(4) Fairey, F. S., Gray, J. W., J . Sci. Med. Assoc., 66,79 (1970). (5) Ter Haar, G., Aronow, R., Enuiron. Health Perspect., 7, 83
blad, V., Creason, J., Lagerwerff, J. V., Ferrard, E. F., paper presented at the 100th Annual Meeting of the American Public Health Association, Atlantic City, N.J., 1972. (9) Bodgen, J. D., Louia, D. B., Bull. Enuiron. Contam. Toxicol., 14, 289 (1975). (10) Wedberg, G. H., Chan, K., Cohen, B. L., Frohlinger, J. O., Enuiron. Sci. Technol., 8, 1090 (1974). (11) Bowman, H. R., Conway, J. G., Asaro, F., Enuiron. Sci. Technol., 6,558 (1972). (12) Rhodes, J. R., Pradzynski, A. H., Hunter, C. B., Payne, J. S., Lindgren, J. L., Enuiron. Sci. Technol., 6,922 (1972). (13) Paciga, J. J., Roberts, T. M., Jervis, R. E., Enuiron. Sci. Technol., 9,1141 (1975). (14) Olson, K. W., Skogerboe, R. K., Enuiron. Sci. Technol., 9,227 (1975). 115) Ter Haar. G.. Bavard, M., Nature (London). 232,553 (1971). (16) Boyer, K. W.’, La-itinen, H. A , , Enuiron. Sci. Techno/., 8, 1093 (1974). (17) Moyers, J. L., Zoller, W. H., Duce, R. A,, Hoffman, G. L., Enuiron. Sci. Techn’ol.,6,68 (1972). (18) Pierrard, J. M., Enuiron. Sci. Technol., 3,48 (1969). (19) Robbins, J. A., Snitz, F. L., Enuiron. Sci. Technol., 6, 164 (1972). (20) Lamb, R. E., Ph.D. Thesis, University of Illinois, Urbana, Ill., 1975. (21) , . Hoake. . P. K.. Lamb. R. E.. Natusch. D. F. S., Enuiron. Sci. Technol.,’followingpaper in this issue. (22) Rabinowitz, M. B., Wetherhill, G. W., Enuiron. Sci. Technol., 6, 705 (1972). (23) McCrone, W. C., Delly, J. G., “The Particle Atlas”, Ann Arbor Science, Ann Arbor, Mich:, 1973. (24) Mason, B., “Principles of Geochemistry”, Wiley, New York, 1966. (25) Linton, R. W., Ph.D. Thesis, University of Illinois, Urbana, Ill., 1977. (26) Joint Committee on Powder Diffraction Standards, “Powder Diffraction File-Inorganic Compounds”, Swarthmore, Pa., 1976. (27) Keyser, T. R., Natusch, D. F. S., Evans, C. A., Jr., Linton R. W., Environ. Sci. Technol., 12,768 (1978).
(1974). (6) Lepow, M. L., Bruckman, L., Gillette, M., Markowitz,S.,Robino, R., Kapish, J., Enuiron. Res., 10,415 (1975). (7) Strehlow, C. D., Barltrop, D., Pediatri. Congr. Int., 14th, II(3), 173 (1975). (8) Pinkerton, C., Hammer, K. E., Hinners, T.,Kent, J. L., Hassel-
Received for reuieu: J u l y 13, 1977. Resubmitted October 14, 1978. Accepted January 15,1979. R. Linton u:as the recipient ofa National Science Foundation energy-related Graduate Traineeship. This research UQS supported by National Science Foundation Grants ERT 74-24276 and DMR 76-01058.
of surface-associated material, which is often highly characteristic of particles derived from a specific source. Thus, automobile exhaust particles have been shown to contain the elements Br, C1, Cr, Mn, Ni, P, Pb, and T1 on their outer surface (25,27). In terms of the actual results obtained, the most important conclusions are that lead derived from automobile exhaust particulates can contribute significantly t o the total lead present in soils and dusts a t a considerable distance from a roadway. Otherwise, it would appear that roadway dusts themselves are likely to contain lead that is almost exclusively derived from automobile exhausts. Acknowledgment The technical assistance of the following individuals is gratefully acknowledged: Professor P. K. Hopke for assistance with INAA procedures; Mr. J. Hartford for sample collection and separations; Professor J. F. Young for access to XRPD instrumentation; Professor H. A. Laitinen for supplying samples of the auto exhaust particles; the technical staff of the University of Illinois Institute for Environmental Studies for assistance with AA and INAA determinations; and the University of Illinois Center for Electron Microscopy for access to SEM/EDS instrumentation. Literature Cited (1) National Academy of Sciences, “Lead: Airborne Lead in Per-
spective”, Washington, D.C., 1972.
(2) Kinnison, R. R., Enuiron. Sci. Technol., 10, 644 (1976). (3) Solomon, R. L., Hartford, J. W., Enuiron. Sei. Technol., 10,773
(1976).
