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Environ. Sci. Techno/. 1995, 29, 1781-1788

Multivariate Statistical Examination of Spatial and Temporal Patterns of Heavy Metal Contamination in New Bedford Harbor Marine Sediments JAMES P. SHINE,* RAVEENDRA V. IKA, AND TIMOTHY E. FORD Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115

Thirteen sediment cores were examined for 10 metals and organic carbon along a pollution gradient from highly contaminated sediments in N e w Bedford Harbor to cleaner sediments in Buzzards Bay. Multiple regression of metal content against organic carbon, aluminum, and iron was significant for all metals. However, partial correlation coefficients indicated that organic carbon explained most of the variance for Cr, Ni, Cu, Zn, Cd, and Pb. Conversely, most of the explained variance in M n was related t o iron, while the explained variance in Co was equally distributed between the three factors. The pattern recognition technique principal components analysis (PCA) was also applied to the data and revealed t w o distinct gradients in the types of metals present in the sediments. The first gradient was between uncontaminated sediments of Buzzards Bay and lesser contaminated sediments from the outer portion of N e w Bedford Harbor. A second gradient revealed temporal and spatial differences in the types of metals present in the contaminated harbor sediments.

Introduction Metals are ubiquitous in the environment. They are introduced naturally through the weathering of rocks as well as from a variety of human activities such as mining, smelting, electroplating,and other industrial processes that have metal residues in their waste streams. Certain metals such as Fe, Zn, Cu, and Mn are essential biological micronutrients required for the growth of aquatic organisms. Other metals such as Hg, Ag, and Pb are not required for growth and are toxic even in trace amounts. All metals exert toxic effects at some concentration, including the micronutrient metals (11,and appropriate caution should therefore be taken concerning the types and amounts of heavy metals released into the environment. Metals discharged into estuarine and coastal marine waters are likely to be scavenged by particles and removed to the sediments (2). The sediments become an important reservoir for metals and may act as a record of the input of these contaminants to the ecosystem. Within an individual sediment core, differences in pollutant concentrations at different depths reflect how heavy metal input and accumulation changes over time (3-5). Comparisons of concentrations between sediment cores from different locations may provide insight on how the accumulation of contaminants may vary in space. An important mitigating factor concerns the post-depositional mobility of metals. Certain metals may not be as effectively bound to particles and may be released from the sediments and transported to distant locations. Other metals may have unique chemistries, for example, the redox speciation of Fe and Mn, that may affect their own mobility and the mobility of other metals within a given core (6, 7). Surveys of metal contamination in aquatic sediments must account for these differences in mobility before conclusions can be drawn concerning the spatial and temporal variability of inputs, transport, and fate ofmetals discharged into coastal marine waters. Studies of heavy metal contamination in the environment are thus complicated by the range of factors that contribute to their transport or distribution. The data sets become large, and multivariate techniques become necessary to probe for interrelationships among the measured variables. Multiple regression and determination of partial correlation coefficients can be valuable to determine relationships between two variables while holding other variables constant. Principal components analysis (PCA) is another multivariate statistical technique used to reduce the dimensionalityof a data set while attempting to preserve relationships present in the original data. It can provide a visual display of the data that is often more enlightening than comparisons of only one or two variables at a time. The technique has been used by a number of investigators to examine multivariate environmental data sets. PCA has been used to examine source-receptor relationships of atmospheric and aquatic pollutants (8-1 I),to characterize PCB congener patterns in marine biological tissue samples (121,and to examine the composition of metals in aquatic sediments and particles (13-15). In this paper, the disposition of metals in the sediments of New Bedford Harbor and Buzzards Bay was investigated * Corresponding author e-mail address: [email protected].

0013-936x/95/0929-1761$09.00/0

@ 1995 American Chemical Society

VOL. 29, NO. 7,1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 1. Location and geographical grouping of sediment core samples taken from New Bedford Harbor and Buzzards Bay.

using multiple regression and PCA. New Bedford Harbor is highly contaminated with PCBs and heavy metals. A portion of the harbor has been designated as a Superfund waste site by the EPA and has been scheduled for remediation. This remediation effort is targeted at an area highly Contaminated with PCBs and does not explicitly address concerns for heavy metals. Remediation efforts are likely however to affect heavy metal fate and distribution. Before the impact of remediation efforts can be judged, fundamental knowledge of the current fate and distribution of heavy metals in the sediments is essential. Multivariate analysis of heavy metals in New Bedford Harbor can provide the fundamental and integrated assessment necessary to understand the current status of metal contamination in the harbor.

