Environ. Sci. Technol. 1984, 18, 923-931
Development and Validation of a Chemical Element Mass Balance for Chicago Peter A. Scheff' Illinois Instkute of Technology, Pritzker Department of Environmental Engineering, Chicago, Illinois 60616
Richard A. Wadden and Robert J. Alien University of Illinois
at Chicago, School of Public Health, Chicago, Illinois 60680
A chemical element mass balance (CEB) receptor model was used to determine the contributions from seven sources to the ambient total suspended and respirable particulate matter (TSP and RP) in Chicago. The CEB was applied as an independent analysis on 33 days of air measurements during a 7-month period from July 1981 through Jan 1982. On a study period average basis, soilderived aerosol makes up the largest fraction of the TSP and RP at 33.5 and 7.3 pg/m3, respectively, followed by the limestone and cement sources, mobile sources, coal combustion, refuse incineration, and steel industries. Strong statistical correlations 0, < 0.01) were found between wind-directional emission strength and the 24-h average contributions from the steel and refuse incineration point sources (r2 = 0.72 and 0.75, respectively). Including total sulfur (particle and gas phases) in the CEB calculations reduced the standard errors and improved the coal and soil predictions. The average unexplained fractions for TSP and RP were approximately equal (19 and 18 pg/m3, respectively), suggesting that this aerosol is concentrated in the small size fraction and represents an upper limit on the secondary contribution to aerosols measured at the receptor site.
Introduction The application of models that relate air pollution emissions and air quality is a necessary step in the development of programs to control and reduce the level of air pollutants in the environment. The identification of air pollution sources and determination of their emission rates are a logical step in this process. However, the relationship between multiple source emissions and resulting air quality is complex, often nonlinear and imperfectly understood. Sophisticated dispersion models have been developed that relate air pollution emissions to ambient concentrations by considering such factors as micro- and macrometeorology, spatial distribution of sources and receptors, emission inventory, topography, chemical reaction, and pollutant deposition. However, determination of the level of control to meet the air quality goals of a region requires accurate estimates of these parameters as well as a trial and error application of the dispersion model. Further complicating this situation is that there are very few monitoring stations compared to the number of sources. In contrast to models based on source dispersion, receptor models are derived solely from monitored air pollution and source chemical characteristics ( I ) . No a priori assumptions about source location and strength, meteorology, or topography are required. These models can use chemical element mass balancing (CEB) coupled with multivariate regression techniques or a variety of multivariate statistical techniques such as principal components analysis. Although receptor models are relatively new and unrefined, they have been applied to a variety of locations (e.g., see ref 2 and 3) and have been used to evaluate and 0013-936X/84/0918-0923$01 S O / O
calibrate the predictions of dispersion models (4). The purpose of this paper is to report on the development of a CEB receptor model for the sources of particulate matter affecting the air quality at the University of Illinois Health Sciences Center in Chicago. Major emphasis is placed on model validation. This includes comparison of CEB predictions to dispersion model predictions, sensitivity analysis of the model's input matrix, and a statistical analysis of modeled results. All analyses were done for both the total and respirable particulate matter fractions. Seven source categories were studied including steel production, mobile sources, soil-derived aerosol, refuse incineration, cement and limestone, oil combustion, and coal combustion.
