Secondary Organic Aerosol Formation from Urban Roadside Air in

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Secondary Organic Aerosol Formation from Urban Roadside Air in Hong Kong Tengyu Liu, Liyuan Zhou, Qianyun LIU, Berto P. Lee, Dawen Yao, Haoxian Lu, Xiaopu Lyu, Hai Guo, and Chak Keung Chan Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06587 • Publication Date (Web): 21 Feb 2019 Downloaded from http://pubs.acs.org on February 21, 2019

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Environmental Science & Technology

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Secondary Organic Aerosol Formation from

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Urban Roadside Air in Hong Kong

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Tengyu Liu1, Liyuan Zhou1, Qianyun Liu2, Berto P. Lee1, Dawen Yao3, Haoxian Lu3,

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Xiaopu Lyu3, Hai Guo3, and Chak K. Chan1,4*

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1School

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China

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2Division

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and Technology, Hong Kong, China

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3Department

of Energy and Environment, City University of Hong Kong, Hong Kong,

of Environment and Sustainability, Hong Kong University of Science

of Civil and Environmental Engineering, The Hong Kong

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Polytechnic University, Hong Kong, China

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4City

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China

University of Hong Kong Shenzhen Research Institute, Shenzhen 518057,

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*Corresponding author:

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Chak K. Chan

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School of Energy and Environment, City University of Hong Kong

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Tel: +852-34425593; Fax: +852-34420688

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Email: [email protected]

1

ACS Paragon Plus Environment


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Abstract

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Motor vehicle emissions are an important but poorly constrained source of secondary

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organic aerosol (SOA). Here, we investigated in situ SOA formation from urban

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roadside air in Hong Kong during winter time using an oxidation flow reactor (OFR),

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with equivalent atmospheric oxidation ranging from several hours to several days. The

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campaign-average mass enhancement of OA, nitrate, sulfate, and ammonium upon

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OFR aging was 7.0, 7.2, 0.8, and 2.6 μg m-3, respectively. To investigate the sources of

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SOA formation potential, we performed multilinear regression analysis between

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measured peak SOA concentrations from OFR and the concentrations of toluene that

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represent motor vehicle emissions and cooking OA from positive matrix factorization

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(PMF) analysis of ambient OA. Traffic-related SOA precursors contributed 92.3%,

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92.4%, and 83.1% to the total SOA formation potential during morning rush hours,

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noon and early afternoon, and evening meal time, respectively. The SOA production

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factor (PF) was approximately 5.2 times of primary OA (POA) emission factor (EF)

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and the secondary particulate matter (PM) PF was approximately 2.6 times of primary

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particles EF. This study highlights the potential benefit of reducing secondary PM

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production from motor vehicle emissions in mitigating PM pollutions.

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1 Introduction

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Motor vehicle emissions are an important source of atmospheric particulate matter

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(PM),1 which influences air quality, human health, and climate.2,

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directly emit PM that mainly comprises primary organic aerosol (POA) and black

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carbon (BC). They also emit volatile organic compounds (VOCs) that can form

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secondary organic aerosol (SOA) through the atmospheric oxidation. Laboratory

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studies found that the amount of SOA generally exceeded that of POA after aging the

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vehicle emissions for less than the equivalent of 1 day at typical atmospheric oxidant

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levels.4-12 However, the contribution of motor vehicle emissions to the SOA burden

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remains largely uncertain due to our limited understanding of SOA precursors and

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yields of vehicle emissions.13

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Motor vehicles

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Smog chambers have been widely used to characterize SOA production from

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diluted emissions of motor vehicles.4-11, 14 Although the chamber studies can capture

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the SOA formation from individual vehicles in several hours, they may not characterize

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the full SOA production potential associated with multiple generations of oxidation

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processes and are also limited to a small number of vehicles and driving conditions. As

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an alternative, the potential aerosol mass (PAM) oxidation flow reactors (OFR)15, 16

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enable the oxidation at equivalent of several hours to weeks. They can be deployed to

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characterize SOA formation from real-world vehicle fleets and driving conditions.17, 18

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Using an OFR, Tkacik et al. 17 observed an order of magnitude higher SOA production

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factors from vehicle emissions in a highway tunnel in Pittsburgh than those reported

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for smog chambers. Saha et al.

18

found that traffic emissions dominated winter-time 3



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SOA production potentials at a near-highway site in North Carolina while SOA

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formation was dominated by biogenic sources during summer time.

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In a highly urbanized environment such as Hong Kong, residents and pedestrians

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have high exposure to roadside air pollutants including PM. Previous studies estimated

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that motor vehicle emissions contributed 20-51% of ambient fine PM (PM2.5) in Hong

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Kong.19-26 Lee et al.

