Atmospheric OA is a combination of species from multiple sources and with different levels of oxidation. Although the bulk characteristics of OA yield some insights into the sources of particulate organics, a more powerful investigation can be achieved via PMF analysis of the organic high resolution spectra to resolve distinct OA factors that may be associated with unique sources and process types [Zhang et al., 2011]. The PMF analysis in this study yields four OA factors. Three of them are found to represent primary emissions: a hydrocarbon-like OA (HOA) factor driven by vehicle emissions, a cooking-influenced OA (COA), and a biomass-burning OA (BBOA) related to the residential wood combustion for heating. In addition, one oxygenated OA (OOA) factor is identified, representing secondarily formed organic species. An overview of the time series, HR spectra, and diurnal patterns of the individual OA factors is shown inFigure 2, along with the time series of tracer species for different sources. In the following sections, we discuss in detail the characteristics, sources, and processes of each factor.
3.2.1. Hydrocarbon-Like OA (HOA)
 During this study, the average (±1σ) mass concentration of HOA is 1.7 (±2.6) μg m−3 and the average contribution of HOA to OA mass is ∼22% (Figure 2m). The mass spectrum of HOA (Figure 2a) is dominated by the CnH2n±1+ ions (82.3% of the total signal; Figure 3a), among which the top 4 ions are C4H9+ at m/z 57, C4H7+ at m/z 55, C3H7+ at m/z 43, and C3H5+ at m/z 41. These ions are the major fragments arising from the 70 eV electron impact (EI) ionization of normal and branched alkanes and cycloalkanes [McLafferty and Turecek, 1993] – the dominant organic components of fuel and lubricating oil [Tobias et al., 2001; Canagaratna et al., 2004]. The abundance of these ions and the picket fence fragmentation pattern are common features in the AMS spectra of POA associated with fossil fuel combustion. The correlation between the Fresno HOA spectrum (after summed to unit mass resolution, UMR) and the standard HOA spectrum derived from 15 ambient HOA profiles [Ng et al., 2011] is very high (R2 = 0.95, Figure S6a). The HOA mass spectrum is also highly similar to the mass spectra of organic particles emitted in diesel [Canagaratna et al., 2004] (R2 = 0.94, Figure S6b) and gasoline vehicle exhausts [Mohr et al., 2009] (R2 = 0.93, Figure S6c).
Figure 3. (a) Average contributions of the six ion families to each OA factor, and (b) average contributions of the four OA factors to the four major ion families.
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 The O/C and OM/OC ratios of HOA are low (0.09 and 1.29) while the H/C ratio is high (1.80), indicating that HOA is mainly composed of chemically reduced hydrocarbon species. In the HRMS, HOA accounts for ∼32% of the CxHy+ signal and less than 10% of the oxygenated CxHyO1+ and CxHyO2+ signals (Figure 3b). The link between HOA and local traffic activities is further supported by its temporal variations: 1) the time series of HOA (Figure 2e) correlates well with those of fuel combustion tracers – CO (R2 = 0.81, Figure S7a), CO2 (R2 = 0.68, Figure S7b) and NO2 (R2 = 0.41, Figure S7c), and 2) the diurnal cycle of HOA concentration (Figure 2i) displays a peak corresponding to the morning rush hour (8:00–10:00) and another peak to the evening rush hour (17:00–20:00). The highest mass fractions of HOA in ambient aerosol occur at 9:00 (∼38% of OA) and 18:00 (∼27% of OA) (Figure 2n).
 As shown in Figure S8, ions that display the tightest correlations with HOA are the CnH2n+1+ ions, particularly C4H9+, C5H11+, C6H13+, C7H15+, and C8H17+, and the CnH2n−1+ ions containing 5 or more carbon atoms. Figure S9a further shows that HOA contributes over 60% of the signal in each one of these ions, corroborating their utilities as the AMS spectral markers for HOA. However, in viewing of integer m/z's, HOA accounts for less than 50% of the signal across the board (Figure S9b), even for m/z 57, which has been identified as an important AMS tracer for HOA in urban environment [Zhang et al., 2005b]. A reason is that this fragment also receives significant contributions from COA and BBOA in this study. Together, HOA, COA, BBOA contribute a total 85% of the signal at m/z 57 (Figure 4e), indicating a strong association of m/z 57 with total POA. In fact, based on comparing the contributions by POA versus those by OOA (Figure S9b), m/z 57 appears to be the most representative POA tracer for the UMR data.
