JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D09204, doi:10.1029/2003JD004199, 2004
Improving source identification of fine particles in a rural northeastern U.S. area utilizing temperature-resolved carbon fractions Eugene Kim Department of Civil and Environmental Engineering, Clarkson University, Potsdam, New York, USA
Philip K. Hopke Department of Chemical Engineering, Clarkson University, Potsdam, New York, USA Received 1 October 2003; revised 21 January 2004; accepted 5 February 2004; published 15 May 2004.
[1] Integrated, 24-hour, ambient PM2.5 (particulate matter 2.5 mm in aerodynamic
diameter) samples were collected at a rural monitoring site in Brigantine, New Jersey, on Wednesdays and Saturdays using Interagency Monitoring of Protected Visual Environments (IMPROVE) samplers. Particulate carbon was analyzed using the thermal optical reflectance method, which divides carbon into four organic carbon (OC), pyrolyzed organic carbon (OP), and three elemental carbon (EC) fractions. A total of 910 samples and 36 variables collected between March 1992 and May 2001 were analyzed using positive matrix factorization, and 11 sources were identified: sulfate-rich secondary aerosol I (48%), gasoline vehicle (13%), aged sea salt (8%), sulfate-rich secondary aerosol II (7%), nitrate-rich secondary aerosol (6%), sulfate-rich secondary aerosol III (5%), sea salt (4%), airborne soil (4%), diesel emission (3%), incinerator (2%), and oil combustion (1%). Temperature-resolved carbon fractions enhanced source separations including three sulfate-rich secondary aerosols and two traffic-related sources that had different abundances of carbon fractions different between sources. Conditional probability functions using surface wind data and deduced source contributions aid in the identification of local sources. Potential source contribution function (PSCF) analysis shows the regional influence of sulfate-rich secondary aerosols. Backward trajectories indicate that the highly elevated airborne soil impacts at INDEX the monitoring site were likely caused by either Asian or Sahara dust storms. TERMS: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0345 Atmospheric Composition and Structure: Pollution—urban and regional (0305); 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation; 4801 Oceanography: Biological and Chemical: Aerosols (0305); KEYWORDS: thermal optical method, carbon fraction, positive matrix factorization, source apportionment, conditional probability function, dust storm Citation: Kim, E., and P. K. Hopke (2004), Improving source identification of fine particles in a rural northeastern U.S. area utilizing temperature-resolved carbon fractions, J. Geophys. Res., 109, D09204, doi:10.1029/2003JD004199.
1. Introduction [2] Many air quality and epidemiology studies have been undertaken since the U.S. Environmental Protection Agency promulgated new national ambient air quality standards for airborne particulate matter [Clarke et al., 2000; Laden et al., 2000; Sarnat et al., 2000]. As part of such studies, advanced source apportionment methods for the airborne particulate matter (PM) are required to understand the relationship between source emissions and human exposure. Also, advanced source apportionment methods for the airborne PM will assist in state implementation plan development by providing data analysis tools for identifying and apportioning airborne PM sources. Positive matrix factorization (PMF) [Paatero, 1997] has been shown to be a powerful Copyright 2004 by the American Geophysical Union. 0148-0227/04/2003JD004199
alternative to traditional receptor models for airborne PM [Huang et al., 1999; Willis, 2000; Qin et al., 2002]. [3] PMF has been used to assess particle source contributions in many studies that utilized total carbon, black carbon, or two carbon fraction (organic carbon and elemental carbon) measurements [Polissar et al., 2001b; Song et al., 2001; Kim et al., 2003a]. In these previous analyses of ambient PM2.5 (particulate matter 2.5 mm in aerodynamic diameter) compositional data, PMF could not always successfully resolve the carbonaceous particle sources, especially traffic-related combustion sources. These sources were extracted as a mixture of traffic sources or as a carbon-rich source combined with others because they had similar chemical profiles and temporal emission patterns were similar. [4] Because the particles from traffic-related combustion sources are mostly carbonaceous material [Watson et al., 1994; Lowenthal et al., 1994], additional compositional data
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Figure 1. Location of the IMPROVE monitoring site in Brigantine, New Jersey. for the carbonaceous particles are required to improve source separation for the traffic-related sources. The Interagency Monitoring of Protected Visual Environments/Thermal Optical Reflectance (IMPROVE/TOR) protocol has been used to analyze carbon mass in the ambient particles [Chow et al., 1993, 2001]. Using these methods, it is possible to separate several individual carbon types including organic and elemental carbon fractions and the pyrolyzed organic carbon with different time-temperature steps. The PM2.5 speciation data, including eight temperatureresolved carbon fractions, were successfully analyzed for the Atlanta and Seattle aerosol studies, in which PMF identified four and two different traffic sources, respectively [Kim et al., 2003b; Maykut et al., 2003]. [5] The objectives of this study are to identify PM2.5 sources, especially traffic-related carbonaceous sources, and estimate their contributions to PM2.5 mass concentrations. In the present study, PMF was applied to an ambient PM2.5 compositional data set of 24-hour integrated samples including eight individual carbon fractions collected during a 10-year period at the IMPROVE monitoring site in Brigantine, New Jersey. The resolved PM2.5 particle sources and their seasonal trends are discussed. The conditional probability function was calculated to help identify the likely locations of the PMF identified sources. The results of this study were compared with the results of previous Brigantine, New Jersey, studies [Song et al., 2001; Lee et al., 2002] that included the two combined organic carbon (OC) and elemental carbon (EC) fractions.
