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Source Apportionment of Molecular Markers and Organic Aerosols1. Polycyclic Aromatic Hydrocarbons and Methodology for Data Visualization A L L E N L . R O B I N S O N , * ,† R . S U B R A M A N I A N , †,⊥ NEIL M. DONAHUE,‡ ANNA BERNARDO-BRICKER,§ AND WOLFGANG F. ROGGE§ Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, Department of Chemistry and Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, Department of Divil and Environmental Engineering, Florida International University, Miami, Florida, Department of Civil and Environmental Engineering, Florida International University, Miami, Florida
Individual organic compounds often referred to as molecular markers are used in conjunction with the chemical mass balance (CMB) model to apportion sources of primary organic aerosol. This paper presents a methodology to visualize molecular marker data; it allows comparison of ambient data and source profiles and allows assessment of chemical stability and aging. The method is intended to complement traditional quantitative source apportionment analysis. The core of the technique involves construction of plots of ratios of species concentrations (ratio-ratio plots) in which source profiles appear as points connected by linear mixing lines. The approach is illustrated using data collected over a 1-year period in Pittsburgh, Pennsylvania. The analysis considers for elemental carbon and a number of high molecular weight polycyclic aromatic hydrocarbons (PAHs) commonly used as molecular markers in CMB: benzo(b+j+k)fluoranthene, benzo(e)pyrene, benzo[g,h,i]perylene, coronene, and indeno(1,2,3-cd)pyrene. In Pittsburgh, the ambient concentrations of these PAHs are higher than in other cities in the United States; they are also strongly correlated consistent with a single, dominant source. Both ratioratio plots and CMB analysis indicate that this source is metallurgical coke production. Although emissions from coke production dominate ambient PAH concentrations, on most study days they contributed little fine particle mass. Ratio-ratio plots are then used to investigate the feasibility of using PAHs to help differentiate between * Corresponding author phone: (412) 268-3657; fax: (412) 2683348; e-mail:
[email protected]. † Department of Mechanical Engineering, Carnegie Mellon University. ‡ Departments of Chemistry and Chemical Engineering, Carnegie Mellon University. § Florida International University. ⊥ Current address: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801. 10.1021/es0510414 CCC: $33.50 Published on Web 11/15/2006
2006 American Chemical Society
gasoline and diesel vehicle emissions. Ambient concentrations of these large PAHs provide little information on the gasoline-diesel split because of the strong influence of local emissions from coke production combined with evidence of photochemical decay of PAHs in the regional air mass. Decay of PAHs will bias estimates of the gasolinediesel split toward diesel emissions.
Introduction Molecular markers are individual organic species used in conjunction with the chemical mass balance (CMB) model to identify sources of primary organic carbon (OC) and fine particle mass (1). Examples of molecular markers include hopanes for vehicular exhaust (2) and levoglucosan for biomass smoke (3). Molecular markers have a high degree of source specificity which makes them attractive for use in source apportionment analysis. The CMB model estimates source contributions by solving a system of equations in which the ambient concentrations of specific chemical constituents are described using a linear combination of source profiles (4). To help the analyst assess the quality of the solution, CMB calculates a number of fitting statistics that describe (a) how well the sources included in the model can explain the ambient data and (b) whether the separation of the sources is statistically significant given the set of fitting species (4). However, different combinations of source profiles and/or fitting species can produce solutions with excellent statistics but significantly different, and even physically unrealistic, results (5-8). This indicates that one or more of the assumptions underlying the model are being violated. Critical assumptions include that the source profiles are representative of the emissions influencing the receptor site, all sources of the marker compounds are included in the model, and the species are chemically stable (4). These assumptions are difficult to evaluate; furthermore, the fitting statistics calculated by CMB do not account for the validity of all of these assumptions. The basic set of molecular markers and sources used in CMB analysis was developed in Southern California (1, 9). Using emission inventories, source oriented models, and other techniques, this research examined a number of the assumptions underlying the CMB approach. More recently, the basic set of markers and source classes developed in Los Angeles has been used for CMB analysis in other locations, implicitly assuming that the assumptions underlying the CMB approach are valid (8, 10, 11). However, these locations often have very different source mixtures and climates, and therefore, present unique challenges. For example, there may be unexpected sources of certain markers. Regional pollutant transport mixes emissions