Environ. Sci. Technol. 2009, 43, 7230–7236
Concentrations of Al, Fe, Mn, Ni, Cu, Cd, Pb, and Zn were measured using DGT (diffusive gradients in thin-films) devices deployed in situ in 34 headwater streams in Northern England. Mean values of filtered samples analyzed by ICP-MS (inductively coupled plasma mass spectrometry) were used, along with DOC (dissolved organic carbon), pH and major ions, to calculate the distribution of metal species using the speciation code WHAM. DGT-measured concentrations, [Me]DGT, of Zn and Cd were generally similar to concentrations in filtered samples, [Me]filt. For the other metals, [Me]DGT was similar to or lower than [Me]filt. Calculation of the maximum dynamic metal from the speciation predicted using WHAM showed that most of the lower values of [Cu]DGT could be attributed to the dominance of Cu-fulvic acid complexes, which diffuse more slowly than simple inorganic species. Similar calculations for Al, Pb, and Mn were consistent with appreciable proportions of these metals being present as colloids that are not simple complexes with humic substances. Differences between WHAM predictions and the measured [Ni]DGT indicated that WHAM used with the default binding parameters underestimates Ni binding to natural organic matter. Plots of [Me]DGT versus the ratio of bound metal to DOC provided slight evidence of heterogeneous binding of Pb and Cu, while results for Mn, Cd, and Zn were consistent with weak binding and complete lability.
total dissolved concentrations using an equilibrium speciation model, and estimates of their diffusion coefficients, to calculate the concentration of metal expected to be measured by DGT (1, 4). Differences between prediction and measurement provide insight into the presence of relatively immobile species (colloids) or complexes which dissociate slowly, as well as an assessment of model validity. When kinetic limitations apply, the species are said to be inert (not measured at all) or partially labile within the characteristic time scale of the measurement. Two approaches have been used to extract kinetic information. When DGT devices with a range of gel layer thicknesses are deployed in situ, information is gained on the overall complex lability for each metal, providing a kinetic signature for the water being studied (5). Information on dissociation rates can be extracted, but the current treatment is restricted to simple complexes which do not represent well the heterogeneous ligands of humic substances that usually dominate freshwaters. Town et al. (6) have considered the role of heterogeneous ligands by examining how the DGT measurement depends on the ratio of the bound metal to the ligand concentration. Their treatment of published DGT data for a range of sites enabled estimation of the degree of heterogeneity of the complexes and their dissociation rate constants. The above measurements and interpretations show promise, but require further testing. This study has examined the capability of DGT for routine metal speciation measurements by performing in situ measurements on 34 headwater streams in Northern England. Measurement of the stream chemistry using traditional sampling procedures allowed calculation of the distribution of metal species using an equilibrium speciation model. Comparison of measurements and predictions allowed conclusions to be drawn about metal complex lability, the presence of inert species and the accuracy of model parametisation. Measurements were not made at different diffusion layer thicknesses, as this approach to obtain kinetic information is analytically challenging for such a large scale monitoring study. Instead the opportunity was taken to examine whether kinetic information could be derived from the ratio of the concentrations of bound metal and ligand. The results form part of a larger study to investigate bioaccumulation of metals and their toxic effects with respect to diverse biota (7).
Introduction
Materials and Methods
The technique of diffusive gradients in thin-films (DGT) is being used increasingly to measure trace metal species in situ (1, 2). The concentration of metal in solution is calculated from the amount that accumulates on a Chelex binding layer after it has diffused through a hydrogel, filter, and diffusive boundary layer in solution (DBL). It is classed as a dynamic technique because it detects a flux of metal species. The measured species must be mobile, that is, able to diffuse easily through the hydrogel and filter, and labile, that is, able to dissociate in the time scale associated with their transport through the diffusion layer (typically minutes) (3). With a dynamic technique the derived concentrations of the collective kinetically available and mobile components require further interpretation. If species in solution are labile, it is possible to use the distribution of species predicted from
Sampling and analysis. Thirty four streams located in three areas of northern England, the Lake District (LD), Teesdale, Tynedale and Weardale (TTW) and Howgill Fells, Ribblesdale, Swaledale and Whernside (HRSW) were sampled during 2006 (Supporting Information (SI) Table S1). They are numbered 1-35 (25 missing) for consistency with Tipping et al. (7). Twenty-six of the streams were in the vicinity of abandoned mine workings and had appreciable concentrations of at least one of Cu, Pb, Zn, Cd, and Ni, but eight sites were unaffected by mines (SI Table S2-9). Total dissolved metal concentrations measured during 2005 were used to determine the deployment time of DGT devices, to avoid problems associated with saturation of the resin. Most DGT devices were deployed for 14 days, but for five streams the sampling time was 24 h (SI Table S1). They were deployed as a “cluster” of six devices with three having 0.8 mm thick, open-pore, (APA) diffusive gels. See refs 8, 9 for precise information on these polyacrylamide gels cross-linked with an agarose derivative (APA). Data from the other three devices, which had different gels, were not used because of problems. The plastic devices
In Situ Speciation Measurements of Trace Metals in Headwater Streams KENT W. WARNKEN,† ALAN J. LAWLOR,‡ STEPHEN LOFTS,‡ EDWARD TIPPING,‡ W I L L I A M D A V I S O N , * ,† A N D H A O Z H A N G † Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster LA1 4YQ, United Kingdom, and Centre for Ecology and Hydrology, Lancaster Environment Centre, Bailrigg, Lancaster LA1 4AP
Received January 13, 2009. Revised manuscript received May 6, 2009. Accepted May 8, 2009.
