Covariations in the concentrations of organic compounds associated with springtime atmospheric aerosols

, Vol. 21. No. Amrorpknr Enoiramrr Printed m Great Bntain.

12, pp. 2549-2%.

ooo4498

1987

Q 1987

I /a7 13.00 + 0.00

Perpmon

Joumak

Ltd

COVARIATIONS IN THE CONCENTRATIONS OF ORGANIC COMPOUNDS ASSOCIATED WITH SPRINGTIME ATMOSPHERIC AEROSOLS RANDALL

C. GREAVES, ROBERT M. BARKLEY and ROBERT E. SIEVERS*

Cooperative Institute for Research in Environmental Sciences and the Department of Chemistry and Biochemistry, Campus Box 215, University of Colorado, Boulder, CO 80309, U.S.A.

and ROBERT R. MEGLEN

Center for Environmental Sdences, Campus Box 136, University of Colorado, Denver, CO 80202, U.S.A. (First received

9 July 1986 and inJinalform 10 March

1987)

Abstract--Organic compounds that can be thermally desorbed from airborne particks change cohesively with time, providing information about sources, photochemical transformations and transport of aerosols. In the spring of 1985,138 airborne particulate samples were colkcted at an urban site in Boukk%,Colorado. Samples were colkcted by drawing approximately 300 / of air, for 58 min, through a small glass tube containing a quartz fiber filter. Particks were subsequently analyzed by direct thermal desorption of volatik organic compounds into a gas chromatographic column followed by separation and detection of compounds with flame ionixation or mass spectrometty. Factor analysis on the concentrations of 42 organic compounds in 138 l-h samples with time and meteorology revealed characteristic chromatograms for photochemical activity, biological sources and motor vehicle sourozs. Organic compounds desorbed from particka include terpenoids from biogenic sources, alkanes from vehicular and biological sourcc$ and aldehydes, ketones, carboxylic acids, &tones and furans from photochemical transformations and other sources. Concentrations of oxygenated specks increased on sunny days relative to cloudy days or nights. Terpenoid concentrations increased when the wind direction was from a forested region west of the sampling site. Odd carbon number n-alkanes increased as temperature increased with the progression of springtime. Key word index: Particles, aerosols. organic compounds, thermal dcsorption, photochemistry, biological, principal factor analysis, transformations, transport, sampling.

INTRODUCXION

It has been much more difficult, until now, to determine what information could be learned about

In trying to better understand the processes that occur in the atmosphere affecting the emission, transport and transformations of chemicals in the atmosphere, scientists have made extensive measurements of the inorganic constituents of particles and used statistical techniques such as factor analysis to learn what they could from these species. These studies have identified factors which measure coal, oil, wood and motor vehicle fuel combustion as well as particles arising from wind blown dust, marine sea spray, refuse burning, smelters and a wide range of individual specific industrial sources (Hopke, 1980; Stevens et ol., 1984; Dzubay et al., 1984; Olmez and Gordon, 1985; Tuncel et ol., 1985). Whik the earlier investigations have been very effective, they are directed principally at inorganic aerosol constituents, and, therefore, do not directly discern atmospheric processes or aerosol sources involving organic compounds.

sources, chemistry and the effect of meteorological parameters on the organic constituents of aerosols. The complexity of classical methods for measurement of organic compound concentrations, with the tedious and lengthy solvent extraction and fractionation, have made it difficult to obtain information about organic composition as detailed as that available for inorganic constituents. While these analytical difliculties are not insurmountable problems, they have been prohibitive to the acquisition of large amounts of data about organic compound composition over short time intervals. A procedure has recently been developed in our laboratory for the analysis of the volatile organic fraction of airborne particulate matter that is faster and less tedious than earlier methods (Greaves et a/.,1985). Unlike earlier procedures, which utilize only a fraction of the collected sample, volatile species from the entire sample arc analyzed, greatly increasing

*Author to whom correspondence should be addressed.

effective sensitivity. This technique involves passing approximately 300 L of ambient air through a small

RANDALLC. GREAVESet al.

2550

glass tube containing a quartz fiber filter supported by a glass frit. After collection, particles are analyzed by direct thermal desorption of volatile organic compounds into a cryogenically cooled gas chromatography column, followed by subsequent heating of the column and detection of the eluting compounds by flame ionization or mass spectrometry. This technique was used to collect and analyze 138 particle samples and to produce organic composition data for aerosol source apportionment by factor analysis. OJ and CO concentrations in the air, temperature, cloud cover, wind speed and wind direction were all monitored continuously during particle sampling. The final data set used in the factor analysis calculation included these variables and the concentrations of 42 organic compounds identified in the airborne particles. Particle samples that showed varying degrees of contributions from natural and anthropogenic sources were observed in this study. Occasionally, when the wind direction was from the west, terpenoids were observed that appeared to be substantially, if not exclusively, from natural sources. In other cases, terpenoids were not readily detected but a significant odd/even HC preference was observed. The concentration of oxygenated organic compounds was found to increase substantially on sunny days as a result of photochemistry, but earlier in the same day alkanes from vehicle and biogenic emissions were also major aerosol constituents. The major objectives of this study were to identify the different sources which contribute to the springtime particulate organic composition of aerosols in an urban/residential environment, and to utilize the rapid analysis features of low volume sampling/direct thermal desorption to observe the hourly fluctuations in sources and particulate organic composition as they change with sunlight, meteorological conditions and time. EXPERIMENTAL Equipment

