Assessment of forest fire impacts on carbonaceous aerosols using complementary molecular marker receptor models at two urban locations in California's San Joaquin Valley

Environmental Pollution 246 (2019) 274e283

Contents lists available at ScienceDirect

Environmental Pollution journal homepage: www.elsevier.com/locate/envpol

Assessment of forest fire impacts on carbonaceous aerosols using complementary molecular marker receptor models at two urban locations in California's San Joaquin Valley* Min-Suk Bae a, c, Matthew J. Skiles a, Alexandra M. Lai a, b, Michael R. Olson a, Benjamin de Foy d, James J. Schauer a, b, * a

Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA Environmental Chemistry and Technology, University of Wisconsin-Madison, Madison, 53706, USA Department of Environmental Engineering, Mokpo National University, Muan, 58554, Republic of Korea d Saint Louis University, Department of Earth & Atmospheric Sciences, Saint Louis, Missouri, 63108, USA b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 March 2018 Received in revised form 5 December 2018 Accepted 6 December 2018 Available online 7 December 2018

Two hundred sixty-three fine particulate matter (PM2.5) samples were collected over fourteen months in Fresno and Bakersfield, California. Samples were analyzed for organic carbon (OC), elemental carbon (EC), water soluble organic carbon (WSOC), and 160 organic molecular markers. Chemical Mass Balance (CMB) and Positive Matrix Factorization (PMF) source apportionment models were applied to the results in order to understand monthly and seasonal source contributions to PM2.5 OC. Similar source categories were found from the results of the CMB and PMF models to PM2.5 OC across the sites. Six source categories with reasonably stable profiles, including biomass burning, mobile, food cooking, two different secondary organic aerosols (SOAs) (i.e., winter and summer), and forest fires were investigated. Both the CMB and the PMF models showed a strong seasonality in contributions of some sources, as well as dependence on wind transport for both sites. The overall relative source contributions to OC were 24% CMB wood smoke, 19% CMB mobile sources, 5% PMF food cooking, 2% CMB vegetative detritus, 17% PMF SOA summer, 22% PMF SOA winter, and 12% PMF forest fire. Back-trajectories using the Weather Research and Forecasting model combined with the FLEXible PARTicle dispersion model (WRF-FLEXPART) were used to further characterize wind transport. Clustering of the trajectories revealed dominant wind patterns associated with varying concentrations of the different source categories. The Comprehensive Air Quality Model with eXtensions (CAMx) was used to simulate aerosol transport from forest fires and thus confirm the impacts of individual fires, such as the Rough Fire, at the measurement sites. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Organic molecular marker Source apportionment CMB PMF

1. Introduction Two well-known source apportionment models (chemical mass balance (CMB) and positive matrix factorization (PMF)) have been used for several decades to identify the complex sources of carbonaceous aerosols, which are composed of elemental carbon and organic carbon (ECOC) (Heo et al., 2014; Jaeckels et al., 2007; Schauer et al., 1996). Traditionally, PMF is useful in understanding the origins of fine aerosols using analytical results of ECOC,

*

This paper has been recommended for acceptance by Eddy Y. Zeng. * Corresponding author. Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53705, USA. E-mail address: [email protected] (J.J. Schauer). https://doi.org/10.1016/j.envpol.2018.12.013 0269-7491/© 2018 Elsevier Ltd. All rights reserved.

secondary inorganic ions, and trace elements because source profiles are not needed (Amato and Hopke, 2012; Sofowote et al., 2015). Knowledge of source profiles is needed in order to find the associations of source factors from PMF, given an assumption about how many significant factors affect the receptor sites. At least one hundred datasets of observations were required to generate proper source profiles with sufficiently low uncertainties (Zhang et al., 2009). A molecular-marker CMB model uses results of previously identified organic tracers that have associations with specific sources of PM2.5 OC (e.g., wood smoke (Schauer et al., 2001; Simoneit et al., 1999), mobile sources (Schauer et al., 2002; Zielinska et al., 2004), and biomass burning (Nolte et al., 2001; Simoneit et al., 1999). Although CMB does not require a minimum set of input data, the selection of the source profile controls the

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

model result sensitivities. Although these model results are becoming more widely adopted by government regulators and researchers, there are still questions about these models because of the different approaches taken for source apportionment. Briefly, CMB is based on an effective variance least squares (EVLS) multilinear regression method. PMF is an explicit point-by-point weighted least squares factor analysis method imposed with non-negativity constraints. In addition, direct result correlations of molecular marker CMB and PMF models are rare due to the large expense of producing sufficiently large organic marker datasets for PMF analysis; thus, it is necessary to conduct comparison studies between source apportionment models as well as air mass transport models. In prior studies, good relationships between an air mass transport model and receptor models in California have been shown (Held et al., 2005). Three receptor models (i.e., CMB, PMF, and principal component analysis (PCA)) were compared to apportion the sources of PM2.5 obtained from a megacity in Asia (Jeong et al., 2017). While these models are widely used, questions (e.g., accuracy of source profiles at the receptor site and secondary products from the primary sources associated with used source profiles that may not have been representative of the sources affecting sites, and unknown sources, which could affect the site) are still raised between the best application of the two models. In this study, samples were collected every third day over 1 year at the Fresno and Bakersfield sites in CA and include observations of more than 160 organic species. These analytical results were used in both CMB and PMF models to apportion sources of PM2.5 OC and directly compare model results. In addition, the Weather Research and Forecasting model (WRF) (Skamarock et al., 2005) was used to drive simulations of smoke transport from potentially significant forest fires using the Comprehensive Air Quality Model with eXtensions (CAMx, Ramboll Environ, 2016) and to simulate back-trajectories using the FLEXible PARTicle dispersion model (FLEXPART and WRF-FLEXPART) (Brioude et al., 2013; Stohl et al., 2005). The CAMx simulations revealed direct impacts from the forest fires on the measurement sites. The back trajectories were used to identify wind transport patterns associated with high concentrations of specific source categories. Samples were analyzed for OC, EC, watersoluble organic carbon (WSOC), and organic molecular markers. CMB and PMF source apportionment models used the results to understand monthly and seasonal source contributions to PM2.5 OC. Source categories, including forest fires, were compared from the results of the CMB and PMF model to PM2.5 OC across the sites. These results presented the validation of models and show additional sources (e.g., forest fire source and SOA) that CMB cannot capture because there are no source profiles. 2. Methods 2.1. Sampling and chemical analysis Two hundred sixty-three PM2.5 samples (24-h integrated samples) were collected every 3 day at the Bakersfield (35.356461
N, 119.062670
W, elevation 400 ft) and Fresno (36.785322
N, 119.774174
W, elevation 340 ft) sites from January 2015 through February 2016. PM2.5 samples were collected on pre-baked 90-mm quartz-fiber filters (Pall Gellman, Ann Arbor, MI) using four URG-3000B medium-volume samplers (URG, Chapel Hill, NC). Two URG samplers were deployed at each site to allow sample collection during collection days scheduled on weekends and holidays from midnight-to-midnight (local Pacific Time) of the sample collection day. Samples were shipped and stored frozen until analysis. A detailed site description can be found elsewhere (Skiles et al., 2018). Briefly, the sampling site in Fresno (population density of

