Environ. Sci. Technol. 2000, 34, 2817-2825
Field Testing a Metal Bioaccumulation Model for Zebra Mussels HUDSON A. RODITI, NICHOLAS S. FISHER,* AND SERGIO A. SAN ˜ UDO-WILHELMY Marine Sciences Research Center, State University of New York, Stony Brook, New York 11794-5000
A kinetic model of trace element bioaccumulation in zebra mussels, employing experimentally determined trace element influx and efflux rates following food and water exposures, was field-tested in the Hudson and Niagara Rivers and Lakes Erie and Ontario. Ultraclean measurements of water column trace element concentrations in these waters and on suspended particles were used to predict metal concentrations in zebra mussels that were measured independently by the NOAA Mussel Watch program. Field concentrations of Ag, Cd, Cr, and Hg ranged from subpicomolar (Ag) to low nanomolar (Cr) and displayed partition coefficients between particulate and aqueous phases of 1-20 × 105 L kg-1. Despite variation in bioaccumulation factors (BAF) between locations by up to 6× (for Ag), our model predicted mean body burdens of Ag, Cr, Hg, and Se that differed from measured tissue concentrations at the same sites by only 30% on average. Cd predictions matched measured values at the two lake sites but exceeded measurements at the three river sites by 2.6-fold. Furthermore, the model predicted that, under all environmental conditions likely to prevail in natural waters, Ag, Cd, and Hg are predominantly accumulated from ingested particles, that Cr is accumulated mostly from the dissolved phase, and that the relative uptake pathway for Se varies with environmental conditions. The highest BAF was for Cd (15-64 × 104) and the lowest BAFs were for Cr and Se (1.2-2.5 × 104 and 0.5-2.8 × 104, respectively), with Ag and Hg being intermediate (2.0-12 × 104 and 1.4-25 × 104, respectively). The good agreement of the model with field measurements suggests, for these elements, that (a) accumulation of these elements in zebra mussels is in fact proportional to influx from food and water (that is, the organism is not actively regulating internal concentrations), and (b) we can account for the processes governing metal bioaccumulation in these animals. We conclude that for these elements the zebra mussel will be effective as a bioindicator of ambient metals in freshwater systems.
Introduction Biomonitoring programs that measure contaminants in the tissues of aquatic organisms have been established in part to overcome the difficulties and shortcomings of measuring metals directly in water. These difficulties include measuring low concentrations accurately, getting representative or * Corresponding author phone: (631)632-8649; fax: (631)632-3049; e-mail:
[email protected]. 10.1021/es991442h CCC: $19.00 Published on Web 06/06/2000
2000 American Chemical Society
“average” samples when trace metal concentrations fluctuate over time, and isolating the bioavailable fraction of the contaminant from unavailable forms. To be used effectively as a biomonitor, an organism must accumulate contaminants proportionately to its exposure and have other demographic and physiological characteristics, including a sufficiently broad geographical distribution, ease of collection, and ability to tolerate elevated contaminant concentrations (1). The zebra mussel (Dreissena polymorpha) holds considerable potential as a bioindicator animal for monitoring freshwater systems because this species is widespread and is unusually effective as a grazer of particulate matter (2). In fact, freshwater monitoring programs have begun to include zebra mussels as a biomonitoring component in a variety of lake and river ecosystems (3, 4). In this way, zebra mussels could serve the same function as other mussel and oyster species that have been used in coastal systems for 3 decades to discern spatial and temporal trends of marine contamination (5, 6). Experience with marine bivalves has shown that data sets of metals can be difficult to interpret without information on metal inputs into a water body and untangling diverse biotic and abiotic processes and pathways that contribute to metal body burdens. Furthermore, quantifying metal uptake parameters may help explain variability in their bioconcentration factors between sites, predict the relative contributions of food- and water-borne metals to body burdens, and estimate the exposure time integrated in an organism’s body burden for specific metals (an index of the exposure history reflected by an organism). Overall, this information can provide a basis for establishing water quality criteria and assessing risk, thereby enhancing the power of biomonitoring data. Previous efforts to model metal accumulation in marine and freshwater bivalves have performed regression analyses to infer that tissue concentrations of metals can be a function of the chemical speciation of the metal (as computed for Cd; 7) or dependent on metal accumulation from food (particularly for Zn, Cd, Cu, and Hg; 8). These studies did not directly quantify the uptake pathways or uptake kinetics of metals from food and water into the bivalves, and their regression analyses relied on assumptions of steady-state conditions. A bioenergetic based kinetic model (see below) has been developed that can help delineate the relative importance of uptake pathways and quantify the effects of discrete environmental variables on metal accumulation in key bioindicator species (9, 10). This model incorporates various environmental and biological conditions likely to be encountered in the environment and treats bioaccumulation in terms of bioenergetic processes. Consequently, the kinetic model is sufficiently flexible that, in principle, it can be applied to a broad spectrum of aquatic environments. We have adapted this model to understand the bioaccumulation of metals in zebra mussels and field tested it at five sites regularly used by NOAA’s National Status and Trends (NS&T) Program for zebra mussel tissues. The field validation procedure consisted of independently measuring metals in water and suspended particles overlying mussels at those sites, running our model with these values and lab-based kinetic parameters for metal uptake (11), and comparing model predictions and independent measurements for metal concentrations in zebra mussel tissues at the same locations. Few studies have field tested metal bioaccumulation models based on laboratory data (9, 12). Reliable measurements of trace metals in water are scarce, in part because concentrations (often picomolar) are often near detection limits and ultraclean techniques are essential to prevent VOL. 34, NO. 13, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
2817
FIGURE 1. Sampling sites on the Great Lakes, Niagara River, and Hudson River where zebra mussel tissues (National Status and Trends Program) and water overlying zebra mussel beds (this study) were sampled for trace metal concentrations within the same week in 1997.