Instruments for measuring OB, CO, wind speed, wind direction and temperature were all ntaintained and calibrated by the Colorado Department of Health. lnfotmation on the cloud cover during sampling was obtained from the National Weather Service in Denver, Colorado. CO was monitored with a Reckman model 866 ambient CO mOmtOriBgsystem (Beckman Instruments Inc., Fullerton, CA) according to the EPAdesigBated reference method. 0s was monitored with a Dasibi Environmentaf Corp. model 1003-RS OS analyzer (Dasibi Environmental Corp., Glendale, CA) according to an EPA-designated QUiWlkBt method. Ouantitation of organic corn~unds was performed with a Hewlett-Packard m-&l 5ggdA gas chr&atograph with flame ionization &tection (GC/FIDL Compo~d Ideatification was performed with a‘H&ktt-Pack& model 5892A gas chromatograph/rnass spectrometer with data syslem (GC/MS/DS), which was moditied such that the end of the chromatographic column extended into the ion source of the mass spectrometer. Roth the GC/FlD mtd GC/MS systems used helium carrier gas through a 25 m x 0.25 met i.d. (0.25 ym film thickness), Ultra Performance fused silica

capillary column, manufactured by Hewktt-Packard. The

linear flow rates through the columns instde the GC/PiD and GG/MSwere3Ocms-‘at 300”Cand4Ocms-‘at 300°C. respectively. The chromatographic oven temperature programwas -60toO°Cat200Cmin~‘thenfromOto300’C at 5 “C min-’ and finally, held at 300 “C for 15 min The injection port temperature of both instruments was 250 “C and the FID temperature was 300 “C. The mass spectrometer

electron impact voltage was 70 eV. Low volume air samples were collected with a Nutech model 221-1A gas sampling pump (Nutech Corp., Durham, NC). The thermal desorption apparatus was constructed from a cylindrical aluminum block (7 cm diameter x 8 cm long with a 2.54 cm hole in the middle) and was fitted with a 165-W heating cartridge connected to an Omega 6000 thermal controller (Omega Engineering, Stamford, CT). Exact operating conditions for the low volume sampling pump and the thermal desorption heating block have been described previously (Greaves et al., 1985). Sampling and analysis Between 18 April 1985 and 20 June 1985,138 air particulate samples were collected at the Colorado Department of Health, Pollution Control Division, Air Monitoring Station in Boulder, Colorado on the campus of the University of Colorado. Partide samples were collected at a flow rate of between 3.5 and 6.5 Cmin-‘, which resulted in filtering between 200 and 400 Cof ambient air. The particle sampling tube was positioned approximately 5 m from ground kvel and within 5 m of the inlets of the 0s aad CO analyzers. Airborne particles were collected for 58 tnin, on the hour, with a 2 ntin per santple interruption used for changing the particle coktion tube. After sampk collection, tubes were again placed inside screw cap culture tubes, covered with aluminum foil, sealed with a screw cap, and stored in crushed ice until they could be transferred to a freezer. The times recorded for sample collection are those when the sampler was removed rather than inserted. Several l-h aerosol stunples were collected consecutively in sessions ranging in duration from 3 to 24 h, with approximately two sessions per week. Larger samples for GC/MS analysis were also collected routinely throughout thii study, but these samples were used only for compound identification. The latter sampies were cofkcted for times ranging from 1 to 12 h resufting in collection volumes ranging from 300 to 4000 d. Field blanks were also obtained during this study by filtration of a token 1OC of ambient air through a new, conditioned particle collection tuk. Samples were stored in a freezer at - 15 “C prior to analysis and were usually analyzed within 72 h after collection. Analysis involved the thermal c&sorption of particulate orgrmiccompounds, at a maximum umtperature of 254 “C for 15 min. directly from the collected particles into a cryogeaicolly cooled ( - 60 “C) fused silica capillary chromatograph column (Greaves et al., 1985). Site description

The,Boulder site was chosen by the Colorado Department

of Health as being typical of an urban. residential environment which is re~mved by several kilometers from any heavy industrial activity. In all directions from the sampling sit& for an approximate radius of 5 km, is the city of Bout&r (popuhtion 8O,flC#) and its surrotmding residentitd areas. Mountains and the Roosevelt National Forest are directly west of Boulder aad there are no large urban or industrial centers to the west for approximately 1000 km. Deave& Colorado, and its large metropolitan area and suburbs populated by approximately 1.5 million inhabitants, lies to the southeast, approximatdy 40 km from the sampling she. ‘Thecountryside castof Boulder is dominated by grassland and cropland with very few trees, while the area west of Boulder has extensive conikr forestation. Several parWe samples were also collcetcd between 20

June 1984 and 3 July 1984 at Niwot Ridge. Colorado. These

2551

Covariations in the concentrations of organic compounds sampks were used to identify the organic constituents of aerosols from the mountainous/forested area lying west of Boulder. This site is at an elevation of 3050 m and is located approximately 8 km cast of the Continental Divide. Engkman spruce and Sub-alpine fir are the two dominant conifers in this region but sign&ant numbers of Ponderom pine and deciduous Aspen are also present. Ground cover comists ofgasses, sedges, Ilowers and various other types of plants. After sample collection, sampks were analyzed according to the standard thermal desorption procedure. Factor analysis and data handling Chromatographic peaks were initially sekcted for integration and used in the factor analysis model based on chromatographic and practical analytical considerations. A peak was used in the factor analysis if chromatographk resolution, compound abundance and contamination levels in the field blanks were all acceptable for that compound. Several common aerosol components, such as polycyclic aromatic hydrocarbons (PAHs) and phthalate esters, were observed in this investigation but were not used in the factor analysis calculation because of analytical interferences. Many carboxylic acids were chromatographically unresolved from other organic constituents and were therefore excluded from the factor analysis data set. In the present study, PAHs were present at concentrations that were too low for reliable flame ionization detection and phthaiatcesters were often observed in field blanks, so neither class of compound was included in the factor analysis. After peak &&on, the integrated area for each compound was transformed into the compound concentration in ambient air (Greaves, 1986)and the resulting matrix was then further transformed by the logarithmic equation, X = (In C/C&in Sd

