Chemical Composition of PM2.5 in October 2017 Northern California Wildfire Plumes

Abstract. Wildfires have become more common and intense in the western US over recent decades due to a combination of historical land management and warming climate. Emissions from large scale fires now frequently affect populated regions such as the San Francisco Bay Area during the fall wildfire season, with documented impacts of the resulting particulate matter on human health. Health impacts of exposure to wildfire emissions depend on the chemical composition of particulate matter, but the molecular composition of the real biomass burning organic aerosol (BBOA) that reaches large population centers remains insufficiently characterized. We took PM2.5 (particles having aerodynamic diameters less than or equal to 2.5 μm) samples at the University of California, Berkeley campus (~60 km downwind of the fires) during the October 2017 Northern California wildfires period, and analyzed molecular composition of OA using a two-dimensional gas-chromatography coupled with high resolution time-of-flight mass spectrometer (GC×GC ToFMS). Sugar-like compounds were the most abundant component of BBOA, followed by mono-carboxylic acids, aromatic compounds, other oxygenated compounds and terpenoids. The vast majority of compounds detected in smoke have unknown health impacts. Regression models were trained to predict the saturation vapor pressure and averaged carbon oxidation state of compounds. The compounds speciated have a wide volatility distribution and most of them are highly oxygenated. In addition, time series of primary BBOA tracers observed in Berkeley were found to be indicative of the types of plants in the ecosystems burned in Napa and Sonoma, and could be used to differentiate the regions from which the smoke must have originated. Commonly used secondary BBOA markers like 4-nitrocatechol were enhanced when plumes aged, but their very fast formation caused them to have similar temporal variation as primary BBOA tracers. Using hierarchical clustering analysis, we classified compounds into 7 factors indicative of their sources and transformation processes, identifying a unique daytime secondary BBOA factor. Chemicals associated with this factor include multifunctional acids and oxygenated aromatic compounds. These compounds have high average carbon oxidation state, and are also semivolatile. We observed no net particle-phase organic carbon formation, which indicates an approximate balance between the mass of evaporated primary and secondary organic carbonaceous compounds to the addition of secondary organic carbonaceous compounds.


plumes was only observed in a few field studies (Hodshire et al., 2019a). Speciated measurements enable us to examine whether primary BBOA marker compounds emitted in fires that differ in fuel type, intensity, or other factors, are suitable for use in source apportionment, and to identify useful marker compounds (and formation pathways) for BB SOA. Speciation of 70 aged wildfire BBOA also informs us about the compounds people were exposed to during the fires.
Traditional speciated measurements of BBOA typically rely on the gas chromatography-mass spectrometric (GC-MS) analyses of filter samples. Due to the limited capability of conventional GC-MS in separating compounds, a high fraction of BBOA is typically assigned as unresolved complex mixture (UCM) (Fine et al., 2004;Hays et al., 2004). For ambient BBOA 75 measurements, low sampling frequency (e.g. one or two samples per day) typically makes it challenging to distinguish BBOA from other pollution sources and to capture short timescale variability indicative of changing emissions, transport, and atmospheric processing. The advances in analytical instruments, such as the two-dimensional GC coupled with high resolution mass spectrometry (GC × GC HRMS) significantly expands the ability to separate, identify and quantify organic compounds in aerosols (Alam and Harrison, 2016;Laskin et al., 2018;Liu and Phillips, 1991;Worton et al., 2017). Using 80 this approach, an unprecedented number of compounds in OA can be now speciated and quantified, and traced back to sources (Zhang et al., 2018). Here we apply GC × GC HRMS to the analysis of 3-4 hour time-resolution samples of extreme levels of ambient particulate matter generated by distant wildfires affecting a populated region.
