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Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers
Atmospheric Environment 43 (2009) 5567–5574
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Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers YuanXun Zhang a, Rebecca J. Sheesley a, James J. Schauer a, *, Michael Lewandowski b, Mohammed Jaoui c, John H. Offenberg b, Tadeusz E. Kleindienst b, Edward O. Edney b a b c
Environmental Chemistry and Technology Program, University of Wisconsin–Madison, 660 North Park Street, Madison, WI 53706, USA National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA Alion Science and Technology, P.O. Box 12313, Research Triangle Park, NC 27709, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 October 2008 Received in revised form 13 February 2009 Accepted 17 February 2009
Monthly average ambient concentrations of more than eighty particle-phase organic compounds, as well as total organic carbon (OC) and elemental carbon (EC), were measured from March 2004 through February 2005 in five cities in the Midwestern United States. A multi-variant source apportionment receptor model, positive matrix factorization (PMF), was applied to explore the average source contributions to the five sampling sites using molecular markers for primary and secondary organic aerosols (POA, SOA). Using the molecular makers in the model, POA and SOA were estimated for each month at each site. Three POA factors were derived, which were dominated by primary molecular markers such as EC, hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs), and which represented the following POA sources: urban primary sources, mobile sources, and other combustion sources. The three POA sources accounted for 57% of total average ambient OC. Three factors, characterized by the presence of reaction products of isoprene, a-pinene and b-caryophyllene, and displaying distinct seasonal trends, were consistent with the characteristics of SOA. The SOA factors made up 43% of the total average measured OC. The PMF-derived results are in good agreement with estimated SOA concentrations obtained from SOA to tracer yield estimates obtained from smog chamber experiments. A linear regression comparing the smog chamber yield estimates and the PMF SOA contributions had a regression slope of 1.01 0.07 and an intercept of 0.19 0.10 mg OC m3 (adjusted R2 of 0.763, n ¼ 58). Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Source apportionment Molecular marker Primary organic aerosol Secondary organic aerosol
1. Introduction Organic aerosols (OAs) consist of thousands of organic compounds originating from a wide range of sources and atmospheric processes. Primary organic aerosols refer to the OA directly emitted from pollution sources. OA that formed in the atmosphere through the photochemical reaction of gas-phase precursors, known as secondary organic aerosol (SOA), is also a major contributor to carbonaceous particulate matter in many locations (Robinson et al., 2007; Turpin and Huntzicker, 1995), and has important influences on atmospheric physicochemical and biochemical properties, including radiative forcing, hydroscopicity and toxicity. SOA is an important contributor to air quality degradation, visibility degradation, climate forcing and adverse impacts on human health (Facchini et al., 1999; Jacobson, 2002; Pope et al., 2004; Tabazadeh, 2005). As a result, there has recently been a great
* Corresponding author. Tel.: þ1 608 262 4495; fax: þ1 608 262 0454. E-mail address: [email protected] (J.J. Schauer). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.02.047
interest in the development of methods to apportion sources of primary and secondary organic aerosols (Cabada et al., 2004; Pandis et al., 1992; Robinson et al., 2007; Strader et al., 1999; Turpin and Huntzicker, 1995). The elemental carbon (EC) tracer method is an important SOA estimation method developed by Turpin et al. (Cabada et al., 2004; Turpin and Huntzicker, 1995; Turpin et al., 1991). It is based on the hypothesis that the ratio of primary organic carbon (POC) to EC in POA is relatively constant, making EC a good tracer for POA. Using the EC tracer method, ambient secondary organic carbon (SOC) is calculated as atmospheric OC minus (POC/EC)*EC, where (POC/EC) is the OC/EC ratio of primary aerosols and EC is the concentration of EC. Previous studies have estimated atmospheric OC/EC ratios during periods when SOA is believed to be small, such as winter, peak morning traffic periods with low solar radiation, periods of intermittent drizzle, periods with low ozone concentrations (Conklin et al., 1981; Turpin and Huntzicker, 1995), or periods after rain episodes (Lim and Turpin, 2002). Ozone, carbon monoxide and nitrogen oxides are often selected as indicators to distinguish POC dominated episodes from the carbonaceous measurement time
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series (Cabada et al., 2004; Strader et al., 1999). Although the EC method has been successfully applied in many places, average primary OC/EC ratios have been shown to vary with location and over time. The molecular marker chemical mass balance (CMB) model has been used to estimate POA contributions using POA tracers for specific sources (Robinson et al., 2006b; Schauer et al., 1996). Although SOA source profiles have not been established, several studies have suggested that the unapportioned OC from molecular marker CMB models is a good estimate of SOA (Schauer et al., 1996; Stone et al., 2007). However, some neglected POA sources, which may occupy a fraction of the unapportioned OC, may introduce biases into the SOA estimation. Moreover, deviations of the input source profiles can also cause substantial uncertainties of SOA estimation (Robinson et al., 2006a). With the recent advances in the measurement of molecular markers for SOA (Edney et al., 2005; Jaoui et al., 2003, 2007; Kleindienst et al., 2007), progress has been made toward the development of methods to estimate SOA from key biogenic and anthropogenic SOA precursors (Lewandowski et al., 2008; Offenberg et al., 2007). The tracer-to-SOA mass fractions were obtained in atmospherically relevant smog chamber experiments (Kleindienst et al., 2007). Using these mass fractions and the measured ambient SOA tracer concentrations, the atmospheric SOA mass contributions can be estimated (Kleindienst et al., 2007; Lewandowski et al., 2008). This SOA yield method has been shown to be compatible with POA source contribution methods (Lewandowski et al., 2008). In this study, SOA tracers and POA tracers are used in a multivariant receptor model, positive matrix factorization (PMF), to examine sources of SOA and POA. By eliminating SOA laboratorybased mass fractions and POA source profiles, the PMF results provide an independent method to assess the contributions of SOA and POA and to compare these to existing tracer models. In the present study, more than eighty organic molecular markers, including the SOA tracers, were measured in five cities in the Midwestern US and were applied for PMF analysis. This is the first reported application of an integrated PMF model that uses primary and secondary organic tracers to apportion primary and secondary sources of organic aerosols. Likewise, the current study provides an independent comparison of SOA contribution estimates from smog chamber mass-fraction data obtained by Lewandowski et al. (2008). 2. Experimental and statistical methods 2.1. Sample collection and analysis The sample collection and chemical analyses used in the current study were previously described in detail by Lewandowski et al. (2008). During the sampling period from March 2004 through February 2005, 24-h ambient PM2.5 (particles less than 2.5 mm in aerodynamic diameter) samples were collected on the EPA 1 in 6 day sampling schedule in five cities in the Midwestern United States including one rural site, Bondville, Illinois, and four urban sites: Detroit, Michigan; Cincinnati, Ohio; Northbrook, Illinois; and East St. Louis, Illinois. OC and EC were measured using the thermal–optical method for each 24-h sample (Birch and Cary, 1996). Fig. S1 illustrates the average fine particle concentrations for these sites, which shows that OC levels in the urban sites are approximately twice the concentrations at the rural site. Samples were combined into monthly composites for each individual site to obtain monthly average atmospheric concentrations of the tracers for 12 months. Primary source tracers were analyzed as described by Sheesley et al. (2004). This approach involved spiking labeled internal
