Chemical composition, sources and secondary processes of aerosols in Baoji city of northwest China

Accepted Manuscript Chemical composition, sources and secondary processes of aerosols in Baoji city of northwest China Y.C. Wang, R.-J. Huang, H.Y. Ni, Y. Chen, Q.Y. Wang, G.H. Li, X.X. Tie, Z.X. Shen, Y. Huang, S.X. Liu, W.M. Dong, P. Xue, R. Fröhlich, F. Canonaco, M. Elser, K.R. Daellenbach, C. Bozzetti, I. El Haddad, A.S.H. Prévôt, M.R. Canagaratna, D.R. Worsnop, J.J. Cao PII:

S1352-2310(17)30158-9

DOI:

10.1016/j.atmosenv.2017.03.026

Reference:

AEA 15237

To appear in:

Atmospheric Environment

Received Date: 15 December 2016 Revised Date:

11 March 2017

Accepted Date: 14 March 2017

Please cite this article as: Wang, Y.C., Huang, R.-J., Ni, H.Y., Chen, Y., Wang, Q.Y., Li, G.H., Tie, X.X., Shen, Z.X., Huang, Y., Liu, S.X., Dong, W.M., Xue, P., Fröhlich, R., Canonaco, F., Elser, M., Daellenbach, K.R., Bozzetti, C., El Haddad, I., Prévôt, A.S.H., Canagaratna, M.R., Worsnop, D.R., Cao, J.J., Chemical composition, sources and secondary processes of aerosols in Baoji city of northwest China, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.03.026. 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.

ACCEPTED MANUSCRIPT

Chemical composition, sources and secondary processes of aerosols in

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Baoji city of northwest China

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Y. C. Wanga,b, R.-J. Huanga,d,e,*, H. Y. Nia, Y. Chena, Q. Y. Wanga, G. H. Lia, X. X. Tiea, Z.

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X. Shenf, Y. Huanga, S. X. Liua, W. M. Dongh, P. Xueg, R. Fröhliche, F. Canonacoe, M.

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Elsere, K. R. Daellenbache, C. Bozzettie, I. El Haddade, A. S. H. Prévôte, M. R.

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Canagaratnah, D. R. Worsnoph, J. J. Caoa,c ,*

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a

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Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China

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University of Chinese Academy of Sciences, Beijing 100049, China

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Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, China

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Centre for Atmospheric and Marine Sciences, Xiamen Huaxia University, Xiamen 361024, China

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Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), 5232 Villigen, Switzerland

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Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

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Baoji Environmental Monitoring Central Station, Baoji 721000, China

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Aerodyne Research, Inc., Billerica, MA, USA

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Key Laboratory of Aerosol Chemistry & Physics, State Key Laboratory of Loess and Quaternary Geology,

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E-mail address: R.-J. Huang ([email protected]) and J.J. Cao ([email protected])

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ACCEPTED MANUSCRIPT Abstract

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Particulate air pollution is a severe environmental problem in China, affecting visibility,

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air quality, climate and human health. However, previous studies focus mainly on large

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cities such as Beijing, Shanghai, and Guangzhou. In this study, an Aerodyne Aerosol

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Chemical Speciation Monitor was deployed in Baoji, a middle size inland city in

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northwest China from 26 February to 27 March 2014. The non-refractory submicron

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aerosol (NR-PM1) was dominated by organics (55%), followed by sulfate (16%), nitrate

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(15%), ammonium (11%) and chloride (3%). A source apportionment of the organic

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aerosol (OA) was performed with the Sofi (Source Finder) interface of ME-2 (Multilinear

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Engine), and six main sources/factors were identified and classified as hydrocarbon-like

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OA (HOA), cooking OA (COA), biomass burning OA (BBOA), coal combustion OA

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(CCOA)，less oxidized oxygenated OA (LO-OOA) and more oxidized oxygenated OA

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(MO-OOA), which contributed 20%, 14%, 13%, 9%, 23% and 21% of total OA,

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respectively. The contribution of secondary components shows increasing trends from

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clean days to polluted days, indicating the importance of secondary aerosol formation

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processes in driving particulate air pollution. The formation of LO-OOA and MO-OOA is

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mainly driven by photochemical reactions, but significantly influenced by aqueous-phase

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chemistry during periods of low atmospheric oxidative capacity.

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Keywords: ACSM, chemical composition, organic aerosol, source apportionment,

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secondary formation processes

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1. Introduction Aerosols scatter and absorb solar radiation, and affect cloud formation, thereby

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influencing visibility and radiative forcing of the Earth’s atmosphere (Watson, 2002;

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Forster et al., 2007). The exposure to ambient aerosols is associated with increased

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respiratory and cardiovascular diseases (Pope and Dockery, 2006; Cao et al., 2012a). In

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China, aerosol pollution is a serious environmental problem (Cao et al, 2012b). A study

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by Chang et al. (2009) on the visibility trends between 1973 and 2007 revealed a

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substantial decrease in visibility in five out of six Chinese megacities. Tie et al. (2016)

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found that the reduction of solar irradiance caused by the aerosol pollution leads to 2%

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decrease in the rice production and 8% decrease in the wheat production in China.