In October 2017, a series of wildfires took place in Napa and Sonoma Counties in Northern California. The emissions caused 85 extreme air pollution conditions with poor visibility throughout the highly populated San Francisco Bay Area for more than 10 days. According to US EPA's measurement data (EPA AirNow), the levels of PM2.5 exceeded 100 μg m -3 at multiple measurement sites within the Bay Area. A recent epidemiological study shows exposure to wildfire smoke in California during 2015-2017, including the 2017 fires measured here, significantly increased the risk of out-of-hospital cardiac arrest (odds ratio 1.70, 95% confidence interval 1.18-2.13) (Jones et al., 2020). These dense smoke periods provide a unique 90 chance to study the speciated chemical composition and transformations of BBOA occurring during the same time health effects have been documented. When these fires first began, we set up a comprehensive set of online measurements at the University of California, Berkeley (UCB) campus, and collected particulate filter samples for offline analysis. This article focuses on the organic aerosols collected on the filters. Main objectives for this study include (1) measuring the chemical composition of BBOA after several hours of atmospheric aging and estimating the volatility and the average carbon 95 oxidation state distribution of BBOA; (2) testing whether marker compounds can be used to distinguish the fuels burned in the fires; (3) testing whether traditional BBSOA tracers can indicate the aging process; (4) using a statistical approach to cluster all the compounds by their temporal behavior to examine their sources and transformation processes; and (5) identifying BBSOA marker compounds, and exploring the temporal trend of BBSOA based on the clustering analysis.
https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License. esters, making the compounds easier to elute from columns. The analytes were then quickly injected into the GC system (Agilent 7890). The first column in the GC×GC system is a semi-polar capillary 60 m × 0.25 mm × 0.25 µm column Restek), which mainly separates compounds by volatility. The temperature of the first GC column was programed to ramp from 40 to 320°C at 3.5°C/min, and to hold for 5 min at 320°C with helium carrier gas flowing at 2 mL min −1 . 135 Analytes eluted from the first column were focused on a guard column (Restek, 1.5m × 0.25 mm, Siltek) in a dual-stage thermal modulator (Zoex) and then loaded onto the second column (Restek,1m × 0.25mm × 250 µm), where the analytes were separated mainly by polarity. A Tofwerk high resolution (m/Δm ≈ 4000) time-of-flight mass spectrometer (HR-ToF-MS) was used as the detector. All samples were analyzed under electron impact ionization (70 eV). Selected samples were analyzed by Vacuum Ultra-Violet (VUV) ionization (10.5 eV) provided by Beamline 9.0.2 at the Advanced 140 Light Source, Lawrence Berkeley National Laboratory. The VUV analyses were for identification only. To minimize fragmentation in VUV, the temperature in the ionization chamber were maintained at 170 °C instead of 270 °C which was used for EI analysis (Isaacman et al., 2012). The GC chromatograms were analyzed using GC Image software (GC Image, LLC).

Compound identification, classification, and quantification 145
We first identified compounds in the samples by matching with authentic standards. A custom-made biomass burning standard mixture of 99 compounds including alkanes, acids, sugars, aromatic compounds, and polycyclic aromatic hydrocarbons (PAHs) was injected onto blank filters and analyzed by the same instrument. The standard compound list has been published in Table S4 in Jen et al. (2019). For compounds not in this standard mixture, we used the NIST MSSEARCH software to compare them with entries in NIST-14, MassBank, Golm Metabolome Database (GMD), Adams Essential Oil, 150 MANE2010 flavor and fragrance mass spectral databases; and the GoAmazon, SOAS and FIREX mass spectral libraries created at UC Berkeley in previous studies using the same instrument as this study Yee et al., 2018;Zhang et al., 2018). Linear retention index (RI) on the 1 st dimension describes the elution order of compounds from the first column (Yee et al., 2018). For compounds analyzed by the same type of column, the elution order is expected to be the same. The RI match is considered in the matching process. The parent ions of the compounds were confirmed with the VUV mass spectra. 155 Positive identifications were achieved for 43% of the speciated compounds. Details for compound identification are discussed in the Supplement.
A total of 572 compounds separated by the GC×GC were classified based on functionality, into mono-carboxylic acid (acid hereafter), alcohol, alkane, aromatic (mono-cyclic only), nitrogen-containing, other oxygenated (with 2 or more -OH or -160 COOH groups), (substituted/oxygenated) PAH, sugar (and sugar derivatives including anhydro-sugars and sugar alcohols), (di-/tri)terpenoid and unknown groups. Detailed procedures for classing unidentified compounds are provided in the Supplement.