Figure 4. Average size distributions of individual OA factors and relevant tracer m/z's: (a) HOA and m/z 57, (b) COA and m/z 55, (c) BBOA and m/z's 60 and 73, and (d) OOA and m/z 44. Fractional contributions of different ions (fion) and the four OA factors (fOA factors) to the average mass of (e) m/z 57, (f) m/z 55, (g) m/z 60 and 73, and (h) m/z 44.
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 Some previous studies reported the size distributions of HOA and OOA based on the measured size distributions of m/z 57 and 44 [Zhang et al., 2005a; Nemitz et al., 2008]. This method seems to work at locations where traffic emissions are predominant sources of POA. However, in the presence of more than one POA sources (such as the case of this study), the measured size distribution of m/z 57 does not represent accurately the size distribution of HOA. We therefore used the multilinear deconvolution method discussed in Section 2.2.2 to estimate the size distributions of individual OA factors (Figures 4 and 5). The mass-based size distribution of HOA shows the typical behavior of traffic-related POA, peaking at around 140 nm (Figures 4a and 5a). In comparison, the distribution of m/z 57 shifts to a larger median due to substantial contributions from BBOA, COA, and OOA (Figures 4a and 4e). Overall, HOA dominates the mass of ultrafine particles (<100 nm) and its fractional contribution gradually decreases with increase of particle size (Figure 5b). It on average occupies less than 10% of the mass in particles larger than 500 nm in Dva.
Figure 5. (a) Size distributions of individual OA factors (data of OOA are multiplied by a factor of 0.5), and (b) fractional contributions of the four OA components to the total OA mass at different size bins, and the mass-based total OA size distributions.
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3.2.2. Cooking-Influenced OA (COA)
 A factor related to cooking activities, COA, was identified. m/z's 55, 41 and 43 are the major peaks in the spectrum (Figure 2b), similar to the COA factors identified in London [Allan et al., 2010], Manchester [Allan et al., 2010], Beijing [Huang et al., 2010], New York City (NYC) [Sun et al., 2011], and Barcelona [Mohr et al., 2012]. As a POA factor, COA has a major contribution from CxHy+ ions, but to a lesser extent (∼72%) compared to HOA (Figure 3a). However, COA contains significantly larger amounts of oxygen-containing ions than HOA does (e.g., CxHyO1+- 19.3%vs. 10.2% and CxHyO2+- 4.7%vs. 3.9%) (Figure 3a) and is thus overall more oxygenated with a higher O/C ratio of 0.11 and a lower H/C ratio of 1.72. The oxygenated ions in the COA spectrum are likely produced from the ionization and fragmentation of oxygenated species such as fatty acids which were detected at significant amount in cooking aerosols [e.g., Zheng et al., 1997; To et al., 2000; He et al., 2004; Mohr et al., 2009]. The O/C ratio of COA in this study is lower than that identified in NYC (0.18) [Sun et al., 2011] and Barcelona (0.21) [Mohr et al., 2012], but similar to that observed in Beijing (0.11) [Huang et al., 2010]. Nevertheless, the overall similarities in the mass spectra between the Fresno COA and the COA factors determined previously are high (R2 = 0.79, 0.90, 0.86, 0.79 and 0.96 for the correlations with the COA factors observed in NYC, London, Manchester, Barcelona and Beijing; Figures S6d–S6i). The Fresno COA spectrum (in UMR) also shows high similarity (R2 of ∼0.9) to the reference aerosol spectra for food cooking reported by Mohr et al.  (Figures S6j–S6m). However, the COA spectrum does not resemble the profile of charbroiling reported by Lanz et al.  (R2 = 0.06, Figure S6n), implying that the Fresno COA is more related to heating of cooking oils rather than burning of meat/food itself, similar to the observation reported by Allan et al. .
 The COA diurnal cycle (Figure 2j) shows increases during noon and early evening coincidental with the lunchtime and dinnertime, corroborating its link with cooking emissions. COA co-varies closely with a few oxygenated ions (such as C3H3O+, C5H8O+, C6H10O+ and C7H12O+; Figures 2f and S8) and it contributes significantly to the signals of those ions (Figure S9a). The correlations between COA and C5H8O+ (R2 = 0.88, Figure S7d) and C6H10O+ (R2 = 0.92, Figure S7e) are especially high, supporting the recommendation by Sun et al.  that these two ions can be used as the high resolution mass spectral markers for ambient COA.
