Environ. Sci. Technol. 2003, 37, 5135-5142
Source Apportionment of PM2.5 at an Urban IMPROVE Site in Seattle, Washington NAYDENE N. MAYKUT Puget Sound Clean Air Agency, 110 Union Street, Seattle, Washington 98101 JOELLEN LEWTAS Human Exposure & Atmospheric Sciences Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 7411 Beach Drive East, Port Orchard, Washington 98366 EUGENE KIM† AND TIMOTHY V. LARSON* Department of Civil and Environmental Engineering, Box 352700 University of Washington, Seattle, Washington 98195
The multivariate receptor models Positive Matrix Factorization (PMF) and Unmix were used along with the EPA’s Chemical Mass Balance model to deduce the sources of PM2.5 at a centrally located urban site in Seattle, WA. A total of 289 filter samples were obtained with an IMPROVE sampler from 1996 through 1999 and were analyzed for 31 particulate elements including temperature-resolved fractions of the particulate organic and elemental carbon. All three receptor models predicted that the major sources of PM2.5 were vegetative burning (including wood stoves), mobile sources, and secondary particle formation with lesser contributions from resuspended soil and sea spray. The PMF and Unmix models were able to resolve a fuel oil combustion source as well as distinguish between diesel emissions and other mobile sources. In addition, the average source contribution estimates via PMF and Unmix agreed well with an existing emissions inventory. Using the temperature-resolved organic and elemental carbon fractions provided in the IMPROVE protocol, rather than the total organic and elemental carbon, allowed the Unmix model to separate diesel from other mobile sources. The PMF model was able to do this without the additional carbon species, relying on selected trace elements to distinguish the various combustion sources.
Introduction Receptor models have been widely used for estimating the source contributions of primary particles in urban areas (1). With an increased focus worldwide on regulating the sources of fine particles, there is a corresponding increased interest in the application of receptor models for apportioning the sources of PM2.5. Traditionally, the U.S. EPA has recommended using the effective variance-weighted chemical mass balance (CMB) receptor model (2, 3) in conjunction with emissions inventories for making these estimates. Recently, two alternative multivariate models, positive matrix factorization (PMF) and Unmix, have been applied to this problem * Corresponding author e-mail:
[email protected]. edu; phone: (206)685-3836; fax: (206)685-3836. † Present address: Department of Chemical Engineering, Clarkson University, Box 5705, Potsdam, NY 13699. 10.1021/es030370y CCC: $25.00 Published on Web 10/17/2003
2003 American Chemical Society
(4-14, 30, 31, 41, 43). In this paper, we compare all three approaches (CMB, PMF, and Unmix) on PM2.5 speciation data taken with the IMPROVE protocol at an urban site in Seattle, WA. The IMPROVE protocol includes temperatureresolved particulate organic and elemental carbon fractions not routinely used in source apportionment analysis. We explore whether including these additional carbon fractions in the PMF and Unmix models improves our ability to resolve the sources of combustion-generated particles.