2. Experiment 2.1. Sample Collection and Chemical Analysis [6] The PM2.5 samples analyzed in this study were collected on Wednesdays and Saturdays at the IMPROVE [Malm et al., 1994] monitoring site located in Brigantine National Wildlife Refuge, New Jersey, as shown in Figure 1. This monitoring site is located near Atlantic Ocean, 12 km northwest of Atlantic City, 90 km southeast of Philadelphia, and 150 km south of New York City. Highways are closely
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situated to the north, south, and west of the monitoring site. Integrated 24-hour PM2.5 samples were collected on Teflon, Nylon, and quartz filters. The Teflon filter was used for mass concentrations and analyzed via particle-induced X-ray emission (PIXE) for Na to Mn, X-ray fluorescence (XRF) for Fe to Pb, and proton elastic scattering analysis (PESA) for elemental hydrogen concentration (University of California at Davis, California). The Nylon filter was analyzed via ion chromatography (IC) for sulfate (SO=4 ), nitrate (NO 3 ), nitrite (NO2 ), and chloride (Cl ) (Research Triangle Institute, North Carolina). [7] The quartz filter was analyzed via the IMPROVE/ TOR protocol [Chow et al., 1993] for eight temperatureresolved carbon fractions (Desert Research Institute, Nevada). This protocol volatilizes organic carbon by four temperature steps in a helium atmosphere: OC1 at 120C, OC2 at 250C, OC3 at 450C, and OC4 at 550C. After OC4 response returns to baseline or a constant value, the pyrolyzed organic carbon (OP) is oxidized at 550C in a mixture of 2% oxygen and 98% helium atmosphere prior to the return of reflectance to its original value. Then three elemental carbon fractions are measured in an oxidizing atmosphere: EC1 at 550C, EC2 at 700C, and EC3 at 850C. Wind direction and speed were measured hourly at the Atlantic City International Airport. [8] Samples for which PM2.5 mass concentrations were not available or for which all eight carbon fractions were not available were excluded from this analysis. Samples in which the PM2.5 mass concentration error flag was not ‘‘NM’’ (normal) were also excluded in this study. Totally, 2.5% of the original data were excluded. XRF sulfur and SO=4 showed excellent correlations (slope = 3.3 ± 0.03, r2 = 0.92), so it is reasonable to exclude SO=4 from the analysis. NH+4 was not included in this study because all values were missing. The reported EC1 concentration in IMPROVE/TOR protocol includes the OP concentration. In this study, the OP was subtracted from EC1 and utilized as an independent variable in the PMF analysis. Thus a total of 910 samples collected between March 1992 and May 2001 and 36 species were used in this study. A summary of PM2.5 speciation data used in this study is shown in Table 1. 2.2. Data Analysis [9] The general receptor modeling problem can be stated in terms of the contribution from p independent sources to all chemical species in a given sample as follows [Miller et al., 1972; Hopke, 1985]: xij ¼
p X
gik fkj þ eij ;
ð1Þ
k¼1
where xij is the jth species concentration measured in the ith sample, gik is the particulate mass concentration from the kth source contributing to the ith sample, fkj is the jth species mass fraction from the kth source, eij is residual associated with the jth species concentration measured in the ith sample, and p is the total number of independent sources. As pointed out by Henry [1987], there are an infinite number of possible solutions to the factor analysis problem because of the free rotation of matrices. To decrease rotational freedom, PMF uses nonnegativity constraints on
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Table 1. Summary of PM2.5 and 35 Species Mass Concentrations Used for PMF Analysis Concentration, ng/m3 Species