(1)

where X is the transformed concentration variableand Csand S, are the geometric mean and the standard devlatlon, respectively (Heidam, 1982).A Chi-square test was conducted at the 90 % con6dence level on the log-transformed concentration data to determine if the resulting variables were normally distributed (Hughes and Grawoig, 1971). Results of this test indicated that, in general, the transformed variables were normally distributed. Histograms and log-probability plots were also constructed for all of the variables and their log-transforms, in order to visually inspect the distribution of each variabk and to identify outliers. In some cases, multiple distributions were observed but in general, these plots showed that the organiccompound concentrations were log-normally distributed and that most of the outlicrs could be attributed to specific analytical errors and transcription errors. Logtransformed concentration data were used in the factor analysis calculation and outliers in the data set were treated as ‘missing data’. Approximately 3 % of the values in the data matrix were missing, so in the factor analysis treatment. these values were replaced by the mean values for those variables. Besides the missing values due to outliers, there are also several ‘missing values’ because compound concentrations were too low for detection. Compounds that were not detected in a particular sample were not treated as missing data, but were reported as the value of the detection limit. Additional transformations of the data matrix were conducted to eliminate variable discontinuities and to add constructed variables. The discontinuity in the wind direction, which occurs at 0 and 360 degrees, was eliminated by a modification of the transformation described by funcel (Tunccl et al., 1985). This procedure results in the formation of two new wind direction variabks, designated west-t and south-north, which can be described as vector components of the original wind direction in either a west toeast or a south to north direction. It should be noted that, in the present convention, wind from the west has positive values (Tuncel et al. gave negative values to west wind), so the west-east variable has a positive rather than negative correlation with

the high terpenoid compound concentrations originating from the forests west of Boulder. The following tramfo~~~ation equations were used (WD = wind din~tion): if wind direction is < 90”. then west-east = - WD/90 (2)

if wind direction is > 90” and $270”, then west-cast

if wind direction is > 270” and < 360”. then west-east = 4 - (WD/90) (4) if wind direction is $ i8V, then south-north

= (WDPO)

-1

(5)

if wind direction is > 180” and $ 360”, then south-north = 3 - (WD/90). (6) Each new variable now represents the component of the wind in a given direction and can be useful individually in the factor model. The discontinuity in the time variable, which occurs at 24 :00 and 00: 00 h, was eliminated by a transform similar to the wind direction transform (Greaves, 1986). For the time variable, the deconvolution procedure also resulted in the formation of two variables, which are designated night-day and dusk-dawn. Two other variabks were also constructed and used in the factor analysis calculation. These transformations were performed on sekcted concentration values according to the following equations. HP1 = 0.5(x,

Cl9 to C31/L;,

Cl8 to C30

to C32) (7) +&dd Cl9 to C31/&,,C20 P ,ym = Z concentrations of the organic compounds in Table 1. (8) In Equation (7) the hydrocarbon preference index, HPI, is analogous to the carbon preference in&x, which indicates the extent of predominance of n-alkanes with odd C numbers over those with even C numbers (Cooper and Bray, 1963). Because of chromatographic interferences, the entire range of normal HCs (n-Cl1 to n-C&, which is normally used to calculate the C preference index, could not be individually measured. Equation (8) is the summation of the total concentrations for all chromatographic peaks used in the factor analysis calculation, for each sampk investigated. Factor analysis computations were conducted &iing the Statistical Packaaes for the Social sciences (SPSS-X) computer program (;Gorusis, 1985). Factors we& ini&ly cxtracted from the Z-scored normally distributed data set by the method of principal components. This resulted in the extraction of nine factors with eigenvalues greater than one. A simple and objective procedure was then used to determine the dimensionality of the factor model (Heidam, 1982). Once the dimensionality of the model was determined, factors were again extracted according to the method of principal components and were subsequently interpreted after varimax rotation. RESULTS AND DISCUSSION Factor

interpretations

A total of 53 variables for 138 samples were used in the factor analysis computation. Forty-two of these variables are the concentrations of organic compounds associated with particles, as measured by low volume

sampling coupled with direct thermal dcsorption. Other factor analysis variables included wind speed, wind direction, cloud cover, temperature, time, CO concentration, O3 concentration, HP1 and P,, The range

and

average

concentrations

of the

organic

RANDALLC. GRMVES et al.

2552

compounds measured, the standard deviations and the temperature programmed chromatographic retention indices are listed in Table 1. There are several methods for determining the dimensionality of the factor model, but most of them are based on rules of thumb, or other subjective methods. The difficulty with such methods is that different rules lead to different solutions, resulting in uncertainty in the overall factor analysis. In the present discussion, the number of factors was determined by a simple and objective procedure (Heidam, 1982). This method involves conducting a series of hypothesis tests, at a selected confidence level, in which the number of factors was decreased by one for each test conducted. The hypothesis states that the M-1 dimensional model is a sufficient factor solution. The decre-

ment and test algorithm process continues until the hypothesis is rejected by showing that a calculated T statistic is greater than an F coefficient. When the hypothesis is rejected it is concluded that, while the Ml solution is not sufficient, the previous M solution is not only sufficient but necessary. Through the use of the Heidam test procedure, the hypothesis was first rejected when four factors were considered (when M = 5, then r = 7.19 and F(0.99) = 7.17). meaning that four factors are not sufficient but five factors are sufficient and necessary. Accepting the five factor solution resulted in rejecting all factors with eigenvalues less than 2.3. A list of the variables which make up these factors, together with their varimax rotated factor loadings, is presented in Table 2. These combined factors account for 70.7 “/, of the total variance in

Table 1. Concentrations of volatile compounds from the thermal desorption of urban airborne particles l

Min. conc.tt (ngm-‘)

Compound Factor 1 ;t;h;ty=nzddehydc(l7)§

1-penten-3_one(lt?)§ 2,Sdimetbyl furan(3)t u-angelicalactone(7) dihydro+dimethyl/ -2(3H)-furanone(9)t n-heptanoic acid(lO)t 1.3~indandione(13)t phthalide( 14)t 2,3-dihydro-5/ -methyl furan(l)t toluene(4)t 4-methyl-3/ -pentene-2-onc(S)t acetic acid(2)t benzaldehyde(S)t S&dimethyl-2/ (SH~furanone§ 2-pentadecanone(l9fl n-nonanoic acid(l2)t n-propionic acid(6)t 2-tridecanone( 15)$ piperitone(1 l)t $6,7,74-tetrahydro 4.4.7&wimethYl-(SU -i(iH)-benxof;r&me(

ML%Iu cont.

