- Email: [email protected]
Wintertime source-reconciliation of ambient organics
.&mqfseric
Emiro~nt
Vat. 23, No. 5, pp. 91 l-920,
1989.
Printed in Great Britain.
WINTERTIME
SOURCE-RECONCILIATION ORGANICS
OF AMBIENT
PAUL F. ARONIAN,* PETER A. SCHEFF~ and RICHARD A. WADDENt *Environmental Engineering Coordinator, UOP, Inc., 25 E. Algonquin Road, Des Plaines, IL60017, U.S.A.and ~Environment~ and Occupational Health Sciences,University of Illinois at Chicago, School of Public Health, P.O. Box 6998, Chicago, IL 60680, U.S.A. (First received 13 May 1988 and
in final form 24 August 1988)
Abstract-The applitition of a Chemical Mass Balance air pollution model to ambient measurements of volatile organic compounds (VOC) is presented. Twenty-six air samples were collected at three sites in the Chicago metropolitan area (suburban, inner-city and industria1) and analyzed for the concentration of 23 compounds including alkanes, aromatics and chlorinated organics. The average daily temperatures on sampling days varied between - 15.0 and 4.4X. Consequently, the potential for significant photochemical reaction was not present. Each ambient sample was evaluated for the cdntribution from eight sources of VOC (vehicles, gasoline vapor, petroleum refineries, architectural coatings, graphic arts, vapor degreasing, dry cleaning and wastewater treatment). The contribution from sources not included in the model was also evaluated. Average contributions from vehicles ranged from 43 gg m - 3 at the suburban location to ‘70pgrnv3 at the inner-city location. Cont~butions from petroleum refineries appeared to reflect the distances from the sources to the monitoring sites with average contributions ranging from 28 to 33 FgrnT3 near the sources (inner-city and industrial receptors, respectively) to 10 pgrn-” away from the sources (suburban receptor). Contributions from architectural coatings, graphic arts, vapor degreasing, wastewater treatment and dry cleaning also suggested this effect of source-receptor geometry. Evaluation of the results shows that the siteaveraged coefficients for vehicles, gasoline vapor and all other unmodeled sources are in close agreement with the State’s emission inventory. Key word index: Air pollution model, receptor model, volatile organic compounds, non-methane organic compounds, emission inventory, vehicle emissions, gasoline vapor, petroleum refinery emissions, architectural coatings, printing, vapor degreasing, dry cleaning.
INTROiXJmION The Chemical Mass Balance (CMB) source-reconciliation model is a method to determine the contribution of each of the major sources of a pollutant to an ambient measurement of the compgsition bf the pollutant. The model is based on a set of mass balances around a group of compounds measured at a receptor site. The calculation requires that the composition of the pollutant be known at the points of emission as well as in the collected sample. The CMB model is typically applied to the major source categories within a given airshed (e.g. vehicle emissions, coal combustion and fugitive soil aerosols), and most applications use from around 7 to 30 tracer compounds to estimate the impacts of four to six sources. The CMB model has been applied to Total Suspended Particulate Matter (TSP) in a variety of locations (Miller et al., 1972; Kowalczyk et al., 1982; Scheff et al., 1984), inhaiable particulate matter < 10 pm aerodynamic diameter (IP) (Dzubay et al., 1988), total hydr~arbon (HC) concentration (THC, defined as the sum of a number of specified HCs) (Mayrsohn and Crabtree, 1976; Feigley and Jeffries, 1979; Nelson et al., 1983), and non-methane hydrocarbons (NMHC) (Wadden et
al., 1986; Scheff and Klevs, 1987; O’Shea and Scheff, 1988). The first of the three NMHC studies was conducted in Tokyo, Japan in which four source categories were fitted with 17 components, ethane to xylenes (Wadden et al.,. 1986). The data set included 192 ambient samples coilected from 300 to 1500 m aloft over Tokyo Bay. The study found that vehicles contributed 7.0%, gasoline vapor 10.5%, petroleum refineries 26.0%, paint solvents 27.2% and all other sources 29.3% of the NMHC sampled. Samples were taken at intervals during a series of flights over 2 July days. The calculated source coefficients were not greatly affected by the reactivity of the components used in the model. A validation of the refinery source coefficient found a high correlation between the CMB prediction and a trajectory analysis for refinery emissions adjusted for dispersion (r2 =0X99). The fractions for paint solvents, gasoline vapors and unidentified sources were also consistent with wind trajectory patterns. A second NMHC study was developed for a data set collected at two New Jersey receptor sites located 16 km apart (Scheff and Klevs, 1987). The study modeled the HC contributions from five sources using
911
912
PAUL F. ARWIAN et ul
24 fitting compounds (C,-C, alkanes along with aromatic solvents). The receptor sites represented intrinsic model validation, as one location was heavily influenced by industrial sources, while the other was a central-city location dominated by vehicle emissions. The results were consistent with the local source-receptor geometry, and the predictions for gasoline storage, paint operations and refineries were generally consistent with wind direction information. A third study, in the Chicago area, used eight HCs as fitting compounds (C,-C, alkanes and benzene) and three source categories; vehicle emissions, petroleum refineries, and gasoline vapor (O’Shea and Scheff, 1988). The sampling program included a one month summertime sampling period in which samples were collected over a 45min period during the noon hour. The results were also in excellent agreement with wind trajectories and source location. These studies demonstrate the usefulness of the method when applied to 0, precursors and toxic organics.
METHODS
Study design This study included a 4-month winter sampling program at three sites in the Chicago Metropolitan Area; a suburban background location approximately 80 km north of downtown Chicago (SUB), an innercity urban site at the University of Illinois at Chicago
located 1.6 km west of downtown Chicago (URB). and an industrial location on Chicago’s southeast side (IND). The sites are roughly located on a north south line and are 21 km and 55 km apart, from IND to URB to SUB, respectively. Four-hour samples (8:OO a.m. to 12:OOp.m.) were collected simultaneously at all three sites. A 20-h sample (1200 p.m. to 8:00 a.m.) was also collected at the central city location for each sampling session. This sampling strategy, therefore, allowed for the evaluation of the impact of major point sources of Non-Methane Organic Compounds (NMOC) at three widely varying receptor locations as well as the evaluation of the long-term average impact at the central location. For example, since all four refineries in the Chicago area are located in the southern portion of the study area, each receptor location will view the refineries with a different emission to collection transport time when the wind is from the south. A set of 23 VOCs was identified as fitting compounds for the CMB calculations and are listed in Table 1. These compounds were selected for a variety of reasons including: (1) they are ubiquitous and, because they are usually above minimum detectable levels in urban environments, 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
Table 1. Source profiles normalized to the fitting compounds (wt%) Vehicles
Gasoline Vapor
Refinery
3.12 18.18 6.12 3.16 7.11 1.17 9.02 7.19 3.20 2.89 1.88 1.31 1.15
0.00 0.00 1.66 0.00 0.00 18.47 41.12 20.24 8.51 2.89 1.48 1.15 0.36
4.81 0.13 21.29 0.80 0.11 4.14 17.60 16.85 7.32 1.22 4.33 3.60 1.66
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.30
0.00 0.00
Benzene Toluene Ethylbenzene p-Xylene n-Xylene Chloroform l,l,l-trichloroethane Carbon tetrachloride Trichloroethylene Perchloroethylene Total THC* as % of NMOC
6.64 14.16 2.01 6.46 3.91 0.00 0.00 0.00 0.00 0.00 100.00 51.21
1.28 1.32 0.36 0.37 0.19 0.00 0.00 0.00 0.00 0.00 100.00 78.22
1.39 4.68 0.58 1.44 0.85 0.00 0.00 0.00 0.00 0.00 100.00 80.47
0.21 78.34 1.36 8.08 8.65 0.00 0.00 0.00 0.00 0.00 lOO.OiI 33.06
3.16 8.42 I .05 0.00 0.00 11.58 31.58 0.00 16.84 27.37
NO, (wt % of THC)
251.29
0.00
0.98
Compounds Ethane Ethylene Propane Propylene Acetylene i-Butane n-Butane i-Pentane n-Pentane 2-Methylpentane 3-Methylpentane n-Hexane 2,CDimethylpentane
* THC =sum of 23 organic fitting compounds.