Materials and Methods Collection of Sediments. Thirteen sediment cores were collected during August and September 1992 in Buzzards Bay and New Bedford Harbor from the research vessel R/V 1782

ENVIRONMENTAL SCIENCE & TECHNOLOGY i VOL. 29, NO 7, 1995

Arctica. The sampling sites were chosen as areas of sediment deposition where minimal disturbance from channel dredging would occur. Three geographical groupings were assigned for sampling purposes: New Bedford Inner Harbor, New Bedford Outer Harbor, and Buzzards Bay. The sampling locations and their geographical groupings are shown in Figure 1. Sediment cores were collected with a 5.1 cm diameter gravity coring device fitted with a PVC barrel and polycarbonate liners (Wildco Corp., Saginaw,MI). Immediately after sampling, the polycarbonate liners containing the cores were removed from the coring device, capped, placed in plastic bags, and transported on ice back to the laboratory for freezing and further processing. In the laboratory, the cores were partially thawed for sectioning. The cores were slowly extruded from the polycarbonate liner, and discrete sediment sections were removed with a PVC blade. Each core was sectioned from the upper surface to the bottom of the core in 2 cm sections for the first 10 cm, then every 3-5 cm below 10 cm. Each section was transferred to an

acid-cleanedpolyethylene bottle and refrozen until sample digestion. Digestion and Analysis of Sediments. Total Cr, Mn, Co, Ni, Cu, Zn, Cd, Pb, Fe, Al, and organic carbon were determined in each sediment section. The sections were digested using the microwave digestion technique described by Wallace et al. (16). Briefly, 0.2 g sediment samples were digested with concentrated HF and HNOBin a microwave digestion bomb (ParrCorp., Moline, IL). The samples were microwaved at 720 W for 3 min and then cooled. A 1.5% boric acid solution was then added, and the samples were microwaved for an additional 2 min. After the samples cooled, they were diluted to a final volume of 100 mL with a 1.5% boric acid solution. This procedure allowed complete dissolution of the sediment. All procedures were carried out in a class 100 clean hood, and trace metal clean reagents and techniques were used throughout the digestions. The limit of detection for metals in the sediments, calculated from replicate blank analyses, ranged from 0.01 to 0.2 mg/ kg dry sediment weight. These levels were several orders of magnitude below the concentrations observed in the actual samples. Samples of a reference marine sediment, BCSS- 1 (NationalResearch Council of Canada),were also analyzed to determine analytical accuracy (n = 15). Deviation of observed values from the certified values was less than 5% for all of the metals. Precision was estimated from the replicate analysis of a subset of the samples (n= 17, chosen randomly). The relative error of replicate measurements for allmetals was less than 5% (mean,4.2%; range, 2.1% for Co and 5.0% for Cd). The metal concentrations in the digestates were determined by inductively coupled plasma-mass spectrometry (ICP-MS, Perkin Elmer Elan 5000). The instrument was calibrated with external standards, and a reference solution provided by the National Institute for Standards and Technology (NIST 1643~)was run after every 10 samples to check for drift in the sensitivity of the instrument. For each element, quantification was based on the most abundant isotope of that element free from analytical interferences. The exception was lead, which was determined as the sum of each isotope ?04Pb, 206Pb,*07Pb,and 208Pb)to allow for possible differences in the isotopic composition between the samples and standards. The concentration of metals in each digestate was determined in triplicate. Organic carbon was determined in each sample with a Perkin Elmer 2400 Series I1 CHN analyzer calibrated with acetanilide. Before analysis, samples of the homogenized sediment were moistened with distilled water and exposed to concentrated HC1 fumes for 48 h to remove carbonates. After drying, approximately 10-20 mg of carbonate free sample was taken for analysis. The limit of detection, based on replicate blank analyses, was 0.02% C, dry weight (n= 38). The mean accuracy, determined from replicate analyses of standard reference material (BCSS-1 marine sediment, National Research Council of Canada) and expressed as percent deviation from certified values, was 0.93% ( n = 20). The mean precision, determined from replicate analyses of samples and reference material and expressed as relative standard deviation, was 0.51% (n = 20).

