## 7.3 Sample Preparation Techniques and Measurement

### 7.3.1 Understanding the Origin of Sediment Source Fingerprints

In all tracer applications, successful implementation must be grounded in a thorough process understanding of the origins of tracer properties and the expected differences between sources. A hypothesis-led approach to source characterisation is recommended to connect the study to established theoretical contexts but also, through the hypothesis testing process, to identify attributes that are unique to the study site in question.

For example, in the context of FRNs and questions of sediment contributions from different land use within the catchment, the starting point is to hypothesise the relative proportions of the three FRNs in each land use based on local environmental knowledge of catchment land use history over recent decades and months. A strategic sample design programme can then be implemented to evaluate consistency of signature within source type areas and confirm differences between types (Fig. 7.3).

In the context of XRF geochemical signatures, a useful starting point is the consideration of the broadest control on differences within the local geology, i.e., weathering profiles and surface versus subsurface material. From here the influence of land use can be hypothesised, e.g., mixing of soil by ploughing to create new signatures and/or amendment by agrochemicals.

In the context of CSSI applications, source samples are targeted to those representing the major land uses in the studied catchment. Sampling should focus mainly on those land uses which are supposed to be susceptible to erosion. To account for spatial variability in $\delta(^{13}C)_{(FA)}$ values, the collection of enough replicates of every source is recommended to properly represent the land use. The CSSI sampling is focused on the uppermost soil layers as this layer is the most susceptible to erosion. Therefore, the sampling depth is usually 20 mm, i.e., the soil layer in which most of the organic matter is found. In some cases, for assessment of gully erosion, road erosion, and riverbank erosion, the sampling could involve also much deeper layers.

A sampling programme that represents all potential sources is of course the goal in any study, but users must also be aware that a missing source will not be identified by the mixing model—the model will always report to a sum of unity whether all sources have been represented by sampling or not.

### 7.3.2 Sampling Strategies

Sampling strategies should naturally be adapted to the specific challenges of each scale of study (Fig. 7.1). With increasing spatial scale comes increased field sampling, processing and analytical demand. Herein, it is useful to consider two broad
approaches to source definition that can be tailored to catchment-specific sample designs.

The *primary source approach* (Fig. 7.2) is a straightforward characterisation of each primary source within the system of interest and comparison to mixtures down-stream in lower-order channels. The use of spatially integrated composite samples from primary sources is widely recognised as a means to ensure representative characterisation of properties. For example, one sample might comprise 10–30 smaller samples taken from a designated area within the source zone. Replication is essential, and the user must satisfy themselves that the number of samples for a source classification is both environmentally and statistically representative to permit effective source discrimination. At the large catchment to basin scale, characterisation of all primary sources can become unwieldy and stretching resources as well as introduce complexity in unmixing model development. In this case, a *tributary approach* might prove useful where the mixtures in higher-order channels or receiving water bodies are compared back to the sediment mixtures in transit or storage in lower-order tributaries upstream (see also Chap. 2). The advantage of the approach is that hydrological processes above the tributary source end member will have created a spatially, and potentially temporally, integrated sample at the subcatchment outlet. A disadvantage is where management practice and erosion response within the lower-order tributary catchment areas are heterogeneous and complex. A hybrid model might use specific tributaries of known land use and/or source type as representative of a specific source end member, assuming other controls on soil properties e.g., geology are uniform. These challenges are explored in the case studies (Sect. 7.4).

### 7.3.3 Laboratory Analysis

#### Measuring FRNs via gamma spectrometry

Detail on gamma spectroscopic methods to determine FRN activity concentrations in soils and sediment is provided in detail by Iurian and Millward (2019). For all tracer applications, users need to carefully consider the particle size fraction that they wish to compare between source and sink (Laceby et al. 2017). Critically, one must compare like with like and further evaluate particle size assemblages to verify.

Once screened to the relevant particle size (e.g., < 63 µm) samples must be sealed in gas-tight and stored for 21 days to permit equilibration between ²¹⁴Pb and its parent radioisotope ²²⁶Ra prior to measurement by gamma spectrometry (Appleby 2001). Activity concentrations of the target radionuclides are measured using a low background HPGe Gamma spectrometry system. Instruments should be calibrated using soil material spiked with certified mixed radioactive standards supplied by a certified provider. All calibration relationships are generally derived using proprietary software and should be verified by inter-laboratory comparison tests, e.g., with reference materials supplied by the IAEA via worldwide proficiency tests. Total ²¹⁰Pb is measured by its gamma emissions at 46.5 keV and its unsupported
component calculated by subtraction of $^{226}$Ra activity, which in turn was measured by the gamma emissions of $^{214}$Pb at 295 and 352 keV. $^{137}$Cs is determined by its gamma emissions at 662 keV (with correction for $^{214}$Bi emissions). Short-lived $^{7}$Be is analysed via its emission at 477 keV. Count times typically range 24–48 h subject to activity concentrations.

## XRF spectrometry for elemental geochemistry

X-ray fluorescence (XRF) spectroscopy enables total element concentrations to be determined without the need for time-consuming and hazardous digestion procedures and has, thus, been widely applied in aquatic and marine contaminant research. XRF spectrometry is often employed as a complementary analytical technique in tracing studies with the potential to provide data for a wide range of elements (typically Na to U) at mg/kg concentrations. There are two major branches of XRF spectrometry: energy dispersive XRF (ED XRF) and wavelength dispersive XRF (WD XRF). Both techniques employ the same fundamental principles of ionisation in the sample material although the techniques differ in their dispersion and detection of secondary X-rays. WD XRF spectrometers provide the advantage of improved resolution and sensitivity for low energy elements (particularly Na to P), whereas the simultaneous nature of ED XRF spectra analyses provides a time advantage over WD XRF (the latter typically employing sequential detection of elements). Despite the differing detection methods, sample matrix and the manner in which the sample is presented to the spectrometer are equally important to both techniques. This is particularly relevant for the analysis of trace elements in sediments, which are often analysed as pressed powder pellets. Sample homogeneity is a key consideration for the operator, and it is crucial that the sample preparation ensures that particle size in the sample is uniform and below ~ 50 µm to reduce particle size and shadowing effects in the analysis layer (Willis et al. 2011). If the particle composition is also heterogeneous, such that an analyte is present in more than one mineral form, mineralogical effects can occur whereby the intensity of the analyte line is affected by differing attenuation properties of the particles. These matrix effects can only be eliminated by fusion methods, which involve mixing the sample with a flux material, typically lithium tetra/metaborate, at high temperature. The resulting glass disc provides a homogeneous geometry for analysis (Willis et al. 2014). Owing to the flux to sample ratios used in the fusion process, XRF analysis of fused materials is usually limited to major elements, which will remain detectable even when diluted by the addition of flux.