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the total OA in urban roadside Hong Kong. However, characterizing SOA formation

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from motor vehicle emissions in Hong Kong remains sparse.

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found that POA from vehicle emissions contributed 26.4% of

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In this study, we investigated in situ SOA formation from urban roadside air in

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Hong Kong using an OFR, with equivalent atmospheric oxidation of several hours to

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several days. We quantified the contribution of motor vehicle emissions to SOA

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formation potential. SOA production factor from in-use vehicle emissions was

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determined and compared to its BC and POA emission factors.

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2 Materials and methods

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2.1 Sampling Setup

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Experiments were conducted at an urban roadside site at the Hong Kong Polytechnic

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University, next to the PUX monitoring station,22, 28-30 situated 1 m adjacent to Hong

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Chong road and 350 m from the 4-lane exit of the Cross Harbour Tunnel (Figure S1 in

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the Supporting Information). The average daily traffic volume was approximately

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115,000 vehicles, consisting of 40.8% gasoline vehicles, 30.3% diesel vehicles, and

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28.9% liquefied petroleum gas (LPG) vehicles.31 The sampling inlet (~2 m long, 3.2

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cm i.d.) equipped with a PM2.5 cyclone on the rooftop at approximately 3.5 m above 4


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the ground operating at a flow rate of 16.7 L/min, with approximately 6 L/min of air

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going into the OFR. The residence time of less than 6 s in the sampling inlet would

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reduce the loss of organic vapors32. Based on the model of Pagonis et al.

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estimated that the loss for compounds with saturation concentrations (C*) of 1×102,

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1×104, and 1×106 μg m-3 in the sampling inlet was less than 26%, 25%, and 1%,

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respectively. The inlet had a minor impact on the sampling of organic vapors and further

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SOA formation. Measurements were performed from 24 Dec 2017 to 15 Jan 2018

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during wintertime. Meteorological conditions were recorded using a weather station

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(Vantage Pro2TM, Davis Instruments Corp., USA) on the rooftop. The average ambient

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temperature, RH, and wind speed during the measurements were 18 ± 3 °C, 67 ± 19%,

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and 0.8 ± 0.3 m/s, respectively.

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2.2 OFR Setup

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SOA formation from the roadside air was investigated in a PAM OFR15, 16 by exposing

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the sampled ambient air to high levels of oxidants. A schematic of the experimental

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setup of OFR and instrumentations is shown in Figure S2. The OFR (Gothenburg

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Potential Aerosol Mass Reactor, Go: PAM) used in this study has been described in

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detail elsewhere.33 Briefly, the Go:PAM OFR is a 7.2 L quartz glass cylindrical tube (1

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m long, 9.6 cm i.d., ~75 s residence time ), equipped with one Philips TUV 30 W

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ﬂuorescent lamp (λ = 254 nm). The reactor is enclosed in a compartment of reflective

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and polished aluminum sheets to provide homogeneous light intensity. The UV lamp

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produces hydroxyl radicals (OH) through the photolysis of O3 in the presence of water

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vapor. O3 was produced by an O3 generator (Model 610, Jelight Inc., USA) via 5


32,

we


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irradiation of pure O2. The input O3 concentration in the OFR was adjusted every two

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days to produce a range of 0.7 ppm to 5.5 ppm. The NO to O3 ratio was estimated to be

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mostly less than 5%, so the perturbation of NO on the input O3 concentrations was

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negligible. Two solenoid valves were used to switch the measurements between

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downstream OFR and roadside air every 30 min.

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OH exposure inside the OFR was calibrated off-line using SO2, following previous

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studies.16, 34 During experiments, the OH exposure may be significantly influenced by

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the external OH reactivity of sampled air.35-37 We estimated the total external OH

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reactivity using the measured concentrations of SO2, CO, NOx, and VOCs (See details

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in SI). For VOCs, the OH reactivity was estimated based on the measured

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concentrations of aromatics and the VOC compositions of vehicle exhaust reported in

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Shing Mun tunnel in Hong Kong.38 The OH estimation equations of Peng et al. 35 were

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validated by the off-line OH calibration (Figure S3) and then applied to estimate the

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OH exposure. The calculated OH exposures ranged from 5.3×1010 to 1.4×1012

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molecules cm-3 s, equivalent to 0.4-10.8 days of photochemical aging, assuming an

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ambient OH concentration of 1.5×106 molecules cm-3.39 The relative importance of

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non-OH chemistry like photolysis at 254 nm was evaluated by taking toluene as a

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surrogate as it is a common SOA precursor found in vehicle emissions.13 The upper

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limit consumption of toluene through photolysis was less than 6% assuming a unity

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quantum yield. Undesired VOC destruction by 254 nm photons was therefore

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considered to be negligible compared to the reaction with OH.

