Environ. Sci. Technol. 2003, 37, 245-255
Global Accounting of PCBs in the Continental Shelf Sediments ANDERS JO ¨ NSSON, O ¨ RJAN GUSTAFSSON,* JOHAN AXELMAN, AND HENRIK SUNDBERG Institute of Applied Environmental Research (ITM), Stockholm University, 106 91 Stockholm, Sweden
The recycling longevity of hydrophobic organic contaminants (HOCs) within the global environment is set by their permanent removal through processes such as degradation and burial in geological reservoirs. More than 90% of the global sediment burial of organic carbon (OC) occurs on the continental shelves, representing 80%) or the data sets are very small (discussed below). Different investigators/laboratories report different detection limits. In many cases, there is insufficient information available to identify each censoring level and the number of observations censored at each level (particularly in the NOAA and EPA databases). Therefore, to account also for the reported nondetectable data, whenever an observation below the detection limit could not be keyed to a single detection limit, the observations were assumed to be singly type 1 left censored at the highest known level (i.e. reported detection limit). The maximum likelihood method was applied to estimate µ and σ from the present type 1 left censored log-normal data sets following refs 21 and 22. Progressive censoring was possible in some of the data sets as all its censored observations were associated with a specified detection limit. All estimations of arithmetic mean, 95% confidence intervals, median, and interquartile ranges for both progressively or type 1 left censored data were done using the ML method with Minitab. When no observations were censored, we used Cox’s approximate method for calculating confidence intervals for the log-normal mean (23). For a few of the populations which were small and/or fully censored data sets, other statistical techniques were more appropriate. These exceptions to the general method of statistical analysis were, however, few and are believed to have only minor influence (e1%) on the global inventory estimates (detailed in Supporting Information Text S2). Sedimentological Properties and Computing of Inventories and Burial Fluxes. To estimate the inventory of PCBs in the recyclable surface sediments of a given area, several sediment properties, in addition to PCB concentration and shelf area, need to be included
I ) C ‚F ‚A ‚zmix ‚(1 - φ)
(1)
where I is the inventory (ton), C is the PCB concentration (ton gdw-1), F is the average density of the dry sediment particles (g cm-3), A is the surface area of the shelf basin (cm2), zmix is the average bioturbated mixing depth (cm), and φ is the average porosity over this mixed depth (dimensionless). The geochemistry literature provides a basis for estimating globally representative values of these sediment properties. Clay minerals have a density between 2 and 2.6, whereas quartz and feldspar have densities of 2.65 and 2.62.75, respectively (24). Hence, in this study the commonly used central value for F of 2.5 (e.g., refs 25 and 26) with an assumed uncertainty range of 2.25-2.75 was applied. Based on a database of short-lived radioisotope (234Th) profiles in surface marine sediment cores (n ) 203), Boudreau has constrained a global value of zmix to 9.8 cm with a standard deviation of 4.5 cm (27). Since this is a global average estimate of zmix this figure already takes into account that a fraction of the shelf sediment area consists of transport and/or erosion bottoms with limited accumulation. Deduced values for the porosity in this mixed layer is frequently in the range 0.7 and 0.8 (e.g., refs 25 and 28-30). A central value for φ of 0.75 was thus employed to estimate the global inventories. The permanent burial flux below the mixing zone was calculated using
F)
I ‚ω zmix
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TABLE 2. Concentrations and Inventories of (a) PCB 52 and (b) PCB 180 in the Ocean Shelf Basins basin
Cseda (ng/g dw)
IQRb
nc
Id (t)
CImine (t)
CImaxf (t)
0.23
2.2
0.032 0.14 0.0023
1.7 0.49 0.081 0.023 1.2 0.94 0.056 0.015 0.0047 0.054 0.057 0.015
(a) PCB 52 Local American Mediterranean Asiatic Mediterranean Baltic Sea Black Sea Indian Ocean Japan Sea Mediterranean Sea North Atlantic Ocean North Pacific Ocean Persian Gulf Red Sea South Atlantic Ocean South Pacific Ocean Yellow and East China Sea sum local sub-basins
