Impact of Secondary Organic Aerosol Tracers on Tracer-based Source

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Impact of Secondary Organic Aerosol Tracers on Tracerbased Source Apportionment of Organic Carbon and PM2.5: A Case Study in the Pearl River Delta, China Qiongqiong Wang, Xiao He, X. H. Hilda Huang, Stephen Miles Griffith, Yongming Feng, Ting Zhang, Qingyan Zhang, Dui Wu, and Jian Zhen Yu ACS Earth Space Chem., Just Accepted Manuscript • DOI: 10.1021/ acsearthspacechem.7b00088 • Publication Date (Web): 06 Oct 2017 Downloaded from http://pubs.acs.org on October 6, 2017

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ACS Earth and Space Chemistry

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Impact of Secondary Organic Aerosol Tracers on

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Tracer-based Source Apportionment of Organic

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Carbon and PM2.5: A Case Study in the Pearl River

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Delta, China

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Qiongqiong Wang,† Xiao He,‡ X. H. Hilda Huang,‡ Stephen M. Griffith,† Yongming Feng,§ Ting Zhang,§

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Qingyan Zhang,§ Dui Wu, and Jian Zhen Yu†,‡,§,*

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†Department of Chemistry, and ‡Division of Environment, The Hong Kong University of Science and

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Technology, Clear Water Bay, Kowloon, Hong Kong, China.

§Atmospheric Research Center, HKUST Fok Ying Tung Graduate School, Guangzhou, China.

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Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China

*Corresponding author: [email protected], 852‐2358‐7389 (Ph)

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ACS Paragon Plus Environment


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Abstract

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Knowledge of the relative abundance of primary organic aerosol (POA) and secondary organic aerosol (SOA)

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forms an important scientific basis for formulating particulate matter (PM) control policies. Taking

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advantage of a comprehensive chemical composition data set of PM2.5 including both POA and SOA tracers

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(most notably SOA tracers of a few biogenic voltaic organic compound precursors), we investigate the

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impact of inclusion of SOA tracers on the source apportionment of organic carbon (OC) and PM2.5 in the

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Pearl River Delta (PRD) region of China using positive matrix factorization (PMF). In PMF runs incorporating

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SOA tracers (PMFw), ten PMF factors were resolved including four secondary factors: (1) SOA_I (α‐pinene,

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β‐caryophyllene and naphthalene derived SOA), (2) SOA_II (isoprene derived SOA), (3) a secondary sulfate

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factor, and (4) a secondary nitrate factor. In PMF tests without SOA tracers (PMFwo), the SOA_I and SOA_II

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factors could not be extracted while the remaining eight source factors were resolved. Among the eight

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common source factors, the industrial emission factor, identified by high loadings of Zn and Pb, showed the

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largest variations between PMFw and PMFwo solutions. The source contributions of SOA_I and SOA_II

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resolved in PMFw were largely shifted to the industry emission source in PMFwo. Secondary organic carbon

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(SOC) summed from the four secondary factors in PMFw contributed ~40% (4.47 μgC/m3) while the SOC

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estimate by PMFwo (3.51 μgC/m3) was 21% lower due to the inability in extracting SOA_I and SOA_II.

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Secondary PM2.5 by PMFwo was 6% lower than that by PMFw (23.7 vs 25.2 μg/m3). The PMFw results

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indicated that SOC from specific precursors may have different formation pathways than secondary sulfate

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and nitrate formation processes and their source contributions could not be properly resolved without the

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indicative tracers included in PMF. This study demonstrates the utility of biogenic SOA tracers in resolving

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isoprene‐derived SOA and highlights the need for more SOA tracers, especially those specific to

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anthropogenic precursors, in improving the source apportionment for those broad OA sources such as

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industrial emissions.

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Key words: Organic aerosol tracers, source apportionment, receptor modeling, secondary aerosol, isoprene SOA, biogenic SOA, positive matrix factorization

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1. Introduction 2


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Increasing exposure to fine particular matter (PM2.5) has been positively associated with adverse health

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effects as documented in many studies.1‐3 Thus, many studies have focused on how to control PM emission,

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especially in locations where air quality standards have still not been reached. Organic Aerosol (OA) is a

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major component of PM2.5 mass, accounting for 20‐90% of the total mass.4‐6 Particulate OA can be directly

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emitted (i.e. primary organic aerosol, POA) or formed through secondary processes in the atmosphere (i.e.

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secondary organic aerosol, SOA). The identification of the relative abundance of POA and SOA is important

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for pollution reduction as their inherently different sources and formation processes dictate different

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control strategies. Compared with POA, the sources of which are commonly known (e.g. vehicle exhaust,

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coal combustion, biomass burning, etc.), knowledge about the properties of SOA have been largely limited

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to laboratory studies.5 SOA formation from certain precursors that result in unique tracers has provided

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valuable insight for aerosol formation schemes. An important subset of these precursors is biogenic

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compounds including isoprene, monoterpenes and sesquiterpenes and has been a focus due to their larger

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emissions and higher reactivity for global SOA formation.7‐9 The difficulty of measuring individual SOA

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products makes it unrealistic to estimate SOA by summing up individual constituents.

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Source apportionment is a widely employed indirect method for estimating SOA contributions, where

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positive matrix factorization (PMF) is successfully used with the advantage of not requiring prior source

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profile information, which is often unavailable for many locations.10 PMF relies on tracers to identify

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different factors (i.e. sources), where the more source‐indicative tracer species that are included, the more

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source types can be potentially resolved. While a variety of source apportionment studies incorporating

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organic tracers in PMF have been carried out at different sampling sites in different time periods for PM11‐

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unexplored. SOA estimated by PMF has primarily relied on inorganic sulfate and nitrate ion inputs,25‐26

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which are best suited to indicate secondary sulfate and nitrate formation processes in the atmosphere.27

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Oxidation products unique to specific hydrocarbon precursors are more suited as SOA tracers. The

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precursor‐specific nature of SOA tracers is inherently promising for indicating sources and aiding source

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separation between SOA and secondary inorganic sources.

