Receptor Modeling of Volatile Organic Compounds. 1. Emisslon Inventory and Validation Peter A. Scheff and Richard A. Wadden
Environmental and Occupational Health Sciences, School of Public Health, University of Illinois at Chicago, P.O. Box 6998, M/C 922, Chicago, Illinois 60680 Air samples, speciated for 23 organics (23-NMOC) by gas chromatography, were collected on 15 days between July 1and September 9,1987, at three sites in Chicago, 20-70 km apart. Four-hour samples were collected simultaneously at all three locations, along with continuous measures of NO/N02 and an additional 20-h sample at one site. The 23-NMOC concentration pattern was evaluated for each of 55 samples using a chemical mass balance (CMB) receptor model and eight source categories: vehicle exhaust, gasoline vapor, petroleum refineries, vapor degreasing, architectural coatings, graphic arts, dry cleaning, and wastewater treatment. The largest contribution was from vehicles (33% of total VOC). Average predictions agreed well with the emission inventory with the exception of petroleum refineries, which were over 5 times the inventory values. Comparison of CMB results with wind trajectories and refinery locations supported this conclusion. Atmospheric reactivity was not a problem for source allocation for the typical 3-5-h time periods between release and sampling. I . Introduction
The chemical mass balance (CMB) source reconciliation model has been found to be a useful tool for the evaluation of the link between emissionsof volatile organiccompounds (VOC) and ambient speciated non-methane organic compound (NMOC) concentration measurements. The end point of the CMB calculation is a sample-specific estimate of the emission inventory that reflects the history of a specific air sample, and by collecting many samples over time, an estimate of the average emission inventory can be developed. The method has been applied in a simplified form to evaluate total hydrocarbon concentrations (THC) in Los Angeles (1,2)and Australia (3) (THC was defined as' the sum of specified "unreactive" hydrocarbons in the atmosphere.). We have evaluated source contributions to the total non-methane hydrocarbon concentration (NMHC) in Tokyo (4),Newark and Linden ( 5 ) , and Chicago (6) and to the total non-methane organic compound concentration in Chicago (7-91,Beaumont (9),and Detroit (9). The application of the CMB model to NMOC differs from previous source-receptor studies of particulate matter in a number of important ways. For our studies in Tokyo,
0013-936X/93/0927-0617$04.00/0
0 1993 American Chemical Society
New Jersey, and Chicago,the chemical fitting compounds comprise the majority of the mass of the categorical pollutant (NMHC or NMOC) modeled. This is in contrast to studies of particulate matter, where the elemental tracers used in the CMB calculations comprise a small fraction of the total mass evaluated (10-13). The modes of generation of VOC are also significantly different from sources of particulate matter. While the elemental signatures of particulate matter sources are directly related to raw material composition (which is, in turn, frequently related to the earth's crust), the composition of VOC sources is often based on the physical and chemical processes that consume or modify the raw material. For example, all petroleum refineries process hydrocarbons with similar unit operations, the emissions of which are not strongly a function of crude oil composition. As a result, we have observed remarkable similarity in the composition of emissions from this source from studies across the United States and Japan (14). In general, uncertainties in emission inventories are greater for VOC than for the criteria pollutants (e.g., particulate matter, CO, S02, and NO,) (15). These uncertainties have proved to be a difficult obstacle to accurate photochemical modeling and to development of effective control strategies for both NMOC and ozone. There is, therefore, a clear need for a source reconciliation technique to evaluate and validate VOC emission inventories developed by traditional survey methods. We have found that the CMB model can help to fill this need. In addition to accurate NMOC inputs, photochemical models need information on the total reactivity of the VOC source inventory. The source fingerprints combined with CMB source calculations provide the first step in developing source-reactivity contributions. And by extending the fingerprints to include highly reactive compounds (materials that may be too reactive to be used as tracer species), source contributions to total reactivity can also be calculated. This is the first part of a two-paper series that reports on a chemical mass balance receptor model evaluation of sources of NMOC in Chicago. In this evaluation, a group of 23 organics and NO, were used to estimate contributions from eight source categories (vehicles,gas vapor, petroleum refineries, printing solvents, architectural coatings, vapor degreasing, wastewater treatment, and dry cleaning).
Environ. Sci. Technol., Val. 27,
No. 4, 1993 817
Details on the development of fingerprints, results from the CMB calculations, and validation of the predictions are presented in this first part. The second paper reports on the use of the CMB model in ozone modeling in the Chicagoregion. In this paper, we demonstrate a procedure to apply the results from the receptor model source allocation of NMOC to the development of source allocation of ambient ozone.
