Particle size and mineralogical composition of inorganic colloids in waters draining the adit of an abandoned mine, Goesdorf, Luxembourg

Applied Geochemistry 24 (2009) 52–61

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Particle size and mineralogical composition of inorganic colloids in waters draining the adit of an abandoned mine, Goesdorf, Luxembourg Montserrat Filella a,b,*, Vincent Chanudet a,c, Simon Philippo d, François Quentel e a

Department of Inorganic, Analytical and Applied Chemistry, University of Geneva, 30 quai Ernest-Ansermet, CH-1211 Geneva 4, Switzerland SCHEMA, Rue Principale 92, L-6990 Rameldange, Luxembourg c Institut F.-A. Forel, University of Geneva, 10 route de Suisse, CH-1290 Versoix, Switzerland d Musée National d’Histoire Naturelle, 25 rue Münster, L-2160 Luxembourg, Luxembourg e Laboratoire de Chimie Analytique, UMR-CNRS 6521, Université de Bretagne Occidentale, 6 avenue V. Le Gorgeu, F-29238 Brest Cedex 3, France b

a r t i c l e

i n f o

Article history: Received 29 April 2008 Accepted 1 November 2008 Available online 24 November 2008 Editorial handling by R. Fuge

a b s t r a c t Particle size distributions and the mineralogy of inorganic colloids in waters draining the adit of an abandoned mine (Goesdorf, Luxembourg) were quantified by single particle counting based on light scattering (100 nm–2 lm) combined with transmission electronic microscopy coupled with energy dispersive spectroscopy and selected area electron diffraction. This water system was chosen as a surrogate for groundwaters. The dependence of the colloid number concentration on colloid diameters can be described by a power-law distribution in all cases. Power-law slopes ranged from 3.30 to 4.44, depending on water ionic strength and flow conditions. The same main mineral types were found in the different samples: 2:1 phyllosilicates (illite and mica), chlorite, feldspars (albite and orthoclase), calcite and quartz; with a variable number of Fe oxide particles. The colloid mineralogical composition closely resembles the composition of the parent rock. Spatial variations in the structure and composition of the rock in contact with the waters, i.e. fissured rock versus shear joints, are reflected in the colloid composition. The properties of the study colloids, as well as the processes influencing them, can be considered as representative of the colloids present in groundwaters. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction The transport and fate of contaminants in soils and groundwater are closely related to the nature and relative abundance of the reactive mineral phases. Since particles in the colloidal size range have a high specific surface, colloids moving through soils, regolith, fractured rock systems and aquifers have been the subject of much interest in a number of pollutant-related studies over many years (McDowell-Boyer et al., 1986; Buddemeier and Hunt, 1988; McCarthy and Zachara, 1989; Puls and Powell, 1992; McCarthy and Degueldre, 1993; Kaplan et al., 1994; Ledin et al., 1994; Honeyman, 1999; Ryan et al., 1999; Geckeis et al., 2003). Consequently, colloids have been studied in a variety of groundwaters from various geological formations ranging from crystalline (Degueldre, 1990; Laaksoharju, 1995; Billon et al., 1991; Vilks et al., 1991; Turrero et al., 1995; Degueldre et al., 1996; Düker and Ledin, 1998) to sedimentary (Longworth et al., 1990; Kim et al., 1992; Vilks et al., 1993) systems, including karstic aquifers (Atteia and Kozel, 1997; Atteia et al., 1998). However, for colloidal material in subsurface

* Corresponding author. Address: Department of Inorganic, Analytical and Applied Chemistry, University of Geneva, 30 quai Ernest-Ansermet, CH-1211 Geneva 4, Switzerland. Fax: +41 22 3796069. E-mail address: montserrat.fi[email protected] (M. Filella). 0883-2927/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.apgeochem.2008.11.010

waters, particle size distributions and a quantitative classification of their mineralogical composition have rarely been published together. One of the main reasons for this is the difficulty in quantitatively assessing both the size and the composition of colloidal materials in natural waters. In order to overcome this problem, a non-perturbing approach that combines a size measuring technique (single particle counting) with the quantification of the size and mineralogical composition of inorganic colloids by transmission electron microscopy (TEM) coupled with energy dispersive spectroscopy (EDS) and selected area electron diffraction (SAED), after the non-perturbing on site preparation of specimen TEM grids, has been developed (Chanudet and Filella, 2006a). This approach has recently been used successfully in the study of a variety of surface waters (Chanudet and Filella, 2006b, 2008). However, in the case of subsurface waters, an additional factor that must be taken into account is the difficulty involved in sampling colloids without causing significant disturbance (McCarthy and Degueldre, 1993). In this study this problem was overcome by sampling water that either percolates through the ceiling of the adit of an abandoned mine or flows along the bottom of the adit. The colloids present in such waters can be considered as being very close to the ones existing in groundwaters, while avoiding sampling artefacts. Along with the ease of sampling, a further advantage is that it makes it possible to study, within a small area, waters with a

M. Filella et al. / Applied Geochemistry 24 (2009) 52–61

different history and chemical composition while maintaining a constant background rock composition, thus allowing the study of the effect of these factors on colloid size and composition.

