Secondary Organic Aerosol Production from ... - ACS Publications

Subscriber access provided by UNIV OF DURHAM

Article

Secondary organic aerosol production from gasoline vehicle exhaust: Effects of engine technology, cold start, and emission certification standard Yunliang Zhao, Andrew T. Lambe, Rawad Saleh, Georges Saliba, and Allen L. Robinson Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05045 • Publication Date (Web): 05 Jan 2018 Downloaded from http://pubs.acs.org on January 5, 2018

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 37

Environmental Science & Technology

1 2 3 4 5 6 7 8 9 10

11 12

Secondary organic aerosol production from gasoline vehicle exhaust: Effects of engine technology, cold start, and emission certification standard Yunliang Zhao1, Andrew T. Lambe2, Rawad Saleh1,3, Georges Saliba1, Allen L. Robinson1,* 1 Department of Mechanical Engineering and Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA, 15213; 2 Aerodyne Research Inc., Billerica, MA, USA, 01821; 3 Now at: College of Engineering, University of Georgia, Athens, GA, USA, 30602.

*Correspondence to: [email protected]

Abstract: Secondary organic aerosol (SOA) formation from dilute exhaust from 16

13

gasoline vehicles was investigated using a Potential Aerosol Mass (PAM)

14

oxidation flow reactor during chassis dynamometer testing using the cold-start

15

unified cycle (UC). Ten vehicles were equipped with gasoline direct injection

16

engines (GDI vehicles) and six with port fuel injection engines (PFI vehicles)

17

certified to a wide range of emissions standards. We measured similar SOA

18

production from GDI and PFI vehicles certified to the same emissions standard;

19

less SOA production from vehicles certified to stricter emissions standards; and,

20

after accounting for differences in gas-particle partitioning, similar effective SOA

21

yields across different engine technologies and certification standards. Therefore

22

the ongoing, dramatic shift from PFI to GDI vehicles in the United States should

23

not alter the contribution of gasoline vehicles to ambient SOA and the natural

24

replacement of older vehicles with newer ones certified to stricter emissions

25

standards should reduce atmospheric SOA levels. Compared to hot operations,

26

cold-start exhaust had lower effective SOA yields, but still contributed more SOA

27

overall because of substantially higher organic gas emissions. We demonstrate

ACS Paragon Plus Environment

1


Page 2 of 37

28

that the PAM reactor can be used as a screening tool for vehicle SOA production

29

by carefully accounting for the effects of the large variations in emission rates.

30 31


2

Page 3 of 37


32 33

1. Introduction Fine particulate matter causes adverse health effects and alters global

34

climate.1, 2 Secondary organic aerosol (SOA), formed through photo-oxidation of

35

organic vapors in the atmosphere, is a major component of fine particulate matter

36

even in urban environments.1, 3 Recent studies suggest that gasoline vehicles

37

may be the dominant source of SOA in urban areas such as Los Angeles.4-7

38

However, SOA formation is complex and uncertain; there is an active, ongoing

39

debate surrounding SOA formation from gasoline vehicles and other on-road

40

sources.8 This uncertainty complicates the development of effective control

41

strategies to reduce human exposure to fine particulate matter.

42 43

Recent studies have investigated SOA formation from dilute gasoline

44

vehicle exhaust primarily using smog chambers9-12 but also with oxidation flow

45

reactors.13, 14 These studies demonstrate that SOA production typically exceeds

46

the direct particulate matter emissions after an hour or two of photo-oxidation at

47

typical daytime conditions.9-12 Chamber experiments also demonstrate that NOx

48

levels can strongly affect SOA formation from gasoline vehicle exhaust.11 Finally,

49

vehicles certified to more stringent emissions produce less SOA than vehicles

50

certified to less stringent standards, qualitatively mirroring trends in organic gas

51

emissions.9, 11

52 53 54

Although past studies have provided substantial insight, important gaps remain in our understanding of SOA formation from gasoline vehicle exhaust due


3


Page 4 of 37

55

to changing engine technology and emissions certification standards. California

56

and the federal government are both phasing in new, more stringent regulations

57

(LEV III and Tier 3, respectively). These standards meet or exceed the most

58

stringent existing regulations, the California super ultra-low emission vehicle

59

(SULEV) standard. In addition, largely driven by increases in the corporate

60

average fuel economy standards15, 16, a dramatic change in gasoline vehicle

61

engine technology is occurring in the United States. Historically, the U.S. fleet

62

has been dominated by vehicles equipped with port-fuel injection engines (PFI

63

vehicles), but the market share of vehicles equipped with gasoline direct injection

64

engines (GDI vehicles) has increased dramatically over the past decade,

65

reaching ~50% of new gasoline vehicles sold in the United States in 2016.15

66 67

SOA production from vehicles certified to the most stringent existing

68

emissions standard, California SULEV, has not yet been quantified through

69

photo-oxidation experiments.11 It has only been estimated using measured

70

chemical composition of organic gas emissions11, 17, but these estimates are

71

uncertain due to a combination of incomplete speciation and uncertainty in SOA

72

models.8, 11

73 74

SOA formation from dilute exhaust from PFI vehicles has been extensively

75

studied across a wide range of emissions standards/model years.9, 11 Previous

76

studies have also characterized the primary emissions from GDI vehicles,

77

including particle number and mass17-22, gaseous pollutants17, 23, 24 and non-


4

Page 5 of 37


78

methane organic gas (NMOG) composition,17, 18, 22-25 but few studies have

79

characterized SOA production from GDI vehicles. One study did not

80

quantitatively measure SOA production from the GDI vehicles11 and the other

81

only measured SOA formation from one GDI vehicle.13, 14 Data for one vehicle

82

are not sufficient as there can be substantial vehicle-to-vehicle variability in

83

emissions due to differences in engine design (e.g. spray versus wall-guided

84

GDI), engine calibration (e.g. spark timing, valve timing, etc.), emission control

85

technologies, and vehicle age and maintenance history.17, 26

86 87

The published chemical composition data of NMOG emissions may

88

provide some insight into the potential differences in SOA formation between GDI

89

and PFI vehicles. Zimmerman et al.18 reports that GDI vehicle exhaust is

90

enriched in aromatics compared to PFI exhaust. Since aromatics are an

91

important class of SOA precursors11, 27, this suggests GDI vehicle exhaust may

92

have enhanced SOA formation potential compared to PFI vehicles. However,

93

they only tested one make and model of GDI vehicles, raising concerns about

94

generalizability. Saliba et al. 17 tested a larger, more diverse fleet and found no

95

systematic differences in SOA precursor emissions between GDI and PFI

96

vehicles, suggesting no differences in SOA production. However, both studies

97

only quantified a fraction of SOA precursors in gasoline exhaust11; furthermore,

98

there are substantial uncertainties in theoretical estimates of SOA formation.1, 11

99


5


100

Page 6 of 37

Our understanding of SOA formation from gasoline vehicle exhaust, such

101

as the effects of changes in NOx levels on SOA formation11, is primarily based on

102

smog chamber experiments. Smog chamber experiments are complex and time

103

consuming (for example, chambers are often cleaned for 12 hr between

104

experiments). Smog chamber experiments are typical batch processes, making it

105

difficult to investigate the effects of operating conditions such as cold-start versus

106

hot operations on SOA formation.14, 28 Improved tools are needed to more

107

routinely assess the SOA formation from gasoline vehicle exhaust.

108 109

Oxidation flow reactors (OFRs) have also been used to study SOA

110

production. OFRs have short residence time, which creates the potential for more

111

real-time and routine measurements.28 OFRs have been used to investigate SOA

112

formation from individual species29, vehicle/engine emissions13, 14, 30, 31 and

113

atmospheric organics.32, 33 SOA composition measured in OFRs strongly

114

resembles that in the atmosphere and similar results can be obtained from OFRs

115

and smog chambers.34, 35 However, OFRs have not been used with vehicles

116

operated over transient cycles sampled using the constant volume samplers –

117

the approached used for vehicle certification testing.

118 119

In this study, we investigated SOA formation from a fleet of 16 on-road

120

gasoline vehicles using a Potential Aerosol Mass OFR (PAM reactor, hereafter)

121

during chassis dynamometer testing. The test fleet consisted of both PFI and

122

GDI vehicles certified to a range of emissions standards from federal Tier0 to


6

Page 7 of 37


123

California super-ultra low emission vehicles (SULEV). We investigate the effects

124

of GDI technology, tightening of emissions standards, and cold-start versus hot-

125

stabilized operations on SOA formation. Finally, we evaluate the use of an OFR

126

as screening tool for SOA production from on-road gasoline vehicle exhaust.