The compound quantification procedure applied has been documented in detail in the main text and supplement of Jen et al. 165 (2019). In brief, we injected multiple known levels of the 99-compound standard mix with the internal standard mix to blank filters and obtained the response curve (based on total ion count) for each compound. Sample compounds within this list were quantified using these curves. Compounds not in this standard mix were quantified using the response curve of the nearest standard compound (preferably in the same class with the compound being quantified) on the GC×GC space. As estimated by Jen et al. (2019), compounds exactly matched with a standard compound have an uncertainty ~ ±10%. 170 Compounds quantified by the nearest compound in the same class have an uncertainty of ~ ±30%. Compounds with unknown functionality have a systematic uncertainty of 200%. We expect compounds with second column retention time > 1.6 s to also have such high uncertainty because there were no standard compounds with that high polarity, and a surrogate standard with lower polarity was used for quantification. However, only 7 reported compounds were in that chromatographic region with extremely high quantitative uncertainty. 175

Supporting measurements
Organic carbon (OC) and elemental carbon (EC) of punched samples from the filters were analyzed on a Sunset Laboratory other VOCs at a 1 Hz sampling frequency. The instrument was calibrated with an authentic VOC gas standard mixture (Apel Riemer Environmental Inc., Miami, FL) containing 23 compounds spanning a wide range of m/z. For compounds not directly calibrated, sensitivity factors derived from known proton transfer rates (Cappellin et al., 2012;Pagonis et al., 2019) and detector transmission were used to convert the response (normalized count rates) to concentration (ppb). Details of the PTR-TOF-MS operation and data processing have been documented elsewhere (Liu et al., 2019;Tang et al., 2016). Hourly 185 concentrations of carbon monoxide (CO) and PM2.5 were continuously measured by the Bay Area Air Quality Management District (BAAQMD) at various sites in the region. Solar radiation data measured at Bethel Island (between the fire sites and Berkeley) were also provided by BAAQMD.

Estimation of compound's volatility and average carbon oxidation state ( ) from GC×GC measurement
Volatility (effective saturation concentration) and average carbon oxidation state distribution are important parameters for 190 predicting the chemistry of OA (Donahue et al., , 2012Kroll et al., 2011). Isaacman et al. (2011) showed that the effective saturation concentrations (C*) and oxygen-to-carbon ratios of compounds can be estimated by their twodimensional retention times. However, that model does not work as well when the sample contains a mixture of aliphatic and aromatic compounds. Also, derivatization can affect the saturation vapor pressure of compounds. We therefore trained two regression models using the MATLAB (version 2019b) Regression Learner to predict the saturation vapor pressure vP of (1) 210 where MWi is the molecular weight of compound i in g mol -1 (assume MW = 200 g mol -1 for compounds with unknown formulae), ξi is the unitless activity coefficient of compound i (assumed to be 1), vP,i is the saturation vapor pressure of compound i (Torr), R is the gas constant (8.21 × 10 −5 m 3 atm mol −1 K −1 ) and T is the temperature (assume 298 K) (Isaacman et al., 2011;Pankow, 1994).

Hierarchical clustering analysis (HCA) 215
Agglomerative hierarchical clustering analysis (HCA) was performed to group the compounds into factors based on their temporal behaviors, using MATLAB Statistics and Machine Learning Toolbox. It has been demonstrated that HCA can identify major groups of compounds (ions) from timelines and patterns of behaviors from chamber measurement data (Koss et al., 2020). A major advantage of the hierarchical clustering analysis over the positive matrix factorization (PMF) method is each compound will only end up in one factor. Ubiquitous biomass burning tracers like levoglucosan will not be split into 220 multiple biomass burning factors.
The concentration timelines were first normalized to prevent all the high (or low) concentration compounds get clustered into the same factor. Then the Euclidean distance between each pair of normalized timelines (e.g. compound concentration vectors u and v) is calculated by: 225 https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License.
where ui and vi are the normalized concentrations of compound u and v at time step i, respectively. The Ward's method was used to cluster the normalized timeline according to the distance (Ward, 1963). This algorithm starts with one compound as a cluster of its own, and then find the nearest compound and merge them (for example, compound u and v were merged in to cluster A). When two clusters A and B are merged, the increase of within-cluster sum of squares is calculated by: 230 where nA and nB are the number of compounds in cluster A and B, ‖ ̅ − � ‖ is the Euclidean distance between the center of cluster A and cluster B. The goal is to find B that minimizes d(A,B). The number of clusters were set at 4-8, and the 7-cluster solution was chosen mainly because of interpretability. The cost of merging (increase in d) was also considered by making sure there was not a jump in d when an extra merge was performed. 235 Figure 1a displays the perimeters of the wildfires in Napa and Sonoma Counties. Figure 1b shows the satellite image and the fire points detected by VIIRS on Oct 12 as an example. The UCB campus site, and many regions in the Bay Area were directly affected by the smoke transported to the Bay Area from these fires. Fuels burned in the Atlas Fire ( Fig. 1c) were 240 dominated by hardwood (including grapevines, various oaks and eucalyptus) and shrubs (chamise and white-leaf manzanita).