## Sample preparation and extraction for CSSI signatures

*Fatty acid (FA) extraction* is the first key step in the process. Several methods can satisfactorily extract FAs, namely Accelerated Solvent Extraction (ASE); Microwave-Assisted Extraction (MAE); Ultrasonic-Assisted Extraction (UAE) and Soxhlet. A detailed description and assessment of the performance, advantages and disadvantages of these methods can be found elsewhere (Jeannotte et al. 2008; Hewavitharana et al. 2020). FAs bound to the soil matrix can be extracted using hot/pressurised nonpolar solvents such as dichloromethane (DCM), or a mixture of solvents (e.g.,
DCM/Methanol) depending on the extraction method used. Samples must be dried before performing any extraction. This can be done by drying at 60 °C in a laboratory oven or using a freeze-dryer. Drying with anhydrous sulphate in direct contact with the wet sample or air-drying is also an alternative when no instruments are available. When using Soxhlet, soil, and sediment samples plus a pinch of anhydrous Na₂SO₄ (used to remove sample moisture) are put in cellulose filter containers, about 250–300 mL of DCM is added to each Soxhlet balloon, and heaters are turned on keeping temperature at 40 °C. The solvent will evaporate and condense in the reflux flask, drip onto the filter container and then the extract is collected into a round-bottom flask. When using ASE, soil and sediment samples are placed in stainless-steel cells and a proper extraction programme is selected. The DCM is injected into the cell, and the sample is heated and pressurised with N₂ (at 100 °C and 2000 PSI). The sample extract is then collected in a sealed vial. For MAE, samples are placed in extraction chambers, and depending on the sample amount, DCM:Metanol (4:1) solvent mixture is added and a proper extraction programme is selected (combining temperature, time and power). When using UAE, samples are transferred into Schott Duran flasks. The sample (10–20 g of dry sample) is then soaked in 100 mL of DCM and placed in an ultrasonic bath for at least 30 min. For both, MAE and UAE, samples must be filtered after the extraction process to remove particles using a suitable filter paper (e.g., Grade 1 Whatman). In addition, it is suggested that the extraction procedure for both (MAE and UAE) can be performed twice to assure quantitative (i.e., complete) extraction of the analytes. In this case, filtrates are combined. To reduce the solvent volume in the solution, a rotary evaporator is used. The temperature is set accordingly (around 40 °C depending on the solvent used), and the reduction process is interrupted when 2–5 mL of the solution remains in the rounded flask. The concentrated sample is then quantitatively transferred to a 10 mL vial using a Pasteur pipette. The vial is then inserted into a water bath at 40 °C, and the extract is subjected to a current of ultrapure N₂ to complete dryness.

Fatty acid derivatisation is the next step. Derivatisation for gas chromatography (GC) analysis is performed to increase volatility, improve separation and reduce tailing of the analytes. Because FAs have high polarity and low volatility, derivatisation is highly recommended (Brondz 2002). In this process, the polar carboxylic acid is converted to a methyl ester. The derivatisation process (i.e., esterification) results in a less polar molecule called Fatty Acid Methyl Ester (FAME). Although, there are several methods reported in the literature for FA derivatisation with reproducible results (Brondz 2002), the method described below is based on the use of methanol as derivatisation agent catalysed with boron trifluoride (BF₃). It is essential to remember that a portion of the methanol used during the derivatisation process must also be sent to the analytical laboratory to obtain the carbon isotopic value of the added methyl group. This will be further used to correct the isotopic values of the FAMES. It is advisable that all reagents should be prepared daily before starting the derivatisation procedure. To the dry extract, 1 mL of 5% of BF₃ in methanol (BF₃-MeOH) is added and mixed on vortex for 2 min. The samples are then placed in a test tube rack in a fan oven/block heater at 70 °C for 20 min and cool down at room temperature. Then, 1 mL distilled water and 1 mL of hexane/DCM (4:1) mixture
are added to the samples and mixed on vortex for 1 min. Posteriorly, samples are let to stand to allow the solvent layers to separate. Carefully, the upper layer (organic phase) is transferred from the 10 mL screw cap test tube to a 2 mL vial by using a Pasteur pipette. The procedure is repeated, but this time adding only 1 mL of hexane/ DCM mixture and transferring the upper layer again. At the end, the vial will contain about 2 mL of the FAME extract. It must be stored at 4 °C until analysis or fully dried with ultrapure N₂ if samples are being analysed in an external laboratory.

The δ(¹³C) values of the FAMEs are adjusted using the following formula to account for the addition of the methyl group added (Gibbs 2008):

$$ \delta(^{13}C)_{\text{FA}} = \frac{\delta(^{13}C)_{\text{FAME}} - (1-X)\delta(^{13}C)_{\text{methanol}}}{X}, $$

where X is the fractional contribution of the free fatty acid to the methyl ester (e.g., 22/(22 + 1) for a FA of 22 carbons); δ(¹³C)ₚmethanol is the isotopic composition of the methanol used during the derivatisation process; δ(¹³C)ₚFA refers to the corrected isotopic composition of the FA, and δ(¹³C)ₚFAME is the isotopic composition of the FAME provided by the instrument.

## IRMS analysis for CSSI

A gas chromatography-combustion-isotope ratio mass spectrometer (GC-C-IRMS) is used to analyse the δ(¹³C) value of fatty acid esters. Samples are injected into the gas chromatograph. Then the sample is vaporised at 280 °C. Thus, the analysed samples in a GC-C-IRMS system must be volatile. Subsequently, the FAMEs are pushed with ultrapure helium gas to pass through the chromatographic column, whose function is to separate them by molecular weight/boiling point and affinity with the stationary phase of the GC column.

From the GC column, the FAMEs go to the combustion reactor, whose function is to convert the compounds into CO₂ and H₂O. This reactor consists of an Alumina tube (Al₂O₃) at 1000 °C. Its internal part contains a wire (composed of copper, nickel, and platinum) that is purged with oxygen allowing oxidative combustion, where nickel acts as a catalyst. The combustion products pass through a moisture trap, where the water present in the gas mixture is removed. This trap consists of a selectively permeable membrane with a dry helium counterflow. The resulting CO₂ is directed to the IRMS device and δ(¹³C) for each fatty acid can be obtained. From an isotope ratio mass spectrometer (IRMS), it is possible to measure isotopic composition at low enrichment and natural abundance levels. It means that low variations in exceedingly small amounts of the heavier (or less abundant) isotope are detected in the presence of large amounts of the lighter isotope with high precision and accuracy.

## Particle size analysis

Tracers often display preferential association with particle size classes in source and channel material, with enrichment in finer particle sizes common for many elements and FRNs (Taylor et al. 2014; Laceby et al. 2017b). Where particle sorting occurs during sediment transport and storage, it is important for the user to account
for particle enrichment during tracing studies. Many studies account for sorting effects by directly analysing transported size fractions in both the source and stored sediment material. Other studies apply particle size correction factors based upon empirically derived relationships between particle size and tracer concentrations (Smith and Blake 2014; Laceby et al. 2017a). Particle size analyses of source and transported material may, therefore, be required. The traditional pipette method is based upon the use of a settling column and Stokes' Law to determine particle size by subsampling the suspension at a predefined depth and time. A more contemporary approach combines the use of Stokes' law with a paralleled X-ray beam to determine sediment concentration in the settling column (SediGraph). Laser diffraction is a commonly applied approach for particle size analysis offering an effective means of analysing large sample numbers and has been shown to provide good agreement with digital image analysis (Bittelli et al. 2019).