Architectural Coating
0.00
Wastewater
0.00 0.00 0.00
0.00 0.00 0.00
0.00 0.00 0.00
0.00 0.00
100.00 9.50 0.00
Graphic arts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Vapor degreasers 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Dry cleaning 0.00 0.00
0.00 0.00 0.00
0.00 0.00
0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00
0.00
0.00
0.00
0.00
0.00
93.09
0.00
0.00
0.00
6.91
0.00
0.00
0.00
0.00 0.00 0.00 0.00
O.Oil 0.00
0.00 0.00
0.00 100.00 11.90 0.00
0.00
0.00
55.06 0.00 33.35 11.59
0.00 0.00 0.00 0.00
100.00 90.17
100.00 100.00 63.00
0.00
0.00
Wintertime source-reconciliation of ambient organics sources studied, (4) the emission data for the compounds are generally consistent from study to study; (5) except for propylene (which is highly reactive) and ethane, acetylene and benzene (which have low reactivities), the HCs have similar OH radical reaction rate coefficients (k,,) (all within an order of magnitude of each other); and (6) the HC fitting compounds have been applied in CMB modeling studies with reasonable success in Japan, New Jersey and Chicago (Wadden et al., 1986; Scheff and Klevs, 1987; O’Shea and Scheff, 1988). In addition to the 23 organics, NO, was collected at the URB receptor location and included as a fitting compound. Eight source categories were selected for the CMB analysis. These are vehicles, fugitive gasoline vapors, petroleum refineries, architectural coatings, graphic arts (printing), waste-water treatment, vapor degreasing and dry cleaning. These sources were selected because they are known to emit significant quantities of NMOC in the Chicago area (IEPA, 1986) and are sources for which reasonable fingerprints could be developed (Scheff et a[., 1989). Environmental sampling Ambient air samples were collected in 6-e electropolished stainless steel canisters (Pleil et al., 1987) and Tenax trap samplers (Krost et al., 1982). A total of 28 4-h samples were collected at the three sites between 13 November 1986 and 8 February 1987. A total of 11 20-h samples were also collected at the URB site during the monitoring period. Each of the canister samples was analyzed for the C,-C, alkane and alkene fitting compounds and the Tenax trap samples were analyzed for the aromatic and chlorinated compounds. Each canister was filled with approximately 9 k of ambient air (1.5 atm. absolute pressure at the end of the sampling period). An all-stainless steel bellows pump (model MB-151, Metal Bellows Corp., Sharon, MA) and mass flow controller (Model R 028, O-100 cm3 min- ‘, Tylan Corporation, Carson, CA) set to deliver 37.75 mlmin-’ for the 4-h sample, or 7.5 ml min - 1 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 (Model N055T1, 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 mlmin-’ (approximately 38 mlmin-’ at - 760 mm Hg vacuum and decreased to 25 ml min- 1 at + 1.5 atm of canister pressure). All canisters were cleaned and evacuated prior to each sampling period. The Tenax samples were collected using low-flow personal sampiing pumps (model P-12SA, DuPont Corp., Wilmington, DE). Flow rates were adjusted to 0.042 emin-- ’ for the 4-h sample and 0.0083 8 min- ’ for the 20-h sample. This corresponds to 10 e of air being sampled for each collection period and was selected to insure no breakthrough for the compounds
913
studied (Krost et al., 1982). The sample cartridges were prepared by packing a 1Ocm x 1.5 cm inner diameter glass tube with 6cm of 40/60 mesh Tenax 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. The Tenax cartridges were stored in a He atmosphere to minimize contamination from ambient air. Oxides of nitrogen were measured at the URB location with a dual channel chemiluminescence analyzer (model 8440E, Monitor Labs Inc., San Diego, CA). The analyzer was calibrated using a 101 ppm standard of NO in N, with a flow dilution system {Matheson Gas Corp., East Rutherford, NJ) and a permeation tube system for NO, test atmosphere generation (models 8861DA and 8861P, Bendix Corp., Lewisburg, WV). The analyzer was calibrated bimonthly during the sampling period at 0.4 ppm and 0.2 ppm of NO and NO,, respectively. Sample analysts Volatile organics collected in the Tenax traps were analyzed by thermal desorption, cryogenic concentration in a liquid nitrogen-cooled Ni capillary trap, followed by high resolution gas chromatographymass spectrometry (GC/MS) (Krost et at., 1982). A field blank was included for each sampling period. The GC/MS system was calibrated to periluorotoluene. The gas canister samples were analyzed using a high resolution 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 (McClenny et aI., 1984). Moisture was removed from the gas sample with a nafion drying tube prior to cryogenic trapping (Pleil et al., 1987). The GC/FID system was calibrated with a certified propane in air standard.
The calculation of the source coefficients requires the multivariate least-squares fit of the composition data with a specified number of coefficients. The general equation is Y=Z/3+E
(1)
where Y is the vector of i molecular concentrations measured at a receptor site, Z is the pollution source molecular composition matrix of i compounds for each of the j sources modeled and E is the vector of i errors (the difference between the measured and predicted molecular compositions). The values of B represent the concentration of NMHC from the modeled sources that was measured at the receptor. In order to have statistical validity, the CMB model requires many more compounds than sources. Specific values of fl can be determined for each unique sample, and a dist~bution of samples over time will provide a distribution of values for each source coefficient.
PAUL F. ARONIANet al.
914
Weighted least squares solutions for /I 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 standard deviation of the measurements for the compounds collected with the canisters (through 2,4-dimethylpentane) were developed from an analysis of ten duplicate samples. The measurement error was calculated as the maximum difference between 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 represents the 95% confidence interval. The measurement errors for the compounds collected with the Tenax traps (benzene to perchloroethylene) were calculated as the standard deviation of repeated analyses of a laboratory calibration standard. Measurement errors are included in Table 2. Source projle development Profiles for the eight sources selected for this study were developed from a review of information including: measurements of the composition of source emissions; personal exposure to the emissions from NMOC sources; predictive models of the emissions froZn sources of NMOC; as well as industrial production and usage data. The source profiles used in Equation 1 are shown in Table 1. A detailed discussion of the profile development has been presented (Scheff et al., 1989). Note that the profiles are normalized to
100% of the 23 organics studied (THC). Therefore, the predicted coefficients (8) represent source contributions at the receptor sites for the specific 23 fitting compounds studied. Also listed in Table 1 is the sum of the 23 fitting VOCs as a per cent of the total NMOC emission. The vehicle emission profile was derived from a dynamometer study of 46 in-use vehicles using the Federal Test Procedure driving cycle (Sigsby et ul., 1987). The gasoline vapor source fingerprint was derived from HC vapors emitted from a winter blend/ no-lead gasoline (USEPA, 1988). The refinery source fingerprint was derived from a plume study of the emissions from a large modern petroleum refinery (Sexton and Westberg, 1983) and is based on an average of six ground level in-plume samples taken on different days 5 km downwind of the refinery and two samples taken at an altitude of 350 m between (I) O-3.5 km and (2) 8-11 km downwind of the refinery on the same day. The architectural coatings fingerprint is based on an extensive survey of product-type consumption in the New York City, State of New Jersey region (Leone et al., 1987), and profiles for composites of three product types (solvent-based coatings, thinning and cleanup solvents and water-based coatings) (USEPA, 1988). The fingerprint was calculated by applying the product consumption data to the compositions in each category and summing the contributions from all categories. Note that the fitting compounds only represent 33.06% of the total emission for this source. Non-fitting compounds make up most of the NMOC
Table 2. Average 4-h ambient concentrations at the three receptor locations (rgmw3) IND Compound
Measurement error
1.64
-.-
.-.-- --Average
URB Range
Ethane Ethylene Propane Propylene Acetvlene i-Butane n-Butane i-Pentane n-Pentane 2-Methylpentane 3-Methylpentane n-Hexane 2,bDimethylpentane
1.64 1.36 0.20 0.38 0.33 1.36 2.13 2.02 2.02 1.91 1.32 4.09
10.9 4.6 8.5 2.3 3.2 4.6 21.9 14.4 9.8 9.6 10.5 7.8 8.1
0.3-41.6 0.9-9.3 2.tS20.2 0.9-3.2 0.84.8 0.3-7.8 2.6-50.9 1.7-34.8 1.1-34.4 0.348.5 0.3-59.1
Benzene Toluene
0.45 0.26
Ethylbenzene p-Xylene o-Xylene
Average
SUB Range
Average
Range
0.345.5
7.9 4.0 5.9 2.5 4.4 2.8 14.4 12.1 11.4 8.5 8.5 7.1 9.7
0.3-35.9 0.3-10.0 1.1-10.2 o.w.7 0.3-11.1 0.3-4.4 2.7-24.0 1.8-34.7 1.2-50.3 0.6-39.6 0.3-43.6 0.9-17.4 0.3-51.2
11.4 2.7 5.1 2.1 3.5 2.6 13.2 9.8 6.2 6.2 6.0 7.3 3.6
0.3-70.3 0.3-6.9 0.3-I 5.0 0.7-3.7 0.3 -8.4 0.8-6.1 2.8-25.3 I &20.-l 1.2-20.8 0.7-21.7 0.6-31.7 2.1 -15.1 0.3S23.8
4.1 9.9
1.5-8.2 O&%.9
7.8 14.6
0.7-23.9 3.341.5
0.26
2.0
0.5-5.9
2.7
0.4-l 1.6
0.54 0.48
5.4 1.4
0.618.6 0.24.0
6.7 1.8
0.6-37.9 0.2-8.2
4.5 6.9 1.3 2.6 1.0
2.3-9.0 2.5-l 5.7 0.5-1.8 1.1-5.3 0.4-1.7
Chloroform I,l,l-Trichloroethane Carbon tetrachloride Trichloroethylene Perchloroethylene
0.89 0.57 0.73 0.45 0.26
2.7 5.2 0.6 0.9 2.3
0.8-5.7 2.8-13.8 0.0-1.5 0.3-2.7 4.2-0.0
4.6 6.7 0.6 1.3 2.6
2.1-9.1 3.4-13.6 [email protected] 0.0-4.8 0.2-7.5
3.1 4.2 0.4 0.8 0.9
1.2-8.8 2.2-6.8 o.cLO.9 0.1.-1.5 O.&l .8
NO, (cone)*
0.25
_
1.7-26.8
_
126.9
33.8-239.4
Wintertime source-reconciliation of ambient organics emissions from this category and include alcohols, ketones, esters, glycols and other chlorinated organics. The graphic arts fingerprint was developed in a similar way. Individual fingerprints for lithography (Flick, 1985), letterpress (Flick, 1985), flexographic (USEPA, 1988), and rotogravure (Flick, 1985; USEPA, 1980a) were combined with a nationwide estimate of the relative fraction of NMOC emissions due to each of these printing operations (USEPA, 1985) to form a composite fingerprint. Note that most of the emissions from printing (88.10%) are also from non-fitting compounds and include naphtha, petroleum distillate, and various alcohols, aldehydes and ketones. Three sources of chlorinated organic were studied. The wastewater treatment source fingerp~nt was developed as a composite of four individual source profiles. Two of the profiles were based on ambient air monitoring at municipal wastewater treatment plants in Cincinnati, OH (Dunovant et al., 1986) and Sayerville, NJ (Harkov et al., 1987). The other two studies’ were based on inlluent wastewater concentrations of volatile compounds at treatment plants in Chicago and a chemical fate model of removal by volatilization, adsorption and biodegradation (Namkung and Rittmann, 1987; Metropolitan Sanitary District of Greater Chicago, 1986). The fingerprint for vapor degreasing is based on a 1985 nationwide chlorinated solvent consumption estimate of 228,800 Mg for this application; and the individual fractions attributed to trichloroethylene, l,l,l-trichloroethane, perchloroethylene and methylene chloride (from the Halogenated Solvents Industry Alliance, Storck, 1987). Note that this fingerprint is only representative of vapor degreasing since no contributions were included from maintenance and manufacturing cold cleaning. The fingerprint for dry cleaners is based on total national solvent usage by perchloroethylene cleaners (55,000 Mga-’ estimated for 1984, USEPA, 1980b) and petroleum based cleaners (31,000 Mga-‘, USEPA, 1982). The petroleum solvents used are composed of Cs to C,, HCs and do not significantly contribute to the fitting compounds.
