# Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements

D. Srivastava, O. Favez, J.-E. Petit, Y. Zhang, U.M. Sofowote, P.K. Hopke, N.

Bonnaire, E. Perraudin, V. Gros, E. Villenave, et al.

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► To cite this version:

D. Srivastava, O. Favez, J.-E. Petit, Y. Zhang, U.M. Sofowote, et al. Speciation of organic fractions does matter for aerosol source apportionment. Part 3: Combining off-line and on-line measurements. Science of the Total Environment, 2019, 690, pp.944-955. 10.1016/j.scitotenv.2019.06.378. hal-03272630

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1 **Speciation of organic fractions does matter for aerosol source apportionment. Part 3: combining off-line and on-line measurements**
2

3
4 D. Srivastava<sup>1,2,3*</sup>, O. Favez<sup>1</sup>, J-E. Petit<sup>4</sup>, Y. Zhang<sup>1,4</sup>, U. M. Sofowote<sup>5</sup>, P.K. Hopke<sup>6,7</sup>, N.
5 Bonnaire<sup>4</sup>, E. Perraudin<sup>2,3</sup>, V. Gros<sup>4</sup>, E. Villenave<sup>2,3</sup>, A. Albinet<sup>1*</sup>

6
7 <sup>1</sup>INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France

8

<sup>2</sup>CNRS, EPOC, UMR 5805 CNRS, 33405 Talence, France

9
3Université de Bordeaux, EPOC, UMR 5805 CNRS, 33405 Talence, France

10
⁴LSCE - UMR8212, CNRS-CEA-UVSQ, Gif-sur-Yvette, France

11
12
⁵Environmental Monitoring and Reporting Branch, Ontario Ministry of the Environment,
Conservation and Parks, Toronto, Ontario, M9P 3V6

13
⁶Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA

14
15
⁷Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY USA

16
17
*Correspondence to: alexandre.albinet@ineris.fr; alexandre.albinet@gmail.com;

18
deepchandra.srivastava@gmail.com

19
20 Submitted to Science of The Total Environment
21 Abstract

The present study proposes an advanced methodology to refine the source apportionment of organic aerosol (OA). This methodology is based on the combination of offline and online datasets in a single Positive Matrix Factorization (PMF) analysis using the multilinear engine (ME-2) algorithm and a customized time synchronization procedure. It has been applied to data from measurements conducted in the Paris region (France) during a PM pollution event in March 2015. Measurements included OA ACSM (Aerosol Chemical Speciation Monitor) mass spectra and specific primary and secondary organic molecular markers from PM₁₀ filters on their original time resolution (30 min for ACSM and 4 h for PM₁₀ filters). Comparison with the conventional PMF analysis of the ACSM OA dataset (PMF-ACSM) showed very good agreement for the discrimination between primary and secondary OA fractions with about 75% of the OA mass of secondary origin. Furthermore, the use of the combined datasets allowed the deconvolution of 3 primary OA (POA) factors and 7 secondary OA (SOA) factors. A clear identification of the source/origin of 54% of the total SOA mass could be achieved thanks to specific molecular markers. Specifically, 28% of that fraction was linked to combustion sources (biomass burning and traffic emissions). A clear identification of primary traffic OA was also obtained using the PMF-combined analysis while PMF-ACSM only gave a proxy for this OA source in the form of total hydrocarbon-like OA (HOA) mass concentrations. In addition, the primary biomass burning-related OA source was explained by two OA factors, BBOA and OPOA-like BBOA. This new approach has showed undeniable advantages over the conventional approaches by providing valuable insights into the processes involved in SOA formation and their sources. However, the origins of highly oxidized SOA could not be fully identified due to the lack of specific molecular markers for such aged SOA.
Keywords: Particulate matter (PM); Secondary organic aerosol (SOA); Aerosol chemical speciation monitor (ACSM); Molecular markers; Source apportionment; Time synchronization

## 1. Introduction

Particulate matter originates from a wide range of sources and atmospheric processes, and has significant impacts on air quality and climate change (Boucher et al., 2013; Heal et al., 2012). Particulate organic matter (POM) makes up a large, and often dominant, fraction (typically 20–60% in the continental mid-latitudes) of fine particulate mass in the atmosphere (Kanakidou et al., 2005). Primary organic aerosol (POA) refers to those organic aerosols (OA) directly emitted from natural or anthropogenic sources. OA that formed in the atmosphere through the oxidation of gas-phase precursors, known as secondary organic aerosol (SOA), also contributes substantially (20-80%) to the carbonaceous aerosol mass (Carlton et al., 2009; Ziemann and Atkinson, 2012). Therefore, the discrimination of POA and SOA sources is crucial to develop and apply efficient air quality control policies.

Positive matrix factorization (PMF), a bilinear factor analytic model that constrains the factors to be non-negative (Paatero et al., 2002; Paatero and Tapper, 1994), has been widely used with traditional speciation data (i.e., organic carbon (OC), elemental carbon (EC), major ions and elements) for source apportionment. The use of molecular markers within PMF has opened new perspectives for filter-based source apportionment studies (Heo et al., 2013; Hu et al., 2010; Shrivastava et al., 2007; Srivastava et al., 2018a; Srivastava et al., 2018c; Wang et al., 2012; Zhang et al., 2009). Nevertheless, short-time resolution of filter measurements is not able to reveal the rapid atmospheric processes (Srivastava et al., 2018a). Development of online instrumentation (e.g. AMS (Aerosol Mass Spectrometer) and ACSM (Aerosol Chemical
67 Speciation Monitor)) (DeCarlo et al., 2006; Jayne et al., 2000; Ng et al., 2011) have successfully 68 improved the real-time measurements of the aerosol chemical composition. The PMF analysis of 69 OA mass spectra from such measurements also permits the differentiation of various POA and 70 SOA sources (Lanz et al., 2007b; Srivastava et al., 2018b; Ulbrich et al., 2009; Zhang et al., 71 2011). However, online approaches often resolve SOA components based on their volatility 72 and/or oxidation state, and do not offer any direct link to the sources.