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Watne et al. 33 quantified the particle wall loss in GO:PAM OFR and found that the

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transmission efficiency for particles with mobility diameters (dm) larger than 25 nm was

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higher than 90%. The particles larger than 25 nm measured downstream OFR in this

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study accounted for greater than 90% of the aerosol mass (Figure S4). Consequently

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wall loss of particles was calculated to be generally less than 3% and particle wall loss

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correction was not applied.

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2.3 Instrumentation

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Gas phase components, including O3 (API model T400), CO (API model 300EU), CO2

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(API model T360), NOx (API model 200E), and SO2 (API model T100U) were

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measured by a series of Teledyne API gas monitors. VOCs were characterized using an

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online proton transfer reaction-mass spectrometry (PTR-MS, PTR-QMS500,

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IONICON Analytik GmbH, Innsbruck, Austria).40 The calibration and operation of this

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instrument has been described elsewhere.41

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A scanning mobility particle sizer (SMPS, model 5.400, GRIMM, Germany) was

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used to measure particle number concentrations and size distributions with a size range

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of 10-807 nm at a 4-min scan interval. Black carbon (BC) was determined by an

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Aethalometer (AE16, Magee, USA) every 1 min. Non-refractory submicron aerosols

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(NR-PM1) were chemically characterized using a high-resolution time-of-flight aerosol

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mass spectrometer (hereafter AMS, Aerodyne Research Incorporated, USA). The

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instrument was operated in the high sensitivity V-mode and the high resolution W-

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mode alternating every 2 min. The molar ratios of hydrogen to carbon (H:C) and oxygen

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to carbon (O:C) were determined with the improved-ambient method.42 Default relative 7



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ionization efficiency (RIE) values for sulfate, nitrate, chloride, and organics were used.

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The RIE for ammonium was determined to be 4.3 from IE calibrations. A collection

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efficiency (CE) of 0.7 was applied for both the roadside air and OFR measurements,

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supported by the mass closures comparison between the sum of AMS and BC mass

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concentrations and concurrent SMPS mass concentrations (Figure S5). AMS CO2+

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signals were corrected for the gas-phase CO2 contributions. The interaction between

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inorganic salts and pre-deposited carbon on the tungsten vaporizer may have an

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interference on the CO2+ signal.43 The inorganic nitrate induced CO2+ signal can be

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subtracted from the organic matrix based on its positive correlation with nitrate-NO+

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signal obtained during IE calibrations.43, 44 In this study, the CO2+ interference induced

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by inorganic nitrate led to less than 3% variations of OA concentrations and O:C ratios.

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Thus AMS data were not corrected for CO2+ interferences from inorganic nitrate to keep

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consistency with previous studies. Hourly average data were reported throughout this

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study unless otherwise specified.

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2.4 SOA production factor

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The pollutant emission factors (EF) and SOA production factor (PF) (mg kg-fuel-1)

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were calculated on a fuel basis:

(

[∆𝐶𝑂2]

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𝐸𝐹 = 106([∆𝑃])

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𝑃𝐹 = 106[𝑆𝑂𝐴𝑡𝑟𝑎𝑓𝑓𝑖𝑐]

𝑀𝑊𝐶𝑂2

(

[∆𝐶𝑂] ―1 𝐶𝑓 𝑀𝑊𝐶

)

+ 𝑀𝑊𝐶𝑂

[∆𝐶𝑂2] 𝑀𝑊𝐶𝑂2

[∆𝐶𝑂] ―1 𝐶𝑓 𝑀𝑊𝐶

)

+ 𝑀𝑊𝐶𝑂

(1) (2)

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where [ΔCO2], [ΔCO], and [ΔP] are the background corrected concentrations of CO2,

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CO, and pollutants (i.e. hydrocarbon-like organic aerosol (HOA) and BC) in μg m-3;

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background CO2 and CO concentrations were concurrently measured at a background 8


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site, Hok Tsui, which is located approximately 10 km upwind of the roadside site,

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providing a good estimation of urban background pollutants levels in Hong Kong.45

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Background HOA and BC concentrations were assumed to be negligible. [SOAtraffic] is

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the estimated potentially formed SOA in μg m-3 from traffic emissions. MWCO2, MWCO,

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and MWC are the molecular weights of CO2, CO, and carbon, respectively. Cf is the

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carbon mass fraction of fuel, adopted to be 0.86.46, 47 EFs of HOA and BC and PF of

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SOA were calculated for periods when traffic-related SOA precursors dominated the

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SOA formation potentials (discussed in Sect. 3.1).