standards on the samples, Soxhlet extraction using 1:1 (v:v) methanol and dichloromethane, concentration, derivatization, and finally GC–MS analysis. Secondary molecular tracers were analyzed following the method as reported by Lewandowski et al. (2008). The samples were sonicated for 1 h using 50 mL 1:1 (v:v) methanol and dichloromethane. The extracts were then rotary evaporated to 1 mL, evaporated to dryness under a gentle stream of ultrapure nitrogen, and derivatized. The samples were then analyzed by GC– ITMS in the methane-CI mode (Kleindienst et al., 2007). 2.2. PMF procedures PMF assumes that concentrations at receptor sites are impacted by the linear combinations of source emissions, which are derived as factors in the model. Large data sets of daily 24-h elemental concentrations have historically been used in PMF models. In the present study, only 12 monthly average measurements were obtained at each site, which is not sufficient for a robust PMF analysis for each individual site. In the current study, we combined all the results from the 5 sites into one model analysis to derive organic aerosol factors that are common across all sites. It is expected that the SOA factors will have some common characteristics such as seasonal variations and SOA tracer distributions. Likewise, it is expected that profiles for mobile sources, biomass burning, and vegetative detritus would be similar for these Midwestern US sites. The presence of local point sources of organic aerosols may lead to poor separation of POA factors; however, the main objective of this study is to split primary and secondary source contributions, and to separate SOA sources. USEPA PMF1.1 was used for the current analysis. This version of the model determines signal-to-noise ratio (S/N) statistics for every input species and allows the user to downgrade the importance or remove species with small S/N values. Among the 85 species measured, 15 species were found to have S/N levels less than or equal to 0.2, and were removed from the computation. The removed species included anthraquinone; all dicarboxylic acids except succinic acid; isophthalic acid; benzenetri- and tetracarboxylic acids; methylphthalic acid; and heneicosanoic acid. Thirty-five species were considered ‘‘weak’’, with S/N ratios less than or equal to 1.0, whose uncertainties were increased by a factor of 3. These species included benzo[j]fluroanthene; perylene; dibenzo[ah]anthrance; picene; methylchrysene; R- and S-5a(H),14a(H),17b(H)-cholestane or ergostane; R-17a(H),21b(H)bishomohopane; trishomohopanes; C24–C36 n-alkanes; succinic acid; phthalic acid; terephthalic acid; and C20, C23–C26 and C28–C29 n-alkanoic acids. Total measured OC is included in the PMF data matrix. To verify the robustness and stability of the PMF model used in this study, PMF2 was used to repeat the analysis originally performed by PMF1.1 and the use of the FPEAK key was used to examine potential rotational ambiguities. The results of the two models yielded results that were in statistical agreement and no rotational ambiguities were found indicating that the model was indeed stable and robust. Detailed discussions about the PMF model have been previously reported in the literature (Jaeckels et al., 2007; Paatero, 1997, 1999; Paatero and Hopke, 2003; USEPA, 2005). 3. Results and discussion 3.1. Determination of PMF solutions A sensitivity analysis of the PMF model was conducted to examine the impact of the number of factors selected for the PMF computation. The 5–8-factor solutions were examined to assess the consistency of the solution with the current understanding of POA
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and SOA sources. The 5-factor solutions produced a factor dominated with n-alkanes and acids which also contained approximately 70% of ambient b-caryophyllinic acid, an atmospheric tracer for b-caryophyllene SOA (Jaoui et al., 2003, 2007). Additionally, this factor contributed more than 40% of ambient EC. Consequently, this factor was associated with both known POA and SOA tracers, suggesting that the 5-factor solution merged primary and secondary sources into a single factor. By increasing the number of allowed factors, the aforementioned factor was split into two factors: one dominated by n-alkanes and acids and contributing approximately 35% of ambient EC, while the other factor was the dominant source of b-caryophyllinic acid. Therefore, solutions having 6 or more factors probed primary and secondary aerosols better than the 5-factor case. The 7- and 8-factor solutions further split the factor associated with both POA and SOA in the 5-factor solution, and generated an isolated n-heptacosanoic acid dominated factor that did not represent any known sources. The 8-factor solution also had two factors with similar species distributions. In this case, the 5- and 8-factor solutions were eliminated from further consideration.
Factor contributions obtained from the 6- and 7-factor solutions are shown in Fig. 1 for each site along with an average across the four urban sites. Fig. 1 also presents the base and alternative cases; the base case included all monthly data, while the alternative case excludes the February and December measurements from Northbrook. These two samples from Northbrook displayed very high concentrations of heavy polycyclic aromatic hydrocarbons (PAHs, e.g., benzo[ghi]perylene and coronene, etc.) which are believed to be associated with local point sources and are not consistent with the measurements at the other sites. The removal of these two months mostly impacted the ‘‘other combustion sources’’ factor, which was the predominate source of the heavy PAHs. Eliminating these two months decreased the impact of this source for the other sites but did not significantly change the overall model results. As discussed previously, the 7-factor solutions generate an n-heptacosanoic acid dominated fatty acid factor which has no significant linkages with any known sources. Furthermore, this 7th factor is generated by splitting the urban primary source factor, with some contributions from SOA factors in Detroit and East St. Louis. This indicates that these two sites might have some different
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Fig. 1. Annual average factor contributions to organic carbon for 6- and 7-factor PMF solutions. Urban sites are Detroit, MI; Northbrook, IL; Cincinnati, OH; and East St. Louis, IL.
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pollution characteristics in comparison to the other three sites. Nevertheless, compound distribution of this fatty acid factor aligns with characteristics of POA with even n-alkane distributions, and approximately 30% EC was contributed by this factor. Since this 7th factor appears to mainly be POA and the base case is too sitespecific for Northbrook, the 6-factor alternative case was selected as the final solution for the purpose of SOA estimation.
