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Application of SAFER model to the Los Angeles PM10 data
Atmospheric Environment 34 (2000) 1747}1759
Application of SAFER model to the Los Angeles PM data 10 Bong Mann Kim!,*, Ronald C. Henry" !South Coast Air Quality Management District, Planning and Policy, 21865 E Copley Dr., Diamond Bar, CA 91765, USA "University of Southern California, Department of Civil/Environmental Engineering, University Park, Los Angeles, CA 90089-2531, USA Received 18 November 1998; accepted 4 August 1999
Abstract The chemical mass balance (CMB) model has been used widely in the PM source apportionment study and the 10 PM state implementation plan (SIP) development. In its modeling application, the CMB model requires source 10 composition pro"les, which can either be measured by source testing or estimated by a multivariate receptor model such as source apportionment by factors with explicit restrictions (SAFER) from the ambient data only. The SAFER model is a multivariate receptor model utilizing a series of linear programming methods to estimate source compositions with explicit physical constraints. The SAFER model was applied to ambient PM data collected in 1986 in the South Coast 10 Air Basin. Source compositions of selected major source categories were estimated. The SAFER model-estimated source pro"les were compared with the measured source pro"les by checking some important elemental ratios. Estimated source compositions were consistent with the measured pro"les. Then the SAFER model-estimated source pro"les were used for CMB analysis to estimate source contributions from each source category. Contributions from the roadway source range from 20 to 34 lg m~3, from the secondary source from 17.75 to 31.40 lg m~3, from the marine source from undetectable to 2.50 lg m~3, and from the crustal source from 4.06 to 8.13 lg m~3. Organic carbon seems to be mainly (81%) contributed by the primary roadway source, and sulfates and nitrates are mainly from the secondary source, although 32% of the sulfate is from primary sources such as roadway, crustal, and marine sources. Published by Elsevier Science Ltd. Keywords: Principal component analysis; SAFER model; Self modeling curve resolution; Additional physical constraints; Stoichiometric constraint; Estimation of source compositions
1. Introduction Because the US EPA (1987) recommends the chemical mass balance (CMB) model as one of the modeling options for the development of a PM state implementa10 tion plan (SIP), the CMB model has been used widely in PM source apportionment study and PM SIP devel10 10 opment in nonattainment areas. The CMB model may be the most reliable model for assessing contributions of primary particles if accurate source compositions are available. Source compositions, however, are almost always uncertain because source compositions may vary
* Corresponding author. Tel.: #1-909-396-3157; fax: #1909-396-3252. E-mail address: [email protected] (Bong M. Kim) 1352-2310/00/$ - see front matter Published by Elsevier Science Ltd. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 3 6 5 - 9
with time, location, raw material and fuel type. Measurement of reliable source compositions is di$cult or sometimes virtually impossible for some sources. Source compositions can be estimated by a multivariate receptor model from the ambient data without direct measurement of the source compositions. The multivariate receptor model, SAFER (source apportionment by factors with explicit restrictions) (Kim, 1989; Henry and Kim, 1990; Kim and Henry, 1999) was applied to PM data 10 collected in 1986 in the South Coast Air Basin (SCAB). Source compositions of the major source categories, such as roadway, secondary, marine, and crustal sources were estimated at six sites in the SCAB and source contributions were determined using the CMB model. The following section is a brief summary of the receptor modeling followed by a model application. The remainder of the paper is devoted to the results of the model application.
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2. Receptor modeling Receptor modeling is a technique for determining emission sources and their contributions to ambient particulate concentrations at speci"c receptor sites. Unlike complex mathematical models which require detailed simulations of physics, chemistry, meteorology, and other processes, receptor models are relatively simple statistical models which require as inputs the particulate data measured at receptor sites and sometimes source data. In the following, the CMB and SAFER receptor models are brie#y explained. 2.1. Chemical mass balance (CMB) model
(Jolli!e, 1986) and the self-modeling curve resolution (SMCR) technique (Lawton and Silvester, 1971). This model leads to source compositions from the ambient data without measuring source compositions, given minimum a priori knowledge. This model is brie#y explained in the following and details can be found in Kim (1989), Henry and Kim (1990), and Kim and Henry (1999). This model di!ers from other factor analysis/principal component analysis-based methods by implementing explicit physical constraints in estimating source compositions. PCA can be explained using the singular-value decomposition (SVD) theorem (Jolli!e, 1986). Any ambient concentrations matrix, C, can be decomposed uniquely as C";D
If p sources exist, the mass concentration of species i, C , measured at the receptor is, i p p C " + S " + a@ S , i"1, 2,2, n, (1) i ij ij j j/1 j/1 where S is the mass of species i from source j and a@ is ij ij the mass fraction of species i in the mass from source j collected at the receptor. Furthermore, a@ "a a , (2) ij ij ij where a is the mass fraction of species i emitted by ij source j as measured at the source and a is the fractionaij tion coe$cient of species i emitted by source j. This coe$cient explains the modi"cation of a to a@ during ij ij transport due to deposition, evaporation and chemical transformation. Usually in the CMB model, a is set to ij 1 under the assumption of conservation of mass. Finally, Eq. (1) reduces to C "a S , i"1, 2,2, n i ij j and in matrix form,
(3)
C"AS#E,
(4)
where C is an ambient concentration matrix of m measurements by n species, ; is the m]m eigenvectors of CCT, D is an m]n diagonal matrix made up of square roots of the corresponding eigenvalues, and < is the n]n eigenvectors of CTC. This result is called the SVD theorem. The < eigenvectors multiplied by the singular values,
2.2. SAFER model The SAFER model is a new type of multivariate receptor model based on principal component analysis (PCA)
(6)
where A is a source composition matrix of n species by p sources and S is p]n source contributions matrix. If A"< and S"(;D)T, Eqs. (5) and (6) are the same. However, < is not the same as A because < is a matrix of eigenvectors. Therefore, abstract eigenvectors, <, need to be transformed to a physically meaningful solution of source compositions, A, by C"(;D¹~1)(¹
where C is an n]1 vector of ambient concentrations, A is an n]p source composition matrix, S is an p]1 vector of source contributions to be estimated, and E is an n]1 vector of measurement errors. The principal approach to solving the resulting mass balance equations has been the least-squares "tting, usually called chemical mass balance (CMB) approach. As noted in the Introduction, CMB may be the most reliable model to estimate source contributions of the primary particle sources if accurate a@ is available. However, generally, a is not equal to ij ij 1 and a@ is not available. Source compositions at a given ij time period and location, a@ , can be estimated from ij a multivariate receptor model.
(5)
(7)
where ¹ is a p]p transformation matrix. Mathematically, there are an in"nite number of ways to transform the principal component solution into another so that the transformed solutions explain the ambient data equally well. There are several di!erent transformation methods, such as orthogonal transformation (VARIMAX, QUARTRIMAX, PROMAX) and oblique transformation (OBLIMIN). However, these transformation methods are not based on physical reality so that they do not guarantee that the transformed results will make sense physically. It is not unusual for the transformed results to have physically impossible negative values of source compositions. The SAFER model uses a new transformation method based on the SMCR technique. Since a unique solution is not possible (Henry, 1987), the SMCR technique restricts the feasible region of the real solution into a small region with explicit physical constraints, such as source compositions must be greater than or equal to zero. Explicit
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
physical constraints form linear inequality constraints in the space spanned by the eigenvectors, <, and these constraints form the feasible region in eigenvectors space, <. Explicit physical constraints, however, are not enough to resolve the sources, and additional information is needed to further restrict the feasible region and to resolve the sources. Prior knowledge of source compositions for each source, e.g., silicon composition in soil dust is between 0.1 and 0.3, could be used directly as additional physical constraints (APCs) in addition to the explicit physical constraints. Any reasonable, not necessarily precise, ranges of source compositions for each source could be used as APCs. APCs also form linear inequality constraints in the eigenvectors space, <, and form a very small feasible region for a speci"c source within the feasible region formed from explicit physical constraints. The feasible region is obtained by "nding all the vertices of the feasible region by a series of linear programming. Any point in the feasible region for a particular source could be the transformation vector for that source. Then any point in the feasible region can be translated into source compositions by A"<¹T
(8)
for that source. Although the feasible region for each source is restricted to a small region, an in"nite number of source compositions is still possible. The source composition at the center of gravity of the feasible region is used as the estimated source composition, and the deviations of the source composition from the source composition of the centroid are provided as "nal solutions of the model. The SAFER model was applied to 1986 PM data 10 which is described in the next section. Source compositions and their source apportionments of the major source categories, such as roadway, marine, crustal, and secondary sources, were estimated at six sites in the SCAB. In this model application, to further reduce the size of the feasible region and to estimate quite precise source compositions, carbon monoxide and ozone gas data were used along with the PM data. These two 10 gases were used as the APCs for the SAFER model application because carbon monoxide and ozone are unique tracers for the motor vehicle and secondary sources, respectively. In addition, a new concept of additional physical constraint, stoichiometric constraint, was also used. This constraint is explained in the next section. 3. Model application 3.1. Data description The Environmental Quality Laboratory of the California Institute of Technology conducted 24-h PM samp10
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ling every six days from January 1986 to December 1986 in the SCAB at nine sampling sites: Burbank, Long Beach, Lennox, Rubidoux, Anaheim, San Nicolas Island, Downtown Los Angeles, Upland, and Tanbark Flats. PM was sampled at all the sites and PM at Down10 2.5 town Los Angeles only. Total mass and 41 species (Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, Mo, Pd, Ag, Cd, In, Sn, Sb, Ba, La, Hg, Pb, Na, Mg, OC, EC, NO , SO , NH ) were analyzed. 3 4 4 A more detailed description of the monitoring site, sampling schedule, and sample analysis can be found in Solomon et al. (1989). To enhance the model results, carbon monoxide and ozone gas data were used together with PM data. Data 10 for CO and O are not available from Tanbark Flats and 3 San Nicolas Island. As a result, two coastal sites } Lennox and Long Beach } and "ve inland sites } Downtown Los Angeles, Burbank, Anaheim, Upland and Rubidoux } have been selected. For model application, ambient data for each site were reduced to two gas parameters (CO and O ) and 24 selected PM species (OC, EC, 3 10 NO , SO , NH , Na, Mg, Al, Si, P, Cl, K, Ca, Ti, V, Cr, 3 4 4 Mn, Fe, Ni, Cu, Zn, Br, Sr, and Pb), due to the uncertainty level in the ambient data. 3.2. Initial source identixcation The number of contributing sources to each site can be estimated from the number of eigenvalues in the correlation matrix of the ambient data. Many analysts use a minimum eigenvalue of 1.0 as the in#uential cuto!; a lower value of 0.5 is used here to ensure that all major in#uences are considered. The number of probable sources identi"ed for each site is six for Rubidoux, seven for Downtown Los Angeles, Long Beach, Upland, eight for Lennox and Anaheim, and nine for Burbank (Kim, 1989). Major source categories identi"ed are motor vehicle, crustal, secondary, and marine sources. The factor analysis (FA) or PCA model cannot distinguish spatially and temporally correlated sources. From the FA or PCA standpoint, spatially and temporally correlated sources are perceived as a single source because they almost always impact the receptor site at the same time. Although these are not a single source, they can be assumed as a pseudo-single composite source. The motor vehicle and road dust sources are spatially and temporally correlated as the road dust is resuspended in the air when the motor vehicle passes over the road. The receptor never experiences motor vehicle tailpipe emissions separate from resuspended road dust, but rather always receives roadway emissions as a mixture of road dust and motor vehicle exhaust. Therefore, source compositions representing this composite roadway source are estimated at all sites from the SAFER model. Secondary species, such as, NH , NO , SO , and sec4 3 4 ondary organic species are usually present as a compound
