Sources of Polycyclic Aromatic Hydrocarbons in Sediments of the Kinnickinnic River, Wisconsin

J. Great Lakes Res. 23(1):61-73 Internal. Assoc. Great Lakes Res., 1997

Sources of Polycyclic Aromatic Hydrocarbons in Sediments of the Kinnickinnic River, Wisconsin Erik R. Christensen*, An Li, Irwan A. Ab Razak, Pichaya Rachdawong, and Jay F. Karls Department of Civil Engineering and Mechanics and Center for Great Lakes Studies University of Wisconsin - Milwaukee Milwaukee, Wisconsin 53201 ABSTRACT. A chemical mass balance (CMB) model was developed and used to apportion both point and nonpoint sources ofpolycyclic aromatic hydrocarbons (PAHs) in six dated sediment cores. The cores were collected in 1994 from a stretch of the Kinnickinnic River between the Becher Street Bridge and the Wisconsin Wrecking Company Wharf, Milwaukee, Wisconsin, and had PAH concentrations of 80 to 1,000 ppm. This work was done in order to identify major PAH sources to this IJC Area of Concern which is important for pollution modeling and management. The source fingerprints were taken from the literature. Coke oven emissions and coal-wood gasification or coal tar are the main local PAH sources, whereas highway dust provides an important nonpoint input since the 1920s. Coal-wood gasification ceases to be a significant source after 1950-'60. Factor analysis based on nonnegative constraint offactor scores and loadings, and oblique rotations, supports this apportionment, but points also to deficiencies in the CMB source profile for coke oven emissions. It appears that the now defunct Milwaukee Solvay Coke Co. was a major source. The influence of geophysical factors and chemical and biological fractionation of the selected PAH compounds appears to be small. INDEX WORDS: PAHs, Milwaukee Harbor, sediments, Lake Michigan, chemical mass balance model, factor analysis, source apportionment.

INTRODUCTION Polycyclic aromatic hydrocarbons (PAHs) are of concern because of their carcinogenic properties (Neff 1979). PAHs have their origin in both natural and anthropogenic processes. However, several studies have indicated that the anthropogenic input of PAHs to aquatic sediments far exceeds natural sources (NAS 1971). Major human activities which produce PAHs include pyrolysis of wood to produce charcoal and carbon black, coke production, manufacturing of gas fuel, power generation from fossil fuels, combustion of fuels in internal combustion engines, incineration of industrial and domestic wastes, oil refinery and chemical engineering operations, aluminum manufacturing, etc .. By-products of these processes which contain significant amount of PAHs have been dumped on the land, in the water, or buried at subsurface sites. Airborne particulates, generated from these processes and carrying

PAHs, are transported worldwide in the atmosphere, and usually find their final destination in soils, and in sediments of aquatic systems. Differences in the pattern of PAH mixtures reflect different sources and pathways, and thus provide a basis for the analysis of their sources. Traditional approaches of PAH source analysis use ratios of specific compounds (for example, phenanthrene to anthracene, fluoranthene to pyrene), alkyl homologue distribution pattern, or the presence of compounds indicative of a particular source (Laflamme and Hites 1978, Lake et aI. 1979, Zhang et aI. 1993, Furlong et aI. 1988, Benner et aI. 1995). These methods, however, involve only a small portion of PAH data produced by an analytical laboratory. Thus, they are often unable to resolve sources at all, or with sufficient accuracy, and may be considered incomplete with respect to the number of PAHs that preferably should be involved in the apportionment. The Milwaukee Harbor estuary is one of the designated Areas of Concern (AOC) of the Interna-

* To whom correspondence may be addressed.

61

Christensen et al.

62

tional Joint Commission (HC). This work was done in conjunction with the Remedial Action Committee of the AOe. Singh et at. (1993), considering PAR compound profiles, used a chemical mass balance model to determine nonpoint sources of PARs in the Milwaukee Rarbor Estuary. Similar approaches have been applied to determine sources of organic pollutants in air (Sexton et al. 1985, Li and Kamens 1993, Venkataraman and Friedlander 1994, Kenski et at. 1995). This approach is extended here to include both point and nonpoint sources of PARs deposited in dated aquatic sediments. An in-depth discussion of model uncertainty, based in part on an alternative constrained factor analytical method, is included. We consider also the horizontal distribution of sediment PARs over of the given stretch of the Kinnickinnic River.

CHEMICAL MASS BALANCE MODEL The two basic assumptions of the chemical mass balance (CMB) model are, (1) the amount of a chemical pollutant in the sediment at a specific site is the sum of the amounts of this chemical from each independent contributing source, and can be expressed by equation [1], and (2) the ratio between the concentration in the sample of a chemical from a given source and the concentration of the same chemical in the source material is the same for all the chemicals under study (no change in source profile between source and receptor), and can be characterized by a single contribution factor a i. The expression for the PARs is, n

Fj

=

L jia + e i

j

(l $: j $: m)

(1)

i=1

where Fj is the measured concentration of the jth PAR compound in the sample,
L ji (M,W')i = L ji j=1

(2)

m

j=1

(m.w.)

ji

where (m.w')"i is the molecular weight of PAR compound j in s6urce i. The contribution factors (a/s) are determined by multiple linear regression using a least-squares method in which the weighted error (equivalent to X2 ) is minimized. The second assumption can also be expressed as a requirement that the source profile, i.e., the relative abundance of PAR compounds, does not change between source and receptor. This definition of a i is more general than the original one (Friedlander 1973) where a i simply is the mass fraction of material originating from source i, and where the sum of all a/s, therefore equals unity. While the latter definition implies mass balance for both bulk material (sediment) and PARs, our version indicates only mass balance for the PARs. The relative contribution Pi of total PARs from source i is calculated for each source according to