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II. Methods The chemical mass balance source-receptor model is based on a set of component mass balances written for tracers of a categorical pollutant. To apply the model, the concentration pattern of a group of tracer or fitting species is measured at the receptor point. The ratios of the amounts of each of the tracer species to the total emission of the categorical pollutant for each of the major sources of the pollutant must also be known. Using the source and ambient data, a set of mass balance equations are developed that describe the measured concentrations at the receptor location as a linear combination of the contributions from the source categories included in the model. Typically many more tracers than fitting species are measured, and a least-squares calculational procedure is used to define a solution of the overdetermined set of simultaneous equations. The major steps of this process, the measurement of ambient concentration, the definition of source composition, and the CMB calculation procedures are described below. A. Ambient Measurements. Ambient samples for the receptor model analysis were collected during a 3-month summer sampling program at three sites in the Chicago metropolitan area: a suburban background 10cation approximately 55 km north of downtown Chicago (SUB),an inner-city urban site at the University of Illinois at Chicago located 2 km west of a downtown Chicago (URB), and an industrial location on Chicago's southeast side (IND) (see Figure 1). The sites are roughly located on a north-south line and are 21 and 55 km apart, from IND to URB to SUB, respectively. Four-hour samples (8:OO a.m.-12:OO p.m.) were collected simultaneously at all three sites on 15 days. An additional 20-h sample was collected at the URB site (12:OO p.m.-8:00 a.m.). A set of 23 organic compounds and NO, were selected for the CMB mass balance calculations. These compounds are listed in Table I and were selected for a variety of reasons including the following: (1) they are ubiquitous and, because they are usually above minimum detectable levels in urban environments, are relatively easy to measure; (2) a number have been identified as toxic organics (benzene, ethylbenzene, the xylenes, l,l,l-trichloroethane, trichloroethylene, tetrachloroethylene, carbon tetrachloride, and chloroform); (3) they make up the majority of the NMOC mass emissions from most of the sources studied; (4) the emission data for these materials are generally consistent from study to study; (5) except for propylene (which is highly reactive) and ethane, and acetylene and benzene (which have low reactivities), the hydrocarbons have similar hydroxyl radical reaction rate coefficients (koH) (all within 1order of magnitude of each other); and (6) the nonchlorinated hydrocarbon fitting compounds had been applied in CMB modeling studies with reasonable success in Japan (41, New Jersey (5), and Chicago (6-8). Ambient air samples were collected in 6-L electropolished stainless-steel canisters (16)and Tenax trap samplers 6181
Environ. Scl. Technol., Vol. 27, No. 4, 1993
Figure 1. Receptor modelingstudy area. Map shows locations of the receptor sltes (m), meteorological stations (A),and printing sources (0).Back-trajectories are shown for a southwest wind arriving at the SPH site at 1O:OO a.m. on July 24, 1987. Each cross-point represents 1 h back in time.
(17). A total of 42 4-h and 13 20-h samples were collected during the summer 1987 (July 1to September 9). Each of the canister samples was analyzed for the C2-C7 alkane and alkene fitting compounds, and the Tenax trap samples were analyzed for the aromatic and chlorinated compounds. Each canister was filled with -9 L of ambient air (1.5 atm absolute pressure at the end of the sampling period). Anall-stainless-steel bellows pump (Model MB-151, Metal Bellows Corp., Sharon, MA) and mass flow controller (Model R 028,O-100 cm3min-', Tylan Corp., Carson, CA) set to deliver 37.75 mL min-' for the 4-h sample, or 7.5 mL min-I for the 20-h sample, were used at the URB receptor site. The 4-h samples at the IND and SUB sites were collected with all-stainless-steel and Teflon diaphragm pumps (ModelN055T1, KNF Nueberger Corp., Princeton, NJ) with flow controlled by calibrated 30-gauge hypodermic needle orifices. The average flow rate over the collection period was 35 mL min-l(-38 mL min-1 at -760 mmHg vacuum and decreased to 25 mL min-' at +1.5 atm canister pressure). All canisters were cleaned and evacuated prior to each sampling period. The Tenax samples were collected using low-flow personal sampling pumps (Model P-l25A, DuPont Corp., Wilmington, DE). Flow rates were adjusted to 0.042 L min-l for the 4-h sample and 0.0083 L min-l for the 20-h sample. This corresponds to 10 L of air being sampled for each collection period and was selected to ensure no breakthrough for the compounds studied (17). The sample cartridges were prepared by packing a 10 cm X 1.5 cm inner diameter glass tube with 6 cm of 40-60-mesh Tenax
Table 1. Source Profiles Normalized to the Fitting Compounds (wt % ) architectural petroleum gasoline coatings refineries vapor vehicles fitting compds
ethane ethylene acetylene propane propylene isobutane n-butane isopentane n-pentane 2-methylpentane 3-methylpentane n-hexane 2,4-dimethylpentane benzene toluene ethylbenzene p-xylene o-xylene chloroform l,l,l-trichloroethane carbon tetrachloride trichloroethylene perchloroethylene total 23-NMOC" (% of VOC) NO, (wt % of VOC) a
3.1 18.2 7.8 6.7 3.2 1.2
9.0 7.2 3.2 2.9 1.9 1.4 1.1 6.6 14.2 2.0 6.5 3.9 0.0 0.0 0.0 0.0 0.0
100 51.2 257
0.0 0.0 0.0 0.39 0.0 13.4 30.2 31.4 13.2 4.9 2.5 2.0 0.3 0.5 0.9 0.04 0.04 0.04 0.0 0.0 0.0 0.0
4.8 0.7 0.1 21.3 0.80 4.7 17.6 16.8 7.3 7.2 4.3 3.6 1.7 1.4 4.7 0.6 1.4 0.85 0.0 0.0 0.0 0.0 0.0
0.0
100
100
91.3 0.0
80.5 0.98
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 3.3 0.27 78.3 1.4 8.1 8.6 0.0 0.0 0.0 0.0 0.0
100
wastewater 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 3.2 8.4 1.0 0.0 0.0 11.6 31.6 0.0 16.8 27.4 100
graphic arts 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
93.1 0.0 6.9 0.0 0.0 0.0 0.0 0.0 0.0
100
vapor degreasers 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 55.1 0.0 33.3 11.6
100
dry. cleaning 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100
100
33.1
26.5
11.9
90.2
63.0
0.0
0.0
0.0
0.0
0.0
23-NMOC, sum of 23 measured ambient compounds.