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in the form of ammonium nitrate, ammonium sulfate, or ammonium bisulfate and secondary organic carbonaceous matter. These secondary compounds are formed in the atmosphere by chemical reactions and typically impact a receptor site at the same time. Therefore, at each receptor site these compounds seem to be coming from the same secondary source. However, this is not a real source, but is the result of chemical reactions from unidenti"ed sources of nitrogen oxides and sulfur oxides. 3.3. Additional physical constraints Setting up additional physical constraints is essential to the SAFER model. This step consists of two substeps: "rst, the selection of the elements and, second, the selection of the source composition ranges for these elements. The general starting points for setting up the appropriate additional physical constraints for each source are the selection of the possible tracers and reasonable ranges for them. The latter are obtained either directly from the source characterization of the source or indirectly from the existing source composition pro"les. In this model application, CO and O gas data have 3 been added to the PM data to minimize the number 10 of additional physical constraints and to enhance the modeling results. CO and O are unique tracers for the 3 motor vehicle and secondary sources, respectively. Therefore, these two gas data can be used as the APCs that are necessary for the SAFER model application; for example, the O fraction for the motor vehicle source 3 must be close to zero and CO for the secondary source must be close to zero. 3.4. Stoichiometric constraint Stoichiometric relations among species are used as APCs for two reasons: to lessen the di$culties of setting up the APCs for each source and to reduce the size of the feasible ranges of the resolved source compositions. Water-soluble ionic species, such as NH , NO , SO , 4 3 4 Na, and Cl, are present as chemical compounds. Therefore, the stoichiometric relationship among species could be used as APCs. For example, NH , NO , and SO 4 3 4 may exist as the compounds NH NO , (NH ) SO , and 4 3 42 4 NH HSO . These three compounds and the secondary 4 4 organic carbon derive mostly from the secondary source. Let the fractions of each compound in the secondary source be assumed as follows: OC : f
1
(9)
NH NO : f 4 3 2
(10)
(NH ) SO : f 42 4 3
(11)
NH HSO : f . 4 4 4
(12)
Then the source compositions of OC (a ), NH (a 4 ), OC 4 NH NO (a 3 ), and SO (a 4 ) in the secondary source can 3 NO 4 SO be expressed as the sum of the fraction of each compound f, i a "0.769 f OC 1
(13)
a 4 "0.225f #0.273f #0.157f 2 3 4 NH
(14)
a 3 "0.775f NO 2
(15)
a 4 "0.727f #0.835f . 3 4 SO
(16)
These expressions can be used directly as additional physical constraints for OC, NH , NO , and SO spe4 3 4 cies; however, the dimensionality will be increased by four and this will require more time to estimate the source compositions. Instead, the above equations are solved for f and the following constraints applied: i f *0, i
(17)
0.9)+ f )1.0, for i"1,2, 4. i
(18)
These physical constraints, derived from the stoichiometric relation among the species, turn out to be very strict in the sense of restricting the feasible region. The concept of using stoichiometry among species as APCs for the secondary source can be extended to the marine and roadway sources. For the marine source, it is well known that NaCl reacts with strong acids such as HNO and H SO to form NaNO , Na SO , and HCl 3 2 4 3 2 4 (Hidy et al., 1974; Hitchcock, 1980). As before, fractions of each compound of NaCl, NaNO , Na SO , and 3 2 4 SO are assumed as f . Then APCs for f are set up. For 4 i i the roadway source, fractions of each compound of NH NO , (NH ) SO , and NH HSO are used to set 4 3 42 4 4 4 up APCs. 3.5. Additional physical constraints for major source categories The APCs used for each source category for each site are summarized in Table 1. Setting up APCs for the composite roadway source is di$cult because the composite pro"le depends on how much of the motor vehicle and crustal sources are mixed together. The fraction of the mixture of the two sources will di!er from site to site. The composite roadway source pro"les were obtained at three di!erent sites (Downtown Los Angeles, Lennox, and Rubidoux) by estimating the best linear combination of the source pro"les that best explain the measured ambient data. In other words, the composite roadway pro"les were obtained by minimizing the di!erence between the linear combination of the motor vehicle and road dust source pro"les and those estimated from the linear combination of the eigenvectors of the ambient data matrix. The solution to the minimization problem
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 1 Additional physical constraints used for the sources for each site Roadway
Secondary
Marine
Crustal
NH NO , (NH ) SO , 4 3 42 4 and NH HSO 4 4
NH NO , (NH ) SO , 4 3 42 4 NH HSO , NaNO , 4 4 3 and OC
NaCl, NaNO , Na SO , 3 2 4 and SO 4
0.0)+ f )0.1 *
0.9)+ f )1.0 *
0.8)+ f )1.0 *
Lennox
0.048)Si)0.088 0.007)Pb)0.018 0.0)O )0.001 3
0.0)CO)0.01 0.0)EC)0.01
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30
Long Beach
0.048)Si)0.088 0.007)Pb)0.018 0.0)O )0.001 3
0.0)CO)0.00001 0.0)EC)0.001
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30 0.0)CO)0.00001
0.0)O )0.001 3 0.05)Al)0.15 0.0)NH )0.001 4 0.0)NO )0.001 3
Los Angeles
0.04)Si)0.07 0.004)Pb)0.008 0.0)O )0.001 3
0.0)CO)0.01 0.0)EC)0.001
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30
0.0)O )0.00001 3 0)OC)0.001 0.0)NH )0.001 4 0.0)NO )0.001 3 0.0)SO )0.001 4 0.05)Al)0.15 0.15)Si)0.35
Anaheim
0.06)Si)0.12 0.002)Pb)0.005 0.026)Al)0.049
0.0)CO)0.00001 0.0)EC)0.001
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30
Upland
0.06)Si)0.12 0.0035)Pb)0.006 0.0)O )0.001 3
0.0)CO)0.00001 0.0)EC)0.001
0.0)CO)0.015 0.0)O )0.00001 3 0)OC)0.001 0.0)EC)0.001 0.0)NH )0.001 4 0.05)Al)0.15 0.15)Si)0.35
Rubidoux
0.06)Si)0.12 0.0035)Pb)0.006
0.0)CO)0.00001 0.0)EC)0.001
0.0)O )0.00001 3 0.0)NH )0.001 4 0.0)NO )0.001 3 0.1)Si)0.3 0.2)Ca)0.4
Stoichiometry among
was found by the application of Lagrange's undetermined multipliers. The complete methodology for obtaining the roadway composite pro"les is discussed in Henry and Kim (1989). Si and Pb were selected for setting up APCs and their ranges were taken from results of the above study. CO and EC were selected to set up APCs for the secondary source. Since it is obvious that CO and EC
have to be negligible, their ranges were set to small values. Na, EC, NH , and CO were used to set up APCs 4 for the marine source and these species, except Na, were set to negligible ranges. APCs for the crustal source were selected from Al, Si, CO, O , OC, EC, NH , NO , and 3 4 3 Ca depending on the site under study. After APCs for each source category were set up, the model was applied to estimate the source compositions
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
Table 2 Estimated source compositions and uncertainties at Downtown Los Angeles Roadway OC EC NH 4 NO 3 SO 4 Na Mg Al Si P Cl K Ca Ti V Cr Mn Fe Ni Cu Zn Br Sr Pb
0.27987 0.15622 0.01532 0.00990 0.04723 0.00732 0.00227 0.01953 0.05498 0.00093 0.01347 0.00786 0.02122 0.00352 0.00034 0.00034 0.00124 0.03437 0.00029 0.00939 0.01028 0.00101 0.00038 0.00613
Secondary (0.08930) (0.03290) (0.00992) (0.02440) (0.03527) (0.01182) (0.00116) (0.00490) (0.01465) (0.00065) (0.01472) (0.00155) (0.00442) (0.00062) (0.00004) (0.00005) (0.00014) (0.00476) (0.00005) (0.00319) (0.00246) (0.00024) (0.00004) (0.00079)
0.12176 0.00051 0.16253 0.37047 0.21186 0.02866 0.00390 0.00549 0.00765 0.00080 0.00179 0.00308 0.00263 0.00046 0.00014 0.00007 0.00001 0.00161 0.00022 0.01107 0.00732 0.00043 0.00001 0.00100
Marine (0.05108) (0.00050) (0.01703) (0.10107) (0.05672) (0.00874) (0.00104) (0.00170) (0.00565) (0.00048) (0.00367) (0.00061) (0.00320) (0.00027) (0.00004) (0.00002) (0.00002) (0.00169) (0.00005) (0.00490) (0.00328) (0.00006) (0.00001) (0.00047)
of these source categories, and source apportionments were made from the CMB model. The model's results for each site are summarized in the next section.
0.01155 0.00050 0.00050 0.20646 0.24962 0.21270 0.02610 0.00415 0.00695 0.00306 0.13489 0.01088 0.01063 0.00072 0.00018 0.00026 0.00090 0.01213 0.00093 0.06815 0.04989 0.00143 0.00028 0.00791
Crustal (0.01509) (0.00050) (0.00050) (0.02180) (0.02176) (0.00440) (0.00045) (0.00197) (0.00631) (0.00041) (0.00875) (0.00062) (0.00209) (0.00026) (0.00001) (0.00002) (0.00009) (0.00193) (0.00003) (0.00168) (0.00143) (0.00003) (0.00002) (0.00061)
0.00039 0.08661 0.00010 0.00047 0.00047 0.00728 0.00177 0.05129 0.15025 0.00213 0.02020 0.01776 0.04921 0.00742 0.00046 0.00062 0.00185 0.06129 0.00034 0.00003 0.00697 0.00030 0.00051 0.00530
(0.00047) (0.00037) (0.00019) (0.00050) (0.00050) (0.00025) (0.00003) (0.00019) (0.00057) (0.00002) (0.00045) (0.00007) (0.00019) (0.00003) (0.00000) (0.00000) (0.00001) (0.00021) (0.00000) (0.00007) (0.00006) (0.00000) (0.00000) (0.00003)
ation between sites is examined for consistency with expected behavior. 4.1. Source composition
4. Results and discussions As an example, the estimated source compositions for Downtown Los Angeles are shown in Table 2 where the estimated source compositions are reported to "ve decimal points. Measured source compositions are usually reported to six decimal points (NEA, 1987,1990). The estimated source compositions are examined below by checking some important elemental ratios. The SAFER model used a very limited amount of a priori knowledge when estimating the source compositions. For example, the Si and Pb ranges are the only a priori knowledge used to estimate the roadway source compositions. If the model estimates the compositions correctly, the estimated compositions should be consistent for all the sites. In other words, the elemental ratios of the estimated source compositions, especially for elements not used as APCs, should be consistent for the sites. These elemental ratios are compared to the ratios expected from source compositions found in the literature. After examining the estimated pro"les, their vari-
4.1.1. Estimated roadway composite source proxle Some elements such as Si, Al, Ca, Pb, Br, and OC are the main elements of the roadway source and were selected to examine the elemental ratios of the estimated roadway source compositions. The elemental ratios of these elements for each site are summarized in Table 3. These elemental ratios are compared with those of other source pro"les. Three pro"les have been taken from the SCAB source composition library (Library Nos. 1}05, 1}39, and 1}43) (NEA, 1987), and one pro"le is taken from Cass and McRae (1983). Two pro"les taken from SCAB Library No. 1}05 and Cass are composite pro"les of several di!erent types of motor vehicles, tire tread and brake linings. SCAB Library Nos. 1}39 and 1}43 are the pro"le composites of paved road dust and soil, respectively. The estimated roadway pro"le is a composite pro"le of motor vehicles and resuspended road dust. Therefore, estimated roadway source compositions cannot be directly compared with composite pro"les of motor vehicles or soil, separately. The ratios of some elements that come
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 3 Elemental ratios of estimated roadway source compositions at each site and comparison with other estimated roadway composite pro"les
Lennox Long Beach Los Angeles Anaheim Upland Rubidoux SCAB! Cass"
Si/Al
Pb/Br
Si/Ca
Si/Pb
Pb/OC
Si/OC
Si/TC
Pb/TC
2.47 2.49 2.82 2.05 2.48 2.50
4.16 5.26 6.07 5.06 5.12 4.52
3.55 2.70 2.59 3.10 2.89 2.11
8.03 7.25 8.97 18.15 17.34 24.71
0.03 0.02 0.02 0.02 0.01 0.01
0.20 0.16 0.20 0.34 0.22 0.34
0.14 0.11 0.13 0.25 0.16 0.26
0.018 0.016 0.014 0.014 0.009 0.010
3.93 2.57
0.03
0.013 0.228
!Adopted from SCAB source composition Library no. 1}05 (NEA, 1987). "Adopted from Cass and McRae (1983).
