RecePtor mod& I
The elemental composition of particulate matter holds a wealth of information on its source. Determining the characteristic “signatures” of dljrferent kinds of sources is the key to unlocking these data
Glen E. Gordon Department of Chemistry Unicersity of M a r y l a n d College P a r k , M d . 20742
Air pollution authorities use models to develop optimal control strategies for air pollutants. Emissions inventories for various sources are used as inputs to plume models, box models, or grid models for prediction of ambient concentrations of total suspended particulate matter (TSP), S02, or other air pollutants. These methods are inadequate for many purposes today and will be even less so for many future needs. Stephen Budiansky has discussed the limitations of these dispersion models, especially for predicting short-term concentrations (ES& T, April 1980). Even if dispersion models were accurate, the source emissions inventories which they rely upon are not. They are inaccurate, especially for sources equipped with pollution controls, and they don’t usually include contributions from fugitive process emissions and dust. Furthermore, future air quality standards will probably require knowledge of sources of particles in certain size ranges, particles bearing certain toxic substances (as is now the case for lead), or particles that have a special role in problems such as visibility degradation or climate modification. Because of the limitations of present methods, there has been increasing interest in and study of receptor models, that is models that assess the contributions from various sources based on observations at sampling sites, the “receptors.” Receptor methods have become feasible because of recent 792
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improvements in sampling and analysis methods for particles. With these tools it has become practical to accumulate data sets that have enormous information content. For example, Jaklevic, Loo, and coworkers at Lawrence Berkeley Laboratory performed automatic x-ray fluorescence (XRF) analyses for about 20 elements in 34 000 samples. The samples, in two size fractions, were obtained from a network of 10 dichotomous samplers operated for two years in and near St. Louis in connection with the Regional Air Pollution Study (R.APS). Data sets of this type contain not only direct information on the concentrations and other parameters measured during each sampling period, but also a wealth of indirect information in the correlations that exist between the many measurements over time and location. Much of the research on receptor models involves the development of statistical techniques for extracting practical information about the sources of T S P and various materials borne by particles from these large data sets.
Chemical element balances Suppose that one knew quite accurately the concentrations of 20 elements on particles released from all of the important types of sources in an urban area. If there are differences in the concentration patterns from one kind of source to another, in theory one could determine the amount of TSP contributed by each kind of source at a particular location and time by analyzing particles collected there for the 20 elements. The observed concentration pattern would be a linear combination of the patterns of particles from the sources, each weighted
by a source-strength term, mJ, the mass per unit volume contributed to TSP by particles from that type of source. If the number of elements measured equalled or exceeded the number of source types, one could obtain the m, values by solving a set of simultaneous equations: c1
=
mJXIJ
(1)
J
where C , is the concentration of element i in the sample and x u is the concentration of the ith element in particulate material from the j t h type of source. This approach is the basis of the most widely used receptor model, chemical element balances (CEBs), originally suggested in the early 1970s by Sheldon Friedlander (now a t UCLA) and George Hidy (Environmental Research and Technology, Inc., ERT) and, independently, by John Winchester (Florida State) and Gordon Nifong (then at the University of Michigan). Of course, the picture presented above is an oversimplification: Particles from many types of sources are poorly characterized and, even if well understood, there are often large variations from one source to another within a given class (e.g., oilfired power plants) and for a given source at various times. Furthermore, Equation (1) has the implicit assumption that all particles are primary (Le., they leave the source as particles) and that there is no modification of the composition between the source and receptor. In fact, we know that a substantial fraction of the fine-particulate mass (< 2.5-ym diameter) is secondary, formed from gases such as SO2 and hydrocarbons. Moreover, particles can be modified before reaching the
0013-936X/80/0914-0792$01 .OO/O @ 1980 American Chemical Society
FIGURE 1
Chemical element balance, Washington, D.C. Marker elements
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0 Results of chemical element balances for Washington, D.C. particles. The top line shows total observed concentrations (horizontal lines) and calculated concentrations (vertical bars) for elements used to determine source strengths ("marker"
0