TABLE 1. Water Characteristics at Sampling Sitesa site
sample ID
sampling infob
POC (µg L-1)
PON (µg L-1)
particulate C:N
DOC (mg L-1)
TSS (mg L-1)
TSS % organic
temp (°C)
pH
Lake Erie Lake Erie Lake Erie Niagara River Lake Ontario Lake Ontario Hudson River (Tivoli) Hudson River (Tivoli) Hudson River (Tivoli) Hudson River (Pok) Hudson River (Pok) Hudson River (Pok)
1 2 3 1&2 1 2 1 2 3 1 2 3
08:00 9/6 12:00 9/6 16:00 9/6 18:00 9/7 14:00 9/8 15:00 9/8 07:00 9/9 SH 10:00 9/9 ME 13:00 9/9 SL 07:00 9/10 SH 10:00 9/10 ME 13:00 9/10 SL
223 ( 16 290 ( 16 270 ( 8 290 ( 22 430 ( 92 327 ( 5 387 ( 103 337 ( 5 320 ( 64 489 ( 37 424 ( 11 389 ( 28
18 ( 4 23 ( 5 10 ( 0 20 ( 0 27 ( 5 20 ( 0 20 ( 8 20 ( 0 13 ( 5 32 ( 3 23 ( 7 22 ( 8
12.7 12.4 27.0 14.5 16.1 16.3 19.3 16.8 24.0 15.2 18.2 17.5
1.76 ( 0.01 1.78 ( 0.02 1.77 ( 0.02 1.81 ( 0.05 2.25 ( 0.11 2.17 ( 0.01 2.98 ( 0.02 2.95 ( 0.01 3.03 ( 0.14 2.73 ( 0.08 3.01 ( 0.06 2.80 ( 0.02
3.0 ( 0.3 3.5 ( 0.1 3.5 ( 0.2 2.2 ( 0.1 1.5 ( 0.1 1.3 ( 0.1 4.7 ( 0.0 4.0 ( 0.2 2.9 ( 0.1 10.3 ( 0.4 5.9 ( 0.3 7.8 ( 0.1
7.5 8.2 7.7 13.2 29.3 25.3 8.3 8.4 11.2 4.7 7.2 5.0
19.5 20.5 21.0 19.5 19.0 19.0 21.5 22.0 22.5 23.0 23.5 23.0
8.4 8.3 8.1 7.8 7.3 7.9 7.3 7.6 7.8 7.3 7.5 7.5
a SH, slack high tide; ME, maximum ebb tide; SL, slack low tide; Pok, Poughkeepsie. Values are means ( 1 SD; n ) 3 (POC, PON); n ) 2 (DOC, TSS). b Year 1997.
contamination. Also, particulate metal data are often not available, and the dissociation of metals from particles under weak acid conditions (potentially important for availability within an organism’s digestive system) is only rarely measured (13, 14). Estimates of total suspended solids (TSS) and trace element partition coefficients (kd) are used to estimate metal concentrations on particles from dissolved measurements when direct measurements are not available, although kd values for a given metal may vary considerably from one location to another (15). We directly measured the dissolved and particulate fraction of metals at these sites, and resolved particulate metals into “labile” (weakly bound) and “refractory” fractions. The NS&T Program began sampling zebra mussels for trace elements and organic contaminants in 1992 in the Great Lakes and the Hudson River. To field test the kinetic bioaccumulation model, we used a subset of this NS&T mussel tissue data by sampling five NS&T sites on the Hudson and Niagara Rivers and Lakes Erie and Ontario for trace metals in water using trace metal clean methods. The kinetic model of metal bioaccumulation we tested is based on rate parameters developed for zebra mussels, including assimilation and absorption efficiencies for solid and dissolved phase contaminants, and efflux or depuration rates (10) as well as mussel growth rates, ingestion rates, and ambient metal concentrations in food and water. We compare the sitespecific modeling predictions with independent NS&T mussel tissue data for each site. This study was motivated by our 2818
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 34, NO. 13, 2000
interest in testing our understanding of the processes governing bioaccumulation and by our interest in enhancing the value of programs that use the zebra mussel as a monitoring organism.