Multielemental Characterization of Urban Roadway Dust Philip K. Hopke Institute for Environmental Studies, University of Illinois, Urbana, Ill. 61801
Robert E. Lamb’ and David F. S. Natusch2* School of Chemical Sciences and Institute for Environmental Studies, University of Illinois, Urbana, 111. 61801
It has now been well established that aerosols and deposited dusts found in urban areas are substantially enriched in many potentially toxic trace elements by comparison with those found in nonurban areas ( I , 2). Consequently, people who reside in urban locations are exposed to much larger amounts of potentially hazardous elements than their rural counterparts. Several studies have been conducted (3-6) to determine the composition of particles derived from those sources which may contribute trace elements to urban aerosols and a number of attempts have been made to relate the observed elemental concentrations in collected aerosol samples to these sources (7-14). However, there still remains considerable uncertainty with respect to the origins of elements such as As, Cd, Cr, Pb, Mn, Ni, and Zn, which are known to be enriched in several ~
Present address, Department of Chemistry, Ohio Northern University, Ada, Ohio 45810. Present address, Department of Chemistry, Colorado State University. Fort Collins, Colo. 80523. I
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Environmental Science & Technology
types of deposited dusts encountered in the urban environment (15-17). Since such dusts may be inhaled, following reentrainment into the air (16, I 8 ) , ingested by children (19), or removed to the aquatic environment by runoff following precipitation ( 2 0 ) ,it is of some importance to determine the sources from which these dusts are derived. Of all of the types of dusts found in the urban environment, one of those most highly enriched in toxic trace elements is roadway dust. Lead concentrations have been reported ( I 7) to reach the percent level in such dusts and several other elements, including Cr, Mn, Ni, and Zn, are present a t disturbingly high levels (16). It is the purpose of this present work, therefore, to investigate one approach to the identification of sources that contribute potentially hazardous elements to roadway dusts. Our approach to elemental source tracing is based on the assumptions that particles derived from a given source exhibit distinguishable size, density, and ferromagnetic characteristics, and that they also exhibit distinguishable interrelation0013-936X/80/0914-0164$01 .OO/O
@ 1980 American Chemical Society
A sample of urban roadway dust was physically fractionated into 30 subsamples, each having unique particle size, density, and ferromagnetic characteristics. Each subsample was then analyzed for 35 elements. The resulting data set was subjected to target transformation factor analysis in order to identify and quantify the sources that contribute to roadway dust. T h e analysis indicated that soil, cement, automobile exhaust emissions, rust, tire wear, and salt were the primary ships between their constituent trace elements. If such characteristics can be identified, it is possible to establish the qualitative and, perhaps, quantitative contributions of individual primary emission sources to deposited roadway dusts. T o this end a sample of roadway dust representative of that encountered in a moderately sized, nonindustrial urban community wqs physically subdivided according to particle size, density, and ferromagnetic susceptibility and each subfraction analyzed for a number of major, minor, and trace elements. T h e resulting data set was then subjected to multivariate statistical analysis to identify the number and nature of the sources and to provide a quantitative estimate of each source contribution to the dust sample.
sources of particulate material, with the last two probably representing mixed components. Semiquantitative estimation of the minor source contributions was achieved. Consideration of graphical representations of elemental mass and concentration distributions, together with the results of the statistical analysis, provides considerable insight into the physical and chemical characteristics of elements in urban dust.
Table 1. Mass Balance of Separated Dust (in Grams) density, glcm3 size, pm
2.9 a
250-500 100-250 75-100 45-75 20-45
8.04 5.07 1.69 1.74 0.34
2.76 3.32 3.27 0.96 0.90
200.21, 1.4 144.89, 1.0 61.42, 3.3 26.80, 1.1 15.30, 0.095
40.19, 7.4 41.73, 5.0 46.32, 6.0 14.01, 1.9 1.76, 0.105
a
Table II. Elemental Analysis of Urban Street Dust element
concn a
element
concn a
Sb
2.2 f 0.3 11 f 1 310 f 54 a4 f 2.7 f 0.5% 1.6 f 0.2 29 f 1 1.1 f 0.2 210 f 20 6.8 f 0.4 1.6 f 0.2 0.4 f 0.03 4.9 f 0.9 5.0 f 0.5 6 2 f 0.5% lo* 1 0.1 f 0.02% 0.16 f 0.4
Mn Hg Ni K Rb Sm
350.0 f 30 0.090 f 0.008 250.0 f 60 0.94 f 0.13% 29.0 f 5 3.4 f 0.5 4.2 f 0.3 1.0 f . 0 . 3 0.2 f 0.09 0.53 f 0.05% 250 f 50 0.44 f 0.13 4.3 f 0.3 3.5 f 0.7 1.0 f 0.2 320 f 30 1 2 0 f 14
Procedures
S a m p l e Collection a n d Separation. A 648-g sample was collected by vacuuming the gutter and roadway on the northwest cornw of the intersection of two moderately traveled (-7000 cardday) streets in Urbana, Ill. The sample was sieved through a 35-mesh screen to remove extraneous rocks, pebbles, and grass and then divided by particle diameter into six physical size fractions. The sample was passed through 60 and 120 mesh (Tyler series) sieves and through three micromesh sieves of nominal cutoffs 7 5 , 4 5 , and 20 pm. Each subsample was further separated into a magnetic and a nonmagnetic fraction by placing a portion of the subsample in a crystallization dish and passing a permanent bar magnet over the bottom in order to isolate the magnetically attracted particles. (This separation is dependent on the strength of the magnet and thus provides an operational definition of magnetic and nonmagnetic particles.) Each nonmagnetic portion was then separated into four density ranges by suspending the sample in mixtures of chloroform (density 1.5 g/cm3) and bromoform (density 2.9 g/cm.l). The four fractions corresponded to 11.5, 1.5-2.2, 2.2-2.9, and >2.Y g/cm3.The magnetic fractions were divided into two density fractions corresponding to 1 2 . 9 and >2.9 g/cmc3. T h e final mass distribution obtained from the entire separation scheme is presented in Table I. E l e m e n t a l Analysis. Lead and cadmium were extracted from a portion of each subsample with 8 N HNO 1 and determined by atomic absorption spectrophotometry. A Jarrel-Ash Model 810 two-channel spectrophotometer with a 10-cm air-acetylene flame was used for these analyses. The analytical lines employed were 283.3 nm for lead and 228.8 nm for cadmium, and background correction was carried out by utilizing a nonabsorbing line source and a second spectrometer channel. Thirty-three elements were determined in another portion of each subsample by instrumental neutron activation analysis. The samples were irradiated for 1 h in a TRIGA Mark 111 reactor a t a neutron flux of 2 X 10l2neutrons cm-2 s-l. The y-emission spectra were observed with a Ge(Li) detector in conjunction with a multichannel analyzer. National Bureau of Standards trace elements in fly ash and trace metals in coal standards were used as multielemental comparator standards. The spectra were read directly onto computer-compatible magnetic tape and analyzed using the PIDAQ program (21).