primarily from the motor vehicle and soil sources are compared only with those of the estimated roadway pro"le. The Si/Al ratio is similar at all the sites: it ranges from 2.05 at Anaheim to 2.82 at Downtown Los Angeles. This ratio is close to the ratios of 2.72 and 2.10 for the SCAB road dust and SCAB soil pro"les, respectively. The Si/Al ratio for average rock ranges from 3.41 to 4.02 and for clay material from 1.04 to 2.07 (Rahn, 1976). A high Si/Al ratio of 2.82 at Downtown Los Angeles was observed in the roadway composite pro"le compared to other sites. This high ratio could be explained by large crustal particles that might come from the activities of construction and demolition of buildings. Large particles settle from the air quickly and form road dust. Upon vehicle activity, this road dust is resuspended in the air and mixed with motor vehicle exhaust to contribute to the receptor at the same time. The low ratio of Si/Al at Anaheim (2.05) could be explained by aluminum-rich clay materials and little construction activity in this area. The Pb/Br ratio is also quite similar at all the sites. It ranges from 4.16 at Lennox to 6.07 at Downtown Los Angeles. This ratio has been compared to the ratios of the SCAB and Cass composite motor vehicle pro"les and the SCAB road dust proi"le. The ratios estimated by the SAFER model are always larger than the ratios of the two composite motor vehicle pro"les of 3.93 for SCAB and 2.57 for Cass. This fact could be explained by Br loss. Br is attacked by acids and evaporates in the HBr form. This means the fractionation coe$cient, a , cannot ij be equal to 1. The pro"le estimated by the SAFER model is a pro"le at the receptor site, a@ ; fractionation coe$ij cient, a , is already incorporated into it, but two comij posite motor vehicle pro"les do not incorporate any fractionation or losses. The Pb/Br ratio for SCAB road dust is much larger than those estimated by the SAFER model. This could also be explained by Br loss. Road
dust is a soil contaminated by motor vehicle exhaust, oil, tire tread, and brake linings. Br in road dust is attacked by acids and evaporates in the HBr form. Br loss in road dust is larger than that of the estimated roadway source because the latter is a mixture of freshly emitted exhaust gas and resuspended road dust which is exposed to the atmosphere for a longer period. Estimated Pb/Br ratios do not show spatial variation. This implies that the roadway source is a local source and that Br loss is a local e!ect; that is, it may not be related to transport from the outside of the local area. The Si/Pb, Si/OC, and Si/TC ratios show strong spatial variation. The Si/Pb ratios at Rubidoux (24.71), Anaheim (18.15), and Upland (17.34) are much larger than at the other sites, which range from 7.25 to 9.76. The roadway source is a mixture of motor vehicle and resuspended road dust sources. Therefore, depending on the mix of the two sources, the roadway source is called crustal rich or crustal lean. It is called crustal rich when the fraction of road dust in the roadway source is more than the motor vehicle fraction, and crustal lean when the reverse is true. The roadway source at the Rubidoux, Anaheim, and Upland sites must be crustal rich, and the other sites must be crustal lean. Long Beach and Lennox are the most crustal-lean sites; Downtown Los Angeles is the next most crustal-lean site. This fact can be con"rmed by the Si/OC and Si/TC ratios, too. These two ratios at Rubidoux, Anaheim, and Upland are larger than those at the other sites. Again, Rubidoux, Anaheim, and Upland are shown to be crustal rich in the roadway source. These three ratios for SCAB road dust are much larger than those estimated by SAFER for any sites. This is obviously because road dust is a crustal-rich source. The Si/Ca, Pb/OC, and Pb/TC ratios are all about the same at all the sites. The Pb/OC and Pb/TC ratios estimated by SAFER are quite similar to the ratios for SCAB road dust pro"le. The Si/Ca ratio of 4.08 for SCAB
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
road dust pro"le and 9.65 for SCAB soil pro"le are larger than the estimated ratios for any of the sites. The estimated Si/Ca ratio is within the range of 2.11}3.55. The lowest Si/Ca ratio is observed at Rubidoux. This low value is related to the unusually high fraction of Ca in the roadway pro"le and is consistent with the high Ca concentration at the Rubidoux site. This fact implies that the crustal source at Rubidoux must be Ca rich. The Pb/OC ratio ranges from 0.01 to 0.03, which is close to the ratio of 0.03 in the SCAB motor vehicle pro"le. The estimated Pb/TC ranges from 0.009 to 0.018 and can be compared with 0.013 (SCAB motor vehicle) and 0.228 (Cass). The Cass and McRae (1983) ratio is very high compared to those of others. Their pro"le is based on the 1976 SCAB emissions. Starting from 1975, leaded fuel was gradually replaced with unleaded fuel and it was completely phased out in 1990. Therefore, the low SAFER estimated ratio of Pb/TC compared to the Cass and McRae ratio could be explained by the reduction in lead concentration that has resulted from replacement of leaded with unleaded fuel. 4.1.2. Estimated secondary source proxle The estimated secondary source compositions are summarized for each site in Table 4. Secondary nitrates are formed in the atmosphere by chemical reactions during transport. Therefore, as expected, the nitrate composition shows strong spatial variations: low values at coastal sites, 0.21313 at Lennox and 0.30630 at Long Beach; high values at inland sites, 0.44563 at Upland and 0.49567 at Rubidoux. The nitrate compositions at Anaheim (0.31157) and Downtown Los Angeles (0.37047) are in the middle of the range. Since ammonium is associated with nitrate, ammonium composition shows exactly the same variation as nitrate: low values at coastal sites and high values at inland sites. Sulfate composition shows variation opposite to that of nitrate: it shows a large fraction at coastal sites and a small fraction at inland sites of Rubidoux and Upland. This does not necessarily mean that the secondary sulfate concentration is high at the coastal sites and low at inland sites. This will be discussed in the source apportionment section. At Long Beach, Lennox, and Rubidoux sites, Na is found in the form of NaNO . This result is consistent 3
with Russell and Cass (1984) and Hildemann et al. (1984). They found that there is not enough pure NH NO to 4 3 explain aerosol nitrate found in coastal areas. They explained the nitrate di!erence as sea salt or soil dust derived NaNO . Recently, this has been veri"ed from 3 a single particle analyzer (Prather et al., 1998). The SAFER model estimated the NaNO in coastal sites, 3 estimating Na fraction at Lennox to be 0.05042 and at Long Beach 0.03875. The estimated amount of NaNO is 3 shown in the next source apportionment section. The NaNO estimated at Rubidoux seems to be unexpected; 3 however, as Hildemann et al. (1984) explained, this could be formed by soil, which is one of the major sources at Rubidoux. 4.1.3. Estimated marine source proxle The strong acids in the atmosphere react with sea-salt aerosol while the atmosphere moves from the ocean to the inland. Therefore, the sea salt composition varies as it moves: the fractionation coe$cient cannot be equal to one. The Cl composition will decrease, while the NO 3 and SO composition will increase and this, in turn, 4 decreases the Na fraction in the sea salt although the actual amount of Na is constant. This decrease of Cl and Na fractions and increase of NO and SO fractions at 3 4 di!erent sites is shown clearly in the estimated source compositions. Therefore, the Cl/Na, NO /Na, and 3 SO /Na ratios will be di!erent at di!erent sites, but, the 4 ratios of Ca/Na, Mg/Na, and K/Na should be consistent over the sites within some allowable ranges. These ratios are summarized in Table 5. The ratios estimated by the SAFER model are compared below with the ratios of sea water (Pytkowicz et al., 1975) and the Portland Aerosol Characterization Study (PACS) marine pro"le (Watson, 1979). As expected, the Cl/Na, NO /Na, and SO /Na ratios show spatial vari3 4 ation over the sites. The sea salt has more time to reach Downtown Los Angeles than the coastal sites of Long Beach and Lennox. Therefore, the Cl/Na ratio in Downtown Los Angeles (0.63) is smaller than those at Long Beach (0.99) and Lennox (0.88). This ratio at Anaheim (0.92) seems to be larger than expected. These ratios can be compared with the sea water and PACS pro"les. The Cl/Na ratio at the coastal sites compared to that of sea
Table 4 Estimated secondary source compositions at each site
OC NH 4 NO 3 SO 4 Na
Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
0.13684 0.12086 0.21313 0.26881 0.05042
0.08228 0.16756 0.30630 0.29102 0.03875
0.12176 0.16253 0.37047 0.21186 *
0.11372 0.13793 0.31157 0.20658 *
0.07022 0.18234 0.44563 0.18154 *
0.06018 0.18383 0.49567 0.13442 0.01196
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 5 Elemental ratios of the estimated marine source compositions at each site and the comparison with the sea water and PACS! marine pro"les Cl/Na
SO /Na 4
NO /Na 3
Ca/Na
Mg/Na
K/Na
Lennox Long Beach Los Angeles Anaheim
0.88 0.99 0.63 0.92
1.56 0.83 1.17 0.53
0.356 0.908 0.971 1.085
0.06 0.04 0.05 0.05
0.12 0.12 0.12 0.11
0.049 0.047 0.051 0.055
Sea water! PACS"
1.80 1.00
0.25 0.25
0.000 0.000
0.04 0.04
0.12 0.12
0.037 0.040
!Adopted from Pytkowicz et al. (1975). "Portland aerosol characterization study (Watson, 1979).
water is 55% at Long Beach and 49% at Lennox. At Downtown Los Angeles, this ratio is only 35%. According to the marine pro"le estimated by SAFER, the Cl/Na ratio used in the PACS pro"le to account for Cl loss seems to be a quite good assumption. The NO /Na and 3 SO /Na ratios increase signi"cantly at all the sites com4 pared to sea water; this increase of NO and SO seems 3 4 to be site speci"c. No spatial variation is observed. As was expected, the Ca/Na, Mg/Na, and K/Na ratios are all stable over the sites and close to the ratios of sea water. The Ca/Na ratio ranges from 0.04 to 0.06, while the sea water ratio is 0.04. The Mg/Na ratio is 0.11 or 0.12, which is close enough to 0.12, the sea water ratio. The K/Na ratio range is from 0.047 to 0.055, which is larger than the ratio of sea water (0.037). The K composition must be overestimated or there must be an additional source from soil particles in the sea spray. 4.1.4. Estimated crustal source proxle The elemental ratios of some elements that come predominantly from the crustal source, Si/Al, Si/Ca, Si/Fe, Mn/Si, and Ti/Si are summarized in Table 6. The Downtown Los Angeles, Long Beach, and Upland sites are fairly consistent for all ratios. At the Rubidoux site, the Si/Al, Si/Ca, and Si/Fe ratios are much less than those at the other sites. However, the Mn/Si and Ti/Si ratios are quite consistent with those at the other sites. This implies that the crustal source at Rubidoux must be highly Ca rich and moderately Fe and Al rich in composition. These "ndings are consistent with earlier studies (Chow et al., 1992; Watson et al., 1994). The high Ca at Rubidoux is likely due to paved and unpaved road dust from nearby cement industries. Rather high fractions of OC (0.063) and EC (0.023) are estimated for the Rubidoux site. Estimated ratios have been compared with those of some other pro"les taken from the SCAB source composition library (Library Nos. 1}39, 1}43, 1}44 and 1}49) (NEA, 1987). The estimated Si/Al ratio is quite similar to
Table 6 Elemental ratios of estimated crustal source compositions at each site and comparison with other crustal compositions Si/Al
Si/Ca
Si/Fe
Mn/Si
Ti/Si
Long Beach Los Angeles Upland Rubidoux
2.46 2.93 2.57 1.80
3.27 3.05 3.15 0.53
2.40 2.45 2.65 1.90
0.010 0.012 0.010 0.013
0.053 0.049 0.039 0.054
SCAB soil! Earth's crust" Limestone#
2.10 3.39 5.71
9.65 6.80 0.08
3.53 5.01 6.32
0.007 0.003 0.046
0.028 0.020 0.017
!Adopted from SCAB source composition Library no. 1}43 (NEA, 1987). "Adopted from SCAB source composition Library no. 1}49 (NEA, 1987). #Adopted from SCAB source composition Library no. 1}44 (NEA, 1987).
those of the SCAB soil and SCAB road pro"les, except at the Rubidoux site. However, Si/Ca and Si/Fe ratios are less than those of the SCAB libraries, and Mn/Si and Ti/Si ratios are greater. The Si in crustal source must be estimated as less than that of the SCAB libraries. A large SO fraction of 0.22399 is estimated at the Long 4 Beach site. This is unusual for a crustal source. However, the estimated composition is consistent with the ambient data as shown by modeling with several di!erent sites of constraints. Therefore, this source at Long Beach will be called crustal-like source. It is crustal in the sense that elemental ratios of major crustal species are similar to crustal material and similar to the crustal source at other sites, as well; however, this is not quite a crustal source because the estimated sulfate composition is too high. Crustal at Long Beach must be mixed with modi"ed sea salts that contain Na SO because the Na fraction at 2 4 Long Beach is high compared to the other sites.
