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Radiometric sand mud characterisation in the Rhine–Meuse Estuary
Geomorphology 43 (2002) 103 – 116 www.elsevier.com/locate/geomorph
Radiometric sand mud characterisation in the Rhine–Meuse Estuary Part B. In situ mapping M. van Wijngaarden a,*, L.B. Venema b, R.J. De Meijer b a
Ministry of Transport, Public Works and Water Management, Institute for Inland Water Management and Waste Water Treatment (RIZA), PO Box 52, 3300 AB Dordrecht, Netherlands b Nuclear Geophysics Division, Kernfysisch Versneller Instituut, University Groningen, Zernikelaan 25, 9747 AA Groningen, Netherlands Received 26 February 2001; received in revised form 14 July 2001; accepted 22 July 2001
Abstract Traditionally, obtaining accurate spatial information about the textural composition of heterogeneous aquatic sediments requires extensive sediment sampling. To avoid a costly and time-consuming operation, a new in situ technique has been investigated. This technique characterises sediment components by the activity concentrations of natural radionuclides. In situ activity concentrations are continuously measured with the Multi-Detector system for Underwater Sediment Activity (MEDUSA), a detector trailed over the bottom by a towing vessel, containing a highly sensitive gamma-ray BGO-detector, a water-depth sensor and a microphone. From the collected g-ray spectra, the activity concentrations of 40K, 238U and 232Th are derived. During two separate field surveys of 5 days each, MEDUSA was applied to the Hollandsch Diep and Haringvliet, two fresh water basins, each approximately 20 km long and 2 – 3 km wide in the Rhine – Meuse Estuary, the Netherlands. In part A of this paper (this issue), it has been illustrated how the radiometric fingerprint of sand and mud in the area was determined. This fingerprint was then used to calculate the sand-mud ratio from the 238U and 232Th activity concentrations of about 25,000 MEDUSA data points. The interpolated sand-mud distribution of the top layer of the aquatic sediment shows a distinct correlation with bathymetry: the deeper channels are mud-rich, whereas the shallow zones are predominantly sandy. In general, the mud content decreases in the seaward direction. This corroborates well with the morphological development of the area in which the deposition of mud is highest near the rivers’ outflow in the east. The absolute total random error in the sand map varies between 6% and 18%, where the largest errors are caused principally by spatial variability of the sediment composition. At present, the radiometric results underestimate the mud content by 10 – 30% due to a higher water content of sediment with high mud contents ( > 60%). The simultaneously recorded friction sound levels provide qualitative information with respect to the sediment composition. Sandy areas show a higher sound level than muddy areas; with the sound and sand map showing a striking similarity. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Sediment composition; Sand-mud ratio; Radiometric sedimentology; In situ mapping
*
Corresponding author. Fax: +31-78-631-5003. E-mail address: [email protected] (M. van Wijngaarden).
0169-555X/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 9 - 5 5 5 X ( 0 1 ) 0 0 1 2 5 - 8
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1. Introduction In aquatic environments, sand and mud can be seen as indicators of the hydromorphological dynamics: in general, mud will be deposited in relatively calm waters, whereas sandy sediments are found in more turbid waters. The sediment bed composition, defined in terms of the sand ( > 63 and < 2000 mm) and mud ( < 63 mm) content, consequently offers information regarding the transport processes and morphological development of the water system. Moreover, sediment transport or morphological models require accurate input regarding the sand-mud composition of the sediment bed, because these parameters are known to strongly influence bed strength and erodibility (Van Rijn, 1993; Houwing, 1999). In addition, muddy sediments often form a potential threat to the ecological functioning of a water system, because pollutants preferably adhere to the mud fraction; especially in Dutch lowland river systems, this regularly turns out to be the case (Den Besten et al., 1995). For a proper ecological risk assessment, detailed information with respect to the spatial distribution of mud deposits is essential. Given the heterogeneity of natural aquatic sediments, numerous samples are needed to provide a reliable and detailed image of the sediment composition. Because sample collection and analysis require a costly and time-consuming operation, in practice, only a limited number of samples are collected and analysed. In situ methods which enable obtaining spatial information of the sediment bed composition, for instance through acoustic reflectance, often require an intensive and difficult calibration, counterbalancing their practical advantage. This state of affairs leaves a distinct need for an accurate measuring methodology for high-density mapping of the sediment composition. In radiometric sedimentology, various sediment components are characterised using the concentration of natural gamma-ray emitting radionuclides (De Meijer et al., 1996; Venema and De Meijer, 2001). It has been demonstrated that a strong relationship exists between the grain size distribution and the geochemical and radiometric composition of sediments. The report in this study is divided into two parts: Part A (this issue) deals
with the derivation of the radiometric fingerprint based on a number of samples taken from the area during a number of surveys. It is shown that in the Hollandsch Diep and Haringvliet (the Netherlands), sand and mud are radiometrically distinguishable. The fingerprint derived for mud ( < 63 mm)/sand ( > 63 mm) is characterised by 46.2 F 1.9 Bq/kg/ 9.3 F 0.9 Bq/kg 238 U and 45.6 F 1.9 Bq/kg/ 9.7 F 0.9 Bq/kg 232Th, respectively. A high correlation coefficient (R2 = 0.96) is obtained between the radiometrically derived mud percentages and the ones obtained in the laboratory by laser diffraction. This paper (part B) reports on the mapping of the underwater bottom utilising the Multi-Detector system for Underwater Sediment Activity (MEDUSA). With this technique, g-rays emitted by the sediment are analysed non-invasively and in situ by trailing the MEDUSA g-ray detector over the sediment bed. Such in situ underwater measurements of natural radioactivity require a detector system in a watertight housing. To avoid uncontrollable effects of adsorption of the g-rays in water layers with varying thickness, the detector system has to maintain contact with the bottom. Jones (2001) gives an overview of various detector systems used for, mainly, high activity levels. To obtain radionuclide specific information with a high spatial resolution, the detector system and the analysis of the data have to be highly efficient. Compared to previous systems, the radiometric g-ray sensor MEDUSA is an order of magnitude more efficient (De Meijer et al., 1996; De Meijer, 1998; Hendriks et al., 2001). The aim of the study presented here is to derive radiometrically a sand-mud map with a high resolution for the Hollandsch Diep and Haringvliet, two semi-stagnant fresh water basins in the southwestern part of the Rhine –Meuse Estuary (the Netherlands). Since the closure of this former estuary, the area is rapidly filling up with riverine sediments. This sedimentation has started near the river’s outflow in the east and is gradually shifting to the west. Starting off from the old sandy estuarine morphology, the area has developed a diversity in the sand-mud composition of the sediment bed, which makes it a suitable test case for sand-mud mapping.