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and OA18‐24, including SOA formation routes through specific organic precursors has been largely

Measuring SOA tracers is technically demanding. The challenge of analyzing a broad spectrum of organic

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species including nonpolar and polar organics along with other PM components has limited most of the

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past studies. Furthermore, due to the sample size requirement for achieving statistically reliable results in

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PMF, the incorporation of these SOA tracers at the necessary scale in PMF has been scarce. Hu et al.23

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incorporated polar SOA tracers with POA tracers in a limited sample size (N=45) together with other

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measured PM components (totaling 21 species) from a short time window (Jul‐Aug 2006) in Hong Kong and

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resolved two SOA related factors, but both mixed with other sources: one with secondary sulfate and nitrate

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and biomass burning, the other mixed with biomass burning and vegetative detritus. More studies,

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especially studies with a larger data set under different meteorological conditions, are needed to evaluate

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the utility of SOA tracers in improving separation of SOA and other PM sources.

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Additionally, the discussion of uncertainties for apportionment results was largely limited to single

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bootstrap method due to the limitation in the previous EPA PMF software. Recently, Brown et al.17

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demonstrated the utility of using new error estimation methods available in PMF 5.0 software to fully

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understand the uncertainties of the results. In this work, for the first time we apply these error estimation

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methods to the PMF analysis and explore the uncertainties to better characterize the results. The objective

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of this study is to evaluate the impact of SOA tracers on PMF source apportionment through incorporating

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an extensive suite of both POA and SOA tracers and to explore the possibility of resolving SOA sources by

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their precursor origin.

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2. Materials and Methods

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2.1 Sampling and chemical analysis

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Details of the sampling and chemical analysis of major species, n‐alkanes, polycyclic aromatic

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hydrocarbons (PAHs), hopanes, levoglucosan and mannosan (including all relevant references) are provided

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in Wang et al.28 A summary is presented here.

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Twenty‐four hour PM2.5 filter samples were collected once every six days in 2012. 164 samples collected

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at three sites in the PRD region were used in this work (Figure S2): the Dongguan site is on the outskirts of

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urban Dongguan, the Guangzhou site is an urban residential site, and Nanhai is a site more influenced by

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industrial activities.

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Chemical analyses of the particulate matter samples included gravimetric determination for total mass,

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ion chromatography for major ions, X‐ray fluorescence analysis for elements, and thermal‐optical method

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for organic carbon (OC) and elemental carbon (EC) concentrations. n‐Alkanes, hopanes and PAHs were

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analyzed by thermal desorption gas chromatography‐mass spectrometry (GC/MS). Levoglucosan and

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mannosan were analyzed by high performance anion‐exchange chromatography with a pulsed

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amperometric detection method.

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The polar SOA tracers, vallinic acid, benzenetricarboxylic acids (BTCAs) and dicarboxylic acids (DCAs) were

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determined by GC/MS with n‐methyl‐n‐(trimethylsilyl) trifluoroacetamide (MSTFA) derivatization method.

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Detailed analytical procedure and their spatial and temporal variation were reported in the thesis by He29

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and will be discussed in an upcoming paper.

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2.2 Positive Matrix Factorization (PMF)

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PMF is a bilinear factor analysis method for estimating the contribution of each source to pollutant

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concentrations at receptor sites.10 PMF decomposes the measured data matrix, xij, into a factor profile

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matrix, fkj, and a factor contribution matrix, gik, (i.e. Equation (1)) by minimizing the scaled residue, Q, with

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the non‐negative constraints on the profile and contribution matrices (i.e. Equation (2)). p

xij   gik f kj  eij k 1

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e  Q    ij  i 1 j 1   uij  (2) n

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(1)

m

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In Equation (1), xij is the observed concentration of the jth species in the ith sample, gik is the source

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contribution of the kth factor to the ith sample, and fkj is the factor profile of jth species in the kth factor. eij is

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the residual concentration for each data point, and uij is the uncertainty for each data point provided by the

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user.

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In this work, the EPA PMF version 5.0 was used to perform the analysis. Three uncertainty estimation

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methods are available in EPA PMF 5.0 software: bootstrapping (BS), displacement (DISP), and bootstrapping

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enhanced with DISP (BS‐DISP).30 BS involves resampling from the original input data set, and generating a

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new PMF solution with each resample resulting in a distribution of PMF model parameters. The variation

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among these bootstrapped model parameters (BS factors) estimates the uncertainty of the initial PMF 5



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solution. DISP and BS‐DISP are new features added in EPA PMF 5.0. DISP displaces each fitted element fij

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from the optimum value far enough to increase Q by a preset value, dQmax. The minimum and maximum

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perturbed values define an uncertainty range for each species in each factor profile. BS‐DISP is a

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combination of BS and DISP, where displacement occurs in source profiles derived from each resampled

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data matrix. The 5th and 95th percentiles of the source profile values define the associated error distribution.

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Another key output from DISP and BS‐DISP analyses is the extent of factor swaps, meaning the change of

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factors between the base and uncertainty runs resulting in an exchange of identity and indicating a not‐

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well‐defined solution. In this work, the three PMF error estimation methods were applied to characterize

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the uncertainty of the PMF solution.

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3. Results and discussion

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PMF modeling assumes mass conservation of input species and constant source profiles. To minimize

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the impact of degradation of organics on the deviation from mass conservation assumption, the less volatile

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and less reactive organic species were chosen as input. The requirement of constant source profiles is not

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strictly met when the receptor model is applied to measurement data covering a long‐time duration (e.g.,

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months or longer). The changing source profile issue exists for both POA and SOA. However,

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understanding/progress can still be gained despite the non‐strict adherence to the requirement of constant

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profiles. The PMF‐resolved source profiles could be considered as average profiles broadly representing the

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underlying sources. Despite the known deviation from the requirement of constant source profiles, many

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past studies have demonstrated the success of PMF source apportionment through comparing source

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apportionment results with CMB modeling.18, 31‐32, 26 For example, Jaeckels et al.18 found good agreement

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of OC explained by the PMF‐resolved biomass burning source with the corresponding biomass burning

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source in CMB (R2=0.88, slope=0.93), based on 24‐h filter samples over 2 years, during which the biomass

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burning source profiles also changed. Nevertheless, it is worthwhile to note the deviations from the

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assumptions underlying the principle of PMF in the ensuing discussion of the PMF‐derived source

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apportionment results.

