- Email: [email protected]
Dissolved arsenic species in the Schelde estuary and watershed, Belgium
Estuarine,
Coastal
and Shelf
Science
(1989) 29,421-433
Dissolved Arsenic Species in the Schelde Estuary and Watershed, Belgium
M. 0. Andreae’
and T. W. Andreae’
Department of Chemistry, University of Antwerp (U.I.A.), Belgium and Department of Oceanography, Florida State Tallahassee, Florida 32306, U.S.A. Received
10 November
Keywords:
1988 and in revised
form
3July
B-2160 University,
Wilrijk,
1989
arsenic; speciation; estuary; river; model; Schelde
The Schelde watershed drains a densely populated and industrialized region in central Europe. The Zenne River, a tributary which flows through the centre of the Brussels industrial region, contributes most of the arsenic to the riverestuary system. Inputs of industrial and domestic effluents create a region of anoxic conditions in the water column of the upper estuary. A study of arsenic concentrations
and speciation
in the rivers
of the Schelde
watershed
shows
that
the ratio of As(II1) to As(V) is elevated in the anoxic part of the estuary and those tributaries that are depleted in oxygen. The combination of a near-constant antbropogenic arsenic emission and seasonally fluctuating water discharge creates a variable arsenic concentration in the river endmember. This variability can explain the non-linearity in the arsenicsalinity relationship in a region without
in the estuary,
where
a pronounced
arsenic
maximum
is seen
local arsenic sources. The As(III)/As(V) ratio in the estuary has typical marine values in the lower estuary and increases sharply at the oxic/anoxic interface near the head of the estuary. The feasibility of using a numerical model incorporating fluctuating river composition and discharge, tidal mixing, zero-order reduction of arsenate and first-order oxidation of arsenite to model the distribution and redox speciation of arsenic in the estuary under non-steady-state conditions is explored.
Introduction The watershed of the Schelde River comprises one of the most densely populated and industrialized regions in central Europe (Wollast, 1982). Large amounts of domestic and industrial wastes are discharged into the Schelde and its tributaries, especially the Zenne River, which flows through the city of Brussels and the adjacent industrial area (Figure I). As a result, the Schelde River and Estuary receive large amounts of organic matter and nutrients as well as inorganic pollutants, e.g. cadmium, zinc, mercury and arsenic (Wollast, 1982; Duinker et al., 1982; Van der Sloot et al., 1985). The intense microbial activity resulting from the input of nutrients and organic matter in domestic and industrial waste coupled with the relatively long residence time of river-derived materials in the “Present F.R.G. 0272-7714/89/050421+
address:
13 $03.00/O
Max
Planck
Institute
for Chemistry,
P.O. Box 3060, D-6500 @ 1989 Academic
Mainz,
Press Limited
422
M.
0. Andreae
&
T. W. Andreae
Figure 1. Arsenic concentrations and speciation in the rivers of the The height of the vertical bars adjacent to the sampling sites inorganic arsenic concentration, the shaded section the fraction of section the amount ofAs(II1). The river kilometers indicated in this locate the sampling positions for the estuarine samples (Table 1).
Schelde watershed. represents the total As(V), and the open figure can be used to
estuary makes the Schelde River and Estuary an ideal system in which to study the interaction of biogeochemical processes with pollutant elements. The speciation of arsenic in natural waters is influenced by a number of biogeochemical processes. In oxygenated waters, the oxidation state + V (arsenate) is thermodynamically stable, but both bacteria and planktonic algae can reduce As(V) to the oxidation state + III (arsenite) even in the presence of dissolved oxygen (Johnson, 1972; Andreae & Klumpp, 1979). This results in the presence of As(II1) at relatively low levels (usually < 10%) in most natural waters. In addition, algae take up As(V) from their environment and excrete a variable fraction of this arsenic in the form of the methylated species, methanearsonic acid and dimethylarsinic acid (Andreae, 1986). Intensive microbial activity in the upper Schelde Estuary results in the almost complete consumption of dissolved oxygen in this region. Previous studies of arsenic speciation in anoxic systems had suggested that not all arsenic was reduced to arsenite even under highly reducing conditions (Andreae, 1979; Andreae & Froelich, 1984). The highest fraction of As(II1) reported was found in the anoxic waters of Saanich Inlet, British Columbia, where 88% of inorganic arsenic was present as arsenite (Peterson & Carpenter,
Arsenic
in Schelde
Estuary
and Watershed
423
1983). However, these studies had been carried out in marine systems where the presence of H,S in the anoxic region made it likely that sulfide complexes with arsenic were also present which could not be reliably detected by the analytical techniques used. The anoxic conditions in the Schelde provided an opportunity to study the redox speciation of arsenic in the absence of H,S. Previous measurements of arsenic concentrations in the Schelde Estuary (which did not include speciation) had shown the existence of strong nonlinearities in the arsenic; salinity relationship which were tentatively related to the removal of As from solution by coprecipitation with ferric hydroxides (Van der Sloot et al., 1985). In other estuaries, however, such removal had not been observed (Waslenchuk & Windom, 1978; Andreae t’r al., 1983; Froelich et al., 1985). In this paper we present new data on the concentration of arsenic species in the Schelde River and its tributaries and on the distribution of arsenic relative to salinity in the Schelde Estuary. We then use these data to explore the feasibility of using the ‘ estuarine analog ’ model (Knox et al., 1984; A. Dickson, 1985, personal communication) to predict and interpret the species distribution in the estuary. While it is obvious that a final analysis of the biogeochemical processes in the Schelde Estuary must await the collection of further data, this paper provides a first case study for the application of a non-steady state, numerical model incorporating non-analytically fluctuating river composition and discharge to describe the time-variant concentration distribution and speciation of a trace element in an estuary. Methods Sampling
The river water samples were collected from the shore by submerging acid-washed polyethylene bottles just below the surface. The bottles were rinsed three times before the sample was collected. The estuarine transect sampleswere taken from the R/V Belgica with standard oceanographic samplersmade from polycarbonate and polyvinyl chloride (Hydrobios, Kiel, West Germany) and transferred into acid-washed polyethylene bottles. The samples were filtered immediately upon return to the laboratory with a nitrogen-pressurized membrane filtering apparatus (0.45 urn pore size, Sartorius GmbH, Goettingen, West Germany), and stored in a refrigerator until analysis. These sampling and handling techniques have been shown to result in no detectable contamination or speciation change for arsenic (Andreae, 1983). Analysis
Arsenic specieswere determined by a combination of the hydride generation technique described by Andreae (1983) with the graphite-furnace atomic absorption detector used for germanium determination by Andreae and Froelich (1981). For the determination of As(II1) the sample was buffered to pH 6 by the addition of tris(hydroxymethylj aminomethane hydrochloride (Tris-HCl) buffer, then sodium borohydride wasadded to the sample in a closed system and the arsine produced from the reduction of As( II I) was swept from the solution by a helium stream. The arsine was collected in a liquid-nitrogen cooled trap filled with a chromatographic packing. After the reaction was complete, the trap was heated and the arsine peak was carried into the graphite furnace (Perkin Elmer HGA 500) through a glasstube attached to the internal argon purge system. The furnace wasbrought to a temperature of 2100 “C about 10 s before the arsine peak eluted and held at this temperature for 30 s. Helium at aflow rate of 100ml min-’ wasused asa carrier gas,
424
M. 0. Andreae Q T. W. Andreae
and argon at the sameflow rate was added to the gasstream before it entered the graphite furnace. The graphite furnace was installed in a Perkin Elmer 5000 atomic absorption spectrophotometer equipped with an electrodeless-discharge As lamp. The analytical peakswere registered on a chart recorder. Total inorganic arsenic was determined using the sameprocedure with the exception that HCl wasadded instead of the Tris-HCI buffer. Standards were prepared by dilution of 0.1 M stock solutions of As(II1) and As(V) to concentrations near those present in the samples.These standards were prepared fresh daily. The precision of the method is c. lo%, the detection limit about 0.01 nM (nmol 1-i). Methylated arsenic compounds (Andreae, 1979) were not observed in this study. Since the analytical system had not been optimized for their detection, we can only state that they did not represent a significant fraction (< 5%) of the dissolved arsenic. In view of the low water temperatures in the estuary, only very low concentrations of methylated specieswere to be expected, basedon the work of Howard et al. (1984). Temperature and dissolved oxygen were determined with an immersed probe and salinity by chloride titration. Results and discussion Arsenic