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Lelieveld et al. (2015) investigated the link between some emission sources and

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premature mortality, and it was estimated that 1.3 million premature deaths were caused

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by outdoor air pollution in China in 2010. These effects are associated with the aerosol

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properties, such as the concentration and chemical composition (Zhang et al., 2015a).

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Numerous field studies have been conducted to identify the composition, sources and

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processes of aerosols in China. Most of the studies are based on the analyses of filter

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samples collected on a daily basis (Chan et al., 2008; Cao et al., 2012c; Huang et al.,

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2014). Recently, several measurements of submicron aerosol composition with high

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time-resolution have been conducted in Chinese cities using online aerosol mass

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spectrometry. The studies with high time-resolution mainly focus on megacities such as

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Shanghai and Beijing (Huang et al., 2012; Sun et al., 2013a; Elser et al., 2016) and big

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cities such as Shenzhen, Nanjing, Xiamen (He et al., 2011; Zhang et al., 2015b; Yan et al.,

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2015), Xi’an and Lanzhou (Xu et al., 2014; Elser et al., 2016). However, middle size

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ordinary cities (with population of around 1 million) constitute the main portion (~70%)

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of Chinese cities and also suffer from serious aerosol pollution. The Chinese MEP

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(Ministry of Environmental Protection) reported that 5 middle size ordinary cities ranked

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the top 10 most polluted cities in China in 2014. Since the Chinese government is

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committed to reducing the urban PM2.5 mass concentration by at least 10% in 2017

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compared to 2012 levels (“Atmospheric Pollution Prevention and Control Action Plan”

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ACCEPTED MANUSCRIPT issued by Chinese State Council in 2013), and aerosol pollution in ordinary cities may

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affect nearby major cities through transport (Sun et al., 2010; Sun et al., 2014; Huang et

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al., 2014), it is necessary to study aerosol properties, sources and processes in ordinary

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prefecture cities for effective emission control strategies. Compared to megacities the

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middle size cities in China, on the other hand, may have different characteristics in

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chemical nature and sources and thus require more studies.

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Baoji is a typical ordinary middle sized city in China (Wu et al., 2004; Xie et al., 2009;

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Xiao et al., 2014). There are a few studies based on filter samples to characterize the

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aerosol composition and its influence on visibility in Baoji (Xie et al., 2009; Wang et al.,

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2010; Xiao et al., 2014). Xie et al. (2009) have illustrated that aliphatic alkanes and

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polycyclic aromatic hydrocarbons (PAHs) in PM10 were much higher in Baoji than those

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in Chinese megacities. Xiao et al. (2014) concluded that ammonium sulfate contributed

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mostly (32.0%) to visibility impairment in Baoji in 2012. However, the filter samples are

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limited by the low temporal resolution and filter sample artifacts (Ng et al., 2011a). In this

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study, measurements with an aerosol chemical speciation monitor (ACSM) were

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conducted at Baoji with 30-minute time resolution, and the objectives were: (1) to

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characterize the temporal evolution, mass fractions and diurnal variations of

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non-refractory PM1 (NR-PM1) species; (2) to study the sources and formation processes

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of organic aerosol; and (3) to identify the main causes of aerosol pollution in Baoji.

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2. Materials and methods

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2.1. Site description

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Baoji is located in the semi-arid region of northwest China (Supplementary Fig. S1). It

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is an ordinary prefecture city of inland China with a population of 1.02 million and 199

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thousand vehicles. An ACSM and other supporting instruments were deployed in the

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Baoji environmental monitoring station (34°21'18.4"N, 107°08'34.7"E). This site is

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classified as a residential and business area. The instruments were installed on the top of a

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fourth-floor building (~15 m high). During the measurement period (26 February-27

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March 2014), the average temperature and RH were 10.6 ± 5.3 ºC (1.0-29.1 ºC) and 50.2

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± 20.7% (13.2-94.5%), respectively. The meteorological condition was very stable with

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average wind speed of 0.63 (± 0.56) m s-1.

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2.2. Instrumentation The quadrupole aerosol chemical speciation monitor (Q-ACSM, Aerodyne Research

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Inc., Billerica, Massachusetts) with unit mass resolution (UMR) and time steps of ~30

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min was deployed for the continuous measurement of non-refractory submicron aerosol

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species (organics, sulfate, nitrate, ammonium and chloride). The ambient aerosol was

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drawn through a 3/8 in. stainless steel tube at a flowrate of ~3 L min-1, and coarse

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particles were removed by an URG cyclone (Model: URG-2000-30ED) with a size cut of

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2.5 µm in front of the sampling inlet. The sample was dried by a nafion dryer

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(MD-110-48S; Perma Pure, Inc., Lakewood, NJ, USA) to reduce uncertainties in the

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collection

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different phase states with different aerosol water content. Detailed descriptions of the

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operational principles and calibration procedures of the ACSM are presented elsewhere

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(Ng et al., 2011a). Briefly, the submicron aerosol (~40–1000 nm vacuum aerodynamic

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diameter) was subsampled into the ACSM with a flow rate of 85 cc min-1. The air was

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sampled through a 100 µm-diameter critical aperture and then focused by the

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aerodynamic lens. The focused particle beam was transmitted to the vaporizer (~600 ºC),

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and the vaporized species were detected and characterized by 70 eV electron impact

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quadrupole mass spectrometry. The combination of an atomizer (Model 9302, TSI Inc.,

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Shoreview, MN, USA), a differential mobility analyzer (DMA, TSI model 3080) and a

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condensation particle counter (CPC, TSI model 3772) was used for the ionization

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efficiency (IE) calibration and to calculate the ACSM response factor (RF). Monodisperse

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ammonium nitrate (NH4NO3) particles were generated and are used for the calibration due

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to the complete vaporization of NH4NO3 (Ng et al., 2011a).