Geometric Meana
Arithmetic Mean
Minimum
Maximum
Number of BDL Values (Percent)b %
Number of Missing Values (Percent)
PM2.5 OC1 OC2 OC3 OC4 OP EC1 EC2 EC3 S NO2 NO3 Al As Br Ca Cl ClCr Cu Fe H K Mg Mn Na Ni P Pb Rb Se Si Sr Ti V Zn Zr
9669 109 275 383 427 153 351 56 15 1101 17 637 42 0.65 3.0 26 284 77 1.9 1.3 30 406 39 56 2.2 229 1.3 13 3.0 0.25 0.96 72 0.23 6.1 4.1 7.3 0.2
11240 161 343 502 533 229 447 72 19 1370 19 880 59 0.76 3.5 30 542 206 2.2 1.7 38 487 45 76 2.7 338 1.7 16 3.9 0.30 1.2 97.2 0.38 8.9 4.9 9.5 0.28
1168 1.5 24 4.5 59 3.0 16 3.0 1.5 146 0.10 54 5.1 0.11 0.35 3.3 3.2 0.20 0.36 0.15 1.2 57 8.0 8.2 0.32 12 0.09 2.9 0.18 0.01 0.06 3.3 0.06 0.87 0.52 0.39 0.03
60550 1436 1584 4418 3105 2141 2070 248 156 8023 141 7018 932 3.7 15.4 293 2288 2682 10 42 415 2583 313 342 12 2512 12 32 18 4.0 6.8 1687 3.3 60 24 54 1.8
0 426 (46.8) 53 (5.8) 40 (4.4) 0 178 (19.5) 17 (1.9) 215 (23.6) 621 (68.2) 0 800 (87.8) 0 468 (51.4) 327 (35.9) 1 (0.1) 3 (0.3) 829 (91.0) 360 (39.5) 654 (71.8) 73 (8.0) 0 0 0 753 (82.7) 490 (53.8) 107 (11.7) 84 (9.2) 902 (99.2) 22 (2.4) 639 (70.1) 23 (2.5) 9 (1.0) 204 (22.4) 209 (22.9) 238 (26.1) 2 (0.2) 810 (88.9)
0 0 0 0 0 0 0 0 0 0 57 (6.3) 57 (6.3) 0 0 0 0 0 149 (16.4) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
a Data below the limit of detection were replaced by half of the reported detection limit values for the geometric mean calculations. b BDL, below detection limit.
the factors. The parameter FPEAK and the matrix FKEY are used to control the rotations [Paatero et al., 2002]. PMF provides a solution that minimizes an object function, Q(E ), based upon uncertainties for each observation [Paatero, 1997; Polissar et al., 1998]. This function is defined as 2 32 p P x g f ij ik kj n X m 6 X 7 k¼1 6 7 ; Qð EÞ ¼ 4 5 uij i¼1 j¼1
ð2Þ
where uij is an uncertainty estimate in the jth constituent measured in the ith sample. [10] The application of PMF depends on the estimated uncertainties for each of the data values. The uncertainty estimation provides a useful tool to decrease the weight of missing data and data below the detection limit in the solution. The procedure of Polissar et al. [1998] was used to assign measured data and the associated uncertainties as the input data to the PMF. The concentration values were used for the measured data, and the sum of the analytical uncertainty and 1/3 of the detection limit value was used as
the uncertainty assigned to each measured value. Values below the detection limit were replaced by half of the detection limit values, and their uncertainties were set at 5/6 of the detection limit values. Missing values were replaced by the geometric mean of the measured values, and their accompanying uncertainties were set at 4 times this geometric mean value. [11] The uncertainty must take into account both the measurement uncertainty and the variability in the source profiles over the 9-year monitoring period. In several cases, in order to take uncertainties into account, larger uncertainties were used to decrease the weight of some specific variables in the model fit [Paatero and Hopke, 2003]. The estimated uncertainties of OC1 were increased by a factor of 2 to reduce the influence of the known positive artifact from the adsorption of gaseous OC [Pankow and Mader, 2001], and the estimated uncertainties of EC1 were increased by a factor of 2 to account for the additional uncertainty from the subtraction of OP. The initial data analysis produced factors with higher aluminum than silicon in the soil profile. This result is highly unlikely [Rahn, 1976]. In order to obtain sensible profiles, it was found necessary to increase the estimated uncertainties of Al by a factor of 3.