Methods Sampling and Analysis. Our data set consisted of 24-h filter measurements collected Wednesdays and Saturdays at an urban site (Beacon Hill) from March 1996 to February 1999. The Beacon Hill site is centrally located within the Seattle urban area on a hilltop, 100 m above sea level (see Figures 1 and 2). The monitor is located within a water reservoir impoundment, southeast of the downtown business district. The area to the immediate north and east of the reservoir is residential. The hill is part of a larger ridge defining the eastern edge of an industrialized valley. The valley includes numerous warehousing facilities as well as a major containerized cargo port with marine diesel sources. Freeways carrying significant amounts of traffic are closely situated to the north and west of the site. The site is considered to be representative of 24-h average PM2.5 levels within a 20-km radius (15). A total of 289 PM2.5 filter sample sets (Teflon, nylon, and quartz filters sampling in parallel) were collected and analyzed using the IMPROVE protocols (16, 17). The filter samples were analyzed at the Crocker Nuclear Laboratory, University of California at Davis (UCD); Desert Research Institute (DRI); and Research Triangle Institute (RTI). The Teflon filter was analyzed at UCD for PM2.5 mass and for elements by proton elastic scattering (H), particle induced X-ray emission (Na to Mn), and X-ray fluorescence (Fe to Zr, Pb) as described by Cahill et al. (18). The denuded nylon filter was analyzed by RTI for nitrate, sulfate, and chloride using ion chromatography (19). Organic and elemental carbon were measured by DRI on the quartz filter via the Thermal/Optical Reflectance (TOR) method as described by Chow et al. (20). This protocol produces carbon thermograms with the following four organic carbon fractions: OC1 at 120 °C, OC2 at 250 °C, OC3 at 450 °C, and OC4 at 550 °C volatilized in a pure He atmosphere. After the OC4 response returns to a constant value, the pyrolyzed carbon (OP) is oxidized at 550 °C in a 2% oxygen/98% He mixture until the reflectance returns to its original value. Additional elemental carbon fractions are then measured at combustion temperatures of 550 °C (EC1), 700 °C (EC2), and 800 °C (EC3). The TOR method defines total organic carbon (OC) and total elemental carbon (EC) as follows: OC ) OC1 + OC2 + OC3 + OC4 + OP and EC ) EC1 + EC2 + EC3 - OP. Table 1 summarizes the particulate species concentrations observed during the 3-yr study period. There were no missing values in this data set.
Model Description The general receptor modeling problem (2, 21) can be stated in terms of the contribution from p independent sources to all chemical species in a given sample as follows: p
xij )
∑g
ik fkj
(1)
k)1
where for airborne particles xij is the jth species concentration (µg/m3) measured in the ith sample, gik is the particulate mass concentration (µg/m3) from the kth source contributing VOL. 37, NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Map of sampling site and surrounding area.
FIGURE 2. Photograph of the Duwamish Valley looking south from the observation deck on the 72nd floor of the Columbia Tower Building in downtown Seattle. to the ith sample, and fkj is the jth species mass fraction (µg/µg) from the kth source. The chemical mass balance model provides a weighted least-squares solution to eq 1 given prior knowledge of both fkj and xij. The weighting involves the uncertainties in the jth species in both fkj and xij (8). Equation 1 can also be solved without prior knowledge of fkj. PMF provides only one of an infinite number of possible solutions to eq 1, that is, only one of many combinations of the g and f matrices (22). PMF provides a solution that minimizes an object function, Q(E), based upon the value of 5136
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each observation and its corresponding uncertainty, subject to nonnegativity constraints (4). Specifically this function is defined as
[ ] p
n
Q(E) )
m
∑∑ i)1 j)1
xij -
∑ k)1
uij
2
gik fkj
(2)
where uij is an uncertainty estimate in the jth element measured in the ith sample. The results of PMF modeling
TABLE 1. Summary of Measured Particulate Species Concentrations (ng/m3) species mass EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP Al As Br SO4 Ca Cl Cr Cu Fe H K Mn Na Ni NO3 Pb Si Ti V Zn Rb Se Sr ClMg P S Zr
mean
median
min
max
8 860 7 520 1 910 33 370 1 080 790 170 7 680 65 50 3 260 18 15 2 100 460 322 14 3 430 620 495 126 2 880 1 000 770 180 5 600 800 610 160 5 150 42 3 0 1 870 71 51 6 700 1.2 0.9 0.2 5.9 2.8 2.4 0.2 9.5 1 270 1 100 130 4 350 43 36 11 220 344 187 3 2 560 3.4 2.9 0.9 13.4 3.8 2.0 0.5 158 78 56 12 459 360 280 90 1 720 66 49 14 1 670 9 6 1 91 320 240 17 2 330 1.8 1.2 0.2 12.9 550 480 52 2 400 7.7 4.7 1.1 78 85 62 7 950 7.9 6.5 0.8 31 7 5 1 41 13 9 2 61 0.3 0.2 0.1 0.9 0.6 0.4 0.1 5.6 0.8 0.5 0.1 24 190 87 0 1 460 7.8 0 0 350 0 0 0 7.7 510 450 52 1 660 0.05 0 0 0.68
BDLa % BDLb 0 0 130 205 27 0 0 0 211 166 141 2 20 1 205 127 1 0 0 0 89 10 56 20 0 3 74 97 0 189 81 39 122 260 288 0 254
0.0 0.0 45.0 70.9 9.3 0.0 0.0 0.0 73.0 57.4 48.8 0.7 6.9 0.3 70.9 43.9 0.3 0.0 0.0 0.0 30.8 3.5 19.4 6.9 0.0 1.0 25.6 33.6 0.0 65.4 28.0 13.5 42.2 90.0 99.7 0.0 87.9
a BDL, number of samples below the detection limit. b % BDL is based on a total of 289 samples.