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2. Morphology of the Hollandsch Diep and Haringvliet The underwater morphology of the area (see Fig. 1) was obtained by a bathymetrical survey with an echosounder at an interline distance of 50 m. The bathymetry shows the deeper former tidal channels surrounded by relatively small shallow shores. Several sand plates still exist in the area: most remarkable is the large plate in the western Haringvliet (location A in Fig. 1), which is a typical relict of the old estuarine morphology. Like this area, most of the shoreline is nowadays protected against severe wave erosion, saving land from destruction and enabling small rivers to re-develop their deltas (location B in Fig. 1). In the eastern part of the Hollandsch Diep, a man-made north – south channel is present (location C in Fig. 1), which is actively kept at depth for shipping purposes. Throughout the area, deep artificial pits occur which are either used for sand mining or function as storage capacity for (muddy) dredged material: the largest dump site ‘Cromstrijen’ is indicated as location F in Fig. 1. Due to the construction of the Haringvliet Dam, flow velocities in the inland waters decreased dramatically, and consequently, sedimentation was initiated in the eastern part of the Hollandsch Diep basin. Approximately 1.2 Mton is annually deposited in the Hollandsch Diep basin (Van Dreumel, 1995). The sedimentation area has a length of approximately 30 km and a width of 1 – 4 km. Actual sediment deposition rates in the Hollandsch Diep range from several cm/year for both ends of the basin to 20 cm/ year in the central parts (Van Eck et al., 1997). In the Haringvliet, sedimentation rates amount to on average 1 cm/year (Van Wijngaarden and Ludikhuize, 1997). The former estuarine morphology consisted mainly of sandy sediments with mud being dynamically deposited in the shallow zones. As the closure resulted in a severe reduction of the tidal range, erosion through wind waves became dominant and prevented mud from settling in shallow areas. Nowadays, the muddy sediments supplied by the rivers accumulate mainly in the former tidal channels of the basin. Recent sandy deposits are found locally directly at the beginning of the Hollandsch Diep (Van Dreumel, 1995, 1997; Van Wijngaarden, 1999). The sand found in the Haringvliet is a relict from the former estuarine
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situation; nowadays, no active sand deposition is found in the Haringvliet. Given the sediment accumulation rates in the area, a gradient in sediment composition of the top layer is expected to shift downstream from predominantly muddy to more sandy sediments.
3. The MEDUSA system MEDUSA (De Meijer et al., 1996; De Meijer, 1998), contains a highly sensitive g-ray BGO-detector, a water pressure gauge, a microphone and accessory electronics to power the sensors and to amplify, digitise and transmit the signals to an on-board computer. The system is connected to a towing vessel by an armoured coaxial cable, for both strength and data transmission. The detectors and electronics are housed in a watertight casing, located at the end of a 30-m long, 10-cm diameter protective PVC hose. The detector is susceptible to radiation emitted by the upper 30 cm of the aquatic sediment. The on-board computer combines the data from the MEDUSA with information from a Digital Global Positioning System (DGPS) system. An algorithm derives the activity concentrations of 40K, 238U and 232Th from the MEDUSA gamma-ray spectra. This procedure is based on a laboratory calibration of the gamma-ray detector (De Meijer et al., 1997). The microphone records the friction sound between the sensor and the sediment bed. Originally, the microphone was installed to monitor the contact between the detector and the bed. It was soon found that soft, muddy, sediment beds produce a lower sound level than hard sandy sediment beds (see Koomans, 2001). This microphone has not yet been accurately calibrated for this area and will therefore yield only qualitative information. 3.1. Surveying the Hollandsch Diep and Haringvliet with MEDUSA The MEDUSA system was deployed in the Hollandsch Diep from the 6th to the 10th of July 1998, and in the Haringvliet from the 9th to the 18th of August 1999. A zigzag pattern perpendicular to the general flow direction with an interline distance of 500 m was navigated, combined with several tracks
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Fig. 1. Bathymetry of the study area (m).
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over the full length of the system. This pattern was chosen to obtain the highest density in the direction where the sediment composition is expected to change most rapidly. The towing speed was kept at 2 m/s, data were integrated over 10 s, resulting in a spatial resolution of 20 m along the survey lines. After the survey, the activity concentrations collected by MEDUSA were first handled by translating the X- and Y-coordinates measured aboard the ship to the coordinates of the detector, taking into account the amount of cable released. Moreover, the ‘off-bottoms’, occurring when the detector loses contact with the bed, were removed from the data. From the measured gamma-ray spectra and total count rate, the separate 40K, 232Th and 238U activity concentrations were derived using spectrum deconvolution (De Meijer et al., 1996; Hendriks et al., 2001). In this procedure, standard spectra of the three nuclides measured in the laboratory were used together with a background contribution, measured in either the Haringvliet or Hollandsch Diep at 10 m water depth for over one hour. A typical example of a 10 s MEDUSA spectrum is given in Fig. 2. Typical counting uncertainties are equal to the square root of the measured number of counts, taking into account the
Fig. 2. Standard gamma ray spectra of
40
K,
232
Th,
238
U and
137
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measurement time. For instance in Fig. 2, the uncertainty in the 40K peak at 1460 keV equals (10 0.30)0.5/10 = 0.17 counts per second. This figure illustrates that the standard spectra of 40K, 232Th and 238U each have their specific shape, while the spectrum measured by MEDUSA is the sum of the activities of the individual radionuclides. The 137Cs spectrum was found to be essential for proper deconvolution of the recorded spectra; the 137Cs spectrum presented in Fig. 2 had been recorded provisionally in the laboratory (see Discussion). The response of the sound measurements turned out to reflect the roughness of the upper sediment layer. In between the two surveys, the set-up was modified to optimise the response of the microphone. By comparing two adjacent areas, a factor of 2.1 between the range of the sound levels between the Hollandsch Diep and the Haringvliet was detected and the Haringvliet data were consequently reduced by this factor. A more extensive description of the operation of the microphone is given in Koomans (2001). Sediment samples taken for fingerprinting are described in part A of this paper (this issue). For a direct calibration of the radiometric data from the
Cs and the in situ measured MEDUSA spectrum (counting time of 10 s).
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MEDUSA to the laboratory data with the HPGe, divers sampled sediment from directly under the MEDUSA sensor, while at the same time the MEDUSA output was registered on-board. Perspex cores of 50 cm height were forced into the ground by the divers, providing fairly undisturbed sediment samples. These sediment cores were taken during the survey of the Haringvliet on eight locations. All samples were analysed on their radionuclide content, main-element composition, organic matter, carbonate content and grain size distribution. To assess the effect on the radioactivity measured by MEDUSA, the divers investigated the depth to which the detector sinks into the sediment bed on both muddy and sandy sediment, under static conditions of the sensor. Fig. 4. Schematic outline of the estimation of the total random error SP for the interpolated point ZP.
4. Interpolation of spatial information The MEDUSA-derived sand/mud composition has been interpolated with the package SURFIS. This package uses multiple linear regression to interpolate between data points in neighbouring quadrants (Fioole et al., 1998). The minimum required data point in each quadrant is set beforehand. Fig. 3 illustrates how the value of an unknown data point ZP is estimated by fitting a plane through the neighbouring data points Z1 to Z4 in each quadrant. In this case, one data point
Fig. 3. Schematic outline of the interpolation procedure by SURFIS for an unknown point ZP.
in each quadrant was set to be required, selecting the four points closest to ZP. The contribution of each point is weighed by its distance to ZP. By carrying out this procedure for each unknown location, a map of the data is produced. A first estimation for the random error S between ZP and a point Z is given by: S = 0.5 | ZP Z|, yielding errors S1 to S4 as presented in Fig. 4. By multiple linear regression, a plane is fitted through these errors, yielding the estimated random error SP in ZP. This error SP is an estimation of the interpolation error but additional errors in the measurement and/or positioning system can be expected. Therefore, this first estimate of the random error has to be validated. Therefore, for a subset of points, each measured value was calculated from four surrounding data points, as presented in Fig. 3. This yields an estimated value for each already measured data point with a corresponding estimated random error. These errors are then classified, while for each class, the standard deviation of the differences between estimated and measured values is determined, the latter being a measure for the total random error. The relation between this total random error and estimated error is presented in Fig. 5 and was used to correct the first estimated random error at each location; the offset in this figure represents the MEDUSA-related measurement error. This exercise finally yields a map of the total random error
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Fig. 5. Relation between the estimated random error and the total random error based on the standard deviation of the difference between measured and estimated values for each data point.
or prediction error, which includes uncertainties in the interpolation, in the MEDUSA sand-mud composition and in the positioning system.