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The main inorganic soil constituents, which come from the weathering of the bedrock (shale), are (starting with the highest concentration): quartz, albite, sericite, illite, chlorite, kaolinite and montmorillonite (Verstraten, 1977). The proportion of each mineral essentially remains constant with depth.

2. Experimental 2.2. Sampling 2.1. Site description 2.1.1. Site location The mine is located 1 km east of the village of Goesdorf on a hill called Weissenstein. According to old maps, Weissenstein means ‘white stone’ and probably refers to quartz veins forming the mineral lode. The village of Goesdorf is located in the Eisleck (490 m asl), north of the Grand Duchy of Luxembourg. The name of the village comes from Giessdorf which etymologically means ‘‘village of smelters”. Mining in the area dates back to Roman times. The mine was exploited until 1944. It consists of a main shaft (70 m deep), 450 m of drift split into five levels, a drain adit at the base of the main shaft, and three smaller extraction shafts. The upper part of the mine (entrance to the shafts and dumps) is at the top of a hill covered in meadowland. The drain adit is located in a valley covered by a deciduous forest (oak and beech trees). The adit is <2 m wide and <2 m high. The location and structure of the mine are shown in Fig. 1a. The water in the adit comes from rain water that has drained through the soil above. Water can either percolate slowly through the pores of the sandstone beds or faster through the faults and the old mine shafts which have collapsed and been partially filled. The area is characterised by a mild, continental climate, with moderately high precipitation. Average rainfall was 862.5 mm for the period 1971–2000 (http://www.aeroport.public.lu/fr/meteo/rapports_clima/index.html). Mean flow values at the exit of the adit are usually lower than 1 L s1 but can periodically increase up to 10 L s1 following heavy rainfall. 2.2.2. Geological setting In order to understand the formation and presence of colloids in water percolating through a geological system, information is needed about rock units, the level of rock fracture and the presence of ore bodies. Much of the area around the Goesdorf mine is made up of massive, grey siltstone (slate) beds 1–2 m thick. They contain fossils, nodules that help to determine the bedding, and ripple marked surfaces. Some rare intercalations of quartz-sandstone beds, 30 cm thick, can also be found. These rocks have been folded. Different amplitudes of folding have been described by Lucius (1950). There are asymmetric, regional folds, inclined with a vergence to the south and a plunge to the east. In the Goesdorf area, minor parasitic folds, with a wavelength ranging from a few kilometres to several metres, have also been mapped. The folding of the area created a slate cleavage in the siltstone which is easily visible in the field. This cleavage is an axial planar cleavage (schistosity). Normal extensional faults, with a NE–SW direction and a strike-slip component, generate broken folds during the folding process (Philippo and Hanson, 2007). Folds are also affected by shear joints. The system also contains some mineralization. Pyritic replacement of fossils and pyritic nodules can be found in the rocks. This pyrite contains high levels of As (3–4% w/w). Some faults are filled by plastic clay produced by a mylonitisation process and they contain elements with low mobility. Other faults are filled by quartz, carbonates (e.g., dolomite) that contain some pyrite and Sb sulfide pockets. The soil in the area is an ochreous brown earth (French classification: Sol brun ocreux; FAO–UNESCO classification: Dystric Cambisol; Belgian classification: Gbbfq and Gbbfi soils series).

