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Characterising the chemistry of micropores in a sodic soil with strong texture-contrast using synchrotron x-ray techniques and LA-ICP-MS.


Soils with strong texture-contrast between surface and B horizons, called duplex soils in Australia, dominate the agricultural zones of the west and south of Australia. The A and E horizons of such soils are usually coarse-textured and of low nutrient content and water-holding capacity. The B horizon has a much finer texture than the surface horizons and can have a bulk density as high as 2 g [cm.sup.-3] (Chittleborough 1992). High soil strength causes mechanical resistance to root penetration. Furthermore, root growth can be impeded because of seasonal waterlogging caused by a perched watertable on the dense, high-strength, sodic B horizon (Adcock et al. 2007). Roots growing through the B horizon of texture-contrast soils can use pores that extend many meters through the profile (Yunusa et al. 2002). These biopores, presumably created by native perennial vegetation, not only provide pathways through the soil otherwise impenetrable by many plants, but also improve exposure to preferential flows of oxygen, water, and nutrients (Bouma 1992; Eldridge and Freudenberger 2005).

Roots change the chemical, physical, and biological properties of the soil in which they grow, and the zone of soil in which these changes occur is called the rhizosphere (Hinsinger et al. 2006). These effects can be direct, such as the exudation of protons, which lower soil pH thereby facilitating access to nutrients, or indirect, such as the exudation of organic molecules, which can be used as a substrate by soil microbes. In a root system, fine roots (<0.8 mm) and root hairs are responsible for water and nutrient uptake (McCully 1999). These roots will be located in the meso- and micropores of a structured soil. Micropores, also called matrix pores, occur between individual mineral grains and soil particles and arc not generally created by soil biota (Eldridge and Freudenberger 2005).

In parallel with development changes in roots, the rhizospheres develop, mature and age. After death of roots, they remain as a relic in soil, commonly as a biopore which, in hard, hostile soils, is occupied by roots of following crops (McCully 2005). Stewart et al. (1999) defined the macropore sheath as the zone around a macropore in which 80% of the roots in the soil are located. In 'hostile' soils, the macropore sheath is small and the roots are concentrated in the immediate vicinity of the macropore. 'Hostile' is a descriptor that has been used to convey the difficulty of many introduced crop and pasture plants to cope with duplex soils having high-strength B horizons. In less hostile soils, the influence of the macropore sheath extends further into the soil matrix, and roots are more evenly distributed in the soil. Few studies have looked at the chemistry of remnant rhizospheres in soil. Most studies have been carried out at the scale of millimetres and on soils in which the natural structure has been destroyed. In the study by Stewart et al. (1999), a 3-mm annulus around the macropore was scraped and separated from the matrix and analysed for several elements and microbiological activity. Studies on duplex soils have shown that the environment around such a macropore has higher organic carbon (C), total nitrogen (N), bicarbonate-extractable phosphorus (P), calcium (Ca), copper (Cu), iron (Fe), and manganese (Mn), and supports higher populations of bacteria, fungi, and actinomycetes (i.e. Pseudomonas spp., Bacillus spp., cellulolytic bacteria, cellulolytic fungi, nitrifying bacteria, and the root pathogen Pythium) than the bulk soil (Pierret et al. 1999; Pankhurst et al. 2002).

Because root growth in hostile subsoils is dependent on pore character, there is a need to understand the distribution of nutrients in relation to pore surfaces. The distribution of micronutrients, the highly heterogeneous nature of soils, and especially their pore surfaces require techniques capable of high resolution and high surface sensitivity. In a previous study (Jassogne et al. 2009), we employed synchrotron-based X-ray techniques to produce high-resolution maps of the distribution of Ca, Mn, Fe, zinc (Zn), and Cu. Synchrotron radiation allowed differentiation of these elements with greater certainty than normal X-ray techniques of lower resolution. Our previous study (Jassogne et al. 2009) showed no detectable difference in speciation of these elements at the pore surface and <500 [micro]m from it. We concluded that, although the influence of the micropore was to concentrate macro- and/or micronutrients within and/or in the immediate vicinity, there was no significant influence of the micropore on the chemical form of these elements. We also concluded that a larger area around the micropore should be studied to investigate whether the influence on the chemical form of these elements varies with scale.