RESULTS
Table 2 lists 4-h average (8:OOa.m. to 1200 p-m.) concentrations of the 23 compounds used in the CMB model fitting procedure for each sampling location. The average is based only on those samples where results from the Tenax and canister samplers are complete. The CMB source coefficients for each complete sample are shown in Table 3. The coefhcients in this table were calculated from source profiles normalized to the sum of the 23 organic fitting compounds. Defining the total HC concentration (THC) as the sum of the 23 fitting compounds, the source coefficients in Table 3 show the concentration of the THC from the eight sources measured at the receptor. The unex-
915
plained fraction shown in the table is defined as the difference between measured THC and the sum of the source coefficients. In this way, the ‘other’ category represents contributions to the THC from all sources which were not included in the model. As shown in Table 3, the other category was positive for 24 of 26 samples. Due to collinearity between certain groups of sources, it was usually not possible to predict positive values for all eight sources simultaneously in the model. When this occurred, the coefficients were recalculated using a procedure discussed below. Sources that could not be resolved on certain days are indicated by zero values in the table. Note the procedure does allow for occasional negative values in the unassigned HC fraction. Coefficients averaged by receptor location and sample duration are shown in Table 4. Other statistics included in the table are the minimum and maximum coefficients and the standard deviation of the daily variation.
DISCUSSION
Ambient hydrocarbon measurements
Mean values of the concentration of the 23 organics are shown in Table 2. Generally, mean VOC concentrations were slightly lower at the suburban site than at the inner-city 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 inner-city site was 14.58 pg m- 3 whereas the mean concentration at the suburban site was 6.92 pg m- 3. The ~rchlor~thylene con~ntrations were also greater at the inner-city and industrial sites than at the suburban site (2.59 and 2.26, compared to 0.86 pg rnm3, respectively). The table also shows that most of the aliphatic HCs had large concentration ranges. This reflects the variability in the source cont~butions from vehicles, gasoline vapors, and refineries, the major sources of these HCs (see Table 1). Note that the average daily temperatures varied between - 15 and 4.4”C, consequently, the potential for significant ambient reaction, which might lead to distortion of source fingerprints, was not present. CM B source coefficients
Equation 1 was solved for each of 26 complete samples (where a complete sample is defined as a valid result from both the Tenax and canister sampling systems). The results are shown in Table 3. Regression diagnostics of the CMB model with all eight sources in the equation identified two problems with collinearity, For a typical run, two of the eight eigenvectors had condition indices > 10. (Condition index is a measure of collinearity and a value > 10 is an indication of a major collinearity, Belsley, 1980.) An examination of the variance proportions and source profile matrix shows the cause of the collinearities. (The variance proportions show for each variable, the proportion of
0.00 9.39 1.17 0.26 0.68 0.40 0.62
18.64 16.22 29.38 19.03
1.42 1.11 2.07 1.42
IND URB URB SUB
2.82 5.32 9.05 5.66
0.00 11.29 0.31 0.31 3.00 3.82 1.04
4.46
0.52 0.00 0.00
0.00 0.00 1.49 0.00 0.00 1.08
94.39 150.02 68.31 36.13 32.2s 55.17 34.37
4.20 5.51 10.60 3.77 -3.14 SO.23 6.10 10.45 3.42 6.16 3.93
0.00 0.09 0.78 0.00 1.41 2.35 1.25 0.51 0.38 0.40 0.98 1.92 0.24 0.28 0.08 0.32 0.11
1.46 1.79 6.24 1.11
1.98 2.46 1.34 3.42 3.24 7.62 2.00 1.48 1.40 2.28 1.72
3.40 6.43
235.57 48.19 278.31 184.45 - 25.85 18.12 8.17 34.07 16.21 31.85 1.04 2.20 1.09 0.62
91.22
41.75
111.87 80.61
137.17 184.32
64.14 77.57 284.88 102.14
453.51 261.37 445.98 325.80
316.73 190.90 132.25
109.32 102.15
29.09 5.10 95.73 95.84 58.06
0.35 0.55 1.91 0.42 0.15
THC
2.78 3.37 7.35 2.24 3.10 7.38 10.27 6.45 3.43
Other
DCL
VDG
* 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, THC = sum of fitting compounds.
12.65 12.40 3.69 0.45 1.02 1.69 1.37
58.80 7.01 42.57
2-8-87 2-8-87 2-8-87 2-8-87
2-6-87 2-6-87 2-6-87
3.s9
13.91 51.05
1.50
9.69 20.56 18.31 42.20 10.58
3.80 2.25 2.94 3.54 7.95 1.65
4 4 20 4 4 4 20 4
URB IND URB SUB
1-29-87
1.03
4
SUB
1-24-87
46.93 17.20
20 4
URB SUB
4.32 7.99
1.17 0.23
0.00
40.71 57.68
4 4
IND URB
3.32 37.99 118.00 26.69 49.67 70.67 51.33 49.23
0.68 1.80 5.40 0.44
0.00 0.00 1.65
35.16 8.06 103.91 17.54
49.91 9.71 39.24 22.30 21.44 5.09 1.62 3.45 4.32
4 4 20 4
IND URB URB SUB
96.26 132.84 110.41 100.93
6.17 9.53 5.65 3.09
12.05 14.10 1.98 0.33
15.93 7.09 40.43 32.53
4 4 20 4
12-17-86 12-17-86 12-17-86 12-17-86 12-21-86 12-21-86 12-21-86 12-21-86 12-26-86 12-26-86 1-21-87 I-21-87
6.39 0.00 0.00 9.72 LO.89 1.65 0.42
7.50 2.07 1.92
0.49 0.00 0.00
54.63 48.70 11.31 69.40 26.26 0.00 0.00
1.09 6.19 6.03
1.42 2.96
41.32 47.14 141.64 35.44 51.67
2.45 3.14
1.59 3.64
21.84 25.77
8.47 10.47
4 20 4 20 4
GA
VEH
WAS
AC
URB URB URB URB SUB IND URB URB SUB
1l-22-86 1l-22-86 12-I3-86 12-13-86 12-13-86
Location
REF
Date
GV
Duration (h)
Table 3. Best-fit source coefficients normalized to THC* (~8 m- ‘)
Wintertime sours-~conciliation
917
of ambient organics
Table 4. Source coefficients averaged by receptor location and duration* (@ m - ‘)
Location Industrial (MD) Average Minimum Maximum Standard deviation Inner-city (URB)_ Average Minimum Maximum Standard deviation Average Minimum Maximum Standard deviation Suburban (SUB) Average Minimum Maximum Standard deviation
Duration (b)
No. of cases
GV
REF
AC
WAS
VEH
GA
VJIG
DCL
Other
4
5
20.1 0.4 50.0 18.4
33.3 2.8 69.4 25.1
2.5 0.0 12.0 5.3
3.2 0,7 6.2 2.2
45.3 3.3 96.3 36.3
2.0 0.0 9.7 4.3
3.4 1.5 7.4 2.4
0.7 0.0 1.4 0.6
46.6 -25.8 235.6 106.8
4
7
4.8 1.0 9.7 3.8
27.8 5.3 57.7 20.8
3.5 0.0 14.1 4.9
4.8 1.1 9.5 3.4
70.2 16.2 141.6 48.6
3.4 0.0 10.9 3.9
4.8 1.4 10.3 3.3
1.0 0.1 2.3 I.0
32.9
16.0 1.6 40.4 16.8
39.5 0.0 103.9 34.1
3.2 0.0 11.3 3.8
4.3 2.1 7.9 2.2
57.0 7.0 118.0 41.7
2.3 0.0 9.4 3.3
4.3 2.0 7.6 2.4
0.9 0.3 1.9 0.6
27::: 100:s
10.5 I.4 32.5 12.0
10.3 0.0 17.5 6.2
0.2 0.0 1.0 0.4
1.7 0.4 3.1 0.8
43.4 13.9 100.9 29.3
0.3 0.0 1.2 0.4
2.2 1.1 3.4 0.9
0.3 0.0 0.6 0.2
43.2 3.4 184.4 65.4
20
4
7
7
3.4 95.7 32.0 64.0
* 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, THC = sum of fitting compounds.
total variance of the estimated regression coefficient that is associated with each of the principal compo-
nents. A collinearity is suggested when more than one variable has a high proportion on a principal component having a high condition index. Two or more proportions > 0.5 suggest a problem, while two or more loadings > 0.9 is a reason for concern.) The largest condition index (representing the smallest eigenvalue) is associated with large variance proportions (> 0.99) for contributions from architectur~i coatings and graphic arts. This collinearity can be seen in Table 1 as these two sources are primarily composed of toluene. Because of this collinearity, it is generally not possible to simultaneously solve for both of the toluene sources. This problem was handled by solving Equation 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 in Table 3, the sum of the two should be interpreted as the combined impact of printing and architectural coating solvents. The exact split between the two sources is less certain. The second highest condition index is associated with a very large variance proportion for wastewater treatment (> 0.99) and large proportions (> 0.85) for vapor degreasing and dry cleaning. This collinearity can be seen in Table 1 as these are the three 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 result in Table 3; the sum of the three sources represents their combined impact. The model was usually able to resolve the vehicle, gas vapor and
refinery sources without problems of negative coefficients. Table 4 lists the average CMB source coefficient by
site and sample duration. Large differences were found between sites. For example, the average gasoline vapor source coefficients are 20.1 for the industrial, 10.5 for the suburban and 4.8 pg m- ’ for inner-city (4 h) locations. The high value for the industrial site is consistent with the fact that the site is close to bulk gasoline storage facilities. The suburban receptor site is located next to the parking lot of a fire station with a gasoline pump and underground storage tank. The relatively high value for gas vapor suggests that the site is influenced by the department’s refueling operation. The average refinery source coefficients are 33.3 for the industrial, 27.8 for the inner-city, and 10.3 pg m - 3 for suburban receptors. This is also consistent with source-receptor geometry as all of the region’s refinery point sources are located in the southern suburbs closest to the industrial site and furthest from the study’s suburban site which is located 55 km north of the city. Table 4 further 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 the site (and much closer to the inner-city and industrial sites), lower values for these sources are expected. It is also interesting to note that the suburban location had the highest % contribution from unexplained sources (38.5% of the average THC measured at the site).