The location of the sampling points is shown in Fig. 1. Their main characteristics are: point A is situated about 50 m from the exit in the small stream which drains the adit and with some added water from other sources; water B flows along the bottom of the adit, with the sampling point located inside the adit but very close to the exit; water C comes through a fault in the ceiling of the adit; waters D and E flow along the floor of the adit, the points are situated about 90 m and 185 m, respectively, from the adit exit; water F trickles out through a landslide that completely plugs the end of the adit and is probably very close to the Sb mineralisation zone; waters G and H drip from the ceiling of the adit and are situated about 4 m apart, just below the mineralised zone. This part of the adit ceiling is covered by black and orange concretions. Water samples for colloid analysis were collected manually using thermostated bottles (2 L). Samples for refractory organic matter (ROM) analysis were collected in clean (Chanudet and Filella, 2007a) polyethylene bottles. Immediately after collection, samples for ROM analysis were acidified to pH 2 with Suprapur grade HCl, stored in a cooler in double plastic bags and kept in a refrigerator until they were measured. Colluvial sediments in direct contact with running water were sampled for XRD and IR characterization. Samples were crushed and analysed as disoriented powder (total rock) in order to record diffractograms of all the minerals present. Representative samples for rock characterization were taken at different points of the adit. Sampling was conducted from downstream to upstream sites to prevent contamination and unwanted sediment incursion into the sample bottles. Two sampling campaigns were carried out: 28th March 2007 (referred to as the ‘‘spring” campaign) and 16th December 2007 (referred to as the ‘‘autumn” campaign). Not all points were sampled in both campaigns: points A, B, E, F and G were sampled in spring and points C, D, F and H in autumn. The sampling campaigns were equivalent in terms of the weather and hydrological conditions: (i) no rainfall was recorded during the 4 days immediately before the sampling and this was preceded by a period of moderate rainfall (8 days in the ‘‘spring” campaign and 12 days in the autumn campaign); (ii) during both periods conditions at night were cold but not freezing and temperatures during the day were mild (10 °C and 5 °C in ‘‘spring” and ‘‘autumn”, respectively). Further sampling for ROM analysis took place on 31st October 2005 and 25th April 2007. 2.3. Measuring methods 2.3.1. Water measurements Major ion (anion and cation) concentrations were measured by ion chromatography (Metrostep Cations 1-2, Metrohm, Switzerland). Alkalinity was measured by a classic acid base titration and use of the Gran method (ASTM, 2005). Conductivity was measured with a LF 325 instrument equipped with a TetraCon 325 conductivity cell (WTW, Germany). Speciation calculations were done using the JESS (Joint Expert Speciation System) modelling package, Version 6.4. This computer software is designed to process thermodynamic data for chemical reactions to automatically attain thermodynamic consistency (May and Murray, 2001). No modifications were made to the JESS database.

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Fig. 1. Situation and planar projection of the Goesdorf mine. Roads and paths are represented by thin lines, the adit by a thicker line. Entries of the (collapsed) shafts are located about 70 m above the adit. Different grey tones indicate different geological units: E1a is schist (siltstone) well stratified with some quartz-sandstone and quartzophyllite, Sg3 is compacted schist with some clayey sandstone. Capital letters indicate the location of the sampling points.

ROM was measured by monitoring the adsorptive stripping voltammetry response of the complex formed by these compounds in the presence of trace amounts of Mo(VI) (Chanudet et al., 2006). This method is particularly well suited to the quantitative determination of low concentrations of humic-type compounds in freshwaters. Standard river fulvic (IHSS Suwannee River fulvic acid standard 1S101F) was used for calibration. Results are expressed as mg C L1. All standard and sample solutions were prepared with 18 MX cm Milli-Q water. 2.3.2. Rock characterization Rock samples were studied with a Siemens D500 diffractometer using Cu Ka1 radiation (k = 1.54056 Å). It was operated at a voltage of 45 kV and a nominal intensity of 35 mA. Samples were sprinkled onto a zero-background quartz plate (from Gem Dugout) and analysed with a petrographic microscope Leica DME POL and an electron microprobe (Cameca SX-50 with a wavelength dispersive

spectrometer, an energy dispersive spectrometer and a matrix correlation with PaP-Program). The electron microprobe was operated with an accelerating voltage of 20 kV and beam current of 10 nA. The trace elements in the argillaceous joints were determined by X-ray fluorescence. 2.3.3. Colluvial sediment characterization Powder X-ray diffraction patterns were obtained with a Bruker D8 diffractometer, using Co Ka1 radiation (k = 1.7902 Å). The diffractometer is equipped with a (h, 2h) goniometer and a position sensitive detector. It was operated at a voltage of 35 kV and a nominal intensity of 45 mA. X-ray diffractograms were collected on powder samples in ambient conditions, within the 2h range [3, 63.5°], with a 0.036° step and 3 s collecting time. Fourier-transform infrared spectra were obtained in transmission mode using a Bruker IFS 55 Fourier IR spectrometer at a resolution of 2 cm1. Pellets were prepared by mixing 50 mg of sample