1.5
6.4
2.7
7.0
13 0.62 0.50
16 1.9
65 1 5
7 110 20
0.037
0.13
13
American Mediterranean Asiatic Mediterranean Baltic Sea Black Sea Indian Ocean Japan Sea Mediterranean Sea North Atlantic Ocean North Pacific Ocean Persian Gulf Red Sea South Atlantic Ocean South Pacific Ocean Yellow and East China Sea sum regional sub-basins
0.24
Regional 0.53
0.14
0.24
11
3.1 1.5 0.16
8.2 3.7 0.62
9 354 239
0.10 0.0094
0.21 0.0051
American Mediterranean Arctic Mediterranean Arctic Ocean Asiatic Mediterranean Baltic Sea Bering Sea Black Sea Indian Ocean Japan Sea Mediterranean Sea North Atlantic Ocean North Pacific Ocean Persian Gulf Red Sea Sea of Okhotsk South Atlantic Ocean South Pacific Ocean Yellow and East China Sea sum remote sub-basins sum global
0.14 0.012 0.016 0.084 0.35 0.35
Remote 0.24 0.013 0.026 0.047 0.65 0.88
0.034
0.055
10
0.49 0.60 0.092
1.7 1.0 0.31
45 391 93
0.034 0.0070
0.0011 0.0076
206
12 5
299 8 30 8 87 9
6 19 2062
0.70 0.0058 0.24 0.32 0.014 0.011 0.79 0.51 0.028 0.0025 0.00080 0.0092 0.010 0.0074 2.6 0.61 0.15 0.12 6.9 0.019 0.65 14 16 2.1 0.0024 0.0010 0.22 0.0088 0.66 42 16 2.2 6.8 26 17 76 34 9.8 8.1 100 272 39 0.89 0.71 13 4.1 1.3 34 660 704
0.35 0.28 0.00043 0.00014 0.0016 0.0016
0.44 0.050 1.83 0.014 0.35 3.7 13 1.1 0.0017 0.00070 0.075 0.0061 0.36
13 1.2 3.8 19 12 17 13 4.1 3.5 37 231 17 0.37 0.29 5.6 4.0 0.67 15 419
0.85 0.29 12 0.028 1.2 24 21 3.9 0.0035 0.0014 0.64 0.013 1.2
21 4.6 12 36 24 339 90 23 19 268 319 107 2.1 1.7 29 4.2 2.6 78 1452
(b) PCB 180s Local American Mediterranean Asiatic Mediterranean Baltic Sea Black Sea Indian Ocean Japan Sea Mediterranean Sea North Atlantic Ocean North Pacific Ocean Persian Gulf Red Sea South Atlantic Ocean South Pacific Ocean Yellow and East China Sea sum local sub-basins 248
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0.64 15
0.99 0.50 0.30
0.061
3.0 19
1.6 1.7
0.23
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65 1 6
8 108 20
13
0.37 0.0077 0.55 0.035 0.029 0.0068 0.086 0.54 0.017 0.0053 0.0017 0.019 0.020 0.0044 1.7
0.12
1.2
0.23 0.0092 0.0041
1.3 0.060 0.21 0.014 0.15 1.1 0.034 0.037 0.012 0.14 0.14 0.0089
0.023 0.27 0.00075 0.00024 0.0027 0.0029
TABLE 2 (Continued) basin
Cseda (ng/g dw)
IQRb
American Mediterranean Asiatic Mediterranean Baltic Sea Black Sea Indian Ocean Japan Sea Mediterranean Sea North Atlantic Ocean North Pacific Ocean Persian Gulf Red Sea South Atlantic Ocean South Pacific Ocean Yellow and East China Sea sum regional sub-basins
0.14
Regional 0.25
0.41
1.1
American Mediterranean Arctic Mediterranean Arctic Ocean Asiatic Mediterranean Baltic Sea Bering Sea Black Sea Indian Ocean Japan Sea Mediterranean Sea North Atlantic Ocean North Pacific Ocean Persian Gulf Red Sea Sea of Okhotsk South Atlantic Ocean South Pacific Ocean Yellow and East China Sea sum remote sub-basins sum global
nc
Id (t)
CImine (t)
CImaxf (t)
206
0.27 0.16 0.68 0.74 0.013 0.54 1.5 12 1.8 0.0016 0.00067 0.52 0.0059 0.55 18
0.21
0.36
0.23
2.1 1.8 0.020 0.98 3.6 14 3.2 0.0025 0.0010 2.3 0.0089 1.0
19
0.12 1.2 0.13
0.29 2.8 0.51
9 354 242
0.14 0.0061
0.39 0.0039
0.070 0.0080 0.0067 0.0061 0.88 0.44
Remote 0.11 0.0083 0.0079 0.0035 1.7 0.61
0.020
0.0000
0.46 0.14 0.063
1.9 0.5 0.23
45 480 93
0.10 0.00090
0.19
6 19
12 5
299 8 31 8 108 9 10
2184
7.6 1.2 2.2 1.9 43 44 54 3.1 6.4 162 304 31 0.28 0.22 10 14 0.16 27 736 756
0.0085 0.30 9.2 0.96 0.0011 0.00044 0.12 0.0039 0.30
6.1 0.67 1.4 1.4 32 21 16 2.7 48 212 13 4.3 1.8 0.073 11 383
9.5 2.3 3.4 2.6 60 94 182 6.2 15 542 435 88 0.56 0.45 24 27 0.35 65 2215
a
Median sediment concentrations. b Interquartile range (i.e., third quartile minus first quartile). c Number of observations used for deriving the population parameters. d Inventory is based on log-normal arithmetic mean concentrations. e The 95% lower confidence limit of the inventory. A blank CImin field next to a defined CImax means that the CImin is undefined. f The 95% upper confidence limit of the inventory. A blank field means that a confidence interval cannot be statistically defined (see text).