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3 Results and Discussion

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3.1 Secondary Aerosol Production

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Figure 1 shows the campaign-average mass concentrations of OA, nitrate, sulfate,

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ammonium, and chloride and their contributions to the total NR-PM1 mass measured

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for urban roadside air and downstream OFR. Overall, the average concentrations for

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ambient OA, nitrate, sulfate, ammonium, and chloride were 6.8, 1.4, 3.0, 1.3, and 0.2

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μg m-3 (53.6%, 11.2%, 23.7%, 10.0%, and 1.5% of the total NR-PM1), respectively.

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After the roadside air was subjected to photochemical aging, the average OA, nitrate,

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sulfate, and ammonium concentrations increased to 13.8, 8.6, 3.8, and 3.9 μg m-3

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(45.8%, 28.5%, 12.5%, 12.8%, and 0.4% of the total NR-PM1) by factors of 2.0, 6.1,

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1.3, and 3.0, respectively, indicating significant production of secondary particles.

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Using the method of Farmer et al.

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OFR was inorganic nitrate. The significant formation of ammonium nitrate was

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expected considering the high levels of precursor gases NOx and ammonia emitted from

48,

we estimated that more than 80% of nitrate in

9



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Hong Kong on-road vehicles.49 In addition, NOx was rapidly oxidized to HNO3 in OFR

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and limited the production of organic nitrate.36, 37 Significant production of ammonium

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nitrate was also observed in previous smog chamber and tunnel studies investigating

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the photochemical aging of vehicle emissions.5, 8, 17, 50-52

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Figure 2a shows the absolute mass enhancement (∆mass = OFR mass – ambient

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mass) of OA, nitrate, sulfate, and ammonium as a function of photochemical age. Data

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are binned by the photochemical age. The OA, nitrate, and ammonium enhancements

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peaked at ~1.5 days of photochemical age and then decreased at higher OH exposures,

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while the sulfate enhancement exhibited a relatively stable trend with the increase of

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photochemical ages. The peak OA, nitrate, and ammonium mass enhancements were

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9.9 μg m-3, 9.1 μg m-3, and 3.2 μg m-3, respectively. The OA enhancement trend is

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qualitatively similar to previous studies on OFR oxidation of ambient air.17, 18, 53-58 This

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trend may be due to the transition of functionalization-dominated reactions and

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condensation at relatively lower photochemical ages to the dominance of fragmentation

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reactions and evaporation at higher ages.17,

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formed from the oxidation of NOx to HNO3 and further neutralized by NH3 from vehicle

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exhaust.49, 59, 60 At the same ozone level and RH, lower vehicle emissions will lead to

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higher OH exposure. The decrease of ammonium nitrate enhancement at higher

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photochemical ages may be due to the lower levels of reagent NH3, supported by the

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good correlations between ΔNH4NO3 and CO, an appropriate proxy of NH3 from

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gasoline and LPG vehicles51 (Figure S6).

53

Ammonium nitrate in the OFR was

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The oxidation of organic gases in the OFR can form low-volatility organic

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compounds (LVOCs), which irreversibly condense on particles and surfaces. In the

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OFR, besides condensation on pre-existing aerosols, LVOCs may undergo other

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processes such as exiting the OFR and loss to the sampling line surfaces, loss to the

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OFR walls, and further reactions with OH to form either condensable or non-

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condensable gas-phase products.53, 55 We performed LVOC fate analysis following the

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method of Palm et al.

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predicted to condense on aerosols, with less than 5% and 10% exiting the OFR and loss

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to the OFR walls. Specifically, condensation on aerosols accounted for as high as 80%

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of the LVOC fate at photochemical age of 0.4-4.0 days. After applying the LVOC fate

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correction for ages less than 5 days, the OA enhancement still showed a similar trend

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to that without LVOC fate correction and peaked at the same photochemical age bin

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(Figure S8).

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(Figure S7). Overall, more than 50% of the LVOCs was

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CO is a commonly used combustion tracer to account for variability due to

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emissions and dilution when characterizing urban SOA formation.61 Figure 2b shows

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the absolute OA mass enhancement (∆OA) normalized by ∆CO as a function of

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photochemical age. After normalized by ∆CO, ∆OA peaked at ~2.4 days of

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photochemical age, comparable with those reported in a highway tunnel in Pittsburgh,17

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urban Beijing,58 and an urban environment in California.53 The peak ∆OA/∆CO value

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was 38 μg m-3 ppm-1, approximately half of that measured in Pittsburgh tunnel17 and 30%

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lower than that at a near-highway site in North Carolina during summer time.18 The

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differences in ∆OA/∆CO values may be attributed to the different fleet compositions,

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sources of precursors, and meteorological conditions in these studies.17, 18

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To further explore the SOA precursor sources in this site, we performed positive

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matrix factorization (PMF) analysis on roadside air OA dataset (See details in SI, Figure