Methods Ambient 24h average particulate samples were collected every sixth day from July 1981 through Jan 1982. Air sampling was carried out on the roof of the School of Public Health building (20 m high) at the University of Illinois Health Sciences Center, located 3 km west of Chicago's Loop. Total suspended particulate matter (TSP) was collected with a standard high volume air sampler (Hi-Vol) (68 m3/h) (5) and respirable particulate matter (cutoff diameter of 2.0 pm) (RP) with a modified Anderson impactor (34 m3/h) (6). All samples were collected on Pallflex quartz filters to control artifact sulfate and nitrate production (7,8)and maintain high collection efficiency (9). Each TSP and RP sample was analyzed for SO -: and NO3- by wet chemical procedures, for the composition of Pb, Ca, Mn, Fe, Al, K, Zn, Cr, Cu, Cd, Na, and Mg by flame atomic absorption analysis (AA), and V, As,Ni, Ba, Co, and Se by high-temperature graphite furnace AA analysis. Details of the sampling analysis procedures have been presented (8). A recovery study of four sets of US. Environmental Protection Agency (USEPA) reference filter strips for SO:- and NO3- showed that recovery was well within the EPA quality control specification of &lo% (IO). A recovery study based on 15 samples of National Bureau of Standards Reference Material 1648 "Urban Particulate Matter" (SRM-1648) showed good precision of the AA measurements (the standard deviation of the fraction recovered from the 15 samples of SRM-1648 was less than 30% of the average fraction recovered for Cr and Ni and less than 20% for the remaining 16 elements) (11). Simultaneous with the particle sampling, continuous measures of pollutant gas and meteorology were taken for 192 consecutive days (including those days with particle collection). The variables measured included SOz (flame photometric/chemiluminescence,Meloy Labs Model SA185-2A) and wind speed and direction (Weather Measure). Details on instrument calibrations have been presented (8). Statistical Considerations. The CEB model is based on the assumption that the concentration of element i at
0 1984 American Chemical Society
Environ. Sci. Technol., Vol. 18, No. 12, 1984
923
the receptor location equals the s u m of the contributions to i from j sources. The model further assumes that mass is conserved: that is, that the fraction of element i in the emissions of source j is unchanged from the time it is released to the environment to the time it is collected at the receptor. With all elemental concentrations expressed as fractions of the total mass (same units for sources and receptors) the CEB model can be written in matrix notation as where Yl,kis a vector of i elemental concentrations with units micrograms of i per gram of receptor particle mass measured on day k, Z,+, is the pollution source elemental concentration matrix with units of micrograms of i per gram of total primary particulate matter emitted from source j (note that j + 1 subscript represents j source vectors plus one intercept term vector of ones), @,+l,k is a vector of j source weights with one intercept term having units of grams of mass from source j per gram of receptor particle mass on day k , and Ekpis a vector of errors on day k (the difference between the measured elemental and predicted elemental concentrations). For CEB solutions, receptor particle mass can be either the TSP or RP fraction. Each of four different source elemental concentration matrices were used to evaluate the TSP and RP data from all 33 particulate sampling days. Since there are usually more elements than sources (i > j ) , an additional condition must be placed on eq 1 to define a unique solution. Solutions to this problem can be defined by minimizing the sum of the squares of the error term weighted by the inverse of the measurement variance of Y. Ordinary least-squares procedures (unweighted by inverse measurement variance) do not consider the contributions from elements with low concentration and, therefore, did not produce reasonable results when applied in this study, and a similar problem was reported in a study of the Washington, DC, area (12). All solutions of the CEB equation for this study were based on the weighted least-squares procedure (WLS). This solution weights the errors E, by the inverse of the variance of the measurement technique for each element i. The variance of vector Y can be directly calculated from a distribution of measurements for each day. This