918
PAUL F. ARONIAN et al.
In addition, Table 4 shows that the 4-h average vehicle contribution at the inner-city site (70.2 pg m -3) is larger than the 2Bh average at the same site (57.0 pg m- ‘1. This difference is consistent with the fact that the major morning drive-time rush hour is contained in the 4-h period and not present in the 20-h sample. ~o~pa~~son of CMB inventory data
source ~o~~c~en~s to omission
A comparison of average source coefficients to emission inventory data (IEPA, 1987) for Chicago is shown in Table 5. The emission inventory data in the table is expressed as kg day-’ and weight per cent of the total VOC emission and is representative of a typical summer day. The inventory includes 61 VOC emission source types (800,509 kgday-I). Note that the inventory does not include specific halogenated compounds (including l,l,l-trichloroethane and freon) and this will affect the comparison for degreasers as this source contains a significant mass fraction of l,l,l-trich~oroethane. Generally, the emission inventory results are in reasonable agreement with the average CMB coefficients. For the case with the CMB coefficients normalized to the THC, the inventory estimates of vehicle emission, gasoline vapor and vapor degreasers are very close to the CMB estimates. In contrast, the average graphic arts contribution of 1.3% is much smaller than predicted by the emission inventory (9.8%). Note, however, that because the CMB estimate for graphic arts is based on a small fraction of the total emission (11.9%) it is under-represented in the results normalized to THC. This was also true for architectural coatings. Normalizing the CMB coefficients in terms of the total NMOC emission brings these predictions much closer together. Table 5 also shows the average CMB coefficients normalized to the total NMOC. The NMOC coeffic-
ients were calculated by dividing the average CMB coefficients by the THC as a fraction of NMOC (see Table 1). Since we do not have a % of NMOC factor for the ‘other’ sources category, the coefficients are expressed as a % of NMOC such that the emission inventory per cent of the seven categories in Table 5 (61%) equals the sum of the normalized CMB estimates. The comparison between the emission inventory as weight % and NMOC normalized CMB estimates for vehicles, gasoline vapor, architectural coatings, graphic arts, vapor degreasing and dry cleaning is very reasonable. The largest difference between the emission inventory and CMB predictions is seen with refinery We suspect this rest&s from contributions. underestimation of the emission inventory values. A previous study in the Chicago area indicated good agreement between calculated refinery coefficients and refinery contributions determined from wind trajectories and refinery capacity (with refinery capacity used as a surrogate for emission); but her agreement when estimates of emissions were substituted for capacity (O’Shea and Scheff, 1988). The inventory values also do not include the contributions from northeast Indiana refineries and other sources. It is encouraging to note that the emission inventory and CMB model’s estimate of ‘other’ sources are very close. This suggests that the calculations do not over-predict the modeled sources nor underpredict the sources not included in the model.
CONCLUSIONS
The Chemical Mass Balance source reconciliation model was applied to 26 ambient measurements of selected VOCs in the Chicago metropolitan area, and the contributions to these compounds From eight source categories were evaluated. Vehicles were the
Table 5. Comparison of emission inventory data to CMB source coefficients CMB Emission inventory Source Vehicles Gasoline vapor Petroleum refineries Architectural coatings Graphic arts Vapor degreasing Dry cleaning Other Total hydrocarbons
(kg day-‘) 286,155 61,179 10,749 44,230 78,268 25,078 758 312,092 800,509
(%I 33.5 7.6 1.3 5.5 9.8 3.1 0.1 39.0 100.0
THC* (pgm-? 54.0. 12.8 27.7 2.4 2.0 3.7 0.7 50.2 153.5
NM00 (%I
(%I
35.2 a.4 18.0 1.5 1.3 2.4 0.5 32.7 100.0
34.6 5.4 11.3 2.3 5.6 1.3 0.4
*Average of the CMB coefficients from the four combinations of site and sampling duration normalized to the 23 fitting compounds. t Weight %, determined from the average CMB coefficients, normalized to NMOC. The sum of the seven souree categories is assumed=61%, which is the sum of the contributions from these categories to the emission inventory.
Wintertime source-reconciliation largest source of the measured compounds accounting for 35.2% of the organics measured, followed by petroleum refineries (l&O%), gasoline vapor (8.4%), vapor degreasing (2.4%), wastewater treatment (2.3%) architectural coatings (l.S%), graphic arts (1.3%) and dry cleaning(OS%). A total of 30.4% of the measured organics were not explained by the eight source categories modeled. The average predictions, adjusted for the contribution of non-fitting compounds in each of the source categories, were generally very consistent with the region’s emission inventory. 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 (O’Shea and Scheff, 1988). It is also useful to note that the method does not overestimate emissions from modeled sources by misallocating residual mass to modeled sources. While the basis of the calculations are somewhat different, the ‘other’ category in the emission inventory accounted for 39.0% of the total emission and the average residual mass from the CMB calculations accounted for 32.7%. We believe, therefore, that the residual mass source category represents actual sources of VOC and has physical meaning. (This observation is also consistent with previous work with both particulate matter and organics (Scheff and Wadden, 1986).) This study demonstrates that the CMB can be applied to ambient air concentrations of organic compounds and be used to evaluate and validate an area’s emission inventory.
919
of ambient organics
Protection Agency (1987) Illinois Reasonable Further Progress Report for 1986: Ozone and Carbon Monoxide. Department of Air Pollution Control
Illinois Environmental
Pub. No. IEPA/APC/187-015, Springfield, IL. Kowalczyk G. S., Gordon G. E. and Rheingrover S. W. (1982) Identification of atmospheric particulate sources in Washington, D.C., using chemical element balances. Enuir. Sci. Technol. 16, 79-90.
Krost K. J., Pellizzari E. D., Walburn S. G. and Hubbard S. A. (1982) Collection and analysis of hazardous organic emissions. Analyt. Chem. 54, 81&817. Leone R. M., Davis E. W. and Jones A. D. (1987) Updating nontraditional VOC source inventories. Pauer 87-58.2. Presented at the 80th Annual Meeting of the Air Pollution Control Association, New York, 21-26 June. Mayrsohn H. and Crabtree J. H. (1976) Source reconciliation of atmospheric hydrocarbons. Atmospheric Environment 10, 137-143. McClenny W. A., Pleil J. D., Holdren M. W. and Smith R. N. (1984) Automated cryogenic preconcentration and gas chromatographic determination of volatile organic compounds in air. Analyt. Chem. 56, 2947-2951. Metropolitan Sanitary District of Greater Chicago (1986) Volatile Organic Compounds at the Calumet Treatment Facility. Chicago, IL.
Miller M. S., Friedlander S. K. and Hidy G. M. (1972) A chemical element balance for the Pasadena aerosol. J. Colloid Interface Sci. 39, 165-176.
Namkung E. and Rittmann B. E. (1987) Estimating volatile organic compound emissions from publicly owned treatment works. J. Wat. Pollut. Control Fed. 59, 67&678. Nelson P. F., Quigley S. M. and Smith M. Y. (1983) Sources of atmospheric hydrocarbons in Sydney: a quantitative determination using a source reconciliation technique. Atmospheric Environment 17, 439449.
O’Shea W. J. and Scheff P. A. (1988) Validation of a source annortionment model for volatile hvdrocarbons. J. Air PoBut. Control Ass. 38, 1020-1026.
_
Pleil J. D., Oliver K. D. and McClennv W. A. (19871 Enhanced performance of nation dryers in removing ‘water from air samples prior to gas chromatographic analysis. J. Air Pollut. Control Ass. 37, 244248.
Acknowledgements-This work was partially supported by the U.S. Environmental Protection Agency by the research grant authorization R811936-01-O and by the National Science Foundation by the grant authorization ECE8502106.
REFERENCES
Belsley D. A., Kun E. and Welsch R. E. (1980) Regression Diagnostics: identifying Influential Data and Sources of Collinearity. John Wiley, New York.
Dunovant V. S., Clark C. S., Que Hee S. S., Hertzberg V. S. and Trapp J. H. (1986) Volatile organics in the wastewater and airspaces of three wastewater treatment plants. J. Wat. Pollut. Control Fed. 58, 886-895.
Dzubay T. G., Stevens R. K., Gordon G. E., Olmez I., Sheffield A. E. and Courtney W. J. (1988) A composite receptor model applied to Philadelphia aerosol. Enoir. Sci. Technol. 22, 4652.
Feigley C. E. and Jeffries J. H. (1979) Analysis of processes affecting oxidant and precursors in the Los Angeles Reactive Pollutant Program. Atmospheric Environment 13, 1369-1384.
Flick E. W. (1985) Printing Ink Formulations. Noyes. Harkov R., Jenks J. and Ruggeri C. (1987) Volatile organic compounds in the ambient air near a large, regional sewage plant in New Jersey. Paper 87-95.1. Presented at the 80th annual meeting of the Air Pollution Control Association, New York, NY. ALE 23/5-8
Scheff P. A., Wadden R. A. and Allen R. J. (1984) Development and validation of a chemical element balance for Chicago. Enuir. Sci. Technol. 18, 923-931. SchetI P. A. and Wadden R. A. (1986) Predicting unidentified and secondary sources with chemical mass balance receptor models. In Trans. Receptor Methods for Source Apportionment: Real World Issues and Applications, pp. 78-93. Air Pollution Control Association, Pittsburgh, PA. Scheff P. A. and Klevs M. (1987) Source-receptor analysis of volatile hydrocarbons. J. Enuir. Enana. 113. 994-1005. Scheff P. A., Wadden R. A., Aronian P.&d Bater B. (1989) Source fingerprints for receptor modeling of volatile organics. J. Air Pollut. Control Ass. (in press). Sexton K. and Westberg H. (1983) Photochemical ozone formation from petroleum refinery emissions. Atmospheric Enuironment 17, 467475.