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S9 and S10). Five factors were identified, including two primary-dominated factors

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(HOA and cooking organic aerosol (COA)) and three oxygenated factors (OOA1,

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OOA2, and OOA3) with different levels of oxidation, contributing 16.5, 18.1, 14.4,

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28.2, and 22.8% to the total OA, respectively. Figure S11 shows the diurnal patterns of

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these five factors. HOA concentrations exhibited a strong morning rush hour peak and

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a relatively weaker evening rush hour peak, while COA concentrations showed

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pronounced daily peaks associated with meal time. Extensive studies have

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demonstrated that vehicle4,

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photochemical aging. Figure 2c and d show scatter plots of ∆OA in peak photochemical

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age bin (1-2 days) vs. toluene and COA concentrations during three specific periods of

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morning rush hours (7:00 – 10:00), noon and early afternoon (12:00 – 15:00), and

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evening meal time (19:00 – 22:00). The data with ∆OA concentrations lower than 5 μg

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m-3, which typically had an OA enhancement ratio (∆OA/ambient OA) lower than 1.5

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(Figure S12), were excluded to minimize the perturbation of variation of ambient OA

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on quantification of ∆OA. During morning rush hours and noontime, the peak ∆OA

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displayed moderate correlations with toluene (R2 > 0.6) as well as HOA (R2 > 0.5,

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Figure S13) but poor correlations with COA (R2 < 0.12), suggesting that toluene and

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other traffic-related precursors with similar diurnal concentration patterns may

5, 7, 8

and cooking emissions62-65 can form SOA during

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dominate the SOA formation potential in these periods. Cooking-related precursors

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were insignificant contributors to the SOA formation potential during morning rush

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hours and noontime despite the COA concentrations were comparable with those of

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HOA during noontime. The cooking-related SOA precursors may have already been

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oxidized during transportation from nearby area to this site and thus decrease the SOA

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formation potential of cooking emissions. During evening meal time, the peak ∆OA

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had moderate correlations with toluene (R2 = 0.64) and COA (R2 = 0.49), suggesting

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that both traffic-related and cooking-related emissions may play important roles in SOA

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formation in OFR in this period.

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The relative contributions of traffic and cooking emissions to SOA formation

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potentials for the above dataset were estimated by applying a multilinear regression

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(MLR) analysis, following the method of Palm et al. 54. Briefly, if we assume that all

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SOA precursors emitted from a given source correlate with the specific VOCs/tracers

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from the same source, the measured SOA concentrations should correlate with the sum

267

of concentrations of VOCs/tracers of each source multiplied by their respective

268

coefficients.54 We assume that the SOA precursors from traffic and cooking emissions

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correlated well with toluene and COA, respectively. A strong correlation (R2 = 0.92)

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was obtained for the measured peak ∆OA formation vs. the amounts predicted using

271

the MLR approach (Figure 3a). The MLR analysis gives a good estimation on the

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relative contribution of traffic and cooking emissions to the SOA formation potential.

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Traffic and cooking emissions contributed 9.8 (92.3%) and 0.8 (7.7%) μg m-3 during

274

morning rush hours, 18.9 (92.4%) and 1.6 (7.6%) μg m-3 during noon and early 13



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afternoon, and 8.4 (83.1%) and 1.7 (16.7%) μg m-3 during evening meal time to the

276

total SOA formation potential, respectively (Figure 3b). Traffic-related SOA precursors

277

dominated the SOA formation potentials, contributing approximately 10, 11, and 4

278

times more potential SOA mass than cooking-related SOA precursors did during

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morning rush hours, noontime, and evening meal time, respectively. It should be noted

280

that these results are not about the sources of pre-existing ambient SOA at this site, but

281

about the source apportionment on potentially formed SOA when the air at this site

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undergoes further oxidation.

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3.2 SOA PF for in-Use Vehicles

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We determined SOA PF for in-use vehicles in Hong Kong based on estimated SOA

285

potentials from traffic emissions during morning rush hours and noontime and

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compared it with those reported from a tunnel study,17 OFR and chamber studies of

287

gasoline vehicles,5, 6, 8, 9, 11, 12, 52, 66 and diesel vehicles4, 7, 10 (Figure 4a). Generally, SOA

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PF of vehicle emissions measured in laboratory is dependent on fuel type, driving mode,

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vehicle types, diﬀerences in regulations, and OH exposures during experiments.13 The

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median SOA PF in this study was approximately 352 mg kg-fuel-1 at 1-2 OH days for

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a fleet composition of 44.1% gasoline vehicles, 41.3% diesel vehicles, and 14.6% LPG

292

vehicles, comparable to ~350 mg kg-fuel-1 at 2-3 OH days in Pittsburgh tunnel with 90-

