Environ. Sci. Technol. 1986, 20, 473-483
(4) Dixon, R. N.; Taylor, C. J. SEM 1979, I , 361-366. (5) Moza, A. K.; Austin, L. G. Fuel 1983, 62, 1468-1473. (6) Bishop, J. K. B.; Biscaye, P. E. Earth Planet. Sci. Lett. 1982, 58, 265-275. (7) Fritz, G. Tracor Northern Inc., 1982, TN-1912. (8) Kelly, J. F.; Lee, R. J.; Lentz, S. SEM 1980, I , 311-322. (9) Henoc, J.; Maurice, F. In “Microanalysis and Scanning Electron Microscopy;” Maurice, F.; Meny, L.; Tixier, R., Eds.; Les Editions de Physique: 1979; p p 281-317. (10) Armstrong, J. T.; Buseck, P. R. Anal. Chem. 1975, 47, 2178-2192. (11) Raeymaekers, B.; Van Espen, P.; Adams, F. Mikrochim. Acta 1984, 11, 437-454. (12) Massart, D. L.; Kaufman, L. “The Interpretation of Analytical Data by the Use of Cluster Analysis”; Wiley: New York, 1983. (13) Van Espen, P. Anal. Chim. Acta 1984,165, 31-49. (14) Anderberg, M. R. “Cluster Analysis for Application”; Academic Press: New York, 1973.
(15) Van Der Plas, L.; Tobi, A. C. Am. J . Sei. 1965,263,87-90. (16) Salomons, W. J. Sed. Petrol. 1975, 45, 440-449. (17) Eisma, D.; Bernard, P.; Boon, J. J.; Van Grieken, R.; Kalf, J.; Mook, W. G. Mitt. Geo1.-Palaeontol. Inst. Uniu. Hamburg 1984,58,397-412. (18) Eisma, D.; Kalf, J.; Veenhuis, M. Neth. J . Sea Res. 1980, 14 (2), 172-191. (19) Eisma, D.; Boon, J. J.; Groenewegen, R.; Ittekkot, V.; Kalf, J.; Mode, W. G. Mitt. Geo1.-Palaeontol. Inst. Univ. Hamburg 1983,55, 295-314.
Received for review December 26, 1984. Revised manuscript received November 4,1985. Accepted December 24,1985. P. B. is indebted to the “Instituut tot Aanmoediging van het wetenschappelijk Onderzoek i n Nijverheid en Landbouw” (I.W. O.N.L.)for financial support. This study was partially financed by the Belgian Ministry of Science Policy under Contracts 8085/10 and 84-89/69.
Source Discrimination of Short-Term Hydrocarbon Samples Measured Aloft Richard A. Wadden”
School of Public Health, University of Illinois, Chicago, Illinois 60680 Itsushi Uno and Shinji Wakamatsu
National Institute for Environmental Studies, P.O. Yatabe, Tsukuba, Ibaraki 305,Japan
rn Weighted least-squares fitting of 17 hydrocarbons was used to estimate ambient contributions from four source categories. The data set consisted of 192 samples collected from 300 to 1500 m over Tokyo, July 16-17,1981. Six runs (flights), 1-1.5 h long, spaced throughout each day, constituted chemical “snapshots” of the urban air. Vehicles contributed 7.0%, gasoline vapor 10.5%, petroleum refinery 26.0%, paint solvents 27.2%, and unexplained sources 29.3% of the total hydrocarbon concentration (based on the 17 components measured and all samples). These coefficients are only representative of the days and conditions sampled and should not be interpreted as annual emission fractions. On a run-averaged basis the correlation between refinery emissions per hour, adjusted for dispersion, and ambient concentrations of total hydrocarbon attributable to petroleum refineries was r2 = 0.899, indicating that the refinery profile was sufficiently unique to be sensed up to 70 km. The fractions for paint solvents, gasoline vapor, and unidentified sources were also consistent with wind trajectory observations. Air-quality models that link source emissions to environmental concentrations are important tools for controlling air pollution. One type of air quality model is the chemical element balance (CEB) or source-reconciliation model. This is a method to determine the relative air pollution contribution of each of the major sources of a categorical pollutant (such as total suspended particulate matter, TSP, or non-methane hydrocarbons,NMHC) from ambient measurements of the composition of the categorical pollutant at one or more receptor sites. The method requires that the composition of the categorical pollutant be known in the emissions of each source category, i.e., source fingerprints, and that the ratio of the concentration of each component to the categorical pollutant concentration be the same in the source emissions and at the receptor point. 0013-936X/86/0920-0473$01.50/0