Multiple Regression. Concentrations of metals in sediments are often normalized against fundamental or conservative properties to understand underlying factors affecting the distribution and variation of metals between

different locations. These normalizing factors have included organic carbon, grain size, aluminum, iron, and lithium (16-19). For this data set, organic carbon, aluminum, and iron were considered as normalizing factors. Organic carbon is valuable in that it accounts for the presence of adsorbed organic ligands, which can bind metals to sediment particles (20,21). Aluminum is important as a tracer of aluminosilicate content of the sediments and concomitant background crustal levels of metals. Iron has been shown to be an important carrier phase for metals in sediments. The behavior of iron in sediments during early diagenesis can have a profound effect on the behavior of metals in sediments (6, 7). Often the metal content is regressed against only one normalizing factor at a time. This can be misleading if the normalizing factor under consideration covaries with other factors or if the distribution of metals is related to more than one single normalizing factor. Multiple regression analysiswas used to regress the metal content of New Bedford Harbor and Buzzards Bay sediments against organic carbon, aluminum, and iron. If regression against the three independent variables was sigdicant, partial correlation coefficients were calculated to show the individual contributions of the independent variables to the total explained variance. A partial correlation coefficient describes the relationship between the dependent and one independent variable while holding the other independent variables constant. For example, the partial correlation coefficient for chromiumwith organic carbon (written as rCrC/M,Fe) gives the correlationbetween chromium and organic carbon after accounting for the interrelationships among chromium, organic carbon, aluminum, and iron and statistically holding them constant. Multiple regression coefficients were calculated using the statistical software S-Plus (Statistical Sciences, Seattle, WA). Partial correlation coefficients were calculated using the procedures described by Sokal and Rohlf (22). Because the raw metal concentration data showed a log-normal distribution, the data set was log transformed before analysis to meet the assumption of normality required for the regression model. Principal Componentshalysis. Details of the methods and goals for the use of PCA with environmental and geochemical data are provided by J6reskog et al. (23) and Legendre and Legendre (24). PCA was used in this study to determine if there were meaningful spatial and temporal differences in the patterns of heavy metals present in the sediments. Differences in the patterns of heavy metals within or between cores may reflect variations in the anthropogenic sources or environmental transport of metals. PCA was used to take the data from the original eight-dimensional space (eight metals in sediments) and project them onto a two-dimensional plane that retains most of the information in the original data. This was done by calculatingvectors (eigenvectorsor p,rincipal axes) along which the eight-dimensional data shows the most variability. The vector along which the eight-dimensionaldata is most variable is calledthe first principal axis. Subsequent principal axes can be calculated orthogonal to each other and retain increasingly smaller variances. In most cases, the first two principal axes are chosen to define the plane onto which the multidimensional data are projected. The position of a data point on a principal axis is called a principal component. In the new coordinate system defined by the first two principal axes, sediment samples that have similar profiles VOL. 29, NO. 7, 1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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of metals plot out near each other. This plot of the samples in the new space, called a principal component or score plot, can often give a better indication of the similarity of heavy metal patterns in the sediment samples than comparisons between individual metals. A strength of this technique is that if most of the variability is incorporated in the first two principal components, the score plot allows a comparison of samples with respect to several variables (in this case eight) rather than one variable at a time. A further strength of the technique is that relationships among the variables (metals) and between the variables and samples can also be examined. A plot of the contribution of the original variables (metals) toward defining the new coordinate system is possible and is called a variable loading plot. As in the principal components plot, variables that plot out near each other on the loadings plot may be closely correlated in the samples. In addition, the location of the variables in the loading plot indicate their effect on the placement of the samples in the principal components plot. For example, a sample with positive scores along the first and second principal axes in the score plot is probably enriched in metals that have positive loadings along the first two principal axes in the loadings plot. In this manner, PCA allows the examination of all the metals together and can describe the effect of all the metals on the placement of the samples in the score plot. An R-mode PCA on the data was accomplished using the PRINCOMP subroutine in the software program S-Plus (Statistical Sciences). Because we were interested in the profile of metals within the samples, the data were row normalized, that is, they were entered as the fraction of total metals (the sum of the eight metals) within each sample. The standard normal deviates of the fraction metal data were loaded into the software program, meaning that all the variables had zero mean and unit variance. These normalization procedures allowed equal weight to be given to each of the variables and avoided domination of the loadings along the principal axes by metals with large variances.