275

1800/km2) is located about 260 km east-southeast of San Francisco and is surrounded by a residential neighborhood, several businesses and restaurants. The sampling site in Bakersfield (population density: 1000/km2) is located 400 km southeast of San Francisco and is surrounded by residential neighborhoods. 2.2. Chemical analysis The samples of organic carbon (OC), elemental carbon (EC), and organic molecular-marker compounds were analyzed by gas chromatography mass spectrometry (GCMS) including filter samples, field blanks, and trip blanks for detailed chemical analyses at the Wisconsin State Laboratory of Hygiene (WSLH). Organic markers included n-alkanes, cycloalkanes, alkanoic acids, resin acids, aromatic diacids, alkanedioic acids, steranes, hopanes, PAHs, oxy-PAHs, phthalates, levoglucosan, and sterols. All data was blankcorrected using field blank data. Uncertainties were estimated using the standard deviations of field blanks and the analytical uncertainty (Skiles et al., 2018). OC and EC were analyzed using the modified NIOSH 5040 ECOC thermal optical transmission analysis (UW-TOT, Sunset Labs; Tigard, OR) at the WSLH (Bae et al., 2017; Bae et al., 2004; Chow et al., 2001; Schauer et al., 2003). WSOC was analyzed at the UW-Madison Water Science and Engineering Laboratory (WSEL) using a Sievers M9 TOC Analyzer (GE Analytical Instruments; Boulder, CO). A 1.5-cm2 punch was extracted in 15 ml Milli-Q water (<18.2 MU,cm), agitated on a shaker table for 6 h, and filtered through a 0.45-mm polypropylene syringe filter into baked and acid-washed analyzer tubes. Samples were analyzed in conjunction with 10% check standards/spikes and lab blanks to assess and maintain QA/QC standards. Dust mass was estimated as the sum of oxides of major crustal elements, including Si, Al, Fe, Mg, Ti, Ca, and water-insoluble K (Cheung et al., 2012). Crustal element concentrations were obtained from the Chemical Speciation Network (CSN), which measures elements using energy dispersive X-ray fluorescence (Solomon et al., 2014). Water-soluble ion concentrations (nitrate, sulfate, ammonium, sodium, and potassium) were also obtained from CSN data and were measured by ion chromatography (Sofowote et al., 2015). All the chemical analysis is based on the EPA protocol (Solomon et al., 2014). For organic speciation, the 90-mm quartz filters collected using medium-volume samplers were sectioned into half-filters for GCMS analysis (Heo et al., 2013; Sheesley et al., 2007a). Each filter section was extracted individually by Soxhlet in a 50:50 methylene chloride (DCM)/acetone mixture. Samples were spiked with 100 mL of internal standard and analyzed in batches, which included laboratory blanks spiked with matrix standards. The final volume for each sample was adjusted to 100 mL and split into two aliquots. Each aliquot was either silylated or methylated prior to analysis. The methylated aliquot was analyzed using gas chromatography electron impact mass spectrometry (GC-EI-MS). Levoglucosan and the secondary organic carbon (SOC) tracers were analyzed using the silylated portion of the extract. These samples were analyzed using gas chromatography positive chemical ionization mass spectrometry (GC-PCIMS) (Bae et al., 2006; Sheesley et al., 2004). 2.3. Source apportionment methods The CMB model (EPA-CMB8.2) was applied to the results obtained during the intensive sampling campaign. The CMB develops a solution based on a linear summation of products at a receptor location based on the abundance of source profiles and source contributions (Watson et al., 2015; Watson et al., 1994). The model attempts to fit ambient speciated results from Bakersfield and Fresno to a specified group of sources with corresponding molecular markers. The U.S. EPA PMF model (EPA-PMF5.0) (Anttila et al.,