over a large spatial domain, greatly expanding the number of sources influencing a receptor site which complicates identification of source profiles. Regional transport also increases the time available for photochemical processing of the emissions. Linear receptor models such as CMB assume that molecular markers are chemically stable; therefore, photochemical decay of the reduced organic species used as molecular markers is potentially a very challenging problem. Another issue is selecting source profiles. Given the substantial resources required for source characterization, source profiles are often taken from the literature and not developed specifically for the region of interest. For important source classes such as motor vehicles or wood combustion, multiple source profiles with speciated organics data have been published. However, these profiles often exhibit VOL. 40, NO. 24, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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significant differences. Colinearity limits the number of source profiles that can be simultaneously included in the model, forcing the analyst to choose among the available profiles. CMB assumes that each profile represents the aggregate emissions from a given source class. Source classes are typically made up of a very large number of individual sources, while the published source profiles are based on emission tests of a single or at best a small number of individual sources. Previous studies have created composite profiles to represent the aggregate emissions of a source class (1). Factor-analysis-based approaches such as UNMIX and PMF offer the possibility to recover directly “unknown” source profiles from ambient data. These are complex and powerful mathematical tools with numerous underlying assumptions and limitations (12). They require large datasets, and therefore, have not been applied using molecular markers. They reveal substantial information about the temporal correlation of pollutant concentrations but not necessarily specific sources. Finally, they describe pollutant concentrations using a linear combination of factors, and therefore, do not account for photochemical decay. This paper is the first in a series of papers that examines the use of CMB with molecular markers to apportion ambient organic aerosol to primary sources. The papers utilize a large dataset of ambient molecular marker concentrations recently developed as part of the Pittsburgh Air Quality Study (13). The unprecedented size of the dataset provides a unique opportunity to critically evaluate the use of molecular markers for source apportionment. Each paper seeks to answer a set of questions for small groups of compounds associated with specific source classes (e.g., levoglucosan, resin acids, and syringols as markers for biomass combustion). Are the ambient molecular marker data organized in a fashion that implies the existence of a well-defined source profile or set of profiles? Which published profiles or combinations of published profiles can explain the ambient data? How well is the amount of ambient OC apportioned to a source class constrained by CMB analysis given the set of viable profile combinations? Is there evidence of photochemical oxidation of molecular markers? To help address these questions, this paper presents a methodology to visualize ambient molecular marker data, to qualitatively evaluate mixing of emissions from different sources, and to assess possible photochemical aging. This technique is used in conjunction with the CMB model to provide a more complete picture of the source apportionment results. To illustrate the approach, we investigate ambient concentrations of a number of high molecular weight polycyclic aromatic hydrocarbons (PAHs). PAHs provide an excellent case study because they are emitted by multiple source classessmotor vehicles, biomass combustion, and industrial processessand multiple source profiles are available for each source class. There is also strong evidence for atmospheric decay of PAHs (14-16). Companion papers apply this approach to molecular marker data for meat cooking emissions (17), biomass smoke (18), and motor vehicle emissions (6). There is considerable merit to examining the PAH data beyond simply illustrating the visualization methodology. Some PAHs are carcinogenic and, therefore, have been the focus of significant source apportionment research (19-21). In the context of CMB modeling of ambient OC and fine particulate matter, PAHs have been used in conjunction with EC to differentiate between emissions of gasoline and diesel vehicles (22, 23). This paper explicitly apportions PAHs to sources, considers the suitability of using PAHs to determine 7804
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FIGURE 1. Scatter and ratio-ratio plots of simulated ambient data (indicated by open circles). The simulated data are based on mixing of emissions from up to three different sources: S1, S2, and S3. Plots (a) and (d) are a one-source scenario; plots (b) and (e) are a twosource scenario; and plots (c) and (f) are a three-source scenario. The arrow in (a) indicates how ambient ratios will change with photochemical aging, as discussed in the text. the gasoline-diesel split, and examines the potential effects of photochemical aging on the source contribution estimates.