calculation ideally requires repeated measurements of vector Yon each day, but such information was not (and is unlikely to be) available. Instead, the variance of Y was based on the results of the SRM-1648 aerosol recovery study. The SRM-1648 recovery study consisted of 15 measurements of vector Yon the standard aerosol. Since each of the 15 measurement vectors was an estimate of the same sample, they are all estimates of the same elemental concentrations and can be viewed as 15 repeated measures of a single day. Therefore, the variance of the measurements of the NBS aerosol represents the variance of the measurement technique at one concentration. The variance of Y for each of the 33 samples of TSP and RP was calculated by normalizing the SRM-1648 aerosol variance to the concentration for each day as where Vi&is the vector of variances for measurement Yi,k on day k ; VSRM.i,, is the vector of variances of the standard aerosol, Y i pis the vector of concentrations on day It, and YSRM-1648 is the vector of concentrations of the standard aerosol. This is consistent with the assumption that the measurement error of an analytical technique is a fixed fraction of the measurement (e.g., *5%) and not an absolute amount. 924
Environ. Sci. Technol., Voi. 18, No. 12, 1984
In addition to the variance model of eq 2, CEB calculations were performed by assuming the variance of each element was constant and equal to the SRM-1648 variance for the element. No adjustment was made for the variability of the elemental concentrations on a day to day basis. Source coefficients for these solutions appeared to be a random distribution of positive and negative values. Consequently, this method of variance estimation was not considered useful, and all results presented in this paper are based on the variance estimation method of eq 2. WLS solutions for /3 were calculated as p = (z’z-’z)-’z’z-’y (3) where Z is a diagonal matrix of the measurement variance Vi. Note that the WLS procedure assumes that the offdiagonal elements of 2, the covariance between elements, are zero. While this may not be true, no justifiable method of calculating them is available. The assumption of zero covariances, therefore, is made for all CEB calculations (e.g., see ref 12). Up to 19 elements were used for various solutions of eq 3 (18 elements measured by AA and sulfur defined as the SO2 and SO4*- expressed as S ) . Source Characterization. Each of four source profile matrices (2)used by eq 3 were developed from published data. Table I lists all of the data used in the definitions of the source matrices. All data selected are based on the elemental composition of samples collected downstream of pollution control devices. In this way, the matrix more accurately reflects actual emissions, and some of the TSP/RP differences are corrected. The steel profile is a composite of four published studies of particles emitted from iron and steel industries (13-16). The mobile source profile is based on tunnel data (17) with sulfur based on road test data (18)and an assumed absolute concentration of lead of 10% by weight (16). The soil data are derived from geological survey data of the composition of surficial materials in the Chicago area (19)and are supplemented by worldwide estimates of soil and crustal composition (20). The refuse profile is based on stack data from three incinerators (21,22). Limestone is a composite of the composition of portland cement ( I , 14), cement plant emissions (13, 16),and crustal limestone (20). Oil is based on six reports on the elemental composition of oil fly ash (3, 23-27). The coal profile is an average of data from nine reports on the elemental composition of coal fly ash (12, 14,2428-32). Sulfur data for steel, refuse, limestone, oil, and coal profiles were supplemented by an EPA emission inventory of SO2 emissions from point sources in the Chicago area (33). Note that because large quantities of sulfur are emitted in the gas phase, concentrations of greater than lo6 pg/g of particulate matter are possible. Four source profile matrices were developed as part of a sensitivity analysis of the CEB procedure. The first matrix, designated as “18-elementn,represents a compilation of the best available data for all 18 elements measured by AA spectroscopy. Solutions based on this matrix will be comparable with the variety of CEB studies from around the country where the inorganic fraction of ambient particulate matter for chemical tracers was used. All elements in Table I except sulfur are contained in this matrix. The second profile matrix, “GE1000”, places the additional condition on the 18-element matrix that an element must contribute at least 0.1 % (lo00 ppm) to the total mass from a source to be considered. If an element’s concentration in a source’s emission was below this level, its contribution was assumed to be zero. In this way, the GElOOO solutions will highlight the contributions of the most important elements for each source. This matrix is