276

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

1995; Jaeckels et al., 2007; Paatero, 1997) was applied to a combination of Bakersfield and Fresno organic molecular markers, ECOC, and WSOC measurements from the 2015/2016 sample campaign to assess the quality of the CMB organics source apportionment. Previous studies (Skiles et al., 2018; Lough and Schauer, 2007b; Sheesley et al., 2007b) have shown sensitivity analyses of CMB methods that were conducted with results from different CMB statistical methods. A method using individual daily measured molecular marker results and uncertainties in daily marker measurements was utilized in this study. PMF calculated both factor contributions and profiles derived by minimizing the objective function (Norris, and Duvall, 2014). Non-negativity constraints are utilized in PMF models. The factor profiles were independently identified as source types and compared to CMB results for better understanding of organic and inorganic contributions to ambient PM. The time series of organic data was also reviewed. Three sampling days (Jan. 3rd in 2015 for Fresno and Jan. 1st in 2016 for both the Fresno and Bakersfield sites) were eliminated in PMF because of strong local holiday biomass burning that PMF cannot separate as a standalone source. Organic compounds, such as light molecular-weight PAHs, light molecular-weight alkanes and alkanoic acids, and cyclo-alkanes, were not used in the PMF calculation due to low data capture and their potential instabilities in PM2.5. A total of 67 organic compounds, which were analytically identified with at least 40% abundance for the sampling periods, were selected for PMF. OC, EC, WSOC, and water-insoluble organic carbon, which is the difference between OC and WSOC, were included in the PMF analysis. PMF allows the user to review concentration statistics, which include the signal-to-noise ratio (S/N) and allows statistically “bad” status species to be removed from the analysis. Twenty PMF runs were made for each model calculation for OC. In this study, used species were marked as “strong”, except for OC that was marked as “weak” in order to calculate total source contributions to OC. From the twenty runs, the goodness-of-fit parameter (the convergent run with the minimum Qrobust, which is the goodness-of-fit parameter calculated excluding points not fit by the PMF model) calculated by PMF was used in the solutions presented in this study. In this study, uncertainties for both CMB and PMF in molecular marker data points were defined as the maximum of two functions of spike recoveries, detection limits, and load blank standard deviations (Skiles et al., 2018). In detail, five organic molecular markers (i.e., cyclopenta(cd)pyrene, perylene, 1,3,5benzetricarboxylic acid, 1,2,4,5-benzenetetracarboxylic acid, and monostearin) were removed from the analysis based on spike recovery testing and acceptable spike recovery definition (Heo et al., 2013). Analytical results were blank corrected using a blank dataset consisting of 14 trip blanks and 14 load blanks from each site. Load blanks at each site were similar, and thus, molecular markers were blank corrected using the average of the 28 load blanks from both sites. Uncertainties in molecular marker data points were shown in Supplemental Information. 2.4. Meteorological simulations To understand source regions impacting the receptor sites, meteorological simulations were performed using the WRF model (ver. 3.8.1) (Skamarock et al., 2005). The simulations were described in Skiles et al. (2018) and follow the methods described in previous studies (de Foy et al., 2015; Heo et al., 2015; Wang et al., 2016). The model was initialized using the Final (FNL) Operational Global Analysis data from the United States National Centers for Environmental Prediction (NCEP, 2000), which has a spatial resolution of 1
, analysis time steps every 6 h, and ingests global meteorological data into the Global Forecast System using the Global Data Assimilation System (GDAS). Two nested domains were

used with 27 and 9 km resolution and 40 vertical levels as shown in Supplemental Information Fig. S6. WRF simulations were performed every 5 days during the entire measurement period with 1.5 days of spin-up time and 5 usable days of meteorological fields. The WRF model options are listed shown in Supplemental Information Table S1. Back-trajectories were calculated for the measurement sites for 6 years at Fresno and for the 2015/2016 sample period at Bakersfield using the WRF simulations and the WRF-FLEXPART model (Brioude et al., 2013). Detailed numerical simulation procedures are described in previous publications (de Foy et al., 2014; de Foy and Schauer, 2015; de Foy et al., 2009; Ashbaugh et al., 1985) and in Supplemental Information. 2.5. Forest fire plume transport simulations To better understand extreme events associated with forest fires, simulations were made with the Comprehensive Air Quality Model with Extensions (CAMx v6.30) model. Black carbon was simulated as a non-reacting species with wet and dry deposition. Emissions were based on the Fire Locating and Modeling of Burning Emissions (FLAMBE) (Hyer and Chew, 2010). Point source releases were made in proportion to the “smoke aerosols” in the FLAMBE inventory and released uniformly between the surface and 1000 m above ground level. In this region, FLAMBE emissions combine hotspot detection from both the East and West Geostationary Operational Environmental Satellites (GOES-East, GOES-West) as well as from the moderate-resolution imaging spectroradiometers (MODIS) on board both the Aqua and Terra satellites. Separate simulations were made for individual large fires that took place in California during the measurement period, as shown in Supplemental Information Fig. S6. 3. Results and discussion 3.1. Speciated PM concentration A total of 71 aerosol species including 67 out of more than 160 organic compounds were evaluated in the PMF model. The time series of PM2.5 major chemical concentrations (PM2.5 mass, OC, EC, nitrate, sulfate, ammonium, and dust) are presented, along with the average OC concentrations related to sources measured at the two sites, in Fig. 1 and Supplemental Information Fig. S2, respectively. Strong seasonality is evident in the measured ambient concentrations of measured PM2.5 major chemical concentrations. Wintertime ambient PM2.5 mass, EC, nitrate, and ammonium concentrations were higher than in summer at both sites. This is significant for the seasonal primary emissions in PM2.5. Levoglucosan, a key tracer molecule widely used to isolate OC from biomass burning (Fine et al., 2004a; Ham and Kleeman, 2011; Heo et al., 2013; Simoneit et al., 1999), was higher in winter than in summer and peaked in December. Monthly averages in December have values of 555 ± 106 ng/m3 in Fresno and 336 ± 46 ng/m3 in Bakersfield. Note that the biomass burning profile used in this study is a residential wood-burning profile. Hopane compounds are thought to be excellent markers for emissions from mobile sources and fuel-oil combustion (Lough et al., 2007a; Schauer et al., 1996). Both sites showed pronounced wintertime peaks in ambient cholesterol, oleic acid, and linoleic acid, which are the markers for particulate emissions released from meat and seed oil cooking (Rogge et al., 1991). 3.2. PMF source contribution OC source contributions obtained from six, seven, and eightfactor PMF solutions were explored at each site, as shown in

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

277

Fig. 1. Time series of PM2.5 chemical concentrations and source contributions to PM2.5 OC for the 6-factor solution from PMF results at the Fresno site.

