Identifying and Estimating the Relative Importance of Sources of Airborne Particulates Michael T. Kleinman Rancho Los Amigos Hospital Campus of the University of Southern California, Environmental Health Service, 7601 East Imperial Highway, Downey, Calif. 90242
Bernard S. Pasternack, Merril Eisenbud, and Theo. J. Kneip" New York University Medical Center, Institute of Environmental Medicine, 550 First Avenue, New York, N.Y. 10016
A regression model has been developed for apportioning the relative contributions of several sources to total suspended particulate matter (TSP) based on trace substance concentrations in aerosol samples. The model when applied to data from New York City indicates that about 40% of the TSP is due to natural or secondary sources. In 1972 to 1975, about 20-25% of the TSP is related to automobile emissions, while fuel oil burning for heat and power contributes about IO%,and incineration about 5%. The model was tested on data collected over several years at urban and nonurban sites. Predicted and observed TSP values agreed usually t o within better than 20%. Demands for energy and the dwindling supply of low sulfur fuel may change the character of the fuels that are being used. I t is imperative that we better understand the relationships between emitted pollutants and their impact on ambient air quality, so that control strategies can be promulgated which will protect the environment and which are cost effective a t the same time. Chemical tracers useful as predictor variables for source apportionment have been selected in other studies a priori from literature data on source emission compositions ( I ) or by analyses of samples obtained from emission sources (2). In either case, the data do not necessarily represent the composition present when the emitted particles have equilibrated with the atmosphere. We sought to develop a method for selection and use of tracers based on the composition of ambient aerosols and independent of source sampling and analysis. Building on previous studies (1-10) that used chemical tracers to link particles emitted by specific sources to aerosol burdens in ambient atmospheres, we have developed a regression model for source apportionment. The model can quantitate source contributions and can also predict the impact on air quality of changes in source emission characteristics.
Methods Air filters (7-day integrated samples) collected during the period 1968 through 1975 in New York City (6, 8, 1 1 ) were analyzed t o determine the concentrations of cadmium, chromium, copper, iron, lead, manganese, nickel, vanadium, and zinc. Anionic species, NO3-, NOn-, and S04:,-, were also determined. Trace metals were determined by atomic absorption spectrophotometry, N03- by reduction on a cadmium amalgam column t o NOz-, and both initial and total NO:, were measured by colorimetric analysis (8, 1 1 ) . In all, about 800 samples from the locations shown in Figure 1 were analyzed and subjected to statistical analysis. The regression model was formulated using multiple regression analysis, which is a statistical technique for predicting or estimating the degree to which changes in independent (predictor) variables will cause changes in a dependent variable. In this case the predictor variables were trace element or anion concentrations in air filter samples, and the depen62
Environmental Science & Technology
Figure 1. Locations of samplers: (1) lower Manhattan; (2) mid-Manhattan: (3) Bronx-Carpenter Hall: (4) Queens College: (5) background sampler: Sterling Forest, Tuxedo, N.Y., about 80 km NW of Manhattan
dent variable was total suspended particulate (TSP) concentrations. Factor analysis was used to aid in selecting the best predictor variables from among the aforementioned candidates. The selection criteria were that the predictor variables should vary independently of one another and that they be reasonably source specific, or primarily associated with a single type of source. Statistical analysis procedures from the University of Michigan's OSIRIS 111 Program package (12) were used to perform the various data management and computational functions required.