Materials and Methods Sampling and Analysis.We measured trace metal concentrations in water overlying zebra mussel beds at five sites sampled by the NS&T Program in September 1997. Water sampling was simultaneous with NS&T’s zebra mussel sampling at these sites. The five sites chosen (Figure 1) were selected from among NS&T’s 26 freshwater sites to represent a range of contamination conditions in both rivers and lakes; sites included two locations on the Hudson River (Tivoli, NY, and Poughkeepsie, NY), the Niagara River (North Tonawanda, NY), Lake Erie (Ashtabula, OH), and Lake Ontario (Oswego, NY). Sampling at a site included simple replication (Niagara River, Lake Ontario), a short time series (Ashtabula, OH), or a tidal cycle (Hudson River: slack high, maximum ebb, and slack low) (Table 1). Water from the Lake Erie and Lake Ontario sites was sampled from rock jetties. The Ashtabula site is not near a river mouth. The Oswego site is near the mouth of the Oswego River, but the site we sampled is not strongly influenced by Oswego River water because there is a major barrier wall that encloses the harbor area, and sampling was done outside of this wall. Water from the Niagara and Hudson River sites was sampled from docks and rock jetties. All sampling employed ultra-trace metal
TABLE 2. Metal Concentrations in Water, Suspended Particles, and Mussel Tissues at Five Sampling Sitesa Ag sample ID Erie Niag
Hudson Tiv
Ont
Cr (× 103)
Cd Hudson Pok
Erie
Niag
Ont
Hudson Tiv
Hudson Pok
Erie
Niag
Ont
1.30 1.22 nd
2.85 2.95 nd
1 2 3
0.95 3.66 4.54 7.95 ( 0.61 5.03 ( 0.68 69.4 0.94 2.57 6.47 7.72 5.38 60.5 1.35 nd nd 7.69 5.52 73.3
52.8 63.3 nd
39.0 36.6 nd
Dissolved (pM) 170.3 ( 28.7 145.0 ( 16.2 0.60 134.4 136.1 0.87 115.9 128.6 0.80
1 2 3
0.50 3.88 1.89 4.10 ( 0.53 3.63 ( 0.13 6.90 0.88 8.29 3.28 2.15 ( 0.28 4.36 ( 0.37 4.98 1.13 nd nd 1.90 4.12 ( 0.44 4.39
16.4 16.2 nd
2.66 4.04 nd
Labile Particulate (nmol (g of TSS)-1) 20.3 ( 1.3 8.87 ( 0.88 0.058 0.057 nd 9.05 ( 0.37 8.37 ( 0.14 0.017 0.006 nd 10.2 8.60 ( 0.42 0.005 nd nd
100
98 ( 1 100
98 ( 2
100
100
100
100
100
VOL. 34, NO. 13, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
0 0 0
0 0 0
0.61 0.19 nd
0 0 nd
2.20 31.9 52.7 51.9 12.1 30.5 4.6 53.0 ( 6.9 4.63 nd nd 33.3
24.4 77.0 23.9
71.5 103.7 65.4
82.7 64.4 nd
31.6 32.9 nd
195.4 165.0 138.4
0.35 1.88 0.73 0.74 4, 5 4 4, 5 5
0.74 5
54.1 44.5 4, 5, 7 4, 5
1 2 3
0 0 0
1 2 3
g
0 0 nd
0 0 nd
0 0 0
25.0 23.4 4, 5, 7 5, 7
Total Unfiltered (pM) 137.4 1.90 181.1 2.77 175.4 2.16
2.60 3.30 nd
2.26 1.38 1.16
Hg Hudson Pok 2.61 2.65 2.34
Erie
Ont
9b 3.4b 1.02c 1.02c 0.75d,e nd
Hudson Hudson Tiv Pok 2.14 4.68 nd
3.80 6.90 nd
0.601 ( 0.08 0.354 ( 0 .087 2.7b,e 0.86b,e 0.194 ( 0 .017 0.181 ( 0 .068 0.82c 0.82c 0.218 0.325 ( 0 .019 0.23d,e nd
2.03 0.84 nd
0.78 0.86 nd
13 ( 6
17 ( 4
nd
nd
nd
nd
nd 1.47 nd
2.18 ( 0.74 1.64 ( 0.2 2.41
1.79 ( 0.02 1.23 ( 0.47 1.09 ( 0.08
nd nd nd
nd nd nd
nd nd nd
nd nd nd
3.49 2.83 nd
5.13 3.29 3.50
7.97 6.34 6.83
18b 1.6c 1.5d
4.6b 1.6c nd
nd nd nd
nd nd nd
0.165 7
0.31 0.39 4, 5, 7 4, 5, 7
0.39 5, 7
0.30 7
Average % Labile 98 ( 0 1 ( 1 1 ( 1 nd
Refractory Particulate (nmol (g of TSS)-1) 0 0.15 ( 0.02 2.78 2.98 0.37 ( 0.02 0.15 ( 0.00 3.97 2.56 0.12 0.14 ( 0.01 2.13 nd
Hudson Tiv
Mussels (nmol g-1)f 30.7 0.142 0.114 0.111 0.240 7 4, 5 4, 5 4, 5, 7 5, 7
a Particulate metals include labile fraction (used in modeling) and refractory fraction (not used in modeling). Means ( 1 SD. Particulate Hg in the Hudson is the sum of labile and refractory pools. Tiv, Tivoli; Pok, Poughkeepsie; nd, not determined. b Data for Lakes Erie and Ontario (23). c Data from Lake Michigan (25). d Data from Lake Erie (24). e Values recalculated from published data. For details on calculations, see Appendix 3. f Measured by NS&T Program; data available at: http://ccmaserver.nos.noaa.gov. g Year 199-.