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with 280 mg of pure KBr. The acquisition was carried out using 200 scans (approximately 90 s). The atmospheric contributions of water and of CO2 were subtracted from the spectra. 2.3.4. Colloid mineralogical characterization and quantification The chemical and mineralogical composition of inorganic colloids was assessed by TEM coupled with EDS and SAED. TEM grids were prepared using a non-perturbing procedure in which specimen grids (Cu 200 mesh, collodion covered, C-coated) were placed at the bottom of centrifuge tubes and specimens were prepared by direct centrifugation of the suspensions in a swing-out rotor (1700 g, 1.5 h) (Perret et al., 1991; Lienemann et al., 1998). Colloids were randomly chosen and observed by TEM (Philips CM12, source W, 80 kV accelerating voltage). Particles were classified into homogeneous chemical classes according to their elemental composition, assessed by EDS (EDAX, DX-4 analyser, EDS spectra collected between 0 and 10 keV with a Si(Li) X-ray detector with a 40 s counting time). Only the elements with an atomic number > 8 (O) were quantified and the detection limit of EDS is about 1%. Their mineralogy was determined by SAED for two or three typical particles of each class. For this purpose, diffraction diagrams were recorded with a CCD camera and interplanar spacings were determined using image analysis software (Roduit, 2006). A detailed description of the methodology used is given in Chanudet and Filella (2006a). About 100 particles were analysed for each sample. For this number of particles, statistical considerations (Chanudet and Filella, 2006a) indicate that results for particles present at less than 10% should only be considered as semi-quantitative. Just for qualitative comparison purposes, the size of the colloids was recorded at the same time as their mineralogy. Particle sizes were taken as being equal to the diameter of a disk with an equivalent surface. A detailed discussion of the limitations of such an approach can be found in Chanudet and Filella (2008). 2.3.5. Colloid particle size distribution measurement Particle size distributions (PSD) of colloidal particles in water were measured with a single particle counter (SPC) in the range 100 nm–2 lm on unfiltered samples. The instrument used (Particle Measuring Systems, Boulder, USA) is made up of single particle counters: a high sensitivity liquid in situ monitor (HSLIS M50) and a volumetric spectrometer LiQuilaz-S02. Both measuring systems are based on the principle of light scattering by single particles. The HSLIS M50 measures colloids in the range 100–200 nm using two channels. The LiQuilaz-S02 measures colloids in the range 200 nm–20 lm using 14 adjustable size classes. An additional particle counter (LiQuilaz-S05) that measures particles in the range 500 nm–20 lm using 14 adjustable size classes was also available in the second campaign. A filtration system (Polygard-CR 500 nm, Durapore 200 nm, Optimizer DI 50 nm) is needed to produce the ultra-filtered water used for the on-line sample dilution. Samples are injected into the dilution line with a high precision

volumetric pump (Desaga KP2000) at different flow rates. High dilution factors make it possible to measure small particles with precision, while low dilution factors are preferred for larger particles. The technique is described in Knollenberg and Veal (1991), Wieland and Spieler (2001) and Rossé and Loizeau (2003). Examples of systematic application can be found in Chanudet and Filella (2006b, 2008). There is no need for any pretreatment or fractionation of the sample.

3. Results 3.1. System characterization 3.1.1. Water composition There are no detailed studies of the hydrology of the mine area. However, useful information can easily be obtained by measuring two parameters: water conductivity and ROM content. Conductivity measurements indicate whether the water has percolated through the highly mineralised zone or not. Values shown in Table 1 clearly demonstrate that the waters that had been in contact with the mineral vein zone (points F and H) have a much higher conductivity than waters percolating through ‘normal’ soil and rocks (point C). The dilution effect is evident in the samples downstream of this water entry (points A and B). A typical composition of the ‘high conductivity’ water is: pH 7.96, conductivity = 516 lS cm1, alkalinity = 4.9, Na = 0.24, K = 0.11, Mg = 3.5,  Ca = 0.51, Cl = 0.25, SO2 4 ¼ 1:4, NO3 ¼ 0:33, (all concentrations in mmol L1, T = 11.2 °C). JESS speciation calculations predict that this water is oversaturated in calcite (SI = 0.1), magnesite (SI = 0.4) and dolomite (SI = 2.6) and show that the ionic strength of the solution (calculated I = 0.0121 mol L1) is mainly due to the presence of Mg (51%), which originated in dolomite; SO2 4 (17%), produced during the dissolution of sulfide-containing min erals, and HCO 3 (19%), with Ca (7%) and NO3 , Na and K (all 1%) each making a minor contribution. ROM, usually known as humic-type substances, has been measured on different occasions in the percolating waters of Goesdorf. Organic matter resistant to degradation is formed in soils by the decomposition of plants. The most soluble part (mostly fulvic acids) is transported into surface and ground waters. The concentrations measured, which are extremely low, are shown in Table 2. The use of a new, extremely sensitive technique, made it possible to measure them (Chanudet et al, 2006). ROM presence proves that the waters have effectively been in contact with the soil where this organic matter is produced. ROM concentrations are surprisingly constant, the only exception being point C. The April sampling campaign was carried out after a period of unusually dry weather and high temperatures that may explain the higher value measured (lower dilution effect). Because of their origin, waters at point C are probably more sensitive to changes in pluviosity.

Table 1 Colloid particle size distribution characteristics, number and mass concentrations. Samplea A B C D E F F H

Campaign Spring Spring Autumn Autumn Spring Spring Autumn Autumn

a b c d

Conductivity (lS cm1 at 20 °C) 266 298 217 541 526 516 545 502

bb 4.25 ± 0.11 4.04 ± 0.11 4.44 ± 0.15 4.07 ± 0.16 3.44 ± 0.11 3.30 ± 0.15 3.60 ± 0.14 3.58 ± 0.12

Colloid numberc (mL1) 6

(11.7 ± 0.26)  10 (3.18 ± 0.04)  106 (4.64 ± 0.29)  106 (2.91 ± 0.09)  106 (9.04 ± 0.08)  106 (1.45 ± 0.02)  106 (3.37 ± 0.16)  106 (4.76 ± 0.11)  106

Sampling locations are indicated in Fig. 2. Slope of the power law distribution (Eq. (1)) and corresponding standard error obtained from linear regression analysis of the data. Measured in the range 100 nm–2 lm. Given error = 1 SD. Calculated in the range 100 nm–2 lm. Calculated by assuming a spherical shape and a density of 2.65 g cm3 (Klein, 2002). Given error = 1 SD.