elements) and for several of the elements used to check the fit ("floating" elements). The other lines show the calculated contributions of the various sources. Source: G s Kowalczyk, t h e w , Univ of Maryland, 1979
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receptor, for example, by preferential fallout of very large particles or by condensation of moderately volatile materials (“condensibles”) that were in the gas phase in hot stack gases. Despite these limitations, the CEB approach has shown considerable promise as a receptor model in several tests. Since it is not, in general, possible to obtain an exact solution to the set of simultaneous equations of Equation ( l ) , the values of rn, are usually determined by performing a leastsquares fit to the observed concentrations of a set of “marker” elementselements whose concentrations are dominated by contributions from certain sources, such as lead from motor vehicles. Ideally, one should use concentrations of all measured nonvolatile elements to obtain the source strengths; however, in testing the CEB method, most investigators try to use a niinimum of carefully chosen marker elements to determine the mj values in order to leave a maximum of “floating” elements whose predicted-toobserved concentration ratios can serve as a measure of the quality of the fit. For example, Gregory Kowalczyk and I performed CEBs on 130 wholefilter samples collected in the Washington, D.C. area in 1976. Largely with the use of components measured by the University of Maryland group, we resolved the observed concentrations into seven components: soil, marine, limestone, motor-vehicle emissions, and combustion of coal, oil, and refuse. Of the approximately 40 measured elements-mostly measured by instrumental neutron activation analysis-nine “marker” elements were selected to be used in determining source strengths. These elements were selected to be sensitive to the particles from the sources: N a (marine), V (oil), Ca (limestone), Zn (refuse), Pb (motor vehicles), AI and Fe (coal soil), Mn (soil), and As (coal). The averages of fits to the marker elements for the 130 samples are shown in Figure 1. Concentrations of Na, V, Ca, Zn, and Pb are so strongly dominated by their principal source components that those elements can be accounted for exactly by adjustment of those principal-component strengths, after contributions from other minor sources are obtained. The remaining elements illustrate one of the classic problems of receptor models: For many elements, the composition patterns of soil and particles emitted from coal-fired plants are so similar that it is difficult to resolve the contributions. Several elements that have higher relative concentrations on
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Environmental Science & Technology
particles from coal-fired plants are not helpful because ambient concentrations are dominated by material from other sources (Zn, Cd, Pb) or they are too volatile to be conservative on particles (Se). We included four marker elements mainly to determine the strengths of these two components: AI and Fe provide measures of the sum of coal and soil components; As is strongly enriched on particles from coal combustion; and Mn is depleted in coal with respect to soil. The latter two are not entirely satisfactory as As is moderately volatile and there is a source of fine-particle Mn that has not been identified. A test of the quality of the fit is the comparison of predicted-to-observed ratios for the elements not used in fitting. In the Washington study, the 29 nonvolatile floating elements were fitted, on the average, to within a factor of two, most errors being underpredictions. The predicted-to-observed ratios for most elements lie between 0.7 and 1.5, but several are seriously underpredicted, notably K, Cr, Cs, Cu, Ni, In, and W. Concentrations of Cs, In, and W in source materials are so poorly known that it is not surprising that they are not well fitted, but the poor fits for K, Cr, Cu, and Ni suggest that an important source has been missed or that there are serious errors in one or more of the components used. Observed and predicted concentrations for several floating elements are shown in Figure 1. The CEB predictions can also be tested by measuring size distributions of particles bearing each element. Size distributions in ambient air should agree with those of the major sources predicted. In general, there was excellent agreement for the Washington CEBs except for Mn. The CEBs predict that most Mn comes from soil, a large-particle source, but about 30% is attached to fine particles, apparently from a source not included in the CEB; for that reason, we reduced the observed Mn concentrations by a factor of 0.7 before performing CEBs. The source strengths indicated by CEBS were tested against box-model predictions based on emissions inventories for power plants, motor vehicles, and refuse incineration, with factor-of-two or better agreement. But because of the uncertainties of emission inventories noted above, how does one know which are the right answers? Although the CEBs performed on the Washington-area data are far from perfect, they yielded better agreement for floating elements than in previous attempts by Friedlander and coworkers for Pasadena and by Donald
,