(3)

The uncertainty 8Pi is calculated from

!(ON)2 + (OD)2 -

op=p!'~

I

where

1';

=

N

(4)

D

NID m

oN = oaiL ji j=l OD = [[oal i

]=1



jl)2 + [oa 2i



j2)2 + ... +

]=1

2]112 [Oan ~ jn ) Nand D are the numerator and denominator, respectively, of equation [3], and the uncertainties 8ai are calculated as in Renry et at. (1984). In order to assess the closeness or goodness of fit between the calculated values of PARs as determined by the model and the measured values, X2 and the multiple linear correlation coefficient R 2 were also determined. The X2 was calculated based on the equation:

PAHs in Milwaukee Harbor Sediments

(5)

where F and F' are measured and calculated concentratibns, reJspectively, of the jth PAH in the sample. This equation is derived from eq. (11) of Henry et al. (1984) using the relative errors (r.e.\ and (r.e.)j of the measurements, and the source p~o­ file i, respectively. We assume that these relative errors do not vary much from compound to compound such that (r.e.)k

crF.

=__J Fj cr ji

(6)

(r.e·)i = - -

ji

where cr indicates standard errors. From statistics it is well known that a good fit between measured and calculated values is obtained when X2 equals the number of degrees of freedom (df) when df> 4. In this case, each term of the sum in eq. [5] corresponding to the number of degrees of freedom, df = m - n assumes a value close to one since F - F' then is comparable to the overall error, i.e., th~ sqJare root of the denominator in eq. [5]. The remaining n terms may be thought of as being zero since n model parameters have been determined from the experimental data. In applying eq. [5], initial
METHODS Sediment Data During June of 1994, six vibra cores, each 2 to 3 meters long, were collected from a stretch of the

63

Kinnickinnic River, close to downtown Milwaukee and between Becher Street Bridge and the Wisconsin Wrecking Company Wharf using RIV Pelagos. During sampling, aluminum irrigation pipes of 6.7 em inner diameter were vibrated down into the sediment through clamps and a flexible coupling to a gasoline engine on board the research vessel. The sampling locations are marked in the map shown in Figure 1. This area was determined to be a "hot spot" of PAH contamination in the Milwaukee Harbor Estuary through our earlier work (Ni et al. 1992). Each core was sliced into 15 sections, and each section was analyzed for porosity, grain size distribution, organic content (loss on ignition), and dated by measuring the 137Cs and 210Pb activities. Sedimentation rate was calculated according to a linear regression of log excess 210Pb activity against cumulative mass of the sediment core. Sixteen PAH compounds were determined for each sample, including naphthalene (NaP), acenaphthylene (AcNP), acenaphthene (AcN), fluorene (Fl), phenanthrene (PhA), anthracene (AN), fluoranthene (PIA), pyrene (Py), benz[a]anthracene (BaA), chrysene (Chy), benzo[b]fluoranthene (BbFlA), benzo[k]fluoranthene (BkFlA), benzo[a]pyrene (BaP), indeno[123-cd]pyrene (IP), dibenz[a,h]anthracene (dBahA), and benzo[ghi]perylene (BghiP). Detailed procedures of sampling, sediment characterization, PAH analysis including quality assurance/quality control (QA/QC), and radioactivity analysis are reported by Li et al. (1995). Complete experimental results of sediment PAHs for the entire Milwaukee Harbor Estuary are presented elsewhere (Li et at. 1996). Only those compounds whose concentrations were determined with satisfactory chromatographic separations and above the method detection limits (0.3-1 ppm) were considered in the CMB modeling. Further reductions in the number of compounds included were made based on available source fingerprint data. Our aim was to have at least four degrees of freedom. Based on this, we selected seven compounds for five cores and eight for one (VC-2). NaP, Fl, PhA, AN, Py, BaA, BaP were chosen for VC-5 and 6, and FI, PhA, AN, FlA, Py, BaA, BaP for VC-l, 3, and 4. NaP is included in the first set because NaP was mainly present in VC-2, 5, and 6. Also, FtA was included in the second set because coal-wood gasification (CWG), for which FlA data were not available, was not a significant source for VC-l, 2, 3, and 4. Thus, for VC-2 we used all eight compounds.

Christensen et al.

64

N

L

t

1 Km Outer Harbor

LAKE MICHIGAN

Milwaukee Solvay Coke Co. --------~

• Sampling Station FIG. 1. Partial map of the Milwaukee Harbor Estuary with sampling sites on the Kinnickinnic River.