Methodology for Visualizing Ambient Data and Source Profiles In this section we present a graphical approach to visualize molecular marker data. The approach uses ratio-ratio plots, which are scatter plots of ratios constructed with data for three speciesstwo target compounds whose concentrations are normalized by the same reference compound. Ambient data and source profiles are displayed on ratio-ratio plots. PAH ratios are commonly used as source indicators (24); here, we extend this basic idea to consider, explicitly, the mixing of emissions from different sources, photochemical aging, and additional molecular markers. Simultaneous consideration of mixing and aging is especially important for receptor sites where regional emissions dominate ambient concentrations, such as in Pittsburgh or other locations in the Eastern U.S. The mathematics underlying the ratio approach are described in Robinson et al. (7). The statistical quality of a CMB solution depends on how well a linear combination of source profiles describes the ambient concentrations of a set of fitting species. For CMB analysis with molecular markers, a small number of organic species (typically of order 20), EC, and certain elements are used as fitting species (1). Since CMB simultaneously considers multiple species, the quality of the fit ultimately depends on the relative distribution of the set of marker species in both the source profiles and ambient data. The ratio-ratio approach allows the analyst to visually compare the distribution of critical marker species in the ambient data to both individual source profiles and linear combinations of multiple profiles, essentially plotting a cross section of the CMB solution space. Although each ratio-ratio plot only considers three species, molecular markers are highly source specific, which means that the CMB source contribution estimate for a given source is largely determined by a small subset of the species considered in the model. Therefore, only one or two ratio-ratio plots are typically needed to visualize the critical set of markers for a particular source. To interpret ratio-ratio plots, one must understand how mixing of emissions from multiple sources (source-source mixing) and photochemical aging influence the organization of the ambient data. Figure 1 shows a set of ratio-ratio plots
constructed with synthetic data to help develop this understanding. First we consider the effects of source-source mixing. The “ambient” ratios of the three hypothetical compounds (A, B, C) shown in Figure 1 are calculated by linear combinations of emissions from up to three different source classes (S1, S2, S3). Each source class has a distinctive source profile with characteristic emission ratios of the three compounds. Source profiles appear as individual points on a ratio-ratio plot and linear mixing lines can be drawn between different profiles (7). Ratio-ratio plots often use logarithmic axes to visualize the wide dynamic range of source profiles; linear mixing lines may appear curved on a log-log plot, so we typically draw them to assist with visualization and interpretation. Figure 1 illustrates how well organized ratio-ratio plots can provide significant insight into the source apportionment problem. Ambient data that cluster to a point on a ratioratio plot can be described by a single source profile. This implies the existence of single dominant source class, as in Figure 1a, or potentially multiple sources with fixed source strengths. Ambient data that organize along a line in a ratioratio plot indicate the existence of two sources with varying source strengths. In this situation the ambient data fall on the mixing line between the two source profiles, as in Figure 1b. Finally, ambient data can appear scattered on a ratio plot. This occurs when the compounds are emitted by three or more source classes with varying strengths. However, the ambient data are still expected to be contained within a mixing region defined by the source profiles, as illustrated in Figure 1c. Photochemical oxidation also influences the organization of ambient data in ratio-ratio plots (7, 25). Briefly, oxidation causes systematic changes in the ambient ratios with increasing photochemical age. For example, if there is a single source of a set of compounds then photochemical decay will cause the ambient data to be distributed along a line in a ratio-ratio plot emanating from the source profile. The line drawn in Figure 1a illustrates the classic scenario used in photochemical age calculations in which the least reactive species is used as the reference compound. In this scenario, ambient data distribute along a diagonal that extends toward the lower-left-hand corner of the plot. The more oxidation the further the ambient ratios will be from the source profile. Combining photochemical decay with mixing of emissions from two or more sources can create disorganized ratioratio plots (7). Ratio-ratio plots allow visual identification of inconsistencies between the ambient data and source profiles. By allowing the analyst to visually compare the ambient data with the entire set of potential profiles, ratio-ratio plots enable rapid identification of viable profile combinations for quantitative CMB analysis. If the ambient data fall outside of the mixing lines created by the source profiles then there could be problems with the source profiles, an unknown source of the compounds, or the compounds could have different atmospheric lifetimes (e.g., one species is being oxidized). Under these conditions, linear source inversions such as CMB will fail. Unfortunately, ratio-ratio plots by themselves (or any other technique that we are aware of) will not tell you if the problem is a missing source photochemistry, or some combination of both. If the problem is a missing source, the organization of the ambient data in the ratioratio plot does provide insight into the composition of the emission of a possible unknown source; its source profile must be located such that it defines a mixing region which encapsulates the ambient data. One must be particularly careful interpreting seasonal patterns in ambient data. For example, ambient data that shifts seasonally along a line in a ratio-ratio plot could indicate seasonally varying source strengths or photochemical aging (7).
