Light absorption of organic carbon and its sources at a southeastern U.S. location in summer

Light absorption of organic carbon and its sources at a southeastern U.S. location in summer

Accepted Manuscript Light absorption of organic carbon and its sources at a southeastern U.S. location in summer Mingjie Xie, Xi Chen, Amara L. Holder...

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Accepted Manuscript Light absorption of organic carbon and its sources at a southeastern U.S. location in summer Mingjie Xie, Xi Chen, Amara L. Holder, Michael D. Hays, Michael Lewandowski, John H. Offenberg, Tadeusz E. Kleindienst, Mohammed Jaoui, Michael P. Hannigan PII:

S0269-7491(18)32814-8

DOI:

10.1016/j.envpol.2018.09.125

Reference:

ENPO 11667

To appear in:

Environmental Pollution

Received Date: 19 June 2018 Revised Date:

24 September 2018

Accepted Date: 25 September 2018

Please cite this article as: Xie, M., Chen, X., Holder, A.L., Hays, M.D., Lewandowski, M., Offenberg, J.H., Kleindienst, T.E., Jaoui, M., Hannigan, M.P., Light absorption of organic carbon and its sources at a southeastern U.S. location in summer, Environmental Pollution (2018), doi: https://doi.org/10.1016/ j.envpol.2018.09.125. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Light absorption of organic carbon and its sources at a Southeastern U.S. location in summer

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Mingjie Xiea,b,c,d,*, Xi Chend, Amara L. Holderd, Michael D. Haysd, Michael Lewandowskie, John H. Offenberge, Tadeusz E. Kleindienste, Mohammed Jaouie, Michael P. Hanniganf

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Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China b State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210023, China c Oak Ridge Institute for Science and Education (ORISE), dNational Risk Management Research Laboratory, eNational Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA f Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA

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*Correspondence to: Mingjie Xie

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E-mail: [email protected]; [email protected];

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Tel: +86-18851903788;

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Fax: +86-25-58731051;

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Mailing address: 219 Ningliu Road, Nanjing, Jiangsu, 210044, China

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ABSTRACT Light-absorbing organic carbon (OC), also referred to as “brown carbon” (BrC), has been

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intensively investigated in atmospheres impacted by biomass burning. However, other BrC

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sources (e.g., secondary formation in the atmosphere) are rarely studied in ambient aerosols. In

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the current work, forty-five PM2.5 filter samples were collected in Research Triangle Park (RTP),

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NC, USA from June 1st to July 15th, 2013. The bulk carbonaceous components, including OC,

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elemental carbon (EC), water soluble OC (WSOC), and an array of organic molecular markers

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were measured; an ultraviolet/visible spectrometer was used to measure the light absorption of

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methanol extractable OC and WSOC. The average light absorption per OC and WSOC mass of

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PM2.5 samples in summer RTP are 0.36 ± 0.16 m2 gC-1 and 0.29 ± 0.13 m2 gC-1, respectively,

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lower than the ambient aerosol samples impacted by biomass burning and/or fossil fuel

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combustion (0.7 – 1.6 m2 gC-1) from other places. Less than 1% of the aqueous extracts

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absorption is attributed to the light-absorbing chromophores (nitroaromatic compounds)

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identified in this work. To identify the major sources of BrC absorption in RTP in the summer,

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Positive Matrix Factorization (PMF) was applied to a dataset containing optical properties and

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chemical compositions of carbonaceous components in PM2.5. The results suggest that the

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formation of biogenic secondary organic aerosol (SOA) containing organosulfates is an

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important BrC source, contributing up to half of the BrC absorption in RTP during the

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summertime.

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This study identified a linkage between atmospheric BrC and biogenic SOA formation

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when biomass burning is not significant in Research Triangle Park, NC during the summer.

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Key words: Brown carbon; Light absorption; Organic molecular marker; Source apportionment;

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Secondary organic aerosol 2

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1. INTRODUCTION The light extinction of atmospheric particulate matter (PM), including absorption and

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scattering, plays an important role in the Earth’s radiative balance (IPCC, 2007; Seinfeld, 2008).

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The organic matter (OM) in PM is commonly treated as purely light scattering in climate models

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(Chung and Seinfeld, 2002; Myhre et al., 2013). However, many existing studies have observed

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light-absorbing organic carbon (OC), also termed as “brown carbon” (BrC), from both primary

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emissions (Saleh et al., 2013, 2014; Washenfelder et al., 2015; Pokhrel et al., 2016; Xie et al.,

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2017b) and secondary formation in the atmosphere (Iinuma et al., 2010; Nakayama et al., 2010;

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Lin et al., 2014; Liu et al., 2016; Xie et al., 2017a). Typical BrC can absorb efficiently in the near

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UV (300 – 400 nm) and visible regions with strong wavelength (λ) dependence (Kirchstetter et

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al., 2004; Saleh et al., 2014; Laskin et al., 2015). The radiative transfer calculations basing on

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aircraft measurements of BrC suggested that BrC accounted for ~20% of carbonaceous aerosol

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(black carbon + brown carbon) warming effect at the tropopause (Liu et al., 2014, 2015; Zhang

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et al., 2017). Due to the lack of information on the emissions, optical and chemical properties,

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and atmospheric transformation of BrC associated with different sources, recent modeling

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studies (Feng et al., 2013; Wang et al., 2014) obtained the optical properties of BrC from

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previous laboratory biomass burning experiments (Chen and Bond, 2010) or ambient

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measurements (Liu et al., 2013; Zhang et al., 2013), and very few modeling study considered the

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BrC absorption from sources (e.g., secondary formation) other than biomass burning (Wang et

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al., 2014) or the loss of BrC from photo-bleaching. Moreover, the source apportionment of BrC

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with receptor models were rarely conducted due to the lack of organic tracers related to specific

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sources (Zhang et al., 2010; Xie et al., 2016). Therefore, the sources and their contributions to

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atmospheric BrC are still unclear.

