occurs as inorganic P (Veneklaas et al. 2012) mainly stored inside the vacuole when plants are growing at a high P status (up to 85–95 %; Raghothama and Karthikeyan (2005)). The remobilization of this stored vacuolar P is one way in which plants can increase their internal P use efficiency (PUE) (Rose and Wissuwa 2012). Re-translocation of P from older to younger or reproductive organs like seeds is another strategy to increase the PUE (Akhtar et al. 2008; Richardson et al. 2011). Compared to the tightly regulated cytosolic inorganic P, the concentrations of other plant P compounds like nucleic acids, phosphorylated metabolites, and phospholipids can change more and the turnover rates of some of these P compounds are less than 1 min (Bieleski and Laties 1963; Veneklaas et al. 2012). After the addition of ³²P to potato plants, it took less than 1 min until half of the ATP in older potato tissue had incorporated the added label (Bieleski and Laties 1963). The replacement of phospholipids by other lipids like sulfo- and galactolipids also increases the PUE of plants (Lambers et al. 2012). Similar to increasing the P acquisition, the expression of intracellular acid phosphatases increases the PUE of plants (O’Gallagher et al. 2021).

Most modern crop varieties are less efficient in acquiring P compared to older varieties or wild types since breeding efforts focused on other traits like increased starch content or yield in general. Nowadays, researchers have investigated the P uptake and utilization efficiency of different plant varieties, and through breeding or genetically modifying crops to create new crop varieties, which have a good balance between yield and P use efficiency (Cong et al. 2020). Examples include potatoes (Balemi and Schenk 2009; Pantigoso et al. 2020), rice (Wissuwa et al. 1998; Irfan et al. 2020), and wheat (Fageria and Baligar 1999; Korkmaz et al. 2009). With these new varieties, it might be possible in future to reduce the dependence on P fertilizers, as well as reduce the amount of legacy P built up in the soil. Other research focuses on soil management strategies such as intercropping (Brooker et al. 2015; Garland et al. 2017) and microorganisms or soil meso- and macrofauna to increase P availability in the soil (Richardson et al. 2009; Blouin et al. 2013; Trap et al. 2016). Intercropping cereals with legumes can have a positive effect on the P availability of cereals (Hinsinger et al. 2011). When intercropping maize (Zea mays) with fava beans (Vicia faba), maize as well as fava bean yield increased by 43% and 26%, respectively (Li et al. 2007).

### 5.1.5 The Next Steps—Where Are the Biggest Gaps in Our Knowledge?

As the previous section illustrates, P cycling in the soil–plant continuum is complex and we still do not fully understand all its aspects. At the same time, dealing with P-related issues (such as declining rock phosphate reserves and pollution of aquatic systems) becomes more urgent. Several research papers recently addressed research gaps and questions regarding the P cycle in the soil–plant continuum (Yang and
Finnegan 2010; Richardson et al. 2011; Kruse et al. 2015; Reed et al. 2015; George et al. 2017). Generally, the gaps can be divided as follows:

i. **Soil intrinsic factors, management, and fauna.** Organic soil P can be an important P source for plants; however, many issues around organic P remain, for instance how to link enzymatic P cycling with operationally defined (organic) P pools or the importance of microbial mechanisms related to organic matter (George et al. 2017). The role of soil pH regarding P availability has recently been under discussion (Barrow 2016; Simonsson et al. 2018). It is also still unclear how microorganisms deal with low P conditions at acidic soil pH (Lidbury et al. 2017). Like soil pH, soil management can also affect the availability of soil P. Soil management strategies include intercropping different plant species with each other and adding specific microorganisms to the soil to enhance P availability. Despite recent advances in this field, there are still many unknowns about how soil management strategies can increase the availability of legacy P and other recalcitrant P (Menezes-Blackburn et al. 2018). Soil management also affects earthworms and other soil fauna, but the role of earthworms for the P nutrition of crops, for example, is a relatively recent field of research (Puissant et al. 2021; Trap et al. 2021).

ii. **Plant physiology and the genetics behind.** Whereas many studies, mainly under experimental conditions in the greenhouse, have examined how P starvation affects plant physiology and what genes are involved, it is still unclear what sets off the phosphate starvation response of plants (Yang and Finnegan 2010). It is also debated how relevant are data obtained in greenhouse studies for field-grown plants (Richardson et al. 2011). Greenhouse studies usually only consider a few variables like the soil P status or soil water content. In contrast, plants grown under field conditions might not only be exposed to low P concentrations but also must adapt to changes in precipitation, temperature, competition about P with microorganisms, or a heterogeneous distribution of P in the soil.

iii. **Incorporation of P into environmental models.** Improving the representation of the P cycle in models can improve the constraints of the predictions about soil C storage under climate change (Helfenstein et al. 2020). However, many model parameters regarding the P cycle are still missing (Reed et al. 2015). One aspect that is recently of high interest is linking the P to other nutrient cycles since modellers have realized the interconnectivity of nutrients (Wang et al. 2007; Reed et al. 2015; George et al. 2017; Bertrand et al. 2019). The C:N:P ratio is an important parameter for investigating the coupling of the C, N, and P cycle (Bertrand et al. 2019). Phosphorus is involved in key metabolic processes, such as providing energy for the transport of nutrients across membranes, and thus, P limitation can also affect the cycling of other nutrients. Cleveland et al. (2002) showed that in tropical forests, the utilization of labile soil organic carbon by microorganisms is P limited.
## 5.2 Current Approaches and Methods to Study P Cycling in the Soil-Plant Continuum

There are still many unknowns when it comes to P cycling in the soil-plant continuum. A detailed overview of current innovations regarding methods to investigate P cycling in the soil-plant continuum can be found in Bünemann et al. (2011), the IAEA TECDOC-1805 (IAEA 2016), Kruse et al. (2015), Neumann et al. (2009), and references therein. Methods to investigate P cycling in the soil-plant continuum can be divided into four groups: chemical extractions, tracers, molecular approaches, and spectroscopic methods (Bünemann et al. 2011). These methods vary in their areas of application and in the information obtained from them (Table 5.2). The chosen method therefore depends on the research questions and hypotheses. However, none of those methods should be used as a stand-alone method but should be combined with other approaches.

Recent developments in the suitability of the different methods have driven attention to approaches commonly used. Recently, the usefulness of chemical fractionations to study P in soils has been debated (Klotzbücher et al. 2019; Barrow et al. 2021; Guppy 2021; Gu and Margenot 2021). In fractionation protocols, fractions are often assigned to certain soil P pools or certain P forms are assigned to certain fractions, like iron-bound P to the NaOH fraction. However, one should be aware that, for instance, a chemically determined P pool does not necessarily adequately represent an environmentally relevant P pool. The resin-extractable P fraction or pool is for instance considered as an approximation for available P, but other fractions also might contain available P. Some methods, like the $\delta(^{18}O)_{PO4}$ technique, however, require knowledge about chemical fractionations (Tamburini et al. 2018), but the limitations of those should then be acknowledged when interpreting the results. It might be useful to combine chemical fractionations with other analytical methods, such as the determination of iron in the NaOH fraction, to validate the results obtained by chemical fractionations (Condron and Newman 2011).

The **extraction and analysis** of P fractions comprise a wide range of methods, from the routine extraction of available P in soils to the isolation of vacuolar P in plants. Most studies investigating P in the soil-plant continuum determine at least the easily extractable inorganic P (considered the available soil P) or the total P in soils. Common methods for the extraction of available P from soil samples include extraction with anion exchange resin membranes (Kouno et al. 1995), bicarbonate (Olsen 1954), water (Van der Paauw 1971), and a sodium fluoride-hydrochloric acid solution (Bray and Kurtz 1945). Not all methods are appropriate for all soil types (Oberson et al. 1997; Nawara et al. 2017; Blackwell et al. 2019), thus leading to erroneous results. Total P in soils and plant material is often determined via chemical extraction, such as acid digestion and sodium carbonate fusion (Sommers and Nelson 1972; Bowman 1988; Bender and Wood 2000; Maathuis 2013). Total P is also determined with less destructive methods like X-ray fluorescence (XRF) (see section below about spectroscopic approaches). While the extraction of available P from soils is relatively straightforward, extracting specific P compounds like phytate
<table><caption>**Table 5.2** Common methods to investigate P cycling in the soil–plant continuum</caption><thead><tr><th>Method</th><th>Basics</th><th>Area of application</th><th>Literature</th></tr></thead><tbody><tr><td rowspan="3">Extraction of P forms and fractions</td><td>Specific P forms</td><td>Extraction of specific P forms like phytate and phospholipids</td><td>Phospholipids from soils as indication for microbial biomass; plant physiology (e.g. changes of P compounds in plants due to P limitation)</td><td>Bieleski and Young (1963), Ehlers et al. (2010), Tjellström et al. (2008)</td></tr><tr><td>Chemical fractionation</td><td>Based on the different chemical extractability of P forms and fractions</td><td>Distribution of P among different P fractions provides rough idea about the P status of a soil</td><td>Adu-Gyamfi et al. (1990), Condrón et al. (1985), Hedley et al. (1982), Levy and Schlesinger (1999), Tiessen et al. (1984)</td></tr><tr><td>Specific P fractions</td><td></td><td>Extraction of available P is used in agriculture to determine the amount of P fertilizer which needs to be applied; metabolic P in plant material</td><td>Fardeau et al. (1988), Nawara et al. (2017)</td></tr><tr><td>Tracers</td><td>Radioisotopes of P</td><td>Application of <sup>32</sup>P or <sup>33</sup>P to soils, plants, etc.; extraction of certain P fractions and determination of radioactivity; labelling P compounds and/or fertilizers with <sup>32</sup>P or <sup>33</sup>P</td><td>Mainly used in greenhouse studies; measuring P fluxes in soils and plants; estimate gross and net organic P mineralization; uptake of P by plants and microorganisms; determination of fertilizer derived P in plants; determination of P availability and exchangeability in soils</td><td>Bünemann (2015,; Fardeau et al. (1995), Frossard et al. (2011), Helfenstein et al. (2020), Pistocchi et al. (2018), Whitfield et al. (2018)</td></tr></tbody></table>

(continued)
<table><caption>**Table 5.2 (continued)**</caption><thead><tr><th>Method</th><th>Basics</th><th>Area of application</th><th>Literature</th></tr></thead><tbody><tr><td rowspan="3">Stable isotopic composition of oxygen (O) associated with P</td><td>Working with natural abundance or labelling; usually extraction of different P fractions via sequential extraction</td><td>Mainly information about biological processes in soils; tracing inorganic P input into aquatic systems</td><td>Amelung et al. (2015), Jaisi and Blake (2014), Pfahler et al. (2020b), Pistocchi et al. (2017), Tamburini et al. (2014a)</td></tr><tr><td>Other tracers</td><td>In some cases, P is associated with other elements like cadmium (Cd) and uranium (U) which can be used to trace P</td><td>Used to study the fate of fertilizer P</td><td>Sun et al. (2020)</td></tr><tr><td></td><td>Labelling P compounds with <sup>13</sup>C</td><td>Studying organic P compounds used by microorganisms</td><td>Gross et al. (2017)</td></tr><tr><td rowspan="2">Molecular approaches</td><td>Omics (meta-genomics, metabolomics, genomics, proteomics, transcriptomics)</td><td>Extraction of metabolites, DNA, and RNA from samples</td><td>Provides information about for example microbial community structure and composition in soils; information about proteins, metabolites, genes, etc., that are involved in a certain process</td><td>Ragot et al. (2015, 2017), Neal et al. (2017)</td></tr><tr><td>Enzyme activities</td><td>Adding different substrates (depending on the targeted enzyme) and measuring, e.g. the amount of phosphate released with time</td><td>Potential enzyme activities in soils; common examples include acid and alkaline phosphatase, phytase</td><td>Araújo et al. (2008), Louche et al. (2010), Nannipieri (2011), Tabatabai and Bremner (1969)</td></tr></tbody></table>

(continued)
<table><caption>**Table 5.2** (continued)</caption><thead><tr><th>Method</th><th>Basics</th><th>Area of application</th><th>Literature</th></tr></thead><tbody><tr><td rowspan="3">Spectroscopic and spectrometric approaches</td><td><sup>31</sup>P-NMR spectroscopy</td><td>Solid or liquid state spectroscopy, liquid state spectroscopy requires extraction prior to analysis</td><td>Used to analyse P-containing compounds, especially organic P in soils</td><td>Annaheim et al. (2015), Cade-Menun and Liu (2014), McLaren et al. (2015), Turner et al. (2012), Vincent et al. (2010)</td></tr><tr><td>X-ray absorption spectroscopy</td><td>Semi-quantitative analysis based on spectral features of different P species</td><td>Mainly used for investigating mineral P forms in soils; potentially useful also for investigating organic P</td><td>Negassa and Leinweber (2009), Schmieder et al. (2020)</td></tr><tr><td>NanoSIMS</td><td></td><td>For example, localization of P in cells</td><td>Gross et al. (2020), Kilburn et al. (2010), Werner et al. (2017)</td></tr></tbody></table>

from soil and plant material is often more laborious (Frank 2013; Reusser et al. 2020; Turner et al. 2020). To quantitatively analyse phytate in soil and plant material, the samples were first extracted with an acid or alkaline solution, followed by isolating phytate from the extract. Methods for isolation include high-performance liquid chromatography (HPLC) and precipitating phytate as an insoluble salt, for example, by adding iron (Frank 2013; Turner 2020).

Using tracers to investigate nutrient cycling is a common research approach, and in the case of P, it includes the two radioisotopes <sup>32</sup>P and <sup>33</sup>P, the stable isotope composition of oxygen (O) associated with P, <sup>13</sup>C-labelled organic P compounds, and using elements that are associated with P, such as uranium (U) in the case of fertilizers. The application areas of P radioisotopes are quite diverse (Frossard et al. 2011) and include determining the residence time of P in different soil P pools (Helfenstein et al. 2020) and tracing P inside plants once it has been taken up (Mimura et al. 1996). Recently, Whitfield et al. (2018) published a method to synthesize inositol hexakisphosphate (IHP) labelled with <sup>32</sup>P, which could advance our understanding of IHP cycling in soils.