Results and Discussion Concentration of Metals in Sediments. Sediment metal concentrations are summarized in Table 1. The concentrations are reported as milligram of metal per kilogram dry weight of sediment. The concentrations of metals in the sediments were high, particularly in the inner part of New Bedford Harbor (cores K, L, and N). Concentrations of Cu were as high as 3400 mg/kg, and Cd concentrations were as high as 76 mg/kg. The concentrations of these elements, when normalized to aluminum content, are enriched approximately 60 and 380 times relative to average background crustal levels, respectively (2.9. In general, there was a decreasing gradient of metal concentration from the inner harbor through the outer harbor to Buzzards Bay, consistent with earlier studies on metals in the surface sediments of the harbor (26).The concentrations of metals observed in the top 2 cm in this study are also consistent with other recent heavy metal measurements from surface sediment grab samples in New Bedford Harbor (26, 27). Profiles of Cd and Pb in three of the cores are shown in Figures 2 and 3. The cores shown are cores A, F, and L and represent typical cores from Buzzards Bay, New Bedford Outer Harbor, and New Bedford Inner Harbor, respectively. An important feature of the profiles is the presence of distinct subsurface maxima in the metal concentrations. 1784

ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29, NO. 7 , 1995

TABLE 1

Summary of Data for Metals and Organic Carbon in Sediment Cores from New Bedford Harbor and Buzzards Bay' Buzzards Bayb

Outer New Bedford HarborC

Inner New Bedford Harbord

40.0 16.0-73.5 250 112-374 4.76 1.64-8.19 13.0 4.44-22.6 9.62 3.75-21.2 74.6 25.3- 182 0.08 0.01 -0.31 23.7 9.21-50.2 2.77 1.33-4.45 9.58 3.96-15.8 1.10 0.54- 1.71

360 38.2-1230 315 222-389 7.03 3.64-9.79 38.1 10.7-107 655 7.01-2080 399 74.3-873 4.50 0.07-20.7 255 14.7-556 4.26 2.67-8.35 12.2 9.59-14.9 4.82 1.51-7.62

426 44.8- 1750 267 166-441 6.38 2.62-10.52 60.0 15.9--159 976 78.6-3420 616 199-1480 8.30 0.49-76.8 261 27.5-621 2.94 1.13-4.77 12.1 6.69-16.8 4.40 0.79-9.5

constituent Cr Mn co

Ni

cu

Zn Cd

Pb Fe

AI organic carbon

mean (mg/kg) range mean (mg/kg) range mean (mg/kg) range mean (mgikg) range mean (mgikg) range mean (mg/kg) range mean (mgikg) range mean (mgikg) range mean (% FezOd range mean (%AIz03) range mean ( % C ) range

"The mean and range from all sections in cores grouped into the three sampling regions are reported. All values are on a dry sediment weight basis. Cores A, B, R, and S. Cores D, F, G, H, I, and J Cores K, L, and N. Cadmium Conc (mgikg)

0

10

20

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40

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-5 f

10

15

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New Bedlord Inner Harbor

- - D - -New Bedlord Outer

Harbor

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FIGURE 2. Vertical profiles of cadmium concentration in three sediment cores from New Bedford Harbor and Buzzards Bay: core A (Buzzards Bay), core F (Outer New Bedford Harbor), and core 1 (Inner New Bedford Harbor).

perhaps indicating that the input of these metals to the harbor and their subsequent accumulation in the sediments have decreased in recent years. In general, the depth of the subsurface maxima of metal concentrations in the New Bedford Inner Harbor cores were deeper than in the New Bedford Outer Harbor cores, which in turn were deeper than in the Buzzards Bay cores. The disparity among the subsurface maxima depths may be due to differences in the sedimentation rates at different locations. Another important feature of the core profiles concerns the differences in metal concentrations between the geographic locations. The profiles of Cd (Figure 2) reveal higher concentrations of metals in the inner harbor than

Lead Concentration (rng/kg)

0

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TABLE 2 500

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Mwltiple and Partial Correlation Coefficients for Regression of Sediment Metal Content against Organic Carbon, Aluminum, and Iron paltial correlation coefficients metal

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Cr Mn

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0.38 0.67 0.85 0.82 0.74 0.86

