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Receptor modeling of the fine aerosol at a residential Los Angeles site
Atmospheric Environment Vol. 26B, No. 4, pp. 473 481, 1992.
0957 1272/92 $5.00+0.00 (f'2 1992 Pergamon Press Ltd
Printed in Great Britain.
R E C E P T O R M O D E L I N G OF THE FINE AEROSOL AT A RESIDENTIAL LOS ANGELES SITE SHERYL H. EHRMAN a n d SOTIRIS E. PRATSINIS* Department of Chemical Engineering, Center for Aerosol Processes, University of Cincinnati, Cincinnati, OH 45221, U.S.A.
and JAMES R. YOUNG Southern California Edison, 2244 Walnut Grove Avenue, Rosemead, CA 91770, U.S.A. (First received 5 February 1992 and in final form 20 April 1992)
Abstract--Receptor modeling on ambient aerosol and air quality data collected at Duarte, CA (a residential site near Los Angeles), in 1983 and 1987 1988 was carried out. A significant change in the ambient concentrations of SO 2 , A1, Si, Mn, Fe, Pb, Br, volatile and organic carbon and fine particle (FP) mass took place from 1983 to 1987-1988. A drastic reduction (~ 80%) of the ambient Pb and Br concentrations took place as the lead content and the usage of leaded gasoline decreased in the Los Angeles Basin during that period. A day-of-the-week analysis indicated that both crustal (Si, Ca, Fe) and transportation (Pb, CO, organic carbon and black carbon) related pollutants exhibit significantly different concentrations between weekdays and weekends of 1987 1988. This indicates that loadings of suspended soil dust are more affected by anthropogenic activities than meteorological patterns. In contrast, sulfate and volatile carbon concentrations seem to be insensitive to that cycle indicating that other sources/processes can be responsible for the ambient levels of these pollutants. Principal component analysis of aerosol and air quality data showed that the major contributions to the variance of the ambient aerosol loadings come from soil, motor vehicles and sulfates. Key word index." Receptor model, fine aerosol, Los Angeles.
INTRODUCTION In order to develop rational and effective strategies for improving air quality, it is necessary to have an understanding of the relationship between the pollutant sources and their impact at receptor sites. Source or dispersion models predict the concentrations of pollutants at a receptor site using diffusion models with emission inventories and meteorological data. Receptor models infer source contributions at receptor sites using statistical models with data taken at the receptor site (Gordon, 1988). Receptor modeling has frequently been used to determine possible sources of population in many sites. In the Los Angeles area, after the classic study of chemical mass balances by Miller et al. (1972), Henry and Hidy (1979) first used multivariate techniques to show that ambient particulate sulfates are related mostly to atmospheric chemistry rather than atmospheric dispersion or SO z emissions. Gray et al. (1986) developed and operated a fine-particle monitoring network in Los Angeles in 1982. They found that particulate carbon emissions were the principal contributors to fine carbonaceous concentrations and accounted for about 40% of the total mass. Kim and *To whom correspondence should be addressed. 473
Henry (1989) developed a statistical model known as source apportionment by factors with explicit restrictions and applied it to P M 1 0 data collected in the South Coast Air Basin during 1986. They showed that particulate nitrates exhibited strong spatial variations over the sampling sites in contrast to sulfates and organic carbon. K a w a m u r a and Kaplan (1987) reported that automobile emissions are the most important primary sources of atmospheric diacids with oxalic acid being the dominant species. Pratsinis et al. (1988) found that about half of the fine aerosol mass and two-thirds of the carbonaceous aerosols can be attributed to motor vehicle emissions during high pollution days in 1983 at Duarte, a residential Los Angeles site. In addition, they showed atmospheric chemistry and even background aerosol (e.g. soil dust) can have significant contributions to the non-black carbonaceous aerosol. Pickle et al. (1990) showed that the carbonyl and aliphatic fraction of the latter aerosol, as determined by transmission F T I R without extraction, are attributed to secondary atmospheric processes and motor vehicle emissions, respectively. This paper presents an analysis of source contributions to the ambient aerosol at Duarte, a residential site in Los Angeles, CA, by receptor modeling using ttest, correlation coefficient and principal component analyses. Possible sources of fine aerosol are deter-
474
S.H. EHRMANet al.
mined and the pattern of the source contributions between 1983 and 1987-1988 is investigated. MEASUREMENTS
Eight-hour (12 8 p.m. PST) samples of fine (dp<3.5 gin) aerosol were collected at Duarte, CA, in 1983 and between June 1987 and June 1988. This is a residential site, located about 35 miles east of downtown Los Angeles. Aerosol samples were collected on 8 x 10 in. quartz filters using two high-volume (Hi-vol) samplers preceded by cyclones with 7.2 and 3.5 gm impactor plates. These filters were subject to organic thermographic analysis, total fine aerosol mass (FP), NO3, SO]- and NH + analysis. Ion chromatography was used to determine sulfate and nitrate concentrations, and indophenol blue chromatography was used for NH,~ (Pratsinis et al., 1988). Thermal analysis was used to determine the carbonaceous component of the aerosol (Dod et al., 1979). Volatile carbon (VC) represents the carbon volatilized below 250~C. Organic carbon (OC) represents the fraction volatilized between 250 and 450°C. The final fraction, carbon volatilized above 450 ° C, is labeled black carbon (BC) (Dod et al., 1979). A Sierra Model 244 dichotomous sampler (single impaction stage, cut point dp< 3.5 #m) was used to collect fine aerosol on Teflon filters for elemental inorganic analysis by PIXE (proton induced X-ray emission spectroscopy). In addition to particulate air quality data, pollutant gas concentrations were obtained from the South Coast Air Quality Management District air quality monitoring station at Azusa (3 km away from the sampling site) by averaging the reported hourly average gas concentrations over the 8-h sampling period. The data were retrieved for statistical analysis from datafiles supplied by Southern California Edison as part of the Los Angeles Aerosol Characterization and Source Apportionment Study (Aerocomp, 1989). Each datafile contained air quality data from a specific chemical analysis of the sampled aerosol. The datafiles were subjected to quality assurance procedures in order to create reliable data sets for statistical analysis. The employed data sets contained 35 and 78 observations (data) for 1983 and 1987-1988, respectively. STATISTICALMETHODS The statistical significance of the difference in mean pollutant concentrations between 1983 and 1987 1988 was investigated (Winkler and Hays, 1975). For the day-of-the-week analysis, the observed data for each pollutant were first transformed into z - s c o r e s (zij are normalized pollutant concentrations in sample i in year j):
(c,j-~) Zij
- - , ~r i
where ~i is the average pollutant concentration in year j, cij is the pollutant concentration in sample i in year j, and ~ is the pollutant concentration standard deviation for year j. A z-score indicates the number of standard deviations a pollutant concentration in sample i is away from its mean in year j. A negative or positive z-score indicates that the pollutant concentration in sample i is below or above, respectively, its mean value in yearj. A z test analysis was performed in order to assess the statistical significance of differences between weekdays and weekends.
The complete set of observations (samples), as well as seasonal subsets of the data were examined using correlation coefficients and principal component analysis (PCA). PCA is frequently used for identifying pollutant sources affecting the air quality (Kleinman et al., 1980). PCA attempts to explain the variance of a large set of intercorrelated variables (the observed air quality data) with a smaller set of independent variables (the principal components). This procedure is advantageous because detailed information regarding atmospheric chemistry and meteorology is not required. PCA was performed using the Factor procedure in the SAS software package (SAS, 1982). The initial set of principal components generated by PCA are not readily interpretable. The first principal component is created to explain as much of the variance as possible. The second principal component tries to explain as much of the remaining variance as possible and so on (Harris, 1975). This analysis results in a n u m b e r of possible distinct sources being grouped together. In order to generate a more interpretable set of components, the initial set of principal components was transformed by Varimax rotation (Hopke, 1982; Thurston and Spengler, 1985). Hopke (1982) has suggested that the elimination of principal components whose eigenvalues are less than one can lead to the exclusion of meaningful principal components. Once the rotated principal components were determined, source identities were assigned based on the correlations between principal components and the original data. In order to have a statistically valid solution, a large number of samples is needed (Thurston, 1983). For a robust solution, the number of degrees of freedom (df) should be larger than 30n (Henry et al., 1984): df =mn-n--
n ( n + 1) 2
where m is the number, of observations (samples) and n is the n u m b e r of pollutants (variables). All principal component analyses presented in this paper satisfied this criterion except for the 1983 data set. Pratsinis et al. (1988), however, found the results of the latter PCA to be robust by means of an empirical statistical analysis. Quantitative estimates of source contributions to the F P and carbonaceous concentrations were obtained by regressing these concentrations to the absolute scores of the principal components using the stepwise procedure (Thurston and Spengler, 1985). The 95% confidence level in the regressions determined whether a regression coefficient assigned to a principal component was meaningful. Principal components were excluded from the regression model if the corresponding parameter estimates were negative~ or if the associated standard error of the regression was greater than one-third of the parameter estimate value and the regression was repeated. This procedure is similar to the one used in chemical mass balance calculations (Miller et al., 1972; Currie et al., 1984).