Source Fingerprints The majority of the PAHs found in the environment originates from incomplete combustion of fossil fuels including wood, coal, and petroleum. Most PAH data used in this study as fingerprints of these sources were drawn from the literature (NRC 1983, Wise et al. 1988, Neff 1979, Singh et al. 1993). They are summarized in Table 1 and illustrated in Figures 2-4. A blank cell in Table 1 means that there is no information about the PAH compound for the given source as for FlA in CWG or Chy in Coke-A and Coke-B. The HWY concentrations represent averages of three samples. Note that the average molecular weights for Coke-B, HWY, CT, and CWG decrease in the given order, probably reflecting the temperature at which the PAHs were formed. The fact that the HWY PAH concentrations are orders of magnitude lower than PAH concentrations of other sources presents no particular difficulty since the a's (equation 1) will be adjusted accordingly by the least-squares procedure. The important thing is that each source profile is distinct

and that its relative error (coefficient of variation) is known and within the same general range (0.03-0.4). In order to improve the comparability of source profiles, and since the apportionment results (Pi' equation 3) do not depend on a constant factor in the source profile, the coke oven (Fig. 2) and coal and coal-wood gasification profiles (Fig. 3) have been normalized to Py (1,000 ppm). Coke production and fuel gas manufacturing were among the major industrial activities in the study area until the mid 1970s. In fact, sediment core VC-6 was taken from a site near the previous Milwaukee Solvay Coke Company, which operated its coke ovens from 1902 until the 1970s. It is likely that the high PAH concentration in the sediment deposited during that period at that location originated from the products of this company, which include metallurgical coke, coal tar, coal gas, and some aromatic chemicals (M.L. Morgan, personal communication, 1994). However, a chemical inventory of the company is not available. Compositions of two types of PAH sources, namely Coke-A and Coke-B,

65

PAHs in Milwaukee Harbor Sediments TABLE 1. Parameter NaP AcNP AcN Fl PhA AN FlA Py BaA Chy BaP BghiP

c.vf M.W.g

PAH concentrations in several sources (ppm). Coke-Aa

CTb

Coke-Ba 17.2h

75.3 680.1 209.3 1,270.0 977.2 536.8

17.2 540.6 391.2 704.4 826.4 3,838.1

566.9

2,147.9

CWGc

1,160.0 252.0 136.0 462.0 101.0 322.0 235.0 102.0 71.7 95.8 53.7 0.03 152.4

0.40 222.7

0.26

236.0 53.6

0.65 0.83 4.75 1.00 5.58 3.95 0.84 1.42 0.82 0.33 0.29 189.0

28.9 94.0 32.7 31.3 6.3 3.8 3.8 0.7 0.20 147.9

972.0 1,770.0 61.7 175.0 31.5 51.0

aAir-filter of coke oven emissions (NRC 1983 p. 2-23) bCoal tar (Wise et al. 1988) cCoal and wood gasification, Ill#6 coal & wood pellets (NRC 1983, pp. 2-13) dGas engine exhaust tar (Neff 1979 p. 36) eHighway dust (Singh et al. 1993) fCoefficient of variation gAverage molecular weight (NaP, Fl, PhA, AN, Py, BaA, BaP) hAssumed upper limit

were obtained from analyzing the samples collected from a glass fiber and a 0.8 /lm porosity silver membrane filter, respectively, of coke oven emissions (Lao et al. 1975). These coke data were also listed in Neff (1979) and NRC (1983). Data listed in Table 1 for Coke-A and Coke-B are the geometric means of two sets of data, i.e., samples 1 and 2 for Coke-A, and samples 3 and 4 for Coke-B (Fig. 2).

In urban areas with heavy traffic, vehicular exhaust and, therefore, highway dust (HWY) can be a significant sources of PAHs. Gasoline engine exhaust tar (GEET) contains significant amount of PAHs including some proven carcinogens such as benzo[a]pyrene and chrysene. Pyrene is the most abundant constituent, accounting for approximately 50% of total PAHs in GEET (Table 1). Other important anthropogenic PAH sources may

PAHs froln coke oven sources

o _ _ _

sample sample sllmple siHllple

3: 4: 1: 2:

PAils from coal and coal-wood gasification (oke Coke [oke Coke

~

13 H A A

Dakota Lignile

CJ

1 8

""""

IOOO~

!lml

am

.' IImlI!!

III

6 & Limestone 6 & Wood

v Z o

v

FIG. 2. PAH concentrations in coke oven emissions, normalized to Py (1,000 ppm) (NRC 1983, pp.2-23)

FIG. 3. PAH concentrations resulting from gasification of various coals and coal blends, normalized to Py (1,000 ppm) (NRC 1983, pp. 2-13)

66 10000 1000

l

Christensen et al.

100 10

'I

100[[j1UlliJ ewe

10

1

0.1

FIG. 4. PAH concentrations in sources used for CMB modeling.

include, but are not limited to, power generation using coal, or oil, petroleum refining, catalyst regeneration, incineration of solid industrial and domestic wastes, as well as leakage and spills of petroleum-based products from various activities. PAHs may also enter the environment from various non-point sources in addition to those considered in this study. Modeling Procedure For each core segment, several cases were considered in which different combinations of sources