0.32 0.26

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Cu

+ New Bedford Inner Harbor

--.--

New Bedford Outer Harbor

Zn

Cd Pb --e--

Buzzards Bay

FIGURE 3. Vertical profiles of lead concentration in three sediment cores from New Bedford Harbor and Buzzards Bay: core A(Bunards Bay), core F (Outer New Bedford Harbor), and core 1 (Inner New Bedford Harbor).

in the outer harbor, which in turn were higher than in the Buzzards Bay sediments. The Pb profiles (Figure3) in cores A, F, and L matched the Cd profiles in that there was a distinct subsurface maximum in concentration. However, the large spatial differencesin concentration observedwith Cd between the inner and outer harbors are not evident for Pb. There is only a slight enrichment of Pb in the inner harbor relative to the outer harbor, which in turn is not as enriched above the concentrations observed in Buzzards Bay as was Cd. When compared to Cd, Pb concentrations seem to be uniformly elevated throughout the harbor, perhaps indicating that the sources or behavior of Pb in the harbor sediments are different than the sources or behavior of Cd. There were no significant downcore trends for Al or Fe in any of the cores. In addition, the concentration of Al and Fe was slightly higher in the New Bedford Harbor cores than the Buzzards Bay cores, but the differences were not significant. The mean Al and Fe concentrations in New Bedford Harbor sediments was 12.2 f 1.5% and 3.81 f 1.14%expressed as A1203 and Fe203,respectively (n= 961, with Al and Fe levels of 9.68 f 1.77%and 2.80 f 0.55% observed in Buzzards Bay (n= 34). Organic carbon levels were higher in New Bedford Harbor than Buzzards Bay and showed distinct subsurface maxima in concentration concomitant with the subsurface maxima of heavy metals. Multiple Regression Analysis. The results of the regressions against organic carbon, aluminum, and iron are given in Table 2. For all eight metals, regression against all three factors was significant, explainingbetween 56 and 87% of the variance. Inspection of the partial correlation coefficients within each metal provides insight as to the relative importance of organic carbon, Al, and Fe on the observed distributions. For the metals Cr, Ni, Cu, Zn, Cd, and Pb, most of the explained variance is due to organic carbon. These metals are positively correlated with organic carbon, indicating that adsorbed organic binding sites on the sediment particles are an important carrier phase for these metals. There is a difference between correlation and causation, and it is possible that organic carbon from anthropogenic sources covaries with anthropogenic metal pollution within and between sediment cores and does not affect metal binding to sediment particles. However, a relationship between organic carbon and metal content

a

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0.66 0.56 0.63 0.64 0.83 0.80 0.65 0.87

ns, not significant ( p > 0.05).

has been shown in sediments at other locations and is consistent with the model of organic carbon coating on particles controlling the sorption of metals (16,20,21,28). In contrast to the above metals, the partial correlation coefficient of Mn with organic carbon was not significant. Rather, variation in Mn was related to variations in Fe and to a lesser extent Al. Iron and manganese have similar redox chemistries and geochemicalbehavior, which explain their covariance in these sediments (6, 7). Cobalt, which had a fairly uniform distribution within individual cores and between Buzzards Bay and New Bedford Harbor sediments, had significant partial correlation coefficients with all three factors indicating that, for this data, organic carbon, Al, and Fe all contributed significantly to the observed distribution of Co in the sediments. Negative partial correlation coefficients were obtained for Ni, Cu, Zn, Cd, and Pb with Fe. This indicates that after controlling for organic carbon and aluminum, decreases in Fe concentration in the sediment correlated with increases in the concentration of these metals. This may reflect the fact that under reducing conditions iron is reduced to Fe(I1) and mobilized from the sediment, while these other metals are effectively deposited as insoluble complexes (6,7, 29, 30). Principal ComponentsAnalysis. The earlier discussion comparing lead and cadmium concentration in the cores underscores a weakness in a univariate or bivariate investigation of the data. By only examining two metals in three cores, the existence of different temporal and spatial behavior between two of the metals is apparent. The Pb/ Cd ratio could be calculated in each sample and could possibly serve as a tracer of metal behavior in the harbor. However, there are data for eight metals in 13 sediment cores. PCA analysis incorporates the data from all the metals in all the cores into one unified comparison and may reveal groupings of samples (or metals) with similar spatial or temporal behavior. In this way, we are looking at multidimensional ratios which may have greater power in resolving metal behavior in New Bedford Harbor and Buzzards Bay. The score plot (the plot of the samples in the new space defined by the first two principal axes) of the percent metal data is shown in Figure 4. Together, the first two principal axes retained 77.9%of the variance from the original eight dimensions. In the score plot, two mixing gradients between three hypothetical end members are evident. The term hypothetical end member is used because true end members defined as identifiable sources (sediment types, VOL. 29, NO. 7,1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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4 Less Conpminated "Outer Harbor" Sedimentsl 34