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Laboratory measurements have demonstrated that biomass burning (BB) can generate

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substantial BrC with strong light-absorbing chromophores (Chen and Bond, 2010; Saleh et al.,

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2014). A number of field studies have confirmed that BB is the dominant contributor to BrC

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absorption in the atmosphere (Washenfelder et al., 2015; Cheng et al., 2016; Zhang et al., 2016).

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Hecobian et al. (2010) investigated the light-absorption characteristics of water-soluble organic

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aerosol (WSOC) in the Southeastern United States (US), and attributed 50% of the yearly

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average light absorption at 365 nm to BB. Washenfelder et al. (2015) found that more than 95%

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of BrC absorption at a rural site located in the Talladega National Forest in central Alabama

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(Southeastern US) was attributable to BB organic aerosol (OA) in the summertime. However,

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light-absorbing WSOC was also observed in urban Los Angeles and Atlanta during the

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summertime (Zhang et al., 2011), under conditions where the BB influence was not significant,

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and was linked with secondary organic aerosol (SOA) formation. In the southeastern US,

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Hecobian et al. (2010) and Zhang et al. (2010) identified a summertime secondary WSOC

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source/factor with a yearly average contribution of 34% to the WSOC absorption by analyzing

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PM2.5 with Positive Matrix Factorization (PMF). Therefore, SOA formation is an important

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source of BrC absorption in the summertime when BB is absent. Despite a number of laboratory

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studies that have identified potential pathways for secondary BrC formation and degradation

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(Updyke et al., 2012; Lee et al., 2014; Zhao et al., 2015; Xie et al., 2017a), the precursors,

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formation mechanisms, compositions, and degradation of secondary BrC in the atmosphere are

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still unclear. Moreover, the aforementioned ambient studies focused on the light absorption of

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WSOC, which typically accounts for less than 50% of the methanol extractable OC (MEOC)

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(Zhang et al., 2013; Cheng et al., 2016).

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To identify the major sources of BrC in summer with no significant BB influences, we

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analyzed the optical and chemical characteristics of OC in PM with aerodynamic diameter less

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than 2.5 µm (PM2.5) collected at the US Environmental Protection Agency (EPA), Research

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Triangle Park (RTP, NC), during the Southern Oxidation and Aerosol Study (SOAS, summer

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2013). We measured daily average total OC, elemental carbon (EC), WSOC and an array of

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polar OMMs; UV/Vis absorbance of the MEOC and WSOC was also measured. PMF was

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applied to optical and selected compositional data to identify and quantify potential sources and

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atmospheric processes contributing to BrC absorption.

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2. METHODS

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2.1 Sampling

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Ambient PM2.5 samples were collected at the EPA’s Ambient Air Innovative Research Site

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in RTP, NC (35.889509 °N, 78.874650 °W). This site is located in the Southeastern US with

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prominent biogenic SOA contribution and influences from anthropogenic pollution (Edney et al.,

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2003; Kleindienst et al., 2007; Hidy et al., 2014; Nguyen et al., 2014). Two PM2.5 samplers

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(Tisch Environmental, Village of Cleves, OH) installed with dual cyclone impactors were used

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for ambient air sampling at a flow rate of 226 L min-1 from June 1 to July 15, 2013. During the

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entire sampling period, the average ambient temperature was 24.1 ± 1.60 °C (range 21.0 –

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26.8 °C), and the relative humidity was 78.9 ± 7.12% (62.5 – 92.8%). Daily integrated PM2.5

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samples were collected onto 90 mm diameter quartz-fiber filters (QF) for 23 h beginning at 7

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AM (EDT). Two sets of forty-five ambient samples and six field blank filters were obtained for

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analysis. The two sets of filter samples were collected in parallel during the same time period.

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Filters were sealed and stored at ≤ -20 °C prior to analysis. One set of filter samples was used for

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OC-EC, WSOC and gas chromatography-mass spectrometry (GC-MS, ThermoQuest, Austin,

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TX) analysis; the other set of filter samples was extracted and analyzed using UV/Vis

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spectrometry (V660, Jasco Incorporated, Easton, MD) and high performance liquid

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chromatography (HPLC, 1200 Agilent Technologies, Santa Clara, CA) /diode array detector

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(DAD, G1315C)- quadruple -time of flight mass spectrometry (Q-ToFMS, Agilent 6520).