## 7.4 Applying Nuclear Tools for Tracking Sediment Pollution from Agriculture (Case Study)

### 7.4.1 Applying FRNs to Identify Agricultural Runoff Source and Connectivity

#### Context

The River Mease, located in central UK, is a mixed agricultural river basin in a temperate maritime climate. The land use comprises a range of cultivated areas growing fodder maize, potatoes and cereal crops with areas of pasture for livestock grazing where field sizes are of the order of 5–15 ha. The topography is gently undulating with low to moderate slopes. At the time of study (Blake et al. 2013), the main river system was classified as being in an ‘unfavourable condition’ for aquatic habitat with respect to high silt and phosphorus concentrations in the riverbed substrate.

Sediment and associated diffuse phosphorus pollution from agriculture were considered to be significant contributors to unfavourable condition. Water quality modelling had suggested that in the region of 40% of dissolved phosphorous, inputs to the catchment were derived from diffuse sources, with agricultural sources likely to be the main contributor. In parallel, sewage treatment works and domestic septic tanks had also been identified as potentially important sources of phosphorus, wherein the former was being tackled with improvements to phosphorus removal. The debate surrounding agricultural versus domestic sources of phosphorus was an important management question but was constrained by evidence being limited to phosphorus in the dissolved phase. Given that the greatest proportion of phosphorus delivered from hill slope to channel is in particulate form (Owens and Walling 2002) and potentially exchangeable with surface water once in aquatic storage (Jarvie et al.
2005a, b), a key research question arose regarding sediment sources in relation to agricultural activities.

## Sampling and analysis

To characterise source materials, ten spatially integrated samples were collected from representative areas of each primary source type that were relevant to FRN characterisation. These were cultivated soil, uncultivated soil, subsurface drains, soil material deposited on roads within the agricultural areas (considered as secondary sources) and stream channel banks.

To characterise sediment mixtures, samples of sediment stored within the channel bed matrix of six tributaries were collected in triplicate using a stilling well methodology early in the winter ‘wet season’ period. Suspended sediment samples were collected via time-integrated samplers for early and later stage rainstorm events during the main winter period. All samples were freeze-dried and gently disaggregated and sieved to < 63 µm. Samples were sealed into 50 mm Petri dishes and stored for 21 days to permit equilibration between $^{214}$Pb and its parent radioisotope $^{226}$Ra prior to measurement by gamma spectrometry (Appleby 2001). Activity concentrations of the target radionuclides were measured using a low background EG&G Ortec planar (GEM-FX8530-S; N-type) HPGe gamma spectrometry system at the University of Plymouth Consolidated Radio-isotope Facility. The instrument was calibrated using soil material spiked with certified mixed radioactive standards supplied by AEA Technology Plc. All calibration relationships were derived using EG&G Gamma Vision software and verified by inter-laboratory comparison tests with materials supplied by the IAEA, namely the worldwide proficiency test using moss soil (IAEA-CU-2009-03). Total $^{210}$Pb was measured by its gamma emissions at 46.5 keV and its unsupported component calculated by subtraction of $^{226}$Ra activity, which in turn was measured by the gamma emissions of $^{214}$Pb at 295 and 352 keV. $^{137}$Cs was determined by its gamma emissions at 662 keV (with correction for $^{214}$Bi emissions). An additional element to this study was analysis for short-lived $^{7}$Be, analysed via its emission at 477 keV. Count times were typically 48 h due to low activity concentrations and low sediment sample mass.

## Results and discussion

The three fallout radionuclides measured provided important information on sediment source dynamics. $^{7}$Be concentrations were observed to be high in all surface soil samples. The other FRN signatures of cultivated and uncultivated surface soil showed a pattern seen typically in previous UK studies (Walling and Woodward 1992) whereby uncultivated soils carried higher activity concentrations of $^{137}$Cs and $^{210}$Pb$_{xs}$ due to the shallow depth profiles of both radionuclides in undisturbed soil. Cultivated soil represents a mix of the surface and shallow subsurface soil materials which effectively lowers the activity concentrations of both FRNs. Channel banks materials had measurable amounts of $^{137}$Cs and $^{210}$Pb, but lower than surface soils but *undetectable* activity concentrations of $^{7}$Be. Material from field drain excavations had very low FRN concentrations across all three FRNs. The road-transported sediment in agricultural areas exhibited the greatest $^{210}$Pb$_{xs}$ activity concentration of
all materials sampled. These high activities were likely due to scavenging of $^{210}$Pb$_{xs}$ by soil in transit from surface pluvial runoff on the impervious surface (i.e., binding to suspended sediment in preference to the surface) and high particulate-water inter-action time (Charlesworth and Foster 2005). Given that the road dust samples were collected from rural lanes at the bridging points where channel access was gained, they are highly representative of material transported to the channel via the road network. The material itself was likely to comprise sediment transported by run-on from agricultural fields, the FRN signature of which is transformed by $^{210}$Pb$_{xs}$ enhancement as above.

The FRN activity concentrations (Table 7.1) of sediment mixtures in the six tributaries showed notable differences between the channel-stored sediment and suspended sediment in terms of $^{7}$Be concentration. The predominant undetectable-to-low activity concentrations of $^{7}$Be in the channel-stored sediment sampled at the beginning of the wet season indicated either a long residence time (i.e., greater than 5 half-lives, ~ 250 days) or a predominant channel bank source. The activity concentrations of $^{137}$Cs and $^{210}$Pb of these samples were within the bank to cultivated land zone (see Fig. 7.4). The switch to $^{7}$Be rich material in most of the suspended sediment material collected indicated first, and most importantly, that a large proportion of the material in transit through the wet season to January was freshly eroded from an exposed soil surface, although there was a wide range in these values. This range could have reflected varying contributions of remobilised ‘old’ sediment from the bed or contributions from subsurface and bank sources (which have been shown to be deficient in $^{7}$Be). The suspended sediment sample from catchment 2 was an exception, with undetectable $^{137}$Cs and low $^{7}$Be suggesting a substantial contribution was made from a subsurface source, whereas the other samples from this time indicated surface soil with varying contributions from cultivated and uncultivated land. In the later period, this system was more in line with the other sites in terms of freshly eroded material in transit. It was notable that the three catchments with the highest $^{7}$Be content in the second set are the three that had the greatest particulate phosphorus concentrations again implying fresh sediment delivery from intensively farmed land. Across all suspended sediment samples, $^{7}$Be showed a weak but positive correlation ($r$ = 0.33), with particulate phosphorus which strengthened markedly ($r$ = 0.8) when low particulate phosphorus outliers from catchments 1 and 5 were not considered. The close affiliation of $^{7}$Be, indicating recent surface erosion, and particulate phosphorous concentrations in catchments 2, 3, 4, and 6 suggested mobilisation of soil from intensively farmed land that was well connected to the stream network via roads (linked to enhanced $^{210}$Pb$_{xs}$).