Fires and fuels, backward trajectories and spread of the fire plumes
Conifer vegetation only accounted for 0.3% of the area within the perimeter of the Atlas Fire. Hardwood forest (various oaks) also dominated the canopies burned in fires in the Sonoma County. However, conifers (Douglas fir, knobcone pine, redwood, ponderosa pine, etc.) contributed 20.9% of the canopy burned. The shrubs (shrubby oaks, chamise, manzanita) constitute 8.9% of canopy burned in the Sonoma County (Fig. 1d). 245 The mean backward trajectory of each cluster is also shown on Fig. 1a. Plumes in cluster 1 arrived from the northeast at relatively low speed. They mainly picked up smoke from the Atlas Fire. Plumes in cluster 2 originated from the west coast.
They picked up smoke from the wildfires (mainly Sonoma County fires) and then transported it to the Bay Area. Smoke in cluster 2 is expected to be more aged than smoke in cluster 3. Plumes in cluster 3 traveled 3-5 hours from the fires to the 250 UCB campus, as estimated from HYSPLIT. They mainly transported smoke from the Sonoma County Fires to the Bay Area. μg m -3 . The Oct 11 nighttime plume and the Oct 17 daytime plume were less dense in terms of PM2.5. The temporal profiles of PM2.5 at different measurement sites clearly show that they were all affected by the same plumes. In Oct 2017, when there was no influence from the wildfires, the PM2.5 at these BAAQMD sites typically stayed below 15 μg m -3 . Therefore, when the smoke came, the BBOA was the dominant component of particulate matter. 260

Chemical composition of particle-phase organic aerosols
The concentrations of different classes of compounds in each sample measured by the GC×GC are displayed in Fig. 3. In the three samples with highest total quantified mass (all from cluster 3 plumes), the compounds quantified can explain 15-20% of total OC by mass. However, in samples with minimal biomass burning influence only 5-10% of OC can be explained. We define periods with total quantified OA above 4 μg m -3 as plume periods (17 samples Table 1. Sugars, dominated by levoglucosan, account for more than a third of total quantified OA in plume periods. Based on the structures, most of these sugars (if underivatized) can fragment into C2H4O2 + (m/z 60) under EI 270 (Fabbri et al., 2002). Terpenoids (especially resin acids) and nitrogen-containing compounds (dominated by nitrocatechols) were also specific to BBOA, while other families of compounds had other sources. For instance, the acids were enriched when smoke was affecting Berkeley, but their fractions in OA are lower than in background plumes because of dilution by the BB specific compounds.

275
In all samples, the fraction of PAHs remained below 0.3% of total quantified OA. The low PAH fraction in OA measured at UCB could be a result of both low PAH emission and photochemical loss. The emission, exposure, and health impacts of PAHs in biomass burning received a great deal of attention in previous studies (Shen et al., 2013;Sun et al., 2018;Tuet et al., 2019). However, recent work has shown that the toxicity of smoke aerosols is better correlated with total PM2.5 or OC than with total PAHs (Bølling et al., 2012;Kim et al., 2018). Other groups of compounds, such as monocyclic aromatic 280 compounds including hydroquinone, catechol and cinnamaldehyde (Leanderson and Tagesson, 1990;Muthumalage et al., 2018), may also make substantial contributions to the toxicity of biomass burning smoke. The effect of compounds other  Table S1. Knowledge of the health impacts of inhaling compounds in this list are still lacking. For example, many 285 nitroaromatic compounds were found to be mutagenic (Purohit and Basu, 2000). The nitro-compounds were found to be the main contributor to the mutagenicity of PM2.5 in Northern Italy (Traversi et al., 2009). The sum of concentrations of (methyl-)nitrocatechols observed at Berkeley exceeded 1.2 μg m -3 . However, no toxicological research of these compounds was found in PubChem. These compounds could be useful candidates for future toxicological studies.
https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License. Figure 4 displays the volatility and distribution of speciated compounds in two relatively fresh samples (with 3-4 hours 290 aging). The two samples have almost equal total quantified OA by mass. In the two samples, the volatility distribution and the distribution were almost identical. The volatility distribution obtained is similar to particle-phase primary BBOA reported by Hatch et al. (2018) in the way that most compounds reside in 10 0 < C* < 10 2 µg m -3 bins. But the standard deviation of log10C* in our study is higher than that in Hatch et al. (2018), which could be related to fuel differences and aging. Compounds in the 10 -2 µg m -3 < C* < 10 -1 µg m -3 bin were mainly aliphatics and tri-terpenoids, while 1 µg m -3 to 10 2 295 µg m -3 bins consist of sugars, aromatic compounds, and other oxygenated compounds. The distributions measured at UCB differ remarkably from that inferred from thermodenuder + AMS measurements of primary BBOA from wood combustion (Donahue et al., 2012;Grieshop et al., 2009c). In that measurement, most of compounds had near -1.5.