 Similar to previous observations [e.g., Mohr et al., 2012], the main spectral differences between COA and HOA include: 1) the COA spectrum has significant contributions from C3H3O+ and C3H5O+ at m/z's 55 and 57 and 2) the signal ratio of m/z 55 to 57 is much higher in COA than in HOA (Figure 2b). To distinguish COA from the other OA factors, Mohr et al.  proposed to use the relationships between the organic mass fractions of m/z's 55 and 57 (i.e., f55 and f57) or between those of C3H3O+ and C3H5O+ (i.e., fC3H3O+ and fC3H5O+) after subtracting contributions from oxygenated OA factors. We made this plot for Fresno OA in Figure 6a, in which the V shape formed by the two broken lines defines the edges of the COA and the HOA factors from several urban AMS data sets [Mohr et al., 2012]. Indeed, COA locates close to the left arm of the V shape, while HOA lies on the right arm, and the slope of f55 versus f57 also appears to be steeper in the afternoon than in the morning (Figure 6a), consistent with the diurnal patterns of COA and HOA (Figures 2i and 2j). Note that unlike in Barcelona [Mohr et al., 2012], in the present study, the fC3H3O+ to fC3H5O+ ratio is almost the same between COA and HOA but the fC3H3O+ in COA is almost 6 times higher than that in HOA (Figure S10a). The fC3H3O+ vs. fC3H5O+ space is thus not useful for distinguishing the two factors for the Fresno data set.
Figure 6. Scatterplots of (a) f55OOA,sub vs. f57OOA,sub, (b) fCO2+ vs. fC2H4O2+ and (c) fCO2+ vs. fC2H3O+. The dots correspond to measured OA data points are colored by time of the day. The corresponding values of the four OA factors identified in this study are also shown. In addition, the LV- and SV-OOA factors identified during the 2009 summer campaign in New York City (NYC) [Sun et al., 2011] are plotted in Figure 6c for reference.
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 The mass-based size distribution of COA is the broadest among the four OA factors and shows two modes centering at ∼200 nm and 450 nm inDva (Figure 5a). This broad size distribution of COA is consistent with previous studies, which observed that the sizes of cooking-related particles vary widely, depending on cooking types, operations, and distance from the cooking sources [e.g.,Li et al., 1993; Dennekamp et al., 2001]. For example, Tu and Knutson  and Tu et al.  found that particles emitted from frying food were generally larger than the ones from gas combustion probably due to scavenging coagulation. Buonanno et al.  reported that the size distributions of particles emitted from different cooking methods (frying and grilling) and foods (fatty and vegetable foods) show a general trend of shifting to a smaller diameter at higher temperatures. The bimodal size distributions of COA observed in this study is similar to the observations by Kamens et al. , in which indoor particles during cooking periods sometimes presented a small size mode in addition to a dominant large size mode. Tu and Knutson  also observed two size modes during cooking soup but only one large size mode when frying food. The size modes of COA of this study (∼200 nm and 450 nm in Dva) are also consistent with previous observations. For example, meat cooking aerosols were found to peak at around 200 nm in mobility diameter [Hildemann et al., 1991] and the volume (or mass) median diameters of submicron aerosols from cooking were found to centered around 300–350 nm [Li et al., 1993; Mohr et al., 2012]. Overall, COA contributes a relatively constant fraction of particle mass in all sizes and it represents a much bigger fraction of mass in accumulation mode particles compared to HOA and BBOA (Figure 5b). As shown in Figure 4b, the size distribution of m/z 55 is quite different from that of COA, because ∼65% of the m/z55 signal is contributed by aerosols of non-cooking sources (Figure S9b) and both C4H7+ and C3H3O+ contribute significantly at m/z 55 (Figure 4f).