The indication from the FRN data is that the material stored in the subcatchment channels has a high residence time (deficient in $^{7}$Be cf Wilson et al. 2007) with activity concentrations of other FRNs in line with catchment soil sources. While this material might comprise bank slumped material, which has been shown to also carry FRN signals similar to cultivated land in this system due to exposure in the past or banks comprising reworked catchment materials, there was little evidence of bank erosion in the field.
<table><caption>**Table 7.1** FRN activity concentrations (Bq kg⁻¹) of channel-stored and suspended sediments sampled in phase 2</caption><thead><tr><th>Subcatchment</th><th><sup>137</sup>Cs</th><th>±</th><th><sup>210</sup>Pb</th><th>±</th><th><sup>7</sup>Be</th><th>±</th></tr></thead><tbody><tr><td colspan="7"><em>Bed sediment early wet season</em></td></tr><tr><td>1</td><td>4</td><td>2</td><td>34</td><td>19</td><td>&lt; 14.99</td><td></td></tr><tr><td>2</td><td>5</td><td>2</td><td>29</td><td>17</td><td>&lt; 48.6</td><td></td></tr><tr><td>3</td><td>6</td><td>2</td><td>44</td><td>18</td><td>&lt; 63.0</td><td></td></tr><tr><td>4</td><td>5</td><td>2</td><td>60</td><td>25</td><td>85</td><td>50</td></tr><tr><td>5</td><td>&lt; 2.49</td><td></td><td>37</td><td>21</td><td>&lt; 42.7</td><td></td></tr><tr><td>6</td><td>7</td><td>1</td><td>57</td><td>17</td><td>79</td><td>28</td></tr><tr><td colspan="7"><em>Suspended sediment early wet season</em></td></tr><tr><td>1</td><td>7</td><td>2</td><td>24</td><td>17</td><td>93</td><td>30</td></tr><tr><td>2</td><td>&lt; 0.91</td><td></td><td>10</td><td>6</td><td>38</td><td>19</td></tr><tr><td>3</td><td>8</td><td>2</td><td>46</td><td>18</td><td>135</td><td>40</td></tr><tr><td>4</td><td>4</td><td>1</td><td>85</td><td>18</td><td>251</td><td>45</td></tr><tr><td>5</td><td>7</td><td>3</td><td>92</td><td>32</td><td>178</td><td>62</td></tr><tr><td>6</td><td>4</td><td>1</td><td>41</td><td>14</td><td>82</td><td>38</td></tr><tr><td colspan="7"><em>Suspended sediment late wet season</em></td></tr><tr><td>1</td><td>11</td><td>4</td><td>55</td><td>28</td><td>164</td><td>73</td></tr><tr><td>2</td><td>5</td><td>2</td><td>52</td><td>26</td><td>147</td><td>41</td></tr><tr><td>3</td><td>11</td><td>4</td><td>80</td><td>37</td><td>251</td><td>87</td></tr><tr><td>4</td><td>7</td><td>2</td><td>77</td><td>27</td><td>257</td><td>51</td></tr><tr><td>5</td><td>7</td><td>2</td><td>44</td><td>17</td><td>84</td><td>26</td></tr><tr><td>6</td><td>8</td><td>2</td><td>50</td><td>23</td><td>148</td><td>39</td></tr></tbody></table>

The <sup>7</sup>Be content and particulate phosphorus loading of the suspended material collected during the major storm events, along with the other FRN signals, demonstrated a substantial contribution from surface soils in catchments 2, 3, 4, and 6, wherein roads were an important connectivity pathway linking the agricultural runoff, containing sediment and phosphorus pollution, to the rivers.

### 7.4.2 Applying XRF Signatures to Evaluate Contribution of Agricultural Runoff to Aquatic Habitat Siltation

#### Context

Freshwater Pearl Mussels (FWPM), *M. margaritifera*, are among the most critically threatened freshwater bivalves worldwide. In addition to their important roles in particle processing, nutrient release, and sediment mixing, they also serve as an ideal target species for evaluation of aquatic ecosystem functioning especially of
<figure><img src="image_47.png" /><figcaption>**Fig. 7.4** FRN signatures for primary source materials where outlines represent range extent and uncertainty is one standard deviation of the mean, shown by the symbols</figcaption></figure>

their symbiotic relationship with Atlantic salmon *Salmo salar* and brown or sea trout *Salmo trutta*. Poor water quality, particularly eutrophication, and siltation are considered major contributory factors in the decline of the species; hence, management of diffuse water pollution from agriculture is a key priority in catchments that host FWPM habitats. The river Clun, UK, is one such system where FWPM have been identified to be under threat (Blake et al. 2016).

FWPM populations have declined dramatically throughout its range in Europe and North America, and recruitment of juvenile mussels only occurs in a small fraction of the streams where populations still persist. There are a range of environmental threats FWPM populations linked to direct pollution and damage to habitat. Indeed, there had been a decline of more than 90% in European populations by the 1990s, and it is accepted that this has continued or even increased since (Geist and Auerswald 2007). Currently, the main concern for FWPM populations in general is that juveniles do not seem to be reproducing, so populations are becoming more elderly. Poor water quality, particularly eutrophication, and siltation are considered major contributory factors in the decline of the species; hence, management of diffuse water pollution from agriculture is a key priority in catchments that host FWPM habitats.

The River Clun is located in central UK and drains a catchment 272 km². The principal land use within the catchment is sheep and cattle grazing although there are notable areas of cultivated land. In the lower reaches, the river is protected as a Special Area of Conservation (SA-C) and Site of Special Scientific Interest (SSSI)
due to the presence of one of the few lowland populations of FWPM in the UK. At the time of study, adult FWMPs were reported to be reproducing in the Clun since glochida had been found on fish but juvenile/young adult FWPMs were, however, generally absent in surveys leading to the hypothesis that they were not surviving in the channel bed substrate to emerge as adults. It was hypothesised that siltation was a key factor so sediment fingerprinting tools were applied to identify the domain sources of polluting sediment.

## *Sampling and analysis*

The sediment fingerprinting approach was applied at two levels in the study basin to determine the principal sources of fine sediment present in the failing FWPM beds in the higher-order channel.

Firstly, at the broad spatial scale, the main tributaries (Fig. 7.5) were treated as integrated source end members and a fingerprinting approach applied through analysis of fine sediment captured and stored in the channel at the stream outlets. This was then compared to material stored in the FWPM reaches. Importantly, this framework also included mainstem channel banks as a potential sediment source. Therefore, this approach allowed the identification of the relative contribution from the different tributaries and mainstem bank erosion to the sediment found within the FWPM beds.

Secondly, the subcatchment scale phase of the sediment fingerprinting attempted to compare sediment collected from the tributary outlets to primary sources in the wider catchment namely: (i) cultivated soil, (ii) uncultivated soil, (iii) channel bank

<figure><img src="image_48.png" /><figcaption>**Fig. 7.5** Schematic of subcatchment scale experimental design showing link between target FWPM reaches on the main channel and subcatchment areas sampled using stream outlet sediment (providing an integrated source signature)</figcaption></figure>
erosion, (iv) farm tracks, (v) road verges, and (vi) road-transported material. Therefore, this approach allowed the identification of the relative contribution from the different sources to the sediment found at the tributary outlets.

Using these two sets of results allows the identification of the tributaries that are significant sources of sediment to the FWPM beds and, within those tributaries, the identification of the primary sources that are most significant.

Channel-stored sediment and suspended sediment were collected in triplicate from the outlet of each tributary. The stilling well method (Lambert and Walling 1988) was used to recover fine sediment stored on and within the uppermost part of the channel bed matrix that was disturbed by water column agitation. Samples were collected during each quarter of the project to characterise spring, summer, autumn, and winter conditions. Suspended sediment was collected using time-integrated samplers (Phillips et al. 2000). These were installed to collect summer (drier season) and winter (wetter season) sample sets. Sampling at the FWPM beds was restricted due to ecological sensitivity of the area. The suspended sediment traps were installed away from FWPM areas but in the representative reaches.

