In the beginning of the 1990’s, the Department of Geochemistry of Utrecht University started a series of geochemical surveys of the Leeward Islands of the Antilles that form part of the Kingdom of the Netherlands. Apart from seeking a nice, warm fieldwork environment, the undertaking was motivated by the deficits in infrastructure and financial resources of these islands, which precluded the authorities to undertake such a survey on their own. By then, the International Geological Correlations Programme (IGCP; Darnley et al., 1995) had just been established to address the need for standardised geochemical databases worldwide; the then Geological Survey of the Netherlands (RGD) - now the TNO-Institute of Applied Geosciences (TNO-NITG) - participated in this programme from the start.

We investigated the spatial variability of the heavy-mineral composition in the Early Pleistocene fluviatile Kedichem Formation in the Netherlands in order to meet the demand for more information about subsurface sediment composition. We first determined the spatial extension and thickness of the sediment body, then used Fuzzy clustering techniques on a database containing approx. 2000 heavy-mineral counts from the Kedichem Formation to map the spatial extension of the various sediment provenances within the formation. Three clusters could be discerned, one representing a combined Meuse-Scheldt source, the other two representing a mixed Rhine-Baltic source. We made slice maps at several depths through the formation, and plotted the cluster memberships.

The maps show an overall dominance of the Meuse-Scheldt source in the south of the Netherlands, whereas the Rhine-Baltic source occurs mainly in the central Netherlands. The methods employed show that it is possible to map and study the 3-D variation in heavy-mineral composition and hence sediment provenance in the Dutch subsurface with the use of simple statistical and visualization techniques.

We started a geochemical mapping campaign in the Early Pleistocene fluviatile Kedichem Formation in the Netherlands in order to meet the demand for more information about subsurface sediment compositions. Geochemical data were collected during a sampling campaign, and about 600 samples from the Kedichem Formation were analyzed. By linking the geochemical data with lithological classifications from the TNO-NITG borehole database, we established a geochemical prediction model.

Elements were divided into classes according to their geochemical behaviour in relation to lithological parameters. For each of the classes, we combined lithological groups in to groups with relevant geochemical differences. By calculating for each element the average composition in each of these groups, we were able to predict the geochemical composition of subsurface sediments by ‘translating’ the spatial lithological data from the TNO-NITG borehole database into geochemical data. We visualized this model by calculating and interpolating the average composition of horizontal slices of the Kedichem Formation. The model performance is fairly good, although it has a tendency to underestimate extreme values.

Unambiguously pristine and largely unpolluted sediments from the Late Weichselian and Holocene infillings of the Meuse residual channels in northern Limburg (the Netherlands) have been sampled to determine the natural compositional variation of the river’s suspended load.

Bulk geochemical and granulometric analyses demonstrate that about 70% of the variation can be ascribed to hydrodynamic mineral sorting. Clay- and fine silt-sized phyllosilicates are the most important deterministic features, hosting the bulk of AI2O3, TiO2, K2O, MgO and trace element variability (notably Ba, Cr, Ga, Rb and V). Quartz is abundant in the fine and coarse sand fractions. Na2O and the Zr-Nb-Nd-Y quartet relate to albitic feldspars and heavy minerals, respectively, in the coarse silt fraction. The granulometry should therefore be quantified if geochemical baseline data for a particular geological unit or region are drawn up and for the evaluation of potentially polluted sediments.

Although provenance has not changed, the composition of Meuse sediments cannot be considered constant over a time frame of 1000–10,000 years, due to climatic change. Weathering of phyllosilicates in both interstadial and interglacial soil environments and changing relative source-area contributions alter the detrital clay-mineral supply to raise the AI2O3 and lower the K2O and MgO contents in Holocene Meuse sediments. Early diagenetic siderite and vivianite formation in gyttjas causes relative accumulations of Fe2O3, MnO, P2O5, Co, Ni and notably Zn above the phyllosilicate background values. These accumulations are natural and show that sediments with elevated trace metal contents are not necessarily polluted. Very early atmospheric pollution in relation to ore mining and smelting activities in the Roman era, however, probably caused elevated Pb contents in Subatlantic humic clays and peat samples, long before the historic pollution of the Industrial Revolution started.

The A12O3, Fe2O3 and CaO contents are used to predict the trace-element values as a function of sample granulometry, siderite/vivianite and lime content, respectively. As such, they can provide a sound basis for environmental researchers to determine baseline values of heavy metals in bulk samples of fine-grained fluvial sediments.