are scaled to the measured mass concentration by a scaling constant, sk. Specifically p
xij )
∑(s g
k ik)
k)1
() fkj sk
(3)
where sk is determined by regressing measured total PM2.5 mass concentration against gik. The Unmix model provides another solution to eq 1 using a self-modeling curve resolution algorithm (5). This algorithm searches for “edges” in the data that define the fkj. These edges exist because some samples are devoid of contributions from at least one source or are dominated by contributions from one source for a particular group of species. The number and direction of the edges derived from Unmix depend on the set of species used. In contrast, the PMF solution is not as sensitive to the choice of input species. Unmix incorporates the algorithm “NUMFACT” that estimates the number of factors in the data using principal component analysis on randomly sampled subsets of the original data (23). In this way, Unmix can guide the choice of the number of factors used in PMF. Conversely, PMF can guide the choice of species used in Unmix. If a multivariate approach such as PMF or Unmix is used, these two models form a natural set of tools to apply to a given receptor modeling problem.
Model Implementation PMF Model. Because of a large fraction of below detection limit values, the following species were not used in the PMF
model: Mg, P, Zr, and OP carbon. Because of its higher analytical precision, we used chlorine via XRF rather than chloride ion even though chlorine was less frequently detected. In addition, we used sulfate ion rather than elemental sulfur although both analytes had comparable detection limits and analytical precision. For preparing the PMF model inputs, we followed the procedure of Polissar et al. (7) to assign overall uncertainties to each measurement. Specifically, the concentration values were used for the measured data, and the sum of the reported analytical uncertainty and one-third of the detection limit value was used as the overall uncertainty assigned to each measured value. Values below the detection limit were replaced by half of the detection limit values, and their overall uncertainties were set at five-sixths of the detection limit values. In the process of testing different uncertainties to find the most physically reasonable solutions, it was necessary to downweight the estimated uncertainty of below detection values by four times their initially assigned uncertainties. There were no missing data. To address the question of how many sources are resolvable by PMF, we examined the following statistical indices as a function of the number of extracted features: (a) the sum of squares of the residuals weighted inversely by the variation of the data points (Q(E) given by eq 2) (10, 11, 24); (b) the maximum individual column mean (IM) and the maximum individual column standard deviation (IS) of the residual matrix (9); (c) the largest number in the rotational matrix (LR) (9); and (d) the sum of the mass fractions of the derived features. The IM, IS, and LR statistical indices provided guidance on the number of features in the data. IM and IS usually show a noticeable decrease when the number of features reaches the appropriate value, whereas LR shows a sudden increase at this point (9). On the basis of these criteria, we considered models having between 5 and 10 sources. After comparing model results with existing source profiles, an eight-source model was finally chosen. Even if the number of features is correctly chosen, the PMF results have some rotational ambiguity (22). A userdefined parameter, FPEAK, allows some “fine-tuning” of the derived rotations (4, 9-11, 25, 42). To find the optimal PMF solution with the most physically reasonable results, it is necessary to run with different numbers of sources and different FPEAK values (Figure S1 in the Supporting Information shows the change of Q value with FPEAK for this eight-source model). A value of FPEAK ) 0.2 provided the most physically