Samples were freeze-dried, disaggregated and sieved to <63 µm to ensure source and mixtures were like-for-like comparison. Elemental geochemistry was analysed via WD XRF using a PanAlytical Axios Max instrument (Sect. 7.4.2). Samples were also analysed for FRNs as described in Sect. 7.4.1. Geochemical unmixing for source apportionment was undertaken using the MixSIAR model as described by Blake et al. (2018).

## 7.5 Results and Discussion

Owing to the ubiquitous nature of the FRN signal across the catchment, these data (Fig. 7.6) provided a broad framework for interpretation of the geochemical unmixing results.

The FRN signatures of primary source material (i.e., cultivated soil, uncultivated soil, channel bank, and road-transported/derived material) conformed to established knowledge based on previous UK studies (Walling and Woodward 1992) wherein uncultivated soils carried higher activity concentrations of ¹³⁷Cs and ²¹⁰Pbxs due to the shallow depth profiles of both radionuclides in undisturbed soil (Figs. 7.7 and 7.8). Cultivated soil was a mix of the surface and shallow subsurface soil materials which effectively lowers the activity concentrations of both FRNs. Channel bank material was deficient in both FRNs due to subsurface nature of material, i.e., much of the channel bank is not labelled by these FRNs. There was also a notable activity concentration of ¹³⁷Cs measured in road sweepings with enhanced ²¹⁰Pb activity concentrations where interaction of pluvial, i.e., surface flow after rainfall, water with fine-grained material offered opportunity for enrichment. In this regard, it is important to also note the elevated ²¹⁰Pbxs activity concentration of road material which was enhanced compared to surface soil. These observations along with the range of ¹³⁷Cs concentrations of road sweepings ranging from uncultivated to cultivated soil imply
<figure><img src="image_49.png" /><figcaption>**Fig. 7.6** FRN signatures for sources and mixtures samples from both tributaries and the high-order river channel</figcaption></figure>

that the soil material sampled from roads originated from surface sources but was subsequently enhanced in $^{210}$Pb$_{xs}$ scavenged from pluvial road runoff water, i.e., tracer fingerprints developed as a consequence of the delivery pathway (Belmont et al. 2014).

Comparison of channel sediment FRN signatures to source materials offers insight into the different nature of main channel sediment compared to tributary sediment. The FRN signals of all material analysed from the main channel reach that hosted the FWMP beds (both upper and lower areas) were suppressed in terms of $^{137}$Cs and $^{210}$Pb$_{xs}$ content and plotted in the range of channel bank material and cultivated material (Fig. 7.8). This suggests channel bank erosion was an important contributor to sediment sampled from the FWMP beds. The tributary sediment also showed an influence of channel bank erosion but the wider spread of FRN signals indicates that in the subcatchments, other sources were also important. Most tributary sediments had a $^{137}$Cs concentration in the cultivated soils range with some elevated $^{210}$Pb$_{xs}$ concentrations although another possible permutation of sources that could lead to this is a combination of pasture and channel bank erosion. There was a general shift towards the lower corner of the FRN bi-plot space (Fig. 7.6) in the autumn/winter set indicating greater subsurface erosion (channel bank) in the more hydrologically active months.

Whichever the main control on surface soil inputs, the FRN data were unequivocal that channel bank erosion was a major sediment source in this system and that the FWMP beds in the main channel of the Clun contained a significant amount of sediment from this source.
<figure><img src="image_50.png" /><figcaption>**Fig. 7.7** Illustrative examples of tracer boxplots comparing source term range (S1, S2...) to mixture range (M1, M2) where Zn demonstrates non-conservative tracer behaviour</figcaption></figure>

Geochemical data (Fig. 7.7) were analysed in the context of broader FRN interpretations. Tracers for application in the mixing model were selected using exploratory data analysis tools (Blake et al. 2018) with elements (examples in Fig. 7.8) that appeared to show non-conservative behaviour (e.g., Zn concentration of suspended sediment falling outside the range of the source materials) being removed from analysis. The FRN inferences of a strong influence of cultivated soil in subcatchments, with a road-sediment signal indicating conveyance path and influence of bank erosion in main channel sediments, were supported by geochemical fingerprinting data (Fig. 7.7). These demonstrated cultivated soil transported by road as a key factor in the most intensively farmed subcatchments (Fig. 7.8 left). While the main channel also showed a strong cultivated source signal in spring-summer, winter signals were dominated by channel bank erosion inputs (Fig. 7.8 right) which is in line with visual evidence of bank collapse on the main channel reaches harbouring FWMPs. While catchment-wide diffuse water pollution from agriculture in the form of sediment input was indicated, and roads provided an efficient transport vector, the localised importance of bank erosion on FWPM habitat was identified as a key factor contributing to population decline. This process though was in turn driven by greater runoff and a flashier hydrological regime linked to runoff generation on both compacted pasture and cultivated land. The results informed management of fine sediment problems relating to both diffuse water pollution and riparian corridor management, e.g., targeted management of channel banks and road crossings.
<figure><img src="image_51.png" /><figcaption>**Fig. 7.8** Unmixing model outputs showing most likely source contributions at the subcatchment (left) and catchment (FWPM beds) (right) scales, in summer and winter</figcaption></figure>

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**William H. Blake** is Professor of Catchment and River Science and Director of the Sustainable Earth Institute at the University of Plymouth. His research focuses on landscape disturbance in river basins, with a particular focus on soil erosion and downstream water pollution and silta-tion. He uses environmental diagnostic tracer approaches, e.g. fallout radionuclides, x-ray fluo-rescence geochemistry, near-infrared spectroscopy, to understand river basin processes and land-water-people connectivity, helping diagnose complex environmental problems. He has undertaken research in Southeast Asia, Australia, East Africa, and Latin America funded by UK Research Councils, European Commission, and environmental charities as well as evaluating applied catch-ment management issues in the UK. He has over 15 years of international collaborative experi-ence with applied research and capacity-building impact in 10+ countries (Africa, Latin America, Southeast Asia) through leadership of UK Research and Innovation Global Challenges Research Fund programmes and European Commission H2020 consortia, and the IAEA (Joint FAO/IAEA Centre) knowledge exchange and training programmes. More recently, his focus has been on development and implementation of interdisciplinary approaches that assess the physical, social, and cultural impacts of soil erosion, which has facilitated co-production of community-owned change to more sustainable land management practices in the global south.

**Alex Taylor** has been working with fallout radionuclide (FRN) tracing techniques for 15 years, with particular focus on beryllium-7 fallout and partitioning behaviour in soils and sediments. He has worked extensively on the application of FRNs for the assessment of soil erosion in the context of sustainable land management practices. As a river catchment scientist, Alex has been involved in numerous sediment source tracing studies in river systems, working with a range of govern-mental and non-governmental organizations to assess contaminant sources, pathways, and recep-tors. His work informs targeted mitigation practice within river catchments, supporting healthy soil and aquatic-marine systems.

**Enrique Munoz-Arcos** is a chemist graduated from Universidad Austral de Chile and a second-year Ph.D. student at the University of Plymouth. His current research project focuses on the use of Fallout Radionuclides (FRNs) to evaluate sediment residence times for improved management of legacy and contemporary river basin pollution. He worked for over three years as a research assistant at Universidad Austral de Chile in several IAEA regional projects (RLA) and a Newton-NERC-funded project focused on the application of environmental nuclear tracers to determine soil erosion sources and impacts on waterbodies.