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[12] The results of PMF were then normalized by a scaling constant, sk, so that the quantitative source contributions as well as profiles for each source were obtained. Specifically, xij ¼
fkj ; ðsk gik Þ sk k¼1
p X
ð3Þ
where sk is determined by regressing total PM2.5 mass concentrations in the ith sample, mi, against estimated source contribution values. mi ¼
p X
sk gik :
ð4Þ
starts for the initial values used in the iterative fitting process (Paatero, downloadable manual, 2000). 2.3. Conditional Probability Function Analysis [15] The conditional probability function (CPF) [Kim et al., 2003a] was calculated to analyze point source impacts from various wind directions using source contribution estimates from PMF coupled with wind direction values measured on site. To minimize the effect of atmospheric dilution, daily fractional mass contribution from each source relative to the total of all sources was used rather than using the absolute source contributions. The same daily fractional contribution was assigned to each hour of a given day to match the hourly wind data. The CPF is defined as
k¼1
CPF ¼
This regression provides useful indicators of the quality of the solution. If the regression produces a negative sk, it suggests that too many sources have been used. If a scaled source profile exceeds unity, then it suggests that too few sources may have been chosen. [13] To determine the number of sources, it is necessary to test different numbers of sources and find the optimal fit with the most physically reasonable results. The robust mode was used to reduce the influence of extreme values on the PMF solution. A data point was classified as an extreme value if the residual exceeded 4 times the error estimate in the process of model iterations. The estimated uncertainties of those extreme values were then increased so that the weight of the extreme values in the solution was decreased. Since rotational ambiguity exists in PMF modeling, the parameter FPEAK and the matrix FKEY are used to control the rotations [Lee et al., 1999; Paatero et al., 2002; P. Paatero, User’s guide for positive matrix factorization programs PMF2 and PMF3, Part 1: Tutorial, 2000, available at ftp://ftp.clarkson.edu/pub/hopkepk/pmf/, hereinafter referred to as Paatero, downloadable manual, 2000]. By setting a nonzero value of FPEAK the routine is forced to add one gik vector to another and subtract the corresponding fkj factors from each other, thereby yielding more physically realistic solutions. PMF was run with different FPEAK values to determine the range within which the objective function Q(E)value in equation (2) remains relatively constant [Paatero et al., 2002]. The optimal solution should lie in this FPEAK range. In this way, subjective bias was avoided to some extent. The external information can be imposed on the solution to control the rotation. If specific species in the source profiles are known to be zero, then it is possible to pull down those values toward lower concentrations through appropriate settings of FKEY, resulting in the most interpretable source profiles. Each element of the FKEY matrix controls the pulling down of the corresponding element in the fkj matrix by setting a nonzero integer value in the FKEY matrix [Lee et al., 1999; Paatero, downloadable manual, 2000]. [14] The final PMF solutions were determined by experiments with different numbers of sources, different FPEAK values, and different FKEY matrices, with the final choice based on the evaluation of the resulting source profiles as well as the quality of the species fits. The global optimum of the PMF solutions was tested by using multiple random
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mDq ; nDq
ð5Þ
where mDq is the number of occurrences from wind sector Dq that exceeded the threshold criterion and nDq is the total number of data from the same wind sector. In this study, 32 sectors were used (Dq = 11.25 degrees). Calm winds ( > 1:0 < 0:7 W nij ¼ 0:4 > > : 0:2
PMF With Eight Carbon Fractions (This Study)
25 < nij 3 < nij 25 : 2 < nij 3 2 nij
1.2 (0.04)
were set to zero, except the following: a value of 5 for NO 3 in nitrate-rich secondary aerosol and sulfate-rich secondary aerosol; a value of 6 for NO 3 in oil combustion; a value of 6 for Na in aged sea salt. The average source contributions of
ð7Þ
3. Results and Discussion [18] A variety of factor number solutions were explored. Diesel emission was not deduced in the 10-source model. In the 12-source model the airborne soil profile was separated into two Si-high sources. Since the profiles and contribution patterns of the two Si-high sources were not interpretable, it seems appropriate to report the 11-source model results. [19] The 11-source model, a value of FPEAK = 0, and a FKEY matrix provided the most physically reasonable source profiles. For the FKEY matrix, values of all elements
Figure 2. Measured versus predicted PM2.5 mass concentration.