127

Detailed primary emissions data from this test campaign are reported in Saliba et

128

al.17 and Drozd et al.25.

129

2. Methods

130

2.1 Test fleet, fuel and test cycle

131

Dilute tailpipe exhaust from gasoline vehicles was photo-oxidized using a

132

PAM reactor during chassis dynamometer testing at the California Air Resources

133

Board’s (CARB) Haagen-Smit Laboratory. The schematic of the experimental

134

setup is shown in Figure S1 in Supporting Information (SI).

135 136

The test fleet consisted of 16 light-duty gasoline vehicles recruited from the

137

California in-use, on-road fleet. Details on the test fleet are in Table S1 in SI. For

138

discussion, vehicles are categorized by emissions certification standard (in

139

parentheses): 1 pre-LEV vehicle (U.S. Tier0), 3 LEV vehicles (California Low

140

emission vehicle), 5 ULEV (California Ultra-low emission vehicle) and 7 SULEV

141

vehicles (California Super ultra-low and partial zero emission vehicles). The

142

number of the GDI vehicle(s) was 1 LEV, 2 ULEV, and 5 SULEV with the

143

remaining vehicles in each category being PFI vehicles.

144


7


145

Although we categorized vehicles based on engine technology (GDI

146

versus PFI), the tailpipe emissions depend on the details of engine design,

147

engine calibration, aftertreatment system, vehicle age, and maintenance history.

148

Vehicles in the same nominal category (e.g. SULEV GDI vehicles) can have

149

important differences, such as spray- versus wall-guided fuel injection systems,

150

that affect tailpipe emissions.19, 36 We characterized tailpipe, not engine-out,

151

emissions because tailpipe emissions are what impact air quality.

Page 8 of 37

152 153

All vehicles were tested using the cold-start Unified Cycle (UC), which is

154

widely used for emissions testing. The speed trace of the UC is shown in Figure

155

1a; it is a transient cycle with two starts, rapid accelerations, and both start-stop

156

and high-speed operations. It is similar in the overall duration and distance to the

157

Federal Test Procedure (FTP), but it was developed specifically to represent

158

driving in Southern California. Like the FTP, the UC is divided into three driving

159

phases, which we refer to as bags (Figure 1a). During each UC bag, dilute

160

exhaust is collected inside separate Tedlar bags; the exhaust in each bag is then

161

analyzed to determine average emissions for each bag. Bag 1 is cold-start (first

162

five minutes of the driving cycle); bag 2 is hot stabilized operation; and bag 3

163

repeats bag 1 but is hot-start. Between bags 2 and 3 there is a 10-min hot soak

164

when the vehicle is not operated and no sampling is performed.

165 166 167

Prior to testing, each vehicle was preconditioned with an overnight soak and without evaporative canister purge. Each vehicle was refueled at the CARB


8

Page 9 of 37


168

Haagen-Smit Laboratory with the same commercial gasoline fuel that met the

169

California summertime fuel standard. Major fuel components included 49%

170

paraffins, 25% aromatics, 14% olefins, and 10% ethanol (wt%); additional fuel

171

composition data are in Saliba et al.17.

172

2.2 Photo-oxidation Experiments The photo-oxidation experiments were carried out using a PAM reactor.30,

173 174

37

175

equipped with four mercury lamps (BHK, Inc) and fluorinated ethylene propylene

176

(FEP) sleeves.30, 37 The mercury lamps have peak emissions intensity at 185 nm

177

and 254 nm, which produces hydroxyl (OH) radicals via the reactions of H2O +

178

hv185 OH + H followed by H + O2 HO2 and O3 + hv254 O(1D) + O2,

179

followed by O(1D) + H2O 2OH. The average residence time inside the PAM

180

reactor was ~100 s.

The PAM reactor is a 13-L cylindrical tube (46 cm L ×22 cm diameter)

181 182

During the dynamometer test the entire tailpipe exhaust from each vehicle

183

was sampled using a constant volume sampler (CVS) following the Code of

184

Federal Regulations Title 40, Chapter 1, Subchapter C, Part 86. The CVS diluted

185

the emissions by a factor of ~10-40 using air treated by high-efficiency particulate

186

(HEPA) filters. A slipstream of the dilute exhaust from the CVS was sampled into

187

the PAM reactor through a heated, silcosteel® stainless steel transfer line,

188

maintained at ~47°C. There was no additional dilution beyond that provided by

189

the CVS.

190


9


191

Page 10 of 37

The particles that exited the PAM reactor were characterized using a

192

scanning mobility particle sizer (SMPS, TSI classifier model 3080, CPC model

193

3772 or 3776) and a high-resolution time-of-flight aerosol mass spectrometer

194

(HR-ToF-AMS, Aerodyne Research, Inc.). SMPS and AMS measurements were

195

performed for the first 1.5 min and 1.0 min every two min, respectively.

196 197

To reduce background levels, the PAM reactor was flushed with HEPA-

198

filter and activated carbon treated air with all four lamps turned on for 20 min

199

prior to each experiment. Following flushing, CVS dilution air with no exhaust

200

was drawn through the reactor for 10 minutes to determine SOA production from

201

background organics. The median SOA production from background organics

202

corresponds to 4%, 9% and 23% of the median SOA measured during bag 1, 2

203

and 3, respectively. However, this dynamic blank may underestimate the SOA

204

production from background organics during an experiment with vehicle exhaust

205

because of differences in the condensational sink of the suspended particles

206

inside the PAM reactor (see SI).

207 208

2.2.1 Estimation of OH Exposure. The integrated OH exposure inside the PAM

209

reactor was determined using off-line calibrations.30, 38 The OH exposure

210

depends strongly on the reactivity (concentration) of the vehicle exhaust inside

211

the PAM reactor.39-41 We used the measured exhaust composition and the

212

method of Peng et al.41 to estimate the OH exposure during each experiment.

213

Calculated OH exposures ranged from 1.1×109 molec cm-3 s to 1.2×1011 molec


10

Page 11 of 37


214

cm-3 s, which was about a factor of 40 lower than when the PAM reactor was not

215

operated with vehicle exhaust (3.7×1011 molec cm-3 s).

216 217

2.2.2 Effects of Gas-Phase CO2 on AMS Organic Aerosol Measurements.

218

The high CO2 concentrations in vehicle exhaust can bias the HR-ToF-AMS

219

measurement at low organic aerosol (OA) concentrations.42 To correct for this

220

interference, we determined the relationship between the CO2 mixing ratio and

221

the AMS signal of m/z 44. These measurements were made while sampling

222

gasoline vehicle exhaust through the PAM reactor with the mercury lamps turned

223

off to eliminate photo-oxidation. The resulting correction is minor given the high

224

OA concentrations during the photo-oxidation experiments, consistent with

225

Collier and Zhang.42

226 227

2.2.3 Split of POA and SOA. We determined the split between primary OA

228

(POA) and SOA during photo-oxidation experiments using the AMS measured

229

C4H9+ mass fragment (m/z 57) as the tracer for POA.43, 44 This fragment is

230

abundant in the AMS mass spectra of gasoline-vehicle POA. Presto et al.43

231

shows that the POA-SOA split for aged gasoline vehicle exhaust determined

232

using m/z 57 as the POA tracer agrees well with other estimates.

233 234

We estimated the POA concentration during the photo-oxidation

235

experiments using the measured mass concentration of m/z 57 and the average

236

fraction of m/z 57 in POA (10.6%). The SOA concentration was the difference


11


Page 12 of 37

237

between the total measured OA and the calculated POA concentration. We

238

determined the mass fraction of m/z 57 in POA (10.6%) by conducting

239

experiments with the PAM reactor mercury lamps turned-off experiments (no

240

photo-oxidation). This fraction of m/z 57 in POA was essentially constant across

241

all lights-off experiments; for example, linear regression of the mass

242

concentrations of m/z 57 to other mass fragments produced by hydrocarbons in

243

POA, such as m/z 43, 71, 85, 41, 55, 69 (SI, Figure S2), yields an R2 >0.9 across

244

the set of experiments. Furthermore, during the photo-oxidation experiments, the

245

ratios between m/z 57 and other hydrocarbon fragments, such as m/z 69 and 71,

246

remain the same as those measured during lights-off experiments, suggesting

247

that m/z 57 is a good POA tracer for gasoline vehicle exhaust.