Using molecular approaches is becoming increasingly accessible. While only a few publications used omics analysis to investigate P cycling in the soil–plant continuum back in 2011 (Wasaki and Maruyama 2011), those methods are nowadays more common and they brought useful insights, for example, into the phosphate starvation response (PSR) of plants (Lopez-Arredondo et al. 2014; Lan et al. 2017). Proteomics revealed that the PSR of plants also affects the enzymes involved in the tricarboxylic acid cycle of plants (Lan et al. 2017).
**Spectroscopic and spectrometric** approaches cover a wide range of techniques (Kruse et al. 2015). Determining total P in soils via XRF instead of chemical extraction often yields more accurate results, as chemical extractions tend to underestimate the concentrations of total soil P (Chander et al. 2008; Wang et al. 2021b). The advantages and disadvantages of ³¹P-NMR are an ongoing debate. One disadvantage is that some organic P compounds might be hydrolyzed prior to or during NMR analysis. By using ¹⁸O-enriched medium this issue could potentially be solved (Wang et al. 2021a). Combining ³¹P-NMR with an ¹⁸O-label is also used to study enzymatic mechanisms (Cohn 1958; Cohn and Hu 1978). Spectroscopic approaches also include other approaches that are currently less common than those mentioned in Table 5.1 (Kruse et al. 2015). One example is nanoscale secondary ion mass spectrometry (NanoSIMS). Rodionov et al. (2020) used NanoSIMS in combination with other techniques to study P cycling in forest subsoils by visualizing the distribution of P in the rhizosphere.

## 5.3 The δ(¹⁸O)PO₄ to Investigate P Cycling in the Soil-Plant Continuum—Current State

### 5.3.1 Background of the Method

In recent years, using the stable isotope composition of oxygen (O) associated with P [δ(¹⁸O)PO₄] to study phosphorus (P) cycling in the soil-plant continuum has become more common. Detailed reviews about the principles of the method and its applications can be found in Adu-Gyamfi and Pfahler (2022), Bauke (2021), Tamburini et al. (2014b), and Jaisi and Blake (2014). In short, the principles of this method are (1) in the environment, P is mainly associated with O and (2) the P-O bond is stable under the absence of biotic processes and at the Earth's surface conditions (Winter et al. 1940). Alterations of δ(¹⁸O)PO₄ values in the environment occur due to processes that lead to cleaving of the P-O bond (mainly biotic processes) or sorting of the heavier and lighter isotopologues (biotic and abiotic processes) (Adu-Gyamfi and Pfahler 2022). Biotic processes include enzymatic activity, such as organic P mineralization through enzymes and P uptake by microorganisms. One of the most important enzymes influencing δ(¹⁸O)PO₄ values is inorganic pyrophosphatase (PPase). This ubiquitous enzyme leads to the progressive exchange of all four O atoms in phosphate with O from surrounding water and causes a temperature-dependent isotopic equilibrium between O in phosphate and in water (Cohn 1958; von Sperber et al. 2017). Equilibrium δ(¹⁸O)PO₄ values can be calculated (Chang and Blake 2015) and are often used as an indicator for microbial P cycling, since the PPase is an intracellular enzyme (Tamburini et al. 2012). Other enzymes, phosphomono- and -diesterases such as acid and alkaline phosphatases among others, lead to an exchange of one to two O atoms between the phosphate moiety and water (Liang and Blake 2006a; von Sperber et al. 2014; Wu et al. 2015). The reported fractionation factors are mostly
negative; i.e., phosphate released from organic P is usually depleted in ¹⁸O compared to the original organic P compound. Acid phosphatase for example has a fractionation factor of approximately −10 ‰ (von Sperber et al. 2014). With a δ(¹⁸O)H₂O value of the surrounding water of around 0 ‰ and organic P with a δ(¹⁸O)PO₄ value of around 20 ‰, phosphate released from organic P via acid phosphatase would have a δ(¹⁸O)PO₄ value of 10 ‰. Abiotic processes like the interaction of phosphate with iron oxides have a rather small fractionation factor compared to biotic processes (Jaisi et al. 2010; Melby et al. 2013a).

To determine the δ(¹⁸O)PO₄ of a P fraction, the targeted fraction needs to be extracted from the environmental sample, e.g. soil, and afterwards isolated to precipitate the final analyte silver phosphate. Nowadays, protocols for the extraction, isolation, and purification of phosphate for determination of δ(¹⁸O)PO₄ values exist for organic and inorganic P forms and fractions in soils, plants, sediments, fertilizers, and water (see Adu-Gyamfi and Pfahler, 2022 for an overview). Most protocols consist of a stepwise purification of an extract/sample (McLaughlin et al. 2004; Tamburini et al. 2010, 2018). The most common protocol for soils and plants consists of four steps: (1) precipitation of ammonium phosphomolybdate (APM), (2) dissolution of APM and precipitation of magnesium ammonium phosphate (MAP), (3) dissolution of MAP and addition of exchange resin to remove cations, (4) removal of resin and addition of silver ammine solution to precipitate silver phosphate. For a more detailed description of the method see also IAEA book, Tamburini et al. (2010), and Tamburini et al. (2018).

### 5.3.2 Calculations

To assure the quality of the obtained isotope data, the following calculations should be performed prior to further data analysis:

* Estimation whether or not **inorganic hydrolysis** of organic or precipitated P occurred during the extraction of a P pool (McLaughlin et al. 2006; Pistocchi et al. 2017).

* **Recovery of a label** like ¹⁸O-labelled phosphate or water added to soils, plants, or nutrient solutions (Gross and Angert 2015; Bauke et al. 2021).

To interpret the obtained isotope data, the following calculations are useful:

* **Isotopic equilibrium** between O in phosphate and in water caused by the PPase (Chang and Blake 2015).

* **Effect of organic P hydrolysis** via phosphomono- and -diesterases on δ(¹⁸O)PO₄ values (see Table 5.1 under “Enzymes”).

The calculated isotopic equilibrium and the effect of organic P hydrolysis can then, for instance, be used in a mass balance to estimate the relative contribution of microbial P cycling and enzymatic hydrolysis of organic P to available soil P. **Mass balances** can also be used to estimate the contribution of different P pools, such as
mineral P and inorganic P in plants to available soil P (Tamburini et al. 2012) (see also Chap. 3).

### 5.3.3 Application of the Method to Study Phosphorus Cycling in the Soil-Plant Continuum

With more protocols available for different P pools and environmental samples, the $\delta(^{18}O)_{PO4}$ method is currently used to investigate a wider range of aspects of the P cycle in the soil-plant continuum (Table 5.3). See also Bauke (2021) for a more detailed discussion about soil $\delta(^{18}O)_{PO4}$ values.

<table><caption>**Table 5.3** Aspects of the phosphorus (P) cycle in the soil-plant continuum to which the $\delta(^{18}O)_{PO4}$ method has been applied so far</caption><thead><tr><th>Aspect of phosphorus cycle</th><th>References</th></tr></thead><tbody><tr><td>Enzymes</td><td></td></tr><tr><td>Acid phosphatase</td><td>von Sperber et al. (2014)</td></tr><tr><td>Alkaline phosphatase</td><td>Blake et al. (2005)</td></tr><tr><td>5'-nucleotidase</td><td>Liang and Blake (2006b)</td></tr><tr><td>Phytase</td><td>von Sperber et al. (2015), Sun et al. (2017)</td></tr><tr><td>Inorganic pyrophosphatase</td><td>Blake et al. (2005), Chang and Blake (2015), von Sperber et al. (2017)</td></tr><tr><td>PDase1</td><td>Liang and Blake (2009)</td></tr><tr><td>DNase</td><td></td></tr><tr><td>Soil processes/ properties</td><td></td></tr><tr><td>Adsorption/ desorption</td><td>Jaisi (2013), Jaisi et al. (2010), Melby et al. (2013a)</td></tr><tr><td>Organic acids</td><td>Wang et al. (2016)</td></tr><tr><td>Soil pH</td><td>Pfahler et al. (2020b)</td></tr><tr><td>Nitrogen</td><td>Bauke et al. (2018), Bi et al. (2018), Frkova et al. (2021), Jing et al. (2021), Shen et al. (2020), Wang et al. (2021b), Weiner et al. (2018)</td></tr><tr><td>Carbon</td><td>Wang et al. (2021b)</td></tr><tr><td>Biopores</td><td>Bauke et al. (2017)</td></tr><tr><td>Soil development</td><td>Helfenstein et al. (2018), Roberts et al. (2015), Tamburini et al. (2012)</td></tr><tr><td>Microbial P cycling</td><td>Pistocchi et al. (2020), Siegenthaler et al. (2020), Tamburini et al. (2012)</td></tr><tr><td>Plants</td><td></td></tr><tr><td>P limitation</td><td>Pfahler et al. (2017, 2020a, b)</td></tr><tr><td>P uptake</td><td>Bauke et al. (2021)</td></tr><tr><td>Fertilizers</td><td>Smith et al. (2021), Sun et al. (2020)</td></tr></tbody></table>
One of the first studies which used ¹⁸O in phosphate to study P cycling in soils was the study by Larsen et al. (1989). They added KH₂PO₄ labelled with ¹⁸O and ³²P to soil, distributed the soil among different pots, and grew grass in those pots for three months. To exclude biotic activity, they also added germicide to some pots for three months. Half of the added ¹⁸O-label was lost in the untreated soils, whereas no loss was observed in the germicide-treated soil, which was attributed to a lack of biotic activity (for more information about labelling experiments see also Chap. 3). In line with this, Tamburini et al. (2012) showed by analysing δ(¹⁸O)ₚO₄ values of soil and plant P pools along a soil chronosequence that prior to being released to the available soil P pool, phosphate is cycled through microbes. In an incubation experiment with organic horizons of two forest soils, differing in their amount of available P, Pistocchi et al. (2020) added ¹⁸O-labelled water to the soils. In the soil with a high P availability, δ(¹⁸O)ₚO₄ values of available P approached equilibrium values, whereas in the low-P soil, the impact of hydrolyzing enzymes was visible in δ(¹⁸O)ₚO₄ values of available P, thus showing that this pool was mostly replenished by P derived from organic P mineralization.

Whereas more and more studies use δ(¹⁸O)ₚO₄ values to investigate P cycling in soils, less is known about plants, i.e. P acquisition by plants and plant internal P cycling, and how these processes affect δ(¹⁸O)ₚO₄ values. Plants play a vital role for P cycling in soils, as they not only take up P from the soil solution, but also exude enzymes and create biopores thereby altering chemical and physical soil properties. Plant litter can also be a major source of P in soils (Sayer et al. 2020); therefore, it is necessary to understand how plants, under different environmental conditions, alter δ(¹⁸O)ₚO₄ values. In a greenhouse experiment with soybeans, Pfahler et al. (2013) showed that δ(¹⁸O)ₚO₄ values of inorganic P, extracted from plant leaves, are more enriched in ¹⁸O compared to P supplied in the nutrient solution. This was most likely caused by the O exchange between phosphate and leaf water, which is usually enriched in ¹⁸O compared to soil water, due to transpiration. In a follow-up study, Pfahler et al. (2017) investigated if δ(¹⁸O)ₚO₄ values can be used to study plant response to P limitation. Combining δ(¹⁸O)ₚO₄ values with radioactive ³²P labelling, they showed that δ(¹⁸O)ₚO₄ values of trichloroacetic acid (TCA)-extractable P from soybean leaves, subjected to P limitation, are less enriched in ¹⁸O compared to the soybeans growing with an ample amount of P in the nutrient solution. This was most likely caused by hydrolyzing enzymes like acid phosphatase, releasing, and then translocating P from senescent to younger leaves. Recently, several studies have investigated whether the δ(¹⁸O)ₚO₄ method can be used to trace P inside plants after uptake (Qin et al. 2018; Hauenstein et al. 2020; Bauke et al. 2021). Qin et al. (2018) used ¹⁸O-labelled phosphate to trace applied phosphate from the soil to maize shoots. They found this possible only when the roots of maize plants were not inoculated with an arbuscular mycorrhizal fungus (AMF). They concluded that the ¹⁸O-label was lost during phosphate metabolism inside the AMF (Qin et al. 2018). Hauenstein et al. (2020) and Bauke et al. (2021) used ¹⁸O-labelled water instead of ¹⁸O-labelled phosphate to investigate P cycling in plants. The ¹⁸O-labelled water was provided to the plants as irrigation water or in the nutrient solution. Hauenstein et al. (2020) used this method to study P nutrition of beech at two forest fertilization experiments
in Germany. Based on the analysis of δ(^{18}O)_{PO4} values of inorganic P in the xylem sap, they postulate that in the P fertilized treatments, the additional P in the xylem was derived from fertilizer P and biologically cycled soil P. Bauke et al. (2021) used spring wheat as a model plant and conducted three different experiments using either hydroponic systems or pots filled with soil. Unlike shoots, root δ(^{18}O)_{PO4} values of TCA P partly preserved the δ(^{18}O)_{PO4} value of the P source due to lower metabolic activity in the roots. They therefore suggested that root P might provide better information on the P source than above-ground biomass, although their study did not consider the effects of mycorrhization, as in Qin et al. (2018).

As shown in the above-mentioned studies, plants can greatly alter δ(^{18}O)_{PO4} values of assimilated phosphate. Hacker et al. (2019) showed that plant diversity, via its effect on evaporation and thus soil water δ(^{18}O)_{H2O} values, can also indirectly impact δ(^{18}O)_{PO4} values of bioavailable soil P. To show this, two waters, differing in their δ(^{18}O)_{PO4} values, were applied to 27 plots in the Jena experiment, which consisted of around 80 plots with different plant species combinations. Microbial P turnover influenced δ(^{18}O)_{PO4} values of bioavailable soil P more in plots with high plant diversity compared to plots with lower plant diversity (Hacker et al. 2019).

Plants can potentially affect soil δ(^{18}O)_{PO4} values in many ways, not just via their impact on evaporation. Plants, like microorganisms, exude enzymes that are known to alter δ(^{18}O)_{PO4} values (see Table 5.1). However, little is known about how other plant acquisition strategies, such as symbiosis with AMF might affect soil δ(^{18}O)_{PO4} values. To the best of our knowledge, only Wang et al. (2016) investigated the impact of low-molecular-weight organic acids (LMWOAs) on δ(^{18}O)_{PO4} values. They used hydroxyapatite and different LMWOAs (acetic, oxalic, and citric acid) in batch and column experiments. The observed fractionation factors were relatively small: -0.3 to 1.1 ‰ (batch experiment) and -1.3 to 1.1 ‰ (column experiment).