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Hourly mass concentration of PM2.5 were measured with a micro-oscillating balance

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(TEOM®1405-DF; Thermo Scientific, Waltham, MA, USA). Hourly average ozone (O3)

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concentrations were measured by a UV photometer (Model 49i Ozone Analyzer, Thermo

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Scientific). The hourly meteorological data, including temperature, relative humidity (RH),

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precipitation, wind speed and wind direction, were measured by an automatic weather

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station (MAWS201, Vaisala, Vantaa, Finland) and a wind sensor (Vaisala Model

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QMW101-M2).

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2.3.1. ACSM data analysis

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The ACSM DAQ software version 1.4.4.5 (Aerodyne Research Inc., Billerica,

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Massachusetts, USA) was used for data acquisition. The ACSM local tool version 1.5.3.5

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(Aerodyne Research Inc., Billerica, Massachusetts, USA) for Igor Pro (WaveMetrics, Inc.,

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Lake Oswego, Oregon USA) was used for ACSM data analysis and export of PMF

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(positive matrix factorization) matrices. Standard relative ionization efficiencies (RIEs)

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were used for organics, nitrate and chloride (i.e. 1.4 for organics, 1.1 for nitrate and 1.3

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for chloride) (Canagaratna et al., 2007) and were estimated from the IE calibrations for

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ammonium (7.2). The RIESO4 was estimated from IE calibration using NH4SO4 before and

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after the measurement campaign, which provided the RIESO4 value of 1.19 and 1.24,

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respectively, very similar to the default value of 1.2 (Canagaratna et al., 2007). We

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therefore expected that the RIESO4 was rather stable during the entire campaign period and

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the value of 1.2 was used. Decreases in the CE are mainly related to the bounce of

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particles when they impact the vaporizer (Middlebrook et al., 2012). The CE is influenced

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by the aerosol acidity, composition and particle phase water content. Because aerosols

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were dried before entering the ACSM, and particles are overall neutralized, the influences

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of particle phase water and acidity are expected to be negligible. Therefore, CE was

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determined as CEdry = max (0.45, 0.0833 + 0.9167×ANMF) (Middlebrook et al., 2012),

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where ANMF represents the mass fraction of ammonium nitrate in NR-PM1.

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2.3.2. Source apportionment

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Commonly used receptor models for organic aerosol (OA) source apportionment

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include PMF (Paatero and Tapper, 1994; Paatero, 1997), multilinear engine (ME-2)

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(Paatero., 1999) and chemical mass balance (CMB, Watson et al., 1997). PMF is a

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bilinear unmixing model that represents the input data as a linear combination of static

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factor profiles and their corresponding time series:

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=

+ (1)

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where X (m х n) refers to the organic spectral matrix containing m organic mass spectra

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(rows) with n ion fragments each (columns), F (p х n) contains the factor profiles (being p

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the number of profiles) and G (m х p) the corresponding time series. The matrix E (m х n)

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contains the model residuals, and scaled residuals are minimized. The model utilizes a

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least squares approach to iteratively minimize the quantity Q, defined as the sum of the

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squared residuals (eij) weighted by their respective uncertainties (σij): (2)

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PMF requires no a priori information about factor profiles or time series. In contrast,

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ME-2 is used to introduce a priori information as an additional model input. With the a

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value approach one or more factor profiles can be constrained as follows: ,

=

± ! · (3)

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where f refers to a row of the matrix F. j represents the mass to charge ratio (m/z) of the

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ions, and the a value determines the extent to which the output profiles can differ from the

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model inputs and it ranges from 0 to 1.

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The PMF receptor model has been widely utilized for the OA source apportionmnet in

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recent years (Lanz et al., 2009; Ulbrich et al., 2009; Jimenez et al., 2009; Ng et al., 2010;

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He et al., 2011; Huang et al., 2012; Dall’Osto et al., 2013; Sun et al., 2013b; Xu et al.,

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2014; Zhang et al., 2015b; Yan et al., 2015). However, it is pointed out that the PMF

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receptor model has difficulties in separating the factors with similar profiles (e.g. cooking

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and traffic, Mohr et al., 2009) or time series (Fröhlich et al., 2015). This problem can be

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solved by the ME-2 or CMB, which introduces a priori information (profiles) for the

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factors (Canonaco et al., 2013; Crippa et al., 2014; Fröhlich et al., 2015).

