# Tracing the Sources and Fate of Contaminants in Agroecosystems

Joseph Adu-Gyamfi, Grzegorz Skrzypek, Gwenaël Imfeld

► To cite this version:

Joseph Adu-Gyamfi, Grzegorz Skrzypek, Gwenaël Imfeld. Tracing the Sources and Fate of Contaminants in Agroecosystems. Springer, 2024, 978-3-031-47265-7. 10.1007/978-3-031-47265-7. hal-04733859

HAL Id: hal-04733859

https://hal.science/hal-04733859v1

Submitted on 13 Oct 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Joseph Adu-Gyamfi
Grzegorz Skrzypek
Gwenaël Imfeld
Lee Heng Editors

<figure><img src="image_3.png" /></figure>

# Tracing the Sources and Fate of Contaminants in Agroecosystems

## Applications of Multi-stable Isotopes

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

IAEA

Joint FAO/IAEA Centre

Nuclear Techniques in Food and Agriculture

OPEN ACCESS

<figure><img src="image_6.png" /></figure>

Springer
Tracing the Sources and Fate of Contaminants
in Agroecosystems
Joseph Adu-Gyamfi · Grzegorz Skrzypek ·
Gwenaël Imfeld · Lee Heng
Editors

Tracing the Sources and Fate
of Contaminants
in Agroecosystems

Applications of Multi-stable Isotopes

<figure><img src="image_7.png" /></figure>

Joint FAO/IAEA Centre
Nuclear Techniques in Food and Agriculture

<figure><img src="image_8.png" /></figure>

IAEA

<figure><img src="image_9.png" /></figure>

Springer
Editors

Joseph Adu-Gyamfi
International Atomic Energy Agency
Vienna, Austria

Grzegorz Skrzypek
The University of Western Australia
Perth, WA, Australia

Gwenaël Imfeld
University of Strasbourg
Strasbourg, France

Lee Heng
International Atomic Energy Agency
Vienna, Austria

<figure><img src="image_10.png" /></figure>

ISBN 978-3-031-47264-0

ISBN 978-3-031-47265-7 (eBook)

https://doi.org/10.1007/978-3-031-47265-7

© IAEA: International Atomic Energy Agency 2024. This book is an open access publication.

The opinions expressed in this publication are those of the authors/editors and do not necessarily reflect the views of the IAEA: International Atomic Energy Agency, its Board of Directors, or the countries they represent.

**Open Access** This book is licensed under the terms of the Creative Commons Attribution 3.0 IGO license (http://creativecommons.org/licenses/by/3.0/igo/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the IAEA: International Atomic Energy Agency, provide a link to the Creative Commons license and indicate if changes were made.

Any dispute related to the use of the works of the IAEA: International Atomic Energy Agency that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IAEA: International Atomic Energy Agency's name for any purpose other than for attribution, and the use of the IAEA: International Atomic Energy Agency's logo, shall be subject to a separate written license agreement between the IAEA: International Atomic Energy Agency and the user and is not authorized as part of this CC-IGO license. Note that the link provided above includes additional terms and conditions of the license.

The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Paper in this product is recyclable.
# Preface

Pollution originating from agriculture, domestic water waste, mining sites, sediment and soil erosion, and other sources has a direct negative impact on human health and water, food, and natural environment quality. Solutes such as nutrients, fertilizers, pesticides, or their by-products disperse through natural ecosystems contaminating streams, rivers, lakes, and groundwater aquifers. Pollutant concentrations frequently increase downstream, and many agroecosystems are contaminated by multiple pollution sources, but traditional techniques based on chemical concentrations do not allow evaluation of the relative contributions from different sources. Complementarily to conventional monitoring, more advanced multi-tracer approaches are required. Stable isotope compositions of nitrates $\delta(^{15}N)_{NO_3}$, $\delta(^{18}O)_{NO_3}$, water $\delta(^{2}H)_{H_2O}$, $\delta(^{18}O)_{H_2O}$, sulphates $\delta(^{34}S)_{SO_4}$, $\delta(^{18}O)_{SO_4}$, phosphates $\delta(^{18}O)_{PO_4}$, and carbon $\delta(^{13}C)_{POM \ or \ DIC}$ combined with advanced hydrochemical methods and models over the last couple of decades have become important methods in forensic investigations of water quality. However, major knowledge gaps in pollution studies in agroecosystems still exist, including the source characterization and separation of mixing from isotope fractionation and equilibration. Therefore, more data, research, and integration of approaches are required. There is also a need for method standardization and the formulation of a scientific *toolbox* that assembles available sampling and analytical techniques useful for identifying sources and transport of agropollutants for better understanding, managing, and improving soil, crop, surface, and groundwater quality in agroecosystems.

The Soil and Water Management and Crop Nutrition (SWMCN) Subprogramme of the Joint Food and Agriculture Organization (FAO)/International Atomic Energy Agency (IAEA) Centre of Nuclear Techniques in Food and Agriculture, through a Coordinated Research Project (CRP) in partnership with national and international research institutes, developed and evaluated a set of analytical techniques, i.e. the *toolbox*. This *toolbox* integrates multiple isotope tracers that provide information on the origins and pathways of multiple pollutants through agroecosystems, thereby providing more accurate guidance on mitigations. The objective of this book is to present protocols, methodologies, and standard operating procedures (SOPs) used for the identification of sources, transport, and fate of agrocontaminants and illustrate
them with several case studies of successful applications. However, land manage-
ment strategies to address and control the transport of pollutants from soil to water
bodies remain the shared responsibility of farm and aquaculture operators, agro-
chemical manufacturers, and policymakers in food and agriculture, as well as the
mining sectors. This book is structured into eight chapters covering (i) an overview
of the book's content, (ii) guidelines for designing water sampling programmes,
(iii) the use of mixing models applicable to tracers for water pollution studies, (iv)
compound-specific isotope analyses to investigate pesticide degradation in agricul-
tural catchments, (v) the use of stable oxygen isotope composition of phosphate
to investigate phosphorous in soil-plant continuum, (vi) the use of stable sulphur
isotopes to disentangle agropollutants from other contaminants, (vii) nuclear tools
used in sediment source apportionment, and (viii) the conclusions and perspectives
forward. The book offers up-to-date information, and we hope it will be a great
source of information and inspiration for students, researchers, and policymakers.

The SWMCN subprogramme and the editors thank all the contributors involved
in the preparation of this publication.

Vienna, Austria

Perth, Australia

Strasbourg, France

Vienna, Austria

Joseph Adu-Gyamfi

Grzegorz Skrzypek

Gwenaël Imfeld

Lee Heng
# Contents

<table><tr><td>1</td><td>Introduction: Stable Isotope Tracers Used for the Identification of Contaminants in Agro-Ecosystems</td><td>1</td></tr><tr><td></td><td></td><td>G. Skrzypek, J. Adu-Gyamfi, G. Imfeld, and L. Heng</td></tr><tr><td>2</td><td>Conceptual Sampling Design for Tracing Agropollutants on a Catchment Scale</td><td>11</td></tr><tr><td></td><td></td><td>G. Skrzypek</td></tr><tr><td>3</td><td>Principles of Mixing and Fractionation Models</td><td>17</td></tr><tr><td></td><td></td><td>G. Skrzypek</td></tr><tr><td>4</td><td>Compound-Specific Isotope Analyses to Investigate Pesticide Transformation in Soil and Water</td><td>33</td></tr><tr><td></td><td></td><td>T. Gilevska and G. Imfeld</td></tr><tr><td>5</td><td>Stable Oxygen Isotope Composition of Phosphates to Investigate Phosphorus Cycling in the Soil-Plant Continuum</td><td>63</td></tr><tr><td></td><td></td><td>V. Pfahler, J. Adu-Gyamfi, Ch. Pistocchi, F. Tamburini, and S. Bauke</td></tr><tr><td>6</td><td>Stable Sulfur and Oxygen Isotope Compositions of Sulfates to Disentangle Agrocontaminants from Other Sources of Sulfur in Agrosystems</td><td>99</td></tr><tr><td></td><td></td><td>G. Skrzypek</td></tr><tr><td>7</td><td>Quantifying Sediment and Associated Pollutants Sources in Agricultural Catchments Using Isotopic Techniques</td><td>127</td></tr><tr><td></td><td></td><td>W. H. Blake, A. Taylor, E. Muñoz-Arcos, L. Ovando-Fuentealba, C. Bravo-Linares, and G. E. Millward</td></tr><tr><td>8</td><td>Conclusion: Stable Isotope Tracers Are Useful for the Identification of Pollutants in Agro-ecosystems</td><td>157</td></tr><tr><td></td><td></td><td>G. Imfeld, G. Skrzypek, J. Adu-Gyamfi, and L. Heng</td></tr><tr><td></td><td>Glossary</td><td>165</td></tr></table>
# Editors and Contributors

## About the Editors

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

Joseph is working as Integrated Soil Fertility Management Specialist providing technical assistance to IAEA national, regional/interregional technical cooperation projects in Africa, Asia, Europe, Latin America, and the Caribbean, on the use of multi-stable isotope fingerprints to identify sources and transport of contaminants in the environment (www.iaea.org). His work involved developing climate-smart agricultural practices to combat and mitigate the impacts of climate change on agri-cultural 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). e-mail: j.adu-gyamfi@iaea.org

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

Grzegorz has 24 years of experience in the field of stable isotope geochemistry, hydrochemistry, and ecology since his Ph.D. (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 several 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. e-mail: grzegorz.skrzypek@uwa.edu.au

**Gwenaël Imfeld** Isotope and Experimental Biogeochemistry, University of Strasbourg, Strasbourg Cedex, France.

Gwenaël is a research professor at the French National Centre for Scientific Research (CNRS) in environmental biogeochemistry, at the Laboratory of Hydrology and Geochemistry of Strasbourg (Ph.D. degree in 2008 at UFZ-Leipzig, Habilitation in 2013, University of Strasbourg). His research interests revolve around pollutant biogeochemistry, microbial transformation and ecology in continental hydrosystems, and freshwater sustainability, resilience and adaptation of socio-hydrosystems. His current research focuses on the degradation and transport of micropollutants in wetlands, soils, and aquifers, combining microbial and stable isotope approaches (CSIA). Gwenaël is leading the research team "Isotope biogeochemistry" with analytical facilities for chemical and isotopic analyses of trace elements and organic compounds. Gwenaël has published > 90 ISI publications in contaminant biogeochemistry and microbial ecology. He is Director of the Research Initiative in Sustainability and the Environment (RISE/FERED) of the University of Strasbourg/CNRS and the Editorial Board of *Chemosphere*. e-mail: gwenael.imfeld@cnrs.fr

**Lee Heng** Soil and Water Management and Crop Nutrition (SWMCN) Section, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency (IAEA), Vienna, Austria.

Lee is working as Section Head of Soil and Water Management and Crop Nutrition (SWMCN) Subprogramme in the Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture (www.iaea.org). She has more than 30 years' experience in soil-plant-water interactions, agricultural water management and water use efficiency, integrated nutrient water interactions, and diffuse pollution control for sustainable agricultural production systems, at both national and international levels. Her work covers countries in Africa, Asia, Europe, and Central and Latin America on sustainable land and water management for climate-smart agriculture and the efficient use and conservation of agricultural resources to enhance food production and environmental sustainability. She is the author and co-author of several books and book chapters and research publications in refereed journals. She has won several awards for her professional achievements, including the IAEA Merit, Promotion (2004) and Superior Achievement Awards (2018), and the
USDA-Agricultural Research Service (ARS) Outstanding Sustained Effort Technology Transfer Award for Outstanding Work in Technology Transfer to Users (2012). e-mail: l.heng@iaea.org

## Contributors

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

**S. Bauke** University of Bonn, Bonn, Germany

**W. H. Blake** University of Plymouth, Plymouth, UK

**C. Bravo-Linares** Universidad Austral de Chile, Valdivia, Chile

**T. Gilevska** Isotope and Experimental Biogeochemistry, University of Strasbourg, Strasbourg Cedex, France

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

**G. Imfeld** Earth & Environment Strasbourg (ITES, UMR7063), University of Strasbourg/CNRS, Strasbourg, France; Isotope and Experimental Biogeochemistry, University of Strasbourg, Strasbourg Cedex, France

**G. E. Millward** University of Plymouth, Plymouth, UK

**E. Muñoz-Arcos** University of Plymouth, Plymouth, UK

**L. Ovando-Fuentealba** University of Plymouth, Plymouth, UK

**V. Pfahler** Formerly Smithsonian Tropical Research Institute, Balboa, Ancon, Panama

**Ch. Pistocchi** Eco and Soils Research Unit—INRAE, CIRAD, IRD, Institut, Agro Montpellier, France

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

**F. Tamburini** Group of Plant Nutrition, D-USYS, ETH, Zurich, Switzerland

**A. Taylor** University of Plymouth, Plymouth, UK
<figure><img src="image_11.png" /></figure>

Chapter 1

# Introduction: Stable Isotope Tracers Used for the Identification of Contaminants in Agro-Ecosystems

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

**Abstract** This chapter provides an overview of the stable isotope tracers used in agro-contaminant studies, summarizing essential information about each tracer, including the range of values, sample collection and preparation, and the most common applications. The chapter provides an overview of the book's concept and contents and refers readers to the chapters of interest.