73 Combining datasets from several measurement systems has emerged as a new opportunity to 74 refine the source apportionment of OA. Slowik et al. (2010) were the first ones to combine the 75 AMS and PTR-MS (proton transfer reaction-mass spectrometer) data, leading to the 76 identification of additional OA factors including additional information on photochemical 77 processes linked to secondary sources. This approach has also been explored in other studies as 78 well (Crippa et al., 2013a). A few researchers have explored the combination of AMS or ACSM 79 data with other measurements, such as ambient and thermally denuded OA spectra (TD-PMF- 80 AMS) (Docherty et al., 2011), or with single particle mass spectrometry (Healy et al., 2013), by 81 merging high resolution mass spectra of organic and inorganic aerosols from AMS measurements 82 (McGuire et al., 2014; Sun et al., 2012), combining off-line AMS data and organic markers or 14C 83 measurements (Huang et al., 2014; Vlachou et al., 2017), or including on-line single particle 84 aerosol time-of-flight mass spectrometry (ATOFMS), on-line EC-OC analysis, and off-line gas 85 chromatography/mass spectrometry with AMS data (Dall'Osto et al., 2014). In addition, the 86 combination of PMF-ACSM outputs with inorganic species and black carbon (BC) measurements 87 (Petit et al., 2014) or ACSM mass spectra with metal concentrations (Sofowote et al., 2018), 88 allowed the source apportionment of PM rather than only OA.

89 In this context, a novel approach has been developed to refine OA sources and to provide 90 more comprehensive information on the associated atmospheric processes. This refinement was
accomplished by performing the PMF analysis using time synchronization multilinear engine (ME-2) algorithm on the combined dataset including the ACSM OA matrix and specific primary and secondary organic molecular markers from PM₁₀ filters on their original time resolutions. The identified OA sources, their oxidation state and formation processes are discussed in this paper. In addition, results from the PMF-combined data analysis are also compared to the conventional PMF-ACSM analysis.

## 2. Methodology

### 2.1. Monitoring site

Measurements were conducted at the SIRTA atmospheric supersite (Site Instrumental de Recherche par Télédétection Atmosphérique, 2.15° E; 48.71° N; 150 m above sea level; http://sirta.ipsl.fr). This site provides long-term, in-situ observations of the chemical, optical, and physical properties of the atmospheric aerosol and illustrates the background air quality of the Paris region (France) and belongs to the European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases, ACTRIS (Crippa et al., 2013a; Haeffelin et al., 2005; Petit et al., 2017a; Petit et al., 2014; Petit et al., 2015; Sciare et al., 2011; Srivastava et al., 2018a). An intensive campaign was performed from 6th - 21st, March 2015 during a severe PM pollution event (PM₁₀ > 50 µg m⁻³ for at least 3 consecutive days). A detailed description of the site and the sampling campaign can be found elsewhere (Petit et al., 2017a; Srivastava et al., 2018a). Similarly, all datasets used for the present work, including filter-based chemical speciation, online measurements and meteorological parameters, have been already reported in these same papers. They are then described only briefly hereafter.
## 2.2. Online instrumentation

PM₁₀ and PM₁ using tapered element oscillating microbalances equipped with the filter dynamic measurement system (TEOM-FDMS, Thermo; 15-min time resolution), black carbon (BC) by multi-wavelength aethalometer (AE33 model, Magee Scientific, 1-min time resolution), NOₓ, and O₃ concentrations using T200UP and T400 monitors (Teledyne API, 15-min time resolution), have been respectively monitored at SIRTA. Further, BC was discriminated between its wood burning and fossil fuel fractions (BCwb and BCff, respectively) using the so-called “aethalometer model” (Drinovec et al., 2015; Sandradewi et al., 2008). The chemical composition of non-refractory submicron aerosols (NR-PM₁) has been measured using ACSM (Aerodyne Research Inc.) at a 30-min time resolution since 2011 (Petit et al., 2015; Zhang et al., 2018). Details on the ACSM measurement principles and operation at SIRTA during the campaign can be found elsewhere (Petit et al., 2017a). Finally, meteorological variables such as temperature, relative humidity (RH), wind direction, and wind speed have been measured at the main SIRTA facility (about 5 km distance).

## 2.3. Filter sample collection and analysis

PM₁₀ samples (Tissuquartz fibre filter, Pallflex, Ø=150 mm) were collected every 4 h from 6-21, March 2015 using a high-volume sampler (DA-80, Digitel; 30 m³ h⁻¹). PM₁₀ filter samples were analysed for a large set of chemical species (n=71) including EC/OC, anions/cations, methanesulfonic acid (MSA), oxalate (Ox, C₂O₄²⁻), cellulose combustion markers (biomass burning) (levoglucosan (Levo), mannosan, and galactosan), 3 polyols (arabitol, sorbitol, and mannitol), 9 polycyclic aromatic hydrocarbons (PAHs), 14 oxy-PAHs, 8 nitro-PAHs and 13 SOA markers (e.g. α-methylglyceric acid (α-MGA), 2,3-dihydroxy-4-oxopentanoic acid (DHOPA),
136 methylnitrocatechols, etc...), following the protocols described elsewhere (Albinet et al., 2019;
137 Albinet et al., 2006; Albinet et al., 2014; Albinet et al., 2013; Cavalli et al., 2010; Guinot et al.,
138 2007; Srivastava et al., 2018a; Srivastava et al., 2018c; Tomaz et al., 2016; Verlhac et al., 2013;
139 Yttri et al., 2015).