Sigsby J. E., Jr, Tejada S., Ray W., Lang J. M. and Duncan J. W. (1987) Volatile organic compound emissions from 46 in-use passenger cars. Enuir. Sci. Technol. 21, 466475. Storck W. (1987) Chlorinated solvent use hurt by federal rules. Chem. Eng. News p. 11, 28 Sept. U.S. Environmental Protection Agency (1980a) Publication
Rotogravure Printing-Background Information for Proposed Standards. EPA 450/3-80-03 1a. U.S. Environmental Protection Agency - _.(1980b) Perchloroethylene Dry Cleaners-Background Information for Proposed Standards. EPA-450/3-79-029a, August. U.S. Environmental Protection Agencv (1982) Control of Volatile Organic Compound Emissions from Earge Petroieum Dry Cleaners. EPA-450/3-82X109, Sept.
U.S. Environmental Protection Agency (1985) 4.9 Graphic
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PAUL F. ARONIAN et al.
Arts. (April 1981).In Compilation ofAir Pollutant Emission Factors. (Vol. 1) AP-42,4th ed., Sept. U.S. Environmental Protection Agency (1988) Air Emissions Species Manual: Volume I, Volatile Organic Compound
Species Projiles. EPA-450/2-88-003a, April. Wadden R. A., Uno I. and Wakamatsu S. (1986) Source discrimination of short-term hydrocarbon samples measwed aloft. Enuir. Sci. Technol. 20, 473-483.
Emiro~nt
Vat. 23, No. 5, pp. 91 l-920,
1989.
Printed in Great Britain.
WINTERTIME
SOURCE-RECONCILIATION ORGANICS
OF AMBIENT
PAUL F. ARONIAN,* PETER A. SCHEFF~ and RICHARD A. WADDENt *Environmental Engineering Coordinator, UOP, Inc., 25 E. Algonquin Road, Des Plaines, IL60017, U.S.A.and ~Environment~ and Occupational Health Sciences,University of Illinois at Chicago, School of Public Health, P.O. Box 6998, Chicago, IL 60680, U.S.A. (First received 13 May 1988 and
in final form 24 August 1988)
Abstract-The applitition of a Chemical Mass Balance air pollution model to ambient measurements of volatile organic compounds (VOC) is presented. Twenty-six air samples were collected at three sites in the Chicago metropolitan area (suburban, inner-city and industria1) and analyzed for the concentration of 23 compounds including alkanes, aromatics and chlorinated organics. The average daily temperatures on sampling days varied between - 15.0 and 4.4X. Consequently, the potential for significant photochemical reaction was not present. Each ambient sample was evaluated for the cdntribution from eight sources of VOC (vehicles, gasoline vapor, petroleum refineries, architectural coatings, graphic arts, vapor degreasing, dry cleaning and wastewater treatment). The contribution from sources not included in the model was also evaluated. Average contributions from vehicles ranged from 43 gg m - 3 at the suburban location to ‘70pgrnv3 at the inner-city location. Cont~butions from petroleum refineries appeared to reflect the distances from the sources to the monitoring sites with average contributions ranging from 28 to 33 FgrnT3 near the sources (inner-city and industrial receptors, respectively) to 10 pgrn-” away from the sources (suburban receptor). Contributions from architectural coatings, graphic arts, vapor degreasing, wastewater treatment and dry cleaning also suggested this effect of source-receptor geometry. Evaluation of the results shows that the siteaveraged coefficients for vehicles, gasoline vapor and all other unmodeled sources are in close agreement with the State’s emission inventory. Key word index: Air pollution model, receptor model, volatile organic compounds, non-methane organic compounds, emission inventory, vehicle emissions, gasoline vapor, petroleum refinery emissions, architectural coatings, printing, vapor degreasing, dry cleaning.
INTROiXJmION The Chemical Mass Balance (CMB) source-reconciliation model is a method to determine the contribution of each of the major sources of a pollutant to an ambient measurement of the compgsition bf the pollutant. The model is based on a set of mass balances around a group of compounds measured at a receptor site. The calculation requires that the composition of the pollutant be known at the points of emission as well as in the collected sample. The CMB model is typically applied to the major source categories within a given airshed (e.g. vehicle emissions, coal combustion and fugitive soil aerosols), and most applications use from around 7 to 30 tracer compounds to estimate the impacts of four to six sources. The CMB model has been applied to Total Suspended Particulate Matter (TSP) in a variety of locations (Miller et al., 1972; Kowalczyk et al., 1982; Scheff et al., 1984), inhaiable particulate matter < 10 pm aerodynamic diameter (IP) (Dzubay et al., 1988), total hydr~arbon (HC) concentration (THC, defined as the sum of a number of specified HCs) (Mayrsohn and Crabtree, 1976; Feigley and Jeffries, 1979; Nelson et al., 1983), and non-methane hydrocarbons (NMHC) (Wadden et
al., 1986; Scheff and Klevs, 1987; O’Shea and Scheff, 1988). The first of the three NMHC studies was conducted in Tokyo, Japan in which four source categories were fitted with 17 components, ethane to xylenes (Wadden et al.,. 1986). The data set included 192 ambient samples coilected from 300 to 1500 m aloft over Tokyo Bay. The study found that vehicles contributed 7.0%, gasoline vapor 10.5%, petroleum refineries 26.0%, paint solvents 27.2% and all other sources 29.3% of the NMHC sampled. Samples were taken at intervals during a series of flights over 2 July days. The calculated source coefficients were not greatly affected by the reactivity of the components used in the model. A validation of the refinery source coefficient found a high correlation between the CMB prediction and a trajectory analysis for refinery emissions adjusted for dispersion (r2 =0X99). The fractions for paint solvents, gasoline vapors and unidentified sources were also consistent with wind trajectory patterns. A second NMHC study was developed for a data set collected at two New Jersey receptor sites located 16 km apart (Scheff and Klevs, 1987). The study modeled the HC contributions from five sources using
911
912
PAUL F. ARWIAN et ul
24 fitting compounds (C,-C, alkanes along with aromatic solvents). The receptor sites represented intrinsic model validation, as one location was heavily influenced by industrial sources, while the other was a central-city location dominated by vehicle emissions. The results were consistent with the local source-receptor geometry, and the predictions for gasoline storage, paint operations and refineries were generally consistent with wind direction information. A third study, in the Chicago area, used eight HCs as fitting compounds (C,-C, alkanes and benzene) and three source categories; vehicle emissions, petroleum refineries, and gasoline vapor (O’Shea and Scheff, 1988). The sampling program included a one month summertime sampling period in which samples were collected over a 45min period during the noon hour. The results were also in excellent agreement with wind trajectories and source location. These studies demonstrate the usefulness of the method when applied to 0, precursors and toxic organics.
METHODS
Study design This study included a 4-month winter sampling program at three sites in the Chicago Metropolitan Area; a suburban background location approximately 80 km north of downtown Chicago (SUB), an innercity urban site at the University of Illinois at Chicago
located 1.6 km west of downtown Chicago (URB). and an industrial location on Chicago’s southeast side (IND). The sites are roughly located on a north south line and are 21 km and 55 km apart, from IND to URB to SUB, respectively. Four-hour samples (8:OO a.m. to 12:OOp.m.) were collected simultaneously at all three sites. A 20-h sample (1200 p.m. to 8:00 a.m.) was also collected at the central city location for each sampling session. This sampling strategy, therefore, allowed for the evaluation of the impact of major point sources of Non-Methane Organic Compounds (NMOC) at three widely varying receptor locations as well as the evaluation of the long-term average impact at the central location. For example, since all four refineries in the Chicago area are located in the southern portion of the study area, each receptor location will view the refineries with a different emission to collection transport time when the wind is from the south. A set of 23 VOCs was identified as fitting compounds for the CMB calculations and are listed in Table 1. These compounds were selected for a variety of reasons including: (1) they are ubiquitous and, because they are usually above minimum detectable levels in urban environments, 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
Table 1. Source profiles normalized to the fitting compounds (wt%) Vehicles
Gasoline Vapor
Refinery
3.12 18.18 6.12 3.16 7.11 1.17 9.02 7.19 3.20 2.89 1.88 1.31 1.15
0.00 0.00 1.66 0.00 0.00 18.47 41.12 20.24 8.51 2.89 1.48 1.15 0.36
4.81 0.13 21.29 0.80 0.11 4.14 17.60 16.85 7.32 1.22 4.33 3.60 1.66
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3.30
0.00 0.00
Benzene Toluene Ethylbenzene p-Xylene n-Xylene Chloroform l,l,l-trichloroethane Carbon tetrachloride Trichloroethylene Perchloroethylene Total THC* as % of NMOC
6.64 14.16 2.01 6.46 3.91 0.00 0.00 0.00 0.00 0.00 100.00 51.21
1.28 1.32 0.36 0.37 0.19 0.00 0.00 0.00 0.00 0.00 100.00 78.22
1.39 4.68 0.58 1.44 0.85 0.00 0.00 0.00 0.00 0.00 100.00 80.47
0.21 78.34 1.36 8.08 8.65 0.00 0.00 0.00 0.00 0.00 lOO.OiI 33.06
3.16 8.42 I .05 0.00 0.00 11.58 31.58 0.00 16.84 27.37
NO, (wt % of THC)
251.29
0.00
0.98
Compounds Ethane Ethylene Propane Propylene Acetylene i-Butane n-Butane i-Pentane n-Pentane 2-Methylpentane 3-Methylpentane n-Hexane 2,CDimethylpentane
* THC =sum of 23 organic fitting compounds.
