Receptor models Development and testing of such models has moved from the research domain into application to practical problems Glen E. Gordon University of Maryland College Park, MD 20742
ation protocols for using receptor models in the development of state implementation plans (SIPS) for PMlO in urban areas. These source assessment tools were direct outgrowths of that early urban-scale research. Not all urban-scale problems have been solved; indeed, there is a need to use organic compounds and characteristics of individual particles observed microscopically to assign sources to more specific categories than is feasible by current analyses for mostly inorganic species. In addition, research is being done on regional-scale receptor modeling to apportion sources of acid deposition and visibility degradation and on anthropogenic particles and gases, which affect long-term global climate. The field has matured to the point that a book on the subject has been written by Hopke (2), which should be consulted for more details than can be covered in this review.
Air pollution authorities use models to develop optimal control strategies for air pollutants. According to the traditional approach, emissions inventories for various sources are used as inputs for plume, box, or grid models to predict ambient concentrations of total suspended particulate matter (TSP), SOz, or other air pollutants. These methods, however, are inadequate for many purposes today and will be even less useful for many future needs. Even if dispersion models were accurate, the source emissions inventories upon which they rely are not. Source emission inventories, especially for sources equipped with pollution controls, usually do not include contributions from fugitive process emissions and dust. Furthermore, air quality standards are beginning to require knowledge of sources of Chemical mass balances The most widely used receptor model particles in certain size ranges (e.g., the new PMlO standard for 10-pm res- is chemical mass balances (CMBs), pirable particles), particles bearing cer- called chemical element balances in my tain toxic substances, or particles that 1980 review following Sheldon have a special role in problems such as Friedlander’s use of the term in 1973 visibility degradation or climate modifi- (3). The term Ghemical mass balances (4) is more general, implying that concation. The limitations of present methods centrations of chemical species are used have contributed to the current interest to apportion mass of airborne particles. The basic idea of CMBs is that comin receptor models, that is, models that assess contributions from various position patterns of emissions from varsources based on observations at sam- ious classes of sources are different pling sites (the “receptors”). Receptor enough that one can identify their conmethods have entered the regulatory tributions by measuring concentrations framework where, along with tradi- of many species in samples collected at tional methods, they will increase the a receptor site. Suppose that one knew reliability of source apportionment. accurately the concentrations of 20 or Both traditional and receptor ap- more species on particles released from proaches will continue to be needed, the important sources in an urban area. for receptor models can never predict The observed concentration pattern of an ambient particulate sample would be the impacts of a proposed source. Much of the early work on receptor a linear combination of the patterns of models is discussed in my 1980 review particles from the sources, each (1). At that time most of the research weighted by a source strength term, mJ, was being done on urban problems. In the mass per unit volume contributed 1987 EPA issued software, technical by particles from that type of source. If guidance, and validation and reconcili- the number of species measured equals 1132 Environ. Sci. Technol., Vol. 22, No. 10, 1988
or exceeds the number of source types, one can obtain the mJ values by solving a set of simultaneous equations: Ct C mj x i j ~ L j (1) J
where C, is the concentration of species i in the sample and xIJis the concentration of the ith element in particulate material from the jth type of source. The term cylj is included as an adjustment for any gain or loss of species i between the source and receptor; the term is assumed to be unity for most species, unless they are volatile enough to be partially in the gas phase, and it is often < 1 for compounds that undergo reactions in the atmosphere. To use Equation 1, one must measure or obtain from the literature the concentration profiles, xrJ,for each important source in the area and measure concentrations of the same species on ambient particles. The unknowns, source strengths mJ, are obtained by solving Equation 1. Usually, more species are measured than the assumed number of sources, so the problem is overdetermined, and a least-squares fit to the concentrations