Sources of pollution in agro-ecosystems are usually difficult to identify using pollu-
tant concentrations alone. Most of these sources are not attributed to a specific loca-
tion (non-point) and are typically in solution (non-particulate). Therefore, compound
concentrations can inform about the level of pollution (low or high), temporal and
spatial dispersal but cannot directly confirm the actual source and transformation
of the pollution. To unambiguously identify sources of pollution, a more advanced
approach is needed, e.g., using tracers that carry unique fingerprints of each poten-
tial source. Frequently stable isotope compositions of elements forming the chemical
compound in pollution are used as environmental tracers. In conjunction with the
major ion ratios and stable hydrogen and oxygen isotope compositions of water

G. Skrzypek (✉)
West Australian Biogeochemistry Centre, School of Biological Sciences, The University of Western Australia, Perth, WA, Australia
e-mail: grzegorz.skrzypek@uwa.edu.au

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

L. Heng
e-mail: l.heng@iaea.org

G. Imfeld
Earth & Environment Strasbourg (ITES, UMR7063), University of Strasbourg/CNRS, Strasbourg, France
e-mail: gwenael.imfeld@cnrs.fr
molecules, these isotope tracers can be successfully used to identify sources of pollutants found in surface and groundwaters (Kendall 1998; Marshall et al. 2008). The application of mass balance calculations using isotope tracers often allows for not only qualitative but also quantitative estimations of the relative contributions from different sources of pollution and the discrimination of agricultural inputs from other contaminants (Kendall 1998). The stable isotope signatures and ion ratios also provide information that is independent of solute concentrations, thereby disentangling the water budget from the pollution budget. An accurate identification of sources and transport of pollutants from soil to water in agro-ecosystems will help develop appropriate soil and water management practices to minimize agropollutants to surface and groundwaters.

Two primary challenges need to be addressed to improve our ability to monitor and quantify sources and transport of pollutants in agro-ecosystems using stable isotope tracers. The first lies in the reliable and routine use of stable isotope methods to identify pollutants and distinguish mixing from stable isotope fractionation. The second challenge lies in the comprehensive implementation of a multi-tracer approach relying on two or more elements and their stable isotope compositions to characterize and quantify sources and reactive transport of solutes in agro-ecosystems. In the present book, we address these two challenges by focusing on the stable H, C, N, O and S isotope compositions in several chemical compounds to trace and monitor sources and transport of macronutrients and micro-contaminants in soil and waters under different land uses and hydro-climatic conditions. Stable isotope tracers have great potential to improve the understanding, management and protection of surface and groundwater and to help in the qualitative and quantitative prediction of water resources. The six chapters included in this book present a critical overview of the stable isotope tracer methods forming a hydrochemical toolbox to be applied in the broad range of agro-contaminant studies. We thus anticipate that this book will stimulate researchers and managers to further develop and use hydrochemical and stable isotope techniques worldwide, complementarily to conventional monitoring and mass balance approaches.

The most commonly used analyses of the stable isotope composition are those of the solutes in water [δ(¹⁵N)NO₃, δ(¹⁸O)NO₃, δ(³⁴S)SO₄, δ(¹⁸O)SO₄, δ(¹³C)DIC, δ(¹⁸O)PO₄, δ(¹³C)pesticide, δ(¹⁵N)pesticide], the particulate organic matter [δ(¹³C)POM, and δ(¹⁵N)POM], together with water molecules [δ(²H)H₂O and δ(¹⁸O)H₂O], as well as chemical ion concentrations, and the major ion ratios (e.g., Na⁺/Ca²⁺, Cl⁻/SO₄²⁻, Ca²⁺/SO₄²⁻). All these analyses are particularly useful for tracing the dynamics of pollution contributions from different sources, usually in reference to local baselines and the original signatures of the contributing pollution sources. The major advantages of stable isotope methods are their relatively low cost and high effectiveness, even when used in areas lacking long-term monitoring or a comprehensive sampling network.

The stable hydrogen, carbon, nitrogen, oxygen and sulfur isotope compositions (HCNOS) of various compounds can be used to study agricultural pollution in surface and groundwaters by applying mass balance calculations and other models to assess the progress of dispersal or decomposition (Chap. 3). Different stable isotope tracers
usually carry different and complementary information about pollution sources, water flows and mixing, as well as about pollutant chemical transformations in the aquatic environment (Chaps. 4–7). Various tracers can be combined to obtain a more unambiguous characterization of pollution sources and to disentangle water budgets from solute budgets, or they can be used independently to address different environmental questions (e.g., Szynkiewicz et al. 2015; Elsner and Imfeld 2016; Dogramaci et al. 2017; Pfahler et al. 2022). This brief overview provides a list of the commonly used stable isotope tracers discussed in this book, along with their major applications, the general ranges of the δ-values observed in nature and the expected analytical uncertainty (Coplen et al. 2002a, b). The analytical uncertainty, along with the natural variability of the δ-values in each source, needs to be considered for the correct solution of the mass balance mixing model equations. One critical consideration is whether the δ-values in the sources potentially contributing to the analysed mixture are significantly different from each other. If the δ-values of different sources are similar, models cannot be solved with high confidence to distinguish sources. On the other hand, transformation processes may be traced over space and time if δ-values in the sources are similar.

The list of tracers presented below has been arranged considering their relevance to studies of agricultural pollution in terrestrial environments. The meaning of the categories is as follows:

* analysed in —medium in which the stable isotope compositions can be analysed.

* range of values —an approximate typical range of the δ-values frequently observed in natural terrestrial environments.

* uncertainty —the typical uncertainty of the stable isotope analysis given as one standard deviation; it varies, respectively to the analytical method used.

* applications —primary applications of the tracer.

* zero value —the reference point for the scale characterized by the δ-value equal 0 ‰.

For the full range of variation in stable isotope compositions in all environments and compounds reported around the world, see the summary by Coplen et al. (2002a, b).

**δ(²H)H₂O and δ(¹⁸O)H₂O—stable hydrogen and oxygen isotope compositions of water**

*analysed in*: surface, ground, precipitation and soil water

*range of values*: δ(²H) – 150 ‰ to + 50 ‰ and δ(¹⁸O) – 20 ‰ to + 10 ‰

*uncertainty*: δ(²H) ~ 0.5–1.0 ‰, δ(¹⁸O) ~ 0.05–0.10 ‰

*applications*: used for tracing sources, mixing models, assessing evaporative losses of water

*zero value*: the stable isotope compositions of the Vienna Standard Mean Ocean Water (VSMOW) are accepted as a zero-reference point (0 ‰) for δ(²H) and δ(¹⁸O)
on the VSMOW2-SLAP2 scale. Therefore, δ(²H) and δ(¹⁸O) values close to zero are typical for seawater and moderately evaporated freshwater.

### δ(¹⁵N)NO₃ and δ(¹⁸O)NO₃—stable nitrogen and oxygen isotope compositions of nitrates

*analysed in:* NO₃⁻ ions dissolved in water or NO₃⁻ in solid substances, e.g., fertilizers, salt precipitates

*range of values:* δ(¹⁵N) -10 ‰ to + 25 ‰ and δ(¹⁸O) -15 ‰ to + 80 ‰

*uncertainty:* δ(¹⁵N) ~ 0.3–0.5 ‰ and δ(¹⁸O) ~ 0.3–0.5 ‰

*applications:* usually used to distinguish N-input sources of surface and ground-water pollution and to assess N-contributions from precipitation, natural plant decomposition, fertilizers, manure and human wastewater; significant fractionation can be caused by the nitrification-denitrification cycle influencing stable isotope composition and substrate concentrations

*zero value:* the stable isotope composition of atmospheric nitrogen is accepted as a zero-reference point for the nitrogen air scale [δ(¹⁵N) = 0 ‰]. The δ¹⁵N value of atmospheric N₂ is constant around the world. The δ(¹⁸O) is usually reported on the VSMOW2-SLAP2 scale, which is the same as water.

### δ(³⁴S)SO₄, δ(¹⁸O)SO₄—stable sulfur and oxygen isotope compositions of sulfates

*analysed in:* SO₄²⁻ ion dissolved in water or SO₄²⁻ in solid substances, e.g., fertilizers, salt precipitates; frequently, for reference, δ(³⁴S)SO₄ is analysed as well in sulfides and various fractions in soil

*range of values:* δ(³⁴S) - 25 ‰ to + 40 ‰ and δ(¹⁸O) - 20 ‰ to 30 ‰

*uncertainty:* δ(³⁴S) ~ 0.2–0.4 ‰, δ(¹⁸O) ~ 0.2–0.4 ‰

*applications:* used to assess inputs from fertilizers and wastewater and to distinguish them from acid rock and mine drainage and atmospheric pollution

*zero value:* the stable isotope composition of Vienna Canyon Diablo Troilite (VCDT), a meteorite mineral, is accepted as a zero reference for the sulfur scale [δ(³⁴S) = 0 ‰]. The δ¹⁸O is usually reported on the VSMOW2-SLAP2 scale.

### δ(¹³C)POM and δ(¹⁵N)POM—stable carbon and nitrogen isotope compositions of particulate organic matter suspended in water (POM)

*analysed in:* particulate organic matter as a suspension in water or sediments, frequently for reference also analysed in plant material, and soil, usually POM is separated on fibreglass filters by water filtration

*range of values:* δ(¹³C) - 30 ‰ to - 10 ‰ and δ(¹⁵N) - 5 ‰ to + 20 ‰

*uncertainty:* δ(¹³C) ~ 0.10 ‰ and δ(¹⁵N) ~ 0.10 ‰
*applications:* primarily used to estimate C-inputs from different vegetation types (especially agricultural crops) and soil erosion to surface water and to distinguish terrestrial and aquatic C-sources; also frequently used to assess C3 to C4 plant input to soil or suspended particles

*zero value:* the stable isotope composition of Vienna Pee Dee Belemnite (VPDB), a carbonate marine fossil, is accepted as a zero-reference point for $\delta(^{13}C)$ stable isotope scale. Therefore, marine carbonates have $\delta(^{13}C)$ close to zero.

### $\delta(^{18}O)_{PO_4}$—stable oxygen isotope compositions of phosphate

*analysed in:* $PO_4^{3-}$ dissolved in water, in plant material, soil, fertilizers, sediments, rocks, detergents, rivers and vegetation

range of values: +6 to +31 ‰

*uncertainty:* ~0.3–0.5 ‰

*applications:* tracing phosphate sources and phosphate turnaround time in soil and surface water to assess fertilizer leaching and turnover

*zero value:* as above for water.

### $\delta(^{13}C)_{DIC}$—stable oxygen isotope compositions of dissolved inorganic carbon (DIC)

*analysed in:* water, usually as total inorganic dissolved carbon (the ratio between $CO_2/HCO_3^-/CO_3^{2-}$ in solution depends on pH)

range of values: –20 ‰ to +5 ‰

*uncertainty:* ~0.10–0.20 ‰

*applications:* primarily used to confirm decomposition of hydrocarbon pollution and methane oxidation and their contribution to DIC; also, to assess the contribution of aquatic organisms to diel cycles of DIC; to detect contributions from deep groundwater

*zero value:* as above for $\delta(^{13}C)_{POM}$.

### $\delta(^{13}C)$ and $\delta(^{15}N)$—stable carbon and nitrogen isotope compositions of pesticide molecules

*analysed in:* water, soil, sediment and plants

range of values: –35 ‰ to 0 ‰ for $\delta(^{13}C)$ and –4 ‰ to +4 ‰ for $\delta(^{15}N)$

*uncertainty:* ~0.5 ‰ for both $\delta(^{13}C)$ and $\delta(^{15}N)$

*applications:* primarily used to evaluate in situ degradation, without considering concentration data of parent compound or transformation products, quantify pollutant degradation and, in some cases, identify reaction pathways
zero value: the stable isotope composition of Vienna Pee Dee Belemnite (VPDB), a carbonate marine fossil, is accepted as a zero-reference point for δ(¹³C) stable isotope scale. The stable isotope composition of atmospheric nitrogen is accepted as a zero-reference point for the nitrogen air scale [δ(¹⁵N) = 0‰]. The δ(¹⁵N) value of atmospheric N₂ is constant around the world.