The calculation procedure is a multivariate least-squares fit of the composition data with a specified number of source coefficients. The general equation is Y=Zp+E (1) where Y is the vector of i elemental or molecular compositions, Z is the pollution source elemental or molecular composition matrix of i components for each of j + 1 sources (including an intercept term vector of ones), p is the vector of j 1source weights or coefficients with one intercept term, and E is the vector of i errors (the difference between the measured and predicted elemental or molecular compositions). The values of p represent the fraction of the categorical component contributed by each emission source type, e.g., automobiles or oil-burning power plants. For statistical validity it is desirable to have many more components than sources. Specific values of p can be determined for each receptor sample. And a distribution of samples over a period of time will provide a distribution of values for each source coefficient. This model has been applied for TSP in a variety of locations (1-4). The method has also been used in somewhat simplified form to evaluate total hydrocarbon concentrations (THC) (5-7). (THC was defined as the sum of a number of specified “unreactive” hydracarbons in the atmosphere.) The composition of specified hydrocarbons were the elements of Y in eq 1,and corresponding source compositions were used for the values of Z. A useful approach for solving eq 1,at least for TSP, has been the weighted least-squares solution p = (Z’ C-lZ)-l(Z’ C-lY) (2)
+
where the prime and superscript minus one indicate transpose and inverse operations, respectively; is a diagonal matrix of the variances for the measurement vector Y. One necessary physical limitation is that, except for the intercept term, none of the p’s can be less than zero.
0 1986 American Chemical Society
Environ. Sci. Technol., Vol. 20, No. 5, 1986
473
Table I. Hydrocarbon Source Fingerprints in the Tokyo Region ( 1 1 ) component ethane ethylene acetylene propane propylene isobutane n-butane isopentane n-pentane 2-methylpentane 3-methylpentane n-hexane benzene to1u en e ethylbenzene p,m-xylene o-xylene
vehicle exhaust
gasoline vapor
paint solvent
petrochemical plant
3.1 12.0 5.5 6.1 5.6 4.6 15.0 10.4 6.0 3.8 2.3 4.4 5.6 10.5 1.6 2.4 1.1
0.0 0.0 0.0 1.8 0.0 15.2 19.1 35.8 13.1 6.3 3.1 3.2 0.9 1.0 0.1 0.3 0.1
3.0 2.9 1.6 20.2 2.8 6.7 16.1 14.4 6.9 3.7 2.1 5.0 4.3 7.1 1.0 1.3 0.9
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 25.7 32.5 30.3 11.5
3.0 50.0 1.8 4.3 3.9 2.6 5.5 4.0 2.8 1.5
100.0
100.0
100.0
100.0
100.0
The objective of this study was to apply the weighted least-squares source-reconciliation technique to a hydrocarbon data set consisting of 192 samples collected aloft over Tokyo, July 16-17,1981, Eighteen components were determined for each sample, most of which were collected at altitudes between 350 and 700 m. Data S e t
The hydrocarbon samples analyzed here were obtained as part of a series of summertime aircraft surveys of photochemical pollutants carried out by the National Institute for Environmental Studies (NIES) since 1978. These airborne surveys were conducted in order to develop a better understanding of photochemical smog formation over Tokyo. More detailed descriptions of this project have been previously reported (8-10). The survey was performed by using two instrumented aircraft and a minicomputer system at the airport. Six runs (flights) were spaced throughout each day starting with sun-up and continuing through midnight. In general, most runs followed nearly the same flight pattern, and consequently run-averaged compositions constitute a chemical “snapshot” of the air over the Tokyo Metropolitan Area. Most of the observation data were collected between altitudes of 350 and 700 m although occasional samples were collected up to 1500 m. Air samples for non-methane hydrocarbon determination (NMHC), and gas chromatography (GC), were collected in the cabin in a Pyrex 1-L glass vessel with two Teflon valves. The sample was pressurized during the flight to about 1.4 atm with a Teflon bellows pump and, after the aircraft