There is a paucity of information at the nano- and micro-scale about the effect of roots on the chemistry of the pore surface and the extent to which synchrotron-based X-ray fluorescence ([micro]-XRF) and X-ray absorption near edge structure (XANES) spectroscopy can aid in these investigations. The X-ray beam can be focussed to a spot size of amplitude a couple of [micro]m to 25-50 [micro]m, depending on the beamline, using a combined harmonic rejection/vertical mirror. A study by Voegelin et al. (2007) used these techniques to investigate the distribution and speciation of arsenic (As) around roots in thin sections of riparian soils. Analysis of soil thin sections by [mu]-XRF and XANES has also been employed to investigate the speciation of Zn in clay soils (Manceau et al. 2004; Isaure et al. 2005) and the geochemistry of As, selenium (Se), and Fe in soil developed in pyritic shale materials (Strawn et al. 2002). The benefit of using thin sections is that the surface is smooth and flat. Thin sections are also easier to handle than intact samples. Nevertheless, impregnating a soil sample with a resin is invasive and the chemistry and structure of the sample could be altered. Drying the sample too quickly with acetone can make roots shrink and can give a misrepresentation of the soil/root contact. However, thin sections can be prepared in such a manner that the interface between soil and root is only minimally perturbed (Van Noordwijk et al. 1992).

In this study, we analyse aggregates of soil in which the original structure has been maintained and thin sections of undisturbed soil at [micro]m-scale in order to resolve the distribution of Ca, Fe, Mn, Zn, and Cu around micropores by [mu]-XRF and their speciation by XANES. The locus of our study was the E horizon--B horizon boundary, the site in the profile of abrupt texture contrast. Because of the novelty of this study, it was important to investigate other techniques that could confirm our findings. In our previous study (Jassogne et al. 2009), some observations by X-ray absorption spectroscopy were confirmed by scanning electron microscopy fitted with an energy-dispersive X-ray analyser (SEM-EDXA), but the instrument was not sensitive enough to study all the elements of interest. Because laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS) can provide spatially resolved information at detection limits of ppm for many elements (Jimenez et al. 2007), this technique was employed. In this paper, LA-ICP-MS was used to determine the distribution of Ca, Mn, Fe, Zn, and Cu along a transect crossing a micropore.

Materials and methods

Intact soil cores (50 cm long, 15 cm diameter) were taken from an agricultural site in southern Australia (33[degrees]54 S, 137[degrees]47 E). The soil was a Red Sodosol in the Australian Soil Classification (Isbell 1996) or a Typic Natrixeralf (Soil Survey Staff 1999). It consisted of A and E horizons of sand texture overlying a sodic B horizon of clay texture at ~35 cm. The general characteristics of this soil are presented in another paper (Jassogne et al. 2009). Intact soil segments (10 cm by 10 cm by 10 cm) were excised from the zone around the E-B boundary (hereafter called the interface). These segments were impregnated with an epoxy resin, and sections of thickness 20 [micro]m were prepared. Soil clods (approx. 1.5 cm by 1.5 cm by 0.7 cm) were isolated from the interface. A criterion for selection of the clods for analysis was that they had distinguishable root channels on their outer surfaces. Micropores were selected on two of the clods. The channels selected for analysis in thin sections were those containing either a decaying root or organic coatings on their surfaces (Fig. 1). In two small pores at the top of the B horizon, it was possible to excise with a fine needle and scalpel a sufficient and coherent amount of organic material for radiocarbon analysis at the Australian Nuclear Science and Technology Facility at Lucas Heights, near Sydney, by accelerator mass spectrometry. Ages were 250 and 450 years BP.

The distributions of Fe, Mn, Cu, Zn, and Ca around the selected pores were mapped by [micro]-XRF, the speciation of Mn, Fe and Zn by [mu]-XANES, and that of Cu by [mu]-X-ray absorption fine structure spectroscopy ([mu]-XAFS). The [mu]-XRF, [mu]-XANES, and [mu]-XAFS data were collected at beamline 13-BM-GSECARS (GeoSoilEnviroConsortium for Advanced Radiation Sources) at the Advanced Photon Source (APS), Argonne National Laboratory, Argonne, IL. The electron storage ring operated at 7 GeV with a top-up fill status. This bending magnet beamline is specialised for earth and environmental science research. The [mu]-XRF maps and [mu]-XANES spectra were collected at ambient temperature in fluorescence mode, except for the [mu]-XANES spectra of the standards, which were collected in transmission mode. The [mu]-XRF microprobe at APS beamline 13-BM is capable of collecting fluorescence data with a 10-30-[micro]m beam spot size range and 10-50 mg [kg.sup.-1] sensitivity, thereby allowing the study of elements at low concentration in complex soil samples.