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Figure 3. Source profiles deduced from PM2.5 samples (prediction ± standard deviation). each source to the PM2.5 mass concentrations between previous Brigantine aerosol studies that included two carbon fractions [Song et al., 2001; Lee et al., 2002] and this study are compared in Table 2. [20] In Figure 2, a comparison of the daily reconstructed PM2.5 mass contributions from all sources with measured PM2.5 mass concentrations shows that the resolved sources effectively reproduce the measured values and account for most of the variation in the PM2.5 mass concentrations (slope = 0.94 ± 0.01, r2 = 0.93). [21] In this study, PMF extracted three different sulfaterich secondary aerosol sources that have a high concentration of S. The identified source profiles (value ± standard deviation) are presented in Figure 3. Figure 4 presents time series plots of estimated daily contributions from each source to PM2.5 mass concentrations. Sulfate-rich secondary aerosol I has the highest source contribution to PM2.5 mass concentrations (48%). Sulfate-rich secondary aerosols II and III account for 7 and 5% of the PM2.5 mass concentration, respectively. Sulfate-rich secondary aerosols I and III correspond to the summer and winter coal-fired power plant
sources identified by Poirot et al. [2001] and seen in the studies of Washington, D. C., and Brigantine, New Jersey [Song et al., 2001; Kim and Hopke, 2004]. Sulfate-rich secondary aerosol II shows a strong association between S and OC. Carbon and tracer elements typically become associated with the secondary aerosol. Aerosol time-offlight mass spectrometry studies of the Atlanta aerosol showed that secondary aerosols were combined with metals, carbons, nitrate, chloride, silicate, and phosphate [Liu et al., 2003]. This association is consistent with previous studies that observed a similar profile in the data from Washington, D. C., and previous Brigantine, New Jersey, data [Song et al., 2001; Kim and Hopke, 2004]. [22] In Figure 5, the averaged seasonal contributions from each source are presented (summer: April – September; winter: October – March). The observed seasonal variations may be due to variation in source strength, in transport conditions, or in both. As shown in Figures 4 and 5, the sulfate-rich secondary aerosol I shows strong seasonal variation with higher concentrations in summer, when the photochemical activity is highest, indicating emissions from
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Figure 4. Time series plot of source contributions. coal-fired power plants in summer [Polissar et al., 2001b; Song et al., 2001]. With seasonal differences of the Se/S concentrations, PMF separated sulfate-rich secondary aerosol III that has higher Se/S concentration and showed higher contributions in winter, indicating emissions from coal-fired power plants in winter [Poirot et al., 2001]. Similarly, PMF separated S into a high-photochemistry source (55 and 53%) and a low-photochemistry source (8 and 13%) in the previous PMF studies as shown in Table 2 [Song et al., 2001; Lee et al., 2002]. These profiles tend to have much less carbon associated with the profiles than has been observed in the prior studies [Song et al., 2001; Lee et al., 2002; Kim et al., 2003a], in which total OC and EC have been used instead of the thermal carbon fractions. The carbon now primarily appears in sulfate-rich secondary aerosol II. [23] Sulfate-rich secondary aerosol II has high carbon concentrations, especially the OP concentration. Elemental carbon has been observed associated with sulfate in individual particles [Liu et al., 2003]. An important question is the nature of the organic carbon associated with these
Figure 5. Seasonal comparison of source contributions to PM2.5 mass concentration (mean ±95% confidence interval).
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Figure 6. Potential source contribution function plot. (a) Sulfate-rich secondary aerosol I. (b) Sulfate-rich secondary aerosol II.
particles. Yu et al. [2002] have observed an association between the water-soluble organic carbon and OP formation in the thermal analysis of Hong Kong and China aerosols. Thus the high OP in the sulfate-rich secondary aerosol II is likely to be secondary organic carbon produced from the oxidation of volatile precursors that has become water soluble in the atmosphere. The PSCF plot of the sulfaterich secondary aerosol II described below shows the longdistance transport of sulfur and organic carbon. [24] It is suggested that this high-carbon sulfate-rich secondary aerosol may be in part the result of heterogeneous acid-catalyzed reactions between the acidic sulfate and gaseous organic compounds, which leads to additional secondary organic aerosol formation [Jang et al., 2003]. There are several possibly mechanisms for the formation of the high-carbon sulfate-rich secondary aerosol, and the exact nature of this factor cannot be deduced from the factor analysis alone. Sulfate particles can also provide a surface onto which semivolatile organic compounds can condense. [25] The PSCF plots for sulfate-rich secondary aerosols I and II are compared in Figure 6, in which PSCF values are displayed in terms of a color scale. As shown in Figure 6a, the PSCF plot of sulfate-rich secondary aerosol I shows the influence of midwestern coal-fired power plants in the Ohio