is used. Equation 1 is solved by an iterative “effective variances” algorithm (3,which weights each species in proportion to its precision in both the receptor measurement and the source profile and propagates all errors to yield uncertainties in the source contribution estimates obtained and in the calculated concentrations of the species. Most CMBs previously were applied to samples collected with air filters, which collect all particles of diameters up to 30 or 40 pm. Because of the inclusion of large particles, typically half of the mass consisted of airborne soil and fly ash from coal ( Q . Most elements dominated by these crustal sources could be easily fitted by CMBs, but they provided little information besides the TSP contributed by innocuous dust. In recent years there has been more interest in particle size groups, as particles below and above about 2.5-pm
0013-936X/88/0922-1132$01.50/0 0 1988 American Chemical Society
lungs. ~urtherm~re, as thei; &tern span the wavelengths of visible light, 6ne particles cause most of the light scattering perceived as haze. Fine particles contain most of the trace elements that provide the test information for identifyiig sources, and most detailed receptor model studies today include the separate collection and analysis of fim and coarse particles with dichotomous samplers. Many species are highly enriched relative to the crustal abundance pattern in the fine fraction; therefore, they are so sensitive to details of the soufces that many enriched elements are not as well fitted as the many crustal elements in the coarse fraction. The nonenriched crustal elements are easy to fit with the equation and provide little information; however, the highly enriched elements (mostly fine) are harder to fit, but provide more information. An example of receptor models a p plied to size-separated samples was organized by EPA in Philadelphia during the summer of 1982 (8). Fine and waw particles were collected at three sites for 12-h intervals for one month. Concentrations of a b u t 40 species in the fine fraction were measured by X-ray fluorescence, instrumental neutron activation analysis, ion chromatography, and thermal optical carbon analysis (for volatile and nonvolatile carbon). Fine fraction samples were subjected to CMBs, as illustrated in Figure 1. Samples from seven local sources (5’) and local soil (10) also were analyzed. Among the eight compnents used in the fit, compositions of all but paint pigment, motor vehicle, and regional sulfate were deterpined from analysis of emissions from local sources.Not all measured species were used in the CMB fit. Some,such as C1 and Br, are too volatile to be conservative on the particles (i.e., (Y < 1). Concentrations of some very trace species such as In, Mo, and Ag are so poorly known in emissions tium sources that it would be risky to attempt to fit them. The fit to these “floating” species often helps identify sources that should have been inclwJed in the fit. The Phiidelphia CMBs illustrate some advances in the technique. The Sb master is a uni ue source of essentially a single trace d m n t . The Sb concentration in the Philadelphia atmos here is much greater than the 1-2 nglm typical of other cities. Refuse incinerators
P
are 0fte.n major sources of Sb, but predicted Sb would have been nearly 100fold too low without inclusion of the Sb roaster, which demonstrates the need for measurements of special local sources.
The calculated motor vehicle Pb contribution is only 180 ngh-far less than the 1-2 pg/m3 typical of urban areas in the days when leaded gasoline was in full use. By 1988 the motor vehicle contribution of Pb, both the calculated and the actual, would be even lower, probably -50 ng/m3. The calculated Br concentration, based on fresh vehicle emissions, is about twice the observed concentration, as some Br leaves the particles. In addition, CMBs may underestimate contributions of nonautomotive Pb. Motor vehicles are also the major source of both forms of carbon. When CMBs were applied to filter samples, the rareearth elements were usually well fitted by crustal materials, but in Philadelphia the rare-earth elements in the fine fraction were enriched relative to crustal material by factors up to 40. The profiles measured from a fluid catalytic cracker of an oil refinery revealed refineries as the source of the enriched rareearth elements. The rareearth elements are present in large amounts in zeolite catalysts used in fluid catalytic crackers (11).The catalyst is retained in the fluid catalytic cracker and reused many times, but small amounts apparently escape up the stack. As shown in Figure 1, some r a r e 4 elements also leave the plant in the product, for oil-fired plants also release these elements. Thus rareearth elements have become useful markers for receptor models. Appreciable amounts of La, Ce, or both also may be released from late-model automobiles quipped with catalyticconverters (12).