Ratio-ratio plots are also useful for comparing source profiles, especially for source classes for which multiple source profiles exist. If all of the profiles for a given source class cluster in a ratio-ratio plot, the statistical quality of the CMB solution will not depend strongly on which profile is used in the model. Alternatively, different profiles for the same source class may scatter in a ratio-ratio plot, causing statistical quality of the CMB solution to depend strongly on which profile is selected. Ratio-ratio plots can also provide insight into how individual source profiles can be combined to create an aggregate profile that is consistent with the ambient data. A key to constructing ratio-ratio plots is to select groups of compounds whose ambient concentrations are dominated by emissions from one or two sources (source-specific sets of compounds). This is possible because molecular markers are often highly source specific; for example, ambient hopanes concentrations are thought to be dominated by motor vehicle emissions (6); ambient levoglucosan, resin acids, and syringols concentrations by biomass smoke emissions (18); or ambient cholesterol, palmitoleic, and oleic acid concentrations by meat cooking emissions (17). To gain a thorough understanding of the CMB solution, one needs to construct a series of ratio-ratio plots with these sourcespecific sets of compounds. CMB analysis assumes that welldefined, stable source profiles exist for each source class; therefore, if this assumption is correct, we expect sourcespecific sets of compounds to produce well organized ratioratio plots. Poorly organized ratio-ratio plots of sourcespecific sets of compounds indicate problems associated with unidentified sources, temporally varying source profiles, or photochemistry. Simple scatter plots of ambient concentrations, often referred to as “edge” plots, are an alternative approach for visualizing ambient data (26). To illustrate some of the advantages of ratio-ratio plots, Figure 1 shows a set of edge plots constructed from the previously discussed set of synthetic data. On an edge plot a source profile appears as a straight line with a slope corresponding to the ratio of the emission rates of the two compounds; therefore, ambient data that organize along a line point to the existence of a single source class (Figure 1d). However, compounds emitted by two or more source classes will create scattered and, therefore, more difficult to interpret edge plots (Figures 1e and f). In contrast, the two-source scenario results in a wellorganized ratio-ratio plot (Figure 1b) because ratio-ratio plots include a third dimension of information. Photochemical aging does not create distinctive patterns in edge plots. Finally, ratio-ratio plots allow the analyst to visually compare multiple profiles for a given source class to help guide development of aggregate source profiles. It is more difficult to compare a large number of source profiles in an edge plot (see, e.g., Figure 2b). One advantage of edge plots is that they compare absolute concentrations and, therefore, emphasize the high concentration data. Measurement uncertainty must be considered to correctly interpret ratio-ratio plots. There are two issues that complicate uncertainty analysis. First, uncertainties of ambient concentrations of individual compounds measured using the same instrumentation are often correlated; for example, errors associated with measuring sample flow rate are the same for all species collected on the same filter. The result is that the actual uncertainty of ratios is often smaller than estimates based on the uncertainty of the individual species and simple error propagation that assumes uncorrelated errors. The magnitude of the benefit associated with correlated errors is illustrated by an analysis of parallel samples collected during the Pittsburgh Air Quality Study. For nonpolar compounds, such as PAHs and n-alkanes, the benefit is small. VOL. 40, NO. 24, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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estimate and, therefore, cannot be simply attributed to measurement uncertainty.
Results
FIGURE 2. Scatter plots of different PAHs. The dashed lines in (a) are linear regressions (R2 > 0.95). The lines in (b) are different source profiles taken from the literature (23, 28-39). The coke production source profile was measured as part of a recent fenceline study in the Pittsburgh region (41). Error bars indicate measurement uncertainty of selected points. Uncertainty of low concentration and coronene data is comparable to the marker size. For example, the actual average relative uncertainty of ratios of PAHs is (9% versus (10% if the errors are assumed to be uncorrelated; for ratios of n-alkanes the same comparison is (17% versus (20%. A much greater benefit is observed if one considers ratios of polar compounds. For example, the actual average relative uncertainty of ratios of n-alkanoic acids is (23% versus (34% if the errors are assumed to be uncorrelated. We attribute this substantial reduction to the fact that the PAQS data did not include an internal standard to track derivitization efficiency; therefore, ratios of polar compounds are substantially more precise than expected because they account for sample-to-sample variation in this efficiency. When comparing data from different studies, ratios may provide a substantial advantage because of cancellation of errors associated with systematic differences in analytical procedures. This potential benefit cannot be evaluated at this time because results from laboratory intercomparisons of organic speciation have not been published. The second complication when considering measurement uncertainty is the fact that a single reference compound is used to normalize the concentrations of the two target compounds. Uncertainty in the reference compound will cause the data to be scattered more along the diagonal and less parallel to the axes. Quantitative accounting for both correlated errors and a single reference compound requires some subtle error analysis, which is not warranted given the qualitative use of ratio-ratio plots as a visualization tool. In this paper, we calculated ratio uncertainties by propagating uncorrelated errors and do not account for the fact that we use a single reference compound. This should provide a reasonable upper-bound uncertainty estimate. As illustrated in this and companion papers, the features in the Pittsburgh ambient data are often orders of magnitude larger than this 7806