The embanked floodplains of the lower Rhine river in the Netherlands contain large amounts of heavy metals, which is a result of many years of deposition of contaminated overbank sediments. The metal pollution varies greatly between the various floodplain sections as well as in vertical direction within the floodplain soil profiles. The present contribution describes the key processes producing the spatial variability of the metal pollution in floodplain soils: (1) spatial patterns of the concentrations and deposition of Cd, Cu, Pb and Zn during a single flood, which have been determined from samples collected after a high-magnitude flood event; (2) the pollution trends of the lower Rhine over the past 150 years, which were reconstructed on the basis of metal concentrations in sediments from small ponds within the floodplain area. During the flood the largest metal depositions (0.03 g/m2 Cd, 0.7 g/m2 Cu, 1.1 g/m2 Pb and 5.0 g/m2 Zn for the Rhine) occurred along the natural levees, decreasing to about one third of these values at larger distance from the river. Deposition of heavy metals occurred since the end of the nineteenth century. Periods of maximum pollution occurred in the 1930s and 1960s, when Cu, Pb and Zn concentrations were about 6–10 times as high as background values.

The resulting metal distribution in the floodplain soil profiles is illustrated by means of typical examples. Maximum metal concentrations in floodplain soils vary from 30 to 130 mg/kg for Cu, from 70 to 490 mg/kg for Pb, and from 170 to 1450 mg/kg for Zn. The lowest metal pollution is found in the distal parts of floodplain sections with low flooding frequencies, where average sedimentation rates have been less than about 5 mm/a. The largest metal accumulations occur in low-lying floodplain sections where average sedimentation rates have been more than 10 mm/a.

Since 1991, several provinces in the Netherlands have put much effort in establishing soil-quality monitoring networks. The purpose of these networks is to provide insight in the trends in (geochemical) soil quality, on which new policies for environmental protection can be based, such as restrictions in certain landuse types and cleaner production processes. The soil quality networks are yet too young to serve this goal. Monitoring efforts are concentrated on micro- and macro-elements, particularly in the top layer of the soil (mainly heavy metals and PAH) as well as phreatic groundwater (mainly nitrates and phosphates) in the various regions of a province. The regional soil-quality monitoring networks focus explicitly on diffuse environmental pollution in the rural areas, which means that sample sites influenced by soil pollution caused by local sources are excluded. Regional differences in soil quality in the rural areas are primarily caused by chemical and physical differences in the natural soil composition and by differences in deposition loads (direct and indirect). Hydrological conditions can also exert a large influence, particularly for nitrate leaching. This leads to three major criteria which the network design is based upon: (1) soil type, (2) landuse (assumed to be representative for deposition), and (3) groundwater tables. Subregions are formed by combining these criteria. Subregions are considered to be more or less homogeneous at a regional scale with respect to the criteria named. Within each region, a pre-calculated number of sites, based on variability of present concentrations, have been sampled and the sample material has been analyzed. Descriptive statistical parameters could thus computed; they are the base for the geochemical soil mapping of the individual, homogeneous subregions.

A recent evaluation of all operational soil-quality monitoring networks shows that these networks are effective instruments to gain insight into the differences in quality of the soil and the phreatic groundwater between the various regions. The understanding of these differences and the processes that caused them provide the provincial authorities with valuable information for policy making and environmental management. The evaluation also reveals differences in network designs, mostly due to local differences in physical-chemical properties and political choices.

It can be concluded from the first results of the networks that the relative high loads of zinc and copper, caused by spreading manure on the farmlands in areas of intensive agricultural landuse, have led to notably higher concentrations of these elements in the top layer of the soil compared to more natural lands like forested areas. The fact that the intensive agricultural landuse is mainly situated on relatively highly permeable sandy soils results in high nitrate concentrations in the phreatic groundwater, up to concentrations far beyond EG drinking-water target levels. First monitoring results signalled several environmental problems of which most of the policy makers were already aware, but could not quantify. Delineation of the most vulnerable areas and/or areas with unacceptably high loads and quantification of concentrations of different elements enable regional governments to take appropriate measures.

The soil-quality monitoring networks will focus in the coming years on the effectiveness of the measures taken in the various areas. Efforts are being made to integrate the relatively new soil-quality monitoring networks and the longer existing groundwater-quality monitoring networks to achieve a better understanding of the (bio)geochemical cycling processes. Tuning the individual regional soil-quality monitoring networks of the various provinces will enable the provision of additional information about soil quality at a larger scale.

A method for designing efficient sampling schemes for reconnaissance surveys of contaminated bed sediments in water courses is presented. The method can be used in networks of water courses, for instance to estimate the total volume of bed sediment of a defined quality class. The water courses must be digitised as arcs in a Geographical Information System.