Gatz (Illinois State Water Survey) for Chicago. In part, the improvement resulted from use of components based on measured compositions of particles for several actual sources in the Washington area. But much of the improvement is because the Washington area has almost no industry; the Washington sources are of types common to most cities and have been studied more carefully than those that are unique to certain localities. One would probably obtain much poorer CEBs for cities such as Chicago, Pittsburgh, and St. Louis, which have a much greater variety of particulate sources. Thomas Dzubay (EPA/ Environmental Science Research Laboratory (ESRL)) has performed CEBs on data from the St. Louis RAPS set, obtaining reasonable fits with the use of seven components: crustal shale, crustal limestone, ammonium%ulfate,motor vehicles, “trace elements,” steel plants, and paintpigment manufacturing. Although Dzubay was able to account for mass concentration in the fine and coarse fractions rather well, and for many major elements, his analysis was not designed to account in detail for trace elements from the many industrial sources in the St. Louis area (see below). One unusual signal was picked up by Dzubay’s analysis. The sampling period he treated (July/August 1976) included July 4, 1976, when there was a large fireworks display near one sampling site. Very high concentrations of AI, K, Sr, Sb, and Ba were observed. The most extensive use of CEBs was done for the Portland, Ore. atmosphere by a group at the Oregon Graduate Center (OGC), including John Cooper, James Huntzicker, and John Watson (now at ERT, Concord, Mass.). This work, plus a similar study of the impact of field burning in the Willamette Valley, was in part supported by the Oregon Dept. of Environmental Quality, where John Kowalczyk and his staff are using the results to help make practical decisions on control of TSP in the area. The O G C group measured concentrations of about 25 elements and species (NO3-, Sod2-, volatile and nonvolatile carbon) in two size fractions of particles from about 30 major sources in the Portland area. Combining their results with data from the literature, they developed components for about 15 types of sources. The group collected ambient particles in two size fractions at six sites in the Portland area and analyzed them for the same species as for source samples. They performed separate CEBs for fine and
FIGURE 2
Sources of urban particles
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4 2 0
Source strengths obtained from CEBs for several urban areas. Several minor components from Portland have been omitted. Categories used by the various groups are not exactly comparable; for example, the auto component from Portland is only for emissions from leaded gasoline
combustion, whereas the Washington and St. Louis CEBs attempt to include all motor-vehicle TSP. In the Portland study, diesel emissions would be included in the carbon categories. The limestone component of Portland is probably included in "urban dust."
Sources: Kowalczyk (see Figure 1); John Watson, thesis, Oregon Graduate Center, 1979; and T G. Dzubay, Ann. N.Y. Acad. Scr. [in press, 1980).
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Volume 14, Number 7, July 1980
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FIGURE 3
Wind direction histograms
000
’ 180 ’ 2kO ’ 360 ’ 360 Mean wind (0, degrees) Histograms of mean wind direction for samples from St. Louis data meeting the two criteria discussed in the text. Mean wind direction ’
060
’ I20
(verticalbars) and standard deviation of a single observation (horizontal
bars) are shown above the clusters.
Source: S.W. Rheingrover and G.E. Gordon in Pmc. 4th lot. Conf. on Nuci. Methods in €0”. ,and Energy Res.. Una. of Missouri, April 1980.
coarse particles for each sampling period. The quality of fits obtained in the Portland studies was similar to that obtained in the Washington, D.C. study. As in Washington, the Portland CEBs failed to account for a majority of the Cu. They also considerably underpredicted Zn because a refuse component was not included; Portland does not permit refuse burning, even though unauthorized combustion may’ go on. It has also been suggested that abrasion of rubber tires, which contain a zinc additive, could contribute to airborne zinc. Studies near highways, though, indicate that the zinc is carried on large particles that travel only a short distance before settling out. The OGC group did not analyze for a number of elements that were most troublesome in the Washington study, e&, Cr, Cs, In, and W. A considerable advantage of the Portland work, however, is that the group obtained data for a number of components that were not previously available. Some could be most useful in other areas; for example, they obtained a vegetativeburning component which is probably needed for Washington, D.C. CEBs and which would produce better fits 796
Environmental Science & Technology
for K, which is considerably underpredicted. The Portland and Washington CEBs are representative of the state of the art in the field today. Although there are still some elements whose concentrations are not well accounted for, those of many elements are now well understood. One important result is that these and similar studies in other areas demonstrate that the largest contribution to TSP, certainly among the coarse particles, is entrained dust. This component is handled differently by different groups. Kowalczyk and I used particles sieved from local soil. The Oregon group used “urban dust,” basically a soil with admixtures of other materials giving it higher concentrations of elements such as Ca and Pb. These dust components are not simply crustal materials blown in from the countryside; the source strengths are usually two to five times greater in urban areas than at background stations, indicating that they are associated with anthropogenic activities. These probably represent various fugitive emissions that are handled poorly, if at all, by sourceemission inventories. They probably