Supplemental Information Fig. S1. The source identification was generally based on the distribution of molecular markers, which are normalized to OC and the temporal distribution. The derived profiles associated with each factor for the six, seven, and eightfactor models presented similar solutions. The SOA winter and mobile factors split into different mixed factors in the seven and eight-factor solutions. Residual OC contributions for each of the different solutions were very consistent. Based on the Pearson correlation coefficients among estimated source contributions, the 6-factor solution was determined as the final source profile. Fig. 1 and Supplemental Information Fig. S2 present daily temporal characteristics of PM2.5 chemical concentrations and source contributions to PM2.5 OC for the 6-factor solution over the entire study period for the Fresno and Bakersfield sites. The SOA winter and wood-combustion factors were significant contributors to OC during winter for both sites. The forest-fire sources had relatively short-term OC contributions during the summer. The mobile source showed no apparent seasonal patterns for both sites. In general, six source categories providing reasonably stable profiles were identified: biomass burning, mobile, food cooking, two different secondary organic carbon factors (i.e., winter and summer), and forest fire. The secondary organic aerosol factor (i.e., both SOA winter and SOA summer in this study) is characterized by alkanoic acids, alkanedioic acids, aromatic diacids, phthalates as well as WSOC; the

biomass burning factor by levoglucosan; the food cooking factor by oleic acid, linoleic acid, and cholesterol; the mobile factor by EC, hopanes, and steranes; the forest-fire by aromatic diacids and alkanoic acids. 3.3. Primary sources based on receptor model comparison Levoglucosan has been used in source profiles of biomass burning source in CMB analyses and is a unique factor in the PMF analysis (Skiles et al., 2018). Both CMB and PMF factors have strong contributions, tracking each other in late fall (November) to early spring (March) and are very low in the summer months for both sites (Supplemental Information Figs. S3 and S4). The PMF biomassburning factor for the optimized model is compared to the CMB biomass-burning source in Fig. 2 with coefficients of divergence (COD), which provide relative measured differences of homogeneity between models (Xie et al., 2012). The comparison shows a high correlation with a r2 of 0.95 and 0.88 for the Fresno and Bakersfield sites, respectively. The correlation result is a strong confirmation of the biomass-burning source both in the CMB and PMF analyses. The food-cooking factor is characterized by oleic acid, linoleic acid, and cholesterol. Most oleic acid and linoleic acid portions are grouped in this factor (Supplemental Information Fig. S5). The

278

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

Fig. 2. Comparison of PMF and CMB OC source concentrations at (a) Fresno (open circles) and (b) Bakersfield (gray circles). (note: coefficients of divergence (COD)).

food-cooking factor is significantly higher in the winter months compared to the summer months for both sites (Supplemental Information Figs. S3 and S4). This factor was plotted against the CMB food-cooking source in Fig. 2, showing good correlation, with a r2 of 0.77 and 0.80 for the Fresno and Bakersfield sites, respectively. However, the lower concentrations (slope of 0.28) in the Fresno site suggest that the CMB cooking source profile, which includes only the primary organic markers, could not explain the secondary products well enough to quantify uncertainties. Previous study showed that air mass transport, as a potential contributor to seasonality in meat smoke apportionment results, depended on the amount of fresh PM concentrations from primary sources due to transport and dilution (Skiles et al., 2018). In addition, prior chamber studies showed that cooking tracers, such as cholesterol and oleic acid, can be degraded by oxidation given specific ozone concentrations, relative humidity, and particles with SOA coatings, which may explain some of the seasonal variability (Weitkamp et al., 2008). The hourly averages of ozone measured at the two sampling sites in 2015 are shown in Supplemental Information Fig. S8. The annual-average ozone concentrations for calendar year 2015 are 32.93 ppb and 33.69 ppb for the Fresno and Bakersfield, respectively. Monthly average data show that January had the lowest ozone concentrations of 8.36 ± 4.81 ppb (average þ standard deviation) for the Fresno and 9.27 ± 4.81 ppb for the Bakersfield. The period of June had the highest ozone concentrations, with an average of 52.58 ± 8.43 ppb (average þ standard deviation) for the Fresno and 49.98 ± 8.43 ppb for the Bakersfield. A comparison of the monthly average ozone concentrations demonstrates that they are statistically indistinguishable between the sites. The mobile source is dominated by EC, water insoluble organic carbon (WIOC), hopanes, and steranes. These organic compounds are known to arise from the crankcase of internal-combustion engines (Schauer et al., 1996). Ambient concentrations of chosen hopanes at the Fresno and Bakersfield sites showed no apparent

seasonal patterns. Hopanes and steranes were also used in source profiles for mobile sources in the CMB analysis. OC contributions from the PMF mobile factor were calculated by measuring the major factors: 17a(H)-21b(H)-30-norhopane, 17a(H)-21b(H)-30hopane, 22S-Homohopane, and 22R-Homohopane. A combination of diesel, gasoline, and smoking vehicle sources was analyzed for the CMB mobile source factor. The PMF mobile factor had poor correlation, with a r2 of 0.27 for the Fresno site and fair correlation, with a r2 of 0.57 for the Bakersfield site. However, the time trends in concentrations track each other during the sampling periods for both sites, as shown in Supplemental Information Figs. S3 and S4. The overall averages of OC concentrations of the mobile source factor were 0.71 ± 0.31 and 1.20 ± 0.97 mg/m3 (mean ± standard deviation) for CMB and PMF, respectively. There are no strong seasonal patterns at either site for OC concentrations of the mobile source factor. The fluctuating OC concentration associated with the mobile source factor may result from changing local/regional mobile emissions with local meteorology and/or boundary layer height. The higher PMF OC concentrations could be due to photochemical processing and SOA formation from the car emissions that the PMF interpolated. However, they do support the notion that mobile source results are reasonable between models. 3.4. PMF forest fire source The forest-fire factor from the PMF model contributed in Fresno and Bakersfield to several episodes in August and September 2015, especially on 16 and 19 August and 9 and 12 September. The factor is characterized by strong source contributions for the two receptor sampling sites. This factor had the highest daily OC contributions in the summer resolved by the PMF model in all cases (6, 7, and 8 factors). From these characteristics, it is clear that these factors are regional sources. The source had several aromatic diacids and alkanoic acids as the dominant species. Forest-fire emission profiles have been shown to contain a much smaller levoglucosan fraction