248 249

2.2.4 Correcting for PAM reactor transit time. To associate the measured

250

SOA production with specific UC bags, we corrected the SOA data for the transit

251

time delay of the exhaust inside the PAM reactor.37 The issue is illustrated in

252

Figure 1c, which plots the measured SOA production from a typical vehicle test.

253

After the vehicle was turned off at the end of bags 2 and 3 it took approximately 3

254

min for the OA signal at the PAM reactor outlet to return to background levels.

255

This delay reflects the time it takes for the exhaust to pass through the PAM

256

reactor.

257 258 259

To better align the measured SOA production with each UC bag, we defined the bag-1 SOA production as the sum of SOA measured during bag 1


12

Page 13 of 37


260

sampling and the SOA formed in the first 3 min of bag 2 (indicated by the

261

hatched area in Figure 1c). We defined bag-2 SOA production as the SOA

262

produced starting 3 min after UC entered bag 2 until 3 min after the conclusion of

263

bag 2. Finally, we defined the bag-3 SOA production as the SOA measured

264

during bag 3 plus that produced 3 min after the conclusion of bag 3.

265 266

Correcting for the transit time delay alters the distribution of SOA

267

production by UC bag. It increases the SOA production attributed to bag 1 (cold

268

start) by about a factor of two, but only reduces the SOA production attributed to

269

bag-2 by about 10%. The simple 3-min shift used here is imperfect correction

270

because the exhaust sample experiences a distribution of transit times inside the

271

PAM reactor. 37 However, it is adequate for characterizing SOA production by UC

272

bag, but not by the many, very rapid individual changes that occur during the UC

273

(Figure 1a). Improving our understanding of the temporal variation of the SOA

274

production during transient vehicle testing will require more detailed

275

characterization of the distribution transit time inside the reactor and potentially

276

improved reactor designs.

277

2.3 Characterization of Primary Emissions

278

The gas-phase primary emissions were characterized by sampling the

279

exhaust directly from the CVS, upstream of the PAM reactor, using an AMA 4000

280

system (AVL North America, Inc.).45 Total gas-phase organics were measured by

281

flame ionization detection (FID), methane by gas chromatography-FID, NOx by

282

chemiluminescence, and CO and CO2 by nondispersive infrared detection.45 The


13


Page 14 of 37

283

gas-phase organics include both hydrocarbons and oxygenated compounds. For

284

discussion, we define the organics measured by FID as total organic gases. Non-

285

methane organic gases (NMOG) are the difference between total organic gases

286

and methane. The primary emissions data from these vehicles are described in

287

Saliba et al.17.

288 Although we speciated the volatile organic compound (VOC) emissions,

289 290

we used the average NMOG composition profile reported by a companion

291

study11 to analyze the SOA production data. Zhao et al.11 reports a much more

292

comprehensive suite of SOA precursor emissions, including intermediate

293

volatility and semi-volatile organic compounds (IVOCs and SVOCs), than

294

analyzed by this study. Zhao et al.11 also tested a substantially larger number of

295

vehicles, using the same test procedures and a very similar fuel as this study.

296

The composition of the VOC emissions measured here (hydrocarbons with

297

carbon number ≤12) agrees well with the average NMOG profile from Zhao et

298

al.11.

299

2.4 Effective SOA yields

300

The effective SOA yield of gasoline vehicle exhaust during each

301

photooxidation experiment is defined as the ratio of the measured SOA mass to

302

the calculated mass (∆M) of reacted SOA precursors,11

303

∆ = ∑[ ] × (1 − ,×[]×∆ )

(1)

304

where [HCi] is the initial concentration of the SOA precursor i (µg m-3); kOH,i is its

305

hydroxyl (OH) radical reaction rate constant (25°C, molecules cm-3); and [OH]×∆t


14

Page 15 of 37


306

is the OH exposure for each experiment calculated using the method of Peng et

307

al.41. [HCi] is calculated using the mass concentration of total NMOG measured

308

in each experiment multiplied by the average mass fraction of HCi in NMOG

309

reported in Zhao et al.11. SVOC concentrations were corrected for the gas-

310

particle partitioning using the measured OA concentration in each experiment

311

and the volatility distribution of POA emissions.46 More discussion of SOA

312

precursors and OH reaction rate constants is provided in SI.

313

2.5 Emission Factors

314

NMOG and OA emissions in this study are reported as fuel-based

315

emission factors, calculated using a fuel-carbon-mass-balance approach.26 For

316

each UC bag, the measured, background-corrected pollutant concentrations

317

were divided by the total fuel carbon in the tailpipe emissions calculated as the

318

sum of the carbon in the measured, background-corrected CO2, CO and NMOG

319

concentrations. The measured fuel-carbon fraction (0.82) was used to convert

320

fuel-carbon into mass of fuel burned.25 Our fuel-based estimates can be

321

converted to distance-based ones using the measured fuel economy for each

322

experiment in Table S1.

323

3. Results and Discussion

324

Figures 1b and 1c show time series of NMOG and OA (SOA+POA)

325

concentrations measured during a photo-oxidation experiment with a SULEV

326

vehicle. The NMOG concentration in bag 1 (cold start) is about a factor of 6

327

greater than those in bags 2 and 3 (hot operation). The POA concentration in bag


15


328

1 is also much higher than those in bags 2 and 3. These trends reflect the high

329

emissions during cold-start operation (bag 1) before the catalytic converter

330

reaches its lights-off temperature.25 However, the SOA concentrations in bag 2

331

are substantially higher than in bag 1. This is unexpected given the trends in

332

NMOG emissions; it suggests that the SOA formation in bag 2 is significantly

333

different from that in bag 1. Similar relative mass distributions of NMOG

334

emissions and SOA production by UC bag were measured across all tests

335

(Figure 2).

336

3.1 Interpreting PAM reactor data and SOA production by UC bag

Page 16 of 37

337

Figures 2a and 2b present box-whisker plots of the distributions of

338

background-corrected NMOG emissions and transit-time corrected estimates of

339

SOA production by UC bag for all tests. The median vehicle produces twice as

340

much SOA in bag 2 than bag 1 while the median NMOG emissions in bag 2 are

341

about ten times lower than in bag 1. This is potentially surprising as one might

342

expect that lower NMOG emissions should lead to less SOA production.

343

However, SOA production depends on the effective SOA yield, the concentration

344

of SOA precursors, and the extent of oxidation. In addition, only a subset of

345

NMOG emissions are SOA precursors.

346 347

The unexpected trend in SOA production versus NMOG emissions by UC

348

bag is primarily driven by how oxidation inside the PAM reactor responds to the

349

large (about a factor of ten) changes in emission rates between cold-start and hot


16

Page 17 of 37


350

operations. This complicates interpretation of PAM reactor data for vehicles

351

operated over transient test cycles like the cold-start UC.

352 353

One major effect of changing emissions rates between cold-start and hot

354

operations is that pollutant concentrations inside the PAM reactor are reduced

355

substantially during the hot operation, which, in turn, changes the extent of

356

oxidation. Figure 2c plots distributions of the OH exposure by UC bag over the

357

entire set of experiments. For the median experiment, the OH exposure in bag 2

358

is five times greater than bag 1 reflecting the changes in OH reactivity inside the

359

PAM reactor between cold-start and hot operations. SOA production increases

360

approximately linearly with the OH exposure for exposures less than 12-h

361

atmospherically equivalent oxidation.5, 30, 32, 47 Therefore, the differences in OH

362

exposures likely contribute about a factor of 5 difference in SOA production

363

between bags 1 and 2 assuming the similar amount and composition of SOA

364

precursors in these two bags. While very important, a factor of 5 is still

365

considerably less than the factor of 20 needed to explain the observed

366

differences in SOA formation between bags 1 and 2, measured by the ratio of

367

SOA production to total NMOG emissions.

368 369

Another factor contributing to the differences in bag-1 and 2 SOA

370

production is changes in the NMOG composition, specifically SOA precursor

371

emissions. Although catalyst light off dramatically reduces the total NMOG

372

emissions (Figure 1c), Zhao et al.46 finds that IVOC emissions as a fraction of


17


Page 18 of 37

373

total NMOG are enriched during the hot operation (such as bags 2 and 3)

374

compared to the cold-start operation (bag 1). IVOCs form SOA efficiently.46, 48-50

375

Therefore, enrichment of IVOCs increases the SOA formation potential of the

376

emissions, potentially increasing the SOA production in bags 2 and 3 compared

377

to bag 1.