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
A large fraction of NO at Upland must be an erron3 eous estimation. For all the sites except Downtown Los Angeles, fairly large fractions of SO are estimated in the 4 crustal source. Therefore, together with the roadway source, the crustal source must be a major primary sulfate source. The primary sulfate is discussed in detail in the next section. 4.2. Source apportionment The source compositions estimated by the SAFER model were applied to the CMB model to estimate source apportionment of one-year average ambient PM concentrations. A summary of the estimated 10 source contributions for six sites using SAFER-estimated source pro"les is shown in Table 7. The source contribution from the roadway source ranges from 19.6 lg m~3 at Lennox to 34.2 lg m~3 at Rubidoux. It explains 44}57% of the annual average PM mass. The remain10 ing sites show approximately the same levels of roadway contribution. The estimated roadway pro"le is a composite pro"le of motor vehicle and road dust. Therefore, the large roadway contribution at Rubidoux must be related to the large fraction of the crustal source in the roadway composite pro"le because Rubidoux has more crustal contributions. Similarly, the small roadway contribution at Lennox could be explained by a small crustal fraction in the composite pro"le. Since secondary compounds are formed in the atmosphere by chemical reactions, the estimated secondary source contribution must be large in areas downwind from the pollution sources. In the SCAB, wind blows from the coast to the inland areas, passing through the highly polluted Downtown Los Angeles area. Therefore, the secondary source contribution at sites downwind from the coast would be larger than that at the coastal sites. Spatial variation of the secondary source contributions is shown in Table 7. The range of secondary contribution is from 17.8 lg m~3 at coastal site, Long Beach, to 31.4 lg m~3 at inland site, Rubidoux. As expected, low contributions at the coastal sites and high contributions
at the inland site, Rubidoux, are observed. Upland is located inland; however, its secondary source contribution is not as high as the Rubidoux site. It is about the same as Downtown Los Angeles. There is a dense array of dairy feedlot ammonia sources in the central part of the SCAB. Rubidoux is located just downwind of this dense array of dairy feedlot ammonia sources; however, Upland is not. Therefore, there is not enough ammonia to neutralize the available nitric acid at Upland, and the ammonium nitrate contribution is not as high as at Rubidoux (see Table 8). The secondary source contribution at Lennox is 22.5 lg m~3, accounting for 50% of the PM mass. This seems to be overestimated compared to 10 other coastal sites, if the expected spatial gradient is maintained. As is shown in Table 8, secondary carbon in Lennox appears to be overestimated as well, compared to other coastal sites. The marine contribution ranges from undetectable to 2.5 lg m~3. The marine contribution shows a spatial variation because the sea-salt composition varies as the sea-salt aerosol moves inland from the ocean. As expected, large contributions are observed in coastal areas and small contributions in inland areas, where the marine contribution is not detectable. The small marine contribution (1.5 lg m~3) observed at Long Beach appears to be underestimated compared to other coastal sites. The marine contribution explains 3}6% of annual average PM mass. 10 The crustal contribution ranges from 4.1 lg m~3 at Long Beach to 8.1 lg m~3 at Rubidoux. Large crustal contributions were observed at Rubidoux and Upland. This crustal contribution accounts for the unexplained portion of the crustal material in the resuspended road dust. This crustal contribution explains 8}13% of PM 10 mass. The secondary source apportionment is di!erentiated to each compound (in the form of ammonium nitrate, ammonium sulfate, ammonium bisulfate, and secondary organic carbonaceous matter) and to each species. These results are summarized in Table 8. The percentage of organic carbon, nitrate, and sulfate from secondary
Table 7 Summary of source apportionment (lg m~3) for each site Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
Roadway Secondary Marine Crustal Sum Unexplained
19.6 22.5 2.5 * 44.6 0.5
25.1 17.8 1.5 4.1 48.4 1.4
27.0 20.8 1.7 6.0 55.5 4.8
28.9 19.4 2.2 * 50.6 2.1
25.4 20.4 * 6.6 52.4 5.6
34.2 31.4 * 8.1 73.7 14.1
Total mass
45.1
49.9
60.3
52.7
58.0
87.8
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 8 Source apportionment of secondary particulate matter Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
As a compound form (lg m~3) SOCM! 4.0 NH NO 2.2 4 3 (NH ) SO 7.9 42 4 NH HSO 0.3 4 4 NaNO 4.2 3
1.9 4.6 7.1 0.0 2.5
3.3 10.0 2.3 3.3 *
2.9 7.8 1.2 3.7 *
1.9 11.7 2.8 2.0 *
2.5 18.8 5.5 0.2 1.4
As a species form (lg m~3) SOC 3.1 NH 2.7 4 NO 4.8 3 SO 6.1 4
1.5 3.0 5.4 5.2
2.5 3.4 7.7 4.4
2.2 2.7 6.1 4.0
1.4 3.7 9.1 3.7
1.9 5.8 15.6 4.2
!Secondary organic carbonaceous matter.
Table 9 Percentage of various species from secondary sources to estimated data
OC! OC" NO 3 SO 4
Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
32.3 25.0 96.2 87.9
14.4 10.7 90.7 69.0
25.1 17.1 92.6 72.3
24.1 18.8 90.0 65.6
15.8 12.1 96.6 59.5
16.5 12.9 96.0 73.7
!Percentage of secondary organic carbon to total organic carbon. "Percentage of secondary organic carbon to total carbon. Gray et al. (1986): 16}22% Azusa and Rubidoux, 1982 for "ne SOC/TC (assume OC at Lennox is all primary). 27}38% SOC/OC. Pratsinis et al. (1984); 17% Lennox for single day SOC/TC. 30% Duarte 10/23/80.
sources to estimated organic carbon, nitrate, and sulfate is shown in Table 9. As expected, nitrates (Table 8) show a spatial distribution with larger values at Rubidoux than at the coast. However, the percentage of the nitrates from the secondary source is quite similar (90}97%). High nitrate concentration at Rubidoux is due to signi"cant ammonia sources, dairy feedlots, in the central part of the SCAB that is located just upwind of Rubidoux. Sulfates in the secondary source are higher at the coast than at inland sites and they range from 3.7 lg m~3 at Upland to 6.1 lg m~3 at Lennox. The percentage of secondary sulfates ranges from 60 to 74%, except at the Lennox site, where it is 88%. Sulfates as ammonium sulfate show high contributions at Lennox, Long Beach and Rubidoux and low contributions at Downtown Los Angeles, Anaheim and Upland while ammonium bisulfate shows the opposite variation. The estimated secondary organic carbon does not show a geographically consistent variation, but seems to vary according to mobile source emission levels. The
range is from 1.9 lg m~3 at Upland to 3.3 lg m~3 at Downtown Los Angeles. The secondary organic carbon at Lennox is not considered because it must be overestimated compared to other coastal sites. The percentage of secondary organic carbon to total carbon ranges from 10.7% at Long Beach to 18.8% at Anaheim; secondary organic carbon to total organic carbon ranges from 14.4 to 25.1%. The fraction of secondary organic carbon at Rubidoux and Upland must be larger than estimated if the secondary organic carbon is formed mainly while being transported from the coast to inland sites. However, the percentage of estimated secondary organic carbon at Rubidoux and Upland must be underestimated compared to Gray et al. (1986) and Pratsinis et al. (1984) as shown in Table 9. Nitrates, sulfates, and organic carbon each have di!erent spatial variations. The secondary portion of the nitrates, sulfates, and organic carbon is about the same for all the sites, averaging 93, 68, and 19%, respectively. The Lennox site is not included in the average because
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
the secondary source compositions at Lennox seem to be substantially overestimated compared with the other coastal sites. This result shows that 7% of nitrates come from major primary sources such as the roadway and marine sources. A fairly large (32%) amount of sulfates is attributed to major primary sulfate sources such as roadway and crustal sources. Organic carbon is mostly (81%) attributed to the primary roadway sources.
5. Conclusions Most of the time, source compositions vary during transport: those measured at the source di!er from those measured at the receptor. Therefore, application of the "xed value of source compositions for di!erent time periods and locations may lead to unreliable results. The SAFER model has been developed based on the SMCR technique to resolve the source compositions from the ambient data with the minimum amount of a priori knowledge. This model estimates the source compositions at the receptor site with the transformation e!ects already incorporated. The SAFER model was applied to the PM data, and 10 the source compositions of some major source categories were estimated. To estimate the source compositions, a limited amount of a priori knowledge was used. For example, stoichiometry among secondary species and negligible concentrations of carbon monoxide and elemental carbon concentrations were used to estimate the secondary source composition pro"les. Pb, Si and ozone concentrations were used to estimate roadway pro"les. The SAFER model-estimated source pro"les were compared with the measured source pro"les by checking some important elemental ratios. Estimated source compositions were very strongly consistent with the measured pro"les, especially the chemical species not used as APCs. As expected, most of the estimated source composition pro"les show spatial variations. The source contributions of each source category have been estimated by the CMB model for each site. Contributions from the roadway source range from 20 to 34 lg m~3, from the secondary source from 17.75 to 31.40 lg m~3, from the marine source from undetectable to 2.50 lg m~3, and from the crustal source from 4.06 to 8.13 lg m~3. Estimated contributions of the secondary and marine sources show spatial variation among the sites: high contributions are obtained from the secondary source (31.40 lg m~3) at an inland site (Rubidoux) and from the marine source (2.50 lg m~3) at a coastal site (Lennox); whereas low contributions are obtained from the secondary source (17.75 lg m~3) at a coastal site (Long Beach) and the marine source (1.67 lg m~3) at an inland site (Downtown Los Angeles). The source apportionment of the secondary particulate matter, including organic carbon, was made and
compared over the sites. Nitrates show strong spatial variation among the sites; sulfates and organic carbons do not. Organic carbon seems to be mainly (81%) contributed by the primary roadway source, and sulfates and nitrates are mainly from the secondary source, although 32% of the sulfates are from primary sources such as roadway, crustal, and marine sources. The SAFER model was shown to be a powerful tool in estimating source compositions from the ambient data with only a limited amount of a priori knowledge; furthermore, the transformation e!ects are already incorporated into the estimated source compositions. Although the uncertainties are associated with the estimated pro"les, the SAFER model-estimated source pro"les should be more representative than the measured pro"les in that the transformation e!ects are incorporated in the estimated source pro"les. Kim et al. (1992) compare the two sets of source apportionment results, one using measured source compositions and the other using SAFER estimated source compositions. The comparison study reveals that the source apportionment made by the two di!erent source compositions, measured and estimated, do not show signi"cant di!erences. Both results were generally in good agreement within an acceptable error range. The SAFER model will have extended application in the future in the estimation of source compositions that are di$cult to measure. Additional research with the SAFER model is needed to further reduce the size of, and better represent the feasible region.
6. Disclaimer The statements, opinions, "ndings, and conclusions of this paper are those of the authors and do not represent the views of the South Coast Air Quality Management District.
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759 Appendix V-I, Air Quality Management Plan 1989 revision, South Coast Air Quality Management District, El Monte. Henry, R.C., Kim, B.M., 1990. Extension of self-modeling curve resolution to mixtures of more than three components: Part 1. Finding the basic feasible region. Chemometrics and Intelligent Laboratory Systems 8, 205}216. Hidy, G.M., Mueller, P.K., Wang, H.H., Karney, J., Twiss, S., Imada, M., Alcocer, A., 1974. Observations of aerosols over Southern California Coastal Waters. Journal of Applied Meteorology 13, 96}107. Hildemann, L.M., Russell, A.G., Cass, G.R., 1984. Ammonia and nitric acid concentrations in equilibrium with atmospheric aerosols: experimental vs. theory. Atmospheric Environment 18, 1737}1750. Hitchcock, D.R., 1980. Sulfuric acid aerosols and HCl release in coastal atmospheres: evidence of rapid formation of sulfuric acid particulates. Atmospheric Environment 14, 165}182. Jolli!e, I.T., 1986. Principal Component Analysis. Springer, New York. Kim, B.M., 1989. Development of a new multivariate receptor model and its application to Los Angeles airborne particle data. Ph.D. Dissertation. University of Southern California, Los Angeles, CA. Kim, B.M., Lewis, R., Hogo, H., Chow, J.C., 1992. Source apportionment by chemical mass balance: a comparison between measured source pro"les and SAFER model estimated source pro"les. Ph.D. Dissertation. University of Southern California, Los Angeles, CA, pp. 146}162. Kim, B.M., Henry, R.C., 1999. Extension of self-modeling curve resolution to mixtures of more than three components: Part 2. Finding the complete solution. Chemometrics and Intelligent Laboratory Systems 49, 61}77. Lawton, C.L., Silvester, E.A., 1971. Self modeling curve resolution. Technometrics 13, 617}630. NEA, 1987. PM source composition library for the South 10 Coast Air Basin. Final Report to the South Coast Air Quality Management District, El Monte, California. NEA, 1990. Enhancement of the South Coast Air Basin source pro"le library for Chemical Mass Balance receptor model
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applications. Final Report to the South Coast Air Quality Management District, El Monte, California. Prather, K.A., Gard, E., Morrical, B., 1998. Aerosol time-of #ight mass spectrometry as a real-time monitor of individual aerosol particles in "eld studies. Final Report to California Air Resources, Contract No. 95-305. Pratsinis, S., Novakov, T., Ellis, E.C., Friedlander, S.K., 1984. The carbon containing component of the Los Angeles Aerosol: source apportionment and contributions to the visibility budget. Journal of the Air Pollution Control Association 34, 643}650. Pytkowicz, R.M., Atlas, E., Culberson, C.H., 1975. Chemical equilibrium in seawater. In: Church, T.M. (Ed.), Marine Chemistry in the Coastal Environment, American Chemical Society Symposium Series, 18. ACS, Washington, DC, pp. 1}24. Rahn, K.A., 1976. Silicon and aluminum in atmospheric aerosols: crust}air fractionation? Atmospheric Environment 10, 597}601. Russell, A.G., Cass, G.R., 1984. Acquisition of regional air quality model validation data for nitrate, sulfate, ammonium ion and their precursors. Atmospheric Environment 18, 1815}1827. Solomon, P.A., Fall, T., Salmon, L., Cass, G.R., Gray, H.A., Davidson, A., 1989. Chemical characteristics of PM 10 aerosols collected in the Los Angeles area. JAPCA 39, 154}163. U.S. Environmental Protection Agency, US EPA, 1987. PM 10 SIP Development Guideline. EPA-450/2-86-001, Research Triangle Park, NC. Watson, J.G. Jr., 1979. Chemical elemental balance receptor model methodology for assessing the sources of "ne and total suspended particulate matter in Portland, Oregon. Ph.D. Dissertation, Oregon Graduate Center, Beaverton, Oregon. Watson, J.G., Chow, J.C., Lu, Z., Fujita, E.M., Lowenthal, D.H., Lawson, D.R., 1994. Chemical mass balance source apportionment of PM during the Southern California Air Qual10 ity Study. Aerosol Science and Technology 21, 1}36.