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3.1 Data pretreatment for PMF analysis A total of 164 PM2.5 samples covering three sites were subject to PMF analysis in this study. The

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combination from three sites provided sufficient samples for the PMF run at the expense of being incapable

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of resolving the potential small site‐specific sources. The species given priority for the PMF analysis were

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those having a high abundance and distinct source characteristics, and particularly for organics also being

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less volatile and less reactive. Highly correlated species (R>0.8), suggesting common sources, were lumped

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together to reduce the number of species and avoid the issue of collinearity in PMF.33 After lumping, 32

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species from 164 samples were introduced to the PMF analysis. The abundance and naming abbreviation

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for the organic tracers used are shown in Table 1, while those of the measured major species are provided

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in Table S1. Briefly, the POA tracers included are: 1) Levoglucosan, mannosan, and vanillic acid all included

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individually; 2) Lumped species: odd_Alk (the sum of n‐C29, n‐C31 and n‐C33 alkanes), even_Alk (the sum of

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n‐C28, n‐C30 and n‐C32 alkanes), hopanes (the sum of five abundant and higher carbon chain hopanes),

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PAHs252 (benzo[b+k]fluoranthene and benzo[e]pyrene), and PAHs276 (indeno[1,2,3‐cd]pyrene and

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benzo[ghi]perylene). The SOA tracers included are: 1) isoprene SOA tracers: 2‐MGA, 2‐MTs and C5‐alkene

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triols; 2) α‐pinT (five lumped organic acid α‐pinene SOA tracers); 3) β‐caryT (β‐caryophyllinic acid, β‐

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caryophyllene SOA tracer); 4) o‐phthalic acid, an anthropogenic SOA tracer derived from naphthalene; and

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5) DCAs (C4‐C6 α,ω‐dicarboxylic acids) and BTCAs (1,2,4‐benzenetricarboxylic acid and 1,3,5‐

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benzenetricarboxylic acid).

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The uncertainties for each data point were calculated according to Equation (3),

uij  ( xij  EF ) 2  (MDL) 2

(3)

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Where MDL is method detection limit; EF is the error fraction determined by the user and associated with

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the measurement uncertainty. 30,34 By convention, PMF modeling adopts a fixed EF for all data points from

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a particular species and the default robust mode. With the intrinsic setting of the robust mode, PMF treats

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occasional high concentration data as “outliers” (defined as scaled residue >4) and “gives them up” in the

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iteration process by replacing its scaled residue with a value of 4. This data treatment resulted in the model

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being unable to reproduce extreme high concentrations of an otherwise low variance time series, which

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characterizes the data variation pattern of the isoprene SOA tracers. Shown in Fig. S1a, all three isoprene

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SOA tracers episodically had concentrations from Aug to Oct that were much higher than the rest of the

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samples, leading the PMF to consider those high data points as outliers to not predict them well when 7



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applying a constant EF in the uncertainty calculation (Figure 1). Appendix 1 in the SI elaborates on how the

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episodic high concentrations in the isoprene SOA data are consistent with the known atmospheric

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chemistry of isoprene. They are “good data”, and it would be erroneous if PMF treated them as “outliers”.

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In order to resolve this issue, but still insist on the robust mode, we employed Equation (4) to calculate a

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varying EF value for Equation (3). The purpose is to give those higher concentration values a greater weight,

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and thus higher priority, in the PMF iteration process:

EF  EF0  a 179

xij

maxxi  (4)

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Where EF0 is now the fixed error fraction related to the analytical measurement precision. EF0 was set as

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0.1 for major components, 0.15 for levoglucosan and mannosan, 0.3 for non‐polar organics, and 0.4 for

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polar secondary organic species. a is associated with the variation between “outliers” and the bulk of the

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data points. The larger a is, the higher adjustment is applied to EFo. Equation (4) was primarily developed

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for polar secondary organic species, due to the poorer model performance when using a fixed EF0 value

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(e.g. C5‐alkene triols, as shown in Figure 1). After trial and error, a was set as 0.3 for C5‐alkene triols.

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Subsequently, based on data variations of individual species, a lower value of a in the range of 0.1‐0.2 was

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set for other polar secondary organic species. For species other than secondary organic species, e.g., ions

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and elements, we did not observe patterns of “outliers”. There was no need to make the special effort to

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determine whether occasional high concentration points were “outliers” due to unknown sources, thus

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obviating the need to run PMF as non‐robust mode. Therefore, Equation (4) with a minimized a value (~0.02)

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was applied to these species. In this study, all input species were set as “strong” due to the signal‐to‐noise

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ratio higher than 1.0 (Table 1 and Table S1).

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Using a bilinear analytic model like PMF, multiple solutions with the same Q value may arise due to the

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matrix transformation (Equation (1) and (2)), and is termed rotational ambiguity. Although non‐negative

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constraints in PMF helps decrease the rotational ambiguity of a solution, ambiguity may still persist in

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environmental source apportionment work. In this study, in order to help reduce the rotational ambiguity,

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constrained PMF runs were performed by setting organic tracers to zero in factor profiles other than their

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known sources and the specific constraints are listed in Table S2. The organic species constrained were

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hopanes, three biomass burning tracers (i.e. levoglucosan, mannosan and vanillic acid). EC was also

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constrained to be present only in primary combustion sources.14, 24

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3.2 Factor numbers determination and model uncertainty

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In PMF, the optimal number of factors is a compromise between identifying factors with the best physical

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explanations and achieving a sufficiently good fit for all species. In PMF solutions of too few factors,

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different sources are combined together, the resolved sources cannot fully explain the individual species.

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While too many factors split one source into multiple uninterpretable factors. Six to twelve factors were

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tested and the final factor numbers were determined by examining the change in Q/Qexp, where Qexp≈n·m‐

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p·(n+m) denotes the degree of freedom of the model solution,30 and the interpretability of each set of

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factors. When the number of factors increases to a certain value, Q/Qexp will change less dramatically.

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Q/Qexp changed by 8% from the 9‐factor to 10‐factor model, less significant than the 10‐11% observed when

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the number of factors varied from 6 to 9 (Figure S3), suggesting the factor number reaching nine was

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needed for explaining the input data. However, when examining factor profiles, the ten‐factor solution

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provides the most reasonable source profiles by separating the coal combustion from vehicle exhaust

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factors. Increasing to eleven factors, oxalate is no longer in the secondary sulfate factor and forms an

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unexplainable oxalate/nitrate factor.

