-Lye, R. C. An algorithm to estimate aircraft cruise black carbon emissions for use in developing a cruise emissions inventory. J. Air Waste Manage. Assoc. 2013, 63 (3), 367–375; DOI 10.1080/10962247.2012.751467. (46) Howard, R.; Hiers, R. S.; Whitefield, P. D.; Hagen, D. E.; Wormhoudt, J. C.; Miake-Lye, R. C.; Strange, R. Experimental characterization of gas turbine emissions at simulated altitude conditions AEDC-TR-96-3; Arnold Engineering Development Center, 1996. (47) The Boeing Company. Flight Planning and Performance Manual 737-800 CFM56-7B26, 3rd ed.; Flight Operation Engineering, Boeing Commercial Airplane Group: Seattle, WA, USA, 2006. (48) Eurocontrol. Base of Aircraft Data (BADA) EUROCONTROL’s Aircraft Performance Model. http://www.eurocontrol.int/sites/default/files/field_tabs/content/documents/sesar/bada-overview.pdf (accessed Sept 1, 2016). (49) Durdina, L.; Lobo, P.; Trueblood, M. B.; Black, E. A.; Achterberg, S.; Hagen, D. E.; Brem, B. T.; Wang, J. Response of real-time black carbon mass instruments to mini-CAST soot. Aerosol Sci. Technol. 2016, 50 (9), 906– 918; DOI 10.1080/02786826.2016.1204423. (50) Abrahamson, J. P.; Zelina, J.; Andac, M. G.; Vander Wal, R. L. Predictive model development for aviation black carbon mass emissions from alternative and conventional fuels at ground and cruise. Environ. Sci. Technol. 2016; DOI 10.1021/acs.est.6b03749.

ACS Paragon Plus Environment

Environmental Science & Technology

485 486 487 488 489 490 491 492 493 494 495

(51) Herndon, S. C. Measurement of gaseous HAP emissions from idling aircraft as a function of engine and ambient conditions; ACRP report 63; Transportation Research Board: Washington, D.C., 2012. (52) Anderson, B. E. Alternative fuel effects on contrails & cruise emissions (ACCESS-2) flight experiment. http://science.larc.nasa.gov/large/data/ACCESS2/presentations/2%20Anderson%20ACCESS%202_Overview%20Talk_09Jan2015--final.pdf (accessed Oct 1, 2016). (53) Giechaskiel, B.; Mamakos, A.; Andersson, J.; Dilara, P.; Martini, G.; Schindler, W.; Bergmann, A. Measurement of Automotive Nonvolatile Particle Number Emissions within the European Legislative Framework: A Review. Aerosol Sci. Technol. 2012, 46 (7), 719–749; DOI 10.1080/02786826.2012.661103. (54) Hallquist, Å. M.; Jerksjö, M.; Fallgren, H.; Westerlund, J.; Sjödin, Å. Particle and gaseous emissions from individual diesel and CNG buses. Atmos. Chem. Phys. 2013, 13 (10), 5337–5350; DOI 10.5194/acp-13-5337-2013.

ACS Paragon Plus Environment

Page 22 of 23

Page 23 of 23

Environmental Science & Technology

Graphical abstract 47x26mm (300 x 300 DPI)

ACS Paragon Plus Environment