3.2. Factor identification Factor compound distributions and the temporal patterns of the factors from the 6-factor alternative case solution are shown in Figs. 2 and 3, respectively, which were used for factor identification. Organic compounds and their potential sources have been reviewed previously (Cass, 1998; Jaeckels et al., 2007; Lewandowski
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Levoglucosan EC Benzo[b]fluoranthene Benzo[k]fluoranthene Benzo[j]fluroanthene Benzo[a]pyrene Benzo[e]pyrene Perylene Indeno[123-cd]pyrene Dibenzo[ah]anthracene Picene Benzo[ghi]perylene Coronene Methylchrysene R-αββ-Cholestane S-αββ-Cholestane R&S-ααα-Cholestane R-αββ-Ergostane S-αββ-Ergostane R-αββ-Sitostane S-αββ-Sitostane α-Trisnorhopane βα-Norhopane Hopane S-αβ-Homohopane R-αβ-Homohopane S-αβ-Bishomohopane R-αβ-Bishomohopane S-αβ-Trishomohopane R-αβ-Trishomohopane Tetracosane Pentacosane Hexacosane Heptacosane Octacosane Nonacosane Triacontane Hentriacontane Dotriacontane Tritriacontane Tetratriacontane Pentatriacontane Hexatriacontane Succinic acid Phthalic acid Terephthalic acid Eicosanoic acid Docosanoic acid Tricosanoic acid Tetracosanoic acid Pentacosanoic acid Hexacosanoic acid Heptacosanoic acid Octacosanoic acid Nonacosanoic acid Triacontanoic acid 2-Methylglyceric acid 2-Methylthreitol 2-Methylerythritol Pinic acid Pinonic acid 3-Acetyl pentanedioic OH-isopropyladipic Acetyl hexanedioic Hydroxyglutaric OH-dimethylglutaric Cyclobutane carboxylic Oxopentanoic β-Caryophyllinic
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Fig. 2. Distribution of molecular markers among factors: 6-factor alternative case solution. R and S represents configurations at C22 in hopanes > C31 or at C20 in steranes. abb or aaa represents 5a(H)14b(H)17b(H) or 5a(H)14a(H)17a(H) configurations in steranes, respectively. a, ba and ab represent 17a(H), 17b(H)21a(H) and 17a(H)21b(H) configurations in hopanes. The complete names of SOA tracers for 3-acetyl pentanedioic through b-caryophyllinic from left to right are 3-acetyl pentanedioic acid; 2-hydroxy-4-isopropyladipic acid; 3-acetyl hexanedioic acid; 3-hydroxyglutaric acid; 2-hydroxy-4,4-dimethylglutaric acid; 3-(2-hydroxy-ethyl)-2,2-dimethyl-cyclobutane-carboxylic acid; 2,3-dihydroxy-4-oxopentanoic acid and b-caryophyllinic acid, respectively.
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Fig. 3. Time series of factor contributions to ambient PM2.5 organic carbon in Midwestern United States from March 2004 through February 2005 (6-factor alternative case solution).
et al., 2008; Schauer et al., 1996). It should be noted that not all the factors specifically represent individual sources. They would be the average among the gradients of all the spatial and temporal observations, and may have minor POC tracers distributed in the SOC factors, and vice versa. Among all the six derived factors, three factors are identified as POA associated factors and three are identified as SOA factors. 3.2.1. POA factors POA factors are characterized with some primary source specific compounds, low contribution of SOA tracers, and relative high contribution to EC. The urban primary factor is characterized with large contribution of n-alkanoic acids, as well as evenly distributed
n-alkanes and some light PAHs associated with a combination of urban primary sources such as cooking emissions, mobile source exhaust and some combustion sources (Cass, 1998). It has little contribution from SOA tracers, but accounts for about 40% of EC contributions. Moreover, no significant seasonal trends (see Fig. 3) are observed. Accordingly, this factor is identified as urban primary sources, which has the largest average OA contributions compared with the other factors (Fig. 1). The next factor is dominated by hopanes and steranes, the tracers for vehicle exhausts (Cass, 1998), as well as PAHs and some lower levels of acids, and is identified as a mobile source-dominated factor. No major seasonal variances were observed; however, differences across cities were apparent. The rural site, Bondville, had a very low mobile source factor
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contribution (Fig. 3). The last POA factor, termed the ‘‘other combustion sources’’ factor, is predominately PAHs and high molecular weight n-alkanes. Like the mobile source factor, it contributes very little to OA in Bondville. Even in the alternative case, Northbrook was heavily influenced by this factor in January 2005. However, this factor has the smallest contributions to OA among the primary factors in several months. 3.2.2. SOA factors Three factors were identified as SOA factors (Figs. 2 and 3) based on their common properties: relatively high contribution of SOA tracers, low contribution of EC, and seasonal trends with peak contributions in the summer. One SOA factor contains high levels of isoprene products (2-methylglyceric acid, 2-methylthreitol and 2-methylerythritol) (Kleindienst et al., 2007) and has peak contributions in July and June at all sampling sites. This indicates high activity of secondary reactions and isoprene yield in the summer. The isoprene SOA factor contributes 0.3–1.0 mg m3 OC to the five cities, averaging 20% of the overall ambient OC concentration, and confirms previous results for isoprene-associated SOA (Zhang et al., 2007). The isoprene SOA factor also consists of many a-pinene oxidation products, thus it is not solely isoprene SOA. The second SOA factor, the a-pinene SOA factor, includes many a-pinene oxidation products, such as 3-(2-hydroxy-ethyl)-2,2-dimethylcyclobutane-carboxylic acid and 3-acetyl hexanedioic acid. Cincinnati, Northbrook and Bondville have relatively small contributions from the a-pinene SOA factor, while in Detroit and East St. Louis, the seasonal trends parallel isoprene SOA. The a-pinene SOA factor contributes on average 5% of the overall ambient OC. The last SOA factor, the b-caryophyllene SOA factor, is characterized by large contributions of b-caryophyllinic acid, the tracer for b-caryophyllene SOA (Jaoui et al., 2007). In addition, it has large contributions of pinic acid and pinonic acid, tracers for a-pinene SOA (Kleindienst et al., 2007), which means that all three SOA factors include contributions from a-pinene SOA. The b-caryophyllene SOA factor also includes n-alkanes and n-alkanoic acids, indicating covariance with some minor primary sources associated with vegetative detritus or some industrial sources. Fig. 3 illustrates that b-caryophyllene SOA has peak contributions in July in Detroit, but no major seasonal trends in the other four cities. The average contribution of b-caryophyllene SOA is 0.5 mg m3 OC, or 19% of the