All stable isotope results in this book are presented in permille (‰) as 1000 isotope delta, on the stable isotope scale respective to the element (e.g., VPDB, VSMOW2-SLAP2, VCDT or AIR). The isotope delta (symbol δ) is defined by the isotope ratios of heavier (i) to lighter (j) isotope of an element (E) in substance (P) and isotope ratio of this element in an international standard (Std), defining the zero point for the international isotope scale, 0‰ (Eq. 1.1):

$$ \delta(^{i/j}\text{E})_{\text{P,Std}} = \frac{R(^{i/j}\text{E})_{\text{P}}}{R(^{i/j}\text{E})_{\text{Std}}} - 1 \quad (1.1) $$

This full notation, for example, for stable sulfur isotope composition in sulfate, respectively to VCDT scale can be expressed as follows (Eq. 1.2):

$$ \delta(^{34/32\text{S}})_{\text{SO}_4, \text{VCDT}} = \frac{R(^{34/32\text{S}})_{\text{SO}_4}}{R(^{34/32\text{S}})_{\text{VCDT}}} - 1. \quad (1.2) $$

To avoid excessive repetition of common terms, we used a shorthand version of the full notation (Eq. 1.3):

$$ \delta(^{34\text{S}})_{\text{SO}_4}. \quad (1.3) $$

The delta values are reported without corrections or adjustments, exactly as in the source publications. Readers should be aware that the isotope delta values assigned for international standards undergo occasional changes, and discrepancies may arise while pooling values published during different years. To warrant the reusability of published data, all new stable isotope results should be published following minimum requirements for publishing HCNOS stable isotope delta results guidelines (Skrzypek et al. 2022) and correct terminology and notations endorsed by the International Union of Pure and Applied Chemistry (Cohen et al. 2007; Brand et al., 2010; BIMP 2019). Each stable isotope data set should be accompanied by a description of (1) analytical procedure, (2) traceability, (3) data processing and (4) uncertainty evaluation.

## Other Tools for Tracing Anthropogenic Contaminants

Other less frequently used tracers for tracing anthropogenic contaminants in water and soil are boron [δ(¹¹B) reflecting ¹¹B/¹⁰B isotope ratio] and strontium [δ(⁸⁷Sr) reflecting ⁸⁷Sr/⁸⁶Sr isotope ratio]. The main anthropogenic source of boron (B) is sodium perborate (NaBO₃), which is used in laundry detergents and household cleaners; thus, boron is commonly found in household sewage. Sewage treatments
generally do not remove boron or cause stable isotope fractionation (Barth 2000). Hence, $\delta(^{11}\text{B})$ can be considered a conservative tracer for partitioning agriculture pollution (e.g., hog manure, cattle feedlot runoff and synthetic fertilizers) from household wastewater. The use of $\delta(^{11}\text{B})$ coupled with $\delta(^{15}\text{N})_{\text{NO}_3}$ and $\delta(^{18}\text{O})_{\text{NO}_3}$ has proved to be an effective means of tracing agricultural nitrate sources to distinguish between two types of sewerage that were indistinguishable using $\delta(^{15}\text{N})$ alone, including washing powders and animal/human manure (Guinoiseau et al. 2018; Kruk et al. 2020). Boron in household pollutants has values like boron minerals [$\delta(^{11}\text{B}) = 5$ to $13\%$] significantly different from values observed in pristine groundwater (~30%) or seawater (~39%) (Vengosh et al. 1994; Vengosh 1998) and can be reflected in plant tissues (Chang et al. 2016).

In natural waters, the geochemistry of dissolved Sr is analogous to that of Ca. The Sr isotope fractionations in geochemical processes are considered negligible due to small differences in isotope masses (~1%), and the $^{87}\text{Sr}/^{86}\text{Sr}$ ratios in natural systems mainly reflect geological age and the initial Rb/Sr ratio in the rocks (Gosselin et al. 2004). Thus, in groundwater, the $^{87}\text{Sr}/^{86}\text{Sr}$ ratio often reflects water-rock interaction time or the aquifer matrix if the groundwater contact time is long enough to attain equilibrium. Strontium isotope composition could be used for assessing salt budget and sources of salinity, particularly to partition fraction coming from rock erosion and precipitation and fertilizers (Hosono et al. 2007; Dogramaci and Skrzypek 2015).

Overall, pollutants may originate from various sources related to different types of agriculture activities, including cultivation, aquaculture, livestock and dairy farms and related food processing industries. Evaluating their respective contributions to soil, surface- and groundwater bodies remains, however, challenging. In this context, stable isotope tracers have untapped potential to quantify agro-contaminant sources, transport and transformation and distinguish them from other sources of contaminants, as presented below.

## References

* Barth SR (2000) Boron isotopic compositions of near-surface fluids: a tracer for identification of natural and anthropogenic contaminant sources. Water Air Soil Pollut 124:49-60

* BIPM (2019) The international system of units (SI), 9th edition. https://www.bipm.org/en/publications/si-brochure. Accessed Nov 18 2021

* Brand WA, Assonov SS, Coplen TB (2010) Correction for the $^{17}\text{O}$ interference in $\delta(^{13}\text{C})$ measurements when analyzing CO2 with stable isotope mass spectrometry (IUPAC Technical Report) Pure Appl. Chem 82:1719-1733

* Chang CT, You CF, Aggarwal SK, Chung CH, Chao HC, Liu HC (2016) Boron and strontium isotope ratios and major/trace elements concentrations in tea leaves at four major tea growing gardens in Taiwan. Environ Geochem Health 38:737-748

* Cohen ER, Cvitas T, Frey JG, Holmstrom B, Kuchitsu K, Marquardt R, Mills I, Pavese F, Quack M, Stohner J, Strauss H, Takami M, Thor AJ (2007) Quantities, units, and symbols in physical chemistry, IUPAC green book, 3rd edn. RSC Publishing

* Coplen TB, Böhlke JK, De Bièvre P, Ding T, Holden NE, Hopple JA, Krouse HR, Lamberty A, Peiser HS, Révész K, Rieder SE, Rosman KJR, Roth E, Taylor PDP, Vocke RD Jr, Xiao YK
(2002a) Isotope-abundance variations of selected elements (IUPAC technical report). Pure Appl Chem 74:1987-2017

Coplen TB, Hopple JA, Böhlke JK, Peiser HS, Rieder SE, Krouse HR, Rosman KJR, Ding T, Vocke Jr RD, Revesz KM, Lamberty A, Taylor P, De Bievre P 2002b. Compilation of minimum and maximum isotope ratios of selected elements in naturally occurring terrestrial materials and reagents. Water-Resources Investigations Report. C6

Dogramaci S, Mclean L, Skrzypek G (2017) Hydrochemical and stable isotope indicators of pyrite oxidation in carbonate-rich environment; the Hamersley Basin, Western Australia. J Hydrol 545:288-298

Dogramaci S, Skrzypek G (2015) Unravelling sources of solutes in groundwater of an ancient landscape in NW Australia using stable Sr, H and O isotopes. Chem Geol 393-4:67-78

Elsner M, Imfeld G (2016) Compound-specific isotope analysis (CSIA) of micropollutants in the environment - current developments and future challenges. Curr Opin Biotechnol 41:60-72

Gosselin DC, Harvey FE, Frost C, Stotler R, Macfarlane PA (2004) Strontium isotope geochemistry of groundwater in the central part of the Dakota (Great Plains) aquifer, USA. Appl Geochem 19:359-377

Guinoiseau D, Louvat P, Paris G, Chen J-B, Chetelat B, Rocher V, Guérin S, Gaillardet J (2018) Are boron isotopes a reliable tracer of anthropogenic inputs to rivers over time? Sci Total Environ 626:1057-1068

Hosono T, Nakano T, Igeta A, Tayasu I, Tanaka T, Yachi S (2007) Impact of fertilizer on a small watershed of Lake Biwa: use of sulfur and strontium isotopes in environmental diagnosis. Sci Total Environ 384:342-354

Kendall C (1998) Tracing nitrogen sources and cycling in catchments In: Kendall C, Mcdonnell KJ (eds) Isotope tracers in catchment hydrology. Elsevier Science BV, Amsterdam

Kruk MK, Mayer B, Nightingale M, Laceby JP (2020) Tracing nitrate sources with a combined isotope approach (δ¹⁵NNO₃, δ¹⁸ONO₃ and δ¹¹B) in a large mixed-use watershed in southern Alberta Canada. Sci Total Environ 703:135043

Marshall JD, Brooks JR, Lajtha K (2008) Sources of variation in the stable isotopic composition of plants. In: Michener R, Lajtha K (eds) Stable isotopes in ecology and environmental science. Blackwell Publishing, Malden

Pfahler V, Adu-Gyamfi J, O'Connell D, Tamburini F (2022) The use of the δ¹⁸Op to study P cycling in the environment. Oxygen isotopes of inorganic phosphate in environmental samples: purification and analysis. Springer International Publishing, Cham

Skrzypek G, Allison C, Böhlke JK, Bontempo L, Brewer P, Camin F, Carter JF, Chartrand MMG, Coplen TB, Gröning M, Hélie J-F, Esquivel-Hernández G, Kraft RA, Magdas DA, Mann JL, Meija J, Meijer HAJ, Moossen H, Ogrinc N, Perini M, Possolo A, Rogers K, Schimmelmann A, Shemesh A, Soto DX, Thomas F, Wielgosz R, Winchester MR, Yan Z, Dunn PJH (2022) Minimum requirements for publishing hydrogen, carbon, nitrogen, oxygen and sulfur stable-isotope delta results. Pure Appl Chem Aop. https://doi.org/10.1515/pac-2021-1108

Szynkiewicz A, Borrok DM, Ganjegunte GK, Skrzypek G, Ma L, Rearick MS, Perkins GB (2015) Isotopic studies of the Upper and Middle Rio Grande. Part 2-Salt loads and human impacts in south New Mexico and west Texas. Chem Geol 411:336-350

Vengosh A (1998) The isotopic composition of anthropogenic boron and its potential impact on the environment. Biol Trace Elem Res 66:145-151

Vengosh A, Heumann KG, Juraske S, Kasher R (1994) Boron isotope application for tracing sources of contamination in groundwater. Environ Sci Technol 28:1968-1974

**G. Skrzypek** has 24 years of experience in the field of stable isotope geochemistry, hydrochemistry, and ecology since his Ph.D. (1999) from The University of Wrocław (UWr). Grzegorz is currently working as Associate Professor and Coordinator of MSc Biology and Ecology specializations at The University of Western Australia (UWA). Grzegorz has published many papers on applications of stable isotope techniques in hydrological, hydrochemical, pollution, ecological,
and paleoclimate studies. He uses stable H, C, N, O, S, and Sr isotopes to trace water budget, nutrient fluxes in natural environments, and dispersal of pollution in environments impacted by mining and agriculture. He held a prestigious Australian Research Council Future Fellowship at UWA (2012–2015) and Foundation for Polish Science Scholarship at UWr (1999–2000). Currently, he is serving as the deputy director of the West Australian Biogeochemistry Centre at UWA (wabc.uwa.edu.au). Grzegorz has been a chief investigator on a number of research projects in Europe and Australia and an expert on IAEA Technical Cooperation Projects and IAEA Technical Meetings.

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

**G. Imfeld** is a research professor at the French National Centre for Scientific Research (CNRS) in environmental biogeochemistry, at Earth & Environment Strasbourg (Ph.D. degree in 2009 at UFZ-Leipzig and University of Neuchâtel (Switzerland), Habilitation in 2013, University of Strasbourg). His research interests revolve around pollutant biogeochemistry, microbial transformation and ecology in continental hydrosystems, and freshwater sustainability, resilience and adaptation of socio-hydrosystems. His current research focuses on the degradation and transport of microluants in wetlands, soils, and aquifers, combining microbial and stable isotope approaches (CSIA). Gwenaël is leading the research team “Isotope biogeochemistry” with analytical facilities for chemical and isotopic analyses of trace elements and organic compounds. Gwenaël has published > 90 ISI publications in contaminant biogeochemistry and microbial ecology. He is Director of the Research Initiative in Sustainability and the Environment (RISE/FERED) of the University of Strasbourg/CNRS and in the Editorial Board of *Chemosphere*.

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

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 3.0 IGO license (http://creativecommons.org/licenses/by/3.0/igo/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the IAEA: International Atomic Energy Agency, provide a link to the Creative Commons license and indicate if changes were made.