were evaluated as possible contributing sources. The number of degrees of freedom (df) which is the difference between the number of PAH compounds involved and the number of possible contributing sources, should as high as possible, and was kept ~ 4. In order to obtain a source apportionment that would be physically meaningful, and comparable from segment to segment, and from core to core, the following objectives in the modeling were maintained: (a) as much as possible to use the same PAH compounds for all segments, (b) to the extent possible to use the same sources for all segments, and (c) to use known information about possible sources, and to have the average source molecular weights distributed evenly and span the range of average sample molecular weights. As discussed above, the first objective was met by using FI, PhA, AN, Py, BaA, BaP for all six cores. In addition, FIA was added for VC-I, 3, 4, NaP for VC-5, 6, and both PIA and NaP for core VC-2. The second and third objectives were met by using Coke-B, HWY, and CWG as general sources for all six cores. We found that the average molecular weights of these sources in all cases spanned the average sample molecular weights. These sources are also in accordance with known information about PAH contamination in the area. Note that coal tar (CT) is almost indistinguishable from CWG, but that the latter source gives slightly lower measured relative errors for X2 = df. The uncertainty of the experimental sediment data, crFj in equation [6], is expressed as the relative standard deviation or error (r.e')k which is calculated by analyzing sample duplicates on different days, several weeks apart. A relative error of 0.3-0.4 was typical. This is then the basis for the earlier statement that the model fit is considered satisfactory for (r.e')k ~ 50%, fair for 50% ~ (r.e')k ~ 70%, and unsatisfactory for (r.e')k ~ 70 %. In an attempt to verify the results obtained by CMB modeling, factor scores and loadings were calculated by principal component analysis (PCA). We used a version of Ozeki et ai's. (1995) method in which the data matrix has only positive elements and where nonnegative constraints are imposed on both the score and the loading matrices. This is achieved through a series of oblique rotations in which the basic factor analysis equation is maintained.IThe advantage of this method over conventional PCA is that factor scores may be directly interpreted as the source contribution of a given sample (segment), and that, similarly, the loading matrix directly provides information

PAHs in Milwaukee Harbor Sediments on the unknown source profiles. The number of sources included depends on the eigenvalue structure of the covariance matrix. Sources corresponding to the largest eigenvalues which account for ~ 80% of the variance of the data were considered significant, and were, therefore, retained. Since core VC-6 goes furthest back in time (1895) and has the most complicated CMB source pattern, the PCA technique was applied to this core. In order to obtain maximum comparability between the CMB and the PCA results, the PAH compounds considered in PCA were the same as those included '; in the CMB model (NaP, FI, PhA, AN, Py, BaA, BaP). A second check on the results of the PAH source analysis for core VC-6 was made by investigating elemental carbon particles of chemically isolated carbon fractions from each layer (Griffin and Goldberg 1983). Oven dried sediment was treated with HCI and HF to remove carbonates and metals and to dissolve silicates. Next, organic compounds were oxidized with H 20 2 in preparation for scanning electron microscopy (Top-Con, ABT-32). The classification of carbon particles according to their source (wood, oil, or coal) was then performed on 100 randomly selected particles for each layer fol-

67

lowing the morphology guidelines of Griffin and Goldberg (1979).

RESULTS AND DISCUSSION A summary of results of radionuclide and PAH analysis for core VC-6, which is used as an example of applying the CMB model, is given in Table 2. From the 210Pb activities it is seen that the sedimentation rate is reasonably constant. Total PAH concentrations for all cores are shown in Figures 5 and 6. From Figure 6, it is seen that core VC-6 spans the time period from 1895 to 1991, and that the maximal total PAH level is 610 ppm occurring in 1902. Levels are also high (590-580 ppm) in 1916 and 1923. The top layer has a moderate PAH concentration of 140 ppm. Relative contributions (P) with uncertainties of Coke-B and CWG to the total PAHs, determined by the CMB model for all six locations in the study area, are also shown in Figures 5 and 6. The HWY contribution is 100% minus the sum of the contributions from Coke-B and CWG. Relative errors of measured concentrations for X2 = df are 11-55% for all cores except VC-2 for which the relative errors are 55-67%. Coke-B, HWY, and CWG are significant sources in VC-5 and 6, whereas only Coke-B

TABLE 2. 2lOPb and 137Cs activities, dates of layers, selected PAH concentrations, and average PAH molecular weights of sediment core VC-6. Net 210Pb 137Cs Sedimentation PAH (ppm)C Depth Activitya Activity Rate b (cm) (dpm/g) (dpm/g) FI Date NaP PhA AN FIA Py BaA 0-17 6.29 0.54 1991 0.76 25.1 0.50 10.5 1.25 21.6 9.4 3.15 17-34 0.47 0.71 1984 0.77 9.3 1.30 23.5 19.9 7.5 34-51 8.19 2.90 1977 0.72 1.68 2.44 65.5 54.7 20.5 22.5 2.20 51-68 2.23 2.54 1970 5.69 31.7 3.49 69.0 55.4 26.0 68-85 3.69 8.52 2.60 1963 0.92 34.2 3.71 66.4 57.1 24.7 85-102 1.64 3.04 1957 7.53 2.43 26.6 3.43 62.2 49.9 30.8 102-119 1.84 0.00 2.34 40.3 1950 3.57 22.6 3.38 48.5 24.5 119-136 0.00 0.05 3.72 1943 36.2 18.8 6.89 60.6 54.8 36.8 136-153 0.36 0.01 1936 49.9 4.36 40.0 6.76 65.5 59.0 53.4 153-170 0.56 0.09 1929 65.0 5.44 43.1 8.76 66.3 63.5 62.1 170-187 1.52 0.06 1923 43.3 5.00 50.7 9.62 87.8 80.5 76.4 187-204 0.57 0.00 4.91 1916 67.4 8.22 78.1 75.4 77.2 48.6 204-221 0.73 0.01 1909 58.6 3.20 22.5 4.77 54.3 53.8 40.3 221-238 0.43 0.00 4.30 1902 144.0 48.0 10.00 66.1 67.3 60.0 238-255 0.47 0.00 1895 50.9 5.65 46.7 6.57 53.0 46.4 52.1 aNet 210Pb Activity = measured 210Pb activity - supported 210Pb activity (1.4 dpm/g). bBased on a constant sedimentation rate of 2.384 g/cm 2/year, determined from 210Pb activity data. cBased on dry weight of sediment sample. dAverage molecular weight (NaP, FI, PhA, AN, Py, BaA, BaP).