Results Annual average trace metal and T S P concentrations measured over the period 1968 through 1975 are summarized in Table I. Years for which there were not enough data to obtain a meaningful annual average were excluded from the table. Weekly TSP, trace metal, and sulfate ion concentrations observed in New York City at the Medical Center during 1972-1975 were analyzed by factor analysis on a year-by-year basis. Pearson product-moment correlation coefficients were computed between all possible pairwise combinations of measured (nonnormalized) variables for weekly TSP, trace metal, and sulfate ion concentrations observed in New York City a t the Medical Center during 1972-1975 on a year-to-year basis, and arranged in matrices, one for each year. Factor analyses were performed on each of the four correlation coefficient matrices. Factor analysis (13)is useful in finding underlying patterns that explain common variations among a set of variables. The 0013-936X/80/0914-62$01.00/0
@
1980 American Chemical Society
Table 1. Concentrations of Trace Metals Measured at Three Locations in New York City (ng/m3) element
Cd Cr
cu
1969
1972
6.0 11.9 63.0 1490 240 27.5 1130 30.7 1370 68.9 380
10.0 33.0 526
Fe K Mn
89.0
Na Ni Pb V Zn
NYU Medical Center 1973 1974
1390 21 10 874 670
7.1 8.9 55.7 1580 358 28.1 1990 45.0 1240 86.0 31 1
1975
6.0 10.8 46.8 1410 37 1 23.1 604 45.4 1400 72.6 338
4.2 8.5 43.9 1010 99.1 19.8 800 35.2 1070 38.8 294
Table II. Clusters of Elements Resulting from Use of Factor Analysis with Maximum Factor Loadings Indicateda 1972
Zn",
Fe"",
CU'
1973
Cd",
Zn",
1974
Fe", Mn',
Fe', Mn",
Fe**,Mn""
SO4'-'
CU"
Cd', Mn'
1975
Cd", Zn'
Zn*, CU'
SO4'-'
Pb', Cr'
Pb, V"
Pb', Cu"'
Cr', SO4"
Ni",
Cr"', Ni
Ni"',
Cd",
so4
v"
Cr
V"
v"'
Pb, Ni'
Elements with a maximum factor loading of 0.6 to 0.8 in the varimax rotated factor matrix. * * Elements with a maximum factor loading of 0.8 or more in the varimax rotated factor matrix. Selected predictor variables are in italics. a *
factors, in this study, are thought to represent physical properties that affect changes in the trace metal concentrations. Thus, a factor might represent an emission source, or the effects of some meteorological phenomenon such as atmospheric dispersion or seasonal temperature changes. We obtained three factors for each of the years 1972, 1974, and 1975 and four factors for 1973. The fourth factor in 1973 had a heavy loading for nickel only and accounted for less than 10% of the variance. We examined the results of the factor analyses and concluded that: (a) several elements, forming clusters, were often loaded in more than one factor; (b) several of these clusters were repeated from year to year. The clusters and factor loading ranges revealed by the factor analyses are summarized in Table 11. These cluster and factor loading results were evaluated in the light of literature data on source compositions, as an aid in selecting the variables most likely t o be independent predictor variables for the multiple regression analysis. The source data indicated which interelement relations were to be expected, and the likelihood that the variables chosen were potentially representative of major sources in the New York City area. For example, earlier studies had indicated that copper was likely to be a useful predictor variable and substitution of zinc for copper resulted in larger residual values in multiple regression trials (8).Analysis of five New York City incinerator fly ash samples confirmed the presence of copper, and a review of sources indicated no other major source of copper in New York, while several possible area sources of zinc were believed present (11). Thus, the overall evaluation indicated that copper was a better choice than zinc as an independent predictor variable for incineration in the New York City area.
1968
Bronx, N.Y. 1969 1972
14.0 49.0 133
9.0 23.0 115
4.0 7.0 60.0 1940
3.5 5.3 52.5 1440
2.6 4.3 21.6 623
1.9 3.2 22.3 481
54.0
40.0
29.0
30.2
11.9
6.9
210 2000 53.0 304
31 1 1580 80.0 289
12.8 839 34.3 198
7.8 707 19.4 181
150 3820 1230 730
122 2760 795 1120
Queens, N.Y. 1974 1975
1973
Lead, in New York City, is predominantly related to automotive emissions and correlates strongly to traffic volume ( I d ) , vanadium concentrations are a function of fuel oil burning emissions from power and heating sources ( 1 5 ) ,and sulfate is formed by the oxidation of SO2 whether from local sources or during transport from more distant sources (16). Manganese was selected as a tracer for soil, or soil-like material (10). Only one element from each cluster was selected because the introduction into a regression model of redundant or highly correlated variables tends to increase the uncertainty in the estimated regression parameters. The weekly data for TSP, Pb, Mn, Cu, V, and Sod2- obtained during 1972 and 1973 were substituted into a series of equations of the form of Equation 1where the concentrations of the above elements replaced the C; terms:
[TSP] = F l C l + FzCz
+
,
. , FiC,
+U
(1)
where C, = concentration of the characteristic element selected as a tracer for source i, F , = constant relating Ci to source effluents (Le., the mass fraction of element i in particles from source i), and where U is that part of the TSP not associated with the modeled sources. The data set for 1972-1973 contained about 100 observations on six variables, taken through all seasons and many meteorological conditions. We could therefore expect the regression estimates to apply on the average over the annual range of the conditions which affect the aerosol. Solving for the parameters ( F s and U s ) with multiple regression analysis, we obtain the equation:
+
[TSP] = 12.O[Pb] 54.O[Cu]
+ 103[V] + 1.66[SOd2-] + 420[Mn] + 26.8
(2)
where the brackets denote ambient air concentrations in pg/m3. The regression equation has a multiple correlation coefficient of 0.76 ( p < 0.001). The standard deviation of the residuals (TSPobsd- TSPcalcd)was determined to be f 1 5 pg/m3. The equation shows that about 27 pg/m3 of particles is not related to emissions from the modeled sources. This value is derived from the constant term of the regression analysis. Industrial emissions, secondary sources which are not coupled to conversion of SO2 or Sod2-, and sea salt aerosols are probably major contributors to this residual. Data for the Medical Center Site in 1969, 1974, and 1975 and data for several years a t other sampling sites were used to test the degree to which the multiple regression coefficients from the 1972-1973 data could be used to apportion TSP for other years and other sites. Application of the regression coefficients to the data from Volume 14, Number
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63
Table 111. Adjusted Regression Coefficients 1969
fuel oil Pb in gasoline % S in
cu Mn Pb
sod'V
1 2 54 420 5 1.66 29
1972
1973
1974
1975
0.3 1 54 420 11 1.66 103
0.3 0.75 54 420 14 1.66 103
0.3 1 54 420 11 1.66 103
0.3 1 54 420 11 1.66 103
other years in the New York City area required consideration of fuel quality changes due to regulatory actions. These changes were believed likely to have affected the coefficients for lead and vanadium. In 1972 and 1973 (the base years of this study), gasoline contained an average of 0.75 g of Pb/gal. For years in which gasoline contained different concentrations of Pb, the regression coefficient for P b was adjusted in inverse proportion to the change in gasoline P b concentrations relative to the base years. In a similar manner, there is a need to adjust the coefficient of V for fuel oil burning as a function of the percent sulfur in the oil burned. Using data from Magee et al. ( 1 7 ) ,a relationship between % S and V is obtained and can be expressed as: ppm of V = 37(% S)- 7.83
(3)
A similar equation can be derived using data summarized by Smith (18)to express the change in ash as a function of change in sulfur content:
+
% ash = 0.045(% S) 0.0092
(4)
During the base years, the average % S in fuel oil was 0.3%. T o adjust the coefficient for a year in which the % S was 1%, one can apply Equations 3 and 4 to obtain a factor of 3.6. We tested the validity of these adjustments against an actual regression analysis on 1969 Medical Center data. During that period the sulfur content of oil was 1%and lead was a t 2 g/gal in gasoline. The V regression coefficient for 1969 was 0.020 f 0.006. The estimate based on correction of the coefficient obtained using 1972-1973 data was 0.029. For Pb, the actual coefficient was 0.007 f 0.005, and the corrected estimate was 0.005. In both cases the adjusted coefficients agreed, within experimental error, with the regression estimates. Using the derived model, including adjusted coefficients where necessary (see Table 111), the contributions of the modeled sources to the TSP were calculated as shown in Table IV for all years a t the Medical Center Site. The error terms were computed using the standard errors associated with the regression coefficients. Regulatory efforts essentially eliminated coal burning be-