**Luis Ovando-Fuentealba** is graduated in Biochemistry and performed a Ph.D. at the University of Plymouth on soil erosion and statistical approaches for soils apportionment. He has worked as a researcher at the Universidad Austral de Chile, and he has published articles in the environmental field.
**Claudio Bravo-Linares** is Senior Lecturer of Environmental Chemistry at the Institute of Chemical Sciences at the Universidad Austral de Chile. His area of interest is the determination of organic contaminants in the environment and finding the sources or precursors using analytical chemistry, fingerprinting methods, and multi-variate statistical tools, as well as proposing and applying methods of treatment and cleaning contaminants in the environment. He has published around 30 scientific articles in different journals related to environmental chemistry. Also, he has written one book and one book chapter on environmental forensics. He has led several national and international research projects on environmental issues. He has been working in the last few years on soil erosion fingerprinting in collaboration with different scientists around the world.

**Geoffrey E. Millward** is Emeritus Professor in the Marine Institute at the University of Plymouth. He works in the ISO9001-accredited Consolidated Radio-isotope Facility (CORiF) where his research concerns radioactivity with an emphasis on environmental contamination by radionuclides, their chemical reactivity, and their impact on the biosphere. He has published over 200 papers on environmental chemical processes and supervised more than 30 candidates for Ph.D. He was presented a Churchill Silver Medal in 1989 by the Queen for his research on North Sea and is presently Trustee for the Estuarine and Coastal Sciences Association.

The opinions expressed in this chapter are those of the author(s) and do not necessarily reflect the views of the IAEA: International Atomic Energy Agency, its Board of Directors, or the countries they represent.

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Chapter 8

# Conclusion: Stable Isotope Tracers Are Useful for the Identification of Pollutants in Agro-ecosystems

G. Imfeld, G. Skrzypek, J. Adu-Gyamfi, and L. Heng

Pollutant management in agro-ecosystems is a complex challenge that requires two key questions to be answered: (i) how to improve identification and quantification of pollutant sources, transport pathways and fate on the catchment scale? and (ii) how to prioritise and combine soil and water management practices to minimise the use and impact of agro-pollutants? An integrative book such as this one, focusing on the stable H, C, N, O and S isotope compositions of a variety of agro-pollutants, may help to address the first question and enable increasingly precise and robust evaluation of pollution in agro-ecosystems. After understanding pollution sources and dispersal, the farm operators and land managers can attempt to answer the second question by considering local conditions and agriculture practices.

This book has highlighted that stable isotope tracers have great potential to
improve the qualitative and quantitative assessment of soil and water pollution, which
is crucial for ecosystem protection plans worldwide. However, stable isotope anal-
yses should be considered as complementary to conventional concentration analysis

G. Imfeld (✉)
Earth & Environment Strasbourg (ITES, UMR7063), University of Strasbourg/CNRS, Strasbourg,
France

e-mail: gwenael.imfeld@cnrs.fr

G. Skrzypek
West Australian Biogeochemistry Centre, School of Biological Sciences, The University of
Western Australia, Perth, WA, Australia

e-mail: grzegorz.skrzypek@uwa.edu.au

J. Adu-Gyamfi · L. Heng
Soil and Water Management and Crop Nutrition (SWMCN) Section, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency (IAEA), Vienna, Austria

e-mail: j.adu-gyamfi@iaea.org

L. Heng
e-mail: l.heng@iaea.org
and mass load calculations. Stable isotope and ion ratios are usually independent of solute concentrations and provide primarily information about pollution sources and transformation processes but not about pollution levels. Pollution levels need to be determined using hydrochemical analyses and load calculations, respectively, to volume of water and pollutant concentrations.

Various stable isotope tracers are often used individually, but the best results are achieved by adopting a multi-tracer approach to obtain more reliable characterisation of pollution sources and to tease apart the water and solute budgets. Thus, combination of the stable isotope and hydrochemical analyses of specific solutes with optimal sampling design, sediment source apportionment and stable isotope composition of water to estimate retention time and evaporation can reinforce the evaluation of sources and transport of pollutants in agro-ecosystems and help to reduce uncertainties of study outcomes. Here, we also provide examples of case studies to show how the stable isotope approaches helped to estimate sources and transport of agro-pollutants and inform decision-making on the catchment scale to control diffuse agricultural pollution and define adapted policies. The ability to enhance comprehension, identification, and quantification of sources and processes related to agro-pollutants is crucial in a global context. Indeed, chemical pollution and nutrient (nitrogen, phosphorus) flows into the biosphere and oceans have exceeded safe limits, breaching the boundaries of global sustainability, which poses a direct threat to humanity (Steffen et al. 2015).

This book also aims to present a scientific toolbox that gathers optimal designs of sampling programmes, considering possible mixing scenarios and multiple isotope tracers of the origin, transport and transformation for a variety of agro-pollutants (Table 8.1). The book’s focus on sampling protocols, current analytical techniques, standard operating procedures, and field examples can contribute to the integration of multi-tracer approaches into routine studies and research practices. It has been demonstrated that establishing a balance between grab sampling and time-integrated sampling is essential in each study to mitigate biases when interpreting agro-pollutant sources and behavior. In the interpretation phase, providing an estimation of uncertainties related to source apportionments and process extent is advisable whenever possible. This includes error propagation to ensure a rigorous interpretation.

Most importantly, the book also addresses requirements and limitations associated with the use of each type of isotope tracer (Table 8.1). A good knowledge of stable isotope signatures of the pollution sources is an essential requirement for efficient application of all stable isotope tracers in mixing or fractionation models. In future, extensive databases for source identification and apportionment of sediment or single agro-pollutant compounds may be available, following Findable, Accessible, Interoperable, Reusable (FAIR) principles. However, agro-pollutants, whose source identification seems possible and is targeted (e.g., PO₄, NO₃), should be distinguished from agro-pollutants, whose degradation is the main process considered (e.g., pesticides). In many cases, sampling and sample handling are sensitive operations that may cause undesired isotope fractionation, as for the δ(¹⁸O)PO₄ method to investigate P cycling in the soil-plant continuum. In addition, the stable isotope composition of
<table><caption>**Table 8.1** Current development and future prospects to evaluate agro-pollutants in the field using stable isotope techniques</caption><thead><tr><th></th><th>Interest in tracing agro-pollutants</th><th>Requirements</th><th>Limitations and challenges</th><th>Advantages and future developments</th></tr></thead><tbody><tr><td>Conceptual design</td><td><ul><li>Establish a sampling design for tracing agricultural pollutants</li><li>Estimation of the relative contributions from pollution sources</li></ul></td><td><ul><li>Hydrochemical and hydrogeological baseline data</li><li>Initial stable isotope compositions at pollution sources</li><li>Hydro-climatic records</li></ul></td><td><ul><li>Requires understanding of the study area (hydrology/land use/source locations)</li><li>Isotope signatures of sources need to be statistically significantly different</li><li>Signatures of potential pollution sources to be analysed at pollution sources</li></ul></td><td><ul><li>Potential use of multiple sampling points and multiple isotope tracers to identify major sources of pollution by estimating pollution budgets in different subcatchments</li></ul></td></tr><tr><td>Mixing and fractionation models</td><td><ul><li>Achieving an optimal sampling design</li><li>increase efficiency: reducing sample number, workload and cost</li></ul></td><td><ul><li>Simple mixing models can be solved algebraically</li><li>Known and significantly different stable isotope signatures of individual sources</li><li>Good mixing of water and pollution assumed</li></ul></td><td><ul><li>Number of tracers used not lower than one less than the number of sources under consideration</li></ul></td><td><ul><li>Advanced modelling for correction of stable isotope fractionations at the time of the reaction</li><li>Probability models for systems with multiple pollution sources</li></ul></td></tr><tr><td>Compound-specific isotope analysis (CSIA) of pesticides</td><td><ul><li>Trace and quantify pesticide sources and degradation</li><li>Evaluate prevailing zones and periods of pesticide dissipation</li></ul></td><td><ul><li>Methods to extract and concentrate pesticides from environmental samples</li><li>Chromatographic separation of all compounds</li><li>Sufficient knowledge regarding hydrological and hydroclimatic conditions and functioning</li></ul></td><td><ul><li>CSIA for low pesticide concentrations in complex environmental matrices</li><li>CSIA restricted to less polar pesticides while using GC-IRMS</li><li>isotope fractionation factors to be derived in the laboratory to evaluate processes</li></ul></td><td><ul><li>Pesticide enrichment and clean-up from complex matrices</li><li>Multi-element CSIA to interpret complex pollution scenarios with multiple pollution sources or different degradation processes</li><li>Comprehensive studies on the catchment scale over long periods</li></ul></td></tr></tbody></table>