The XRF maps were taken at two energies. The high-energy map was taken at 10 500 keV and showed the distribution of Fe, Zn, and Cu. The low-energy map was taken at an energy of 7050 eV. This is below the absorption edge of Fe to avoid interference from background Fe fluorescence for elements (in our study, Mn) with an absorption edge less than that of Fe and located close to the Fe absorption edge.

The intact samples and the thin sections were mounted on the rotation axis of an X-Y-[theta] stepping motor stage. Fluorescence data were collected for an area 10 000 [micro]m by 200 [micro]m on the first intact sample, an area 10 000 [micro]m by 950 [micro]m on the second intact sample, and two areas 2400 [micro]m by 1000 [micro]m on the thin section. The step size was 50 [micro]m for the intact sample and 25 [micro]m for the thin sections using a solid-state energy dispersive X-ray detector that allowed simultaneous detection of fluorescence signals from multiple elements. Aluminium foil was used to diminish the background fluorescence from Fe. The fluorescence signal from a given element is proportional to the integrated number of atoms of that element along the transect of the synchrotron beam.

Hotspots (zones of relatively high concentration) of the elements of interest were chosen based on the XRF maps.

Selecting these points allowed collection of XAFS spectra, especially for elements present in very low concentrations. Hotspots were randomly selected for each element (Mn, Fe, Cu, and Zn), some close to the pore surface, some further into the soil matrix. A similar procedure was adopted for the thin section analysis. Three [mu]-XANES spectra were collected over the energy range of -200 to + 600 eV above the K-edge. The XANES and XAFS spectra were collected around the absorption edges of the elements of interest: Mn, 6539 eV; Fe, 7112 eV; Cu, 8979 eV; Zn, 9659 eV. Additionally, the XANES and XAFS spectra of Fe, Mn, Cu, and Zn standards were collected. Standards were chosen carefully according to the knowledge of the type of soil. For example, the soil had a strong red colour, which indicated that it potentially contained much oxidised Fe. The standards selected for Fe were fayalite ([Fe.sub.2]Si[O.sub.4]), magnetite ([Fe.sub.3][O.sub.4]), goethite (FeOOH), siderite (FeC[O.sub.3]), vivianite ([Fe.sub.3](P[O.sub.4]).8[H.sub.2]O), hematite ([Fe.sub.2][O.sub.3]), green rust-Cl ([(Fe, [Mg.sup.2+]).sub.6] [([Fe.sup.3+]).sub.2][(OH).sub.18].4[([H.sub.2]O).sub.18]Cl), and green rust-S[O.sub.4] ([(Fe,[Mg.sup.2+]).sub.6] [([Fe.sup.3+]).sub.2][(OH).sub.18].4[([H.sub.2]O).sub.18]S[O.sub.4]). The standards selected for Mn were birnessite [([(Na, Ca).sub.0.5]([Mn.sup.4+], [Mn.sup.3+]).sub.2] [O.sub.4].1.5[H.sub.2]O), hureaulite ([(Mn, Fe).sub.5][H.sub.2][(P[O.sub.4]).sub.4].4[H.sub.2]O), manganocalcite (Mn-CaC[O.sub.3]), Mn-carbonate (MnC[O.sub.3]), Mn-sulfate (MnS[O.sub.4]), bixbyite ([Mn.sub.2][O.sub.3]), pyrolusite (Mn[O.sub.2]), and switzerite ([(Mn, Fe).sub.3][(P[O.sub.4]).sub.2].7[H.sub.2]O). The standards selected for Cu were azurite ([Cu.sub.3][(C[O.sub.3]).sub.2][(OH).sub.2]), calcosiderite (Cu,[Fe.sub.6][(P[O.sub.4]).sub.4][(OH).sub.8].4([H.sub.2]O O)), cuprite ([Cu.sub.2]O), libethenite ([Cu.sub.2](P[O.sub.4])(OH)), malachite ([Cu.sub.2](C[O.sub.3])[(OH).sub.2]), nissonite ([Cu.sub.2][Mg.sub.2][(P[O.sub.4]).sub.2][(OH).sub.2].5([H.sub.2]O)), pseudomalachite ([Cu.sub.5][(P[O.sub.4]).sub.2][(OH).sub.4]), tenorite (CuO), and CuS[O.sub.4]. The standards selected for Zn were ferrihydrite adsorbed Zn (Zn-[Fe.sub.5][O.sub.3][(OH).sub.9]), franklenite (Zn,[Mn.sup.2+],[Fe.sup.2+]) [([Fe.sup.3+],[Mn.sup.3+]).sub.2][O.sub.4]), hopeite ([Zn.sub.3][(P[O.sub.4]).sub.2].2([H.sub.2]O)), hydrozincite ((Zns[(C[O.sub.3]).sub.2][(OH).sub.6]), scholzite (Ca[Zn.sub.2][(P[O.sub.4]).sub.2]. 2([H.sub.2]O)), smithsonite (ZnC[O.sub.3]), willemite ([Zn.sub.2]Si[O.sub.4]), and Zn-sulfate (ZnS[O.sub.4]).