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The source profile of the constrained ten‐factor solution is shown in Figure 2. Factor identification was

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based on the highest loading species, and their indicative sources are summarized in Table S3. Six primary

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sources were resolved, namely biomass burning identified by levoglucosan, mannosan and vanillic acid;

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vehicle exhaust by hopanes and EC; coal combustion by PAHs276 and hopanes; industrial emission by Zn

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and Pb; ship emission by V and Ni; and dust by crustal elements Al, Si and Fe. Four secondary sources were

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resolved, they are secondary sulfate factor, secondary nitrate factor, SOA_I factor representing SOA derived

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from α‐pinene, β‐caryophyllene and naphthalene, which are subject to similar ozonolysis and/or OH radical‐

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initiated oxidation,8 while SOA_II factor denoting SOA derived from isoprene oxidation, the products of

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which vary under high/low NOx conditions.9 The DISP uncertainties of the PMF‐resolved factor profiles are

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shown in Figure 2 as grey error bars. They were derived from base PMF runs without any constraints,

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representing the largest rotational uncertainty range for each species in each factor profile. The uncertainty

9



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estimates indicate some presence of sulfate in SOA_I factor (1‐9%) and SOA_II factor (2‐8%), reflecting the

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incomplete separation of secondary inorganic aerosol and SOA.

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The model stability of the 10‐factor solution is summarized briefly here (see details in Table S4 and

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Appendix 2 in the SI). All BS factors, except for vehicle exhaust factor, mapped to the base factors in > 95%

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of the runs (minimum correlation R‐value: 0.8). The vehicle exhaust factor was mapped in 89% of the runs.

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No factor swaps or decrease of Q occurred with DISP. The BS‐DISP results suggest that there is still some

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factor interdependence and rotational ambiguity. 64% of the BS‐DISP runs were accepted and only a 0.2%

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decrease of the Q value was observed. The most numerous factor swaps observed were for SOA_II and coal

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combustion factors (35% and 22% of the accepted runs at the lowest dQmax level) due to their different data

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characteristics. For the SOA_II factor, the factor swaps may be attributed to large variations in the source

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contributions during the sampling period as can be seen from the sharp peaks of the observed

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concentrations of isoprene SOA tracers from Aug to October (Figure S1a). While for coal combustion, it may

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arise from the many common elements with vehicle exhaust, as this source combines with vehicle exhaust

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in the 9‐factor solution.

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For the minimum a value adopted in this study, the non‐fixed EF method showed similar factor profiles

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to the fixed EF0 method, while having the advantage of improving the model performance of the high

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concentration SOA tracer data without distorting the low concentration data performance (see factor

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profile comparison for 10‐factor solution in Figure S4).

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The constrained BS, DISP and BS‐DISP uncertainties in OC contribution are shown in Figure 3. Generally,

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the DISP method showed the smallest uncertainty range and BS‐DISP showed largest uncertainty range,35

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with the largest DISP interval of 1.20 μgC/m3 for secondary nitrate compared with 4.08 μgC/m3 BS‐DISP

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interval for vehicle exhaust. All uncertainty methods support the general conclusion that the secondary

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sulfate and vehicle exhaust are major OC mass contributors, but uncertainty estimates show a broad range

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of possible values, especially for the vehicle exhaust (0.50‐4.58 μgC/m3 in BS‐DISP). This may be due to the

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hopanes input species, as it is a common tracer for vehicle exhaust and coal combustion, which combine in

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the 9‐factor run. The industrial emission factor also showed a broad range for OC contribution (0‐3.10

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μgC/m3 in BS‐DISP), which may be due to a lack of unique tracers for this factor. The larger uncertainty

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range in the BS‐DISP method for the yearly organic tracer‐based PMF analysis is likely from the high

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variability of the sources during the sampling period.

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It should also be noted that departure from mass conservation in organic tracers and the variability in

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SOA profiles during the study period lead to deviations in the PMF model assumption and thus increase the

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uncertainty of the results.

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3.3 OC and PM2.5 source apportionment

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The monthly variation of individual factor contributions to OC and reconstructed PM2.5 for each site is

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shown in Figure 4. The PMF modeled concentrations, reconstructed using Equation (5) from PMF‐modeled

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individual components16, matched well with the measured concentrations (Figure S6).

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Reconstructed PM2.5 = [Na+]+[sulfate]+[nitrate]+[ammoniumn]+[Cl‐]+[K+]+1.4[OC]+[EC]+[crustal]+[other

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trace metal]; [crustal]=2.2[Al]+2.49[Si]+2.42[Fe].

264

OC contribution from the secondary sulfate, secondary nitrate, SOA_I (SOA derived from α‐pinene, β‐

265

caryophyllene and naphthalene) and SOA_II (SOA derived from isoprene) factors were assumed as

266

secondary OC (SOC), while OC from the other factors were assumed to be primary OC (POC). The time series

267

of the OC contribution from the four secondary factors are shown in Figure S5. At the three sites, SOC

268

contributed 40‐60% to OC in Aug to Oct compared to the other months, and was likely a driving force behind

269

heavier pollution during those periods. The secondary sulfate factor contributes the most to SOC, which

270

can be seen from the correlation of the respective species with OC. OC has a good correlation with NH4+,

271

sulfate, and oxalate (R: 0.67‐0.80). The SOC associated with secondary sulfate and nitrate factors accounted

272

for 1.20 μgC/m3 (17%) (Jul) to 7.17 μgC/m3 (43%) (Oct) on average across all sites, with the fall and winter

273

months (Jan, Sep and Oct) showing higher contributions than the summer months (Jun‐Jul). SOC from the

274

two SOA factors ranged from 0.40 μgC/m3 (6%) (Feb) to 2.96 μgC/m3 (18%) (Oct) on average across all sites.

275

The highest contributions were found from Aug to Oct, especially for the isoprene derived SOC, and

276

contributed up to 11% of the total SOC in Aug. This was probably due to two reasons: 1) higher isoprene

277

emissions at higher temperature lead to more formation of isoprene SOA tracers,36 and 2) the atmospheric

278

conditions during these sampling days were more favorable for SOA formation and characterized by an

279

overlap of more intense solar radiation, higher aerosol acidity and continental back‐trajectories. The high

(5)

11



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

280

loading of SOC in the secondary sulfate factor compared with two SOA factors may indicate the potential

281

mix of the SOA in secondary sulfate factor due to the limited organic tracers included.