where F is the PCB burial flux in the global continental shelf (t yr-1) and ω is the average sedimentation rate (cm yr-1). The sedimentation rate has been determined at many different locations of the continental shelf by using as particle transport tracers natural radionuclides with half-lives of the same order as this deposition process. Based on a large global database of such 210Pb profiles (n ) 220), Middelburg and co-workers (31) established an empirical relationship between ω and ocean depth, which predicts values of 1-2 mm/ yr on the continental shelf. This is consistent with other largescale assessments placing a typical continental shelf ω at 1-3 mm/yr (e.g., refs 28, 32, and 33), and we have thus selected 2 mm/yr as a central value.
Results and Discussion Estimates of the total inventories (Figure 1a,b) and median concentrations (Figure 2) in each basin are illustrated for the two congeners PCB52 and PCB180. The estimates of the median concentration and mean inventory of these two target congeners of varying physicochemical properties for each sub-basin, along with their corresponding measures of dispersion, the interquartile range (IQR) and 95% confidence limit (95% CI), are given in Table 2a,b. The corresponding information for the other six target congeners is provided in the Supporting Information. Uncertainty Analysis. The estimated inventories are products of the factors PCB concentration, continental shelf
area, sediment depth, solids concentration (1 - porosity), and density of the solid particles (eq 1). The lumped parameter sediment depth, solids concentration, and solids density, and its combined uncertainty, is the same for each inventory estimate (i.e., global, as opposed to basin-specific values, were used for these sedimentological parameters). The relative uncertainty of the solids concentration (1-φ), elaborated above, corresponds to (20% around the central value of 0.25. Similarly, the relative uncertainty of the solids density was (10% around 2.5 g cm-3. Assuming a normal distribution, the 95% confidence interval for the mixing depth could be estimated based on the reported standard deviation of 4.5 cm and a sample size of n ) 203 (27). The 95% uncertainty range was thus 9.4-10.2 cm or 9.8 ( 6.2% cm. Hence a conservative estimate of the total uncertainty range for the product of sediment depth, solids concentration, and solids density (i.e., area-specific solids inventory in the mixed surface sediment) was estimated by propagation to be 6.1 ( 23% g cm-2. Area and concentration estimates were specific for each sub-basin. Menard and Smith (13) gave four significant digits for their area estimates. Data from the DCW used to calculate the area of Local and Regional sub-basins, and indirectly of the Remote sub-basins, was considered to be of similar quality. No additional uncertainty was therefore added as a result of area calculations. The effect of including Aroclor-derived estimates of individual congener concentrations in the data set subjected VOL. 37, NO. 2, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Inventories (metric ton) in the ocean shelf sediments for PCB52 (a) and PCB180 (b) partitioned between the different basins. Basin abbreviations are explained in Table 1 and area extents, defined in ref 13, are indicated with thick black lines. The diameters of the pie charts are proportional to the total inventory in each basin (listed above pie chart). The contribution of each sub-basin class to the total inventory is indicated by the size of the pie slices as white (remote), gray (regional), and black (local). It is evident that the Remote shelf sediments on Earth host the vast majority of shelf sedimentary PCBs. The gray-marked zone of the continental margins represent the extent of the 0-200 m depth continental shelf as defined by the General Bathymetric Chart of the Ocean (GEBCO). to modeling was evaluated (5% of data set was from Aroclor reports; Table 1). The difference between the Aroclorexcluded relative to the Aroclor-included Basin inventories for the eight target congeners were for the three Remote Basins of the North Atlantic, the South Pacific, and the Mediterranean -1 ( 7%, -8 ( 98%, and -2 ( 24%, respectively. Since these effects are small relative to the estimate of the total uncertainty (e.g., Table 2), it was deemed that it was beneficial to consider this existing data on PCB in shelf sediments. The 95% confidence intervals of the mean concentration were employed for the uncertainty estimate of the inventory. The width of the confidence intervals varied with the total number of samples (N) and number of uncensored samples (n). Since the maximum likelihood method was used to calculate mean and variance, the confidence interval for the estimates of the mean concentrations were dependent on 250