 On average, COA (average ± 1σ = 1.5 ± 2.3 μg m−3) accounts for ∼19% of the total OA mass (Figure 2m) and 33% of the POA mass (POA = HOA + COA + BBOA). The fractional contribution of COA is comparable to that of HOA in winter Fresno, similar to HR-AMS observations in NYC, where COA and HOA were found to contribute 16% and 14% of OA mass, respectively [Sun et al., 2011], and in Barcelona, where the values were 17% and 16%, respectively [Mohr et al., 2012]. Significant amounts of COA, which are comparable to or higher than the HOA loadings, were also reported in Beijing, China [Huang et al., 2010], and in two large cities in UK – Manchester and London [Allan et al., 2010]. In fact, prior to the AMS reports, source apportionment studies based on the molecular tracer technique repeatedly reported the identification of cooking aerosols in urban areas. For example, cooking emissions were estimated to account for 5–12% of the OA mass in some southeastern U.S. cities [Zheng et al., 2002], 21% of the primary fine organic carbon particulate mass in the Los Angeles area [Rogge et al., 1991], and 5–19% of PM2.5 mass during wintertime in Fresno [Chow et al., 2007]. All these results emphasize the importance of cooking emissions in urban areas. These aerosols thus deserve more attention in future air pollution control strategies, especially in densely populated area since all COA factors identified so far are from aerosols sampled from metropolises.
3.2.3. Biomass-Burning OA (BBOA)
 In this study, we identified that regional residential wood combustion for heating is another important POA source, in addition to vehicular and cooking emissions, in Fresno during wintertime. This factor, which is termed as biomass-burning OA (BBOA), includes considerable signals from oxygenated ions of CxHyO1+ (35.8%) and CxHyO2+ (14.7%) (Figure 3a); it thus consists of a higher concentration of oxidized species than HOA and COA do. The O/C of BBOA is the highest (0.33) and its H/C lowest (1.56) among three POA factors, similar to the findings by Aiken et al.  and Mohr et al. . The BBOA profile (Figure 2c) has strong signals at m/z 60 (100% of which is C2H4O2+) and m/z 73 (95% of which is C3H5O2+), which are the known fragment ions in the EI-MS of levoglucosan - a biomass-burning aerosol tracer [Alfarra et al., 2007; Aiken et al., 2008]. The BBOA spectrum of this study is also similar to the AMS BBOA factors observed at other locations and the correlation with the standard BBOA spectral profile derived from the average of two BBOA spectra obtained in Mexico city and Houston [Ng et al., 2011] is tight (R2 = 0.85, Figure S6o). Recently, Jimenez et al.  proposed a graphical method taking f60 as a primary marker for BBOA and f44 as a tracer for SOA or aged POA, to investigate the atmospheric evolution of BBOA. We demonstrated this graph in Figure 6b using fCO2+ vs. fC2H4O2+ instead of f44 vs. f60. Note that for this data set, since ions other than CO2+ and C2H4O2+ contribute very little to signals at m/z 44 and 60 (Figures 4g and 4h), respectively, the f44 versus f60 plot (Figure S10b) is very similar to Figure 6b. The triangle shape is adopted from that reported by Jimenez et al. , which was determined from the measurement data influenced by fire plumes. The Fresno BBOA locates near to the right edge of the triangular region, separated well from the other OA factors which situate outside the triangle shape, owing to low fC2H4O2+ in them (Figures 2a–2d).
 The mass-based average size distribution of BBOA (Figure 5a) peaks around 160 nm, resembling more to the size distributions of HOA than to OOA, despite the larger difference in the oxidation degrees between BBOA and HOA (0.33 versus 0.09) than between BBOA and OOA (0.33 versus 0.42). Similar to HOA, BBOA contributes a large fraction of the small particle mass (Figure 5b). Since m/z 60 is only composed of C2H4O2+ and m/z 73 is dominated by C3H5O2+ (95%), their size distributions are thus highly similar to that of BBOA (Figures 4c and 4g).
 The average (±1σ) BBOA mass concentration is ∼1.3 (±2.2) μg m−3 (16% of OA mass), slightly lower than those of COA and HOA. BBOA presents a distinct diurnal pattern that shows substantially elevated concentrations in the evening from 19:00–24:00 (Figure 2k), indicating its direct link to the residential wood burning activities. The time series of BBOA correlates very well with that of C2H4O2+ (R2 = 0.95, Figure S7f) and C3H5O2+ (R2 = 0.91, Figure S7g), which are the AMS spectral markers for BBOA. It also has a moderate correlation with K+ (R2 = 0.38, Figure S7h), another important biomass-burning marker [Chow et al., 2007]. BBOA has the highest N/C ratio (0.021) among the four OA components (Figures 2a–2d), and it shows particularly good correlations with CN+ (R2 = 0.46, Figure S7i) and CHN+ (R2 = 0.58, Figure S7j), consistent with enhanced nitrile emissions during burning of biomasses [Simoneit et al., 2003].