* Corresponding phone: +441524593935; fax: +441524593985; e-mail:
[email protected]. † Lancaster Environment Centre, Lancaster University. ‡ Centre for Ecology and Hydrology. 7230
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 19, 2009
10.1021/es900112w CCC: $40.75
2009 American Chemical Society
Published on Web 05/29/2009
were cleaned by soaking them overnight (∼20 °C) in (1) 2% Decon; (2) 1.5 M HNO3 (Anal-R); (3) 1.5 M HNO3 (Aristar), and (4) 4 M HCl (Aristar). They were completely rinsed (18.2 MΩ deionized water, DIW) between each overnight step. To remove all residual acid from the devices (pH < 7), they were soaked in DIW over several days. All devices were assembled under Class-100 clean room conditions. The cluster, which was held together using Dyna Cable Teflon coated fishing line, was suspended within a plastic pipe (22 cm length × 15 cm width), using plastic cable ties, and placed on the streambed. The pipe was anchored on the sides and top using rocks collected from the streams, being careful not to restrict flow through the pipe. Recovery of the DGT devices required only cutting of the cable ties and immediately placing them into acid cleaned zip-lock bags. On return to the laboratory, the DGT devices were rinsed with DIW and opened on a sample cart in front of a class-100 laminar flow cabinet, within a class-1,000 clean room. The resin-gels were placed in a vial containing 1 mL of 1 M HNO3. Eluates from samples collected from sites 1-26 were analyzed using a Perkin-Elmer Elan DRC II ICP-MS (inductively coupled plasma mass spectroscopy) and the other eluates were analyzed using a Thermo XSeries2 ICP-MS. Concentrations of metals measured by DGT, [Me]DGT, were calculated using eq 1, where M is the mean accumulated mass of metal, ∆g is the thickness of the gel (0.08 cm), ∆f is the thickness of the filter (0.014 cm), and δ is the thickness of the diffusive boundary layer (10). A representative value for a flowing stream of δ ) 0.029 cm was assumed (10). The diffusion coefficients of metals in the APA gels used here, Dgel have been found to be related to the diffusion coefficients in water, Dw, by Dgel ) 0.85Dw (8). Values appropriate to the mean measured water temperature at deployment and retrieval were used. The diffusion coefficients of each metal in the filter (Df) were assumed to be the same as those in the APA gel. The effective exposed area of the filter overlying the gel, Ae, was taken to be 3.8 cm2 and t was the exposure time in seconds. In practice the concentrations obtained using this complete approach were within 4% of those calculated using the regular DGT equation (eq 2) using a geometric area, Ag, of 3.14 cm2, a total diffusion layer thickness, ∆dl, of 0.094 cm and disregarding the diffusive boundary layer. Lateral diffusion in the gel causes Ae to be greater than Ag (10). M CDGT )
(
∆f ∆g δ + + Dgel Df Dw Aet
CDGT )
M∆dl DgelAgt
)
(1)
(2)
Two procedures were used to determine the concentrations of trace metals in filtered water, [Me]filt, collected at the times of DGT deployment and retrieval. At 26 sites (SI Table S1) duplicate samples were collected directly into plastic syringes and filtered through precleaned, plastic filter holders loaded with 0.4 µm Nuclepore membranes, into 15 mL precleaned polyethylene bottles containing 0.3 mL of HNO3, which yielded a final acid concentration of 2% (v/v). The bottles were placed into double acid-cleaned zip-lock bags for transport back to the laboratory where they were analyzed by ICP-MS (Thermo XSeries2). For all sites, close to the syringe sampling where applicable, samples were collected by hand immersion of 500 mL acid-washed polyethylene bottles. They were sealed in polyethylene bags and stored in cool boxes for transport to the laboratory. Within 24 h they were filtered using 0.45 µm polypropylene filter devices (Whatman, Puradisc) and acidified with 1% HNO3 (Baker, Ultrex II).