Factor aoalysis The regional sulfate component illustrates an attempt to deal with a weakness of the original form of CMBs, namely the assumption that all particles are primary and of the same composition as those released from the sources (unless as other than unity are used). As in most areas of the eastern United States, the tine fraction in Philadelphia is strongly dominated by sulfate. Sulfur is most often released as S a gas and slowly converted to sulfate, so normal CMBs cannot handle sulfate. Dmbay (13accounted for the mass contributed by sulfate by adding a pure ("4)2SO4 component to his CMB of the St. Louis aerosol. However, pure ("&SO4 does not include the appreciable amounts of several other elements associated with sulfate (Figure 1). 1134 Environ. Sci.Technol., MI. 22.No. 10, 1988
Development of the regional sulfate component illustrates other receptor model methods. In contrast with CMBs, factor analysis requires no a priori estimates of the number and compositions of components, for it searches the data set for groups of species (the "factors") whose collective variations account for most of the fluctuation of the species included. The first step in factor analysis is the normaliition of all parameters:
zq. = ( x . - X.)/o. 0 , "
(2)
where xij is the concentration of species i in sample j and ui is the standard deviation of the concentration for all samples included in the factor analysis, and xiis the average concentration. Weighting factors are not needed because all variables have the same z distributions centered at zero with standard deviations of unity. Because of the normalization, any quantifiable variables such
as wind speed, wind direction, or relative humidity can be included along with concentrations. The objective in factor analysis is to h d a minimum number of factors that explain most of the variance of the system, that is, one unit per variable with the normalization of Equation 2. In theory, the variance explained by each factor remains large for the several factors associated with important sources and drops sharply beyond. In practice, however, the variance explained drops off gradually, and one must decide how many to retain. Usually those factors that explain the variance of at least one variable are retained, and rarely do data
sets sustain more than
six factors. Factor loadings for 18 elements collected on filters in Boston and measured by instrumental neutron activation analysis in 1970 are shown in Figure 2 (14). The square of the factor loading indicates the fraction of the variance of the element that is accounted for by variation of the strength of the factor. The factors can usually be associated with sources of the heavily loaded elements. For example, factor 1 in Figure 2 is surely soil and other crustal materials, for it is heavily loaded with elements that are expected to come primarily from crustal sources. Factor 2, loaded with Na and C1, is marine aerosol, per-
haps with some mad salt contribution. The heavy loading of V on factor 3, with some Co and Cr, indicates oil combustion. Bromine on factor 4 indicates a motor vehicle component. The presence of appreciable rare-earthelement loadings is not understood, as rare-earth element catalysts were not in wide use in 1970. Factor 6, containing Zn and Sb, apparently results from refuse incineration. Factor 5 was not understood at the time; Hopke et al. were aware of no source dominated by Mn and Se, although the mixed fine and coarse size distribution of Mn (la) suggested that it had a he-particle source in addition to the coarse material of factor 1. The mystery of factor 5 was clarified several years later when Thurston and Spengler (1.7) performed X-ray tluorescence analyses of fine-fraction samples from Watertown, MA, in the Boston suburbs. Their results, shown in Figure 3, revealed a factor loaded with S , which came into the area with winds from the southwest. They attributed this factor, which also contained appreciable loadings of Mn and Se, to coal combustion in the Midwest. This Sloaded factor is probably the same as Hopke's factor 5 , but because instrumental neutron activation analysis does not measure S well, Hopke and colleagues were unaware of its presence. At first it was difficultto accept the idea that sources as far away as loo0 km could contribute appreciable fractions of certain trace elements in the middle of an eastern city, but that result has been confirmed in subsequent studies, including the Philadelphia study. Because Watertown is a fairly urban area, Thurston and Spengler's coalrelated component is apparently contaminated with V and Pb from oil combustion and motor vehicles, respectively (17). Factors can arise from any kind of correlation, not just origination from a common source. Correlations caused by wind direction also can occur when two or more sources are located in the same general direction from the receptor. As in this case, motor vehicle emissions are frequently "smeared" into factors from major sources located in the same direction as major highways. Note that Thurston and Spengler went beyond the usual use of factor analysis to assign variance: They developed a method, absolute principal component scores, for extracting factor compositions; therefore, the results shown in Figure 3 are in the same form as results from CMBs. Target transformation factor analysis ("FA) combines features of CMBs and factor analysis (2). With TTFA, concentrations are normalized relative to zero rather than the average concenEnviron. Sci. Technal., Vol. 22,No. 10, 1988 1135
trations, so one obtains concentrations directly rather than by the indirect methods noted for ordinary factor analysis. The solutions obtained in factor analyses can be thought of as vectors in n-dimensional space, where iz is the number of samples. It is common to rotate the raw solutions to obtain final solutions that have as much physical meaning as possible (e.g., to make them orthogonal). If the compositions of some components are known, they can be used as starting vectors for TTFA, and the starting vectors are adjusted by the TTFA to improve the representation of data. Chang et al. (18) used TTFA to extract concentration profiles of sources in St. Louis from groups of ambient samples strongly influenced by emissions from those sources. The samples had been identified by Rheingrover and Gordon (19), who used wind trajectory analysis to determine the profiles by linear regressions against the concentration of a prominent element from each source. Linear regressions implicitly assume that the uncorrelated portions of each species concentration are randomly distributed, whereas the more powerful TTFA resolves uncorrelated portions into components associated with other sources. Despite the wide use of factor analysis, Henry (20) has shown that a wide range of solutions is possible, even when applied to very simple simulated data sets. How do we reconcile his apparently sound criticisms of the method with the physically reasonable results that many investigators have obtained? Henry suggests that the success of the method is partly the result of the investigator’s skill in selecting the species to include in the data set and partly a result of the frequent use of Varimax rotations, which produce final factors as distinct as possible from each other. Perhaps because the composition patterns of the actual components tend to be similarly distinct, the results obtained are reasonable. Note that factor analysis cannot be successfully applied to all data sets. Its success depends on the presence of considerable independent sample-to-sample fluctuation of source strengths. In contrast, CMBs are performed on one sample at a time and their success depends only on having source profiles that closely match those of components actually present.