Receptor modeling of fine aerosol RESULTS AND DISCUSSION
Temporal comparisons Comparisons were made between the yearly average air pollutant concentrations in 1987-1988 and 1983 at Duarte (Fig. 1). A statistical analysis indicated that the difference in means between the 1983 and 1987-1988 data was significant, with 99% confidence for Fe, Mn, Br, Pb, SO 2-, Si, Al, FP and VC, and with 90% confidence for OC. Lead and bromine exhibit the most dramatic difference between 1 9 8 3 and 1987-1988. The five-fold decrease of the ambient concentration of these elements in this time period is attributed to the lower usage of leaded gasoline and the progressive reduction of Pb-Br-containing compounds in that gasoline. Clearly these data are in agreement with Fig. 2 that shows the average Pb concentration in leaded gasoline in the United States through 1987 (Dickson et al., 1987). The decrease of SO~ , and carbonaceous species concentrations from 1983 to 1987-1988 can be at least partly attributed to the different procedures for selection of sampling days. In 1983, sampling took place on days of distinctly
475
poor air quality, while in 1987-1988, sampling was conducted on randomly selected days. A day-of-the-week analysis was performed on selected pollutants to explore their origins further in 1987-1988. Figure 3a shows the z-scores of Pb, CO and S plotted against the day of the week on which sampling took place. The mean values for both Saturday and Sunday were compared with the mean value for the midweek days, and the difference in means was found to be significant at the 99% confidence level for Pb and CO, but not for S. Lead and CO mostly come from automobile emissions that are higher on weekdays than on weekends. On the other hand, particulate sulfur results from atmospheric conversion of SO2, a process clearly not dependent on the weekly cycle of human activities (Henry and Hidy, 1979). The results of this analysis are in agreement with the current knowledge regarding the sources of these pollutants. The origins of crustal elements in the 1987-1988 data set were also investigated by a z-score analysis in Fig. 3b. It is apparent that these elements exhibit large negative z-scores on the weekends while they fluctuate
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IVln Fe Ni Zn Br
Pb
VC
CC
BC
FP/10
NH4
NO3
SO4
~E
=ft.
4
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03 x 100
CO
SO2 x 100
Fig. 1. Yearly average concentrations of (a) fine (dp < 3.5 #m) aerosol components (#g cm- 3) and (b) carbonaceous and ionic components (#g cm- 3 at Duarte, CA, in 1983and 1987-1988,(c) ambient pollutant gases (ppm) at Azusa, CA, in 1983 and 1987-1988.
476
S.H. EHRMANet al. 25"
(a) oc 2.0 m 15
~o ° 05"
& ca 00
. . . 1972 . 1974 . . 1976 . 1978 1980 19 8 2 1984 ' ' 1966 1968 1970 1986 1988
Year
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Fig. 2. Average Pb content in leaded gasoline in the United States through 1987.
SU around the average during the weekdays. The difference in mean concentrations between weekdays and weekends for Si, Ca and Fe was significant with greater than 99% confidence. Soil dust is of natural origin. However, it appears as an anthropogenic component of the aerosol because the loadings clearly follow the weekly pattern of activities. Driving motor vehicles (e.g. resuspended road dust) and construction work could result in increased loadings of soil dust and support observed temporal pattern of crustal element concentrations. A z-score analysis was also performed on the three carbon components and FP, in Fig. 3c. The concentration of volatile carbon fluctuates around the mean and does not seem to be affected by the day of the week. A statistical test on the difference between the weekend and weekday mean VC concentrations did not indicate any significant difference. This indicates that a fraction of the volatile carbon is non-anthropogenic and possibly biogenic since it is not sensitive to the weekly cycle of human activities. Organic carbon, on the other hand, appears as an anthropogenic component and the difference between the weekend and weekday mean concentrations was significant at the 98% level. This is very intriguing because it is known that organic carbon is closely related to the products of atmospheric photochemical activity. It is possible that fast atmospheric reactions lead to the formation of secondary organic carbon so its ambient concentration is closely tied to primary organic vapor emissions rather than meteorology. Black carbon also appears as an anthropogenic component which is not surprising since BC is a combustion product closely tied to primary particulate emissions. Finally, FP has large zscores on the weekends and mostly positive scores on the weekdays. The weekend and weekday mean concentrations were significantly different at the 95% level indicating that the FP mass is clearly related to anthropogenic activities.
Correlations amon9 air quality variables
In 1987-1988, high correlations (r2>0.90) were found between NO 3 and NH~-, and among Si, Ca and
II
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TH
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SA
TU
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SA
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Fig 3. Average (a) lead, carbon monoxide and sulfur; (b) silicon, calcium and iron; (c) volatile carbon, organic carbon, black carbon and fine particle mass z-scores for every day of the week for 1987-1988 in Duarte, CA.
Fe. Good correlations (re> 0.80) were found between S and SO ] - , and among A1, K and Ti. In 1983, high correlations were found between Pb and Br, between VC and OC, and between S and S O l - . Good correlations were found among VC, OC, BC, Br, Pb, CO and NO2, among Pb, NH,~ and NO3, among S, SO] , and NH 2- and among Si, Ca and Fe (Pratsinis et al.,
Receptor modeling of fine aerosol 1988). It is worth noting the reduction in the correlation coefficient between motor vehicle tracers from 1983 to 1987 1988. To investigate seasonal changes in the observed air quality variables in 1987-1988, the sampling dates were divided roughly according to the seasons of the year and the correlations between air quality variables were explored. Forty-nine samples collected in the months of April to October were included in the summer subset. Twenty-nine samples collected in the months of November to March made up the winter subset. In the summer subset, the NH~ concentration correlated strongly (r2=0.97) with SO42 concentration. In the winter subset, NH,~ correlated weakly (r2=0.62) with SO 2- and strongly (r2=0.98) with NO 3. The variables associated with automobile emissions (CO, NOx Pb) showed generally higher correlations with each other in winter than they did in the summer. Lead correlated strongly (r 2 > 0.80) with CO, NOx, NO3, OC, Mn, Zn and Br in the winter set, and showed a low correlation (r 2 < 0.55) with other variables in the summer. The carbonaceous components showed no correlations other than with each other in the summer. In the winter set, correlations (r 2 >0.70) were found among VC, Zn, Br, CO and NOx; among OC and K, NH~, VC (r2 >0.70), Mn, Fe, Zn, Br, Pb, NOx, NO 3 (r2> 0.80) and CO (r 2 >0.90); and among black carbon and Zn, NH~ (r2>0 70) and CO, NOx, VC and OC (r2> 0.80).
Principal component analysis The results from the principal component analysis on the 1987 1988 data set are shown in Table 1. Sodium and chlorine were excluded because of difficulties in analysing their concentrations. PIXE is not a
477
good analytical technique for measuring sodium concentrations. Chlorine has a high equilibrium vapor pressure and easily volatilizes from the collected samples. Variables such as Br, Ni, Ti and SO2 were also excluded from the analysis since they were not detected in more than 10% of the samples collected in 1987-1988 (Pratsinis et al., 1988). A principal component correlating strongly (r 2 >0.90) with CO and significantly (r2>0.70) with Pb, OC, BC and NOx appeared as the first principal component. The sampling site is located downwind from downtown Los Angeles, an area of high primary motor vehicle emissions. Crustal elements dominated the second principal component while the third principal component correlated with sulfur and sulfates. Principal components with single loadings of N H 4 / N O 3, 03, Zn and VC appeared after the abovementioned principal components. Ozone did not appear until the fifth principal component and the corresponding eigenvalue was low. The ozone has a much stronger showing in the principal component analysis of the 1983 data at Duarte (Pratsinis et al., 1988). This is attributed to the meteorology of 1983. The sixth component correlated with zinc which has been linked to refuse incineration (Hopke et al., 1982; Johnson, 1982) and tire dust (Miller et al., 1972). The latter seems more probable for the Los Angeles Basin. PCA was also performed on a data set including Br, Ti, and on another data set including Br, Ni, Ti, SO 2. As these variables were included, singular principal components associated with those variables appeared (Roscoe et al., 1982). Aside from these changes, the overall structure of the principal components remained identical to that obtained in the absence of these elements. The 1983 PCA results are shown in Table 2. In 1983, variables associated with primary auto emissions (Pb,
Table 1. Principal component analysis, 1987-1988 air quality data Motor vehicles AI Si
-0.12 0.07
S
Eigenvalue
0.87 0.99
Sulfates Nitrates
0.10 0.06 0.49 0.12 0.58 0.72 0.94 0.76 -0.08 0.57 0.57 0.10 0.64 0.89 0.85
0.89 0.98 0.58 0.97 0.28 0.09 -0.03 0.07 0.21 -0.03 0.02 0.00 - 0.08 0.17 -0.1
-0.03 -0.09 -0.05 0.02 0.26 -0.01 -0.03 0.01 0.44 0.27 -0.10 0.96 0.20 0.08 0.05
--0.11 --0,04 0.00 0.08 -0.01 0.23 0.03 0.10 0.29 0.16 0.23 -0.28 0.74 0.77 0.08 0.13 0.16 0.23
5.2
5
2.3
1.6
0.00
K Ca Mn Fe Zn Pb CO NO~ 03 NHg NO3 SO2VC OC BC
Soil dust
-0.01
0.17 0.01 0.96
Bold values indicate component loadings > 0.70.