Concentrations were determined using a Perkin-Elmer Elan DRC II ICP-MS for laboratory filtered samples. For the five sites with 24 h DGT deployments, samples were only collected on retrieval. Water samples for major cations and anions (laboratory filtered), pH, and dissolved organic carbon (DOC) were also collected at all sites within four time periods during 2006 (March 6-8, March 20-22, April 3-5, April 17-19). Sampling and analysis procedures have been described (7). Samples for trace metals (laboratory filtered) were also collected at these times, with the April dates coinciding with DGT deployment and retrieval. Modeling. Calculations of streamwater chemical speciation were performed using WHAM (11) incorporating Humic Ion-Binding Model VI (12), as described in detail for the same study sites (7). Input concentration and pH data for the model were averages of the measured values at the start and finish of the DGT deployments, except in cases where the deployments were for one day only, for which analytical data from a single water sample were used. The concentrations of Na, Mg, K, Ca, Cl, NO3, SO4 and trace metals Ni, Cu, Zn, Cd, and Pb in filtrates were assumed to represent truly dissolved components, that is inorganic species and metal bound to dissolved organic matter (DOM). Any metal associated with mineral colloids was assumed to be negligible. We assumed that aluminum in filtrates was in true solution, unless the preliminary speciation calculation based on this total dissolved value showed Al3+ activity exceeded that expected from a solubility product of 108.5 (at 25 °C) for the reaction Al(OH)3 + 3H+ ) Al3+ + 3H2O. In that case the activity of Al3+ was assumed to be controlled by Al(OH)3 solubility, corrected for temperature using a reaction enthalpy of -107 kJ mol-1 (13, 14). The activity of Fe3+ was estimated from the empirical equation of Lofts et al. (15), which is an improvement on estimation from a single solubility product, used by Tipping et al. (7). The “binding activity” of DOM was estimated to be equivalent to that of average isolated fulvic acid at a concentration of 65% of the DOM concentration, assumed to be twice the DOC concentration.
Results and Discussion General Water Chemistry. The chemistry of the streams was wide-ranging, with pH varying from 4.1 to 8.3, DOC from 0.6 to 13.2 mg L-1and alkalinity by Gran titration from near zero to 2.33 milli-equivalents L-1 (SI Table S1). Site 11 was strongly influenced by a local disused Pb mine, and its high sulfate concentration and low pH (4.1) suggest that pyrite oxidation is occurring. Site 21 also had high sulfate, again probably from pyrite, but the high pH (7.8) is consistent with weathering of Mg, K, and Ca, which were all high. The relative standard deviations (RSDs) for the duplicate samples of total dissolved metals collected at the start and end of DGT deployment were usually less than 5% (SI Tables S2-S9). Due to its very low concentrations, the RSDs for Cd were higher, but they were all less than 18%. Generally the mean measured concentrations at the start and end of DGT deployment were well within 50% of one another. However, for Al, Mn, and Ni the difference could be as high as 100% for sites 13, 14, and 32, indicating that at these sites concentrations changed appreciably during deployment, presumably due to changing flows, as indicated by different water levels on retrieval and deployment. Concentrations of Pb, Zn, and Cd spanned approximately 4 orders of magnitude; Al, Mn, and Ni 3 orders of magnitude; and Cu less than 2 orders of magnitude. Zn concentrations were in some cases very high and greatly exceeded those of Pb and Cu, even though there is a history of Cu and Pb mining in parts of the region. The highest concentrations of all metals except Fe occurred at site 11. VOL. 43, NO. 19, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
7231
FIGURE 1. Log of the DGT measured concentration of each metal plotted against the log mean concentration in filtrates for each deployment. The ideal 1:1 line is shown. Data are grouped according to pH: triangles >7, circles 6-7, squares 3.4 mg L-1. Dependence of DGT Measurements on Filterable Metal. All DGT devices were successfully retrieved and analyzed. DGT measurements were generally reproducible, with average RSDs varying between 6% for Cd and 12% for Cu and Pb. Plots of log [Me]DGT versus log[Me]filt were used to compare the paired data sets (Figure 1). [Me]filt was the mean for samples collected at DGT deployment and retrieval, except for the 24 h deployments when only the means of the duplicates at retrieval were used. For both Zn and Cd, [Me]DGT and [Me]filt were generally very similar, as indicated by the close adherence to the 1:1 line. This good correspondence is consistent with predictions using WHAM that in most cases less than 20% of the metal is complexed with DOC (SI Table S9). For Zn, there was 1 very clear outlier (site 16) and two less marked (sites 22, 30) above the line, and one outlier below (site 29). Site 22 is one of the three Nenthead streams (21-23) leaving a 7232