Application of SAFER model to the Los Angeles PM data 10 Bong Mann Kim!,*, Ronald C. Henry" !South Coast Air Quality Management District, Planning and Policy, 21865 E Copley Dr., Diamond Bar, CA 91765, USA "University of Southern California, Department of Civil/Environmental Engineering, University Park, Los Angeles, CA 90089-2531, USA Received 18 November 1998; accepted 4 August 1999
Abstract The chemical mass balance (CMB) model has been used widely in the PM source apportionment study and the 10 PM state implementation plan (SIP) development. In its modeling application, the CMB model requires source 10 composition pro"les, which can either be measured by source testing or estimated by a multivariate receptor model such as source apportionment by factors with explicit restrictions (SAFER) from the ambient data only. The SAFER model is a multivariate receptor model utilizing a series of linear programming methods to estimate source compositions with explicit physical constraints. The SAFER model was applied to ambient PM data collected in 1986 in the South Coast 10 Air Basin. Source compositions of selected major source categories were estimated. The SAFER model-estimated source pro"les were compared with the measured source pro"les by checking some important elemental ratios. Estimated source compositions were consistent with the measured pro"les. Then the SAFER model-estimated source pro"les were used for CMB analysis to estimate source contributions from each source category. Contributions from the roadway source range from 20 to 34 lg m~3, from the secondary source from 17.75 to 31.40 lg m~3, from the marine source from undetectable to 2.50 lg m~3, and from the crustal source from 4.06 to 8.13 lg m~3. Organic carbon seems to be mainly (81%) contributed by the primary roadway source, and sulfates and nitrates are mainly from the secondary source, although 32% of the sulfate is from primary sources such as roadway, crustal, and marine sources. Published by Elsevier Science Ltd. Keywords: Principal component analysis; SAFER model; Self modeling curve resolution; Additional physical constraints; Stoichiometric constraint; Estimation of source compositions
1. Introduction Because the US EPA (1987) recommends the chemical mass balance (CMB) model as one of the modeling options for the development of a PM state implementa10 tion plan (SIP), the CMB model has been used widely in PM source apportionment study and PM SIP devel10 10 opment in nonattainment areas. The CMB model may be the most reliable model for assessing contributions of primary particles if accurate source compositions are available. Source compositions, however, are almost always uncertain because source compositions may vary
* Corresponding author. Tel.: #1-909-396-3157; fax: #1909-396-3252. E-mail address: [email protected] (Bong M. Kim) 1352-2310/00/$ - see front matter Published by Elsevier Science Ltd. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 3 6 5 - 9
with time, location, raw material and fuel type. Measurement of reliable source compositions is di$cult or sometimes virtually impossible for some sources. Source compositions can be estimated by a multivariate receptor model from the ambient data without direct measurement of the source compositions. The multivariate receptor model, SAFER (source apportionment by factors with explicit restrictions) (Kim, 1989; Henry and Kim, 1990; Kim and Henry, 1999) was applied to PM data 10 collected in 1986 in the South Coast Air Basin (SCAB). Source compositions of the major source categories, such as roadway, secondary, marine, and crustal sources were estimated at six sites in the SCAB and source contributions were determined using the CMB model. The following section is a brief summary of the receptor modeling followed by a model application. The remainder of the paper is devoted to the results of the model application.
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
2. Receptor modeling Receptor modeling is a technique for determining emission sources and their contributions to ambient particulate concentrations at speci"c receptor sites. Unlike complex mathematical models which require detailed simulations of physics, chemistry, meteorology, and other processes, receptor models are relatively simple statistical models which require as inputs the particulate data measured at receptor sites and sometimes source data. In the following, the CMB and SAFER receptor models are brie#y explained. 2.1. Chemical mass balance (CMB) model
(Jolli!e, 1986) and the self-modeling curve resolution (SMCR) technique (Lawton and Silvester, 1971). This model leads to source compositions from the ambient data without measuring source compositions, given minimum a priori knowledge. This model is brie#y explained in the following and details can be found in Kim (1989), Henry and Kim (1990), and Kim and Henry (1999). This model di!ers from other factor analysis/principal component analysis-based methods by implementing explicit physical constraints in estimating source compositions. PCA can be explained using the singular-value decomposition (SVD) theorem (Jolli!e, 1986). Any ambient concentrations matrix, C, can be decomposed uniquely as C";D
If p sources exist, the mass concentration of species i, C , measured at the receptor is, i p p C " + S " + a@ S , i"1, 2,2, n, (1) i ij ij j j/1 j/1 where S is the mass of species i from source j and a@ is ij ij the mass fraction of species i in the mass from source j collected at the receptor. Furthermore, a@ "a a , (2) ij ij ij where a is the mass fraction of species i emitted by ij source j as measured at the source and a is the fractionaij tion coe$cient of species i emitted by source j. This coe$cient explains the modi"cation of a to a@ during ij ij transport due to deposition, evaporation and chemical transformation. Usually in the CMB model, a is set to ij 1 under the assumption of conservation of mass. Finally, Eq. (1) reduces to C "a S , i"1, 2,2, n i ij j and in matrix form,
(3)
C"AS#E,
(4)
where C is an ambient concentration matrix of m measurements by n species, ; is the m]m eigenvectors of CCT, D is an m]n diagonal matrix made up of square roots of the corresponding eigenvalues, and < is the n]n eigenvectors of CTC. This result is called the SVD theorem. The < eigenvectors multiplied by the singular values,
2.2. SAFER model The SAFER model is a new type of multivariate receptor model based on principal component analysis (PCA)
(6)
where A is a source composition matrix of n species by p sources and S is p]n source contributions matrix. If A"< and S"(;D)T, Eqs. (5) and (6) are the same. However, < is not the same as A because < is a matrix of eigenvectors. Therefore, abstract eigenvectors, <, need to be transformed to a physically meaningful solution of source compositions, A, by C"(;D¹~1)(¹
where C is an n]1 vector of ambient concentrations, A is an n]p source composition matrix, S is an p]1 vector of source contributions to be estimated, and E is an n]1 vector of measurement errors. The principal approach to solving the resulting mass balance equations has been the least-squares "tting, usually called chemical mass balance (CMB) approach. As noted in the Introduction, CMB may be the most reliable model to estimate source contributions of the primary particle sources if accurate a@ is available. However, generally, a is not equal to ij ij 1 and a@ is not available. Source compositions at a given ij time period and location, a@ , can be estimated from ij a multivariate receptor model.
(5)
(7)
where ¹ is a p]p transformation matrix. Mathematically, there are an in"nite number of ways to transform the principal component solution into another so that the transformed solutions explain the ambient data equally well. There are several di!erent transformation methods, such as orthogonal transformation (VARIMAX, QUARTRIMAX, PROMAX) and oblique transformation (OBLIMIN). However, these transformation methods are not based on physical reality so that they do not guarantee that the transformed results will make sense physically. It is not unusual for the transformed results to have physically impossible negative values of source compositions. The SAFER model uses a new transformation method based on the SMCR technique. Since a unique solution is not possible (Henry, 1987), the SMCR technique restricts the feasible region of the real solution into a small region with explicit physical constraints, such as source compositions must be greater than or equal to zero. Explicit
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
physical constraints form linear inequality constraints in the space spanned by the eigenvectors, <, and these constraints form the feasible region in eigenvectors space, <. Explicit physical constraints, however, are not enough to resolve the sources, and additional information is needed to further restrict the feasible region and to resolve the sources. Prior knowledge of source compositions for each source, e.g., silicon composition in soil dust is between 0.1 and 0.3, could be used directly as additional physical constraints (APCs) in addition to the explicit physical constraints. Any reasonable, not necessarily precise, ranges of source compositions for each source could be used as APCs. APCs also form linear inequality constraints in the eigenvectors space, <, and form a very small feasible region for a speci"c source within the feasible region formed from explicit physical constraints. The feasible region is obtained by "nding all the vertices of the feasible region by a series of linear programming. Any point in the feasible region for a particular source could be the transformation vector for that source. Then any point in the feasible region can be translated into source compositions by A"<¹T
(8)
for that source. Although the feasible region for each source is restricted to a small region, an in"nite number of source compositions is still possible. The source composition at the center of gravity of the feasible region is used as the estimated source composition, and the deviations of the source composition from the source composition of the centroid are provided as "nal solutions of the model. The SAFER model was applied to 1986 PM data 10 which is described in the next section. Source compositions and their source apportionments of the major source categories, such as roadway, marine, crustal, and secondary sources, were estimated at six sites in the SCAB. In this model application, to further reduce the size of the feasible region and to estimate quite precise source compositions, carbon monoxide and ozone gas data were used along with the PM data. These two 10 gases were used as the APCs for the SAFER model application because carbon monoxide and ozone are unique tracers for the motor vehicle and secondary sources, respectively. In addition, a new concept of additional physical constraint, stoichiometric constraint, was also used. This constraint is explained in the next section. 3. Model application 3.1. Data description The Environmental Quality Laboratory of the California Institute of Technology conducted 24-h PM samp10
1749
ling every six days from January 1986 to December 1986 in the SCAB at nine sampling sites: Burbank, Long Beach, Lennox, Rubidoux, Anaheim, San Nicolas Island, Downtown Los Angeles, Upland, and Tanbark Flats. PM was sampled at all the sites and PM at Down10 2.5 town Los Angeles only. Total mass and 41 species (Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, Mo, Pd, Ag, Cd, In, Sn, Sb, Ba, La, Hg, Pb, Na, Mg, OC, EC, NO , SO , NH ) were analyzed. 3 4 4 A more detailed description of the monitoring site, sampling schedule, and sample analysis can be found in Solomon et al. (1989). To enhance the model results, carbon monoxide and ozone gas data were used together with PM data. Data 10 for CO and O are not available from Tanbark Flats and 3 San Nicolas Island. As a result, two coastal sites } Lennox and Long Beach } and "ve inland sites } Downtown Los Angeles, Burbank, Anaheim, Upland and Rubidoux } have been selected. For model application, ambient data for each site were reduced to two gas parameters (CO and O ) and 24 selected PM species (OC, EC, 3 10 NO , SO , NH , Na, Mg, Al, Si, P, Cl, K, Ca, Ti, V, Cr, 3 4 4 Mn, Fe, Ni, Cu, Zn, Br, Sr, and Pb), due to the uncertainty level in the ambient data. 3.2. Initial source identixcation The number of contributing sources to each site can be estimated from the number of eigenvalues in the correlation matrix of the ambient data. Many analysts use a minimum eigenvalue of 1.0 as the in#uential cuto!; a lower value of 0.5 is used here to ensure that all major in#uences are considered. The number of probable sources identi"ed for each site is six for Rubidoux, seven for Downtown Los Angeles, Long Beach, Upland, eight for Lennox and Anaheim, and nine for Burbank (Kim, 1989). Major source categories identi"ed are motor vehicle, crustal, secondary, and marine sources. The factor analysis (FA) or PCA model cannot distinguish spatially and temporally correlated sources. From the FA or PCA standpoint, spatially and temporally correlated sources are perceived as a single source because they almost always impact the receptor site at the same time. Although these are not a single source, they can be assumed as a pseudo-single composite source. The motor vehicle and road dust sources are spatially and temporally correlated as the road dust is resuspended in the air when the motor vehicle passes over the road. The receptor never experiences motor vehicle tailpipe emissions separate from resuspended road dust, but rather always receives roadway emissions as a mixture of road dust and motor vehicle exhaust. Therefore, source compositions representing this composite roadway source are estimated at all sites from the SAFER model. Secondary species, such as, NH , NO , SO , and sec4 3 4 ondary organic species are usually present as a compound