The XANES spectra of the randomly chosen hotspots were averaged, the edge energy was calibrated, and the spectrum normalised. Linear combination fitting was applied using IFEFFIT software on the pre-processed XANES spectra of the hotspots (Newville 2001). For each selected hotspot, the combination with the lowest [chi square] was chosen as the most likely combination of compounds in that hotspot. The accuracy of the fitting depends on how well the standards represent the data. A reduced [chi square] < 1 indicated a reliable fit. Owing to the limited number of standards, the best fit composition may not give the true composition, although it can provide an indication of primary forms of the element of interest and describe the chemical differences among the selected hotspots in a spatially resolved manner.

Subsequently, impregnated soil samples were chemically analysed with an Agilent 7500cs ICP-MS (Agilent Technologies, Santa Clara, CA). The regions of interest were ablated using a high-performance New Wave Nd:Yag 213 UV laser ablation system (ESI, Portland, OR). An optical microscope was used to find pores in the impregnated samples with a thickness of ~0.5 cm and a length of 3 cm. The pores did not always obviously contain organic matter.

With the laser, the samples were ablated across the micropores over a length of 2 mm. The laser ablated at a speed of 10 [micro]m [s.sup.-1] and the spot size was 30 [micro]m. The sensitivity was 4.7 mg [L.sup.-1] for Ca, 280 ng [L.sup.-1] for Mn, 86 [micro]g [L.sup.-1] for Fe, 1.9 [micro]g L.sup.-1] for Zn, and 290 ng [L.sup.-1] for Cu. Measurements were qualitative and only gave a representation of the depletion or accumulation of elements along the micropore. For quantitative measurements, calibration is necessary. This could be done with homogeneous samples. However, this would have defeated the purpose of the study, as our objective was to characterise the heterogeneity of the elements in the immediate vicinity of the micropores of interest. Another reason why quantitative measures were not possible was that the depth to which the laser ablated, and hence the volume of soil nebulised, was not always constant (Weis et al. 2005).

Results and discussion

Of the few pores from the intact samples and the thin sections that were studied, only one representative of each sample type was selected for consideration in this section of the paper. An intact sample containing a black decaying root was scanned over an area of 1 cm by 0.2 cm. The XRF images showed that Ca was concentrated in the channel containing the root (Fig. 1). The pore selected contained organic matter from a decaying root, and this may have been a source of the Ca, given that roots can accumulate Ca (Singh and Jacobson 1979). Another source may be Ca from the soil solution adsorbed onto the organic matter. Pores in the thin sections did not always contain decaying organic matter but pores selected always had coatings of organic matter (Fig. 2). As shown on the distribution maps, Ca was also concentrated in the pores (Fig. 3). In this case, Ca could have been adsorbed from the soil solution onto the pore surface. So, it seems that root activity concentrates Ca at pore walls and in pores solely by organic matter decay, or water extraction by roots can also be responsible for accumulation of Ca in and around pore walls. With the techniques used in the current study (synchrotron-based hard X-ray absorption spectroscopy) it was not possible to directly obtain chemical form(s) of Ca accumulated in and at the surface of pore walls. These soils were alkaline and pH generally increases with depth. Co-located elements (i.e. Cu) were mainly in carbonate forms, and therefore, it is possible that Ca accumulated, at least in part, as Ca-carbonate. There is ample of evidence of Ca-carbonate precipitation occurring in root biopores, and in the rhizosphere (Jaillard 1982; Callot et al. 1983; Hinsinger 1998).