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

than aerosols from residential wood burning (Sullivan et al., 2008) and are not always apportioned as part of the biomass-burning profile (Heo et al., 2013). By confirming regional forest fires, it is clear that the PMF model agreed with severe spikes in OC concentrations, depending on forest-fire days (Supplemental Information Figs. S3 and S4). Figure S6 in the Supplemental Information shows the location of the individual fires that were simulated using CAMx. These include the fires in the Shasta-Trinity National Forest and Six Rivers National Forest in northern California, the Oak Fire, the Rocky Fire, and the Rough Fire, which was one of the largest fires in California's recent history. National Ambient Air Quality Standards were used for fine particulate matter to assess regional wildland fire smoke and air quality management. Fig. 3 presents time series of forest fire impacts using CAMx simulations and FLAMBE emissions of “smoke aerosol” alongside the results of PMF forest fire source at the Fresno and Bakersfield sites. To convert from “smoke aerosol” concentrations to black carbon concentrations that would match the PMF analysis, we applied a heuristic scaling factor. For the Rough Fire and Rocky Fire, we found that scaling the smoke aerosols by 0.1

279

yielded a good match with the measurements. For both the Shasta Fire and the Oak Fire, the concentrations were scaled by a factor of 0.2. As discussed in Hyer and Chew (2010), there are uncertainties in the emissions inventories due to uncertainties in both the mapping of fuels and the fuel models used. The varying scaling factors could be seen as an indication that varying emissions among the fires impacted the measurement sites during this study. Overall, the time series shows that there is strong agreement between the simulations and the measurements, which confirms the identification of the forest fire source using the PMF analysis. 3.5. Secondary sources based on receptor model comparison Two different secondary organic aerosol (SOA) factors are characterized based on the seasonal patterns. The SOA winter is classified with the most alkanes, alkanoic acids, and benzenecarboxcylic acids with higher OC concentrations in the winter. The SOA summer is classified with the pionic acid with the higher OC concentrations in the summer. Previous work has suggested aliphatic acids and aromatic di-, tri-, and tetra-acids to be indicators

Fig. 3. Time series of forest fires using CAMx simulations with the results of the PMF forest-fire source at the Fresno and Bakersfield sites.

280

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

of secondary organic aerosols (SOA) (Fine et al., 2004b; Rogge et al., 1993; Sheesley et al., 2004). A combination of SOA winter, SOA summer, and forest-fire sources, which cannot be quantified in the CMB model, was compared to the CMB “other source” factor (Skiles et al., 2018). There is a fair correlation between the two models (r2 ¼ 0.46 and 0.50 for the Fresno and Bakersfield sites, respectively), suggesting that the two secondary organic factors and forest fires are of importance. The plot has good slope agreement (m ¼ 0.96 and 0.80 for the Fresno and Bakersfield sites, respectively) suggesting that the different approaches between the PMF and the CMB sources present good overall results. 3.6. Combined source contribution results Overall, the PMF analysis agrees well with CMB in terms of both tracers of and contributions from biomass burning. Although the PMF and CMB models differed in their source contributions of food/ meat cooking and mobile sources, the two agreed in terms of the chemical tracers for those sources. The CMB model yielded the detailed mobile sources (i.e., gasoline, diesel, and smoke) and vegetative detritus that the PMF could not calculate their source profiles. The PMF model identified the forest fire sources that the CMB could not capture due to lack of a source profile. Further, the PMF analysis identified three other important factors: SOA winter, SOA summer, and forest fire. While these sources could not be identified directly in the CMB model, their total source contributions corresponded well with the overall secondary OC contributions estimated from CMB results. The PMF food cooking factor produced more realistic values than the CMB due to the possibility of degradation by oxidation. For these reasons, a total of seven sources (i.e., CMB wood smoke, CMB mobile, CMB vegetative detritus, PMF food cooking, PMF SOA summer, PMF SOA winter, and PMF forest fire) were chosen. The combination of the two models can certainly provide reasonable quantitative information on important sources of OC at the two sampling sites. Additionally, this comparison between models is a useful context for interpreting CMB results in studies where molecular marker-based PMF would not be feasible. It may be true that the mathematical integration of the results leads to complex error structures that are difficult to properly integrate. However, from an applied science perspective, the integration between two model results is an important advance for atmospheric sciences and air quality management. Monthly averages of combined PMF and CMB OC source contributions at the Fresno and Bakersfield sites are shown in Supplemental Information Tables S2 and S3 and Fig. S9. As a result, total source contributions derived from the CMB and PMF methods to total PM2.5 OC mass at Fresno were 1.21 mg/m3 CMB wood smoke, 0.70 mg/m3 CMB mobile sources, 0.13 mg/m3 PMF food cooking, 0.73 mg/m3 PMF SOA summer, 0.90 mg/m3 PMF SOA winter, and 0.58 mg/m3 PMF forest fire. Total source contributions at Bakersfield were 0.67 mg/m3 CMB wood smoke, 0.73 mg/m3 CMB mobile sources, 0.24 mg/m3 PMF food cooking, 0.59 mg/m3 PMF SOA summer, 0.78 mg/m3 PMF SOA winter, and 0.37 mg/m3 PMF forest fire. The overall relative source contribution percentages to OC were 23.5% CMB wood smoke, 18.6% CMB mobile sources, 4.9% PMF food cooking, 2.3% CMB vegetative detritus, 17.0% PMF SOA summer, 21.7% PMF SOA winter, and 12.0% PMF forest fire.