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 19, 2009
mine area. Its zinc concentration is noticeably lower than the very high values of the other two streams, where high sulfate reflected pyrite oxidation. As the deployment time was only one day, and only one sample for total dissolved metals was taken, meaningful comparison of DGT and spot samples is questionable. For site 16, [Zn]filt measured on samples collected at the start and end of the 14 day DGT deployment were within 10%. There is no obvious rationale for the discrepancy between [Zn]DGT and [Zn]filt for sites 16 and 30. Mean [Zn]filt at site 29 decreased slightly during deployment from 1.58 to 1.22 µmol L-1, but the DGT measurement was much lower at 0.2 µmol L-1. Explanation in terms of complexation is unlikely, as the WHAM calculation suggests that only 8% is organically complexed (SI Table S9). Except for site 16, the Cd plot had the same obvious outliers as identified for Zn. This agreement between these two metals with similar chemistries is consistent with systematic rather than random
FIGURE 2. Log of the DGT measured concentration of Cu, Pb, Mn, and Al plotted against the log dynamic maximum concentration. The ideal 1:1 line is shown. Data are grouped according to pH: triangles >7, circles 6-7, squares 3.4 mg L-1. causes for the observed discrepancies between [Me]DGT and [Me]filt, as discussed above. Apart from two obvious outliers above the line, corresponding, as for Zn and Cd, to sites 21, 22, and 30, most Mn data lie on or below the line. The reasons for the systematic spread of data below the 1:1 line, which is more marked in the cases of Ni, Cu, Pb, and Al, are discussed fully in the next section. For Ni and Cu there are no outliers above the line and for Pb there is only one, corresponding to site 16. The two outliers above the line for Al (sites 1 and 21) do not correspond to those observed for other metals. When [Fe]filt < 3.10-6 M, [Fe]DGT was fairly constant at 5-16 10-8 M, only approaching [Fe]filt at the lowest measured values. For this group of data, DOC was less than 3.4. When [Fe]filt > 3.10-6 M, [Fe]DGT spanned the range 0.067-7.2.10-6 M, approaching the 1:1 line. DOC was greater than 3.4 for these data except for the point actually on the 1:1 line, which was for site 11, with the exceptionally low pH of 4.1. It appears that at very low pH or Fe concentrations, Fe is mainly in solution and therefore fully measured by DGT. As pH and/or Fe concentration increases, colloidal forms dominate, with the proportion that can be maintained in solution increasing in the presence of DOC, due to the formation of dissolved complexes with fulvic acid. Predicting the DGT Measured Concentrations. The calculation of the DGT concentration using eqs 1 or 2 uses only the diffusion coefficient of the free metal ion. However, for a fully labile system, where all species are assumed to be in equilibrium, the amount of metal accumulated by DGT is proportional to the sum of the concentration of each species times their diffusion coefficients (16). In freshwaters the dominant ligand is usually fulvic acid. Experiments with diffusion cells have shown that, to a good approximation, the mean diffusion coefficients of metal-fulvic-acid complexes in the APA gel are typically 20% of the diffusion coefficients of the free metal ions (8). Consequently, in solutions where a high proportion of the metal is bound to fulvic acid, the DGT measured concentration would be expected to be less than the total dissolved metal. The DGT-measured metal has been referred to as the dynamic metal because it reflects the transport rate of species (3). The dynamic metal has its maximum value, termed [Me]dyn max, when all species are fully
labile (able to dissociate rapidly and be measured (4). If there are only simple inorganic species, Minorg, and metal bound to fulvic acid, MFA, it can be calculated using eq 3. dyn [Me]max )
∑ (M
inorg
+ 0.2MFA)
(3)