Nonmagnetic fraction is listed first, followed by the magnetic fraction.
As Ba Br Ca Cd Ce cs Cr co DY Eu
Ga Hf Fe La Pb Lu a
sc Se Ag Na Sr Tb Th U Yb Zn Zr
Fg/g unless otherwise specified
Table 111. Concentration of Lead in Separated Dust
(cLs/s) size, pm
2.9 a
1400, 9200 4200, 13 000 3700, 22 000 4500. 19 000 9000, 13 000
Nonmagnetic fraction is listed first, followed by the magnetic fraction.
The overall composition of the bulk dust sample is presented, in terms of the 35 elements determined, in Table 11. T h e analyses also establish the way in which each of the elements is distributed among the 30 subsamples. These data can be presented either in terms of the elemental concentration in each subsample, as illustrated for lead in Table 111, or by combining the data in Tables I and 111, in terms of the percentage of each elemental mass present in each subsample (Table IV). Inclusion of all such data sets herein is inappropriate; however, they are available on request from the authors. Volume 14, Number 2, February 1980
165
{XI = MlFJ
Table IV. Mass Balance of Lead in Separated Dust
(%I denslty. g1cm3
sire, pm
250-500 100-250 75-100 45-75 20-45 a
2.9 a
5.8, 7.1 18, 6.7 1.8, 11 6.3, 3.5 0.01, 0.13
Nonmagnetic fraction is listed first, followed by the magnetic fraction
An alternative, and for the present purposes more meaningful, method of data presentation is to generate a threedimensional graphical projection of elemental concentration as a function of particle size and density. Such projections are presented, together with that for mass distribution, in Figures 1 and 2 for the elements Pb, Fe, Zn, Cd, and Hg. As discussed in a later section, this type of graphical presentation is helpful in deciding which elements are associated with particles having the same physical characteristics and thus likely to be derived from the same source. Statistical Analysis. Our approach to statistical analysis of the data generated in this study is based on the readily justifiable assumption that the present roadway dust sample is a mixture of materials derived from a number of independent sources. Thus, the amount of any given element present in the total sample can be expressed as the sum of cont,ributions from each of these sources. Mathematically this is expressed, for the case of lead, as: M ( P b ) = MI(Pb)
+ M2(Pb) + . . . Mk(Pb)
(1)
where M ( P b ) is the total mass of lead present and Mk (Pb) is the mass of lead contributed by the kth source. However, each source contributes a total mass of material, F k , of which only a fraction is lead. Mk (Pb) can, therefore, be represented by the product: Mk(Pb) = ak(Pb)Fk
(2)
where a k (Pb) is the concentration of lead in the total mass of material contributed by the k t h source. If the compositions of material derived from all of the sources which contribute to the overall dust sample were known a priori, the present data could be fitted by standard multiple regression techniques and the contribution of each source calculated. However, since these data are not known, it is necessary to invoke multivariate statistical analysis techniques. Several such techniques have been applied to the present data and the most appropriate was found to be that of factor analysis (21). In order to apply factor analysis, it is necessary to have a data set for a series of samples, each of which has different amounts of the various source materials in it. We are assuming that the physical separation processes do not change the effective source composition but merely distribute the different source materials into the various physically distinct fractions. The data on the 30 subsamples described herein constitute such a set. Thus, the amount of each element in each physically fractionated subsample should be related to a sum of products analogous to the chemical element balance equation of Friedlander (12),Le.,
It is the task of factor analysis, then, to determine the value of m (the number of terms in Equation 3 that are required to reproduce the original data set), and the values of one of the two product matrices on the right-hand side of Equation 4. The value of the other matrix can be calculated from knowledge of the original data and the results of the factor analysis. In an earlier study (7), factor analysis was used to identify the sources of air particulates in the Boston metropolitan area. The analysis in that case employed data that were standardized to a mean value of zero and a standard deviation of unity for each variable. T h a t analysis then attempted to reproduce the variance in the system. There are several problems that were raised in that work. The actual concentration profiles of the sources could not be calculated, and there was no method to determine the quantitative contribution of each source to the total particulate loading. The method used in that study results in a model that does not reproduce the data but rather the deviation of the data point from the mean value. In order to overcome these difficulties, a different approach has been used in this present study. The analysis can be made on the untransformed data by calculating an alternative correlation coefficient between the pairs of samples. The correlation about the origin is defined as:
In the previously published applications of factor analysis to environmental systems (7-91, the analysis was made on the matrix of correlations about the mean between the variables. In the terminology of Rozett and Peterson (22),the examination of the interrelationships between variables is termed an R analysis. The examination of the interrelationships between samples is termed a 4 analysis. They both aim for the same ultimate result: the determination of the {a]and (F} matrices in Equation 4. The R analysis calculates the {a}matrix first, while the 4 analysis obtains the IF)matrix. The results obtained by Ritter and co-workers (23) and Malinowski and co-workers (24-26) have shown that for many cases the 4 analysis provides a clearer indication of the number of factors involved and so its use is being investigated. The matrix of correlations can be represented as: IRI = lVITIXI'rlXIIVJ
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Environmental Science & Technology
(6)
where {XITis the transpose of the original data matrix and (V) is a diagonal matrix containing the inverse of the square root of the sum of the squared data values. The linear relationships described by Equation 4 can be obtained from this matrix of correlation coefficients in the following manner. A matrix {B}which will diagonalize the {RJmatrix is determined such that: IBI-'IRJ{BJ= lAl
(7)
where {AI is a diagonal matrix of eigenvalues. If there are no two identical eigenvalues, then the {B]matrix is orthogonal and its transpose, {BIT,is equal to its inverse, 1BI-I:
lB)-'lvl'rixlTlx)lvIIB] = lAl
(8)
Substituting {BITfor {B}-lyields: /B)'r"l''{XI'rIX)(VJIB) = {AI
where Xij represents the amount of the i t h element present in the j t h physically fractionated subsample, and a and F are as defined in Equations 1and 2. In matrix notation this series of equations can be written as:
(4)
Defining
(9)
Then:
1x1 = la)~v)-lIBIT
(10)
which can be transformed into Equation 4 by defining:
{V)-l(B)T = (F) Thus, if any matrix (B)which will diagonalize the correlation matrix can be found, then the elements of the {E)matrix can be calculated. In theory, p , the number of sources in the system, is equal to the number of nonzero eigenvalues obtained from the diagonalization. The remaining n-p eigenvalues should be equal to zero and can be excluded from the analysis since they contribute no information. In practice, however, the presence of sampling, analytical, and calculational errors makes the determination of the proper number of factors difficult. Several different tests to aid in the determination of the dimensionality of the data have been developed. A large decrease in the magnitude of the eigenvalues produced in Equation 8 is one indicator of the correct number of factors. In another test, the data are reproduced with only the first factor and compared point-by-point with the original data. An average error, X-square, and Exner value are calculated. The Exner function (35) is defined by:
where xy is a n original datum point, x,,P is the point reproduced with p retained factors, and x o is the value of the average point. Close agreement between the reproduced and original matrix is indicated by an Exner value of zero. T h e eigenvalues and Exner function for the first ten factors are given in Table V. The data are next reproduced with both the first and second factors and again compared point-by-point with the original data. This procedure is repeated, each time with one additional factor, until the data are reproduced with the desired precision. If p is the minimum number of factors needed to adequately reproduce the data, then the remaining n-p factors can be eliminated from the analysis. These tests do not provide unequivocal indicators of the number of factors t h a t should be retained. Some judgment becomes necessary in evaluating all of the test results and deciding upon a value for p . In this manner the dimension of the (A)and {Flmatrices is reduced from n to p . The analysis shows that the present data set can reasonably be reproduced by retaining between five and seven terms in Equation 3. Thus, the number of factors, m , which determine the overall sample composition is between five and seven. I t should be noted, however, that no universally applicable method for establishing m has been established (27),and that it is necessary to discard a number of small, but nonzero, eigenvalues in the matrix in order to establish the probable number of contributing source factors. T h e (a{matrix is calculated from the original data and the reduced size {FI matrix.
Table V. Criteria for Factor Number Selection factor no.
1 2 3 4 5
factor eigenvalue
Exner
no.
elgenvdlue
Exner
24.8 3.98 1.13 0.036 0.019
0.477 0.081 0.057 0.029 0.013
6
0.0091 0.00066 0.00649 0.00030 0.00025
0.0066 0.0052 0.0044 0.0035 0.0013
7
8 9 10
The analysis described above shows that any matrix t h a t will provide the diagonalization of the correlation matrix is a mathematically acceptable solution. However, the resulting {a)and (F)matrices may not be interpretable in terms of the system being studied. In order to assign physical meanings to each of the mathematically determined source factors, it is helpful to rotate the eigenvectors in the reduced factor space. In the earlier applications of factor analysis ( 7 ) ,the rotation was designed to make the {a)matrix (normalized elemental concentration) entries in Equation 4 either zero or unity (varimax rotation). This criterion, known as simple structure (7), implies t h a t a given element is contributed to the total sample by only one source. In the present instance, however, such a criterion seems unacceptable, and orthogonal rotation was found to be of minimal use in obtaining source factors with values that can be associated with concentrations of the various elements from the various source materials. An alternative approach involving rotation of the eigenvectors so as to maximize their alignment (target transformation) with a set of test vectors was therefore employed. These test vectors represent the normalized elemental concentrations of materials derived from several specific sources considered to be likely contributors to roadway dust and are given in Table VI. In addition, vectors which contributed only a single element were tested for each of the 35 elements under consideration. A computer program written by Malinowski e t al. (28) to perform this target transformation analysis was employed. Refinement was performed iteratively so as to minimize the differences between the source factors and the test vectors. The quality of alignment of each of the rotated source factors and each test vector could be measured with the Exner function and was used to decide whether a given source factor could be related to a test vector. The rotation also provides a prediction of the a values (i.e., the elemental contributions). Once a test vector has been identified as a possible source, it can be refined by replacing the input value for a given element with the value predicted by the target transformation. This procedure provides a feedback mechanism whereby tho concentration profiles can be directed by the analysis toward the best possible fit. As a further check on the degree of overlap, values for elements purposely omitted from the input test vector can be predicted and compared with known values. Although this procedure does require initial estimates for possible source emission elemental profiles, the procedure provides a method to determine if such a profile is a reasonable source in the particular system under study as well as a method to systematically modify the initial vectors to allow for variation in Composition of the local sources from the generalized sources used as the test vectors. The best overall fit to the data was obtained for the six refined test vectors listed in Table VII, which also lists the precision with which the concentration of each element in the total sample is approximated by the combined test vectors. Before these vectors can be used to calculate the mass contribution of each source to each subsample, they must be scaled to reflect the actual concentration in micrograms of element per gram of source material. T o illustrate the need for this scaling, consider the source vectors for soil and iron. T h e iron concentrations in typical solids range from approximately 2 to 6%, while the iron concentration in small iron or rust iron particles might be expected to be in the range of 60 to 80%.Thus, to have the vector values for iron equal to 1 for both sources will yield an overestimation of the mass of the iron source by an order of magnitude. I t is possible to utilize the masses of each subsample to calculate the scaling factors for these vectors by a multiple regression technique. The total mass of the subsample can be obtained by: Volume 14, Number 2, February 1980
167
Table VI. Unscaled Input Test Vectors element
Sb As Ea Br
soil 1 a
SOll
2b
soit 3
auto 1
auto 2 b
brakes
motor oil
cement
marine d
0.037
Ca
0.014 1.9x 10-4 0.13
Cd Ce
1.9x 10-3
0.013 0.27
0.013 0.38 0.05
4.6 x 10-4 0.024
0.20
cs
0.32
0.96
0.69
1 .o
1.0
0.038
0.061
1.9 X
0.34 5.3x 10-3
1.6x 10-3
Cr
co DY Eu Ga Hf Fe La Pb
1 .o
2.0x 10-4 1 .o
1 .o
3.0x 10-4
0.05
0.01
1.0
1.0
1 .o
Lu Mn
0.025
Hg Ni K Rb Sm sc Se Ag Na Sr Tb Th
U Yb Zn Zr
0.022
0.67 0.026
0.036
1 .o
2.9 x 10-4
0.084
0.20
0.17
2.2x 10-3
3.3 x 10-3
1.3x 10-3
0.015
Reference 34. Gatz, D. F., Atmos. Environ., 9, 1-18 (1975).
Mj=
3.5 x 10-3
6 F k /
SI, where MJis the mass of the physically fractionated subsample, FhJ is the mass concentration obtained from the factor matrix, and Sk is the scaling factor for the h t h source to be determined. From a regression analysis for each of the subsample masses for each of the elements, the scaling factors to give the elemental concentrations in percent are calculated and are given in Table VII. T h e calculated masses from these scaled vectors are compared t o the experimental values in Table VIII. T h e contributions of each of the sources listed in Table VI1 to the overall dust sample were obtained by summing the source factors over all of the samples calculating the percentage contribution to the total sample mass of each source. The results indicate that the composition of this roadway dust sample is 76% soil, 5.0% cement, 0.3% salt, 1.5% automobile exhaust particles, 7.7% iron, and 7.2% automobile tire wear particles, with 2.3% of the mass unaccounted for by this analysis.
Discussion The first question to be addressed is whether the six sources of roadway dust particles indicated in Table VI1 are reasonEnvironmental Science & Technology
0.11
0.80
Reference 2. dReference 5.
k=l
168
1.0
able. T h e presence of a soil component is certainly t o be expected and is strongly indicated by the data, even though the soil test vector employed does not exactly represent the soil composition a t the point the dust sample was collected. The only unusual aspect of this soil component is that it contains a n extremely high concentration of lead. This fact suggests that a significant fraction of the total lead associated with automobile exhaust particles has become physically or chemically associated with soil particles. A similar finding has been reported by Olson and Skogerboe (29). If correct, this behavior will result in the underestimating of the automobile exhaust component in the present analysis. T h e contribution of automobile exhaust particulates to roadway dust is also expected. The observed association of lead with magnetic iron indicated in Figure 1 has also been reported by Olson and Skogerboe (29), and the ratio of bromine to lead for the automobile exhaust component is very close to that obtained by Ondov ( 5 )in his study of particulate material collected in the Baltimore Harbor Tunnel. I t is interesting to note that the chromium and manganese concentrations in the automobile exhaust component are similar to those found by Ondov ( 5 ) in deposited dusts, but are about a factor of 20 lower than he observed in airborne particles. I t would appear, therefore, that the automobile exhaust component reflects that portion of exhaust particulates which