Standard

retention

(ngm-‘)

dev.$$

index@

(ngm-‘)

Chrom.

< 19 < 19

50 64

26 26

7.1 10

1576 1583

< 6.4 < 32 < 19

17 84 58

8.7 46 27

2.1 12 7.9

768 870 988

170 99 58 160

59 40 34 43

32 11 8.8 29

1109 1342 1348 709

49 48

17 24

8.4 5.5

788 805

1200 44 50

420 21 21

220 5.9 7.1

123 960 956

<30 < 26 < 25 < 15 < 8.5 < 18 < 210 < 19 < 19 < 8.0 < 26 c260 < 8.0 < 5.7 < 0.7

16 56 590 22 28 14

9.8 34 340 12 9.1 2.1

2.0 7.1 69 3.2 4.0 2.1

1706 1287 817 1496 1253 1541

< < < < <

5.7 5.1 5.7 6.5 5.7

33 130 150 40 110

9.7 18 29 9.4 16

5.0 21 28 5.8 17

1182 1123 1226 1283 1189

< 5.7 < 6.1

25 53

8.9 13

4.5 8.7

1165 1173

< < < < < < <

35 15 11 71 32 20 72

7.5 4.1 4.2 23 5.2 5.5 17

5.2 3.0 2.1 17 4.1

2900 3100 2700 1847 2100 2300 1470

16)s

Factor 2 terpenoid(23)WI terpenoid(20)#** tcrpcnoid(25)P bornyl acetate(26)t l-(1,4dimethyL3qclo/ hexen-1.yl)-etbanone(24)$ camphor(2l)t y-heptalactone(22)t Factor 3 n-nonacoaane(32)t n-hentriacontane(33)t n-he@acosane(3l)t phyt&c(28)§ n-heneicosane(29)t n-tricosane(30)t ydecalactone(27)$

Max. COW.

0.7 0.7 0.7 0.3 0.3 0.3 6.0

4.2 11

Covariations in the concentrations of organic compounds

2553

Table 1 (Contd.) Min. conc.tt (ngm-?

Compound Factor 4 n-tetracosane(37)t n-octaco.sanc(rlO)t n-hexacosane(39)t n-docosane(36)t n-pentacosane(38)t n-triacontane(4l)t branched acid(U)11 ncicosane(35)t

< < < < < < < <

Max. cont. (ngm-‘)

0.3 0.3 0.3 0.3 0.3 0.7 0.3 0.3

Mean cont. (ngm-“)

Standard dev.$$

2.1 2.0 1.5 4.3 5.0 2.9 3.9 2.9

6.3 13 8.3 14 16 15 26 11

1.1 1.8 1.5 2.7 3.1 2.1 4.2 2.1

Chrom. retention index@8

2400 2800 2600 2200 2500 3000 1826 2txH3

l Numbers in parentheses refer to chromatographic peaks in Fig. 1 and in Fig. 5. GC/FID was used for compound quantitations using calibration curves derived by measuring responses from authentic standards. For compounds for which authentic standards were unavailable, quantitation was performed using calibration plots from analogous compounds, assuming a similar flame ionization response. factor. Unknown terpenoids were. estimated using the camphor calibration plot. t Compounds were identified by comparison of their mass spectra to literature mass spectra and by comparison of their mass spectra and retention times to those of authentic standards measured in our laboratory. $ Compounds were only tentatively identifkd by comparison of their mass spa%ra to literature mass spectra and by interpolation of the expected retention time of the compounds from the retention times of homolog compounds. QCompounds were tentatively identified by comparison of their mass spectra to literature mass spectra. 1Compound type was identified by mass spectral inkrpretation and comparing the mass spectrum to the mass spectra of analogous compounds 1138m/zwasthchighatmpuiothe~spectrs l * 150 m/z was the highest mass in this mass spectra. tt Values listed are the detection limits (Gabrick, 1970). $3 Values represent the standard deviation of the ambient compound concentrations in aerosols over time. Analytical precision of mpetitive quantitations, as measured by percent relative standard deviation, ranges between 15 and 30%. §§The programmed temperature chromatographic retention index is relative to n-alkanes. The equation used in this calculation is given in Poole (Poole and Schuette, 1984).

Table 2. Matrix of factor loadings of chemical and meteoroloaical variables* Factors Fl Factor 1 ethenyl henzaldchyde 1-phenyl-I-penten-3-one 2,5-dimethyl furan a-angelicalactone dihydro+dimethyl/ -2(3Hbfuranone n-beptanoic acid 1.3~indandione phthalidc 2,3-di-hydro-5/ -methyl furan t01uene 4-methyl-3/ -pentene-2-one acetic acid benzaldehyde 5,5dimethyl-2(5H) -fluanone 2-pentadeanone n-nonaaoic acid n-propanoic acid 2-tridecanone pipe&one OZOlK

5,6,7,7a-tetrahydro-/ 4,4,7a-trimethyl-( -2(4H~benzofuranone

P .-.

cloud

cover

F2

F3

F4

F5

0.90

0.88 0.87 0.85

0.30

0.84 0.84 0.83 0.83

0.25 0.27

0.82 0.81 0.81 0.77 0.73 0.71 0.68 0.67 0.64 0.64 0.63 0.60 0.58 0.47 -0.41

0.28 0.3 1

0.25 0.35

0.29

0.31 0.28 0.58 0.46

- 0.38 0.44

0.57 - 0.36

0.37

- 0.26 - 0.34 0.50

0.53 0.42

0.35

2554

RANDALLC. GREAVESet al. Table 2 (Contd.) Factors

Factor 2 terpenoid terpenoid terpenoid bornyl acetate I-(l+dimethyl-3cyclo/ hexen-1-yl)-ethanone west-east camphor y-heptahtctone night-day Factor 3 a-nonacosane a-hentriacontane n-heptacosane phytone HP1 n-heneicosane temperature n-tricosane y-decalactone