5. Sand-mud distribution in the Hollandsch Diep and Haringvliet
For both Hollandsch Diep and Haringvliet, the fingerprints based on 232Th and 238U of mud and sand were derived with a correllation coefficient, R2 = 0.96. The results in Table 1 show that the characteristic activity concentrations differ by a factor of 5. This large factor allows a distinction of small mud admixtures.
5.1. Results fingerprint 6. Morphological analysis of sand/mud maps The results from sample analysis (part A) can be summarised as: 1. 2. 3.
4.
232
Th and 238U can be used to fingerprint the sediments in the area under study 40 K is not suitable for fingerprinting Variety in age and/or provenance results in radiometric differences, which however, do not interfere with the mud and sand fingerprint A diversity of sand-mud mixtures were sampled showing a decreasing contribution of the coarse fraction towards the Haringvliet.
Fig. 6(a) presents the sand map obtained by interpolating the sand contents determined along the 500m interdistant survey lines. The values represent a Table 1 Characteristic radiometric fingerprints of sand and mud. The values are derived from the 238U and 232Th activity concentrations of untreated total samples Sediment type
238
232
Sand ( > 63 mm) Mud ( < 63 mm)
9.3 F 0.9 46.2 F 1.9
9.7 F 0.9 45.6 F 1.9
U-series
Th-series
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Fig. 6. (a) Sand content top layer (%).
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Fig. 6. (b) Absolute total random error in sand content (%).
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weighed average over the top 30 cm. The weighing is based on the adsorption of gamma rays by the sediment and favours the top layer; the values presented are based on the assumption of a homogeneous top layer. Preliminary results of sediment cores taken near the MEDUSA sensor indicate that even for situations with clearly layered sediments, MEDUSA estimates the average mud content over the top layer with acceptable accuracy. Moreover, the sound measurements indicate that the sinking of the detector is limited; this will be discussed later. The sand content was calculated from the radionuclide concentrations using the radiometric fingerprints as listed in Table 1 and using the relation that mass fractions of sand and mud add up to unity. The sediments investigated consist solely of mud and sand, coarser sediment (gravel) is found only further upstream. It should be noted that according to Fig. 6(a), the minimum sand content is 40%, whereas from the sample analysis, values close to 0% were measured. We will return to this issue later. Despite the 10 times larger interline distance of the radiometric survey, a clear correlation is observed between the sand content in Fig. 6(a) and the bathymetry map in Fig. 2. The muddy sediments are concentrated in the deeper sections and the sandy deposits in the shallow nearshore zones and plates. Especially in the Hollandsch Diep, the channels are clearly recognisable by their low sand contents; it is known that mud layers of several metres thickness exist in these areas. In general, the sand content increases from east to west, which is consistent with the morphological development of the area after its disconnection from the North Sea (Van Dreumel, 1995; Van Eck et al., 1997; Van Wijngaarden and Ludikhuize, 1997). Moreover, model calculations illustrate that even during fairly high Rhine discharges (around 5000 m3/s), sand will not be deposited beyond the channel in the eastern end (location C) (Van Wijngaarden, 1999). Fig. 6(a) implies that this sand is deposited predominantly as a point bar along the outer bend of the Nieuwe Merwede (location D in Fig. 6(a)). Sandy sediments are dominant at the borders and in the shallow parts, this is evident for the Haringvliet at both locations A and B: a sandy plate and a sandy river delta. It is known that in the latter area, the zone behind the shore-protection works (too small in size to
show on the maps presented here) is slowly recovering its original muddy character. Just in front of these protection works, wind-induced waves prevent mud from settling or may even erode sediment. The erosive effect of wind waves was estimated by calculating the shear stress at the bottom as a function of local depth, fetch length and wind speed (Philips, 1969). Wave characteristics were simulated according to CERC (1977) and the critical shear stress for erosion was estimated to be around 0.2 N/m2 (Kuijper et al., 1993; Terwindt, 1995). Results show that in shallow areas up to 3 m water depth, in situations when fetchlengths exceed several kilometres, the described effect of wind waves will be significant. Wind wave activity can therefore be considered an important factor in the development of the shallow zones. The high sand concentration found in the Hollandsch Diep at location C may be sand-deposited during (extremely) high discharges of the Rhine and/ or by erosion from the inflowing Dordtsche Kil. Analysis of sediments dredged from this channel show a fairly coarse-grained sediment with d50 values around 300 mm (Ludikhuize, pers. comm.), which is similar to the Rhine sand samples from this study. These dredging data also demonstrate the occurrence of sand in the northern part of this channel (location C) and muddy sediments in the southern part (location E), both indicated in Fig. 6(a). The location ‘Cromstrijen,’ where dredged muddy sediment are dumped (location F in Fig. 2) can also be recognised by its high mud contents in Fig. 6(a) (location F). The area is enclosed by shallow sandy plates, which seem to emphasise the anthropogenic nature of such a pit. Although the main channel is nowadays situated near the southern shore (indicated by location G in Figs. 1 and 6(a)), the area in which Cromstrijen is situated was originally built up from predominantly shallow sandy plates. Fig. 6(b) represents the random total error in absolute values to the sand content map in Fig. 6(a). The uncertainties are typically of the order of 12% (median value) in the actual sand concentrations, coinciding with a median sand concentration of 55%. Fig. 6(b) shows that errors are small (8%) in areas with a uniform sediment composition (for instance location G), whereas larger errors (up to 18%) appear in those areas with large fluctuations in bathymetry and sediment composition (for instance,
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Fig. 7. Recorded sound level (arbitrary units).
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near location F). A minimum error of approximately 4% is always present in the data, which is the average error in the MEDUSA measurement, as discussed previously, being the offset in Fig. 5. Higher errors therefore reflect the error introduced in the interpolation procedure and originate from small scale changes in sediment composition. Accordingly, the latter contribution to the total error can be reduced by surveying the area at a denser line pattern. The interpolated sound intensity measurements are presented in Fig. 7. The sound intensity is generated by the friction between the protective PVC hose and the top of the sediment layer (few millimetres to centimeters). The signal has been illustrated to provide information on the grain size of the very top layer (Koomans, 2001). The sound correlates well with the sand content: deeper, muddy sections have low sound levels, whereas shallow, sandy zones produce a much higher sound level. The sound map implies that deeper areas will be predominantly muddy, whereas the shallow zones are dominated by sandy deposits. Some interesting differences can be noted while correlating sound with either the sand or the bathymetrical map. For example in the Haringvliet between location B and the Haringvliet Dam (Fig. 6(a)), one finds a band, indicated by location H, with slightly reduced sand contents. Fig. 7 shows, however, that the sound intensity is considerably lower in this band when compared to the surrounding sediments. The combination of the two effects points towards a thin mud layer on top of a sandy sediment bed. This also implies that the extent to which the detector sinks into the sediment is limited because the sound measurements did not detect a sandy layer, which should be expected to be present deeper in the profile, but still within the top 30 cm.