The correlation graphs originating from the XRF distribution maps showed that Mn and Zn were always strongly correlated with Fe in the intact samples ([R.sup.2] for Fe and Mn = 0.93, Fig. 1) and the thin sections ([R.sup.2] for Fe and Mn = 0.92, Fig. 3). The correlations of Mn and Fe were based on the low-energy maps taken below the absorption edge of Fe. Calcium and Cu were much less positively correlated with Fe than Mn and Zn. Calcium was mainly accumulated in pores, whereas Fe, Mn, Cu, and Zn were mainly accumulated in soil. Calcium, Mn, Zn, and Cu were always more correlated with Fe in the intact samples than in the thin sections, a result that has its explanation in the difference in effective sampling depth of the two sample types. The pores selected were always those exposed on the surface of the samples. Fluorescence X-ray signals measured in these experiments could have escaped from a maximum sample depth of ~50 [micro]m. Given that intact samples were ~10mm thick, spectral information will have been gathered, not only from the pore surface but also from the soil matrix. Because the soil contains a total Fe concentration of ~4%, a considerable contribution to the Fe spectral signatures will have come from the matrix. The thickness of the thin sections was only 20 [micro]m; therefore, the influence of matrix Fe would have been less significant.

Data from hotspots suggested that most of the Mn existed in reduced form (Table 1). More than 50% of Mn occurred as Mn-phosphate-like species (hureaulite and switzerite), and those species could also contain reduced Fe. In contrast, the Mn hotspots selected in the intact samples had a significant fraction of Mn as Mn(IV) oxides (birnessite and Mn[O.sub.2]) in addition to Mn-phosphate-like species. This was not observed in the thin sections. It is, however, not certain whether this was an artefact of thin-section preparation, beam-induced reduction of Mn in soil thin sections (i.e. due to interaction with resin), or due to the fact that larger soil volume was exposed in the intact sample XANES data collection. Furthermore, the measurements close to the pore surface did not differ from the ones further into the soil matrix (Fig. 4).

In most Sodosols, only a small proportion of Fe is available for plants because of the oxidised form in which the Fe is present. The three chemical conditions and processes primarily affecting Fe availability to plants are pH, redox status, and chelation (McFarlane 1999). The distribution maps showed that there was no enrichment of Fe around the selected pores (Figs l and 3) but that it was distributed randomly throughout the areas chosen for analysis. The XANES spectra suggested that Fe was mostly present in oxidised form (Table 2). Oxide-like bindings such as those of goethite and hematite were found in the hotspots selected in the intact samples and the thin sections. Some spots in the intact samples appeared to contain green rust-Cl-like and green rust-S[O.sub.4]-1ike bindings, but these forms were not found in the thin sections. In contrast, magnetite was always found in the thin sections (except for one hotspot) but never in the intact samples. Green rust [((Fe, [Mg.sup.2+]).sub.6][([Fe.sup.3+]).sub.2][(OH).sub.18].4[([H.sub.2]O).sub.18]) and magnetite ([Fe.sub.3][O.sub.4]) are both oxides with a mixture of oxidised and reduced Fe. The only conclusion that could be made was that in the selected hotspots, a mixture of oxidised and reduced Fe was present. The hotspots selected in the thin sections always had a higher proportion of mixed oxidation forms of Fe than hotspots selected on the intact samples. Therefore, it could not be concluded that the Fe speciation in the intact sample was different from the ones in the thin sections. The amount of standard used in this type of study is limited. Given that soil is highly heterogeneous, we could not state that the bindings in the hotspots were exactly the same as the bindings of the standards. We concluded that, in all of these hotspots, FeO-like minerals were present and that these were a mixture of Fe in the II and III oxidation states similar to the ones found in the standards (Fig. 5). In only one hotspot in the thin section was Fe found in a phosphate binding (vivianite).

Zinc sometimes accumulated around the micropores but was also present in higher concentrations away from the pore (Figs 1 and 3). Linear combination fitting of the XANES and XAFS region of the absorption spectra showed that the speciation of Zn for the hotspot at the edge of the pore and further into the soil matrix chosen in the intact samples was very similar and in forms resembling hydrozincite, Zn-sulfate, and willemite (Table 3, Fig. 6). The same was found for the hotspots selected on the thin sections; zinc was always found associated with sulfates at the pore surface. Franklenite-like forms were found at the pore edge, whereas Zn adsorbed on ferrihydrite was found in the soil matrix. Only one instance of smithsonite-like bindings was found, and this was at the pore surface. This could be due to the higher C[O.sub.2] levels inside soil pores, which favour the formation of carbonates. The one occurrence of scholzite was in the soil matrix.