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River Valley [Poirot et al., 2001]. Potential source areas and pathways that give rise to the high contribution to the Brigantine site are located in southern Indiana and northern Kentucky. These identified areas also include areas where the sulfate-rich secondary aerosol I was formed in addition to areas where the sources were located. There are also areas of potential influence in Louisiana and southern Mississippi. There is a significant petrochemical industry in Louisiana, but the detailed nature of these source areas is uncertain. The PSCF plot for sulfate-rich secondary aerosol III that shows a similar influence of the Ohio River Valley is not shown here. [26] In Figure 6b, the PSCF plot of sulfate-rich secondary aerosol II shows high values around eastern Tennessee, northeastern Georgia, and western South Carolina as potential source areas. These areas are likely to be related to the volatile organic carbon emissions from biogenic sources that then give rise to secondary organic aerosol. There remain some potential source areas in the Ohio River Valley as well as areas in southern Louisiana, Mississippi, and Alabama that might be associated with sulfur emissions. In addition, there are other small high-potential areas around Hudson Bay in Canada, suggesting the potential for secondary organic carbon contributions from Canadian forest fires. The PSCF plot of sulfate-rich secondary aerosol II indicates regional influences of the biogenic as well as anthropogenic secondary aerosol. [27] The fractions of two sulfate-rich secondary aerosol sources identified in previous studies were 63% [Song et al., 2001] and 66% [Lee et al., 2002]. The fraction of three sulfate-rich secondary aerosol sources identified in this study was 60%. This is consistent with the 60% contribution from the three sulfate-rich secondary aerosols identified in closely situated Washington, D. C. [Kim and Hopke, 2004]. The average source contributions from sources to the PM2.5 mass contributions are compared between weekday and weekend in Figure 7. In this study, sulfate-rich secondary aerosols do not show strong weekday/weekend variations. In contrast, sulfate-rich secondary aerosol that has a high concentration of carbon fractions has weekend-high variations in Atlanta and Washington, D. C., studies [Kim et al., 2003b; Kim and Hopke, 2004]. The reasons for this apparently anomalous weekday/weekend variations of the sulfate-rich secondary aerosol observed in Atlanta, Washington, D. C., and this study need further investigation. [28] Gasoline vehicle and diesel emissions have high carbon concentrations, whose abundances differ between these sources. Gasoline vehicles and diesel emissions account for 13% and 3% of the PM2.5 mass concentration, respectively. In the previous analyses including two carbon fractions, traffic-related sources were extracted as motor vehicle emissions that account for 6% of the PM2.5 mass concentration [Song et al., 2001] or motor vehicle/mixed combustion and diesel/Pb-Zn sources accounting 8% and 6% of the PM2.5 mass concentration, respectively [Lee et al., 2002]. The PMF-extracted fractional carbon profiles are presented for the three main combustion sources in Figure 8. Gasoline vehicles emissions have high concentrations of the OC fractions. In contrast, diesel emissions were tentatively identified on the basis of the high concentration of EC. These profiles are consistent with previous
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Figure 7. Comparisons of model-resolved contributions between weekday and weekend (mean ± 95% confidence interval). measurements [Watson et al., 1994, 2001; Watson and Chow, 2001; Lowenthal et al., 1994]. Specifically, the gasoline vehicle source has large amounts of OC3 and OC4. Diesel emissions contain high concentrations of EC1. These results are similar to those estimated in Atlanta [Kim et al., 2003b], Washington, D. C. [Kim and Hopke, 2004], and Seattle [Maykut et al., 2003] studies where the eight carbon fractions were included in the analysis.
[29] CPF values for each source are plotted in polar coordinates in Figure 9. The CPF plot of gasoline vehicle source points to the north and southwest, which are the directions of closely located highway 9. Diesel emissions appear to have contributions from the northwest, where Philadelphia and highways carrying a large amount of traffic between New York City and Washington, D. C., are situated. In Figure 7, gasoline vehicle emissions do not
Figure 8. Fractional carbon profiles for combustion sources (prediction ± standard deviation). 9 of 13
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Figure 9. CPF plots for the highest 25% of the mass contributions. show strong weekday/weekend variations. Diesel emissions show weak weekday-high variations, demonstrating that diesel emissions are mostly from vehicles operating primarily on weekdays [Lewis et al., 2003]. The weak variations are thought to be caused by a long distance between sources and the monitoring site. [30] The ratio of the average contributions of diesel emissions relative to gasoline vehicle of 0.26 (=0.37 mg/m3 diesel emissions/1.41 mg/m3 gasoline vehicle emissions) is different from modeled ratios of 3.2 in Pasadena, 3.0 in West Los Angeles [Schauer et al., 1996], 3.7 in San
Gorgonio National Park, California (W. Zhao and P. K. Hopke, Source apportionment for ambient particles in the San Gorgonio National Monument, submitted to Journal of Geophysical Research, 2003), and 2.3 in Atlanta [Kim et al., 2003b], indicating limited diesel emission in the vicinity of Brigantine, New Jersey. The fraction of mass contributions from gasoline vehicle and diesel emissions is 16% of the PM2.5 mass concentration. This total in a rural area is smaller than 21% from the fraction of gasoline vehicle and diesel emission sources estimated in an Atlanta aerosol study [Kim et al., 2003b]. Gasoline vehicles and diesel
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Figure 10. Backward trajectories arriving at Brigantine, New Jersey, calculated from NOAA Air Resources Laboratory. (a) 9 – 22 April 2001. (b) 18– 29 June 1994.