overall ambient OC concentration on average. Fig. 2 also shown that there are some primary tracers distributed in the SOA profiles, which is common for most PMF results (Bullock et al., 2008; Jaeckels et al., 2007; Shrivastava et al., 2007). The relative contributions of key primary source tracers in the SOA profiles and the relative source contributions of POA and SOA suggest that the contributions of primary sources to the SOA source concentration estimates could be up to 10–20% of the SOA contributions. 3.3. Relative contributions of POA and SOA Monthly source apportionment results of all the five cities are listed in Table S1 in the supporting materials. Primary sources, on average, account for 57% of overall ambient OC concentrations, ranging from 44% in Bondville, the rural site, to 67% in East St. Louis. In contrast, secondary sources contribute an average of 43% of overall ambient OC, ranging from 29% in East St. Louis to 56% in Bondville. These results indicate a relatively even annual split between POA and SOA in the Midwestern US, with SOA likely dominant in rural areas and POA dominant in urban centers. Comparison of the individual SOA subgroup contributions demonstrates poor agreements between this study and the tracer yield method, which indicates that the SOA factors are not cleanly split across source types and that there is overlap across the SOA factors. Therefore, to evaluate the estimates of the relative contributions of POA and SOA from the current PMF model, all the secondary contributions were summed and compared with the results obtained via SOA tracer yield method derived from the same data set (Lewandowski et al., 2008). Fig. 4 shows the time series of SOC in all the cities computed via the two different methods, which displays good agreement in most months and locations. Statistical analysis demonstrates good correlation between the two methods except for a few months with high secondary contributions, such as July–September in Detroit and October in East St. Louis. However, these sites are likely to be biased by local industrial sources during these time periods. Previous studies have identified infrequent but very significant impacts of OC point sources in East St. Louis (Jaeckels et al., 2007). As shown in Fig. 5, linear regression between the two data sets results in a slope of 1.01 0.07 with an intercept of 0.19 0.10 mg OC m3. The adjusted R2 is 0.76 (n ¼ 58) which indicates that the two methods are highly consistent. Fig. 5 also
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Fig. 4. Time series of estimated secondary organic carbon concentrations for five sampling cities from March 2004 through February 2005.
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PMF SOC (μg m-3, This study)
Protection Agency, through its Office of Research and Development, funded and collaborated in the research described under Contract EP-D-05-065 to Alion Science and Technology. The manuscript has been subjected to Agency Review and approved for publication.
Slope: 1.01 +/- 0.07 Intercept: 0.19 +/- 0.10 Adjusted R2: 0.763 (n=58)
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Appendix. Supplementary data 2
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Tracer Yield SOC (μ μg m-3, Lewandowski et al., 2008) Fig. 5. Comparison of estimated secondary organic carbon concentrations for five sampling cities from March 2004 through February 2005: PMF apportionment versus tracer yield technique.
illustrates that when the secondary contributions are very low, the PMF method underestimates the secondary contributions compared with the tracer yield method, which may be because of the non-linearity of the yield curve or errors introduced by PMF due to the error structure of data at these very low levels, or the selected ‘‘alowlim’’ value (USEPA, 2005), 0.1 in this study, which may induce underestimation for some low contribution observations. A possible explanation for the large biases observed in Detroit, as well as the smaller biases in the other urban areas, are the impacts of local industrial sources. The industrial sources in Detroit are believed to emit significant amounts of carbonaceous particulate matter, which does not match well with either the primary or secondary source profiles and tends to lead to the redistribution of OC from these sources to sources as a function of low specificity tracers such as n-alkanoic acids. Such tracers are associated with both primary and secondary sources and appear to be important in the distribution of the industrial source emissions. Further work is needed to better understand the role of these industrial sources on source apportionment models. Primary contributions estimated in this study were used to estimate the POC/EC ratio using linear regression. Results show that the average POC/EC ratio for all the five cities is 2.26 0.39 with an intercept of 0.36 0.22 mg OC m3. For comparison, POC/EC ratios were reported as 2.4 in Los Angeles (Gray et al., 1986) and 2.2 0.2 in Los Angeles (Turpin and Huntzicker, 1995; Turpin et al., 1991), 1.7 for Los Angeles during winter morning traffic periods (Conklin et al., 1981), 2.4 0.35 in the San Joaquin Valley (Strader et al., 1999), and ranging from 0.9 to 3.1 in Pittsburgh (Cabada et al., 2004). Interestingly, the average POC/EC ratio in this study is similar to the general Los Angeles ratios; however there are too few locations presented in the literature to make any generalizations. PMF results appear to provide a method to separate regional POA and SOA and this approach should be integrated in future studies that allow the comparison of tools to distinguish POA and SOA for further evaluation. Acknowledgement The EC/OC and POA tracer measurements made in this study were made possible by funding from the Lake Michigan Air Directors Consortium, LADCO. The United States Environmental
A map of sampling sites and the PMF source apportionment monthly results of each location are shown in the supplemental material. Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atmosenv.2009.02.047. References Birch, M.E., Cary, R.A., 1996. Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol Science and Technology 25 (3), 221–241. Bullock, K.R., Duvall, R.M., Norris, G.A., McDow, S.R., Hays, M.D., 2008. Evaluation of the CMB and PMF models using organic molecular markers in fine particulate matter collected during the Pittsburgh Air Quality Study. Atmospheric Environment 42 (29), 6897–6904. Cabada, J.C., Pandis, S.N., Subramanian, R., Robinson, A.L., Polidori, A., Turpin, B., 2004. Estimating the secondary organic aerosol contribution to PM2.5 using the EC tracer method. Aerosol Science and Technology 38, 140–155. Cass, G.R., 1998. Organic molecular tracers for particulate air pollution sources. TrAC – Trends in Analytical Chemistry 17 (6), 356–366. Conklin, M.H., Cass, G.R., Chu, L.-C., Macias, E.S., 1981. In: Hopke, P.K. (Ed.), Atmospheric Aerosol: Source/Air Quality Relationships Macias ES. Chemical Society, Washington, DC, pp. 235–250. Edney, E.O., Kleindienst, T.E., Jaoui, M., Lewandowski, M., Offenberg, J.H., Wang, W., Claeys, M., 2005. Formation of 2-methyl tetrols and 2-methylglyceric acid in secondary organic aerosol from laboratory irradiated isoprene/NOx/SO2/air mixtures and their detection in ambient PM2.5 samples collected in the eastern United States. Atmospheric Environment 39 (29), 5281–5289. Facchini, M.C., Mircea, M., Fuzzi, S., Charlson, R.J., 1999. Cloud albedo enhancement by surface-active organic solutes in growing droplets. Nature 401 (6750), 257–259. Gray, H.A., Cass, G.R., Huntzicker, J.J., Heyerdahl, E.K., Rau, J.A., 1986. Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles. Environmental Science & Technology 20 (6), 580–589. Jacobson, M.Z., 2002. Control of fossil-fuel particulate black carbon and organic matter, possibly the most effective method of slowing global warming. Journal of Geophysical Research – Atmospheres 107 (D19). 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. Environmental Science & Technology 41 (16), 5763–5769. Jaoui, M., Leungsakul, S., Kamens, R.M., 2003. Gas and particle products distribution from the reaction of beta-caryophyllene with ozone. Journal of Atmospheric Chemistry 45 (3), 261–287. Jaoui, M., Lewandowski, M., Kleindienst, T.E., Offenberg, J.H., Edney, E.O., 2007. Betacaryophyllinic acid: an atmospheric tracer for beta-caryophyllene secondary organic aerosol. Geophysical Research Letters 34 (5). Kleindienst, T.E., Jaoui, M., Lewandowski, M., Offenberg, J.H., Lewis, C.W., Bhave, P.V., Edney, E.O., 2007. Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern US location. Atmospheric Environment 41 (37), 8288–8300. Lewandowski, M., Jaoui, M., Offenberg, J.H., Kleindienst, T.E., Edney, E.O., Sheesley, R.J., Schauer, J.J., 2008. Primary and secondary contributions to ambient PM in the Midwestern United States. Environmental Science & Technology 42 (9), 3303–3309. Lim, H.J., Turpin, B.J., 2002. Origins of primary and secondary organic aerosol in Atlanta: results of time-resolved measurements during the Atlanta supersite experiment. Environmental Science & Technology 36 (21), 4489–4496. Offenberg, J.H., Lewis, C.W., Lewandowski, M., Jaoui, M., Kleindienst, T.E., Edney, E.O., 2007. Contributions of toluene and a-pinene to SOA formed in an irradiated toluene/a-pinene/NOx/air mixture: comparison of results using 14C content and SOA organic tracer methods. Environmental Science & Technology 41 (11), 3972–3976. Paatero, P., 1997. Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems 37 (1), 23–35. Paatero, P., 1999. The multilinear engine – a table-driven, least squares program for solving multilinear problems, including the n-way parallel factor analysis model. Journal of Computational and Graphical Statistics 8 (4), 854–888. Paatero, P., Hopke, P.K., 2003. Discarding or downweighting high-noise variables in factor analytic models. Analytica Chimica Acta 490 (1–2), 277–289. Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H., 1992. Secondary organic aerosol formation and transport. Atmospheric Environment: Part A – General Topics 26 (13), 2269–2282. Pope, C.A., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D., Godleski, J.J., 2004. Cardiovascular mortality and long-term exposure to
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Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers YuanXun Zhang a, Rebecca J. Sheesley a, James J. Schauer a, *, Michael Lewandowski b, Mohammed Jaoui c, John H. Offenberg b, Tadeusz E. Kleindienst b, Edward O. Edney b a b c
Environmental Chemistry and Technology Program, University of Wisconsin–Madison, 660 North Park Street, Madison, WI 53706, USA National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA Alion Science and Technology, P.O. Box 12313, Research Triangle Park, NC 27709, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 October 2008 Received in revised form 13 February 2009 Accepted 17 February 2009
Monthly average ambient concentrations of more than eighty particle-phase organic compounds, as well as total organic carbon (OC) and elemental carbon (EC), were measured from March 2004 through February 2005 in five cities in the Midwestern United States. A multi-variant source apportionment receptor model, positive matrix factorization (PMF), was applied to explore the average source contributions to the five sampling sites using molecular markers for primary and secondary organic aerosols (POA, SOA). Using the molecular makers in the model, POA and SOA were estimated for each month at each site. Three POA factors were derived, which were dominated by primary molecular markers such as EC, hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs), and which represented the following POA sources: urban primary sources, mobile sources, and other combustion sources. The three POA sources accounted for 57% of total average ambient OC. Three factors, characterized by the presence of reaction products of isoprene, a-pinene and b-caryophyllene, and displaying distinct seasonal trends, were consistent with the characteristics of SOA. The SOA factors made up 43% of the total average measured OC. The PMF-derived results are in good agreement with estimated SOA concentrations obtained from SOA to tracer yield estimates obtained from smog chamber experiments. A linear regression comparing the smog chamber yield estimates and the PMF SOA contributions had a regression slope of 1.01 0.07 and an intercept of 0.19 0.10 mg OC m3 (adjusted R2 of 0.763, n ¼ 58). Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Source apportionment Molecular marker Primary organic aerosol Secondary organic aerosol
1. Introduction Organic aerosols (OAs) consist of thousands of organic compounds originating from a wide range of sources and atmospheric processes. Primary organic aerosols refer to the OA directly emitted from pollution sources. OA that formed in the atmosphere through the photochemical reaction of gas-phase precursors, known as secondary organic aerosol (SOA), is also a major contributor to carbonaceous particulate matter in many locations (Robinson et al., 2007; Turpin and Huntzicker, 1995), and has important influences on atmospheric physicochemical and biochemical properties, including radiative forcing, hydroscopicity and toxicity. SOA is an important contributor to air quality degradation, visibility degradation, climate forcing and adverse impacts on human health (Facchini et al., 1999; Jacobson, 2002; Pope et al., 2004; Tabazadeh, 2005). As a result, there has recently been a great
* Corresponding author. Tel.: þ1 608 262 4495; fax: þ1 608 262 0454. E-mail address: [email protected] (J.J. Schauer). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.02.047
interest in the development of methods to apportion sources of primary and secondary organic aerosols (Cabada et al., 2004; Pandis et al., 1992; Robinson et al., 2007; Strader et al., 1999; Turpin and Huntzicker, 1995). The elemental carbon (EC) tracer method is an important SOA estimation method developed by Turpin et al. (Cabada et al., 2004; Turpin and Huntzicker, 1995; Turpin et al., 1991). It is based on the hypothesis that the ratio of primary organic carbon (POC) to EC in POA is relatively constant, making EC a good tracer for POA. Using the EC tracer method, ambient secondary organic carbon (SOC) is calculated as atmospheric OC minus (POC/EC)*EC, where (POC/EC) is the OC/EC ratio of primary aerosols and EC is the concentration of EC. Previous studies have estimated atmospheric OC/EC ratios during periods when SOA is believed to be small, such as winter, peak morning traffic periods with low solar radiation, periods of intermittent drizzle, periods with low ozone concentrations (Conklin et al., 1981; Turpin and Huntzicker, 1995), or periods after rain episodes (Lim and Turpin, 2002). Ozone, carbon monoxide and nitrogen oxides are often selected as indicators to distinguish POC dominated episodes from the carbonaceous measurement time