The predicted distribution of species available from the WHAM calculation provides concentrations of Minorg and MFA and dyn hence an estimate of [Me]max , which can be compared to [Me]DGT (2). All inorganic species are reasonably assumed (17) to have the same diffusion coefficient as the free metal ion. The simple approach of eq 3 does not accommodate the difference in the ratio of the diffusion coefficients in the DBL, but as this accounts for only 24% of the total thickness any associated error will be less than 10%. The dependence of the DGT measured concentration dyn is shown in Figure 2 for Al, Mn, Cu, and Pb. For on[Me]max Ni, Cd, Zn, and Fe there was very little difference between these plots and the plots of [Me]DGT versus [Me]filt. Outliers identified for Zn and Cd appeared in both data sets. The negligible difference is not surprising, as the proportion of metal bound to fulvic acid has to be high before there will be a noticeable effect. For example when 50% of the metal dyn is 60% of the value when there is no is bound to FA, [Me]max dyn plot would only binding to FA. Therefore use of the [Me]max be expected to move the data point 0.22 log units closer to the 1:1 line. If there is only 25% bound, the shift would be 0.1 log units. Less than 3% of the total Fe present in the filtrate is predicted to be bound for all sites (SI Table S5), with most of the remaining 97% being colloidal iron oxide. Only at site 13 is Ni more than 25% bound (SI Table S7). Zn and Cd are also predicted to be more than 50% bound at site 13, but for most of the remaining sites less than 25% is bound (SI Tables S3 and S9). dyn for four The DGT measurement may be less than [Me]max main reasons: (1) there may be colloidal metal with substantially lower diffusion coefficients than the metal fulvic acid complexes (note that here we define colloidal metal as filterable, but not in true solution or bound to FA), (2) the release of metal ions from the fulvic acid binding sites may be insufficiently quick to sustain the demand for metal by DGT, (3) there may be some inert metal species that do not VOL. 43, NO. 19, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
7233
FIGURE 3. Log of the DGT measured concentration of Cu, [Cu]DGT, and [Cu]DGT minus the concentration of the free ion, [Cu′]DGT, plotted against log([Cu]filt/DOC) and log([Cu]org/DOC). Linear regression lines are shown. Data are grouped according to pH: triangles >7, circles 6-7, squares 3.4 mg L-1. dissociate within the measurement time, or (4) WHAM may underestimate metal binding to FA. dyn . A substantial proportion of [Ni]DGT is less than[Ni]max Except for one site (34), all the substantial deviations below the line are for sites with DOC > 3.4 mg L-1. Recalculation dyn , assuming 100% complexation, moves most points of [Ni]max very close to the line. Moreover, dissociation of Ni from its complexes is known to be slow compared to other transition metals (19). If a very small proportion is bound, as predicted, this would make little difference, but if there is more bound than predicted, this kinetic limitation would lower [Ni]DGT dyn compared to [Ni]max .. Compared to Ni, a slightly greater proportion of Mn is predicted, as previously suggested (18), to be bound (>40% for nine of the sites (SI Table S6)). This has the effect of slightly improving the agreement between [Mn]DGT and dyn over that between [Mn]DGT and [Mn]filt. Other than [Mn]max samples not being comparable, there is no reasonable reason for [Mn]DGT to be greater than [Mn]filt and the two sites corresponding to the data points that lie above the 1:1 line have already been identified as outliers. A substantial proportion of the data lie below the 1:1 line. Inert species are not expected for Mn and complexes with fulvic acid dissociate rapidly (5). The likely candidate is colloidal oxides, as suggested by the bryophyte studies of these waters (7). The high proportion of Cu that is bound to FA has the dyn effect of bringing the points for the [Cu]DGT versus [Cu]max plot much closer to the 1:1 line than for the [Cu]DGT versus [Cu]filt plot. However, for a high proportion of the measuredyn . Kinetic limitation ments, [Cu]DGT was less than[Cu]max associated with a proportion of Cu occupying strong binding sites has been reported previously (5). Most of the data below the line are for sites with DOC > 3.4 mg L-1 (SI Table S4). The exceptions are at very low Cu concentrations. It would be expected that kinetic limitation would be greatest when Cu concentrations are low, favoring a greater proportion occupying strong binding sites of FA. While there is generally a high proportion of Pb bound to FA, it is not predicted to be as dominant as for Cu (SI Tables S4 and S8). Compared to the plot of [Pb]DGT versus [Pb]filt, a higher proportion of the points on the plot of [Pb]DGT dyn versus [Pb]max are close to the 1:1 line, However, there is a set of data where [Pb]DGT is approximately an order of 7234