~

'Clean Buzzards Bay Sediments

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pollutant sources) with specific geochemical meanings do not exist. Rather, these end members represent a distinct profile of metals in the sediment that may have resulted from a number of sources or processes occurring within the sediments. Spatially, samples from similar geographical locations form clusters around the hypothetical end members and are labeled in Figure 4. In general, the first principal component has separated the clean Buzzards Bay samples from the contaminated samples in New Bedford Harbor. The second principal component has further separated the samples from New Bedford Harbor based on the types of metals present in the sediments. The less contaminated sediments from the outer harbor generally have positive loadings along the second principal component, whereas the highly contaminated sediments from the inner portion of the harbor have negative loadings along the second principal component. Based on the overlapping data clusters, there appears to be a mixing gradient between the Buzzards Bay sediment cluster and the New Bedford Outer Harbor sediment cluster. A second gradient appears between the sediments of the inner and outer portions of New Bedford Harbor. There is no mixing gradient evident between the inner harbor sediments and the Buzzards Bay sediments. Using the information from all the metals, it is apparent that there are different spatial patterns of heavy metal contamination within the sediments of New Bedford Harbor with gradients between the different contaminant profiles. The variable loading plot, which characterizes the role of metals in defining the hypothetical end members, is shown in Figure 5 . The loading plot shows three clusters of metals, each with similar loadings as the geographical clusters in the score plot. The first cluster (Co,Mn, and Ni) has similar loadings to the samples from Buzzards Bay. This reflects the fact that sediments from Buzzards Bay are enriched in these metals relative to New Bedford Harbor sediments. However, it does not necessarily mean that these sediments have higher concentrations of Co, Mn, and Ni. Because the clean Buzzards Bay sediments do not have high concentrations of the contaminant metals (Zn, Pb, Cr, Cd, and CUI, Co, Mn, and Ni represent a higher percentage of total metals in these sediments. That Co and Ni behave similarly is consistent with previous studies ENVIRONMENTAL SCIENCE &TECHNOLOGY

.060-,

0 60

.O 40

A Cd

I

~

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-0 20

0 00

0 20

,

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Principal Component 1

FIGURE 4. Principal component score plot of sediment samples from New Bedford Harbor and Buzzards Bay in the new coordinate space defined by the first two principal components. The retained variance of each principal component is shown in parentheses, and the labeled clusters represent samples from similar geographic regions.

1786

I

-0 4 0 1

VOL 29, N O 7 , 1995

FIGURE 5. Loadings plot showing the relationships among metals in the samples from New Bedford Harbor and Buzzards Bay and their role in defining the new coordinate space.

on the behavior of these two metals in sediments. Co and Ni have been shown to have similar geochemistries in marine sediments (30, 311, and previous work has also shown that manganese oxides may play a significant role in the cycling of Co and Ni in sediments (30, 32, 33). Based on the loadings of Pb and Zn in the variable loading plot (Figure 51, it appears that the cluster of samples from the outer portion of New Bedford Harbor are enriched in these two metals. These sediments were generally more contaminated than the Buzzards Bay sediments and less contaminated than sediments from the inner portion of New Bedford Harbor (see Table 1). As with Co, Ni, and Mn in the Buzzards Bay sediments, the outer harbor sediments do not necessarily have higher levels of Pb and Zn than other sediments. As seen in Figure 3, concentrations of Pb in core F from the outer harbor were not higher than the observed Pb concentrations in core L from the inner harbor. Instead, these sediments may contain higher levels of Pb andlor Zn than Buzzards Bay sediments but are not as enriched in other metals (Cr, Cu, and Cd) as sediments from the inner harbor. Finally, the cluster of metals with positive loadings along the first principal component axis and negative loadings along the second principal component axis (Cu, Cd, and Cr) are enriched in the samples from contaminated sediments from the inner portion of New Bedford Harbor. The three points to the extreme lower right of the score plot come from the three samples with the highest concentrations of metals: core L at 10-16 cm depth, the depth of the subsurface maxima in metal concentrations. It is clear from the principal component score plot that there are distinct spatial patterns of metal contamination in New Bedford Harbor based on the types of metals present in the sediments. In addition, individual cores also show significant temporal variability in the types of metals accumulating in the sediments. As an example, a score plot with only the samples from core H is shown in Figure 6. The points in the plot are labeled with the mean depth of that sample within the core. The plot shows that sediments from deep within the core resemble clean Buzzards Bay sediments and perhaps date to aperiod before there was substantial industrial discharge of metals to the harbor. At shallower depths in the core, metal contamination becomes evident. Based on the location of the samples in the score plot, the deeper sediments in core H first became contaminated with Zn and/or Pb only. The