originate from many activities: entrainment by vehicles, demolition of buildings, abrasion of highways and buildings, construction of buildings, etc. Aside from their nuisance value, these mostly large particles probably have little direct effect on people; but this is an important finding for air pollution authorities because of their mandate to keep T S P within compliance. Although the present CEB approach can provide some practical guidance to air pollution agencies, it is still under development. Several weaknesses of the method must be addressed before the method can become routinely useful: The compositions of components are not well enough known. Many important sources have received little study; those that have been studied may differ from one area to another. For example, oil- and coal-combustion components depend on the composition of the fuel and the nature of the plant, especially of pollution controls. Emissions from other kinds of sources, such as metals industries, will similarly depend on the composition of the feedstock and the kinds of processes used. Even such a ubiquitous source as the soil component is poorly characterized; one can analyze soil particles that pass through a sieve, but the composition is not the same for the finer particles that become airborne. Even if one has very accurate composition data for in-stack particles, they may not reflect the composition of airborne particles from the source at some distance away. In-stack measurements will include neither fugitive emissions from the source nor, in high-temperature sources, all of the “condensibles” that may initially be in the vapor state but will later condense. Also, the in-stack measurements may include material borne by very large particles that fall out near the source. Of course, CEBs do not handle secondary materials well; for. example, sulfates, which typically make up about half of the fine-particle mass, are included without any indication of their source. While some of the best CEBs appear to provide a reasonably accurate idea of the contributions to T S P from major classes of sources, most are not definitive enough to identify contributions of individual sources within a class.
Multivariate methods In order to avoid some of these problems with CEBs, several groups have applied techniques known as factor analysis, multivariate analysis,
or pattern recognition. According to factor analysis, concentrations of each element in each sample are normalized with respect to the mean value and standard deviation for the element. This set of normalized concentrations is treated with complex statistical methods in order to find a small number of factors whose collective variations account for most of the observed variations of the elements. This approach has several advantages relative to CEBs. One need make no a priori assumptions about the number and compositions of the components; any secondary or fugitive material associated with a source should be revealed by the analysis. Also, one can include parameters other than concentrations in the data set, such as particle size, wind speed, temperature, and light scattering. Philip Hopke (University of Illinois) and co-workers applied factor analysis to concentrations of I8 elements in a set of 90 samples from the Boston area. They were able to resolve six factors. The first factor has heavy loadings for Al, Sc, rare earths, and several other elements and appears to be a soil component, perhaps with some admixture of fly ash from coal-fired plants. The second factor has high loadings of N a and C1, representing marine aerosols. The third contains V and Co, probably a residual-oil component; the fourth, Br, from motor vehicles; and the fifth, with Zn and Sb, may represent refuse combustion. The sixth factor, mainly loaded with M n and Se, is a mystery, perhaps obtained as a correction to other components (e.g., for mixing coal emissions into factor 1). Variance not accounted for in an element is called “unique variance” and arises from analytical errors or other conditions applying to the specific element, such as volatility that leads to exchange with the vapor phase as is observed with bromine and selenium. Jarvis Moyers and co-workers (University of Arizona) performed factor analysis on concentrations of 24 elements and species from a network of 11 Hi-Vol samplers in and near Tucson, Ariz. At most locations, the analysis yielded six factors, and a total of 10 factors was obtained from the entire data set. The major factor at all sites was a soil component and the second factor a t most sites had heavy loadings of the secondary species NH4+ and S042-plus minor loadings of Ni, Cd, Zn, Pb, and NO3-. A third comporient was associated with automotive emissions. Other components were less clear-cut, but appeared to be associated with mining activities,
FIGURE 4
Particle trajectories
A map showing fine-particle Mn trajectories (solid arrows) from stations 105, 106, 112, and 118 locating an iron works. Dashed lines represent g(0)for the Station 118 cluster.