### 5.3.4 Natural Abundance Versus Labelling Experiments

Most field studies work with the natural abundance of ^{18}O in the environment. However, the differences between δ(^{18}O)_{PO4} values of P pools between treatments or between different P pools are sometimes within 1 or 2 ‰ and it might be difficult to draw sound conclusions, especially when replication is missing (Pfahler et al. 2020b). One possibility is to work with strong environmental gradients, as in the pioneering field study by Tamburini et al. (2012) at a soil chronosequence. In other cases, the addition of an ^{18}O-labelled P source or ^{18}O-labelled water is advisable. Labelling experiments have great potential to bring further insights into the P cycle in the soil–plant continuum. Following the work by Larsen et al. (1989) in soils, Melby and co-authors conducted several studies using ^{18}O-labelled phosphate (Melby et al. 2011, 2013a, b). In the first study, Melby et al. (2011) described a method how to produce ^{18}O-labelled phosphate to be used in other studies. They then conducted two studies in which they used ^{18}O-labelled phosphate to investigate biological P cycling (Melby et al. 2013b) and phosphate sorption (Melby et al. 2013a). In recent years,
more and more studies, including the ones mentioned before, have applied an ¹⁸O-label to soils/plants, either via phosphate or via water. Most of them are conducted under controlled environmental conditions (i.e., greenhouse or growth chambers), since the application of a labelled compound in the open environment requires more care and might not always be suitable. In areas with high precipitation like tropical rainforests or high evaporation like deserts, ¹⁸O-labelled water applied to the soil might rapidly be altered, which needs to be accounted for when interpreting the obtained data (Kendall and McDonnell 2012; Beyer and Penna 2021). In addition to adding a ¹⁸O-labelled compound, also the addition of other labels like ³³P/³²P (Pfahler et al. 2017; Siegenthaler et al. 2020) and ¹³C can bring useful insights into P cycling (Gross and Angert 2017). In an incubation study, Siegenthaler et al. (2020) applied ³²P as well as ¹⁸O-labelled water to soil samples from a climatic gradient in Hawaii. They found that both techniques complement each other like in case of NaOH-EDTA P where ³²P and δ(¹⁸O)ₚO₄ values both indicated that NaOH-EDTA P was only partly cycled during the incubation experiment. Combining δ(¹⁸O)ₚO₄ with labelling an organic P compound with ¹³C, Gross and Angert (2017) showed that this combination could help elucidating both, C and P, cycling in the environment.

## 5.4 Challenges of the δ(¹⁸O)ₚO₄ Method Application

The major pitfalls of the δ(¹⁸O)ₚO₄ method can be grouped into three categories: sample handling mistakes, issues during purification, and lack of additional data for results interpretation.

### 5.4.1 Sample Handling Mistakes

δ(¹⁸O)ₚO₄ is strongly influenced by biotic processes. It is therefore essential to reduce biological activity immediately after sampling to not alter the δ(¹⁸O)ₚO₄ of any targeted P pool or compound. Drying soil samples might be effective in reducing biological activity, but drying and re-wetting are known to lyse microbial cells and release microbial P into the available P pool (Dinh et al. 2017). Thus, analysing δ(¹⁸O)ₚO₄ of available P extracted from dried soils is different from δ(¹⁸O)ₚO₄ values determined using fresh soils. A relatively simple way to reduce biological activity is to store the samples on ice in a freezer/polystyrene box. However, prolonged storage of fresh soil samples, even at 4 °C, might affect δ(¹⁸O)ₚO₄ values, especially those of more labile P pools. Storing fresh soil samples for a minimum amount of time, even in the fridge, is thus recommended.
## 5.4.2 Extraction Issues

One issue when working with plants and $\delta(^{18}O)_{PO4}$ is that enzymatic activity needs to be stopped during extraction, otherwise $\delta(^{18}O)_{PO4}$ could be altered (Bieleski 1964). The combination of low temperatures and trichloroacetic acid was relatively efficient in stopping enzymes during extractions. Similarly, resin and microbial P extractions for $\delta(^{18}O)_{PO4}$ analysis are usually conducted at 4 °C (Tamburini et al. 2012).

Another issue associated with extracting P for $\delta(^{18}O)_{PO4}$ analysis from soil samples is that sometimes large soil volumes are required to obtain a sufficient amount of P (10–20 µmol P) for the purification protocol (Pfahler et al. 2020a). This is challenging for two reasons: (1) keeping the original soil-to-solution ratio for an extraction might be difficult and (2) reducing large volumes of extracts is laborious and needs to be done carefully (Adu-Gyamfi and Pfahler 2022). Keeping the original soil-to-solution ratio is important, as changes to it might result in extracting more or less P compared to the original ratio (Weiner et al. 2011; Pfahler et al. 2020a) and thus comparing results between studies is difficult. Large volumes of extracts need to be concentrated, for instance via brucite precipitation to approximately 100 mL to purify it and precipitate silver phosphate as the final analyte. Several brucite precipitations might be necessary, which could result in a loss of P if not done carefully (Adu-Gyamfi and Pfahler 2022).

## 5.4.3 Issues During the Purification Protocol

Adu-Gyamfi and Pfahler (2022) addressed the issues during the purification protocol in detail. In brief, issues during the purification of extracts are often due to high quantities of organic matter in the extracts or low P concentrations. There are ways to deal with those issues, but one must be aware of them first. An additional step with a DAX resin or (several) brucite precipitations could be included to remove more organic matter (Adu-Gyamfi and Pfahler 2021). Chemicals used in the purification protocol could potentially contain small impurities of P. Also, some detergents used in laboratories to clean equipment contain P. Thoroughly checking for P contaminations is therefore a good practice, especially when handling samples with low P concentrations.

Soils and soil extracts rich in calcium, iron, and silica might cause problems during the purification protocol (Adu-Gyamfi and Pfahler 2022; Tamburini et al. 2018). Knowledge of the chemistry of the studied soils is thus recommended to be able to prevent and face these problems.
### 5.4.4 Pitfalls in ¹⁸O-labelling Experiments

In addition to the above-mentioned pitfalls, there are additional ones when setting up labelling experiments with ¹⁸O-labelled water or a ¹⁸O-labelled P compound/pool. One pitfall might be the sensitivity and linearity of isotope ratio mass spectrometry (IRMS) measurements, as observed in the case of other isotope studies (Blessing et al. 2008). Measuring ¹⁸O-labelled and unlabelled samples in separate IRMS runs is advisable to avoid carry-over memory effects. Most available silver phosphate reference materials (store-bought) do not cover a wide range of δ(¹⁸O)ₚO₄ values. Typically, they range between 14 and 23 ‰. It might therefore be necessary to produce an in-house standard (Lecuyer et al. 1999), covering the expected range of δ(¹⁸O)ₚO₄ in the experiment (Halas et al. 2011; Watzinger et al. 2021).

Like for other labelling experiments, background information of the soil- or plant-inherent P concentration and isotope values is necessary to allow choosing an adequate labelling strength. Especially in samples with high P concentrations, the added label may be strongly diluted, and the intended processes will not become apparent. It is recommended to perform a preliminary test consisting of four steps: (1) analysis of δ(¹⁸O)H₂O of soil water, (2) analysis of δ(¹⁸O)ₚO₄ in different soil P pools, (3) calculate theoretical equilibrium δ(¹⁸O)ₚO₄ values using different values for δ¹⁸O of soil water, and (4) choose a δ(¹⁸O)H₂O of soil water which would result in a well distinct, for instance, by a factor of 5 or 6, δ(¹⁸O)ₚO₄ equilibrium value. For label application, a homogenous distribution of the labelled substance (water or P compound/pool) to a soil (in the field or pot) is crucial for ¹⁸O-labelling experiments. Sample homogeneity could also be an issue when using ¹⁸O-labelled water or P compounds. During the experiment and subsequent purification or sample treatment procedures, labelled samples should also be handled with care and contact with unlabelled samples should be avoided to avoid any carry-over of the label.

### 5.4.5 Lack of Additional Information

Phosphate mainly exchanges O with water, and δ(¹⁸O)H₂O of water plays an important role in interpreting δ(¹⁸O)ₚO₄ values. Analysing δ(¹⁸O)H₂O of water in soils and plants requires the extraction of the water, usually via cryogenic distillation. For more information about cryogenic distillation and discussions about it see, for example, Orlowski et al. (2016) and references therein. Evapotranspiration influences δ(¹⁸O)H₂O of soil and plant water. Putting sub-samples of the soil and plant samples into appropriate containers directly at the time of sampling is therefore recommended. Because the equilibrium promoted by PPase is temperature-dependent, measuring the ambient/soil/leaf temperature is highly recommended. One of the major aspects of P cycling in the soil–plant continuum is the P availability. Determining at least the concentrations of available P (soils), total P (soils, plants), and inorganic P (plants)
is thus recommended. Knowledge of other soil properties, such as soil pH, organic matter content, and bulk density, might also help in interpreting $\delta(^{18}O)_{PO4}$ values.

## 5.5 Way Forward and Prospects of the $\delta(^{18}O)_{PO4}$ Method for Plant P Cycling

### 5.5.1 Earth System Models and $\delta(^{18}O)_{PO4}$

Recently, P has been incorporated into Earth system models in addition to carbon (C) and nitrogen (N) (Reed et al. 2015). Advances in those models could be made if we have a better understanding of P mineralization, how P could limit C cycle processes, P sorption, and the coupling between P, C, and N cycling (Reed et al. 2015). $\delta(^{18}O)_{PO4}$ has the potential to advance our understanding in these aspects, especially when biotic processes are involved. Studies conducted so far indicate that $\delta(^{18}O)_{PO4}$ could provide useful information about the role of soil pH, enzymes, and soil microorganisms for P cycling in the soil-plant continuum, the effect of P limitation, the interaction of P with other nutrients such as N, and the residence times of P in different soil P pools (see Sect. 5.3). The first attempts to use $\delta(^{18}O)_{PO4}$ for modelling/estimating P fluxes exist (Tamburini et al. 2012; Jaisi et al. 2017; Pfahler et al. 2017; Helfenstein et al. 2018; Bauke et al. 2021), but further research is needed.

### 5.5.2 $\delta(^{18}O)_{PO4}$ Values of Single Organic Phosphorus Compounds

To advance Earth system models even further, knowing $\delta(^{18}O)_{PO4}$ values of specific organic P compounds might be useful. Knowing those values, one could more accurately estimate the parameters associated with P mineralization. The determination of $\delta(^{18}O)_{PO4}$ of organic P usually requires treating the pure organic P compound with UV light to hydrolyze the organic P and release inorganic P (Blake 2005; Tamburini et al. 2018). Wu et al. (2015) and Sun et al. (2017) determined $\delta(^{18}O)_{PO4}$ of phytate via direct pyrolysis in the TC/EA-IRMS. So far, studies which analysed $\delta(^{18}O)_{PO4}$ values of single organic P compounds have always used pure compounds and did not extract them from environmental samples. The next step might be to investigate whether some of the available methods for extracting single organic P compounds from environmental samples are suitable for the determination of $\delta(^{18}O)_{PO4}$ values. Phytate can be isolated from soil samples via hypobromite oxidation (Turner 2020). However, due to the experimental conditions, it is necessary first to adapt this method to the phosphate purification protocols currently available, so that reliable soil phytate $\delta(^{18}O)_{PO4}$ values can be obtained.
### 5.5.3 More Laboratory and Glasshouse Studies Needed to Advance the Application of $\delta(^{18}O)_{PO4}$ in the Field

So far $\delta(^{18}O)_{PO4}$ studies have focused on hydrolyzing enzymes but data on the fractionation factors of synthesizing enzymes and phosphotransferases, to name a few, for the $\delta(^{18}O)_{PO4}$ are missing. Some papers, however, report isotopic effects of $^{18}O$ on the rate constants of these enzymes, for example Hengge et al. (2002). Information about those enzymes is vital, especially for the P cycle in microorganisms and plants. How microorganisms deal with low soil pH and low soil P conditions simultaneously is also not well understood (Lidbury et al. 2017). The $\delta(^{18}O)_{PO4}$ might help shed light on this, as indicated in the study of Pfahler et al. (2020b). Glasshouse studies excluding variables such as changes in the vegetation but focusing mainly on the soil pH might therefore be useful. As mentioned by Reed et al. (2015), better knowledge about P acquisition strategies is necessary to improve models. Excreting organic acids is one P acquisition strategy, and one study showed that the dissolution of hydroxyapatite by organic acids (oxalic, citric, and acetic acid) leads to a small isotopic fractionation (Wang et al. 2016). As organic acids mainly affect processes in the rhizosphere, studies determining $\delta(^{18}O)_{PO4}$ values in the rhizosphere are encouraged. This is challenging due to the small amounts of rhizosphere soil that can be collected. Rodionov et al. (2020) found no differences in $\delta(^{18}O)_{PO4}$ values of HCl-extractable P between bulk and rhizosphere soil samples. $\delta(^{18}O)_{PO4}$ values of the more actively cycled P pools, such as microbial and available P, extracted from rhizosphere samples have not yet been reported.

### 5.5.4 Lack of Field Studies Including Plants and Ecosystems

In general, information about $\delta(^{18}O)_{PO4}$ of plants is still limited (see Sect. 5.3). Detailed plant studies are thus far only available for soybeans (Pfahler et al. 2013, 2017), maize (Qin et al. 2018), and spring wheat (Bauke et al. 2021). Additionally, Tamburini et al. (2012) analysed different plant species along a soil chronosequence at the Damma Glacier in Switzerland, Helfenstein et al. (2018) reported $\delta(^{18}O)_{PO4}$ values for different plant species at a climatic gradient in Hawaii, and Pfahler et al. (2020b) determined $\delta(^{18}O)_{PO4}$ of bulk vegetation samples from a grassland experiment in the UK. Those studies provided useful hints/insights about using $\delta(^{18}O)_{PO4}$ in plant studies, but data is still limited. The effect of P limitation has so far only been investigated in soybeans, and the study by Pfahler et al. (2020b) indicates that P limitation has a similar effect on grasslands. Studies about the effect of P limitation on major crops, such as maize, rice, and wheat, especially under field conditions, are still missing. Likewise, there are no studies using $\delta(^{18}O)_{PO4}$ and investigating the effect of crop rotation or intercropping on P cycling. Crop rotations or intercropping often combine plants with different P acquisition strategies to increase the P use efficiency of crops, and it could be expected to have an influence on the $\delta(^{18}O)_{PO4}$ of soil and plants.
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**Verena Pfahler** Verena's area of expertise covers phosphorus (P) dynamics and impacts on the environment and the use of stable and radioisotopes in environmental studies. She is a postdoctoral research fellow focusing on phosphorus cycling in the soil/plant system and P cycling in agricultural systems and alternative P fertilizers. She extensively uses stable oxygen isotopes in phosphates (δ(¹⁸O)PO₄). Verena has over 10 years of experience in using the δ(¹⁸O)PO₄ protocols and adapting them to different soil and plant extracts. She is a co-author of a book chapter and 13 peer-reviewed journal papers. Her recent work includes P cycling in tropical rainforests.