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2.2 OC-EC, WSOC and OMMs analysis

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Details of the bulk OC, EC, WSOC and OMM analysis are given in supplemental

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information. Briefly, the bulk OC and EC were measured using a thermal optical instrument

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(Sunset Laboratory, Portland, OR) operated with a modified NIOSH method 5040 protocol

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(NIOSH, 1999). WSOC was extracted from an aliquot of each PM2.5 sample using Milli-Q water

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and measured on a total organic carbon (TOC) analyzer (TOC-V, CSN, Shimadzu) after

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filtration. The concentrations of OC-EC and WSOC are given in Table 1. The OMMs in each

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PM2.5 sample were extracted and analyzed with a GC-MS and a HPLC/DAD-Q-ToFMS. As

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shown in Table 2, the OMMs analyzed by GC-MS include three isoprene SOA tracers, eight

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monoterpene SOA tracers and levoglucosan. Four nitro-aromatic compounds (NACs), two

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isoprene derived organosulfates (iOS) and five monoterpene derived organosulfates (mOS) were

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measured using HPLC/DAD-Q-ToFMS. The authentic standards and surrogates used for OMMs

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quantification are provided in supporting information and Table S1. Their recoveries and method

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detection limits are shown in Table S2. Field blanks were analyzed using identical methods for

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blank correction, and no contamination was observed for the target compounds.

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2.3 Light absorption measurement

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Water extracts. Details of the filter extraction and light absorption measurement are similar to

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those in Xie et al. (2017b). Briefly, an aliquot of filter (6 cm2) was extracted in 5 mL pure water

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(HPLC grade) ultrasonically for 15 min, and then filtered through a 25 mm diameter

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polytetrafluoroethylene (PTFE) filter with a 0.2 µm pore size (National Scientific Company).

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The light absorption of water extracts was measured with a UV-Vis spectrometer over the

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wavelength (λ) range of 200 nm to 900 nm. To ensure data quality, the wavelength accuracy (±

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0.3 nm) and repeatability (± 0.05 nm) were monitored periodically. Solvent background was

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subtracted with a reference cuvette containing pure water.

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The aqueous extract absorption (Aλ,w) measured by UV/Vis spectrometer is converted to light

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absorption coefficient (Absλ,w, Mm-1) by:

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Abs, = ( , − , ) ×

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where A700,w is subtracted from Aλ,w to correct for systematic baseline drift, Vl (m3) is the solvent

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volume (5 mL) used for extraction, Va (m3) is the air volume sampled by the extracted filter area,

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L (0.01 m) is the optical path length, and ln (10) converts the absorption coefficient in units of m-

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1

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(MAEλ,w, m2 gC-1) is a measure of absorption efficiency (Zhang et al., 2013; Liu et al., 2016),

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and is calculated by:

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from log base-10 to natural log (Hecobian et al., 2010). The bulk mass absorption efficiency

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where WSOC is the concentration of WSOC in each sample (µg m-3). The spectral dependence

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of light absorption, parameterized as the absorption Ångström exponent (Åw), is determined from

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the slope of linear regression of log10(Absλ,w) vs. log10(λ) over the λ range of 300 to 550 nm.

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Methanol extracts. The method of sample extraction in methanol and light absorption

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measurement are the same as those for the water extracts. After extraction, the filter was air dried

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in the fume hood overnight and analyzed with the Sunset thermal-optical analyzer to measure the

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residual OC. The extraction efficiency (η, %) of OC by methanol is calculated by:

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!# $ !%

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"=

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where OCb is the OC content of each PM2.5 filter before extraction and OCr is the OC content in

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the residual filter after extraction.

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The light absorption coefficient for methanol extracts (Absλ,m, Mm-1) is calculated by:

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Abs,' = ( ,' − ,' ) ×

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and the bulk mass absorption efficiency (MAEλ,m, m2 gC-1) for methanol extracts is calculated by:

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MAE,' =

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where Coc is the mass concentration of extracted OC (OCb – OCr) for each filter sample (µg m-3).

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The absorption Ångström exponent for methanol extracts (Åm) is determined from the slope of

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the linear regression of log10(Absλ,m) vs. log10(λ) over the λ range of 300 to 550 nm. In the

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current work, Absλ and MAEλ were focused at 365 nm, since BrC can have significant absorption

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at this wavelength, and λ = 365 nm is long enough to avoid the influence from other species (e.g.,

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nitrate) (Hecobian et al., 2010). This wavelength is also used in most of the previous studies on

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ambient BrC absorption in solution (Zhang et al., 2011, 2013; Cheng et al., 2016). The

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measurement uncertainty was evaluated by replicate analysis in our previous work (Xie et al.,

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2017b), which suggested an uncertainty lower than 5% for light absorption measurement.

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2.4 PMF analysis and data selection

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A PMF2 model (Paatero, 1998a, b) coupled with a bootstrap technique (Hemann et al., 2009)

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was applied to attribute BrC absorption (Abs365) to specific sources or atmospheric processes.

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Details of this method have been introduced in several previous studies (Xie et al., 2012, 2013a,

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b). Briefly, PMF resolves factor profiles and contributions from an array of compositional data of

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ambient particles by minimizing the sum of squared, scaled residues (Q). One thousand replicate

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data sets were generated from the original data set with the stationary black bootstrap technique 8

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and analyzed with PMF. The factor profiles of bootstrap solutions are compared to that of the

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base case solution for matching rate calculation. A high factor matching rate reflects the

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uniqueness of base case factors and robustness of the solution to input data (Xie et al., 2012).