131 cm

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FIGURE 6. Principal componentscore plot showing only the samples from core H. Each point has been labeled from the depth of that sample in the core. Two replicate samples at depths of 7,9, and 17.5 cm have been included in the plot

gradient from 35 to 17.5 cm depth is analogous to the positions of the Co, Mn, Ni, and PblZn clusters in the loadings plot. At shallower depths in the core, from 17.5 to 9 cm, the sediments become more enriched in Cr, Cd, and Cu relative to Zn and Pb. The sediments at 9 cm in core H are at the subsurface metal concentration maxima and indeed are highly enriched in Cr, Cd, and Cu relative to other depths. Above 9 cm, the sediment concentrations of all metals decreased (as in Figures 2 and 31, but the metal profile did not revert back to the ‘clean’profile observed deep in the core or in Buzzards Bay, but rather reverted back to a profile characterized by relative enrichment in Pb and/or Zn. Apparently the rates of decrease in Zn and Pb toward the surface of the cores are less than the rates of decrease for Cr, Cd, and Cu, resulting in relative enrichment of Zn and Pb in the surface sediments. This temporal pattern was present in most of the cores from New Bedford Harbor, particularly those from the outer portion of the harbor. There are a number of possible explanations for the observed spatial and temporal patterns of metals in New Bedford Harbor and Buzzards Bay sediments. One explanation may be that there was a point source of Cr, Cd, and Cu to the Inner Harbor. The inputs of these metals occurred over a finite period of time and may have been effectively retained in the sediments near the sources rather than resuspended and distributed uniformly throughout the harbor. Distinct from the sources of Cr, Cd, and Cu may have been non-point sources of Pb and Zn, beginning before the inputs of Cr, Cd, and Cu, as Zn and Pb are enriched deeper in the cores. These two metals are also more uniformly distributed through the harbor, consistent with a non-point source such as atmospheric deposition and stormwater runoff. The accumulation of these two metals in the sediments, although decreasing, has not decreased as much as the rates for Cr, Cd, and Cu, suggesting continued loading to the harbor. Post-depositionalmobilityof the metals may also explain the observed temporal and spatial patterns. There may have been point sources for all of the metals in New Bedford Harbor, particularly in the inner harbor. However, once the metals were sequestered to the sediments, it is possible that Zn and Pb were released back into the water column and therefore became more uniformly redistributed than