7. Discussion After the MEDUSA activity concentrations were converted to sand-mud contents, two remarkable effects appear. (1) In general, the MEDUSA activity concentrations were lower than the values of samples from nearby locations measured in the laboratory. This effect was most clearly observed for samples with a high mud content (>80%) in the laboratory. For areas
with pure mud, the MEDUSA-derived mud content did not exceed 60%. (2) In situ, an asymmetry in the ratio 238U/232Th was observed, whereas these nuclides occur in almost equal amounts in the laboratory measurements. To investigate these two effects, a special set of samples was collected by divers during the Haringvliet survey. As the samples were collected very close to the actual location of the MEDUSA detector, the two results ought to be identical. In the laboratory analysis of the samples, considerable 137Cs activity concentrations were observed (up to 35 Bq/kg). In the initial MEDUSA analysis 137Cs was not included. Because 137Cs standard spectra cannot be measured directly, in the laboratory, a spectrum was recorded for a point source placed in a sandy environment. A proper 137Cs standard spectrum should be recorded using Cs-active material (sand), however such material is very difficult to obtain. The procedure followed therefore resulted in a qualitative 137Cs spectrum. Nevertheless, the inclusion of this spectrum improved the quality of the deconvolution procedure, but was insufficient to bring the HPGe and MEDUSA data in agreement. In the laboratory, freeze-dried samples were measured. Wetting the samples resulted in higher values, removing the asymmetry in the U/Th activity concentrations. This indicated that the samples were insufficiently sealed, allowing the gaseous 238U decay product, 222Ra, to escape from the sample. This experience has resulted in an improved sealing procedure in which the samples were kept sealed for 3 weeks prior to treatment. The values quoted in this part and in part A for the fingerprints are those obtained with the proper sealing procedure. After this correction had been made, the discrepancy in the U/Th ratio disappeared but the low values of mud contents of predominantly muddy samples remained. The mud contents from laboratory analysis of the sediment samples obtained by the divers and those from radiometry of the exact same location with MEDUSA were now in reasonable agreement. However, these measurements were conducted under static conditions; scuba divers observed by eye that in case of high mud contents, the detector was almost completely submerged in mud. Under static conditions, two effects counteract. The submersion exposes MEDUSA to more mud than if the detector is placed
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on the sediment bed. This effect will lead to higher apparent activity concentrations. This effect is counteracted by the lower density of mud as the standard spectra used in the analysis of the g-ray spectra are obtained for dry sediment with a density of about 1800 kg/m3. In sediments with a lower density, the larger amount of water reduces the amount of g-rays reaching the detector and hence the detector notices lower activity concentrations. The extent to which counterbalancing occurs is smaller under dynamic towing conditions than under static conditions as it may be expected that due to the motion, the degree of submersion will be reduced. The density effect is expected to be large enough to cause a reduction in the MEDUSA-derived sand and mud contents up to 30%. This means that sediment with mud contents over 60% will not occur within these data. Integrating the effect of density in the processing of the data is the only way to overcome this uncertainty, which can be estimated by simulation techniques.
8. Conclusion The aim of this study was to investigate whether a novel technique could replace high-density sampling to obtain detailed sand-mud ratios of aquatic sediments. The novelty of the technique is in its ability to measure with a high spatial resolution the activity concentrations of natural radionuclides with high precision and to convert these concentrations to sand and mud contents. Comparison to samples, partly taken by divers around the detector being at rest on the sediment bed, and assessment of uncertainties via an interpolation procedure were used to evaluate the statistic and systematic uncertainty of the method. The interpolation procedure confirmed that the statistical error in the sand content is about 4% at a measuring time of 10 s. Other factors, such as the interpolation and spatial variability of the sediment composition add on average an error of about 4%, but in some areas even 14%. These results indicate that the accuracy of the MEDUSA system is not directly the limiting factor, but that the present 500 m interline distance in certain areas introduces a larger error due to small scale variations in the sediment composition.
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On the one hand, this calls for an even smaller interline distance, as this indicates that apparently even the 25,000 data points obtained by this survey are not sufficient to obtain a higher accuracy than 10– 20%. However, traditional sampling and laboratory analysis of such an amount of samples would be prohibited by the required time and associated costs. The MEDUSA survey, on the other hand, including sampling comprised about 160 h, with relatively low additional costs involved. The comparison between sample analysis and MEDUSA data revealed three sources of systematic uncertainty. Insufficient sealing of the samples for laboratory analysis allowed radon gas, produced in the nuclear decay of 238U, to escape. Since the 238U concentrations are determined from gamma rays of radon decay products, these values turned out systematically too low by about 20%. Proper sealing has removed this anomaly. The second source of systematic uncertainty is the presence of 137Cs in the aquatic sediment. The contribution of this radionuclide to the gamma ray spectra was initially ignored. In a later stage, a 137Cs standard spectrum was constructed, which improved the deconvolution of the spectra; still proper standard spectra have to be obtained either by measurement or simulation. The third source of systematic uncertainty was observed for areas with almost purely muddy sediment. For these areas, the maximum mud content is about 60%. The comparison with the samples taken by divers pointed to a systematic under-evaluation of the activity concentrations for sediments with a low density. At present, the system is calibrated for dry bulk sediment densities of about 1700 kg/m3. For lower densities, the volume ‘‘seen’’ by the detector contains less mass of sediment and more, absorbing water. The result is an ‘apparent’ lower mud content. To remove this systematic derivation, two actions have to be taken: 1. 2.
the detector has to be equipped with an in situ densitometer simulation calculations have to be developed to correct for the low densities
Both topics are beyond the scope of the present investigation, but will be addressed in future research.
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Acknowledgements The authors wish to acknowledge the support of the crew on-board the Rijkswaterstaat vessel ‘The Nes.’ Mr. G.v.d. Berg of RIZA, Mr. L.P. Groen and Mr H. Limburg of the of KVI have been very helpful during the MEDUSA surveys. Special gratitude is for Mr. A. Fioole of RIZA for the interpolation and statistical analysis of the data.