However, our measurement shows although there is a peak of OA with between -2 and -1.5 contributed mainly by mono-carboxylic acids, sugars and oxygenated species cause a larger peak of between -0.5 and 0.5. The fragmentation 300 probability is an important parameter for simulating SVOCs in fire plumes (Alvarado et al., 2015). The probability of an SVOC compound to fragment when reacting with OH can be estimated by p = (O:C) 0.25 (Donahue et al., 2013). Assuming ≈ 3( : ) − 2, for compounds with between -0.5 and 0.5, the probability for them to fragment and form more volatile compounds are roughly 0.84 -0.96. Stronger fragmentation could reduce net growth of mass of particle-phase BBOA in aging processes. 305 Figure 5 shows the timelines of OC, commonly used BB marker compounds and the backward trajectory clusters throughout the campaign. Levoglucosan and mannosan, being the decomposition products of cellulose and hemicellulose, respectively, are emitted in the combustion of most plants Nolte et al., 2001;Simoneit, 2002). Levoglucosan is the most abundant BBOA species measured in this campaign. Its abundance reached around 20% of total quantified OA in the plume 310 periods. Comparing Fig. 5d and 5e, when there was a peak of OC, there were usually peaks of levoglucosan as well. The levoglucosan to mannosan mass ratio can be used to differentiate hardwood and softwood (conifer) fires. Hardwood fires usually have emission ratios of levoglucosan/mannosan around 20, while for softwood fires this ratio is usually less than 5 (Cheng et al., 2013). The levoglucosan to mannosan ratio stayed above 20 in most samples, which confirms the dominance of hardwood as fuel in the October 2017 Northern California wildfires as shown in Figure 1 (c) and (d). 315

Primary BBOA markers can indicate vegetation burned
Diterpenoids including resin acids are unique markers in biomass burning emissions for conifer combustion (Hays et al., 2002). Figure 5c shows the time series of dehydroabietic acid (DHAA), di-dehydroabietic acid (di-DHAA), abietic acid and retene. The concentration of the most abundant resin acid, dehydroabietic acid, reached over 0.8 μg m -3 in a plume. These conifer tracers mainly showed up in three plumes on Oct 11, 12 and 13. Figure 5a shows those plumes were associated with 320 backward trajectories in cluster 2 and 3 which mainly transported smoke form the fires in Sonoma County, in which more https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License. than 20% of the vegetation burned was conifer. In contrast, the BB plumes on October 10 and 17 were not accompanied by peaks of these conifer fire makers. That agrees with the fact that those plumes originated in the northeast (Fig. 5a), which mainly transported smoke from the Atlas Fire with little conifer combustion. Dimethopxyphenols and amyrins are markers of hardwood. They were enriched in the plumes on Oct 10 and 17, as well as the plumes on Oct 11-13, which further 325 confirms the ubiquity of hardwood fuels in all the fires affecting Berkeley. Hydroquinone and two other compounds were also shown to be good tracers for manzanita fires . They were present in most of the plumes, which is in line with the prevalence of manzanita in that region.