Colloid massd (lg L1) 111.1 ± 0.7 37.1 ± 0.2 36.7 ± 0.5 29.5 ± 0.9 121.9 ± 0.3 40.2 ± 0.1 57.3 ± 0.7 84.4 ± 2.0

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Table 2 Refractory organic matter (ROM) concentrations expressed in mg C L1.

Table 3 Mineralogical composition of colluvial sediments.

Sampling campaign

Sampling point

ROM (mg C L1)a

Sample

XRD

IR

31 October 2005

H Creekb

0.028 2.0

B

25 April 2007

C D F G

0.14 0.040 0.060 0.004c

Quartz Mica Calcite Illite Kaolinite

16 December 2007

C D F H

0.070 0.050 0.059 0.028

Quartz Mica Chlorite Calcite Siderite Jacobsite Feldspars (trace amounts) Possible dolomite

D

Quartz Mica Calcite Chlorite Siderite Jacobsite Feldspars (trace amounts)

Quartz Mica Calcite Illite Kaolinite

E1

Quartz Mica Siderite Chlorite and/or kaolinite Siderite Jacobsite Feldspars (trace amounts)

Quartz Mica Calcite

E2

Quartz Mica Chlorite Siderite (partially substituted) Jacobsite Feldspars (trace amounts)

Quartz Mica Calcite Kaolinite

a

1 SD = 0.01 mg L1. This sampling point was located in the creek draining the mine about 50 m from the adit exit. This creek runs through a thick coniferous forest. c This value can be considered only as indicative because it corresponds to the detection limit of the technique. b

3.1.2. Rock mineralogy The main rock in Goesdorf is slate with graded bedding. It is a siltstone with angular, moderately sorted grains composed of quartz, clay and mica. The quartz-sandstone intercalations are mature sandstones, classed as arenite, mainly composed of well sorted and angular quartz grains, with some mica, zircon and feldspar. The argillaceous joints are between 5 and 10 cm thick. They are mainly formed of kaolinite, clinochlore and illite. The main trace elements present are (all values in wt %): Ti (1.010), Ba (0.040), Zr (0.030), Cr (0.015), Rb (0.015), Sr (0.015), Ni (0.007) and rare earth elements, i.e. La (0.015) and Ce (0.020). The presence of these trace elements can be explained by some minor minerals found in the argillaceous joints such as zircon (ZrSiO4 containing some rare earth elements), rutile (TiO2 with some Cr), chromite (FeCr2O4) and pyrite (FeS2 with some Ni). The composition of the ore body with the Sb mineralization varies from the wall to the centre. The wall is composed of quartz, carbonates, clinochlore and pyrite rich in As, Cu and Ni. The centre of the mineralization is composed of Sb ore where: stibnite (Sb2S3), berthierite (FeSb2S4), zinkenite (Pb9Sb22S42) and sphalerite (ZnS) have been observed. When exposed to the circulation of water, the Sb ore is oxidized and oxides and sulphates are formed. Those found in Goesdorf are: gypsum (CaSO4nH2O), hematite (Fe2O3), senarmontite (Sb2O3), valentinite (Sb2O3) and stibiconite III V ðSb Sb2 O6 ðOHÞÞ. 3.1.3. Colluvial sediment mineralogy XRD and IR measurements revealed the presence of the minerals listed in Table 3 in colluvial sediments sampled at points B, D and E (two different samples, E1 and E2, located about 3 m apart). XRD and FTIR spectra are presented in Fig. 2. The height of the background noise in XRD was relatively high (17 cps on average). This is probably due to the presence of Fe or Mn in poorly crystallised forms that fluoresce with the radiation used (Co Ka1). The presence of chlorite is suspected in XRD (peak at 14 Å) but was not confirmed by the IR measurements. The signal of chlorite, which is probably present only in small quantities, is masked by the signals of the other phases present. In contrast, the threshold of IR detection of kaolinite is very low and this phase could clearly be identified in samples B, D and E2. 3.2. Colloid mineralogical composition The mineral classes considered when quantifying the mineralogical composition of the colloids by the method described in Chanudet and Filella (2006a) were: feldspars (albite and orthoclase), 2:1 phyllosilicates (mica, illite), chlorite, quartz, calcite, oxides