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The measurement days resampled in each bootstrapped data set were recorded to evaluate the

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bias and variability due to random resampling error in the PMF solution. In the current work, the

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factor number was determined by the interpretability of different PMF solutions (3 – 5 factors)

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and factor matching rate (at least > 50%). Details of the simulation statistics for 3- to 5-factor

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solutions are provided in Table S3 of the supporting information.

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Due to the limited sample size (N = 45), OMMs with identical sources and significant

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correlations (p < 0.05) were added together as one input for PMF analysis. The final input

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species include Abs365,m, Abs365,w, OC, EC, WSOC, isoprene SOA tracers (sum of the three

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tracers in Table 2), iOS (sum of the two iOS tracers in Table 2), mOS (sum of the five mOS

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tracers in Table 2) and levoglucosan. The NACs and monoterpene SOA tracers were not

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included for PMF analysis due to the uninterpretable PMF solution. One possible explanation is

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that the NACs identified in this work can be generated from both biomass burning (Lin et al.,

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2016, 2017) and photo-oxidation of aromatic VOCs (Iinuma et al., 2010; Xie et al., 2017a);

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besides biogenic emissions, biomass burning also emits monoterpenes as precursors for SOA

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formation (Greenberg et al., 2006), so the contribution of monoterpene SOA cannot be separated

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from biomass burning during PMF analysis. GC-MS analysis was unavailable for two samples

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collected on July 2nd and 14th 2013, and these two samples were excluded from the PMF analysis.

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The estimation of uncertainties for input species concentrations and treatment of missing values

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are provided in Xie et al. (2016). All measurements used in PMF analysis were above the limit of

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detection.

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3. RESULTS

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3.1 Bulk components and BrC absorption The statistics of bulk OC, EC and WSOC concentrations and the light absorption of WSOC

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and MEOC are provided in Table 1. The average concentration of OC was 1.77 ± 0.74 µg m-3,

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ranging from 0.72 to 3.61 µg m-3. Concentrations of EC were more than 10 times lower than OC

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with an average concentration of 0.092 ± 0.051 µg m-3. WSOC accounted for 17.9 – 80.2%

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(average 48.9 ± 15.1%) of OC, much lower than the fraction of OC extractable by methanol

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(84.9 ± 7.57%).

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The Abs365,w ranged from 0.067 to 0.46 Mm-1, with an average value of 0.21 ± 0.097 Mm-1;

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the corresponding average MAE365,w and Åw are 0.29 ± 0.13 m2 gC-1 and 9.33 ± 1.13,

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respectively. The MEOC had a significantly (p < 0.05) higher average light absorption

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coefficient (Abs365,m, 0.52 ± 0.23 Mm-1) and mass absorption efficiency (MAE365,m, 0.36 ± 0.16

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m2 gC-1) compared to that of WSOC. The ranges of Abs365,m and MAE365,m are 0.17 – 0.99 Mm-1

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and 0.18 – 1.44 m2 gC-1. While the average Åm (5.49 ± 0.88) is significantly (p < 0.05) lower

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than Åw.

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3.2 Speciated OMMs in PM2.5

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The statistics of the polar OMMs concentrations are listed in Table 2. Among the four

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identified NACs, 4-nitrocatechol has the highest concentrations (0.057 ± 0.042 ng m-3), followed

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by 4-nitrophenol (0.018 ± 0.027 ng m-3). Two sulfate esters from isoprene oxidation (Surratt et

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al., 2006, 2007) and five from monoterpene oxidation (Surratt et al., 2007, 2008; Stone et al.,

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2012) were detected in all PM2.5 samples, with average concentrations ranging from 0.66 ± 0.50

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– 3.23 ± 2.38 ng m-3 (Table 2). The three identified isoprene SOA tracers, 2-methylglyceric acid,

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2-methylthreitol and 2-methylerythritol, had average concentrations of 8.71 ± 8.22, 40.3 ± 39.7

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and 96.8 ± 97.1 ng m-3, respectively. The 3-methyl-1,2,3-butanetricarboxylic acid (30.9 ± 20.3

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ng m-3) is the most abundant species among the eight monoterpene SOA tracers, followed by 3-

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hydroxyglutaric acid (19.5 ± 14.7 ng m-3) and 3-acetyl pentanedioic acid (14.8 ± 12.5 ng m-3).

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Levoglucosan, a widely used organic tracer for BB (Simoneit et al., 1999; Zhang et al., 2009a), is

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detected in our samples at concentrations ranging from 1.47 to 23.3 ng m-3.

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3.3 PMF results

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A four-factor solution (anthropogenic emission, organosulfate formation, biomass burning

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and isoprene SOA formation) was chosen due to the most interpretable factors and high factor

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matching rate (70.2%) between the bootstrapped and base case solutions. The 3-factor solution

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combined the anthropogenic emission and biomass burning factors into one factor (Fig. S1); the

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5-factor solution had a factor matching rate lower than 50% (Table S3), and the majority of bulk

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solution absorption could not be related to any specific source (Fig. S2). Figs. 1 and 2 exhibit the

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factor profiles and factor contributions to Abs365,m based on the 4-factor PMF solution. The

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average relative factor contributions to Abs365,m, Abs365,w, OC, EC and WSOC are given in Fig.