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96% of light-duty gasoline vehicles.17 It also fell within the range of 194-472 mg kg-

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fuel-1 for cold start gasoline direct injection vehicles measured at 2.2-4.5 OH days in

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OFR experiments.52 Compared with experiments performed at relatively low

296

photochemical ages (< 0.6 days), the SOA PF in this study was higher than those 14


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(typically lower than 80 mg kg-fuel-1) for gasoline vehicles,5, 8, 9, 11, 12, 66 except for a

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Euro 5 gasoline vehicle (340 mg kg-fuel-1) operated with a New European Driving

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Cycle,6 which was within the values for diesel vehicles (76-1133 mg kg-fuel-1).4, 7, 10

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Figure 4b shows the EFs of BC and HOA, and PF of secondary particulate matter

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(SPM) for the same periods in Figure 4a. SPM concentrations were the sum of

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secondary nitrate, ammonium, and SOA concentrations. The median EFs for BC, HOA,

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and SPM were 261, 68, and 852 mg kg-fuel-1, respectively. The SOA PF was

304

approximately 5.2 times of POA EF and the SPM PF was approximately 2.6 times of

305

primary particles (BC+HOA) EF, suggesting that the mass of secondary particles

306

formed from vehicle emissions downwind may dominate over its original primary

307

particle mass. The BC EF was lower than reported values for diesel vehicles (400-2200

308

mg kg-fuel-1) in Hong Kong67-69 but significantly higher than those for gasoline vehicles

309

(58-100 mg kg-fuel-1) in California and Toronto70, 71 and LPG ones (90 mg kg-fuel-1) in

310

Hong Kong.67 Diesel vehicles may dominate BC emissions during morning rush hours

311

and noontime considering the abundance of diesel vehicle fleets in these periods and

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relatively high BC EFs of diesel vehicles. The HOA EFs were within the range of

313

reported values for gasoline and diesel vehicles.72, 73

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3.3 OA Composition and Evolution in OFR

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AMS elemental ratio data have been extensively used to explore OA aging.74, 75 In

316

Figure 5 we plot the H:C and O:C molar ratios for ambient and OFR OA on a Van

317

Krevelen diagram. The H:C and O:C ratios and the estimated average carbon oxidation

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state (OSC) (OSC ≈ 2×O:C – H:C)76 were 1.4–1.9, 0.2–0.8 and -1.6–0.3 for ambient OA 15



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and 1.0–1.8, 0.3–1.7 and -1.1–2.3 for OFR OA, respectively. The ambient and OFR

320

data generally fell within the ambient data obtained from multiple field studies.77 Upon

321

aging, the O:C ratios and OSC increased and H:C ratios decreased, consistent with

322

previous OFR ambient studies.17, 18, 53-56 The ambient and OFR data followed slightly

323

different trends, with slopes of -0.77 and -0.58, respectively. A slope between -1 and -

324

0.5 may indicate OA evolution chemistry involving the addition of both carboxylic acid

325

and alcohol/peroxide functional groups without fragmentation and/or the addition of

326

carboxylic acid functional groups with fragmentation.75,

327

ambient and OFR slopes may have resulted from the low-NO chemistry in OFR, which

328

may lead to more peroxide production.

329

3.4 Atmospheric Implications

330

Over the years, China has implemented aggressive and strict regulations to reduce

331

primary particle emissions from on-road vehicles, such as introducing stricter emission

332

standards for in-use and new vehicles and improving the fuel quality.78 However, the

333

effects of such measures targeting primary emissions from vehicle emissions on

334

secondary PM formation has not been much discussed. Our results suggest that on-road

335

vehicles in Hong Kong, a megacity in China, may form a significant amount of

336

secondary PM. After photochemical aging, the secondary PM PF for vehicle emissions

337

was 2.6 times of their primary PM emissions. Particularly, the SOA PF was

338

approximately 5.2 times of POA EF. Despite the limited data set in this study in Hong

339

Kong, one can extrapolate the results of this study to estimate the potential impacts of

340

SOA production from traffic emissions. In 2013, the road transport sector was estimated 16


77

The differences in the

Page 17 of 30


341

to emit 27.4 Tg yr-1 of CO and 0.37 Tg yr-1 of PM2.5 in China.79 Based on the median

342

∆OA/∆CO value (40 μg m-3 ppm-1) for vehicle emissions determined from morning

343

rush hours and noon time in this study, we estimated an SOA production of 1.3 ± 0.9

344

Tg yr-1 from the road transport sector in China in 2013, almost 3.5 times of its primary

345

PM2.5 emissions. While it is understood that such extrapolation has a lot of uncertainties,

346

the benefits of reducing vehicle emissions should include that of reducing SPM

347

production. In this study, SOA formation from vehicle emissions was investigated

348

during wintertime. Further work will be required to investigate the dependence of

349

secondary PM formation from vehicle emissions on fuel types, driving mode, vehicle

350

types, regulations, and ambient temperature to make suitable measures in controlling

351

vehicle secondary PM production.