(mainly Fe and Ti) and ‘others’. The mineralogical composition of the main colloids present in the different samples is shown in Fig. 3a for samples B to G. The mineralogical composition of sample A was not investigated. The anomalous presence of a large amount of particular Fe oxides in sample H, which was not observed in the other samples, precludes any direct comparison of the mineralogical composition of this sample, in percentage terms, with that of the other samples. Most of the particles classified as ‘others’ in Fig. 3a were identified but were not abundant enough to warrant the creation of an independent class. Particular attention was paid to the identification of ‘rare’ (synonymous here with less abundant) particles in the second sampling campaign (samples C, D, F and H). The class ‘Fe oxides’ includes a large variety of colloid types. Only about half of the colloids in this category were ‘pure’ Fe oxides (except in H, see below). A significant proportion was associated with aluminosilicates (mostly illite) and a few with organic matter (e.g., bacteria debris). Some were associated with trace elements (Mn, Ti, Cr) present in large enough quantities to be detected by EDS. It is important to point out that, as explained in detail in Chanudet and Filella (2006a), the measuring method does not allow the exact mineralogy of Fe oxyhydroxides to be determined, even if they are present in crystallized forms (e.g., hematite, goethite). Moreover, since C is not adequately measured by EDS, the possibility of Fe-carbonate being identified as Fe oxyhydroxides cannot be ruled out. The composition of the samples analysed has the following in common: (i) 2:1 phyllosilicates are the most abundant minerals; (ii) quartz is always present; (iii) when present, the amount of feldspars is not negligible; (iv) Fe oxyhydroxides are systematically present; (v) kaolinite, classified in the ‘others’ category because it is a small proportion, is present in all the samples. However, despite being relatively similar in terms of colloid composition, some differences also exist among samples, namely: (i) A higher presence of feldspars in sample H. Fig. 3b shows the percentages of the different minerals present in samples B to H when Fe oxides are not considered. This allows for the inclusion

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Fig. 3. Mineralogical composition of inorganic colloids from the Goesdorf mine determined by a combination of EDS and SAED. (a) Distribution of all mineral types in samples B, C, D, E, F and G. (b) Distribution of the different mineral types in samples B, C, D, E, F, G and H once Fe oxides have been excluded. F–s: spring sampling, F–a: autumn sampling.

Fig. 2. XRD pattern (a) and IR spectra (b) obtained from Goesdorf mine colluvial sediments. C: calcite, Cl: chlorite, F: feldspars, J: jacobsite, M: micas, Q: quartz, S: siderite. In (b): Ba = 878, b = 838, c = 781, d = 754; Da = 917, b = 781, c = 753, d = 713; E1a = 752.

of sample H in the comparison. This figure clearly shows that sample H contains a higher proportion of feldspars (mostly, albite) compared to all other samples. (ii) The size of the same mineral in the different samples. TEMmean size of colloids classified as ‘illites’ (2:1 phyllosilicates) in the different sampling points was: 590 nm (sample C), 760 nm (sample D), 1260 nm (sample F-autumn), 1120 (sample H). In sample H, which is the only one that contained a high proportion of feldspars, the mean size for albite was 510 nm. These values correspond to individual particles, not to aggregates. Moreover, as discussed in Chanudet and Filella (2007b), sizes measured by TEM are surface-based and since particles are preferentially deposited onto grids on their largest surface (the most stable configuration), TEM sizes are probably overestimated compared to SPC sizes. Nevertheless, TEM-derived values are useful to compare samples.

(iii) The presence of ‘rare’ particles and aggregates. The composition of water in point C, which does not percolate through the mineralized vein, was distinct in that it did not contain any ‘rare’ particles or large aggregates. In contrast, waters in samples D and F were extremely rich in both. In sample D a few phases of altered aluminosilicates of the smectite type, dolomite, brucite and ankerite, were present. Aggregates made up of different types of minerals were observed in this sample. Sample F contained some colloidal-size particles of chromite and titanite as well as aggregates. An example is shown in Fig. 4 (top). As mentioned above, sample H contained a large number of colloids with the composition of Fe oxides. Morphologically, they looked very different from Fe oxide particles observed in all other samples. Most of them were individual particles about 100 nm in size but they often formed aggregates that looked similar to those frequently observed in the oxic–anoxic interface of stratified lakes (Perret et al., 2000). An example of these Fe oxides is shown in Fig. 4 (bottom). Not all of them had the same O:Fe relative composition. Within the limits imposed by the semi-quantitative character of EDS measurements, at least three different types were observed (values in atomic percentages): the most abundant with 82.9 ± 0.4 O:17.1 ± 0.5 Fe (n = 13), a second with a higher percentage of O (84.9:15.1) and a third, with a lower percentage

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Fig. 4. (Top) TEM micrograph showing an aggregate in sample F–a; the EDS composition of some components of the aggregate are shown. (Bottom) TEM micrograph showing a typical aggregate of Fe oxyhydroxide in sample H.