Traditional specific secondary BBOA markers
We focus on the behaviors of two groups of BB-specific SOA compounds here. 7-oxo-dehyroabietic acid (7-oxo-DHAA) 330 was proposed to be an aging product of resin acids (Yan et al., 2008). However, other studies have suggested that DHAA can thermally degrade to 7-oxo-DHAA and finally retene in fires, and the differences in the abundance of these compounds can be attributed to the combustion temperature (Ramdahl, 1983;Simoneit et al., 1993;Standley and Simoneit, 1994). To figure out whether this process mainly occurs in the fires or in the atmosphere, a comparison between source and receptor profiles of these compounds is needed. The ratios and concentration timelines of DHAA, 7-oxo-DHAA and retene are shown in 335 Figure 6a and 6b. Both the 7-oxo-DHAA/DHAA ratio and the retene/DHAA ratio reached peak after the peak of DHAA. In addition, as shown in Figure S4, the 7-oxo-DHAA/DHAA ratio and the retene/DHAA ratio in the primary BBOA were far below the ratios detected in the ambient samples at Berkeley. Therefore, it is likely that the conversion to 7-oxo-DHAA (and retene) mainly happened through oxidation during transport in the atmosphere. We conclude that the 7-oxo-DHAA to DHAA ratio provides a useful indicator for the formation of BB SOA. 340 Another group of compounds often used as tracers for BBSOA in source apportionment are the nitro-aromatic compounds (Watson et al., 2016). When catechol or methyl-catechols react with OH, NO3 or HONO with the presence of NO2, 4nitrocatechol or methyl-nitrocatechols will be produced (Bertrand et al., 2018;Finewax et al., 2018;Iinuma et al., 2010;Vidović et al., 2018). The low vapor pressures of these compounds cause them to be mainly in the particle phase, and 345 therefore are expected to be useful BB SOA markers (Finewax et al., 2018). Figure 6d shows the concentration time series of the nitrocatechols. The sum of the nitrocatechols reached 1.37 μg m -3 in the early morning on Oct 13, which is 9.1% of total quantified OA. The nitrocatechol/OC ratios in daytime plumes (Oct 11, Oct 12 & 13 afternoons) were lower than in the nighttime/early morning plumes (Fig. 6c). Since the plumes came from the same fires, the diel difference was either caused by the differences in day/night combustion processes or oxidation chemistry. The concentration of catechol and 4-350 nitrocatechol are shown in Fig. S5. In the daytime plumes, there were more catechol relative to 4-nitrocatechol than in the nighttime/early morning plumes. Finewax et al. (2018) has shown that the molar yield of 4-nitrocatehcol when catechol reacts with OH and NO3 are 0.3 ± 0.03 and 0.91 ± 0.06, respectively. The difference in oxidation mechanism is thus a more plausible explanation to the diel difference of nitrocatechols/OC ratio. https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License.
Qualitatively, the nitrocatechols are good markers for BB SOA. However, assuming [OH] = 2 × 10 6 molecules cm -3 , and 355 [NO3] = 5 × 10 8 molecules cm -3 , the lifetimes of catechol against OH or NO3 oxidation are only 1.4 h and 20 s, respectively (Finewax et al., 2018). As shown in Fig. 6b and 6d, the timelines of nitrocatechols are very similar to the primary BBOA tracers like dehydroabietic acid, especially in the nighttime plumes. Nitrocatechols and dehydroabietic acid were categorized into the same timeline factor in all 4-factor to 8-factor solutions in the HCA analysis (see Sections 2.6 and 3.4). Although these smoke plumes traveled approximately 3-4 hours from the fires to Berkeley, it was long enough for the formation of 360 substantial amounts of nitrocatchols. In addition, 4-nitrocatechol was also detected in fresh BBOA in the Fire Lab study . This issue could be a challenge of using nitrocatechols as BB SOA markers in timeline-based source apportionment analyses.

Clustering of compounds by HCA
To reduce the complexity of interpreting the time series of each of the 572 compounds, we simplified them into 7 factors 365 based on the similarity in temporal behavior. Figure 7a displays the dendrogram of the factors. The predicted volatility and calculated/predicted of each compound are displayed in Fig. 7b, colored by the factor the compound belongs to. Factor 1 (N = 85) compounds include levoglucosan, mannosan, vanillic acid, 4-hydroxybenzoic acid and pentacosanoic acid, etc.
These compounds are universally emitted in burning most kinds of biomass fuels. The grass fire tracer, p-Coumaryl alcohol (Nolte et al., 2001;Oros et al., 2006;Simoneit et al., 1993), is also in this factor. It indicates the presence of grass as 370 understory fuel in all the fires. This factor contributes around 1/3 of the total quantified mass (Fig. 8). Factor 2 (N = 78) is also dominated by primary BBOA compounds. Most aliphatic acids, alcohols, and alkenes above C20 (including nnonacosan-10-ol) are in this factor. The most abundant alkane measured, the n-nonacosane, is also in this factor. These compounds are likely from the plant wax sources (Medeiros and Simoneit, 2008;Simoneit, 2002). Like Factor 1, compounds in the second factor were present in almost all BB plumes. Factor 3 (N = 100) is also a primary BBOA factor. But it is hard 375 to associated with it with any specific fuels based on the chemical composition, and it is therefore called the "primary BBOA unknown" factor. Compounds in this factor were more abundant in the Oct 11 daytime plume than the Oct 12 and 13 plumes. This factor contains both hardwood tracers like syringic acid, as well as galactosan and pinitol, which are more abundant in conifer combustion emissions (Medeiros and Simoneit, 2008;Munchak et al., 2011). Other sugar alcohols, such as myo-inositol, ononitol, erythritol and xylitol are in this factor. These compounds are more abundant in green (moist) 380 vegetation fires (Medeiros and Simoneit, 2008;Schmidl et al., 2008). The most abundant sugar alcohol myo-inositol is present in both plants and animals as well (Loewus and Murthy, 2000;Medeiros and Simoneit, 2008). Its concentration reached 0.28 μg m -3 in the Oct 11 plume originated from the Sonoma County.