282

The correlation of SOC contribution from the two SOA factors vs secondary sulfate and secondary nitrate

283

factors are examined in Figure S7, color coded with temperature as the z‐variable. Generally, SOA_II was

284

positively correlated with temperature and likely driven by temperature‐dependent isoprene emissions

285

while secondary nitrate was negatively correlated due to the gas‐particle partitioning equilibrium of nitrate

286

shifting to the gas phase at higher temperature. A moderate correlation between SOA_I and secondary

287

sulfate factor was found (R=0.62), indicating some commonality in their formation processes. Many studies

288

have documented the enhancement of biogenic SOA production by anthropogenic species through creating

289

a more acidic environment in the aerosol.37

290

3.4 Impact of SOA tracers on PMF

291

This study also tested the PMF model without including the polar SOA tracers (PMFwo) to compare with

292

the base PMF (PMFw), which includes the polar SOA tracers. In PMFwo, eight factors were resolved (see

293

source profiles in Figure S8), but the two SOA factors could not be extracted due to the lack of the

294

corresponding SOA tracers. The utility of isoprene SOA tracers was especially noteworthy in resolving the

295

isoprene SOA factor (i.e., SOA_I). The importance of including the biogenic SOA tracers would be even more

296

outstanding in places with strong SOA contributions such as southeastern US and in forest locations.e.g.,38‐

297

40

298

The comparison of species loading in each factor profile for the same species from PMFw and PMFwo is

299

shown in Figure 5. Generally, for the eight factors, the secondary sulfate, secondary nitrate and industrial

300

emission factors showed the largest variation between PMFw and PMFwo, while the other five factors were

301

more stable, especially in their markers’ loading. For individual species, the species which were constrained

302

in the two PMF runs are marked with asterisks and show less variation. For the remaining species, Na+,

303

NH4+, K+, and the dust and ship emission related species Al, Si, Fe, and V and Ni, showed relatively less

304

variation, while obvious bias was observed for Cl‐, sulfate, nitrate, Zn and Pb between the PMF runs. With

305

the incorporation of SOA tracers, Cl‐ and nitrate shifted from the secondary nitrate factor to industrial

306

emission, while less sulfate, Zn and Pb was observed in the industrial emission factor in PMFw. For the un‐

12


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307

constrained organics (n‐alkanes and PAHs), their loading in the major sources was consistent and the total

308

loading in the secondary factors was comparable between the two PMF runs. Oxalate showed consistent

309

loading in the total secondary factors but shifted from secondary sulfate factor to the two SOA factors in

310

PMFw. For OC, higher loadings in the secondary factors were observed in PMFw, but with slightly lower

311

loading in secondary sulfate and obviously lower loading in the industrial emission factor.

312

The correlation of the factor contributions for each factor between PMFwo and PMFw is shown in Table 2.

313

Generally, the eight common factors correlated well between the two runs (R: 0.94‐1.00), indicating the

314

robustness of the resolved factors. Among the eight factors, industrial emission, secondary sulfate and

315

secondary nitrate factors showed relatively lower precision to reproduce PMFw results, especially industrial

316

emission (R=0.94). The difference of the industrial emission contribution between the two PMF runs plotted

317

against the sum of the two extra SOA factors from PMFw for OC and reconstructed PM2.5 is further shown

318

in Figure 6. It can be seen that the apportioned mass difference from the industrial emission factor between

319

two PMF runs is well reproduced by the two SOA factors (slope ≈1), indicating that the two SOA factors

320

likely reduce the contribution from the original industrial emission in PMFwo. The variability of the

321

secondary sulfate factor was probably due to its complex inter‐dependence with biogenic SOA formation

322

processes, especially with SOA_I (R=0.62). For the variation in secondary nitrate factor, it was probably a

323

result of the fluctuation of the secondary sulfate and industrial emission factor, as they were all influenced

324

by regional transport (nitrate, sulfate, Zn and Pb generally showed moderate correlations: R=0.59‐0.75). For

325

the rest of the factors, the good correlation can be attributed to a greater dependence on unique tracers,

326

which determines the factor contribution variations. In industrial emission, Zn and Pb were used to identify

327

the factor. While considerable OC and EC were resolved by PMF to be present in this source, there was no

328

known organic tracers to help track this source.

329

3.5 Implication on SOC and PM2.5 apportionment

330

The differences of the apportioned OC and PM2.5 mass contributions from PMFw and PMFwo are shown

331

in Figure 7. Generally, the bias was more obvious for OC than for PM2.5. The largest differences (PMFwo ‐

332

PMFw) were found for the industrial emission factor, with an average bias reaching 0.93 μgC/m3 for OC and

333

2.85 μg/m3 for PM2.5. The secondary sulfate factor showed less average bias (0.21 μgC/m3 and 0.00 μg/m3

13



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

334

for OC and PM2.5, respectively), but with a relatively higher inter‐quartile range: 0.19‐0.57 μgC/m3 for OC

335

and ‐1.70‐1.98 μg/m3 for PM2.5. The other factors showed less bias except for vehicle exhaust in OC

336

contribution and secondary nitrate factor in PM2.5 contribution. SOC from secondary sulfate showed only a

337

small decrease from 2.99 to 2.72 μgC/m3, while secondary nitrate factors were negligibly different between

338

PMFw and PMFwo (0.52 vs 0.53 μgC/m3). After including SOA tracers, the estimated SOC increased to 4.47

339

μgC/m3, indicating SOC estimated from PMFwo may lead to an underestimation of 21% (3.51 vs 4.47

340

μgC/m3). The apportioned PM2.5 mass from the four secondary sources was biased by ~6% (25.18 vs 23.71

341

μg/m3).

342

The results of this study suggest that SOC from specific precursors probably have different formation

343

pathways than secondary sulfate and nitrate processes, and should be considered in the PMF modeling.

344

Otherwise their source contributions could not be resolved and would be partly counted as POC instead.