reasonable source profiles. In addition, the associated Q value is approximately equal to the number of elements in xij minus the number of elements in gik, implying that the standard deviations of the data have been wellcharacterized. The model was run in the default robust mode without removal of any of the seven potential outliers discussed in the next section. Finally, the global optimum of the eight-source model with FPEAK ) 0.2 was tested. Changing the seed of pseudo-random values for the PMF model produced nearly identical source contributions and source profiles. Unmix Model. Unmix Version 2.3 was provided by Dr. Gary Norris of the U.S. EPA National Exposure Research Laboratory. From the set of all measured species, the user must select that subset of input species that results in a feasible solution as defined by the model. To do this, we examined the plots of total mass versus species concentrations and chose an initial subset with well-defined edges (5). Seven measurements were removed from the original data set in order to produce sharp, consistent edges. Two potassium values were removed from the samples collected July 5, 1997, and July 4, 1998, that were unusually high and probably due to fireworks. One Pb value was similarly removed on July 4, 1998. The Al, Si, S, and Fe measurements VOL. 37, NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Species concentrations plotted against PM2.5. These data were used in the six-source Unmix solution. taken on April 29, 1998, were also removed. This turned out to be the day of maximum impact from an Asian dust event that simultaneously impacted other western U.S. cities, including Spokane, WA (26). Finally, we removed one carbon measurement, OC2, taken on December 17, 1997, that was an outlier from an otherwise well-defined edge. We have no obvious explanation for this latter, high value. From the edited data, we eventually obtained a six-factor solution using 15 species (H, Na, S, Si, Al, Fe, Ca, V, Ni, K, Pb, and several carbon fractions; OC2, OC3, OC4, and EC1) along with the Unmix default filter parameters (0.15, 0.25) and weighting parameters (0.15, 1). The minimum R2 was 0.72 with an estimated minimum signal-to-noise ratio of 2.01. A 6-factor solution was not feasible if we included the lowest temperature carbon fraction, OC1, or if we replaced S with SO42-. Figure 3 shows scatterplots of PM2.5 versus each of species used in the six-source Unmix solution (S is reported as equivalent SO42- for comparison with PMF). A given species was retained if it had a well-defined upper edge on these plots, indicative of the fact that each is an important component of at least one source (27). CMB Model. We used the EPA Chemical Mass Balance Model-Version 8. The sources used in the analysis were based upon those identified in an inventory of 1996 PM2.5 emissions in King County (28). The source profiles of the major source categories used for the majority of model runs are listed in Table S1 in the Supporting Information. These profiles are described in detail in ref 29. Separate profiles were included for secondary sulfate and nitrate-enriched sea salt. The vegetative burning profile most often used came from emissions tests in California while the motor vehicle profiles most often used came from Phoenix, AZ. Because of the collinearity of the diesel and gasoline mobile source profiles, we used a composite profile (PHRD) taken from 10 roadway samples in Phoenix. Due to a lack of adequate source profiles, the temperature-resolved fractions and the particulate hydrogen were not included in the CMB analysis. Although there are source test data of temperature-resolved carbon fractions reported for some of the combustion sources, there is not sufficient information available for all sources to use these carbon fractions in the CMB analysis. The standard CMB model fitting criteria were used for each sample: χ2 < 1, R2 > 0.8, and percent of attributable mass between 80 and 120%.