Table VII. Refined Test Vectors element
soil
Sb
1.1 x 10-4 3.3 x 10-4 5.5 x 10-3 1.5x 10-3 0.26 1.5x 10-5 1.2x 10-3 4.5 x 10-5 4.5 x 10-3 5.5 x 10-3 3.5 x 10-5 1.6x 10-5 3.6 x 10-4 5.5 x 10-5 1.0 3.2 x 10-4 0.11 3.2X 0.019 5.5 x 10-7 4.4 x 10-5
As Ba Br Ca Cd Ce cs Cr co DY Eu Ga Hf Fe La Pb Lu Mn
Hg Ni K Rb Sm sc Se Ag Na Sr Tb Th U Yb Zn Zr scaling factor
0.55
1.5 x 2.0 x 9.1x 3.2 x 1.8 X 0.27 6.8 X 1.5 x 2.6x 5.5 x 5.0 x 0.02 2.3 x
10-3 10-4 10-5 10-5
10-5 10-5 10-5 10-5 10-3
2.2
iron
7.7x 0.0 1.7 X 1.2 x 0.0 3.0 X 3.9 x 1.6 X 1.4x 6.9 x 6.4X 0.0 1.3x 3.3 x 1 .o 4.7x 7.7 x 0.0 1.3x 3.3x 5.7x 0.0 4.0 x 0.0 8.0 X 0.0 1.0 x 1.3x 1.2 x 2.4X 0.0 1.7 X 5.9 x 0.0 0.0
10-6
cement
2.7 x 10-5 0.0
10-4
9.8x 10-4 1.7x 10-4
lop6
1.o 0.0
10-6 10-4 10-5
10-5 10-6 10-5 10-6 10-4 10-8 10-5 10-5
10-6 10-4 10-5
10-6
70.0
5.8 x 0.0 0.0 0.0 4.7x 3.8 x 0.0 1.4x 0.0 1.9x 3.8 x 1.6 X 9.6 x
10-4
10-6 10-7 10-5 10-4 10-4 10-3
0.0. 0.0
2.4x 10-4 9.9x 10-4 4.5 x 10-3 0.0
1.4x 1.1 x 5.6x 9.0 x 0.011 1.4x 1.7x
10-4 10-4 10-3 10-5 10-3 10-4
10-4 10-4
10-5 10-6 10-4 10-3
10-6 10-4 10-3
45.0
settle rapidly following emission but which retain bromine to about the same extent as particles that have a substantial airborne residence time. At first sight the existence of a component similar to marine salt is surprising. However, the dust sample studied was collected in early spring from a location where a considerable amount of salt is used for deicing during the winter months. I t is noteworthy t h a t this salt component exhibits substantially elevated levels of several elements including arsenic, chromium, cobalt, nickel, and zinc. This observation leads one to suspect that the salt component may represent a mixed factor t h a t incorporates particles rich in the elements mentioned. Such a situation could result from adherence of foreign particles to the salt crystals or simply from lack of resolving power of the statistical technique employed. The assignment of a component due to tire wear is based primarily on the presence of zinc, whose concentration ranges from 8000 to 12 000 pg/g in the rubber ( 5 ) .The relative concentrations of chromium, barium, and manganese associated with this so-called tire wear component are, however, higher than found in tire rubber (5).Since these elements are reported to be present in brake linings (5),it is possible that this component may be due to a combination of brake lining and tire wear. An alternative explanation is that particles containing extremely high concentrations of zinc in the form of
4.9 x 10-4 4.5x 10-3 0.011 0.039 0.0 1.1 x 10-4 2.3x 10-4 8.0 x 10-5 5.5 x 10-3 1.3x 10-3
salt
0.0 0.0 0.0 0.0
3.9x 10-3 0.0 0.0
4.2 x 10-5 8.7 x 10-4 0.0
0.0
4.2 x 10-5 0.0
9.7 x 10-5
1.3x 10-5 3.6 x 10-4
0.0
0.0
0.0
0.0 8.3 x 10-4 0.0 1.1 x 10-5 0.13
7.3x 10-4 2.9 x 10-4 6.7 x 10-7 0.087 2.3x 10-7 0.0 6.7x 10-4 0.0
6.5 X loW6 2.0 x 10-3 0.0 0.0 9.4x 10-3 0.0 0.0 0.035 1.2x 10-3
0.0
0.0
0.0
0.0
4.4 x 2.7x 8.9X 8.4 X 1.7x 2.0x 4.0 x 2.4x 5.6X 5.6X 0.0 7.3 x 6.4x 3.8 x
auto
tire
8.9 x 10-4 9.4 x 10-5 0.0 0.0
8.1x 10-4 2.6 x 10-4 0.0
3.6 x 10-3 0.027 0.0 0.0 1.3x 10-3 0.0 1 .o 0.011 0.88
1 .o
6.7x 3.6x 4.7 x 5.4x 0.0
10-5 10-5 10-6 10-4
1.0
x
10-3
2.4x 10-4 0.0 0.0 1 .o 7.7x 10-3 0.0 0.0
0.0 0.0
2.5x 10-5 1.9x 10-5 0.0
5.0 x 10-4 15.0
1.3x 10-5 0.0 0.0 0.0
av % error
59.0 59.0 64.0 87.0 0.02 83.0 41.0 53.0 54.0 74.0 50.0 52.0 40.0 53.0 0.002 57.0 0.75 59.0 27.0 89.0 80.0 0.09 44.0 63.0 52.0 52.0 63.0 0.62 53.0 58.0 77.0 54.0 51.0 6.9 73.0
31.0
galvanizing chips are present. Such particles have been observed in closely related dust samples ( 2 5 ) . The so-called “iron” component probably occurs as a result of accumulation in street dust of small rust particles derived from automobiles and other iron objects. Chromium and manganese are the only other elements which make any significant contribution to this component, which further substantiates the idea that rusting of automobiles constitutes the primary source factor. The factor designated as cement corresponds to the test vector that contains pure calcium. Since aluminum, silicon, and magnesium (which are the primary matrix elements in concrete) were not determined in this study, there is little supporting evidence for identifying the calcium factor as cement; however, scanning electron microscopic studies (30, 31) indicate the presence of a large number of particles that are apparently derived from abrasion of concrete. In addition, Rahn and Harrison ( 1 6 ) have presented evidence t h a t indicated the presence of cement particles in roadway dusts. These particles are also enriched in lead relative to pure cement from agglomeration with the primary automobile exhaust aerosol. I t is apparent from the above considerations t h a t the six source factors identified are both credible and consistent with observation, although possible limitations in the resolving Volume 14,Number 2,February 1980
169
Table VIII. Comparison of Actual and Calculated Mass Distributions no.