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series (Cabada et al., 2004; Strader et al., 1999). Although the EC method has been successfully applied in many places, average primary OC/EC ratios have been shown to vary with location and over time. The molecular marker chemical mass balance (CMB) model has been used to estimate POA contributions using POA tracers for specific sources (Robinson et al., 2006b; Schauer et al., 1996). Although SOA source profiles have not been established, several studies have suggested that the unapportioned OC from molecular marker CMB models is a good estimate of SOA (Schauer et al., 1996; Stone et al., 2007). However, some neglected POA sources, which may occupy a fraction of the unapportioned OC, may introduce biases into the SOA estimation. Moreover, deviations of the input source profiles can also cause substantial uncertainties of SOA estimation (Robinson et al., 2006a). With the recent advances in the measurement of molecular markers for SOA (Edney et al., 2005; Jaoui et al., 2003, 2007; Kleindienst et al., 2007), progress has been made toward the development of methods to estimate SOA from key biogenic and anthropogenic SOA precursors (Lewandowski et al., 2008; Offenberg et al., 2007). The tracer-to-SOA mass fractions were obtained in atmospherically relevant smog chamber experiments (Kleindienst et al., 2007). Using these mass fractions and the measured ambient SOA tracer concentrations, the atmospheric SOA mass contributions can be estimated (Kleindienst et al., 2007; Lewandowski et al., 2008). This SOA yield method has been shown to be compatible with POA source contribution methods (Lewandowski et al., 2008). In this study, SOA tracers and POA tracers are used in a multivariant receptor model, positive matrix factorization (PMF), to examine sources of SOA and POA. By eliminating SOA laboratorybased mass fractions and POA source profiles, the PMF results provide an independent method to assess the contributions of SOA and POA and to compare these to existing tracer models. In the present study, more than eighty organic molecular markers, including the SOA tracers, were measured in five cities in the Midwestern US and were applied for PMF analysis. This is the first reported application of an integrated PMF model that uses primary and secondary organic tracers to apportion primary and secondary sources of organic aerosols. Likewise, the current study provides an independent comparison of SOA contribution estimates from smog chamber mass-fraction data obtained by Lewandowski et al. (2008). 2. Experimental and statistical methods 2.1. Sample collection and analysis The sample collection and chemical analyses used in the current study were previously described in detail by Lewandowski et al. (2008). During the sampling period from March 2004 through February 2005, 24-h ambient PM2.5 (particles less than 2.5 mm in aerodynamic diameter) samples were collected on the EPA 1 in 6 day sampling schedule in five cities in the Midwestern United States including one rural site, Bondville, Illinois, and four urban sites: Detroit, Michigan; Cincinnati, Ohio; Northbrook, Illinois; and East St. Louis, Illinois. OC and EC were measured using the thermal–optical method for each 24-h sample (Birch and Cary, 1996). Fig. S1 illustrates the average fine particle concentrations for these sites, which shows that OC levels in the urban sites are approximately twice the concentrations at the rural site. Samples were combined into monthly composites for each individual site to obtain monthly average atmospheric concentrations of the tracers for 12 months. Primary source tracers were analyzed as described by Sheesley et al. (2004). This approach involved spiking labeled internal
standards on the samples, Soxhlet extraction using 1:1 (v:v) methanol and dichloromethane, concentration, derivatization, and finally GC–MS analysis. Secondary molecular tracers were analyzed following the method as reported by Lewandowski et al. (2008). The samples were sonicated for 1 h using 50 mL 1:1 (v:v) methanol and dichloromethane. The extracts were then rotary evaporated to 1 mL, evaporated to dryness under a gentle stream of ultrapure nitrogen, and derivatized. The samples were then analyzed by GC– ITMS in the methane-CI mode (Kleindienst et al., 2007). 2.2. PMF procedures PMF assumes that concentrations at receptor sites are impacted by the linear combinations of source emissions, which are derived as factors in the model. Large data sets of daily 24-h elemental concentrations have historically been used in PMF models. In the present study, only 12 monthly average measurements were obtained at each site, which is not sufficient for a robust PMF analysis for each individual site. In the current study, we combined all the results from the 5 sites into one model analysis to derive organic aerosol factors that are common across all sites. It is expected that the SOA factors will have some common characteristics such as seasonal variations and SOA tracer distributions. Likewise, it is expected that profiles for mobile sources, biomass burning, and vegetative detritus would be similar for these Midwestern US sites. The presence of local point sources of organic aerosols may lead to poor separation of POA factors; however, the main objective of this study is to split primary and secondary source contributions, and to separate SOA sources. USEPA PMF1.1 was used for the current analysis. This version of the model determines signal-to-noise ratio (S/N) statistics for every input species and allows the user to downgrade the importance or remove species with small S/N values. Among the 85 species measured, 15 species were found to have S/N levels less than or equal to 0.2, and were removed from the computation. The removed species included anthraquinone; all dicarboxylic acids except succinic acid; isophthalic acid; benzenetri- and tetracarboxylic acids; methylphthalic acid; and heneicosanoic acid. Thirty-five species were considered ‘‘weak’’, with S/N ratios less than or equal to 1.0, whose uncertainties were increased by a factor of 3. These species included benzo[j]fluroanthene; perylene; dibenzo[ah]anthrance; picene; methylchrysene; R- and S-5a(H),14a(H),17b(H)-cholestane or ergostane; R-17a(H),21b(H)bishomohopane; trishomohopanes; C24–C36 n-alkanes; succinic acid; phthalic acid; terephthalic acid; and C20, C23–C26 and C28–C29 n-alkanoic acids. Total measured OC is included in the PMF data matrix. To verify the robustness and stability of the PMF model used in this study, PMF2 was used to repeat the analysis originally performed by PMF1.1 and the use of the FPEAK key was used to examine potential rotational ambiguities. The results of the two models yielded results that were in statistical agreement and no rotational ambiguities were found indicating that the model was indeed stable and robust. Detailed discussions about the PMF model have been previously reported in the literature (Jaeckels et al., 2007; Paatero, 1997, 1999; Paatero and Hopke, 2003; USEPA, 2005). 3. Results and discussion 3.1. Determination of PMF solutions A sensitivity analysis of the PMF model was conducted to examine the impact of the number of factors selected for the PMF computation. The 5–8-factor solutions were examined to assess the consistency of the solution with the current understanding of POA
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and SOA sources. The 5-factor solutions produced a factor dominated with n-alkanes and acids which also contained approximately 70% of ambient b-caryophyllinic acid, an atmospheric tracer for b-caryophyllene SOA (Jaoui et al., 2003, 2007). Additionally, this factor contributed more than 40% of ambient EC. Consequently, this factor was associated with both known POA and SOA tracers, suggesting that the 5-factor solution merged primary and secondary sources into a single factor. By increasing the number of allowed factors, the aforementioned factor was split into two factors: one dominated by n-alkanes and acids and contributing approximately 35% of ambient EC, while the other factor was the dominant source of b-caryophyllinic acid. Therefore, solutions having 6 or more factors probed primary and secondary aerosols better than the 5-factor case. The 7- and 8-factor solutions further split the factor associated with both POA and SOA in the 5-factor solution, and generated an isolated n-heptacosanoic acid dominated factor that did not represent any known sources. The 8-factor solution also had two factors with similar species distributions. In this case, the 5- and 8-factor solutions were eliminated from further consideration.