378 379

To quantify the effects of differences in the OH exposure and NMOG

380

composition, we converted the SOA production data into effective SOA yields

381

(Figure 2d). We first calculated the effective SOA yields using the bag-specific

382

OH exposure and the cold-start NMOG composition data (i.e. not accounting for

383

differences in composition by UC bag). The median effective SOA yield of bag 1

384

is 0.2, which is similar to estimates from chamber experiments under high-NOx

385

conditions.11 However, a median effective SOA yield of 1.0 is needed to explain

386

bag-2 SOA production. This is substantially higher than yields of dilute gasoline

387

exhaust measured in chamber experiments even under low-NOx conditions.11

388 389

The second yield estimate shown in Figure 2d accounts for differences in

390

NMOG composition between cold-start and hot operations. For this estimate, we

391

include the additional IVOC emissions from hot operations measured by Zhao et

392

al. 46 in our calculation of bag-2 and 3 effective SOA yields. This means there is

393

more reacted SOA precursor mass, which reduces the bag-2 effective yield to

394

0.6, about a factor of 3 higher than the effective SOA yield for bag 1 exhaust

395

(Figure 2d). The higher bag-2 SOA yield reflects the IVOC enrichment of the


18

Page 19 of 37


396

NMOG emission during hot operations. The differences in SOA yields are not

397

due to changes in gas/particle partitioning as the median OA concentration in

398

bag 2 is less than in bag 1.

399 400

The bag 2 data may overestimate the hot-operation SOA yield due to

401

uncertainty in the correction for background organics (see SI). The median

402

contribution of background organics to total NMOG across all vehicle tests was

403

7% and 66% in bags 1 and 2, respectively. The relatively high levels of

404

background organics in bag 2 are due to a combination of the CVS dilution air

405

not being treated with activated carbon and the very low emissions during hot

406

operations, especially for vehicles certified to stringent standards. Although

407

Figure 2b shows background-corrected SOA production, we measured the

408

background SOA production using CVS dilution air without exhaust. This

409

approach likely underestimates the contribution of background organics because

410

of differences in condensational sinks (see SI). For comparison, the NMOG

411

composition data of Zhao et al. 46 suggests that the effective SOA yield of hot-

412

operation NMOG emissions is about 1.6 higher than that of cold-start emissions

413

at an OA concentration of 10 µg m-3.

414 415

The median effective SOA in bag 3 is expected to be similar to bag 2 as

416

both are hot-operations. We attribute the lower bag-3 effective SOA yield plotted

417

in Figure 2 to differences OA concentration.

418


19


419 420

Page 20 of 37

3.2 SOA production by vehicle class Given the complexity in interpreting PAM-reactor data by UC bag, we

421

focus our comparison of SOA production by vehicle class on the bag 1 data (cold

422

start). This provides a consistent set of data; the same conclusions are reached if

423

you compare data for different bags, for example using bag 2 instead of bag 1

424

data. In addition, the high emissions during cold start reduce the relative

425

importance of background organics and create a relatively high condensational

426

sink which ensures that semivolatile oxidation products reach partitioning

427

equilibrium inside the PAM reactor (Figure S3). The cold-start SOA production

428

corresponds to approximately 2 hours of atmospheric oxidation at an average

429

OH concentration of 1.5 x 106 molecules cm-3.

430 431

Figure 3 presents the measured bag-1 SOA production (Figure 3a) and

432

NMOG emissions (Figure 3b) by vehicle class. Figure 3a shows less SOA

433

production from vehicles that meet more stringent emissions standards. For

434

example, SOA production from median SULEV certified vehicle is 90% lower

435

than the median pre-LEV vehicles. The reduction is 60% from LEV to SULEV.

436

Therefore tightening NMOG emissions standards reduces SOA production,

437

which is consistent with both smog chamber studies and expectations based on

438

NMOG emission rates.11, 17 However, previous studies did not quantify SOA

439

formation from SULEV vehicles because the exhaust concentrations were too

440

low relative to the background.11

441


20

Page 21 of 37

442


Figure 3a also provides the first comparison of measured SOA production

443

between PFI and GDI vehicles across the wide range of emissions standards.

444

There are no statistically significant differences in SOA production from PFI and

445

GDI vehicles certified to the same emission standard. This suggests that the

446

ongoing, dramatic shift from PFI to GDI vehicles in the US vehicle fleet should

447

not alter atmospheric SOA production.

448 449

The reductions in the measured SOA production are moderately

450

correlated (R2 =0.4) with changes in NMOG emissions (Figure S4). This

451

underscores that tightening of NMOG emissions standards reduces SOA

452

production from gasoline vehicles effectively.

453 454

To illustrate the influence of other factors beyond NMOG emissions on

455

SOA production, Figure 4 plots the bag-1 effective SOA yields versus OA

456

concentrations. Converting the SOA production data to yields accounts for any

457

experiment-to-experiment differences in OH exposure. The effectives yields are

458

correlated to OA concentrations (R2=0.6) with higher SOA yields at higher OA

459

concentrations. We attribute this to shifts in gas/particle partitioning of

460

semivolatile organics.51

461 462

Figure 4 indicates that there are no systematic differences in effective

463

SOA yields for GDI or PFI vehicles. This confirms the hypothesis of Saliba et al.17

464

that there are no differences in SOA formation between PFI and GDI vehicles.


21


Page 22 of 37

465 466 467

3.3 Comparisons with smog chamber data Figure 4 compares the effective SOA yields measured with the PAM

468

reactor to smog chamber data from our previous study focusing on PFI

469

vehicles.11 The PAM results are consistent with the high-NOx chamber data;

470

however, the low NOx chamber experiments have much higher yields than the

471

PAM measurements. This underscores the importance of both atmospheric

472

chemistry and gas/particle partitioning on SOA formation.

473 474

Figure 4 also compares the PAM-reactor yields with published

475

parameterizations of SOA formation from single ring aromatics and n-alkanes

476

derived from smog chamber experiments (see SI). The PAM-reactor yields are

477

more sensitive to the OA concentration than chamber based parameterizations,

478

suggesting that SOA formed in the PAM reactor is more volatile than the

479

published parameterizations.

480 481

The apparent inconsistency between our measurements and single

482

compound data could be due to many factors. For example, chamber

483

experiments indicate that the SOA volatility varies widely by compound.52, 53

484

However, SOA parameterizations are only available for a small subset of the

485

organics in vehicle exhaust.48, 50, 53 Therefore the published parameterizations for

486

single compounds might not represent SOA formation from the complex mixture

487

of gasoline vehicle exhaust. In addition, the higher surface area/condensational


22

Page 23 of 37


488

sinks of suspended particles in the PAM reactor experiments promotes

489

condensation of organic vapors, potentially resulting in less oxidized SOA.54

490

Future studies are needed to investigate the relationship of the SOA volatility and

491

chemical evolution of real emissions with the OH exposure and condensational

492

sink of suspended particles.

493

4. Implications

494

The PAM reactor, as well as other OFRs, can be used to characterize

495

SOA formation from motor vehicle exhaust, including identification of high-

496

emitters, comparison of vehicles equipped with different engine and

497

aftertreatment technologies, and quantification of the effects of vehicle operations.

498

However, the SOA yield analysis presented in Figures 2 and 4 highlight the

499

complexity of interpreting the PAM-reactor data and other measurements of SOA

500

formation. The data illustrate the significant effects of OH reactivity, chemical

501

composition of SOA precursors, OA concentrations, and condensational sinks of

502

suspended particles on SOA production. One can draw robust conclusions

503

among a set of vehicles by carefully accounting for these factors. Interpretation is

504

especially challenging for transient vehicle testing, which often features very

505

large changes in pollutant emission rates between cold start and hot operations.

506

Future OFR studies of vehicle exhaust should consider using additional dilution

507

to increase the OH exposure of cold-start emissions and activated carbon treated

508

dilution air in the CVS to reduce the contribution of background organics during

509

hot operations.


23


Page 24 of 37

510 511

Our results indicate that SOA production from both PFI and GDI vehicles

512

has been substantially reduced through the tightening of NMOG emissions

513

standards. Our SULEV data suggest that this trend will continue with the

514

implementation of the new federal Tier 3 and California LEV III standards. In

515

addition, we find no difference in SOA production between PFI and GDI vehicles

516

certified to the same standard. This suggests that the ongoing, dramatic shift

517

from PFI to GDI vehicles in the US vehicle fleet should not alter SOA production.