2018). At Fresno, clusters 1 and 2 show transport from the Pacific Ocean past the San Francisco Bay and down the Central Valley to the receptor site. Wind transport during cluster 2 is slightly more northerly than during cluster 1 and also has weaker winds than cluster 1, as can be seen from the thicker RTA trace close to the receptor site. Clusters 3 and 4 are characteristic of southward transport along the valley. The RTA trace is larger closer to Fresno for cluster 3 whereas it is larger north of Sacramento in cluster 4, suggesting that wind transport is slower during cluster 3 than during cluster 4. Clusters 5 and 6 show slow local flows from the southern part of the Central Valley towards Fresno: during cluster 5 the stagnation is more pronounced, with an RTA trace very close to the site, whereas for cluster 6 there is regional component that is more pronounced. At Bakersfield, there is much weaker wind transport than at Fresno. This is consistent with the lower wind speeds and appears as more localized tracers in the Residence Time Analysis with less pronounced transport directions than the Fresno Clusters. Nevertheless, the Bakersfield clusters follow a similar pattern to the Fresno Clusters, with clusters 1 to 4 representing flow from the north and clusters 5 and 6 representing local flow from the southern end of the Central Valley. The differences between the clusters are more related to slight changes in local wind speed and direction, in contrast to Fresno where the differences were more related to regional transport. To understand the source regions associated with six of the PMF factors, boxplots of cluster analyses related to the PMF factors are shown in Fig. 4. PMF biomass burning and food cooking sources have a clear pattern based on wind transport clusters: concentrations are minimal for clusters 1 and 2, and highest for cluster 5 at Fresno and cluster 4 at Bakersfield. Cluster 5 represents days with the weakest wind transport where winds are calm and the measurement sites are impacted by local emissions. This suggests that at Fresno and Bakersfield, both biomass burning and food cooking aerosols are predominantly from local sources. The mobile source PMF factor also has low concentrations associated with clusters 1 and 2, but the highest concentrations are associated with cluster 3 at Fresno and cluster 4 at Bakersfield. This suggests that at both sites, the highest concentrations of mobile source aerosols are associated with weak transport from the north. When the wind is strong enough, there is sufficient dilution to offset the impact of the urban areas to the north. Likewise, the SOA winter factor is associated with weak transport from the north: the maximum occurs during cluster 4 at Fresno and for clusters 3 and 4 at Bakersfield. In contrast, the SOA summer factor is associated with clusters 1 and 2 which indicate longer range transport from the northwest, including the urban areas of San Francisco and Sacramento. This suggests that these are secondary aerosols that have formed in the urban plumes and have been transported to the measurement site. Their concentrations are much lower for clusters 4, 5 and 6 which represent fresh urban plumes from local emissions. The forest fire PMF factor only occurs during specific forest fire events and hence is not expected to have a strong dependence on the clusters. Higher concentrations in distributions associated with clusters 1 and 2 were observed as these corresponded to the plume transport from some of the large fires impacting the sites. This suggests that at both sites, the highest concentrations of forest fire aerosols are associated with transport from the same area as shown in Supplemental Information Fig. S6.

3.7. Potential source regions Figure S7 in the Supplemental Information shows six primary cluster analyses calculated from WRF-FLEXPART back-trajectory simulations of the measurement period for both sites (Skiles et al.,

4. Summary and conclusion To understand temporal source contributions to PM2.5 OC in

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

281

Fig. 4. Boxplots of cluster-classified high-source regions related to each identified PMF source. (Note: The central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the 'þ' symbol.).

Fresno and Bakersfield, California, a unique 14-month dataset of organic molecular markers measured in Fresno and Bakersfield, California, was evaluated with PMF and CMB models. Source contributions for both sites were obtained from molecular marker CMB and PMF models and were found to yield comparable estimates. The PMF model identified six source categories with stable profiles: biomass burning, mobile, food cooking, two types of secondary organic carbon (SOC) (winter and summer), and forest fires. This study also confirmed that very large forest fires were a major source distinct from the other sources. The impacts of the fires on OC levels at the measurement site were confirmed using CAMx simulations based on FLAMBE emissions. In addition, clusters of wind transport patterns calculated using WRF-FLEXPART identified the source regions for all the PMF factors. Good agreement of biomass burning between CMB and PMF source contributions was obtained. This can provide weight of evidence for biomass burning contributions. PMF is in fair agreement with the CMB analysis of tracers for food

cooking and mobile sources. PMF analysis confirmed that the molecular-marker CMB model can accurately quantify the contributions from SOA productions during winter and summer. Overall, the combined model estimated the percentage contribution of each source group to OC levels as follows: 23.5% CMB wood smoke, 18.6% CMB mobile sources, 4.9% PMF food cooking, 2.3% CMB vegetative detritus, 17.0% PMF SOA summer, 21.7% PMF SOA winter, and 12.0% PMF forest fire. In conclusion, this study shows that the CMB and PMF models yield results that are comparable to each other and sufficiently reliable for identifying pollutant sources. Acknowledgments We acknowledge the support of the California Air Resources Board (Contract Number 14-754) and the support of Min-Suk Bae from the National Research Foundation of Korea (NRF) (NRF2017R1D1A1B03029517 & NRF-2017M3D8A1092222).