352

In addition, our results suggest that cooking emissions could also be an important

353

SOA contributor in urban areas. This is consistent with previous laboratory62, 64 and

354

model studies80,

355

photochemical aging. However, the contribution of cooking to urban SOA still remains

356

poorly constrained due to the lack of information on cooking SOA precursors and their

357

corresponding SOA yields in a city. Given that vehicle emissions are subjected to

358

stricter regulations than cooking emissions, further work is needed to give insights on

359

the relative contributions of vehicle and cooking emissions to SOA in urban areas.

81

that cooking emissions would form substantial SOA upon

17



360

ASSOCIATED CONTENT

361

Supporting Information

362

Description of OH exposure estimation (Text S1), OA source apportionment (Text S2),

363

satellite imaginary of the sampling site (Figure S1), schematic of the experimental setup

364

of OFR and instrumentations (Figure S2), comparison of measured and modeled OH

365

exposures (Figure S3), volume size distributions for aged particles (Figure S4),

366

comparison between AMS and BC mass vs. SMPS mass (Figure S5), scatter plot of

367

∆NH4NO3 and CO concentrations (Figure S6), LVOC fate (Figure S7), ∆OA with and

368

without LVOC fate correction (Figure S8), diagnostic plots of the PMF analysis (Figure

369

S9), mass spectra and mass time series of all OA factors (Figure S10), diurnal patterns

370

of all OA factors (Figure S11), correlations between peak ∆OA and toluene

371

concentrations (Figure S12), and correlations between peak ∆OA and HOA

372

concentrations (Figure S13).

373

Acknowledgments

374

Chak K. Chan would like to acknowledge the support of the Environment and

375

Conservation Fund (ECF Project 81/2016) and the Science Technology and Innovation

376

Committee of Shenzhen Municipality (project no. JCYJ20160401095857424). Hai Guo

377

would like to acknowledge the support of the Research Grants Council of

378

the Hong Kong Special Administrative Region via grant CRF/C5004-15E, and the

379

Strategic Focus Area (SFA) scheme of The Research Institute for Sustainable Urban

380

Development at The Hong Kong Polytechnic University (PolyU) (1-BBW9). We thank

381

the Hong Kong Observatory for providing the CO2 data at Hok Tsui. 18


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Page 25 of 30


Roadside

OFR 1.5%

10.0%

12.8% 3.9 µg m-3

0.2 µg m-3

1.3 µg m-3

0.1 µg m-3

12.5%

23.7%

3.8 µg m-3

53.6%

3.0 µg m-3

11.2%

28.5%

1.4 µg m-3

8.6 µg m-3

OA

Nitrate

45.8% 13.8 µg m-3

6.8 µg m-3

643

0.4%

Sulfate

Ammonium

Chloride

644

Figure 1. Campaign-average mass concentrations of OA, nitrate, sulfate, ammonium, and chloride and

645

their contributions to the total NR-PM1 mass for urban roadside air and OFR.

25



-3

OH exposure (molec. cm s) 11

89

2

3

4

10

12

8

5 6 7 89

10 5 0

6 7 89

2

3

4

5 6 7 89

11 2

3

4

5 6 7 8

10

12

80 60 40 20 0

6 7 8

2

3

4

5 6 7 8

1 10 Eq. Photochemical age (days)

1 10 Eq. Photochemical age (days) 6

10

100

-3

OA Nitrate Sulfate Ammonium

15

-1

-3

Mass enhancement (µg m )

10

-3

OH exposure (molec. cm s)

(b)

OA/CO (µg m ppm )

(a)

Page 26 of 30

6

-3

-3

1 day = 1.5 × 10 molec day cm OHexp 1 day = 1.5 × 10 molec day cm OHexp (c) 80 (d) 80 2 2 7:00 - 10:00 y = 9.96x - 5.54 R = 0.65 y = 9.01x - 4.06 R = 0.64

40 20

-3

-3

Peak OA (µg m )

2

60

y = 3.48x + 7.04 R = 0.12

Peak OA (µg m )

12:00 - 15:00 19:00 - 22:00

2

y = 5.29x + 1.45 R = 0.95

2

60

y = 2.32x + 4.95 R = 0.49

40 20 0

0 0

646

7:00 - 10:00 12:00 - 15:00 19:00 - 22:00

2

y = 4.32x + 10.7 R = 0.05

2

4 6 8 Toluene (ppb)

10

0

12

1

2

3

4 -3

5

6

COA (µg m )