03
Zn
Carbon
0.12 0.05 0.16 0.10 0.00 -0.11 0.02 -0.01 -0.04 -0.08 -0.06 0.82 -0.16 -0.22 0.11 0.09 0.05 0
0.12 0.05 0.11 -0.09 0.02 0.36 0.10 0.69 0.20 0.01 0.17 -0.01 0.05 0.06 0.00 0. i 1 0.10 0.09
-0.07 --0.01 0.00 -0.02 0.01 0.06 0.01 0.11 0.07 -0.04 0.09 0.06 0.09 0.05 0.10 0.71 0.17 0.15
0.8
0.8
0.6
478
S.H. EHRMAN et al. Table 2. Principal component analysis, 1983 air quality data Motor vehicles Na Si S K Ca Ti Fe Ni Zn Br Pb CO 03 NO 2 NH~ NO 3 SO ]-
VC OC BC Eigenvalue
Soil dust
Sulfates
03
Marine aerosol
Fuel oil, fly ash
-0.15 0.17 0.19 0.07 0.07 0.19 0.46 0.48 0.64 0.82 0.90 0.76 0.09 0.85 0.67 0.93 0.19 0.71 0.76 0.85
0.08 0.94 0.08 0.59 0.93 0.66 0.79 0.13 0.30 0.28 0.21 0.21 0.25 0.16 0.00 0.07 -0.04 0.14 0.31 0.07
0.08 0.06 0.94 0.06 - 0.05 0.03 0.08 0.11 0.28 0.21 0.29 0.21 0.25 0.17 -0.01 0.07 0.96 0.36 0.16 0.12
0.12 0.09 0.20 0.23 0.09 0.00 0.16 -0.01 0.12 0.19 0.05 0.17 0.87 0.06 -0.08 -0.28 0.18 0.31 0.38 0.12
0.98 -0.12 0.09 0.17 0.20 0.03 0.03 0.08 0.07 -0.15 -0.11 0.06 0.10 0.05 -0.08 --0.05 0.05 -0.16 0.05 - 0.07
0.05 0.02 0.07 0.14 0.08 0.03 0.09 0.86 0.11 0.17 0.14 0.09 0.13 0.14 -0.02 -0.04 0.02 0.06 0.09 0.01
7.0
3.7
3.1
1.3
1.1
1.0
Bold values indicate component loadings>0.70.
Table 3. Principal component analysis, winter 1987-1988
AI Si S K Ca Mn Fe Zn Pb CO NOx 03 NH,~ NO 3
SO ] VC OC BC Eigenvalue
Motor vehicles
Soil dust
Sulfates
03
Nitrates
0.11 0.16 0.29 0.39 0.25 0.70 0.62 0.78 0.79 0.90 0.90 -0.06 0.65 0.67 0.20 0.75 0.85 0.82
0.92 0.97 - 0.20 0.79 0.92 0.50 0.74 0.37 0.27 0.23 0.19 --0.08 0.09 0.17 - 0.04 0.18 0.39 0.13
-0.1! -0.08 0.91 0.18 -0.10 -0.01 0.05 0.10 0.09 0.22 0.33 0.07 0.45 0.30 0.95 0.17 0.14 0.38
-0.01 -0.02 0.11 -0.03 -0.09 -0.09 - 0.06 -0.07 -0.21 - 0.11 -0.04 0.99 -0.13 -0.13 0.130 0.19 -0.04 0.22
0.03 -0.01 0.08 0.22 0.06 0.11 0.04 0.09 0.24 0.09 0.08 --0.07 0.59 0.64 0.11 0.15 0.18 0.14
6.9
4.6
2.5
1.2
1
Bold values indicate component loadings > 0.70.
CO, NOx and carbon) showed m u c h higher correlations with nitrates than in 1987-1988. Lead had a c o m p o n e n t loading of 0.90 in 1983 and it d r o p p e d to 0.72 in 1987-1988. In 1983, the m o t o r vehicle principal c o m p o n e n t emerged as the one explaining the largest a m o u n t of the variance. In 1987-1988, the soil dust c o m p o n e n t was almost equally significant. This shift p r o b a b l y comes from the decreased loadings of primary automotive variables (Pb, Br, C). Furthermore, the Ni factor which is usually associated with fuel oil
c o m b u s t i o n was not present since ambient Ni concentrations d r o p p e d below the detection level in 36 out of 78 samples in 1987-1988. This could be attributed to the fact that some power plants in southern California shifted from burning fuel oil to natural gas in the early 1980s. P C A was performed on seasonal subsets of the 1987-1988 data. The results are shown in Tables 3 and 4. In the winter subset, tracers of m o t o r vehicle emissions showed high loadings in the first principal
Receptor modeling of fine aerosol
479
Table 4. Principal component analysis, summer 1987 1988
Al Si S K Ca Mn Fe Zn Pb CO NO~ 03 NH~ NO 3
SO4z VC OC BC Eigenvalue
Soil dust
Sulfates
Carbon
NO x
0.90 0.99 -0.10 0.87 0.98 0.84 0.98 0.30 0.05 -0.08 0.09 0.14 -0.04 0.34 0.06 -0.16 0.14 0.14
0.05 --0.06 0.93 -0.16 -0.13 0.12 0.00 0.42 0.11 0.04 0.05 0.26 0.94 0.29 0.95 0.35 0.17 0.13
-0.15 -0.05 0.02 0.00 0.30 0.06 0.03 0.31 0.29 0.35 0.17 0.26 0.11 0.01 0.18 0.49 0.82 0.92
0.05 0.02 0.09 -0.08 0.01 0.20 0.05 0.30 0.22 0.13 0.94 0.14 0.02 -0.03 0.00 0.21 0.16 0.08
-0.15 0.01 -0.11 0.02 0.02 0.19 0.06 0.24 0.89 0.20 0.18 0.10 0.13 0.01 0.11 0.15 0.23 0.13
5.5
3.2
2.2
1.2
1.1
Pb
03 0.02 0.03 0.06 0.12 0.01 0.02 0.03 0.02 0.10 0.16 0.12 0.89 0.11 0.02 0.12 0.18 0.21 0.11 1
CO
Nitrates
0.06 -0.07 0.04 0.01 -0.02 0.03 - 0.05 0.15 0.19 0.86 0.10 0.14 0.01 --0.16 0.01 0.09 0.14 0.21
-0.04 0.09 0.00 0.15 0.06 0.15 0.10 0.01 0.01 -0.17 -0.02 0.03 0.21 0.88 0.09 -0.07 0.02 0.02
0.9
0.9
Bold values indicate component loadings>0.70.
component. The carbonaceous aerosol concentrations also correlated with this principal component. Soil dust, sulfates and 0 3 principal components followed in descending order. In the summer, soil dust emerged as the first principal component. Sulfates emerged second in the order followed by a distinct carbonaceous principal component. The m o t o r vehicle variables were split into singular principal components of carbonaceous fractions, Pb, CO, nitrates and NOx. Ozone did not appear until the sixth principal component and nitrates did not appear until the eighth principal component. These results indicate that during the winter, when low photochemical activity takes place, NOx, nitrates and carbonaceous aerosols are closely associated with primary m o t o r vehicle emissions. In the summer, however, meteorology plays an important role in the formation of these pollutants so their ambient concentrations do not follow tracers of primary emissions. Clearly, active atmospheric chemistry during the summer creates different pathways for generation of secondary carbonaceous particulates. The location of the N H ~ ion in the PCA varied from summer to winter. In the summer, N H ~ correlated strongly with the sulfate principal component. In the winter, it correlated with the nitrates.