landed, connected to the analyzer (Shimadzu, Inc., Model HCM-3AS) to measure NHMC concentration, Individual hydrocarbons were subsequently measured by using a GC analyzer (Shimadzu, Inc., Model GC-4CM). Hydrocarbons were analyzed with a 99% confidence interval corresponding to a precision of f 5 % of the mean value of repeated analyses for Cz-C5 and &lo% of the mean for C6 and heavier. These values correspond to a/% of 0.019 and 0.039 for C2-Cb and CC’, respectively, and were used to determine the elements of in eq 2. The vertical wind profile was also monitored at about 20 sites by using pilot-balloon observations. Wind data were collected at each 100-m interval up to 3000 m. These were integrated at the airport to determine the areal wind profile for subsequent trajectory estimation. 474
weight % petroleum refinery
Envlron. Sci. Technol., Vol. 20,No. 5, 1986
1.1
4.3 3.4 7.7 1.6 1.4 1.1
Source Characterization
Initially five source categories were selected: vehicle exhaust, gasoline vapor, petroleum refineries, petrochemical plants, and paint solvents. The source compositions are given in Table I(11). These sources were chosen because fingerprint estimates for the Japanese setting could be developed for their description. It was recognized that some major source categories were not included (notably printing solvents and other solvents used in bonding, degreasing, dry cleaning and rubber production, and processing operations). Consequently, it was not expected that the calculated source coefficients should add up to 1.0; nor was this result desirable ( 4 , 12). The vehicle exhaust profile is based on extensive ambient monitoring in Kanagawa Prefecture, part of the study area (11). The data set consisted of 36 samples collected once per month from 1978 to 1980. This profile generally agreed with a simulated vehicle exhaust composition developed from 10-mode driving-cycle emission tests on 14 automobiles conforming to 1973-1978 Japanese emission standards (11). The major difference between the average ambient measurement and the simulated tailpipe composition (adjusted for a 20% diesel contribution) was in the contribution from propane (6.1% vs. 0.370, respectively) and butane (19.6% vs. 10.170,respectively). These differences were not unreasonable since the Japanese taxi fleet is LPG-fueled (LPG consists of propane and butanes) and this was not accounted for in the simulated fingerprint. (The difference is not a measure of fingerprint variation). Consequently, the ambient composition was chosen as being more representative of the actual vehicle mix. The petroleum refinery emission composition was an average of 36 ambient samples collected once per month from 1978 to 1980 in the vicinity of a refinery in Kawasaki City (11). This fingerprint was within a factor of 3 for most components compared with one developed from downwind measurements near a U.S. refinery (13). The comparison was not unreasonable given the fact that all the Japanese measurements were not necessarily downwind, and the average may consequently contain some background contribution. The petrochemical fingerprint was similarly based on 36 monthly measurements near a polyethylene plant (11). The gasoline vapor profile is based on samples from the air space of gasoline storage tanks of three brands of regular, unleaded gasoline. This grade constitutes over
Table 11. Paint Solvent Compositions
component
composite analysis from usage data"
ethane ethylene acetylene propane propylene isobutane n-butane isopentane n-pentane 2-methylpentane 3-methylpentane n-hexane benzene toluene ethylbenzene p,m-xylene o-xylene
-
single ambient analyses, Kanagawa Prefecture" single ambient analyses, U.S. Kanagawa Prefecture" vehicle ship paint plant painting shop painting yard emissionsb
25.7 32.5 30.3 11.5
0.3 1.0 0.6 0.7 0.4 0.7 0.7 0.5 0.4 0.2 0.1 0.4 0.3 6.2 26.2 43.9 17.4
100.0
100.0
-
-
0.2 0.7 0.7 18.8 32.5 28.6 9.9
1.0 1.0 1.8 67.4 6.2 17.1 5.5
average of composite and U.S. paint plant 0.5 0.5 0.9 46.5 19.4 23.7 8.5
100.0
100.0
100.0
-
0.7 1.0 1.2 1.2 0.4 0.6 1.1 1.1 0.8
-
0.5
"Reference 11. bReference 14.