9
2819
TABLE 3. Kinetic Model Parameters for Ag, Cd, Cr, Hg, and Se (11) Modified To Consider Mussel Pumping Activity for 17.4 h d-1 a element
AE (%)
Ku (L g-1 d-1)
Kef (d-1)
Kew (d-1)
Ag Cd Cr(III) Cr(VI) Hg Se(IV) Se(VI)
4.5 ( 0.7 22.7 ( 3.7 1.5 ( 0.5 nd 3.6 ( 1.3 23.0 ( 4.5 nd
3.67 ( 0.63 1.98 ( 0.16 0.95 ( 0.21 0.53 ( 0.14 2.3 ( 0.2 0.14 ( 0.02 0.43 ( 0.11
0.070 ( 0.014 0.012 ( 0.001 0.019 ( 0.003 nd 0.050 ( 0.005 0.026 ( 0.002 nd
0.088 ( 0.017 0.011 ( 0.002 nd 0.011 ( 0.006 nd 0.035 ( 0.003 nd
a
Means ( standard error. nd, not determined.
clean techniques (16); further details are given in Appendix 1 (see Supporting Information). NS&T mussel tissue data available for the five study sites (Table 2) were compared with model predictions. Model and Kinetic Parameters. Both water and food are potential sources of trace metals to zebra mussels. A kinetic model (9) describes uptake from two or more independent pathways as
dCm/dt ) (KuCw) + (AE × IR × Cf) - (ke + g)Cm (1) where Cm is the metal concentration in mussel soft tissues (nmol g-1 dry wt), t is the exposure time (d), Ku is the dissolved uptake rate constant (L g-1 dry wt d-1), Cw is the dissolved metal concentration (nM), AE is the assimilation efficiency of ingested metal (%), IR is the ingestion rate (g g-1 dry wt d-1), Cf is the metal concentration in ingested solids (nmol g-1 dry wt), ke is the efflux or depuration rate (d-1), and g is the growth rate (d-1). Under steady-state conditions, eq 1 can be solved as
Cmss ) (KuCw)/(kew + g) + (AE × IR × Cf)/(kef + g)
(2)
where Cmss is the steady-state trace metal concentration in the mussel soft parts (nmol g-1 dry wt), and kew and kef are efflux rate constants (d-1) following metal uptake from the dissolved phase or food, respectively. Table 3 presents measured values for each of the model parameters determined with laboratory radiotracer experiments. The IR value used in this model (0.35 g g-1 dry wt d-1) is a mean of values from two studies using different methods (11, 17) (Appendix 2 provides detailed information on IR and growth rate determinations; see Supporting Information). The mean IR assumes that TSS loads do not limit ingestion rates (17), an assumption that is probably met in the waters we sampled given values g1.4 mg L-1 (Table 1). These IR values should apply to a range of mussel sizes since ingestion capacity is unaffected by body weight (18). A growth rate (g) of 0.0019 ( 0.0002 d-1 was employed, based on an annual production-to-biomass ratio of 1 (18, 19). Other kinetic parameters used in the modeling are taken from Roditi and Fisher (11), although some modifications have been made. Cr Ku values for (III) and (VI) oxidation states differ by almost a factor of 2, but we did not determine Cr speciation in water samples, only total dissolved Cr. Therefore, at our lake sites we assume the same speciation as found previously in Lake Ontario (20), where 75-85% of the dissolved Cr was Cr(VI). No similar study was performed on the Hudson or Niagara Rivers; we assume at these sites that Cr(III) and Cr(VI) species are present in equal concentrations. We also modified the Ku value for Se(IV) (to 0.14 L g-1 d-1) from that reported in Roditi and Fisher (11) as further experimentation showed that the earlier value was underestimated by a factor of 2.8. In addition, Ku values reported in Table 3 are slightly reduced (11) because they have been scaled down to an active day of 17.4 h (21, 22) from 24 h. 2820
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 34, NO. 13, 2000
With the exception of Hg in the Hudson, we did not measure Hg and Se at our sites and rely on published values for these elements (for Hg, refs 23-25; for Se, ref 26). Se concentrations were not available for most study sites, so sites were grouped into “river” and “lake” sites. Se concentrations (nM) used for river sites were 1.7 nM Se(IV) and 0.73 nM Se(VI) and for lake sites 9.7 nM Se(IV) and 3.1 nM Se(VI) (26). Particulate Se for both groups was assumed to be 8.1 nmol g-1 seston (26). Data from other studies generally lack the spatial and temporal coincidence with NS&T mussel tissue data sets of our own water column measurements and also do not describe the physical and chemical speciation of the metals. Consequently, our modeling efforts for Hg and Se are more tentative than for the other metals and should be revisited when additional field measurements are available. Appendix 3 provides further details on application of published Hg and Se measurements to our model (see Supporting Information). Model parameter error terms were determined by error propagation analysis (27). Standard errors of 20% were assumed for trace metal measurements in this exercise.