518

However, SOA production also depends on atmospheric conditions, specifically

519

the NOx regime (Figure 4); therefore only tightening NMOG emissions standards

520

may not be enough to reduce urban SOA levels.11

521 522

We find that the effective SOA yields of hot operations are about a factor

523

of 3 higher than cold-start operations. However, cold-start emissions contribute

524

90% of the total UC NMOG emissions. Therefore, we expect cold-start emissions

525

contribute the majority of the SOA production from the UC cycle. Although this

526

conclusion appears to contradict the measured distribution of SOA production

527

shown in Figure 2, one must account for differences in oxidant exposure, OA

528

concentration, and precursor concentrations.

529 530

Following the approach of Saliba et al.17 (see equation (2) in Saliba et

531

al.17), we estimated the distance that must be traveled under hot operations to

532

match the SOA production from one cold start. The analysis requires multiplying


24

Page 25 of 37


533

the bag-1 and bag-2 SOA precursor emission rates by their corresponding

534

effective SOA yields. The median vehicle tested here must be driven about 30

535

miles to match the SOA production from one cold start. Given that we likely

536

overestimate the bag-2 effective SOA yield (see SI), 30 miles may be a lower

537

bound. As a reference, the daily average trip length in the United States is 9.7

538

miles.55 Therefore, if the UC is representative of US driving patterns, specifically

539

commuting, and if our test fleet is representative of the in-use fleet, then our

540

results indicate that the majority of SOA formation from gasoline vehicle exhaust

541

is from cold-start emissions.

542 543

Supporting Information.

544 545 546 547

Estimation of effective SOA yields, condensational sinks of suspended particles and background correction, and six figures and one table.

548

The authors declare no competing financial interest.

Notes


25


Page 26 of 37

549

Acknowledgements:

550 551 552 553 554 555 556 557 558

The authors would like to thank the excellent and dedicated personnel at the California Air Resources Board, especially at the Haagen–Smit Laboratory. Financial support was provided by the California Air Resources Board (Contract #12-318 and #14-345) and US Environmental Protection Agency (Assistance Agreement RD83587301). The California Air Resources Board also provided substantial in-kind support for vehicle procurement, testing, and emissions characterization. A. T. Lambe acknowledges support from the Atmospheric Chemistry Program of the National Science Foundation under grant AGS1537446. The views, opinions, and/or findings contained in this paper are those of the authors and should not be construed as an official position of the funding agencies.

559

References:

560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591

1. Hallquist, M.; Wenger, J. C.; Baltensperger, U.; Rudich, Y.; Simpson, D.; Claeys, M.; Dommen, J.; Donahue, N. M.; George, C.; Goldstein, A. H.; Hamilton, J. F.; Herrmann, H.; Hoffmann, T.; Iinuma, Y.; Jang, M.; Jenkin, M. E.; Jimenez, J. L.; Kiendler-Scharr, A.; Maenhaut, W.; McFiggans, G.; Mentel, T. F.; Monod, A.; Prevot, A. S. H.; Seinfeld, J. H.; Surratt, J. D.; Szmigielski, R.; Wildt, J., The formation, properties and impact of secondary organic aerosol: current and emerging issues. Atmos. Chem. Phys. 2009, 9, (14), 5155-5236. 2. HEI Health Effects Institute. State of Global Air 2017. Special Report.; Boston MA: Health Effects Institiute, 2017. 3. Jimenez, J. L.; Canagaratna, M. R.; Donahue, N. M.; Prevot, A. S. H.; Zhang, Q.; Kroll, J. H.; DeCarlo, P. F.; Allan, J. D.; Coe, H.; Ng, N. L.; Aiken, A. C.; Docherty, K. S.; Ulbrich, I. M.; Grieshop, A. P.; Robinson, A. L.; Duplissy, J.; Smith, J. D.; Wilson, K. R.; Lanz, V. A.; Hueglin, C.; Sun, Y. L.; Tian, J.; Laaksonen, A.; Raatikainen, T.; Rautiainen, J.; Vaattovaara, P.; Ehn, M.; Kulmala, M.; Tomlinson, J. M.; Collins, D. R.; Cubison, M. J.; Dunlea, E. J.; Huffman, J. A.; Onasch, T. B.; Alfarra, M. R.; Williams, P. I.; Bower, K.; Kondo, Y.; Schneider, J.; Drewnick, F.; Borrmann, S.; Weimer, S.; Demerjian, K.; Salcedo, D.; Cottrell, L.; Griffin, R.; Takami, A.; Miyoshi, T.; Hatakeyama, S.; Shimono, A.; Sun, J. Y.; Zhang, Y. M.; Dzepina, K.; Kimmel, J. R.; Sueper, D.; Jayne, J. T.; Herndon, S. C.; Trimborn, A. M.; Williams, L. R.; Wood, E. C.; Middlebrook, A. M.; Kolb, C. E.; Baltensperger, U.; Worsnop, D. R., Evolution of Organic Aerosols in the Atmosphere. Science 2009, 326, (5959), 1525-1529. 4. Bahreini, R.; Middlebrook, A. M.; de Gouw, J. A.; Warneke, C.; Trainer, M.; Brock, C. A.; Stark, H.; Brown, S. S.; Dube, W. P.; Gilman, J. B.; Hall, K.; Holloway, J. S.; Kuster, W. C.; Perring, A. E.; Prevot, A. S. H.; Schwarz, J. P.; Spackman, J. R.; Szidat, S.; Wagner, N. L.; Weber, R. J.; Zotter, P.; Parrish, D. D., Gasoline emissions dominate over diesel in formation of secondary organic aerosol mass. Geophys. Res. Lett. 2012, 39, L06805. 5. Hayes, P. L.; Ortega, A. M.; Cubison, M. J.; Froyd, K. D.; Zhao, Y.; Cliff, S. S.; Hu, W. W.; Toohey, D. W.; Flynn, J. H.; Lefer, B. L.; Grossberg, N.; Alvarez, S.; Rappenglueck, B.; Taylor, J. W.; Allan, J. D.; Holloway, J. S.; Gilman, J. B.; Kuster, W. C.; De Gouw, J. A.; Massoli, P.; Zhang, X.; Liu, J.; Weber, R. J.; Corrigan, A. L.; Russell, L. M.; Isaacman, G.; Worton, D. R.; Kreisberg, N. M.; Goldstein, A. H.;