Any dispute related to the use of the works of the IAEA: International Atomic Energy Agency that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IAEA: International Atomic Energy Agency's name for any purpose other than for attribution, and the use of the IAEA: International Atomic Energy Agency's logo, shall be subject to a separate written license agreement between the IAEA: International Atomic Energy Agency and the user and is not authorized as part of this CC-IGO license. Note that the link provided above includes additional terms and conditions of the license.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

<figure><img src="image_12.png" /></figure>
Chapter 2

Conceptual Sampling Design for Tracing
Agropollutants on a Catchment Scale

<figure><img src="image_13.png" /></figure>

Check for
updates

G. Skrzypek ID

**Abstract** This chapter provides guidelines for designing a sampling programme for tracing agricultural pollutants using stable isotope techniques at different catchment scales. This sampling template could be considered for the application of various tracers with respect to local needs and study objectives. The template is based on a series of overlapping mixing models that use ‘triple sampling points’ to identify major sources of pollution by estimating pollution budgets in different subcatchments along the water flow pathways. This framework can be further specified, and a more detailed sampling network can be developed after analysing the results obtained from the initial sampling. Step-by-step technical instructions on how to collect and preserve water samples for stable isotope analyses can be found in a separate document published by the International Atomic Energy Agency as a Standard Operating Procedure (Skrzypek in Sampling and isotope analysis of agricultural pollutants in water IAEA TECDOC SERIES. International Atomic Energy Agency, Vienna, pp. 2–21, 2018).

**2.1 Conceptual Case Study—Sampling Design Outline**

Water quality progressively deteriorates in a river supplying a lake, but the upstream catchment lacks hydrological infrastructure and monitoring points. The land use is diversified, and discharged pollution that impacts the lake water quality may originate from one or all activities conducted in the catchment, including dairy production, chicken farms, crops, fruit cultivation or mining. A sampling programme designed to identify pollution sources and their relative contributions to lake water quality requires optimised selection of sampling points across the whole catchment (see also the case study presented in Chap. 7).

The potential contributions from various sources of pollution are determined by
dividing the catchment into a few subcatchments with expected different types and

G. Skrzypek (✉)

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

e-mail: grzegorz.skrzypek@uwa.edu.au
levels of pollution based on land use and infrastructure (Fig. 2.1). Each major tributary is sampled above and below potential sources of pollution and above and below the mouths of various order tributaries (mixing points). In this conceptual example, a total of 15 surface and 4 groundwater sampling points are used. The samples collected above mixing points (e.g., *b2*, *b3*) represent tracer concentrations and stable isotope compositions in the water outflowing from each subcatchment. The sample collected below the mixing point (e.g., *b1*) reflects a mean value that is proportional to the values of the samples (*b2* and *b3*) collected above the mixing point and the volume of water arriving from each subcatchment.

### Mean Signature of the Catchment

Concentrations of pollution at sampling point *a1* represent the mean value for the entire catchment. Some parts of catchments can be heavily polluted, whereas others can be pristine, and the pollution concentrations can be distinctive at different locations. Therefore, the mean concentrations will reflect the pollution loads and the volumes of water inflowing from different subcatchments that are characterised by different levels of pollution. The stable isotope compositions observed in water and water solutes reflect a mean value proportional to the stable isotope compositions and contributions from all significant pollution sources across the whole catchment. The

<figure><img src="image_14.png" /><figcaption>**Fig. 2.1** Map of the conceptual sampling design on the catchment scale. This framework can serve as a template for selecting sampling points, although it requires accounting for local conditions, land use and research objectives</figcaption></figure>
stable isotope compositions could also be further modified by secondary processes that cause stable isotope fractionation during the chemical transformation of pollution or its removal.

## Baseline Background Values

All major ion concentrations and stable isotope compositions of chemical compounds that may occur in waters naturally or can be delivered with rainfall should be considered to understand the baseline values and to separate pollution originating from land use from natural concentrations and inputs from outside the catchment (Chap. 3). The baseline values can be established by sampling water in the section of tributaries above the expected sources of pollution (e.g., above a mine site, point *e*1) or from pristine areas (e.g., points *g*1, *h*1, *c*1, Fig. 2.1). Points *h*1 and *g*1 are located in the pristine area of a nature reserve and are not directly impacted by either agricultural or industrial activity. If these data are consistent with those obtained for other likely unpolluted points *e*1 and *c*1, they constitute a general baseline for this area and characterise the natural variability in solute concentrations and their stable isotope compositions.

The sampling of spring water or groundwater could also be considered. Additionally, the sampling of rainfall may provide useful information about the δ(²H)H₂O and δ(¹⁸O)H₂O values delivered to the study sites and for estimating evaporative losses. Rainwater chemical and stable isotope compositions can be useful for partitioning airborne pollution in regions where air pollution is a serious concern.

## Subcatchment Division with Respect to Expected Pollution Sources

The studied catchment can be divided into a few subcatchments that separate sections of the major creek lines impacted by a specific type of pollution. These creek sections are named AAA, BBB to FFF. Surface water sampling points are labelled *a*1, *b*1 to *k*2; groundwater sampling points *x*1 to *x*4 (Fig. 2.1).

The river section EEE flows through the orchard, and if polluted, it can be expected to carry primarily fertilisers, pesticides and the other agrochemicals used in this area. The water quality of EEE can be analysed at the bottom of the subcatchment (*k*2) before it mixes with FFF to account for the pollutants originating from the orchard only. To understand the actual net input of pollution from the orchard to EEE, the quality of water entering the subcatchment should be considered and point *h*1 located in pristine area can be used to establish a baseline for water hydrochemistry entering this catchment. The difference between concentrations and stable isotope compositions between *h*1 and *k*2 reflects the added pollution loads from the orchard.

In contrast to EEE, the river section FFF may be impacted by pollution from two potential sources: the dairy factory and the orchard. Therefore, the difference between the results obtained for *k*3 and *g*1 (Section FFF) reflects the potential inputs from these two sources. The results for FFF can be further compared with the results for EEE. If the baseline values at *h*1 and *g*1 are not significantly different, then the difference between *k*2 and *k*3 could reflect the potential input from the dairy factory to FFF. The direct signature of potential pollution from the dairy factory can also be verified using groundwater (bore *x*4).
The proportional contribution of FFF and EEE to KKK and to lake pollution could be further calculated using mass balance calculations and the results for individual tracers from the triple sampling point $k3 + k2 = k1$. The concentrations and stable isotope compositions at sampling point $k1$ will reflect those at $k3$ and $k2$ with respect to the contribution from FFF and EEE. A simple verification procedure can be designed using the mass balance model (see calculation examples in Chap. 3) and the following equation (Eq. 2.1):

$$ \delta_{k1} = x \times \delta_{k2} + y \times \delta_{k3}, \qquad (2.1) $$

where $\delta_{k1}$, $\delta_{k2}$ and $\delta_{k3}$ are stable isotope compositions of analysed samples collected at $k1$, $k2$ and $k3$. Assuming contributions from only two water sources, the proportional contributions $x$ and $y$ will equal 1 (Eq. 2.2).

$$ x + y = 1. \qquad (2.2) $$

Solving these simultaneous equations, we can calculate the relative contributions $x$ and $y$ that reflect the inputs from EEE and FFF, respectively. This calculation can be repeated for various tracers, including stable isotope composition, ion concentrations, to calculate the relative contributions of various pollutants, ions and volumes of water. Combining these mass balance calculations allows estimation of the relative loads of pollution from both subcatchments, even without directly measuring the volumes of inflowing water, which is often challenging in the field.

A similar approach can be adopted for other subcatchments of the same order. The River Section BBB flows through an area with multiple industrial poultry farms. The potential influence of the farms on water quality in the river section BBB can be verified by comparing the results from $b3$ located downstream and $c1$ located upstream of the farms. The potential impact of the farms and the stable isotope signature of pollution can also be verified by sampling local drains ($b4$) or shallow groundwater bores ($x1$, $x2$ and $x3$). The potential impact of the mining site on water quality in the river section DDD can be verified by analysing the difference in water quality between sampling point $e1$ located upstream in relation to the mine and point $d3$. The influence of agrochemicals used for crop farming can be analysed by comparing the results from $b2$ and $d1$. Knowing the water quality change through each of the selected creek and river sections, the contributions to the catchment pollution budget can be further calculated using other triple mixing points ($d3 + d2 = d1$ for mixing between DDD and KKK and $b3 + b2 = b1$ for mixing CCC and BBB).
## 2.2 Other Input Data

The ranges of the stable isotope compositions typical of the various types of pollutants can be obtained from the literature; however, these values should be verified locally. If possible, the signatures of potential pollution sources should be analysed directly in the study area at the source of pollution (e.g., by obtaining fertilisers and discharged wastewater directly from animal farms or water draining from mining sites, etc.) (see also, Chap. 6).

The use of this simplifying sampling design template requires some understanding of the study area, particularly its hydrology and land use, and the acquisition of basic GIS information about the locations of potential sources of pollution. Any hydro-chemical or hydrogeological information, if available, will help improve the interpretation of the results. The suggested multiple mixing models that cover different orders of catchments will allow the calculation of the relative contributions of the pollution from each listed source if the stable isotope signatures in the sources are significantly different. Understanding the local climate and obtaining rainfall records are also important factors. Fieldwork for hydrochemical studies is usually recommended during dry periods, at least a few weeks following substantial precipitation events. High-volume precipitation may dilute solute concentrations after the initial flushing down of pollutants accumulated and retained in water pools or soil. However, the major ion ratios and stable isotope signatures will not be directly impacted by dilution with rainwater if the concentrations of solutes are a few orders of magnitude higher than those in precipitation.

## Reference

* Skrzypek G (2018) Water sampling and preservation for stable isotope analyses of water molecules and selected solutes. In: Dercon G, Heng L, Heiling M (ed) Sampling and isotope analysis of agricultural pollutants in water IAEA TECDOC SERIES. International Atomic Energy Agency, Vienna, pp 2–21. Available from: https://www.iaea.org/publications/12374/sampling-and-isotope-analysis-of-agricultural-pollutants-in-water

**G. Skrzypek** has 24 years of experience in the field of stable isotope geochemistry, hydrochemistry, and ecology since his Ph.D. (1999) from The University of Wrocław (UWr). Grzegorz is currently working as Associate Professor and Coordinator of MSc Biology and Ecology specializations at The University of Western Australia (UWA). Grzegorz has published many papers on applications of stable isotope techniques in hydrological, hydrochemical, pollution, ecological, and paleoclimate studies. He uses stable H, C, N, O, S, and Sr isotopes to trace water budget, nutrient fluxes in natural environments, and dispersal of pollution in environments impacted by mining and agriculture. He held a prestigious Australian Research Council Future Fellowship at UWA (2012–2015) and Foundation for Polish Science Scholarship at UWr (1999–2000). Currently, he is serving as the deputy director of the West Australian Biogeochemistry Centre at UWA (wabc.uwa.edu.au). Grzegorz has been a chief investigator on a number of research projects in Europe and Australia and an expert on IAEA Technical Cooperation Projects and IAEA Technical Meetings.
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.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 3.0 IGO license (http://creativecommons.org/licenses/by/3.0/igo/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the IAEA: International Atomic Energy Agency, provide a link to the Creative Commons license and indicate if changes were made.

Any dispute related to the use of the works of the IAEA: International Atomic Energy Agency that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IAEA: International Atomic Energy Agency's name for any purpose other than for attribution, and the use of the IAEA: International Atomic Energy Agency's logo, shall be subject to a separate written license agreement between the IAEA: International Atomic Energy Agency and the user and is not authorized as part of this CC-IGO license. Note that the link provided above includes additional terms and conditions of the license.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

<figure><img src="image_15.png" /></figure>
Chapter 3

Principles of Mixing and Fractionation
Models

<figure><img src="image_16.png" /></figure>

Check for
updates

G. Skrzypek ID

**Abstract** Upfront consideration of possible mixing scenarios is required to achieve an optimal sampling design and deliver a successful project. This will also help increase efficiency by reducing the number of samples, workload and cost without compromising the final outcomes. Chapter 2 summarised key information about a conceptual framework for devising a sampling design for tracing non-particulate agricultural pollutants in surface and groundwaters on a catchment scale. This chapter provides a short introduction to mixing models applicable to tracers for water pollution studies.

**3.1 The Mixing Model Concept**

The stable isotope composition of an element in a chemical compound dissolved in water (i.e., a solute) reflects its origin/source and, to some extent, the stable isotope fractionation processes that may have modified its initial signature during its transport and retention in solution. Failure to account for stable isotope fractionation processes may thus negatively affect the final conclusions of partitioning contributions from various sources of pollution.