140

141 **2.4. Source apportionment**

142 **2.4.1. Model description**

143
144 Receptor modelling, including PMF, is based on the principle of mass conservation. A mass
145 balance between the measured species concentrations and source profiles is solved as a linear
146 combination of factors *p*, species profile *f* of each source and the amount of mass *g* contributing
147 to each individual sample (Hopke, 2016; Paatero and Tapper, 1994). "Multi-time/time
148 synchronization" factor analysis has been developed to merge datasets with different time
149 resolutions, and take each measured concentration data into account (Crespi et al., 2016; Kuo et
150 al., 2014; Liao et al., 2015; Liao et al., 2013; Ogulei et al., 2005; Sofowote et al., 2018; Zhou et
151 al., 2004). To achieve this, the main source-receptor model equation has been modified as below
152 (Zhou et al., 2004) (Eq. (1)).

$$
153 \qquad X_{sj} = \frac{1}{t_{s2} - t_{s1} + 1} \sum_{k=1}^{p} f_{kj} \sum_{ts1}^{ts2} (g_{sk} \eta_j) + e_{sj} \quad (1)
$$

154 where Xsj represents measured data for species j in sample s, and esj is the residual of each
155 sample/species not fitted by the model. j represents the measured species, ts1 and ts2 are the
156 starting and the ending times for sample s, respectively. The time unit (i.e. time resolution of the
157 ME-2 outputs) corresponds to the shortest sampling interval from the available data (30 min in
158 this work). ηj is an adjustment factor for replicated species measured by more than one analytical
method with different time resolutions. Since it was not the case in this work, there was no duplication between ACSM and filter measurements, $\eta_i$ has been set to 1.

A smoothing parameter was used in order to eliminate unrealistic residuals, as explained by Ogulei et al. (2005) and implemented by Sofowote et al. (2018).

### 2.4.2. Error estimation/ Input matrix

Uncertainties related to the ACSM OA dataset were calculated according to the procedure usually used for PMF analysis on AMS data (Ng et al., 2011). “Weak” and “bad” variables were defined based on the signal-to-noise ratio lower than 3 and 1, respectively.

The calculation and optimization of the uncertainties for the filter molecular markers were performed following the procedure explained by Sofowote et al. (2018). The uncertainties ($u_{sj}$) have been calculated using the ME-2 error model (Eq. (2)) (Paatero, 2000) (error code -14 in ME-2 was used).

$$u_{sj} = c_1 + c_3 \max(|x_{sj}|, |y_{sj}|) \qquad (2)$$

where, $c_3$ is a multiplier to add an extra uncertainty (set to 0.1 here), $x_{sj}$ and $y_{sj}$ are the observed and modelled values, respectively, and $c_1$ is the measurement or estimated error. Adjustments to the detection limit (DL) have then been done accordingly in order to get distributions of scaled residuals centred around 0 with an acceptable range between -5 and 5 (Zhou et al., 2004). Instrumental DL of the molecular markers (evaluated from standard solutions) analysed on the 4-hr PM$_{10}$ filter samples were used as first estimates for $c_1$. DL for actual samples are usually 10 times higher or greater (Saadati et al., 2013). Therefore, DL of the molecular markers were adjusted by a factor ranging from 10 to 100. Higher adjustment factors were used for very low concentration compounds (pg, nitro-PAHs), while for the compounds with DL based on field blanks (MSA and Ox)
and standard solutions, a factor from 1 to 10 was considered. For the compounds with DL obtained from inter-laboratory comparisons (levoglucosan and EC) (Chiappini et al., 2014; Panteliadis et al., 2015; Verlhac et al., 2013), no major adjustments were required. The original and adjusted DLs are given in the supplementary material (SM, Table S1).

The scaled residuals obtained for each molecular marker are presented in Fig. S1. In the ideal scenario, these scaled residuals should be normally distributed between -3 to 3. However, large residuals can be expected when using this kind of complex models. Here, we chose to keep all the data points - i.e., even the ones with high residuals for one or several molecular markers - in order to avoid any alteration in the physical meaning of the given dataset. For ACSM m/z's, each of the major ones has been included in the input matrix except m/z 29 which caused too high instability within the combined PMF results. It was assumed that no scaling factor was needed to adjust for m/z 29 and other minor ones not accounted within the combined PMF analysis. Eventually, the input data matrix included 774 discrete time units, 866 samples (rows), and 72 variables (57 m/z from the ACSM, 14 organic molecular markers and EC) (Table S2).

Note, the organic markers used here were analysed in the PM₁₀ extracts while the PM₁ fraction was measured using the ACSM. However, it has been demonstrated that the organic molecular markers including secondary and primary markers such as levoglucosan, PAHs, oxy-PAHs, nitro-PAHs, MSA, nitrophenols, dicarboxylic acids (oxalic acid, phthalic acid and succinic acid) are mainly associated (>90%) with the fine aerosol fraction (below PM₁) (Agarwal et al., 2010; Albinet et al., 2008; Allen et al., 1996; Kerminen et al., 1997; Li et al., 2016; Pszenny, 1992; Ringuet et al., 2012; van Drooge and Grimalt, 2015). Hence, the assumption of using ACSM measurements and PM₁₀ organic markers in a combined source apportionment approach is acceptable.
207 **2.4.3. Selection and optimization of the final solution**

208
209 The selection of the factors was based on the investigation of the chemical profiles, the temporal evolutions of the contributions, and the prior knowledge available for the same site (Crippa et al., 2011; Fröhlich et al., 2015; Petit et al., 2017b; Srivastava et al., 2018a) (Figs. S2 to S7). Solutions with a number of factors below or equal to eight were less explanatory. Therefore, only solutions with more than eight factors were assessed. Solution with nine, ten, and eleven factors were carefully examined including the study of G-plots (Figs. S8-S10 and discussion in SM (S.B sub-section)). The ten-factor solution eventually provided the most reasonable solution for this combined ME-2 analysis.