Factor contributions obtained from the 6- and 7-factor solutions are shown in Fig. 1 for each site along with an average across the four urban sites. Fig. 1 also presents the base and alternative cases; the base case included all monthly data, while the alternative case excludes the February and December measurements from Northbrook. These two samples from Northbrook displayed very high concentrations of heavy polycyclic aromatic hydrocarbons (PAHs, e.g., benzo[ghi]perylene and coronene, etc.) which are believed to be associated with local point sources and are not consistent with the measurements at the other sites. The removal of these two months mostly impacted the ‘‘other combustion sources’’ factor, which was the predominate source of the heavy PAHs. Eliminating these two months decreased the impact of this source for the other sites but did not significantly change the overall model results. As discussed previously, the 7-factor solutions generate an n-heptacosanoic acid dominated fatty acid factor which has no significant linkages with any known sources. Furthermore, this 7th factor is generated by splitting the urban primary source factor, with some contributions from SOA factors in Detroit and East St. Louis. This indicates that these two sites might have some different
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Fig. 1. Annual average factor contributions to organic carbon for 6- and 7-factor PMF solutions. Urban sites are Detroit, MI; Northbrook, IL; Cincinnati, OH; and East St. Louis, IL.
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pollution characteristics in comparison to the other three sites. Nevertheless, compound distribution of this fatty acid factor aligns with characteristics of POA with even n-alkane distributions, and approximately 30% EC was contributed by this factor. Since this 7th factor appears to mainly be POA and the base case is too sitespecific for Northbrook, the 6-factor alternative case was selected as the final solution for the purpose of SOA estimation.
3.2. Factor identification Factor compound distributions and the temporal patterns of the factors from the 6-factor alternative case solution are shown in Figs. 2 and 3, respectively, which were used for factor identification. Organic compounds and their potential sources have been reviewed previously (Cass, 1998; Jaeckels et al., 2007; Lewandowski
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Fig. 2. Distribution of molecular markers among factors: 6-factor alternative case solution. R and S represents configurations at C22 in hopanes > C31 or at C20 in steranes. abb or aaa represents 5a(H)14b(H)17b(H) or 5a(H)14a(H)17a(H) configurations in steranes, respectively. a, ba and ab represent 17a(H), 17b(H)21a(H) and 17a(H)21b(H) configurations in hopanes. The complete names of SOA tracers for 3-acetyl pentanedioic through b-caryophyllinic from left to right are 3-acetyl pentanedioic acid; 2-hydroxy-4-isopropyladipic acid; 3-acetyl hexanedioic acid; 3-hydroxyglutaric acid; 2-hydroxy-4,4-dimethylglutaric acid; 3-(2-hydroxy-ethyl)-2,2-dimethyl-cyclobutane-carboxylic acid; 2,3-dihydroxy-4-oxopentanoic acid and b-caryophyllinic acid, respectively.
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Fig. 3. Time series of factor contributions to ambient PM2.5 organic carbon in Midwestern United States from March 2004 through February 2005 (6-factor alternative case solution).
et al., 2008; Schauer et al., 1996). It should be noted that not all the factors specifically represent individual sources. They would be the average among the gradients of all the spatial and temporal observations, and may have minor POC tracers distributed in the SOC factors, and vice versa. Among all the six derived factors, three factors are identified as POA associated factors and three are identified as SOA factors. 3.2.1. POA factors POA factors are characterized with some primary source specific compounds, low contribution of SOA tracers, and relative high contribution to EC. The urban primary factor is characterized with large contribution of n-alkanoic acids, as well as evenly distributed
n-alkanes and some light PAHs associated with a combination of urban primary sources such as cooking emissions, mobile source exhaust and some combustion sources (Cass, 1998). It has little contribution from SOA tracers, but accounts for about 40% of EC contributions. Moreover, no significant seasonal trends (see Fig. 3) are observed. Accordingly, this factor is identified as urban primary sources, which has the largest average OA contributions compared with the other factors (Fig. 1). The next factor is dominated by hopanes and steranes, the tracers for vehicle exhausts (Cass, 1998), as well as PAHs and some lower levels of acids, and is identified as a mobile source-dominated factor. No major seasonal variances were observed; however, differences across cities were apparent. The rural site, Bondville, had a very low mobile source factor
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contribution (Fig. 3). The last POA factor, termed the ‘‘other combustion sources’’ factor, is predominately PAHs and high molecular weight n-alkanes. Like the mobile source factor, it contributes very little to OA in Bondville. Even in the alternative case, Northbrook was heavily influenced by this factor in January 2005. However, this factor has the smallest contributions to OA among the primary factors in several months. 3.2.2. SOA factors Three factors were identified as SOA factors (Figs. 2 and 3) based on their common properties: relatively high contribution of SOA tracers, low contribution of EC, and seasonal trends with peak contributions in the summer. One SOA factor contains high levels of isoprene products (2-methylglyceric acid, 2-methylthreitol and 2-methylerythritol) (Kleindienst et al., 2007) and has peak contributions in July and June at all sampling sites. This indicates high activity of secondary reactions and isoprene yield in the summer. The isoprene SOA factor contributes 0.3–1.0 mg m3 OC to the five cities, averaging 20% of the overall ambient OC concentration, and confirms previous results for isoprene-associated SOA (Zhang et al., 2007). The isoprene SOA factor also consists of many a-pinene oxidation products, thus it is not solely isoprene SOA. The second SOA factor, the a-pinene SOA factor, includes many a-pinene oxidation products, such as 3-(2-hydroxy-ethyl)-2,2-dimethylcyclobutane-carboxylic acid and 3-acetyl hexanedioic acid. Cincinnati, Northbrook and Bondville have relatively small contributions from the a-pinene SOA factor, while in Detroit and East St. Louis, the seasonal trends parallel isoprene SOA. The a-pinene SOA factor contributes on average 5% of the overall ambient OC. The last SOA factor, the b-caryophyllene SOA factor, is characterized by large contributions of b-caryophyllinic acid, the tracer for b-caryophyllene SOA (Jaoui et al., 2007). In addition, it has large contributions of pinic acid and pinonic acid, tracers for a-pinene SOA (Kleindienst et al., 2007), which means that all three SOA factors include contributions from a-pinene SOA. The b-caryophyllene SOA factor also includes n-alkanes and n-alkanoic acids, indicating covariance with some minor primary sources associated with vegetative detritus or some industrial sources. Fig. 3 illustrates that b-caryophyllene SOA has peak contributions in July in Detroit, but no major seasonal trends in the other four cities. The average contribution of b-caryophyllene SOA is 0.5 mg m3 OC, or 19% of the