background or marine aerosol, and unknown urban sources. One factor contained loadings of Zn, K, Ca, Na, Fe, and Mg. The Arizona study was performed by chemical separations in which these six elements were analyzed from the same aliquot. Apparently this step induces a small mutual correlation that is detected by the factor analysis. Charles Lewis (EPA/ESRL) and Edward Macias (Washington University) applied factor analysis to 19 elements in fine and coarse fractions from five dichotomous samplers. The samples were operated at the same site in Charleston, W.V. for 24-hour periods from Aug. 25 to Sept. 14, 1976. Four factors were resolved: soil (with some automotive contamination), ammonium sulfate, automotive emissions, and a component that appeared to contain a mixture of materials from anthropogenic sources (C, N , Si, K, Ca, Fe, Zn, Se, and Sr). It is noteworthy that no coal-combustion component was resolved, despite its apparent importance in Charleston, indicated by a very high As concentration (26 ng/m3 in the fine fraction). Unfortunately, As was not included in the
factor analysis because of a large standard deviation (51%) among samples collected simultaneously in the five samplers. It would be interesting to see if a coal component would be resolved from the soil component if As were included in factor analysis. In its normal form, factor analysis has several weaknesses which limit its usefulness: Because of the extensive normalization of raw data, one cannot extract the concentrations of species of a component. The results indicate only the fraction of a species’ variation that is explained by the component. Since the method operates on variations rather than absolute values, it is not effective when applied to data sets that have little variation in time or space. If a very strong, but almost constant component were present among other weaker, but more variable components, the former might be undetected by factor analysis. Factor analysis is apparently not able to resolve components that have similar concentrations for most elements. As noted above, the classic problem for all receptor models in areas where coal is burned is that of Volume 14, Number 7, July 1980
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separating coal and soil components. The factor analysis of Charleston aerosols yielded a single component that seemed to represent both soil and the lithophile portion of coal emissions.
e
A hybrid approach Daniel Alpert and Philip Hopke have developed an approach that has some of the best features of CEBs and factor analysis. According to this method, target-transformation factor analysis (TTFA), the elemental concentrations are not normalized to center on zero at the average concentration as in ordinary factor analysis. Thus, one retains information about absolute concentrations, but in doing so, must sacrifice the ability to include variables other than concentrations. The solutions obtained in T T F A (or ordinary factor analysis) may be thought of as vectors in n-dimensional space, where n is the number of samples treated. It is common practice in factor analysis to “rotate” the raw solutions in order to obtain final solutions that have as much physical meaning as possible. If one has a good knowledge of the composition patterns of some components, the Alpert/Hopke TTFA allows one to rotate the solutions so that some of them line up with the known components. Using the same data set for Boston that had been used for ordinary factor analysis (see above), Alpert and Hopke used TTFA to account for the data with six sources: soil, residual fuel oil, refuse, motor-vehicle emissions, marine aerosol, and road dust. One has considerable flexibility with TTFA, as various sets of input components can be used as test vectors in order to settle upon a set that yields the best represention of the data. After the set is fixed, further adjustments in the make-up of components can be made to obtain “refined source profiles” that further improve the fit. After these operations were performed, the average error between predicted and observed concentrations was about 2070, a very impressive figure. The Boston data set is incomplete; only 18 elements were measured, several of which were rare earths. Several key elements (Pb, As, Ca) were not included and the mass concentration was not determined. Recently, Alpert and Hopke performed TTFA on the July/August 1976 data from Station 112 of the St. Louis RAPS data set. They resolved four factors in the coarse fraction (soil, limestone, sulfate, paint-pigment emissions) and, when they excluded samples from July 4 and 5 , five factors in the fine fraction 798
Environmental Science & Technology
(motor-vehicle emissions, sulfate, fly ash/soil, paint pigment, and refuse). When the July 4 and 5 data are included, a fireworks component emerges (with strong contributions from K, AI, Zn, Ba, and Sr) and the pigment and refuse components are no longer resolved. These results demonstrate that TTFA is capable of detecting a component that affects only a few of the samples. With the July 4 and 5 samples excluded, the appearance of a refuse component is curious. It contains rather high concentrations of Zn, Cu, Fe, Mn, Pb, K, and CI and may be trying to account for a mixture of particles from zinc, copper, and steel plants, all of which are generally to the east of the sampling site.