emissions contributed more to the PM2.5 mass concentration in the winter as shown in Figure 5. [31] For the next source, aged sea salt is suggested because the profile is characterized by its high mass fractions of Na and S. The lack of chlorine in the profile is caused by chloride displacement by acidic gases. This source accounts for 8% of the PM2.5 mass concentration, which is consistent with estimations in the previous two studies [Song et al., 2001; Lee et al., 2002]. This source does not show a strong seasonal pattern. In Figure 9 there are indications of higher contributions from the direction of the Atlantic Ocean. [32] The nitrate-rich secondary aerosol is represented by its high concentration of NO 3 . This source accounts for 6% of the PM2.5 mass concentration. The previous Brigantine aerosol study [Song et al., 2001] estimated an 8% contribution to the PM2.5 mass concentration. This source has seasonal variation with maxima in winter as shown in Figures 4 and 5. These peaks in winter indicate that low temperature and high relative humidity help the formation of nitrate aerosols in Brigantine. This seasonal variation is consistent with a previous study for Atlanta, Georgia [Kim et al., 2003b]. The CPF plot shows the contributions from the west and northwest. This direction is thought to indicate the contributions from Philadelphia, Pennsylvania. [33] The fresh sea-salt factor has a high concentration of Na and Cl, accounting for 4% of the PM2.5 mass concentration. This source does not show a strong seasonal pattern. In Figure 9 the CPF plot shows the contributions from the
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direction of the Atlantic Ocean, which is consistent with the source direction of aged sea salt. [34] Airborne soil is represented by Si, Al, Fe, Ca, and K [Watson et al., 2001; Watson and Chow, 2001], contributing 4% to the PM2.5 mass concentration. This result is consistent with a 3% contribution of airborne soil to the Atlanta PM2.5 mass concentration. The previous Brigantine studies showed 1% [Song et al., 2001] and 2% [Lee et al., 2002] contributions of airborne soil to the PM2.5 mass concentration. Crustal particles could be contributed by unpaved roads, construction sites, and wind-blown soil dust. The airborne soil shows seasonal variation, with higher concentrations in the dry summer season. The carbon content in this source and main contributions from northwest and southwest suggest that the airborne soil is mainly crustal particles resuspended by road traffic. Also, the reduced construction activity on weekends could help show the weekday-high variation. [35] The HYSPLIT model (Draxler and Rolph, Web source, 2003; Rolph, Web source, 2003) was used to calculate the air mass backward trajectories for days with high impacts of this source in Figure 4. Backward trajectories were calculated with a starting height of 500 m above sea level using the vertical mixing model. As shown in Figure 10, the elevated contribution on 29 June 1994 was likely to be caused by a Sahara dust storm. Also, the peaks on 26 August 1998, 3 July 1999, and 2 August 2000 have similar backward trajectories indicating Sahara dust storms. In contrast, the peak on 22 April 2001 was likely to be caused by Asian dust storm that developed over Mongolia on 7 April 2001 (NASA, Earth probe Total Ozone Mapping Spectrometer aerosol index, 2001, available at http:// toms.gsfc.nasa.gov/aerosols/aerosols.html). The ratio of the average concentrations of Al relative to Si of 0.61 for 910 samples is consistent with 0.55 for both dust storm samples. In contrast, the ratios of Ca to Si of 0.12 for the four Sahara dust storm samples and 0.17 for a Asian dust storm sample are significantly smaller than the ratio of 0.30 for all samples. This suggests that relatively small concentration ratios of Ca to Si can be an indication of global dust storm effects. [36] The incinerator factor is identified by carbon, Si, K, Zn, and Pb, contributing 2% to the PM2.5 mass concentration. A previous Brigantine aerosol study attributed 5% of the PM2.5 mass concentration to this source, in which some fraction of diesel emission was thought to be mixed [Song et al., 2001]. Waste incineration mixed with industrial sources was identified in another Brigantine aerosol study attributing 5% of the PM2.5 mass concentration [Lee et al., 2002]. This source has a seasonal trend with higher values in winter. The CPF plot for incinerator points to the west and northwest, where municipal solid wasted incinerators are located. [37] Oil combustion is characterized by carbon fractions, V, and Ni. This source contributes 1% to the PM2.5 mass concentration. The previous Brigantine aerosol studies estimated 2% [Song et al., 2001] and 4% [Lee et al., 2002] contributions to the PM2.5 mass concentration. As shown in Figure 8, this source profile has a large amount of EC1 carbon fractions, reflecting residual oil combustion for the utilities and industries. The CPF plot of this source in Figure 9 points to the north and southeast, indicating New
