Secondary Organic Aerosol Production from Gasoline Vehicle Exhaust

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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

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Environmental Science & Technology

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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

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

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

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

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

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

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

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

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

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yields across different engine technologies and certification standards. Therefore

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

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

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replacement of older vehicles with newer ones certified to stricter emissions

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

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

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overall because of substantially higher organic gas emissions. We demonstrate

ACS Paragon Plus Environment

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

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

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

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

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

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

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

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

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

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sources.8 This uncertainty complicates the development of effective control

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strategies to reduce human exposure to fine particulate matter.

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Recent studies have investigated SOA formation from dilute gasoline

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

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

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

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

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

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vehicles certified to more stringent emissions produce less SOA than vehicles

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

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emissions.9, 11

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


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to changing engine technology and emissions certification standards. California

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

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

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

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

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

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

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

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

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

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

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SOA production from vehicles certified to the most stringent existing

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

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

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

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

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models.8, 11

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SOA formation from dilute exhaust from PFI vehicles has been extensively

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

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studies have also characterized the primary emissions from GDI vehicles,

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


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

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characterized SOA production from GDI vehicles. One study did not

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quantitatively measure SOA production from the GDI vehicles11 and the other

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

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

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

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

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

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The published chemical composition data of NMOG emissions may

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

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

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

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

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

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

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

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systematic differences in SOA precursor emissions between GDI and PFI

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

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

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

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Our understanding of SOA formation from gasoline vehicle exhaust, such

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

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smog chamber experiments. Smog chamber experiments are complex and time

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

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

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

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

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routinely assess the SOA formation from gasoline vehicle exhaust.

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

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

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

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

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

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

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

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operated over transient cycles sampled using the constant volume samplers –

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the approached used for vehicle certification testing.

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

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

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

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


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

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

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

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

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

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al.17 and Drozd et al.25.

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2. Methods

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2.1 Test fleet, fuel and test cycle

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

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PAM reactor during chassis dynamometer testing at the California Air Resources

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

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

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

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

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discussion, vehicles are categorized by emissions certification standard (in

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

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

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

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

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remaining vehicles in each category being PFI vehicles.

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Although we categorized vehicles based on engine technology (GDI

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

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

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

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

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

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emissions because tailpipe emissions are what impact air quality.

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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California summertime fuel standard. Major fuel components included 49%

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

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composition data are in Saliba et al.17.

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

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

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

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

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

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

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reactor was ~100 s.

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

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During the dynamometer test the entire tailpipe exhaust from each vehicle

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

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

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

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

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

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

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the CVS.

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The particles that exited the PAM reactor were characterized using a

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

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

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

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

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

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

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

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

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background organics. The median SOA production from background organics

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

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

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production from background organics during an experiment with vehicle exhaust

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because of differences in the condensational sink of the suspended particles

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inside the PAM reactor (see SI).

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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OA concentrations during the photo-oxidation experiments, consistent with

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Collier and Zhang.42

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

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

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

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

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

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

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We estimated the POA concentration during the photo-oxidation

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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reactor.

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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


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

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

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

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

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

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Correcting for the transit time delay alters the distribution of SOA

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

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

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

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

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

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

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

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production during transient vehicle testing will require more detailed

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characterization of the distribution transit time inside the reactor and potentially

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improved reactor designs.

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2.3 Characterization of Primary Emissions

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The gas-phase primary emissions were characterized by sampling the

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

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

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

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


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

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

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

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

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Saliba et al.17.

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

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we used the average NMOG composition profile reported by a companion

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

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comprehensive suite of SOA precursor emissions, including intermediate

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

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

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

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The composition of the VOC emissions measured here (hydrocarbons with

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

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al.11.

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2.4 Effective SOA yields

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The effective SOA yield of gasoline vehicle exhaust during each

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

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

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∆ = ∑[ ] × (1 − ,×[]×∆ )

(1)

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

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


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

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

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

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

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particle partitioning using the measured OA concentration in each experiment

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

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precursors and OH reaction rate constants is provided in SI.

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2.5 Emission Factors

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

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

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

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

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

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

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

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

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experiment in Table S1.

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3. Results and Discussion

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

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concentrations measured during a photo-oxidation experiment with a SULEV

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

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


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

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

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

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

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

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

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

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(Figure 2).

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3.1 Interpreting PAM reactor data and SOA production by UC bag

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

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

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

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

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

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

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

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

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NMOG emissions are SOA precursors.

346 347

The unexpected trend in SOA production versus NMOG emissions by UC

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

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


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operations. This complicates interpretation of PAM reactor data for vehicles

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

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

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operations is that pollutant concentrations inside the PAM reactor are reduced

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

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

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

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

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

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

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

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

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

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

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

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

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SOA production to total NMOG emissions.

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

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production is changes in the NMOG composition, specifically SOA precursor

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emissions. Although catalyst light off dramatically reduces the total NMOG

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


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

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

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

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

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to bag 1.

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

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

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

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

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

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

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

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

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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

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

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

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

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

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

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


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

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

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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

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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.


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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


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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


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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)


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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)


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


Secondary Organic Aerosol Production from Gasoline Vehicle Exhaust

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