concentrations in the Oct 17 plumes were comparable to or even higher than in the Oct 11-13 plumes. Since the Oct 17 plume mainly came from the Atlas Fire, the high abundances of these tracers were expected. However, it is unclear why other hardwood BBOA markers like syringaldehyde and sinapaldehyde did not follow this trend. The concentrations of 390 syringol and pyrogallol were found to increase moderately in aging experiments due to partitioning or chemical formation (Bertrand et al., 2018;Fortenberry et al., 2018). Since the Oct 17 plume were more aged than the Oct 11-13 ones, it is possible that the two compounds were formed during transport. Factor 5 (N = 124) is a BBOA factor. Its concentrations exceeded Factor 1's in the densest plumes on Oct 11-13. This factor represents BBOA from the Sonoma County fires according to the backward trajectory cluster. It consists of many hardwood tracers (such as sinapaldehyde, lupeol, α-amyrin 395 and syringaldehyde) and softwood tracers (such as DHAA, pimaric acid, sandaracopimaric acid, abietic acid and retene), as well as fast-forming SOA such as nitrocatechols. The co-occurrences of both hardwood and softwood tracers indicates these fuels were burned simultaneously in the fires in Sonoma County.
Factor 6 (N =100) is the daytime BBSOA factor. This factor has a moderate correlation with levoglucosan (r = 0.68). The 400 dendrogram shows this factor has large distance (small correlation) from any other factor (Fig. 7a). Figure 9 displays the concentration of this factor versus time. The major peaks of this factor either happened in the afternoon or in the plume that arrived in Berkeley at night but previously experienced daytime aging. Higher daytime concentrations of these compounds indicate stronger aging processes of BBOA in daytime. The Oct 11 afternoon plume was relatively fresh, with ~3 hours daytime aging (in backward trajectory cluster 2). The Oct 12 & 13 afternoon plumes were more aged (in backward trajectory 405 cluster 3). Although the OC level in the Oct 11 plume was the highest, the concentration of Factor 6 was higher in Oct 12 & 13 afternoon plumes. This factor accounted for 9-14% of total quantified OA in those plumes. More than 90% of the mass of this factor resides in C* volatility bins between 1 and 10 3 μg m -3 , which indicates the semivolatile nature of this factor.
Positively identified BBSOA compounds include multifunctional aliphatic acids and oxygenated aromatic compounds ( Table   2). All the multifunctional aliphatic acids have less than 10 carbon atoms. Although they were abundant in aged biomass 410 burning plumes, many of them are not specific tracers of BB SOA. For instance, malic acid and tartaric acid were found in aged wood smoke in oxidation experiments (Hartikainen et al., 2020). However, they can be produced in the oxidation of 1,3-butadinene and isoprene (Claeys et al., 2004;Jaoui et al., 2014). Malic acid can also be produced by the hydroxylation of succinic acid, an oxidation product of long-chain unsaturated fatty acids (Kawamura et al., 1996;Kawamura and Ikushima, 1993). Pinic acid and 3-methyl-1,2,3-butanetricarboxylic acid (MBTCA), commonly used as biogenic (monoterpene) SOA 415 tracers (Jenkin et al., 2000;Szmigielski et al., 2007;Zhang et al., 2018), are also in this factor. Monoterpenes and oxymonoterpenes can account for more than 5% of total non-methane organic gas emission in certain conifer fires . The biogenic SOA could be oxidation products of the terpenes emitted in fires. It is also possible that biomass burning emissions enhanced the formation of biogenic SOA. As for aromatic compounds listed in Table 2, phthalic acid, 4,7dimethyl-1,3-isobenzofurandione and 1,3-dyhdroxynaphthalene could be PAH oxidation products (Wang et al., 2007). 420 Protocatechuic acid and gallic acid are likely more specific to biomass burning. Protocatechuic acid is a product of coniferyl https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License. alcohol and coniferyl aldehyde ozonolysis, and the vanillic acid + NO3 reaction (Liu et al., 2012;Net et al., 2010Net et al., , 2011.