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S3. The factor profiles presented in Fig. 1 are normalized by

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∗ ,-. = ∑4

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where F*kj is the relative weight of species j in factor k to the other factors. The simulated sum of

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factor contributions to Abs365,m agreed with the measured Abs365,m (PMF factor

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contributions/observation = 0.91 ± 0.19, r = 0.88, t test p <0.01).

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In Fig. 1, the anthropogenic emission factor is characterized by the highest percentages of

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EC and OC. The organosulfate formation factor contains the highest percentages of WSOC, iOS

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and mOS. Levoglucosan is almost uniquely loaded in the BB factor with low percentages of

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Abs365,m and Abs365,w. Very low percentages of bulk components and BrC absorption are 11

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attributed to the isoprene SOA formation factor. As shown in Fig. S3, the organosulfate

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formation factor has the highest average contributions to Abs365,m (47%), Abs365,w (46%) and

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WSOC (46%) among the four factors, followed by the anthropogenic emission factor (Abs365,m

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35%, Abs365,w 40% and WSOC 30%).

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4. DISCUSSION

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4.1 Comparison of light-absorbing properties to other studies

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In the current study, MAE365,m (0.36 ± 0.16 m2 gC-1) and MAE365,w (0.29 ± 0.13 m2 gC-1)

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for ambient PM2.5 samples were much lower than those obtained for winter in Beijing (MAE365,m

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1.45 ± 0.26 m2 gC-1, MAE365,w 1.22 ± 0.11 m2 gC-1) (Cheng et al., 2016) and summer in Los

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Angeles (MAE365,m ~1.6 m2 gC-1, MAE365,w 0.71 m2 gC-1) (Zhang et al., 2013), where BB and

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anthropogenic emissions contributed substantially to BrC absorption. MAE365,m and MAE365,w

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values similar to this study were obtained in sampling sites located in Southeastern US during

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summer periods (between ~0.2 and ~0.5 m2 gC-1) (Hecobian et al., 2010; Zhang et al., 2011; Liu

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et al., 2013). This might be due to the fact that biogenic SOA makes up a substantial portion of

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the organic aerosol in Southeastern US and has weak or no absorption (Zhang et al., 2011). The

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Åm and Åw can be used to reflect the spectral dependence of BrC absorption in methanol and

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water extracts, respectively. In the current work, the average Åm (5.49 ± 0.88) is much lower

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than Åw (9.33 ± 1.13), and similar results were also obtained in winter Beijing (Åm 6.99 ± 0.27,

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Åw 7.28 ± 0.24) (Cheng et al., 2016) and summer Los Angeles (Åm 4.82 ± 0.49, Åw 7.58 ± 0.49)

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(Zhang et al., 2013), suggesting that the absorption of strong BrC chromophores might be less

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dependent on wavelength than weak BrC chromophores in the same aerosol sample.

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The light-absorbing properties obtained in this work are derived from the measurements of

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aqueous extracts using a UV-Vis spectrometer, and cannot reflect the BrC absorption in airborne

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particles directly. The atmospheric aerosol absorption not only depends on the BrC mass

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concentration and the structures of BrC chromophores, but also the size distribution, shapes, and

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mixing state of airborne particles (Bond et al., 2006; Lack and Cappa, 2010; Cappa et al., 2012;

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Saleh et al., 2014; Laskin et al., 2015). However, the solution-based measurements of BrC

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absorption are not subject to black carbon (BC) or other absorbers, since the BrC components are

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isolated, thereby permitting a direct measurement of BrC chromophores absorption (Liu et al.,

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2013). The particle-based measurements with optical instruments typically quantify the

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absorption of all components, including BC, BrC, and dust, and differentiating BrC absorption

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from the total can be difficult due to large contribution from BC. Moreover, the assumptions

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associated with Mie calculations for optical properties of airborne particles may lead to

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substantial uncertainty, such as the mixing state of particles (external, internal, or a mixture). Liu

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et al. (2013) found high correlations (r2 > 0.7) between the bulk solution absorption and Mie-

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predicted BrC absorption, and inferred that the actual atmospheric absorption is approximately 2

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times as the bulk solution absorption. Therefore, the light-absorbing properties obtained in this

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work can represent the absorbing characteristics of BrC chromophores isolated from ambient

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aerosols.

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4.2 Contributions of NACs to Abs365

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NACs are a group of light absorbing chromophores that have been observed in BB

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emissions (Claeys et al., 2012; Mohr et al., 2013; Lin et al., 2016, 2017) and SOA from the

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oxidation of certain aromatic volatile organic compounds (VOCs) under high NOX conditions

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(Iinuma, et al., 2010; Lin et al., 2015; Xie et al., 2017a). NACs are also detected in a variety of

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atmospheric environments with a variety of contributing sources (Desyaterik et al., 2013; Kahnt

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et al., 2013; Zhang et al., 2013; Teich et al., 2017). The contribution of total NACs to the light

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absorption of aqueous extracts of ambient particles can be more than 3% (Zhang et al., 2013;

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Teich et al., 2017). In the current work, the light absorption of individual NACs in sample

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extracts was estimated as the product of the concentrations of NACs and the MAE365 of the

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individual compounds in methanol and water. The MAE365,m and MAE365,w for individual NACs

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were obtained from Xie et al. (2017a) and Zhang et al. (2013), respectivley. The MAE365,w for

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C6H6NO3 is not available in Zhang et al. (2013), and thus the MAE365,m for C6H6NO3 reported in