The method comprises six steps: (1) stratifying the water courses; (2) choosing a variogram; (3) calculating the parameters of the variance model; (4) choosing a compositing scheme; (5) choosing the values for the cost-model parameters; and (6) optimising the sampling scheme. The method is demonstrated with a survey of the main water courses in the reclaimed areas of Oostelijk Flevoland and Zuidelijk Flevoland.

A soil-quality map is at present an important tool to integrate laws on soil quality with regional infrastructural works. Basic data are commonly available, but soil quality is an indicator that has to be derived from these data, including site-specific environmental standards. We propose three geostatistics-based methods for the comparison of interpolated contaminant concentrations and standards.

The study is illustrated by data from a part of the Betuwe railroad transect, which extends over 12 km in the western Netherlands. As it turns out, a useful procedure is to combine interpolated contaminant concentrations with interpolated threshold values.

The Groundwater resources on the Caribbean Islands of Aruba, Bonaire and Curaçao are limited and of poor quality. The groundwater of the islands is brackish, due to both seawater mixing and the semi-arid climate of the islands. Two hundred and thirty water samples were collected to relate chemical variations in the groundwater of the three islands to the underlying differences in geology, and to define the natural versus anthropogenic influences. Both the chemical and isotopic (δ180, δD, and δ37Cl) compositions of samples were determined.

Several geochemical processes are recognised in the chemistry of the groundwater samples. The most important processes are calcite dissolution, cation exchange, silicate weathering and potassium fixation. In (sub)urban areas anthropogenic influences affect the groundwater quality: high nitrate concentrations were measured. Infiltrating domestic and agricultural (waste)water replenishes the aquifer, and has a desalinization effect on the groundwater quality. This phenomenon is primarily seen on Curaçao, the most populated island.

Oxygen and hydrogen isotopie compositions of groundwaters from Curaçao and Bonaire show that the samples are either meteoric water, or are affected by evaporation or seawater mixing. No distinction could be made between the last two processes. Only a few samples were measured for the Cl-isotope composition; all showed that no physical processes have taken place.

A stream-sediment survey was carried out for the island of Aruba. Concentrations in stream sediments represent the abundance of chemical elements in the drainage basins. A geochemical atlas was created from the collected data and natural background values were established. Cluster analysis and pattern recognition techniques were used to gain a better understanding of the data set.

Two cluster models were selected to study the various geochemical controls on the sediments and to establish a spatial basis of environmental-quality settings for the development of future environmental policies. The first cluster model was suitable to recognise in some detail the reflection of the geology on the geochemistry of the stream sediments. The second, coarser cluster model stressed the importance to distinguish between the two main lithological units of the island when instituting natural background values. Not one uniform value per element is valid, but the value depends on the lithology.

A geochemical soil survey was carried out on the island of Curaçao in October 1992 as part of an overall geochemical characterisation of the three Leeward islands of the Netherlands Antilles. The aim of this soil survey was to provide systematic geochemical data of the rural soils of Curaçao for characterisation, geochemical mapping and environmental application. A total of 122 samples was collected from the topsoils (upper 0.1 m) within a rectangular grid (2 km2). All samples were sieved to a grain size of < 2 mm and analysed for 24 elements by ICP-AES, after decomposition with hot aqua regia. A small, selected set of soil samples was subjected to a more extensive study of soil properties.

It appears from statistical data interpretation of the chemical compositions with techniques like non-linear mapping and cluster analysis that the topsoils of Curaçao can be divided into six homogeneous groups (soil types) with contrasting chemical signatures. Labelled according to their dominant geology, soil process or most striking characteristic, these soil types are defined as follows: sandy limestone soil type, Midden Curaçao soil type, arid/calcareous soil type, basalt West soil type, agriculture-influenced soil type, and the basalt East soil type.

Geochemical contour maps were made that provide a basis for environmental issues on the island. Finally, background levels (baseline data) were derived from the chemical soil composition of several elements, taking into consideration the correlation with the Fe or Al content of these soils. The defined baselines can be used to establish background values for future soil surveys.

The Late Glacial landscape of the Netherlands was a landscape with changing geomorphology and vegetation. Glacial, eolian and fluvial processes in the time before the Late Glacial initially had formed the main landscape types that still existed during the Late Glacial. In these landscape types, geomorphological processes persisted, particularly during intervals when the vegetation cover was discontinuous. Vegetation development initiated soil formation and stabilised the substratum. On the other hand, the abiotic landscape influenced vegetation development, and particularly vegetation patterns.

The Late Glacial vegetation patterns, changing in time, have been reconstructed on the basis of a palynological database containing the data from over 250 pollen diagrams from the Netherlands. Patterns of vegetation and abiotic landscape appear to compare to each other in many instances, indicating the close interrelationship between vegetation and the abiotic landscape.