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3. Results and discussion

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3.1. Concentrations and chemical composition of NR-PM1

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Fig. 1a shows the time series of mass concentrations of NR-PM1 chemical species. The

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variation of OA mass concentration has short-term fluctuations and shows a distinct

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diurnal profile. The increase rates are 7.3 (3.0-13.3) µg m-3 h-1 from the early morning to

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the morning rush hour (5:00-10:00 Local time, LT) and 17.7 (4.0-38.2) µg m-3 h-1 from

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afternoon to the evening (16:00-22:00 LT). The higher mass increase rate from late

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afternoon could be a consequence of decreasing boundary layer and increasing emissions

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during this period. The relative contribution of the aggregated primary OA (POA,

ACCEPTED MANUSCRIPT including HOA, BBOA, COA and CCOA) to total OA is generally high in the morning

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and evening peaks (62 ± 11% for the morning peak, 70 ± 11% for the evening peak) and

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low (49 ± 12% for the morning valley, 38 ± 14% for the evening valley) for the

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corresponding valleys. Therefore, the high mass increase rates of OA could be associated

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with the enhanced primary emissions. Most of the OA events are not correlated with

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inorganic events, which do not have many short term fluctuations and appear to be more

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regional. Fig. 1b represents the relative contribution of each constituent to the NR-PM1

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mass during the entire study period. On average, NR-PM1 consists of 55% (31-88%)

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organic, 16% (3-35%) sulfate, 15% (2-45%) nitrate, 14% (0-32%) ammonium and 2%

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(0-20%) chloride. The average mass concentration of total NR-PM1 species is 54 (± 28)

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µg m−3. As shown in Fig. 2a, the time series of NR-PM1 correlates strongly with PM2.5

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(R2=0.77), yielding a regression slope of 0.71. This indicates the dominance of NR-PM1

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in PM2.5. Fig. 2b summarizes the NR-PM1 mass concentrations from AMS (aerosol mass

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spectrometer) or ACSM measurement at five cities in Asia during springtime. NR-PM1

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concentrations measured in Tokyo and Hong Kong are less than 15 µg m-3, while those

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measured in inland China (Beijing, Nanjing and Baoji) exceed 25 µg m-3. The highest

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concentration is found in Baoji, which is comparable to the highly aerosol polluted city –

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Beijing (Sun et al., 2015), more than 2 times higher than in Nanjing (Wang et al., 2016),

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and ~4 times higher than in Hong Kong (Li et al., 2015) and Tokyo (Takegawa et al.,

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2006). The higher NR-PM1 concentration in Baoji emphasizes the heavily polluted air in

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this city.

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In order to obtain the information about the NR-PM1 sources and formation, the diurnal

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variation of NR-PM1 species is discussed. As shown in Fig. 3, OA exhibits two major

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peaks. The first one appears at 8:00-10:00 LT coinciding with the morning traffic rush

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hour and the enhanced OOAs concentrations (as described in Sect.3.2), while the second

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one, which is ~2 times higher, at 20:00 LT. The evening peak is due to the PBL change,

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enhanced primary emissions and the effect of relative humidity, as discussed in the next

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section. Sulfate shows peaks in the late morning, likely due to the enhance of SO2

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oxidation from increased atmospheric oxidative capacity. Nitrate presents an evident

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increase from 7.6 µg m-3 at 8:00 LT to 10.0 µg m-3 at 10:00 LT, which is probably due to

ACCEPTED MANUSCRIPT the enhanced production of nitric acid (HNO3). Indeed, the NO2 concentration is enhanced

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in the morning traffic rush hour. Nitrous acid (HONO) is accumulated at night and

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photolyzed in the morning after sunrise producing OH and NO (Alicke et al., 2003; Li et

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al., 2010). HNO3 is produced from the photochemical reactions of NO2 + OH. Nitrate

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shows a valley between 13:00 and 16:00 LT, indicating that the photochemical production

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of nitrate cannot overcome the vertical dilution from PBL development. By contrast, the

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variation of sulfate is rather flat in the afternoon, indicating the large production of sulfate

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which compensates for the dilution from PBL development.

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3.2. Sources of organic aerosol

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In the PMF solutions from 2 to 8 factors, the 6-factor solution is most interpretable,

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which includes HOA, BBOA, COA, CCOA, LO-OOA and MO-OOA. BBOA is mixed

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with other factors in PMF soultuons with a lower number of factors and is splitted into

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two factors of similar profiles in the 7-factor PMF solutions (Supplementary Fig. S2).

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Further separation of factors does not enhance the interpretation of the data, as the new

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factor cannot be associated with distinct sources or peocesses. In the 6-factor solutions,

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the BBOA profile is characterized by the presence of signals at m/z 60 and m/z 73. The

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time series of BBOA is highly correlated with m/z 60 (R2 >0.85) and m/z 73 (R2 >0.83).

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Although the BBOA profile is well-defined in PMF solutions, the mixing between other

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sources is obvious (Supplementary Fig. S2). Specifically, HOA profile contains rather

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high contributions at the PAH-related m/z’s, such as m/z 77, 91 and 115, indicating the

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mixing between HOA and CCOA. COA profiles contains a higher than expected

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contribution at m/z 44 (CO2+), indicating the mixing between COA and OOAs.

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To separate HOA from CCOA in ME-2, we constrained the HOA profile from Ng et al.

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(2011b), which is the average over 15 sites all over the world (including China, Japan,

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Europe and United States). Although the vehicle in China and Enropean are different, e.g.