Results Derived Source Profiles. Figures 4 and 5 show the source profiles derived from the PMF and Unmix models, respec5138
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tively (the exact values of the mass fractions and their associated uncertainties for the four primary combustion sources are listed in Table S2 in the Supporting Information). Figure 4 compares the profiles derived by PMF from the data that includes only OC and EC (black bars) versus the profiles derived by PMF using the temperature-resolved carbon fractions (hatched bars). PMF produced similar profiles for most sources using either temperature-resolved fractions or simply OC and EC. In contrast, Unmix gave a five-source rather than a six-source solution unless selected temperatureresolved fractions (OC2, OC3, OC4, EC1) were used instead of OC and EC. When these fractions were included, Unmix resolved a “gasoline” and a “diesel” profile; when only OC and EC were used, Unmix could only resolve a single motor vehicle profile. In Figure 5, the six-source profiles derived from the temperature-resolved carbon data via Unmix (gray bars) are compared to the corresponding PMF profiles (hatched bars). The marine, soil and sulfate sources were readily identified by comparison of their profiles with those used in the CMB analysis (Table S1 in Supporting Information). The fuel oil profile is distinguished by the relatively high abundance of Ni and V as well as sulfate and EC, similar to previously reported PMF-derived profiles (7, 10, 13, 30). The vegetative burning profile is distinguished by the relatively high level of OC versus EC as well as the relatively low levels of all trace elements except K and is also similar to previously reported PMF-derived profiles (7, 10, 13, 14, 30). The diesel profile is similar to that reported in Phoenix (10, 31), distinguished by higher EC than OC and relatively high levels of both Mn and Fe. As discussed later, this source also shows higher weekday versus weekend source contributions as also seen in Phoenix (31). The motor vehicle/gasoline profile is identified by its high abundance of organic carbon. The motor vehicle/ gasoline profile is also similar to that found in Phoenix (10). The carbon fraction profiles derived from both PMF and Unmix analysis found EC1 to be a dominant feature of the source we identified as diesel, although EC1 was also significantly present in the vegetative burning and fuel oil profiles and to a lesser extent in the soil profile (Figure 5). Comparing the diesel and gasoline profiles between the two models, PMF predicts greater abundances of EC1 and Fe in diesel relative to gasoline, whereas Unmix predicts a greater abundance of OC4 in gasoline relative to diesel. In addition, the PMF model predicts a substantial amount of Mn in the diesel profile relative to the other combustion profiles (Figure 4), whereas Unmix does not accept Mn in its six-source
FIGURE 4. Comparison of source profiles derived from PMF. The hatched bars represent the profiles derived from the temperature-resolved OC and EC fractions; the black bars are the profiles derived using total OC and EC. solution. Similarly, PMF predicts an enrichment of As in the vegetative burning profile relative to other sources, whereas Unmix does not accept this species. Both models predict significantly greater abundances of both Ni and V in the fuel oil profile relative to other sources (Figure 5). Source Contribution Estimates (SCE). There are seasonal differences in the SCEs for some sources and not others (Figure S2 in the Supporting Information shows the individual SCE values predicted by PMF and Unmix plotted vs time). This is summarized in Figure 6 where the average PMFderived SCEs are plotted with their 95% confidence intervals as a function of both season and day of week. Recall that
samples were taken on Wednesdays and Saturdays. There is a marked seasonal effect for the vegetative burning, soil and secondary sulfate sources, and a strong day of the week effect for both the diesel and the vegetative burning sources. A similar pattern was observed for the Unmix-derived SCEs. Predicted versus measured PM2.5 mass is highly correlated for all three models (r > 0.94; see also Figure S3 in the Supporting Information). The average SCE during the study period for each source from all three models is listed in Table 2. In addition, we have included the percentage of the total PM2.5 mass estimated from the 1996 King County Emissions Inventory VOL. 37, NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 5. Comparison of Unmix (gray bars) and PMF (hatched bars) source profiles derived using temperature-resolved carbon fractions.