BaP

M.W.d

10.8 7.0 24.1 22.9 26.0 22.6 17.3 18.6 17.6 22.6 34.3 28.1 15.7 23.4 17.0

206.4 203.0 206.9 200.5 204.4 200.7 202.3 183.9 181.2 179.3 190.1 182.4 175.1 164.8 179.3

Christensen et al.

68

Year

Year M

m m

~

m

~

~

m

ro

m

~ ~

~

~

~ ~

~

m

m

~

m

~

~

ro

m

M

~

~

1000 r·L-.L..--l---l.I---l.--'---'----L---L~1 - - ' - - ' - - ' - ' - ' 100

100

1000

VC4

VCl

~

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----8

----8

0.. 0..

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m m

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~t-c:. f-t~-= ·: }=t:·~ ·f~· ~c:.~·f-;·:-·: trJ:·-; I"._ '.~,'_.I-,/_:'~r-+

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>:::

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.........

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116 137 158 179 200 221 242 263 284 305 ~ ~

0

M

m

m

~

m

~

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m

~

M

ro

m

m

N

m 0

N 0

;

I I I 1 OOO+I--L--L--L---.L--'--L--L--L-~~-'--'--'--'--'--ti 100

VC3

!

I

I

~

50 500

I

I

~

I

o

./ ,f.

r- - r- -,'" 9

26

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1

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60

77

-

,t--/

t

i

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III

50

i

i

0

128 145 162 179 196 213 230 247

Depth (em)

Depth (em) .. Total PAH (ppm)

r

.......,.

I

0

30

0 ..... +-'


.

L-,_.~.---.- . -.--

~

1000

500

>::: ()

VC2

I

0

+-'

0

i

0 ()

+-'

,.0 ..... ....

+-'

>:::

>::: 0 ..... ;:1

.... Q) ()

0.. 0..

~

Coke B (%)

... PAH ,ppm)

-

Coke B (%)

•

CWG (%)

FIG. 5. Total PAH concentration and Coke-B contribution vs. depth and year of cores VC-l, 2, and 3.

FIG. 6. Total PAH concentration and Coke-B & CWG contribution vs. depth and year of cores VC4,5, and 6.

and HWY are important for cores VC-l, 2, 3, and 4. Note that there is a rough correlation between total PAH concentrations and the sum of Coke-B and CWG contributions. This would be expected since the latter sources are local. In particular, the highest PAH concentrations of 800-1,020 ppm in VC-2 are accompanied by high Coke-B contributions of 50 to more than 90% (Fig. 5). Figure 7 shows a detailed comparison for VC-6 of the contributions of the HWY, CWG, and CokeB along with scores calculated from PCA using the three highest eigenvalues. As may be seen from this figure, there is a remarkable agreement between the CMB and factor analysis (FA) results for CWG. The loading vectors, determined by PCA, and corresponding to these scores are correctly categorized, based on a Q2 criterion for log of the

normalized vectors (Kuik et oZ. 1993), according to the assumed CMB source profiles (Fig. 8). Thus, the HWY loading vector fits in fact best with the HWY source profile used in CMB modeling, and the CWG and Coke-B loadings provide similarly optimal fits to the corresponding actual source profiles. NaP and FI are excluded in the HWY and CWG profiles, respectively, because their values became negative as a result of the oblique rotations. Also, actual values of NaP and FI were treated as outliers for the Coke-B fit. This may indicate that the Coke-B source profile used in CMB modeling (Fig. 2) is deficient in that the low-molecular weight compounds are underestimated. One possible reason is that these compounds primarily are in the vapor phase, and, therefore, are not strongly retained on the filter.

PAHs in Milwaukee Harbor Sediments

.-

HWY

Year

Core VC6 ~

a

M

~

a

ID

M

M

ID

10 ~-----------------,

~

'"'" '"'" '" '" "''" '" '" "'" '" '"'" '" '" '"'"a '"a '" ~

~

~

~

69

M

N

N

N

IlIll8

ID

ITIIIl

Predicted Actual

:::

S m

'"

0.1

V :::

"

8

0.01

CWG 1000 ~-----------------,

III OIIIJ

Predicted Actual

CWG

:::

10

" ""o '-'

100

+---'----'----'----'----'---L.--'------L-.~~~~~~+___t_

Coke B

a ;;;

50

Coke B 10000 ~ 1000

Ell

Predicted

o

Outlier

mm Actual

}--.-m-----1i!----IIt---

100 -J---Jlllf---JIII!---

.y

9

26

43

60

77

94

111

.....

128 145 162 179 196 213 230 247

10 -J---m++-lIIIIf--H--

"-m Z

Z

."

Depth (em) ...... FA model

-CMB model

FIG. 7. Percent contribution to PAHs of HWY, CWG, and Coke-B sources vs. depth and year of core VC-6. Results obtained by CMB modeling are compared to scores derived from factor analysis (FA).

PAH Sources From Figures 5 and 6 it is seen that the HWY source in the top layers is highest (= 65%) in cores VC-l, 3, and 4, and 35-40% in the other cores. Also, this source increases toward the top in cores VC-2 and 6, to some extent so in VC-l and 3, but not in VC-4 and 5. The latter anomaly may be caused by redistribution of PAHs within the system. CWG, which is found in VC-5 and 6, becomes small after 1950-60, and appears to reflect the actual history of coal gasification or possibly coal tar production in the area. The highest PAH concentrations are associated with high contributions of Coke-B and CWG (VC-2 and 6).