A solute analysed in a water sample often originates from more than one source and therefore represents a mixture. This mixture is characterised by a mean stable isotope composition that is proportional to the relative contributions from all sources and the original stable isotope signatures of those sources. If the stable isotope signatures of individual sources are known and if they differ significantly from each other, then the stable isotope composition of the mixture can be used to estimate the relative contribution from each source. The calculation algorithm for this estimation is known as a stable isotope mass balance, and it can be applied to various elements in various solutes analysed in a single sample. However, an unambiguous solution of mass balance equations requires that the number of tracers used be not lower than one less

<footnote>G. Skrzypek (✉)
West Australian Biogeochemistry Centre, School of Biological Sciences, The University of
Western Australia, Perth, Australia
e-mail: grzegorz.skrzypek@uwa.edu.au</footnote>
than the number of sources under consideration (e.g., if three sources are contributing to a mixture, we need a minimum of two tracers; if four sources are considered, we need a minimum of three tracers, etc.). If fewer than the required number of tracers is available, these tracers can still be used for mass balance calculations, but an accurate calculation of the exact contributions will not be possible. Instead, possible ranges of contributions from each source can be obtained. The concept of mixing models is illustrated in the following three examples, which show different scenarios according to the different numbers of tracers available.

## 3.2 Calculation Examples

### 3.2.1 Example 1—Two-Source Mixing Using One Tracer

This is a simplified example of calculations for a two-source mixing model using only one stable isotope tracer (fractionation is not considered). The stable sulfur isotope composition, δ(^34S), has been analysed in a water sample collected from a river at a site below a large area of agricultural farmland where high sulphate concentrations were detected. Two sources of sulphate were considered to be major contributors to the elevated sulfate concentrations in the river (SO₄²⁻ = 800 mg/L): (1) oxidation of pyrite (FeS₂) that naturally occurred in the metamorphic rocks common in the study area and (2) potash fertiliser (potassium sulphate K₂SO₄), applied in large quantities by farmers.

**Input values**

δ(^34S)ₚyr = 1.5 ± 0.5 ‰; the mean signature of pyrite samples collected from several rock outcrops.

δ(^34S)ₚer = 6.2 ± 0.6 ‰; the signature analysed in fertiliser samples collected from a few bags purchased by the farmers.

**Mixture values**

δ(^34S)ₘpl = 5.2 ‰ the signature of sulfates in the river water sample.

**Mass balance**

The following set of simultaneous equations can be used for mass balance calculations of the relative contributions of fertiliser and pyrite to elevated sulfate concentrations in the river:

$$ \delta(^{34}S)_{\text{Spl}} = x_{\text{Pyr}} \times \delta(^{34}S)_{\text{Pyr}} + x_{\text{Fer}} \times \delta(^{34}S)_{\text{Fer}} \quad (3.1) $$

$$ 1 = x_{\text{Pyr}} + x_{\text{Fer}}, \quad (3.2) $$

where xₚyr and xₚer are the fractions of pollution (SO₄²⁻) from pyrite and fertiliser, respectively. Since only two sources are considered, the sum of xₚyr and xₚer is equal
to 1 (by fraction) or 100%; therefore, these equations can be easily solved, as follows:

$$x_{\text{Fer}} = 1 - x_{\text{Pyr}} \qquad (3.3)$$

and, by substitution

$$\delta(^{34}\text{S})_{\text{Spl}} = x_{\text{Pyr}} \times \delta(^{34}\text{S})_{\text{Pyr}} + (1 - x_{\text{Pyr}}) \times \delta(^{34}\text{S})_{\text{Fer}} \qquad (3.4)$$

and this is finally rearranged to

$$x_{\text{Pyr}} = \frac{\delta(^{34}\text{S})_{\text{Spl}} - \delta(^{34}\text{S})_{\text{Fer}}}{\delta(^{34}\text{S})_{\text{Pyr}} - \delta(^{34}\text{S})_{\text{Fer}}}. \qquad (3.5)$$

Using the input and mixture data given above in the example and Eq. 3.5, the following can be calculated:

$$x_{\text{Pyr}} = \frac{5.2 - 6.2}{1.5 - 6.2} = 0.21 \qquad (3.6)$$

$$x_{\text{Fer}} = 1 - 0.21 = 0.79. \qquad (3.7)$$

This means that the rock weathering and resulting pyrite oxidation contributes only to 21% of the total sulfate concentration of 800 mg/L observed in the river water, whereas the majority of the sulphate (79%) originates from fertiliser. This can be translated into loads respective to concentrations of 168 mg/L originating from pyrite and 632 mg/L from fertiliser. If the outflow from the catchment is known, then the total load of sulfate originating from agricultural pollution discharged downstream can be estimated (e.g., mg/h or kg/day) by multiplying the outflow (e.g., L/h) by the concentration (e.g., mg/L). However, one drawback that must be kept in mind is that these numbers reflect the hydrochemical conditions at the time of sampling. As a result, they may change seasonally due to variability in hydrological conditions, the timing of fertiliser applications and whether crops are being planted or harvested. If more frequent (e.g., monthly) and long-term monitoring of the hydrochemical and stable sulfur isotope composition is conducted, and if the total annual load of fertiliser in the catchment is known, the annual agricultural pollution loads and the efficiency of fertiliser utilisation by plants can be estimated (see, e.g., Szynkiewicz et al. 2015a, b).

Note that for a two-source mixing model, the δ-value of the mixture must fall between the δ-values of the two considered sources (Fig. 3.1). If the δ-value of the mixture is outside the range covered by the δ-values of two sources, then the following scenarios are likely: (1) the sources have been incorrectly identified; (2) a third additional source is contributing to the mixture; or (3) the original stable isotope compositions of the sources have been significantly modified by stable isotope
<figure><img src="image_17.png" /><figcaption>**Fig. 3.1** Two-source mixing model concept (data as for Example 1). The $\delta(^{34}S)$ value of the mixture must lie between the $\delta(^{34}S)$ values of the sources. The size of the plotted bar is inversely proportional to the contribution from each source; therefore, a larger distance between the signature of the source and the signature of the mixture implies a lower contribution to the mixture</figcaption></figure>

fractionation or equilibration processes, so the $\delta$-values used are not a true reflection of the source signatures.

### 3.2.2 Example 2—Three-Source Mixing Using Two Tracers

More than two major sources often contribute simultaneously to water pollution. In these cases, more tracers need to be analysed in each sample to allow calculation of the exact contributions from individual sources (e.g., if three sources are considered, a minimum of two different isotope tracers need to be used). This simplified example considers a three-source mixing model using two stable isotope tracers (fractionation was not considered). The stable nitrogen and oxygen isotope compositions [$\delta(^{15}N)$ and $\delta(^{18}O)$] have been analysed in a water sample collected from a river outflowing from an agricultural area where high nitrate concentrations were detected. Three major $NO_3^-$ sources were considered: precipitation, fertiliser and manure.

**Input values**

$\delta(^{15}N)_{Prec} = 5.2 \text{ ‰}$ and $\delta(^{18}O)_{Prec} = 40.8 \text{ ‰}$; the mean signatures of $NO_3$ analysed in precipitation.

$\delta(^{15}N)_{Fer} = -2.3 \text{ ‰}$ and $\delta(^{18}O)_{Fer} = 5.0 \text{ ‰}$; the mean signatures of $NO_3$ analysed in fertiliser.

$\delta(^{15}N)_{Man} = 25.0 \text{ ‰}$ and $\delta(^{18}O)_{Man} = 5.4 \text{ ‰}$; the mean signatures of $NO_3$ analysed in manure.

**Mixture values**

$\delta(^{15}N)_{Spl} = 16.9 \text{ ‰}$ and $\delta(^{18}O)_{Spl} = 8.1 \text{ ‰}$; the signatures of a water sample from the river.

**Mass balance**

Equations for mass balance calculations can be prepared in the same way as depicted in Example 1, assuming that the stable isotope compositions of both nitrogen and
oxygen remain unchanged and fully identify the initial signatures of the sources.

$$ \delta^{15}N_{\text{Spl}} = x_{\text{Prec}} \times \delta(^{15}\text{N})_{\text{Prec}} + x_{\text{Fer}} \times \delta(^{15}\text{N})_{\text{Fer}} + x_{\text{Man}} \times \delta(^{15}\text{N})_{\text{Man}} \quad (3.8) $$

$$ \delta(^{18}\text{O})_{\text{Spl}} = x_{\text{Prec}} \times \delta(^{18}\text{O})_{\text{Prec}} + x_{\text{Fer}} \times \delta(^{18}\text{O})_{\text{Fer}} + x_{\text{Man}} \times \delta(^{18}\text{O})_{\text{Man}} \quad (3.9) $$

$$ 1 = x_{\text{Prec}} + x_{\text{Fer}} + x_{\text{Man}} \quad (3.10) $$

where $x_{\text{Prec}}$, $x_{\text{Fer}}$ and $x_{\text{Man}}$ are the fractions of NO₃ originating from precipitation, fertiliser and manure, respectively. Since the contributions are assumed to arise from only these three sources, the sum of $x_{\text{Prec}} + x_{\text{Fer}} + x_{\text{Man}}$ is equal to 1 (by fraction) or 100 %. These simultaneous equations (Eqs. 3.8, 3.9 and 3.10) can be mathematically solved by substitutions.

The solution of this mass balance model, using the input and mixing data for Example 2, is as follows:

$$ x_{\text{Prec}} = 0.08 $$

$$ x_{\text{Fer}} = 0.24 $$

$$ x_{\text{Man}} = 0.68 $$

In Example 2, the majority of the NO₃⁻ pollution detected in the river originates from manure, at 68 %, and fertilisers, at 24 %, whereas precipitation delivers only 8 %.

Note that for a three-source mixing model, the δ-value of the mixture must lie within the triangle (or polygon if more sources are considered) restricted by the δ-values of the three considered sources. The triangle shows the range of all possible δ-values which can originate from mixing of the considered sources proportionally to their contributions (Fig. 3.2).

### 3.2.3 Example 3—Three-Source Mixing Using One Tracer Only

Often, more than two major sources contribute to water pollution, but an inadequate number of tracers is available (e.g., if three sources are considered, but only one isotope tracer is available). In this scenario, the exact contribution from each source cannot be calculated. Instead, ranges of contributions may be calculated that can provide information about possible maximum and minimum contributions. Example 3 presents the same scenario with the same three sources discussed in Example 2, but only one tracer, δ(^15N) has been used for the calculations.
<figure><img src="image_18.png" /><figcaption>**Fig. 3.2** Three-source mixing model concept (data as for Example 2). The signature of the mixture, in proportion to the contributed fractions, must fall between the signatures of the three sources; therefore, it will lie within the yellow mixing triangle. For simplification, fractionation was not considered in this example</figcaption></figure>

### Input values

$\delta(^{15}\text{N})_{\text{Prec}} = 5.2 \, \text{‰}$; a mean signature of NO₃ analysed in precipitation.

$\delta(^{15}\text{N})_{\text{Fer}} = -2.3 \, \text{‰}$; a mean signature of NO₃ analysed in fertiliser.

$\delta(^{15}\text{N})_{\text{Man}} = 25.0 \, \text{‰}$; a mean signature of NO₃ analysed in stored manure.

### Mixture values

$\delta(^{15}\text{N})_{\text{Spl}} = 16.9 \, \text{‰}$; the signature of a water sample from the river.

### Mass balance

The mass balance equations for this scenario cannot be mathematically solved because three unknowns cannot be calculated using only two equations. Instead, the ranges and the combination of values for solving these equations can be obtained using, for example, IsoSource software (Phillips and Gregg 2003) (See input data in Appendix 2).

$$ \delta(^{15}\text{N})_{\text{Spl}} = x_{\text{Prec}} \times \delta(^{15}\text{N})_{\text{Prec}} + x_{\text{Fer}} \times \delta(^{15}\text{N})_{\text{Fer}} + x_{\text{Man}} \times \delta(^{15}\text{N})_{\text{Man}} \quad (3.11) $$

$$ 1 = x_{\text{Prec}} + x_{\text{Fer}} + x_{\text{Man}} \quad (3.12) $$
where $x_{Prec}$, $x_{Fer}$ and $x_{Man}$ are fractions of NO$_3$ originating from precipitation, fertiliser and manure, respectively. Since the contributions from three sources are considered, the sum of $x_{Prec} + x_{Fer} + x_{Man}$ is equal to 1 (by fraction) or 100 %.

The solution of this mass balance model, using the input and mixing data for Example 3, is as follows:

$$x_{Prec} = 0 \text{ to } 0.41 \text{ (mean } 0.20)$$

$$x_{Fer} = 0 \text{ to } 0.30 \text{ (mean } 0.15)$$

$$x_{Man} = 0.59 \text{ to } 0.70 \text{ (mean } 0.65).$$

These calculations do not provide exact values; however, the obtained information can still be very useful as it confirms that the major source of nitrate in the polluted river is manure and that its contribution is not lower than 59 % and not higher than 70 % with mean 65 %. The remaining contribution, between 15 and 20 %, originates from fertiliser and precipitation, which can vary from 0 to 41 % and 0 to 30 %, respectively.