FIGURE 6. PMF-derived source contributions by season and day of the week. (28). The PMF and Unmix values are in better agreement with the inventory estimates than they are with the CMB estimates. One exception is the apportionment of PM2.5 to a number of smaller contributions from specific industrial source categories whose presence was deduced from the 5140
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local agency emission inventory for use in the CMB model. The PMF and Unmix models were unable to unambiguously resolve these relatively minor sources. For example, when a Kraft pulp mill profile was included in the CMB model, the model detected Kraft Mill impacts; subsequent wind analyses
TABLE 2. Estimated Average Source Contributionsa (µg/m3) source vegetative burning diesel vehicles gasoline vehicles mobile sources (gas + diesel)
CMB
Unmix
PMF
inventoryb
1.4 (16%)
3.3 (37%)
2.5 (28%)
28%
3.9 (44%)
1.7 (19%) 0.8 (9%) 2.5 (28%)
1.6 (18%) 0.4 (4%) 2.0 (22%)
18% 5% 23%
1.3 (15%)
0.9 (10%)
2%
fuel oil industryc
0.6 (7%)
soil
0.3 (4%)
0.5 (6%)
1.2 (14%)
marine (sea salt) Na rich secondary nitrate
0.6 (7%)
1.1 (12%)
0.3 (3%) 0.4 (5%)
secondary sulfate secondaryd
1.5 (17%)
3% 3%
0.4 (4%) 1.6 (18%) 34%
a
Values in parentheses are percent of predicted PM2.5 mass. b 1996 King County Emission Inventory (Puget Sound Clean Air Agency, 1996); primary emission percentages are adjusted for secondary contribution (see footnote d). c Industries include steel blast furnace, ferromanganese furnace, crude oil boiler, and Kraft paper mill. d The average percent contribution of sulfate, nitrate, marine, and Na-rich features for all samples as estimated by both PMF and CMB.
pointed directly to the three Kraft mills in the area (data not shown here). The PMF model produced a chemically similar feature that we named “Na-rich” because its source profile is very similar to not only a Kraft pulp mill enriched in sodium sulfate but also an aged marine aerosol enriched in sodium nitrate typical of urban coastal areas. To examine the extent to which certain species were acting as unique tracers of a given source, we compared the modelderived source contribution estimates on a given day with the concentration of measured species on that same day. In terms of the primary combustion sources, the PMF-derived estimates of vegetative burning, fuel oil, and diesel impacts are highly correlated with As, Ni, and Fe (r ) 0.84, 0.93, and 0.89, respectively; a more detailed summary of these correlations is found in Table S3 in the Supporting Information). The As correlation may be due to the burning of chemically treated lumber scraps in residential wood stovessAs is a major component of chromated copper arsenate wood preservative. In contrast, the Unmix-derived estimates show only the Ni/fuel oil correlation; the Fe correlation is split between gasoline and diesel, and the As correlation is diminished (r ) 0.66). It should be noted that As is not explicitly included in the Unmix model (when it is added, no feasible six-source solution can be found), that 49% of the As values are below the detection limit, and that, unlike PMF, Unmix does not include measurement uncertainties. There is a slightly higher correlation between the PMF-derived diesel contribution and the Fe in excess of that attributed to soil (r ) 0.92 for Feexcess; where Feexcess ) Fe - R[Si]; here R ) 0.41 based upon the PMF-derived source profile in Figure 4).