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To illustrate the use of scatter and ratio-ratio plots as visualization tools, we examine the contribution of different sources to ambient concentrations of EC and a number of high molecular weight PAHs in Pittsburgh, PA: benzo(b+j+k)fluoranthene, benzo(e)pyrene, benzo[g,h,i]perylene, coronene, and indeno(1,2,3-cd)pyrene. These compounds primarily exist in the particle phase and have been previously used as molecular markers in CMB analysis of fine particulate matter. Unless otherwise noted, “PAHs” refers specifically to this specific set of compounds and not to the broader compound class. Ambient PAH and EC concentrations were measured on 96 days between July, 2001 and June, 2002 in an urban park in Pittsburgh (13). Daily measurements were made in July 2001 and most of January 2002; during other periods, 24-hr samples were collected on a 1-in-6-day schedule. The PAH measurements were made using a medium volume quartz-PUF sampler. The samples were solvent-extracted and analyzed using gas chromatographymass spectrometry. Additional details on the dataset are provided in the Supporting Information. First we consider simple scatter plots of data to display the range of measured PAH concentrations in Pittsburgh. Figure 2a plots several PAHs versus indeno[1,2,3-cd]pyrene. On most days, the ambient PAH concentrations are less than a few ng/m3; however, greatly elevated concentrations were intermittently observed in non-winter months (AprilNovember). On average, PAH concentrations in Pittsburgh are comparable to those measured in Birmingham, AL (11), but 2-6 times higher than those measured in Atlanta, GA, Pensacola, FL, Gulfport, MS, and Houston, TX (11, 27). Figure 2a reveals that the ambient PAH concentrations are strongly correlated (R2 > 0.95); only coronene and benzo[g,h,i]perylene show a small, but statistically significant, intercept. Strong linear correlations exist among all 16 threering and larger PAHs in the Pittsburgh dataset (the average R2 value is almost 0.9). These strong correlations suggest a single dominant source for these compounds. Removing the high concentration days from the dataset does not alter the strong correlations among the PAHs. Given the large number of source profiles with PAH data, it is difficult to compare ambient data to source profiles using scatter plots. This is illustrated in Figure 2b, which plots ambient concentrations of benzo[e]pyrene versus indeno[1,2,3-cd]pyrene and a large number of source profiles (23, 28-39). The ambient concentrations can be explained by combining any two profiles that bracket the data; however, the large number of viable source profile combinations makes it difficult to develop scenarios for CMB analysis. Ratio-ratio plots provide a clearer picture of the ambient data; Figure 3 presents one possible PAH ratio-ratio plot: indeno(1,2,3-cd)pyrene and benzo(g,h,i)perylene normalized by benzo(e)pyrene. As expected from the strong correlations shown in Figure 2a, the ambient PAH data tightly cluster to a point in Figure 3 consistent with a single dominant source. A large number of source profiles are also plotted in Figure 3. The ambient distribution of the three PAHs is similar to the metallurgical coke production source profile and a number of the biomass smoke profiles. Notably, the ambient PAH data cannot be explained by the mixing of gasoline and diesel emissions. For example, the indeno(1,2,3-cd)pyreneto-benzo(e)pyrene ratio of many of gasoline vehicle profiles is similar to the ambient data, but these profiles are substantially enriched in benzo(g,h,i)perylene compared to the ambient data. Many of the biomass smoke source profiles cluster together in Figure 3, as do many of the gasoline vehicle source profiles, indicating that the PAHs emitted by these
FIGURE 3. Ratio-ratio plot comparing ambient PAH to source profiles. The lines indicate the slopes of linear regressions shown in Figure 2a and the gray areas indicate fifth and ninety-fifth percentiles of the data. Source profiles are from refs 23, 28-39. Error bars are shown for a limited number of points to indicate typical level of measurement uncertainty. Profiles plotted on the y-axis and/or x-axis do not report indeno(1,2,3-cd)pyrene and/or benzo(g,h,i) perylene emissions or have emission ratios outside the range of the plot. source classes have characteristic distributions. This clustering supports averaging profiles to create aggregate profiles for a given source class. Additional ratio-ratio plots can be constructed, similar to the one shown in Figure 3, using different PAHs. These plots (not shown) exhibit essentially the same pattern as the one shown in Figure 3, the ambient data cluster to a point that corresponds to the coke production profile. A CMB analysis was performed on the Pittsburgh data to apportion molecular markers, OC, and fine particulate matter to primary sources. The analysis is similar to previous applications of CMB with molecular marker data (1). The model considers four PAHs: benzo[e]pyrene, indeno[1,2,3cd]pyrene, benzo[g,h,i]perylene, and coronene. Additional species in the model are four n-alkanes, iso-hentriacontane, anteiso-dotriacontane, four hopanes, two alkanoic acids, palmitoleic acid, cholesterol, levoglucosan, resin