Results Metal Measurements. Metal concentrations in dissolved and total unfiltered pools were in the low to mid picomolar range for Ag, Hg, and Cd and in the low nanomolar range for Cr (Table 2). The labile fraction accounted for essentially all of the particle-bound Ag and Cd but 0.2 µm) were measured directly, the percentage of total metal associated with particles increased in the order Cd < Ag < Hg e Cr, with averages across sites for these elements of 26 ( 10%, 67 ( 18%, 81 ( 7%, and 86 ( 5%, respectively (Table 4). Calculating particulate metal as the difference between metal concentration in total unfiltered water and dissolved metal concentrations yielded 14 ( 14%, 82 ( 10%, and 62 ( 6% for Cd, Ag, and Cr, respectively. Partition coefficients (Kd) of metals between the aqueous and solid phases were calculated as Kd (L kg-1) ) [(mol of metal)(g of particle)-1/(mol of metal)(mL of water)-1], in which the numerator only considers the labile pool. Kd values were in the order Cr e Cd < Hg < Ag, with mean values (× 105) for all sites of 0.94 ( 0.69, 1.2 ( 0.8, 3.6 ( 3.4, and 8.9 ( 6.4, respectively (Table 4). If refractory particulate metal is considered as well, the Cr Kd increases to 20 ( 11 × 105, but the Ag and Cd Kd values do not change. Table 1 presents characteristics of the sampling sites, including concentrations of POC, PON, DOC, TSS loads, and pH. TSS loads were in the range 1.3-10.3 mg L-1 with the lowest values in Lake Ontario (mean ) 1.4 ( 0.1 mg L-1) and the highest at Hudson River sites (mean ) 5.9 ( 2.5 mg L-1). POC was highest in the Hudson River and Lake Ontario (327489 µg L-1), which, combined with the TSS data, suggests that a larger fraction of TSS was organic at the Ontario site than the other sites. DOC was highest in the Hudson River (mean ) 2.9 ( 0.1 mg L-1) and lowest in Lake Erie (1.8 ( 0.01 mg L-1). Model Calculations. The mean model predictions of Ag, Cr, Hg, and Se differed from concentrations in zebra mussel tissues independently measured at the same sites by 27%, 37%, 28%, and 29%, respectively (Figure 2). Measured and modeled Ag and Cr in tissues were generally within uncertainty ranges, and the model displayed no bias toward overor underestimation (overestimates at one site were balanced by underestimates at others, such that net deviation of the
TABLE 4. Metal Association with Suspended Particles (>0.2 µm)a Ag
Cd
Cr
Hg
Site
% on particles
Kd (× 105)
% on particles
Kd (× 105)
% on particles
Kd (× 105)
% on particles
Kd (× 105)
Erie Niagara Ontario Hudson T Hudson P mean
71 ( 7 79 ( 9 39 ( 1 55 ( 12 85 ( 2 67 ( 18
7.7 ( 1.8 21 ( 11 4.6 ( 0.5 3.5 ( 1.2 7.6 ( 0.4 8.9 ( 6.4
21 ( 3 39 ( 3 11 ( 2 26 ( 7 33 ( 5 26 ( 10
0.81 ( 0.16 2.8 ( 0.3 0.89 ( 0.21 0.78 ( 0.10 0.63 ( 0.03 1.2 ( 0.8
93 ( 2 83 ( 1 nd 85 ( 1 83 ( 6 86 ( 5
0.41 ( 0.39 0.24 ( 0.20 nd 2.0 ( 0.5 1.1 ( 0.3 0.94 ( 0.69
nd nd nd 81 ( 7 nd 81 ( 7
nd nd nd 3.6 ( 3.4 nd 3.6 ( 3.4
a % on particles represents labile + refractory pools. Metal K values describing partitioning between dissolved and particulate phases are based d on the labile fraction only. Hudson T, Tivoli site; Hudson P, Poughkeepsie site. Means ( 1 SD. nd, not determined.