97 % of Japanese gasoline usage. Vapor compositions were determined at 20 and 30 OC, and the profile is an interpolated value at 25 OC, representative of typical summer temperatures (11). The paint solvent composition is a weighted value based on the usage amounts for all of Japan of the following: varnish; enamel; veneer, epoxy, urethane, polyester, anticorrosive and other paints; and the respective composition of each type of coating. The composite analysis is compared in Table I1 with single ambient measurements near a vehicle painting shop and a ship painting yard, both in Kanagawa Prefecture. Measurements near a U.S. paint plant are also included (14). The Japanese data reflect some disparity in toluene concentration, but comparison of the other components is not unreasonable given the nature of ambient sampling. The U S . data present a somewhat different profile, but all the fingerprints contain over 90% of the weight in the toluene and heavier fraction which is in contrast with profiles for the other source categories.
Emission Inventory The annual average emission inventory data from the Japanese Environmental Agency are shown in Table 111. These values reflect annual usage patterns, and all hydrocarbons, rather than only the 18 hydrocarbon components used for source characterizations. Consequently,the emission fractions are not directly compara6le with calculated source fractions which reflect diurnal variation and particular wind trajectories. Recognizing these limitations, the inventory values do provide a general point of reference for reviewing the model results.
Results All sample and source analyses were converted to weight fractions. Equation 2 was solved for each individual sample and for the average composition based on all the samples. Since m- and p-xylene were not differentiated in the source fingerprints, these compositions were combined, and only 17 components were used. Coefficients were also calculated from the average composition by run. Individual samples were only representative of a limited air
Table 111. Total Hydrocarbon Emission Inventory ( I 1 )
total hydrocarbon emission rate (1978 basis) mobile sources stationary sources petroleum refineries petrochemical plants paint solvents printing solvents other solvents all stationary sources all sources
Of Japan metric tonslyear %
Tokyo metropolitan area metric tonslyear %
722000
38.9
195000
31.6
162800 30 600 616500 99200 226600 1135700 1857700
8.8 1.6 33.2 5.3 2.2 61.1 100.0
56400 13 900 227800 47000 77600 422 700 617700
9.1 2.3 36.9 7.6 12.6 68.4 100.0
mass. But the series of 12 runs, each described by an average composition, was essentially a repeated sampling of the same space over Tokyo at different times through the 2 days. The only restriction on the solution of eq 2 was that coefficients had to be 10. If more than one acceptable solution was found for eq 2, the combinationof coefficients that gave maximum r2 was chosen. When all five sources were included, the vehicle source coefficient became negative. This occurred primarily because of the collinearity between the petrochemical and vehicle fingerprints. Because only two major petrochemical sources were identified, and vehicle contributions were expected to usually be present, we subsequently limited the sources to vehicle exhaust, gasoline vapor, petroleum refinery, and paint solvent. Table IV summarizes the calculated source coefficients based on the overall average and on the run averages. The mean values from the distribution of coefficients calculated from individual sample compositionsare also shown. The averaging procedure by run (weight percent average, Table IV) does not take into account variation in individual component compositions. Analysis by sample does take this variation into account, and the sample coefficient average is the mean of the distribution of p's calculated from individual samples. For most of the comparisons of Environ. Sci. Technol., Vol. 20, No. 5, 1986
475
h
h
0.1
m
h
h
h
0
"1
m I
1
v
v
v
m
m
2
v
2
v
m
m
0
m
0
h
0
0
3
0
4
a*
0
2
2
0
m
0 m
t0
0.1
2
"1
W t-
0
2
2
h
m
h
2
N
":
f
0
m
(9
N
2
h
0
2
m
0
2
2
W
t(D w
v
m
x
h
2
0
v
1
0
*
h
8
0.1
I
II c!