polymer using glass wool at both ends to provide support. The tubes were purified with methanol, dried under vacuum conditions, and packaged in Kimax culture tubes for storage and shipping. (We do not recommend this procedure, and we no longer use it, as it led tomany cracked and unusable sample tubes.) The Tenax cartridges were stored in a helium atmosphere to minimize contamination from ambient air. Oxides of nitrogen were measured with a dual-channel chemiluminescenceanalyzer (Model84403, Monitor Labs Inc., San Diego, CA). Volatile organics collected in the Tenax traps were analyzed by thermal desorption, cryogenic concentration in a liquid nitrogen-cooled nickel capillary trap, followed by high-resolution gas chromatography-mass spectrometry (GC-MS) (17). A field blank was included for each sampling period. The GC-MS system was calibrated to perfluorotoluene. The gas canister samples were analyzed using a highresolution gas chromatograph/flame ionization detection (GC/FID) system. The air samples were transferred from the canisters to the gas chromatographic column through an injection system containing a cryogenic trap (18). Moisture was removed from the gas sample with a Nafion drying tube prior to cryogenic trapping (16). The GC/ FID system was calibrated with a NIST traceable propane in air standard. B. Source Characterization. The source fingerprint describes the chemical composition of the emissions of a categorical pollutant such as VOC or PMlo. For the model to reflect the overall emissions in an air shed, the fingerprints used in the CMB model should represent the emissions from a class of sources rather than from individual processes within a source class. For example, the fingerprint for graphic arts should represent a composite of all of the major printing operations (rotogravure, lithography, letterpress, and flexographic) in proportion
to their emissions in the study area. In order to develop general descriptions of source composition for this study, a variety of types of information including ambient measurements, downwind plume characterization studies, data from source tests, product usage information, and product composition information were all used for the development of fingerprints. Table I shows the source fingerprints used for this study normalized to 100% of the fitting compounds. Included in the table is the s u m of the fitting compounds as apercent of the total VOC emission and NO, as a percent of the total VOC emission. A detailed discussion of the development of the source compositions is available elsewhere (14).The motor vehicle emission fingerprint represents the average tail-pipe emission composition from the FTP driving cycle test for 46 in-service vehicles collected under laboratory conditions (note that the vehicles were tested without any modifications or engine tuning) (19). The gasoline vapor fingerprint is the headspace composition of summer-blend unleaded gasoline (20). The refinery fingerprint is the average composition from (1)six groundlevel, in-plume samples and (2) two in-plume measurementa taken at 350 m aloft in the vicinity of a large modern petroleum refinery (21). The architectural coatings fingerprint is based on an extensive survey of product-type consumption in the New York City-state of New Jersey region (22)and profiles for composites of three product types (solvent-based coatings, thinning and cleanup solvents, and water-based coatings) (20). Note that the fitting compounds only represent 33.06% of the total emission for this source. Nonfitting compounds make up most of the NMOC emissions from this category and include alcohols,ketones, esters, glycols, and chlorinated organics (14). The graphic arts fingerprint is based on compositions for offset Envlron. Sci. Technol., Vol. 27, No. 4, 1993 610
12 I F T P tailpipe
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=Atlanta
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lithography (23),letterpress (23), flexographic (ZO), and rotogravure (23,24)that were combmedusing anationwide estimate of the relative fraction of NMOC emissions due to each of these printing operations (25). Note that most of the emissions from printing are also from nonfitting compounds and include naphtha, petroleum distillate,and various alcohols, aldehydes, and ketones (14). The wastewater fingerprint is derived from two ambient monitoring studies at municipal wastewater treatment plants (26,27) and modeling studies of volatilization of organics at treatment plants in Chicago (28). The fingerprintforvapordegreasingisbasedona1985nationwide chlorinated solvent consumption estimate of 228 800 metrictons for thisapplication,and theindividual fractions attributed to trichloroethylene, l,l,l-trichloroethane,perchloroethylene, and methylene chloride (product usage information from the Halogenated Solvents Industry Alliance) (29). The data for dry cleaners is based on total solvent usage by perchloroethylene cleaners (55 000 Mg yr-') (30)and petroleum-based cleaners (31 000 Mg yi-9 (31). The data selected for the fingerprints represented the best available information on summer 1987 conditions when the ambient sampling was carried out. T w o recent studies of mobile source emissions measured the composition of NMOC in tunnels in Atlanta and Chicago (32, 33). Figure 2 shows the average tail-pipe vehicle composition used in this study and the average mobile source compositionsmeasuredin Atlantaand Chicago. Although the bases of the measurements are different (tail-pipe composition vs total running emissions), the patterns of the compositions are remarkably similar and show that themobile sourcefingerprinthasnot changedsignificantly since 1987. C. CMB Model Calculations. The receptor model calculation requires the multivariate least-squares fit of the composition data with a specified number of sources 620 Environ. Sd.TeCmol.. VOI. 27. No. 4. 1993
and fitting compounds. The general equation is