(continued)
<table><caption>**Table 8.1** (continued)</caption><thead><tr><th></th><th>Interest in tracing agro-pollutants</th><th>Requirements</th><th>Limitations and challenges</th><th>Advantages and future developments</th></tr></thead><tbody><tr><td>Stable oxygen isotope composition of phosphates</td><td><ul><li>Stable isotope composition of oxygen bond to phosphorus δ(¹⁸O)PO₄</li><li>Investigate P cycling in the soil-plant continuum</li></ul></td><td><ul><li>Extraction, isolation and purification for δ(¹⁸O)PO₄ determination from organic and inorganic P forms and fractions in soils, plants, sediments, fertilisers and water</li><li>Calculation of isotopic equilibrium and the effect of organic P hydrolysis</li><li>δ(¹⁸O)H₂O of water required to interpret δ(¹⁸O)PO₄</li></ul></td><td><ul><li>Small differences between δ(¹⁸O)PO₄ values of P pools between treatments or between different P pools</li><li>δ(¹⁸O)PO₄ strongly influenced by biotic processes. Enzymatic activity to be stopped during extraction</li><li>High quantities of organic matter or low P concentrations complicate extraction</li></ul></td><td><ul><li>δ(¹⁸O)PO₄ to interpret the coupling between P, C and N cycling</li><li>Knowing δ(¹⁸O)PO₄ values of specific organic P compounds to advance earth system models</li><li>Laboratory and glasshouse studies to apply δ(¹⁸O)PO₄ in the field</li></ul></td></tr></tbody></table>

(continued)
<table><caption>**Table 8.1 (continued)**</caption><thead><tr><th></th><th>Interest in tracing agro-pollutants</th><th>Requirements</th><th>Limitations and challenges</th><th>Advantages and future developments</th></tr></thead><tbody><tr><td>Stable sulphur and oxygen isotope composition of sulphates</td><td><ul><li>Generation, transport and mixing of sulphate and stable sulphur and oxygen isotope compositions used as tracers of various sources</li></ul></td><td><ul><li>Processes contributing to the stable isotope fractionation of sulphates in soil and aquatic environments should be considered</li><li>δ(2H)H₂O, δ(¹⁸O)H₂O data support interpretation of δ(³⁴S)SO₄ and δ(¹⁸O)SO₄ values to partition the fate of water and solutes</li></ul></td><td><ul><li>Stable isotope composition of leachates, and especially δ(¹⁸O)SO₄, can be modified by bacterial processes</li><li>In catchments contaminated with pollutants from several sources, stable isotope signatures of overlapping values may be ambiguous</li></ul></td><td><ul><li>Denser sampling to determine pollution budgets in individual subcatchments</li><li>Application of more advanced probabilistic models</li></ul></td></tr><tr><td>Sediment quantification</td><td><ul><li>Source apportionment of (fine) sediment and associated co-pollutants</li><li>Sediment mobilisation, transport, storage and yield to evaluate pollutant source dynamics</li></ul></td><td><ul><li>Tracing studies require large sample numbers for elemental concentrations</li><li>CSIA technique offers the possibility to discriminate different land uses</li></ul></td><td><ul><li>Assumption of instantaneous ¹³⁷Cs, ²¹⁰Pb and ⁷Be binding at the soil surface is site-dependent</li><li>Particle sorting and enrichment during sediment transport and storage should be accounted for</li></ul></td><td><ul><li>Evaluating heterogeneous and complex response to management practice and erosion within the lower-order tributary catchment areas</li><li>Expanding the application and sensitivity of XRF analysis</li></ul></td></tr></tbody></table>
the agro-pollutant of interest needs to be considered in relation to the other chem-
ical compounds present in the environment, which may contribute to stable isotope
exchange, equilibration or chemical synthesis, thus altering the original signatures
of the pollution source. In situations where degradation or transformation is antici-
pated, understanding the associated isotope fractionation is crucial for interpreting
field data. If this information is not yet available in the literature, isotope fraction-
ation may be deduced from reference experiments (e.g., biodegradation in native
sediment, photolysis in river water). Moreover, separation fractionation from mixing
is the primary challenge and may sometimes lead to ambiguous results. Finally,
the interpretation of agro-pollutant transformation is constrained by the absence of
reference field studies, encompassing vegetation and ecosystem dynamics. There-
fore, laboratory microcosms and glasshouse studies will be instrumental in advancing
the application of stable isotope tracers in agro-ecosystems. Additionally, they will
contribute to the development of novel, mechanistic mixing concepts and models to
enhance studies in complex and dynamic agro-ecosystems.

Despite the acknowledged limitations, stable isotope techniques provide addi-
tional dimensions to the interpretation of agro-pollutants, enabling advanced studies
that are not achievable through traditional hydrochemical methods alone. This book
contributes to the systematization, standardization, and optimization of stable isotope
methods in routine studies of agro-pollutants on a catchment scale. We anticipate that,
in the future, stable isotope methods will steadily become the routine and preferred
approach in many research areas, including the tracing of agro-pollutants.