Fl

F2

0.26 0.28

0.91 0.90 0.88 0.85

F4

F5

0.80

- 0.36 0.30 0.48

0.77 0.67 0.67 0.58

0.29

0.30 - 0.33 0.80 0.79 0.75 0.69

0.42

0.66

0.28 0.40 0.33

0.32

Factor 4 n-tetracosane n-octawsane n-hexacosane ndocosane carbon monoxide

0.62 0.57 0.52 0.37

0.43

n-pentaccmne

n-triacontane branched acid ncicosane Factor S dusk-dawn wmd speed south-north

F3

0.42

0.55 0.26 0.32 0.44

0.33 - 0.34 0.37 0.50

0.78 0.77 0.71 0.68 0.65 0.59 0.50 0.47 0.46

- 0.28 0.29

0.34 0.54 0.49

0.33

0.40 0.40 - 0.30 0.71 0.56 0.51

* Only factor loadings with absolute magnitudes greater than 0.25 are reported in the table.

the data set, with 60.5 % of the variance accounted for by the first three factors. Although the five-factor solution can be statistically justified, it is not necessarily unique, because other confidence intervals, such as 0.95 or 0.975, could have been used in the hypothesis test. The 0.99 confidence interval solution was chosen instead of the 0.95 solution because when both solutions were compared, it was found that the first four factors in either solution could be interpreted “ording to the general discussion presented below, i.e. Factors 14 were interpreted as containing concentrations of @totochemical-oxygenates, biogenic-terpenoids, biogenicodd HCs and vehicular-HCs, respectively. As expected, the 0.975 conlidence interval solution (when M = 8, then T = 6.00 and F(0.975) = 5.32) accounts for a greater percentage of the variance in the data set and has higher variable communalities than the five factor solution, but the eight factor model does not contain significant additional chemical information.

This data set was also investigated using a ninefactor solution (Sievers et al., 1986). In this study the statistical methods used to arrive at the dimensionality of the model were not as formal&d as in the present case, but the information and resulting factors were similar to the five-factor model. In the previous investigation, data were not logarithmically transformed, the number of factors were determined by inspection of the scree plot and two additional variables (ambient air concentration of n-oc@kane and n-nonadecane), which were later determined to he affected by chromatographic interferences, were included. The present treatment is statistically more precise, but there is still information to be obtained by consideration of the previous study. Each factor listed in Table 2 was interpreted according to the meteorological and chemical elements it contains and according to the interaction between these variables. Factor 1 was interpreted to be a photochemical factor; Factors 2 and 3 are related to

Covariations in the concentrations of organic compounds

different biogenic sources; Factor 4 was interpreted as a vehicular source factor and Factor 5 could not be chemically interpreted in terms of a clearly identifiable source. It is possible that some pyrolysis may accompany thermal desorption of volatile organic compounds during analysis. However, thermal decomposition under the conditions selected appears to be negligible (Greaves, 1986), and most of the compounds identified have been observed previously in the analysis of particles by techniques not involving thermal desorption. Plwtochemical factor Factor 1, accounting for 38.8 % of the variance in the data set, is the most important factor reflecting changes in the organic compound concentrations in airborne particulste matter. Many of the organic compounds in Factor 1 were interpreted as arising principally from photochcmicai processes. in designating this as a ‘photochemical’ factor, it is recognized that these compounds may arise from several processes: photolysis, secondary oxidation involving OS, NO, OH, etc., and to some extent other sources such as combustion may contribute.

3

2555

Factor 1 includes 0, and oxygen-containing organic compounds such as carboxylic acids, aldehydes, furans, lactones and ketones. 0,. acetic acid, propanoic acid, n-heptanoic acid, n-nonanoic acid, phtbalide and ethenyl benzaldehyde are all components that are known (or suspected) to be produced by photochemical activity (Friedlander, 1977; Grosjean et al., 1978; Cronn et al., 1977). Other Factor 1 chemical species, such as the furans and 4-methyl-3-penten-2one, could be inferred to be photochemical byproducts based upon the presence of relevant precursors and analogous chemical reactions (Gu et al., 1985). By contrast, Factor 1 does not contain any of the saturated nalkanes. Further evidence for the interpretation of Factor I as a photochemical factor is obtained by observing the magnitude of the Factor 1 scores as a function of time of day, wind direction and atmospheric conditions for individual samples and for a large number of samples collected in consecutive hours. Inspection of the Factor 1 scores for individual samples showed that a small number of samples were dominated by a specific individual factor rather than contribution from several, or all, factors, resulting in chromatograms with distinctive ‘signatures’. Figure 1A shows a chromato-

IA FACTOR I

Fig. 1. Chronu~paphic signatures of perticIcs aGctcd on different days dominated by organic compounds from aerosol sources dictated byzphotochemistry (JA) and wind direction (1B). Tbc numbers on this figure refer to Table 1.

RANDALL C. GREAVES et of.