Y=Zj3+E (1) where Y is the vedor of i molecular concentrations measured at a receptor site, Z is the pollution source molecular compmition matrix of i compounds for each of the j sources modeled, and E is the vector of i errors (the differencebetween the measured and predicted molecular compositions). The values of @ represent the concentration of NMOC from the sources in eq 1that were measured at the receptor. Specific values of j3 can be determined for each speciated ambient sample, and a distribution of samples over time will provide a distribution of values for each source coefficient. Weighted least-squares solutions for j3 were calculated for each ambient sample. This procedure weights the regression analysis by the ambient measurement error. The calculations were performed on a personal computer using the SYSTAT statistical package (SYSTAT Inc., Evanston, IL). The measurement errors for the compounds collected with the canisters (through 2,4-dmethylpentane) were developed from an analysis of 10 duplicatesamples. The measurement error was calculated as the maximum differencebetween the replicate analyses divided by the Student's t with 9 degrees of freedom at the 95% confidence level. (Note that this calculation assumes that the maximum difference between the 10 replicate analyses repersentsthe 95 % confidenceinterval.) The measurement errors for the compounds collected on the Tenax traps (benzenethrough perchloroethylene)were calculated as the standard deviation of repeated analyses of a laboratory calibration standard. Equation 1was solved for each of 55 complete samples (wherea complete sample is defined as a valid result from both the Tenax and canister sampling systems). Each solution,therefore,representsthe emission history for each ambientsample. RegressiondiagnosticaooftheCMBmodel
~~
Table 11. Average Concentrations (fig m-3)
URB compound
IND
4h
20 h
SUB
ethane ethylene propane propylene acetylene isobutane n-butane isopentane n-pentane 2-methylpentane 3-methylpentane n-hexane 2,4-dimethylpentane
0.36 0.40 2.10 1.34 1.47 4.42 10.8 19.3 13.8 5.89 4.01 4.73 1.19
0.37 0.51 2.11 1.53 0.92 2.16 8.44 13.7 9.55 5.04 3.49 4.96 1.12
0.59 0.54 1.52 1.31 0.61 1.41 5.96 15.37 13.9 6.52 4.59 7.36 1.66
0.36 0.39 1.59 1.44 0.52 2.12 3.85 6.46 5.31 5.71 6.05 3.29 0.76
benzene toluene ethylbenzene p-xylene a-xylene
8.27 10.97 3.46 6.57 2.19
6.83 9.63 2.39 5.95 1.99
10.9 10.3 2.38 4.70 1.65
6.62 6.66 1.93 2.58 0.92
chloroform l,l,l-trichloroethane carbon tetrachloride trichloroethylene perchloroethylene
9.08 21.9 1.13 1.13 1.06
4.02 11.6 0.87 1.16 2.52
5.17 15.9 0.97 0.82 1.46
6.88 13.1 0.68 0.52 0.56
NO,
97.0
78.3
72.4
44.4
no. of samples
14
15
13
13
least-squares calculation with all eight sources in eq 1 identified two problems with collinearity. (A collinearity problem was defined as an eigenvector with a condition index greater than 10(34).) An examination of the variance proportions and source profile matrix shows the cause of the collinearities. The largest condition index (representing the smallest eigenvalue) is associated with large variance proportions (>0.99) for contributions from architectural coatings and graphic arts. This collinearity can be seen in Table I as these two sources are primarily composed of toluene. Because of this, it was not possible to simultaneously solve for both of the toluene sources. This problem was handled by solving eq 1 with either architectural coatings or graphic arts in the model and averaging the two solutions. Although the results for these sources are shown separately, we interpret the sum of the two as the combined impact of printing solvents and architectural coatings. The evaluation of the split between the two sources is discussed in section 1V.B. The second highest condition index is associated with a very large variance proportion for wastewater treatment, vapor degreasing, and dry cleaning. This collinearity can be seen in Table I as these are all sources of chlorinated organics. As was the case for the solvent sources, the sources of chlorinated organic were estimated separately and the three solutions averaged to give the final result. The model was able to resolve the vehicle, gas vapor, and refinery sources without problems caused by profile collinearity.
III. Results Mean values of the concentrations of the 23 organics are shown in Table 11. Generally, mean NMOC concentrations were lower at the suburban site than at the innercity or industrial sites. For example, due to the large number of printing operations in the central area of the
city, the 4-h mean toluene concentration at the urban site was 9.63 pg m-3 whereas the mean concentration at the suburban site was 6.66 pg m-3. The perchloroethylene concentrations were also greater at the urban and industrial sites than at the suburban site (2.51 and 1.06 pg m-3, compared to 0.56 pg m-3, respectively). CMB source coefficients were calculated for each individual ambient sample. The coefficients were calculated using the source profiles in Table I that are normalized to the sum of the 23 organic fitting compounds. Since we did not have a good measure of total ambient NMOC, we defined the total as the sum of the 23 fitting compounds (23-NMOC). The CMB source coefficients represent the concentration of 23-NMOC from individual sources measured at the receptor sites. Average coefficients for each sampling site and averaging time are shown in Table 111. The unexplained fraction shown in the table is the measured 23-NMOC not explained by the model and is calculated as the difference between measured 23NMOC and the sum of the source coefficients. In this way, the “other” category represents contributions to the 23-NMOC from all sources which were not included in the model. The average refinery source coefficients in Table I11are highest for the industrial and lowest for the suburban receptor site. This is consistent with source-receptor geometry. Chicago area refineries are located in the southern part of the study area, closest to the industrial site and furthest from the study’s suburban site. Table I11 also shows that the suburban location had the smallest impacts from architectural coatings,wastewater treatment, graphic arts solvents, vapor degreasing, and dry cleaning. Since most of the major printing, industrial, and wastewater sources are located far south of this site (and much closer to the inner-city and industrial sites), lower values for these sources are expected.