FIG. 8. Comparison of source profiles for HWY, CWG, and Coke-B used in CMB modeling with normalized loading vectors derived from factor analysis (FA).

Figure 7 provides a confirmation of the CMB results for CWG, partial confirmation for HWY, and an indication that Coke-B before 1923 and after 1943 are overestimated by the CMB model. The latter result is based on the fact that the FA model for Coke-B follows the actual activities of the now defunct Milwaukee Solvay Coke Co. and that the Coke-B source profile for CMB modeling appeared deficient with respect to the low-molecular weight compounds Fl and NaP (Fig. 8). Figure 9 provides an independent confirmation of the HWY results of Figure 7 in that oil particles are much more abundant in the upper layers. Also, the results for wood particles indicate that coal rather than coal-wood gasification was predominant. There is not much difference between the corresponding source profiles (Fig. 3).

Christensen et al.

70 Year

Core VC6 0;

...

~

~

<<~

aJ

0

<-

~

.,. '" '"~ '"~ '"~ ~ '"'"~ '"~ i:J'" '"'" ~ '" '"'" M

100 lfJ

'1 ... '1 ... '1

(J)

p, lfJ ~

a

M

0

N

01

0

aJ

'1 .. - 'V

'V

U

0

''1 .. ,.'1 . '1 ....'1 '.'1 __ '1 .... '1_"

.'V

-~

<-

50

Carbon Particles 0 Wood e Oil 'V Coal

,(J)

c

e

h

(J)

~

/e-e-e /

'-..

0 9

26

43 60 77

Depth (em)

FIG. 9. Percent carbon particles from wood, oil, and coal burning.

larger than one, it is reasonable to assume that smaller particles of high PAR content have replaced larger particles of low PAR content at the receptor compared to the source. This is consistent with the fact that larger particles, e.g., sand, have a lower PAR content than smaller organic carbon-silt particles « 74 11m) (Ab Razak et al. 1996), and that the larger particles would tend to settle out before reaching the sample sites due to gravity according to Stokes law. A comparison of measured and calculated PAR concentrations of the above sections of VC-6 is shown in Figure 10. The calculated concentrations are obtained from n

F;= LcDji(Xi i=l

Statistical Analysis Statistical results for selected sections of core VC-6 are presented in Table 3. From this table it is seen that the multiple correlation coefficient R2 is between 0.51 and 0.71 and that the relative error for X2 = df is between 40 and 53%. The sum of ai'S ranges from 0.92 to 3.25. Since this sum mainly is

TABLE 3.

using the previously introduced notation. Measured values are shown with error bars for X2 = df. Note that although source profile errors are not shown in this figure, they are implicitly included in the calculated values. In view of these conditions, the agreement between measured and calculated PAR concentrations is quite good.

Statistical results of CMB modeling for selected sections of sediment core VC-6 (df = 4). R.E.a

Core Section

PAH Sources

6-2

Coke-B CWG HWY

6-5

Coke-B CWG HWY

0.00594 ± 0.0027 -0.000194 ± 0.0023 3.240 ± 1.216

6-7

Coke-B CWG HWY

6-9

x2

R2

La

(0/0)

5.25

0.604

0.93

45

0.535 ± 0.289 0.0010 ± 0.011 0.466 ± 0.224

5.81

0.705

3.25

46

0.00558 ± 0.0024 0.0112 ± 0.0068 2.60 ± 1.07

0.539 ± 0.284 0.0603 ± 0.0406 0.401 ± 0.202

3.95

0.633

2.61

40

Coke-B CWG HWY

0.00884 ± 0.00384 0.126 ± 0.00825 2.02 ± 3.08

0.464 ± 0.276 0.367 ± 0.283 0.170 ± 0.268

6.06

0.547

2.15

43

6-11

Coke-B CWG HWY

0.0163 ± 0.00638 0.132 ± 0.0770 2.65 ± 3.27

0.585 ± 0.301 0.263 ± 0.177 0.152±0.194

4.65

0.644

2.80

43

6-14

Coke-B CWG HWY

0.0125 ± 0.00507 0.106 ± 0.169 3.11 ± 5.57

0.534 ± 0.385 0.253 ± 0.429 0.213 ± 0.402

8.14

0.510

3.23

53

aRelative error at X2

= df

a 0.00203 ± 0.00086 0.00201 ± 0.00150 0.925 ± 0.365

Pi 0.561 ± 0.286 0.0309 ± 0.025 0.408 ± 0.199

PAHs in Milwaukee Harbor Sediments

1~~ ~

VC6-dIUIUIIIJ

0.1

100~J VC6-5

10

1

~

0.1

~

Measured conc. Calculated conc.

FIG. 10. Measured and calculated PAH concentrations for selected sections of core VC-6.