The mixing models all assume good mixing of water and pollution, but this is not always the case. Inhomogeneity may introduce additional uncertainty into mass balance calculations. In addition, none of the examples presented considered stable isotope fractionations. A fractionation may occur during chemical reactions, physical phase changes, or biological processes, particularly if these processes are not completed and not all substrates are exhausted. The effects of stable isotope fractionations can be corrected, but this will require more advanced modelling and obtaining specific fractionation factors for each stable isotope, chemical compound and physicochemical condition at the time of the reaction (e.g., Lewicki et al. 2022). All results, calculations and interpretations reflect the conditions at the time of sampling, and these can vary over seasons and can be particularly impacted by changes in the hydrological conditions (Dogramaci and Skrzypek 2015; Szynkiewicz et al. 2015a; Dogramaci et al. 2017).

## 3.3 Overview of Available Software for Mass Balance and Fractionation Models

Simple mixing models that use the number of tracers lower by one than the number of sources ($t = s-1$) can be solved algebraically and do not require advanced software. The solution can be derived from simultaneous equations, as presented in examples 1 and 2 above, and the final equation is then solved manually or implemented in a calculation spreadsheet. These options will provide an exact mathematical solution, and the uncertainty of the final calculation will be associated with the laboratory analyses and accurate determination of the signatures of sources and mixtures, but not with the calculation formula that provides the exact solution. The algebraic solution is not applicable in cases where fewer tracers are available ($t \le s-2$). That situation
usually requires the calculation of a range of possible combinations of values solving equations, or alternatively, a probability calculation can be attempted. A few models have been published in recent years using different calculation methods, although most of these models have been developed for assessing dietary contributions in food web studies (e.g., Phillips et al. 2014). Although they were intended for ecological studies, they can easily be adopted in pollution studies. Here, the most popular models are presented: IsoSource, MixSIAR and FRAME.

### 3.3.1 IsoSource—Algebraic Solution for Complex Models

The IsoSource mixing model was developed to allow the calculation of the proportional contributions from a large number of sources using an algebraic algorithm. The software (v 1.3.3) works on Microsoft Windows systems (including Windows 10) and accepts input values for up to five tracers/isotopes and for a maximum of ten sources. All possible scenarios are calculated at user-defined increments and tolerances. The results are returned as a table with fractions of the relative contributions that solve the mixing model equations. Minimum, maximum and mean values are calculated and allow the determination of the possible range of contributions from each source. The software allows the calculation of fractions for one mixture at a time and does not assess uncertainty. The results are recorded as separate report files and need to be combined manually by the user. The system is easy to use, and all inputs are entered manually into a provided window. IsoSource does not have the option of uploading input files.

Website: https://www.epa.gov/eco-research/stable-isotope-mixing-models-estimating-source-proportions

Primary reference: Phillips and Gregg (2003)

Alternatives: LP_Tracer (Bugalho et al. 2008); Moore-Penrose pseudoinverse (Hall-Aspland et al. 2005); SOURCE/STEP (Lubetkin and Simenstad 2004).

### 3.3.2 MixSIAR—Bayesian

MixSIAR is an R package that uses Bayesian statistical methods for (1) determining the proportional contribution of different sources to a mixture and (2) assessing dispersion in the isotope space (Stock et al. 2018). This package, like its earlier version, SIAR, and like IsoSource, was developed for stable isotope studies of food webs. The first functionality (1) can be easily adopted for any mixing model, whereas the second functionality (2), although it is used to assess the distribution of stable isotope results, is specific for ecological studies and not directly applicable to pollution studies. Bayesian statistical distributions are used for both the characterisation of source contributions and uncertainties. The package is a ready-to-use product and
could be used in R-Studio, but elementary knowledge of R coding is required. The model accepts input files, and batches of data can be processed.

Website: http://brianstock.github.io/MixSIAR/

Primary reference: Stock et al. (2018)

Alternatives: MixSIR (Moore and Semmens 2008; Ward et al. 2010); SIAR (Parnell et al. 2010); IsotopeR (Hopkins and Ferguson 2012) and FRUITS (Fernandes et al. 2014), isoWater (Bowen et al. 2018).

### 3.3.3 FRAME—Bayesian, Markov Chain Monte Carlo

FRAME (isotope Fractionation And Mixing Evaluation) is a newly developed software that works on the Windows platform. The FRAME mathematical algorithm uses the Markov chain Monte Carlo model to estimate the contribution of individual sources and processes, as well as the probability distributions of the calculated results. It has a user-friendly graphical interface that can simultaneously determine mixing proportions and the progression of the fractionating process. The fractionation can be defined by the user for specific requirements of the particular isotope system (e.g., an open or closed system). The model can integrate up to three isotopic signatures for each compound used to identify sources contributing to the mixture. This model has been specifically developed for the assessment of mixing and emissions in agriculture pollution studies; therefore, it can be used for any mixing model for pollution studies. The software is easy to use, and datasets can be uploaded using the provided spreadsheet templates.

Website: https://malewick.github.io/frame/

Primary reference: Lewicki et al. (2022).

Alternatives: isoWater (Bowen et al. 2018) (https://cran.r-project.org/web/packages/isoWater/index.html)

### 3.3.4 Other Models and Calculation Spreadsheets

A few other calculation spreadsheets and models can be applicable to agro-contaminant studies (e.g., Hydrocalculator, EasyIsoCalculator). Many studies require estimations of different processes, including evaporative losses in different parts of a catchment, retention times, or evaporation over inflow ratio in lakes or dam reservoirs. Evaporative loss also informs about the evapo-concentration of solutes and can confirm evaporation as a process driving high concentrations of solutes. These calculations are rather too complex to solve manually and can be easily performed using Hydrocalculator (Skrzypek et al. 2015).

Tracer studies that use compounds with artificially elevated concentrations of heavier isotopes are used to track pollution dispersal or chemical uptake by biological organisms. In these studies, stable isotope results are reported using different
expressions (e.g., fraction, ratio or delta). The EasyIsoCalculator spreadsheet allows fast and effortless recalculation between the main expressions (Skrzypek and Dunn 2020a, b).

### 3.3.5 Hydrocalculator—Estimating Evaporative Losses

The Hydrocalculator software (v 1.3) works on the Microsoft Windows system and allows the calculation of evaporative losses from a water body using the stable hydrogen and oxygen isotope composition of the water. The mathematical algorithm is based on a modified version of the Craig-Gordon model. The user can choose between a steady-state and non-steady-state model and select one of three options for estimating the stable isotope composition of atmospheric moisture. The software has an easy-to-use graphical interface. The data can be entered manually in calculator mode or uploaded as a template file for batch conversion. Outputs are recorded in a spreadsheet file (CSV, comma-separated values).

Website: http://hydrocalculator.gskrzypek.com

Primary reference: Skrzypek et al. (2015).

Alternatives: FRAME (Lewicki et al. 2022), isoWater (Bowen et al. 2018).

### 3.3.6 EasyIsoCalculator—Recalculating Between Fractions, Delta Values and ppm

EasyIsoCalculator is a calculation spreadsheet that allows recalculation of the main expressions of isotope compositions (isotope ratio, fraction and delta) for five light elements: hydrogen, carbon, nitrogen, oxygen and sulphur. The spreadsheet allows the selection of one of many absolute ratios defining zero points on isotope delta scales. The spreadsheet has an easy-to-use interface with dropdown menus, and its open code allows the addition of new values. Up to fifty results can be calculated at once using the copy-and-paste option.

Website: http://easyisocalculator.gskrzypek.com/

Primary reference: Skrzypek and Dunn (2020a, b)

Alternatives: n/a.

## Appendix 1

How to use IsoSource software (data as for mixing model, Example 2, presented in Chap. 3).
1. Download and install the software from https://www.epa.gov/eco-research/sta
ble-isotope-mixing-models-estimating-source-proportions (Philips and Gregg
2003).

2. Start the software and enter the δ(^15N) and δ(^18O) data in the table, inputting values for your water sample into the blue cells, and values for pollution sources into the white cells (increment 1 % and tolerance 0.1).

<figure><img src="image_19.png" /></figure>

3. Click 'Calc' on the upper toolbar and save your result file in your folder using the name of a sample as the name of your file.

4. Now you can open your results. Click View (a new window will pop up) and then File.

5. Open the file containing your results of calculations with extension *.tot (e.g., sampename.tot)

6. The results are at the bottom of the file. For this particular example, the mean, maximum and minimum contributions are equal.
<figure><img src="image_20.png" /></figure>

7. Transfer or type your results into an appropriate table in an Excel spreadsheet.

8. Calculate for each sample separately, one sample at the time.

## Appendix 2

1. Input Values for IsoSource Software from Example 3 in Chap. 3

<figure><img src="image_21.png" /></figure>
2. The results are at the bottom of the \*.tot file and show the possible ranges for each source, which solves Eqs. 3.11 and 3.12. Calculate for each sample separately, one sample at the time.

<figure><img src="image_22.png" /></figure>

References

Bowen GJ, Putman A, Brooks JR, Bowling DR, Oerter EJ, Good SP (2018) Inferring the source of evaporated waters using stable H and O isotopes. Oecologia 187:1025–1039.

Bugalho M, Barcia P, Caldeira M, Cerdeira J (2008) Stable isotopes as ecological tracers: an efficient method for assessing the contribution of multiple sources to mixtures. Biogeosciences Discussions 5

Dogramaci S, Skrzypek G (2015) Unravelling sources of solutes in groundwater of an ancient landscape in NW Australia using stable Sr, H and O isotopes. Chem Geol 393–4:67–78

Dogramaci S, Mclean L, Skrzypek G (2017) Hydrochemical and stable isotope indicators of pyrite oxidation in carbonate-rich environment; the Hamersley Basin, Western Australia. J Hydrol 545:288–298

Fernandes R, Millard AR, Brabec M, Nadeau M-J, Grootes P (2014) Food reconstruction using isotopic transferred signals (FRUITS): a Bayesian model for diet reconstruction. PLoS ONE 9:e87436

Hall-Aspland SA, Hall AP, Rogers TL (2005) A new approach to the solution of the linear mixing model for a single isotope: application to the case of an opportunistic predator. Oecologia 143:143–147

Hopkins Iii JB, Ferguson JM (2012) Estimating the diets of animals using stable isotopes and a comprehensive Bayesian mixing model. PLoS ONE 7:e28478

Lewicki MP, Lewicka-Szczebak D, Skrzypek G (2021) FRAME—Monte Carlo model for evaluation of the stable isotope mixing and fractionation. PLoS ONE 17(11):e0277204
Lubetkin SC, Simenstad CA (2004) Multi-source mixing models to quantify food web sources and pathways. *J Appl Ecol* 41:996-1008

Moore JW, Semmens BX (2008) Incorporating uncertainty and prior information into stable isotope mixing models. *Ecol Lett* 11:470-480

Parnell AC, Inger R, Bearhop S, Jackson AL (2010) Source partitioning using stable isotopes: coping with too much variation. *PLoS ONE* 5:e9672

Phillips DL, Gregg JW (2003) Source partitioning using stable isotopes: coping with too many sources. *Oecologia* 136:261-269

Phillips DL, Inger R, Bearhop S, Jackson AL, Moore JW, Parnell AC, Semmens BX, Ward EJ (2014) Best practices for use of stable isotope mixing models in food-web studies. *Can J Zool* 92:823-835

Skrzypek G, Dunn PJ (2020a) The recalculation of stable isotope expressions for HCNOS—Easy-IsoCalculator. *Rapid Commun Mass Spectrom* 34:e8892

Skrzypek G, Dunn P (2020b) Absolute isotope ratios defining isotope scales used in isotope ratio mass spectrometers and optical isotope instruments. *Rapid Commun Mass Spectrom* 34:e8890

Skrzypek G, Mydłowski A, Dogramaci S, Hedley P, Gibson JJ, Grierson PF (2015) Estimation of evaporative loss based on the stable isotope composition of water using Hydrocalculator. *J Hydrol* 523:781-789

Skrzypek G, Allison C, Bohlke JK, Bontempo L, Brewer P, Camin F, Carter JF, Chartrand MMJ, Coplen TB, Gröning M, Hélie J-F, Esquivel-Hernández G, Kraft RA, Magdas DA, Mann JL, Meija J, Meijer HAJ, Moossen H, Ogrinc N, Perini M, Possolo A, Rogers K, Schimmelmann A, Shemesh A, Soto DX, Thomas F, Wielgosz R, Winchester MR, Yan Z, Dunn PJH (2021) Minimum requirements for publishing HCNOS stable isotope delta results. *Pure and App Chem* (in review)

Stock BC, Jackson AL, Ward EJ, Parnell AC, Phillips DL, Semmens BX (2018) Analyzing mixing systems using a new generation of Bayesian tracer mixing models. *PeerJ* 6:e5096

Szynkiewicz A, Borrok DM, Ganjegunte GK, Skrzypek G, Ma L, Rearick MS, Perkins GB (2015a) Isotopic studies of the upper and middle rio grande. Part 2-Salt loads and human impacts in south New Mexico and west Texas. *Chem Geol* 411:336-350

Szynkiewicz A, Borrok DM, Skrzypek G, Rearick MS (2015b) Isotopic studies of the upper and middle rio grande. Part 1—Importance of sulfide weathering in the riverine sulfate budget. *Chem Geol* 411:323-335

Ward EJ, Semmens BX, Schindler DE (2010) Including source uncertainty and prior information in the analysis of stable isotope mixing models. *Environ Sci Technol* 44:4645-4650

**Grzegorz Skrzypek** Grzegorz has 24 years of experience in the field of stable isotope geochemistry, hydrochemistry and ecology since his PhD (1999) from The University of Wroclaw (UWr). Grzegorz is currently working as Associate Professor and Coordinator of MSc Biology 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.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 3.0 IGO license (http://creativecommons.org/licenses/by/3.0/igo/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the IAEA: International Atomic Energy Agency, provide a link to the Creative Commons license and indicate if changes were made.