IV. Analysis A. Coefficient Consistency with the Emission Inventory. CMB model results are most useful if they can be shown to be consistent with independent observations of source strength and meteorology. For the validation of CMB calculations, we use both seasonal average and short-term time scales to compare CMB predictions to emission inventory and meteorological information. For example, NMOC samples collected over varying meteorological conditions at a receptor site that is not heavily influenced by a nearby industrial or vehicular source will reflect the emission inventory of the area surrounding the receptor. Combining results from receptors across the region reflects the average emission inventory. This analysis for the Chicago metropolitan region is shown in Table IV. The second and third columns of the table show the Chicago area’s emission inventory for a “typical” summer day (35). The individual source types in the inventory have been combined to be consistent with the CMB source categories. The fourth and fifth columns show the average of the CMB coefficients from the three monitoring sites shown on Figure 1. These concentrations were calculated on the basis of the 23 measured fitting compounds and do not include the NMOC from other organics. The sixth column shows the results normalized to the total VOC emission. These estimates are calculated Environ. Sci. Technoi., Vol. 27, No. 4, I993 621
Table 111. Mean Source CoefficientsP (pg m-3) location
duration (h)
industrial urban urban suburban
4 4 20 4
VEH
GV
44.7 37.4 45.0 22.4
23.0 12.3 6.0 9.7
REF
AC
GA
WAS
VDG
DCL
other
23-NMOC
16.9 2.3 1.8 8.4 8.1 0.35 120 226 11.5 2.5 1.8 6.5 5.1 0.84 101 179 14.9 1.9 1.5 6.9 6.1 0.49 105 188 9.9 1.9 1.6 5.0 4.8 0.19 67 122 GV, gasoline vapor; REF, petroleum refinery; AC, architectural coatings; WAS, wastewater treatment; VEH, vehicles; GA, graphic arts; VDG, vapor degreasing; DCL, dry cleaning; other, sum of residual organic; 23-NMOC, fitting compounds. Table IV. Comparison of Emission Inventory Data to CMB Source Coefficients
UIC study source vehicles gasoline vapor petroleum refineries architectural coatings graphic arts vapor degreasing dry cleaning other total hydrocarbons
Illinois EPA emission inventory (kg d a y 9 (%) 286 155 61 179 10 749 44 230 78 268 25 078 758 312 092 800 509
33.5 7.6 1.3 5.5 9.8 3.1 0.1
39.0 100.0
23-NMOC' (rg m-9 (%) 37.4 12.7 13.3 2.2 1.7 6.0 0.5 105.2 179.0
20.9 7.1 7.4 1.2
1.0 3.4 0.3 58.7 100.0
USEPA ( % NMHCc) VOCb (%)
33.8 6.4 7.6 3.1 6.6 3.1 0.4
LaSalle (n = 20)
total (n = 39)
45.0 16.6 10.8 4.6 7.6
39.2 13.5 12.3 5.5 9.4
15.3
19.9
'Average of the CMB coefficients from the four combinations of site and sampling duration normalized to the 23 fitting compounds.
* Weight percent, determined from the average CMB coefficients, normalized to the total VOC emission. The sum of the seven source categories
is assumed to be 61% , which is the sum of the contributions from these categories in the emission inventory. Average of 20 6:OO-9:00 a.m. samples collected during the summer 1987 at a downtown receptor site and the average of 39 samples collected at both the LaSalle and top of the Sears tower receptor sites. CMB calculations are on the basis of total non-methane hydrocarbon concentration.
by dividing the CMB concentration in column 4 by the fraction of the total VOC represented by the measured compounds. (The 23-NMOC/VOC fractions are listed in Table I.) The sum of the resulting VOC concentrations was set to 61% ' ,which is the sum of the contributions from these categories in the region's emission inventory. Estimates for the fractional contributions from vehicles, fugitivegasolinevapor, architectural coatings,graphic arts, and vapor degreasing are very similar. Although the absolute value of the inventory emissions may be in error, the relative emissions from these sources appear to be well represented. In contrast, the emissions from petroleum refineries in the inventory are substantially below the CMB estimated concentrations based on the ambient measurements in the Chicago region. This finding was also seen in a previous study in the Chicago area (6)and points to an area where the inventory underestimates the actual emissions. The comparisons between inventory and model were remarkably consistent for architectural coatings, graphic arts, and vapor degreasing even though these were calculated in the presence of severe collinearities. Also included in Table IV are average CMB model predictions from measurements of NMHC made independently by the USEPA during summer 1987 in Chicago (36). These data were collected as part of a larger study of NMHC in 39 cities with ozone in excess of the NAAQS (37). The CMB predictions represent 6:OO-9:00 a.m. averages representing two downtown Chicago sites: (I) LaSalle St., a ground-level street canyon site and (2) the top of the Sears tower (-365 m). The LaSalle St. site is strongly influenced by traffic in the downtown business area. Results from CMB modeling of five sources are listed in the last two columns of Table IV. These calculations are on the basis of total measured NMOC (not just the sum of the fitting compounds) and are shown for the ground-level site and an average from the two sites. The results are consistent with our UIC CMB study and 622
Environ. Scl.Technol., Vol. 27, No. 4, 1993
receptor site location. For example, the LaSalle St. site is heavily influenced by mobile sources and shows the highest contribution from vehicles. However, the average of both sites for mobile sources is in good agreement with the emission inventory. The estimates for architectural coatings and graphic arts are also very close to the emission inventory. As with our UIC study, the actual concentration from petroleum refineries is substantially higher than the emission inventory value. B. Coefficient Consistency with Meterology and Source Strength. A second approach to model validation is to compare short-term predictions with meterological characteristics. For example, a receptor location downwind of a large printing company will see a higher graphic arts concentration than when the wind is from the opposite direction. The underlying assumption behind this comparison is that the composition of pollutants in an airsample is directly related to the sources that contribute to the air sample. When this comparison is made on a seasonal basis, we would expect agreement with the emission inventory; this is what we have observed for Chicago. When this evaluation is made on a sample-tosample basis, however, the regional emission inventory is no longer an appropriate reference. The source-receptor impact can vary significantly as a function of source geometry and wind direction. We have found that the careful use of ground-based trajectories can be helpful in the evaluation of CMB predictions for short-term grab samples (minutes to a few hours duration). Previous work in Tokyo ( 4 )and Chicago (6)has demonstrated the utility of this approach. In this paper, we show the application of ground-based trajectories for CMB predictions for graphic arts and petroleum refineries measured at the UIC receptor site. If we consider the dispersion of emissions from a point source, and use the Gaussian plume model, the receptor
concentration, C, due to a particular elevated source is
1000 r
xE *
1 ,
I
P c
!