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the vehicle is dominated by gasoline in China and diesel in Europe, the HOA spectra from

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Europe and China are similar (Ng et al., 2011b; Elser et al., 2016), indicating that traffic

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emissions from different type of vehicles have similar profiles. To decrease the influence

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of OOA on the appointment of COA, the COA profile from Paris (Crippa et al., 2013) was

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constrained. While some variabilities are expected between Chinese and European

ACCEPTED MANUSCRIPT cooking activities, high similarities are found between the COA profile from Paris and

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four COA profiles from different types of Chinese cooking (He et al., 2010; Crippa et al.,

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2013; Elser et al., 2016). To minimize the effect of non-local input profiles (both HOA

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and COA), a value approach was used to adjust the input profiles in a certain extent. For

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ME-2 results with constrained HOA and COA profiles, one unconstrained factor was

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always present and was characterized by high signals at m/z 43, m/z 44, m/z 60 and m/z 73,

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indicating the mix of OOA with BBOA (Supplementary Fig. S3). To separate BBOA from

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OOA, we constrained BBOA profile from the 6-factor PMF solution in Baoji with a value

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of 0. The BBOA profile is totally constrained because it is a well-defined local profile as

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discussed above. 121 possible results can be obtained by limiting a values for HOA and

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COA profiles between 0 and 1 with an interval of 0.1.

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A set of three criteria for optimization of OA source appointment are as follows: (1) The COA diurnal pattern. In the urban area, COA usually has peaks in the mealtimes. This diurnal pattern can be used for the identification of COA. (2) Minimization of m/z 60 and 115 in HOA. The threshold for the maximal fractional

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contributions is derived from multiple ambient data sets (mean + 2σ) (Ng et al.,

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2011b). The upper limit of fractional contribution of m/z 60 and 115 in HOA is 0.006

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and 0.004, respectively.

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(3) The consistency of unconstrained factors with previous studies. Specifically, CCOA

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should present high signals at PAH-related m/z’s, such as m/z 77, 91 and 115. OOAs

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should have abundant peaks at m/z 44 and have lower signals at PAH-related m/z’s

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than CCOA .

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8 solutions conform to the criteria above. The final profiles and time series of

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individual factors are the average from these 8 solutions. Error bars are the standard

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deviation of each m/z. An overview of the time series and mass spectrometric profile of

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each factor is shown in Fig. 4, including HOA, BBOA, COA, CCOA and two oxygenated

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OA factors (i.e., LO-OOA and MO-OOA). The diurnal profiles and mass fractions of

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individual OA factors are shown in Fig. 5. The characteristics of each factor are discussed

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in detail in this section.

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The average concentration of HOA is 5.7 (±5.0) µg m-3, and the mass fraction of HOA

ACCEPTED MANUSCRIPT in OA is 20%. The Baoji HOA spectra is highly correlated with the mass spectra of

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organic particles emitted from diesel (R2 = 0.94) (Canagaratna et al., 2004) and gasoline

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vehicles (R2 = 0.92) (Mohr et al., 2009). As shown in Supplementary Fig. S4, HOA

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co-varies closely with the CnH2n-1 and CnH2n+1 ions, particularly m/z 41, 43, 55, 57, 69, 71,

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83, 85, 97 and 111. Further, HOA contributes 25.5–47.2% of the signal in these m/z’s,

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higher than the contribution from other OA components. The diurnal pattern of HOA

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displays a peak in the morning rush hour (8:00) and the other peak in the evening rush

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hour (20:00).

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The average mass concentration of COA is 4.0 (±3.2) µg m-3, and it contributes 14% of

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total OA mass. The correlations in the mass spectra between the Baoji COA and the COA

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factors identified in previous studies are high (R2 = 0.81, 0.93 and 0.94 for the COA

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factors determined in Barcelona, Paris and Beijing, respectively) (Mohr et al., 2012;

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Crippa et al., 2013; Elser et al., 2016). The mass spectra of COA is characterized with

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high m/z 41/43 ratio (1.7) and m/z 55/57 ratio (1.9) which have been shown to be robust

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markers for COA (He et al., 2010; Mohr et al., 2012; Crippa et al., 2013; Elser et al.,

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2016). The enhanced signals at m/z 41 and 55 can be from the heating of seed oil (Allan et

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al., 2010). The COA diurnal cycle shows peaks during morning (7:00), noon (12:00) and

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early evening (20:00), consistent with the breakfast, lunch and dinner time.

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The average BBOA mass concentration is 3.9 (±4.2) µg m-3 with a contribution of 13%

317

to total OA mass. The correlations in the mass spectra between the Baoji BBOA and the

318

BBOA from Ng et al (2011b) are high (R2 = 0.83). This factor shows high signals at m/z

319

60 (C2H4O2+) and 73 (95% of which is C3H5O2+) (Lanz et al., 2008; Mohr et al., 2009).