FIGURE 2. Comparison of model-predicted and independently measured concentrations of Ag, Cd, Cr, Hg, and Se in zebra mussel tissues at each site. Black bars represent predicted body burdens, except for Hg (Lakes Erie and Ontario sites) where several predictions are made, each employing a different reported Hg concentration. For Se, river and lake sites are grouped together for comparison. Error bars represent standard errors. model from actual measurements was 3% for Ag, 13% for Cr, 3% for Se, and as low as 1% for Hg). Model predictions for Cd matched measured values at the two lake sites, but on average exceeded measurements by 2.6-fold at the three river
sites. Measured and modeled Hg in tissues are similar, particularly in the Hudson River where our direct Hg measurements are available. The model both over- and underpredicts in Lake Erie when different published data VOL. 34, NO. 13, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
2821
TABLE 5. Bioaccumulation Factors of Metals in Zebra Musselsa bioaccumulation factors (× 104)
a
site
Ag
Cd
Cr
Hg
Se
Erie Niagara Ontario Hudson Tivoli Hudson Poughkeepsie Mean
10 ( 3 12 ( 3 8.5 ( 1.6 4.3 ( 1.2 2.0 ( 0.3 7.4 ( 3.7
64 ( 4 47 ( 2 59 ( 1 15 ( 1 15 ( 2 40 ( 21
1.4 ( 0.3 1.5 ( 0.1 nd 2.5 ( 0.6 1.2 ( 0.4 1.7 ( 0.5
14 ( 9 nd 17 ( 8 1.4 ( 0.2 1.8 ( 0.2 11 ( 9
0.51 2.8 0.63 1.6 2.2 1.6 ( 0.9
Values for Hg are based on mean values from refs 23-25. Values for Se are based on values from ref 26. Means ( 1 SD. nd, not determined.
+ particulate [labile + refractory]) (10). Mean BAFs for all sites were in the order Cd > Hg g Ag > Se g Cr (Table 5).
Discussion
FIGURE 3. Relationship between model-predicted and independently measured (NS&T Program) concentrations of Ag, Cd, Cr, Hg, and Se in zebra mussel tissues. Line drawn displays a hypothetical 1:1 fit between predicted and measured values. Data points are means ( 1 standard error. sets (23, 24) are used, likely reflecting the influence of the data set used on model predictions and underscoring the importance of a spatial and temporal match in water and mussel tissue data. Modeled Se in tissues based on previously reported Se data generally agreed with field measurements, with predictions being slightly high for lakes and slightly low for rivers. An attempt was made to assess the fine-scale variations in zebra mussel tissue concentrations among sites for each metal. For the five sites examined and the three metals for which there is site-specific data, regression analysis of Ag predictions vs Ag measurements yielded a significant r 2 value of 0.74, but for Cr (r 2 ) 0.27) and Cd (r 2 ) 0.01), regressions were not significant. Thus, while the model predicts tissue metal concentrations to within approximate concentrations measured by the NS&T Program (Figure 2), it appears that the model fares more poorly in predicting the relatively small differences among sites for Cr and Cd. Clearly data from more sample times for water and particle measurements from each site and from more sites overall need to be evaluated to assess the model’s predictive capabilities of variance in metal concentrations among sites. In addition to this within-metal variability among sites, for inter-metal comparisons the model performed well over a range of 3 orders of magnitude in metal concentrations in predicting metal levels in zebra mussel tissues on a sitespecific basis (Figure 3). Predicted and measured concentrations were lowest for Ag and Hg (generally 100 nmol g-1) and overall were within 30% of the independent measurements. Biological concentration of individual metals in mussels was calculated as follows: bioaccumulation factor (BAF) ) mol of metal g-1 dry wt soft parts (given in Table 2)/(total mol of metal)(mL of water)-1 (where total metal ) dissolved 2822
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 34, NO. 13, 2000
Metal Measurements. The trace metal measurements made to field test our model are some of the first measurements made for these elements in these bodies of water. Studies have rarely undertaken weak and strong acid digestions of particulate metals, and “particulate” metals are often estimated by the difference between total unfiltered and dissolved metal pools. Our dissolved Ag measurements (0.94-7.95 pM) are generally below those reported in a survey of 11 rivers (including the Hudson), for which a “baseline” concentration of dissolved Ag of 10 pM was estimated (28) and are also generally below values reported (4.7-40.9 pM) for the freshwater portion of the urbanized lower estuary (29). Luoma et al. (30) found that 55-85% of particulate Ag was extractable with 0.5 N HCl, while we observed 100% extraction with 0.6 N HCl. Smith and Flegal (31) report a Kd of 7.9 × 105 in the Sacramento and San Joaquin Rivers, comparable to our value of 8.9 × 105 (Table 4). Coale and Flegal (32) report mean dissolved Cd concentrations for Lakes Erie and Ontario of 76.8 ( 14.2 and 17.6 ( 5.6 pM, comparable to our values of 68 ( 5 and 38 ( 1 pM, respectively. Nriagu et al. (33) reported dissolved Cd in Lake Erie between 5 and 82 pM and in Lake Ontario between 4.4 and 71 pM (fall transect), within the range of our values. Dissolved Cd concentrations in 11 Lake Michigan tributaries averaged 90 pM (34). Our dissolved Cd concentrations for the tidal freshwater Hudson River (116-170 pM) compare closely with the range (110-290 pM) measured in the Hudson, where 31-78% of Cd was associated with TSS (35), as compared to our range of 26-33%. The mean Kd value for Cd in the five