Table 5. Source apportionment of fine particle mass (%) by PCA-SR at Duarte, CA
Motor vehicles Soil Sulfates VC
78 samples
60 samples
47 i 5
10 + 1 23 _+4 16 _+4 12+3
16 _+4
r2
63
%3 :z
77
"~
1
0 20
40
60
80
Sampling days, 1987-88
Fig. 4. Comparison of daily FP and silicon aerosol concentration at Duarte, CA, in 1987-1988.
Source contributions The fine aerosol mass and the carbonaceous aerosol fractions were apportioned to the principal components by stepwise regression on the components' scores (Thurston and Spengler, 1985; Pratsinis et al., 1988). A summary of these results is given in Table 5. Of the fine particle (FP) mass accounted for by the model in 1987-1988, the m o t o r vehicle principal component accounted for 47% of the total while the sulfate
principal component accounted for 16%. These results are in agreement for these sources with an earlier study at the same site in 1983. It might seem odd,' however, that soil dust has no contribution to the total aerosol mass in 1987-1988. To investigate this, Fig. 4 is presented showing daily loadings of F P and Si, a typical crustal element. Beginning in May of 1988
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(sampling days 60-78), the Si loadings increased ap- fractions from 1983 to 1987 1988. Thus loadings of proximately three-fold over their average, indicative of elements associated with soil dust (Si, Ca, Fe) varied some soil upturning event taking place near the sam- with human activity as did loadings of Pb, CO, BC, pling site. Similar behavior was obtained for other OC and FP. The loadings for volatile carbon (VC), on crustal elements such as A1, Ca and Fe. In contrast, the the other hand, were not dependent on the weekly FP did not show a significant increase during this time human cycle, suggesting a non-anthropogenic and period. PCA and stepwise regression (PCA-SR) ana- possibly biogenic source for VC. lyses were also performed on a data set comprised only Motor vehicles and soil dust emerged as the princiof samples collected until April 1988 (the first 60 pal components explaining the largest amount of samples of 1987 1988). The structure of the principal variance in the 1987-1988 data. The motor vehicle components was consistent with the previous analysis principal component also explained the largest with the addition of a weak (eigenvalue = 1) principal amount of variance in 1983. However, the loadings of component highly loaded with AI. Results of regres- variables associated with primary motor vehicle emission analysis on the set of 60 samples are also shown in sions decreased in 1987 1988. The ozone principal Table 5. The most apparent difference between the 78 component showed a large decrease in the amount of and the 60 PCA-SR sample analysis of the FP was the variance accounted for from 1983 to 1987-1988. The appearance of a soil dust and volatile carbon contribu- air quality in 1987-1988 was better than in 1983 tion of 23 and 12%, respectively, of the total while the primarily due to favorable meteorology in the Southern California Basin. motor vehicle contribution was reduced to 10%. Seasonal analysis revealed differences in the correlIn 1983 the PCA-SR revealed contributions to FP by ozone photochemistry and marine aerosol (Prat- ations and factor patterns of winter and summer data sinis et al., 1988). The difference in the meteorology subsets. In the winter, a small set of principal componand data analysis may explain these results. The lack ents explained most of the data variance. Pollutants of contribution by ozone photochemistry in emitted by motor vehicle variables (Pb, CO, NOx, 1987-1988 may be attributed to meteorology and carbonaceous fractions) appeared clustered in one weather patterns. Marine aerosol contributions could factor in winter, while each one of these variables not be determined because separate chemical analysis emerged as a singular principal component in the for Na was not carried out in the 1987 1988 samples. summer. Regression analyses, performed on the full data set Similar results (to PCA-SR) were also obtained when simple multiple linear regression (Kleinman et of 78 observations, showed that motor vehicles (47%) al., 1980) was used employing trace elements for each and sulfates (16%) accounted for the fine particle mass source (Si or Ca for soil, CO or Pb for motor vehicles, at Duarte, CA, in 1987 1988. There were also found NO 3 for nitrates, S for sulfates and 0 3 for photo- unusually high concentrations of crustal elements at chemistry) for the set of 78 and 60 samples, respect- the end of that sampling period. When only the first ively. The regression results for the 1987-1988 data set 60 samples were included in the analysis, soil dust containing all 78 samples and just the first 60 samples (23%), sulfates (16%), motor vehicles (10%) and volashow some limitations in the use of multiple linear tile carbon (12%) accounted for the fine mass at the regression techniques when a source exhibits a drastic same site. The latter source estimates are more in line change in its contributions during the sampling with the current understanding of pollution source period. Furthermore, the present source estimates contributions in residential areas in Los Angeles should be considered rather qualitative because of the though still a large aerosol mass fraction was not low coefficient of determination (r 2) of the regression accounted for (~40%). The above results indicate, model (Kleinman et al., 1980) and the fact that a large however, that the regression procedures possibly are mass fraction ( ~ 40%) is not accounted for. In order to sensitive to dramatic, non-random changes in the perform a quantitative source apportionment analysis contributions by a single source during the period of on data where the source receptor relationship varies sampling, and the performance of the regression can greatly but not randomly during the sampling period, be adversely affected by these changes. as in the case of soil dust in the 1987 1988 data, another method such as chemical mass balances on Acknowledgements--This research was sponsored by Southern California Edison. S. H. E. was supported in part by the each sample might be more appropriate. National Science Foundation through a Research Experiences for Undergraduates Grant, EID-9000779. We also gratefully acknowledge contributions on the statistics of this paper by Dr Eleni Pratsini and discussions with Mr J. D. Landgrebe (Procter & Gamble) and Prof. Sheldon K. FriedCONCLUSIONS lander (UCLA). Receptor modeling of the fine (dp < 3.5 #m) aerosol at Duarte, CA, was conducted for samples collected in 1983 and 1987-1988. A t-test analysis revealed significant differences in the ambient concentrations of SO ] - , A1, Mn, S, Si, Fe, Pb, Br and the carbonaceous
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Aerocomp (1989) Los Angeles aerosol characterization and source apportionment study: background, contents and
Receptor modeling of fine aerosol data retrieval procedures. Aerocomp Inc. prepared for Southern California Edison, November. Currie L. A., Gerlach R. W., Lewis C. W., Balfour G. E., Cooper J. A., Dattner S. L., De Cesar R. T., Gordon G. E., Heisler S. L., Hopke P. K., Shah J. J., Thurston G. D. and Williamson H. J. (1984) Interlaboratory comparison of source apportionment procedures: results for simulated data sets. Atmospheric Environment 18, 1517-1537. Dickson C. L., Woodward P. W. and Bingstad P. L. (1987) Trends of petroleum fuels. DE87-001237, topical report prepared by lIT Research Institute, National Institute for Petroleum and Energy Research, for the U.S. Dept. of Energy. Dod R. L., Rosen H. and Novakov T. (1979) Optico-thermal analysis of the carbonaceous fraction of aerosol particles in atmospheric aerosol. Research Annual Report 1977-1978, LBL-8696, Lawrence Berkeley Laboratory, Berkeley, CA. Gordon G. E. (1988) Receptor models. Envir, Sci. Technol. 22, 1132-1142.