2556

gram of a sample with a very high Factor 1 score. The organic compounds constituting Factor 1 are labeled on this chromatogram and the numbers on Fig. 1 refer to the compounds listed in Table 1. This particular sample was collected on the afternoon of 19 June 1985, between 15:OOh and 16:00 h. The wind direction was from the Denver metropolitan area (southeast) at a speed of 4 km h- ‘. The temperature was 26.7 “C, there were no clouds to obstruct the sunlight, and the 0, concentration was relatively high at 90 ppbv (ambient Oa concentrations observed in this study ranged from 2 to 100 ppbv with an average of 42 ppbv). In general, when samples showed large concentrations of Factor 1 compounds, they were those collected during, or immediately after, atmospheric conditions which favor high photochemical activity (i.e. hot, sunny afternoons with high OJ concentrations). A plot of Factor 1 scores vs time of day is presented in Fig. 2. This plot shows low Factor 1 scores during the night and high Factor 1 scores during the day, which is consistent with the photochemical interpretation of this factor. The persistence of Factor 1 past sunset is expected because of the lag time for particle dilution, transport and removal. In Fig. 2, the solid line shows the change in Factor 1 over time for one particular sampling session, on 18 June 1985. During this sampling session, temperature, O:, concentration and solar radiation were also increasing in a manner similar to Factor 1 scores. The incident near-surface radiation during the sampling period was estimated by cloud cover, as reported by the National Weather Service at Stapleton airport, and confirmed by visual observations in Boulder. Cloud cover was measured on a scale from 0 to 10, with 0 indicating no clouds present, and 10 indicating complete cloud cover. Cloudy conditions (6 and higher cloud cover) and sunny conditions (4 and lower cloud cover) are designated by C and S, respectively, in Fig. 2.

20 INCREASE

OF

i

1 s

VO

C

-I2

S

C

c

S

S ss

p

St

S:C,p

c

S S

MY

“c r:

c S cc

Organic compounds associated with springtime urban aerosols are often related to anthropogenic activity (Daisey, 1980; Broddin et al., 1980), but a significant organic compound contribution can sometimes arise from natural sources (Hahn, 1980; Cronn et ol., 1977). These natural contributions are often detected by the presence of tracer compounds that are known to be produced by biogenic processes. Studies in rural areas have shown that these biogenic tracers are terpenoids, sesquiterpenoids, n-alkanes, esters and ketones (Simoneit, 1984; Simoneit and Maxurek, 1982). Another measure of natural contributions to an aerosol is the predominance of the odd-C numbered nalkanes over the even-C numbered n-alkanes. Alkanes of recent biogenic origin show a significant odd/even HC preference (Copper and Bray, 1963), and the alkanes, n-CltHS6, n-C19H60, n-Cj,H6.,, are often present in the highest concentrations. The n-alkanes from the combustion of fossil fuels do not have a

WITH

3

ii

I2

of

Biogenic factors

PHOTOCMUCAL

awGENAlES

TIME

A comparison of Factor 1 scores (from 6:oO h to 22:00 h) with cloud cover showed that 80% of the sunny days had Factor 1 scores greater than zero and 72 % of the cloudy days had Factor 1 scores less than zero. In Fig. 2 some of the hours with partial or full cloud cover that exhibited anomalously high Factor 1 scores were on afternoons following periods with extensive sunshine earlier in the same day. During days with full cloud cover (cloud cover = lo), only 26% of the samples analyzed showed nheptanoic acid concentrations greater than 40 ng m- 3 while 90% of the samples from sunny days (cloud cover = 0) had n-heptanoic acid concentrations greater than 40 ng m- 3. This suggests a very strong predominance of photochemical sources for particulate n-heptanoic acid. Other Factor 1 organic compounds showed similar relationships to cloud cover.

c

s

s

ss

::

C

C

Cs~:C~:C;S~

C

C

C

c

sc

Es

j;iCslS

S

C

c

C

-_ 5

c

c

C c

ti

io

TIME: CX=WY

c

C

i0

( hours)

Fig. 2. A large number of high Factor 1 sums during sunny days and low Factor 1 saxes during the ni&t and cloudy days (S=low cloud cover, C = high cloud cover).The line connects values during the course of one sunny day.

2557

Covariations in the concentrations of organic compounds odd/even

carbon number predominance. During spring and summer, aerosol samples from smaller less industrialized urban areas show varying degrees of influence from biogenic and anthropogenic sources (Simoneit, 1984; Simoneit and Maxurek, 1982). Factor 2 contains several biogenic terpenoids as well as y-heptalactone, the night-day time variable and the west to east wind direction. The ambient air concentrations of camphor, bomyl acetate and y-heptalactone are components of Factor 2, and are all known constituents of the trees and plants (Nicholas, 1963; Masada, 1963; Graedel, 1979) indigenous to the forested areas west of Boulder, Colorado. Terpenoids are well known biogenic compounds and the predominance of these compounds, coupled with meteorological variables discussed below, indicate that Factor 2 is a biogenic factor, probably forest-related. Inspection of Fig. 3 shows that Factor 2 is small when the wind is from the east and large when the wind is from the west. This supports the contention that Factor 2 arises from the natural emissions of terpenoids and other natural organic compounds from the large number of trees and plants in the forested mountain regions west of Boulder. Many of the same terpenoid-emitting plants and trees are also present in Boulder, and contribute to a background level of these compounds irrespective of wind direction, but the rapid increase in the concentration of these terpenoids is only obwved when the wind direction shifts and air is transported from the large forested area to the west of Boulder. A chromatogram that shows domination by Factor 2 compounds when the wind is from the west is presented in Fig 1B. This sample was collected between 4:00 and 5:oO h on 22 May 1985. The wind was almost directly from the west (265 “) at a speed of 2 km h-l, the 0, concentration was only 19 ppbv and

significant

n-alkane

the CO concentration was below the detection limit (ambient CO concentrations observed in this study ranged from 0.1 to 3.4 ppmv with an average of 0.8 ppmv). The air mass being transported to Boulder from the west is characteristic of relatively clean continental air. Samples comprised of mostly Factor 2 compounds were often observed just after sunset, when air was moving downslope from the mountains into Boulder. The occurrence of the night-day time variable in Factor 2 is probably due to this prevailing wind from the west during the night. Factor 3 was interpreted as a second distinct biogenie factor related to biogenic activity surrounding the sampling site. It consists of the odd-C numbered hydrocarbons, n-heneicosane (&H,), n-tricosane (t&H&, n-heptacosane (Cs,H&, n-nonacosane (C&H&, n-hentriacontane (CJIHW), as well as the HC preference index (HPI), the temperature, phytone and y-decalactone. Odd-C HCs are constituents of several types of plants, and are present in relatively high concentrations in grasses and broad-leaf plants (Simoneit, 1984). The odd-C n-alkane predominance is often cited as indicating natural source contributions to the airborne particulate matter (Simoneit, 1984; Simoneit and Mazurek, 1982). Factor 3 was interpreted as arising from sources that are at least partly independent of the biogenic Factor 2, because Factor 3 is not correlated with the wind direction, and it contains normal alkanes with odd-C numbers that were not observed in concentrations significantly higher than in the forest at Niwot Ridge, in contrast with observations in Boulder. Temperature was also included in Factor 3 and the effect of temperature on biogenic emissions of the oddC number HCs was elucidated by inspection of the correlation matrix. Table 3 shows the relevant correlation coefficients between temperature and the individual HCs between n-e&sane and hentriacontane.