 Additionally, BBOA should also contain various phenolic compounds which are important pyrolysis products of lignin in woods [e.g., Simoneit et al., 1993; Shakya et al., 2011]. Since lignin is a complex compound, unlike the low molecular weight methoxyphenols, polyphenols may also be emitted and present in aerosols from wood-burning [Gonçalves et al., 2011]. Specially, in this study, the time series of two fragment ions representative of polyphenols, C14H14O4+ (m/z 246) for guaiacol dimer and C16H18O6+ for syringol dimer (m/z 306) [Sun et al., 2010], correlate well with BBOA (R2 = 0.82 and 0.78 for m/z 246 and 306; Figure 7), indicating their utilities as tracers for biomass burning aerosols. However, these two ions were also observed in the AMS HRMS of the SOA products of phenolic compounds formed via photochemical aqueous-phase reactions [Sun et al., 2010], suggesting that the BBOA observed in this study may contain secondary organic species as well. In a single-particle mass spectrometry study conducted in Fresno during wintertime,Qin and Prather also detected biomass-burning particles of which the diurnal pattern was very similar to that of the BBOA observed in this study. They hypothesized that the diurnal profile was due to direct biomass emissions followed by gas-to-particle partitioning of semi-volatile species and aqueous-phase processing. Similar processes likely occurred during this campaign given the wet and cold atmospheric conditions throughout the campaign [Ge et al., 2012]. As a result, the BBOA factor of this study may contain secondary species, but likely only in a minor amount since no enhancement of BBOA concentration was observed during nighttime fog events, during which all secondary species including OOA, nitrate, and sulfate increased significantly [Ge et al., 2012]. In addition, the diurnal pattern of the BBOA factor also suggests that it is mainly governed by direct wood burning emissions, i.e., mainly of a primary origin. Overall, the average nighttime BBOA concentration (e.g., ∼2.9 μg m−3 between 19: 00 – 24:00) is more than 7 times higher than the average daytime BBOA concentration (e.g., ∼0.4 μg m−3 between 11:00 – 16:00).
Figure 7. (a) Time series of BBOA, m/z 246 and m/z 306 and scatterplots of BBOA vs. (b) m/z 246 and (c) m/z 306. The data points in Figures 7b and 7c are colored by measurement time.
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3.2.4. Oxygenated OA (OOA)
 In addition to three POA factors, a secondary, oxygenated OA factor was identified and found to account for an average 43% of the OA mass (Figure 2m). OOA is a ubiquitous and often dominant aerosol component in the atmosphere [Zhang et al., 2007; Jimenez et al., 2009]. In many cases, OOA can be further separated into a low-volatility/more oxygenated OOA (LV-OOA/MO-OOA) and a semi-volatile/less oxygenated OOA (SV-OOA/LO-OOA), which represent different aging and oxidization degrees [Jimenez et al., 2009; Ng et al., 2010, and references therein]. However, in this study only one OOA factor was identified. The most abundant peaks in the OOA mass spectrum (Figure 2d) are CO2+ at m/z 44 and C2H3O+ at m/z43. It is characterized with an overall dominance of oxygen-containing ions (51.5% in total: 37.9% is CxHyO1+ and 13.6% is CxHyO2+) though CxHy+ signals still occupy the largest portion (42.1%) among the six ion categories (Figure 3a). The Fresno OOA spectrum (in UMR) shows overall good agreements with the standard spectra of OOA, LV-OOA and SV-OOA derived from 15 urban data sets [Ng et al., 2011] (R2 = 0.81 – 0.86; Figures S6p–S6r). Ng et al.  discovered that all OOA components fell into a triangular region defined by f44 versus f43 and that the positions within the triangle corresponds to their oxidation degrees. Since the HRMS allows us to exclude interferences from other ions (such as C3H7+ at m/z 43), we examined fCO2+ versus fC2H3O+ instead (Figure 6c). OOA is close to the bottom of the triangular region, approximately at the same position as the SV-OOA identified from NYC does [Sun et al., 2011] and in the region within which the SV-OOA factors usually falls [Ng et al., 2010]. The f44 (∼9%) and the O/C ratio (0.42) of the Fresno OOA are in the range of the SV-OOA factors observed worldwide too (e.g., 7 ± 4% forf44 and 0.35 ± 0.14 for O/C ratio) [Jimenez et al., 2009]. These results imply that the OOA factor identified in winter Fresno is more similar to SV-OOA than to LV-OOA. On the other hand, HOA and COA are both outside of the triangular region, denoting their different chemical characteristics; BBOA lies the closest to OOA in the triangle, consistent with its higher content of oxygenated species. Compared to thef44 versus f43 space (Figure S10c), the fCO2+ versus fC2H3O+ triangle apparently improves the separation of OOA from other primary OA factors.