acids, syringaldehyde, acetosyringone, titanium, iron, and elemental carbon. Source profiles for eight source classes are included in the model: diesel vehicles, gasoline vehicles, road dust, biomass combustion, cooking emissions, coke production, vegetative detritus, and cigarettes. Additional details of the CMB calculations are provided in the Supporting Information. The CMB model apportions more than 80% of the four PAHs on all study days to coke production. This is not unexpected given the clustering of the ambient data around the coke production source profile in ratio-ratio plots, such as the one shown in Figure 3. There are two large metallurgical coke production facilities located within 15 km of the monitoring site, one to the northwest and the other to the southeast. One of these facilities is the largest in North America, and both facilities are old and routinely fined for emission violations. Although the CMB analysis only explicitly considered four PAHs, coke production is likely the dominant source for many other PAHs in Pittsburgh given the strong linear correlation among the different PAHs. Recent source apportionment studies performed have attributed the majority of these PAHs in urban areas to either road dust (21) or motor vehicle emissions (20). These studies were performed in other cities and coke production is a relatively unique source. In addition, the ambient data considered here were collected more recently, so the vehicle
fleet was somewhat newer than that of previous studies. Interestingly, there is also a major coke production facility located in Birmingham, AL, another city in the United States with high PAH levels comparable to Pittsburgh (11). Although coke production is the dominant source of these PAHs in Pittsburgh, on most days it is only a minor source of OC and fine particle mass. CMB estimates a median daily contribution of coke production emissions of 40 ( 5 ng-C m-3 to ambient OC and 100 ( 11 ng m-3 to PM2.5 mass. On 18% of the study days, the CMB model apportions more than 200 ng-C m-3 of ambient OC to coke production with a maximum daily contribution of 1570 ( 180 ng-C m-3. Its maximum daily contribution to ambient PM2.5 mass is 3920 ( 440 ng m-3. These spikes occurred on days when PAH concentrations were high and the meteorology indicated a local inversion and/or transport from one of the local coke production facilities. If one excludes the highest concentration days and certain PAHs from the CMB model, combinations of other, noncoke profiles can describe the ambient PAH data within the performance criteria for CMB. However, the source contribution estimates from these solutions to both PAHs and fine particulate matter are often very different than those that include the coke production source profile. For example, they often estimate a significantly larger contribution of gasoline vehicle emissions. This underscores the importance of including all major sources in the CMB model. Coke production is a source that has not been previously considered in CMB analysis with molecular markers, even in locations where they are present (11). Gasoline-Diesel Split. PAHs in combination with EC have been used to differentiate between gasoline and diesel vehicle fine particle emissions (22, 23). Diesel vehicles are the dominant source of EC in urban environments while gasoline vehicle emissions are significantly enriched in certain PAHs compared to diesel emissions. The feasibility of separating gasoline and diesel emissions using PAHs and EC can be evaluated using the ratio-ratio plots shown in Figure 4, which present the indeno[1,2,3-cd]pyrene and benzho[g,h,i]perylene data normalized by EC. The ambient PAH and EC concentrations are only weakly correlated as the PAH are dominated by coke oven emissions; this causes the data to be distributed along the diagonal in the ratio-ratio plot. The error bars in Figure 4 indicate that the variation in the ambient data is much larger than the measurement uncertainty. To understand potential source contributions, Figure 4 also compares the ambient PAH-to-EC ratios to a number of source profiles. The biomass smoke profiles have a similar distribution of PAH-to-EC ratios as the ambient data indicating that different mixtures of biomass smoke can explain both the ambient PAH and EC data. However, biomass smoke is not a major source in Pittsburgh (18). The diesel profiles, with relatively low PAH-to-EC ratios, and the coke production profile, with a relatively high PAH-to-EC ratio, bracket the ambient data. Finally, the gasoline profiles are either depleted in indeno[1,2,3-cd]pyrene or enriched in benzho[g,h,i]perylene compared to the ambient data. To eliminate any complications due to wood smoke, Figure 4b shows a PAH-to-EC ratio-ratio plot of data on days when ambient concentrations of wood smoke markers (levoglucosan, resin acids, syringols) were low. On these days motor vehicles and coke production are expected to be the dominant sources of PAH and EC. Mixing lines are drawn in Figure 4b to illustrate how different vehicle fleets influence the data. Each line is based on different combinations of the average gasoline, average diesel, and coke production source profiles. The average profiles are simple arithmetic averages of the published source profiles for each source class (profiles that do not report for certain species, e.g., diesel profiles with no indeno[1,2,3-cd]pyrene, are included in the average VOL. 40, NO. 24, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Ratio-ratio plots focused on using PAH and EC data to determine the gasoline-diesel split: (a) complete ambient dataset; (b) 64 days with low biomass smoke marker concentrations. The stars indicate average emission profiles. The lines in (b) indicate mixing scenarios as described in the text, and the filled symbols in (b) indicate mixtures in which coke production contributes 1 and 5% of the OC