in the Schelde watershed
The collection sitesfor the estuarine and river samplesare shown in Figure 1, the analytical results in Table 1. The arsenic concentrations in the rivers of the Schelde watershed (with the exception of the Zenne River) fall in the range of c. 10to 50 nM, which is typical of European rivers (Andreae et al., 1983; Seyler, 1985). They do not appear to be significantly elevated relative to the world average (c. 20 nM: Andreae et al., 1983; Martin dz Meybeck, 1979; Andreae, unpublished data), which is somewhat surprising in view of the high density of population and industry in this watershed. The Zenne River, however, contained almost 400 nM As, an order of magnitude higher than the other tributaries. At the time of sampling, the Zenne wasvisibly polluted with large amounts of sewage;it must be assumedthat either municipal or industrial dischargesinto this river are responsible for the elevated arsenic levels. In terms of its water discharge, the Zenne is a minor contributor to the Schelde watershed-during mid-November 1984, it contributed only about 9qb of the total discharge of the Schelde (Antwerpse Zeediensten, 1985). At the sametime, however, it was responsible for over 60% of the arsenic input. The Zenne also carries extremely high levels of organic matter: BOD values in excessof 100 mg 1-l and COD values near 400 mg 1-I have been reported (Instituut voor Hygiene en Epidemiologie, 1984). The speciation of arsenic showsa clear relationship to water quality, asexpressedby the dissolved oxygen content. The lowest ratios of As(II1) to total As were found in the Zenne Canal and the Nete Canal (Table 1). These canals serve as shipping routes and receive little pollution, and consequently their waters are near oxygen saturation. In contrast, the low oxygen levels in the rivers of the Schelde watershed tend to coincide with high ratios of As(II1) to total As. The total arsenic levels in the canals,which receive their water from the Maas and Sambre river system, are comparable to the values in the Schelde tributaries. The input of waste into the Schelde watershed (with the exception of the Zenne), therefore, doesnot appear to result in a significant increasein total arsenic, but rather in a speciation shift towards As(II1). The presence of significant amounts of arsenite even where oxygen has not been completely depleted, shows that the As(III)/As(V) ratio does not reflect thermodynamic
Arsenic
in Schelde
Estuary
and Watershed
425
TABLE 1. Arsenic species concentrations and supporting parameters in the Schelde watershed and Estuary
Location Zenne Canal, Hombeek Nete Canal, Lier Leie, Afsnee Schelde, Eke Schelde, Appels Ferry Dender, Aalst Grote Nere, Lier Nete, Duffel Dijle, Muizen Zenne, Hombeek Rupel, Niel Ferry Schelde, Kruibeke Schelde, Kruibeke Estuary, km 95 Estuary, km 85 Estuary, km 80 Estuary, km 78 Estuary, km 73 Estuary, km 68 Estuary, km 64 Estuary, km 53 Estuary, km 62 Estuary, km 48 Estuary, km 43 Estuary, km 40 Estuary, km 36 Estuary, km 28 Estuary, km 19 Estuary. km 10 Estuary, km 3 Estuary, km 2 Estuary, km 2
Date
Salinity
12.12.84 18.11.84 28.11.84 18.11.84 18.11.84 18.11.84 18.11.84 18.11.84 18.11.84 12.12.84 18.11.84 18.11.84
0.08
12.12.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84
Temp (YX)
pH
02 As(II1) (mgl ‘J (nM)
5.6 9.0
8.3 10.9
14.4 11.2
1.4 0.4
8.0 7.8
6.0 5.6
11
0.18 0.40
9.9 9.9
0.4 0.6
110
0.48 0.30
10.0
0.8 0.4
0.17
0.10 0.10 0.10 0.21 0.66 2.2 2.6 2.9 3.5 8.6 13.3 15-3 21-8 24.3
27.1 29.2 30.8 33.3 33.7
7.3
5 15 5 6
370 77
46 8
33 39
80 40
24 28 37 43 46 45 49 50 56 64 52
28 34 41 46 48 47 50 52 57 65 53 5' 47 47 36 31 33 21 39
9.7
7.32
10.3
7.40
3.0
10.4 10.3
7.45
4.9
9.5
7.52
11.4
9.5
7.80
13.0
4-5 5-9 4-7 3-o 23 l-4 l-4 l-4 l-3 l-5 1.4 1-3 l-3
12.1
1-l 11 l-2
10 9.6 9.5
12.2 11-2
16 13
36
6 6
7.41 7.40
7.78
16 12
As(tota1) As(II1) (nM1
39 29 34 9 1.4 7 6 260 41
8.9 8.9
9.7
0.7 0.7
O-04 1 1
As(V) (nM)
O-8
1-o
51 46 46 34 30 32 24 28
45 44 39 15
13 13 13
equilibrium (Andreae, 1979). This suggests that the reduction of As(V) to As(III%! does not take place abiotically as a result of low redox-levels in the water, but rather is biologically mediated. Since the ability of both bacteria and planktonic algae to reduce arsenate has been shown in several studies (Johnson, 1972; Andreae & Klumpp, 1979; Sanders 8r Windom, 1980; Sanders, 1983), it is likely that the reduction of arsenic is a result of biological processes. Furthermore, in the presence of even small amounts of oxygen, arsenite oxidation can take place simultaneously with microbially mediated arsenate reduction. The oxidation process is also microbially catalyzed and its rate, therefore, also depends on the type and number of bacteria present (Myers et al., 1973; Osborne & Ehrlich, 1976; Phillips &Taylor, 1976; Sanders, 1979; Scudlark & Johnson, 1982). Myers et ~1. (1973) showed that sewage microorganisms and sewage treatment effluents can reduce arsenate to arsenite, or oxidize arsenite to arsenate, depending on the availability of oxygen. In particular, the fact that the two oxidation states of arsenic are always simultaneously present at significant levels suggest that the observed As(III)/As(V‘, ratios are the result of the balance of multiple microbial and abiotic reduction and oxidation
426
M. 0. Andreae & T. W. Andreae
IO 0
IO
20
30
Sallnlty
Figure 2. Distribution of dissolved total inorganic arsenic in the Schelde Estuary as a function of salinity. The data points represent the measurements made on the samples collected in the estuary during a cruise of the R/V Belgica on 27 November 1984. The lines show the distributions predicted by the estuarine model for the early morning high tides of 27 November ( - - -- ) and 28 November ( ), which bracket the sampling period. (The arsenic/salinity distribution is independent of the tidal cycle).
processes.Consistent with this argument, we find only a general tendency for this ratio to be lower in oxygenated canals and higher in the oxygen-depleted rivers, but not a clear correlation between O,-saturation and the oxidation state of arsenic. The absence of complete reduction of arsenic even in sulphide-free environments suggeststhat the failure to find complete reduction in marine systems (Andreae, 1979; Andreae & Froelich, 1984; Peterson & Carpenter, 1983) is not due to analytical problems in the presence of sulphide, but rather to the complex redox kinetics of arsenic in natural aquatic systems. The distribution of arsenicin the ScheldeEstuary The distribution of arsenic in the Schelde Estuary on 27 November 1984 is shown as a function of salinity in Figure 2. A pronounced maximum is evident in the upper estuary at a salinity of c. 7. Such maxima have been interpreted asevidence of input of arsenic into estuarine waters by industrial dischargesor by diffusion from sediment porewaters (Van der Sloot et al., 1985;Andreae et al., 1983; Knox et al., 1984; Seyler, 1985; Sanders, 1985). However, the maximum occurs near kilometer 50, in a region where no known industrial or municipal discharges of arsenic are present. The suspendedparticulate and bottom sediment data for arsenic presented by Van der Sloot et al. (1985) suggestthat the sediments in the Schelde Estuary do not have the extremely high arsenic levels which can account for the arsenic input by desorption from sediments into porewater and diffusion into the overlying waters, as observed in the Tamar Estuary (Knox et al., 1984). Desorption of arsenic from suspendedriverine particulates entering the salinity gradient is not likely to occur; on the contrary, most authors have found evidence for the coprecipitation of arsenic with ferric hydroxides in the low-salinity region of estuaries (Howard et al., 1984; Langston, 1983; Van der Sloot et al., 1985; Carpenter et al., 1978). We shall therefore examine whether the arsenic distribution observed by usreally requires an input in the region of the maximum or whether it can be explained on the basis of fluctuations in the river discharge and composition.
Arsenic
in Schelde
Estuary
and Watershed
-127
‘0 Distance
(km!
Figure 3. Comparison between the observed distribution of salinity (0) in the estuary on 27 November 1984 and that predicted by the estuarine model. The dashed line ( - - - - I represents the model prediction for the early morning high tide (04.25 h at Vlissingen), the solid line ( __ ) the evening low tide (23.04 h), between which the samples were taken.