Gray H. A., Cass G. R., Huntzicker J. J., Heyerdahl E. K. and Rau J. A. (1986) Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles. Envir. Sci. Technol. 20, 580-589. Harris R. J. (1975) A Primer of Multivariate Statistics. Academic Press, New York. Henry R. C. and Hidy G. M. (1979) Multivariate analysis of particulate sulfate and other air quality variables by principal components--Part I. Annual data from Los Angeles and New York. Atmospheric Environment 13, 1581 1596. Henry R. C., Lewis C. W., Hopke P. K. and Williamson H. J. (1984) Review of Receptor Model Fundamentals. Atmospheric Environment 18, 1499-1506. Hopke P. K. (1982) Letter to the Editors. Atmospheric Environment 16, 1279-1280. Hopke P. K., Severin K. G. and Chang S. (1982) Applications and verifications studies of target transformation factor analysis as an aerosol receptor model. In Proc. Receptor Models Applied to Contemporary Pollution Problems, pp, 110-126. Air Pollution Control Association, Danvers, MA. Johnson D. L. (1982) A particle class balance receptor model
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lor aerosol apportionment in Syracuse, NY. In Proc. Receptor Models Applied to Contemporary Pollution Problems, pp. 238-248. Air Pollution Control Association, Danvers, MA. Kawamura K. and Kaplan I. R. (1987) Motor exhaust emissions as a primary source for dicarboxylic acids in Los Angeles ambient air. Envir. Sci. Technol. 21, 105-I10. Kim B. M. and Henry R. C, (1989) Source apportionment by factors with explicit restrictions (SAFER). Proc. Air Waste Man. Ass. 9, 89-145.6. Kleinman M. T., Pasternack B. S., Eisenbud M. E. and Kneip T. J. (1980) Identifying and estimating the relative importance of sources of airborne particulates. Envir. Sci. Tech nol. 14, 62-65. Miller M. S., Friedlander S. K. and Hidy G. M. (1972) A chemical element balance for the Pasadena aerosol. J. Colloid. lnterf Sci. 39, 165-176. Pickle T., Allen D. T. and Pratsinis S. E. (1990) The sources and size distributions of aliphatic and carbonyl carbon in Los Angeles aerosol. Atmospheric Environment 24A, 2221-2228. Pratsinis S. E., Zeldin M. D. and Ellis E. C. (1988) Source resolution of the fine carbonaceous aerosol by principal component - stepwise regression analysis. Envir. Sci. Technol. 22, 212-216. Roscoe B. A., Hopke P. K., Dattner S. L. and Jenks J. M. (1982) The use of principal component factor analysis to interpret particulate compositional data sets. J. Air Pollut. Control Ass. 32, 637-642. SAS (1982) SAS User's Guide. Statistics. SAS Institute, Cary, NC. Thurston G. D. (1983) A source apportionment of particulate air pollution in metropolitan Boston. Ph.D. thesis, Harvard School of Public Health, Boston, MA. Thurston G. D. and Spengler J. D. (1985) A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmospheric Environment 19, 9-25. Winkler R. L. and Hays W, L. (1975) Statistics, Probability, Inference and Decision, 2nd edn. Holt, Rinehart and Winston, New York.
0957 1272/92 $5.00+0.00 (f'2 1992 Pergamon Press Ltd
Printed in Great Britain.
R E C E P T O R M O D E L I N G OF THE FINE AEROSOL AT A RESIDENTIAL LOS ANGELES SITE SHERYL H. EHRMAN a n d SOTIRIS E. PRATSINIS* Department of Chemical Engineering, Center for Aerosol Processes, University of Cincinnati, Cincinnati, OH 45221, U.S.A.
and JAMES R. YOUNG Southern California Edison, 2244 Walnut Grove Avenue, Rosemead, CA 91770, U.S.A. (First received 5 February 1992 and in final form 20 April 1992)
Abstract--Receptor modeling on ambient aerosol and air quality data collected at Duarte, CA (a residential site near Los Angeles), in 1983 and 1987 1988 was carried out. A significant change in the ambient concentrations of SO 2 , A1, Si, Mn, Fe, Pb, Br, volatile and organic carbon and fine particle (FP) mass took place from 1983 to 1987-1988. A drastic reduction (~ 80%) of the ambient Pb and Br concentrations took place as the lead content and the usage of leaded gasoline decreased in the Los Angeles Basin during that period. A day-of-the-week analysis indicated that both crustal (Si, Ca, Fe) and transportation (Pb, CO, organic carbon and black carbon) related pollutants exhibit significantly different concentrations between weekdays and weekends of 1987 1988. This indicates that loadings of suspended soil dust are more affected by anthropogenic activities than meteorological patterns. In contrast, sulfate and volatile carbon concentrations seem to be insensitive to that cycle indicating that other sources/processes can be responsible for the ambient levels of these pollutants. Principal component analysis of aerosol and air quality data showed that the major contributions to the variance of the ambient aerosol loadings come from soil, motor vehicles and sulfates. Key word index." Receptor model, fine aerosol, Los Angeles.
INTRODUCTION In order to develop rational and effective strategies for improving air quality, it is necessary to have an understanding of the relationship between the pollutant sources and their impact at receptor sites. Source or dispersion models predict the concentrations of pollutants at a receptor site using diffusion models with emission inventories and meteorological data. Receptor models infer source contributions at receptor sites using statistical models with data taken at the receptor site (Gordon, 1988). Receptor modeling has frequently been used to determine possible sources of population in many sites. In the Los Angeles area, after the classic study of chemical mass balances by Miller et al. (1972), Henry and Hidy (1979) first used multivariate techniques to show that ambient particulate sulfates are related mostly to atmospheric chemistry rather than atmospheric dispersion or SO z emissions. Gray et al. (1986) developed and operated a fine-particle monitoring network in Los Angeles in 1982. They found that particulate carbon emissions were the principal contributors to fine carbonaceous concentrations and accounted for about 40% of the total mass. Kim and *To whom correspondence should be addressed. 473
Henry (1989) developed a statistical model known as source apportionment by factors with explicit restrictions and applied it to P M 1 0 data collected in the South Coast Air Basin during 1986. They showed that particulate nitrates exhibited strong spatial variations over the sampling sites in contrast to sulfates and organic carbon. K a w a m u r a and Kaplan (1987) reported that automobile emissions are the most important primary sources of atmospheric diacids with oxalic acid being the dominant species. Pratsinis et al. (1988) found that about half of the fine aerosol mass and two-thirds of the carbonaceous aerosols can be attributed to motor vehicle emissions during high pollution days in 1983 at Duarte, a residential Los Angeles site. In addition, they showed atmospheric chemistry and even background aerosol (e.g. soil dust) can have significant contributions to the non-black carbonaceous aerosol. Pickle et al. (1990) showed that the carbonyl and aliphatic fraction of the latter aerosol, as determined by transmission F T I R without extraction, are attributed to secondary atmospheric processes and motor vehicle emissions, respectively. This paper presents an analysis of source contributions to the ambient aerosol at Duarte, a residential site in Los Angeles, CA, by receptor modeling using ttest, correlation coefficient and principal component analyses. Possible sources of fine aerosol are deter-
474
S.H. EHRMANet al.
mined and the pattern of the source contributions between 1983 and 1987-1988 is investigated. MEASUREMENTS
Eight-hour (12 8 p.m. PST) samples of fine (dp<3.5 gin) aerosol were collected at Duarte, CA, in 1983 and between June 1987 and June 1988. This is a residential site, located about 35 miles east of downtown Los Angeles. Aerosol samples were collected on 8 x 10 in. quartz filters using two high-volume (Hi-vol) samplers preceded by cyclones with 7.2 and 3.5 gm impactor plates. These filters were subject to organic thermographic analysis, total fine aerosol mass (FP), NO3, SO]- and NH + analysis. Ion chromatography was used to determine sulfate and nitrate concentrations, and indophenol blue chromatography was used for NH,~ (Pratsinis et al., 1988). Thermal analysis was used to determine the carbonaceous component of the aerosol (Dod et al., 1979). Volatile carbon (VC) represents the carbon volatilized below 250~C. Organic carbon (OC) represents the fraction volatilized between 250 and 450°C. The final fraction, carbon volatilized above 450 ° C, is labeled black carbon (BC) (Dod et al., 1979). A Sierra Model 244 dichotomous sampler (single impaction stage, cut point dp< 3.5 #m) was used to collect fine aerosol on Teflon filters for elemental inorganic analysis by PIXE (proton induced X-ray emission spectroscopy). In addition to particulate air quality data, pollutant gas concentrations were obtained from the South Coast Air Quality Management District air quality monitoring station at Azusa (3 km away from the sampling site) by averaging the reported hourly average gas concentrations over the 8-h sampling period. The data were retrieved for statistical analysis from datafiles supplied by Southern California Edison as part of the Los Angeles Aerosol Characterization and Source Apportionment Study (Aerocomp, 1989). Each datafile contained air quality data from a specific chemical analysis of the sampled aerosol. The datafiles were subjected to quality assurance procedures in order to create reliable data sets for statistical analysis. The employed data sets contained 35 and 78 observations (data) for 1983 and 1987-1988, respectively. STATISTICALMETHODS The statistical significance of the difference in mean pollutant concentrations between 1983 and 1987 1988 was investigated (Winkler and Hays, 1975). For the day-of-the-week analysis, the observed data for each pollutant were first transformed into z - s c o r e s (zij are normalized pollutant concentrations in sample i in year j):
(c,j-~) Zij
- - , ~r i
where ~i is the average pollutant concentration in year j, cij is the pollutant concentration in sample i in year j, and ~ is the pollutant concentration standard deviation for year j. A z-score indicates the number of standard deviations a pollutant concentration in sample i is away from its mean in year j. A negative or positive z-score indicates that the pollutant concentration in sample i is below or above, respectively, its mean value in yearj. A z test analysis was performed in order to assess the statistical significance of differences between weekdays and weekends.