26

Page 27 of 37

592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637


Thalman, R.; Waxman, E. M.; Volkamer, R.; Lin, Y. H.; Surratt, J. D.; Kleindienst, T. E.; Offenberg, J. H.; Dusanter, S.; Griffith, S.; Stevens, P. S.; Brioude, J.; Angevine, W. M.; Jimenez, J. L., Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign. J. Geophys. Res. 2013, 118, (16), 9233-9257. 6. Jathar, S. H.; Woody, M.; Pye, H. O. T.; Baker, K. R.; Robinson, A. L., Chemical transport model simulations of organic aerosol in southern California: model evaluation and gasoline and diesel source contributions. Atmos. Chem. Phys. 2017, 17, (6), 43054318. 7. Platt, S. M.; El Haddad, I.; Pieber, S. M.; Zardini, A. A.; Suarez-Bertoa, R.; Clairotte, M.; Daellenbach, K. R.; Huang, R. J.; Slowik, J. G.; Hellebust, S.; TemimeRoussel, B.; Marchand, N.; de Gouw, J.; Jimenez, J. L.; Hayes, P. L.; Robinson, A. L.; Baltensperger, U.; Astorga, C.; Prevot, A. S. H., Gasoline cars produce more carbonaceous particulate matter than modern filter-equipped diesel cars. Scientific Reports 2017, 7. 8. Gentner, D. R.; Jathar, S. H.; Gordon, T. D.; Bahreini, R.; Day, D. A.; El Haddad, I.; Hayes, P. L.; Pieber, S. M.; Platt, S. M.; de Gouw, J.; Goldstein, A. H.; Harley, R. A.; Jimenez, J. L.; Prevot, A. S. H.; Robinson, A. L., Review of Urban Secondary Organic Aerosol Formation from Gasoline and Diesel Motor Vehicle Emissions. Environ. Sci. Technol. 2017, 51, (3), 1074-1093. 9. Gordon, T. D.; Presto, A. A.; May, A. A.; Nguyen, N. T.; Lipsky, E. M.; Donahue, N. M.; Gutierrez, A.; Zhang, M.; Maddox, C.; Rieger, P.; Chattopadhyay, S.; Maldonado, H.; Maricq, M. M.; Robinson, A. L., Secondary organic aerosol formation exceeds primary particulate matter emissions for light-duty gasoline vehicles. Atmos. Chem. Phys. 2014, 14, (9), 4661-4678. 10. Platt, S. M.; El Haddad, I.; Zardini, A. A.; Clairotte, M.; Astorga, C.; Wolf, R.; Slowik, J. G.; Temime-Roussel, B.; Marchand, N.; Jezek, I.; Drinovec, L.; Mocnik, G.; Mohler, O.; Richter, R.; Barmet, P.; Bianchi, F.; Baltensperger, U.; Prevot, A. S. H., Secondary organic aerosol formation from gasoline vehicle emissions in a new mobile environmental reaction chamber. Atmos. Chem. Phys. 2013, 13, (18), 9141-9158. 11. Zhao, Y. L.; Saleh, R.; Saliba, G.; Presto, A. A.; Gordon, T. D.; Drozd, G. T.; Goldstein, A. H.; Donahue, N. M.; Robinson, A. L., Reducing secondary organic aerosol formation from gasoline vehicle exhaust. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, (27), 6984-6989. 12. Nordin, E. Z.; Eriksson, A. C.; Roldin, P.; Nilsson, P. T.; Carlsson, J. E.; Kajos, M. K.; Hellen, H.; Wittbom, C.; Rissler, J.; Londahl, J.; Swietlicki, E.; Svenningsson, B.; Bohgard, M.; Kulmala, M.; Hallquist, M.; Pagels, J. H., Secondary organic aerosol formation from idling gasoline passenger vehicle emissions investigated in a smog chamber. Atmos. Chem. Phys. 2013, 13, (12), 6101-6116. 13. Timonen, H.; Karjalainen, P.; Saukko, E.; Saarikoski, S.; Aakko-Saksa, P.; Simonen, P.; Murtonen, T.; Dal Maso, M.; Kuuluvainen, H.; Bloss, M.; Ahlberg, E.; Svenningsson, B.; Pagels, J.; Brune, W. H.; Keskinen, J.; Worsnop, D. R.; Hillamo, R.; Ronkko, T., Influence of fuel ethanol content on primary emissions and secondary aerosol formation potential for a modern flex-fuel gasoline vehicle. Atmos. Chem. Phys. 2017, 17, (8), 5311-5329. 14. Karjalainen, P.; Timonen, H.; Saukko, E.; Kuuluvainen, H.; Saarikoski, S.; Aakko-Saksa, P.; Murtonen, T.; Bloss, M.; Dal Maso, M.; Simonen, P.; Ahlberg, E.;


27


638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682

Page 28 of 37

Svenningsson, B.; Brune, W. H.; Hillamo, R.; Keskinen, J.; Ronkko, T., Time-resolved characterization of primary particle emissions and secondary particle formation from a modern gasoline passenger car. Atmos. Chem. Phys. 2016, 16, (13), 8559-8570. 15. Davis, S. C.; Williams, S. E.; Boundy, R. G.; Moore, S. 2015 Vehicle Technologies Market Report.; https://www.osti.gov/scitech/biblio/1255689-vehicletechnologies-market-report, 2016. 16. USEPA; USDOT, United States Environmental Protection Agency and Department of Transportation. 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards. Fed. Regist. 2012, 77, (199), 62623–63200. 17. Saliba, G.; Saleh, R.; Zhao, Y.; Presto, A. A.; Lambe, A. T.; Frodin, B.; Sardar, S.; Maldonado, H.; Maddox, C.; May, A. A.; Drozd, G. T.; Goldstein, A. H.; Russell, L. M.; Hagen, F.; Robinson, A. L., Comparison of Gasoline Direct-Injection (GDI) and Port Fuel Injection (PFI) Vehicle Emissions: Emission Certification Standards, Cold-Start, Secondary Organic Aerosol Formation Potential, and Potential Climate Impacts. Environ. Sci. Technol. 2017, 51, (11), 6542-6552. 18. Zimmerman, N.; Wang, J. M.; Jeong, C. H.; Ramos, M.; Hilker, N.; Healy, R. M.; Sabaliauskas, K.; Wallace, J. S.; Evans, G. J., Field Measurements of Gasoline Direct Injection Emission Factors: Spatial and Seasonal Variability. Environ. Sci. Technol. 2016, 50, (4), 2035-2043. 19. Bahreini, R.; Xue, J.; Johnson, K.; Durbin, T.; Quiros, D.; Hu, S.; Huai, T.; Ayala, A.; Jung, H., Characterizing emissions and optical properties of particulate matter from PFI and GDI light-duty gasoline vehicles. J. Aerosol Sci. 2015, 90, 144-153. 20. Fushimi, A.; Kondo, Y.; Kobayashi, S.; Fujitani, Y.; Saitoh, K.; Takami, A.; Tanabe, K., Chemical Composition and Source of Fine and Nanoparticles from Recent Direct Injection Gasoline Passenger Cars: Effects of Fuel and Ambient Temperature. Atmos. Environ. 2016, 124, 77-84. 21. Khalek, I. A.; Bougher, T.; Jetter, J. J., Particle Emissions from a 2009 Gasoline Direct Injection Engine Using Different Commercially Available Fuels. SAE Int. J. Fuels Lubr 2010, 3, 623-637. 22. Liang, B.; Ge, Y.; Tan, J.; Han, X.; Gao, L.; Hao, L.; Ye, W.; Dai, P., Comparison of PM Emissions from a Gasoline Direct Injected (GDI) Vehicle and a Port Fuel Injected (PFI) Vehicle Measured by Electrical Low Pressure Impactor (ELPI) with Two Fuels: Gasoline and M15 Methanol Gasoline. . J. Aerosol Sci. 2013, 57, 22-31. 23. Cole, R. L.; Poola, R. B.; Sekar, R., Exhaust Emissions of a Vehicle with a Gasoline Direct-Injection Engine. SAE Tech. Pap. 1998, doi: 10.4271/982605. 24. Myung, C. L.; Choi, K.; Kim, J.; Lim, Y.; Lee, J.; Park, S., Comparative Study of Regulated and Unregulated Toxic Emissions Characteristics from a Spark Ignition Direct Injection Light-Duty Vehicle Fueled with Gasoline and Liquid Phase LPG (Liquefied Petroleum Gas). Energy 2012, 44, 189-196. 25. Drozd, G. T.; Zhao, Y. L.; Saliba, G.; Frodin, B.; Maddox, C.; Weber, R. J.; Chang, M. C. O.; Maldonado, H.; Sardar, S.; Robinson, A. L.; Goldstein, A. H., Time Resolved Measurements of Speciated Tailpipe Emissions from Motor Vehicles: Trends with Emission Control Technology, Cold Start Effects, and Speciation. Environ. Sci. Technol. 2016, 50, (24), 13592-13599.