In order to simulate the behaviour of arsenic in the Schelde Estuary, we adapted a model which had been developed to simulate the effects of mixing and chemical processes in the Tamar Estuary (Knox et al., 1984; A. Dickson, 1985, personal communication). In this model, the estuary is simulated as a sequence of perfectly mixed tank reactors through which water is transported as a result of river discharge and tidal flow. The magnitude of the river discharge and its composition can be changed as a function of time, using either analytical functions or arrays of values. In addition, zero and first order reactions have been included which proceed at specified rates within each box to represent the effect of chemical processes within the estuary. The geometry of the Schelde Estuary was based on data from a 1971 survey of the estuary (Laforce et al., 1974). In order to obtain an accurate representation of the mixing characteristics of the estuary, the salinity distribution as a function of distance along its axis was computed by the model for different values of river discharge and compared with survey data (Peters & Sterling, 1976). The length of the boxes into which the estuary is divided was then varied until a best fit was obtained (0.66’7 km, dividing the 100 km length of the estuary into 150 segments). The measured discharge as a function of time during 1984 was then introduced and the model run for the period between 1 January through 27 November of that year. The predicted salinity distribution for the high and low tides closest to the sampling period is shown in Figure 3 together with the observed salinity data, showing that the model produced a satisfactory fit. It should be noted that due to the sharp discharge maximum on 26 November, the salinity distribution in the estuary is highly time-dependent for several days following this date. Consequently, the model predicts salinity distributions significantly different from the observed values for the preceding and following days. The arsenic concentrations in the river end-member had been measured on several days in November and December 1984. We therefore had to interpolate the time-dependent river end-member composition required as input to the estuarine model by using a dilution equation. In view of the fact that all tributaries other than the Zenne had low arsenic concentrations which fell into a narrow range, we assumed that the river end-member could be represented by a simple dilution of a constant, namely, anthropogenic input (I)
428
M. 0. Andreae & T. W. Andreae
200 (b)
0
100
200
300
Day
Figure 4. (a) Discharge of the Schelde River below the confluence with the Rupel during 1984, and (b) the arsenic concentration in the riverine end-member predicted by a dilution model (see text).
via the Zenne
with
river
water
of a constant
background arsenic concentration and
discharge (Q):
The parameters in this equation can be derived by fitting the As concentrations measured in the end-member on three different days to the dischargesfor the samedays. From the data in Table 1 we obtained a value of 19 nM for [Aslbpclrground and 5.4 mmol set-’ for Z(r2=0+37). This corresponds to an anthropogenic input of about 13 tons of As per year into the Zenne. Alternatively, we can average the As concentrations in the tributaries other than the Zenne (24 nM) and obtain the Zenne input from its measuredconcentration and discharge (4.2 mm01s-l). The fact that these two approachesgive comparable results supports the validity of our dilution equation. For the prediction of the end-member time series, we prefer to use the parameters from the least-squaresfit, however, since they represent a longer time period than the single measurement in the Zenne. (Note that the end-member concentrations predicted by this approach are derived completely independently from the estuarine distribution of arsenic). The resulting discharge and concentration curves are shown in Figure 4 (discharge data are from Antwerpse Zeediensten (1985). Ten-day averages of river discharge were used with the exception of the ten days preceding the sampling campaign, where we used daily values. It is evident from Figure 4 that the cruise took place during a pronounced discharge transient, so that highly nonsteady conditions are to be expected.
Arsenic
::I 0
in Schelde
Estuary
and Watershed
, , , , / / , , 20
40
60
80
IO0
Figure 5. Comparison between the observed distribution of dissolved total inorganic arsenic (0) in the estuary on 27 November 1984 and that predicted by the estuarine model. The dashed line ( - -- - ) represents the model prediction for the early morning high tide (04.25 h at Vlissingen), the solid line ( ) the evening low tide (23.04 h), between which the samples were taken. Error bars are not shown; they would be the same as in Figure 2.
Y
I
0
20
40 Dhmce
60 (km)
80
Y
10 100
Figure 6. Distribution of dissolved As(II1) in the Schelde Estuary as observed on 25 November 1984 (0; representative error bar shown for one sample only), and as predicted by the estuarine model for different assumptions for the rate of arsenite oxidation and arsenate reduction. The model calculations are shown for mean tidal conditions on 27 November. The solid line ( ) represents the predicted distribution in the absence of redox transformations between As(II1) and As(V). The other lines use various arsenite oxidation rates (all in h I): -0.004 (abiotic oxidation only), - 0.038 (laboratory experiments by Scudlark &Johnson, 1982), -0-019 (half that rate to account for lower temperatures in the Schelde), and -0.09 (rate suggested as ‘ typical ’ for estuaries by Scudlark & Johnson, 1982). The distribution of dissolved oxygen in the estuary is represented by squares (0).
The arsenic distribution in the estuary can now parameters as for salinity, but introducing a constant of 30 nM As and the time-dependent river end-member The result is shown in Figure 5 for the high and low the model distribution shows good agreement with
be modeled using the same mixing seawater end-member concentration concentration shown in Figure 4. tides on 27 November 1984. Again, the observed one. When the model
430
M. 0. Andreae & T. W. Andreae
arsenicconcentrations are plotted against salinity (Figure 2), a maximum is evident below a salinity of 10, somewhat lower than actually observed, but still in reasonableagreement. These results show that estuarine inputs are not required to explain the arsenic distribution which we observed in the Schelde Estuary. Similar behaviour is to be expected in any system where the concentration of a speciesin the riverine end-member is strongly dependent on the discharge. This applies in particular to situations where a pollutant is added to a river through sometype of discharge, since the pollutant flux is either constant or at least doesnot vary proportionally to the river discharge. In such cases,the shapeof the speciesvs. salinity plot cannot be interpreted without knowledge of the river endmember composition as a function of time. This has been recognized by the authors who proposed the use of species/salinity relationships to determine non-conservative behaviour in estuaries, e.g. Officer (1979), but is often disregarded in the literature, e.g. Sanders (1985). Officer (1979), however, was only able to state that his analytical, tidal-averaged, steady-state model was not suitable for time-variant conditions. Later variants of the Officer model introduced river end-member variations, e.g. Kaul and Froelich (1984), but were limited to simple periodical functions. In contrast to previous studies, the approach proposed here eliminates both the requirements for steady state and for tidal averaging. It makes it possible to model the estuarine distribution of a specieswhen both the river discharge and the river composition are time-variant, even under conditions where that time-dependence is non-analytical, as is the casehere. It also lends itself to conducting sensitivity studies where the variation of the estuarine distribution of a speciesresulting from perturbation of the input parameters is evaluated. Arsenic redox speciation When the same estuarine model is applied to As(III), it is obvious that a conservative mixing model (Figure 6, solid line) cannot explain the observed distribution. (Based on the data in Table 1, a seawater As(II1) concentration of 1.0 nM and a river As(II1) concentration of 25% of total As were assumedasmodel parameters). Our measurements show a sharp, non-linear decreaseof As(II1) in the region below km 80, where dissolved oxygen begins to increase in the estuarine waters. The large difference between the measuredAs(II1) values and the predictions from a conservative model can be explained by the oxidation of As(II1) to As(V) in the estuary. In order to represent this phenomenon in the model, we have included various first-order arsenite oxidation rate constants in the model. This made a satisfactory fit in the region above 60 km possible, but also brought As(II1) below 50 km to almost zero. In order to obtain the observed near-constant As(II1) concentration in the lower estuary, we added a pseudo-zero-order As(V) reduction rate constant to the model equations. Choosing a zero-order reduction rate was basedon the assumption that reduction was not As(V) controlled, but limited by the presence of biological catalysts. This resulted in the curves shown in Figure 6 for first-order oxidation rate constants of - 0.004 to - 0*090h-l; in each case,the zero-order reduction rate constants were assignedthe samenumerical value and opposing sign. These reduction rate constants are comparable to those determined experimentally by Johnson and Burke (1978) and Sanders and Windom (1980). The best agreement between model and observation is obtained for oxidation rate constants between - 0.019 and - 0.090 h-l. In experiments with Narragansett Bay water to which 1.2 PM As(II1) had been added, Scudlark and Johnson (1982) measured a microbially catalyzed oxidation rate constant of c. -0.038 h-‘. They found that this rate
Arsenic
in Schelde
Estuary
and Watershed
431
constant increased with decreasing initial As(II1) concentration, and suggested a value of -0.09 as ‘ typical ’ for productive surface waters. In view of the fact that the water temperatures in the Schelde Estuary were considerably lower (c. 10 “C vs. 25 “C) than those used by Scudlark and Johnson (1982), we fmd that the range of rate constants required to obtain a reasonable fit in our model agrees well with their observations. It is also clear that a rate constant of -0.004 h-*, which represents abiotic oxidation by dissolved oxygen only (based on the data of Johnson & Pilson, 1975), is not sufficient to explain the observed distribution of As(II1) in the Schelde Estuary. Heterogeneous abiotic reactions, on the other hand, may also contribute to the oxidation of arsenite in the Schelde Estuary. Oscarson et al. (1981) and Peterson and Carpenter (1983) have shown that the presence of Mn(IV) oxides significantly enhances arsenite oxidation. Rate constants of - 0.025 to - 0.034 h-’ were observed in the presence of d-MnO, at 25 “C and values from - 0.041 to - 0.069 h-’ in the presence of lake sediment at 5 ~‘C (Oscarson et al., 1981). Peterson and Carpenter (1983) found a rate constant of -0.014 h-’ at 6 ‘C in water from a layer rich in particulate manganese collected above the oxic/anoxic interface in Saanich Inlet. This rate decreased by about one order of magnitude when the sample was poisoned with an antibiotic, but still remained significantly higher than values measured in manganese-poor, but unpoisoned surface water. Manganese oxide precipitation is taking place in the Schelde in the same region as arsenite oxidation (Duinker et al., 1979), both processes being driven by the redox gradient which is present in the estuary. This results in particulate Mn concentrations in the Schelde which are similar to or higher than the concentrations in Peterson and Carpenter’s (1979 j experiments. Under these conditions, it is likely that both microbial and abiotic processes are contributing to arsenite oxidation. It is clear from the preceding discussion, that the limited data set available does not make it possible to rigorously verify the applicability of the model or the uniqueness of the solutions obtained. Further profiles measured under different flow conditions will be required before it can be considered that the major features of the biogeochemical arsenic cycle in the Schelde estuary have been conclusively established. We feel, however, that the proposed niodel can serve as a useful tool in the interpretation of the distribution and speciation behaviour of an element in the estuarine environment, particularly under nonsteady-state conditions, when both the composition of the river end-member and the river discharge are allowed to fluctuate in a non-analytical fashion. We are able to show that the estuarine distribution of As(II1) can be predicted by the model, when, and only when, the rate constants derived from independent laboratory measurements are introduced. Therefore, this approach makes it possible to verify redox rate constants in temporally and spatially variant estuarine systems. We hope that this study may stimulate future, more detailed research combining field studies of estuarine trace element speciation and nonsteady-state numerical modelling. Acknowledgements
The authors are indebted to A. Dickson for the original version of the estuarine analog program and helpful advice on how to use it. We thank F. Adams for making possible a sabbatical stay at the University of Antwerp (Universitaire Instelling Antwerpen). We are grateful to F. Dehairs for collecting the estuarine samplesand to W. Van Mol and R. Van Cleuvenbergen for help in the laboratory. We acknowledge E. Smets, who provided the data on the estuary geometry and H. Belmans, who supplied information on the discharge
432
M.