 This semi-volatile characteristic of the Fresno OOA is further verified by its higher correlation with nitrate - a semi-volatile secondary species (R2 = 0.86, Figure S7k), than with sulfate - a low-volatility secondary species (R2 = 0.71, Figure S7l). Actually, the correlation coefficients between OOA and any secondary inorganic species (i.e., nitrate, sulfate, chloride, and ammonium) are higher than those between the OOA and the POA factors (i.e., HOA, COA, and BBOA; Table 1). OOA correlates especially well with total secondary inorganic anions (= sulfate + nitrate + chloride) (R2 = 0.87, Figure 8a) while the correlation between POA and secondary inorganic anions is poor (R2 = 0.13, Figure 8b). In addition, OOA does not correlate with combustion tracer gases such as CO (R2 = 0.10, Figure S7m), CO2 (R2 = 0.19, Figure S7n) and NO2 (R2 = 0.07, Figure S7o), nor with POA (R2 = 0.12, Figure 8c). These results are consistent with the association of OOA to secondary origins.
Table 1. Pearson's R Coefficients for Linear Regressions Between OA Factors and Gas Pollutants and Aerosol Species Measured at Fresno in Winter 2010a
|Ammonium + sulfate + nitrate + chloride||0.29||0.27||0.32||0.93||0.35|
|Methanesulfonic acid (MSA) or mesylate||0.31||0.37||0.37||0.85||0.42|
Figure 8. Scatterplots of (a) OOA versus total secondary inorganic anions (= Sulfate + Nitrate + Chloride), (b) POA (= HOA + COA + BBOA) versus total inorganic anions, and (c) OOA versus POA. Data points are colored by the ambient relative humidity values in Figures 8a and 8b. Data points are colored by the mass fractions of OA in NR-PM1in Figure 8c, and the marker size is proportional to the total NR-PM1 concentration.
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 The diurnal cycle of OOA (Figure 2k) is relatively flat without distinct peaks. The slightly higher concentrations of OOA during nighttime are likely due to: 1) enhanced gas-to-particle partitioning favored by lower temperature and higher RH at night, and 2) aqueous-phase production of secondary aerosol species in the presence of nighttime fogs [Ge et al., 2012]. Particularly, CH2SO2+ and CH3SO2+, which are the characteristic AMS spectral ions for methanesulfonic acid (MSA) [Ge et al., 2012] – a well-known secondary species, correlate tightly with OOA (R2 = 0.60 and 0.71, respectively, Figure S8). In addition, these two ions are almost contributed by OOA alone (Figure S9a. These results further support the hypothesis that the OOA factor represents SOA. The average mass-based size distribution of OOA is similar to those of secondary inorganic species [Ge et al., 2012], peaking at an accumulation mode size of ∼460 nm in Dva (Figures 4d and 5a). The mass fraction of OOA gradually increases with the increase of particle size and oxygenated species dominates the mass of particles larger than 300 nm in Dva (Figure 5b). The size distribution of m/z 44 is broader than that of OOA and extends deeper into the ultrafine size range (Figure 4d), because of significant contributions from POA (Figure 4h).