from vehicle emissions plus coke production. Profiles plotted on the y-axis and/or x-axis do not report indeno(1,2,3-cd)pyrene and/or benzo(g,h,i)perylene emissions or have emission ratios outside of the range of the plot. Source profiles are from refs 23, 28-39. Error bars are shown for a limited number of points to indicate typical level of measurement uncertainty. as a zero). The mixing lines represent four of the many possible mixing scenarios. The two solid lines indicate 100% gasoline or 100% diesel emissions mixed with coke production emissions. The two dashed lines are mixtures of emissions from coke production with emissions from two different mixed-vehicle fleets. The composition of the mixed-vehicle fleet is defined by the fraction of motor vehicle OC contributed by each vehicle type (in a 2:1 diesel:gasoline fleet, diesel vehicles emit twice as much OC as gasoline vehicles). The relative contribution of coke production varies along each line with the lower and upper ends of each line corresponding to coke production contributing 0 and 100% of the combined OC from these two sources, respectively. Figure 4b indicates that the mixing line connecting the average diesel and the coke production source profiles passes through the entire ambient data set. Therefore, these two sources can describe the ambient EC and PAH data. However, other mixing lines collapse along the diagonal defined by the ambient data when coke production contributes just 5% of the combined OC from vehicle emissions and coke production, regardless of the vehicle fleet composition. For 7808
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example, Figure 4b indicates that ambient data with PAHto-EC ratios greater than 1 can correspond to vehicle fleets ranging from 100% diesel to a 2:1 diesel-gasoline split. This occurs because emissions from coke production are much richer in PAHs compared to the other sources, which severely limits the feasibility of using the PAHs in combination with EC to specify the gasoline-diesel split. Figure 4b does suggest that on a reasonable number of days diesel emissions dominate gasoline emissions because a large fraction of the data with low PAH-to-EC ratios falls below even the 2:1 diesel:gasoline mixing line. Notably there is not even a hint of spread toward the average gasoline source profile in Figure 4b, suggesting minimal contribution of gasoline vehicles to ambient OC on many study days. However, this conclusion is based on the assumption that PAHs are stable in the atmosphere. Photochemical Aging and Regional Transport. PAHs are known to photochemically decay (14-16) while EC is chemically stable (except for deposition, which we assume affects PAHs and EC equally). Therefore, photochemical degradation of PAHs will reduce the PAH-to-EC ratio in the ambient air compared to fresh emissions. In this section we examine the ambient data for evidence of photochemical aging of PAHs and discuss implications of this aging on source contribution estimates. Assuming constant source strengths, photochemical aging should create a seasonal pattern in the ambient data, PAHto-EC ratios are expected to be lower in the summer than in the winter due to seasonal changes in photochemical activity. We examined the ambient data for such a pattern; however, the analysis is complicated by the intermittent large spikes in PAH concentrations. These spikes are a clear sign of variable source strengths, which is to be expected since the contribution of the coke plants is strongly dependent on local meteorology. If one removes the occasional day with large spikes in PAH concentrations from the dataset then the PAH-to-EC ratios measured at the Pittsburgh site exhibit a strong seasonal pattern consistent with aging. For example, median summer PAH-to-EC ratios are roughly a factor of 2 lower than median winter PAH-to-EC ratios. Another important consideration when assessing the effects of photochemical aging is the spatial distribution of sources. This is especially important in locations like Pittsburgh that are strongly influenced by regional transport. Photochemistry will have a much larger effect on PAHs in the regional air mass compared to those emitted locally because of the differences in transport times. Therefore, PAHto-EC ratios are expected to be lower in the regional air mass than in locations strongly influenced by local emissions. PAH concentrations in the regional air mass were measured at a rural site in Florence, PA, 40 km west-southwest of Pittsburgh (13). Thirteen paired sets of samples for organic speciation were collected from at the Florence and Pittsburgh sites during January 2002 and four paired sets during July 2002. Florence is almost always upwind of the city and fine particle mass and bulk composition measured at the Pittsburgh, Florence and other sites in Southwestern PA are very similar, underscoring the regional character of the fine particulate matter (40). Figure 5 presents a ratio-ratio plot that compares the data collected simultaneously in Florence and Pittsburgh. The summertime PAH-to-EC ratios in Florence are smaller than those measured in Pittsburgh, particularly on two of the days. The ratios on these two days are comparable to the lowest values observed in Pittsburgh during the entire study. During the wintertime, the PAH-to-EC ratios in Pittsburgh and Florence are essentially the same, with a range of values indicated by the arrow in Figure 5. The winter PAH-to-EC ratios in Florence are, on average, a factor of 4 higher than the summer ratio. All of these features are consistent with
Discussion