00

INCREASE OF MGENIC

2

0

CCMPOUNDS WITH

WIND FROM THE WEST

0

0 0

O 0

0

8

I

1 -0.a

0

as

(E&l

&%l) WEST TO EAST WIN0 OIRECTION COMPONENT

Fig. 3. The incrcasc in Factor 2 scores with wind direction from the west.

2558

RANDALL C. GREAVES et al. Table 3. Correlation coetlicients between ambient air temperature and hydrocarbon levels in sprin&me airborne particles Odd-carbon

numbers

corr. coeff.

Even-carbon numbers

corr. coeff.

0.47 0.55 0.65 0.62 0.73 0.48

n-triacontane n-octacosane n-hexacosane n-tetracosane n-docosane ncicosane

0.14 0.08 0.11 0.27 0.39 0.07

n-hentriawntane n-nonacosane n-heptacosane n-pentacosane n-tricosane n-heneicosane

The striking feature of these data is the consistently better correlations between temperature and concentrations of odd-C numbered HCs as contrasted with the virtual absence of correlation between temperature and concentrations of even-C number compounds. This pattern appears to be the result of increased biological productivity, with correspondingly higher emission of odd-C number compounds at warmer temperatures as springtime progressed to the onset of summer. The daily average of Factor 3 generally increased from April to June; as illustrated in Fig. 4. This supports the hypothesis of source strengths of odd-C numbered HCs being dependent upon biological productivity. There are, of course, anthropogenie contributions of both odd and even-C alkanes with no apparent odd/even discrimination but for the springtime airborne particles in Boulder, the odd HCs represent a larger influence than the even-C numbered HCs. Artifacts such as loss of a portion of volatile compounds during the sampling were also considered when examining concentration variations as a function of temperature. However, concentrations of n-C3 1HeO, for example, became higher, rather than lower, as the temperature increased during sampling.

A chromatogram of a sample with a high Factor 3 score is presented in Fig. SA. The constituents of Factor 3, mostly odd-C numbered HCs, are numbered on this figure and refer to the compounds listed in Table 1. This sample was collected on 29 May 1985, between 9:00 h and 10:00 h. The wind direction was from the northwest at a speed of 3 km h-‘. The temperature was 18.9 “C, and there was almost complete cloud cover. In general, most of the samples that showed high Factor 3 scores were collected during the later portion of this investigation, as the average temperature increased with the onset of summer. Phytone (6,10,14-trimethylpentadecan-2-one) was also contained in Factor 3 and is a common constituent of plants, insects and animal manure and it is, therefore, difficult to attribute a particular source to this compound. This ketone had one of the highest concentrations and was among the most ubiquitous constituents of the organic compounds in airborne particles observed in this study, and it has been observed by other investigators (Simoneit and Maxurek, 1982). This compound also has the highest correlation coefficient with temperature of any compound (r = 0.70), which may account for its inclusion in Factor 3.

0

8 0

i 0 0 0

INCREASE

OF ooDCAl?8ON NUMBERU)HYDR-RBONS

WITHPRoGREssloNOF SVtMZM

FIR.4. increase in oddcarbon numbered hydrocarbon concentrations with the progression of springtime.

2559

Covariations in the concentrations of organic compounds

Ld---LL !O

30

40

TIME

FlELD BLANK

( min 0fEr

so

70

rYesorption initiotim 1

00

i

Fig.5 Chromatograms showing organic compounds from aerosol souses dominated br odd-carbon numbered hydrocarbons (SA)and vehicular hydrocarbons (5B).The numbers on this &we refer to Table 1. Vehicular fbctor Factor 4 was interpreted to be from motor vehicle sources. it contains CO, seven saturated HCs and an unknown branched carboxylic acid. Saturated HCs and CO are well-known constituents of automotive combustion and their occurrence in Factor 4 supports the vehicular source interpretation of this factor. The absence of odd numbered HCs in this factor is a result of their larger source strength from biogenic emissions, rather than a lmrckof emission from vehicular sources. The characteristic feature of the signature for vehicular sources is the presence of large n-alkaoe chromatographic peaks in the 60-70 min range, Fig. 5B. This sample was collected on 19 June 1985 between 7:OO h and 8:00 h. The wind was from the southeast (Denver) at a speed of 4 km h-r. Theambient CO concentration was 0.6 ppm and the ambient O3 concentration was 28 ppb. The interpretation of Factor 4 as arising principally from automobiles is further supported by the relationship between the Factor 4 scores and time of day. The connected line in Fig. 6 shows an increase in the Factor 4 score between 7:00 and 930 MST, which is the expected result, assuming the normal urban traffic flow. Most samples containing high concentrations of Factor 4 compounds were collected between 8 : 00 and 9:00 MST.

Short-term ffuctwtions aerosols

in the organic co~~sir~~

of

Many of the rapid changes in the ~m~sition of particulate matter observed in this study would not have been observed using techniques with particle collection times > 1 h. In several instances, large changes were observed in the concentrations of organic compounds in the particles over a l-h period. Several of these changes were the result of wind direction shifts bringing terpenoid compounds from the west to rep& the existent airborne particles, Other rapid changes were observed in the organic constituents of particles when Factor 4 vehicular source compounds incmased during the morning traffic movement. The concentrations of Factor 1 compounds usually increased relatively slowly, over a period of several hours (Fig. 2), but occasionally twofold increases were observed during a l-h time period at midday.