345

OC from the industrial emission factor was biased most (1.54 vs 2.74 μgC/m3) between the two PMF runs,

346

suggesting that some SOC was grouped into POA factors associated with industrial emission, making it

347

necessary to find unique tracers for this source type. In this study, limited to the filter‐based chemical

348

speciation data sampled once every six days, we had to combine multi‐site year‐long data to generate a

349

sufficient dataset for the PMF analysis. As a consequence, only averaged source factor profiles over the

350

sampling period and multiple sites were generated.

351

The analysis results indicate that certain SOA sources (e.g., SOA_II factor, the isoprene SOA source factor)

352

could be resolved from the sulfate and nitrate source factors, while the resolved SOA_I source factor more

353

likely portrays a convolutedly mixed SOAs of different precursors. The utility of the biogenic SOA tracers

354

was especially noteworthy, which will be particularly important for regions with strong biogenic SOA

355

contributions. With still a fairly limited set of SOA tracers, a large portion of SOA remains un‐separated and

356

still grouped in the secondary sulfate and nitrate factors. Future studies using higher time resolution data

357

should be pursued to resolve the dynamic and seasonally variable SOA profiles. Also, more SOA tracers,

358

especially those from anthropogenic precursors, are needed to evaluate the interaction of anthropogenic

359

SOA with sulfate and nitrate, as a large portion of SOC still resides in the secondary sulfate and nitrate

360

factors.

14


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361 362

Supporting Information

363

Temporal variation of isoprene SOA tracers, abundance statistics of major PM2.5 components, list of

364

tracers used for source identification, map locations of the sampling sites and auxiliary PMF results. This

365

material is available free of charge via the Internet at http://pubs.acs.org.

366 367

Acknowledgements

368

This work was partially supported by Natural Science Foundation of China (21177031 and 91543130). We

369

gratefully acknowledge the Fok Ying Tung Foundation for funding to the Atmospheric Research Center at

370

HKUST Fok Ying Tung Graduate School and Prof. Alexis Lau for initiating the PM monitoring project.

371 372

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482


Table of content (TOC) graph SOA_I Secondary sulfate

SOA_II Secondary nitrate

w/wo: with/without SOA tracers

PMFwo PMFw

Secondary PM2.5

Primary PM2.5

PMFwo PMFw Secondary OC 0%

20%

Primary OC

40%

60%

80%

100%

Factor contribution (%)

483 484

19



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

485

Table 1. Abundance and naming of measured organic tracers (ng/m3) used in the PMF analysis. Naming

Grouping

Dongguan avg. ± stdev

Guangzhou avg. ± stdev

Nanhai avg. ± stdev

S/N*

Odd_Alk

n‐C29, n‐C31 and n‐C33 alkane

17.78 ± 11.08

18.74 ± 12.55

25.62 ± 22.46

4.8

Even_Alk

n‐C28, n‐C30 and n‐C32 alkane

13.76 ± 8.47

14.04 ± 8.72

20.72 ± 16.70

4.8

2.66 ± 1.81

1.79 ± 0.93

2.73 ± 2.66

6.4

1.51 ± 1.48 0.70 ± 0.68

1.43 ± 1.22 0.65 ± 0.55

2.53 ± 2.51 0.92 ± 0.88

3.3 3.1

PAHs252 PAHs276

αβ‐hopane, αβS&R‐homohopane and αβS&R‐bishomohopane benzo[b+k]fluoranthene and benzo[e]pyrene indeno[1,2,3‐cd]pyrene and benzo[ghi]perylene

Levoglucosan

/

114.3 ± 125.8

89.63 ± 73.47

183.7 ± 165.7

5.3

Mannosan

/

7.53 ± 9.08

6.82 ± 6.54

15.36 ± 14.64

4.1

Vanillic acida

/

0.42 ± 0.45

0.51 ± 0.51

1.01 ± 1.03

1.6

DCAsa

succinic acid, glutaric acid and adipic acid

16.37 ± 13.14

17.74 ± 14.03

21.16 ± 16.46

4.2

BTCAsa

1,2,4‐benzenetricarboxylic acid and 1,3,5‐benzenetricarboxylic acid

1.83 ± 2.83

3.66 ± 4.12

6.63 ± 9.85

2.6

o‐Phthalic acida

/

8.83 ± 7.27

11.64 ± 10.35

22.73 ± 17.38

1.9

2‐MGAa

2‐methylglyceric acid

1.01 ± 1.17

1.54 ± 2.10

1.19 ± 1.28

1.8

2‐MTsa

2‐methylthreitol and 2‐methylerythritol

11.23 ± 14.45

24.12 ± 47.70

17.05 ± 24.51

2.8

35.63 ± 84.96

36.05 ± 97.49

30.55 ± 88.81

2.9

6.30 ± 7.06

8.62 ± 10.32

10.54 ± 13.91

3.3

4.59 ± 4.33

6.07 ± 5.28

11.36 ± 9.61

1.8

Hopanes

C5‐alkene triolsa

α‐pinTa β‐caryTa 486 487 488

Page 20 of 28

cis‐2‐methyl‐1,3,4‐trihydroxy‐1‐butane, 3‐methyl‐2,3,4‐trihydroxy‐1‐butane and trans‐2‐methyl‐1,3,4‐trihydroxy‐1‐butane 3‐hydroxyglutaric acid, 3‐hydroxy‐4,4‐dimethylglutaric acid, 3‐methyl‐1,2,3‐butanetricarboyxlic acid, 3‐acetylglutaric acid and 3‐isopropylglutaric acid β‐caryophyllinic acid

*Signal to noise ratio: (concentration‐uncertainty)/uncertainty. a Species not included in PMF without SOA tracers (PMF

wo).

20


Page 21 of 28

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

489 490


Table 2. Correlation (R2) of source contributions for each factor from PMFwo with PMFw. The correlation of the corresponding factors between two PMF runs were highlighted in bold and gray shaded. PMFwo PMFw Secondary sulfate Secondary nitrate Biomass burning Vehicle exhaust Coal combustion Industrial emission Ship emission Dust

491

Secondary sulfate 0.890 0.086 0.029 0.007 0.044 0.181 0.032 0.011

Secondary nitrate 0.026 0.909 0.192 0.305 0.013 0.115 0.082 0.001

Biomass burning 0.016 0.257 0.993 0.342 0.084 0.245 0.028 0.032

Vehicle exhaust 0.000 0.343 0.308 0.964 0.046 0.221 0.006 0.029

Coal combustion 0.029 0.062 0.128 0.044 0.939 0.142 0.021 0.044

21


Industrial emission 0.002 0.332 0.258 0.243 0.253 0.882 0.000 0.041

Ship emission 0.067 0.033 0.052 0.000 0.061 0.008 0.947 0.002

Dust 0.042 0.001 0.023 0.010 0.036 0.053 0.000 0.987


700 600

Measured conc.