Architectural Coating
0.00
Wastewater
0.00 0.00 0.00
0.00 0.00 0.00
0.00 0.00 0.00
0.00 0.00
100.00 9.50 0.00
Graphic arts 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Vapor degreasers 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Dry cleaning 0.00 0.00
0.00 0.00 0.00
0.00 0.00
0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00
0.00
0.00
0.00
0.00
0.00
93.09
0.00
0.00
0.00
6.91
0.00
0.00
0.00
0.00 0.00 0.00 0.00
O.Oil 0.00
0.00 0.00
0.00 100.00 11.90 0.00
0.00
0.00
55.06 0.00 33.35 11.59
0.00 0.00 0.00 0.00
100.00 90.17
100.00 100.00 63.00
0.00
0.00
Wintertime source-reconciliation of ambient organics sources studied, (4) the emission data for the compounds are generally consistent from study to study; (5) except for propylene (which is highly reactive) and ethane, acetylene and benzene (which have low reactivities), the HCs have similar OH radical reaction rate coefficients (k,,) (all within an order of magnitude of each other); and (6) the HC fitting compounds have been applied in CMB modeling studies with reasonable success in Japan, New Jersey and Chicago (Wadden et al., 1986; Scheff and Klevs, 1987; O’Shea and Scheff, 1988). In addition to the 23 organics, NO, was collected at the URB receptor location and included as a fitting compound. Eight source categories were selected for the CMB analysis. These are vehicles, fugitive gasoline vapors, petroleum refineries, architectural coatings, graphic arts (printing), waste-water treatment, vapor degreasing and dry cleaning. These sources were selected because they are known to emit significant quantities of NMOC in the Chicago area (IEPA, 1986) and are sources for which reasonable fingerprints could be developed (Scheff et a[., 1989). Environmental sampling Ambient air samples were collected in 6-e electropolished stainless steel canisters (Pleil et al., 1987) and Tenax trap samplers (Krost et al., 1982). A total of 28 4-h samples were collected at the three sites between 13 November 1986 and 8 February 1987. A total of 11 20-h samples were also collected at the URB site during the monitoring period. Each of the canister samples was analyzed for the C,-C, alkane and alkene fitting compounds and the Tenax trap samples were analyzed for the aromatic and chlorinated compounds. Each canister was filled with approximately 9 k of ambient air (1.5 atm. absolute pressure at the end of the sampling period). An all-stainless steel bellows pump (model MB-151, Metal Bellows Corp., Sharon, MA) and mass flow controller (Model R 028, O-100 cm3 min- ‘, Tylan Corporation, Carson, CA) set to deliver 37.75 mlmin-’ for the 4-h sample, or 7.5 ml min - 1 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 (Model N055T1, 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 mlmin-’ (approximately 38 mlmin-’ at - 760 mm Hg vacuum and decreased to 25 ml min- 1 at + 1.5 atm of canister pressure). All canisters were cleaned and evacuated prior to each sampling period. The Tenax samples were collected using low-flow personal sampiing pumps (model P-12SA, DuPont Corp., Wilmington, DE). Flow rates were adjusted to 0.042 emin-- ’ for the 4-h sample and 0.0083 8 min- ’ for the 20-h sample. This corresponds to 10 e of air being sampled for each collection period and was selected to insure no breakthrough for the compounds
913
studied (Krost et al., 1982). The sample cartridges were prepared by packing a 1Ocm x 1.5 cm inner diameter glass tube with 6cm of 40/60 mesh Tenax 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. The Tenax cartridges were stored in a He atmosphere to minimize contamination from ambient air. Oxides of nitrogen were measured at the URB location with a dual channel chemiluminescence analyzer (model 8440E, Monitor Labs Inc., San Diego, CA). The analyzer was calibrated using a 101 ppm standard of NO in N, with a flow dilution system {Matheson Gas Corp., East Rutherford, NJ) and a permeation tube system for NO, test atmosphere generation (models 8861DA and 8861P, Bendix Corp., Lewisburg, WV). The analyzer was calibrated bimonthly during the sampling period at 0.4 ppm and 0.2 ppm of NO and NO,, respectively. Sample analysts Volatile organics collected in the Tenax traps were analyzed by thermal desorption, cryogenic concentration in a liquid nitrogen-cooled Ni capillary trap, followed by high resolution gas chromatographymass spectrometry (GC/MS) (Krost et at., 1982). A field blank was included for each sampling period. The GC/MS system was calibrated to periluorotoluene. The gas canister samples were analyzed using a high resolution 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 (McClenny et aI., 1984). Moisture was removed from the gas sample with a nafion drying tube prior to cryogenic trapping (Pleil et al., 1987). The GC/FID system was calibrated with a certified propane in air standard.
The calculation of the source coefficients requires the multivariate least-squares fit of the composition data with a specified number of coefficients. The general equation is Y=Z/3+E
(1)
where Y is the vector of i molecular concentrations measured at a receptor site, Z is the pollution source molecular composition matrix of i compounds for each of the j sources modeled and E is the vector of i errors (the difference between the measured and predicted molecular compositions). The values of B represent the concentration of NMHC from the modeled sources that was measured at the receptor. In order to have statistical validity, the CMB model requires many more compounds than sources. Specific values of fl can be determined for each unique sample, and a dist~bution of samples over time will provide a distribution of values for each source coefficient.
PAUL F. ARONIANet al.
914
Weighted least squares solutions for /I 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 standard deviation of the measurements for the compounds collected with the canisters (through 2,4-dimethylpentane) were developed from an analysis of ten duplicate samples. The measurement error was calculated as the maximum difference between 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 represents the 95% confidence interval. The measurement errors for the compounds collected with the Tenax traps (benzene to perchloroethylene) were calculated as the standard deviation of repeated analyses of a laboratory calibration standard. Measurement errors are included in Table 2. Source projle development Profiles for the eight sources selected for this study were developed from a review of information including: measurements of the composition of source emissions; personal exposure to the emissions from NMOC sources; predictive models of the emissions froZn sources of NMOC; as well as industrial production and usage data. The source profiles used in Equation 1 are shown in Table 1. A detailed discussion of the profile development has been presented (Scheff et al., 1989). Note that the profiles are normalized to
100% of the 23 organics studied (THC). Therefore, the predicted coefficients (8) represent source contributions at the receptor sites for the specific 23 fitting compounds studied. Also listed in Table 1 is the sum of the 23 fitting VOCs as a per cent of the total NMOC emission. The vehicle emission profile was derived from a dynamometer study of 46 in-use vehicles using the Federal Test Procedure driving cycle (Sigsby et ul., 1987). The gasoline vapor source fingerprint was derived from HC vapors emitted from a winter blend/ no-lead gasoline (USEPA, 1988). The refinery source fingerprint was derived from a plume study of the emissions from a large modern petroleum refinery (Sexton and Westberg, 1983) and is based on an average of six ground level in-plume samples taken on different days 5 km downwind of the refinery and two samples taken at an altitude of 350 m between (I) O-3.5 km and (2) 8-11 km downwind of the refinery on the same day. The architectural coatings fingerprint is based on an extensive survey of product-type consumption in the New York City, State of New Jersey region (Leone et al., 1987), and profiles for composites of three product types (solvent-based coatings, thinning and cleanup solvents and water-based coatings) (USEPA, 1988). The fingerprint was calculated by applying the product consumption data to the compositions in each category and summing the contributions from all categories. Note that the fitting compounds only represent 33.06% of the total emission for this source. Non-fitting compounds make up most of the NMOC
Table 2. Average 4-h ambient concentrations at the three receptor locations (rgmw3) IND Compound
Measurement error
1.64
-.-
.-.-- --Average
URB Range
Ethane Ethylene Propane Propylene Acetvlene i-Butane n-Butane i-Pentane n-Pentane 2-Methylpentane 3-Methylpentane n-Hexane 2,bDimethylpentane
1.64 1.36 0.20 0.38 0.33 1.36 2.13 2.02 2.02 1.91 1.32 4.09
10.9 4.6 8.5 2.3 3.2 4.6 21.9 14.4 9.8 9.6 10.5 7.8 8.1
0.3-41.6 0.9-9.3 2.tS20.2 0.9-3.2 0.84.8 0.3-7.8 2.6-50.9 1.7-34.8 1.1-34.4 0.348.5 0.3-59.1
Benzene Toluene
0.45 0.26
Ethylbenzene p-Xylene o-Xylene
Average
SUB Range
Average
Range
0.345.5
7.9 4.0 5.9 2.5 4.4 2.8 14.4 12.1 11.4 8.5 8.5 7.1 9.7
0.3-35.9 0.3-10.0 1.1-10.2 o.w.7 0.3-11.1 0.3-4.4 2.7-24.0 1.8-34.7 1.2-50.3 0.6-39.6 0.3-43.6 0.9-17.4 0.3-51.2
11.4 2.7 5.1 2.1 3.5 2.6 13.2 9.8 6.2 6.2 6.0 7.3 3.6
0.3-70.3 0.3-6.9 0.3-I 5.0 0.7-3.7 0.3 -8.4 0.8-6.1 2.8-25.3 I &20.-l 1.2-20.8 0.7-21.7 0.6-31.7 2.1 -15.1 0.3S23.8
4.1 9.9
1.5-8.2 O&%.9
7.8 14.6
0.7-23.9 3.341.5
0.26
2.0
0.5-5.9
2.7
0.4-l 1.6
0.54 0.48
5.4 1.4
0.618.6 0.24.0
6.7 1.8
0.6-37.9 0.2-8.2
4.5 6.9 1.3 2.6 1.0
2.3-9.0 2.5-l 5.7 0.5-1.8 1.1-5.3 0.4-1.7
Chloroform I,l,l-Trichloroethane Carbon tetrachloride Trichloroethylene Perchloroethylene
0.89 0.57 0.73 0.45 0.26
2.7 5.2 0.6 0.9 2.3
0.8-5.7 2.8-13.8 0.0-1.5 0.3-2.7 4.2-0.0
4.6 6.7 0.6 1.3 2.6
2.1-9.1 3.4-13.6 [email protected] 0.0-4.8 0.2-7.5
3.1 4.2 0.4 0.8 0.9
1.2-8.8 2.2-6.8 o.cLO.9 0.1.-1.5 O.&l .8
NO, (cone)*
0.25
_
1.7-26.8
_
126.9
33.8-239.4
Wintertime source-reconciliation of ambient organics emissions from this category and include alcohols, ketones, esters, glycols and other chlorinated organics. The graphic arts fingerprint was developed in a similar way. Individual fingerprints for lithography (Flick, 1985), letterpress (Flick, 1985), flexographic (USEPA, 1988), and rotogravure (Flick, 1985; USEPA, 1980a) were combined with a nationwide estimate of the relative fraction of NMOC emissions due to each of these printing operations (USEPA, 1985) to form a composite fingerprint. Note that most of the emissions from printing (88.10%) are also from non-fitting compounds and include naphtha, petroleum distillate, and various alcohols, aldehydes and ketones. Three sources of chlorinated organic were studied. The wastewater treatment source fingerp~nt was developed as a composite of four individual source profiles. Two of the profiles were based on ambient air monitoring at municipal wastewater treatment plants in Cincinnati, OH (Dunovant et al., 1986) and Sayerville, NJ (Harkov et al., 1987). The other two studies’ were based on inlluent wastewater concentrations of volatile compounds at treatment plants in Chicago and a chemical fate model of removal by volatilization, adsorption and biodegradation (Namkung and Rittmann, 1987; Metropolitan Sanitary District of Greater Chicago, 1986). The fingerprint for vapor degreasing is based on a 1985 nationwide chlorinated solvent consumption estimate of 228,800 Mg for this application; and the individual fractions attributed to trichloroethylene, l,l,l-trichloroethane, perchloroethylene and methylene chloride (from the Halogenated Solvents Industry Alliance, Storck, 1987). Note that this fingerprint is only representative of vapor degreasing since no contributions were included from maintenance and manufacturing cold cleaning. The fingerprint for dry cleaners is based on total national solvent usage by perchloroethylene cleaners (55,000 Mga-’ estimated for 1984, USEPA, 1980b) and petroleum based cleaners (31,000 Mga-‘, USEPA, 1982). The petroleum solvents used are composed of Cs to C,, HCs and do not significantly contribute to the fitting compounds.