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these two variables and the standard deviation as well as the difference between the estimated mean of the congener for the sub-basin and the detection limit for the particular congener. The 95% confidence interval for the mean concentration was highly variable between congeners and subbasins. However, the most important basin in terms of PCB inventories, the North Atlantic (15% of the Earth’s total continental shelf area, Table 1), was also best covered in terms of PCB data density and had therefore a relatively narrow 95% CI for most congeners (Tables 2 and S3). For instance, the average relative uncertainty (95% limit) around the mean concentration for the eight target PCBs in the North +26% (average n ) 420). Atlantic Remote sub-basin was -20% We used a conservative method of estimating the uncertainty in the global inventory estimate as a result of these concentration uncertainties. The upper uncertainty limit for the total inventory was calculated as the sum of the upper
FIGURE 2. The median, or typical, concentrations of PCB52 (striped) and PCB180 (black) found in the Remote class sediments of the different ocean shelf basins (median concentrations of PCB52 listed above bars). Large spatial variations are seen both in the absolute sedimentary concentrations as well as of the relative concentrations of the two congeners. 95% confidence limit of the inventories of each sub-basin based on the upper 95% confidence limit for concentration and mid values of the sedimentological parameters in eq 1. The procedure was repeated for the lower 95% confidence limit of the estimated inventories. In this way, the uncertainties in concentration for the different sub-basins were weighted for its area and absolute concentration. The total relative uncertainty of the global inventories due to the +101% concentration parameter was at an average -39% (average n ) 2086) for the eight target congeners (Tables 2 and S3). Hence, because of the size of the data set, particularly in the most receiving shelf basins, the inventories appear to be constrained within roughly a factor of 2 in either direction. Another, potentially large, uncertainty not included in the above estimate, is the effect of sampling bias. A considerable amount of the total number of PCB concentrations publicly available may be biased toward positions in proximity to populated areas. As detailed above, our strategy to correct for this bias was to classify the continental margins of each basin into Local, Regional, and Remote sub-basins based on their distance from the assumed sources of PCBs (approximated by the outer boundary of population centers in DCW). The procedure appeared effective since the median concentration was almost always highest in the local class and lowest in the remote class (Tables 2 and S3). For a few basins, notably the North Atlantic and the Mediterranean, the Regional class exhibited for several congeners a higher concentration than the Local class. As discussed elsewhere (e.g., ref 34), this may be related to the lithology of the sediment transported by rivers. While coarser sediment fall out near river mouths (Local), the finer sediment particles, presumably more effective at transporting PCBs, may be deposited a “Regional” distance (i.e. 1-10 km) off the river mouth. The distinguishing classification of each basin is further supported by findings indicating that PCBs concentrations in areas far from population centers are relatively evenly distributed. For example, PCB concentrations in Baltic Sea sediments taken more than 10 km off the coast did not show any correlation with distance from coast or population centers/latitude (ref 35 and Axelman et al., unpublished results). The efficiency of this classification scheme in minimizing “hotspot” bias for the estimate of inventory in
TABLE 3. Global Inventories and Burial Fluxes in the Continental Shelf Sediments inventorya
global (ton) global burial fluxb (ton/yr)
PCB 28
PCB 52
PCB 153
PCB 180
460910 290
7001500 420
12002100 720
7602200 380
9.620 3.9
1431 5.1
2447 8.8
1546 4.7
a Sub- and superscripts indicate the lower and upper 95% confidence limits, respectively. b Sub- and superscripts indicate the lower and upper 95% confidence limits propagated from the inventory, sedimentation rate, and sediment mixed layer depth.