Copper was only present in small amounts in the soil (<10 [micro]g [g.sup.-1] in the whole soil profile). The XRF maps of the thin sections showed that Cu was enriched at the edges of the areas where Ca was located or in the same areas (Fig. 3). These areas of enrichment were coincident with organic matter coatings. Previous studies have found that Cu is associated with organic matter (Jacobson et al. 2007). In this study, only two hotspots in the area close to the pore in the thin sections could be analysed because of the low concentration of Cu in the soil. The components resulting from the linear combination fitting were different for both hotspots. However, both were composed of ~70% carbonate and 30% phosphate (Table 4). Again, this could be due to higher levels of C[O.sub.2] in, and in the vicinity of, soil pores, favouring the formation of carbonates.

The distribution of elements of interest across a section of a micropore in impregnated samples was measured by LA-ICP-MS and compared with XANES and XAFS data of the same section. Measurements from 0 s to l0 s at the beginning of the x-axis could not be accounted for, as the instrument always needed a period to adjust (Figs 7 and 8). By viewing the ablating point on the sample on the screen of the microscope and comparing it with the counts of the elements detected, it was established that the decrease in counts of silicon (Si) was a sensitive measure of the location of the micropore, because we can expect Si distribution abundantly all throughout soil but low or closer to background concentrations wherever we have pores. The point with the lowest counts was the middle of the pore and this can be attributed to pore geometry, i.e. approximate cylindrical shape of pores. Although all of the [mu]-XRF maps indicated that Ca was concentrated in the micropores, Ca was detected in only some pores by the LA-ICP-MS. The graphs, however, show that wherever there was an accumulation of Ca, there was also an accumulation of Fe and Mn (see Fig. 7 ~20 s, Fig. 8 ~10-50 s). This contrasts with the distribution maps by [mu]-XRF. The differences are probably a result of the different volumes of soil material sampled during measurement; sampling depth (effective fluorescence signal depth) for [mu]-XRF was 50 [micro]m, whereas that for LA-ICP-MS was greater. Because Fe is relatively depleted at the very surface of the micropore, LA-ICP-MS will detect a greater proportion of Fe than [mu]-XRF. There were accumulations of Mn, Zn, and Cu in proximity of the micropore. Note that care must be taken when interpreting results obtained by LA-ICP-MS. Elemental fractionation depends on characteristics of the sample, such as optical absorption behaviour. In a heterogeneous medium such as soil, this will vary between samples, and therefore, overcoming this problem for matrix-independent quantification becomes a problem (Weis et al. 2005). The high degree of heterogeneity of the elements in the samples and their inhomogeneous distribution makes it impossible to have precise and accurate results that allow quantification (Jimenez et al. 2007).

Every pore created by roots and used by subsequent roots has a different history. The inhomogeneity in elemental concentration and spatial distribution will be greater at smaller scale than at larger scale. Rhizosphere chemistry will depend on the type of root (e.g. root hair, mature root), state of decomposition, extent and diversity of occupancy of pore, and types of plants. Further complexity arises from transport of particles in suspension and solutions, a process dependent on a range of factors such as pore size and pore continuity. Surface analytical techniques such as those employed here have a significant role to play in refining our understanding of nutrient form, concentration, and availability and how plant roots affect these in space and time.

In this investigation we attempted to study microstructure, in as undisturbed condition as possible, by using intact soil aggregates and thin sections prepared following vacuum impregnation with resin. However, many surface-sensitive techniques require a flat surface. Intact samples cannot be polished, and when surfaces are flattened, smearing occurs, which alters the organisation of soil particles and could lead to problems when the chemistry of the surface is studied. Soils of low coherence fragment readily. We attempted to study the chemical nature of the rhizosphere across the E horizon-B horizon boundary, but the samples fragmented and our study was confined to the upper B horizon.