References

Steffen W, Richardson K, Rockström J, Cornell SE, Fetzer I, Bennett EM, Biggs R, Carpenter SR, de Vries W, de Wit CA, Folke C (2015) Planetary boundaries: guiding human development on a changing planet. Science 347(6223):1259855. https://doi.org/10.1126/science.1259855.ISSN0036-8075

G. Imfeld is a research professor at the French National Centre for Scientific Research (CNRS) in environmental biogeochemistry, at the Earth & Environment Strasbourg (PhD degree in 2009 at UFZ-Leipzig and University of Neuchâtel, Habilitation in 2013, University of Strasbourg). His research interests revolve around pollutant biogeochemistry, microbial transformation and ecology in continental hydrosystems, and freshwater sustainability, resilience and adaptation of socio-hydrosystems. His current research focuses on the degradation and transport of micropollutants in wetlands, soils, and aquifers, combining microbial and stable isotope approaches (CSIA). Gwenaël is leading the research team “Isotope biogeochemistry” with analytical facilities for chemical and isotopic analyses of trace elements and organic compounds. Gwenaël has published > 90 ISI publications in contaminant biogeochemistry and microbial ecology. He is Director of the Research Initiative in Sustainability and the Environment (RISE/FERED) of the University of Strasbourg/CNRS and the Editorial Board of *Chemosphere*.
**G. Skrzypek** has 24 years of experience in the field of stable isotope geochemistry, hydrochemistry, and ecology since his PhD (1999) from The University of Wrocław (UWr). Grzegorz is currently working as Associate Professor and Coordinator of MSc Biology and Ecology specializations at The University of Western Australia (UWA). Grzegorz has published many papers on applications of stable isotope techniques in hydrological, hydrochemical, pollution, ecological, and paleoclimate studies. He uses stable H, C, N, O, S, and Sr isotopes to trace water budget, nutrient fluxes in natural environments, and dispersal of pollution in environments impacted by mining and agriculture. He held a prestigious Australian Research Council Future Fellowship at UWA (2012–2015) and Foundation for Polish Science Scholarship at UWr (1999–2000). Currently, he is serving as the deputy director of the West Australian Biogeochemistry Centre at UWA (wabc.uwa.edu.au). Grzegorz has been a chief investigator on a number of research projects in Europe and Australia and an expert on IAEA Technical Cooperation Projects and IAEA Technical Meetings.

**J. Adu-Gyamfi** is working as Integrated Soil Fertility Management Specialist providing technical assistance to IAEA national, regional/interregional technical cooperation projects in Africa, Asia, Europe, Latin America, and the Caribbean, on the use of multi-stable isotope fingerprints to identify sources and transport of contaminants in the environment (www.iaea.org). His work involved developing climate-smart agricultural practices to combat and mitigate the impacts of climate change on agricultural systems and food security, soil and water quality in developing countries. He has > 30 years of experience in the use of isotopes to measure and monitor interactions between soil, water, and nutrients in cropping systems at different research, academic, national, and international organizations and development consultant firms in both developing and developed countries. He is the author and co-author of four books, several book chapters, and research publications in refereed journals. He has won several awards for his professional achievements including the ICRISAT Outstanding Research Scientist 2002, Special Award on *Root research on drylands* (Japanese Society of Root Research), IAEA Superior Achievement (2018), and Merit Awards (2022).

**L. Heng** is working as Section Head of Soil and Water Management and Crop Nutrition (SWMCN) Subprogramme in the Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture (www.iaea.org). She has more than 30 years’ experience in soil–plant–water interactions, agricultural water management and water use efficiency, integrated nutrient water interactions, and diffuse pollution control for sustainable agricultural production systems, at both national and international levels. Her work covers countries in Africa, Asia, Europe, and Central and Latin America on sustainable land and water management for climate-smart agriculture and the efficient use and conservation of agricultural resources to enhance food production and environmental sustainability. She is the author and co-author of several books and book chapters and research publications in refereed journals. She has won several awards for her professional achievements, including the IAEA Merit, Promotion (2004) and Superior Achievement Awards (2018), and the USDA-Agricultural Research Service (ARS) Outstanding Sustained Effort Technology Transfer Award for Outstanding Work in Technology Transfer to Users (2012).
The opinions expressed in this chapter are those of the author(s) and do not necessarily reflect the views of the IAEA: International Atomic Energy Agency, its Board of Directors, or the countries they represent.

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<figure><img src="image_54.png" /></figure>
Glossary

³¹P-NMR ³¹P nuclear magnetic resonance. Spectroscopic technique, based on the spins of atomic nuclei, which is used to study mainly organic P in soils and plants.

**Adsorption** Bonding of ions, molecules, or atoms in the soil solution or soil air to
sorption sites of solids through physical and chemical processes.

**ARD** Acid Rock Drainage, sulfide oxidation process leading to generation of sulfates and acidification of water.

**Biopores** Pores that formed through the activity of roots and/or soil macro-fauna like earthworms.

**CSIA** Compound-specific isotope analysis. CSIA refers to the measurement of the isotope ratios (typically carbon, hydrogen, oxygen, nitrogen, sulfur, or chlorine) of individual organic compounds extracted from complex environmental mixtures.

**Desorption** Release of ions, molecules, or atoms sorbed to solids into the soil solution or air. Reverse process to adsorption.

**DGT** Diffusive gradient in thin film technique. Used as a measure for plant-available phosphorus.

**Dissolution** It refers to the disintegration of solid minerals into individual ions/
complexes and released into the soil solution. Opposite process of precipitation.
Influenced by factors like soil pH and concentration.

**EU15** Number of member countries of the European Union prior to its enlargement on May 1, 2004. It includes the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and UK.

**Genomics** Analysis of the complete set of DNA (including its genes) of an organism.

**Immobilization** Conversion of an element, compound, or ion into an unavailable form. In the case of P, it often refers to the uptake of P by microorganisms.

**Isotopologue** Molecules such as phosphate and phytate which differ in their isotopic composition are called isotopologues. For example, a phytate containing only ¹⁸O and a phytate molecule containing only ¹⁶O are two isotopologues of the same molecule.

**Metabolomics** Investigating cellular processes involving metabolites like nucleotides.
**Metagenomics** Extraction of the genome directly from environmental samples and analysis of the genome without prior cultivation of microorganisms.

**Mineralization** Conversion of an element, compound, or ion from an unavailable form into a bioavailable form.

**Mycorrhizal fungi** Symbiosis between certain soil fungi and the roots of higher plants.

**NanoSIMS** Nano-scale secondary ion mass spectrometry. Spectrometric method with a high resolution which allows, for example, the measurement of different isotopes on a cellular and sub-cellular level.

**Omics** Umbrella term for research areas ending with -omics like metagenomics and proteomics.

**Phosphorus use efficiency** The ability of a plant to grow well under P limitation. In agriculture, it relates to the ability of a crop to acquire P and produce a relatively high yield also under P limitation. It is often calculated based on the total amount of P in a plant and the amount of applied fertilizer P.

**Porosity** It refers to the volume of pores in soils or sediments, expressed in percent. The pores include air and water-filled pores.

**Precipitation of minerals** It refers to the precipitation of dissolved ions from the soil solution when, for example, the soil pH or the concentration changes. Opposite process of dissolution.

**Proteomics** Analysis of all proteins within a cell/organism at a certain time. It is often used to investigate what proteins are expressed when organisms are exposed to stress, such as phosphorus limitation.

**PSR** Phosphate starvation response of organisms. Includes expression of certain genes under P starvation and changes in root architecture.

**Rhizosphere** The soil volume which is affected by root activity. It typically relates to the first few mm around roots.

**Rock phosphate** The main raw material for mineral phosphorus fertilizer.

**Sorption** General term for processes that lead to the attachment of one substance to another. Includes adsorption, absorption, and ion exchange. Reverse process to desorption.

**SPE** Solid phase extraction.

**Transcriptomics** Investigating the transcriptome (sum of RNA transcripts) of an organism.

**XRF** X-ray fluorescence analytical technique used for identification of minerals.