0. Andreae
& T. W. Andreae
of the Schelde. W. Maenhaut was helpful in tracking down information which would otherwise have been difficult to obtain. We thank M. Dancy for help with the preparation of the manuscript and figures. This research was supported by the Belgian Ministry of Science Policy (grant 84-89/69) and by the National Fund for Scientific Research Brussels.
References Andreae, M. 0. 1979 Arsenic speciation in seawater and interstitial waters: the influence of biologicalchemical interactions on the chemistry of a trace element. Limnology and Oceanography 24,440-452. Andreae, M. 0. 1983 Arsenic, In: Methods of Seawater Analysis, 2nd edition (Grasshoff, K., Erhardt, M., Kremling, K., eds.). Weinheim: Verlag Chemie, 1983, pp. 218-225. Andreae, M. 0. 1986 Organoarsenic compounds in the environment, In: Organometallic compounds in the environment (Craig, P. J., ed.). Harlow: Longman, pp. 198-228. Andreae, M. O., Byrd, J. T. & Froelich, P. N. 1983 Arsenic, antimony, germanium and tin in the Tejo Estuary, Portugal: modeling a polluted estuary. EnoironmentaZ Science and Technology 17,731-737. Andreae, M. 0. & Froelich, P. N. 1984 Arsenic, antimony and germanium biogeochemistry in the Baltic Sea. Tellus 36B, 101-l 17. Andreae, M. 0. & Froelich, P. N. 1981 Determination of germanium in natural waters by graphite furnace atomic absorption spectrometry with hydride generation. Analytical Chemistry 53,287-291. Andreae, M. 0. & Klumpp, D. W. 1979 Biosynthesis and release of organoarsenic compounds by marine algae. Environmental Science and Technology 13,738741. Antwerpse Zeediensten 1985 De Afvoer van de Schelde in 1984, Antwerp. Carpenter, C., Peterson, M. L. & Jahnke, R. A. 1978 Sources, sinks, and cycling of arsenic in the Puget Sound Region, In: Estuarine Interactions, (Wiley, M. L. ed.). New York: Academic Press, pp. 460-479. Duinker, J. C., Wollast, R. & Billen, G. 1979 Behaviour of manganese in the Rhine and Scheldt Estuaries, II. Geochemical Cycling. Estuarine and Coastal Marine Science 9,727-738. Duinker, J. C., Nolting, R. F. & Michel, D. 1982 Effects of salinity, pH and redox conditions on the behaviour of Cd, Zn, Ni and Mn in the Scheldt Estuary. ThaZussiaJugosZawica l&191-202. Froelich, P. N., Kaul, L. W., Byrd, J. T., Andreae, M. 0. & Roe, K. K. 1985 Arsenic, barium, germanium, tin, dimethylsulfide and nutrient biogeochemistry in Charlotte Harbor, Florida, a phosphorus-enriched estuary. Estuarine, Coastal and Shelf Science 20,239-264. Howard, A. G., Arbab-Zavar, M. H. St Apte, S. C. 1984 The behaviour of dissolved arsenic in the estuary of the River Beaulieu. Estuarine, Coastal and Shelf Science 19,493-504. Instituut voor Hygiene en Epidemiologie 1984 Kwaliteitsverzicht van een Aantal Belgische Oppervlakte Wateren in 1982 en 1983, Brussels. Johnson, D. L. 1972 Bacterial reduction of arsenate in {eawater. Nature 240,44-45. Johnson, D. L. & Burke, R. M. 1978 Biological mediation of chemical speciation: II Arsenate reduction during marine phytoplankton blooms. Chemosphere 8,645-648. Johnson, D. L. & Pilson, M. E. Q. 1975 The oxidation of arsenite in seawater. Environmental Letters 8, 157-171. Kaul, L. W. & Froelich, P. N. 1984 Modeling estuarine nutrient geochemistry in a simple system. Geochimica et Cosmochimica Actu 48,1417-1433. Knox, S., Langston, W. J., Whitfield, M., Turner, D. R. & Liddicoat, M. I. 1984 Statistical analysis of estuarine profiles: II. Application to arsenic in the Tamar Estuary (S.W. England). Estuarine, Coastal and Shelf Science l&623-638. Laforce, E., Roovers, P. & Sterling, A. 1974 Thermische Waterveroutreiniging in de Westerschelde. Waterbouwkundig Laboratorium, Borgerhout, Belgium. Langston, W. J. 1983 The behaviour of arsenic in selected United Kingdom estuaries. CanadiunJournal of Fisheries and Aquatic Science 40,143-150. Martin, J. M. & Meybeck, M. 1979 Elemental mass-balance of material carried by major world rivers. Marine Chemistry 7,173-206. Myers, D. J., Heimbrook, M. E., Osteryoung, J. & Morrison, S. M. 1973 Arsenic oxidation state in the presence of microorganisms: examination by differential pulse polarography. Environmental Letters 5(l), 53-61. OtXicer, C. B. 1979 Discussion of the behaviour of non-conservative dissolved constituents in estuaries. Estuarine and Coastal Marine Science 9,91-94. Osborne, F. H. & Ehrlich, H. L. 1976 Oxidation of arsenite by a soil isolate of Alcaligenes. Journal of Applied Bacteriology 41,295-305. Oscarson, D. W., Huang, P. M., Defosse, C. & Herbillon, A. 1981 Oxidative power of Mn(IV) and Fe(II1) oxides with respect to As(II1) in terrestrial and aquatic environments. Nature 291,50-51.
Arsenic
in Schelde
Estuary
and Watershed
Peters, J. J. & Sterling, A. 1976 Hydrodynamique et transports de sediments de I’estuaire de l’Escaut, In: L’Estuaire de I’Escaut, (Nihoul, J. C. J., Wollast, R. eds.). Projet Mer, Program. Pol. Scientifique, Brussels, Vol. 10. Peterson, M. L. & Carpenter, R. 1983 Biogeochemical processes affecting total arsenic and arsenic species distributions in an intermittently anoxic fjord. Marine Chemistry 12,295-321. Phillips, S. E. & Taylor, M. L. 1976 Oxidation of arsenite to arsenate by Alcalzgenes jaecalis. .4pplied and Envwonmental Microbiology 32,392-399. Sanders, J. G. 1979 Microbial role in the demethylation and oxidation of methylated arsenicals in seawater Chemosphere 3,135-137. Sanders, J. G. 1983 Role of marine phytoplankton in determining the chemical speciation and biogeochem& cycling of arsenic. CanadianJournal of Fisheries and Aquatic Science 40,192-196. Sanders, J. G. 1985 Arsenic geochemistry in Chesapeake Bay: dependence upon anthropogenic inputs and phytoplankton species composition. Marine Chemistry 17, 329-340. Sanders, J. G. & Windom, H. L. 1980 The uptake and reduction of arsenic species by marine algae. S.\ru~zrt~~~ and Coastal Marine Science 10, 555-567. Scudlark, J. R. & Johnson, L. D. 1982 Biological oxidation of arsenite in seawater. Esruari~ze, C:~~~>rtii ~r~i She(f Science 14,693-706. Seyler, I’. 1985 Formes chimiques et comportement de l’arsenic en milieu estuarien. Ph. D. Disserta:ic>n. University of Paris 6. \‘an der Sloot, H. A., Hoede, D., Wijkstra, J., Duinker, J. C. & Nolting, R. F. 1985 Anionic species of V, As, Se, MO, Sb, Te and W in the Scheldt and Rhine estuaries and the Southern Bight (North Sea). ESTIU&~. (Toastal and Shelf Science 21,633-65 1. Waslenchuk, D. G. & Windom, H. L. 1978 Factors controlling the estuarine chemistry of arsemc. E~trc~rrm dnd Coastal Marine Science 7,455-464. Wollast, R. 1982 Behaviour of organic carbon, nitrogen and phosphorus in the Scheldt estuarv. T/z&s.\:~ hrgoslavica 18, 1 l-34.