217 The ME-2 algorithm allows the implementation of constraints. The general framework for applying constraints to PMF solutions has already been discussed elsewhere (Amato and Hopke, 2012; Amato et al., 2009). A subsequent run was performed on the base results in which different constraints and number of constraints were applied (Table S3). The ratio of observed Qaux, i.e., auxiliary terms in the object function (Q) that is minimized in PMF, to the expected Qaux (Qaux-o/Qaux-e) (see SM for details) can be investigated to decide the number of constraints that could be applied (Sofowote et al., 2015). Finally, only one constraint has been applied. 1-Nitropyrene (1-NP, marker for diesel emissions) (Keyte et al., 2016; Schulte et al., 2015; Zielinska et al., 2004a) was pulled up maximally in the primary traffic OA (PTOA) factor and the fractional expected change in the target was set to 0.4.

227 Diel profiles of PTOA factors from the unconstrained and constrained solutions are shown in Fig. S11. The constrained PTOA displayed more pronounced morning and evening rush-hours peaks than the unconstrained solution. The unconstrained PTOA showed higher concentrations
suggesting that the use of the constraint on 1-NP led to a better separation of POA factors (Srivastava et al., 2018a; Zhang et al., 2019). In addition, no notable change was observed between the base and the constrained factor profiles and temporal evolutions (Figs. S12 and S13).

Details are presented in the SM.

234

## 3. Description of the factors obtained from the PMF-combined data analysis

The PMF-combined data analysis allowed the deconvolution of 10 OA factors including 3 POA (primary traffic OA (PTOA), biomass burning OA (BBOA), and oxidized BBOA (OBBOA)) and 7 SOA factors (2 biogenic-SOA, 4 anthropogenic-SOA and 1 highly processed/aged SOA) (Fig. 1). The identified OA factors have been classified in terms of oxidation state, sources and/or precursors. Overall, aged (highly processed) SOA (SOA-5) was the main contributor (34%) to the OA mass during the campaign. Oxidized biomass burning (OBBOA) (14%), biogenic SOA (BSOA-1, marine-rich) (14%), anthropogenic SOA (ASOA-2, nitro-PAHs) (10%) and primary traffic OA (PTOA) (8%) made significant contributions to the total OA mass.

244

As for PMF-ACSM (Fig. 2), the results obtained showed that about 75% of OA mass was secondary in origin. Details on the PMF-ACSM analysis performed in the frame of the present study are given in the SM (section S.C. Results from PMF-ACSM analysis, Figs. S14-S19).

Using the combined data matrix, the origin of 54% of the total SOA fraction was clearly identified. Specifically, 28% of the total SOA fraction was found to be related to anthropogenic SOA (ASOA-1, ASOA-2, ASOA-3 and ASOA-4) from combustion sources such as biomass burning and traffic emissions.

245

246

247

250
The identified OA sources, their chemical profiles and temporal evolutions are shown on Figs. 3 and 4. They are discussed individually together with their diel cycles (Fig. 5).

## 3.1. POA factors

### 3.1.1. Primary traffic OA (PTOA)

The PTOA factor accounted for 8% of the OA mass during the studied period (Fig. 1). It was identified based on its high proportion of aliphatic hydrocarbons, especially m/z 27, 41, 55, 57, 69, and 71 (Aiken et al., 2009) together with a significant amount of EC (43% in this factor) and 1-nitropyrene (1-NP) (35% of species in this factor) (Fig. 3). These mass fragments are consistent with the mass spectral characteristics found for the primary combustion sources (i.e., fossil fuel) and have been commonly used in urban environment as a proxy of traffic emissions (Lanz et al., 2007a; Mohr et al., 2009; Ulbrich et al., 2009; Zhang et al., 2019).

The use of 1-NP, known to be a good marker of diesel emissions (Keyte et al., 2016; Schulte et al., 2015; Zielinska et al., 2004a; Zielinska et al., 2004b) to constraint this factor, clearly improved its separation from other primary sources (see section 2.4.3). PTOA also showed a fair correlation with $BC_{fr}$ ($r^2$= 0.39; n= 774) (Fig. S20), thereby corroborating this factor link with traffic emissions. In addition, the diel cycle of the resolved source exhibited two pronounced peaks in agreement with the traffic rush hours in the morning and in the evening (Fig. 5).

### 3.1.2. Biomass burning OA (BBOA)

BBOA source profile included high contributions of m/z 60 and 73 (Fig. 3). Thee ions are typical fragments of anhydrous sugars, such as levoglucosan, which are produced during cellulose pyrolysis (Alfarra et al., 2007; Lanz et al., 2007b; Simoneit et al., 1999). Levoglucosan, a well-known marker for biomass burning, was mainly present (67%) in this factor. In addition, this
factor included noticeable contributions of other species often emitted by wood combustion, such as benzo[a]fluorenone (B[a]Fone), benzo[b]fluorenone (B[b]Fone), and 9-nitroanthracene (9-NA) (Nalin et al., 2016).

Overall, BBOA contributed only 3% of the OA mass (Fig. 1). Slightly higher concentrations were measured at the beginning of the PM pollution event, a period that was highly affected by local emissions and notably by residential heating (Petit et al., 2017b; Srivastava et al., 2018a). The diel cycle of BBOA illustrated significant increases from the late afternoon until the night corresponding to residential heating (Fig. 5).

### 3.1.3. Oxidized BBOA (OBBOA)

The source profile of this factor contained m/z, 43, 44, 45, 59, 60, 71 and 73, together with a significant contribution of oxidized species like oxalate (38% of the OBBOA in mass concentration) (Fig. 3). This source accounted for 14% of the OA mass for the study period (Fig. 1). This factor was found to be correlated with levoglucosan and potassium ($K^+$) (levoglucosan, $r^2$= 0.48; $K^+$, $r^2$= 0.46; n= 92), both, tracers for biomass burning activities (Cachier et al., 1995; Simoneit et al., 1999) (Fig. S21). The observed diel profile of this factor also illustrated the same behaviour as BBOA with high concentrations during the night corresponding to wood burning activities (Fig. 5). Therefore, this factor was considered as another biomass-burning source with oxidized characteristics and named as OBBOA. In addition, the oxidized organic masses m/z, 45, 59, 71, and 73 are commonly found in aged biomass burning particles, formed via the photochemical oxidation of organic acids followed by gas-to-particle partitioning (Zhang et al., 2017), supporting the connection of this source with biomass burning activity.