0
where Qj is the emission rate from a particular source, u is the windvelocity, uyand aZare the dispersion Coefficients in the vertical (2) and horizontal (y) direction, and H is the source height and plume rise. For large downwind distances (typically 10-70 km from the trajectories), plume rise will no longer be a factor. This is particularly the case for many VOC sources where leaks are major emission sources. In addition, back-trajectories for each sample are based on plume center lines (Le., intersection with the emission source site), so y = 0. For many conditions the dispersion coefficients can be approximated by a, = Axa and uz = GxY, where x is the downwind distance (or travel time) and A, G, a,and y are empirically determined. For long downwind distances, the exponential terms in eq 2 will become -1.0. If in addition we assume that u is relatively constant (the mean wind speed for the 15days was 2.7 f 1.0 m s-l for the 8-11 a.m. period), then we find from eq 2 that
C a QJlxa'Y
'1 , O O i w
l
ti
0
CMB Refinery Coefficients,pg/m3 Figure 3. Relationship between trajectory score based on emissions and CMB coefficient for summer-time refinery emissions at the SPH receptor slte. Vertlcal llnes represent the range of scores from two trajectory paths.
(3)
+
where 1 I a y I 2. Consequently, if a + y = 1, the concentration should be proportional to the emission rate divided by the downwind distance from source to receptor (or alternatively, the travel time). The totalconcentration allocated to all sources in a particular category will be the sum of each of the emission contributions adjusted for upwind distance. Trajectory scores, defined as the sum of all Qlx values for a particular source category over which a trajectroy passed, were determined for each hour of the 4-h samples collected a t the UIC receptor site. Q / x values were included if they fell within a virtual plume area defined by the back-trajectory from the receptor f22.5' from the plume center line. This is illustrated in Figure 1,which shows the back-trajectory (dotted line) for a southwest wind arriving at the SPH site at 1O:OO a.m. on July 24, 1987. The area within the trajectory represents a f22.5O arc. Sources on this map (+) are large (greater than 25 tons per year) printing operations in the Chicago area. (The use of tracing paper for overlaying the trajectory on a map of the emission inventory greatly aided this laborintensive process.) The dotted lines across the trajectories represent hours back in time. Sources within the backplume area were scored as hits for the hour. Each hit was divided by the appropriate trajectory distance between source and receptor. The total hits for each source category were then summed for the 4-h sample-averaging time. Plumes typically took 8-12 h to leave the study area. Scores with low values of Q l x represent runs during which the air sampled followedtrajectories that infrequently passed over point sources or were far removed in time from such sites. The point a t Q l x = 0 represents a run during which none of the sampled air had trajectories intersecting modeled source locations. Our goal with this analysis was to develop trajectories which described where the air mass, measured at the monitoring site, came from. One complicating factor for meteorologyin Chicago is Lake Michigan,which influences the near-shore wind vector on most days of the year. Back-
CMB Refinery Coefficient, pg/m3 Figure 4. Relationship between trajectory score based on capaclty and CMB coefficient for summertime refinery emissions at the SPH receptor stte.
trajectories away from the lake need to be drawn so as to not be influenced by the near-shore effect. Areview of all available meteorological data showed that most of the monitoring stations in the region are within the area influenced by the lake. To avoid problems from the lake effect, wind vector data from three monitoring stations outside of this area were used (CLAN, south of the lake, BRAI, southwest, and ARBO, west of the lake). These locations are also shown in Figure 1. Trajectories for each sample were drawn based on the two meteorological monitoring sites that were closest to the actual upwind direction for the sampling period. The two trajectories were individually scored, and the difference between scores shows the variability inherent to the technique. Scores representing the emissions from graphic arts and petroleum refineries (tons yr-l km-l) were developed and compared to CMB predictions. In addition, scores representing total throughput for refineries (thousands of barrels day' km-l) as surrogates for emissions were developed and compared to CMB predictions. Figures 3 and 4 showthe comparisonbetween summertime trajectory scores and CMB coefficients for refineries for the SPH receptor site. Plotting symbols in the middle of the vertical lines represent the average, and the line represents the Envlron. Sci. Technol., Vol. 27, No. 4, 1993 628
250
7 ~
sun
I
sat
I
e
.-E
50
01 0
+
I ‘ 1 0.5
1
1.5
I 2
2.5
3
I 3.5
4
4.5
5
CMB Graphic Arts Coefficlent,pg/m3 Flgure 5. Relationship between trajectory score and CMB coefficient for summertime graphic arts emissions at the SPH receptor site.