Model Limitations and Potential Geochemical and geophysical factors can influence the PAH concentrations found in sediments. The source resolution could conceivably also be affected by these factors through particle size. However, since properties such as loss on ignition (7-12%), porosity (0.5-0.6), and grain size distrib-

71

ution (clay 8-10%, silt 25-40%, and sand 50-60%) were fairly similar for all six cores, except VC-l (Li et al. 1995), we believe that the results are minimally influenced by these factors. For VC-l, there was a virtual absence of clay and silt in layers 4, 5, 7, 8, and 9. The loss on ignition « 3%) and the porosity (0.35-0.4) were also low there. As a result, the corresponding PAH levels were depressed (Fig. 5). If sediment mixing were significant, it could influence the results of the chemical mass balance model by homogenizing the sediment. From 7Be measurements (Li et al. 1995), we found, however, that mixing is restricted to the upper -2 cm which is much smaller than the typical sediment interval thickness of 10-20 cm. Thus, mixing is not likely to influence the results. A fundamental requirement of the chemical mass balance model as expressed in equation [1] is that the PAH compound profile from a given source is the same at the source and the receptor. Any fractionation, i.e., differential alteration of the PAH profile between source and receptor, must be known in order to be taken into account. While we allow all PAH compounds to be changed with the same factor, as in the case of addition or subtraction of large inert particles to the sample, we assume here that there is no fractionation. This assumption seems to be justified by (a) the sums of the source contribution factors a are fairly close to one and, (b) the marker compounds NaP, Fl, PhA, An, FlA, Py, BaA, and BaP (Table 2) are fairly conservative and do not appear to be subject to significant volatilization, biodegradation, or photooxidation. Compounds such as naphthalene and anthracene which can be volatilized (NaP) or photodegraded (AN) may here be suitable marker compounds because the transport path in the environment is short, so that the PAHs are quickly buried in the sediments. While PAH degradation is known to occur in the water column by microbial attack or photolysis (Callahan et al. 1979), most of the PAHs would rapidly be incorporated in the sediments where they are protected form further degradation (Hinga et al. 1980, Saylor and Sherill 1983). According to Hinga and Pilson (1987), BaA buried in sediment may remain stable in anoxic layers. The rapid sedimentation rate (2.8-9.8 cm/yr) in the Kinnickinnic River, and the shallowness of the river itself, may therefore explain why fractionation of the PAHs is of minor significance in this system. The development of CMB models for PAHs in

72

Christensen et al.

aquatic systems merits further attention. Improved source resolution can be achieved by expanding the list of suitable marker compounds and by measuring corresponding source fingerprints. The use of alternative source resolution techniques to confirm the sources is desirable. CONCLUSIONS

A chemical mass balance model for PAHs in aquatic sediments has been developed. The model is similar to CMB models currently used in air quality studies (Sexton et ai. 1985, Kenski et ai. 1995). Source fingerprints for Coke-A, Coke-B, coal tar (CT), coal-wood gasification (CWG), gas engine exhaust tar (GEET), or highway dust (Hwy) were taken from the literature (NRC 1983, Neff 1979, Singh et ai. 1993, Wise et ai. 1988). The number of marker compounds is here either eight (NaP, FI, PhA, AN, FlA, Py, BaA, and BaP), or seven when either NaP or FlA are left out. The importance of consistent geochemical and geophysical factors as well as the stability of marker compounds is emphasized. The model is successfully applied to dated sediments of the Kinnickinnic River between the Becher Street Bridge and the Wisconsin Wrecking Company Wharf. This area is heavily contaminated with PAHs (80-1,000 ppm). One major source appears to be the former Milwaukee Solvay Coke Co. which produced coke and performed coal gasification from around 1900 to the late 1970s. Other possible sources include railroads and highway runoff. The horizontal and vertical distribution of PAHs is in accordance with this picture. PARs from highway runoff peaks in the upper layers of most cores. Coal-wood gasification or coal tar is highest in cores VC-5, and VC-6 close to the former Milwaukee Solvay Coke Co. Coke-B peaks in VC-2. Maximum total PAH concentrations are found in cores (VC-2, 6) with high Coke-B and CWG contributions. The value of using CMB models in dated cores is demonstrated for core VC-6 which spans the time period from 1895 to 1991. From 1895 to 1950 the PAHs came from Coke-B and coal-wood gasification or coal tar. Highway runoff becomes important in the 1920s, decreases during 1930-'40, and peaks in the most recent layers. This would be expected based on the historical development of automobile traffic. The source apportionment for core VC-6 is supported by a principal component analysis using

nonnegative constraints on factor scores and loadings, and by elemental carbon particle analysis. ACKNOWLEDGMENTS