Table I. Source Profile Matrix" (rg/g) element
steel
A1 Asc Ba Ca Cd coc Cr cu Fe K Mi3 Mn Na Ni Pb Sb SeC V Zn
20 000 25 75c 43 000 25c 6 5 250 5 000 360 000 10 000 12 000 37 000 3OOc
50OC 7 000 230 OWd 45 60OC 8 100
mobile sources 7 100 0
1500 11 000 24OC 11 75c 72OC 8 000 0
7 500 3OOc 0 8OC 100 000 4 635d 8.1 0
1700
soil 30 000 5 5OOc 8 000 0.5c 20 5OC 3OC 25 000 20 000 7 000
30OC 6 300 3OC
3OC 260d 0.1 l0OC 5OC
refuse
limestone
14 000 240 70OC 17 000 1500 6.6 49OC 1700 6 500
20 000 12 l0OC 330 000 2ac 3
0 13 000
73OC 82 000 15OC 81 000 1 960 OOOd 37 3lC 120 000
oil 4 700 35 1623 26 000 3.6c 1000
58OC 85OC
l0OC
15OC 12 200 20 000 18 200 62OC 4 300 6OC
14 000 1270 18 000 27OC 28 000 14 500 49OC 3 350 000 43 41 100 73OC
lOOC
210000d 10 3OC 5OC
coal 120 000 280 2 800 43 700 19c 55 35OC 39OC 85 000 10 400 8 200 41OC 9 500 215c 38OC 3 882 000 330 52OC 51OC
" SULFUR profile. *Set equal to zero in 18-element and GElOOO profiles. Set equal to zero in GElOOO profile. Set equal to zero in S-FOSSIL profile. represented by the 18-elementmatrix, replacing all values less than 1000 ppm with zero. The third profile matrix, "SULFUR", adds the best available estimate of total sulfur to the 18-element matrix. Sulfur is defined as the sum of gaseous (SO,) and particulate (Sod2-) sulfur compounds expressed as sulfur. Receptor concentration of sulfur is defined in the same way. Given this definition, sulfur will be conserved even if a fraction of the SOp released by a source is transformed to SO4,- before it reaches the receptor. Note that conservation of mass is a basic requirement of the CEB method. This matrix facilitated testing the effect of adding a major gas-phase element to the CEB procedure. It was hoped that it would be particularly useful in resolving the coal and soil components, two sources that are traditionally difficult to separate because of their similar elemental profiles. This matrix is represented by all the data in Table I and was considered to be the best estimate of source elemental characteristics. The fourth profile matrix, "S-FOSSIL", includes the assumption that 100% of the total sulfur emission is derived from the coal and oil combustion sources. Thus, sulfur emissions from mobile sources, steel, soil, refuse, and limestone are assumed to be zero. Solutions based on this matrix, therefore, will represent an upper limit to the contributions of fossil fuel combustion. Equations 1and 3 were solved for the source weights /3 for the RP and TSP fractions of the ambient aerosol on each of the 33 particle sampling days for the four source prbfile matrices. For these calculations the variance of sulfur was taken as 1% of the concentration squared. This is equivalent to the relationship between Fe variance and Fe concentration and was selected because of the high analytical precision in both the Fe and sulfur measurements. Occasionally the WLS procedure estimated a negative source coefficient, which is physically meaningless. It is therefore necessary to assume that all negative coefficients are zero and to reestimate the other nonzero coefficients with the zero source removed from the analysis. In general, negative regression coefficients were a minor problem. The only two cases where there were a significant number of negative estimates were for oil contributions and RP coal contributions when sulfur was not included in the model.
Table 11. Study Period Average Concentrations of Particulate Matter, Sulfur Dioxide, and Meteorology measure of particulate matter
no. of days
TSP, rg/m3 RP, pg/m3 SO-: (TSP), rg/m3 SO-: (RP), pg/m3 SOZ,ppb wind speed, mph wind direction, deg
33 33 35 35 194 193 193
mean"
SD"
40.8 75.7 36.7 18.1 9.32 7.52 4.95 7.11 13.0 14.5 0.68b 235b
range" 32.2-243.5 11.9-89.1 2.09-40.3 1.18-20.2 2.50-96.6 0.5-15.0
"Based on 24-h average concentrations. *Vector average.