Tracking the source Scott Rheingrover and I are also working on the St. Louis data set. Before performing CEBs, we need compositions of particles from the many large point sources in the St. Louis area. Since we don’t have access to any stack samples collected during RAPS, we are attempting to develop components from the ambient samples themselves. T o identify samples heavily influenced by particles from dominant sources of various elements, we have searched the data set for samples which have very high concentrations of each element (usually about 3 c greater than the average for the element at the particular station). By applying a second criterion in the selection of samples, namely small standard deviation of the wind direction during the sampling period, we found that for many of the elements, the mean wind directions for the selected sampling periods cluster strongly about one or two directions. These directions point toward dominant sources of the element. Examples of histograms of wind directions obtained from all 6-hour samples meeting both criteria at Station 103 are shown in Figure 3. The events obtained for Ti in both fine and coarse fractions cluster around angles of about 21 1’. The trajectories indicated for Ti, for example, point to the well-known, major Ti source in St. Louis, a paintpigment plant to the southwest of Station 103. Some elements show clusters around more than one angle, and the fine and coarse fractions often yield different results. For example, the coarse-particle Zn distribution in Figure 3 has only one cluster, at 188’, associated with a primary Zn smelter S S W of the station. By contrast, the fine-particle Zn distribution has one cluster a t IO’ and another at 194’.
The former is associated with iron and steel production N N E of the station, and the latter with the primary Zn smelter (perhaps with some influence by a primary Pb smelter located at an angle more southwest). For some elements, such as coarse Si and C a in Figure 3, there are often no narrow clusters. These elements are mostly lithophile (crustal) elements which have so many sources, such as fugitive dust associated with traffic, that there are no dominant point sources at specific angles. Maps were made for each element showing the wind trajectories from each of the stations. Trajectories for fine-particle Mn, pointing to an iron works located near Station 106, are shown in Figure 4. With a(@ values of about 3’ and trajectories as short as 2 km, we were able to locate the source of the high concentrations of fineparticle Mn in Figure 4 to within a city block. Additional trajectories for fine-particle Mn (not shown) point to an iron and steel plant located between Stations 103 and 108. Once the samples containing large contributions from the various sources are identified, we will obtain samples from the archives to perform analyses for several additional elements that are useful for characterizing sources. The question will then arise: How can we use these analyses to determine compositions of particles from the sources? One approach may be to compare linear regressions of the concentrations of various elements with that of the element used as a marker for the source. For example, scatter plots of the concentrations of fine-particle Fe and Ti versus fine-particle Mn, along with their regression lines, are shown in Figure 5. The samples used in these regressions are the 27 fine-particle Mn-criteria samples which made up the four station trajectories shown in Figure 4. The relationship between Fe and Mn is quite strong, with a probability of no correlation P < 0.001, whereas Ti versus Mn has P > 0.5. Several other elements, including Si, K, and Cr, are strongly correlated with Mn. As a first approximation for elements that are strongly correlated, the slopes of the regression lines could be taken as the concentrations of elements relative to Mn on particles from the source. A more reliable approach, however, would probably be to apply the TTFA method to the collection of samples affected by the source in order to extract the source of interest from the other sources contributing to the samples. If particle compositions from the sources can be obtained in this way
from network samples, this approach will have many advantages over the traditional methods: Most condensihles will have condensed before reaching the sampling site and very large particles will have fallen out. The component received a t the sampling stations should include fugitive particles small enough to remain airborne for a significant time. The source components are determined at the same time as the ambient samples are taken, and with the same apparatus. This method identifies the imnact of specific point sources, not just the class of source. Thus, it may be possible to distinguish emissions of three or so major sources within a class. Since we are selecting samples containing very high concentrations of elements, the method may he effective for-elements whose concentrations are often below detection limits The wind-trajectory method may thus provide components that are appropriate for use in receptor models. If successful, it will complement other methods, but not completely supplant them. It is unclear whether it will be able to handle com.ponents from sources with very tall stacks. It may also be unsuited for sources that are widely distributed, such as motor vehicles, home heating, and gravel quarries. Thus it will probably still be necessary to make collections from sources followed by detailed analyses of the composition of particles and vapors collected.