Any dispute related to the use of the works of the IAEA: International Atomic Energy Agency that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IAEA: International Atomic Energy Agency's name for any purpose other than for attribution, and the use of the IAEA: International Atomic Energy Agency's logo, shall be subject to a separate written license agreement between the IAEA: International Atomic Energy Agency and the user and is not authorized as part of this CC-IGO license. Note that the link provided above includes additional terms and conditions of the license.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /></figure>

Chapter 4

Compound-Specific Isotope Analyses
to Investigate Pesticide Transformation
in Soil and Water

T. Gilevska and G. Imfeld

**Abstract** This chapter provides an overview of approaches employed in tracking pesticide degradation within agricultural catchments, focusing on the existing challenges and burgeoning prospects afforded by pesticide compound-specific isotope analyses (CSIA). The discussion centers on the development of CSIA for low concentrations of pesticides in environmental matrices. Additionally, the chapter explores the viability of implementing pesticide CSIA in field applications for tasks such as for source apportionment, discerning transformation reactions, and quantifying the extent of degradation on a catchment scale.

4.1 Overview of Pesticide Dissipation Processes in Soil and Water

The transport and distribution of pesticides in the environment are determined by numerous factors, including the physicochemical properties of the active compounds, the mechanisms of formulation, the rate of application, as well as local conditions such as topography, the soil and vegetation characteristics, and the hydro-climatic conditions. Pesticide behaviour in soil and water is determined by chemical, physical, and biological processes, such as sorption–desorption, volatilization, degradation, uptake by plants, and leaching, all of which contribute to pesticide dissipation, which includes degradative and non-degradative processes. The relative significance and cumulative effect of these processes vary according to both pesticide and matrix properties. Transformation of pesticides involves both biotic processes (e.g., microbial degradation) and abiotic processes (e.g., photochemical reactions). The contribution, rate, and extent of the transformation processes for a given pesticide are determined by its chemical structure and environmental conditions. While evaluating the specific contributions of pesticide transformation pathways is essential for risk assessment and decision-making on the catchment scale, robust approaches

<footnote>T. Gilevska · G. Imfeld (✉)
Isotope and Experimental Biogeochemistry, University of Strasbourg, Strasbourg Cedex, France
e-mail: gwenael.imfeld@cnrs.fr</footnote>
to characterize and constrain these processes pesticide transformation are mostly missing.

For instance, physicochemical properties of the soil (pH and SOM content, redox gradients in soils), biological properties (activity and distribution of microorganisms), and environmental conditions controlling soil temperature and moisture generally can affect both biotic or abiotic transformations. However, within soils biotic transformation of pesticides typically dominates over abiotic transformation, e.g., photochemical transformations which are typically restricted to only the top sub-millimetre layers of soil. In contrast, in shallow surface waters, phototransformation (with indirect photolysis being more relevant to pesticide degradation) can significantly contribute to pesticide degradation. For some pesticides, e.g., organophosphate pesticides, hydrolysis can also be a relevant abiotic reaction in the aqueous phase. However, for most pesticides, hydrolysis requires specific conditions such as high or low pH or low-redox environments. Given the multitude of processes influencing pesticide dissipation in the environment, accurate tracking of degradation as the primary process for removing parent compounds necessitates an approach grounded in multiple lines of evidence. This approach should encompass the integration of various complementary methods, which are briefly described below.

## 4.2 Evaluating Pesticide Transformation in Soil and Water: Current Approaches and Limitations

### 4.2.1 Pesticide Concentration Analysis

The rapid development of analytical instrumentation has enabled the detection and monitoring of multiple pesticide compounds in soil and water at low environmental concentrations (Alder et al. 2006; He and Aga 2019). Prior to analysis, different methods are commonly used to extract and concentrate pesticides from environmental samples, including solid-phase extraction (SPE) for water and quick, easy, cheap, effective, rugged, and safe procedure (QuEChERS) for soil (Anastassiades et al. 2003; Bonansea et al. 2013). Analyses by gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-tandem mass spectrometry (LC-MS/MS) are the two most common methods for monitoring pesticide transformation in soil and water.

Pesticide concentration analysis is typically used in routine pesticide monitoring (e.g., by environmental agencies) and for examining in situ pesticide degradation. For instance, the concentration-based approach of monitoring 76 pesticide residues in 317 soil samples across the European Union revealed that over 80% of the soil samples contained pesticide residues (Silva et al. 2019). The fungicide boscalid was the most common pesticide molecule detected. Indeed, boscalid is very persistent in the soil matrix, with a dissipation half-life (DT<sub>50</sub>) ranging from 130 to 337 days (Karlsson et al. 2016). However, while monitoring approaches based on concentration analysis
alone enable quantification of pesticide residues in the environment, they remain
limited in distinguishing between physical and degradative processes, both of which
can contribute to overall pesticide dissipation. Consequently, a comprehensive frame-
work to examine pesticide transformation and dilution in the environment, and espe-
cially process-based information, is mostly lacking. Although a substantial volume
of data on pesticide degradation has been gathered through regulatory testing, the
capacity to anticipate and quantify the extent of degradation and the specific path-
ways involved in various field conditions still poses a considerable challenge (Fenner
et al. 2013).

Incorporating an examination of not only the parent pesticide molecules but also
their transformation products (TPs) offers a valuable approach to relate concen-
tration monitoring and the degradation processes taking place in the field, thus
enabling a more comprehensive identification and quantification of these in situ
processes. Additionally, TPs may alter the ecotoxicological effects of pesticides on
ecosystems. Through targeted and non-targeted methods, i.e., used to characterize
chemical structures of unknown compounds, this complementary analysis is thus
fundamental to evaluate the environmental fate and effects of pesticides. TPs anal-
ysis is easier when TPs are known, and more challenging when non-target analyses
are used. Nevertheless, recent advances in the interpretation of the non-target anal-
ysis allow for the identification of previously unidentified TPs. For instance, suspect
screening of 242 pesticide TPs combined with national monitoring data has recently
identified previously undetected TPs, even in the absence of the parent compound
(Menger et al. 2021). However, this approach introduces additional complexity, as
TPs are liable to not only be produced but degraded as well in a natural environment.
Hence, even when TP-based analyses are applied, quantifying degradation based on
concentration alone is subject to large uncertainty. Concentration monitoring of both
parent compounds and their TPs is frequently integrated with numerical modeling
approaches to simulate the degradation of parent compounds and the subsequent
production and breakdown of resulting TPs within a specific study site.

4.2.2 Modelling Approaches

Computational models can be used to predict variables related to pesticide persistence and mobility in soil and water. As a result, they provide a means to describe and predict the reactive transport of pesticides with various degrees of uncertainty based on the input parameters (Goumenou et al. 2021). Numerous studies have attempted to model the behaviour and distribution of pesticides in the environment, ranging from the micro-environment to the global scale. Different modelling approaches have been evaluated in several review papers (Mottes et al. 2014; Wang et al. 2019). Typically, in most models, degradation is one of the most sensitive model parameters determining the fate of pesticides in soil and losses to surface water and groundwater (Dubus
et al. 2003). Accurately capturing the dynamics of pesticide and TPs degradation is therefore particularly critical in the context of risk assessment using fate models since this can introduce considerable uncertainty into model predictions (Lindahl et al. 2008). In addition, scaling up pesticide dissipation processes requires conceptual simplification of localized processes with a minimal loss of information (Imfeld et al. 2021). For instance, understanding the relationship between degradation rates and measured field parameters (e.g., microbial activity, temperature and soil moisture) allows for the application of pesticide fate models at larger scales (Ghafoor et al. 2011). Reasonable constraints on these field parameters can be made by iterating between model development, sampling, and measurement schemes. In recent years, the Soil and Water Assessment Tool (SWAT) (Arnold et al. 1993) has been proposed as a comprehensive modelling technique to relate processes affecting pesticides in sediment-water interfaces to catchments and river basins (Imfeld et al. 2021).

### 4.2.3 Biomolecular Analysis

The structural and functional characteristics of microbial communities are also poten-
tial indicators for monitoring and evaluating the degradation of a given pollutant in the
environment. Following environmental DNA (eDNA) extraction, mainly quantitative
polymerase chain reaction (qPCR) and functional gene arrays can be used to examine
the response of microbial communities to exposure to pesticides and the potential of
microbial population to adapt and degrade specific pesticides (Fenner et al. 2013).
However, both techniques rely on knowledge of the biologically driven pesticide
degradation pathway and gene sequence coding, which are not typically available
(Imfeld and Vuilleumier 2012). In addition, detection and quantification of pesticide-
degrading genes from environmental DNA generally feature the genetic potential for
pesticide degradation but not its expression. In some cases, RNA transcripts may help
follow up target active microorganisms involved in the degradation process (Monard
et al. 2013). However, RNAs remain difficult to retrieve and conserve from complex
environmental samples such as soil. In future, next-generation approaches may build
on approaches involving specific degradation genes and their expression to also
include patterns of microbial diversity using metagenomics and meta-transcriptomics
(Jeffries et al. 2018; Rodríguez et al. 2020; Malla et al. 2022). This could be applied
to both probe the effect of pesticides on soil or water microorganisms, as well as to
evaluate the pesticide degradation functions.

Beyond these approaches, compound-specific stable isotope analysis (CSIA)
already offers a complementary approach with untapped potential to evaluate the
sources and transformations of pesticides in the environment.
### 4.2.4 Compound-Specific Stable Isotope Analysis (CSIA)

#### 4.2.4.1 Principles

Degradation processes (biotic or abiotic) typically lead to changes in the ratio of stable isotopes (e.g., ¹³C/¹²C) in organic molecules. This change is related to the kinetic isotope effect (KIE) and depends on the preferential rate of bond breakage for weaker bonds (those with light isotopes, e.g., ¹²C for carbon) compared to bonds bearing a heavy isotope (¹³C for carbon). This may lead to an enrichment in the remaining contaminant in ¹³C relative to the initial isotope composition (Hunkeler et al. 1999; Meckenstock et al. 1999; Sherwood Lollar et al. 1999). The isotope effects produced during physical processes, such as volatilization, dissolution, dispersion, and sorption are typically smaller, usually insignificant (relative to analytical uncertainty) compared to the large KIE that may be produced by bond cleavage (Hunkeler et al. 2008; Kuntze et al. 2020 and reference therein). Therefore, change of ¹³C/¹²C isotope ratios during pesticide degradation can provide direct evidence of its degradation. The CSIA approach has been extensively applied over the past two decades to measure and quantify the degradation of legacy pollutants in industrial-contaminated sites. Industrial sites, unlike agricultural fields, are typically characterized by very high concentrations of the pollutants due to the presence of non-aqueous phase liquids (Hunkeler et al. 2008).

#### 4.2.4.2 Application of CSIA

To quantify the magnitude of the isotope fractionation during the given degradation process, isotope fractionation factors ($ε$) need to be derived in the laboratory. To do so, change in the isotope signatures, e.g., $δ(^{13}C$), can be related to the extent of degradation with the Rayleigh equation (Eq. 4.1):

$$ \ln\left(\frac{\delta(^{13}C)_t + 1}{\delta(^{13}C)_0 + 1}\right) = \varepsilon \times \ln(f), \quad (4.1) $$

where $δ(^{13}C)_0$ and $δ(^{13}C)_t$ represent the isotope signatures at time 0 and $t$ of the degradation, respectively, whereas $f$ is the fraction of the remaining pesticides at time $t$ (Fig. 4.1a).