(80.8:19.2). Many of the second and third colloid types contained Cr concentrations high enough to be detected by EDS. The rock through which this water percolates is covered in a layer of blackish concretions, rich in Mn and Fe. The presence of these concretions suggests that the percolating waters are highly charged with Fe and Mn which easily oxidize when they come into contact with air. Their composition and the role they may play in pollution attenuation are studied in another paper (Filella et al., 2008). 3.3. Colloid particle size distributions Fig. 5 shows the colloid size distribution determined for the samples. It shows that the colloid concentration in samples continuously increases as size decreases. The observation that the number PSD depends on colloid diameters is a common feature of natural waters (Filella, 2007) and is usually described by a power-law distribution:

dN=du ¼ Aub

ð1Þ

where N = colloid number, u = colloid diameter, A = parameter related to the total colloid concentration and b = the opposite of the power-law slope on a log–log basis. Table 1 gives the calculated colloid numbers and b values. The b values obtained fall within the values measured in natural water, usually between 2.5 and 4.5 (Filella, 2007). Irrespective of the sampling campaign, b values obtained for water samples closer to the exit of the adit look higher than those for water samples closer to the far end. A simple statistical analysis (ANOVA, p < 0.05) shows that samples A and C differ significantly

from samples E, F (spring, autumn) and H. Sample A is located outside the mine and receives waters from other sources. Sample C has already been shown (ROM concentration variations) to behave differently. The total colloid number concentrations in samples from the adit are very low (Table 1), usually below 1  107 particles mL1. Total colloid mass concentrations in the size range measured can be calculated by totalling up the concentrations in all size classes after assuming a spherical shape and a common density of 2.65 g cm3 (Klein, 2002). Mass concentrations below 100 lg L1 are found with this procedure. The main assumptions on which such an approach is based, and its limitations, have been discussed in detail in Chanudet and Filella (2007b). Two samples show slightly higher concentrations: samples A and E. As mentioned earlier, sample A intercepts waters from outside the adit and therefore cannot be directly compared with the rest. Sample E is suspected to have a minor contamination problem caused by colluvial sediments when sampling. The PSD was not measured by SPC in sample G. Nevertheless, a qualitative estimate of the colloid concentration in this sample can be made from the TEM image included in Fig. 5 which shows that the number of colloids in this sample is extremely low. A new particle counter that measures colloids in the range 500 nm–20 lm was used in the second sampling campaign. The b values obtained in this size range for the different samples were: 3.55 ± 0.17 (SE) (sample C), 3.58 ± 0.12 (sample D), 3.41 ± 0.06 (sample F) and 3.76 ± 0.05 (sample H). In samples C and D, b values in the 500 nm–2 lm range are significantly lower than those in the 100 nm–20 lm range (Table 1) while samples F and H behave

M. Filella et al. / Applied Geochemistry 24 (2009) 52–61

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Fig. 5. Particle size distributions (100 nm–2 lm) in waters from the Goesdorf mine. TEM micrographs are also shown for some samples. Scale bar: 10 lm.

differently. Better linearity between 500 nm and 20 lm, as evidenced by lower slope associated standard errors, also characterises samples F and H. These results suggest that particles of sizes outside the colloidal range (‘big’ particles > 2 lm) are being lost in samples C and D, which is not happening in samples F and H. It is important to mention, however, that measurements in the higher size range should be considered with caution because they are based on extremely low particle counts, particularly in waters which contain a very low particle charge, as is the case with Goesdorf waters. 4. Discussion The composition of the colloids in the different samples (2:1 phyllosilicates, feldspars, quartz) is quite similar and fairly accurately reflects the composition of the solid matrix. Mobile colloids are generated in soils and groundwaters by a number of mechanisms including (i) erosion and mechanical resuspension of noncemented small sized grains and (ii) dissolution of cementation agents composed of fine-grained crystalline and poorly-crystalline secondary minerals. Thus the predominance of illite colloids (the largest proportion of the 2:1 phyllosilicates) in most samples is easily explained by the predominance of this mineral in the rock shear joints; because of its fragmented structure, small, solid components of such joints will be mobilized more easily than the components of unaltered rocks. The different composition of sample H (a higher concentration of feldspars) would therefore suggest that waters at this point do not percolate through a shear joint but through fissured rocks (richer in feldspars). It is interesting to note that waters in close physical proximity (points G and H) can have different colloid compositions which just reflect the heterogeneity of the solid matrix. A third mechanism by which colloids are generated in subsurface systems is the precipitation of colloidal size phases. Dolomite, calcite and Fe oxides are examples of this colloid formation process in the system studied. As shown by speciation calculations, both calcite and dolomite are oversaturated in the waters that have