To further investigate the evolution of OA factors, the triangle plot $f_{44}$ vs. $f_{43}$ (fraction of m/z 44 and m/z 43 in OA, respectively) was investigated as suggested by Ng et al. (2010). The $f_{44}$ can
be considered as indicator of atmospheric ageing due to photochemical processes leading to the increase of f₄₄ in the atmosphere while f₄₃ is an indicator of the degree of hydrogenation and thus of primary combustion processes. The triangle plot is presented in Fig. 6.

Both, PTOA and BBOA factors, identified above, showed low oxidative properties (low f₄₄) with high hydrogenated ones (high f₄₃) (both located at the bottom right of the triangle plot) in agreement with previous studies (Sun et al., 2012; Zhang et al., 2015). Alternatively, the present factor showed similarities with oxidized primary OA (OPOA) resulting from the rapid oxidation in the gas phase of low volatility and/or semi-volatile organics (Grieshop et al., 2009), together with the characteristics of partially oxidized biomass burning with mid f₆₀ values typical of primary BBOA (Figs. 6 and S22). Thus, this resolved source represents a good example of OPOA from wood burning probably formed between the emission and the introduction in ambient air, as already shown previously (Nalin et al., 2016). In addition, this factor could also be part of the burn-out phase of combustion when there is a lot of pyrolysis, followed by the formation of oxidized OA and humic-like substances (HuLIS). Finally, OBBOA seemed to be more oxidized at the end of the campaign due to enhanced atmospheric processing (Fig. S23).

## 3.2. SOA factors

As described previously, the f₄₄ vs. f₄₃ plot provides valuable information on the photochemical ageing of the evolved SOA components (Ng et al., 2010). All SOA components were found in the upper half part of the triangle plot (Fig. 6). The variability observed in f₄₄ and f₄₃ suggested the role of various sources, precursors, and different chemical pathways involved in their formation as discussed below.

### 3.2.1. Biogenic SOA-1 (marine-rich) (BSOA-1)
This factor was identified based on high contributions of m/z 44 and methanesulfonic acid (MSA, being fully present in this factor). MSA is a known secondary oxidation product of dimethylsulfide (DMS), which is emitted by phytoplankton and several types of anaerobic bacteria in the oceanic environment (Charlson et al., 1987; Chasteen and Bentley, 2004; Zorn et al., 2008). Due to low signal-to-noise ratio from the ACSM measurements, typical MSA mass fragments such as m/z 78 and 79 (Crippa et al., 2013b) were not present in the factor profile.

This factor accounted for 14% of the OA mass, with a high contribution during the final days of the campaign (Figs. 1 and 4). These results were in agreement with the impact of long range transport and ageing processes highlighted previously for the same pollution event during this period together with high NO₃⁻ and SO₄²⁻ concentrations and high wind speed (Petit et al., 2017a; Srivastava et al., 2018a) (Fig. S24). In addition, a phytoplankton bloom hotspot near the North Sea in March 2015 in agreement with the origin of air masses at the end of the campaign (Fig. S25) been previously reported, confirming the marine influence (Srivastava et al., 2018a).

This factors also included a significant contribution of oxalate (43% in this factor), an ultimate by-product of photochemical oxidation processes. Secondary formation routes for oxalic acid (quantified as oxalate) are thought to be driven by the photochemical decomposition of gaseous anthropogenic (e.g., cycloalkanes) and biogenic organic compounds (Carlton et al., 2007; Carlton et al., 2009; Hatakeyama et al., 1987; Kawamura et al., 1996), followed by partitioning onto the condensed phase (Martinelango et al., 2007; Sullivan and Prather, 2007). Heterogeneous formation, which includes in-cloud processing (Pun et al., 2000), also represents a formation pathway of this oxidized species. Therefore, the observations suggest that BSOA-1 is principally linked to marine SOA, but the significant impacts of other sources (anthropogenic/ biogenic) should not be excluded. This possibility was further confirmed by the results obtained from the Concentration-Weighted Trajectory (CWT) (Petit et al., 2017a) analysis showing a geographical
origin of this factor from the North Sea including substantial influence of air masses from the continental Eastern Europe (Fig. S26). Note that, terrestrial sources of DMS (from soil and trees) were reported recently (in the Amazon rain forest) by Jardine et al. (2015).

### 3.2.2. Biogenic SOA-2 (isoprene-rich) (BSOA-2)

The identification of this factor was based on oxygenated mass fragments (m/z 44 and m/z 43) and the presence of two isoprene oxidation products (Carlton et al., 2009), i.e., α-MGA (60% in this factor) and 2-MT (2-methylerythritol, 99% in this factor, as shown in Fig. 4. BSOA-2 accounted for 5% of the total OA mass (Fig. 1). Such low contributions during the late winter and early spring periods is expected due to low biogenic emissions in this season. Finally, BSOA-2 was found to be significantly less oxidized compared to previous BSOA-1 (Fig. 6).

### 3.2.3. Anthropogenic SOA-1 (nitro-PAHs) (ASOA-1)

This factor was characterized by the presence of 2-nitrofluoranthene (2-NFlt, 100% in this factor) and oxygenated mass fragments (m/z 44 and m/z 43) (Fig. 4). 2-NFlt is an exclusive secondary oxidation product from the gas phase reaction between fluoranthene and NO₂ initiated by OH (day-time) or NO₃ (night-time) radicals (Arey et al., 1986; Atkinson et al., 1987). Therefore, this factor was considered to be associated with PAH SOA from anthropogenic sources i.e., biomass burning and traffic.