Discussion Including temperature resolved carbon fractions rather than the traditional OC/EC split in the PMF model does not appear to make any significant difference to the number and type of sources identified and only small differences to their respective contributions to PM2.5. The average contribution of vegetative burning to PM2.5 mass concentration decreased from 36% when only OC and EC were used to 28% when the carbon fractions were used. The contribution of diesel emissions increased from 15% to 18% and that of gasoline vehicle decreased from 5% to 4%. In contrast, the Unmix model was unable to separate the diesel and gasoline profiles unless several of the temperature resolved carbon fractions were used. One possible reason for this difference between models is that most of the PMF-derived SCEs are highly correlated with the concentration of at least one trace element. The PMF algorithm separates the sources without
depending strongly on the carbon measurements. In contrast the Unmix-derived SCEs are not strongly correlated with as many specific tracer species and therefore relies on the carbon fractions to separate the diesel and gasoline sources. PMF also creates a dedicated factor for secondary sulfate, whereas Unmix distributes the sulfur among several factors. This is shown in Figure 5 where all of the Unmix profiles have larger abundances of sulfate than the corresponding PMF profiles. In addition, the PMF-resolved sulfate factor has more accompanying carbon, especially OC3, OC4 and EC1, when the temperature-resolved fractions are used than when they are not (see Figure 4). Although the two mobile source categories appear to be the most difficult to clearly resolve by either model, there is good agreement between both models SCEs and the local emission inventory (see Table 2). The ratios of the average PM2.5 contributions of diesel relative to gasoline at this site are 4.0 and 2.1 for the PMF and Unmix models, respectively. This range of values is consistent with (a) the local emissions inventory (28) value of 3.6; (b) a nationally derived value of 2.3 for 1997 PM2.5 emissions (32); and (c) values of 3.2 in Pasadena and 3.0 in West Los Angeles derived from CMB using organic source markers (33). It should also be noted that the local emissions inventory includes a substantial offroad diesel contribution (about 50% of the total diesel emissions). In addition, the EC1 in soil implies a contribution to the soil contributions from resuspended road dust (Figure 6). The presence of substantial Mn and Fe in the derived diesel profile (both PMF and Unmix) is consistent not only with that found in Phoenix (10, 31) but also with the fact that these compounds are major components of diesel fuel additives. The marked decrease in the diesel contribution on Saturday versus Wednesday is due to the lower number of diesel vehicles on weekends versus weekdays (31). Even though poorly running spark ignition engines can potentially have emissions similar to diesel emissions, they would presumably not show this marked weekday/weekend difference. We also would like to compare our model-derived mobile source profiles directly with source (tailpipe) measurements. However, the availability of carbon source profiles using the TOR method is currently limited to project reports from DRI (34, 35) and motor vehicle inspection and maintenance and roadway data from Phoenix, AZ, collected primarily in January 1990 (36). The profiles for light-duty gasoline-fueled vehicle exhaust (PHAUTO), heavy-duty diesel-fueled vehicle exhaust VOL. 37, NO. 22, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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(PHDIES), and a roadside profile (PHRD) were constructed by averaging the results of several individual samples reported by Chow et al. (37). These profiles differ substantially from the organic carbon profiles we report here derived from multivariate analysis of ambient samples. Also the source test studies on diesel and gasoline vehicles differ from ambient samples in the proportion of EC in EC1 versus EC2. A recent CRC/EPA (34) study reported gasoline vehicle EC profiles that were similar to the PMF profiles reported here. This same study found that for diesel EC2 was the dominant form of EC. These differences between source and ambient profiles for carbon may be due to sampling artifacts (38-40), analysis artifacts (44), and/or actual changes that occur as organic compounds react as they travel from the tailpipe to the receptor. The total carbon collected in source sampling studies using quartz filters exceeds the PM2.5 mass collected on Teflon filters (34), suggesting that adsorbed low molecular weight OC (e.g., OC1) may also contribute to this problem. The major difference between the CMB and the multivariate (PMF and Unmix) model predictions is the relative contribution of vegetative burning versus motor vehicles. This difference may be due, in part, to a lack of locally available source profiles for use in the CMB model (the vegetative burning and mobile source profiles used in the CMB model were based on measurements in California and Arizona, respectively), and, as mentioned earlier, the fact that we cannot use the temperature- resolved carbon fractions (or the hydrogen values) that are available to the PMF and Unmix models. Despite this, there is relatively good agreement between the PMF and CMB-derived secondary aerosol mass. However, the lack of temperature-resolved carbon source profiles for use in the CMB model might be a reason for its inability to separate gasoline from diesel sources (these source profiles are considered collinear in the model when using the traditional OC/EC split).
Acknowledgments This paper has been reviewed in accordance with the U.S. Environmental Protection Agency’s peer and administrative review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. The U.S. EPA partially funded the research described here under a contract with Puget Sound Clean Air Agency. Part of this study was funded by the UW/EPA Northwest Research Center for Particulate Air Pollution and Health (R827355).
Supporting Information Available Three tables and three figures. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review February 14, 2003. Revised manuscript received August 11, 2003. Accepted August 26, 2003. ES030370Y