Dongguan

EF0 method

Guangzhou

EF method

Nanhai

500 400

493 494 495 496 497

y = 0.558x + 0.004 Rp= 0.929 y = 0.263x + 0.005 Rp= 0.870

300 200 100 0 1-Jan-12 5-Feb-12 12-Mar-12 17-Apr-12 23-May-12 24-Jun-12 3-Aug-12 8-Sep-12 14-Oct-12 19-Dec-12 12-Jan-12 17-Feb-12 24-Mar-12 29-Apr-12 4-Jun-12 10-Jul-12 28-Jul-12 27-Aug-12 2-Oct-12 13-Nov-12 19-Dec-12 18-Jan-12 23-Feb-12 11-Apr-12 16-Jul-12 2-Sep-12 14-Oct-12 25-Nov-12

C5-alkene triols, ng/m3

492

Modeled conc.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 28

Measured conc.

Date

Figure 1. Time series and scatter plot of C5‐alkene triols at three sites: measured data and PMF predicted data from using fixed EF0 method and non‐fixed EF method.

22


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 % of species

498 499 500

Sulfate Oxalate OC EC Al Si V Fe







Zn

Pb


Zn

Pb


Ni

Zn

OC

EC

Al

Si

V

Fe

Ni

Zn

Nitrate

Nitrate

Nitrate

Nitrate

Nitrate

Nitrate

Nitrate

Nitrate

Nitrate

Sulfate

ClNitrate

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Cl-

Oxalate

K+

K+

K+

K+

K+

K+

K+

K+

K+ Cl‐

K+

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+ Ammonium

Na+

100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0

Ammonium

Ship emission Industrial emission

PAHs252 PAHs276

PAHs252 PAHs276

Even_Alk Hopanes PAHs252

Hopanes

PAHs252

23

ACS Paragon Plus Environment α‐pinT β‐caryT

α-pinT

β-caryT

β-caryT

β-caryT

α-pinT

2-MTs

α-pinT

2-MTs

2-MGA

o-Phthalic acid

BTCAs

C5-alkene triols

2‐MTs

2-MTs

2-MGA

o-Phthalic acid

BTCAs

Mannosan

C5‐alkene triols C5-alkene triols

2‐MGA

2-MGA

C5-alkene triols

o‐Phthalic acid

o-Phthalic acid

DCAs

DCAs

DCAs BTCAs

DCAs

Mannosan

β-caryT

α-pinT

C5-alkene triols

2-MTs

2-MGA

o-Phthalic acid

BTCAs

DCAs

Vanillic acid

Mannosan

Levoglucosan

PAHs276

PAHs252

Hopanes

Even_Alk

DCAs BTCAs o-Phthalic acid 2-MGA 2-MTs C5-alkene triols α-pinT β-caryT

DCAs BTCAs o-Phthalic acid 2-MGA 2-MTs C5-alkene triols α-pinT β-caryT

Mannosan Vanillic acid


PAHs276

PAHs252

PAHs252

Levoglucosan

Hopanes

Hopanes PAHs276

Even_Alk

Even_Alk

Levoglucosan

Pb

Odd_Alk

Pb

Odd_Alk

Ni

β-caryT

α-pinT

C5-alkene triols

2-MTs

2-MGA

o-Phthalic acid

BTCAs

DCAs

Vanillic acid

Mannosan

Levoglucosan

PAHs276

PAHs252

Hopanes

Even_Alk

Odd_Alk

Pb

Zn

Ni

β-caryT

α-pinT

C5-alkene triols

2-MTs

2-MGA

o-Phthalic acid

BTCAs

DCAs

Vanillic acid

Mannosan

Levoglucosan

PAHs276

PAHs252

Hopanes

Even_Alk

Odd_Alk

Pb

Zn

β-caryT

α-pinT

C5-alkene triols

2-MTs

2-MGA

o-Phthalic acid

BTCAs

DCAs

Vanillic acid

Mannosan

Levoglucosan

PAHs276

PAHs252

Hopanes

Even_Alk

Odd_Alk

Pb

Zn

Ni

Secondary nitrate formation process

BTCAs

Vanillic acid

Vanillic acid


Mannosan

Levoglucosan

Vanillic acid

Levoglucosan

PAHs276 Levoglucosan

PAHs276

Figure 2. Factor profiles (percentage of each species in factor) for 10‐factor constrained run, grey error bars indicate the largest DISP uncertainty range from base run without any constraints.

Levoglucosan

Hopanes

Even_Alk

Pb

Odd_Alk

Ni

Zn

Ni

Vehicle exhaust

Zn

Biomass burning

Hopanes

Even_Alk

Odd_Alk

Even_Alk

Odd_Alk

Pb

Dust Odd_Alk

Ni

Coal combustion

Zn

SOA_II

Pb

Ni

SOA_I

Odd_Alk

Ni

Page 23 of 28 ACS Earth and Space Chemistry

Secondary sulfate formation process


6.0

BS Error Estimate

Base Value BS Median

4.0

Dust

Ship emission Ship emission

Dust

Dust

Coal combustion Coal combustion

Ship emission

DISP Average

Coal combustion

Vehicle exhaust Vehicle exhaust

Industrial emission

Industrial emission

Biomass burning Biomass burning

Vehicle exhaust

BS-DISP Error Estimate

Base Value

Industrial emission

SOA_II SOA_II

SOA_II

DISP Error Estimate

Biomass burning

SOA_I SOA_I

SOA_I

2.0

Secondary nitrate

4.0

Secondary nitrate

0.0 6.0

Secondary nitrate

2.0

Secondary sulfate

4.0

Secondary sulfate

0.0 6.0

Secondary sulfate

2.0

OC contribution, μgC/m3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 28

Base Value BS-DISP Average

0.0

501 502 503 504 505

Figure 3. OC contribution (μgC/m3) from each factor determined by the constrained run, with error bars (whiskers) representing the corresponding uncertainty ranges for the BS, BS‐DISP and DISP method. Whiskers representing the 5th and 95th percentiles for the BS, 5th and 95th percentiles for the BS‐DISP at the smallest pre‐ set dQmax value and the minimum and maximum values at the smallest pre‐set dQmax value for DISP.