range of the two trajectory scores developed for each sample. The wide range on the plots shows the sensitivity of the scoring technique to the specification of wind direction. Figure 3 scores are based on refinery emissions (from the region V inventory), and Figure 4 scores are based on refinery capacity as a surrogate for emissions. While both figures show an increasing trend between trajectory score and CMB estimates, the relationship based on capacity shows a clearer relationship. This is consistent with Table IV and our previous observations in the Chicago region that suggest serious problems with the emission estimates for refineries (6). Figure 5 shows the comparison between trajectory score and CMB coefficients for the large graphic arts sources. Except for the five positive trajectory scores when the CMB prediction was zero, the plot shows a clear linear trend between upwind emission and CMB allocation to the graphic arts source category. A review of the day of the week shows that these five outlying points represent weekend or Monday morning sampling periods. Trajectory scores were developed by assuming the sources were emitting at a constant rate throughout the study period. While this may be an appropriate assumption for petroleum refineries, this is not true for graphic arts and printing operations which often reduce or shut down production over the weekend. Therefore, trajectories that pass over printing operations between late Friday afternoon and Monday morning may not pick up additional VOC emissions in proportion to the emission inventory. This would cause an overscoring for graphic arts for Saturday, Sunday, and Monday morning sampling periods. Adjusting for day- and timespecific emissions for graphic arts would move the five points toward the origin on the plot. In other words, the CMB model gave the correct prediction. We recognize that the separation between graphic arts and architectural coatings may be a problem. However, the emission inventory and CMB analysis show that graphic arts is 64 % and 68 % of the total graphic arts plus architectural coatings emissions,respectively. In addition, the pattern in Figure 5 is consistent with the conclusion that the graphic arts-architectural coatings split is correctly predicted on a sample-to-sample basis.
V. Conclusions The chemical mass balance source reconciliation model was applied to 55 ambient summertime measurements of 624
Environ. Scl. Technol., Vol. 27, No. 4, 1993
selected NMOC in the Chicago metropolitan area, and the contributions to these compounds from eight source categorieswere evaluated. Vehicles were the largest source of the measured compounds followed by petroleum refineries, gasoline vapor, vapor degreasing, wastewater treatment, architectural coatings, graphic arts, and dry cleaning. The average predictions, adjusted for the contribution of nonfitting compounds in each of the source categories, were generally very consistent with the region’s emission inventory. This consistency with emission inventory had also been observed for wintertime data in Chicago (7). The agreement for both cold and warm seasons (including days with high ozone) suggests that reactivity of the fitting organics was not a problem in the CMB evaluation. Of all of the sources modeled, only the prediction for petroleum refineries was substantially different from the emission inventory allocation. A previous study in Chicago has shown that the emissions for petroleum refineries are often underreported, and the findings from this study corroborate that finding (6). Short-term 4-h predictions for two large point source categories, petroleum refineries and graphic art printing, were evaluated using surface wind trajectories. A comparison of air mass history with CMB predictions showed reasonable agreement with upwind source strength and CMB estimate for graphic arts, confirming the model’s capability to resolve this source from the complex mixture of organics in urban air. Similar results were found for refineries when plant capacity was used as a surrogate for emissions. The relation with upwind emissions was not as clear, further suggestingerrors in the emission inventory for this source. This study demonstrates that the CMB can be applied to ambient air concentrations of organic compounds and is useful for evaluating and validating an area’s emission inventory. Acknowledgments This work was partially supported by the USEPA grant authorization R811936-01-0“Developmentand Validation of a Source-Receptor Air Pollution Model for Hydrocarbons and Toxic Organics” from the Office of Exploratory Research, by the National Science Foundation grant authorization ECE-8502106“Development of a Chemical Mass Balance Air Pollution Model for Primary and Secondary Particulate Matter”, and by the USEPA grant authorization R-814715-01-0“Determination of Air Toxic Emission Inventory from Source Allocation of Ambient PMlO and NMOC”. We recognize all the students at the University of Illinois and the Illinois Institute of Technology who worked so competently on the project, and particularly Barbara Bates, Bruce Hegberg, Paul Aronian, Qi Zhou, Donna Kenski, and Hak Sung Lee. The views of the authors are their own and do not purport to reflect the position of the US. Environmental Protection Agency or National Science Foundation. Literature Cited Mayrshon, H.; Crabtree, J. H. Atmos. Enuiron. 1976, 10, 137-143. Feigley, C. E.; Jeffries, J. H. Atmos. Environ. 1979,13,13691384. Nelson, P. F.; Quigley, S. M.;Smith, M. Y. Atmos. Enuiron. 1983, 17,439-449. Wadden, R. A.; Uno, I.; Wakamatsu, S. Enuiron. Sei. Technol. 1986,20, 473-483.