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Xie et al., (2017a) is used as a surrogate. The average contributions to Abs365,m and Abs365,w from

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the four NACs detected in this work are 0.079 ± 0.083% and 0.21± 0.16%, respectively. The

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estimated average contribution of total NACs to Abs365,w is comparable to those obtained under

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acidic conditions during the summer in Germany (Waldstein and Melpitz, 0.13 ± 0.05 – 0.15 ±

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0.10) and in China (Xianghe and Wangdu, 0.46 ± 0.24 – 0.56 ± 0.31) (Teich et al., 2017), but

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lower than that observed in the Los Angeles summer (~3%) (Zhang et al., 2013). Among the four

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NACs in this work, C6H5NO4 (4-nitrocatechol) has the highest contribution to bulk extracts

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absorption, accounting for 67.2 ± 11.8% and 68.7 ± 12.0% of total NACs contributions to

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Abs365,m and Abs365,w, respectively. However, the NACs only account for a very small fraction of

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BrC absorption in ambient PM, and are subject to atmospheric processes (e.g., aging and photo-

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bleaching) (Lee et al., 2014; Zhao et al, 2015). Therefore, further studies are needed to determine

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which additional light absorbing chromophores, such as highly conjugated compounds (e.g.,

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polycyclic aromatic compounds and high molecular weight humic like substances), are

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responsible for BrC absorption in the atmosphere.

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4.3 Correlation matrix analysis

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Because the NACs identified in this work had minor contributions (< 1%) to Abs365,m and

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Abs365,w, and the other OMMs were less likely to contribute to BrC absorption due to their non-

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conjugated, chain-like structures, a correlation matrix plot (Fig. 3) was used to examine the

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relationships of all measurements conducted in this work, especially for BrC absorption versus

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organic composition. As shown in Fig. 3, the NACs weakly correlate (r < 0.4) with most other

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OMMs, including levoglucosan. Possible reasons are as follows: the observed NACs are not

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from BB; NACs and levoglucosan undergo different formation processes during BB, or have

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different atmospheric lifetimes or atmospheric processing mechanisms. One potential source of

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ambient NACs in these samples is the oxidation of gaseous aromatics with NOX (Xie et al.,

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2017a). However, high time resolution concentration data of NACs, volatile aromatics and other

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gaseous pollutants (e.g, NOX, O3) are needed for validation. The iOS and mOS, representing the

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formation of organosulfates from photo-oxidation of biogenic VOCs, correlated significantly (r =

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0.53 – 0.94, p < 0.01). Guo et al. (2015) estimated a pH range of 0.5 to 2 for particle water in the

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Southeastern US during the summer, and such acidic conditions favor the formation of

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organosulfates (Surratt et al., 2008). The three isoprene SOA tracers listed in Table 1 correlated

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strongly (r = 0.77 – 0.97, p < 0.01); however, only four of the eight monoterpene SOA tracers (3-

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acetyl pentanedioic acid, 3-acetyl hexanedioic acid, 3-methyl-1,2,3-butanetricarboxylic acid and

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3-hydroxyglutaric acid) showed strong correlation (r > 0.7). Except 2-hydroxy-4-isopropyladipic

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acid and 3-hydroxy-4,4-dimethylglutaric acid, the other monoterpene SOA tracers correlate with

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levoglucosan significantly (r = 0.50 – 0.60, p < 0.01). This is because biomass burning can also

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generate monoterpenes serving as precursors for SOA formation (Greenberg et al., 2006), and

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the monoterpene SOA tracers are linked to both biogenic SOA and biomass burning. The

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average concentration of levoglucosan (9.25 ± 5.40 ng m-3) in this study is much lower than

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other locations in Southeastern US (summer 18.7 ± 44.7 ng m-3; winter 170 ± 180 ng m-3)

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(Zhang et al., 2010), and no significant (p > 0.05) correlation was observed between

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levoglucosan and Abs365, suggesting that BB is not the dominant BrC source in this work.

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4.4 Sources of BrC absorption As shown in Figs. 1, 2 and S1, the organosulfate formation factor accounts for major BrC

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absorption and contributes 47% of Abs365,m and 46% of Abs365,w on average. The organosulfate

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factor is characterized by the highest loadings of WSOC, iOS and mOS, indicating that a

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substantial fraction of BrC absorption is from biogenic SOA. However, the organosulfate

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molecules identified in this work might not be chromophores themselves (Nguyen et al. 2012).

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Song et al. (2013) and Nguyen et al. (2012) investigated the absorption of laboratory generated

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biogenic SOA inferring that the light-absorbing compounds were aldol condensation oligomers

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with nitroxy organosulfate groups formed in acidic aerosols. Presently, two iOS and five mOS

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species were identified, and three of the five mOS species are nitroxy organosulfates (Table 1).

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The PMF results of this work support the theory that the organosulfate formation process is

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closely related to BrC absorption in Southeastern US. The organosulfates measured in this work

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are formed through the reactions of biogenic VOCs and sulfuric acid (or SO2) (Surratt et al.,

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2006, 2007, 2008), and an association has been observed between anthropogenic emission and

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SOA formation from isoprene and monoterpenes (Budisulistiorini et al., 2015; Xu et al., 2015).