CONCLUSIONS

Factor analysis constitutes an effective method for determining the natural associations between changes in the chemical constituents of aerosols and grouping these components to reveal common sources, trans-

2560

RANDALL C. C&EAVES et al.

INCREASE MOTOR WITH

OF VEHICLE

TIME

o

/ HYDROCARBONS

o

OF DAY o

W

16-

0 0

I5

IO

5

TIME

08

o

20

OF DAY (hours)

Fig. 6. Rapid increase in Factor 4 scores during periods of heavy traffic volume (6:OOto 9:00 in the morning).

port and transformations. Factor 1 contained the ambient O:, concentration, and a variety of particulate organic oxygenates, some of which are known photochemical by-products, so this was interpreted as a photochemical factor. Particle samples collected during periods of full sunlight showed much higher concentrations of oxygenated compounds than samples collected at night or on cloudy days. During afternoons the particulate concentrations of acetic acid, n-heptanoic acid and other oxygenates increase, mirroring the Oa concentrations in ambiint air. Factor 2 was strongly dependent upon wind direction and contained several terpenoids in the particles; it was interpreted as a transportdependent biagenic factor arising principally from the conifers in the forested regions west of the sampling site. Factor 3 contained the odd-C numbered n-alkanes and was interpreted as arising from higher biological productivity as temperatures increase with the onset of summer. Factor 4 contained the ambient CO concentration and the saturated n-alkanes in aerosol particles, and it was interpreted as a vehicular factor. The concentrations of these HCs increased rapidly during the morning rush hour. Low volume samphng with direct thermal desorption allowed hourly fluctuations of the concentrations of organic compounds desorbed from airborneparti&atobemeuunsdandshorttermphenomena to he obaerva% At midday, two-fold incmases in the concentrations of oxygenates in particles over a l-h period were occasionally observed. Many of the aerosol source interpretations presented in this study would not have been possible using traditional techniques with sample collection times > 1 h. Acknowk$gments-We are grateful to the National Science Foundation for support of this research under the Grant ATM-8317948. We would also like to thank the Colorado

Department of Health and the National Weather Service for providing access to their data. The a&tanee of Steve Arnold, Don Barbaric and Susan Martino from the Colorado Department of Health, AF Pollution Control Division was greatly appreciated. Technical assistance provided by Pamela Veltkamp and Gene Lutter is grrtehtlly aeknow&@ed. R. E. S. aclmowkdges the support of the Univ. of Colorado Council on Research and Creative Work for a faculty fellowship spent as a Visiting Seltolar at the Scripps Institution of Oceanography, U.C. San Diego.

REFERENCES

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Covariations in the concentr rations of organic compounds Greaves R. C, Barkky R. M. and Sievers R. E. (1985) Rapid sampling and analysis of volatile constituents of airborne particulate matter. Analyr. Chem. 57.2807-2815. Grosjean D., Van Cauwenberghe K, Schmid J. P., Kelley P. E. and Pitts J. N, Jr (1978) Identification of Cs-C,, aliphatic dicarboxylic acids in airborne particulate matter. E&r. Sci. Technoi. 12, 313-317. _ Gu C, Rvnard C. M.. Hendrv D. G. and Mill T. (19851 Hyd’mxil radical oxidation oiisoprene. Enuir. Sci. Te‘chnoi 19, 151-155. Hahn J. (1980) Organic constituents of natural aerosols. Ann. N. Y. Acad. Sci. (NYAS) 338.359-376. Heidam N. Z. (1982) Atmospheric aerosol factor models, mass and missing data. Atmospheric Environment 16, 1923-1931. Hopke P. K. (1980) Source identification and resolution through application of factor analysis and cluster analysis. Ann. N. Y. Acad. Sci. (NYAS) 338, 103-115. Hughes A. and Grawoig D. (1971)Statistics: A Foundationfor ktalysis, pp. 223-23k Addison-Wesley, Reading. Masada Y. (19631 Analvsis of Essential Oils bv Gas Chromatogrhphy btd M&s Sp&tromerry. John Wile& New York. Nicholas H. J. (1963) Biogenesis of Natural Compounds (Editedby Bernfeld P.), p. 641. Pergamon Press, Oxford. Norusis M. J. (1985) SPSS-X Advanced Statistics Guide, pp. 125-165. McGraw-Hill, New York.

2561

Olmex I. and Gordon G. E. (1985) Rare earths: atmospheric signatures for oil-fired power plants and refineries. Science 229,966-970. Poole C. F. and Schuette S. A. (1984) Contemporary Practices o/Chromatography, p. 25. Elsevier Science, New York. Sieve.rs R. E., Greaves R. C., Barkley R. M. and Meglen R. R. (1986) Signatures of atmospheric aerosols: cohesion of changes in organic compound concentrations. Proceedings of the 1986 EPAJAPCA Symposium on Measurement of Toxic Air Pollutants, Report EPA-600/9-86-013, Raleigh, North Carolina, April, pp. 287-303. Simoneit B. R. T. (1984) Organic matter in the troposphereIII. Characterization and sources of petroleum and pyrrogenic residues in aerosols over the Western United States. Atmospheric Environment l&51-67. Simoneit B. R. T. and Maxurek M. A. (1982) Organic matter of the troposphen-II. Natural background of biogenic lipid matter in aerosols over the rural Western United States. Atmospheric Environment 16, 2139-2159. Stevens R. K., Dzubay T. G., Lewis C. W. and Shaw R. W., Jr (1984) Source apportionment methods of the origin of ambient aerosols that affect visibility in forested areas. Atmospheric Environnvnt 18.261-272. Tuna1 S. G.. Olmez I.. Parrinaton J. R.. Gordon G. E. and Stevens R..K. (1985)Comp&ion of ine particle regional sulfate component in Shenandoah Valley. Enuir. Sci. Technol. 19, 529-537.