**Joseph Adu-Gyamfi** Joseph is working as an 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).
**Chiara Pistocchi** Chiara is an Associate Professor in soil biogeochemistry at the Institut Agro Montpellier (FRANCE). Her work focuses on: (i) soil biological processes, in particular the relationships between soil organisms (diversity and functions) and carbon, nitrogen and phosphorus dynamics and availability for plants; (ii) what is the interplay between nutrients cycling and soil carbon storage in low-input diversified agroecosystems and; (iii) the efficiency and impact on soils nutrient cycles of innovative fertilizers from recycled materials. Since has several years of experience in the application of stable and radioisotopes in phosphorus biogeochemistry studies. She is the co-author of 23 papers in peer-reviewed journals and several book chapters.

**Federica Tamburini** Federica is a Researcher, working and managing the stable isotope facility of the group of Plant Nutrition (D-USYS) of the ETH (Eidgenössische Technische Hochschule), Zurich. Her research area is phosphorus cycle in the soil/plant system focusing on the use of oxygen stable isotopes in phosphate as a tracer to understand biological processes in P cycling. She is an expert in stable isotope geochemistry; inorganic geochemistry; chemical extraction and purification of environmental samples. She has supervised/Co-supervised MSc, PhD students and post-Doctoral and research fellows. Federica is CoPI in several national and international research projects dealing with phosphorus cycling in the soil/plant system and has 47 publications in refereed journals, books and book chapters.

**Sara L. Bauke** Sara is a postdoctoral research fellow at Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES) –Soil Science and Soil Ecology at the University of Bonn. Her research focuses on patterns of nutrient storage and cycling in soil compartments such as subsoil horizons, aggregates, rhizosphere or biopores, considering single nutrients (phosphorus, nitrogen, sulfur) as well as for interactions among multiple nutrients and between nutrients and water across a range of different ecosystems. Her studies mainly rely on isotope analyses as tracers for nutrient recycling and uptake and she has won the early career Fritz-Scheffer Award of the German Soil Science Society for her work on phosphorus cycling in subsoils using stable oxygen isotopes and phosphorus radioisotopes.

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<figure><img src="image_33.png" /></figure>

# Chapter 6
## Stable Sulfur and Oxygen Isotope Compositions of Sulfates to Disentangle Agrocontaminants from Other Sources of Sulfur in Agrosystems

G. Skrzypek

**Abstract** This chapter summarizes key information about the sulfur cycle and stable sulfur isotope biogeochemistry. The overview includes a description of the range of the stable sulfur isotope compositions in natural environments, potential pollutants and fertilizers used in agriculture. The primary focus is on the generation, transport, and mixing of sulfate and on its stable sulfur and oxygen isotope compositions that can be used as tracers. Several case studies that apply stable sulfate isotope analyses for the identification and quantification of pollutants in natural environments are used to provide an overview of successful applications and highlight major challenges. These are followed by the principles of sulfate sampling and analytical techniques.

### 6.1 Overview of Stable Sulfur Isotope Biogeochemistry

Sulfur, as one of the macro-elements (H, C, N, O and S) is required for the biosynthesis of methionine and cysteine, two canonical amino acids that are incorporated into proteins (Brosnan and Brosnan 2006) and therefore is essential for the biosphere. Sulfur exists in natural environments at several oxidation states, but the most common are -2, +2, +4, and +6. The highest oxidation state (+6) is the most important for sulfur assimilation by plants because sulfur is mainly taken up as the sulfate ion, SO₄²⁻ (Davidian and Kopriva 2010). Sulfate is also an abundant ion (~2.7 g/L) in marine environments (Andrulionis and Zavialov 2019), but some terrestrial ecosystems can be sulfur limited, despite a highly dynamic sulfur cycle and significant exchanges between land and sea (Fig. 6.1). The sulfate budget of the global ocean is controlled by: (1) river discharge (Burke et al. 2018); (2) sulfate reduction and sulfide re-oxidation; and (3) burial of sulfur-bearing minerals (Jørgensen et al. 2019). The terrestrial sulfur budget reflects primary inputs from (1) oceans via the atmosphere

<footnote>G. Skrzypek (✉)
School of Biological Sciences, West Australian Biogeochemistry Centre,
The University of Western Australia, Perth, WA, Australia
e-mail: grzegorz.skrzypek@uwa.edu.au</footnote>
as inorganic sulfate and biogenic origin dimethylsulfide (Alon et al. 2013); (2) rock erosion resulting in sulfate dissolution and sulfide oxidation (Szynkiewicz et al. 2015b); and (3) organic matter decomposition (Schroth et al. 2007; Joo et al. 2022). In addition to these natural sources, the sulfur pool in agrosystems is supplemented by various anthropogenic sources, such as: (1) industrial outputs (Górka et al. 2017); (2) mining (Dogramaci et al. 2017); and (3) agriculture pollution (Chalk et al. 2017). Therefore, the use of chemical concentrations alone does not allow partitioning of sulfur sources or assessment of pollution inputs. This challenge can be addressed by including stable sulfur isotope analyses in hydrochemical analyses, as the stable sulfur isotope composition provides fingerprints of the original S-sources or leading processes and allows calculation of the proportional contribution of these sources.

Sulfur has four stable isotopes with a broad range of natural abundances: $ ^{32}S = 95.04 \% $, $ ^{33}S = 0.75 \% $, $ ^{34}S = 4.20 \% $ and $ ^{36}S = 0.01 \% $ as mole fraction percentages, and its stable isotope composition widely varies across different environments (Coplen et al. 2002a). Because the majority of the stable sulfur isotope fractionations are mass dependent, the ratio between the two most abundant stable isotopes $ ^{34}S/^{32}S $ is measured primarily using isotope ratio mass spectrometry (IRMS) (Sharp 2017). The results are usually reported in permilles (‰) as 1000 isotope delta values $ \delta(^{34}S) $ with respect to the absolute abundance in the Cañon Diablo Troilite (VCDT) standard, which is the international zero point for the VCDT scale (Kaplan and Hulston 1966; Skrzypek and Dunn 2020b). In addition to $ \delta(^{34}S) $, the stable oxygen

<figure><img src="image_34.png" /><figcaption>**Fig. 6.1** Principles of sulfur biogeochemical cycling. Oxidation of hydrogen sulfide $H_2S$ occurs in the presence of molecular $O_2$, leading to the production of sulfate ($SO_4$), with minor stable isotope fractionation. Sulfate ($SO_4$) can be utilized by plants via assimilatory reduction, again with minor stable isotope fractionation, and the resulting organic matter can be decomposed, leading to desulfurization. $SO_4$ is the compound preferred by plants uptake. Dissimilatory and assimilatory reduction can be conducted under anoxic conditions by a few groups of microorganisms using $SO_4$ as a biochemical substrate or during respiration. These biological processes are extremely $ ^{32}S $ selective and result in a very low $ \delta(^{34}S) $ for $H_2S$ and a very high $ \delta(^{34}S) $ for residual $SO_4$</figcaption></figure>
isotope composition of the oxygen bonded in sulfates $\delta(^{18}\text{O})_{\text{SO}_4}$ also provides valuable information about the sulfate origin. The $\delta(^{18}\text{O})_{\text{SO}_4}$ value is typically analyzed in relation to the stable oxygen isotope composition of the water in which the sulfate is dissolved and that of atmospheric oxygen. Oxygen has three stable isotopes with different abundances of $^{18}\text{O} = 0.20\%$, $^{17}\text{O} = 0.04\%$ and $^{16}\text{O} = 99.76\%$ as mole fraction percentages (Coplen et al. 2002a). In the majority of studies, isotope delta values $\delta(^{18}\text{O})$ reflecting the $^{18}\text{O}/^{16}\text{O}$ ratio are used and expressed in permille (\%) in relation to the international water standard representing the zero point on the Vienna Standard Mean Ocean Water (VSMOW) scale.

The range of $\delta(^{34}\text{S})_{\text{SO}_4}$ and $\delta(^{18}\text{O})_{\text{SO}_4}$ values observed in the environment arises from stable isotope fractionations occurring during chemical, physical, or biological processes. In general, the stable sulfur isotope fractionation is relatively small during sulfur oxidation processes (~0–2 \%) and negligible during sulfate dissolution (Clark and Fritz 1997). By contrast, this fractionation is very large during bacterial sulfur reduction processes, at up to ~40 \%, because bacteria preferentially take up the lighter $^{32}\text{S}$ isotope, thereby leaving the residual sulfur enriched in the heavy $^{34}\text{S}$ isotope (Habicht and Canfield 1997). $^{32}\text{SO}_4^{2-}$ also reacts up to 1.07 faster than $^{34}\text{SO}_4^{2-}$ (Weyer et al. 1979). During sulfur oxidation, oxygen forms a strong and stable chemical bond with sulfur and does not easily exchange with water under natural conditions (Fig. 6.1). Its $\delta(^{18}\text{O})_{\text{SO}_4}$ reflects the stable isotope composition of the oxygen source available during oxidation, which is usually atmospheric molecular oxygen or oxygen from water molecules. The isotope fractionation that occurs during the oxidation process depends on the source of oxygen and equals –11.4 \% between atmospheric oxygen and sulfate and +4.1 \% between water and sulfate (Taylor et al. 1984; Dogramaci et al. 2017).

## 6.2 Stable Isotope Composition of Sulfur Sources in Agro-ecosystems

Agro-ecosystems are usually studied on the catchment scale according to land use and water flow direction (Chap. 2). Therefore, the stable isotope and hydrochemical composition of pollution and natural solutes are monitored along waterways, in groundwater and pore water, and, if possible, at pollution sources or directly in contaminating compounds before their dispersal into the environment. The hydrochemical and stable isotope compositions of sulfate in water will therefore represent a mixture of various sources, both natural and anthropogenic, and the usual objective is to identify the fractional contributions from different sources and determine their locations. Hence, tracer studies very frequently use mass balance mixing models (Chap. 3) and sometimes include a stable isotope fractionation component (Lewicki et al. 2022). Mixing models can provide a fair assessment of the potential relative
contributions from different sources if the hydrochemical and stable isotope signatures of each source are determined with reasonable accuracy. This is why the identification and characterization of potential sources are important before designing a specific sampling plan. The optimal method is to analyze the signature of each source at the studied location; however, the overview provided below can give benchmarks for the expected range of values.

The stable isotope studies using $\delta(^{34}S)_{SO_4}$ and $\delta(^{18}O)_{SO_4}$ as stable isotope hydrochemical tracers usually require consideration of the following potential sources of sulfur that contribute to waterways:

*   **Natural sources**

- Precipitation and sea spray

- Rock weathering

- Sediments and soils

- Decomposing organic matter

*   **Anthropogenic sources**

- Atmospheric pollution

- Acid rock drainage due to mining or other excavations

- Industrial pollution discharge

- Agriculture fertilizers.

### 6.2.1 Natural Sources

The sea spray contribution to the terrestrial sulfur budget is significant and occurs through the direct delivery of seawater droplets in coastal regions or by influencing the hydrochemical composition of precipitation. Sea spray delivers sulfate and dimethyl sulfate (DMS) at very high concentrations, but its delivery is restricted to coastal regions (Crawford et al. 2019; Lewis and Schwartz 2004). By contrast, precipitation that carries ocean signatures condenses across all land masses. It delivers large quantities of water, but the solutes are usually present at very low concentrations, with mean sulfate contents <3 mg/L and varying with distance from the coast and pollution sources; in pristine areas, the content is <0.3 mg/L (Vet et al. 2014). The generated loads of solutes are enormous on the landscape scale, but for sulfate surface budgets, these loads matter only in places with a long water retention time and a high evaporation rate (Skrzypek et al. 2013). In wet climates with high runoff, rainfall primarily causes solute dilution in the surface water, and its $\delta(^{34}S)_{SO_4}$ and $\delta(^{18}O)_{SO_4}$ are irrelevant due to the very low sulfate concentration (Vet et al. 2014). However, rainfall solute loads have a long-term impact on groundwater hydrochemistry in many regions, especially in arid and semiarid areas (Skrzypek et al. 2013, 2016), and in the absence of sulfur-bearing minerals in geological formations, rainfall may constitute a hydrochemical stable isotope baseline for sulfate in local pristine waters (Dogramaci and Skrzypek 2015).
The sulfate concentration in flood water depends on the evaporation and disso-
lution of salts deposited earlier on the ground; therefore, its concentration varies
across a broad range and is not itself indicative of precipitation input. To make a
meaningful estimation, other tracers can be used. For example, the ratio between
sulfate and chloride ($SO_4/Cl$), expressed in milliequivalents (meq/L) in the range
between 0.15 and 0.70, can be attributed directly to marine origin (Fig. 6.2). Higher
ratios will suggest inputs other than rainfall sulfate, while lower ratios will imply
sulfur removal by sulfate reduction or excessive halite dissolution. Oceans also have
a very distinct compositions, with $\delta(^{34}S)_{SO_4} \sim 21.1$ ‰ and $\delta(^{18}O)_{SO_4} \sim 8.6$ ‰, and
these are highly consistent around the world (Rees et al. 1978; Holser et al. 1979). The
unique $\delta(^{34}S)_{SO_4}$ values progressively decrease from 21 ‰ at the coast to ~14 ‰ in
inland locations, but they usually remain above these values for other sulfur sources
(Chivas et al. 1991). This range of values can be used as a basic reference baseline for
local studies (Fig. 6.2). In this example, samples with $\delta(^{34}S)_{SO_4} \sim 12$ ‰ fall within
the $SO_4/Cl$ ratio typical for rainfall, suggesting precipitation as the dominant sulfate
source for these waters (Fig. 6.2) (Dogramaci et al. 2017). By contrast, samples with
a $SO_4/Cl$ ratio significantly higher than 0.7 have low $\delta(^{34}S)_{SO_4} < 8$ ‰, suggesting an
input of sulfur with a low $\delta(^{34}S)_{SO_4}$.