actual mass, g
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
8.040 2.760 200.2 10 40.190 5.070 3.320 144.890 41.730 1.690 3.270 61.420 46.320 1.740 0.960 26.800 14.010 0.340 0.900 15.300 1.760 1.400 7.400 1.000 5.000 3.300 6.000 1.100 1.900 0.095 0.105
sample
calcd mass, g
8.3 2.3 143.0 46.0 5.4 2.9 176.0 20.0 1.8 4.6 85.0 29.0 2.7 1.3 27.0 8.6 0.53 1.7 32.0 1.6 2.2 8.4 1.5 6.7 3.3 7.8 0.55 2.2 0.085 0.12
% error
-2.7 15.0 29.0 -14.7 -7.1 14.0 -21.0 51.0 -6.4 -39.0 -33.0 37.0 -57.0 -35.0 -2.3 39.0 -57.0 -92.0 -112.0 9.4 -6 1.O -14.0 -51.0 -34.0 0.92 -30.0 48.0 -18.0 10.0 -19.0
power of the statistical analysis are indicated. T h e second question to be addressed, then, is whether the calculated contributions of each source to this roadway dust are meaningful. The differences between calculated and measured elemental concentrations and subsample masses are presented in Tables VI1 and VIII, respectively. The average error between the calculated and actual concentration for particular elements in a given sample is 33%. T h e worst point error is approximately 100%. Consideration of the individual errors in Tables VI1 and VI11 indicates poor agreement for the elements Hg and Ni (and, to a lesser extent, for Ag, As, Co, Eu, Hf, Se, Sm, Tb, T h , U, and Zr) and for the masses of high-density magnetic particles. Since all of these constituents combined make up only a small fraction of the overall samples, the imprecision is clearly of less significance than a t first sight. The data in Table VI11 show t h a t the agreement between actual and calculated subsample masses is reasonably good and that the general characteristics of the mass distribution are quite well reproduced. In this regard it should be noted that the analysis is based on knowledge of only 11%of the total sample, since matrix elements such as Al, Si, Mg, C, and 0 were not determined. The major bias in the analysis appears to yield too high a value for soil in samples 18 and 19 and too low for samples 3 and 8. Information about the physicochemical characteristics of individual elements present in this roadway dust sample can be obtained by consideration of distribution data such as those presented in Figures 1and 2 and Tables I11 and IV. Also, the sources of individual elements can be established from a consideration of the test vectors in Table VI in cases where 170
Environmental Science & Technology
I
Pb
nonmagnetic
mognetic
Fe
nonmagnetic
Tx 10
magnetlC
Figure 1. Plot of the mass distributionand lead and iron concentrations of the physically fractionated street dust samples as a function of particle size and density for the nonmagnetic and magnetic subfractions
there is acceptable agreement between calculated and measured concentrations. Thus, an insight can be obtained into probable environmental behavior as illustrated for the following elements of environmental significance. It is apparent from the data in Table VI that automobile exhaust particles constitute the primary source of lead. T h e distribution of bromine is very similar to the lead distribution shown in Figure 1. Both elements are found to be most concentrated in high-density, magnetic particles. Consideration of the mass distribution of lead, as presented in Table IV, however, shows that some 30%of the total lead present is associated with the particles of intermediate density characteristic of soil and cement. The results of the statistical analysis together with the similar lead and bromine distributions indicate t h a t this soil association is due either to incomplete
P
mmagnetic
nonmagnetic
nonmagnetic
magnetic
magnetic
magWtIC
Figure 2. Plot of the zinc, cadmium, and mercury concentrations as a function of particle size and density for the nonmagnetic and magnetic subfractions
particle separation or, as suggested earlier, to association of automobile exhaust particles with soil and cement particles. Such association raises the possibility of small, but possibly significant, amounts of lead being reentrained into the atmosphere as a result of turbulence. T h e distribution profiles shown in Figure 2 for zinc concentration suggest that this element exists in three distinct forms. T h e assignments in Table VI indicate that, if these forms are characteristic of zinc sources, then the sources are tire wear, soil, and cement. There are, however, some uncertainties regarding these assignments. T h e highest zinc concentration (-6300 pg/g) is associated with large, high-density, nonmagnetic particles whose densities are somewhat higher than expected for tire wear. The suggestion, made earlier, that these particles represent chips of galvanizing materials is more consistent with the raw data. It is the “tire wear” component t h a t has its highest amount for the subsample. Density considerations also mitigate against the small, high-density, magnetic particles that contain zinc being derived from any of the assigned sources. A more likely source would appear to be from automobile exhaust. T h e large, low-density, nonmagnetic zinc fraction is, however, consistent with the soil and
cement sources and is seen to contain most of the mass of zinc in the sample. Clearly, there is considerable uncertainty associated with the origins of zinc and, consequently, with the assignment of tire wear as the factor based primarily on its zinc concentration. I t should also be noted that, by contrast with lead, zinc can be leached quite readily from roadway dust with water ( 3 2 ) .Thus, the observed zinc distribution may be the result of secondary chemical association with particles other than those originally deposited. If this is the case then the observed distribution may only partially reflect the characteristics of source particulates. T h e concentration distribution profiles for arsenic, cadmium, chromium, cobalt, and manganese all resemble that of cadmium illustrated in Figure 2. These elements are apparently derived from two sources, which are assigned as automobile exhaust and tire wear particles. T h e former source is strongly supported by the similarity observed between the lead and cadmium profiles for high-density magnetic and nonmagnetic particles. In addition, the presence of each of these elements in fresh automobile exhaust particles has been established by ion microprobe analyses ( 3 3 ) . The association of arsenic, cadmium, chromium, cobalt, and