This study shows that one pore can be drastically different from another. However, there was no difference in chemistry of these elements at the pore surface and <500 [micro]m (Jassogne et al. 2009) to 10000 [micro]m from it (current study). As observed for micropores in our previous study (Jassogne et al. 2009), it appeared that the influence of the micropore was to concentrate Zn, Mn, and Cu within and in the immediate vicinity of it but that there was no significant influence of the micropore on the chemical form of these elements. The chemical form of these three elements was similar at the pore surface and in the matrix. A larger area around the micropore may need to be studied to see whether the influence on the chemical form of these elements varies at a larger scale (>1 cm).

The difference in micro-spatial chemistry between the thin sections and the intact samples can be attributed to the thickness of the sample analysed. This resulted in Mn, Zn, and Cu having stronger correlations with Fe in the thin sections than in the intact samples, suggesting that thinness of samples is important to define 'real' elemental relationships. Differences observed between [mu]-XRF and LC-ICP-MS can be mainly attributed to lower detection limit of LA-ICP-MS compared with synchrotron based [mu]-XRF and the differences in effective sampling depths by these techniques. The combination of these non-invasive techniques, especially synchrotron based X-ray techniques, has given more insights in root--soil interactions. 10.1071/SR11312


We thank the Australian Government for the International Postgraduate Research Scholarship of Laurence Jassogne. We also thank Professor Hans Lambers for support. This work was performed at GeoSoilEnviro CARS (GSECARS), Sector 13, Advanced Photon Source (APS), Argonne National Laboratory. GSECARS is supported by the National Science Foundation Earth Sciences (EAR-1128799) and Department of Energy--Geosciences (DE-FG02-94ER14466). Use of the Advanced Photon Source was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract No. DE-AC02-06CH11357. We especially want to thank Matt Newville at GSECARS for the invaluable suggestions for sample setup and support for XRF/XAS data collection. The authors would also like to thank Angus Netting for the help with LA-ICP-MS analysis at Adelaide Microscopy. The authors would also like to thank the Australian Institute for Nuclear Science and Engineering (AINSE Ltd) for providing financial assistance (Award No. AINGRA06030P) to enable work on carbon dating of organic matter to be conducted. This work was supported by the Australian Synchrotron Research Program (ASRP) which is funded by the Commonwealth of Australia under Major National Research Facilities Program. The Cooperative Research Centre for Plant-based Management of Dryland Salinity also partly funded this research.

Received 26 November 2011, accepted 18 July 2012, published online 15 August 2012


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Laurence Jassogne (A), Ganga Hettiarachchi (B,C,E), Ann McNeill (C), and David Chittleborough (D)

(A) School of Plant Biology, University of Western Australia, Crawley, WA 6907, Australia.

(B) Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA.

(C) School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA 5064, Australia.

(D) School of Earth and Environmental Sciences, University of Adelaide, Waite Campus, PMB 1, Glen Osmond, SA 5064, Australia.

(E) Corresponding author. Email:

Table l. Fractions of manganese species in selected Mn-hotspots
in the area close to and far from the soil micropore in an intact
soil sample and in a thin section

Chi-square statistic: [chi square]=[summation][[(fit -
data)/[epsilon]].sup.2]/(Ndata - Ncomponents); here, [epsilon] is the
estimated uncertainty in the normalised XANES data (taken as 0.01
for all data); the sum is over Ndata points, and Ncomponents is
the number of components in the fit. The total percentage was
constrained to be 100% in all fits. Typical uncertainties in the
fractions listed for each standard component are 5%

                           Binessite    Hureaulite     Switzerite

Close                1    0.00          0.00          0.76
Far                  2    0.11          0.64          0.25
                     3    0.056         0.00          0.58
Close thin section   4    0.00          0.36          0.64

                          [Mn.sub.2]                    Reduced
                           [O.sub.3]    Mn[O.sub.2]   [chi square]

Close                1    0.24          0.00           0.011
Far                  2    0.00          0.00           0.018
                     3    0.00          0.36          <0.010
Close thin section   4    0.00          0.00          <0.010

Table 2. Fractions of iron species in selected Fe-hotspots in the
area close and far from the soil micropore in an intact soil
sample and in a thin section

Chi-square statistic: [chi square] = [summation][[(fit -
data)/[epsilon]].sup.2] /(Ndata - Ncomponents); here, [epsilon] is
the estimated uncertainty in the normalised XANES data (taken as
0.01 for all data); the sum is over Ndata points, and Ncomponents
is the number of components in the fit. The total percentage was
constrained to be 100% in all fits. Typical uncertainties in the
fractions listed for each standard component are 5%