ASOA-1 showed significant contributions to the OA mass (10%) (Fig. 1). The diel cycle showed an increase of the concentrations from early night until early morning indicating the predominance of night-time processes as previously shown by Srivastava et al. (2018a) (Fig. 5). This factor is oxidized but also included a significant fraction of f₄₃ (Fig. 6).
372 **3.2.4. Anthropogenic SOA-2 (oxy-PAHs) (ASOA-2)**

373 High proportions of dibenzo[b,d]pyran-6-one (6H-DPone, almost exclusively present in this 374 factor) and oxygenated mass fragments (m/z 44 and m/z 43) were observed in this source profile 375 (Fig. 4). 6H-DPone is a by-product from phenanthrene photooxidation and is considered to be a 376 good marker of PAH SOA formation (Lee and Lane, 2010; Tomaz et al., 2017). This factor also 377 included B[a]Fone and B[b]Fone (33% and 47% in this factor, respectively) that may originate 378 from both primary and secondary processes (Albinet et al., 2007; Srivastava et al., 2018a; Tomaz 379 et al., 2017) (Fig. 4). Thus, this factor was treated as another PAH SOA source accounting for 4% 380 of the OA mass (Fig. 1).

381 ASOA-2 appeared slightly less oxidized than the other PAH SOA (ASOA-1) (Fig. 6), 382 suggesting different chemical processes involved in the formation of nitro- and oxy-PAHs 383 (Srivastava et al., 2018a). The diel cycle of ASOA-2 also illustrated a different pattern with 384 noticeable morning and evening peaks (Fig. 5), which may suggest an influence from traffic, but 385 a more detailed identification of the sources linked to this factor is difficult to obtain. 386

386

387 **3.2.5. Anthropogenic SOA-3 (phenolic compounds oxidation) (ASOA-3)**

388 The source profile of this factor incorporated high proportions of 4-methyl-5-nitrocatechol (4-Me5Nc) (100% in this factor) and 3-methyl-5-nitrocatechol (3-Me5Nc) (91% in this factor) 389 together with high m/z 44 and m/z 43 (oxidized mass fragments) (Fig. 4). Nitrocatechols are by-products from the photooxidation of phenolic compounds (i.e., cresols, methoxy phenols) largely 390 emitted by biomass burning (Bruns et al., 2016; Iinuma et al., 2010). In addition, this factor also 391 included significant contributions of m/z 60, m/z 73 and levoglucosan (17% in this factor). The 392 ASOA-3 factor followed a distinctive temporal evolution with higher concentrations at the 393 beginning of the sampling campaign in agreement with the BBOA pattern (Figs. 3 and 4). The 394 395
396 diel cycle showed concentration peaks in the early evening concurrently with the wood burning emissions (Fig. 5). Therefore, this factor illustrated the characteristics of anthropogenic SOA 397 linked to biomass burning emissions. This assignment was also supported by the f₄₄ vs. f₆₀ plot 398 (Fig. S22) showing very high f₆₀ values comparable to the BBOA factor. 399

400 ASOA-3 showed a similar oxidation level to both previously identified anthropogenic SOA 401 factors (ASOA-1 and ASOA-2) (Fig. 6) but significantly less hydrogenated (low f₄₃). Finally, 402 ASOA-3 had a low contribution to the OA mass (3% on average) (Fig. 1). 403

404 3.2.6. Anthropogenic SOA-4 (toluene oxidation) (ASOA-4) 405

406 The source profile of this factor showed a high proportion of m/z 44, including 100% 407 contribution of DHOPA (Fig. 4). DHOPA is a SOA marker of toluene oxidation (Kleindienst et 408 al., 2004). Therefore, this factor seemed to be another anthropogenic SOA from combustion 409 sources notably biomass burning and traffic, as they both emit large quantities of toluene (Baudic 410 et al., 2016; VanderSchelden et al., 2017).

410 Small contributions of m/z 60 and levoglucosan (~10% in this factor) could also be observed 411 (Fig. 4). The diel cycle of ASOA-4 showed a pronounced peak at night concurrent with 412 residential heating activities (Fig. 5). Thus, ASOA-4 seemed another anthropogenic SOA factor 413 mainly linked to biomass burning emissions. 414

414 This factor was also more oxidized (high f₄₄ content) with different f₄₃ and f₆₀ fractions than 415 the other biomass burning SOA (ASOA-3) (Figs. 6 and S22), showing that different chemical 416 processes were involved. Finally, the contribution of ASOA-4 to total OA was about 5% (Fig. 1). 417

418 3.2.7. SOA-5 (aged SOA) 419
High contributions of m/z 44 and m/z 43 were observed for this factor. This pattern is similar to the more oxidized OA factors determined at other urban/suburban sites from AMS/ACSM PMF analyses (Fig. 4) (Ng et al., 2010; Ulbrich et al., 2009). Interestingly, no organic markers have been observed to be associated with this factor except a small contribution of α-MGA (30% in this factor), an isoprene oxidation by-product. This factor was the predominant one over the PM pollution event, accounting for about 34% of the OA mass (Fig. 1) and clearly very oxidized (Fig. 6).

In addition, SOA-5 showed good correlations with secondary inorganic species ($r^2_{\text{sulfate}}$= 0.67; $r^2_{\text{nitrate}}$= 0.81; $r^2_{\text{ammonium}}$= 0.83; n= 774) (Fig. S27) and a distinctive temporal variation, with very high concentrations at the end of the campaign (Fig. 4). Its high oxidized properties (Fig. 6), together with the impact of long range transport observed during this period, suggested that SOA-5 may contained highly processed aerosol, as also supported by previous findings for the same campaign (Petit et al., 2017a; Srivastava et al., 2018a).