References CERC (Coastal Engineering Research Centre), 1977. Shore Protection Manual, vol. 1. Department of U.S. Army, U.S. Government Printing Office, Washington, DC. De Meijer, R.J., 1998. Heavy minerals from ‘Edelstein’ to Einstein. Journal of Geochemical Exploration 63, 81 – 103. De Meijer, R.J., Ta´nczos, I.C., Stapel, C., 1996. Radiometry as a technique for use in coastal research. In: De Batist, M., Jacobs, P. (Eds.), From Geology of Siliciclastic Shelf Seas. Geological Society Special Publication, vol. 117, pp. 289 – 297. De Meijer, R.J., Stapel, C., Jones, D.G., Roberts, P.D., Rozendaal, A., MacDonald, W.G., 1997. Improved and new uses of natural radioactivity in mineral exploration and processing. Exploration and Mining Geology 6, 105 – 117. Den Besten, P.J., Schmidt, C.A., Ohm, M., Ruys, M.M., Van Berghem, J.W., Van de Guchte, C., 1995. Sediment quality assessment in the delta of the Rivers Rhine and Meuse based on field observations, bioassays and food chain implications. Journal of Aquatic Ecosystem Health 4, 257 – 270. Fioole, A., Houwing, E.J., van der Heijdt, L.M., 1998. Surfis: a tool for designed and optimising dredging schemes. Water Science and Technology 37 (6 – 7), 103 – 107. Hendriks, P.H.G.M., Limburg, J., De Meijer, R.J., 2001. Full spectrum analysis of natural gamma ray spectra. Journal of Environmental Radioactivity 53, 365 – 380. Houwing, E.J., 1999. Determination of the critical erosion threshold of cohesive sediments on intertidal mudflats along the Dutch
Wadden sea coast. Estuarine, Coastal and Shelf Science 49, 545 – 555. Jones, D.G., 2001. Development and application of marine gammaray measurements: a review. Journal of Environmental Radioactivity 53, 313 – 333. Koomans, R.L., 2001. Sand in motion; effects of density and grainsize. PhD Thesis; University of Groningen, The Netherlands, ISBN-90-367-1338-2. Kuijper, C., Cornelisse, J.M., Winterwerp, J.C., 1993. Erosiegedrag van Haringvliet slib, Report Z705. WL Delft Hydraulics, Delft. Philips, O.M., 1969. The Dynamics of the Upper Ocean. Cambridge Univ. Press, Cambridge. Terwindt J.H.J., 1995. Salt marshes and sea level rise: plant dynamics relation to accretion enchancement techniques. National Research Programme on Global Air Pollution and Climate Change, Report 410.100.077, Utrecht. Van Dreumel, P.F., 1995. Zand en slib transport in het Noordelijk Deltabekken. Ministry of Transport, Public Works and Water Management Directorate Southern Holland, Rotterdam. Van Dreumel, P.F., 1997. Het Noordelijk Deltabekken: metingen van zwevend stof transport en troomsnelheid tijdens een hoge afvoer van de Rijn en Maas; januari/februari 1995. Ministry of Transport, Public Works and Water Management Directorate Southern Holland, Rotterdam. Van Eck, G.T.M., Zwolsman, J.J.G., Saeijs, H.L.F., 1997. Influence of compartimentization on the water quality of reservoirs: lessons learned from the enclosure of the Haringvliet Estuary, The Netherlands. Proceedings of the Dix-neuvieme Congres des Grand Barrages, Florence, pp. 647 – 669. Van Rijn, L., 1993. Principles of Sediment Transport in Rivers, Estuaries and Coastal Seas. Aqua Publications, Amsterdam, 658 pp. Van Wijngaarden, M., 1999. A two dimensional model for suspended sediment transport in the Rhine – Meuse Estuary (The Netherlands). Earth Surface Processes and Landforms 24, 1173 – 1188. Van Wijngaarden, M., Ludikhuize, D., 1997. Morfologie en sediment kwaliteit in het Noordelijk Deltabekken; MER Haringvlietsluizen. RWS report 98.094. Venema, L.B., De Meijer, R.J., 2001. Natural radionuclides as tracers of the dispersal of dredge spoil dumped at sea. Journal of Environmental Radioactivity 55 (3), 221 – 239.
Radiometric sand mud characterisation in the Rhine–Meuse Estuary Part B. In situ mapping M. van Wijngaarden a,*, L.B. Venema b, R.J. De Meijer b a
Ministry of Transport, Public Works and Water Management, Institute for Inland Water Management and Waste Water Treatment (RIZA), PO Box 52, 3300 AB Dordrecht, Netherlands b Nuclear Geophysics Division, Kernfysisch Versneller Instituut, University Groningen, Zernikelaan 25, 9747 AA Groningen, Netherlands Received 26 February 2001; received in revised form 14 July 2001; accepted 22 July 2001
Abstract Traditionally, obtaining accurate spatial information about the textural composition of heterogeneous aquatic sediments requires extensive sediment sampling. To avoid a costly and time-consuming operation, a new in situ technique has been investigated. This technique characterises sediment components by the activity concentrations of natural radionuclides. In situ activity concentrations are continuously measured with the Multi-Detector system for Underwater Sediment Activity (MEDUSA), a detector trailed over the bottom by a towing vessel, containing a highly sensitive gamma-ray BGO-detector, a water-depth sensor and a microphone. From the collected g-ray spectra, the activity concentrations of 40K, 238U and 232Th are derived. During two separate field surveys of 5 days each, MEDUSA was applied to the Hollandsch Diep and Haringvliet, two fresh water basins, each approximately 20 km long and 2 – 3 km wide in the Rhine – Meuse Estuary, the Netherlands. In part A of this paper (this issue), it has been illustrated how the radiometric fingerprint of sand and mud in the area was determined. This fingerprint was then used to calculate the sand-mud ratio from the 238U and 232Th activity concentrations of about 25,000 MEDUSA data points. The interpolated sand-mud distribution of the top layer of the aquatic sediment shows a distinct correlation with bathymetry: the deeper channels are mud-rich, whereas the shallow zones are predominantly sandy. In general, the mud content decreases in the seaward direction. This corroborates well with the morphological development of the area in which the deposition of mud is highest near the rivers’ outflow in the east. The absolute total random error in the sand map varies between 6% and 18%, where the largest errors are caused principally by spatial variability of the sediment composition. At present, the radiometric results underestimate the mud content by 10 – 30% due to a higher water content of sediment with high mud contents ( > 60%). The simultaneously recorded friction sound levels provide qualitative information with respect to the sediment composition. Sandy areas show a higher sound level than muddy areas; with the sound and sand map showing a striking similarity. D 2002 Elsevier Science B.V. All rights reserved. Keywords: Sediment composition; Sand-mud ratio; Radiometric sedimentology; In situ mapping
*
Corresponding author. Fax: +31-78-631-5003. E-mail address: [email protected] (M. van Wijngaarden).
0169-555X/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 9 - 5 5 5 X ( 0 1 ) 0 0 1 2 5 - 8
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1. Introduction In aquatic environments, sand and mud can be seen as indicators of the hydromorphological dynamics: in general, mud will be deposited in relatively calm waters, whereas sandy sediments are found in more turbid waters. The sediment bed composition, defined in terms of the sand ( > 63 and < 2000 mm) and mud ( < 63 mm) content, consequently offers information regarding the transport processes and morphological development of the water system. Moreover, sediment transport or morphological models require accurate input regarding the sand-mud composition of the sediment bed, because these parameters are known to strongly influence bed strength and erodibility (Van Rijn, 1993; Houwing, 1999). In addition, muddy sediments often form a potential threat to the ecological functioning of a water system, because pollutants preferably adhere to the mud fraction; especially in Dutch lowland river systems, this regularly turns out to be the case (Den Besten et al., 1995). For a proper ecological risk assessment, detailed information with respect to the spatial distribution of mud deposits is essential. Given the heterogeneity of natural aquatic sediments, numerous samples are needed to provide a reliable and detailed image of the sediment composition. Because sample collection and analysis require a costly and time-consuming operation, in practice, only a limited number of samples are collected and analysed. In situ methods which enable obtaining spatial information of the sediment bed composition, for instance through acoustic reflectance, often require an intensive and difficult calibration, counterbalancing their practical advantage. This state of affairs leaves a distinct need for an accurate measuring methodology for high-density mapping of the sediment composition. In radiometric sedimentology, various sediment components are characterised using the concentration of natural gamma-ray emitting radionuclides (De Meijer et al., 1996; Venema and De Meijer, 2001). It has been demonstrated that a strong relationship exists between the grain size distribution and the geochemical and radiometric composition of sediments. The report in this study is divided into two parts: Part A (this issue) deals
with the derivation of the radiometric fingerprint based on a number of samples taken from the area during a number of surveys. It is shown that in the Hollandsch Diep and Haringvliet (the Netherlands), sand and mud are radiometrically distinguishable. The fingerprint derived for mud ( < 63 mm)/sand ( > 63 mm) is characterised by 46.2 F 1.9 Bq/kg/ 9.3 F 0.9 Bq/kg 238 U and 45.6 F 1.9 Bq/kg/ 9.7 F 0.9 Bq/kg 232Th, respectively. A high correlation coefficient (R2 = 0.96) is obtained between the radiometrically derived mud percentages and the ones obtained in the laboratory by laser diffraction. This paper (part B) reports on the mapping of the underwater bottom utilising the Multi-Detector system for Underwater Sediment Activity (MEDUSA). With this technique, g-rays emitted by the sediment are analysed non-invasively and in situ by trailing the MEDUSA g-ray detector over the sediment bed. Such in situ underwater measurements of natural radioactivity require a detector system in a watertight housing. To avoid uncontrollable effects of adsorption of the g-rays in water layers with varying thickness, the detector system has to maintain contact with the bottom. Jones (2001) gives an overview of various detector systems used for, mainly, high activity levels. To obtain radionuclide specific information with a high spatial resolution, the detector system and the analysis of the data have to be highly efficient. Compared to previous systems, the radiometric g-ray sensor MEDUSA is an order of magnitude more efficient (De Meijer et al., 1996; De Meijer, 1998; Hendriks et al., 2001). The aim of the study presented here is to derive radiometrically a sand-mud map with a high resolution for the Hollandsch Diep and Haringvliet, two semi-stagnant fresh water basins in the southwestern part of the Rhine –Meuse Estuary (the Netherlands). Since the closure of this former estuary, the area is rapidly filling up with riverine sediments. This sedimentation has started near the river’s outflow in the east and is gradually shifting to the west. Starting off from the old sandy estuarine morphology, the area has developed a diversity in the sand-mud composition of the sediment bed, which makes it a suitable test case for sand-mud mapping.