the Remote basins was further tested quantitatively. The variance of the data decreased markedly in going away from Local to Regional and Remote data sets. For instance, the mean relative standard deviation (rsd) for Local, Regional, and Remote sub-basins were for American Mediterranean 73, 2.2, and 1.5, respectively, and the same trend was seen for the North Atlantic classes at 17, 8.6, and 3.4. The relatively small rsd in the Remote sub-basins suggest that their estimated Inventories are not compromised by inclusion of near-source sediments. Nevertheless, more data on PCB concentrations in very remote sediments would be beneficial. Spatial Distribution Patterns of Inventories. The total inventories of four target congeners in the global shelf sediments were in the range 400 to 1200 ton each (Table 3). An overwhelming fraction (95-99%) of the estimated global shelf PCBs resides in the northern hemisphere (NH) sediments (Figure 1a,b). It should be recognized in this context that, while most of the total ocean area is in the southern hemisphere (SH), this vast water only overlies 18% of the global continental shelf area (calculated from Table 1; with 50% of Indian Ocean shelf taken to be in SH). This quantitative estimate of PCB inventory in a major environmental matrix, showing a vast dominance of NH over SH, is qualitatively in agreement with measurements of PCBs in NH and SH butter (36) and other organochlorine contaminants in tree bark (3). Within the NH shelf basins, the North Atlantic sediments contain 40-50% of the estimated global shelf inventory of PCBs (Figure 1a,b). The largest part of the world’s historical VOL. 37, NO. 2, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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PCB production (37) and emissions (38) also occurred in the Eastern U.S.A. and Western Europe. Hence, the North Atlantic coastal ocean is likely to have been exposed historically to significantly higher emissions than other ocean basins, both via atmospheric and runoff processes. Diffuse emissions still contribute with elevated concentrations in runoff water to the Western North Atlantic shelf (39). Semienclosed basins off the North Atlantic such as the Mediterranean Sea and the Baltic Sea, both located in the 30-60 °N latitudinal PCB source band (38), are also predicted to host significant portions of the global shelf sediment burden (Figure 1a,b), qualitatively consistent with results from basin-wide mass balance investigations of these areas (35, 40). We have recalculated the sum-PCB inventory from the Baltic Sea study of Axelman and co-workers for PCB52 (35), and their estimate of a Baltic inventory agrees within a factor of 2 with that of the current study. It has been discussed that “poleward distillation” of organochlorines may lead to significant accumulation of POPs in the Arctic environment (e.g., refs 41 and 42). Comparison of PCB patterns in limited sediment samples at increasing latitudes extending into the Arctic are suggesting a distillationlike fractionation in the relative congener abundance but decreasing absolute concentrations/fluxes toward the north (43, 44). While the vast Arctic Ocean shelf makes up 14% of the global shelf area, its inventory of PCB52 and PCB180 is here estimated to be merely 1 and 0.3%, respectively, of the global shelf inventory of these PCBs. Our estimate of the PCB inventories in the global continental shelf sediments is higher than an earlier estimate of the inventories in NH shelf sediments, for which Axelman and Broman (4) used sediment PCB data from only four regions (Baltic Sea, North Sea, Mediterranean, and the Arctic Ocean). It appears that a major factor behind this difference is the definition of the reservoir boundaries. Axelman and Broman (4) did not calculate an area-specific load of PCBs, to be extrapolated over the NH shelf area, but instead used OC-normalized PCB concentrations in combination with a global estimate of OC burial in shelf sediments (45). For the estimate of inventories, the key diverging assumption was that they assumed that the PCB pool available for recycling back to the water column corresponds to only 10 years of sediment accumulation, assumed to equal only the 0-1 cm sediment depth (4). However, it is known that shelf sediments are mixed/bioturbated down, on average, 9.8 cm (27). Bioturbation