The complete set of observations (samples), as well as seasonal subsets of the data were examined using correlation coefficients and principal component analysis (PCA). PCA is frequently used for identifying pollutant sources affecting the air quality (Kleinman et al., 1980). PCA attempts to explain the variance of a large set of intercorrelated variables (the observed air quality data) with a smaller set of independent variables (the principal components). This procedure is advantageous because detailed information regarding atmospheric chemistry and meteorology is not required. PCA was performed using the Factor procedure in the SAS software package (SAS, 1982). The initial set of principal components generated by PCA are not readily interpretable. The first principal component is created to explain as much of the variance as possible. The second principal component tries to explain as much of the remaining variance as possible and so on (Harris, 1975). This analysis results in a n u m b e r of possible distinct sources being grouped together. In order to generate a more interpretable set of components, the initial set of principal components was transformed by Varimax rotation (Hopke, 1982; Thurston and Spengler, 1985). Hopke (1982) has suggested that the elimination of principal components whose eigenvalues are less than one can lead to the exclusion of meaningful principal components. Once the rotated principal components were determined, source identities were assigned based on the correlations between principal components and the original data. In order to have a statistically valid solution, a large number of samples is needed (Thurston, 1983). For a robust solution, the number of degrees of freedom (df) should be larger than 30n (Henry et al., 1984): df =mn-n--
n ( n + 1) 2
where m is the number, of observations (samples) and n is the n u m b e r of pollutants (variables). All principal component analyses presented in this paper satisfied this criterion except for the 1983 data set. Pratsinis et al. (1988), however, found the results of the latter PCA to be robust by means of an empirical statistical analysis. Quantitative estimates of source contributions to the F P and carbonaceous concentrations were obtained by regressing these concentrations to the absolute scores of the principal components using the stepwise procedure (Thurston and Spengler, 1985). The 95% confidence level in the regressions determined whether a regression coefficient assigned to a principal component was meaningful. Principal components were excluded from the regression model if the corresponding parameter estimates were negative~ or if the associated standard error of the regression was greater than one-third of the parameter estimate value and the regression was repeated. This procedure is similar to the one used in chemical mass balance calculations (Miller et al., 1972; Currie et al., 1984).
Receptor modeling of fine aerosol RESULTS AND DISCUSSION
Temporal comparisons Comparisons were made between the yearly average air pollutant concentrations in 1987-1988 and 1983 at Duarte (Fig. 1). A statistical analysis indicated that the difference in means between the 1983 and 1987-1988 data was significant, with 99% confidence for Fe, Mn, Br, Pb, SO 2-, Si, Al, FP and VC, and with 90% confidence for OC. Lead and bromine exhibit the most dramatic difference between 1 9 8 3 and 1987-1988. The five-fold decrease of the ambient concentration of these elements in this time period is attributed to the lower usage of leaded gasoline and the progressive reduction of Pb-Br-containing compounds in that gasoline. Clearly these data are in agreement with Fig. 2 that shows the average Pb concentration in leaded gasoline in the United States through 1987 (Dickson et al., 1987). The decrease of SO~ , and carbonaceous species concentrations from 1983 to 1987-1988 can be at least partly attributed to the different procedures for selection of sampling days. In 1983, sampling took place on days of distinctly
475
poor air quality, while in 1987-1988, sampling was conducted on randomly selected days. A day-of-the-week analysis was performed on selected pollutants to explore their origins further in 1987-1988. Figure 3a shows the z-scores of Pb, CO and S plotted against the day of the week on which sampling took place. The mean values for both Saturday and Sunday were compared with the mean value for the midweek days, and the difference in means was found to be significant at the 99% confidence level for Pb and CO, but not for S. Lead and CO mostly come from automobile emissions that are higher on weekdays than on weekends. On the other hand, particulate sulfur results from atmospheric conversion of SO2, a process clearly not dependent on the weekly cycle of human activities (Henry and Hidy, 1979). The results of this analysis are in agreement with the current knowledge regarding the sources of these pollutants. The origins of crustal elements in the 1987-1988 data set were also investigated by a z-score analysis in Fig. 3b. It is apparent that these elements exhibit large negative z-scores on the weekends while they fluctuate
12"
(b) I0"
1"0 T ( a ,
[7
[I-1 1983
[ ]
°"t Ill ~ "~
o
.
'
'
6
~
i"
0.4
0.2
0.0
0
AI
Si S/4 K
Ca Ti
IVln Fe Ni Zn Br
Pb
VC
CC
BC
FP/10
NH4
NO3
SO4
~E
=ft.
4
NOx
03 x 100
CO
SO2 x 100
Fig. 1. Yearly average concentrations of (a) fine (dp < 3.5 #m) aerosol components (#g cm- 3) and (b) carbonaceous and ionic components (#g cm- 3 at Duarte, CA, in 1983and 1987-1988,(c) ambient pollutant gases (ppm) at Azusa, CA, in 1983 and 1987-1988.
476
S.H. EHRMANet al. 25"
(a) oc 2.0 m 15
~o ° 05"
& ca 00
. . . 1972 . 1974 . . 1976 . 1978 1980 19 8 2 1984 ' ' 1966 1968 1970 1986 1988
Year
>,<
Fig. 2. Average Pb content in leaded gasoline in the United States through 1987.
SU around the average during the weekdays. The difference in mean concentrations between weekdays and weekends for Si, Ca and Fe was significant with greater than 99% confidence. Soil dust is of natural origin. However, it appears as an anthropogenic component of the aerosol because the loadings clearly follow the weekly pattern of activities. Driving motor vehicles (e.g. resuspended road dust) and construction work could result in increased loadings of soil dust and support observed temporal pattern of crustal element concentrations. A z-score analysis was also performed on the three carbon components and FP, in Fig. 3c. The concentration of volatile carbon fluctuates around the mean and does not seem to be affected by the day of the week. A statistical test on the difference between the weekend and weekday mean VC concentrations did not indicate any significant difference. This indicates that a fraction of the volatile carbon is non-anthropogenic and possibly biogenic since it is not sensitive to the weekly cycle of human activities. Organic carbon, on the other hand, appears as an anthropogenic component and the difference between the weekend and weekday mean concentrations was significant at the 98% level. This is very intriguing because it is known that organic carbon is closely related to the products of atmospheric photochemical activity. It is possible that fast atmospheric reactions lead to the formation of secondary organic carbon so its ambient concentration is closely tied to primary organic vapor emissions rather than meteorology. Black carbon also appears as an anthropogenic component which is not surprising since BC is a combustion product closely tied to primary particulate emissions. Finally, FP has large zscores on the weekends and mostly positive scores on the weekdays. The weekend and weekday mean concentrations were significantly different at the 95% level indicating that the FP mass is clearly related to anthropogenic activities.
Correlations amon9 air quality variables
In 1987-1988, high correlations (r2>0.90) were found between NO 3 and NH~-, and among Si, Ca and
II
-1
NO
TU
WE
TH
FR
SA
NO
TU
WE
TH
FR
SA
TU
WE
m
SA
(bl
su
•
•
1
(c)
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? N
•
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114
Fig 3. Average (a) lead, carbon monoxide and sulfur; (b) silicon, calcium and iron; (c) volatile carbon, organic carbon, black carbon and fine particle mass z-scores for every day of the week for 1987-1988 in Duarte, CA.
Fe. Good correlations (re> 0.80) were found between S and SO ] - , and among A1, K and Ti. In 1983, high correlations were found between Pb and Br, between VC and OC, and between S and S O l - . Good correlations were found among VC, OC, BC, Br, Pb, CO and NO2, among Pb, NH,~ and NO3, among S, SO] , and NH 2- and among Si, Ca and Fe (Pratsinis et al.,
Receptor modeling of fine aerosol 1988). It is worth noting the reduction in the correlation coefficient between motor vehicle tracers from 1983 to 1987 1988. To investigate seasonal changes in the observed air quality variables in 1987-1988, the sampling dates were divided roughly according to the seasons of the year and the correlations between air quality variables were explored. Forty-nine samples collected in the months of April to October were included in the summer subset. Twenty-nine samples collected in the months of November to March made up the winter subset. In the summer subset, the NH~ concentration correlated strongly (r2=0.97) with SO42 concentration. In the winter subset, NH,~ correlated weakly (r2=0.62) with SO 2- and strongly (r2=0.98) with NO 3. The variables associated with automobile emissions (CO, NOx Pb) showed generally higher correlations with each other in winter than they did in the summer. Lead correlated strongly (r 2 > 0.80) with CO, NOx, NO3, OC, Mn, Zn and Br in the winter set, and showed a low correlation (r 2 < 0.55) with other variables in the summer. The carbonaceous components showed no correlations other than with each other in the summer. In the winter set, correlations (r 2 >0.70) were found among VC, Zn, Br, CO and NOx; among OC and K, NH~, VC (r2 >0.70), Mn, Fe, Zn, Br, Pb, NOx, NO 3 (r2> 0.80) and CO (r 2 >0.90); and among black carbon and Zn, NH~ (r2>0 70) and CO, NOx, VC and OC (r2> 0.80).