field sites of our study (1.2 ( 0.8 × 105) was comparable to a mean value of 1.4 × 105 reported for Cd in 10 Lake Michigan tributaries but an order of magnitude lower than 1.8 × 106 reported for an 11th Lake Michigan tributary (34). Beaubien et al. (20) report mean total dissolved Cr for Lakes Erie and Ontario at 2.6 and 6.8 nM, which are close to other reported values (33) of 2.6 and 7.2 nM. These concentrations are 2-3× higher than our values of 0.8 ( 0.1 and 2.9 ( 0.1 nM for Lakes Erie and Ontario, respectively, although the pattern of 2-3× higher Cr concentrations in Lake Ontario than Lake Erie is evident in all three studies. Cr(VI) in Lake Ontario makes up 75-85% of total dissolved Cr, with Cr(III) and particulate Cr below detection levels (20). While we did not examine Cr speciation, our direct measurements of particulate Cr show that 83-93% of total water column Cr at our sites was associated with suspended solids. This contrast between our particulate Cr results and those of Beaubien et al. (20) may be a result of higher TSS loads in our near-shore samples, which might also cause reduction of Cr(VI) to Cr(III). The particulate Cr fraction has important implications for Cr bioaccumulation in zebra mussels because
TABLE 6. Percent of Total Bioaccumulated Metal Obtained from Food, Based on Model Calculationsa % bioaccumulated from food
Lake Erie Niagara River Lake Ontario Hudson Tivoli Hudson Poughkeepsie average biological half-life (d)
Ag
Cd
Cr
Hg
Se
79 ( 4 90 ( 5 71 ( 2 63 ( 7 80 ( 1 77 ( 9 9.1
75 ( 4 91 ( 1 76 ( 4 77 ( 4 71 ( 1 78 ( 7 51.6
15 ( 13 9(7 nd 45 ( 7 33 ( 7 25 ( 14 45.4
62-82b
24 61 24 61 61 46 ( 18 21.2
nd 62-82b 57 ( 16 57 ( 16 65 ( 8 13.4
a Retention half-times (d) were determined using average depuration rates (Table 3) weighted by the contribution of particulate and dissolved metals pools to body burdens and growth rates (g). b 62% using Hg data from refs 23, 24; 82% using data from ref 25. Means ( 1 SD. nd, not determined.
our model predicts that 9-45% of Cr body burdens is from particulate Cr. Our range of dissolved Cr concentrations in the freshwater Hudson River (1.2-2.7 nM) is at the low end of concentrations reported for this system (2.3-11.6 nM) (36); our particulate Cr values (sum of labile and refractory pools, 1.4-2.8 µmol g-1) are in the range of other measurements in the Hudson (0.7-1.3 µmol g-1) (36). The dissolved Hg concentrations we report for the freshwater Hudson River (2.1-6.9 pM) are comparable to those for the Sacramento River (4.7 pM), the Columbia River (2.2 pM) (23), and 11 Lake Michigan tributaries (5.2 pM) (34). Kd values for Hg on particles in Wisconsin lakes ranged from 0.2 to 10 × 105 (mean ) 1.8 × 105) (37) and in Lake Michigan tributaries ranged from 0.2 to 77 × 105 (mean ) 3.2 × 105) (34), within the range reported here (mean ) 3.6 × 105). Partition coefficients (Kd) are reported in this work because metal partitioning onto suspended solids is a key factor influencing metal bioaccumulation in zebra mussels. In this study, we consider the partitioning of labile metal onto solids to calculate Kd rather than both labile + refractory metals since the labile metal is exchangeable and is presumably the only fraction that desorbs from ingested particles in mussel guts that are mildly acidic (38) and, consequently, is the best predictor of bioavailable metal from particles. Model Verification. The generally close agreement between our model’s predictions and measured zebra mussel tissue concentrations of metals is comparable to the success of this model when field tested for the marine mussel Mytilus edulis (9) and for marine copepods (12). Furthermore, this agreement indicates that (a) the rate parameters developed in laboratory radiotracer experiments adequately describe in situ rates and (b) we can account for the processes governing bioaccumulation of these elements in zebra mussels in natural waters. Because metal concentrations in zebra mussels are proportional to measurable fluxes of metals in to and out of tissues, which in turn are proportional to ambient metal concentrations in bioavailable pools, we now have quantitative support for the application of zebra mussels as bioindicators of metal contamination in freshwater. The versatility of the model under varying proportions of dissolved to particulate metals between sites is indicative of one of the kinetic model’s strengths. It is not the total metal concentration but rather the partitioning of the metal between dissolved and particulate (specifically labile particulate) metal fractionss with the different bioavailabilities of each to zebra musselss that ultimately determines body burdens at a specific site and determines BAFs. In fact, BAFs of Ag and Cr in zebra mussels vary from site to site by factors of approximately 6 and 3, respectively, which is predicted by the model. Thus, an important component of our field validation approach is direct measurement of metals both in the dissolved phase and on particles. This is best illustrated with Ag at the Niagara River site, where an examination of dissolved Ag concentrations (average ) 3.1 pM) shows this site to be