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 19, 2009
dyn magnitude less than [Pb]max . These correspond to most of the HRSW sites and some of the TTW sites. For all sites in the outlying set of data DOC is fairly high (>3.4) and pH is quite high, suggesting that there may be more organically bound Pb than predicted. However, it is most probable that these lower values of [Pb]DGT are due to the presence of colloidal Pb. A similar large difference between [Pb]DGT and dyn was attributed previously to colloidal Pb (4). Pb binds [Pb]max strongly to particles generally, but particularly to MnO2. The dyn was used to estidifference between [Me]DGT and [Me]max mate colloidal Fe and Mn. Calculations using WHAM showed that all the colloidal Pb could be accounted for by adsorption to MnO2, with adsorption to iron oxides being relatively unimportant. dyn are only slightly Data in the plot of [Al]DGT versus [Al]max more regularly grouped about the 1:1 line than observed for the [Al]DGT versus [Al]filt plot. There are a set of data for high dyn . pH and DOC sites where [Al]DGT is much less than[Al]max Where the WHAM calculation indicated supersaturation with respect to Al(OH)3, the Al concentrations were calculated assuming equilibrium with Al(OH)3. However, the consisdyn for the set of outlying data tently lower [Al]DGT than [Al]max suggest that the solubility of Al(OH)3 in these streams may be less than assumed, which is not unreasonable given that reported values of logKso for Al(OH)3 range ( 1 of the value used (14). Alternatively, fine clay colloids may be present. Only for some of the sites is the binding of Al to FA sufficient dyn , but to account for the difference between [Al]DGT and [Al]max it is possible that the WHAM calculation may have underestimated the binding. Kinetic Limitations and Heterogeneity Effects. As a representative constituent of natural organic matter, FA can be regarded as a heterogeneous ligand with a distribution of binding sites. The occupancy of the sites and therefore the effective binding strength depends on the metal to ligand ratio (20). The heterogeneity can be described by a heterogeneity parameter, Γ, which provides a measure of the variation in the affinities of binding sites that can be available to a metal (21, 22). Town et al. (6) have shown that it is possible to obtain Γ from DGT measurements. They assumed that metal, Me, is distributed between i sites of natural organic matter, Si, of total concentration ∑i[Me]i so that the total bound species are ∑i[MeS]i. The average dissociation rate
constant for the sites, kjd, is then given by eq 5, where ka is the characteristic association rate constant for Me and B is a constant related to complex stability.
(∑ ) (∑ ) [MeS]i
kd[MeS]i ) kaB-1/Γ
1/Γ
i
[S]i
(1-Γ)/Γ
(5)
i
When most of the metal is complexed and there is slow dissociation, the mass accumulated by DGT is primarily under kinetic control. Then the DGT measured flux and interpreted concentration are directly proportional to kjd[MeS]i provided (∑i[S]i)(1-Γ)/Γ does not vary (3). Town et al. (6) have shown that for Cu, which is strongly complexed, the filterable metal (in mol L-1) can be used as a reasonable approximation for ∑i[CuS]i. They also found pragmatically that the concentration of DOC (in g L-1) can be substituted for (∑i[S]i)(1-Γ)/Γ. As the only concern is the scatter of the data and the slope, it is acceptable to mix units. The slope of this plot effectively provides 1/Γ because the DOC term varies much less than filterable metal. A straight line could be reasonably fitted to the plot of log[Cu]DGT versus log([Cu]filt/DOC) (Figure 3), with the slope (SI Table S10) corresponding to Γ ) 1.03. For a true kinetically limited situation, a value of Γ approaching 1 indicates a homogeneous ligand. Progressively lower values indicate increasing heterogeneity, with 0.5 or less being typical of established heterogeneous binding. When the data for the other metals were treated similarly, the fits were generally as good as for Cu (SI Figure S1) and the derived values of Γ were scattered about 