                          [Fe.sub.2]               Green     rust-S
                          [O.sub.3]    Goethite   rust-Cl   [O.sub.4]

Close                 1   0.64         0.12       0.069     0.17
                      2   0.49         0.51       0.00      0.00
                      3   0.69         0.31       0.00      0.00
Far                   4   0.58         0.42       0.00      0.00
                      5   0.72         0.23       0.054     0.00
                      6   0.05         0.95       0.00      0.00
Close thin section    7   0.69         0.00       0.00      0.00
                      8   0.59         0.41       0.00      0.00
                      9   0.24         0.52       0.00      0.00
                     10   0.26         0.56       0.00      0.00
                     11   0.00         0.77       0.00      0.00
Far thin section     12   0.18         0.61       0.00      0.00
                     13   0.34         0.55       0.00      0.00

                          [Fe.sub.3]                 Reduced
                          [O.sub.4]    Vivianite   [chi square]

Close                 1   0.00         0.00        <0.010
                      2   0.00         0.00        <0.010
                      3   0.00         0.00        <0.010
Far                   4   0.00         0.00        <0.010
                      5   0.00         0.00        <0.010
                      6   0.00         0.00        <0.010
Close thin section    7   0.13         0.18        <0.010
                      8   0.00         0.00        <0.010
                      9   0.23         0.00        <0.010
                     10   0.18         0.00        <0.010
                     11   0.23         0.00        <0.010
Far thin section     12   0.21         0.00        <0.010
                     13   0.11         0.00        <0.010

Table 3. Fractions of zinc species in selected Zn-hotspots in the
area close and far from the soil micropore in an intact soil
sample and in a thin section

Chi-square statistic: [chi square]=[summation][[(fit -
data)/[epsilon]].sup.2]/(Ndata-Ncomponents); here, [epsilon] is
the estimated uncertainty in the normalised XANES data (taken as
0.01 for all data); the sum is over Ndata points, and Ncomponents
is the number of components in the fit. The total percentage was
constrained to be 100% in all fits. Typical uncertainties in the
fractions listed for each standard component are 5%

                 Franklenite   Hydrozincite   Zn-sulfate   Ferihydrite
                                                           adsorbed Zn

Close        1   0.00          0.66           0.00         0.00
             2   0.00          0.23           0.00         0.00
Far          3   0.00          0.18           0.60         0.00
Close        4   0.00          0.49           0.12         0.00
  thin       5   0.00          0.34           0.22         0.00
  section    6   0.29          0.39           0.17         0.15
             7   0.00          0.00           0.38         0.10
             8   0.29          0.00           0.40         0.00
Far thin     9   0.00          0.00           0.51         0.087
  section   10   0.00          0.49           0.00         0.00
            11   0.00          0.00           0.00         0.30

                  Willemite    Smithsonite    Scholzite      Reduced
                                                           [chi square]

Close        1   0.34          0.00           0.00         0.035
             2   0.00          0.77           0.00         0.15
Far          3   0.23          0.00           0.00         0.013
Close        4   0.39          0.00           0.00         0.00
  thin       5   0.44          0.00           0.00         0.00
  section    6   0.00          0.00           0.00         0.00
             7   0.52          0.00           0.00         0.00
             8   0.00          0.31           0.00         0.00
Far thin     9   0.40          0.00           0.00         0.00
  section   10   0.16          0.00           0.35         0.016
            11   0.70          0.00           0.00         0.064

Table 4. Fractions of copper species in selected Cu-hotspots in
the area close to the soil micropore in a thin section

Chi-square statistic: [chi square] = [[(fit -
data)/[epsilon]].sup.2]/(Ndata - Ncomponents); here, [epsilon] is
the estimated uncertainty in the normalised XANES data (taken as
0.01 for all data); the sum is over Ndata points, and Ncomponents
is the number of components in the fit. The total fraction was
constrained to be 100% in all fits. Typical uncertainties in the
fractions listed for each standard component are 5%

                         Malachite   Nissonite   Azurite

Close thin section   1     0.69        0.31       0.00
                     2     0.00        0.00       0.70

                         Calcosiderite     Reduced
                                         [chi square]

Close thin section   1       0.00            19.55
                     2       0.30           121.63
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Author:Jassogne, Laurence; Hettiarachchi, Ganga; McNeill, Ann; Chittleborough, David
Publication:Soil Research
Geographic Code:8AUST
Date:Aug 1, 2012
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