tween 1969 and 1972,reduced incineration, controlled the lead level in gasoline, and required the use of low sulfur oil by 1972. Overall declines in the airborne concentrations of Mn, Cu, Pb, and V reflect these regulatory actions ( 1 1 ) . The estimated TSP values agree with the measured values to within about 10%.In the worst case, 1975, the model overestimates the TSP by 13 pg/m3. The model, therefore, is reasonably good for predicting the effects that changes in source emission patterns have on TSP, and can be used to determine the relative importances of the modeled pollution sources. The contributions do not add up to exactly the average measured mass because the model does not explicitly include all of the sources of particulate pollution in the city, and the unallocated source contributions (industrial, commercial, and other unidentified sources) are treated as a constant. Thus, in some years the model overestimates, while in other years it underestimates TSP, but on the average, the modeled sources account for about 75% of the TSP. The changes in New York City's air pollutant regulations are reflected in the decrease of contributions attributed to several sources of the TSP shown in Table IV. The particulate contributions of automobiles and natural sources appear to have remained essentially constant on a mass basis, and represent a larger fraction of the lower total mass of contemporary aerosols. As a test of the model's generality, we used trace metal data obtained a t sites other than the Medical Center, including data from a forested rural site in Tuxedo, N.Y. In this nonurban area, which has little or no commercial or industrial activity, one can assume that the constant term should approach zero. Sulfate data were not available for all of the years tested, but we used an average value of 6 pg/m3, which was determined from 1974 data. The measured and computed TSP values are shown in Table V. Fair agreement was obtained for the three years tested for Tuxedo data, and the differences between actual and predicted values for the other sites are less than 15%of measured values with the exception for Queens. This may represent an overestimate of the residual as the TSP levels in Queens are generally lower than for Manhattan. These tests of the model on data from two urban stations and one nonurban station confirm that the model has a general utility in predicting TSP values based on airborne trace metal concentrations and apportioning the sources of the TSP.
Conclusions The total suspended particulate matter is usually assumed to be an additive function of source emissions. In principle, one can inventory the emissions from each source and estimate the fraction which each will contribute to a pollutant, e.g., total suspended particulate matter (TSP). In practice, however, this is an extremely time consuming and difficult endeavor, because one must determine the particulate emissions from
Table IV. Calculated Contributions to TSP of Air Pollution Sources (Suspended Particle Concentrations in pg/m3) source
automobiles fuel oil burning incineration soil sulfates other calcd TSP obsd TSP deviation
tracer
Pb V
cu Mn
sod'-
1969
10.5 f 3.38 29.0 f 12.0 28.6 f 14.1 37.0 f 18.0 (14.9 f 3.7)b 26.8 147 f 26.3 134 4-13
1972
1973
1974
1975
15.4 f 4.8 6.9 f 2.9 3.5 f 1.7 11.7 f 5.9 14.6 f 3.6 26.8 79 f 9.1 82 -3
18.2 f 5.7 8.2 f 3.4 3.0 f 1.5 11.7 f 5.9 16.4 f 4.1 26.8 84 f 9.9 80 +4
15.4 f 4.8 7.4 f 3.1 2.5 f 1.2 9.6 f 4.8 15.1 f 3.8 26.8 76 f 8.5 71
12.0 f 3.7 4.0 f 1.7 2.3 f 1.1 8.3 f 4.1 13.4 f 3.3 26.8 65 f 6.7 52 +13
a All error terms are calculated from the standard error of the regression coefficient. through 1975.
64
Environmental Science & Technology
+5
1969 sulfate data are estimated as the average of the data from 1972
Table V. Comparison of Predicted TSP from Aerosol Apportionment Model with Measured TSP at Urban and Nonurban Locations Carpenter Hall predicted measured deviation YO deviation
1968=
1969
1972
115 113 +2 1.7
110 108 +2
73 82 -11
125 125
118 104 14 13
1.8
1973
.
13
1974
82 74 +8 11
HASL
predicted measured deviation YO deviation Queens College predicted measured deviation % deviation Sterling Forest predicted measured deviation YO deviation
0 0
a
(1) Friedlander, S.K., Enuiron. Sei. Technol.. 7. 235 (1973). (2) Kowalczyk, G. S., Choquette, C. E., Gordon, G. F., Atmos Envi-
26 38 -12 32
average deviation (predicted vs. measured) =
Acknowledgments This work was completed while Dr. Kleinman was affiliated with New York University Medical Center’s Institute of Environmental Medicine. The authors thank Dr. David M. Bernstein, Mr. James Miller, Mr. John Gorczynski, Ms. Marie Ann Leyko, and Mr. R. Peter Mallon for their help in the collection and analysis of the ambient air samples cited in this report. Literature Cited
69 44 +25 57 31 37 -6 16
analysis program is continuing in an effort to determine the usefulness of the method in defining source contributions within each season.