1. These results provide little evidence for kinetic limitation. By assuming that the speciation using WHAM is correct, the approximated estimate of ∑i[MeS]i can be improved. Rather than use [Me]filt, we can use [Me]org, the total concentration of the metal fulvic species. With correlation coefficients (R2) of 0.15 and 0.13 for the Fe and Ni plots respectively, use of the regression coefficient was not justified (SI Table S10). For the other metals there was no evidence of heterogeneity. To exclude the metal which is not complexed, the concentration of the free ion was subtracted from the DGT-measured concentration. This procedure had little effect on Γ values when [Me]filt/DOC was used. When the free ion correction and [Me]org/DOC were used together (SI Table S10) the lowest values of Γ were obtained for Pb and Cu. They would be expected to show a degree of heterogeneity as there is generally substantial bound metal. However, the Γ values of 0.85 (Cu) and 0.84 (Pb) were not substantially different from the values for the other metals. They are a little higher than those obtained previously using DGT of 0.7 (Cu) and 0.8 (Pb) (Town et al., 2009) and substantially higher than estimates from voltammetry (22-24). The Γ values for Zn, Cd, and Mn of 0.92-1.39 are consistent with weak complexation and high lability. In the above treatment, data for sites 21-23, where the comparability of [Me]DGT and [Me]filt data is questionable, were omitted, but this had little effect on Γ values. For the five sites where Al(OH)3 was supersaturated, [Al]filt used the calculated total Al. This approach to investigate heterogeneity should only be used for a set of data with a narrow pH range (6). However, including all data in the plots against [Me]filt/ DOC did not affect Γ appreciably. As the low pH site (11) was a clear outlier on the plots against Corg/DOC, it was omitted. Eliminating data for sites with DOC < 3.4 to favor a greater proportion of complexed and potentially kinetically limited metal did not appreciably change Γ. dyn Interpretation of this substantial data set in terms of Cmax showed there was a high degree of consistency between the speciation measured using DGT and predicted by WHAM
from the total filtered concentrations. Exceptions for Al, Pb, and Mn were consistent with the presence of colloidal (not MFA) forms, whereas for Ni the evidence suggests that, when using the default binding parameters, WHAM underestimates organic complexation. Except for Cu, there was generally little evidence that complex dissociation kinetics affected the DGT measurement in these waters. As noted by others (25), the combined use of these approaches can greatly increase confidence in assessing water quality.
Acknowledgments We are grateful to S. A. Thacker and C. D. Vincent for field assistance, the CEH Lancaster Environmental Analytical Group for their assistance with streamwater chemical analyses, J. Hamilton-Taylor for helpful discussions, and R. Town for input on heterogeneity aspects. This work was undertaken within the project “Environmental Quality Standards for trace metals in the aquatic environment” jointly funded by the Environment Agency of England and Wales, the European Copper Institute, European Nickel Industry Association, International Cadmium Association, International Zinc Association (Europe), Rio Tinto, and the Scottish Environment Protection Agency.
Supporting Information Available Site locations, pH, DOC, major ion and metal concentrations, DGT measured concentrations with deployment times and dates, values of the heterogeneity factor, plots relating to binding heterogeneity for each metal and derived values of Γ. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) Meylan, S.; Odzak, N.; Behra, R.; Sigg, L. Speciation of copper and zinc in natural freshwater: comparison of voltammetric measurements, diffusion gradients in thin-films (DGT) and chemical equilibrium models. Anal, Chim. Acta 2004, 510, 91– 100. (2) Gimpel, J.; Zhang, H.; Davison, W.; Edwards, A. C. In situ trace metal speciation in lake surface waters using DGT, dialysis and filtration. Environ. Sci. Technol. 2003, 37, 138–146. (3) van Leeuwen, H. P.; Town, R. M.; Buffle, J.; Cleven, R. F. M. J.; Davison, W.; Puy, J.; van Riemsdijk, W. H.; Sigg, L. Dynamic speciation analysis and bioavailability of metals in aquatic systems. Environ. Sci. Technol. 