647

Figure 2. (a) Absolute mass enhancement (∆mass = OFR mass – ambient mass) of OA, nitrate, sulfate,

648

and ammonium from the oxidation of urban roadside air as a function of photochemical age. Data are

649

binned by the photochemical age. Error bars represent the standard deviations when averaging mass

650

concentrations and photochemical ages for each bin. (b) Absolute OA mass enhancement (∆OA)

651

normalized by background-corrected CO (∆CO) as a function of photochemical age. Background CO

652

concentrations were measured at a background site, Hok Tsui. (c) Correlations between peak ∆OA (1-2

653

days of photochemical aging) and toluene concentrations during morning rush hours (7:00 – 10:00), noon

654

and early afternoon (12:00 – 15:00), and evening meal time (19:00 – 22:00). Data with ∆OA less than 5

655

µg m-3 are excluded. (d) Correlations between peak ∆OA (1-2 days of photochemical aging) and COA

656

concentrations during morning rush hours (7:00 - 10:00), noon and early afternoon (12:00 – 15:00), and

657

evening meal time (19:00 – 22:00). 26



-3

(a)

Measured peak OA (µg m )

Page 27 of 30

80

2

R = 0.92 1:1

60 40 20 0 0

20

40

60

80

Predicted SOA from (b)

Morning rush hours Noon and early afternoon

Evening meal time 16.9%

7.6%

7.7%

1.6 µg m-3

0.8 µg m-3

92.3% 9.8 µg

658

-3

multilinear regression (µg m )

m-3

1.7 µg m-3

92.4% 18.9 µg

Traffic

83.1%

m-3

8.4 µg m-3

Cooking

659

Figure 3. (a) Measured peak ∆OA vs. SOA predicted from multilinear regression analysis during

660

morning rush hours (7:00 - 10:00), noon and early afternoon (12:00 – 15:00), and evening meal time

661

(19:00 – 22:00). (b) The estimated contributions of traffic and cooking emissions to the SOA formation

662

potential during morning rush hours, noon and early afternoon, and evening meal time.

27



17

Tkacik et al.

(a)

Nordin et al. Platt et al.

8

7

Chirico et al. Deng et al.

4

10

12

Pieber et al.

-1

66

11

Gordon et al.

9

Peng et al.

SOA PF (mg kg-fuel )

Du et al.

6

Gordon et al. Liu et al.

Zhao et al.

5

Page 28 of 30

52

This study

1000 6 4 2

100 6 4 2

10

0

1

2

3

4

5

Eq. Photochemical age (days) 6

-3

1 day = 1.5 × 10 molec day cm OHexp 2

-1

EF or PF (mg kg-fuel )

(b) 1000 4 2

100 4 2

10

BC

HOA

SOA

SPM

663 664

Figure 4. (a) SOA PF for in-use vehicles determined in this study and comparison with those reported

665

for a tunnel study,17 gasoline vehicles,5, 6, 8, 9, 11, 12, 52, 66 and diesel vehicles.4, 7, 10 Yellow, red, and

666

blue symbols represent the tunnel, gasoline vehicles, and diesel vehicles studies, respectively. (b) EFs

667

for BC, HOA, and secondary particulate matter (SPM). For box-and-whisker plots, the top and bottom

668

line of the box are 75th and 25th percentiles of the data, the line inside the box is the median, and the top

669

and bottom whiskers are 95th and 5th percentiles.

28


Page 29 of 30


OS=-2

2.0

2

Roadside S = -0.77, R = 0.96 2

OFR S = -0.58, R = 0.95

0

H:C

1.5 OS=-1

1.0 0.5

-0.5

2 4 6 8 10 Eq. Photochemical age 6

-3

(days, [OH] = 1.5×10 molec. cm ) OS=0

0.0 0.0

-1

-2 OS=2

OS=1

0.5

1.0

1.5

O:C

670 671

Figure 5. Van Krevelen diagram of urban roadside air and OFR OA measurements. Average carbon

672

oxidation states from Kroll et al.

673

reference. Blue and red dash lines represent the OA data region observed in multiple ambient

674

measurements.77 Dot and square symbols represent roadside air and OFR data, respectively. OFR data

675

are colored by photochemical age in days (at [OH] = 1.5×106 molecule. cm-3). Linear orthogonal distance

676

regression fit slope and R2 are also shown.

76

and functionalization slopes from Heald et al.

29


75

are shown for


677

TOC Art

-3

Mass Conc (µg m )

25 20 15

Potential SOA from motor vehicle emissions Potential SOA from cooking emissions HOA COA

10 5 0

Morning rush hour Noon time Evening meal time

678 679

30


Page 30 of 30

Secondary Organic Aerosol Formation from Urban Roadside Air in

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