A similar OOA factor has also been observed at other European sites in winter with the same characteristics, i.e., high correlation with long-range transported secondary inorganic species (Daellenbach et al., 2017; Lanz et al., 2007b), supporting the assignment of this source.

## 4. Comparison between PMF-ACSM and PMF-combined data outputs

Results from PMF-combined data were compared here to those of PMF-ACSM to investigate the consistency and the benefits of the developed OA source apportionment methodology over the conventional approaches. Briefly, four factors were identified from the PMF-ACSM analysis: 2 POA factors - i.e., HOA (hydrocarbon-like OA, 16%) and BBOA (biomass burning OA, 14%) - and 2 SOA factors, i.e., LO-OOA (low oxidized-oxygenated OA, 15%) and MO-OOA (more
442 oxidized-OOA, 55%) (Fig. 2). These results notably highlighted the enhanced formation of SOA
443 during this PM pollution event as shown previously (Petit et al., 2017a).

444

445 **4.1. POA sources**

446 The primary traffic emissions and biomass burning-related OA factors from both PMF
447 analyses are presented in Fig. 7. The primary biomass burning factor from the PMF-combined
448 data analysis was considered to be the sum of both identified biomass burning-related factors:
449 BBOA and OBBOA (see details in section 3.1). Relatively good agreement was observed for this
450 comparison (r²= 0.62, slope= 0.90, n= 774, y-axis= [BBOA + OBBOA]combined, x-axis=
451 BBOAACSM), with diel cycles notably presenting maximum concentrations during the night (Fig.
452 S28). A good agreement was also noticed between the traffic OA factors (r²= 0.75, slope= 0.68,
453 n= 774, y-axis= PTOA, x-axis= HOA). However, higher night time concentrations were noticed
454 for the PMF-ACSM HOA factor. This result could be attributed to the influence of biomass
455 burning PM on the HOA mass concentrations as suggested in section 3.1 and previously shown at
456 SIRTA during this time of the year (Petit et al., 2014; Srivastava et al., 2018a; Zhang et al.,
457 2019).

458 The PMF-combined data analysis seemed better able to resolve primary traffic OA than PMF-
459 ACSM, whereas HOA should only be considered as a proxy of road transport OA. Discrepancies
460 were observed for biomass burning emissions between both approaches at the end of the
461 campaign, a period highly affected by oxidized species as previously specified. OBBOA may
462 contain more secondary species than primary ones in this period, explaining the observed
463 discrepancies. Overall, the primary biomass burning source was well resolved by both
464 approaches, although the PMF-combined data analysis allowed the distinction of POA and
465 OPOA from wood combustion, which was not possible with the conventional PMF-ACSM
466 approach. In addition, the results obtained showed that about 80% of the BBOA was composed of
467 OBBOA suggesting that BBOA in the atmosphere was probably more oxidized in nature rather
468 being like the primary BBOA found in this study.

469

470 **4.2. SOA fractions**

471 The comparison of the total SOA estimates (total OOA factors) from both approaches is
discussed here (Fig. S29). The total SOA estimates from the PMF-combined data analysis was
472 the sum of the 7 OOA (Σ₇OOA) factors (BSOA-1, BSOA-2, ASOA-1, ASOA-2, ASOA-3, ASO4
473 and SOA-5), while only 2 OOA factors (MO-OOA and LO-OOA) were resolved using the PMF-
474 ACSM analysis. Overall, both approaches showed very good agreement (r²= 0.96, slope= 1.10,
475 n= 774, y-axis= Σ₇OOA, x-axis= MO-OOA + LO-OOA) highlighting that the PMF performed on
476 the combined dataset has given valuable insights into the secondary sources with 7 sources
477 resolved instead of only 2. This result is discussed into details below.
478

479

Individual SOA sources from both approaches have been compared to provide comprehensive
479 information on the SOA formation processes/sources. No direct association was observed
480 between the individual SOA factors obtained from both approaches, except between the highly
481 oxidized OOA factors (MO-OOA, BSOA-1 and SOA-5; Table S4). MO-OOA was found to be
482 significantly correlated with both, BSOA-1 and SOA-5 (Table S4; r²= 0.76-0.89, n= 774).
483 Further, the sum of BSOA-1 and SOA-5 was compared with MO-OOA, and the results showed a
484 very good agreement (r²= 0.96, slope= 1.13, n= 774, y-axis= BSOA-1 + SOA-5, x-axis= MO-
485 OOA) with similar temporal evolutions (Fig. S29). Hence, MO-OOA was mainly composed of
486 two factors, BSOA-1 and SOA-5, identified from the PMF-combined data analysis. However, the
487 clear identification of the sources linked to MO-OOA is still difficult to achieve. As explained
488

481

482

483

484

485

486

487

488
489 previously, SOA-5 could not be identified using any molecular markers and was mainly
490 explained as being processed aerosols and long-range transport. In addition, BSOA-1 contained a
491 part of the marine SOA but with substantial influences from other biogenic or anthropogenic
492 sources that cannot be ruled out. This source profile also included nonspecific oxidized species
493 such as oxalate. Therefore, a detailed description of MO-OOA, and more generally of aged SOA,
494 is not yet possible to achieve since this SOA fraction is represented mainly by ultimate oxidation
495 by-products (i.e. oxalate) and highly processed PM.

496 Finally, no evident correlation was observed for LO-OOA with any of the SOA sources identified
497 by PMF-combined data analysis, confirming that LO-OOA may not stand for a single source,
498 precursor or specific atmospheric process.