282

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2018.12.013. References Amato, F., Hopke, P.K., 2012. Source apportionment of the ambient PM2.5 across st. Louis using constrained positive matrix factorization. Atmos. Environ. 46, 329e337. Anttila, P., Paatero, P., Tapper, U., Jarvinen, O., 1995. Source identification of bulk wet deposition in Finland by positive matrix factorization. Atmos. Environ. 29, 1705e1718. Ashbaugh, L.L., Malm, W.C., Sadeh, W.Z., 1985. A residence time probability analysis of sulfur concentrations at grand-canyon-national-park. Atmos. Environ. 19, 1263e1270. Bae, M.S., Schauer, J.J., DeMinter, J.T., Turner, J.R., Smith, D., Cary, R.A., 2004. Validation of a semi-continuous instrument for elemental carbon and organic carbon using a thermal-optical method. Atmos. Environ. 38, 2885e2893. Bae, M.S., Schauer, J.J., Lee, T., Jeong, J.H., Kim, Y.K., Ro, C.U., Song, S.K., Shon, Z.H., 2017. Relationship between reactive oxygen species and water-soluble organic compounds: time-resolved benzene carboxylic acids measurement in the coastal area during the KORUS-AQ campaign. Environ. Pollut. 231, 1e12. Bae, M.S., Schauer, J.J., Turner, J.R., 2006. Estimation of the monthly average ratios of organic mass to organic carbon for fine particulate matter at an urban site. Aerosol Sci. Technol. 40, 1123e1139. Brioude, J., Arnold, D., Stohl, A., Cassiani, M., Morton, D., Seibert, P., Angevine, W., Evan, S., Dingwell, A., Fast, J.D., Easter, R.C., Pisso, I., Burkhart, J., Wotawa, G., 2013. The Lagrangian particle dispersion model FLEXPART-WRF version 3.1. Geosci. Model Dev. (GMD) 6, 1889e1904. Cheung, K., Olson, M.R., Shelton, B., Schauer, J.J., Sioutas, C., 2012. Seasonal and spatial variations of individual organic compounds of coarse particulate matter in the los angeles basin. Atmos. Environ. 59, 1e10. Chow, J.C., Watson, J.G., Crow, D., Lowenthal, D.H., Merrifield, T., 2001. Comparison of IMPROVE and NIOSH carbon measurements. Aerosol Sci. Technol. 34, 23e34. de Foy, B., Cui, Y.Y., Schauer, J.J., Janssen, M., Turner, J.R., Wiedinmyer, C., 2015. Estimating sources of elemental and organic carbon and their temporal emission patterns using a least squares inverse model and hourly measurements from the st. Louis-midwest supersite. Atmos. Chem. Phys. 15, 2405e2427. de Foy, B., Heo, J., Schauer, J.J., 2014. Estimation of direct emissions and atmospheric processing of reactive mercury using inverse modeling. Atmos. Environ. 85, 73e82. de Foy, B., Schauer, J.J., 2015. Origin of high particle number concentrations reaching the st. Louis, midwest supersite. J. Environ. Sci. 34, 219e231. de Foy, B., Zavala, M., Bei, N., Molina, L.T., 2009. Evaluation of WRF mesoscale simulations and particle trajectory analysis for the MILAGRO field campaign. Atmos. Chem. Phys. 9, 4419e4438. Fine, P.M., Cass, G.R., Simoneit, B.R.T., 2004a. Chemical characterization of fine particle emissions from the fireplace combustion of wood types grown in the midwestern and western United States. Environ. Eng. Sci. 21, 387e409. Fine, P.M., Chakrabarti, B., Krudysz, M., Schauer, J.J., Sioutas, C., 2004b. Diurnal variations of individual organic compound constituents of ultrafine and accumulation mode particulate matter in the los angeles basin. Environ. Sci. Technol. 38, 1296e1304. Ham, W.A., Kleeman, M.J., 2011. Size-resolved source apportionment of carbonaceous particulate matter in urban and rural sites in central California. Atmos. Environ. 45, 3988e3995. Held, T., Ying, Q., Kleeman, M.J., Schauer, J.J., Fraser, M.P., 2005. A comparison of the UCD/CIT air quality model and the CMB source-receptor model for primary airborne particulate matter. Atmos. Environ. 39, 2281e2297. Heo, J., de Foy, B., Olson, M.R., Pakbin, P., Sioutas, C., Schauer, J.J., 2015. Impact of regional transport on the anthropogenic and biogenic secondary organic aerosols in the los angeles basin. Atmos. Environ. 103, 171e179. Heo, J., Schauer, J.J., Yi, O., Paek, D., Kim, H., Yi, S.-M., 2014. Fine particle air pollution and mortality: importance of specific sources and chemical species. Epidemiology 25, 379e388. Heo, J.B., Dulger, M., Olson, M.R., McGinnis, J.E., Shelton, B.R., Matsunaga, A., Sioutas, C., Schauer, J.J., 2013. Source apportionments of PM2.5 organic carbon using molecular marker positive matrix factorization and comparison of results from different receptor models. Atmos. Environ. 73, 51e61. Hyer, E.J., Chew, B.N., 2010. Aerosol transport model evaluation of an extreme smoke episode in southeast Asia. Atmos. Environ. 44, 1422e1427. Jaeckels, J.M., Bae, M.-S., Schauer, J.J., 2007. Positive matrix factorization (PMF) analysis of molecular marker measurements to quantify the sources of organic aerosols. Environ. Sci. Technol. 41, 5763e5769. Jeong, J.-H., Shon, Z.-H., Kang, M., Song, S.-K., Kim, Y.-K., Park, J., Kim, H., 2017. Comparison of source apportionment of PM2.5 using receptor models in the main hub port city of east Asia: busan. Atmos. Environ. 148, 115e127. Lough, G.C., Christensen, C.G., Schauer, J.J., Tortorelli, J., Mani, E., Lawson, D.R., Clark, N.N., Gabele, P.A., 2007. Development of molecular marker source profiles for emissions from on-road gasoline and diesel vehicle fleets. Air Waste Manag. Assoc. 57, 1190e1199. Lough, G.C., Schauer, J.J., 2007. Sensitivity of source apportionment of urban