Coastal
and Shelf
Science
(1989) 29,421-433
Dissolved Arsenic Species in the Schelde Estuary and Watershed, Belgium
M. 0. Andreae’
and T. W. Andreae’
Department of Chemistry, University of Antwerp (U.I.A.), Belgium and Department of Oceanography, Florida State Tallahassee, Florida 32306, U.S.A. Received
10 November
Keywords:
1988 and in revised
form
3July
B-2160 University,
Wilrijk,
1989
arsenic; speciation; estuary; river; model; Schelde
The Schelde watershed drains a densely populated and industrialized region in central Europe. The Zenne River, a tributary which flows through the centre of the Brussels industrial region, contributes most of the arsenic to the riverestuary system. Inputs of industrial and domestic effluents create a region of anoxic conditions in the water column of the upper estuary. A study of arsenic concentrations
and speciation
in the rivers
of the Schelde
watershed
shows
that
the ratio of As(II1) to As(V) is elevated in the anoxic part of the estuary and those tributaries that are depleted in oxygen. The combination of a near-constant antbropogenic arsenic emission and seasonally fluctuating water discharge creates a variable arsenic concentration in the river endmember. This variability can explain the non-linearity in the arsenicsalinity relationship in a region without
in the estuary,
where
a pronounced
arsenic
maximum
is seen
local arsenic sources. The As(III)/As(V) ratio in the estuary has typical marine values in the lower estuary and increases sharply at the oxic/anoxic interface near the head of the estuary. The feasibility of using a numerical model incorporating fluctuating river composition and discharge, tidal mixing, zero-order reduction of arsenate and first-order oxidation of arsenite to model the distribution and redox speciation of arsenic in the estuary under non-steady-state conditions is explored.
Introduction The watershed of the Schelde River comprises one of the most densely populated and industrialized regions in central Europe (Wollast, 1982). Large amounts of domestic and industrial wastes are discharged into the Schelde and its tributaries, especially the Zenne River, which flows through the city of Brussels and the adjacent industrial area (Figure I). As a result, the Schelde River and Estuary receive large amounts of organic matter and nutrients as well as inorganic pollutants, e.g. cadmium, zinc, mercury and arsenic (Wollast, 1982; Duinker et al., 1982; Van der Sloot et al., 1985). The intense microbial activity resulting from the input of nutrients and organic matter in domestic and industrial waste coupled with the relatively long residence time of river-derived materials in the “Present F.R.G. 0272-7714/89/050421+
address:
13 $03.00/O
Max
Planck
Institute
for Chemistry,
P.O. Box 3060, D-6500 @ 1989 Academic
Mainz,
Press Limited
422
M.
0. Andreae
&
T. W. Andreae
Figure 1. Arsenic concentrations and speciation in the rivers of the The height of the vertical bars adjacent to the sampling sites inorganic arsenic concentration, the shaded section the fraction of section the amount ofAs(II1). The river kilometers indicated in this locate the sampling positions for the estuarine samples (Table 1).
Schelde watershed. represents the total As(V), and the open figure can be used to
estuary makes the Schelde River and Estuary an ideal system in which to study the interaction of biogeochemical processes with pollutant elements. The speciation of arsenic in natural waters is influenced by a number of biogeochemical processes. In oxygenated waters, the oxidation state + V (arsenate) is thermodynamically stable, but both bacteria and planktonic algae can reduce As(V) to the oxidation state + III (arsenite) even in the presence of dissolved oxygen (Johnson, 1972; Andreae & Klumpp, 1979). This results in the presence of As(II1) at relatively low levels (usually < 10%) in most natural waters. In addition, algae take up As(V) from their environment and excrete a variable fraction of this arsenic in the form of the methylated species, methanearsonic acid and dimethylarsinic acid (Andreae, 1986). Intensive microbial activity in the upper Schelde Estuary results in the almost complete consumption of dissolved oxygen in this region. Previous studies of arsenic speciation in anoxic systems had suggested that not all arsenic was reduced to arsenite even under highly reducing conditions (Andreae, 1979; Andreae & Froelich, 1984). The highest fraction of As(II1) reported was found in the anoxic waters of Saanich Inlet, British Columbia, where 88% of inorganic arsenic was present as arsenite (Peterson & Carpenter,
Arsenic
in Schelde
Estuary
and Watershed
423
1983). However, these studies had been carried out in marine systems where the presence of H,S in the anoxic region made it likely that sulfide complexes with arsenic were also present which could not be reliably detected by the analytical techniques used. The anoxic conditions in the Schelde provided an opportunity to study the redox speciation of arsenic in the absence of H,S. Previous measurements of arsenic concentrations in the Schelde Estuary (which did not include speciation) had shown the existence of strong nonlinearities in the arsenic; salinity relationship which were tentatively related to the removal of As from solution by coprecipitation with ferric hydroxides (Van der Sloot et al., 1985). In other estuaries, however, such removal had not been observed (Waslenchuk & Windom, 1978; Andreae t’r al., 1983; Froelich et al., 1985). In this paper we present new data on the concentration of arsenic species in the Schelde River and its tributaries and on the distribution of arsenic relative to salinity in the Schelde Estuary. We then use these data to explore the feasibility of using the ‘ estuarine analog ’ model (Knox et al., 1984; A. Dickson, 1985, personal communication) to predict and interpret the species distribution in the estuary. While it is obvious that a final analysis of the biogeochemical processes in the Schelde Estuary must await the collection of further data, this paper provides a first case study for the application of a non-steady state, numerical model incorporating non-analytically fluctuating river composition and discharge to describe the time-variant concentration distribution and speciation of a trace element in an estuary. Methods Sampling
The river water samples were collected from the shore by submerging acid-washed polyethylene bottles just below the surface. The bottles were rinsed three times before the sample was collected. The estuarine transect sampleswere taken from the R/V Belgica with standard oceanographic samplersmade from polycarbonate and polyvinyl chloride (Hydrobios, Kiel, West Germany) and transferred into acid-washed polyethylene bottles. The samples were filtered immediately upon return to the laboratory with a nitrogen-pressurized membrane filtering apparatus (0.45 urn pore size, Sartorius GmbH, Goettingen, West Germany), and stored in a refrigerator until analysis. These sampling and handling techniques have been shown to result in no detectable contamination or speciation change for arsenic (Andreae, 1983). Analysis
Arsenic specieswere determined by a combination of the hydride generation technique described by Andreae (1983) with the graphite-furnace atomic absorption detector used for germanium determination by Andreae and Froelich (1981). For the determination of As(II1) the sample was buffered to pH 6 by the addition of tris(hydroxymethylj aminomethane hydrochloride (Tris-HCl) buffer, then sodium borohydride wasadded to the sample in a closed system and the arsine produced from the reduction of As( II I) was swept from the solution by a helium stream. The arsine was collected in a liquid-nitrogen cooled trap filled with a chromatographic packing. After the reaction was complete, the trap was heated and the arsine peak was carried into the graphite furnace (Perkin Elmer HGA 500) through a glasstube attached to the internal argon purge system. The furnace wasbrought to a temperature of 2100 “C about 10 s before the arsine peak eluted and held at this temperature for 30 s. Helium at aflow rate of 100ml min-’ wasused asa carrier gas,
424
M. 0. Andreae Q T. W. Andreae
and argon at the sameflow rate was added to the gasstream before it entered the graphite furnace. The graphite furnace was installed in a Perkin Elmer 5000 atomic absorption spectrophotometer equipped with an electrodeless-discharge As lamp. The analytical peakswere registered on a chart recorder. Total inorganic arsenic was determined using the sameprocedure with the exception that HCl wasadded instead of the Tris-HCI buffer. Standards were prepared by dilution of 0.1 M stock solutions of As(II1) and As(V) to concentrations near those present in the samples.These standards were prepared fresh daily. The precision of the method is c. lo%, the detection limit about 0.01 nM (nmol 1-i). Methylated arsenic compounds (Andreae, 1979) were not observed in this study. Since the analytical system had not been optimized for their detection, we can only state that they did not represent a significant fraction (< 5%) of the dissolved arsenic. In view of the low water temperatures in the estuary, only very low concentrations of methylated specieswere to be expected, basedon the work of Howard et al. (1984). Temperature and dissolved oxygen were determined with an immersed probe and salinity by chloride titration. Results and discussion Arsenic