499
500 5. Conclusions
501

501 In this study, an advanced approach has been developed to investigate the OA sources using
502 ME-2 algorithm applied to a combined dataset including ACSM OA matrix (30-min time
503 resolution) and organic molecular markers from PM₁₀ filter-based chemical analyses (4-h time
504 resolution) in their original time resolutions. This new methodology allowed resolution of 10 OA
505 factors, including POA sources such as primary traffic OA (PTOA), biomass burning OA
506 (BBOA), and one uncommonly-resolved OPOA factor related to wood combustion (OBBOA), as
507 well as 7 SOA factors (2 biogenic-SOA, 4 anthropogenic-SOA and1-highly processed SOA). A
508 clear identification of 54% of the total SOA fraction has been achieved thanks to this new
509 approach. Specifically, 28% of the total SOA fraction from 4 different SOA factors, seemed to be
510 directly related to combustion sources (biomass burning and/or traffic emissions), notably the
511 oxidation processes of toluene and phenolic compounds.
512 Overall, good agreement for both, primary and secondary sources was observed between the
513 PMF combined data analysis and the conventional PMF-ACSM approach. An improvement in
514 the source apportionment of traffic emissions has been obtained with the clear identification of
515 the primary traffic OA while the PMF-ACSM elucidates a proxy (HOA) for this source. The
516 PMF-combined data analysis also indicated that OBBOA could represent more than 80% of the
517 primary biomass burning fraction, suggesting that wood combustion PM is mainly present in the
518 atmosphere as OPOA under the conditions during the study period.

519 For secondary sources, the comparison with PMF-ACSM highlighted the significant
520 advantage of the PMF-combined data analysis by resolving 7 SOA factors instead of 2 SOA
521 factors (MO-OOA, LO-OOA). Moreover, the results confirmed that MO-OOA was mainly
522 associated with ultimate oxidation by-products and highly processed aerosols, also possibly
523 accounting for a small fraction from marine SOA.

524 Finally, the use of the combined dataset provided valuable insights into the processes involved
525 in the SOA formation and their sources. It should then be promoted and further developed (e.g.,
526 using more molecular markers, such as organo-sulphates or organo-nitrates) to conduct future OA
527 source apportionment studies.

529 Acknowledgements

530 This work has been supported by the French Ministry of Environment and the national
531 reference laboratory for air quality monitoring (LCSQA), as well as by the H2020 ACTRIS-2
532 project (grant agreements no. 654109). The authors gratefully acknowledge François Truong
533 (LSCE) and Robin Aujay-Plouzeau (INERIS) for taking care of samples and instrumentation and
534 other staff at the SIRTA observatory for providing weather-related data used in this study. They
535 also thank Patrick Bodu for the graphical abstract design.
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929
**Fig. 1.** Average contributions (left) and temporal evolution (right) of the identified sources to OA fraction. PTOA: primary traffic OA; BBOA: biomass burning OA; OBBA: oxidized BBOA; BSOA-1: biogenic SOA-1 (marine-rich); BSOA-2: biogenic SOA-2 (isoprene-rich); ASOA-1: anthropogenic SOA-1 (nitro-PAHs); ASOA-2: anthropogenic SOA-2 (oxy-PAHs); ASOA-3: anthropogenic SOA-3 (phenolic compounds oxidation); ASOA-4: anthropogenic SOA-4 (toluene oxidation); SOA-5 (aged SOA).

**Fig. 2.** Average contributions (left) and temporal evolution (right) of the identified sources to OA mass concentrations from PMF-ACSM.

**Fig. 3.** Chemical profiles and temporal evolutions of OA factors identified at SIRTA. Chemical profiles: left part, ACSM mass fragments (log scale); right part, organic marker contributions. PTOA: primary traffic OA; BBOA: biomass burning OA; OBBOA: oxidized BBOA; BSOA-1: biogenic SOA-1 (marine-rich); BSOA-2: biogenic SOA-2 (isoprene-rich).

**Fig. 4.** Chemical profiles and temporal evolutions of OA factors identified at SIRTA (continued). Chemical profiles: left part, ACSM mass fragments (log scale); right part, organic marker contributions. ASOA-1: anthropogenic SOA-1 (nitro-PAHs); ASOA-2: anthropogenic SOA-2 (oxy-PAHs); ASOA-3: anthropogenic SOA-3 (phenolic compounds oxidation); ASOA-4: anthropogenic SOA-4 (toluene oxidation); SOA-5 (aged SOA).

**Fig. 5.** Diel profiles of anthropogenic OA factors resolved from the PMF-combined data analysis at Paris-SIRTA. Error bars represent ± 2 SD (standard deviation). PTOA: primary traffic OA; BBOA: biomass burning OA; OBBOA: oxidized BBOA; ASOA-1: anthropogenic SOA-1 (nitro-PAHs); ASOA-2: anthropogenic SOA-2 (oxy-PAHs); ASOA-3: anthropogenic SOA-3 (phenolic compounds oxidation); ASOA-4: anthropogenic SOA-4 (toluene oxidation).

**Fig. 6.** Triangle plot showing f<sub>44</sub> vs. f<sub>43</sub>. f<sub>44</sub> and f<sub>43</sub> represent the fraction of m/z 44 and m/z 43 in OA, respectively. The dots are coloured according to the sampling dates. PTOA: primary traffic emissions; BBOA: biomass burning OA; OBBOA: oxidized BBOA; BSOA-1: biogenic SOA-1 (marine-rich); BSOA-2: biogenic SOA-2 (isoprene-rich); ASOA-1: anthropogenic SOA-1 (nitro-
PAHs); ASOA-2: anthropogenic SOA-2 (oxy-PAHs); ASOA-3: anthropogenic SOA-3 (phenolic compounds oxidation); ASOA-4: anthropogenic SOA-4 (toluene oxidation); SOA-5 (aged SOA).

**Fig. 7.** Temporal evolutions of primary biomass burning- (BBOA<sub>ACSM</sub> vs [BBOA + OBBOA]<sub>combined</sub>) and primary traffic emissions-related (HOA vs PTOA) POA factors identified using both PMF-ACSM and PMF- combined data analyses.
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