Rock weathering occurs naturally and can represent an important source of sulfur in waterways (Calmels et al. 2007; Das et al. 2012; Szynkiewicz et al. 2015b). The common form of sulfur is sulfide (e.g., pyrite or chalcopyrite) in many types of rock, especially metamorphic and sedimentary rocks (Dogramaci et al. 2017). Elemental sulfur occurs less frequently in, for example, volcanic rocks and as sulfate

<figure><img src="image_35.png" /></figure>

**Fig. 6.2** Stable sulfur isotope composition and chloride concentration relative to the $SO_4/Cl$ ratio (meq/L). The typical $SO_4/Cl$ ratio for rainfall varies between 0.15 and 0.7. Samples with $SO_4/Cl$ ratios >0.7 usually have additional S derived from sources other than precipitation. Samples with $SO_4/Cl$ ratios <0.1 are subject to S removal due to sulfate precipitation [$\delta(^{34}S)_{SO_4}$ 10–15‰] or bacterial reduction [$\delta(^{34}S)_{SO_4}$ > 30‰]. Groundwater—black points, surface water—blue points. Data from Pilbara, Western Australia, figure modified after Dogramaci et al. 2017
in evaporative deposits or salt domes (gypsum). These different geological sources of sulfur frequently show distinct $\delta(^{34}S)_{SO_4}$ and $\delta(^{18}O)_{SO_4}$ values that can aid in the identification of the contribution to ground and surface waters. However, the natural variability has a broad range (Coplen et al. 2002b), so obtaining local values is important whenever practical (Szynkiewicz et al. 2015b).

Sulfides usually have very low $\delta(^{34}S)$, often below 0 ‰ (Krouse and Grinenko 1991; Clark and Fritz 1997), but values above zero have also been reported, such as -8.9 to 2.7 ‰ for the hydrothermal sulfides of the San Juan and Sangre de Cristo Mountains in the USA (Nordstrom et al. 2007; Campbell and Lueth 2008) or -1.9 to 4.4 ‰ for Pilbara in Western Australia (Dogramaci et al. 2017). In contrast to sulfides, the $\delta(^{34}S)_{SO_4}$ sulfates of marine origin evaporites are usually near or above the seawater values and are typically >15 ‰ (Claypool et al. 1980). However, most evaporites from old rock formations can be partitioned from modern marine sulfates deposited with rainfall or sea spray based on $\delta(^{18}O)_{SO_4}$. The evaporites $\delta(^{18}O)_{SO_4}$ values are usually much higher than the theoretically expected value of 16 ‰ (Holser et al. 1979) due to diagenetic alterations, and their values are significantly above those of marine sulfates (8.6 ‰). The $\delta(^{34}S)_{SO_4}$ values observed in sulfides may overlap with volcanic sulfur, which usually has values slightly above zero in both basalt rocks (Torssander 1988, 1989) and volcanic gases (De Moor et al. 2022).

The stable sulfur isotope composition of **soil and terrestrial sediments** primarily reflects geological sources and inputs from rain and sea spray (Mizota and Sasaki 1996; Stack and Rock 2011; Tanikawa et al. 2022) and consists of a mixture of minerals, organic sulfur or dissolved sulfates in porewater (Chalk et al. 2017). Under anoxic conditions (Fig. 6.1), if bacterial sulfate reduction occurs, the residual sulfate tends to have very high values, frequently >30‰ (Bottrell and Novak 1997; Oleinik et al. 2003; Schroth et al. 2007). Plant stable isotope composition usually reflects sulfur sources because the isotope fractionation occurring during sulfate uptake is usually negligible (Skrzypek et al. 2008; Chalk et al. 2017; Gałuszka et al. 2020). Therefore, plants do not provide unique signatures unless abundant organic matter decomposes under anoxic conditions, such as in wetlands or lake sediments, when the major driver of $\delta(^{34}S)$ is bacterial reduction causing very high $\delta(^{34}S)_{SO_4}$ for residual sulfates (Trust and Fry 1992; Bottrell and Novak 1997; Skrzypek et al. 2010).

## 6.2.2 Anthropogenic Sources

### 6.2.2.1 Atmospheric Pollution

Atmospheric pollution in industrial areas frequently overwhelms the natural $\delta(^{34}S)_{SO_4}$ signature of precipitation. Very high sulfate loads are still observed around the world, despite desulfurization technologies introduced in the 1980s–1990s to address the acid rain problem (Jędrysek et al. 2002a, b), and they can exceed 4 kg/ ha for sulfur (e.g., in the Mideastern US), compared to ~0.1 kg/ha for sulfur in pristine areas (e.g., the Rocky Mountains) (https://enviroatlas.epa.gov). A similar or even
<figure><img src="image_36.png" /><figcaption>**Fig. 6.3** Range of δ(^34S)SO₄ variation in precipitation collected in the city of Wrocław (SW Poland) with respect to the values of potential sulfur sources. The observed lack of correlation between the SO₄ concentration and its δ(^34S)SO₄ suggests that pyrite oxidation during coal burning in a power plant is the main source of atmospheric sulfur, dominating sulfur budget in the atmosphere. Figure from Górka et al. (2017)</figcaption></figure>

higher deposition of sulfur was observed in the industrial centers of Asia and Europe. The primary source of SO₂ emission in the atmosphere is coal combustion in power plants and smelters (Jědrysek et al. 2003). Sulfur in coal originates primarily from sulfides and therefore has a very low δ(^34S) (close to 0‰). During high-temperature combustion, whole sulfur is oxidized, and stable isotope fractionation for sulfur is low (Eldridge et al. 2016; Górka et al. 2017; Holt et al. 1982; Fig. 6.3). Sulfur is oxidized with atmospheric oxygen with a globally constant δ(^18O) value of 23.5–23.8 ‰ (Kroopnick and Craig 1972; Horibe et al. 1973), and with the addition of atmospheric moisture, this forms SO₄ characterized by a variable δ(^18O), but usually below 0‰ (Araguás-Araguás et al. 2000). As a consequence of these processes, the δ(^34S)SO₄ in precipitation in polluted areas is low and usually varies from 0 to 5‰, with seasonal variations and variations due to inputs from other sources of pollution (Jenkins and Bao 2006; Górka et al. 2017; Kim et al. 2018; Wu et al. 2021).

### 6.2.2.2 Acid Rock Drainage Due to Mining and Ground Disturbance

Sulfide oxidation occurs naturally through erosion processes of geological formations, which contribute to elevated concentrations of sulfates in the surface and groundwater (Cortecci et al. 2002; Brenot et al. 2007; Turchyn et al. 2013; Szynkiewicz et al. 2015b). The process occurring naturally on the ground surface primarily involves atmospheric oxygen, with only a small addition of oxygen from
water molecules, at a proportion of 7:1 (Eq. 6.1). Therefore, during this reaction, only two moles of reduced hydrogen ($H^+$) are produced per mole of $FeS_2$ oxidized, and this contributes only moderately to water acidity. However, this process can escalate to a catastrophic scale when large amounts of pyrite are rapidly oxidized during large disturbances, such as those caused by mining, waste rock deposition, or construction work, that lead to acid rock drainage (ARD) (Dold 2017). This accelerated oxidation of a large amount of pyrite exposed to the surface during a disturbance results in a much lower pH than is observed with natural erosion processes occurring on the surface and on a small scale. Pyrite oxidation immobilizes iron, so the concentration of ferrous iron ($Fe^{2+}$) increases in the solution. Ferrous iron can subsequently be oxidized to ferric iron ($Fe^{3+}$) in the presence of oxygen at a low pH (Eq. 6.2), and this reaction can occur spontaneously or can be catalyzed by microorganisms. Ferric iron can also catalyze further pyrite oxidation under anoxic conditions using oxygen exclusively from water molecules and generating an eightfold higher acidity (Eq. 6.3) compared to oxidation involving atmospheric oxygen (i.e., 16 mol compared to 2 mol of $H^+$) (Eq. 6.1).

$$FeS_2(s) + 7/2O_2(g) + H_2O(l) = Fe^{2+}(aq) + 2SO_4^{2-}(aq) + 2H^+(aq) \quad (6.1)$$

$$4Fe^{2+}(aq) + O_2(g) + 4H^+(aq) = 4Fe^{3+}(aq) + 2H_2O(l) \quad (6.2)$$

$$FeS_2(s) + 14Fe^{3+}(aq) + 8H_2O(l) = 15Fe^{2+}(aq) + 2SO_4^{2-}(aq) + 16H^+(aq). \quad (6.3)$$

These hydrochemical reactions will trigger elevated concentrations of sulfate and iron and low pH in waters impacted by ARD, which is accelerated by the presence of ferric iron. Low pH can be naturally neutralized in rock formations that contain large amounts of carbonates; therefore, acidity and sulfate concentrations alone cannot be used to verify the extent of ARD. For this reason, major ion hydrochemistry is frequently combined with stable isotope analyses. Pyrite in most geological formations has very low $\delta(^{34}\text{S})$ usually close to 0 ‰ and the ARD process does not cause stable isotope fractionation (Taylor et al. 1984; Gałuszka et al. 2020). Therefore, the original $\delta(^{34}\text{S})$ of pyrite is reflected in the initial $\delta(^{34}\text{S})_{SO_4}$ value of the sulfate. However, some bacterially mediated reactions could be split into two phases, with some fractionation occurring during the initial stage (Brunner et al. 2008).

The $\delta(^{34}\text{S})_{SO_4}$ value of produced sulfate usually does not overlap with other sources of natural sulfur in the environment or fertilizers. However, the $\delta(^{34}\text{S})$ alone may not be indicative of different secondary sources of sulfates originating from sulfide oxidation. For instance, ARD usually cannot be partitioned from pyrite oxidation resulting from natural erosion or from air pollution carrying the signature of pyrite combusted with coal in power plants if the $\delta(^{34}\text{S})$ values of the pyrite sources are similar. However, sulfate produced during sulfur oxidation in different environments will have a different $\delta(^{18}\text{O})_{SO_4}$ that reflects the stable oxygen isotope composition in these environments. For example, sulfate oxidized with atmospheric
oxygen will have values close to those of the oxygen in the atmosphere, which is constant around the globe (~23.5 ‰) (Kroopnick and Craig 1972) minus an isotope fractionation factor of −11.4 ‰ (Taylor et al. 1984). The stable oxygen isotope composition of surface water and groundwater is much more variable (usually −10 to +10 ‰) and depends on several factors, such as climate, distance from the sea, and evaporation losses (Gat 1997). Consequently, the δ(^{18}O)SO₄ will reflect the signature of the water available at the time of oxidation plus a fractionation factor of +4.1 ‰ (Taylor et al. 1984).

Oxygen and sulfur form strong chemical bonds in sulfate; therefore, both δ(^{34}S)SO₄ and δ(^{18}O)SO₄ are well preserved in solution unless sulfate is subject to biological processes and significant reduction, especially in anoxic conditions (Fritz et al. 1989, Fig. 6.1). Based on the stable isotope signatures of contrasting sources of oxygen contributing to sulfide oxidation, the contributions from the two leading processes to ARD can be partitioned (Eqs. 6.1–6.3).

### 6.2.2.3 Agriculture Fertilizers

The stable sulfur and oxygen isotope compositions of fertilizers vary in relatively large ranges according to their chemical compositions, manufacturing processes used for synthetic fertilizers and mineralogy of natural fertilizers (Mizota and Sasaki 1996; Coplen et al. 2002c; Vitòria et al. 2004; Chalk et al. 2017). This variability in δ(^{34}S)SO₄ and δ(^{18}O)SO₄ underscores the importance of understanding what types of fertilizers are used locally and, if possible, analyzing their stable isotope compositions. However, both δ(^{34}S)SO₄ and δ(^{18}O)SO₄ fertilizer signatures have been successfully used in several studies to partition agriculture sources from other sulfur sources by applying mixing models and estimating inputs from agrocontaminants (Mizota and Sasaki 1996; Hosono et al. 2007; Nakano et al. 2008; Szynkiewicz et al. 2015a; Urresti-Estala et al. 2015).

The dissolution and leaching of fertilizers to the surface and groundwater do not cause isotope fractionation, so the generated dissolved sulfate reflects the initial signature of the fertilizers (Chalk et al. 2017). However, the stable isotope composition of leachates, and especially δ(^{18}O)SO₄, can be further modified by bacterial processes. Hence, besides the initial stable isotope composition of fertilizers, processes contributing to the stable isotope fractionation of sulfates in soil and aquatic environments should also be considered. Stable isotope fractionation caused by microbiological processes adds uncertainty to the calculations, but the fertilizers usually still have a distinct composition that partitions them from other sources of sulfur in agrosystems if both δ(^{34}S)SO₄ and δ(^{18}O)SO₄ were analyzed at the same time (Fig. 6.4). The δ(^{34}S)SO₄ values are usually reported in the literature (Mizota and Sasaki 1996; Chalk et al. 2017), but relatively limited δ(^{18}O)SO₄ data are available (Vitòria et al. 2004). The median δ(^{34}S)SO₄ value reported by Vitòria et al. (2004) in the data compilation from a few countries, but primarily from Spain, was 5.7 ‰. The majority of fertilizers (25 of the 27 analyzed) from Spain had δ(^{34}S)SO₄ between −
<figure><img src="image_37.png" /><figcaption>**Fig. 6.4** Range of stable sulfur and oxygen isotope compositions of fertilizers based on the compi-
lation made by Vitòria et al. (2004), and Urresti-Estala et al. (2015), in reference to other typical
sources of sulfate found in agrosystems. Note that fertilizers are represented by two separate green
polygons according to origin</figcaption></figure>

1.8 and 11.7 ‰ and $\delta(^{18}\text{O})_{\text{SO}_4}$ between 7.7 and 16.5 ‰, thereby distinguishing these fertilizers from many other sulfur sources.

The type of applied fertilizer depends on the type of agricultural production, soil, and climate. Thus, the relatively broad range of $\delta(^{34}\text{S})_{\text{SO}_4}$ and $\delta(^{18}\text{O})_{\text{SO}_4}$ presented in Fig. 6.4 could be much more restricted for local studies on the catchment scale. Further, the subdivision of catchments according to the water flow direction and land use (Chap. 2) can provide even narrower value ranges for local use in mixing models developed for a particular location (Chap. 3 and Fig. 6.5).