Results Table I1 lists study-period averages, standard deviations, and ranges of particulate matter and sulfate in the TSP and RP size fractions, as well as SO2 concentrations and meteorology. Using a two-sided t test, no significant differences (a = 0.05) in the continuously collected variables were found between study-period averaged values and averages from particle sampling days only, either on the basis of 24-h averaged values or on the basis of noonhour values. Table I11 lists study-period average elemental concentrations in the TSP and RP fractions. Elemental measurements are corrected for filter background and recovery efficiency. Minimum concentrations (shown in parentheses) are also corrected for filter background and recovery and further assume that any concentration less than two standard deviations above the blank mean is actually zero. For elemental concentrations near the filter background the two values of concentration will bracket the actual value. Table IV lists averages and standard deviations of TSP and RP source coefficients for the 33 study period days for each of the four source profile matrices. Six of the seven sources are included in Table IV. The oil source is not presented here and not carried through the discussion because it always contributed less than 0.5% to either the RP or TSP fractions. The negative finding for the oil combustion contribution is consistent with CEB studies for a variety of locations (1-3,12,27, 34,35) that found, at best, a small oil combustion component. The average total particulate matter fraction explained Environ. Scl. Technol,, Vol. 18, No. 12, 1984 925
Table 111. Elemental TSP and RP Concentrations (ng/m*) element
meann
A1 (TSP)b 1630 (RP) AS (TSP) (RP) Ba (TSP) (RP) Ca (TSP) (RP) Cd (TSP) (RP) CO (TSP) (RP) Cr (TSP) (RP)
435 4.33 (4.28) 0.58 (0.07) 41.5 8.96 3420 639 3.12 (2.96) 1.97 (0.98) 1.67 (1.43) 2.28 (1.75) 95.0 (46.3) 25.7 (10.0) Cu (TSP) 128.0 (RP) 50.8 Fe (TSP) 2310 (RP) 474 (425) K (TSP) 958 (RP) 620 Mg (TSP) 1730 (RP) 485 Mn (TSP) 89.1 (RP) 38.1 Na (TSP) 2540 (RP) 1670 (1660) Ni (TSP) 20.2 (19.4) (RP) 24.8 (22.1) Pb (TSP) 490 (RP) 319 Se (TSP) 2.01 (RP) 0.62 (0.44) V (TSP) 8.74 (RP) 3.44 Zn (TSP) 304 (RP) 211
SD"
range"
909 335 4.68 (4.73) 0.76 (0.41) 30.2 5.79 3290 744 2.27 (2.46) 1.77 (2.12) 1.07 (1.31) 2.57 (2.86) 187 (193) 65.4 (59.4) 54.0 35.4 1530 373 (419) 630 416 1380 520 71.3 28.9 2480 1890 18.0 (18.9) 27.4 (29.2) 230 154 1.46 0.66 (0.73) 6.39 2.87 288 180
585-5360 131-1680 0.99 (0.00)-26.5 0.00-2.44 12.3-172 1.66-33.4 961-19800 116-4490 0.70 (0.00)-10.6 0.00-8.04 0.39 (0.00)-4.21 0.00-10.5 0.0-1090 0.0-351 50.6-280 10.6-189 750-6260 119 (0.0)-1710 250-3000 210-2110 545-8740 88.3-2560 16.1-281 6.2-106 345-10000 243 (0.0)-7750 3.25 (0.00)-66.9 0.00-95.3 198-1020 104-704 0.29 (0.00)-7.60 0.00-1.97 0.83-31.0 0.26 (0.00)-14.8 62.0-1500 54.1-959
"Based on 35 24-h samples; if typical concentrations are within 2a of the detection limit, minimum values of the mean and standard deviation are given in parentheses by assuming measured values below 2u are zero. bSize fraction containing sample: TSP, total suspended particulate matter; RP, respirable particles ( 5 2 um).
by the six sources is also included in Table IV. All coefficienh were calculated by the WLS procedure and have units of grams from source category j collected on the filter per gram of particulate matter at the receptor collected on the filter. Table V expands on the SULFUR matrix solutions by adding ranges and source contributions in micrograms per cubic meter.
Discussion As indicated in Table V, soil-derived aerosol makes up the largest fraction of TSP and RP at 44% and 20%, respectively, at the University of Illinois Health Sciences Center. On the basis of a study period average TSP of
75.7% pg/m3 and RP of 36.7 pg/m3, soil-derived aerosol represents 33.5 pg/m3 of TSP, 7.3 pg/m3 of which is in the RP range. This is consistent with the fact that soil-derived aerosols ordinarily contain a substantial coarse particle fraction. Refuse combustion appears to be the only source found completely in the RP size fraction (2.0 out of 2.2 pg/m3 was RP). Coal and mobile source contributions are both approximately 55% in the RP fraction. This finding is consistent with the definition of the mobile source profile that contains both combustion particles (all in the RP range) and mechanically generated particles (in the coarse particle range). Steel industries contribute an average 1.9 pg/m3 to the TSP, 0.4 pg/m3 of which is in the RP fraction. Cement- and limestone-derived