Methodological limitations

Soils and sediments provide their own set of challenges for using pXRF, either in or ex situ. This section considers the major known limitations; water or moisture content, sample heterogeneity, sample geometry and in situ analysis before considering instrumental elemental range.

Water content

This is a major inhibitor for analysis. Water attenuates the radiation both from the x-ray source, and the emitted radiation by a factor which varies according to the elements of interest. Bastos et al. (2012) found moisture content attenuated the signal by up to 20% compared to dried and ground samples when measuring Mn, Ni, Zn, Br, Y, Nb, Ti, Fe, Zr and Pb. Berger et al. (2009) demonstrated that when measuring the lighter elements, S, Al, Ca, P, Si, Fe, in sediment samples using a helium purge, water content was negatively correlated with element count. The difference in measured values between a dry sample and samples with 50% water was over 50%

in some cases, but the degree varied by sample and element. The difference of 30% in findings for the degree of attenuation is probably due to using the helium purge and a focus upon lighter elements. A further study by Coronel et al. (2014) used pXRF on four samples with differing properties (sand, clay loam, high organic matter, low organic matter) with varying degrees of moisture saturation. For the elements Cu, Zn, Sr and Zr, the results were significantly lower in the saturated soil in all cases. The effects were greater in the high organic soil for all elements except Fe, due to the soil’s ability to retain a large water volume. However, on the other soil

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types, Mn and Fe increased with moisture content, attributed to finer particles being held in solution closer to the instrument than in dry samples. They conclude that Cu, Fe, Mn, Sr and Zn levels in soils with low organic content could be measured in situ or without drying. This partly contradicts Berger et al. (2009), and Bastos et al. (2012), and as the study is based upon four samples, definitive conclusions cannot be drawn. A further example is provided by Crooks et al.

(2006), who conclude that moisture content, if known, can be corrected for, although they found a direct correlation between moisture content and lower readings in samples when measuring heavy metal concentrations. Therefore, there is a degree of attenuation with both lighter and heavier elements in moist samples.

In a larger study by Schneider et al. (2015), 215 samples from differing environmental conditions tested both the correlation between elemental results from aqua regia digestion using ICP-AES and pXRF. Going further, the samples were then tested using pXRF dry, recently wetted, and two days after wetting, in which time the samples had been left in ambient air conditions. The results strongly suggest that all elements were affected by moisture content, although in differing degrees. The attenuation by moisture content could be fixed using the Lambert-Beer equation, that is to say the attenuation is to some degree predictable based upon known moisture content for all elements measured in their study.

These published studies also clearly demonstrate the link between moisture content, particle size and signal diffraction, meaning wet, coarse samples are poor representatives of actual content (Berger et al., 2009). Ge et al. (2005) suggest this can be corrected using a formula based upon the direct correlation between the back-scattered radiation and the water content of the sample. This formula can be applied to samples with up to 20% moisture. Schneider et al. (2015) also concluded it was feasible to apply moisture corrections to elemental concentrations from samples. However for this to be possible, for in situ analysis, the soil water content has to be known for later data correction. It is also questionable whether one formula can correct for the effects of water in varied soil conditions, given that other mentioned studies found that the soil type strongly affected the elemental results when moist.

Sample heterogeneity

To return to the study mentioned above, Coronel et al. (2014) also studied the effect of grain size on pXRF readings. They conclude that sample heterogeneity, especially in situ, is a large source of error. However, their study concludes that sieving to 2 mm and grinding the samples in a porcelain mortar is adequate to compensate for this effect with the majority of elements.

Harking back to the previous section on representative sampling, how representative is 1 g or even 0.1 g of dried and crushed soil? This issue is not avoided by any other method employed in

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archaeological geochemistry, and the best solution is to increase sample density and be consistent in sampling method. An alternative solution can be to grind, homogenise and pelletize the samples prior to analysis to create a more uniform sample in terms of surface and texture; it also removes the air spaces within the samples to improve light element detection with or without helium purge when using pXRF. Obviously, this can significantly increase the sample processing time and cost, and does not remove the issue of soil heterogeneity over a site or archaeological surface.

Using XRF, the penetration for the analysis of heavier elements can be up to 2 millimetres in highly porous samples, whereas for the lighter elements only the surface is excited due to the lower KeV required (Berger et al., 2009). Davis et al. (2011) analysed obsidian samples using a lab XRF to examine the effect of sample dimensions. The minimum width of the sample in their study is 10-25 mm, however, this is dictated by the sample window, which ideally should be covered by the sample and the elements to be analysed. The thickness, they suggest, should be over 1.2 mm, but they note this is dependent upon the excitation energy used and the composition of the sample. This is based on ideal conditions, when results are to be as precise as possible, and the study does note that less than ideal sample sizes can produce viable results.