6.2.2.4 Other Industrial Pollution

Some other industrial pollutants impact the environment and influence $\delta(^{34}\text{S})_{\text{SO}_4}$;
however, these impacts occur less frequently and on more restricted scales. Some of
these pollutants (e.g., hydrocarbons), even those characterized by low sulfur concen-
trations, can create anoxic conditions that accelerate microbiological sulfur reduc-
tion, thereby leading to substantial stable isotope fractionation. Specific chemical
compounds in industrial pollution can also be used to determine dispersal pathways
(Santucci et al. 2018; Schroth et al. 2001; Torssander et al. 2006).
<figure><img src="image_38.png" /><figcaption>**Fig. 6.5** Stable sulfur and oxygen isotope compositions of 43 fertilizer samples reported for Texas (Szynkiewicz et al. 2015a), Spain (Vitòria et al. 2004) and Western Australia (Data from Department of Water and Environmental Regulation and Department of Primary Industries and Regional Development of Western Australia). The gray area represents 80 % of the sample number with respect to the value range. Symbols indicate countries, and colors indicate chemical compositions</figcaption></figure>

## 6.3 Disentangling Acid Rock Drainage from Agrocontaminants Using $\delta(^2H)_{H_2O}$, $\delta(^{18}O)_{H_2O}$, $\delta(^{34}S)_{SO_4}$ and $\delta(^{18}O)_{SO_4}$

The stable isotope analyses of sulfate usually are accompanied by analyses of stable hydrogen and oxygen isotope composition of water. The obtained $\delta(^2H)_{H_2O}$, $\delta(^{18}O)_{H_2O}$ results can support interpretation of $\delta(^{34}S)_{SO_4}$ and $\delta(^{18}O)_{SO_4}$ values and better partition the fate of water molecules from the fate of solutes. This is particularly important for interpretation of $\delta(^{18}O)_{SO_4}$ for which understanding of $\delta(^{18}O)_{H_2O}$ is required and variability in water stable isotope composition of major water sources.

### 6.3.1 Principles of $\delta^2H(H_2O)$ and $\delta^{18}O(H_2O)$ Interpretation

The stable hydrogen and oxygen isotope composition of water, $\delta(^2H)_{H_2O}$ and $\delta(^{18}O)_{H_2O}$, can inform us about the water sources contributing to a water body and
distinguish inputs originating from four usually considered sources: (1) precipitation, (2) groundwater, (3) surface runoff, and (4) ocean water. Each of these sources can have very distinct $\delta(^2H)_{H_2O}$ and $\delta(^{18}O)_{H_2O}$, but their values vary spatially and temporarily with respect to the major drivers governing precipitation, groundwater recharge conditions, and evaporation, reflecting retention time on the ground surface (Gat 1997). All these values should be considered based on local conditions and the Local Meteoric Water Line (LMWL) and Local Evaporation Line (LEL), as defined by $\delta(^2H)-\delta(^{18}O)$ relationship, as well as the precipitation pattern prior to sampling (Fig. 6.6; Craig and Gordon 1965; Rozanski et al. 1993).

<figure><img src="image_39.png" /></figure>

**Fig. 6.6** Conceptual plot of the stable hydrogen and oxygen isotope composition of major water budget components considered for the evaluation of water source mixing and the progress of evaporation. The presented numerical range areas are those observed for the Pilbara region of Western Australia; these ranges will vary according to the local conditions. The slope of the Local Meteoric Water Line (LMWL) usually varies by ~8 to ~7 and the Local Evaporation Line (LEL) slope varies by ~5 to ~4. The dominant precipitation resulting from large-volume events or precipitation during cold periods tends to have negative values (RW). Groundwater (GW) usually reflects the values of the dominant precipitation, lies close to LMWL and has negative values. However, it is higher than RW and reflects the evaporative loss occurring between precipitation and infiltration into the groundwater aquifer. The stable isotope compositions of lakes and other water bodies are distributed along the LEL, and their position reflects the progress of evaporation, with higher delta values for more evaporated waters. Water bodies with significant additions of GW tend to lie between LEL and LMWL and close to GW. The crossing point between LEL and LMWL indicates the initial value for water prior to evaporation and lies close to RW.
Precipitation varies seasonally according to temperature, humidity, source of moisture, rainout effect, elevation, distance from the coast, and other parameters (Dansgaard 1964; Rozanski et al. 1992, 1993). According to the International Atomic Energy Agency (IAEA) guidelines, the most appropriate method is to obtain LMWL locally from the monthly mean precipitation collected over a period of a minimum of three years (Gröning et al. 2012; Hughes and Crawford 2012; IAEA 1992). However, if local data are not available, several locations around the world have at least some precipitation data accessible from the Global Network of Isotopes in Precipitation (GNIP) database (Schotterer et al. 1996, https://www.iaea.org/services/networks/gnip) or national datasets (for example, in Australia: Hollins et al. 2018, in Canada: Peng et al. 2004, in Nigeria: Ohwoghere-Asuma et al. 2021, in the USA: Putman and Bowen 2019; Tian and Wang 2019 and in Vietnam: Le Duy et al. 2018). For regions lacking local data, approximate values can be obtained using an interpolation algorithm, as proposed by Bowen et al. (Bowen 2019; Bowen and Revenaugh 2003; https://waterisotopes.org).

Groundwater primarily reflects the stable isotope composition of the dominant precipitation contributing to recharge, such as large-volume events during the wet season (Skrzypek et al. 2019), snowmelt (Jasechko et al. 2017b) or past periods of abundant precipitation forming paleogroundwater (Jasechko et al. 2017a). These large-volume events or wet periods tend to have negative signatures. Similarly, at higher latitudes, the isotope composition is biased toward precipitation during the cold season and therefore also tends to have more negative values than during other seasons (Marchetti and Marchetti 2019). Therefore, groundwater frequently has low $\delta(^2H)_{H_2O}$ and $\delta(^{18}O)_{H_2O}$ compared to surface water, and these values are consistent on a regional or basin scale (Dogramaci et al. 2012a).

The surface water stable isotope composition in lakes and rivers is governed by evaporation and primarily reflects retention time (Fellman et al. 2011). The progress of evaporation reflects climate conditions, and retention time, since precipitation or water influx can be estimated by applying the Craig-Gordon model (Craig and Gordon 1965). Sampling inflow and outflow from a lake and comparing $\delta(^2H)_{H_2O}$ and $\delta(^{18}O)_{H_2O}$ allows for the estimation of evaporation over the inflow ratio. Similarly, repetitive sampling of a lake, pond, or waterhole as the water level decreases due to evaporation allows estimation of the water fraction loss (Fellman et al. 2011; Nitzsche et al. 2017; Paulsson and Widerlund 2020). Evaluation of evaporative losses helps to define the water budget for surface water bodies (Yapiyev et al. 2020) and to partition the fate of water molecules from the fate of solutes. The calculation procedure employing the C-G model has been simplified and standardized by the introduction of Hydrocalculator software (Skrzypek et al. 2015).

The ocean water $\delta(^2H)_{H_2O}$ and $\delta(^{18}O)_{H_2O}$ values are usually constant over a long time at a given location, and they reflect latitude. The mean ocean water $\delta(^2H)_{H_2O}$ and $\delta(^{18}O)_{H_2O}$ values are equal to 0‰ and define the zero point for the VSMOW2-SLAP2 international stable hydrogen and oxygen isotope scale (Coplen 1995). However, the surface water may vary locally with respect to evaporation, freshwater input, and ice melt, with values usually between -1 and 2‰ for $\delta(^{18}O)_{H_2O}$, and between -3 and 10 ‰ for $\delta(^2H)_{H_2O}$ (Schmidt 1999; Benetti et al. 2017). The ocean stable
isotope composition could be used to assess mixing in estuaries (Price et al. 2012) or groundwater intrusion (Heilweil et al. 2009; Carreira et al. 2014) and to assess solute inputs in coastal regions.

### 6.3.2 Combined Approach to Partition Sulfate Sources Using Hydrochemical Composition, $\delta(^{18}\text{O})_{\text{H}_2\text{O}}$ and $\delta(^{18}\text{O})_{\text{SO}_4}$

Stable isotope analyses of $\delta(^{2}\text{H})_{\text{H}_2\text{O}}$ and $\delta(^{18}\text{O})_{\text{H}_2\text{O}}$ have become routinely used tools to supplement major ion analyses. They can be coupled with analyses of changes in salinity, especially targeting conservative ions such as Cl to distinguish evapoconcentration from inputs other than precipitation (e.g., recycled accumulated salt or salt originating from geological formation or ions contributing from pollution) (Skrzypek et al. 2013). In pollution studies, $\delta(^{18}\text{O})_{\text{H}_2\text{O}}$ can also be used to track the process of sulfate formation because $\delta(^{18}\text{O})_{\text{SO}_4}$ primarily reflects the stable isotope composition of water and, to some extent, atmospheric oxygen, which is characterized by very constant values of ~23.5 ‰ (Kroopnick and Craig 1972). Following Eqs. 6.1–6.3, the contribution from these two oxygen sources to sulfate formation can be used to assess the dominant reaction for sulfate formation and the risk of ARD.

The potential of acid rock drainage is usually investigated in six steps and can be further used to partition other sources of sulfates in waterways:

1. The $\text{SO}_4/\text{Cl}$ ratio (Fig. 6.2a) indicates the possibility of sulfate addition above those attributed to rainfall input. If the $\text{SO}_4/\text{Cl}$ is higher than 0.7, then part of the sulfate could originate from a source that does not contribute to other major conservative ion concentrations (e.g., conservative Cl). This is informative regarding the excessive input above that is observed in precipitation, but it does not specifically identify the source. However, other hydrochemical models can provide additional information about potential sources (Dogramaci et al. 2012b).

2. The decrease or increase in $\delta(^{34}\text{S})_{\text{SO}_4}$ correlated with an increased concentration of $\text{SO}_4$ could also be attributed to the signature of the dominant sulfur input source (Fig. 6.7). Sulfides usually tend to have low or negative $\delta(^{34}\text{S})_{\text{SO}_4}$. Therefore, a decrease in values is frequently associated with leaching from pyrite oxidation (e.g., García-Lorenzo et al. 2014; Dogramaci et al. 2017; Chudy et al. 2021). The best option is to confirm the local signatures of pyrite by directly analyzing minerals from the rock matrix in the study area.

3. In many cases, pyrite has a distinct signature that is lower than that of many other sources of sulfur. Therefore, $\delta(^{34}\text{S})_{\text{SO}_4}$ alone could be sufficient to partition pyrite sulfur input from many other sources (García-Lorenzo et al. 2014; Chalk et al. 2017; Chudy et al. 2021). However, the values can overlap with fertilizers, which also tend to have low $\delta(^{34}\text{S})_{\text{SO}_4}$ in some cases (Fig. 6.4). Fertilizer contributions can be separated from pyrite contributions using $\delta(^{18}\text{O})_{\text{SO}_4}$, as the $\delta(^{18}\text{O})_{\text{SO}_4}$
<figure><img src="image_40.png" /><figcaption>**Fig. 6.7** Increases in sulfide concentration correlate with decreases in δ(^34S)SO₄ in a case study from Pilbara, Western Australia. The pH at most locations was neutral because of buffering by carbonates. Acid rock drainage had to be confirmed using a combination of various hydrochemical and stable isotope tracers (blue points—surface water, black points—groundwater). Figure from Dogramaci et al. (2017)</figcaption></figure>

originating from pyrite oxidation is usually low (<10‰) and therefore different from most fertilizers (>10‰) (Fig. 6.4).

4. The extent of ARD can be further confirmed using δ(^18O)SO₄ linked with δ(^18O)H₂O of local ground or surface water in which sulfates are dissolved (Toran 1987; Taylor et al. 1984). The model proposed by Taylor et al. (1984) allows assessment of what portions of oxygen in the generated sulfate originate from the atmosphere and from water with respect to Eqs. 6.1–6.3 (Fig. 6.8). However, δ(^18O)SO₄ can be prone to microbial alterations by sulfuric bacteria, so δ(^18O) will be replaced by the value of water available for the metabolic processes of bacteria in those cases; therefore, it should be treated with caution particularly when sulfate concentrations in water are low (Brunner et al. 2008). This method is still useful for assessment of ARD extent if uncertainty associated with potential bacterial reduction is kept in mind.

5. The results of the ARD sulfate contribution could be used as input values for mixing models to calculate the proportional contributions from all determined
<figure><img src="image_41.png" /><figcaption>**Fig. 6.8** Estimation of the acid rock drainage (ARD) contribution to groundwater sulfates based on the stable oxygen isotope compositions of water and sulfate. Low $\delta(^{18}\text{O})_{\text{SO}_4}$ and $\delta(^{18}\text{O})_{\text{H}_2\text{O}}$ tend to suggest higher inputs from ARD (points between 50 and 100 %). Figure after Dogramaci et al. (2017)</figcaption></figure>

sulfur sources, as discussed in Chap. 3. To account for uncertainty in the calculation, the average value for each considered source should be used, as well as a range of values reflecting the natural variability in each source $\delta(^{34}\text{S})_{\text{SO}_4}$ and $\delta(^{18}\text{O})_{\text{SO}_4}$.

The potential of ARD using the above principles can be qualitatively evaluated using a few categories with respect to simple hydrochemical parameters and sulfate and water stable isotope compositions, using the classification proposed by Dogramaci et al. (2017).

The principles summarized above have been successfully used in several variants of studies that endeavored to partition ARD sulfur sources from other inputs, usually to complement hydrochemical analyses of major ions and nutrients. The application of multiple tracers at the same time allows a more robust determination of contributions from various sources and a clearer estimation of calculation uncertainty. These outcomes help to evaluate what types of factors and human activities impact water quality. Szynkiewicz et al. (2015a, b) comprehensively assessed several sulfur sources in the Rio Grande catchment by partitioning sulfide inputs from agriculture fertilizer leachates, and they reported that the $\text{SO}_4$ fluxes in Rio Grande at El Paso, at
200–450 metric tons/day, were originating from agrocontaminates primarily associ-
ated with irrigation periods linked with intensive fertilizer application. By contrast,
this flux was only 30–80 metric tons/day during the postharvest winter season, when
the majority of sulfate in the river was delivered by natural brines seeping into the
catchment. At the same time, in the upper part of the Rio Grande catchment, sulfides
contributed 83–94 % of the total sulfate budget (Szynkiewicz et al. 2015a, b). Toran
(1987) was able to identify an area of sulfate contaminatiońn in groundwater from
a carbonate-hosted mine on farmland in Wisconsin. Using a mixing model, Toran
(1987) also applied hydrochemical modeling using the USGS computer program
PHREEQE and determined the contribution from acid rock drainage despite the
neutral pH due to buffering by carbonate rocks. Newman et al. confirmed under-
ground seepage from an acidic pit lake to a perennial stream system. The acid rock
drainage water input elevated the sulfate concentration and lowered the pH, which
contributed to the mobility of potentially toxic metals (Al, Cd and Zn). The use of
stable isotope methods allowed the evaluation of the extent of mining impacts in the
catchments downstream from the mine and the partitioning of mining sources from
other sulfur sources (Newman et al. 2020) and separate mining inputs from other S-
sources including agriculture. A similar approach was adopted by Chudy et al. (2021)
who assessed the impact of an abandoned iron ore mine on water quality. The extent of
the mining influence and separation from the geological background could be further
evaluated using bioindicator methods and plant tissue analyses (e.g., Migaszewski
and Gałuszka 2019; Gałuszka et al. 2020).