among the least contaminated. However, mussel body burdens are the highest here because particles at this site are more contaminated with Ag than at any other site. It is noteworthy that an attempt to predict particulate Ag by applying an average Kd value to the dissolved Ag concentration at this site would have underpredicted body burdens because there is a uniquely high fraction of Ag on particles at this site (Kd ) 21 × 105 at the Niagara River, 3× higher than at any other study site). Furthermore, it also appears that the labile (0.6 N HCl-extractable) particulate metal fraction best predicts bioaccumulation. This is apparent in considering the bioaccumulation of Cr, which is the only element with a significant refractory pool. Cr body burdens predicted with the total particulate (labile + refractory) Cr pool, instead of the labile pool only, exceed actual body burdens by 406% on average. As noted above, the labile (0.6 N HCl extraction) fraction may be the best predictor of bioaccumulation (39) because this weak acid extraction may be comparable in pH to the zebra mussel gut, particularly the digestive gland (38). Modeled Hg body burdens in zebra mussels using our Hg data from the Hudson River agree closely with measured body burdens at the Hudson River. This close agreement is usually also observed when reported Hg concentrations are used at the lake sites. The only thorough investigation of Hg reported for the Great Lakes is for Lake Michigan (25); when these concentrations are used, modeled body burdens match measured mussel tissue concentrations well in Lakes Erie and Ontario. The Hg value reported for Lake Erie (23), which is considerably higher than other reported values for Lake Erie, produces the poorest fit, overpredicting actual body burdens by a factor of 3. Our model’s Se predictions, based on reported Se concentrations and assumptions concerning redox speciation and particle loads, are comparable to measured values. As with Hg, Se results are more tentative than for the other metals. It will be particularly useful to obtain measurements of Se redox speciation in the field as well as Se partitioning between aqueous and solid phases. Comparisons of kinetic rate parameters between D. polymorpha and the marine mussel, M. edulis, show that assimilation of Se from food is generally 2-3× higher in M. edulis but absorption efficiencies of selenite from solution are 2-3× higher in D. polymorpha (0.084 as compared to 0.03) (11). Also, while selenate uptake is expected to be negligible for M. edulis in seawater due to the dilution effect of sulfate in seawater, selenate absorption efficiency in freshwater by zebra mussels (0.25%) is greater than selenite uptake. Consequently, our model predicts that the dissolved phase will contribute 39-76% (Table 6) of the total Se body burden in the zebra mussel, as compared to only 1.6-3.6% in M. edulis (9). Among the five elements studied, the poorest fit for model predictions with measured values is observed with Cd at the three river sites, where the model predictions are higher than measured values by a factor of 2-3; in contrast Cd predictions VOL. 34, NO. 13, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
2823
for the two lake sites are directly comparable to field measurements. Several explanations may account for the discrepancy between predictions and measurements at the river sites, including differences in seston quality and DOC concentration between riverine and lacustrine environments, although it is not clear how seston quality would uniquely affect Cd bioaccumulation among the metals studied here. Alternatively, this discrepancy may be due to the assumption of steady-state conditions between Cd in overlying water and in zebra mussel tissues, which is least likely to hold for Cd of the metals examined given the slow turnover time of this metal in zebra mussel tissues. The biological half-life of the metal (time required for loss of 50% of the metal from the animals) is longest for Cd (52 d) and shortest for Ag (9 d), with the relative sequence for all elements in this study being Cd > Cr > Se > Hg > Ag (Table 6). These half-lives were calculated using metal depuration rates from food and water exposures, the relative contribution of food and water as uptake pathways, and the mussel’s growth rate. These values suggest that Cd body burdens least reflect a synoptic (short-term) view of the water column while best reflecting a time-averaged (2-month) view; in contrast, Ag might best reflect a synoptic view of the water column, and indeed model predictions for Ag tissue concentrations closely match field measurements. Generally, among the three metals for which we had our own water column measurements for all sites, variance between model predictions of tissue metal concentrations and independent field measurements for each site increased with the biological half-lives of the metals in the mussels. This suggests that our assumption of steadystate conditions between metals in mussel tissues and ambient water holds best for elements with short half-lives in mussel tissues. The variance in the model’s predictive power for Cd in river and lake environments may be a function of the relative constancy of the environment in these two ecosystems. If some water parameters that influence metal distribution such as TSS in a river are more variable due to resuspension by winds and tides and runoff from watersheds, this environment could be less constant than that of a lake, and the constancy required for steady-state assumed in our model might be better realized in the lakes in this study. In the limited number of field sites considered in this study, no significant correlations were observed between water parameters (POC, PON, C:N, POC:TSS, DOC, or TSS) and site-specific BAFs for Ag, Cd, and Cr, although weak correlations appeared in several instances. For example, low TSS (