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2. Morphology of the Hollandsch Diep and Haringvliet The underwater morphology of the area (see Fig. 1) was obtained by a bathymetrical survey with an echosounder at an interline distance of 50 m. The bathymetry shows the deeper former tidal channels surrounded by relatively small shallow shores. Several sand plates still exist in the area: most remarkable is the large plate in the western Haringvliet (location A in Fig. 1), which is a typical relict of the old estuarine morphology. Like this area, most of the shoreline is nowadays protected against severe wave erosion, saving land from destruction and enabling small rivers to re-develop their deltas (location B in Fig. 1). In the eastern part of the Hollandsch Diep, a man-made north – south channel is present (location C in Fig. 1), which is actively kept at depth for shipping purposes. Throughout the area, deep artificial pits occur which are either used for sand mining or function as storage capacity for (muddy) dredged material: the largest dump site ‘Cromstrijen’ is indicated as location F in Fig. 1. Due to the construction of the Haringvliet Dam, flow velocities in the inland waters decreased dramatically, and consequently, sedimentation was initiated in the eastern part of the Hollandsch Diep basin. Approximately 1.2 Mton is annually deposited in the Hollandsch Diep basin (Van Dreumel, 1995). The sedimentation area has a length of approximately 30 km and a width of 1 – 4 km. Actual sediment deposition rates in the Hollandsch Diep range from several cm/year for both ends of the basin to 20 cm/ year in the central parts (Van Eck et al., 1997). In the Haringvliet, sedimentation rates amount to on average 1 cm/year (Van Wijngaarden and Ludikhuize, 1997). The former estuarine morphology consisted mainly of sandy sediments with mud being dynamically deposited in the shallow zones. As the closure resulted in a severe reduction of the tidal range, erosion through wind waves became dominant and prevented mud from settling in shallow areas. Nowadays, the muddy sediments supplied by the rivers accumulate mainly in the former tidal channels of the basin. Recent sandy deposits are found locally directly at the beginning of the Hollandsch Diep (Van Dreumel, 1995, 1997; Van Wijngaarden, 1999). The sand found in the Haringvliet is a relict from the former estuarine
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situation; nowadays, no active sand deposition is found in the Haringvliet. Given the sediment accumulation rates in the area, a gradient in sediment composition of the top layer is expected to shift downstream from predominantly muddy to more sandy sediments.
3. The MEDUSA system MEDUSA (De Meijer et al., 1996; De Meijer, 1998), contains a highly sensitive g-ray BGO-detector, a water pressure gauge, a microphone and accessory electronics to power the sensors and to amplify, digitise and transmit the signals to an on-board computer. The system is connected to a towing vessel by an armoured coaxial cable, for both strength and data transmission. The detectors and electronics are housed in a watertight casing, located at the end of a 30-m long, 10-cm diameter protective PVC hose. The detector is susceptible to radiation emitted by the upper 30 cm of the aquatic sediment. The on-board computer combines the data from the MEDUSA with information from a Digital Global Positioning System (DGPS) system. An algorithm derives the activity concentrations of 40K, 238U and 232Th from the MEDUSA gamma-ray spectra. This procedure is based on a laboratory calibration of the gamma-ray detector (De Meijer et al., 1997). The microphone records the friction sound between the sensor and the sediment bed. Originally, the microphone was installed to monitor the contact between the detector and the bed. It was soon found that soft, muddy, sediment beds produce a lower sound level than hard sandy sediment beds (see Koomans, 2001). This microphone has not yet been accurately calibrated for this area and will therefore yield only qualitative information. 3.1. Surveying the Hollandsch Diep and Haringvliet with MEDUSA The MEDUSA system was deployed in the Hollandsch Diep from the 6th to the 10th of July 1998, and in the Haringvliet from the 9th to the 18th of August 1999. A zigzag pattern perpendicular to the general flow direction with an interline distance of 500 m was navigated, combined with several tracks
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Fig. 1. Bathymetry of the study area (m).
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over the full length of the system. This pattern was chosen to obtain the highest density in the direction where the sediment composition is expected to change most rapidly. The towing speed was kept at 2 m/s, data were integrated over 10 s, resulting in a spatial resolution of 20 m along the survey lines. After the survey, the activity concentrations collected by MEDUSA were first handled by translating the X- and Y-coordinates measured aboard the ship to the coordinates of the detector, taking into account the amount of cable released. Moreover, the ‘off-bottoms’, occurring when the detector loses contact with the bed, were removed from the data. From the measured gamma-ray spectra and total count rate, the separate 40K, 232Th and 238U activity concentrations were derived using spectrum deconvolution (De Meijer et al., 1996; Hendriks et al., 2001). In this procedure, standard spectra of the three nuclides measured in the laboratory were used together with a background contribution, measured in either the Haringvliet or Hollandsch Diep at 10 m water depth for over one hour. A typical example of a 10 s MEDUSA spectrum is given in Fig. 2. Typical counting uncertainties are equal to the square root of the measured number of counts, taking into account the
Fig. 2. Standard gamma ray spectra of
40
K,
232
Th,
238
U and
137
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measurement time. For instance in Fig. 2, the uncertainty in the 40K peak at 1460 keV equals (10 0.30)0.5/10 = 0.17 counts per second. This figure illustrates that the standard spectra of 40K, 232Th and 238U each have their specific shape, while the spectrum measured by MEDUSA is the sum of the activities of the individual radionuclides. The 137Cs spectrum was found to be essential for proper deconvolution of the recorded spectra; the 137Cs spectrum presented in Fig. 2 had been recorded provisionally in the laboratory (see Discussion). The response of the sound measurements turned out to reflect the roughness of the upper sediment layer. In between the two surveys, the set-up was modified to optimise the response of the microphone. By comparing two adjacent areas, a factor of 2.1 between the range of the sound levels between the Hollandsch Diep and the Haringvliet was detected and the Haringvliet data were consequently reduced by this factor. A more extensive description of the operation of the microphone is given in Koomans (2001). Sediment samples taken for fingerprinting are described in part A of this paper (this issue). For a direct calibration of the radiometric data from the
Cs and the in situ measured MEDUSA spectrum (counting time of 10 s).