**6.4 Sample Preparation Techniques and Measurements**

The sample of sulfate used for $\delta(^{34}S)_{SO_4}$ and $\delta(^{18}O)_{SO_4}$ analyses is usually precipi-
tated from water as barium sulfate (BaSO$_4$). Several variants of the procedure have
been described in the literature, but all are based on the steps originally designed for
the classical method for the analysis of sulfate concentrations (e.g., Carmody et al.
1998; Skrzypek 2018):

1. Usually, between 0.5 and 4 L of water sample is collected (for $SO_4^{2-}$ concentra-
tion, this range is for 50 and 5 mg/L of sulfate, respectively) to clean high-density
polyethylene (HDPE) bottles. Bottles should be filled full, without headspace, and
well-sealed. These water volumes are sufficient to precipitate ~50 mg of BaSO$_4$.
For a single stable isotope analysis, usually up to 1 mg of BaSO$_4$ is required,
but handling such small aliquots is impractical during washing and transferring.
Water samples should be filtered through 0.45 µm filters in the field or as soon
as delivered to the laboratory. Sulfate samples are prone to stable isotope frac-
tionation caused by bacterial reactions; therefore, they should be stored chilled
on ice in a portable ice chest box in the field and later in a fridge at ~+ 5 °C.
2. Before BaSO₄ is precipitated, the pH of the filtered sample should be adjusted by adding a few milliliters of HCl to lower the pH to 2-3. The volume of acid required depends on the HCl concentration used and water alkalinity, but typically ~3-5 mL of 5 N HCl is sufficient.

3. To accelerate crystal formation, warming the sample to ~40 °C is recommended. Precipitation of 50 mg of BaSO₄ (as per the volumes and concentrations listed in point 1) requires the addition of ~1.2 mL of 10 % BaCl₂; this volume contains approximately three times more barium than required by the stoichiometric reaction to ensure fast and complete precipitation and also to take into consideration the potential uncertainty in the prediction of sulfate concentration. After vigorous stirring with a glass rod, the solution needs to be left for several hours to allow barium sulfate formation and deposition at the bottom of the beaker.

4. The following day, white crystals should cover the bottom of the beaker and the supernatant should be transparent. The completeness of sulfate precipitation can be tested by adding a small amount of BaCl₂ solution to see if further precipitation occurs, appearing as foggy tracers in the supernatant. In the next step, the supernatant needs to be removed and the white BaSO₄ crystals washed with deionized water at least three times. The supernatant removal procedure can be performed using a simple siphon tube and then the beaker is refilled with deionized water. Again, the sample needs to be left overnight for crystal sedimentation. This process needs to be repeated three times. Alternatively, to accelerate the washing process, the sample can be transferred to centrifuge tubes, spun and the supernatant removed and replaced with deionized water. Using a centrifuge at 5000 rpm accelerates the separation of crystals and the supernatant and eliminates the overnight wait time. The washing with deionized water and centrifugation procedure should be repeated three times to remove traces of HCl and any unreacted excess of BaCl₂.

5. After washing, the collected BaSO₄ sample can be transferred to a small evaporative dish and dried at 110 °C in a laboratory oven. After drying and cooling, the samples can be stored in small glass or plastic vials for several years. Pure BaSO₄ should be white (or light gray) powder, like fine kitchen salt.

6. BaCl₂ is toxic, particularly for aquatic organisms. Therefore, all wastewater from washing needs to be neutralized by adding H₂SO₄ to precipitate the residual barium remaining in solution, and then the pH can be adjusted to neutral using sodium carbonate (e.g., soda ash).

The procedure presented above does not account for potential interferences that can be caused by compounds other than sulfate sulfur and oxygen-bearing compounds present in water samples (Skrzypek 2018). Therefore, modification of this procedure may be required to address the following potential problems:

1. Samples that contain large concentrations of dissolved organic carbon (DOC) may be contaminated by coprecipitated organic fractions, as indicated by frequent changes in sample color from white to dark gray or brown. To address this issue, prefiltration through a 0.21 µm filter can be considered. Contaminated samples can also be further processed by adding 10 % hydrogen peroxide (H₂O₂) to an
evaporation dish with dry BaSO₄ to oxidize organic matter. The small addition of organic matter usually has a minor influence on δ(^34S)SO₄ but could significantly change δ(^18O)SO₄.

2. If the H₂S concentration in water samples is >0.5 mg/dm³ (characterized by a distinctive rotten-egg odor), H₂S needs to be removed by precipitation and filtration to separate it from SO₄²⁻. The water sample must first be transferred to a glass beaker (500 or 1000 mL depending on the sample volume) and 5% zinc acetate solution added in a 1:10 ratio (e.g., 50 mL zinc acetate solution to 500 mL of water sample volume). After mixing, at least 1 h should be allowed for precipitation and sedimentation (Carmody et al. 1998).

3. Analyses of δ(^18O)SO₄ in water samples that contain high concentrations of nitrates can be impacted by coprecipitation of nitrates that inoculate crystals of BaSO₄. Nitrates will yield oxygen during the analysis, and they will be added to the oxygen yield from sulfates. In coprecipitated NO₃/SO₄ mixtures where molar ratios are above 2, the oxygen bonded in nitrates may contribute up to 7% of the total yields and cause changes in δ(^18O)SO₄ of up to 5‰ (Michalski et al. 2008). Nitrates can be removed from water before precipitation of barium sulfate using the anion-exchange resin method (Le Gendre et al. 2017) or after precipitation using a chelating solution to redissolve and then reprecipitate barium sulfate (Bao 2006).

The stable sulfur and oxygen isotope compositions of sulfate in barium sulfate samples are routinely analyzed using IRMS in traditional offline systems or in continuous flow mode (Sharp 2017). In the currently more popular continuous flow systems, BaSO₄ for δ(^34S)SO₄ analyses is decomposed at a high temperature (1000 °C) in elemental analyzers adapted for sulfur. After purification and water removal, SO₂ gas in the helium stream is introduced to the ion source of a mass spectrometer. Analyzing SO₂ compared to the SF₆ gas traditionally used in offline systems is challenging due to isobaric interferences caused by the variable stable oxygen isotope composition. This arises from the physical properties of SO₂, as different combinations of sulfur and oxygen isotopes can result in the same masses of SO₂ molecule (Sharp 2017). This issue can be partially overcome using various correction factors and utilizing IRMS systems with three collectors that analyze simpler molecules (e.g., fragmented ions, such as SO⁺ with possible masses 48, 49 and 50). This analytical approach simplifies the required isobaric corrections and reduces analytical uncertainty. The analyzed samples are normalized to the VCDT scale for presentation as isotope delta values (Skrzypek et al. 2022) using at least two, but preferably three, well-calibrated laboratory or international standards, such as NBS127, IAEA-S-1, IAEA-S-2, and IAEA-S-3 (Skrzypek 2013). The usual combined analytical uncertainty of δ(^34S)SO₄ analysis varies between 0.3 and 0.4‰. The stable oxygen isotope composition of sulfates, δ(^18O)SO₄, can also be analyzed using continuous flow IRMS systems that differ from those used for δ(^34S)SO₄. For δ(^18O)SO₄ analysis, high-temperature pyrolysis (HTP) at 1450 °C is required, utilizing a thermal conversion elementary analyzer (TC/EA). BaSO₄ is reduced in the presence of hot graphite and glassy carbon, and carbon monoxide (CO) is produced. CO is separated from other gases using a gas
chromatography column and introduced to the ion source of the IRMS. After multi-point normalization (Skrzypek 2013), usually based on international standards (e.g., IAEA-SO5, IAEA-SO6 and NBS127), the results are reported on the VSMOW stable isotope scale with a combined analytical uncertainty usually 0.30–0.40‰, one sigma (Skrzypek and Sadler 2011).

## 6.5 Limitations and Challenges

Stable isotope delta results are reported with respect to entirely artifact-based scales that occasionally require updates (e.g., VSMOW2-SLAP2 or VPDB). To ensure the longevity of published data, a comprehensive description must be provided of (1) the analytical procedure; (2) traceability; (3) data processing; and (4) the uncertainty evaluation. For the minimum requirements for publishing stable isotope results, please refer to the guidelines prepared by the International Atomic Energy Agency and the International Union of Pure and Applied Chemistry (Skrzypek et al. 2022). If recalculations are required between different expressions of the stable isotope composition (e.g., isotope ratio, isotope fraction, isotope delta), the differences between the absolute values of the zero points for stable isotope scales adopted in the manufacturing software setup need to be considered and the calculations adjusted accordingly (Skrzypek and Dunn 2020a, b).

In catchments contaminated with pollutants originating from several sources, the major challenge is clarifying the ambiguous stable isotope signatures of overlapping values. To a large extent, this issue can be addressed using denser sampling to determine pollution budgets in individual subcatchments and by applying more advanced probabilistic models rather than simple mass balance arithmetic calculations (Lewicki et al. 2022).

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**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 M.Sc 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.
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 7
## Quantifying Sediment and Associated Pollutants Sources in Agricultural Catchments Using Isotopic Techniques

W. H. Blake, A. Taylor, E. Muñoz-Arcos, L. Ovando-Fuentealba, C. Bravo-Linares, and G. E. Millward

**Abstract** This chapter provides an overview of nuclear tools used in sediment source apportionment studies, summarising essential information about the use of fallout radionuclides, elemental geochemistry, and compound-specific stable isotopes. Exemplar studies are described to show how the method can inform decision making at farm and basin scales to control diffuse water pollution by agriculture.

### 7.1 Overview of Sediment and Associated Pollutants in Agricultural Catchments

In the context of agricultural pollution, sediment derived from soil erosion is widely regarded as a major pollutant both in its own right and in its role as a vector for particle-reactive agrochemicals. The widespread and costly environmental impacts of sediment transfer from agricultural land into downstream aquatic ecosystems are recognised by river basin managers and policy makers worldwide (Evans 2010), yet

W. H. Blake (✉) · A. Taylor · E. Muñoz-Arcos · L. Ovando-Fuentealba · G. E. Millward
University of Plymouth, Plymouth, UK
e-mail: william.blake@plymouth.ac.uk

A. Taylor
e-mail: alex.taylor-34@plymouth.ac.uk

E. Muñoz-Arcos
e-mail: enrique.munozarcos@plymouth.ac.uk

L. Ovando-Fuentealba
e-mail: luis.ovandofuentealba@plymouth.ac.uk

G. E. Millward
e-mail: G.Millward@plymouth.ac.uk

C. Bravo-Linares
Universidad Austral de Chile, Valdivia, Chile
e-mail: cbravo@uach.cl
the complexity of sediment production and delivery processes renders it a complex challenge to mitigate. Soil erosion by water poses a serious threat to water and nutrient retention, biodiversity, and plant primary productivity on agricultural land (Pimentel 2006). Downstream, sediment, and nutrient export to rivers and lakes contributes to eutrophication and contamination of water resources by algae and associated natural toxins, affecting the functioning of wetland and lake ecosystems. Furthermore, siltation enhances the habitats that promote water-borne disease vectors and siltation of reservoirs compromises water supply and hydroelectric power (HEP) generation (Boardman and Poesen 2006). Herein, it is clear that enhanced runoff and soil erosion have a major impact on food, water and energy security (Blake et al. 2018). Future land-management decisions require quantification of the anthropogenic amplification of runoff and soil erosion processes, e.g., sediment yield from different agricultural land uses, so that hotspots of soil erosion can be targeted and controlled specifically by mitigation measures.

Application of tools to support source apportionment should be grounded in a thorough understanding of hydrological and soil erosion processes (Morgan 2005) that lead to sediment generation from agricultural land for specific study sites. The mode of overland flow development, i.e., infiltration excess vs saturation overland flow can have a critical bearing on tracer behaviour in some circumstances and the particle size selectivity of soil erosion (Issa et al. 2006) can strongly influence source signatures.

Sediment dynamics in river systems involve complex processes and their quantitative assessment faces many uncertainties. River sediment budgeting approaches provide understanding of sediment mobilisation, transport, storage, and yield (Walling and Collins 2008), a useful framework within which to evaluate evidence on sediment and pollutant source dynamics. River sediment budgets can be understood, in simplistic terms, as the mass balance between the sediment sources, deposition areas, and outputs. One of the most significant findings from budgetary studies is the importance (and magnitude) of sediment storage in river systems. From the total amount of sediment produced (i.e., eroded) in upland surfaces (i.e., sources), only a fraction makes its way to the basin outlet (i.e., sediment yield). This discrepancy has been named the ‘sediment delivery problem’ (Walling 1983), and it has been argued that various sediment storage mechanisms operating within a catchment explain this discrepancy (Trimble 1983; Walling 1983; Fryirs 2013). Since then, increasing research has been carried out to explain and disentangle the mechanisms, pathways, and fates of eroded sediment within a river basin, and evidence on source dynamics is a crucial ingredient to support management decisions at the basin scale.

In many catchments, sediment spends significant time stored in riverine compartments which can compound challenges of source apportionment within specific time frames. Its delivery is therefore controlled by storage and sporadic remobilisation at various timescales (Muñoz-Arcos et al. 2022). These storage units can be defined as transient, short-lived landforms that are frequently reworked by episodic events such as bars, lateral deposits, and the streambed where they play a key role in the (dis)connectivity of catchment sediment cascades (Fryirs 2013). Spatial and
<figure><img src="image_44.png" /><figcaption>**Fig. 7.1** Scales of complexity in sediment tracing applications (Koiter et al. 2013)</figcaption></figure>

temporal aspects of sediment connectivity from hillslope to channel are important considerations in all source apportionment studies (Fig. 7.1).