particulate matter to uncertainty in motor vehicle emissions profiles. Air Waste Manag. Assoc. 57, 1200e1213. National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce, 2000. Updated Daily. NCEP FNL Operational Model Global Tropospheric Analyses, Continuing from July 1999. Research Data Archive at the National Center for Atmospheric Research. Computational and Information Systems Laboratory. https://doi.org/10.5065/D6M043C6. Nolte, C.G., Schauer, J.J., Cass, G.R., Simoneit, B.R., 2001. Highly polar organic compounds present in wood smoke and in the ambient atmosphere. Environ. Sci. Technol. 35, 1912e1919. Norris, G., Duvall, R., 2014. EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. U.S. Environmental Protection Agency, office of Research and Development, Washington, DC 20460, USA. Paatero, P., 1997. Least squares formulation of robust non-negative factor Analysis. Chemometr. Intell. Lab. 37, 23e35. Ramboll Environ, 2016. CAMx User's Guide Comprehensive Qir Quality Model with Extensions, Version 6.3. Technical Report, March 2016. Rogge, W.F., et al., 1993. Quantification of urban organc aerosols at a molecularlevel-identification, abundance and seasonal-variation. Atmos. Environ. Part A. 27, 1309e1330. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simonelt, B.R.T., 1991. Sources of fine organic aerosol .1. Charbroilers and meat cooking operations. Environ. Sci. Technol. 25, 1112e1125. Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R., 2001. Measurement of emissions from air pollution sources. 3. C1-C29 organic compounds from fireplace combustion of wood. Environ. Sci. Technol. 35, 1716e1728. Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R., 2002. Measurement of emissions from air pollution sources. 5. C1-C32 organic compounds from gasolinepowered motor vehicles. Environ. Sci. Technol. 36, 1169e1180. Schauer, J.J., Mader, B.T., Deminter, J.T., Heidemann, G., Bae, M.S., Seinfeld, J.H., Flagan, R.C., Cary, R.A., Smith, D., Huebert, B.J., Bertram, T., Howell, S., Kline, J.T., Quinn, P., Bates, T., Turpin, B., Lim, H.J., Yu, J.Z., Yang, H., Keywood, M.D., 2003. ACE-asia intercomparison of a thermal-optical method for the determination of particle-phase organic and elemental carbon. Environ. Sci. Technol. 37, 993e1001. Schauer, J.J., Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T., 1996. Source apportionment of airborne particulate matter using organic compounds as tracers. Atmos. Environ. 30, 3837e3855. Sheesley, R.J., Schauer, J.J., Bean, E., Kenski, D., 2004. Trends in secondary organic aerosol at a remote site in Michigan's upper peninsula. Environ. Sci. Technol. 38, 6491e6500. Sheesley, R.J., Schauer, J.J., Meiritz, M., DeMinter, J.T., Bae, M.S., Turner, J.R., 2007a. Daily variation in particle-phase source tracers in an urban atmosphere. Aerosol Sci. Technol. 41, 981e993. Sheesley, R.J., Schauer, J.J., Zheng, M., Wang, B., 2007b. Sensitivity of molecular marker-based CMB models to biomass burning source profiles. Atmos. Environ. 41, 9050e9063. Simoneit, B.R.T., Schauer, J.J., Nolte, C.G., Oros, D.R., Elias, V.O., Fraser, M.P., Rogge, W.F., Cass, G.R., 1999. Levoglucosan, a tracer for cellulose in biomass burning and atmospheric particles. Atmos. Environ. 33, 173e182. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., Powers, J.G., 2005. A Description of the Advanced Research WRF Version 2, Boulder, CO. Skiles, M.J., Lai, A.M., Olson, M.R., Schauer, J.J., Foy, B.d., 2018. Source apportionment of PM2.5 organic carbon in the san joaquin valley using monthly and daily observations and meteorological clustering. Environ. Pollut. 237, 366e376. Sofowote, U.M., Su, Y., Dabek-Zlotorzynska, E., Rastogi, A.K., Brook, J., Hopke, P.K., 2015. Sources and temporal variations of constrained PMF factors obtained from multiple-year receptor modeling of ambient PM2.5 data from five speciation sites in ontario, Canada. Atmos. Environ. 108, 140e150. Solomon, P.A., Crumpler, D., Flanagan, J.B., Jayanty, R.K.M., Rickman, E.E., McDade, C.E., 2014. U.S. National PM2.5 chemical speciation monitoring networksdCSN and IMPROVE: description of networks. Air Waste Manag. Assoc. 64, 1410e1438. Stohl, A., Forster, C., Frank, A., Seibert, P., Wotawa, G., 2005. Technical note: the Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys. 5, 2461e2474. Sullivan, A.P., Holden, A.S., Patterson, L.A., McMeeking, G.R., Kreidenweis, S.M., Malm, W.C., Hao, W.M., Wold, C.E., Collett, J.L., 2008. A method for smoke marker measurements and its potential application for determining the contribution of biomass burning from wildfires and prescribed fires to ambient PM2.5 organic carbon. J. Geophys. Res. Atmos. 113, 14. Wang, Y., Zhang, Y., Schauer, J.J., de Foy, B., Guo, B., Zhang, Y., 2016. Relative impact of emissions controls and meteorology on air pollution mitigation associated with the asia-pacific economic cooperation (APEC) conference in beijing, China. Sci. Total Environ. 571, 1467e1476. Watson, J.G., Chow, J.C., Lowenthal, D.H., Chen, L.W.A., Shaw, S., Edgerton, E.S., Blanchard, C.L., 2015. PM2.5 source apportionment with organic markers in the southeastern aerosol Research and characterization (SEARCH) study. Air Waste Manag. Assoc. 65, 1104e1118. Watson, J.G., Chow, J.C., Lu, Z.Q., Fujita, E.M., Lowenthal, D.H., Lawson, D.R., Ashbaugh, L.L., 1994. Chemical mass-balance source apportionment of PM10 during the southern California air-quality study. Aerosol Sci. Technol. 21, 1e36. Weitkamp, E.A., Hartz, K.E.H., Sage, A.M., Donahue, N.M., Robinson, A.L., 2008. Laboratory measurements of the heterogeneous oxidation of condensed-phase

M.-S. Bae et al. / Environmental Pollution 246 (2019) 274e283 organic molecular makers for meat cooking emissions. Environ. Sci. Technol. 42, 5177e5182. Xie, M., Coons, T.L., Dutton, S.J., Milford, J.B., Miller, S.L., Peel, J.L., Vedal, S., Hannigan, M.P., 2012. Intra-urban spatial variability of PM2.5-bound carbonaceous components. Atmos. Environ. 60, 486e494. Zhang, Y., Sheesley, R.J., Bae, M.-S., Schauer, J.J., 2009. Sensitivity of a molecular

283

marker based positive matrix factorization model to the number of receptor observations. Atmos. Environ. 43, 4951e4958. Zielinska, B., Sagebiel, J., McDonald, J.D., Whitney, K., Lawson, D.R., 2004. Emission rates and comparative chemical composition from selected in-use diesel and gasoline-fueled vehicles. Air Waste Manag. Assoc. 54, 1138e1150.