( 5 ) Scheff, P. A.; Klevs, M. J. Environ. Eng. 1987, 113, 9941005. (6) O’Shea, W. J.; Scheff, P. A. J . Air Pollut. Control. Assoc. 1988,38, 1020-1026. (7) Aronian, P.; Scheff, P. A.; Wadden, R. A. Atmos. Environ. 1989,23,911-920. (8) Hegberg, B. A.; Scheff, P. A,; Wadden, R. A.; Bates, B. A.; Aronian, P. F.; Qi, Z. Presented a t the 82nd Annual Meeting of the Air and Waste Management Association, Anaheim, CA, June 25-30, 1989; Paper 89-104.4. (9) Kenski, D. M.; Wadden, R. A.; Scheff, P. A,; Lonneman, W. A. Presented at the 84th Annual Meeting of the Air and Waste Management Association, Vancouver, BC, Canada, June, 1991; Paper 91-82.3. (10) Miller, M. S.; Friedlander, S. K.; Hidy, G. M. J . Colloid Interface Sei. 1972, 39, 165-176. (11) Kowalczyk,G. S.; Gordon, G. E.; Rheingrover,S.W. Environ. Sci. Technol. 1982, 16, 79-90. (12) Scheff, P. A,; Wadden, R. A.; Allen, R. J. Environ. Sci. Technol. 1984,18,923-931. (13) Dzubay, T. G.; Stevens, R. K.; Gordon, G. E.; Olmez, I.; Sheffield, A. E.; Courtney, W. J. Environ. Sci. Technol. 1988,22,46-52. (14) Scheff, P. A.; Wadden, R. A.; Bates, B. A.; Aronian, P. F. J. Air Pollut. Control Assoc. 1989, 39,469-478. (15) National ResearchCouncil. Rethinking the Ozone Problem in Urban and Regional Air Pollution: National Academv Press: Washington, DC, 1991. (16) Pleil, J. D.; Oliver, K. D.; McClenny, W. A. J . Air Pollut. Control Assoc. 1987,37, 244-248. (17) Krost, K. J.; Pellizzari, E. D.; Walburn, S. G.; Hubbard, S. A. Anal. Chem. 1982,54,810-817. (18) McClenny, W. A.; Pleil, J. D.; Holdren, M. W.; Smith, R. N. Anal. Chem. 1984,56, 2947-2951. (19) Sigsby, J. E., Jr.; Tejada, S.;Ray, W.; Lang, J. M.; Duncan, J. W. Environ. Sci. Technol. 1987,21, 466-475. (20) Air Emissions Species Manual: Volume I. Volatile Organic Compound Species Profiles;EPA-450/2-88-003a;US.Environmental Protection Agency, April, 1988. (21) Sexton, K.; Westberg, H. Atmos. Environ. 1983, 17, 467475. (22) Leone, R. M.; Davis, E. W.; Jones, A. D. Presented at the 80th Annual Meeting of the Air Pollution Control Association, New York, June 21-26, 1987; Paper 87-58.2. (23) Flick,E. W.PrintingInkFormulations; Noyes: ParkRidge, NJ, 1985.
(24) Publication Rotogravure Printing-Background Znformation for Proposed Standards; EPA 45013-80-031a; U.S. Environmental Protection Agency, Oct 1980. (25) 4.9 Graphic Arts (April 1981) In Compilation of Air Pollutant Emission Factors, 4th ed.; U.S.Environmental Protection Agency, 1985; Vol. 1, AP-42. (26) Donovant, V. S.; Clark, C. S.; Que Hee, S. S.; Hertzberg, V. S.; Trapp, J. H. J.-Water Pollut. Control Fed. 1986, 58, 886-895. (27) Harkov, R.; Jenks, J.; Ruggeri, C. Presented at the 80th Annual Meeting of the Air Pollution Control Association, New York, 1987; Paper 87-95.1. (28) Namkung, E.; Rittmann, B. E. J.-Water Pollut. Control Fed. 1987,59,670-678. (29) Storck, W. Chem. Eng. News 1987,65 (Sept 28)) 11. (30) Perchloroethylene Dry Cleaners-Background Information for Proposed Standards; EPA-450/3-79-029a;US.Environmental Protection Agency, Aug 1980. (31) Control of Volatile Organic Compound Emissions from Large Petroleum Dry Cleaners; EPA-45013-82-009;US. Environmental Protection Agency, Sept. 1982. (32) Lonneman, W. A.; Seila, R. L.; Daughtridge, J. V.; Richter, H. G. Presented at the 84th Annual Meeting of the Air and Waste Management Association, Vancouver, BC, Canada, June 1991; Paper 91-68P.2. (33) Scheff, P. A.; Porter, J. A.; Doskey, P. Presented at the 84th Annual Meeting of the Air and Waste Management Association, Vancouver, BC, Canada, June 1991; Paper 91-79.5. (34) Belsley, D. A.; Kun,E.; Welsch, R. E. RegressionDiagnostics; John Wiley and Sons: New York, 1980. (35) lllinois Reasonable Further Progress Report for 1986 Ozone and Carbon Monoxide; IEPA/APC/87-015; Illinois Environmental Protection Agency, Division of Air Pollution Control, 1987. (36) Kenski, D. M.; Wadden, R. A.; Scheff, P. A.; Lonneman, W. A. Presented a t the 84th Annual Meeting of the Air and Waste Management Association, Vancouver, BC, Canada, June 1991; Paper 91-82.3. (37) Seila, R. L.; Lonneman, W. A. Presented a t the 81st Annual Meeting of the Air Pollution Control Association, Dallas, TX, 1988; Paper 88-150.8.
Received for review January 30, 1992. Revised manuscript received July 14, 1992. Accepted November 23, 1992.
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