RESULTS
Table 2 lists 4-h average (8:OOa.m. to 1200 p-m.) concentrations of the 23 compounds used in the CMB model fitting procedure for each sampling location. The average is based only on those samples where results from the Tenax and canister samplers are complete. The CMB source coefficients for each complete sample are shown in Table 3. The coefhcients in this table were calculated from source profiles normalized to the sum of the 23 organic fitting compounds. Defining the total HC concentration (THC) as the sum of the 23 fitting compounds, the source coefficients in Table 3 show the concentration of the THC from the eight sources measured at the receptor. The unex-
915
plained fraction shown in the table is defined as the difference between measured THC and the sum of the source coefficients. In this way, the ‘other’ category represents contributions to the THC from all sources which were not included in the model. As shown in Table 3, the other category was positive for 24 of 26 samples. Due to collinearity between certain groups of sources, it was usually not possible to predict positive values for all eight sources simultaneously in the model. When this occurred, the coefficients were recalculated using a procedure discussed below. Sources that could not be resolved on certain days are indicated by zero values in the table. Note the procedure does allow for occasional negative values in the unassigned HC fraction. Coefficients averaged by receptor location and sample duration are shown in Table 4. Other statistics included in the table are the minimum and maximum coefficients and the standard deviation of the daily variation.
DISCUSSION
Ambient hydrocarbon measurements
Mean values of the concentration of the 23 organics are shown in Table 2. Generally, mean VOC concentrations were slightly lower at the suburban site than at the inner-city 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 inner-city site was 14.58 pg m- 3 whereas the mean concentration at the suburban site was 6.92 pg m- 3. The ~rchlor~thylene con~ntrations were also greater at the inner-city and industrial sites than at the suburban site (2.59 and 2.26, compared to 0.86 pg rnm3, respectively). The table also shows that most of the aliphatic HCs had large concentration ranges. This reflects the variability in the source cont~butions from vehicles, gasoline vapors, and refineries, the major sources of these HCs (see Table 1). Note that the average daily temperatures varied between - 15 and 4.4”C, consequently, the potential for significant ambient reaction, which might lead to distortion of source fingerprints, was not present. CM B source coefficients
Equation 1 was solved for each of 26 complete samples (where a complete sample is defined as a valid result from both the Tenax and canister sampling systems). The results are shown in Table 3. Regression diagnostics of the CMB model with all eight sources in the equation identified two problems with collinearity, For a typical run, two of the eight eigenvectors had condition indices > 10. (Condition index is a measure of collinearity and a value > 10 is an indication of a major collinearity, Belsley, 1980.) An examination of the variance proportions and source profile matrix shows the cause of the collinearities. (The variance proportions show for each variable, the proportion of
0.00 9.39 1.17 0.26 0.68 0.40 0.62
18.64 16.22 29.38 19.03
1.42 1.11 2.07 1.42
IND URB URB SUB
2.82 5.32 9.05 5.66
0.00 11.29 0.31 0.31 3.00 3.82 1.04
4.46
0.52 0.00 0.00
0.00 0.00 1.49 0.00 0.00 1.08
94.39 150.02 68.31 36.13 32.2s 55.17 34.37
4.20 5.51 10.60 3.77 -3.14 SO.23 6.10 10.45 3.42 6.16 3.93
0.00 0.09 0.78 0.00 1.41 2.35 1.25 0.51 0.38 0.40 0.98 1.92 0.24 0.28 0.08 0.32 0.11
1.46 1.79 6.24 1.11
1.98 2.46 1.34 3.42 3.24 7.62 2.00 1.48 1.40 2.28 1.72
3.40 6.43
235.57 48.19 278.31 184.45 - 25.85 18.12 8.17 34.07 16.21 31.85 1.04 2.20 1.09 0.62
91.22
41.75
111.87 80.61
137.17 184.32
64.14 77.57 284.88 102.14
453.51 261.37 445.98 325.80
316.73 190.90 132.25
109.32 102.15
29.09 5.10 95.73 95.84 58.06
0.35 0.55 1.91 0.42 0.15
THC
2.78 3.37 7.35 2.24 3.10 7.38 10.27 6.45 3.43
Other
DCL
VDG
* 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, THC = sum of fitting compounds.
12.65 12.40 3.69 0.45 1.02 1.69 1.37
58.80 7.01 42.57
2-8-87 2-8-87 2-8-87 2-8-87
2-6-87 2-6-87 2-6-87
3.s9
13.91 51.05
1.50
9.69 20.56 18.31 42.20 10.58
3.80 2.25 2.94 3.54 7.95 1.65
4 4 20 4 4 4 20 4
URB IND URB SUB
1-29-87
1.03
4
SUB
1-24-87
46.93 17.20
20 4
URB SUB
4.32 7.99
1.17 0.23
0.00
40.71 57.68
4 4
IND URB
3.32 37.99 118.00 26.69 49.67 70.67 51.33 49.23
0.68 1.80 5.40 0.44
0.00 0.00 1.65
35.16 8.06 103.91 17.54
49.91 9.71 39.24 22.30 21.44 5.09 1.62 3.45 4.32
4 4 20 4
IND URB URB SUB
96.26 132.84 110.41 100.93
6.17 9.53 5.65 3.09
12.05 14.10 1.98 0.33
15.93 7.09 40.43 32.53
4 4 20 4
12-17-86 12-17-86 12-17-86 12-17-86 12-21-86 12-21-86 12-21-86 12-21-86 12-26-86 12-26-86 1-21-87 I-21-87
6.39 0.00 0.00 9.72 LO.89 1.65 0.42
7.50 2.07 1.92
0.49 0.00 0.00
54.63 48.70 11.31 69.40 26.26 0.00 0.00
1.09 6.19 6.03
1.42 2.96
41.32 47.14 141.64 35.44 51.67
2.45 3.14
1.59 3.64
21.84 25.77
8.47 10.47
4 20 4 20 4
GA
VEH
WAS
AC
URB URB URB URB SUB IND URB URB SUB
1l-22-86 1l-22-86 12-I3-86 12-13-86 12-13-86
Location
REF
Date
GV
Duration (h)
Table 3. Best-fit source coefficients normalized to THC* (~8 m- ‘)
Wintertime sours-~conciliation
917
of ambient organics
Table 4. Source coefficients averaged by receptor location and duration* (@ m - ‘)
Location Industrial (MD) Average Minimum Maximum Standard deviation Inner-city (URB)_ Average Minimum Maximum Standard deviation Average Minimum Maximum Standard deviation Suburban (SUB) Average Minimum Maximum Standard deviation
Duration (b)
No. of cases
GV
REF
AC
WAS
VEH
GA
VJIG
DCL
Other
4
5
20.1 0.4 50.0 18.4
33.3 2.8 69.4 25.1
2.5 0.0 12.0 5.3
3.2 0,7 6.2 2.2
45.3 3.3 96.3 36.3
2.0 0.0 9.7 4.3
3.4 1.5 7.4 2.4
0.7 0.0 1.4 0.6
46.6 -25.8 235.6 106.8
4
7
4.8 1.0 9.7 3.8
27.8 5.3 57.7 20.8
3.5 0.0 14.1 4.9
4.8 1.1 9.5 3.4
70.2 16.2 141.6 48.6
3.4 0.0 10.9 3.9
4.8 1.4 10.3 3.3
1.0 0.1 2.3 I.0
32.9
16.0 1.6 40.4 16.8
39.5 0.0 103.9 34.1
3.2 0.0 11.3 3.8
4.3 2.1 7.9 2.2
57.0 7.0 118.0 41.7
2.3 0.0 9.4 3.3
4.3 2.0 7.6 2.4
0.9 0.3 1.9 0.6
27::: 100:s
10.5 I.4 32.5 12.0
10.3 0.0 17.5 6.2
0.2 0.0 1.0 0.4
1.7 0.4 3.1 0.8
43.4 13.9 100.9 29.3
0.3 0.0 1.2 0.4
2.2 1.1 3.4 0.9
0.3 0.0 0.6 0.2
43.2 3.4 184.4 65.4
20
4
7
7
3.4 95.7 32.0 64.0
* 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, THC = sum of fitting compounds.