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These might explain the presence of EC in the organosulfate formation as well as isoprene SOA

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factors.

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The anthropogenic emission factor has the second highest average contribution to WSOC

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(30%), Abs365,m (35%) and Abs365,w (40%). Weber et al. (2007) suggested that WSOC from

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PM2.5 in the Southeastern US is mainly SOA. So it may be that the anthropogenic emission

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factor has a contribution from anthropogenic SOA. However, due to the lack of anthropogenic

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SOA tracers, the anthropogenic SOA contribution was unresolved here and likely lumped into

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the anthropogenic emission factor. Previous studies have demonstrated that both primary motor

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vehicle emissions and SOA formation with aromatic VOCs emitted from anthropogenic sources

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contain BrC chromophores (Xie et al., 2017a, b). In the absence of BB emissions, we tentatively

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suggest that biogenic SOA containing organosulfates formed under acidic conditions is an

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important source of BrC absorption in RTP during the summer. However, the BrC absorption

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attributable to primary and secondary anthropogenic emissions, respectively, needs further study.

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In the current work, the small sample size might result in large uncertainties in PMF results.

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Zhang et al. (2009b) found that OMM-based PMF analysis could derive stable factors with high

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OC contributions from data set with a small sample size (50 – 60), but their factor contributions

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were subject to considerable uncertainties. As such, the two PMF factors with high contributions

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to OC (organosulfate formation and anthropogenic emission) resolved in this work are valid, but

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their contributions to bulk solution absorption should be treated with caution. Moreover, the

392

grouping of OMMs with identical sources for PMF analysis neglected the variability between

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species. To understand the impact of species grouping on PMF results, individual OMMs were

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used instead of grouped OMMs (e.g., iOS) for an additional PMF simulation, and a 4-factor

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solution was chosen due to the most interpretable factors with a factor matching rate of 18.8%.

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Similar as the PMF analysis using grouped OMMs, the factor profiles and contributions

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presented in Figs. S4 and S5 suggest a substantial contribution of Abs365,m (49%) form

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organosulfate formation factors. These results validated the grouping of OMMs for PMF analysis

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in this work, which enhanced the robustness of PMF solution by increasing the factor matching

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rate between the bootstrapped and base case solutions.

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5.0 CONCLUSION To understand the sources of BrC absorption during the summer in RTP, NC, when the

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influence of BB was less significant, the light absorption of MEOC and WSOC in PM2.5 and the

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chemical compositions of OC were measured and analyzed with a PMF receptor model. The

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average MAE365,m and MAE365,w observed in the current study are 0.36 ± 0.16 m2 gC-1 and 0.29

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± 0.13 m2 gC-1, respectively, comparable to the places where organic aerosols are dominated by

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biogenic SOA in summer (e.g., Southeastern US), but weaker than the locations with

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considerable influences from BB or anthropogenic emissions (e.g., Beijing, Los Angeles). The

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four NACs identified in PM2.5 samples account for less than 1% of the aqueous extracts

411

absorption, and further studies are warranted to explore the unexplained BrC chromophores. The

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weak correlation between Abs365 and levoglucosan (p > 0.05) suggests that BB is not an

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important BrC source in RTP during the summer. A 4-factor PMF solution was resolved from

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the data set with grouped OMMs, and the orgnaosulfate formation factor had average

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contributions of 47% and 46% to Abs365,m and Abs365,w, respectively. These results were

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validated by PMF analysis using individual OMMs, and suggested that BrC chromophores could

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be generated during the formation process of biogenic SOA containing organosulfate under

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acidic conditions. However, the BrC absorption attributable to primary and secondary

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anthropogenic emissions requires further work.

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ACKNOWLEDGEMENTS/DISCLAIMER

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This research was supported by the State Key Laboratory of Pollution Control and

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Resource Reuse Foundation (No. PCRRF17040), the National Natural Science Foundation of

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China (NSFC, 41701551), the Startup Foundation for Introducing Talent of NUIST (No.

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2243141801001). The U.S. Environmental Protection Agency, through its Office of Research

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and Development, also funded and collaborated in the research described here under Contract

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EP-C-15-008 to Jacobs Technology, Inc. This research was supported in part by an appointment

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to the Postdoctoral Research Program at the National Risk Management Research Laboratory

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administered by the Oak Ridge institute for Science and Education through Interagency

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Agreement No. 92433001 between the U.S. Department of Energy and the U.S. Environmental

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Protection Agency. Data used in the writing of this manuscript is available at the U.S.

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Environmental Protection Agency’s Environmental Dataset Gateway (https://edg.epa.gov). The

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views expressed in this article are those of the authors and do not necessarily represent the views

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or policies of the U.S. Environmental Protection Agency.

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aerosol in Los Angeles and Atlanta: A contrast in secondary organic aerosol. Geophysical Research Letters 38, L21810. doi:10.1029/2011GL049385. Zhang, Y., Sheesley, R.J., Bae, M.-S., Schauer, J.J., 2009. Sensitivity of a molecular marker based positive matrix factorization model to the number of receptor observations. Atmospheric Environment 43, 4951-4958. Zhang, Y., Sheesley, R.J., Schauer, J.J., Lewandowski, M., Jaoui, M., Offenberg, J.H., Kleindienst, T.E., Edney, E.O., 2009. Source apportionment of primary and secondary organic aerosol using Positive Matrix Factorization (PMF) of molecular markers. Atmospheric Environment 43, 5567-5574. Zhao, R., Lee, A.K.Y., Huang, L., Li, X., Yang, F., Abbatt, J.P.D., 2015. Photochemical processing of aqueous atmospheric brown carbon. Atmospheric Chemistry and Physics 15, 6087-6100.