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
in the form of ammonium nitrate, ammonium sulfate, or ammonium bisulfate and secondary organic carbonaceous matter. These secondary compounds are formed in the atmosphere by chemical reactions and typically impact a receptor site at the same time. Therefore, at each receptor site these compounds seem to be coming from the same secondary source. However, this is not a real source, but is the result of chemical reactions from unidenti"ed sources of nitrogen oxides and sulfur oxides. 3.3. Additional physical constraints Setting up additional physical constraints is essential to the SAFER model. This step consists of two substeps: "rst, the selection of the elements and, second, the selection of the source composition ranges for these elements. The general starting points for setting up the appropriate additional physical constraints for each source are the selection of the possible tracers and reasonable ranges for them. The latter are obtained either directly from the source characterization of the source or indirectly from the existing source composition pro"les. In this model application, CO and O gas data have 3 been added to the PM data to minimize the number 10 of additional physical constraints and to enhance the modeling results. CO and O are unique tracers for the 3 motor vehicle and secondary sources, respectively. Therefore, these two gas data can be used as the APCs that are necessary for the SAFER model application; for example, the O fraction for the motor vehicle source 3 must be close to zero and CO for the secondary source must be close to zero. 3.4. Stoichiometric constraint Stoichiometric relations among species are used as APCs for two reasons: to lessen the di$culties of setting up the APCs for each source and to reduce the size of the feasible ranges of the resolved source compositions. Water-soluble ionic species, such as NH , NO , SO , 4 3 4 Na, and Cl, are present as chemical compounds. Therefore, the stoichiometric relationship among species could be used as APCs. For example, NH , NO , and SO 4 3 4 may exist as the compounds NH NO , (NH ) SO , and 4 3 42 4 NH HSO . These three compounds and the secondary 4 4 organic carbon derive mostly from the secondary source. Let the fractions of each compound in the secondary source be assumed as follows: OC : f
1
(9)
NH NO : f 4 3 2
(10)
(NH ) SO : f 42 4 3
(11)
NH HSO : f . 4 4 4
(12)
Then the source compositions of OC (a ), NH (a 4 ), OC 4 NH NO (a 3 ), and SO (a 4 ) in the secondary source can 3 NO 4 SO be expressed as the sum of the fraction of each compound f, i a "0.769 f OC 1
(13)
a 4 "0.225f #0.273f #0.157f 2 3 4 NH
(14)
a 3 "0.775f NO 2
(15)
a 4 "0.727f #0.835f . 3 4 SO
(16)
These expressions can be used directly as additional physical constraints for OC, NH , NO , and SO spe4 3 4 cies; however, the dimensionality will be increased by four and this will require more time to estimate the source compositions. Instead, the above equations are solved for f and the following constraints applied: i f *0, i
(17)
0.9)+ f )1.0, for i"1,2, 4. i
(18)
These physical constraints, derived from the stoichiometric relation among the species, turn out to be very strict in the sense of restricting the feasible region. The concept of using stoichiometry among species as APCs for the secondary source can be extended to the marine and roadway sources. For the marine source, it is well known that NaCl reacts with strong acids such as HNO and H SO to form NaNO , Na SO , and HCl 3 2 4 3 2 4 (Hidy et al., 1974; Hitchcock, 1980). As before, fractions of each compound of NaCl, NaNO , Na SO , and 3 2 4 SO are assumed as f . Then APCs for f are set up. For 4 i i the roadway source, fractions of each compound of NH NO , (NH ) SO , and NH HSO are used to set 4 3 42 4 4 4 up APCs. 3.5. Additional physical constraints for major source categories The APCs used for each source category for each site are summarized in Table 1. Setting up APCs for the composite roadway source is di$cult because the composite pro"le depends on how much of the motor vehicle and crustal sources are mixed together. The fraction of the mixture of the two sources will di!er from site to site. The composite roadway source pro"les were obtained at three di!erent sites (Downtown Los Angeles, Lennox, and Rubidoux) by estimating the best linear combination of the source pro"les that best explain the measured ambient data. In other words, the composite roadway pro"les were obtained by minimizing the di!erence between the linear combination of the motor vehicle and road dust source pro"les and those estimated from the linear combination of the eigenvectors of the ambient data matrix. The solution to the minimization problem
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 1 Additional physical constraints used for the sources for each site Roadway
Secondary
Marine
Crustal
NH NO , (NH ) SO , 4 3 42 4 and NH HSO 4 4
NH NO , (NH ) SO , 4 3 42 4 NH HSO , NaNO , 4 4 3 and OC
NaCl, NaNO , Na SO , 3 2 4 and SO 4
0.0)+ f )0.1 *
0.9)+ f )1.0 *
0.8)+ f )1.0 *
Lennox
0.048)Si)0.088 0.007)Pb)0.018 0.0)O )0.001 3
0.0)CO)0.01 0.0)EC)0.01
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30
Long Beach
0.048)Si)0.088 0.007)Pb)0.018 0.0)O )0.001 3
0.0)CO)0.00001 0.0)EC)0.001
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30 0.0)CO)0.00001
0.0)O )0.001 3 0.05)Al)0.15 0.0)NH )0.001 4 0.0)NO )0.001 3
Los Angeles
0.04)Si)0.07 0.004)Pb)0.008 0.0)O )0.001 3
0.0)CO)0.01 0.0)EC)0.001
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30
0.0)O )0.00001 3 0)OC)0.001 0.0)NH )0.001 4 0.0)NO )0.001 3 0.0)SO )0.001 4 0.05)Al)0.15 0.15)Si)0.35
Anaheim
0.06)Si)0.12 0.002)Pb)0.005 0.026)Al)0.049
0.0)CO)0.00001 0.0)EC)0.001
0.0)EC)0.001 0.0)NH )0.001 4 0.05)Na)0.30
Upland
0.06)Si)0.12 0.0035)Pb)0.006 0.0)O )0.001 3
0.0)CO)0.00001 0.0)EC)0.001
0.0)CO)0.015 0.0)O )0.00001 3 0)OC)0.001 0.0)EC)0.001 0.0)NH )0.001 4 0.05)Al)0.15 0.15)Si)0.35
Rubidoux
0.06)Si)0.12 0.0035)Pb)0.006
0.0)CO)0.00001 0.0)EC)0.001
0.0)O )0.00001 3 0.0)NH )0.001 4 0.0)NO )0.001 3 0.1)Si)0.3 0.2)Ca)0.4
Stoichiometry among
was found by the application of Lagrange's undetermined multipliers. The complete methodology for obtaining the roadway composite pro"les is discussed in Henry and Kim (1989). Si and Pb were selected for setting up APCs and their ranges were taken from results of the above study. CO and EC were selected to set up APCs for the secondary source. Since it is obvious that CO and EC
have to be negligible, their ranges were set to small values. Na, EC, NH , and CO were used to set up APCs 4 for the marine source and these species, except Na, were set to negligible ranges. APCs for the crustal source were selected from Al, Si, CO, O , OC, EC, NH , NO , and 3 4 3 Ca depending on the site under study. After APCs for each source category were set up, the model was applied to estimate the source compositions
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
Table 2 Estimated source compositions and uncertainties at Downtown Los Angeles Roadway OC EC NH 4 NO 3 SO 4 Na Mg Al Si P Cl K Ca Ti V Cr Mn Fe Ni Cu Zn Br Sr Pb
0.27987 0.15622 0.01532 0.00990 0.04723 0.00732 0.00227 0.01953 0.05498 0.00093 0.01347 0.00786 0.02122 0.00352 0.00034 0.00034 0.00124 0.03437 0.00029 0.00939 0.01028 0.00101 0.00038 0.00613
Secondary (0.08930) (0.03290) (0.00992) (0.02440) (0.03527) (0.01182) (0.00116) (0.00490) (0.01465) (0.00065) (0.01472) (0.00155) (0.00442) (0.00062) (0.00004) (0.00005) (0.00014) (0.00476) (0.00005) (0.00319) (0.00246) (0.00024) (0.00004) (0.00079)
0.12176 0.00051 0.16253 0.37047 0.21186 0.02866 0.00390 0.00549 0.00765 0.00080 0.00179 0.00308 0.00263 0.00046 0.00014 0.00007 0.00001 0.00161 0.00022 0.01107 0.00732 0.00043 0.00001 0.00100
Marine (0.05108) (0.00050) (0.01703) (0.10107) (0.05672) (0.00874) (0.00104) (0.00170) (0.00565) (0.00048) (0.00367) (0.00061) (0.00320) (0.00027) (0.00004) (0.00002) (0.00002) (0.00169) (0.00005) (0.00490) (0.00328) (0.00006) (0.00001) (0.00047)
of these source categories, and source apportionments were made from the CMB model. The model's results for each site are summarized in the next section.
0.01155 0.00050 0.00050 0.20646 0.24962 0.21270 0.02610 0.00415 0.00695 0.00306 0.13489 0.01088 0.01063 0.00072 0.00018 0.00026 0.00090 0.01213 0.00093 0.06815 0.04989 0.00143 0.00028 0.00791
Crustal (0.01509) (0.00050) (0.00050) (0.02180) (0.02176) (0.00440) (0.00045) (0.00197) (0.00631) (0.00041) (0.00875) (0.00062) (0.00209) (0.00026) (0.00001) (0.00002) (0.00009) (0.00193) (0.00003) (0.00168) (0.00143) (0.00003) (0.00002) (0.00061)
0.00039 0.08661 0.00010 0.00047 0.00047 0.00728 0.00177 0.05129 0.15025 0.00213 0.02020 0.01776 0.04921 0.00742 0.00046 0.00062 0.00185 0.06129 0.00034 0.00003 0.00697 0.00030 0.00051 0.00530
(0.00047) (0.00037) (0.00019) (0.00050) (0.00050) (0.00025) (0.00003) (0.00019) (0.00057) (0.00002) (0.00045) (0.00007) (0.00019) (0.00003) (0.00000) (0.00000) (0.00001) (0.00021) (0.00000) (0.00007) (0.00006) (0.00000) (0.00000) (0.00003)
ation between sites is examined for consistency with expected behavior. 4.1. Source composition
4. Results and discussions As an example, the estimated source compositions for Downtown Los Angeles are shown in Table 2 where the estimated source compositions are reported to "ve decimal points. Measured source compositions are usually reported to six decimal points (NEA, 1987,1990). The estimated source compositions are examined below by checking some important elemental ratios. The SAFER model used a very limited amount of a priori knowledge when estimating the source compositions. For example, the Si and Pb ranges are the only a priori knowledge used to estimate the roadway source compositions. If the model estimates the compositions correctly, the estimated compositions should be consistent for all the sites. In other words, the elemental ratios of the estimated source compositions, especially for elements not used as APCs, should be consistent for the sites. These elemental ratios are compared to the ratios expected from source compositions found in the literature. After examining the estimated pro"les, their vari-
4.1.1. Estimated roadway composite source proxle Some elements such as Si, Al, Ca, Pb, Br, and OC are the main elements of the roadway source and were selected to examine the elemental ratios of the estimated roadway source compositions. The elemental ratios of these elements for each site are summarized in Table 3. These elemental ratios are compared with those of other source pro"les. Three pro"les have been taken from the SCAB source composition library (Library Nos. 1}05, 1}39, and 1}43) (NEA, 1987), and one pro"le is taken from Cass and McRae (1983). Two pro"les taken from SCAB Library No. 1}05 and Cass are composite pro"les of several di!erent types of motor vehicles, tire tread and brake linings. SCAB Library Nos. 1}39 and 1}43 are the pro"le composites of paved road dust and soil, respectively. The estimated roadway pro"le is a composite pro"le of motor vehicles and resuspended road dust. Therefore, estimated roadway source compositions cannot be directly compared with composite pro"les of motor vehicles or soil, separately. The ratios of some elements that come
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
1753
Table 3 Elemental ratios of estimated roadway source compositions at each site and comparison with other estimated roadway composite pro"les
Lennox Long Beach Los Angeles Anaheim Upland Rubidoux SCAB! Cass"
Si/Al
Pb/Br
Si/Ca
Si/Pb
Pb/OC
Si/OC
Si/TC
Pb/TC
2.47 2.49 2.82 2.05 2.48 2.50
4.16 5.26 6.07 5.06 5.12 4.52
3.55 2.70 2.59 3.10 2.89 2.11
8.03 7.25 8.97 18.15 17.34 24.71
0.03 0.02 0.02 0.02 0.01 0.01
0.20 0.16 0.20 0.34 0.22 0.34
0.14 0.11 0.13 0.25 0.16 0.26
0.018 0.016 0.014 0.014 0.009 0.010
3.93 2.57
0.03
0.013 0.228
!Adopted from SCAB source composition Library no. 1}05 (NEA, 1987). "Adopted from Cass and McRae (1983).