total variance of the estimated regression coefficient that is associated with each of the principal compo-
nents. A collinearity is suggested when more than one variable has a high proportion on a principal component having a high condition index. Two or more proportions > 0.5 suggest a problem, while two or more loadings > 0.9 is a reason for concern.) The largest condition index (representing the smallest eigenvalue) is associated with large variance proportions (> 0.99) for contributions from architectur~i coatings and graphic arts. This collinearity can be seen in Table 1 as these two sources are primarily composed of toluene. Because of this collinearity, it is generally not possible to simultaneously solve for both of the toluene sources. This problem was handled by solving Equation 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 in Table 3, the sum of the two should be interpreted as the combined impact of printing and architectural coating solvents. The exact split between the two sources is less certain. The second highest condition index is associated with a very large variance proportion for wastewater treatment (> 0.99) and large proportions (> 0.85) for vapor degreasing and dry cleaning. This collinearity can be seen in Table 1 as these are the three 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 result in Table 3; the sum of the three sources represents their combined impact. The model was usually able to resolve the vehicle, gas vapor and
refinery sources without problems of negative coefficients. Table 4 lists the average CMB source coefficient by
site and sample duration. Large differences were found between sites. For example, the average gasoline vapor source coefficients are 20.1 for the industrial, 10.5 for the suburban and 4.8 pg m- ’ for inner-city (4 h) locations. The high value for the industrial site is consistent with the fact that the site is close to bulk gasoline storage facilities. The suburban receptor site is located next to the parking lot of a fire station with a gasoline pump and underground storage tank. The relatively high value for gas vapor suggests that the site is influenced by the department’s refueling operation. The average refinery source coefficients are 33.3 for the industrial, 27.8 for the inner-city, and 10.3 pg m - 3 for suburban receptors. This is also consistent with source-receptor geometry as all of the region’s refinery point sources are located in the southern suburbs closest to the industrial site and furthest from the study’s suburban site which is located 55 km north of the city. Table 4 further 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 the site (and much closer to the inner-city and industrial sites), lower values for these sources are expected. It is also interesting to note that the suburban location had the highest % contribution from unexplained sources (38.5% of the average THC measured at the site).
918
PAUL F. ARONIAN et al.
In addition, Table 4 shows that the 4-h average vehicle contribution at the inner-city site (70.2 pg m -3) is larger than the 2Bh average at the same site (57.0 pg m- ‘1. This difference is consistent with the fact that the major morning drive-time rush hour is contained in the 4-h period and not present in the 20-h sample. ~o~pa~~son of CMB inventory data
source ~o~~c~en~s to omission
A comparison of average source coefficients to emission inventory data (IEPA, 1987) for Chicago is shown in Table 5. The emission inventory data in the table is expressed as kg day-’ and weight per cent of the total VOC emission and is representative of a typical summer day. The inventory includes 61 VOC emission source types (800,509 kgday-I). Note that the inventory does not include specific halogenated compounds (including l,l,l-trichloroethane and freon) and this will affect the comparison for degreasers as this source contains a significant mass fraction of l,l,l-trich~oroethane. Generally, the emission inventory results are in reasonable agreement with the average CMB coefficients. For the case with the CMB coefficients normalized to the THC, the inventory estimates of vehicle emission, gasoline vapor and vapor degreasers are very close to the CMB estimates. In contrast, the average graphic arts contribution of 1.3% is much smaller than predicted by the emission inventory (9.8%). Note, however, that because the CMB estimate for graphic arts is based on a small fraction of the total emission (11.9%) it is under-represented in the results normalized to THC. This was also true for architectural coatings. Normalizing the CMB coefficients in terms of the total NMOC emission brings these predictions much closer together. Table 5 also shows the average CMB coefficients normalized to the total NMOC. The NMOC coeffic-
ients were calculated by dividing the average CMB coefficients by the THC as a fraction of NMOC (see Table 1). Since we do not have a % of NMOC factor for the ‘other’ sources category, the coefficients are expressed as a % of NMOC such that the emission inventory per cent of the seven categories in Table 5 (61%) equals the sum of the normalized CMB estimates. The comparison between the emission inventory as weight % and NMOC normalized CMB estimates for vehicles, gasoline vapor, architectural coatings, graphic arts, vapor degreasing and dry cleaning is very reasonable. The largest difference between the emission inventory and CMB predictions is seen with refinery We suspect this rest&s from contributions. underestimation of the emission inventory values. A previous study in the Chicago area indicated good agreement between calculated refinery coefficients and refinery contributions determined from wind trajectories and refinery capacity (with refinery capacity used as a surrogate for emission); but her agreement when estimates of emissions were substituted for capacity (O’Shea and Scheff, 1988). The inventory values also do not include the contributions from northeast Indiana refineries and other sources. It is encouraging to note that the emission inventory and CMB model’s estimate of ‘other’ sources are very close. This suggests that the calculations do not over-predict the modeled sources nor underpredict the sources not included in the model.
CONCLUSIONS
The Chemical Mass Balance source reconciliation model was applied to 26 ambient measurements of selected VOCs in the Chicago metropolitan area, and the contributions to these compounds From eight source categories were evaluated. Vehicles were the
Table 5. Comparison of emission inventory data to CMB source coefficients CMB Emission inventory Source Vehicles Gasoline vapor Petroleum refineries Architectural coatings Graphic arts Vapor degreasing Dry cleaning Other Total hydrocarbons
(kg day-‘) 286,155 61,179 10,749 44,230 78,268 25,078 758 312,092 800,509
(%I 33.5 7.6 1.3 5.5 9.8 3.1 0.1 39.0 100.0
THC* (pgm-? 54.0. 12.8 27.7 2.4 2.0 3.7 0.7 50.2 153.5
NM00 (%I
(%I
35.2 a.4 18.0 1.5 1.3 2.4 0.5 32.7 100.0
34.6 5.4 11.3 2.3 5.6 1.3 0.4
*Average of the CMB coefficients from the four combinations of site and sampling duration normalized to the 23 fitting compounds. t Weight %, determined from the average CMB coefficients, normalized to NMOC. The sum of the seven souree categories is assumed=61%, which is the sum of the contributions from these categories to the emission inventory.
Wintertime source-reconciliation largest source of the measured compounds accounting for 35.2% of the organics measured, followed by petroleum refineries (l&O%), gasoline vapor (8.4%), vapor degreasing (2.4%), wastewater treatment (2.3%) architectural coatings (l.S%), graphic arts (1.3%) and dry cleaning(OS%). A total of 30.4% of the measured organics were not explained by the eight source categories modeled. The average predictions, adjusted for the contribution of non-fitting compounds in each of the source categories, were generally very consistent with the region’s emission inventory. 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 (O’Shea and Scheff, 1988). It is also useful to note that the method does not overestimate emissions from modeled sources by misallocating residual mass to modeled sources. While the basis of the calculations are somewhat different, the ‘other’ category in the emission inventory accounted for 39.0% of the total emission and the average residual mass from the CMB calculations accounted for 32.7%. We believe, therefore, that the residual mass source category represents actual sources of VOC and has physical meaning. (This observation is also consistent with previous work with both particulate matter and organics (Scheff and Wadden, 1986).) This study demonstrates that the CMB can be applied to ambient air concentrations of organic compounds and be used to evaluate and validate an area’s emission inventory.
919
of ambient organics
Protection Agency (1987) Illinois Reasonable Further Progress Report for 1986: Ozone and Carbon Monoxide. Department of Air Pollution Control
Illinois Environmental
Pub. No. IEPA/APC/187-015, Springfield, IL. Kowalczyk G. S., Gordon G. E. and Rheingrover S. W. (1982) Identification of atmospheric particulate sources in Washington, D.C., using chemical element balances. Enuir. Sci. Technol. 16, 79-90.
Krost K. J., Pellizzari E. D., Walburn S. G. and Hubbard S. A. (1982) Collection and analysis of hazardous organic emissions. Analyt. Chem. 54, 81&817. Leone R. M., Davis E. W. and Jones A. D. (1987) Updating nontraditional VOC source inventories. Pauer 87-58.2. Presented at the 80th Annual Meeting of the Air Pollution Control Association, New York, 21-26 June. Mayrsohn H. and Crabtree J. H. (1976) Source reconciliation of atmospheric hydrocarbons. Atmospheric Environment 10, 137-143. McClenny W. A., Pleil J. D., Holdren M. W. and Smith R. N. (1984) Automated cryogenic preconcentration and gas chromatographic determination of volatile organic compounds in air. Analyt. Chem. 56, 2947-2951. Metropolitan Sanitary District of Greater Chicago (1986) Volatile Organic Compounds at the Calumet Treatment Facility. Chicago, IL.
Miller M. S., Friedlander S. K. and Hidy G. M. (1972) A chemical element balance for the Pasadena aerosol. J. Colloid Interface Sci. 39, 165-176.
Namkung E. and Rittmann B. E. (1987) Estimating volatile organic compound emissions from publicly owned treatment works. J. Wat. Pollut. Control Fed. 59, 67&678. Nelson P. F., Quigley S. M. and Smith M. Y. (1983) Sources of atmospheric hydrocarbons in Sydney: a quantitative determination using a source reconciliation technique. Atmospheric Environment 17, 439449.
O’Shea W. J. and Scheff P. A. (1988) Validation of a source annortionment model for volatile hvdrocarbons. J. Air PoBut. Control Ass. 38, 1020-1026.
_
Pleil J. D., Oliver K. D. and McClennv W. A. (19871 Enhanced performance of nation dryers in removing ‘water from air samples prior to gas chromatographic analysis. J. Air Pollut. Control Ass. 37, 244248.
Acknowledgements-This work was partially supported by the U.S. Environmental Protection Agency by the research grant authorization R811936-01-O and by the National Science Foundation by the grant authorization ECE8502106.
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