overall ambient OC concentration on average. Fig. 2 also shown that there are some primary tracers distributed in the SOA profiles, which is common for most PMF results (Bullock et al., 2008; Jaeckels et al., 2007; Shrivastava et al., 2007). The relative contributions of key primary source tracers in the SOA profiles and the relative source contributions of POA and SOA suggest that the contributions of primary sources to the SOA source concentration estimates could be up to 10–20% of the SOA contributions. 3.3. Relative contributions of POA and SOA Monthly source apportionment results of all the five cities are listed in Table S1 in the supporting materials. Primary sources, on average, account for 57% of overall ambient OC concentrations, ranging from 44% in Bondville, the rural site, to 67% in East St. Louis. In contrast, secondary sources contribute an average of 43% of overall ambient OC, ranging from 29% in East St. Louis to 56% in Bondville. These results indicate a relatively even annual split between POA and SOA in the Midwestern US, with SOA likely dominant in rural areas and POA dominant in urban centers. Comparison of the individual SOA subgroup contributions demonstrates poor agreements between this study and the tracer yield method, which indicates that the SOA factors are not cleanly split across source types and that there is overlap across the SOA factors. Therefore, to evaluate the estimates of the relative contributions of POA and SOA from the current PMF model, all the secondary contributions were summed and compared with the results obtained via SOA tracer yield method derived from the same data set (Lewandowski et al., 2008). Fig. 4 shows the time series of SOC in all the cities computed via the two different methods, which displays good agreement in most months and locations. Statistical analysis demonstrates good correlation between the two methods except for a few months with high secondary contributions, such as July–September in Detroit and October in East St. Louis. However, these sites are likely to be biased by local industrial sources during these time periods. Previous studies have identified infrequent but very significant impacts of OC point sources in East St. Louis (Jaeckels et al., 2007). As shown in Fig. 5, linear regression between the two data sets results in a slope of 1.01 0.07 with an intercept of 0.19 0.10 mg OC m3. The adjusted R2 is 0.76 (n ¼ 58) which indicates that the two methods are highly consistent. Fig. 5 also
4
PMF SOC (This study)
3
2
East St. Louis
Northbrook
Bondville
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
Cincinnati
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
Mar Apr May Jun Jul Aug Sep Oct Nov Jan
Detroit
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
0
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
1
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
SOC Concentration (μg m-3)
Tracer Yield SOC (Lewandowski et al., 2008)
Average
Fig. 4. Time series of estimated secondary organic carbon concentrations for five sampling cities from March 2004 through February 2005.
Y. Zhang et al. / Atmospheric Environment 43 (2009) 5567–5574
PMF SOC (μg m-3, This study)
Protection Agency, through its Office of Research and Development, funded and collaborated in the research described under Contract EP-D-05-065 to Alion Science and Technology. The manuscript has been subjected to Agency Review and approved for publication.
Slope: 1.01 +/- 0.07 Intercept: 0.19 +/- 0.10 Adjusted R2: 0.763 (n=58)
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3
Appendix. Supplementary data 2
Detroit Cincinnati East St. Louis Northbrook Bondville
1
0
0
1
2
3
Tracer Yield SOC (μ μg m-3, Lewandowski et al., 2008) Fig. 5. Comparison of estimated secondary organic carbon concentrations for five sampling cities from March 2004 through February 2005: PMF apportionment versus tracer yield technique.
illustrates that when the secondary contributions are very low, the PMF method underestimates the secondary contributions compared with the tracer yield method, which may be because of the non-linearity of the yield curve or errors introduced by PMF due to the error structure of data at these very low levels, or the selected ‘‘alowlim’’ value (USEPA, 2005), 0.1 in this study, which may induce underestimation for some low contribution observations. A possible explanation for the large biases observed in Detroit, as well as the smaller biases in the other urban areas, are the impacts of local industrial sources. The industrial sources in Detroit are believed to emit significant amounts of carbonaceous particulate matter, which does not match well with either the primary or secondary source profiles and tends to lead to the redistribution of OC from these sources to sources as a function of low specificity tracers such as n-alkanoic acids. Such tracers are associated with both primary and secondary sources and appear to be important in the distribution of the industrial source emissions. Further work is needed to better understand the role of these industrial sources on source apportionment models. Primary contributions estimated in this study were used to estimate the POC/EC ratio using linear regression. Results show that the average POC/EC ratio for all the five cities is 2.26 0.39 with an intercept of 0.36 0.22 mg OC m3. For comparison, POC/EC ratios were reported as 2.4 in Los Angeles (Gray et al., 1986) and 2.2 0.2 in Los Angeles (Turpin and Huntzicker, 1995; Turpin et al., 1991), 1.7 for Los Angeles during winter morning traffic periods (Conklin et al., 1981), 2.4 0.35 in the San Joaquin Valley (Strader et al., 1999), and ranging from 0.9 to 3.1 in Pittsburgh (Cabada et al., 2004). Interestingly, the average POC/EC ratio in this study is similar to the general Los Angeles ratios; however there are too few locations presented in the literature to make any generalizations. PMF results appear to provide a method to separate regional POA and SOA and this approach should be integrated in future studies that allow the comparison of tools to distinguish POA and SOA for further evaluation. Acknowledgement The EC/OC and POA tracer measurements made in this study were made possible by funding from the Lake Michigan Air Directors Consortium, LADCO. The United States Environmental
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