primarily from the motor vehicle and soil sources are compared only with those of the estimated roadway pro"le. The Si/Al ratio is similar at all the sites: it ranges from 2.05 at Anaheim to 2.82 at Downtown Los Angeles. This ratio is close to the ratios of 2.72 and 2.10 for the SCAB road dust and SCAB soil pro"les, respectively. The Si/Al ratio for average rock ranges from 3.41 to 4.02 and for clay material from 1.04 to 2.07 (Rahn, 1976). A high Si/Al ratio of 2.82 at Downtown Los Angeles was observed in the roadway composite pro"le compared to other sites. This high ratio could be explained by large crustal particles that might come from the activities of construction and demolition of buildings. Large particles settle from the air quickly and form road dust. Upon vehicle activity, this road dust is resuspended in the air and mixed with motor vehicle exhaust to contribute to the receptor at the same time. The low ratio of Si/Al at Anaheim (2.05) could be explained by aluminum-rich clay materials and little construction activity in this area. The Pb/Br ratio is also quite similar at all the sites. It ranges from 4.16 at Lennox to 6.07 at Downtown Los Angeles. This ratio has been compared to the ratios of the SCAB and Cass composite motor vehicle pro"les and the SCAB road dust proi"le. The ratios estimated by the SAFER model are always larger than the ratios of the two composite motor vehicle pro"les of 3.93 for SCAB and 2.57 for Cass. This fact could be explained by Br loss. Br is attacked by acids and evaporates in the HBr form. This means the fractionation coe$cient, a , cannot ij be equal to 1. The pro"le estimated by the SAFER model is a pro"le at the receptor site, a@ ; fractionation coe$ij cient, a , is already incorporated into it, but two comij posite motor vehicle pro"les do not incorporate any fractionation or losses. The Pb/Br ratio for SCAB road dust is much larger than those estimated by the SAFER model. This could also be explained by Br loss. Road
dust is a soil contaminated by motor vehicle exhaust, oil, tire tread, and brake linings. Br in road dust is attacked by acids and evaporates in the HBr form. Br loss in road dust is larger than that of the estimated roadway source because the latter is a mixture of freshly emitted exhaust gas and resuspended road dust which is exposed to the atmosphere for a longer period. Estimated Pb/Br ratios do not show spatial variation. This implies that the roadway source is a local source and that Br loss is a local e!ect; that is, it may not be related to transport from the outside of the local area. The Si/Pb, Si/OC, and Si/TC ratios show strong spatial variation. The Si/Pb ratios at Rubidoux (24.71), Anaheim (18.15), and Upland (17.34) are much larger than at the other sites, which range from 7.25 to 9.76. The roadway source is a mixture of motor vehicle and resuspended road dust sources. Therefore, depending on the mix of the two sources, the roadway source is called crustal rich or crustal lean. It is called crustal rich when the fraction of road dust in the roadway source is more than the motor vehicle fraction, and crustal lean when the reverse is true. The roadway source at the Rubidoux, Anaheim, and Upland sites must be crustal rich, and the other sites must be crustal lean. Long Beach and Lennox are the most crustal-lean sites; Downtown Los Angeles is the next most crustal-lean site. This fact can be con"rmed by the Si/OC and Si/TC ratios, too. These two ratios at Rubidoux, Anaheim, and Upland are larger than those at the other sites. Again, Rubidoux, Anaheim, and Upland are shown to be crustal rich in the roadway source. These three ratios for SCAB road dust are much larger than those estimated by SAFER for any sites. This is obviously because road dust is a crustal-rich source. The Si/Ca, Pb/OC, and Pb/TC ratios are all about the same at all the sites. The Pb/OC and Pb/TC ratios estimated by SAFER are quite similar to the ratios for SCAB road dust pro"le. The Si/Ca ratio of 4.08 for SCAB
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
road dust pro"le and 9.65 for SCAB soil pro"le are larger than the estimated ratios for any of the sites. The estimated Si/Ca ratio is within the range of 2.11}3.55. The lowest Si/Ca ratio is observed at Rubidoux. This low value is related to the unusually high fraction of Ca in the roadway pro"le and is consistent with the high Ca concentration at the Rubidoux site. This fact implies that the crustal source at Rubidoux must be Ca rich. The Pb/OC ratio ranges from 0.01 to 0.03, which is close to the ratio of 0.03 in the SCAB motor vehicle pro"le. The estimated Pb/TC ranges from 0.009 to 0.018 and can be compared with 0.013 (SCAB motor vehicle) and 0.228 (Cass). The Cass and McRae (1983) ratio is very high compared to those of others. Their pro"le is based on the 1976 SCAB emissions. Starting from 1975, leaded fuel was gradually replaced with unleaded fuel and it was completely phased out in 1990. Therefore, the low SAFER estimated ratio of Pb/TC compared to the Cass and McRae ratio could be explained by the reduction in lead concentration that has resulted from replacement of leaded with unleaded fuel. 4.1.2. Estimated secondary source proxle The estimated secondary source compositions are summarized for each site in Table 4. Secondary nitrates are formed in the atmosphere by chemical reactions during transport. Therefore, as expected, the nitrate composition shows strong spatial variations: low values at coastal sites, 0.21313 at Lennox and 0.30630 at Long Beach; high values at inland sites, 0.44563 at Upland and 0.49567 at Rubidoux. The nitrate compositions at Anaheim (0.31157) and Downtown Los Angeles (0.37047) are in the middle of the range. Since ammonium is associated with nitrate, ammonium composition shows exactly the same variation as nitrate: low values at coastal sites and high values at inland sites. Sulfate composition shows variation opposite to that of nitrate: it shows a large fraction at coastal sites and a small fraction at inland sites of Rubidoux and Upland. This does not necessarily mean that the secondary sulfate concentration is high at the coastal sites and low at inland sites. This will be discussed in the source apportionment section. At Long Beach, Lennox, and Rubidoux sites, Na is found in the form of NaNO . This result is consistent 3
with Russell and Cass (1984) and Hildemann et al. (1984). They found that there is not enough pure NH NO to 4 3 explain aerosol nitrate found in coastal areas. They explained the nitrate di!erence as sea salt or soil dust derived NaNO . Recently, this has been veri"ed from 3 a single particle analyzer (Prather et al., 1998). The SAFER model estimated the NaNO in coastal sites, 3 estimating Na fraction at Lennox to be 0.05042 and at Long Beach 0.03875. The estimated amount of NaNO is 3 shown in the next source apportionment section. The NaNO estimated at Rubidoux seems to be unexpected; 3 however, as Hildemann et al. (1984) explained, this could be formed by soil, which is one of the major sources at Rubidoux. 4.1.3. Estimated marine source proxle The strong acids in the atmosphere react with sea-salt aerosol while the atmosphere moves from the ocean to the inland. Therefore, the sea salt composition varies as it moves: the fractionation coe$cient cannot be equal to one. The Cl composition will decrease, while the NO 3 and SO composition will increase and this, in turn, 4 decreases the Na fraction in the sea salt although the actual amount of Na is constant. This decrease of Cl and Na fractions and increase of NO and SO fractions at 3 4 di!erent sites is shown clearly in the estimated source compositions. Therefore, the Cl/Na, NO /Na, and 3 SO /Na ratios will be di!erent at di!erent sites, but, the 4 ratios of Ca/Na, Mg/Na, and K/Na should be consistent over the sites within some allowable ranges. These ratios are summarized in Table 5. The ratios estimated by the SAFER model are compared below with the ratios of sea water (Pytkowicz et al., 1975) and the Portland Aerosol Characterization Study (PACS) marine pro"le (Watson, 1979). As expected, the Cl/Na, NO /Na, and SO /Na ratios show spatial vari3 4 ation over the sites. The sea salt has more time to reach Downtown Los Angeles than the coastal sites of Long Beach and Lennox. Therefore, the Cl/Na ratio in Downtown Los Angeles (0.63) is smaller than those at Long Beach (0.99) and Lennox (0.88). This ratio at Anaheim (0.92) seems to be larger than expected. These ratios can be compared with the sea water and PACS pro"les. The Cl/Na ratio at the coastal sites compared to that of sea
Table 4 Estimated secondary source compositions at each site
OC NH 4 NO 3 SO 4 Na
Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
0.13684 0.12086 0.21313 0.26881 0.05042
0.08228 0.16756 0.30630 0.29102 0.03875
0.12176 0.16253 0.37047 0.21186 *
0.11372 0.13793 0.31157 0.20658 *
0.07022 0.18234 0.44563 0.18154 *
0.06018 0.18383 0.49567 0.13442 0.01196
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 5 Elemental ratios of the estimated marine source compositions at each site and the comparison with the sea water and PACS! marine pro"les Cl/Na
SO /Na 4
NO /Na 3
Ca/Na
Mg/Na
K/Na
Lennox Long Beach Los Angeles Anaheim
0.88 0.99 0.63 0.92
1.56 0.83 1.17 0.53
0.356 0.908 0.971 1.085
0.06 0.04 0.05 0.05
0.12 0.12 0.12 0.11
0.049 0.047 0.051 0.055
Sea water! PACS"
1.80 1.00
0.25 0.25
0.000 0.000
0.04 0.04
0.12 0.12
0.037 0.040
!Adopted from Pytkowicz et al. (1975). "Portland aerosol characterization study (Watson, 1979).
water is 55% at Long Beach and 49% at Lennox. At Downtown Los Angeles, this ratio is only 35%. According to the marine pro"le estimated by SAFER, the Cl/Na ratio used in the PACS pro"le to account for Cl loss seems to be a quite good assumption. The NO /Na and 3 SO /Na ratios increase signi"cantly at all the sites com4 pared to sea water; this increase of NO and SO seems 3 4 to be site speci"c. No spatial variation is observed. As was expected, the Ca/Na, Mg/Na, and K/Na ratios are all stable over the sites and close to the ratios of sea water. The Ca/Na ratio ranges from 0.04 to 0.06, while the sea water ratio is 0.04. The Mg/Na ratio is 0.11 or 0.12, which is close enough to 0.12, the sea water ratio. The K/Na ratio range is from 0.047 to 0.055, which is larger than the ratio of sea water (0.037). The K composition must be overestimated or there must be an additional source from soil particles in the sea spray. 4.1.4. Estimated crustal source proxle The elemental ratios of some elements that come predominantly from the crustal source, Si/Al, Si/Ca, Si/Fe, Mn/Si, and Ti/Si are summarized in Table 6. The Downtown Los Angeles, Long Beach, and Upland sites are fairly consistent for all ratios. At the Rubidoux site, the Si/Al, Si/Ca, and Si/Fe ratios are much less than those at the other sites. However, the Mn/Si and Ti/Si ratios are quite consistent with those at the other sites. This implies that the crustal source at Rubidoux must be highly Ca rich and moderately Fe and Al rich in composition. These "ndings are consistent with earlier studies (Chow et al., 1992; Watson et al., 1994). The high Ca at Rubidoux is likely due to paved and unpaved road dust from nearby cement industries. Rather high fractions of OC (0.063) and EC (0.023) are estimated for the Rubidoux site. Estimated ratios have been compared with those of some other pro"les taken from the SCAB source composition library (Library Nos. 1}39, 1}43, 1}44 and 1}49) (NEA, 1987). The estimated Si/Al ratio is quite similar to
Table 6 Elemental ratios of estimated crustal source compositions at each site and comparison with other crustal compositions Si/Al
Si/Ca
Si/Fe
Mn/Si
Ti/Si
Long Beach Los Angeles Upland Rubidoux
2.46 2.93 2.57 1.80
3.27 3.05 3.15 0.53
2.40 2.45 2.65 1.90
0.010 0.012 0.010 0.013
0.053 0.049 0.039 0.054
SCAB soil! Earth's crust" Limestone#
2.10 3.39 5.71
9.65 6.80 0.08
3.53 5.01 6.32
0.007 0.003 0.046
0.028 0.020 0.017
!Adopted from SCAB source composition Library no. 1}43 (NEA, 1987). "Adopted from SCAB source composition Library no. 1}49 (NEA, 1987). #Adopted from SCAB source composition Library no. 1}44 (NEA, 1987).
those of the SCAB soil and SCAB road pro"les, except at the Rubidoux site. However, Si/Ca and Si/Fe ratios are less than those of the SCAB libraries, and Mn/Si and Ti/Si ratios are greater. The Si in crustal source must be estimated as less than that of the SCAB libraries. A large SO fraction of 0.22399 is estimated at the Long 4 Beach site. This is unusual for a crustal source. However, the estimated composition is consistent with the ambient data as shown by modeling with several di!erent sites of constraints. Therefore, this source at Long Beach will be called crustal-like source. It is crustal in the sense that elemental ratios of major crustal species are similar to crustal material and similar to the crustal source at other sites, as well; however, this is not quite a crustal source because the estimated sulfate composition is too high. Crustal at Long Beach must be mixed with modi"ed sea salts that contain Na SO because the Na fraction at 2 4 Long Beach is high compared to the other sites.