Excess fine sediment (silt and clay fractions < 63 µm) poses a risk to receiving waters owing to both its physical presence as well as the potential for transport of co-contaminants. Many surface water systems are sensitive to changes in the transport and deposition of fine sediments owing to the impact of increased turbidity in the water column and siltation of channel bed habitats. For example, there is potential for siltation to affect oxygen demand and subsequently impact upon salmonid spawning grounds (Jensen et al. 2009; Sear et al. 2017). Excess suspended and stored sediment may also affect other, sessile biota such as the freshwater pearl mussel (*Margaritifera margaritifera*), which is critically endangered in Europe, according to the International Union for the Conservation of Nature (IUCN). Freshwater pearl mussels are sensitive to changes in water parameters, preferring oligotrophic and low turbidity conditions. For successful recruitment of juvenile freshwater pearl mussels, it is essential that the sediment in which juveniles establish has an open, well-oxygenated structure, enabling clear exchange between the interstices and the overlying water column. Excess fine sediment can negatively impact upon channel bed habitat by
embedding coarse material and preventing exchange between interstices and surface (Geist and Auerswald 2007).

Transport of fine sediment from agricultural settings to receiving waters can also impact upon water quality through increased inputs of co-contaminants, which may exert direct or indirect effects depending on toxicity and bioaccumulations factors, and influence upon trophic structure. In relation to the latter, surface waters can be sensitive to elevated nutrient inputs associated with increased sedimentation, particularly with regard to phosphorus (P), often the growth-limiting nutrient in freshwater systems. Agricultural inputs can potentially increase the bioavailable pool of P, leading to eutrophication and associated ecological impacts (Withers and Jarvie 2008). Given that P is strongly associated with sediments, there is now a clear recognition of the need for improved management practices to reduce soil and associated phosphorus inputs to river channels (Ballantine et al. 2009; Ockenden et al. 2014). Significant improvements have been made to reduce the amount of dissolved P that enters watercourses from point source discharges across the EU. However, legacy sediment stored within channel and floodplain systems has the potential to act as a secondary source of P to the water column following disturbance (van der Perk et al. 2007) or in response to changes in condition of overlying waters (Jarvie et al. 2005b; Neal et al. 2012). Desorption and remobilisation processes are highly dependent upon environmental factors such as pH and oxidation-reduction conditions but, nevertheless, could offset the benefit of reductions in P inputs in the short term (Burns et al. 2015).

Sediment associated contaminants from agricultural settings are not limited to nutrients but can be wide ranging and related to biological (faecal) contamination, metals, and an array of organic compounds used in crops such as pesticides, herbicides and fungicides, and veterinary pharmaceutical treatments. Many organic compounds are highly particle reactive and persistent and are, thus, now restricted in their usage. However, the persistence of legacy inputs in sediment storage zones may present a continued threat to aquatic systems (Holden et al. 2017).

## 7.2 Approaches and Theory for Sediment Source Tracing

### 7.2.1 Core Principles of the Methodology

Among the sediment source tracing methods, the sediment fingerprinting approach has been widely used to apportion sediment sources at different landscapes and catchment characteristics (Walling 2005; Owens et al. 2016; Collins et al. 2017, 2020). Sediment fingerprinting approaches are based on identification of statistically significant differences in soil properties between target river basin sediment source areas. If signatures of downstream mixtures can also be derived, and the key assumption that signatures are not altered during hydrological and fluvial transport (i.e., conservatively), then qualitative or quantitative comparison between source and mixture
<figure><img src="image_45.png" /><figcaption>**Fig. 7.2** Example experimental designs to link primary sources to sediment mixtures (Blake et al. 2018) where rivers flow downward, filled circles represent nodes at which sediment mixture (Mix = M) samples could be collected, and dashed grey lines delineate watersheds (S) denoted by subscript numbers, and subscript letters indicate unique sources. (A) Simple watershed with three sources, SA-C, and one mixture location at the outflow, M. (B) Longitudinal system with four sources, SA-D, and multiple mixture locations at the outlet of each nested subwatershed, M1–4. (C) Distributed system with mixtures at the outflow of each of three subwatersheds, M1, M2, and M4, four sources (SA-D), as well as mixtures on the main channel: M3 and M5. *Note* not all sources are present in all subwatersheds</figcaption></figure>

fingerprints can elucidate dominant source contributions (Fig. 7.2). Differences in source properties and ‘fingerprintsm can occur due to a range of natural and anthro- pogenic processes generally linked to the geological substrate, cultivation practice, land use, and atmospheric depositions or pollution.

Properties that have been used to discriminate sources include: (i) fallout radionu- clides (FRNs) (e.g., ¹³⁷Cs, ⁷Be, and excess ²¹⁰Pbₓ₅ defined as the difference in activity concentrations between ²¹⁴Pb and total ²¹⁰Pb) which effectively label surface soil material permitting discrimination of surface, subsurface, and cultivated sources (e.g., Mabit et al. 2008; Wilkinson et al. 2009); (ii) major and minor element geochem- istry which is related to geological substrate but is also modified by soil formation processes and weathering and therefore can discriminate between land use (e.g., Collins et al. 2010; Laceby and Olley 2015); (iii) Compound Specific Stable Isotopes (CSSIs) which are organic fingerprints related to soil plant cover (e.g., Blake et al. 2012; Bravo-Linares et al. 2018; Upadhayay et al. 2018b); (iv) mineral magnetic properties which are sensitive to soil formation processes and pollution (e.g., Ley and Devon 1998; Blake et al. 2006); (v) contaminants from industrial or other anthro- pogenic activities, e.g., heavy metals (e.g., Rothwell et al. 2005); and (vi) sediment colour (e.g., Pulley and Collins, 2021, Barthod et al. 2015).

Collins et al. (2020) provide comprehensive reviews of the wider methodology and key considerations in source apportionment applications for the user community.
Here, we focus specifically on practical application of techniques that fall within the ‘nuclear tools’ domain: fallout radionuclides (FRNs), X-ray fluorescence (XRF) elemental geochemistry, and Compound Specific Stable Isotopes (CSSI).

### 7.2.2 The ‘Nuclear Tools’ Tracer Toolkit

Fallout radionuclides (FRNs) are delivered to surface soil via wet and dry fallout mechanisms. The three FRNs applied in source apportionment studies are ¹³⁷Cs, ²¹⁰Pb, and ⁷Be. Cs-137 is a legacy radioisotope derived from thermonuclear weapons testing in the twentieth century with most fallout occurring during the 1950s and 1960s, and locations affected by fallout from nuclear power plant accidents, notably the Chernobyl incident in the 1980s. Fallout ²¹⁰Pb is the unsupported component of total ²¹⁰Pb present in soil and sediment. The supported component is in equilibrium with its geogenic parents in the uranium series. The unsupported fallout component enters the atmosphere via diffusion of its intermediate parent ²²²Rn from regional bedrock and weathered substrates. Be-7 is produced by cosmic ray spallation of oxygen and nitrogen in the upper atmosphere.

All three FRNs are delivered to the surface predominantly by rainfall with the assumption of instantaneous binding at the soil surface. As with all tracer tools, such assumptions need careful consideration and evaluation in the specific context of any study site. For example, FRNs have been shown to be preferentially associated with fine-grained materials (He and Walling 1997; Taylor et al. 2012) raising critical questions about size selectivity of transport and influence on concentration-based tracer signatures (Laceby et al. 2017a). The different half-lives of the three isotopes and hence residence time in the soil profile is a key controlling factor of their utility as source tracers. With its half-life of ~ 30 years, ¹³⁷Cs delivered to the soil profile are subjected to downward translocation by both bioturbation and geochemical diffusion and migration. Furthermore, ploughing of the soil surface mixes ¹³⁷Cs uniformly through the plough layer. Similarly, the ~ 22-year half-life of ²¹⁰Pb means its fallout component in surface soil is subject to similar processes although it is less geochemically mobile than ¹³⁷Cs, and it tends to have a shallower depth profile in the soil. Ploughing of course leads to uniform mixing as for ¹³⁷Cs. The short half-life of ⁷Be, 53 days, means it has a uniquely different profile in the soil compared to the longer-lived FRNs. The FRN remains present in the uppermost millimetres of the soil since activity concentrations of material bioturbated are rendered below detection limits by radioactive decay. If a soil surface is ploughed, the ⁷Be activity of the surface is diluted to below detection limits effectively resetting the surface signature. The ⁷Be signature of the soil surface is also highly dependent on fallout dynamics linked to spatial and temporal rainfall patterns (Taylor et al. 2016).

There are two useful models of FRN-based source signatures. The first (Wallbrink and Murray 1993) was developed for uncultivated scenarios representing degraded land subject to sheet wash and incision processes (Fig. 7.3a). In this context, variability in the activity concentrations of ⁷Be and ¹³⁷Cs of downstream sediment can be
linked back to sediment production processes. The second (Walling and Woodward 1992) is based on typical agricultural conditions (Fig. 7.3b) with a mixed cultivated and uncultivated land cover allowing apportionment to land use and process drivers.

<figure><img src="image_46.png" /><figcaption>**Fig. 7.3** Models of FRN application as a source tracers a for degraded landscapes subject to sheet wash and incision (Wallbrink and Murray 1993) and b agricultural settings with mixed cultivated and uncultivated land cover (Walling 2012)</figcaption></figure>
XRF elemental geochemistry

Analysis of stable elements offers complementary data to CSSI and FRNs to provide multi-parameter datasets capable of improved source discrimination. Within a river catchment, source materials can be identified a priori based upon hydrological connectivity of land (source) units or via field observation. Element profiles may vary naturally in line with underlying geology or be altered by contaminant inputs such is the case for road-derived or mining-related materials for example. Agricultural practices can influence surface soil signatures through enhanced weathering of cultivated soils, such that differences between surface and subsurface element concentrations can often be detected. This may be further enhanced by atmospheric deposition at the soil surface (Smith and Blake 2014). Field survey sample numbers are dependent on the size of the study area, the range of potential sources, and gaining representative samples involves repeat sampling of each source type. Subsequently, tracing studies often require large sample numbers to be processed and characterised for element concentrations. The choice of analytical technique, therefore, requires careful consideration. Techniques such as inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) have the advantage of low detection limits (≤ µg/kg), which offer potential for a wide range of tracers to be measured. However, these techniques measure elements in the aqueous phase, which requires the use of hazardous (e.g., strong acids) and time-consuming digestion methods prior to measurement. X-ray fluorescence (XRF) spectrometry offers an alternative and potentially rapid approach for measuring a broad range of elements in solid materials such as soils and sediments. XRF spectrometry provides total element concentrations and can, if required, be undertaken in a non-destructive manner on loose powder material, which is particularly useful where sample mass is limited. As well as laboratory-based instrumentation, the use of portable XRF instruments offers an opportunity for in situ screening of contaminant hotspots and source materials (Turner and Taylor 2018).

*Compound Specific Stable Isotopes (CSSI)* offer complementary/specific information to FRNs and geochemical approaches. Elemental geochemistry, fallout radionuclides, and mineral magnetism are sediment fingerprints that have been successfully applied to identify and apportion sediment sources within a catchment. However, they are limited when determining specific land use-derived sediment sources. The CSSI technique offers the possibility of refining other fingerprinting techniques by potentially allowing discrimination of different land uses based on the stable carbon isotopic composition of fatty acids δ($^{13}$C)($_{FA}$). CSSI refers to isotopes of individual compounds in complex mixtures rather than the isotopic signature of the bulk sample. Sources of different land uses can be identified based on stable isotope characteristics of specific molecules in vegetation communities. Soil contains a wide variety of organic compounds, which vary significantly in stability and structure, and therefore, the isotopic signature of a bulk soil is not expected to be conservative. Consequently, the need to target specific organic compounds appears. Plants synthesise complex organic molecules during photosynthesis using carbon dioxide. They can produce the same compounds (e.g., fatty acids) but with different isotopic compositions because
of isotopic fractionation which will reflect the different photosynthetic pathways of the plants (Chikaraishi and Naraoka 2003; Reiffarth et al. 2016). Plant-derived FAs are incorporated into the soil through rhizodeposition and decomposition of organic matter transferring the δ(¹³C) signature of the vegetation from which they are released. Fatty acids become bound to mineral and clay soil particles making them persistent for long periods of time (Gibbs 2008; Reiffarth et al. 2016; Upadhayay et al. 2017a). Although FA concentrations may change overtime due to degradation by microorganisms, volatilisation, dilution, and dispersion, they do not cause significant isotopic fractionation (Gibbs 2008). However, the use of the very long-chain fatty acids (VLCFAs) is particularly recommended for this purpose since they are predominantly plant-derived and more resistant to degradation in soils and sediments which avoids the influence of external sources such as contamination by microorganisms and algae and reduces the risk of isotopic fractionation (Reiffarth et al. 2016; Upadhayay et al. 2017b). Due to this fingerprinting method based upon qualitative data (δ(¹³C)(FA)), a further correction by the concentrations of each fatty acid in the source material considered in the fingerprinting approach is required (Upadhayay et al. 2018a). Different relative tracer concentrations in potential sources influence apportionment calculations, and the need to consider concentration-dependency in isotope mixing is strongly recommended (Upadhayay et al. 2018a).

### 7.2.3 Linking Source Materials to Downstream Mixtures

Quantitative comparison of source signatures to downstream mixtures is achieved through application of mixing models. With increasing attention to challenges of uncertainty and representativeness alongside the need to harmonised approaches, methodologies have emerged with exemplar structures and data to support user application. Recent innovations include application of MixSIAR (Stock et al. 2018) in river basin contexts (Blake et al. 2018); FingerPro purpose designed for the soil sediment continuum (Lizaga et al. 2020a), and the SIFT framework (Pulley and Collins 2018). Recent publications all provide code and/or open-source software to support user implementation. Nevertheless, a key element before performing any calculation is the careful choice of tracers (see also Chap. 3). Tracer selection is a crucial step for any fingerprinting study in which tracer conservativeness, source discrimination, and mixing model performance should be evaluated. Several strategies have been employed to select the most suitable tracers for sediment source apportionment such as boxplots, biplots, multivariate analyses, Kruskal-Wallis, and discriminant function analysis (DFA) tests, among others (Collins et al., 2020). For discussion about the performance of different tracer selection methods, the reader is directed to other works (Palazón and Navas 2017; Smith et al. 2018; Lizaga et al. 2020b).