Multiple linear regression Another technique used in the Philadelphia study was multiple linear regressions. Kleinman et al. (21) ran multiple linear regressions of mass vs. concentrations of several elements that were markers for certain types of sources. The resulting coefficients were 1136 Environ. Sci. Technol., Vol. 22, No. 10, 1988
used to determine average masses contributed by those sources and the concentration of each marker element in particulate matter from its associated source. Dzubay et al. (8) regressed mass vs. motor vehicle Pb and the S of regional sulfate in Philadelphia to determine the concentration of Pb in particles from motor vehicles and the concentration of S in the regional sulfate. The Pb concentration in motor vehicle particles obtained was 6.9 k 1.9 % . This value has been gradually decreasing as Pb is being phased out of gasoline: Values as high as 40% Pb have been used in the past (3). The resulting S concentration in regional sulfate was 20.4k 1.4%, somewhat smaller than the values of 24.2% and 27.9% predicted from the stoichiometries of (NH&S04 and NH4HS04, respectively. Values ranging from 12% to 20%, all below the expected values for those compounds, had been obtained by previous workers, which suggests that some of the mass is water, carbonaceous material, etc. Dzubay et al. also calculated contributions of coal- and oil-fired power plants to regional sulfate by regressing its concentration vs. those of Se and V (or Ni), respectively, and found that coal contributes 72 % and oil contributes 16%, with 12% arising from other sources.
Regulatory applications The first regulatory use of receptor modeling in the United States was done from 1977 to 1980 by the Oregon Department of Environmental Quality to determine cost-effective ways to attain compliance with TSP standards in some areas of Portland, and, later, in Eugene and Medford (22). Results of CMBs of ambient samples were compared with predictions based on dispersion models. For some sources such as motor vehicles, there was good agreement between models. Dispersion model road dust contributions were much lower than the CMB contribution, and the wood smoke contribution was not predicted at all by dispersion models. After source emissions inventories were modified in light of the CMB results, dispersion model predictions agreed well with CMBs. Results could be quoted in terms of the cost of control by various methods per pglm3 reduction of TSP Various measures ranged from a cost savings of $lOO,OOO per pg/m3 by reduction of traffic in certain areas to a cost of $4.1 million per pg/ m3 by adoption of a limit of 0.5% sulfur content for residual oil used by power plants. The Portland community chose to reduce traffic in the area of high road dust contributions. Core et al. (22) note that this approach met the legal standard but did nothing to im-
prove regional visibility or public health. Receptor models are currently being used to develop PMlO control strategies in major urban areas such as Portland, OR, Las Vegas, NV, Los Angeles, Denver, Chicago, Newark, NJ, and Seattle and in small communities such as Reno, NV, and Kalispell, MT. For example, a recent receptor model study of the wintertime haze condition called the Denver Brown Cloud by Lewis et al. (23) revealed that motor vehicles account for 40 % of fineparticle mass and 40% of daytime light extinction. Electric power generation was responsible for 27 % and 34%, respectively, and wood smoke for 8 % and 12%* The impetus for this widespread application of receptor models results from the recently promulgated PMlO standard for particles. Regulations associated with the PMlO standard will require most communities to use conventional source-based models to devise SIPSfor nonattainment areas; however, they are strongly urged to use receptor models, especially CMBs, as well. Authorities in 50 of the 64 affected areas have indicated their plans to use receptor modeling when devising SIPS. In preparation for promulgation of the PMlO standard, Thompson G. Pace, of the EPA Office of Air Quality Planning and Standards, directed efforts of EPA staff and other investigators to prepare material to help state and local authorities use CMBs. John Watson of the Desert Research Institute developed CMB software for IBM PCcompatible computers. The CMB software is interactive, for the user can see and print results of each sample CMB and change parameters (mainly add or remove sources) and repeat the CMB if necessary. Several types of data are displayed for evaluation of the results: calculated