320

These two ions (C2H4O2+ and C3H5O2+) are well-known fragments of levoglucosan and

321

mannosan, which are products of incomplete combustion and pyrolysis of cellulose and

322

hemicelluloses from biomass burning (Alfarra et al., 2007). BBOA contributes 47 % of

323

m/z 60 and 45 % of m/z 73, much higher than the contributions from other factors

324

(Supplementary Fig. S4). The time series of BBOA correlates very well with that of m/z

325

60 (R2 = 0.90) and m/z 73 (R2 = 0.85). It also has a high correlation with COA (R2 = 0.63,

326

Supplementary Table S1), indicating that some cooking activities may use wood or straw

327

for combustion. The diurnal variation of BBOA is consistent with that of chloride, both of

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which shows morning (9:00) and evening (20:00) peaks. The two peaks are associated

329

with increased heating and cooking activities. The average mass concentration of CCOA is 2.6 (±2.3) µg m-3, accounting for 9% of

331

the total OA mass. The CCOA contribution is lower compared to BBOA, which is similar

332

to previous study in a nearby city Xi’an during the wintertime heating season (Elser et al.,

333

2016). The CCOA mass spectra is dominated by unsaturated hydrocarbons. It is

334

correlated well with the ambient CCOA mass spectra in Beijing and Xi’an (R2 = 0.66)

335

(Elser et al., 2016). The signal of m/z 44 in Baoji CCOA is similar with those observed in

336

2014 winter Beijing (Elser et al., 2016; Sun et al., 2016), but much lower than those in

337

2010 winter Beijing (Hu et al., 2016) and 2014 winter Lanzhou (Xu et al., 2016). The

338

spectral differences were probably due to different burning conditions and aging

339

processes. Zhou et al (2016) found that the change in burning types of coals can produce

340

different CCOA spectra, particularly for m/z 44 and 73. The mass spectra of CCOA can be

341

further validated by the high signals at PAH-related m/z, such as m/z 77, 91 and 115

342

(Dall'Osto et al., 2013; Hu et al., 2013). Not surprisingly, the temporal variation of CCOA

343

correlates well with m/z 77, 91 and 115, with correlation coefficient R2 >0.65

344

(Supplementary Fig. S4). It should be noted that CCOA presents the highest contributions

345

to (PAH)-related ions (Supplementary Fig. S4). The diurnal of CCOA shows a peak (not

346

the maximum) at 8:00 LT, which might be associated with the breakfast cooking activities

347

which often use coal, particularly for the on-street breakfast cooking. The CCOA morning

348

peak was also observed in other locations, e.g., Beijing and Xi’an (Elser et al., 2016).

349

CCOA started increase in the evening and reached the maximum at the midnight,

350

consistent with enhanced emissions from residential coal combustion at night.

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Two oxygenated OA factors were resolved, i.e., LO-OOA and MO-OOA, which

352

account for 23% and 21% of the total OA mass, respectively. The sum of OOAs

353

correlates well with SIAs (secondary inorganic aerosols, including sulfate, nitrate and

354

ammonium) (R2 = 0.82), indicating likely a more regional nature due to the difference in

355

their formation rates (Huang et al., 2014). The correlation of time series between

356

LO-OOA and MO-OOA is low (R2 = 0.39, Supplementary Table S1), but the resulting

357

linear combination indicates the evolution of secondary OA (SOA). The most abundant

ACCEPTED MANUSCRIPT peaks in the mass spectra of the OOAs are at m/z 44 and 43. LO-OOA contributes 36% of

359

m/z 43 and 31% of m/z 44 signals, while MO-OOA contributes 3% of m/z 43 and 60% of

360

m/z 44 signals. Ion intensities at m/z 44 and 43 (representing SOA) were retrieved by

361

removing the POA contribution from the measured m/z 44 and 43 following the approach

362

described in Canonaco et al. (2015). LO-OOA is highly correlated (R2 = 0.97) with SOA

363

m/z 43 (an indicator of less oxidized oxygenated organics), while MO-OOA is well

364

correlated (R2 = 0.90) with SOA m/z 44 (an indicator of more oxidized oxygenated

365

organics). The diurnal cycle of two OOAs peaks in the morning (10:00 LT) and increases

366

in the evening (18:00-20:00). The peak in the morning is likely due to the enhanced

367

production of OH radicals from e.g. HONO, H2O2 and CH3OOH after sunrise (Alicke et

368

al., 2003; Li et al., 2010). The increase in the evening is likely due to the decrease of PBL

369

height and the effect of regional transport. Besides, both fLO-OOA (the mass fraction of

370

LO-OOA in OA) and fMO-OOA (the mass fraction of MO-OOA in OA) are positively

371

correlated with RH when RH >60% (Supplementary Fig. S5), indicating that the

372

formation of LO-OOA and MO-OOA is also influenced by relative humidity (RH) in the

373

nighttime.

374

3.3. Atmospheric processes of secondary aerosol

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Fig. 6 shows the mass fractions of NR-PM1 species and OA sources in clean days,

376

less-polluted days and polluted days. Here, the days with NR-PM1 daily average mass

377

concentration below the 25th quartile (i.e., 43 µg m-3) are denoted as the clean days, within

378

the 25-75th interquartile range (i.e., 43-75 µg m-3) as the less-polluted days, and higher

379

than the 75th quartile (i.e., 75 µg m-3) as the polluted days. The mean concentration of

380

NR-PM1 is 84.9 µg m-3 in the polluted days, about 3 times higher than in clean days.