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
A large fraction of NO at Upland must be an erron3 eous estimation. For all the sites except Downtown Los Angeles, fairly large fractions of SO are estimated in the 4 crustal source. Therefore, together with the roadway source, the crustal source must be a major primary sulfate source. The primary sulfate is discussed in detail in the next section. 4.2. Source apportionment The source compositions estimated by the SAFER model were applied to the CMB model to estimate source apportionment of one-year average ambient PM concentrations. A summary of the estimated 10 source contributions for six sites using SAFER-estimated source pro"les is shown in Table 7. The source contribution from the roadway source ranges from 19.6 lg m~3 at Lennox to 34.2 lg m~3 at Rubidoux. It explains 44}57% of the annual average PM mass. The remain10 ing sites show approximately the same levels of roadway contribution. The estimated roadway pro"le is a composite pro"le of motor vehicle and road dust. Therefore, the large roadway contribution at Rubidoux must be related to the large fraction of the crustal source in the roadway composite pro"le because Rubidoux has more crustal contributions. Similarly, the small roadway contribution at Lennox could be explained by a small crustal fraction in the composite pro"le. Since secondary compounds are formed in the atmosphere by chemical reactions, the estimated secondary source contribution must be large in areas downwind from the pollution sources. In the SCAB, wind blows from the coast to the inland areas, passing through the highly polluted Downtown Los Angeles area. Therefore, the secondary source contribution at sites downwind from the coast would be larger than that at the coastal sites. Spatial variation of the secondary source contributions is shown in Table 7. The range of secondary contribution is from 17.8 lg m~3 at coastal site, Long Beach, to 31.4 lg m~3 at inland site, Rubidoux. As expected, low contributions at the coastal sites and high contributions
at the inland site, Rubidoux, are observed. Upland is located inland; however, its secondary source contribution is not as high as the Rubidoux site. It is about the same as Downtown Los Angeles. There is a dense array of dairy feedlot ammonia sources in the central part of the SCAB. Rubidoux is located just downwind of this dense array of dairy feedlot ammonia sources; however, Upland is not. Therefore, there is not enough ammonia to neutralize the available nitric acid at Upland, and the ammonium nitrate contribution is not as high as at Rubidoux (see Table 8). The secondary source contribution at Lennox is 22.5 lg m~3, accounting for 50% of the PM mass. This seems to be overestimated compared to 10 other coastal sites, if the expected spatial gradient is maintained. As is shown in Table 8, secondary carbon in Lennox appears to be overestimated as well, compared to other coastal sites. The marine contribution ranges from undetectable to 2.5 lg m~3. The marine contribution shows a spatial variation because the sea-salt composition varies as the sea-salt aerosol moves inland from the ocean. As expected, large contributions are observed in coastal areas and small contributions in inland areas, where the marine contribution is not detectable. The small marine contribution (1.5 lg m~3) observed at Long Beach appears to be underestimated compared to other coastal sites. The marine contribution explains 3}6% of annual average PM mass. 10 The crustal contribution ranges from 4.1 lg m~3 at Long Beach to 8.1 lg m~3 at Rubidoux. Large crustal contributions were observed at Rubidoux and Upland. This crustal contribution accounts for the unexplained portion of the crustal material in the resuspended road dust. This crustal contribution explains 8}13% of PM 10 mass. The secondary source apportionment is di!erentiated to each compound (in the form of ammonium nitrate, ammonium sulfate, ammonium bisulfate, and secondary organic carbonaceous matter) and to each species. These results are summarized in Table 8. The percentage of organic carbon, nitrate, and sulfate from secondary
Table 7 Summary of source apportionment (lg m~3) for each site Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
Roadway Secondary Marine Crustal Sum Unexplained
19.6 22.5 2.5 * 44.6 0.5
25.1 17.8 1.5 4.1 48.4 1.4
27.0 20.8 1.7 6.0 55.5 4.8
28.9 19.4 2.2 * 50.6 2.1
25.4 20.4 * 6.6 52.4 5.6
34.2 31.4 * 8.1 73.7 14.1
Total mass
45.1
49.9
60.3
52.7
58.0
87.8
Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
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Table 8 Source apportionment of secondary particulate matter Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
As a compound form (lg m~3) SOCM! 4.0 NH NO 2.2 4 3 (NH ) SO 7.9 42 4 NH HSO 0.3 4 4 NaNO 4.2 3
1.9 4.6 7.1 0.0 2.5
3.3 10.0 2.3 3.3 *
2.9 7.8 1.2 3.7 *
1.9 11.7 2.8 2.0 *
2.5 18.8 5.5 0.2 1.4
As a species form (lg m~3) SOC 3.1 NH 2.7 4 NO 4.8 3 SO 6.1 4
1.5 3.0 5.4 5.2
2.5 3.4 7.7 4.4
2.2 2.7 6.1 4.0
1.4 3.7 9.1 3.7
1.9 5.8 15.6 4.2
!Secondary organic carbonaceous matter.
Table 9 Percentage of various species from secondary sources to estimated data
OC! OC" NO 3 SO 4
Lennox
Long Beach
Los Angeles
Anaheim
Upland
Rubidoux
32.3 25.0 96.2 87.9
14.4 10.7 90.7 69.0
25.1 17.1 92.6 72.3
24.1 18.8 90.0 65.6
15.8 12.1 96.6 59.5
16.5 12.9 96.0 73.7
!Percentage of secondary organic carbon to total organic carbon. "Percentage of secondary organic carbon to total carbon. Gray et al. (1986): 16}22% Azusa and Rubidoux, 1982 for "ne SOC/TC (assume OC at Lennox is all primary). 27}38% SOC/OC. Pratsinis et al. (1984); 17% Lennox for single day SOC/TC. 30% Duarte 10/23/80.
sources to estimated organic carbon, nitrate, and sulfate is shown in Table 9. As expected, nitrates (Table 8) show a spatial distribution with larger values at Rubidoux than at the coast. However, the percentage of the nitrates from the secondary source is quite similar (90}97%). High nitrate concentration at Rubidoux is due to signi"cant ammonia sources, dairy feedlots, in the central part of the SCAB that is located just upwind of Rubidoux. Sulfates in the secondary source are higher at the coast than at inland sites and they range from 3.7 lg m~3 at Upland to 6.1 lg m~3 at Lennox. The percentage of secondary sulfates ranges from 60 to 74%, except at the Lennox site, where it is 88%. Sulfates as ammonium sulfate show high contributions at Lennox, Long Beach and Rubidoux and low contributions at Downtown Los Angeles, Anaheim and Upland while ammonium bisulfate shows the opposite variation. The estimated secondary organic carbon does not show a geographically consistent variation, but seems to vary according to mobile source emission levels. The
range is from 1.9 lg m~3 at Upland to 3.3 lg m~3 at Downtown Los Angeles. The secondary organic carbon at Lennox is not considered because it must be overestimated compared to other coastal sites. The percentage of secondary organic carbon to total carbon ranges from 10.7% at Long Beach to 18.8% at Anaheim; secondary organic carbon to total organic carbon ranges from 14.4 to 25.1%. The fraction of secondary organic carbon at Rubidoux and Upland must be larger than estimated if the secondary organic carbon is formed mainly while being transported from the coast to inland sites. However, the percentage of estimated secondary organic carbon at Rubidoux and Upland must be underestimated compared to Gray et al. (1986) and Pratsinis et al. (1984) as shown in Table 9. Nitrates, sulfates, and organic carbon each have di!erent spatial variations. The secondary portion of the nitrates, sulfates, and organic carbon is about the same for all the sites, averaging 93, 68, and 19%, respectively. The Lennox site is not included in the average because
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Bong M. Kim, R.C. Henry / Atmospheric Environment 34 (2000) 1747}1759
the secondary source compositions at Lennox seem to be substantially overestimated compared with the other coastal sites. This result shows that 7% of nitrates come from major primary sources such as the roadway and marine sources. A fairly large (32%) amount of sulfates is attributed to major primary sulfate sources such as roadway and crustal sources. Organic carbon is mostly (81%) attributed to the primary roadway sources.
5. Conclusions Most of the time, source compositions vary during transport: those measured at the source di!er from those measured at the receptor. Therefore, application of the "xed value of source compositions for di!erent time periods and locations may lead to unreliable results. The SAFER model has been developed based on the SMCR technique to resolve the source compositions from the ambient data with the minimum amount of a priori knowledge. This model estimates the source compositions at the receptor site with the transformation e!ects already incorporated. The SAFER model was applied to the PM data, and 10 the source compositions of some major source categories were estimated. To estimate the source compositions, a limited amount of a priori knowledge was used. For example, stoichiometry among secondary species and negligible concentrations of carbon monoxide and elemental carbon concentrations were used to estimate the secondary source composition pro"les. Pb, Si and ozone concentrations were used to estimate roadway pro"les. The SAFER model-estimated source pro"les were compared with the measured source pro"les by checking some important elemental ratios. Estimated source compositions were very strongly consistent with the measured pro"les, especially the chemical species not used as APCs. As expected, most of the estimated source composition pro"les show spatial variations. The source contributions of each source category have been estimated by the CMB model for each site. Contributions from the roadway source range from 20 to 34 lg m~3, from the secondary source from 17.75 to 31.40 lg m~3, from the marine source from undetectable to 2.50 lg m~3, and from the crustal source from 4.06 to 8.13 lg m~3. Estimated contributions of the secondary and marine sources show spatial variation among the sites: high contributions are obtained from the secondary source (31.40 lg m~3) at an inland site (Rubidoux) and from the marine source (2.50 lg m~3) at a coastal site (Lennox); whereas low contributions are obtained from the secondary source (17.75 lg m~3) at a coastal site (Long Beach) and the marine source (1.67 lg m~3) at an inland site (Downtown Los Angeles). The source apportionment of the secondary particulate matter, including organic carbon, was made and
compared over the sites. Nitrates show strong spatial variation among the sites; sulfates and organic carbons do not. Organic carbon seems to be mainly (81%) contributed by the primary roadway source, and sulfates and nitrates are mainly from the secondary source, although 32% of the sulfates are from primary sources such as roadway, crustal, and marine sources. The SAFER model was shown to be a powerful tool in estimating source compositions from the ambient data with only a limited amount of a priori knowledge; furthermore, the transformation e!ects are already incorporated into the estimated source compositions. Although the uncertainties are associated with the estimated pro"les, the SAFER model-estimated source pro"les should be more representative than the measured pro"les in that the transformation e!ects are incorporated in the estimated source pro"les. Kim et al. (1992) compare the two sets of source apportionment results, one using measured source compositions and the other using SAFER estimated source compositions. The comparison study reveals that the source apportionment made by the two di!erent source compositions, measured and estimated, do not show signi"cant di!erences. Both results were generally in good agreement within an acceptable error range. The SAFER model will have extended application in the future in the estimation of source compositions that are di$cult to measure. Additional research with the SAFER model is needed to further reduce the size of, and better represent the feasible region.
6. Disclaimer The statements, opinions, "ndings, and conclusions of this paper are those of the authors and do not represent the views of the South Coast Air Quality Management District.
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