in the Schelde watershed
The collection sitesfor the estuarine and river samplesare shown in Figure 1, the analytical results in Table 1. The arsenic concentrations in the rivers of the Schelde watershed (with the exception of the Zenne River) fall in the range of c. 10to 50 nM, which is typical of European rivers (Andreae et al., 1983; Seyler, 1985). They do not appear to be significantly elevated relative to the world average (c. 20 nM: Andreae et al., 1983; Martin dz Meybeck, 1979; Andreae, unpublished data), which is somewhat surprising in view of the high density of population and industry in this watershed. The Zenne River, however, contained almost 400 nM As, an order of magnitude higher than the other tributaries. At the time of sampling, the Zenne wasvisibly polluted with large amounts of sewage;it must be assumedthat either municipal or industrial dischargesinto this river are responsible for the elevated arsenic levels. In terms of its water discharge, the Zenne is a minor contributor to the Schelde watershed-during mid-November 1984, it contributed only about 9qb of the total discharge of the Schelde (Antwerpse Zeediensten, 1985). At the sametime, however, it was responsible for over 60% of the arsenic input. The Zenne also carries extremely high levels of organic matter: BOD values in excessof 100 mg 1-l and COD values near 400 mg 1-I have been reported (Instituut voor Hygiene en Epidemiologie, 1984). The speciation of arsenic showsa clear relationship to water quality, asexpressedby the dissolved oxygen content. The lowest ratios of As(II1) to total As were found in the Zenne Canal and the Nete Canal (Table 1). These canals serve as shipping routes and receive little pollution, and consequently their waters are near oxygen saturation. In contrast, the low oxygen levels in the rivers of the Schelde watershed tend to coincide with high ratios of As(II1) to total As. The total arsenic levels in the canals,which receive their water from the Maas and Sambre river system, are comparable to the values in the Schelde tributaries. The input of waste into the Schelde watershed (with the exception of the Zenne), therefore, doesnot appear to result in a significant increasein total arsenic, but rather in a speciation shift towards As(II1). The presence of significant amounts of arsenite even where oxygen has not been completely depleted, shows that the As(III)/As(V) ratio does not reflect thermodynamic
Arsenic
in Schelde
Estuary
and Watershed
425
TABLE 1. Arsenic species concentrations and supporting parameters in the Schelde watershed and Estuary
Location Zenne Canal, Hombeek Nete Canal, Lier Leie, Afsnee Schelde, Eke Schelde, Appels Ferry Dender, Aalst Grote Nere, Lier Nete, Duffel Dijle, Muizen Zenne, Hombeek Rupel, Niel Ferry Schelde, Kruibeke Schelde, Kruibeke Estuary, km 95 Estuary, km 85 Estuary, km 80 Estuary, km 78 Estuary, km 73 Estuary, km 68 Estuary, km 64 Estuary, km 53 Estuary, km 62 Estuary, km 48 Estuary, km 43 Estuary, km 40 Estuary, km 36 Estuary, km 28 Estuary, km 19 Estuary. km 10 Estuary, km 3 Estuary, km 2 Estuary, km 2
Date
Salinity
12.12.84 18.11.84 28.11.84 18.11.84 18.11.84 18.11.84 18.11.84 18.11.84 18.11.84 12.12.84 18.11.84 18.11.84
0.08
12.12.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84 27.11.84
Temp (YX)
pH
02 As(II1) (mgl ‘J (nM)
5.6 9.0
8.3 10.9
14.4 11.2
1.4 0.4
8.0 7.8
6.0 5.6
11
0.18 0.40
9.9 9.9
0.4 0.6
110
0.48 0.30
10.0
0.8 0.4
0.17
0.10 0.10 0.10 0.21 0.66 2.2 2.6 2.9 3.5 8.6 13.3 15-3 21-8 24.3
27.1 29.2 30.8 33.3 33.7
7.3
5 15 5 6
370 77
46 8
33 39
80 40
24 28 37 43 46 45 49 50 56 64 52
28 34 41 46 48 47 50 52 57 65 53 5' 47 47 36 31 33 21 39
9.7
7.32
10.3
7.40
3.0
10.4 10.3
7.45
4.9
9.5
7.52
11.4
9.5
7.80
13.0
4-5 5-9 4-7 3-o 23 l-4 l-4 l-4 l-3 l-5 1.4 1-3 l-3
12.1
1-l 11 l-2
10 9.6 9.5
12.2 11-2
16 13
36
6 6
7.41 7.40
7.78
16 12
As(tota1) As(II1) (nM1
39 29 34 9 1.4 7 6 260 41
8.9 8.9
9.7
0.7 0.7
O-04 1 1
As(V) (nM)
O-8
1-o
51 46 46 34 30 32 24 28
45 44 39 15
13 13 13
equilibrium (Andreae, 1979). This suggests that the reduction of As(V) to As(III%! does not take place abiotically as a result of low redox-levels in the water, but rather is biologically mediated. Since the ability of both bacteria and planktonic algae to reduce arsenate has been shown in several studies (Johnson, 1972; Andreae & Klumpp, 1979; Sanders 8r Windom, 1980; Sanders, 1983), it is likely that the reduction of arsenic is a result of biological processes. Furthermore, in the presence of even small amounts of oxygen, arsenite oxidation can take place simultaneously with microbially mediated arsenate reduction. The oxidation process is also microbially catalyzed and its rate, therefore, also depends on the type and number of bacteria present (Myers et al., 1973; Osborne & Ehrlich, 1976; Phillips &Taylor, 1976; Sanders, 1979; Scudlark & Johnson, 1982). Myers et ~1. (1973) showed that sewage microorganisms and sewage treatment effluents can reduce arsenate to arsenite, or oxidize arsenite to arsenate, depending on the availability of oxygen. In particular, the fact that the two oxidation states of arsenic are always simultaneously present at significant levels suggest that the observed As(III)/As(V‘, ratios are the result of the balance of multiple microbial and abiotic reduction and oxidation
426
M. 0. Andreae & T. W. Andreae
IO 0
IO
20
30
Sallnlty
Figure 2. Distribution of dissolved total inorganic arsenic in the Schelde Estuary as a function of salinity. The data points represent the measurements made on the samples collected in the estuary during a cruise of the R/V Belgica on 27 November 1984. The lines show the distributions predicted by the estuarine model for the early morning high tides of 27 November ( - - -- ) and 28 November ( ), which bracket the sampling period. (The arsenic/salinity distribution is independent of the tidal cycle).
processes.Consistent with this argument, we find only a general tendency for this ratio to be lower in oxygenated canals and higher in the oxygen-depleted rivers, but not a clear correlation between O,-saturation and the oxidation state of arsenic. The absence of complete reduction of arsenic even in sulphide-free environments suggeststhat the failure to find complete reduction in marine systems (Andreae, 1979; Andreae & Froelich, 1984; Peterson & Carpenter, 1983) is not due to analytical problems in the presence of sulphide, but rather to the complex redox kinetics of arsenic in natural aquatic systems. The distribution of arsenicin the ScheldeEstuary The distribution of arsenic in the Schelde Estuary on 27 November 1984 is shown as a function of salinity in Figure 2. A pronounced maximum is evident in the upper estuary at a salinity of c. 7. Such maxima have been interpreted asevidence of input of arsenic into estuarine waters by industrial dischargesor by diffusion from sediment porewaters (Van der Sloot et al., 1985;Andreae et al., 1983; Knox et al., 1984; Seyler, 1985; Sanders, 1985). However, the maximum occurs near kilometer 50, in a region where no known industrial or municipal discharges of arsenic are present. The suspendedparticulate and bottom sediment data for arsenic presented by Van der Sloot et al. (1985) suggestthat the sediments in the Schelde Estuary do not have the extremely high arsenic levels which can account for the arsenic input by desorption from sediments into porewater and diffusion into the overlying waters, as observed in the Tamar Estuary (Knox et al., 1984). Desorption of arsenic from suspendedriverine particulates entering the salinity gradient is not likely to occur; on the contrary, most authors have found evidence for the coprecipitation of arsenic with ferric hydroxides in the low-salinity region of estuaries (Howard et al., 1984; Langston, 1983; Van der Sloot et al., 1985; Carpenter et al., 1978). We shall therefore examine whether the arsenic distribution observed by usreally requires an input in the region of the maximum or whether it can be explained on the basis of fluctuations in the river discharge and composition.
Arsenic
in Schelde
Estuary
and Watershed
-127
‘0 Distance
(km!
Figure 3. Comparison between the observed distribution of salinity (0) in the estuary on 27 November 1984 and that predicted by the estuarine model. The dashed line ( - - - - I represents the model prediction for the early morning high tide (04.25 h at Vlissingen), the solid line ( __ ) the evening low tide (23.04 h), between which the samples were taken.