28

Page 29 of 37

683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727


26. May, A. A.; Nguyen, N. T.; Presto, A. A.; Gordon, T. D.; Lipsky, E. M.; Karve, M.; Gutierrez, A.; Robertson, W. H.; Zhang, M.; Brandow, C.; Chang, O.; Chen, S. Y.; Cicero-Fernandez, P.; Dinkins, L.; Fuentes, M.; Huang, S. M.; Ling, R.; Long, J.; Maddox, C.; Massetti, J.; McCauley, E.; Miguel, A.; Na, K.; Ong, R.; Pang, Y. B.; Rieger, P.; Sax, T.; Truong, T.; Vo, T.; Chattopadhyay, S.; Maldonado, H.; Maricq, M. M.; Robinson, A. L., Gas- and particle-phase primary emissions from in-use, on-road gasoline and diesel vehicles. Atmos. Environ. 2014, 88, 247-260. 27. Gentner, D. R.; Isaacman, G.; Worton, D. R.; Chan, A. W. H.; Dallmann, T. R.; Davis, L.; Liu, S.; Day, D. A.; Russell, L. M.; Wilson, K. R.; Weber, R.; Guha, A.; Harley, R. A.; Goldstein, A. H., Elucidating secondary organic aerosol from diesel and gasoline vehicles through detailed characterization of organic carbon emissions. Proc. Natl. Acad. Sci. U. S. A. 2012, 109, (45), 18318-18323. 28. Simonen, P.; Saukko, E.; Karjalainen, P.; Timonen, H.; Bloss, M.; Aakko-Saksa, P.; Ronkko, T.; Keskinen, J.; Dal Maso, M., A new oxidation flow reactor for measuring secondary aerosol formation of rapidly changing emission sources. Atmospheric Measurement Techniques 2017, 10, (4), 1519-1537. 29. Lambe, A. T.; Onasch, T. B.; Croasdale, D. R.; Wright, J. P.; Martin, A. T.; Franklin, J. P.; Massoli, P.; Kroll, J. H.; Canagaratna, M. R.; Brune, W. H.; Worsnop, D. R.; Davidovits, P., Transitions from Functionalization to Fragmentation Reactions of Laboratory Secondary Organic Aerosol (SOA) Generated from the OH Oxidation of Alkane Precursors. Environ. Sci. Technol. 2012, 46, (10), 5430-5437. 30. Tkacik, D. S.; Lambe, A. T.; Jathar, S.; Li, X.; Presto, A. A.; Zhao, Y. L.; Blake, D.; Meinardi, S.; Jayne, J. T.; Croteau, P. L.; Robinson, A. L., Secondary Organic Aerosol Formation from in-Use Motor Vehicle Emissions Using a Potential Aerosol Mass Reactor. Environ. Sci. Technol. 2014, 48, (19), 11235-11242. 31. Jathar, S. H.; Friedman, B.; Galang, A. A.; Link, M. F.; Brophy, P.; Volckens, J.; Eluri, S.; Farmer, D. K., Linking Load, Fuel, and Emission Controls to Photochemical Production of Secondary Organic Aerosol from a Diesel Engine. Environ. Sci. Technol. 2017, 51, (3), 1377-1386. 32. Ortega, A. M.; Hayes, P. L.; Peng, Z.; Palm, B. B.; Hu, W. W.; Day, D. A.; Li, R.; Cubison, M. J.; Brune, W. H.; Graus, M.; Warneke, C.; Gilman, J. B.; Kuster, W. C.; de Gouw, J.; Gutierrez-Montes, C.; Jimenez, J. L., Real-time measurements of secondary organic aerosol formation and aging from ambient air in an oxidation flow reactor in the Los Angeles area. Atmos. Chem. Phys. 2016, 16, (11), 7411-7433. 33. Palm, B. B.; Campuzano-Jost, P.; Ortega, A. M.; Day, D. A.; Kaser, L.; Jud, W.; Karl, T.; Hansel, A.; Hunter, J. F.; Cross, E. S.; Kroll, J. H.; Peng, Z.; Brune, W. H.; Jimenez, J. L., In situ secondary organic aerosol formation from ambient pine forest air using an oxidation flow reactor. Atmos. Chem. Phys. 2016, 16, (5), 2943-2970. 34. Bahreini, R.; Middlebrook, A. M.; Brock, C. A.; de Gouw, J. A.; McKeen, S. A.; Williams, L. R.; Daumit, K. E.; Lambe, A. T.; Massoli, P.; Canagaratna, M. R.; Ahmadov, R.; Carrasquillo, A. J.; Cross, E. S.; Ervens, B.; Holloway, J. S.; Hunter, J. F.; Onasch, T. B.; Pollack, I. B.; Roberts, J. M.; Ryerson, T. B.; Warneke, C.; Davidovits, P.; Worsnop, D. R.; Kroll, J. H., Mass Spectral Analysis of Organic Aerosol Formed Downwind of the Deepwater Horizon Oil Spill: Field Studies and Laboratory Confirmations. Environ. Sci. Technol. 2012, 46, (15), 8025-8034.


29


728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772

Page 30 of 37

35. Bruns, E. A.; El Haddad, I.; Keller, A.; Klein, F.; Kumar, N. K.; Pieber, S. M.; Corbin, J. C.; Slowik, J. G.; Brune, W. H.; Baltensperger, U.; Prevot, A. S. H., Intercomparison of laboratory smog chamber and flow reactor systems on organic aerosol yield and composition. Atmospheric Measurement Techniques 2015, 8, (6), 2315-2332. 36. Zhang, S.; McMahon, W., Particulate Emissions for LEV II Light-Duty Gasoline Direct Injection Vehicles. SAE Int. J. Fuels Lubr 2012, 5, 637-646. 37. Lambe, A. T.; Ahern, A. T.; Williams, L. R.; Slowik, J. G.; Wong, J. P. S.; Abbatt, J. P. D.; Brune, W. H.; Ng, N. L.; Wright, J. P.; Croasdale, D. R.; Worsnop, D. R.; Davidovits, P.; Onasch, T. B., Characterization of aerosol photooxidation flow reactors: heterogeneous oxidation, secondary organic aerosol formation and cloud condensation nuclei activity measurements. Atmospheric Measurement Techniques 2011, 4, (3), 445-461. 38. Kang, E.; Root, M. J.; Toohey, D. W.; Brune, W. H., Introducing the concept of Potential Aerosol Mass (PAM). Atmos. Chem. Phys. 2007, 7, (22), 5727-5744. 39. Li, R.; Palm, B. B.; Ortega, A. M.; Hlywiak, J.; Hu, W. W.; Peng, Z.; Day, D. A.; Knote, C.; Brune, W. H.; de Gouw, J. A.; Jimenez, J. L., Modeling the Radical Chemistry in an Oxidation Flow Reactor: Radical Formation and Recycling, Sensitivities, and the OH Exposure Estimation Equation. J. Phys. Chem. A 2015, 119, (19), 4418-4432. 40. Peng, Z.; Day, D. A.; Stark, H.; Li, R.; Lee-Taylor, J.; Palm, B. B.; Brune, W. H.; Jimenez, J. L., HOx radical chemistry in oxidation flow reactors with low-pressure mercury lamps systematically examined by modeling. Atmospheric Measurement Techniques 2015, 8, (11), 4863-4890. 41. Peng, Z.; Day, D. A.; Ortega, A. M.; Palm, B. B.; Hu, W. W.; Stark, H.; Li, R.; Tsigaridis, K.; Brune, W. H.; Jimenez, J. L., Non-OH chemistry in oxidation flow reactors for the study of atmospheric chemistry systematically examined by modeling. Atmos. Chem. Phys. 2016, 16, (7), 4283-4305. 42. Collier, S.; Zhang, Q., Gas-Phase CO2 Subtraction for Improved Measurements of the Organic Aerosol Mass Concentration and Oxidation Degree by an Aerosol Mass Spectrometer. Environ. Sci. Technol. 2013, 47, (24), 14324-14331. 43. Presto, A. A.; Gordon, T. D.; Robinson, A. L., Primary to secondary organic aerosol: evolution of organic emissions from mobile combustion sources. Atmos. Chem. Phys. 2014, 14, (10), 5015-5036. 44. Sage, A. M.; Weitkamp, E. A.; Robinson, A. L.; Donahue, N. M., Evolving mass spectra of the oxidized component of organic aerosol: results from aerosol mass spectrometer analyses of aged diesel emissions. Atmos. Chem. Phys. 2008, 8, (5), 11391152. 45. USGPO, U.S. Government Publishing Office, Electronic Code of Federal Regulations: Title 40, Chapter 1, Subchapter C, Part 86: Control of Emissions From New and In-use Highway Vehicles and Engines. In http://www.ecfr.gov/cgi-bin/textidx?SID=c56ff4e0ab7f442c7e8babf29cc6e4c2&mc=true&node=pt40.19.86&rgn=div5, 2014. 46. Zhao, Y.; Nguyen, N. T.; Presto, A. A.; Hennigan, C. J.; May, A. A.; Robinson, A. L., Intermediate Volatility Organic Compound Emissions from On-Road Gasoline Vehicles and Small Off-Road Gasoline Engines. Environ. Sci. Technol. 2016, 50, 45544563.