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MEDUSA to the laboratory data with the HPGe, divers sampled sediment from directly under the MEDUSA sensor, while at the same time the MEDUSA output was registered on-board. Perspex cores of 50 cm height were forced into the ground by the divers, providing fairly undisturbed sediment samples. These sediment cores were taken during the survey of the Haringvliet on eight locations. All samples were analysed on their radionuclide content, main-element composition, organic matter, carbonate content and grain size distribution. To assess the effect on the radioactivity measured by MEDUSA, the divers investigated the depth to which the detector sinks into the sediment bed on both muddy and sandy sediment, under static conditions of the sensor. Fig. 4. Schematic outline of the estimation of the total random error SP for the interpolated point ZP.
4. Interpolation of spatial information The MEDUSA-derived sand/mud composition has been interpolated with the package SURFIS. This package uses multiple linear regression to interpolate between data points in neighbouring quadrants (Fioole et al., 1998). The minimum required data point in each quadrant is set beforehand. Fig. 3 illustrates how the value of an unknown data point ZP is estimated by fitting a plane through the neighbouring data points Z1 to Z4 in each quadrant. In this case, one data point
Fig. 3. Schematic outline of the interpolation procedure by SURFIS for an unknown point ZP.
in each quadrant was set to be required, selecting the four points closest to ZP. The contribution of each point is weighed by its distance to ZP. By carrying out this procedure for each unknown location, a map of the data is produced. A first estimation for the random error S between ZP and a point Z is given by: S = 0.5 | ZP Z|, yielding errors S1 to S4 as presented in Fig. 4. By multiple linear regression, a plane is fitted through these errors, yielding the estimated random error SP in ZP. This error SP is an estimation of the interpolation error but additional errors in the measurement and/or positioning system can be expected. Therefore, this first estimate of the random error has to be validated. Therefore, for a subset of points, each measured value was calculated from four surrounding data points, as presented in Fig. 3. This yields an estimated value for each already measured data point with a corresponding estimated random error. These errors are then classified, while for each class, the standard deviation of the differences between estimated and measured values is determined, the latter being a measure for the total random error. The relation between this total random error and estimated error is presented in Fig. 5 and was used to correct the first estimated random error at each location; the offset in this figure represents the MEDUSA-related measurement error. This exercise finally yields a map of the total random error
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Fig. 5. Relation between the estimated random error and the total random error based on the standard deviation of the difference between measured and estimated values for each data point.
or prediction error, which includes uncertainties in the interpolation, in the MEDUSA sand-mud composition and in the positioning system.
5. Sand-mud distribution in the Hollandsch Diep and Haringvliet
For both Hollandsch Diep and Haringvliet, the fingerprints based on 232Th and 238U of mud and sand were derived with a correllation coefficient, R2 = 0.96. The results in Table 1 show that the characteristic activity concentrations differ by a factor of 5. This large factor allows a distinction of small mud admixtures.
5.1. Results fingerprint 6. Morphological analysis of sand/mud maps The results from sample analysis (part A) can be summarised as: 1. 2. 3.
4.
232
Th and 238U can be used to fingerprint the sediments in the area under study 40 K is not suitable for fingerprinting Variety in age and/or provenance results in radiometric differences, which however, do not interfere with the mud and sand fingerprint A diversity of sand-mud mixtures were sampled showing a decreasing contribution of the coarse fraction towards the Haringvliet.
Fig. 6(a) presents the sand map obtained by interpolating the sand contents determined along the 500m interdistant survey lines. The values represent a Table 1 Characteristic radiometric fingerprints of sand and mud. The values are derived from the 238U and 232Th activity concentrations of untreated total samples Sediment type
238
232
Sand ( > 63 mm) Mud ( < 63 mm)
9.3 F 0.9 46.2 F 1.9
9.7 F 0.9 45.6 F 1.9
U-series
Th-series
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Fig. 6. (a) Sand content top layer (%).
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Fig. 6. (b) Absolute total random error in sand content (%).
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weighed average over the top 30 cm. The weighing is based on the adsorption of gamma rays by the sediment and favours the top layer; the values presented are based on the assumption of a homogeneous top layer. Preliminary results of sediment cores taken near the MEDUSA sensor indicate that even for situations with clearly layered sediments, MEDUSA estimates the average mud content over the top layer with acceptable accuracy. Moreover, the sound measurements indicate that the sinking of the detector is limited; this will be discussed later. The sand content was calculated from the radionuclide concentrations using the radiometric fingerprints as listed in Table 1 and using the relation that mass fractions of sand and mud add up to unity. The sediments investigated consist solely of mud and sand, coarser sediment (gravel) is found only further upstream. It should be noted that according to Fig. 6(a), the minimum sand content is 40%, whereas from the sample analysis, values close to 0% were measured. We will return to this issue later. Despite the 10 times larger interline distance of the radiometric survey, a clear correlation is observed between the sand content in Fig. 6(a) and the bathymetry map in Fig. 2. The muddy sediments are concentrated in the deeper sections and the sandy deposits in the shallow nearshore zones and plates. Especially in the Hollandsch Diep, the channels are clearly recognisable by their low sand contents; it is known that mud layers of several metres thickness exist in these areas. In general, the sand content increases from east to west, which is consistent with the morphological development of the area after its disconnection from the North Sea (Van Dreumel, 1995; Van Eck et al., 1997; Van Wijngaarden and Ludikhuize, 1997). Moreover, model calculations illustrate that even during fairly high Rhine discharges (around 5000 m3/s), sand will not be deposited beyond the channel in the eastern end (location C) (Van Wijngaarden, 1999). Fig. 6(a) implies that this sand is deposited predominantly as a point bar along the outer bend of the Nieuwe Merwede (location D in Fig. 6(a)). Sandy sediments are dominant at the borders and in the shallow parts, this is evident for the Haringvliet at both locations A and B: a sandy plate and a sandy river delta. It is known that in the latter area, the zone behind the shore-protection works (too small in size to
show on the maps presented here) is slowly recovering its original muddy character. Just in front of these protection works, wind-induced waves prevent mud from settling or may even erode sediment. The erosive effect of wind waves was estimated by calculating the shear stress at the bottom as a function of local depth, fetch length and wind speed (Philips, 1969). Wave characteristics were simulated according to CERC (1977) and the critical shear stress for erosion was estimated to be around 0.2 N/m2 (Kuijper et al., 1993; Terwindt, 1995). Results show that in shallow areas up to 3 m water depth, in situations when fetchlengths exceed several kilometres, the described effect of wind waves will be significant. Wind wave activity can therefore be considered an important factor in the development of the shallow zones. The high sand concentration found in the Hollandsch Diep at location C may be sand-deposited during (extremely) high discharges of the Rhine and/ or by erosion from the inflowing Dordtsche Kil. Analysis of sediments dredged from this channel show a fairly coarse-grained sediment with d50 values around 300 mm (Ludikhuize, pers. comm.), which is similar to the Rhine sand samples from this study. These dredging data also demonstrate the occurrence of sand in the northern part of this channel (location C) and muddy sediments in the southern part (location E), both indicated in Fig. 6(a). The location ‘Cromstrijen,’ where dredged muddy sediment are dumped (location F in Fig. 2) can also be recognised by its high mud contents in Fig. 6(a) (location F). The area is enclosed by shallow sandy plates, which seem to emphasise the anthropogenic nature of such a pit. Although the main channel is nowadays situated near the southern shore (indicated by location G in Figs. 1 and 6(a)), the area in which Cromstrijen is situated was originally built up from predominantly shallow sandy plates. Fig. 6(b) represents the random total error in absolute values to the sand content map in Fig. 6(a). The uncertainties are typically of the order of 12% (median value) in the actual sand concentrations, coinciding with a median sand concentration of 55%. Fig. 6(b) shows that errors are small (8%) in areas with a uniform sediment composition (for instance location G), whereas larger errors (up to 18%) appear in those areas with large fluctuations in bathymetry and sediment composition (for instance,
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Fig. 7. Recorded sound level (arbitrary units).