particles contribute 11.6 pg/m3 to the average TSP concentration and only 5.1 pg/m3 to the average RP fraction. This suggests that the steel and limestone sources are both significant contributors of coarse particulate matter. It is important to note that the contributions from the noncontrollable sources (e.g., soil- and wind-derived limestone and cement particles) make up an average 56.9% of the TSP and 33.9% of the RP explained by the CEB model. Sources that are usually considered controllable (e.g., steel, mobile sources, refuse, and coal) account for much smaller fractions of the TSP and RP explained by the model (15.2% and 16.6%, respectively). These results also show that 19.1 and 18.2 pg/m3 of the TSP and RP fractions, respectively, are not explained by the six sources modeled. This unexplained particulate matter is the second largest component of TSP and largest component of RP. Because the unexplained mass concentrations are approximately equal, it is reasonable to assume that they are derived from the same sources and principally exist in the RP size range. The unexplained aerosol fraction is an important characteristic of the CEB result. Assuming that the sources of unexplained aerosol do not significantly contribute to the elements used in the statistical analysis, the estimation of source coefficients will be independent of the unexplained fraction. The unexplained fraction, therefore, will consist of unidentified primary sources, secondary sources, and experimental error. For instance, the sulfur concentrations measured at the receptor influence both primary sources with significant sulfur emissions as well as the unexplained particulate matter fraction, some of which originates from distant sources out of the region. Further assuming that all significant primary sources are included in the model, it can be estimated that the 24-h average secondary contribution is less than or equal to the unexplained aerosol concentration (19.1 and 18.2 pg/m3 for TSP and RP, respectively). Identification of minor sources of particulate matter and experimental error will decrease this upper limit for the secondary contribution. If it is assumed that the unexplained fraction contains secondary particulate matter, this fraction should be
Table IV. Distribution of TSP and RP Source Coefficients Based on 33 24-h Samples particle fraction
mean source coefficient, % (standard deviation) steel soil mobile sources refuse
profile
limestone
TSP
18-element GElOOO SULFUR S-FOSSIL
15.9 (8.2) 17.1 (8.0) 15.3 (11.4) 17.5 (10.9)
2.5 3.2 2.5 2.5
(1.7) (1.8) (1.7) (1.6)
44.2 (18.5) 42.8 (18.7) 44.3 (17.1) 38.6 (15.3)
4.8 4.7 4.7 4.8
(2.7) (2.8) (2.6) (2.7)
2.8 2.8 2.9 2.7
RP
18-element GElOOO SULFUR S-FOSSIL
11.6 (11.1) 10.7 (10.5) 13.9 (10.0) 13.5 (12.3)
1.2 (1.2) 1.6 (1.3) 1.1 (1.2) 1.1 (1.2)
35.4 (17.6) 34.8 (16.1) 20.0 (14.7) 14.6 (14.3)
5.9 (3.6) 5.8 (3.7) 4.5 (3.4) 5.5 (3.6)
4.0 4.3 5.5 4.4
(I
Total fraction = sum of six sources shown.
026
Environ. Sci. Technol., Vol. 18, No. 12, 1984
coal
total4
(1.5) (1.7) (1.8) (1.5)
5.5 (4.9) 4.7 (4.9) 5.1 (3.7) 7.9 (3.9)
75.7 75.3 74.8 74.0
(2.4) (2.4) (3.5) (2.4)
0.4 (1.8) 0.3 (1.5) 5.5 (3.4) 7.9 (3.9)
58.5 57.5 50.5 47.0
Table V. TSP and RP Source Coefficients based on 19 Elements and 33 days of Observation particle fraction source
source coefficient mean SD range
Table VII. Correlation of CEB Predictions and Upwind Source Contributions
r2
source contribution,” fig/m3
CEB source category
TSP
RP
coal combustion refuse incineration steel industry limestone composite
0.060 0.752b 0.718b 0.014
0.013 0.687b 0.34gb 0.002
TSP limestone steel soil mobile sources refuse coal total unexplained RP limestone steel soil mobile sources refuse coal total unexplained
0.153 0.025 0.443 0.047 0.029 0.051 0.748 0.252
0.114 0.017 0.171 0.026 0.018 0.037 0.231 0.231
0.000-0.514 0.033-0.069 0.116-0.773 0.006-0.105 0.008-0.086 0.000-0.147 0.404-1.442 0.000-0.596
11.6 1.9 33.5 3.6 2.2 3.7 56.6 19.1
0.139 0.011 0.200 0.045 0.055 0.055 0.505 0.495
0.100 0.012 0.147 0.034 0.035 0.034 0.190 0.190
0.000-0.389 0.000-0.052 0.000-0.589 0.000-0.122 0.015-0.147 0.000-0.141 0.183-0.936 0.064-0.817
5.1 0.4 7.3 1.7 2.0 2.0 18.5 18.2
” Source contribution calculated by multiplying the average source coefficient by the average particle concentration.
“CEB estimates of contributions to TSP and RP are based on SULFUR source profile matrix calculations. Slope of regression line significantly greater than zero (p