coefficients (Db), commonly on the order of 10 cm2 yr-1 (e.g., ref 32), suggest that hydrophobic compounds such as PCBs may be exchanged from throughout a 9.8 cm mixed interval to the overlying water column over a decadal time scale. Hence, the entire mixed sediment layer should be considered part of the environmentally recycling pool of PCBs and similar compounds. Another intriguing finding of the present study was that the many infamous and highly contaminated surface sediments of urban harbors and estuaries of contaminated rivers cannot be of importance as a secondary source to sustain the concentrations observed in the remote sub-basins. The summed PCB inventory of the Local sediments is only a small fraction (0.2-0.5%; Table 2) of the global shelf PCB inventory, which instead is dominated by the vast areas taken up by the Remote sediments (87-97%; Table 2). To illustrate, from published PCB release rates from the U.S. Superfund Site New Bedford Harbor of sum-PCB at 2.2 kg/yr (46), it may be estimated that it would take for PCB52 (taken as 5% of an assumed Arochlor 1254 composition (12)) over 6000 years from 1000 such Superfund Sites to build up the present global shelf sediment inventory of this congener. Rather, it is likely that the Remote inventories originate from dispersal of the historical diffuse discharges to rivers and atmosphere. Spatial Distribution Patterns of Concentrations. Assessment of the individual PCBs in this global shelf database 252
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affords the possibility to establish a “typical” PCB concentration and to diagnose important fate processes, leaving a fractionating imprint on the relative congener distribution. The typical (i.e. median) concentration of PCB52 in marine sediments of the dominant Remote class of the North Atlantic and the neighboring semienclosed basins, the American Mediterranean, the Baltic Sea and the Mediterranean Sea, is in the range 0.1-0.6 ng gdw-1 (Figure 2). About an order of magnitude lower concentrations are typical for Remote shelves in the Southern Hemisphere and in the Arctic Ocean and the Arctic Mediterranean. Intermediate median concentrations in the vicinity of 0.1 ng gdw-1 are found on the remote Asiatic Mediterranean and North Pacific shelves (Figure 2). The surprisingly high median concentrations of PCB52 in the Bering Sea of 0.4 ng gdw-1 is likely to reflect samples taken in the vicinity of naval or fishing harbors on the Aleutian Islands and/or shore-based facilities of the region’s offshore industry, which are not classified as population centers by the Digital Chart of the World GIS database and therefore incorrectly assigned to the Remote class. For enclosed shelf basins where one may expect that runoff is a significant entry route, such as the Baltic Sea (35) and the Mediterranean Sea (34), there is a suggestion that the ratio of PCB180:PCB52 is higher than the average (Figure 2). This is consistent with a scenario of sedimentation of riverborne particles, preferentially carrying the more hydrophobic congeners. In contrast, the lower PCB180:PCB52 ratio of the Pacific, Atlantic, and Indian Oceans is likely to reflect the preferential longer-range transport of the more volatile and water-soluble lower-chlorinated congener. An alternative modeling approach, to the direct and unassuming approach selected in this study, would have been to assume that the PCBs were “equilibrated” with organic carbon (OC) in sediments and couple such OCnormalized PCB concentrations to geochemical information on the large-scale organic carbon cycle. Indeed, some previous studies have demonstrated that a correlation between sedimentary OC and levels of PCBs may be significant within a specific region (e.g., ref 34 for the Northwestern Mediterranean; ref 44 for the Arctic Ocean). However, large-scale signals of PCB distribution deduced from the present global database reveal a somewhat different picture. Already the fact that median PCB congener concentrations in sediments of the Arctic Ocean, American Mediterranean, and the Southern Hemisphere Basins may be factors 5 to > 100 lower than in the 30-60 °N sediments (Figure 2, Table 2, and SI Table S3) suggests that there are large variations in OC-normalized PCB concentrations in the Remote sediments of the world. The OC content in shelf sediments does not follow