FIGURE 5. Ratio-ratio plot comparing Pittsburgh data and regional background air measured at an upwind site in Florence, PA. Stars are the average source profiles shown in Figure 4. The dashed lines correspond to the mixing scenarios shown in Figure 4b. The heavy solid line indicates mixing between the coke production source profile and aged regional background air, as described in the text. Only the summer data are shown; winter data distribute along the same line as summer data over the range indicated by labeled bar. The 13 winter data points are not plotted explicitly, because they overlap each other. photochemical degradation of PAHs in the regional air mass during the summer. An interesting feature of the Florence data plotted in Figure 5 is that it falls on the 100% diesel-coke production mixing line. This suggests that emissions from diesel vehicles dominate the gasoline-diesel split in the regional mass and that coke production emissions also contribute to PAH concentrations. There is a large coke production facility in Follansbee, WV, which is roughly 25 km west of the Florence site. The presence of a coke facility upwind of Florence potentially complicates the interpretation of the PAH data from the site. For example, the seasonal pattern in the Florence data could be due to a stronger wintertime influence of the Follansbee coke production facility. This seems unlikely given the large number of wintertime data points combined with the available meteorology data - on some sampling days the wind direction measured at the Florence site does not point toward the Follansbee plant. Photochemical degradation has important implications for using PAHs to help infer the gasoline-diesel split. As previously discussed, one interpretation of Figure 4b is that on many days the gasoline-diesel split is dominated by diesel emissions. However, the organization of the ambient data is also entirely consistent with the mixing of relatively fresh emissions from coke production with photochemically aged, PAH-depleted emissions in the regional air mass. Such mixing will disperse the ambient data along a diagonal line extending from the coke production source profile toward the lowerleft-hand corner of the ratio-ratio plotsexactly as the heavy, solid mixing line shown in Figure 5. This mixing line connects the coke production source profile and the Florence data with the lowest PAH-to-EC ratio (a reasonable estimate of photochemically aged regional air). Therefore, photochemical decay of PAHs will bias estimates of the gasoline-diesel split based on PAH data toward diesel emissions. The extent of this bias cannot be determined from the ratio-ratio plots, but it is a significant concern in Pittsburgh and other locations dominated by regional transport. In addition, photochemical decay will also reduce CMB estimates of coke production emissions to ambient OC and fine particle mass.
This paper illustrates the use of ratio-ratio plots to investigate the effects of source-source mixing and photochemical aging on ambient molecular marker concentrations. By comparing the ambient data to source-source mixing lines one can gain substantial insight into potential CMB solutions. In the PAH example, they revealed how a potentially unexpected source, metallurgical coke production emissions, exert a significant influence on the ambient PAH concentrations, greatly diminishing the feasibility of using the PAH to help specify the gasoline-diesel split. The organization of the PAH data also points to significant photochemical decay of the PAHs in the region air mass, biasing the gasoline-diesel split toward diesel emissions. The companion papers in this series consider other source-specific sets of molecular markers (6, 17, 18). Each paper illustrates different strengths and challenges of using molecular markers for source apportionment and provides additional case studies of the ratio-ratio approach. Ambient data of the cooking markers (cholesterol, palmitoleic acid, oleic acid, palmitc acid, and stearic acid) form reasonably well-organized ratio-ratio plots, implying the existence of a well-defined source profile (17). However, the data do not agree with any known profiles creating challenges for CMB analysis. Motor vehicle markers (hopanes and EC) also form well-organized ratio-ratio plots, but the data exhibit a distinct seasonal pattern (6). This seasonality causes unexpected shifts in the gasoline-diesel split and, therefore, points to photochemical aging. Unlike molecular markers for other primary sources, the biomass burning markers (levoglucosan, syringols, and resin acids) are not well organized in the ratioratio plots (18). Therefore, biomass burning is an example of a source class without a distinct source profile. A consistent theme throughout the other papers is how source profile variability can be the main source of error in CMB analyses. The ratio-ratio approach is intended to be used in conjunction with existing quantitative source apportionment models such as CMB or PMF. It is not a standalone source apportionment tool. The value added by the ratio-ratio approach is that it allows the analyst to visualize a portion of the solution space without making any assumptions regarding source contributions or chemistry. Although the Pittsburgh dataset is quite large, the approach can be used even if only a small dataset is available.
Acknowledgments This research was conducted as part of the Pittsburgh Air Quality Study, which was supported by the U.S. Environmental Protection Agency under contract R82806101 and the U.S. Department of Energy, National Energy Technology Laboratory under contract DE-FC26-01.NT41017. This research was also supported by the EPA STAR program through the National Center for Environmental Research (NCER) under grant R832162. This paper has not been subject to EPA’s required peer and policy review, and therefore does not necessarily reflect the views of the Agency. No official endorsement should be inferred.
Supporting Information Available Additional details of the sample data set and CMB calculations, This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review June 2, 2005. Revised manuscript received August 29, 2006. Accepted September 11, 2006. ES0510414