3.2.5. Relative Importance of Primary and Secondary OA in Winter Fresno
 Figure 8c demonstrates that the high PM loading periods are generally associated with high POA concentrations and high mass fractions of organic aerosols, indicating the important roles that primary aerosol emissions play in aerosol pollutions in Fresno during wintertime. On average, POA dominates the OA mass (∼57%) with HOA, COA and BBOA being equally important (Figure 2m). The diurnal patterns of these three types of POA, however, are substantially different (Figures 2i–2k), indicating that the average POA composition varies considerably during the day. Primary emissions, in particular, play a leading role in aerosol pollution during nighttime, contributing an average 80% of the total OA at around 20:00 (Figure 2n), owing to elevated emission from evening traffic, dinner cooking and residential wood burning compounded with the collapsing of mixed layer height. Enhancements of POA are observed during morning rush hour and around noontime too, reflecting impacts from vehicle emissions and lunch cooking, respectively. A survey of some prior studies about PM (mostly PM2.5) sources in Fresno during wintertime is listed in Table 2. Generally, our findings about PM1 are consistent with those studies, highlighting the importance of wood burning, vehicle emission, and cooking as sources of aerosol pollution in Fresno.
Table 2. Prior Studies About the Sources of Fine PM in Fresno During Wintertime
|References||Method||Primary Sources of PM|
|Magliano et al. ||Chemical Mass Balance (CMB) model using PM2.5 and PM10 collected in Fresno during 1995 wintertime||Wood burning, mobile and geological materials (fugitive dust) were found.|
|Schauer and Cass ||CMB model using PM2.5 collected in Fresno during two severe wintertime (1995) air pollution episodes||Hardwood and softwood combustion (31 % of PM2.5), diesel (6 % of PM2.5) and gasoline-motor vehicles (8 % of PM2.5), and meat cooking (5 % of PM2.5) were found. Contributions from road dust and natural gas combustion were small but measurable.|
|Gorin et al. ||Measurements on daily PM2.5 and PM10 in Fresno during winter 2003–2004||Wood smoke (41 % of OC and 18 % of PM2.5), vehicle emissions (25 % of OC) and meat cooking (∼5 % of OC) were found.|
|Chen et al. ||CMB model using PM2.5 measurements from 23 sites in SJV during 2000–2001 wintertime||Primary sources including residential wood combustion (23–24 % of PM2.5), motor vehicle (10–15 % PM2.5), cooking (3–5 % PM2.5), agriculture-diary (2 % PM2.5) and fugitive dust (3–5 % PM2.5) were found.|
|Chow et al. ||CMB model using PM2.5 measurements in Fresno during 5 Dec. 2000–3 Feb. 2001||Residential wood combustion (31 % of PM2.5: 16–17 % of hardwood and 12–15 % of softwood); Motor vehicle (9–15 % of PM2.5: 3–10 % from gasoline and 5–6 % from diesel), and cooking (5–19 % of PM2.5) were found.|
 In addition, polycyclic aromatic hydrocarbons (PAHs), are known byproducts of incomplete combustion from sources like diesel and gasoline engines, biomass burning and wood smoke, etc., and many of them are potent mutagens and carcinogens [Marr et al., 2006]. The concentrations of particle-bound PAHs in this study (Figure 9a) were estimated based on the fragmentation table reported by Dzepina et al. . The average PAHs concentration is determined at 16 ng m−3. The time series of PAHs shows very good correlations with POA (R2 = 0.72, Figure 9b) (Table 1). In addition, PAHs shows a better correlation with BBOA (R2 = 0.70, Figure 9e) than with HOA (R2 = 0.44, Figure 9c) and COA (R2 = 0.41, Figure 9d) (Table 1), indicating that residential wood combustion is a significant source of PAHs in Fresno.
Figure 9. (a) Time series of PAHs and POA (= HOA + COA + BBOA), scatterplots of PAHs with (b) POA, (c) HOA, (d) COA and (e) BBOA. The data points are colored by measurement time.
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 OOA/SOA on average accounts for 43% of the OA mass during this study (Figure 2m). Similarly, OOA was found to account for less than half of the OA mass at three other urban locations (i.e., NYC, Tokyo, and Manchester) during winter months [Zhang et al., 2007]. In contrast, a dominant contribution from OOA was usually observed at urban locations during warm seasons [Zhang et al., 2011]. The slower secondary aerosol formation kinetics and the stronger accumulation of primary pollutants due to lower mixed layer depth during winter all contribute to a large mass fraction of POA in aerosol particles. During this study, OOA only outweighs POA at early morning (00:00–06:00) when all the anthropogenic primary emissions are largely reduced (Figure 2n).