the PCB distribution patterns, and variations in OC between different basins are much smaller than the spatial PCB variations (e.g., ref 47). Since we had entered sedimentary OC content into the database when it was reported for the same sediment sample as for target PCBs, the present data set allowed testing of the role of OC in affecting the large-scale sedimentary PCB distribution. Linear regressions of OC versus concentrations of each of four PCB congeners (PCB28, PCB52, PCB153, and PCB180) were performed for the Remote regimes containing the largest number of coupled OC-PCB data (AmM, AtN, Ba, Me, and PaN). At the 95% confidence limit, there were only significant correlations for two out of the 20 populations (PCB28 and PCB52 in the Mediterranean Sea with r2 of 0.86 and 0.27, respectively). Even when the regressions were performed on log-transformed PCB concentrations, there were only significant (95% C.I.) positive correlations in six out of 20 cases (with average r2 of 0.25 for these six sediments). An expected large variability in sorption abilities between coastal sedimentary organic matter of terrestrial versus marine origin,
FIGURE 3. Estimated magnitudes of the global cumulative historical production (black), global cumulative historical emission (white), and global shelf sediment inventory (grey) in metric ton of selected individual PCB congeners. Inspection of the error bars provided in a figure in ref 37 places the uncertainty of the production estimates at around 30%, whereas the estimated uncertainties of the two other masses are larger (as indicated by error bars in the figure). further amended with different amounts of pyrogenic and other anthropogenic carbon forms (e.g., ref 33), may certainly contribute to this decoupling. Perhaps an even more significant contributor to the overwhelming lack of OC-PCB correlations in marine sediments may be sought in a combination of these chemicals’ physicochemical properties and the biogeochemistry of carbon export. In contrast to presumed equilibration of PCBs in the atmosphere with surface soils, the sluggishness in ocean circulation may prevent water-borne PCBs in near-source regions to equilibrate with distant sediments before they are significantly scavenged out of the water column by settling organic-rich particles (e.g., refs 6 and 26). Sharp gradients in POCnormalized seawater PCB concentrations have been observed along coastal to open ocean transects (e.g., refs 6 and 48). Clearly, the large spatial variations in PCB concentrations of Remote sediments cannot be explained by varying OC contents but is rather a testament to a world not near equilibrium. Sediment Inventories in Relation to Cumulative Emissions. A significant fraction of the estimated total amount of PCBs emitted to the environment can be accounted for in the sediments. Cumulative historical emissions of individual PCB congeners have been given with a large uncertainty interval spanning over 3 orders of magnitude between high and low ranges (38). Earlier estimates (49, 50) have recently been recalculated to annual emissions to the Northern Hemisphere for PCB28, PCB52, PCB153, and PCB180 (4). These earlier figures are well in accordance only with the upper uncertainty limit of the recent estimates by Breivik and co-workers (38). In Figure 3, the global continental shelf inventories of PCBs are compared to global historical cumulative estimates of both PCB production and emission. The inventories of the target PCBs in the global shelf sediments correspond to 1-6% of their estimated industrial production. The estimated sediment inventory is higher than the presumed “best” value for the emission and indicates that actual cumulative emissions are closer to the upper limit of the emission suggested by these workers (38). The agreement in terms of absolute numbers was also reasonable; however, sediments alone cannot be expected to account for all emitted PCBs since other environmental compartments, in particular soils, also constitute significant pools of PCBs (4, 50, 51). The soils inventories of the Northern hemisphere for PCB28, PCB52, PCB153, and PCB180 have been recently estimated to be 190, 210, 1220, and 580 ton, respectively (51). Our estimates of the PCB inventories in the global continental shelf surface sediments (Table 3) are equal to or higher than these soil estimates.