24


Page 25 of 28

(a) OC mass contribution, μg/m3 Dust Ship emission Industrial emission Coal combustion Vehicle exhaust Biomass burning SOA_II SOA_I Secondary nitrate formation process Secondary sulfate formation process Measured

Guangzhou

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

Dongguan

May

Mar

Jan

0.0

Feb

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

May

Mar

0.0

Jan

0.0

May

5.0

Ship emission 20.0Industrial emission Coal combustion Vehicle exhaust 15.0 Biomass burning SOA_II 10.0SOA_I Secondary nitrate formation process 5.0Secondary sulfate formation process Measured

Mar

10.0

25.0Dust

Ship emission 20.0Industrial emission Coal combustion Vehicle exhaust 15.0 Biomass burning SOA_II 10.0SOA_I Secondary nitrate formation process 5.0Secondary sulfate formation process Measured

Jan

15.0

25.0Dust

Feb

20.0

Feb

OC contribution, μgC/m3

25.0

Nanhai, Foshan

(b) PM2.5 mass contribution, μg/m3

506 507 508 509

Dongguan

Guangzhou

Dec

Nov

Oct

Dust Ship emission Industrial emission Coal combustion Vehicle exhaust Biomass burning SOA_II SOA_I Secondary nitrate formation process Secondary sulfate formation process Measured

Sep

Aug

Jul

Jun

May

Apr

Mar

Jan

Feb

Dec

Nov

Oct

90.0Dust 80.0Ship emission 70.0Industrial emission Coal combustion 60.0Vehicle exhaust 50.0Biomass burning 40.0SOA_II SOA_I 30.0Secondary nitrate formation process 20.0Secondary sulfate formation process 10.0Measured 0.0

Sep

Aug

Jul

Jun

May

Apr

Mar

Jan

Feb

Dec

Nov

Oct

Sep

Aug

Jul

Jun

May

Apr

Mar

90.0Dust 80.0Ship emission 70.0Industrial emission Coal combustion 60.0Vehicle exhaust 50.0Biomass burning 40.0SOA_II SOA_I 30.0Secondary nitrate formation process 20.0Secondary sulfate formation process 10.0Measured 0.0

Jan

90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

Feb

PM2.5 contribution, μg/m3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


Nanhai, Foshan

Figure 4. Monthly variation of individual factor contribution to (a) OC and (b) PM2.5 reconstructed from PMF‐modeled components. Measured OC and PM2.5 are shown as filled circles.

25



100% 90% w

w

w w

w w w w w w

w

w

w

w

80%

w w w

w w ww w ww ww w ww w w w w w ww w ww ww

w ww

ww w

ww ww w ww ww w w w ww w ww ww w ww ww w ww ww w ww ww w ww ww w ww ww w ww w w w w w ww w ww

w w w

w w ww w ww ww w ww w w w w w ww w ww ww

w ww

ww w

ww ww w ww ww w w w ww w ww ww w ww ww w ww ww w ww ww w ww ww w ww ww w ww w w w w w ww w ww

70%

% of species

60% 50%

w

w w w w w

40% 30%

Dust Ship emission ww w ww emission w w w w w Industrial Coal combustion Vehicle exhaust Biomass burning SOA_II ww w ww w w w w w SOA_I Secondary nitrate formation process Secondary sulfate formation process

20% 10%

w

wo

Mannosan*

w

wo

w

wo

PAHs276

w

wo

PAHs252

w

wo

Hopanes*

w

wo

Even_Alk

w

wo

Odd_Alk

w

wo

Pb

w

wo

Zn

w

wo

Ni

w

wo

Fe

w

wo

V

w

wo

Si

w

wo

Al

w

wo

EC*

w

wo

OC

w

wo

Oxalate

w

wo

Sulfate

w

wo

Nitrate

w

wo

Cl-

w

w

wo

wo

K+

Levoglucosan*

510 511

Ammonium

w

wo

0%

Na+

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 26 of 28

Figure 5. Factor loading of common species in PMFw and PMFwo. The species which marked with stars were constrained species in PMF runs.

26


Page 27 of 28

25 1:1 line

SOA_I+SOA_II (PMFw)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


20

15

y=1.01x+0.009 Rp=0.736 y=1x-0.365 Rp=0.712

10

5

3

3*OC (µgC/m ) 3

PM2.5 (µg/m ) 0 0

512 513 514 515

5

10

15

20

25

Industrial emission (PMFwo-PMFw)

Figure 6. Comparison of the difference of industrial emission factor contribution for OC (μgC/m3) and PM2.5 (μg/m3) between the two PMF runs with the sum of the two extra SOA factors.

27



3

12

Dust Ship emission Industrial emission Coal combustion Vehicle exhaust Biomass burning SOA_II SOA_I Secondary nitrate formation process Secondary sulfate formation process SOC

OC, μgC/m3

ΔOC, μgC/m

3

2 1 0

-1 -2

6 3

5 0

Dust Ship emission Industrial emission Coal combustion Vehicle exhaust Biomass burning SOA_II SOA_I Secondary nitrate formation process Secondary sulfate formation process Secondary

40 30 20 10

-5

0

PMF w w PMF wo wo

SOA_II

SOA_I

Dust

Ship emission

Industrial emission

Coal combustion

Vehicle exhaust

Biomass burning

Secondary nitrate

Secondary sulfate

-10

516 517 518 519 520

PMF w w PMF wo wo

50

PM2.5, μg/m3

3

9

0

-3 10

ΔPM2.5, μg/m

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 28

Figure 7. Box plot of daily difference of individual factor contribution to OC (μgC/m3) and to PM2.5 (μg/m3) from PMFw and PMFwo. Squares and horizontal lines in the box denote average and median, respectively. Lower and upper boundaries of boxes represent 25% and 75% percentile values, and the whiskers represent the 10%‐90% percentile range.

28


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