masses contributed by each source and their uncertainties, calculated and observed concentrations of species and their uncertainties and t-statistic values, correlation coefficient between calculated and measured concentrations of species, x2, degrees of freedom, and clusters of sources with high collinearities, One can view a matrix that shows amounts of each species from each source and store the results in files for treatment by other programs. Core et al. produced a source composition library (24, 25) containing data for 50-odd sources, with data given separately for fine and coarse fractions. Users are encouraged to perform analyses of particles from important sources in their areas; however, if this cannot be done, they should find sources in the
library as similar as possible to those in their areas. Also, Sheffield and I (26) developed a research-quality source composition library that contains data from more than 20 coal-fired power plants, 9 oil-fired plants, 5 incinerators, and many others, including considerable data from Japanese studies. Chow and Watson summarize the availability and contents of several source composition libraries (27). For one involved in receptor model development and testing since the early 1970s, I find it gratifying to see this method move from the research domain into application to practical problems. Although Pace and others have provided excellent information for local authorities, I have concerns about the ways in which the methods will be used. The availability of various source composition libraries may cause some users to rely exclusively on the literature rather than making measurements on local sources. There are no universal sources with emissions that have about the same compositions in various cities. For example, there is enormous variation in the composition of particles released by coal-fired power plants, depending on the type of coal burned, the boiler design, and the type and efficiency of pollution controls. If possible, measurements should be made at major sources that may be unique or variable. Another concern is that most people will use only X-ray fluorescence data for receptor modeling, for it is a fast, highly automated, reliable analytical method. The most definitive information about some sources is provided by trace elements such as Se, As, Sb, and rare-earth elements, which show up marginally, at best, by X-ray fluorescence. Although it would be impractical to routinely analyze large numbers of samples by instrumental neutron activation analysis, selected samples should perhaps be analyzed by instrumental neutron activation analysis or other techniques for some key species that are not accurately analyzed by Xray fluorescence. Monthly PMlO samples from all sites in the Japanese National Air Sampling Network are analyzed by instrumental neutron activation analysis (28).
Methods under development Organic compounds. There is a strong need for receptor modeling based on airborne organic compounds, because there are no good inorganic markers for some important sources such as gasoline-powered vehicles (now that Pb, Br, and C1 are being removed from gasoline), diesel engines, petrochemical industries, and solvent plants. Even wood smoke presents a
problem because its main elemental marker is K; however, it is risky to use total K for wood combustion unless one has extensive knowledge of the many other sources of K, such as lime kilns, soil, and incinerators. Soluble K is present in greater quantities in wood smoke than in particles from other sources. Furthermore, source apportionment of organic species is itself important because many organic species are mutagenic, carcinogenic, or otherwise toxic. Daisey et al. applied receptor models to organic species (29-31) by estimating source contributions to benzo(a)pyrene using a CMB model (31). Later, Daisey and Kneip used factor analysis and multiple linear regressions because of the lack of organic source emissions data (32).About one-third of the nonpolar organic mass could not be traced with the usual elemental tracers (33). Oil-burning and motor vehicles were identified as the major sources of nonpolar particulate organics, and polar organics were strongly associated with sulfate-related aerosol and resuspended soil. Several investigators have developed receptor models for gaseous hydrocarbons. Nelson et al. found that vehicle exhaust and evaporative emissions of gasoline account for more than twothirds of the hydrocarbons in Sydney, Australia (34). In contrast, these sources in Tokyo accounted for