We thank Donald F. Gatz and an anonymous reviewer for several helpful suggestions to improve and clarify the manuscript. This research was supported by the U.S. Army Corps of Engineers. Additional support was obtained from U.S. National Science Foundation, Grant No. BES-9314725. REFERENCES Ab Razak, 1. A., Li, A., and Christensen, E. R. 1996. Association of PAHs, PCBs, 137Cs, and 210Pb with clay, silt, and organic carbon in sediments. Water Science and Technology 43(7-8): 29-35. Benner, B. A., Jr., Wise, S. A, Currie, L. A., Klouda, G.A, Klinedinst, D. B., Zweidinger, R. B., Stevens, R. K., and Lewis, C. W. 1995. Distinguishing the contributions of residential wood combustion and mobile source emissions using relative concentrations of dimethylphenanthrene isomers. Environ. Sci. Technol. 29: 2382-2389. Callahan, M. A., Slimak, M. W., and Gabel, N. W. 1979. Water Related Environmental Fate of 129 Priority Pollutants. U. S. EPA. EP 1.2 : P76/18/V. 2. Friedlander, S. K. 1973.Chemical element balances and source identification of air pollution sources. Environ. Sci. Techno!. 7: 235-240. Furlong, E. T., Carter, D. S., and Hites, R. A. 1988. Organic contaminants in sediments from the Trenton Channel of the Detroit River, Michigan. J. Great Lakes Res. 14: 489-501. Griffin, J. J., and Goldberg, E. D. 1979. Morphologies and origin of elemental carbon in the environment. Science 206: 563-565. _ _, and Goldberg, E. D. 1983. Impact of fossil fuel combustion on sediments of Lake Michigan: a reprise. Environ. Sci. Technol. 17(4) : 244-245. Henry, R. C., Lewis, C. W., Hopke, P. K., and Williamson, H. J. 1984. Review of receptor model fundamentals. Atmos. Environ. 18 (8): 1507-1515. Hinga, K. R., and Pilson, M. E. Q. 1987. Persistence of benz(a)anthracene degradation products in an enclosed marine ecosystem. Environ. Sci. Techno!. 21: 648-653. _ _, Pilson, M. E. Q., Lee, R. F., Farrington, J. W., Tjessem, K., and Davis, A. C. 1980. Biogeochemistry of benzanthracene in an enclosed marine ecosystem. Environ. Sci. Techno!. 14: 1136-1143. Kenski, D. M., Wadden, R. A, Scheff, P. A, and Lonneman, W. A. 1995. Receptor modeling approach to VOC emission inventory validation. J. Environ. Eng. July 483-491. Kuik, P., Blaauw, M., Sloof, J. E., and Wolterbeek, H.

PAHs in Milwaukee Harbor Sediments Th. 1993. The use of Monte Carlo methods in factor analysis. Atmos. Environ. 27A (13): 1967-1974. Laflamme, R. E., and Hites, R. A. 1978. The global distribution of polycyclic aromatic hydrocarbons in recent sediments. Geochimica et Cosmochimica Acta 42: 289-303. Lake, J., Norwood, c., Dimock, C., and Bowen, R. 1979. Origins of polycyclic aromatic hydrocarbons in estuarine sediments. Geochimica et Cosmochimica Acta 43: 1847-1854. Lao, R. c., Thomas, R. S., and Monkman, J. L. 1975. Computerized gas chromatographic- mass spectrometric analysis of polycyclic aromatic hydrocarbons in environmental samples. J. Chromatogr. 112: 681-700. Li, A, Ab Razak, I. A., and Christensen, E. R. 1995. Toxic Organic Contaminants in the Sediments of the Milwaukee Harbor Estuary. Phase Ill-Kinnickinnic River Sediments. Final Report to U.S. Army Corps of Engineers, Detroit. University of WisconsinMilwaukee. _ _, Ab Razak, I. A., Ni, F., Gin, M. F., and Christensen, E. R. 1996. Polycyclic aromatic hydrocarbons in the sediments of the Milwaukee Harbor Estuary, Wisconsin, USA. Water, Air, and Soil Pollution. In press. Li, C. K., and Kamens, R. M. 1993. The use of polycyclic aromatic hydrocarbons as source signatures in receptor modeling. Atmos. Environ. 27A (4): 523-532 National Academy of Sciences (NAS). 1971. Particulate Polycyclic Aromatic Matter. Washington, D.C. National Research Council. 1983. Polycyclic Aromatic Hydrocarbons: Evaluation of Sources and Effects. National Academy Press, Washington, D.C. Neff, J. M. 1979. Polycyclic Aromatic Hydrocarbons in the Aquatic Environment: Sources, Fates, and Biological Effects. London: Applied Science Publishers. Ni, F., Gin, M. F., and Christensen, E. R. 1992. Toxic

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Organic Contaminants in the Sediments of the Milwaukee Harbor Estuary. Final Report to the Milwaukee Metropolitan Sewerage Dis.trict. University of Wisconsin-Milwaukee. Ozeki, T., Koide, K., and Kimoto, T. 1995. Evaluation of sources of acidity in rainwater using a constrained oblique rotational factor analysis. Environ. Sci. Technolo 29 (6): 1638-1645. Saylor, G. S., and Sherill, T. W. 1983. Bacterial Degradation of Coal-Conversion By Products (Polycyclic Aromatic Hydrocarbons) in Aquatic Environments. Final Report. Office of Water Research and Technology, U. S. Department of Interior. Sexton, K., Liu, K. S., Hayward, S. B., and Spengler, J. D. 1985. Characterization and source apportionment of wintertime aerosol in wood burning community. Atmos. Environ. 19: 1225-1236. Singh, A. K., Gin, M. F., Ni, F., and Christensen, E. R. 1993. A source-receptor method for determining nonpoint sources of PAHs to the Milwaukee Harbor Estuary. Water Science and Technology 28: 91-102. Venkataraman, c., and Friedlander, S. K. 1994. Source resolution of fine particulate polycyclic aromatic hydrocarbons using a receptor model modified for reactivity. J. Air & Waste Manage. Assoc. 44:1103-1108 Wise, S. A., Benner, B. A., Byrd, G. D., Chesler, S. N., Rebbert, R. E., and Schantz, M. M. 1988. Determination of polycyclic aromatic hydrocarbons in a coal tar standard reference material Anal. Chem.60: 887-894. Zhang, X., Christensen, E. R., and Yan, L.- Y. 1993. Fluxes of polycyclic aromatic hydrocarbons to Green Bay and Lake Michigan sediments. J. Great Lakes Res. 19(2): 429-444. Submitted: 29 February 1996 Accepted: 15 January 1997