Where do we stand? Although receptor models for ambient particles are still under development, it is alreadyclear that they will a t least serve as a useful complement to more classical modeling procedures. The high interest in this subject was evident a t a Receptor Model Workshop a t Quail Roost Conference Center in North Carolina, Feb. 25-28, 1980, sponsored by EPA and organized by Environmental Research and Technology. This workshop brought together more than 40 researchers and users from industry, universities, and several levels of government. The official report of the workshop is being prepared by E R T and is expected to appear in September. Several personal observations on the workshop are offered here. First, the results of receptor-model applications are beginning to be used in local decisions aimed a t achieving compliance with T S P standards. In addition to the Oregon studies mentioned, Nicholas Kolak of the New
FIGURE 5
Linear regressions over “marker” element concentrations
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York State Department of Environmental Conservation is using CEBs to assess steel-mill contributions in the Buffalo-Lackawanna area. John Woodward of Exxon (Florham Park, N.J.) reported that his group is using CEBs to determine the impact of some of their large plants on the surroundings. One major unresolved question discussed at the workshop was: Given a large data set, do the various interpretive methods yield the same results in terms of numbers and types of components? Since no one knows the true answers for sets of real data, it would probably he best to test the mathematical approaches with the use of made-up data sets, constructed with the aid of random-number generators. These would contain typical experimental errors and sample-to-sample variations. These made-up data sets would he distributed in a “blind round-robin” exercise to the various groups that are developing interpretive methods, and each group’s results would be compared to the “true” source strengths to test the procedures. This exercise would, of course, test only the ability of the methods to ex-
tract components from data sets containing typical errors and fluctuations; it would not test the overall receptormodel approach. Unless some urban area can be subjected to very careful receptor-model study and, simultaneously, a very accurate emissions inventory can he developed, it is not clear that a trueoverall test can bedone. In most cases, the receptor-model results are probably already more accurate than predictions based on existing source-emissions data. A second major concern expressed was the need far much better quality components for use in receptor models. Millions of dollars are spent each year on stack testing, but nearly all of the results are inadequate for these applications. In most tests, only mass concentrations (occasionally broken down by particle size) and pollutant gases are measured. Regrettably, after spending huge sums for the required stack tests, the people responsible do not spend modest additional funds for chemical analyses. Even if they are willing to do analyses, the results would usually be inadequate because of the high blank values of the glassfiber filters typically used in standard Volume 14. Number 7, July 1980
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methods. Source-testing groups argue that filter materials with low blank values are mostly organic materials that would not survive in high-temperature stacks. Even if stack particles were collected and analyzed perfectly, how would one handle “condensibles”? These problems suggest that a new design of source measurements is needed, for example, measurements in the plume far enough from the stack for condensation of condensibles and collection on organic or other lowblank filters.
New approaches In terms of statistical treatments of data, there are several additional approaches that might be useful. Target-transformation factor analysis has only begun to be exploited and holds considerable promise. Little has been done to exploit time-series analysis. Theodore Kneip’s group (New York University) has demonstrated the value of data sets that extend over several years, allowing one to observe seasonal and yearly changes. Their observations were especially interesting during the late 1960s and early OS, when changes in concentrations of several elements occurred due to new stringent requirements on the quality of fuels. Various groups have made use of diurnal or weekend/ weekday variations to help associate elements with activities, but there is room for a great deal more work. Additional methods, such as ridge regression and other constrained methods, should be investigated for possible use. One area that is vital to the ultimate practical use of receptor models is that of detailed error analysis. Alan Dunker (General Motors, Warren, Mich.) and John Watson (ERT) have looked a t this problem carefully and demonstrated the need for modelers to attach realistic uncertainties to concentrations of the elements used in the components. When these are included, the uncertainties of the strengths of many sources become much larger, in some cases to the point where one can no longer be certain that some components are present at all. Before such errors and fluctuations can be properly handled, however, we need much more investigation of the compositions of particles from various sources and from given sources as a function of time. Perhaps the greatest opportunities for improving receptor models lie in the area of detailed analyses of carbonaceous materials. The previous emphasis was on the use of concentrations of elements to identify sources. 800
Environmental Science & Technology
This does not work very well for sources that release mainly carbonaceous particles. At present, there are no reliable elemental “signatures” for emissions from some important sources, such as diesel engines, oil and gas furnaces, and coking ovens. Research is needed to develop identification methods based on organic compounds and other forms of carbon. There are classes of organic compounds such as large hydrocarbons or polynuclear aromatic hydrocarbons that have different abundance patterns for different sources. Before techniques involving compounds can become successful, one must establish that their relative abundances are not altered between release and detection, e.g., because of differing chemical reactivities or volatilities, or by sampling artifacts. One very important new method, developed in connection with the Portland study by Lloyd Currie and George Klouda (NBS) and the OGC group, is the use of 14C/total carbon measurements to distinguish between carbonaceous aerosols from “modern” sources (recently living material) and fossil-fuel combustion. Measurements of I4C/C indicated that surprisingly large amounts of fine carbonaceous particles in the Portland atmosphere resulted from wood combustion in fireplaces and wood stoves. Particles from many types of sources are so similar in composition to that ofcrustal material that it may be impossible to resolve all of them just on the basis of elemental concentrations. It may be very useful to develop microscopic methods to identify them, especially if the methods can be automated.