2005, 39, 8545–8556. (4) Unsworth, E.; Warnken, K. W.; Zhang, H.; Davison, W.; Black, F.; Buffle, J.; Cao, J.; Cleven, R. F. M. J.; Galceran, J.; Gunkel, P.; Kalis, E. J. J.; Kistler, D.; Van Leeuwen, H. P.; Martin, M.; Noel, S.; Nur, Y.; Odzak, N.; Puy, J.; Van Riemsdijk, W. H.; Sigg, L.; Temminghoff, E. J. M.; Tercier-Waeber, M.-L.; Toepperwien, S.; Town, R. M.; Weng, L. P.; Xue, H. Model predictions of metal speciation in freshwaters compared to measurements by in situ techniques. Environ. Sci. Technol. 2006, 40, 1942–1949. (5) Warnken, K. W.; Davison, W.; Zhang, H.; Galceran, J.; Puy, J. In situ measurements of metal complex exchange kinetics in freshwater. Environ. Sci. Technol. 2007, 41, 3179–3185. (6) Town, R. M.; Chakraborty, P.; van Leeuwen, H. P. Dynamic DGT speciation analysis and application to natural heterogeneous complexes. Environ. Chem., in press. (7) Tipping, E.; Vincent, C. D.; Lawlor, A. J.; Lofts, S. Metal accumulation by stream bryophytes, related to chemical speciation. Env. Poll. 2008, 156, 936–947. (8) Scally, S.; Davison, W.; Zhang, H. Diffusion coefficients of metals and metal complexes in hydrogels used in diffusive gradients in thin films. Anal. Chim. Acta 2006, 558, 222–229. (9) Warnken, K. W.; Davison, W.; Zhang, H.; Galceran, J.; Puy, J. Interpretation of in situ speciation measurements of inorganic and organically complexed trace metals in freshwater by DGT. Environ. Sci. Technol. 2008, 42, 6903–6909. (10) Warnken, K. W.; Zhang, H.; Davison, W. Accuracy of the diffusive gradients in thin-films technique: diffusive boundary layer and effective sampling area considerations. Anal. Chem. 2006, 78, 3780–3787. VOL. 43, NO. 19, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
7235
(11) Tipping, E. WHAM-a chemical equilibrium model and computer code for waters, sediments and soils incorporating a discrete site/electrostatic model of ion-binding by humic substances. Comput. Geosci. 1994, 20, 973–1023. (12) Tipping, E. Humic ion-binding Model VI: an improved description of the interactions of protons and metal ions with humic substances. Aquat. Geochem. 1998, 4, 3–48. (13) Tipping, E.; Rey-Castro, C.; Bryan, S. E.; Hamilton-Taylor, J. Al(III) and Fe(III) binding by humic substances in freshwaters, and implications for trace metal speciation. Geochim. Cosmochim. Acta 2002, 66, 3211–3224. (14) Tipping, E. Modelling Al competition for heavy metal binding by dissolved organic matter in soil and surface waters of acid and neutral pH. Geoderma 2005, 127, 293–304. (15) Lofts, S.; Tipping, E.; Hamilton-Taylor, J. The Chemical Speciation of Fe(III) in Freshwaters. Aquat. Geochem. 2008, 14, 337– 358. (16) Zhang, H. In situ speciation of Ni and Zn in freshwaters: comparison between DGT and speciation models. Environ. Sci. Technol. 2004, 38, 1421–1427. (17) Buffle, J.; Zhang, Z.; Startchev, K. Metal flux and dynamic speciation at (bio)interfaces. Part I: Critical evaluation and compilation of physico-chemical parameters for complexes with simple ligands and fulvic/humic substances. Environ. Sci. Technol. 2007, 41, 7609–7620. (18) Van Laer, L.; Smolders, E.; Degryse, F.; Janssen, C.; Schamphelaere, K. A. C. Speciation of nickel in surface waters measured
7236
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 43, NO. 19, 2009
(19) (20) (21) (22) (23)
(24)
(25)
with the Donnan membrane technique. Anal. Chim. Acta 2006, 578, 195–202. Morel, F. M. M.; Hering, J. G. Principles and Applications of Aquatic Chemistry; Wiley: New York, 1993; p 400. Tipping, E. Cation Binding by Humic Substances; Cambridge University Press: Cambridge, 2002. Buffle, J. Complexation Reactions in Aquatic Systems: an Analytical Approach; Ellis Horwood: Chichester, 1998. Town, R. M.; Filella, M. Dispelling the myths: is the existence of L1 and L2 ligands necessary to explain metal ion speciation in natural waters. Limnol. Oceanogr. 2000, 45, 1341–1357. Filella, M.; Town, R. M. Determination of metal ion binding parameters for humic substances. Part 1. Application of a simple calculation method for extraction of meaningful parameters from reverse pulse polarograms. J. Electroanal. Chem. 2000, 485, 21–33. Chakraborty, P.; Fasfous, I. I.; Murimboh, J.; Chakarabarti, C. L. Simultaneous determination of speciation parameters of Cu, Pb, Cd, and Zn in model solutions of Suwannee River fulvic acid by pseudopolarography. Anal. Bioanal. Chem. 2007, 388, 463–474. Balistrieri, L. S.; Blank, R. G. Dissolved and labile concentrations of Cd, Cu, Pb, and Zn in the South Fork Coeur d’Alene River, Idaho: Comparisons among chemical equilibrium models and implications for biotic ligand models. Appl. Geochem. 2008, 23, 3355–3371.
ES900112W