Principal component analysis The results from the principal component analysis on the 1987 1988 data set are shown in Table 1. Sodium and chlorine were excluded because of difficulties in analysing their concentrations. PIXE is not a
477
good analytical technique for measuring sodium concentrations. Chlorine has a high equilibrium vapor pressure and easily volatilizes from the collected samples. Variables such as Br, Ni, Ti and SO2 were also excluded from the analysis since they were not detected in more than 10% of the samples collected in 1987-1988 (Pratsinis et al., 1988). A principal component correlating strongly (r 2 >0.90) with CO and significantly (r2>0.70) with Pb, OC, BC and NOx appeared as the first principal component. The sampling site is located downwind from downtown Los Angeles, an area of high primary motor vehicle emissions. Crustal elements dominated the second principal component while the third principal component correlated with sulfur and sulfates. Principal components with single loadings of N H 4 / N O 3, 03, Zn and VC appeared after the abovementioned principal components. Ozone did not appear until the fifth principal component and the corresponding eigenvalue was low. The ozone has a much stronger showing in the principal component analysis of the 1983 data at Duarte (Pratsinis et al., 1988). This is attributed to the meteorology of 1983. The sixth component correlated with zinc which has been linked to refuse incineration (Hopke et al., 1982; Johnson, 1982) and tire dust (Miller et al., 1972). The latter seems more probable for the Los Angeles Basin. PCA was also performed on a data set including Br, Ti, and on another data set including Br, Ni, Ti, SO 2. As these variables were included, singular principal components associated with those variables appeared (Roscoe et al., 1982). Aside from these changes, the overall structure of the principal components remained identical to that obtained in the absence of these elements. The 1983 PCA results are shown in Table 2. In 1983, variables associated with primary auto emissions (Pb,
Table 1. Principal component analysis, 1987-1988 air quality data Motor vehicles AI Si
-0.12 0.07
S
Eigenvalue
0.87 0.99
Sulfates Nitrates
0.10 0.06 0.49 0.12 0.58 0.72 0.94 0.76 -0.08 0.57 0.57 0.10 0.64 0.89 0.85
0.89 0.98 0.58 0.97 0.28 0.09 -0.03 0.07 0.21 -0.03 0.02 0.00 - 0.08 0.17 -0.1
-0.03 -0.09 -0.05 0.02 0.26 -0.01 -0.03 0.01 0.44 0.27 -0.10 0.96 0.20 0.08 0.05
--0.11 --0,04 0.00 0.08 -0.01 0.23 0.03 0.10 0.29 0.16 0.23 -0.28 0.74 0.77 0.08 0.13 0.16 0.23
5.2
5
2.3
1.6
0.00
K Ca Mn Fe Zn Pb CO NO~ 03 NHg NO3 SO2VC OC BC
Soil dust
-0.01
0.17 0.01 0.96
Bold values indicate component loadings > 0.70.
03
Zn
Carbon
0.12 0.05 0.16 0.10 0.00 -0.11 0.02 -0.01 -0.04 -0.08 -0.06 0.82 -0.16 -0.22 0.11 0.09 0.05 0
0.12 0.05 0.11 -0.09 0.02 0.36 0.10 0.69 0.20 0.01 0.17 -0.01 0.05 0.06 0.00 0. i 1 0.10 0.09
-0.07 --0.01 0.00 -0.02 0.01 0.06 0.01 0.11 0.07 -0.04 0.09 0.06 0.09 0.05 0.10 0.71 0.17 0.15
0.8
0.8
0.6
478
S.H. EHRMAN et al. Table 2. Principal component analysis, 1983 air quality data Motor vehicles Na Si S K Ca Ti Fe Ni Zn Br Pb CO 03 NO 2 NH~ NO 3 SO ]-
VC OC BC Eigenvalue
Soil dust
Sulfates
03
Marine aerosol
Fuel oil, fly ash
-0.15 0.17 0.19 0.07 0.07 0.19 0.46 0.48 0.64 0.82 0.90 0.76 0.09 0.85 0.67 0.93 0.19 0.71 0.76 0.85
0.08 0.94 0.08 0.59 0.93 0.66 0.79 0.13 0.30 0.28 0.21 0.21 0.25 0.16 0.00 0.07 -0.04 0.14 0.31 0.07
0.08 0.06 0.94 0.06 - 0.05 0.03 0.08 0.11 0.28 0.21 0.29 0.21 0.25 0.17 -0.01 0.07 0.96 0.36 0.16 0.12
0.12 0.09 0.20 0.23 0.09 0.00 0.16 -0.01 0.12 0.19 0.05 0.17 0.87 0.06 -0.08 -0.28 0.18 0.31 0.38 0.12
0.98 -0.12 0.09 0.17 0.20 0.03 0.03 0.08 0.07 -0.15 -0.11 0.06 0.10 0.05 -0.08 --0.05 0.05 -0.16 0.05 - 0.07
0.05 0.02 0.07 0.14 0.08 0.03 0.09 0.86 0.11 0.17 0.14 0.09 0.13 0.14 -0.02 -0.04 0.02 0.06 0.09 0.01
7.0
3.7
3.1
1.3
1.1
1.0
Bold values indicate component loadings>0.70.
Table 3. Principal component analysis, winter 1987-1988
AI Si S K Ca Mn Fe Zn Pb CO NOx 03 NH,~ NO 3
SO ] VC OC BC Eigenvalue
Motor vehicles
Soil dust
Sulfates
03
Nitrates
0.11 0.16 0.29 0.39 0.25 0.70 0.62 0.78 0.79 0.90 0.90 -0.06 0.65 0.67 0.20 0.75 0.85 0.82
0.92 0.97 - 0.20 0.79 0.92 0.50 0.74 0.37 0.27 0.23 0.19 --0.08 0.09 0.17 - 0.04 0.18 0.39 0.13
-0.1! -0.08 0.91 0.18 -0.10 -0.01 0.05 0.10 0.09 0.22 0.33 0.07 0.45 0.30 0.95 0.17 0.14 0.38
-0.01 -0.02 0.11 -0.03 -0.09 -0.09 - 0.06 -0.07 -0.21 - 0.11 -0.04 0.99 -0.13 -0.13 0.130 0.19 -0.04 0.22
0.03 -0.01 0.08 0.22 0.06 0.11 0.04 0.09 0.24 0.09 0.08 --0.07 0.59 0.64 0.11 0.15 0.18 0.14
6.9
4.6
2.5
1.2
1
Bold values indicate component loadings > 0.70.