381

Organics account for 67% of NR-PM1 mass in clean days, but this fraction decreases to

382

56% in less polluted days and further to 48% in polluted days. In terms of the OA sources,

383

while POA dominates OA in the clean days accounting for 65% of OA mass, its

384

contribution decreases to 56% in less polluted days and to 48% in polluted days. The

385

contribution of two OOAs increases correspondingly, i.e., from 16% in clean days to 24%

386

in less polluted days and further to 29% in polluted days for LO-OOA, and from 19% in

387

clean days to 20% in less polluted days and further to 23% in polluted days for MO-OOA.

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ACCEPTED MANUSCRIPT Similar to LO-OOA and MO-OOA, the secondary inorganic species including sulfate,

389

nitrate and ammonium also show increasing trends from clean days to polluted days (Fig.

390

6). It should be noted that the increase of two OOA fractions and SIA are accompanied

391

with increased RH and decreased temperature and wind speed (Fig. 6), suggesting the

392

important effects of meteorological conditions on the formation of secondary aerosol

393

during particulate air pollution period in Baoji.

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The strong increase of sulfate in polluted days is associated with high RH, suggesting

395

aqueous-phase oxidation of SO2 could be an important process for sulfate formation as

396

reported in previous studies (Sun et al., 2013b; Elser et al., 2016). The large increase of

397

nitrate from 9% during clean days to 18% during polluted days, however, is likely

398

associated with the decrease of temperature which facilitates the gas-to-particle

399

partitioning of semivolatile NH4NO3. Compared to the SIAs, the formation of OOAs is

400

still very uncertain, especially during particulate pollution period. Here the correlation of

401

two OOAs with atmospheric oxidative capacity and meteorological conditions are further

402

evaluated to better understand the factors governing SOA formation.

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Data at the daily maximum solar radiation ± 2 h are analyzed to capture the period of

404

highest photochemical activities. As shown in Fig. 7, the increases of fLO-OOA (the mass

405

fraction of LO-OOA in OA) and fMO-OOA (the mass fraction of MO-OOA in OA) are

406

accompanied with the increased odd oxygen (Ox=O3+NO2) and solar radiation, indicating

407

that the formation of OOAs is driven by the photochemical reactions. The fitted line for

408

the fLO-OOA vs. Ox has a slope of 0.0019, while that for fMO-OOA vs. Ox is 0.0014. This

409

suggests that the formation of LO-OOA is faster than MO-OOA, likely due to the

410

enhanced emissions of volatile organic compounds (VOCs) in the pollution area which

411

compete with the production of MO-OOA. Fig. 8 shows that the formation of LO-OOA

412

and MO-OOA is also influenced by relative humidity (RH). When RH >60% which is

413

typical value during haze pollution events, both fLO-OOA and fMO-OOA are positively

414

correlated with RH. In particular at low atmospheric oxidative capacity (i.e., Ox=40-70 µg

415

m-3), fLO-OOA and fMO-OOA are linearly correlated with RH, suggesting the importance of

416

aqueous-phase chemistry in the formation of SOA.

417

4. Conclusions

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ACCEPTED MANUSCRIPT An Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research Inc.) was

419

deployed in Baoji from 26 February to 27 March 2014. The non-refractory submicron

420

aerosol (NR-PM1) is dominated by organics (55%), followed by sulfate (16%), nitrate

421

(15%), ammonium (11%) and chloride (3%). Six OA factors were identified and classified

422

as HOA, COA, BBOA, CCOA, LO-OOA and MO-OOA, which contributed 20%, 14%,

423

13%, 9%, 23% and 21%, to total OA respectively. The contribution of secondary

424

components shows increasing trends from clean days to polluted days. The strong

425

increase of sulfate in polluted days is associated with high RH, suggesting aqueous-phase

426

oxidation of SO2 could be an important process for sulfate formation. The large increase

427

of nitrate is likely associated with the decrease of temperature which facilitates the

428

gas-to-particle partitioning of semivolatile NH4NO3. The formation of LO-OOA and

429

MO-OOA are mainly driven by photochemical reactions, but influenced by the

430

aqueous-phase process particularly at low atmospheric oxidative capacity. Our study

431

suggests the importance in reduction of the precursors of secondary aerosols to mitigate

432

the PM pollution in Baoji. Further, due to the fact that middle size ordinary cities (e.g.,

433

Baoji) constitute the main portion (~70%) of Chinese cities, the measurement and data

434

analysis techniques and even some results found in this study may be applicable to other

435

middle size cities in China, potentially facilitating their efforts to design effective

436

mitigation measures.

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Acknowledgements

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This research is supported by the National Science Foundation of China (No. 91644219,

440

No. 41403110 and No. 41673134), the Shaanxi Goverment (2012KTZB03-01-01),

441

projects from "Strategic Priority Research Program" of the Chinese Academy of Science

442

(Grant No. XDB05060500 XDA05100401) and projects from Ministry of Science and

443

Technology (2013FY112700).

444

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Mass Conc. (µg m )

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3/25

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Fig. 1. (a) Time series of NR-PM1 species, (b) relative contributions of NR-PM1 species for the entire study period. The number on the top of the pie chart is the average mass concentration of total NR-PM1 species.