Gatz, D. F., “Relative contributions of different sources of urban aerosols: application of a new estimation method to multiple sites in Chicago.” Atmos. Enuiron. 1975, 9, 1-18. Hidy, G . M.; Friedlander, S. K., “The nature of the Los Angeles aerosol.” In “Proc. of the Second Int. Clean Air Congress.” H. M. Englund and W. T. Beery, Eds.; Academic Press: London, 1971, p. 39 1. Hopke, P. K.; Gladney, E. S.; Gordon, G. E.; Zoller, W. H.; Jones, A. G., “The use of multivariate analysis to identify sources of selected elements in the Boston urban aerosol.” Atmos. Enuiron. 1976, 10, 1015-25. Kneip, T. J.; Loy, P. J., Eds., “Aerosols: anthropogenic and natural, sources and transport.” Annals N.Y. Acad. Sci. 1980, 338, 1-618. Kowalczyk, G. S.; Choquette, C. E.; Gordon, G. E., “Chemical element balances and identification of air pollution sources in Washington, D.C.” Atmos. Enciron. 1978,12, 1143-53. Lewis, C. W.; Macias, E. S., “Composition of size-fractionated aerosol in Charlestown, W.Va.” A t m o s . Enuiron. 1980, 14, 185- 194. Loo, B. W.; French, W. R.; Gatti, R. C.; Goulding, F. S.;Jaklevic, J. M.; Llacer, J.; Thompson, A. C., “Large-scale measurement of airborne particulate sulfur.” Atmos. Enciron. 1978,12, 759-7 1. National Research Council. “Controlling Airborne Particles.” National Academy of Sciences: Washington, D.C., 1980. Stevens, R. K.; Dzubay, T. G.; Russwurm, G.; Rickle, D., “Sampling and analysis of atmospheric sulfates and related species.” Atmos. Environ. 1978, 12, 55-68. Winchester, J. W.; Nifong, G. D., “Water pollution in Lake Michigan by trace elements from pollution aerosol fallout.” Wat., Air Soil Pollut. 1971, I , 50-64.
Additional reading Alpert, D. J.; Hopke, P. K., “A quantitative determination of the sources in the Boston urban aerosol.” Atmos. Enuiron. (in press, 1980). Cooper, J. A,; Currie, L. A.; Klouda, G. A., “Application of carbon- 14 measurements to impact assessment of contemporary carbon sources on urban air quality.” Enuiron. Sci. Technol. (in press, 1980). Dzubay, T. G.;Stevens, R. K., “Ambient air analysis with dichotomous sampler and x-ray fluorescence spectrometer.” Enuiron. Sci. Technol. 1975,9, 663-68. Friedlander, S. K., “Chemical element balances and identification of air pollution sources.” Enuiron. Sci. Technol. 1973, 7, 235-40. Gaarenstroom, P. D.; Perone, S. P.; Moyers, J. L., “Application of pattern recognition and factor analysis for characterization of atmospheric particulate composition in the Southwest Desert atmosphere.” Enuiron. Sci. Technol. 1977, 1 1 , 795-800.
Glen E. Gordon is professor of chemistry at the Uniuersity of Maryland. He received his B.S. in chemistry at the University of Illinois in 1956 and his Ph.D. f r o m the Unicersity of California at Berkeley in 1960. He was a member of the faculty at M I T f r o m 1960 to 1969. Originally incolced in fundamental studies of nuclear chemistry, in recent years Gordon and ProJ: William Zoller, also of Maryland, and their students have developed a number of nuclear analytical methods f o r measuring trace elements on atmospheric particles and in air-pollution source materials and used the analyses to identify sources of ambient particles. For his contributions to the development of nuclear analytical methods, Gordon receiued the 1977 A C S Award f o r Nuclear Applications in Chemistry.