18 24 -6 25 16.8%
Calculated using same regression coefficients as for 1969
each individual source, or alternatively, the aggregate particulate output from each class of pollutant sources. We have derived an air pollution model based on long-term data for a single site, which successfully allocates approximately 75% of the TSP to five principal sources. The model has been used successfully to apportion the sources for several other sites where similar long-term data were available. The method used to develop the model should be applicable to other pollutants. For example, it might be used to apportion sources of organic compounds in urban aerosols. The applicability of this modeling approach is broadened because the regression coefficients can be adjusted as demonstrated above to reflect changes in source terms. Thus, the coefficients determined and tested in New York may prove to be reasonably valid for other cities with suitable modification for known source differences. For cities with either more or less industrialization, some adjustment of the constant term (in pglm“) or addition of suitable tracers might be necessary in order to apply the model. If data are available, the techniques described can be applied and a new model can be derived. Atmospheric dispersion, degree days, and source emissions vary greatly season by season in New York City. The data
ron., 12,1143-53 (1978). (3) Kneip, T. J., Kleinman, M. T., Eisenbud, M., paper published in the Proceedings of the Third International Clean Air Congress, Dusseldorf, Federal Republic of Germanv. 1974. (4) Blifford, I. H., Jr., Meeker, G. O., A t k o s Enuiron, 1, 147-57 (1967). ( 5 ) Kneip, T. J., Eisenbud, M., Strehlow, C. D., Freudenthal, P. C., J Air Pollut Control Assoc, 20,144 (1970). (6) Eisenbud, M., Kneip, T. J., “Trace Metals in the Atmosphere”, New York State Department of Environmental Conservation, Technical Paoer No. 16. Julv 1971. (7) Miller, M. S:, Friedlander, S . K., Hidy, G. M., J . Colloid Interface Sci., 39, 165 (1972). (8) Eisenbud, M., Kneip, T. J., “Trace Metals in Urban Aerosols: Final Report to Electric Power Research Institute”, October 1975, NTIS No. Pb-248-324. 1976. (9) Hopke, P. K., Gladney, E. S., Gordon, G. E., Zoller, W. H., Atmos. Enuiron., 10, 1015-25 (1976). (10) Gaarenstroom, P . D., Perone, S. P., Moyers, J. L., Enuiron. Sci. Technol., 11,795 (1977). (11) Kleinman. M. T.. Ph.D. Thesis. New York Universitv. 1977. (12) University of Michigan, Institute for Social Researih, OSIRIS 111 System and Program Description, Vol. I, 1973. (13) Harmon, H. H., “Modern Factor Analysis”, 3rd ed., University of Chicago Press, Chicago, 1976. (14) Bernstein, D. M., Rahn, K. A,, in A n n . N . Y . Acczd. Sei., 322, 87-97 (1979). (15) Kleinman. M. T.. Bernstein. D. M.. Kneia. T . J.. Air Pollut. Control Assoc. J., 27,65-7 (197b). (16) Wolff, G. T., Liov, P. J.. Leaderer. B. P.. Bernstein. D. M.. Kleinman, M. T., Ann. N . Y . Acad. Sci., 322, g7-71 (1979). (17) Magee, E. M., Hall, H. J.,Varga, G. M., Jr., “Potential Pollutants in Fossil Fuels”, Office of Research and Monitoring, US.Environmental Protection Agency, Report No. EPA-RZ-73-249, 1973. (18) Smith, W. S.,“Atmospheric Emissions from Fuel Oil Combustion”, U.S. Department of Health, Education and Welfare, Public Health Service Division of Air Pollution, Cincinnati, Ohio, 1962. Receiued for reuiew October 11, 1978. Accepted September 28, 1979. This study uas funded by grants f r o m the Electric Power Research Institute (Grant N o . RP439-I) a n d the American Petroleum Institute, and is part of a Center Program supported by Grant N o . ES00260 from the National Institute of Environmental Health Sciences and Grant No. CA13343from the National r a n t e r Institute.
Volume 14, Number
1, January 1980
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