CO, NOx and carbon) showed m u c h higher correlations with nitrates than in 1987-1988. Lead had a c o m p o n e n t loading of 0.90 in 1983 and it d r o p p e d to 0.72 in 1987-1988. In 1983, the m o t o r vehicle principal c o m p o n e n t emerged as the one explaining the largest a m o u n t of the variance. In 1987-1988, the soil dust c o m p o n e n t was almost equally significant. This shift p r o b a b l y comes from the decreased loadings of primary automotive variables (Pb, Br, C). Furthermore, the Ni factor which is usually associated with fuel oil
c o m b u s t i o n was not present since ambient Ni concentrations d r o p p e d below the detection level in 36 out of 78 samples in 1987-1988. This could be attributed to the fact that some power plants in southern California shifted from burning fuel oil to natural gas in the early 1980s. P C A was performed on seasonal subsets of the 1987-1988 data. The results are shown in Tables 3 and 4. In the winter subset, tracers of m o t o r vehicle emissions showed high loadings in the first principal
Receptor modeling of fine aerosol
479
Table 4. Principal component analysis, summer 1987 1988
Al Si S K Ca Mn Fe Zn Pb CO NO~ 03 NH~ NO 3
SO4z VC OC BC Eigenvalue
Soil dust
Sulfates
Carbon
NO x
0.90 0.99 -0.10 0.87 0.98 0.84 0.98 0.30 0.05 -0.08 0.09 0.14 -0.04 0.34 0.06 -0.16 0.14 0.14
0.05 --0.06 0.93 -0.16 -0.13 0.12 0.00 0.42 0.11 0.04 0.05 0.26 0.94 0.29 0.95 0.35 0.17 0.13
-0.15 -0.05 0.02 0.00 0.30 0.06 0.03 0.31 0.29 0.35 0.17 0.26 0.11 0.01 0.18 0.49 0.82 0.92
0.05 0.02 0.09 -0.08 0.01 0.20 0.05 0.30 0.22 0.13 0.94 0.14 0.02 -0.03 0.00 0.21 0.16 0.08
-0.15 0.01 -0.11 0.02 0.02 0.19 0.06 0.24 0.89 0.20 0.18 0.10 0.13 0.01 0.11 0.15 0.23 0.13
5.5
3.2
2.2
1.2
1.1
Pb
03 0.02 0.03 0.06 0.12 0.01 0.02 0.03 0.02 0.10 0.16 0.12 0.89 0.11 0.02 0.12 0.18 0.21 0.11 1
CO
Nitrates
0.06 -0.07 0.04 0.01 -0.02 0.03 - 0.05 0.15 0.19 0.86 0.10 0.14 0.01 --0.16 0.01 0.09 0.14 0.21
-0.04 0.09 0.00 0.15 0.06 0.15 0.10 0.01 0.01 -0.17 -0.02 0.03 0.21 0.88 0.09 -0.07 0.02 0.02
0.9
0.9
Bold values indicate component loadings>0.70.
component. The carbonaceous aerosol concentrations also correlated with this principal component. Soil dust, sulfates and 0 3 principal components followed in descending order. In the summer, soil dust emerged as the first principal component. Sulfates emerged second in the order followed by a distinct carbonaceous principal component. The m o t o r vehicle variables were split into singular principal components of carbonaceous fractions, Pb, CO, nitrates and NOx. Ozone did not appear until the sixth principal component and nitrates did not appear until the eighth principal component. These results indicate that during the winter, when low photochemical activity takes place, NOx, nitrates and carbonaceous aerosols are closely associated with primary m o t o r vehicle emissions. In the summer, however, meteorology plays an important role in the formation of these pollutants so their ambient concentrations do not follow tracers of primary emissions. Clearly, active atmospheric chemistry during the summer creates different pathways for generation of secondary carbonaceous particulates. The location of the N H ~ ion in the PCA varied from summer to winter. In the summer, N H ~ correlated strongly with the sulfate principal component. In the winter, it correlated with the nitrates.
Table 5. Source apportionment of fine particle mass (%) by PCA-SR at Duarte, CA
Motor vehicles Soil Sulfates VC
78 samples
60 samples
47 i 5
10 + 1 23 _+4 16 _+4 12+3
16 _+4
r2
63
%3 :z
77
"~
1
0 20
40
60
80
Sampling days, 1987-88
Fig. 4. Comparison of daily FP and silicon aerosol concentration at Duarte, CA, in 1987-1988.
Source contributions The fine aerosol mass and the carbonaceous aerosol fractions were apportioned to the principal components by stepwise regression on the components' scores (Thurston and Spengler, 1985; Pratsinis et al., 1988). A summary of these results is given in Table 5. Of the fine particle (FP) mass accounted for by the model in 1987-1988, the m o t o r vehicle principal component accounted for 47% of the total while the sulfate
principal component accounted for 16%. These results are in agreement for these sources with an earlier study at the same site in 1983. It might seem odd,' however, that soil dust has no contribution to the total aerosol mass in 1987-1988. To investigate this, Fig. 4 is presented showing daily loadings of F P and Si, a typical crustal element. Beginning in May of 1988
480
S.H. EHRMANet al.
(sampling days 60-78), the Si loadings increased ap- fractions from 1983 to 1987 1988. Thus loadings of proximately three-fold over their average, indicative of elements associated with soil dust (Si, Ca, Fe) varied some soil upturning event taking place near the sam- with human activity as did loadings of Pb, CO, BC, pling site. Similar behavior was obtained for other OC and FP. The loadings for volatile carbon (VC), on crustal elements such as A1, Ca and Fe. In contrast, the the other hand, were not dependent on the weekly FP did not show a significant increase during this time human cycle, suggesting a non-anthropogenic and period. PCA and stepwise regression (PCA-SR) ana- possibly biogenic source for VC. lyses were also performed on a data set comprised only Motor vehicles and soil dust emerged as the princiof samples collected until April 1988 (the first 60 pal components explaining the largest amount of samples of 1987 1988). The structure of the principal variance in the 1987-1988 data. The motor vehicle components was consistent with the previous analysis principal component also explained the largest with the addition of a weak (eigenvalue = 1) principal amount of variance in 1983. However, the loadings of component highly loaded with AI. Results of regres- variables associated with primary motor vehicle emission analysis on the set of 60 samples are also shown in sions decreased in 1987 1988. The ozone principal Table 5. The most apparent difference between the 78 component showed a large decrease in the amount of and the 60 PCA-SR sample analysis of the FP was the variance accounted for from 1983 to 1987-1988. The appearance of a soil dust and volatile carbon contribu- air quality in 1987-1988 was better than in 1983 tion of 23 and 12%, respectively, of the total while the primarily due to favorable meteorology in the Southern California Basin. motor vehicle contribution was reduced to 10%. Seasonal analysis revealed differences in the correlIn 1983 the PCA-SR revealed contributions to FP by ozone photochemistry and marine aerosol (Prat- ations and factor patterns of winter and summer data sinis et al., 1988). The difference in the meteorology subsets. In the winter, a small set of principal componand data analysis may explain these results. The lack ents explained most of the data variance. Pollutants of contribution by ozone photochemistry in emitted by motor vehicle variables (Pb, CO, NOx, 1987-1988 may be attributed to meteorology and carbonaceous fractions) appeared clustered in one weather patterns. Marine aerosol contributions could factor in winter, while each one of these variables not be determined because separate chemical analysis emerged as a singular principal component in the for Na was not carried out in the 1987 1988 samples. summer. Regression analyses, performed on the full data set Similar results (to PCA-SR) were also obtained when simple multiple linear regression (Kleinman et of 78 observations, showed that motor vehicles (47%) al., 1980) was used employing trace elements for each and sulfates (16%) accounted for the fine particle mass source (Si or Ca for soil, CO or Pb for motor vehicles, at Duarte, CA, in 1987 1988. There were also found NO 3 for nitrates, S for sulfates and 0 3 for photo- unusually high concentrations of crustal elements at chemistry) for the set of 78 and 60 samples, respect- the end of that sampling period. When only the first ively. The regression results for the 1987-1988 data set 60 samples were included in the analysis, soil dust containing all 78 samples and just the first 60 samples (23%), sulfates (16%), motor vehicles (10%) and volashow some limitations in the use of multiple linear tile carbon (12%) accounted for the fine mass at the regression techniques when a source exhibits a drastic same site. The latter source estimates are more in line change in its contributions during the sampling with the current understanding of pollution source period. Furthermore, the present source estimates contributions in residential areas in Los Angeles should be considered rather qualitative because of the though still a large aerosol mass fraction was not low coefficient of determination (r 2) of the regression accounted for (~40%). The above results indicate, model (Kleinman et al., 1980) and the fact that a large however, that the regression procedures possibly are mass fraction ( ~ 40%) is not accounted for. In order to sensitive to dramatic, non-random changes in the perform a quantitative source apportionment analysis contributions by a single source during the period of on data where the source receptor relationship varies sampling, and the performance of the regression can greatly but not randomly during the sampling period, be adversely affected by these changes. as in the case of soil dust in the 1987 1988 data, another method such as chemical mass balances on Acknowledgements--This research was sponsored by Southern California Edison. S. H. E. was supported in part by the each sample might be more appropriate. National Science Foundation through a Research Experiences for Undergraduates Grant, EID-9000779. We also gratefully acknowledge contributions on the statistics of this paper by Dr Eleni Pratsini and discussions with Mr J. D. Landgrebe (Procter & Gamble) and Prof. Sheldon K. FriedCONCLUSIONS lander (UCLA). Receptor modeling of the fine (dp < 3.5 #m) aerosol at Duarte, CA, was conducted for samples collected in 1983 and 1987-1988. A t-test analysis revealed significant differences in the ambient concentrations of SO ] - , A1, Mn, S, Si, Fe, Pb, Br and the carbonaceous
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