FIGURE 4. The fraction of the recently estimated maximum cumulative historical emissions of PCBs (38) that are accounted in the inventories of the global shelf sediments as a function of the chlorination degree of the individual congeners. Interestingly, there was a strong congeneric trend in the ratio of Shelf Inventory to Cumulative Emission (Figure 4). A larger relative fraction of the higher-chlorinated congeners seems to be found still remaining in the environment, in agreement with recent NH budget calculations (4). This strongly indicates that a sink, favoring the removal of lowerchlorinated congeners such as tropospheric reaction with hydroxy radical (5), acts on the global environmental (or recycling) pool of these lower-chlorinated PCBs. Whereas the shelf inventories only correspond to about 10% of emission estimates for the lower-chlorinated (tri- and tetrachlorinated) congeners, this figure increases with chlorination degree to suggest that shelf sediments account for nearly 80% of the total cumulative emissions of heptachlorinated congeners such as PCB180 (Figure 4). Sediment Burial Fluxes and Effect on Environmental Longevity of PCBs. The environmental persistency is a key element in chemical risk assessment. The environmental longevity of substances subjected to long-range transport such as the PCBs is set by their global permanent removal fluxes. Given that the mixed surface layer of continental shelf sediments is a large reservoir of the globally recycling PCB pool, it behoves us to assess the magnitude of the burial flux of PCBs to deeper sediment strata. Combining the PCB inventory with relevant geochemical parameters such as sediment accumulation rate (eq 2) affords estimate of the global burial flux of individual PCB congeners (Table 3). This removal sink is on the order of 8-24 ton/yr for the eight individual target congeners of this study. For lowerchlorinated congeners such as PCB28 and PCB52, this sediment burial sink is apparently overshadowed by the inferred global-scale removal flux through atmospheric hydroxy radical reaction (4, 5). It must be noted that such a large hydroxy-radical mediated sink has recently been questioned based on mass balance arguments and diagnostic fingerprints of the historical evolution of the congeneric fingerprint of the environmentally cycling PCBs (e.g., refs 4 and 7). However, for higher-chlorinated congeners such as PCB153 and PCB180, similar such hydroxy radical reaction sink estimates are equal to or smaller than our estimate of the sediment burial. Based on very limited data, estimates of deep-sea export of individual congeners suggest that the deep-sea sink flux may be of similar magnitude as the now constrained sediment burial flux (4, 6), but elucidation of the open ocean settling sink awaits results from recent largescale oceanic surveys (Schulz-Bull and Gustafsson, manuscript in preparation). For the purpose of risk assessment, it appears reasonable to estimate the average environmental residence time for the higher-chlorinated, and more bioaccumulable, PCB congeners from a total global removal flux equaling that VOL. 37, NO. 2, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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constrained for shelf sediment burial. Hence, the environmental persistency of PCBs may be estimated using (52, 53)
τres )
Itotal Fsink
(3)
where τres is the mean environmental residence time (yr), Itotal is the inventory of the recycling PCB pool in the global environment (ton), and Fsink is the flux due to any permanent removal process such as burial into deeper layer of shelf sediments (ton/yr). Given the reasonable agreement for higher-chlorinated congeners between the global shelf inventory and the upper estimate of cumulative historical emissions, considering the presence of an additional soil inventory, the upper emission numbers were selected as conservative estimates of Itotal. Hence, a governing permanent removal flux into deeper shelf sediments of PCB153 and PCB180 suggests that the environmental residence times of these bioaccumulable and ecotoxicologically significant pollutants are on the order of 110 and 70 years, respectively. Such high longevities are further consistent with the fact that their cumulative emissions can still be largely accounted for in the global recycling reservoirs such as the mixed surface layer of shelf sediments. Since hydroxy radical reactions would remove the lighter PCB congeners more efficiently, the residence time of, for instance, PCB28 is expected to be much shorter. Such large variations in expected lifetimes between different PCB congeners could be taken into account in their relative risk estimates. This analysis suggests that humans and the rest of the global ecosystem will continue to be exposed to the more bioaccumulable PCB congeners for decades to centuries to come even after production and direct releases have been halted.
Acknowledgments Insightful discussions on aspects of the oceanic and largescale cycling of persistent organic pollutants with Dag Broman, Ian Cousins, and Detlef Schulz-Bull are much appreciated. Many colleagues around the world have kindly provided sediments and/or data to help build the PCB database. This work was financially supported by funding from EU DG XII, contract no. ENV4-CT97 (GLOBAL-SOC) and contract no. EVK1-CT-2001-00101 (ABACUS), and from the Swedish Strategic Environmental Research Fund (MISTRA contract no. 98538).
Supporting Information Available Source references and statistical properties of the PCB database as well as estimates of concentrations and inventories for additional congeners. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review July 10, 2002. Revised manuscript received November 1, 2002. Accepted November 6, 2002. ES0201404
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