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718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753

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R = 0.77 Slope = 0.71

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Hongkong Nanjing

Beijing

Baoji

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Fig.2. (a) Comparison of PM2.5 and NR-PM1 mass concentrations in this study, (b) NR-PM1 mass concentrations at five cities in Asia during springtime. The data of other cities are taken from Takegawa et al. (2006), Li et al. (2015), Sun et al. (2015) and Wang et al. (2016).

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Fig.3. Diurnal variations for NR-PM1 species. Data of sulfate, nitrate and ammonium are multiplied by a factor of 3, data of chloride is multiplied by a factor of 10.

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Fig. 4. Mass spectrums (left) and time series (right) of six resolved OA sources. Error bars of mass spectrums represent the standard deviation of each m/z over all accepted solutions..

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HOA BBOA COA CCOA LO-OOA MO-OOA

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(b) Less-polluted days

o

RH = 33.3%, T = 12.5 C, Wind Speed = 0.72 m/s Mass Conc. (NR-PM1) = 28.8 µg m

(c) Polluted days

o

o

RH = 56.8%, T = 10.2 C, Wind Speed = 0.60 m/s

-3

Mass Conc. (NR-PM1) = 54.5 µg m

RH = 72.0%, T = 6.6 C, Wind Speed = 0.52 m/s

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Mass Conc. (NR-PM1) = 84.9 µg m

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Fig. 6. Mass fractions of NR-PM1 species and OA sources in (a) clean days, (b) less-polluted days and (c) polluted days. We denote the days with NR-PM1 daily average mass concentration below the 25th quartile as the clean days, within the 25-75th interquartile range as the less-polluted days, and higher than the 75th quartile as the polluted days.

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Fig. 7. (a) Scatter plots of fLO-OOA versus Ox concentration at the daily maximum solar radiation ± 2 h,

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(b) Scatter plots of fMO-OOA versus Ox concentration at the daily maximum solar radiation ± 2 h. Data

901

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fMO-OOA versus RH (>60%) in the daytime (8:00-18:00). Data are color coded according to the Ox

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concentration (µg m-3). The black lines fit over data with low Ox (40-70 µg m-3).

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Chemical compositions, sources and secondary process of aerosols in

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Baoji city of northwest China

954

Y. C. Wanga,b, R.-J. Huanga,d,e,*, H. Y. Nia, Y. Chena, Q. Y. Wanga, G. H. Lia, X. X. Tiea, Z.

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X. Shenf, Y. Huanga, S. X. Liua, W. M. Dongh, P. Xueg, R. Fröhliche, F. Canonacoe, M.

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Elsere, K. R. Daellenbache, C. Bozzettie, I. El Haddade, A. S. H. Prévôte, M. R.

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Canagaratnah, D. R. Worsnoph, J. J. Caoa,c ,*

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a

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Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China

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b

University of Chinese Academy of Sciences, Beijing 100049, China

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c

Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, China

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d

Centre for Atmospheric and Marine Sciences, Xiamen Huaxia University, Xiamen 361024, China

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e

Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), 5232 Villigen, Switzerland

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f

Department of Environmental Sciences and Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

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g

Baoji Environmental Monitoring Central Station, Baoji 721000, China

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h

Aerodyne Research, Inc., Billerica, MA, USA

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Key Laboratory of Aerosol Chemistry & Physics, State Key Laboratory of Loess and Quaternary Geology,

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E-mail address: R.-J. Huang ([email protected]) and J.J. Cao ([email protected])

Supplementary Material

970 971 972

Supplementary Table S1. Correlation coefficient (R2) between the resolved OA component.

1

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LO-OOA MO-OOA HOA COA BBOA CCOA

MO-OOA

EP

LO-OOA

973 974 975 976 977 978 979 980 981

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0.39 1

HOA

COA

BBOA

CCOA

0.08 0.14 1

0.07 0.10 0.27 1

0.12 0.19 0.48 0.63 1

0.09 0.01 0.55 0.13 0.20 1

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Supplementary Fig. S2. PMF profiles of OA sources for 5, 6 and 7 factor solution.

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Supplementary Fig. S3. ME-2 profiles of OA sources. The COA profile is from that of Crippa et al.

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(2013), and the HOA profile is from that of Ng et al. (2011b). The others are unconstrained factors.

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Supplementary Fig. S4. Correlations of OA factors with the m/z’s. The m/z’s with high correlations

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are marked with circles. The triangles represent the relative contributions of OA factors to the m/z’s.

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Supplementary Fig. S5. (a) Scatter plots of fLO-OOA versus RH (>60%) in the nighttime (19:00-7:00),

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(b) Scatter plots of fMO-OOA versus RH (>60%) in the nighttime (19:00-7:00).

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1021 Fig. S1 Geographical location of Baoji city.

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Fig. S3 ME-2 profiles of OA sources. The COA profile is from that of Crippa et al. (2013), and the

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HOA profile is from that of Ng et al. (2011b). The others are unconstrained factors.

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R (POA factors vs m/z's)

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Highlights: · The high NR-PM1 concentration in Baoji emphasizes the heavily polluted air. · The secondary aerosol contribution increased from clean to polluted days.

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· The formation of OOAs is influenced by aqueous-phase chemistry.