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York City and Atlantic City as major contributors of this source. Previous backward trajectory analyses for the Vermont aerosol study indicated that major sources of oil combustion were located along the northeastern urban corridor between Washington, D. C., and Boston, Massachusetts [Polissar et al., 2001b]. As shown in a Washington, D. C., study [Kim and Hopke, 2004], the impacts of oil combustion gradually decrease in the time series plot of Figure 4. This source shows a winter-high seasonal trend in Figure 5. [38] Watson et al. [1994] reported the eight temperatureresolved carbon fractions measured by the IMPROVE/TOR protocol based on chassis dynamometer tests of Phoenix light-duty gasoline vehicles and heavy-duty diesel vehicles in 1989. In their results, gasoline vehicles have high concentrations of OC fractions, and diesel vehicles contain high concentrations of EC fractions. Lowenthal et al. [1994] reported similar profiles for the diesel trucks and buses based on the measurements in 1992. The PMF-derived source profiles of the particulate carbon fractions in this study can be compared with these limited available source profiles. Similar to the measurements, the gasoline vehicle emissions resolved in this study includes the lower temperature carbon fractions. The diesel emissions contain large amounts of the elemental carbon fractions as shown in Figure 8. [39] Comparison of the PMF-derived carbon fraction profiles with measured source test profiles reveals interesting differences: Measured gasoline and diesel particles contain a larger amount of OC1 fraction than those of PMF estimations. This difference may be due to the source-sampling artifact that is caused by the adsorption of fresh semivolatile organic compounds by quartz filters [Pankow and Mader, 2001]. Another possible explanation is that the atmospheric chemical processing of lower molecular weight organic compounds between source and sampling sites reduces the OC artifact. Also, the measured source test profiles showed that the gasoline vehicle emissions have a large amount of OC4 and the diesel emissions contain a high concentration of EC2. In contrast, the PMF-derived carbon fraction profiles for both this study and an Atlanta aerosol study [Kim et al., 2003b] have a large amount of OC3 and OC4 for the gasoline vehicle emissions and EC1 for the diesel emissions. The change in the evolution of carbon fractions in the thermal analysis may be influenced by the presence of other aerosol constituents. Transition metal oxides in the atmospheric aerosol may catalyze the oxidation of OC and EC fractions at lower temperature, which results in higher concentrations of OC3 for the gasoline vehicle emissions and EC1 for the diesel emissions [Fung et al., 2002].
4. Conclusion [40] Integrated PM2.5 compositional data from samples collected at a rural monitoring site in Brigantine National Wildlife Refuge, New Jersey, were analyzed through PMF. Including 8 temperature-resolved carbon fractions, PMF effectively resolved 11 sources of the PM2.5. In contrast to the previous Brigantine PM2.5 studies that included two carbon fractions [Song et al., 2001; Lee et al., 2002], diesel emissions were separated from gasoline vehicle
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emissions by utilizing eight carbon fractions whose abundances differ between two sources. The diesel emissions contain a large amount of the elemental carbon fractions. The gasoline vehicle emissions show carbon fractions without significant elemental carbon. Sulfate-rich secondary aerosols are the largest PM2.5 source in Washington, D. C., accounting for 60% of the mass concentration during the study period. The PSCF analysis shows the potential source areas and pathways of sulfate-rich secondary aerosols, especially the regional influences of the biogenic as well as anthropogenic secondary aerosol. Backward trajectories indicate that the elevated airborne soil impact on Brigantine was likely to be caused by both Asian and Sahara dust storms. The impacts from the local sources are clearly seen using PMF results combined with the conditional probability functions plots. [41] Acknowledgments. This work was supported by the United States Environmental Protection Agency (US EPA) under a subcontract to Clarkson University by State University of New York at Albany (SUNY) and by the Science to Achieve Results (STAR) program under grant R831078. Although the research described in this article has been funded in part by US EPA through cooperative agreement R82806001 to SUNY, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (http:// www.arl.noaa.gov/ready.html) used in this publication.
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P. K. Hopke, Department of Chemical Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USA. (
[email protected]) E. Kim, Department of Civil and Environmental Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USA. (
[email protected])
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