Surface geometry

The instrument assumes an infinitely thick, homogenous sample with a smooth surface (Charlton, 2013), which of course many archaeological samples are not, and cannot be without very undesirable damage or lengthy processing. Newer instruments also have modes or settings which automatically select the filter and elemental range suitable for the material, which can also have inbuilt assumptions over the sample texture. For example, the Niton XL3t GOLDD used in this research has a soils mode, which is designed for environmental monitoring. This mode assumes the sample is not ideal, but porous with an uneven surface geometry, whereas the metals mode assumes a homogenous and flat surface. Therefore the results are not purely a measure of sample processing, but of instrument setting and assumptions with the newer portable instruments. Additionally, Hunt and Speakman (2015) note that for ED-XRF instruments such as portable XRF instruments, the effect of surface geometry on sediment and clay samples is often small.

This stands opposed to studies by Crooks et al. (2006) and Coronel et al. (2014) who both suggest that increasing particle size is negatively correlated with elemental count for the studied elements. Technological developments have advanced since 2006, and the recommendation by Crooks et al. (2006) that sampled should be sieved to 125 µm is a reflection of this. In Coronel et al. (2014), 2 mm was deemed sufficient for dried samples analysed in a field laboratory. This

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change is perhaps a reflection of both the purposes of the study (land contamination and ethnographical geochemical survey), and the improvements in fundamental parameter calibration algorithms.

In the previously mentioned study, Davis et al. (2011) analysed the effect of surface angle on samples, and concluded this had a minimal effect for all but one of the elements they selected (Fe). This experiment, as they note, was on modern samples that have not been subject to weathering etc. as archaeological samples are. This experiment was also on obsidian, which as stated previously, is close to ideal. When considering soil and sediments, surface geometry is closely linked to sample heterogeneity, and can be partly mitigated by sample preparation methods such as sieving.

Trace element accuracy on site

There is a general consensus that pXRF instruments are internally stable and fairly precise (Nazaroff et al., 2010, Shackley, 2010). The accuracy has been questioned, which will be discussed further below and in chapter 4. Instrument precision and accuracy is easily measured and reconciled with the correct use of standard reference materials (SRM). Accuracy on-site and in situ can be problematic. Moisture content of the sample will vary from area to area due to a profusion of natural and man-made variations, which are not always quantifiable. In addition, holding four kilos perfectly steady for minutes at a time invites human error. With using pXRF, especially on-site, measuring absolutes cannot be an objective. As with all geochemical analysis in general, in situ results are not suitable for inter-site comparisons. It is the intra-site variability that is interpreted. Misinterpretation can occur, however, if the figures are affected by moisture, porosity and uneven surface geometry to the degree the results become unrepresentative or unreliable. So why not always do the analysis in a laboratory, where samples can be processed to be consistent, and suitable samples can be easily used without the added worry of contamination or other hindrances? The ability to answer questions on site is invaluable for targeting sampling or even excavation, and aiding in situ interpretation in order to improve recovery (Donais and George, 2013). As long as the limitations of the analysis are known, can data be useful without being accurate?

In a pilot study comparing pXRF on wet and dry samples to ICP using weak acid digestion, Nolan and Hill (2014) found that the wet samples analysed using pXRF were more broadly comparable to the ICP results than the dry samples. As Middleton (2004) suggested in his much referenced paper, when including the whole sample via total acid digestion for ICP, the geological signal can

‘drown’ out the anthropogenic enhancement. Nolan and Hill (2014) suggest this is the case with dry samples using pXRF, but offer no explanation as to why wet samples would be comparable

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to acid extracted samples for ICP. Logically, the attenuation of the signal from moisture, or indeed sample heterogeneity, would have equal effect on both the weakly held potentially anthropogenic elements and the soil matrix (see 2.4.2). However, they do note that dry pXRF samples did not appear to mirror the known distribution of archaeology on the test site, whereas wet samples and ICP results did. The fact that results do not meet predictions does not automatically mean the results are invalid; it could equally be the case that the predictions, based upon unexcavated archaeological features, were misinterpreted at the start. It is within the realms of possibility that archaeology has affected moisture retention, creating a ‘false’

reading with the wet samples that could be misinterpreted as elemental values alone, as opposed to physical sample properties. This illustrates the importance of carefully recording the soils during sampling in situ (and in cases where the sample is measured in laboratory conditions), as many desirable and undesirable effects on elemental retention can occur over short distances.

All the factors in this section were considered prior to and during the analysis of case study samples. Chapter 4 contains results from tests undertaken to assess instrumental accuracy and precision, moisture content and analytical time.