Protocatechuic acid and gallic acids were also found to be the Fenton-like oxidation products of biomass burning-related small aromatic acids in the atmospheric aqueous phase (Santos et al., 2016a(Santos et al., , 2016b. These compounds could be monitored in future field and lab studies to verify whether they are suitable BB SOA tracers. 425 Factor 7 (N = 51) is an urban OA factor. Palmitic acid, stearic acid, benzophenone, 6,10,14-trimethyl-2-pentadecanone are all in this factor. The concentration of this factor was more constant over time than the others. It did not sharply increase in the fire plumes. EC (not included in Fig. 8) was also grouped into this factor, which indicates that EC measured at the UCB campus was not dominated by EC from fires. 430

Effect of aging on the mass of fire induced PM2.5 and OC
To investigate whether PM2.5 mass increased or decreased during transport and aging, the ΔPM2.5/ΔCO ratio at multiple BAAQMD sites affected by the Oct 11, 12 and 17 plumes are compared in Figure 10. The levels of PM2.5 at the sites considered here reached peak 0-5 hours after the Napa site. The ΔPM2.5/ΔCO ratios in each fire plume intercepted at different sites have a narrow distribution, epecially in the Oct 11 noon time plume. PM2.5 were diluted by 2 to 3 times in 2 435 hours. In this process, the evaporation induced by dilution must have approximately balanced SOA formation. Minimal increase of particle OC mass in aging were observed at UCB campus as well. Acetonitrile is a stable compound that is commonly used as a biomass burning tracer (Gilman et al., 2015;Holzinger et al., 2005). Figure 11 shows that particle OC is linearly correlated with acetonitrile. The relationship was not affected by the fraction of the BB SOA factor in total quantified OA or day/night difference. The strong correlation and other indicators suggest that although substantial chemical 440 transformation happened, there must have been near balance between evaporation and secondary OC formation in terms of the particle OC budget. The evaporation (and fragmentation type oxidation) of primary BBOA could reduce the net increase of PM2.5 and OC mass. Also, since many BB SOA compounds are also semivolatile, their evaporation and further oxidation could also reduce the net increase of particle mass. Since aged BBOA has slightly higher O/C ratio, it can be inferred that aging still resulted in a small increase of BBOA mass in aging. However, this increase was much smaller than the results 445 reported by Vakkari et al. (2018), in which PM1 mass more than doubled in only three hours of daytime aging.

Conclusions
The chemical composition of organic aerosol during the October 2017 Northern California wildfires was characterized in detail, tracking nearly 600 chemicals at 3-4-hour time resolution. We demonstrated that using easily obtained parameters from GC × GC measurements, the volatility and of compounds can be satisfactorily predicted. The OA consisted of 450 compounds spanning 10 orders of magnitude in volatility. The BBOA had high , and possibly high chance to fragment.
We found that the time series of primary BBOA tracers at the receptor site can be used to trace back the fuels burned, and https://doi.org/10.5194/acp-2020-910 Preprint. Discussion started: 23 September 2020 c Author(s) 2020. CC BY 4.0 License.
the timelines of BB SOA markers can indicate the transformation processes. Through hierarchical clustering analysis, we traced back the sources of the OA measured at Berkeley and discovered a unique daytime BB SOA factor. Compounds in that factor are highly oxygenated but are still semi-volatile. Using the PM2.5 and CO measured by the BAAMD network, we 455 found the growth of particle mass during aging was small. Similar analysis could be used to study other fires. Relatively fresh and aged samples had similar OC/acetonitrile ratio, which indicates the evaporation of particle organic compounds and the condensation of gas phase organic compounds were balanced in terms of carbon.
The thermal desorption GC × GC measurements remarkably improved our ability to separate and quantify the chemicals 460 residents in the San Francisco Bay Area were exposed to. We hope the resulting database can contribute to more accurate exposure assessments from wildfire smoke in general. Also, with better speciation of compounds in wildfires, more targeted toxicological studies could be carried out to elucidate the health impacts of BBOA.