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Table 1. Concentrations of bulk carbonaceous components and light-absorbing properties.

1.62 0.087 0.82 49.8 86.9

0.21 0.29 9.33 0.52 0.36 5.49 0.42 0.84

b

Min

Max

% of Missing values

0.74 0.051 0.45 15.1 7.57

0.72 0.0091 0.20 17.9 58.2

3.61 0.29 2.24 80.2 97.4

0.00 2.22 26.7 26.7 0.00

0.21 0.26 9.37

0.097 0.13 1.13

0.067 0.12 5.79

0.46 0.67 11.9

0.00 26.7 0.00

0.49 0.34 5.25 0.097 0.77

0.23 0.16 0.88 0.43 0.37

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1.77 0.092 0.84 48.9 84.9

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0.17 0.18 4.29 0.26 0.35

0.99 1.14 9.10 0.70 1.98

0.00 0.00 0.00 0.00 26.7

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standard deviation; calculated as missing No. of observations/total sample number (N = 45) × 100%.

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Bulk components -3 OC (µg m ) -3 EC (µg m ) -3 WSOC (µg m ) WSOC/OC (%) MEOC/OC (%) WSOC absorption -1 Abs365,w (Mm ) 2 -1 MAE365,w (m gC ) Åw MEOC absorption -1 Abs365,m (Mm ) 2 -1 MAE365,m (m gC ) Åm Abs365,w/Abs365,m MAE365,w/MAE365,m

Mean

1

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Table 2. Speciated organic compounds (ng m-3) in PM2.5. Std

Min

Max

% of Missing values

0.018 0.0068 0.057 0.0082

0.0073 0.0048 0.059 0.0056

0.027 0.0092 0.042 0.0090

0.0007 0.0004 0.0060 0.0010

0.12 0.060 0.16 0.046

11.1 2.22 22.2 4.44

1.59 3.23

1.54 2.45

0.96 2.38

0.12 0.069

3.90 8.56

0.00 0.00

1.17 1.69 2.66 0.66 2.20

0.98 1.55 2.25 0.60 1.96

1.20 1.25 2.09 0.50 1.67

0.027 0.074 0.086 0.027 0.029

7.68 4.13 8.80 1.85 6.39

0.00 0.00 0.00 0.00 0.00

14.8 4.11 30.9 19.5 4.06 4.30 3.80 3.79 9.25

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8.71 40.3 96.8

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Median

5.51 31.2 70.1

8.22 39.7 97.1

0.40 2.53 7.80

45.3 177 438

4.44 4.44 4.44

10.7 2.88 29.7 15.4 3.08 3.88 3.18 3.07 9.09

12.5 3.10 20.3 14.7 5.03 3.18 2.62 2.41 5.40

1.97 0.62 3.11 1.31 0.23 0.38 0.38 0.34 1.47

46.7 10.6 73.1 48.9 31.9 11.3 10.1 8.85 23.3

6.67 8.89 4.44 4.44 4.44 26.7 6.67 20.0 6.67

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Quantified using authentic standards; b quantified as 2-methyl-4-nitrophenol; c quantified as 2-methyl-4-nitroresorcinol; d quantified as camphor sulfonic acid; e quantified as cis-ketopinic acid.

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Nitroaromatic compounds (NACs) a C6H5NO3 (4-Nitrophenol) b C7H7NO3 a C6H5NO4 (4-Nitrocatechol) c C7H7NO4 d Isoprene sulfate ester (iOS) C2H4O6S C5H12O7S d Monoterpene sulfate esters (mOS) C10H18O5S C10H16O7S C10H17NO7S C10H17NO8S C10H17NO10S e Isoprene SOA tracers 2-Methylglyceric acid 2-Methylthreitol 2-Methylerythritol e Monoterpene SOA tracers 3-Acetyl pentanedioic acid 3-Acetyl hexanedioic acid 3-Methyl-1,2,3-butanetricarboxylic acid 3-Hydroxyglutaric acid 2-Hydroxy-4-isopropyladipic acid 3-Hydroxy-4,4-dimethylglutaric acid Pinic acid Pinonic acid a Levoglucosan

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Figure Captions Fig. 1. Median factor profiles for the 4-factor solution. The whiskers represent the variability in the factor profile derived from the bootstrapped PMF solutions (one standard deviation).

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Fig. 2. Temporal variations in factor contributions to Abs365,m derived from the 4-factor PMF solution.

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Fig. 3. Correlation contour plot of the Pearson’s correlation coefficient (r) between the optical and chemical measurements of organic aerosol. Compounds are grouped by class.

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Figure 1

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Figure 2

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Figure 3

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Highlights

> BrC absorption in summer at RTP, NC is similar to other Southeastern US locations. > Less than 1% of BrC absorption is attributed to identified nitroaromatic compounds.

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> Biomass burning is not the dominant source for BrC absorption in summer at RTP, NC.

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> Biogenic secondary organic aerosol formation is a major BrC source in this work.