+g e:9 0 h
*
h
h
h
p*
m m
v
v
Y
8
v
m m
3
(D
3
8
8
(D
8 8
t-
8 8
9
m
t-
9
0
t-
(D W
m m
m 0,
t0.1
8
8
8
8
3
ri
tm
m t-
2
m
B
Environ. Sci. Technol., Vol. 20, No. 5, 1986
a
2
m (D
2 i p*
I
0
t-
0
m
s0.1 ***
1
0
v
B
-
0)
2 m
lr: t-
-*
8
Y
88
476
m m
m
1
0
t-
N
Table V. Comparison of Measured a n d Calculated Compositions i n Weight Percent Using 17 a n d 14 Components
component ethane ethylene acetylene propane propylene isobutane n-butane isopentane n-p en tan e 2-methylpentane 3-methylpentane n-hexane benzene toluene ethylbenzene p,m-xylene o-xylene
study average 14 11 components components measd (c) calcd measd ealcd
run 21 14 11 components components m e d (e) calcd m e a d calcd
3.6 (1.8) 2.4 (1.7) 2.9 (1.2) 7.7 (5.6) 0.41 (0.75) 3.1 (1.1) 5.7 (2.1) 6.4 (2.$ 5.0 (2.0) 3.0 (0.9) 1.5 (1.2) 5.8 (5.0) 7.9 (3.7) 25.2 (6.8) 6.6 (3.0) 8.6 (6.2) 4.1 (2.9)
8.1 11.8) 3.8 2.2 (0.6) 12.0 (11.4) 0.91 (0.86) 3.0 (1.0) 5.9 (1.6) 5.8 (1.6) 4.1 (1.6) 3.0 (0.6) 1.6 (0.1) 4.0 (0.9) 6.3 (1.8) 24.3 (8.1) 6.1 (1.9) 8.1 (1.7) 4.0 (0.9)
99.91
0.46 1.1 0.26 5.3 0.58 3.1 6.7 7.7 3.0 1.4 0.50 1.4 1.1 9.1 8.1 8.2 2.9 61.50
2.4
1.3
7.7 0.41 3.1 5.7 6.4
5.6 0.49 2.9 1.3 7.8 3.0 1.1 0.11 1.3
5.0
3.0 1.5 5.8
9.8 8.8 8.4 2.7
25.2 6.6
8.6 4.1 85.51
60.66 100.07
no. of samples in av 192
P
ii..ii
1.6 1.8 3.8 1.1 8.1 12.0 0.97 1.6 3.0 3.1 5.9 7.0 5.8 6.3 4.7 3.2 3.0 1.9 1.6 1.3 4.0 2.4 2.2 9.0 24.3 1.9 6.1 1.6 8.7 3.3 4.0 69.4
87.87
0.799
measd ( r ) calcd
5.0 (0.9) 1.3 (0.9) 4.2 (0.8) 8.0 9.5 (1.0) 0.02 (0.08) 1.3 2.9 3.7 (0.5) 7.4 6.6 (1.2) 5.8 (1.4) 6.6 4.4 (0.8) 3.1 1.6 2.7 (0.7) 0.79 1.2 (0.2) 2.1 3.1 (1.4) 11.1(3.2) 9.5 25.4 (4.4) 8.1 6.0 (2.3) 1.1 6.1 (3.3) 2.5 (1.5) 3.0 1.8
63.89 99.82
23 0.584
run 25 14 11 components cohponents measd
calcd
1.3
1.89
9.5 0.02 3.7 6.6 5.8 4.4 2.7 1.2 3.1
6.0 0.02 2.9
0 0.78 0 5.3 0.02 3.0 1.4 8.5 2.9
0.88 0
0.93 0.51 8.4 25.4 7.2 6.0 6.9 6.1 2.5 1.9 54.6R 78.92
1.8 8.5 2.9 0.79
0 0.95 8.8 7.3 7.0 1.8 55.65
11
0.582
0.146
0.602
0.813
@’s calculated from the two averaging techniques the re-
sults are not numerically different (i.e., Ag