In order to simulate the behaviour of arsenic in the Schelde Estuary, we adapted a model which had been developed to simulate the effects of mixing and chemical processes in the Tamar Estuary (Knox et al., 1984; A. Dickson, 1985, personal communication). In this model, the estuary is simulated as a sequence of perfectly mixed tank reactors through which water is transported as a result of river discharge and tidal flow. The magnitude of the river discharge and its composition can be changed as a function of time, using either analytical functions or arrays of values. In addition, zero and first order reactions have been included which proceed at specified rates within each box to represent the effect of chemical processes within the estuary. The geometry of the Schelde Estuary was based on data from a 1971 survey of the estuary (Laforce et al., 1974). In order to obtain an accurate representation of the mixing characteristics of the estuary, the salinity distribution as a function of distance along its axis was computed by the model for different values of river discharge and compared with survey data (Peters & Sterling, 1976). The length of the boxes into which the estuary is divided was then varied until a best fit was obtained (0.66’7 km, dividing the 100 km length of the estuary into 150 segments). The measured discharge as a function of time during 1984 was then introduced and the model run for the period between 1 January through 27 November of that year. The predicted salinity distribution for the high and low tides closest to the sampling period is shown in Figure 3 together with the observed salinity data, showing that the model produced a satisfactory fit. It should be noted that due to the sharp discharge maximum on 26 November, the salinity distribution in the estuary is highly time-dependent for several days following this date. Consequently, the model predicts salinity distributions significantly different from the observed values for the preceding and following days. The arsenic concentrations in the river end-member had been measured on several days in November and December 1984. We therefore had to interpolate the time-dependent river end-member composition required as input to the estuarine model by using a dilution equation. In view of the fact that all tributaries other than the Zenne had low arsenic concentrations which fell into a narrow range, we assumed that the river end-member could be represented by a simple dilution of a constant, namely, anthropogenic input (I)
428
M. 0. Andreae & T. W. Andreae
200 (b)
0
100
200
300
Day
Figure 4. (a) Discharge of the Schelde River below the confluence with the Rupel during 1984, and (b) the arsenic concentration in the riverine end-member predicted by a dilution model (see text).
via the Zenne
with
river
water
of a constant
background arsenic concentration and
discharge (Q):
The parameters in this equation can be derived by fitting the As concentrations measured in the end-member on three different days to the dischargesfor the samedays. From the data in Table 1 we obtained a value of 19 nM for [Aslbpclrground and 5.4 mmol set-’ for Z(r2=0+37). This corresponds to an anthropogenic input of about 13 tons of As per year into the Zenne. Alternatively, we can average the As concentrations in the tributaries other than the Zenne (24 nM) and obtain the Zenne input from its measuredconcentration and discharge (4.2 mm01s-l). The fact that these two approachesgive comparable results supports the validity of our dilution equation. For the prediction of the end-member time series, we prefer to use the parameters from the least-squaresfit, however, since they represent a longer time period than the single measurement in the Zenne. (Note that the end-member concentrations predicted by this approach are derived completely independently from the estuarine distribution of arsenic). The resulting discharge and concentration curves are shown in Figure 4 (discharge data are from Antwerpse Zeediensten (1985). Ten-day averages of river discharge were used with the exception of the ten days preceding the sampling campaign, where we used daily values. It is evident from Figure 4 that the cruise took place during a pronounced discharge transient, so that highly nonsteady conditions are to be expected.
Arsenic
::I 0
in Schelde
Estuary
and Watershed
, , , , / / , , 20
40
60
80
IO0
Figure 5. Comparison between the observed distribution of dissolved total inorganic arsenic (0) in the estuary on 27 November 1984 and that predicted by the estuarine model. The dashed line ( - -- - ) represents the model prediction for the early morning high tide (04.25 h at Vlissingen), the solid line ( ) the evening low tide (23.04 h), between which the samples were taken. Error bars are not shown; they would be the same as in Figure 2.
Y
I
0
20
40 Dhmce
60 (km)
80
Y
10 100
Figure 6. Distribution of dissolved As(II1) in the Schelde Estuary as observed on 25 November 1984 (0; representative error bar shown for one sample only), and as predicted by the estuarine model for different assumptions for the rate of arsenite oxidation and arsenate reduction. The model calculations are shown for mean tidal conditions on 27 November. The solid line ( ) represents the predicted distribution in the absence of redox transformations between As(II1) and As(V). The other lines use various arsenite oxidation rates (all in h I): -0.004 (abiotic oxidation only), - 0.038 (laboratory experiments by Scudlark &Johnson, 1982), -0-019 (half that rate to account for lower temperatures in the Schelde), and -0.09 (rate suggested as ‘ typical ’ for estuaries by Scudlark & Johnson, 1982). The distribution of dissolved oxygen in the estuary is represented by squares (0).
The arsenic distribution in the estuary can now parameters as for salinity, but introducing a constant of 30 nM As and the time-dependent river end-member The result is shown in Figure 5 for the high and low the model distribution shows good agreement with
be modeled using the same mixing seawater end-member concentration concentration shown in Figure 4. tides on 27 November 1984. Again, the observed one. When the model
430
M. 0. Andreae & T. W. Andreae
arsenicconcentrations are plotted against salinity (Figure 2), a maximum is evident below a salinity of 10, somewhat lower than actually observed, but still in reasonableagreement. These results show that estuarine inputs are not required to explain the arsenic distribution which we observed in the Schelde Estuary. Similar behaviour is to be expected in any system where the concentration of a speciesin the riverine end-member is strongly dependent on the discharge. This applies in particular to situations where a pollutant is added to a river through sometype of discharge, since the pollutant flux is either constant or at least doesnot vary proportionally to the river discharge. In such cases,the shapeof the speciesvs. salinity plot cannot be interpreted without knowledge of the river endmember composition as a function of time. This has been recognized by the authors who proposed the use of species/salinity relationships to determine non-conservative behaviour in estuaries, e.g. Officer (1979), but is often disregarded in the literature, e.g. Sanders (1985). Officer (1979), however, was only able to state that his analytical, tidal-averaged, steady-state model was not suitable for time-variant conditions. Later variants of the Officer model introduced river end-member variations, e.g. Kaul and Froelich (1984), but were limited to simple periodical functions. In contrast to previous studies, the approach proposed here eliminates both the requirements for steady state and for tidal averaging. It makes it possible to model the estuarine distribution of a specieswhen both the river discharge and the river composition are time-variant, even under conditions where that time-dependence is non-analytical, as is the casehere. It also lends itself to conducting sensitivity studies where the variation of the estuarine distribution of a speciesresulting from perturbation of the input parameters is evaluated. Arsenic redox speciation When the same estuarine model is applied to As(III), it is obvious that a conservative mixing model (Figure 6, solid line) cannot explain the observed distribution. (Based on the data in Table 1, a seawater As(II1) concentration of 1.0 nM and a river As(II1) concentration of 25% of total As were assumedasmodel parameters). Our measurements show a sharp, non-linear decreaseof As(II1) in the region below km 80, where dissolved oxygen begins to increase in the estuarine waters. The large difference between the measuredAs(II1) values and the predictions from a conservative model can be explained by the oxidation of As(II1) to As(V) in the estuary. In order to represent this phenomenon in the model, we have included various first-order arsenite oxidation rate constants in the model. This made a satisfactory fit in the region above 60 km possible, but also brought As(II1) below 50 km to almost zero. In order to obtain the observed near-constant As(II1) concentration in the lower estuary, we added a pseudo-zero-order As(V) reduction rate constant to the model equations. Choosing a zero-order reduction rate was basedon the assumption that reduction was not As(V) controlled, but limited by the presence of biological catalysts. This resulted in the curves shown in Figure 6 for first-order oxidation rate constants of - 0.004 to - 0*090h-l; in each case,the zero-order reduction rate constants were assignedthe samenumerical value and opposing sign. These reduction rate constants are comparable to those determined experimentally by Johnson and Burke (1978) and Sanders and Windom (1980). The best agreement between model and observation is obtained for oxidation rate constants between - 0.019 and - 0.090 h-l. In experiments with Narragansett Bay water to which 1.2 PM As(II1) had been added, Scudlark and Johnson (1982) measured a microbially catalyzed oxidation rate constant of c. -0.038 h-‘. They found that this rate
Arsenic
in Schelde
Estuary
and Watershed
431
constant increased with decreasing initial As(II1) concentration, and suggested a value of -0.09 as ‘ typical ’ for productive surface waters. In view of the fact that the water temperatures in the Schelde Estuary were considerably lower (c. 10 “C vs. 25 “C) than those used by Scudlark and Johnson (1982), we fmd that the range of rate constants required to obtain a reasonable fit in our model agrees well with their observations. It is also clear that a rate constant of -0.004 h-*, which represents abiotic oxidation by dissolved oxygen only (based on the data of Johnson & Pilson, 1975), is not sufficient to explain the observed distribution of As(II1) in the Schelde Estuary. Heterogeneous abiotic reactions, on the other hand, may also contribute to the oxidation of arsenite in the Schelde Estuary. Oscarson et al. (1981) and Peterson and Carpenter (1983) have shown that the presence of Mn(IV) oxides significantly enhances arsenite oxidation. Rate constants of - 0.025 to - 0.034 h-’ were observed in the presence of d-MnO, at 25 “C and values from - 0.041 to - 0.069 h-’ in the presence of lake sediment at 5 ~‘C (Oscarson et al., 1981). Peterson and Carpenter (1983) found a rate constant of -0.014 h-’ at 6 ‘C in water from a layer rich in particulate manganese collected above the oxic/anoxic interface in Saanich Inlet. This rate decreased by about one order of magnitude when the sample was poisoned with an antibiotic, but still remained significantly higher than values measured in manganese-poor, but unpoisoned surface water. Manganese oxide precipitation is taking place in the Schelde in the same region as arsenite oxidation (Duinker et al., 1979), both processes being driven by the redox gradient which is present in the estuary. This results in particulate Mn concentrations in the Schelde which are similar to or higher than the concentrations in Peterson and Carpenter’s (1979 j experiments. Under these conditions, it is likely that both microbial and abiotic processes are contributing to arsenite oxidation. It is clear from the preceding discussion, that the limited data set available does not make it possible to rigorously verify the applicability of the model or the uniqueness of the solutions obtained. Further profiles measured under different flow conditions will be required before it can be considered that the major features of the biogeochemical arsenic cycle in the Schelde estuary have been conclusively established. We feel, however, that the proposed niodel can serve as a useful tool in the interpretation of the distribution and speciation behaviour of an element in the estuarine environment, particularly under nonsteady-state conditions, when both the composition of the river end-member and the river discharge are allowed to fluctuate in a non-analytical fashion. We are able to show that the estuarine distribution of As(II1) can be predicted by the model, when, and only when, the rate constants derived from independent laboratory measurements are introduced. Therefore, this approach makes it possible to verify redox rate constants in temporally and spatially variant estuarine systems. We hope that this study may stimulate future, more detailed research combining field studies of estuarine trace element speciation and nonsteady-state numerical modelling. Acknowledgements
The authors are indebted to A. Dickson for the original version of the estuarine analog program and helpful advice on how to use it. We thank F. Adams for making possible a sabbatical stay at the University of Antwerp (Universitaire Instelling Antwerpen). We are grateful to F. Dehairs for collecting the estuarine samplesand to W. Van Mol and R. Van Cleuvenbergen for help in the laboratory. We acknowledge E. Smets, who provided the data on the estuary geometry and H. Belmans, who supplied information on the discharge
432
M.