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near location F). A minimum error of approximately 4% is always present in the data, which is the average error in the MEDUSA measurement, as discussed previously, being the offset in Fig. 5. Higher errors therefore reflect the error introduced in the interpolation procedure and originate from small scale changes in sediment composition. Accordingly, the latter contribution to the total error can be reduced by surveying the area at a denser line pattern. The interpolated sound intensity measurements are presented in Fig. 7. The sound intensity is generated by the friction between the protective PVC hose and the top of the sediment layer (few millimetres to centimeters). The signal has been illustrated to provide information on the grain size of the very top layer (Koomans, 2001). The sound correlates well with the sand content: deeper, muddy sections have low sound levels, whereas shallow, sandy zones produce a much higher sound level. The sound map implies that deeper areas will be predominantly muddy, whereas the shallow zones are dominated by sandy deposits. Some interesting differences can be noted while correlating sound with either the sand or the bathymetrical map. For example in the Haringvliet between location B and the Haringvliet Dam (Fig. 6(a)), one finds a band, indicated by location H, with slightly reduced sand contents. Fig. 7 shows, however, that the sound intensity is considerably lower in this band when compared to the surrounding sediments. The combination of the two effects points towards a thin mud layer on top of a sandy sediment bed. This also implies that the extent to which the detector sinks into the sediment is limited because the sound measurements did not detect a sandy layer, which should be expected to be present deeper in the profile, but still within the top 30 cm.
7. Discussion After the MEDUSA activity concentrations were converted to sand-mud contents, two remarkable effects appear. (1) In general, the MEDUSA activity concentrations were lower than the values of samples from nearby locations measured in the laboratory. This effect was most clearly observed for samples with a high mud content (>80%) in the laboratory. For areas
with pure mud, the MEDUSA-derived mud content did not exceed 60%. (2) In situ, an asymmetry in the ratio 238U/232Th was observed, whereas these nuclides occur in almost equal amounts in the laboratory measurements. To investigate these two effects, a special set of samples was collected by divers during the Haringvliet survey. As the samples were collected very close to the actual location of the MEDUSA detector, the two results ought to be identical. In the laboratory analysis of the samples, considerable 137Cs activity concentrations were observed (up to 35 Bq/kg). In the initial MEDUSA analysis 137Cs was not included. Because 137Cs standard spectra cannot be measured directly, in the laboratory, a spectrum was recorded for a point source placed in a sandy environment. A proper 137Cs standard spectrum should be recorded using Cs-active material (sand), however such material is very difficult to obtain. The procedure followed therefore resulted in a qualitative 137Cs spectrum. Nevertheless, the inclusion of this spectrum improved the quality of the deconvolution procedure, but was insufficient to bring the HPGe and MEDUSA data in agreement. In the laboratory, freeze-dried samples were measured. Wetting the samples resulted in higher values, removing the asymmetry in the U/Th activity concentrations. This indicated that the samples were insufficiently sealed, allowing the gaseous 238U decay product, 222Ra, to escape from the sample. This experience has resulted in an improved sealing procedure in which the samples were kept sealed for 3 weeks prior to treatment. The values quoted in this part and in part A for the fingerprints are those obtained with the proper sealing procedure. After this correction had been made, the discrepancy in the U/Th ratio disappeared but the low values of mud contents of predominantly muddy samples remained. The mud contents from laboratory analysis of the sediment samples obtained by the divers and those from radiometry of the exact same location with MEDUSA were now in reasonable agreement. However, these measurements were conducted under static conditions; scuba divers observed by eye that in case of high mud contents, the detector was almost completely submerged in mud. Under static conditions, two effects counteract. The submersion exposes MEDUSA to more mud than if the detector is placed
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on the sediment bed. This effect will lead to higher apparent activity concentrations. This effect is counteracted by the lower density of mud as the standard spectra used in the analysis of the g-ray spectra are obtained for dry sediment with a density of about 1800 kg/m3. In sediments with a lower density, the larger amount of water reduces the amount of g-rays reaching the detector and hence the detector notices lower activity concentrations. The extent to which counterbalancing occurs is smaller under dynamic towing conditions than under static conditions as it may be expected that due to the motion, the degree of submersion will be reduced. The density effect is expected to be large enough to cause a reduction in the MEDUSA-derived sand and mud contents up to 30%. This means that sediment with mud contents over 60% will not occur within these data. Integrating the effect of density in the processing of the data is the only way to overcome this uncertainty, which can be estimated by simulation techniques.
8. Conclusion The aim of this study was to investigate whether a novel technique could replace high-density sampling to obtain detailed sand-mud ratios of aquatic sediments. The novelty of the technique is in its ability to measure with a high spatial resolution the activity concentrations of natural radionuclides with high precision and to convert these concentrations to sand and mud contents. Comparison to samples, partly taken by divers around the detector being at rest on the sediment bed, and assessment of uncertainties via an interpolation procedure were used to evaluate the statistic and systematic uncertainty of the method. The interpolation procedure confirmed that the statistical error in the sand content is about 4% at a measuring time of 10 s. Other factors, such as the interpolation and spatial variability of the sediment composition add on average an error of about 4%, but in some areas even 14%. These results indicate that the accuracy of the MEDUSA system is not directly the limiting factor, but that the present 500 m interline distance in certain areas introduces a larger error due to small scale variations in the sediment composition.
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On the one hand, this calls for an even smaller interline distance, as this indicates that apparently even the 25,000 data points obtained by this survey are not sufficient to obtain a higher accuracy than 10– 20%. However, traditional sampling and laboratory analysis of such an amount of samples would be prohibited by the required time and associated costs. The MEDUSA survey, on the other hand, including sampling comprised about 160 h, with relatively low additional costs involved. The comparison between sample analysis and MEDUSA data revealed three sources of systematic uncertainty. Insufficient sealing of the samples for laboratory analysis allowed radon gas, produced in the nuclear decay of 238U, to escape. Since the 238U concentrations are determined from gamma rays of radon decay products, these values turned out systematically too low by about 20%. Proper sealing has removed this anomaly. The second source of systematic uncertainty is the presence of 137Cs in the aquatic sediment. The contribution of this radionuclide to the gamma ray spectra was initially ignored. In a later stage, a 137Cs standard spectrum was constructed, which improved the deconvolution of the spectra; still proper standard spectra have to be obtained either by measurement or simulation. The third source of systematic uncertainty was observed for areas with almost purely muddy sediment. For these areas, the maximum mud content is about 60%. The comparison with the samples taken by divers pointed to a systematic under-evaluation of the activity concentrations for sediments with a low density. At present, the system is calibrated for dry bulk sediment densities of about 1700 kg/m3. For lower densities, the volume ‘‘seen’’ by the detector contains less mass of sediment and more, absorbing water. The result is an ‘apparent’ lower mud content. To remove this systematic derivation, two actions have to be taken: 1. 2.
the detector has to be equipped with an in situ densitometer simulation calculations have to be developed to correct for the low densities
Both topics are beyond the scope of the present investigation, but will be addressed in future research.
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Acknowledgements The authors wish to acknowledge the support of the crew on-board the Rijkswaterstaat vessel ‘The Nes.’ Mr. G.v.d. Berg of RIZA, Mr. L.P. Groen and Mr H. Limburg of the of KVI have been very helpful during the MEDUSA surveys. Special gratitude is for Mr. A. Fioole of RIZA for the interpolation and statistical analysis of the data.
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