30

Page 31 of 37

773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808


47. de Gouw, J. A.; Brock, C. A.; Atlas, E. L.; Bates, T. S.; Fehsenfeld, F. C.; Goldan, P. D.; Holloway, J. S.; Kuster, W. C.; Lerner, B. M.; Matthew, B. M.; Middlebrook, A. M.; Onasch, T. B.; Peltier, R. E.; Quinn, P. K.; Senff, C. J.; Stohl, A.; Sullivan, A. P.; Trainer, M.; Warneke, C.; Weber, R. J.; Williams, E. J., Sources of particulate matter in the northeastern United States in summer: 1. Direct emissions and secondary formation of organic matter in urban plumes. J. Geophys. Res. 2008, 113, (D8). 48. Chan, A. W. H.; Kautzman, K. E.; Chhabra, P. S.; Surratt, J. D.; Chan, M. N.; Crounse, J. D.; Kurten, A.; Wennberg, P. O.; Flagan, R. C.; Seinfeld, J. H., Secondary organic aerosol formation from photooxidation of naphthalene and alkylnaphthalenes: implications for oxidation of intermediate volatility organic compounds (IVOCs). Atmos. Chem. Phys. 2009, 9, (9), 3049-3060. 49. Lim, Y. B.; Ziemann, P. J., Effects of Molecular Structure on Aerosol Yields from OH Radical-Initiated Reactions of Linear, Branched, and Cyclic Alkanes in the Presence of NOx. Environ. Sci. Technol. 2009, 43, (7), 2328–2334. 50. Presto, A. A.; Miracolo, M. A.; Donahue, N. M.; Robinson, A. L., Secondary Organic Aerosol Formation from High-NOx Photo-Oxidation of Low Volatility Precursors: n-Alkanes. Environ. Sci. Technol. 2010, 44, (6), 2029-2034. 51. Donahue, N. M.; Robinson, A. L.; Stanier, C. O.; Pandis, S. N., Coupled partitioning, dilution, and chemical aging of semivolatile organics. Environ. Sci. Technol. 2006, 40, (8), 2635-2643. 52. Carlton, A. G.; Bhave, P. V.; Napelenok, S. L.; Edney, E. D.; Sarwar, G.; Pinder, R. W.; Pouliot, G. A.; Houyoux, M., Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environ. Sci. Technol. 2010, 44, (22), 8553-8560. 53. Ng, N. L.; Kroll, J. H.; Chan, A. W. H.; Chhabra, P. S.; Flagan, R. C.; Seinfeld, J. H., Secondary organic aerosol formation from m-xylene, toluene, and benzene. Atmos. Chem. Phys. 2007, 7, (14), 3909-3922. 54. Lambe, A. T.; Chhabra, P. S.; Onasch, T. B.; Brune, W. H.; Hunter, J. F.; Kroll, J. H.; Cummings, M. J.; Brogan, J. F.; Parmar, Y.; Worsnop, D. R.; Kolb, C. E.; Davidovits, P., Effect of oxidant concentration, exposure time, and seed particles on secondary organic aerosol chemical composition and yield. Atmos. Chem. Phys. 2015, 15, (6), 3063-3075. 55. FHA, United States Department of Transportation Summary of Travel Trends: 2009 National Household Travel Survey. http://nhts.ornl.gov/download.shtml%5Cnhttp://scholar.google.com/scholar?hl=en& btnG=Search&q=intitle:2009+National+Household+Travel+Survey#9


31


Page 32 of 37

809

Figures

810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852

Figure 1. Results from a typical experiment. (a) Driving speed for the UC with vertical dashed lines indicating the different UC driving phases, defined as bags 1, 2 and 3; (b) concentrations of NMOG entering the PAM reactor and (c) concentrations of OA (SOA +POA) exiting the reactor during a photo-oxidation experiment with a SULEV vehicle. The concentrations of NMOG, OA, SOA and POA presented here are not corrected for background organics. OA concentrations were measured every other minute for one minute. We assume that the 1-min average represents the OA concentration in the previous and subsequent 30 s of the measurement. SOA production measured in the first 180 s in bag 2 (hatched bars) is considered as SOA production from the exhaust from the bag 1 due to the transit time delay inside the PAM reactor. Figure 2. Box-whisker plots from all PAM experiments of (a) NMOG emissions, (b) SOA production, and (c) OH exposure by UC bag. (d) Median effective SOA yield by UC bag. Results in (a) and (b) are expressed as mass fraction of total NMOG emissions or SOA production across the entire UC cycle. The atmospheric equivalent aging time in (c) is calculated assuming an average OH concentration of 1.5×106 molecules cm-3. The effective SOA yield in each bag in (d) is shown for two cases: 1) differences in OH exposure (grey bar) and (2) differences in OH exposure and chemical composition (blue bar). The boxes represent the 25th and 75th percentiles with the centerline being the median. The whiskers represent the 10th and 90th percentiles.

Figure 3. Box-whisker plots of the cold start (bag 1) (a) SOA production and (b) NMOG emissions sorted by vehicle emission certification standard. The number of vehicles in each category (the number of GDI vehicles in parentheses) is 1(0), 3 (1), 5 (2), 7(5) for pre-LEV, LEV, ULEV and SULEV, respectively. Symbols indicate the average ± one standard deviation for GDI and PFI vehicles in each category. The boxes represent the 25th and 75th percentiles with the centerline being the median. The whiskers represent the 10th and 90th percentiles. Figure 4. Scatter plot of effective SOA yields versus OA concentration measured during the cold start operation (bag 1). The dashed line is a linear regression (y=0.0038*x; R2=0.6). The average effective SOA yields and OA concentrations (± one standard deviation) for pre-LEV, LEV and ULEV vehicles measured during smog chamber experiments are also shown for comparison11. The thick grey line indicates the relationship between the effective SOA yields and OA concentration using published parameterizations derived from chamber experiments with individual aromatic compounds and n-alkanes. The thickness of the grey line indicates the variation in the predicted SOA yields due to differences in OH exposures -- the thicker the line the more variation. For the table of content use only


32

Page 33 of 37


Figure 1. Results from a typical experiment. (a) Driving speed for the UC with vertical dashed lines indicating the UC driving phases, defined as bag 1, 2 and 3; (b) concentrations of NMOG entering the PAM reactor and (c) concentrations of OA (SOA +POA) exiting the reactor during a photo-oxidation experiment with a SULEV vehicle. The concentrations of NMOG, OA, SOA and POA presented here are not corrected for background organics. OA concentrations were measured every other minute for one minute. We assume that the 1-min average of OA represents the OA concentration in the previous and subsequent 30 s of the measurement. SOA production measured in the first 180 s in the bag 2 (hatched bars) is considered as SOA production from the exhaust from the bag 1 due to the transit time delay inside the PAM reactor. 206x169mm (300 x 300 DPI)



Figure 2. Box-whisker plots from all PAM experiments of (a) NMOG emissions, (b) SOA production, and (c) OH exposure by UC bag. (d) Median effective SOA yield by UC bag. Results in (a) and (b) are expressed as mass fraction of total NMOG emissions or SOA production across the entire UC cycle. The atmospheric equivalent aging time in (c) is calculated assuming an average OH concentration of 1.5×106 molecules cm3. The effective SOA yield in each bag in (d) is shown for two cases: 1) differences in OH exposure (grey bar) and (2) differences in OH exposure and chemical composition (blue bar). The boxes represent the 25th and 75th percentiles with the centerline being the median. The whiskers represent the 10th and 90th percentiles. 192x139mm (300 x 300 DPI)


Page 34 of 37

Page 35 of 37


Figure 3. Box-whisker plots of the cold start (bag 1) (a) SOA production and (b) NMOG emissions sorted by vehicle emission standard. The number of vehicles in each category (the number of GDI vehicles in parentheses) is 1(0), 3 (1), 5 (2), 7(5) for pre-LEV, LEV, ULEV and SULEV, respectively. Symbols indicate the average ± one standard deviation for GDI and PFI vehicles in each category. The boxes represent the 25th and 75th percentiles with the centerline being the median. The whiskers represent the 10th and 90th percentiles. 70x105mm (300 x 300 DPI)



Figure 4. Scatter plot of effective SOA yields versus OA concentration measured during the cold start operation (bag 1). The dashed line is a linear regression (y=0.0038*x; R2=0.6). The average effective SOA yields and OA concentrations (± one standard deviation) for pre-LEV, LEV and ULEV vehicles measured during smog chamber experiments are also shown for comparison11. The thick grey line indicates the relationship between the effective SOA yields and OA concentration using published parameterizations derived from chamber experiments with individual aromatic compounds and n-alkanes. The thickness of the grey line indicates the variation in the predicted SOA yields due to differences in OH exposures -- the thicker the line the more variation. 132x143mm (300 x 300 DPI)


Page 36 of 37

Page 37 of 37


For the table of content use only 76x70mm (300 x 300 DPI)


Secondary Organic Aerosol Production from ... - ACS Publications

Recommend Documents