manganese with low-density, nonmagnetic particles of intermediate size is consistent with the expected characteristics of particles derived from tire wear. However, the fact that most of the mass of these elements is associated with this fraction leads one to suspect tire wear as the sole source. T h e earlier suggestion that the tire wear component is, in fact, a mixed factor involving particles derived from brake linings and, perhaps, motor oil is more acceptable on physical grounds. T h e observed association of chromium and manganese in the salt component (Table VII) is surprising a t the substantial concentrations involved. However, manganese may be present a t quite high levels in raw salt. The uncertainties associated with chromium are such that the calculated relative source contributions must be treated with caution. Finally, it is interesting to note that cadmium and zinc exhibit quite different concentration profiles indicating that cadmium does not occur simply as a zinc impurity. However, Lagerwerff and Specht ( 1 5 )found wide variation in the Zn/Cd and Pb/Cd ratios for four different roadside soil samples indicating the possibility of multiple sources related to the highway but not just automotive exhaust particles. This observation of different sources could also indicate that neither element is migrating in the dust, as previously suggested for zinc, since their chemistries are closely similar. Although present a t extremely low levels, the distribution of mercury concentrations is interesting because of its resemblance to that of the total mass distribution. This suggests t h a t metallic mercury present in the gas phase may become associated with dust particles simply on the basis of their availability. The observed weighting toward somewhat smaller particles than that of the total mass distribution could thus reflect the slight increase in surface-to-volume ratio of particles with decreasing size in the range considered. Conclusion The studies reported herein have shown that a substantial fraction of the particulate matter present in urban roadway dust is anthropogenic in origin. Thus, automobiles contribute direct exhaust emissions, tire wear particles, and iron particles, which are probably due to body rusting or ablation from the interior of the exhaust system. Cement particles are derived from the road surface by abrasion, and there is a residue of salt used for roadway deicing. T h e remaining material is natural soil. Volume 14, Number 2, February 1980
171
I t would appear that the statistical treatment employed herein is capable of defining the primary sources that contribute to roadway dust and of achieving semiquantitative estimates of those anthropogenic sources of primary environmental significance. T h e resolving power of the analysis is, however, insufficient t o distinguish between sources that produce particulate matter having closely similar physical characteristics. Better definition of test vectors and/or more extensive sample fractionation is suggested as a means of improving resolving power. Quantitative assessment of the major source components is generally poor, and it is clear that analytical data for bulk matrix elements are necessary for improved quantitation. Finally, it must be stressed that, while statistical analysis provides a more detailed and more quantitative description of the data, graphical presentation of the mass and concentration distributions of individual elements in a fractionated particulate sample is frequently more useful in assigning physical significance. I t is strongly recommended, therefore, that both interpretive approaches be employed in a n interactive configuration. Acknowledgment
We wish to thank the operating staff of the Illinois Advanced TRIGA Reactor facility for their help in making the irradiations and the Illinois State Geological Survey for access to their counting equipment on which most of the reported analyses were performed. L i t e r a t u r e Cited (1) Lee, R. E., Jr., Goransen, S. S., Enrione, R. E., Morgan, G. B., Enuiron. Sci. Technol., 6,1025-30 (1972). ( 2 ) Rahn, K. A,, “The Chemical Composition of the Atmospheric Aerosol”, Technical Report, Graduate School of Oceanography, University of Rhode Island, 1976. ( 3 ) Gladney, E. S., Small, J. A,, Gordon, G. E., Zoller, W. H., Atmos. Enuikon., 10, 1071-7 (1976). ( 4 ) Mroz, E. J.,Ph.D. Thesis, University of Maryland, 1976, unpublished. (5) Ondov, J . M., Ph.D. Thesis, University of Maryland, 1974, unpublished. (6) Greenberg, R. R., Zoller, W. H., Gordon, G. E., Enuiron. Sci. Technol., 12,566-73 (1978);Greenberg, R. R., Zoller, W. H., Jacko, R. B., Neuendorf, D. W., Yost, K. J., ibid., 12, 1329-32 (1978). ( 7 ) Hopke, P. K., Gladney, E. S., Gordon, G. E., Zoller, U’. H., Jones, A. G., Atmos. Enoiron., 10,1015-25 (1976). (8) Gaarenstroom, P. D., Perone, S. P., Poyers, J. L., Enuiron. Sci. Techno/., 11,795-800 (1977). (9) Gatz. D. F., J . Appl. M e t e o r . , 17,600-8 (1978). (10) Bogen. J., Atmos. Enuiron., 7, 1117-25 (1973). (11) Gartrell, G., Jr., Friedlander, S. K., Atmos. Enuiron., 9,279-99 (1975).
Photooxidation of the Propylene-NO,-Air Transform Infrared Spectrometry
(12) Friedlander, S. K., Enuiron. Sci. Technol., 7,235-40 (1973). (13) Miller, M. S., Friedlander, S. K., Hidy, G. M., J . Colloid Interface Sci., 39, 165-76 (1972). (14) Kowalczyk, G. S.,Choquette, C. E., Gordon, G. E., Atmos. Enuiron., 12, 1143-53 (1978). (15) Lagerwerff, J. V., Specht, A. W., Enuiron. Sci. Technol., 4,583-6 (1970). (16) Rahn, K. A., Harrison, R. P., Proceedings of the Conference on Atmosphere-Surface Exchange of Particulate and Gaseous Pollution, CONE-740921, NTIS, 1974, p p 557-69. (17) Solomon, R. L., Hartford, J . W., Enciron. Sci. Techno/., 10, 773-7 (1976). (18) Draftz, It. G., “Types and Sources of Suspended Particulates in Chicago”, Report No. IITRI-C9914-C01, IIT Research Institute, Chicago, 1975. (19) Lepow, M. L., Brickman, L., Rohino, R. A,, Markowitz, G., Gillete, M., Kapish, J., Enuiron. Health Perspect.. 7, 99-102 (1974). (20) Beeton, A. M., Limnoi. Oceanogr., 10,240 (1965). (21) Maney, J. P., Fasching, J. L., Hopke. P. K., Comput. Chem., 1, 257-64 (1977). (22) Rozett, R. W., Peterson, E. M., Anal. Chem., 47, 1301-8 (1975). (2:i) Ritter, G. L., Lowery, S.R., Isenhour, T. L., Wilkins, C. L., Anal. (‘hem., 48, 591-