been in contact with the mineralised vein and their formation could therefore be expected, if kinetic factors are ignored. Iron oxides are present in all samples. They have a variety of morphological aspects; many were sorbed onto other mineral and organic phases. The diversity of amorphous Fe oxides formed in natural waters is well-known (Davison and De Vitre, 1992; Perret et al., 2000). Ferrihydrite, the most widespread poorly crystalline Fe oxide, is ambiguous in its identity and often consists of a mixture of particles with varying degrees of ordered structures and hydration (Majzlan et al., 2004). Moreover, even if crystalline forms were present, as discussed in detail in Chanudet and Filella (2006a), the measuring method would not allow for assigning crystallographic structures to these oxides (e.g., hematite, goethite, lepidocrocite, etc.). The abundance of fresh Fe oxyhydroxides in sample H merits particular mention. Morphologically they looked different from the Fe oxides observed in the other samples. It is not possible to ascertain whether these colloids were formed before, during or after sampling but they were absent in other waters with apparently similar chemical characteristics and origin (samples F and G) when the same sampling and conservation procedures were used. Although colloids in all samples show a common log–log linear number versus size relationship, different slope values have been measured. They show that the proportion of bigger versus smaller particles is not constant among the samples. A number of different factors need to be considered in combination in order to understand the differences observed. First, two different types of water sample have been measured: waters that come directly from the ‘‘rock” (points C, F, H and G) and waters that flow along the floor of the adit (samples B, D and E). For the first group of water samples, different slope values may be due to the effect of: (i) water flow rate: higher flow rates may modify mobilization, transport and coagulation processes; (ii) mineralogical composition: different types of material may erode in different ways or precipitate in different sizes; (iii) the solid matrix structure: the presence of material that is more or less fractured or porous and physical factors related to the relative size of the colloids

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and pores and pore throats; (iv) differences in water chemistry: a higher ionic strength favours coagulation and attachment between colloids and between colloids and matrix minerals. Sample C is characterised by a higher SPC b value, the absence of aggregates (TEM) and the presence of smaller TEM-sized illites as compared to sample F. Considering that there are no significant differences in factor (ii), the different features can be explained by the combined effect of a higher water flow and a lower ionic strength at point C. The effect of the slightly higher ROM level (compared to other samples) at point C could also be cited, but the well-known colloid stabilising effect of ROM compounds (Filella, 2007) is probably negligible in this case because ROM concentration in C is still very low in absolute terms. Waters at G and H share very similar conductivities (high) and flows (very low) but, as discussed above, they differ significantly in terms of their colloidal composition (presence of feldspars and fresh Fe oxides in H). Unfortunately, their b values cannot be compared because PSD was not measured at point G by SPC. However, it is important to mention that, as shown by TEM images, the total number of colloids in this sample is extremely low while the composition is still typical of the other samples, although with bigger illite particles. Thus, the combination of high ionic strength and very slow flow conditions would favour the retention of smaller particles in the porous media while bigger particles flow through preferential paths. For the second group of samples (B, D and E), slope values remain relatively constant in the waters flowing through the adit prior to the input of water at C (points D and E) while water in B shows a distinctively higher b value. Colloid PSD in any surface water is the result of the combined effect of the PSD of the incoming waters, entrainment of colloids previously settled in the colluvial sediments, losses through aggregation–sedimentation and aggregate breakage. In the samples studied, although a possible disaggregating effect brought about by a decrease in the ionic strength after the input of waters at point C cannot be disregarded, variations in b values seem to very closely reflect the PSD of the incoming waters. 5. Conclusions Coupling particle size determination by light scattering with mineralogical characterization and quantification by TEM-EDSSAED has recently proved to be a powerful tool in the study of colloids in surface waters (Chanudet and Filella, 2008) and ice (Chanudet and Filella, 2006b). In this study, the same approach has been applied to the study of waters draining the adit of an old mine. This system was chosen as a close surrogate of groundwater media. While variations in PSD of these subsurface waters are mostly due to variations in water ionic strength and flow rates, the colloid mineralogical composition reflects the composition and structure of the parent rock. Similar factors can be expected to influence the size and mineralogical composition of groundwater colloids. Acknowledgements We would like to thank Dina Andriamahady (ROM), Isabelle Bihannic (XRD) and Odile Barres (IR) for laboratory assistance. References ASTM, 2005. D1067-02 Test Methods for Acidity or Alkalinity of Water, vol. 11.01. ASTM Book of Standards. Atteia, O., Kozel, R., 1997. Particle size distributions in waters from a karstic aquifer: from particles to colloids. J. Hydrol. 201, 102–119. Atteia, O., Perret, D., Adatte, T., Kozel, R., Rossi, P., 1998. Characterization of natural colloids from a river and spring in a karstic basin. Environ. Geol. 34, 257–269. Billon, A., Caceci, M., Della Mea, G., Dellis, T., Dran, J. C., Moulin, V., Nicholson, S., Petit, J. C., Ramsay, J., Russel, P., Theyssier, M., 1991. The role of colloids in the

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