Cr, Cd, or Cu. A study on the post-depositional mobility of metals in marsh sediments by Zwolsman et al. (33) showed that Cr is efficiently retained by sediments once deposited. In that study however, Cu and Cd were not as effectively retained in the sediments and showed similar post-depositional mobilities as Zn and Pb. The subtidal estuarine sediments in this study may have very different chemical and physical characteristics than marsh sediments. The depth of the oxic layer in marsh sediments is in the centimeter to decimeter range (33). The depth of the oxic layer in New Bedford Harbor is less than 1 cm (Shineand Ford, manuscript in preparation). Factors such as sulfide chemistry may have a prominent effect on the retention of metals in New Bedford Harbor and influence the post-depositional mobility of metals leading to the observed spatial patterns in the harbor sediments. Experiments are currently under way in our laboratory to compare the post-depositional mobility of metals in New Bedford Harbor and Buzzards Bay sediments. The PCA showed that sediments from different areas have characteristic eight-dimensional ‘fingerprints’based on the concentrations of metals. These fingerprintscan be used to study the transport and fate of metals in New Bedford Harbor and Buzzards Bay. Within New Bedford Harbor, sediment samples from different sites have different patterns of metals. Heterogeneity at these relatively short distance scales (less than 1 km) indicates that metals in New Bedford Harbor may be rapidly sequestered to the sediments close to their original sources. The metals are effectively retained in the sediments, and processes such as early diagenetic release of metals or sediment transport apparently do not lead to a homogeneous distribution throughout the harbor. These observations would not be apparent from comparisons of one or two metals at a time and would be difficult to attain from a correlation matrix of metal concentrations. These fingerprints can also be used to observe the effects of the USEPA Superfund remediation on the long-term recovery of New Bedford Harbor. This integrated approach can show how dredging efforts affect the transport of contaminated sediments throughout the harbor and can determine which metals in particular have changed in space and time. A qualifier in this analysis is the absence of sedimentation rate data for each of the individual cores. This limits the discussion of trends within individual cores to qualitative terms, not allowing placement of quantitative temporal labels on the observed qualitative changes in the distribution of metals. It also does not allow the comparison of samples with similar ages from different cores, which could provide quantitative insight on spatial transport of metals through New Bedford Harbor and Buzzards Bay. A second consideration is the effect of sedimentation rate on the early diagenesis of metals. Miiller and Suess (34) have shown that there is a direct correlation between sedimentation rate and the flux of labile organic matter to sediments. Ahigher sedimentation rate will therefore limit the diffusion of oxygen into the sediments, depleted due to respiration of organic carbon, while simultaneously increasing the influx of respirable organic matter. The result is that higher sedimentation rates in New Bedford Harbor may cause these sediments to become anoxic closer to the sedimentwater interface than sediments in Buzzards Bay and may in turn have a profound effect on the early diagenesis and retention of metals in sediments. However, in the absence of sedimentation rate data, multivariate analysis of the VOL. 29, NO. 7,1995 /ENVIRONMENTAL SCIENCE &TECHNOLOGY

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concentration data can still provide valuable qualitative information on the disposition of sediments in New Bedford Harbor and Buzzards Bay and the factors which potentially control the observed metal distributions.

Conclusion In this study, multivariate statistical techniques were used to examine metal contamination in sediment cores from New Bedford Harbor and Buzzards Bay. Multiple regression of heavy metal content against organic carbon, Al, and Fe indicated that organic carbon may be a significant factor controlling the distribution of Cr, Ni, Cu, Zn, Cd, and Pb. Variation in levels of Mn were best explained by variation in Fe, while Co had significant partial correlation coefficients with all three factors indicating that, for this data, organic carbon, Al, and Fe all contributed significantly to the observed distribution of Co in the sediments. The multivariate technique PCA was used as an exploratory technique to examine the patterns of metals present within individual samples. The PCA score and variable loading plots simplified the data from eight dimensions to two dimensions while preserving 78% of the variability, and the two-dimensional projection of the data contained more information than could be acquired by examining only a few cores with a few metals at a time. The data showed spatial variation of the metals in New Bedford Harbor based on the types of metals present. The metals Pb and Zn were relatively uniformly elevated throughout the harbor, consistent with a non-point source. However, the sediments from the inner portion of New Bedford Harbor were characterized by relative enrichment in Cr, Cd, and Cu, particularly at the subsurface maxima in metal concentrations. The PCA analysis indicated that PblZn contamination occurs deeper in the cores than Cr, Cd, and Cu. Although concentrations of all metals have decreased at the surface of the cores, the rate of decline for Pb and/or Zn is less than that observed for Cr, Cd, and Cu and has persisted to the surface of the cores. Together these techniques suggested distinct hypotheses concerning the sources and environmental fate of metals in the sediments. They provided a unified picture of the disposition of metals in the sediments that might not otherwise be apparent with a univariate or bivariate approach. This integrated approach provides a framework for future scientific research and can serve as a basis for investigations of the efficacy of environmental remediation efforts.

Acknowledgments This work was supported by Grant P42ES0947 from the National Institute of Environmental Health Sciences Superfund Program. We thank Jonathan Sorci, Elizabeth Kay, and Elizabeth LaPointe for their assistance in various phases of the study, and we greatly appreciate the thoughtful input of four anonymous reviewers.

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Received for review August 19, 1994. Revised manuscript receiued March 9, 1995. Accepted March 23, 1995.* ES940531L e Abstract published in Advance ACS Abstracts, May 1, 1995