0. Andreae
& T. W. Andreae
of the Schelde. W. Maenhaut was helpful in tracking down information which would otherwise have been difficult to obtain. We thank M. Dancy for help with the preparation of the manuscript and figures. This research was supported by the Belgian Ministry of Science Policy (grant 84-89/69) and by the National Fund for Scientific Research Brussels.
References Andreae, M. 0. 1979 Arsenic speciation in seawater and interstitial waters: the influence of biologicalchemical interactions on the chemistry of a trace element. Limnology and Oceanography 24,440-452. Andreae, M. 0. 1983 Arsenic, In: Methods of Seawater Analysis, 2nd edition (Grasshoff, K., Erhardt, M., Kremling, K., eds.). Weinheim: Verlag Chemie, 1983, pp. 218-225. Andreae, M. 0. 1986 Organoarsenic compounds in the environment, In: Organometallic compounds in the environment (Craig, P. J., ed.). Harlow: Longman, pp. 198-228. Andreae, M. O., Byrd, J. T. & Froelich, P. N. 1983 Arsenic, antimony, germanium and tin in the Tejo Estuary, Portugal: modeling a polluted estuary. EnoironmentaZ Science and Technology 17,731-737. Andreae, M. 0. & Froelich, P. N. 1984 Arsenic, antimony and germanium biogeochemistry in the Baltic Sea. Tellus 36B, 101-l 17. Andreae, M. 0. & Froelich, P. N. 1981 Determination of germanium in natural waters by graphite furnace atomic absorption spectrometry with hydride generation. Analytical Chemistry 53,287-291. Andreae, M. 0. & Klumpp, D. W. 1979 Biosynthesis and release of organoarsenic compounds by marine algae. Environmental Science and Technology 13,738741. Antwerpse Zeediensten 1985 De Afvoer van de Schelde in 1984, Antwerp. Carpenter, C., Peterson, M. L. & Jahnke, R. A. 1978 Sources, sinks, and cycling of arsenic in the Puget Sound Region, In: Estuarine Interactions, (Wiley, M. L. ed.). New York: Academic Press, pp. 460-479. Duinker, J. C., Wollast, R. & Billen, G. 1979 Behaviour of manganese in the Rhine and Scheldt Estuaries, II. Geochemical Cycling. Estuarine and Coastal Marine Science 9,727-738. Duinker, J. C., Nolting, R. F. & Michel, D. 1982 Effects of salinity, pH and redox conditions on the behaviour of Cd, Zn, Ni and Mn in the Scheldt Estuary. ThaZussiaJugosZawica l&191-202. Froelich, P. N., Kaul, L. W., Byrd, J. T., Andreae, M. 0. & Roe, K. K. 1985 Arsenic, barium, germanium, tin, dimethylsulfide and nutrient biogeochemistry in Charlotte Harbor, Florida, a phosphorus-enriched estuary. Estuarine, Coastal and Shelf Science 20,239-264. Howard, A. G., Arbab-Zavar, M. H. St Apte, S. C. 1984 The behaviour of dissolved arsenic in the estuary of the River Beaulieu. Estuarine, Coastal and Shelf Science 19,493-504. Instituut voor Hygiene en Epidemiologie 1984 Kwaliteitsverzicht van een Aantal Belgische Oppervlakte Wateren in 1982 en 1983, Brussels. Johnson, D. L. 1972 Bacterial reduction of arsenate in {eawater. Nature 240,44-45. Johnson, D. L. & Burke, R. M. 1978 Biological mediation of chemical speciation: II Arsenate reduction during marine phytoplankton blooms. Chemosphere 8,645-648. Johnson, D. L. & Pilson, M. E. Q. 1975 The oxidation of arsenite in seawater. Environmental Letters 8, 157-171. Kaul, L. W. & Froelich, P. N. 1984 Modeling estuarine nutrient geochemistry in a simple system. Geochimica et Cosmochimica Actu 48,1417-1433. Knox, S., Langston, W. J., Whitfield, M., Turner, D. R. & Liddicoat, M. I. 1984 Statistical analysis of estuarine profiles: II. Application to arsenic in the Tamar Estuary (S.W. England). Estuarine, Coastal and Shelf Science l&623-638. Laforce, E., Roovers, P. & Sterling, A. 1974 Thermische Waterveroutreiniging in de Westerschelde. Waterbouwkundig Laboratorium, Borgerhout, Belgium. Langston, W. J. 1983 The behaviour of arsenic in selected United Kingdom estuaries. CanadiunJournal of Fisheries and Aquatic Science 40,143-150. Martin, J. M. & Meybeck, M. 1979 Elemental mass-balance of material carried by major world rivers. Marine Chemistry 7,173-206. Myers, D. J., Heimbrook, M. E., Osteryoung, J. & Morrison, S. M. 1973 Arsenic oxidation state in the presence of microorganisms: examination by differential pulse polarography. Environmental Letters 5(l), 53-61. OtXicer, C. B. 1979 Discussion of the behaviour of non-conservative dissolved constituents in estuaries. Estuarine and Coastal Marine Science 9,91-94. Osborne, F. H. & Ehrlich, H. L. 1976 Oxidation of arsenite by a soil isolate of Alcaligenes. Journal of Applied Bacteriology 41,295-305. Oscarson, D. W., Huang, P. M., Defosse, C. & Herbillon, A. 1981 Oxidative power of Mn(IV) and Fe(II1) oxides with respect to As(II1) in terrestrial and aquatic environments. Nature 291,50-51.
Arsenic
in Schelde
Estuary
and Watershed
Peters, J. J. & Sterling, A. 1976 Hydrodynamique et transports de sediments de I’estuaire de l’Escaut, In: L’Estuaire de I’Escaut, (Nihoul, J. C. J., Wollast, R. eds.). Projet Mer, Program. Pol. Scientifique, Brussels, Vol. 10. Peterson, M. L. & Carpenter, R. 1983 Biogeochemical processes affecting total arsenic and arsenic species distributions in an intermittently anoxic fjord. Marine Chemistry 12,295-321. Phillips, S. E. & Taylor, M. L. 1976 Oxidation of arsenite to arsenate by Alcalzgenes jaecalis. .4pplied and Envwonmental Microbiology 32,392-399. Sanders, J. G. 1979 Microbial role in the demethylation and oxidation of methylated arsenicals in seawater Chemosphere 3,135-137. Sanders, J. G. 1983 Role of marine phytoplankton in determining the chemical speciation and biogeochem& cycling of arsenic. CanadianJournal of Fisheries and Aquatic Science 40,192-196. Sanders, J. G. 1985 Arsenic geochemistry in Chesapeake Bay: dependence upon anthropogenic inputs and phytoplankton species composition. Marine Chemistry 17, 329-340. Sanders, J. G. & Windom, H. L. 1980 The uptake and reduction of arsenic species by marine algae. S.\ru~zrt~~~ and Coastal Marine Science 10, 555-567. Scudlark, J. R. & Johnson, L. D. 1982 Biological oxidation of arsenite in seawater. Esruari~ze, C:~~~>rtii ~r~i She(f Science 14,693-706. Seyler, I’. 1985 Formes chimiques et comportement de l’arsenic en milieu estuarien. Ph. D. Disserta:ic>n. University of Paris 6. \‘an der Sloot, H. A., Hoede, D., Wijkstra, J., Duinker, J. C. & Nolting, R. F. 1985 Anionic species of V, As, Se, MO, Sb, Te and W in the Scheldt and Rhine estuaries and the Southern Bight (North Sea). ESTIU&~. (Toastal and Shelf Science 21,633-65 1. Waslenchuk, D. G. & Windom, H. L. 1978 Factors controlling the estuarine chemistry of arsemc. E~trc~rrm dnd Coastal Marine Science 7,455-464. Wollast, R. 1982 Behaviour of organic carbon, nitrogen and phosphorus in the Scheldt estuarv. T/z&s.\:~ hrgoslavica 18, 1 l-34.