Accurate determination of $ε$ values is key to quantifying degradation processes in situ. From different forms of the Rayleigh distillation equation standard linear regression without forcing through zero is preferred (Scott et al. 2004) (Fig. 4.1b).

The extent of biodegradation (%) of an organic compound can be calculated in situ using the determined $ε$ according to Eq. 4.2, without concentration data, eliminating the influence of physical processes.
<figure><img src="image_25.png" /><figcaption>**Fig. 4.1** a—Change of stable carbon-isotope compositions during degradation; b—Rayleigh equation for the same dataset</figcaption></figure>

$$B = \left( 1 - \left( \frac{\delta(^{13}\text{C})_t + 1}{\delta(^{13}\text{C})_0 + 1} \right)^{\frac{1}{\varepsilon_{\text{bulk,C}}}} \right) \times 100 \quad (4.2)$$

For GC- and LC-isotope ratio mass spectrometry (IRMS) analysis, organic compounds are converted into a simple gas for which the isotope ratio is determined (e.g., CO₂ for CSIA of carbon, N₂ for CSIA of nitrogen). Due to the total conversion of the molecule to a measuring gas, position-specific changes in the isotope composition of a target compound cannot be ascertained. Moreover, the isotope fraction is diluted with any additional atom that is not directly involved in the reaction. In the case of large molecules due to the number of carbon atoms per molecule, a large extent of degradation is required to measure significant stable carbon-isotope fractionation (Δ(^13C) > 2 ‰) to identify degradation in the field (Hunkeler et al. 2008). The isotope fractionation criterion for positive identification of in situ degradation of Δ(^13C) > 2 ‰ is typically established based on total analytical uncertainty (±0.5 ‰), incorporating accuracy and reproducibility (Sherwood Lollar et al. 2007), which may vary among compounds and elements analysed.

Even fractionation experiments under conditions controlled in the laboratory can be influenced by several phenomena, including additional rate-limiting steps, transport across the cell membrane (Renpenning et al. 2015; Ehrl et al. 2019), low substrate bioavailability (Sherwood Lollar et al. 2010), or substrate-enzyme binding (Mancini et al. 2006). These rate-limiting steps can “mask” the ε normally associated with the particular bond-cleavage involved. In some cases, this can lead to an underestimation of isotope fractionation, making it difficult to determine the appropriate ε to use for quantifying the biodegradation of contaminants.

This highlights the relevance of multi-element CSIA (ME-CSIA) to circumvent the limitation of only one element CSIA approach. In theory, all elements in the molecule are affected in a similar way by the masking processes mentioned above, eliminating the bias observed for ε.
#### 4.2.4.3 Multi-element CSIA (ME-CSIA)

ME-CSIA can provide additional support for interpreting in situ results, specifically when complex pollution scenarios with multiple pollution sources or different degradation processes occur. Many pesticides contain nitrogen atoms (e.g., carbamates, acetanilides, thiabendazoles, triazoles), and therefore nitrogen CSIA in addition to carbon CSIA may help tracing pesticides degradation. In such a case, transformation mechanisms are identifiable from dual-isotope plots [e.g., $\delta(^{13}C)$ versus, e.g., $\delta(^{15}N)$], reflecting underlying carbon- and nitrogen-isotope effects. For example, C-N dual-CSIA allows the identification of degradation mechanisms of six chloroacetanilide and acylalanine pesticides during abiotic hydrolysis (Masbou et al. 2018a). In ME-CSIA lambda ($\Lambda$) is the slope of the dual-isotope plot, and it reflects the changes in the stable isotope composition of one element versus the second element, which can be more specific to a reaction, and thus inform about transformation processes in the laboratory or in the field. The York method, which incorporates uncertainty in both variables (Ojeda et al. 2019), better adapts to the wide set of data conditions observed for dual-isotope data, and the natural logarithmic form is used for large isotope fractionation (Höhener and Imfeld 2021) (Fig. 4.2.).

Laboratory derived $\varepsilon$ and $\Lambda$ values for various pesticides and different degradation pathways can be found in recent review papers (Elsner and Imfeld 2016; Kuntze et al. 2020; Cui et al. 2021; Won et al. 2021). Nevertheless, CSIA datasets for pesticides during biotic and abiotic degradation processes are still currently limited, mainly due to analytical challenges (see below).

<figure><img src="image_26.png" /><figcaption>**Fig. 4.2** Dual-isotope plot of **a**—raw $\Delta$ values according to (Ojeda et al. 2019) and **b**—ln-transformed $\delta$ values according to (Höhener and Imfeld 2021) for the same dataset</figcaption></figure>
## 4.3 Compound-Specific Isotope Analysis (CSIA) of Pesticides in Soil and Water

### 4.3.1 Potential of Pesticide CSIA

Current research on pesticides focuses on toxicity, degradability, degradation pathways, and the formation of transformation products. However, novel management strategies relying on the natural and engineered degradation of pesticides and following the precautionary principle are warranted. Synthetic pesticides generally undergo degradation tests prior to use. The recurrent detection of herbicides in groundwater and soil emphasizes the difficulties in extrapolating laboratory tests to environmental conditions. Beyond the outcome of regulatory testing, current approaches are limited in their ability to (i) predict over relevant long time scales the degradation of micropollutants in the environment, (ii) evaluate, beyond environmental monitoring, the prevailing dissipation zones and periods ("hot/cold spots and moments") in integrative studies on the catchment scale (Elsner and Imfeld 2016). Pesticides CSIA can provide additional assistance in this regard.

Although the analytical challenges currently limit field studies, including pesticide CSIA, applications in the environment are now within reach and can be foreseen in different scenarios of increasing complexity:

*   *Point source scenario.* The fate of organochlorine pesticides, such as hexachlorocyclohexane (HCH), has been evaluated in several contaminated field sites with point source pollution using CSIA (Bashir et al. 2015; Chartrand et al. 2015; Liu et al. 2017, 2021; Wu et al. 2018, 2019a; Qian et al. 2019). In analogy to steady-state assessments of legacy contaminants, continuous pesticide release from a single point source related, for example, to pesticide spills during production, can be studied.

*   *Event-based scenario.* Usually, pesticide pollution in agricultural fields does not conform to single point source scenarios. Instead, it often manifests as pesticide pulses, which can be traced in event-based studies to elucidate the dynamics of "hotspots" and "hot moments" (e.g., Riml et al. 2013). The isotope data of pesticides in surface water may enhance the interpretation of transformation pathways in heterogeneous reactive compartments. The first field studies have recently been reported (Schreglmann et al. 2013; Alvarez-Zaldivar et al. 2018).

*   *Large-scale studies.* Comprehensive studies on the catchment scale may also integrate multiple pesticide sources and events over multiple seasons (Moschet et al. 2013). Pesticide CSIA may thus also serve comprehensive studies on a larger spatial scale with multiple sources and events over a season but may miss where, when, and how degradation occurs. For example, the stable carbon-isotope signature was used to evidence the migration and degradation of DDTs in a large-scale study of arable soils across China (Niu et al. 2016). In this respect, although multi-element CSIA may also be desirable to further improve characterization,
its feasibility is challenged by the sampling window, where quantification limits
can be achieved.

Additionally, the combination of passive sampling and pesticides CSIA opens novel opportunities. Passive sampling techniques have been widely used to integrate mean concentrations of organic contaminants over extended sampling periods (< 50 days) and to pre-concentrate micropollutants, allowing their detection in trace amounts in surface waters (10–50 pg/L). A passive sampling approach combined with CSIA has been previously applied for vapour intrusion studies of legacy contaminants, such as benzene (Goli et al. 2017), and has recently been tested for pesticide in wetlands in agricultural fields (Gilevska et al. 2022). δ(¹³C) and δ(¹⁵N) of pesticides in Polar Organic Chemical Integrative Sampler (POCIS) deployed in surface water would reflect their degradation in both water and hydrologically connected soil. Therefore, when compared to the initial isotope signature of the pesticide formulation applied, isotope signatures from POCIS can indicate degradation in both water and soil in the entire agricultural catchment.

Complementarily, pesticide chirality can provide yet another tracer to understand
the fate of chiral pesticides in the environment. One-third of marketed pesticides
are chiral, and most of them are used as racemates, despite the fact that the desired
activity usually depends on one enantiomer, while the other(s) has adverse environ-
mental effects (Maia et al. 2017). As many degradation processes are enantioselec-
tive, monitoring changes in the enantiomeric fraction (EF) can be used to follow
degradation in situ. Furthermore, combining CSIA and enantioselective analysis
techniques for the enantioselective stable isotope analysis (ESIA) may enhance the
evaluation of sources and transformation processes of individual pesticide enan-
tiomers in the environment (Milosevic et al. 2013; Elsner and Imfeld 2016; Jin and
Rolle 2016; Masbou et al. 2018b, 2023). For example, combined evidence from
concentrations, enantiomer ratios, and isotope composition confirmed the degrada-
tion of the herbicide dichlorprop and its metabolite in the hotspot at the contaminated
site in Denmark (Milosevic et al. 2013).

4.3.2 Challenges of Pesticide CSIA

The occurrence of very low (sub-µg/L) concentrations of pesticides and their polarity are two major analytical challenges to be addressed to expand CSIA approaches to pesticides. First, the enrichment of sufficient analyte (typically a few ng of C or N on column per injection are needed) requires the extraction of large amounts of water, soil, or vegetal material. The detection limits for nitrogen CSIA of pesticides are much higher than for carbon, as typically there are fewer atoms of N than C in the pesticide molecule, making it difficult to apply ME-CSIA in situ (Elsner and Imfeld 2016). Any scaling up of the extraction method should be monitored for potential isotope effects. As shown by Melsbach et al. (2021), increasing the volume of water during SPE above 10 L may alter the δ(¹³C) of atrazine.
For accurate and precise CSIA, complete chromatographic separation of all compounds is required. Therefore, any chromatographic interferences can strongly limit the applicability of the CSIA. As the majority of pesticides are non-volatile or semi-volatile on-column liquid injection is typically used as a sample introduction technique when pesticides are analysed with GC-IRMS. This technique, in contrast to static or dynamic headspace sampling used for volatile organic compounds, can introduce many matrix interferences from compounds with similar physicochemical properties from soil or water co-enriched during extraction procedures. For example, during large-volume SPE, non-volatile matrix components from environmental samples are thus enriched together with the target compounds, which is defined here as the matrix effect. Extract clean-up procedures are thus often necessary prior to the application of CSIA to environmental samples (see Sect. 4.4.2.3).

The second challenge lies in the polarity of the more polar pesticides and their transformation products. This generally requires the use of a derivatization step prior to GC separation or the use of LC-IRMS, which is currently limited to $\delta(^{13}C)$ analyses. LC-IRMS is further constricted due to high detection limits (at least one order of magnitude higher than GC-IRMS). LC-IRMS has also limited application due to the incompatibility of the method with organic solvents, such as methanol, typically used for chromatographic separation (Gilevska et al. 2014; Perini and Bontempo 2021). Therefore, the most common derivatization step is chosen to circumvent the polarity of the parent compound or daughter products. The choice of the derivatization method strongly depends on the chemical structure of the pesticide. Methylation of hydroxyl and amino groups is typically achieved with trimethylsilyldiazomethane (TMSD) and trimethylsulfonium hydroxide (TMSH) (Reinnicke et al. 2010; Mogusu et al. 2015; Melsbach et al. 2019; Torrentó et al. 2019). Any additional step, such as derivatization, requires strict screening for any isotope fractionation during the sample preparation. Additionally, the stable carbon-isotope composition should be corrected if additional carbon atoms are introduced during derivatization.

## 4.4 Soil and Water Sample Preparation and Measurements with GC-IRMS for Stable Carbon- and Nitrogen-Isotope Composition of Pesticides

### 4.4.1 Sampling Strategy

The following sampling strategy is suggested to quantify pesticide degradation extent under field conditions in small agricultural catchments (i.e., 10–100 ha). Both sampling and interpretation should be adapted for larger scales to include multiple sources and events over an agricultural season.

First, sufficient knowledge regarding hydrological and hydro-climatic conditions and functioning is mandatory for the application of pesticide CSIA at the catchment scale (Table 4.1). Hydro-climatic data should provide sufficient resolution to evaluate
(i) mean daily rainfall, (ii) mean rainfall intensity, (iii) total rainfall, (iv) mean daily reference evapotranspiration, (v) mean daily temperature, (vi) mean daily discharge normalized by the total catchment area, (vii) time of concentration, and (viii) proportion of days in a month when rainfall occurred (% Wet Days). Subsurface travel time should be defined precisely, possibly with preliminary hydrological studies using the stable hydrogen and oxygen isotope composition of water [δ(²H) and δ(¹⁸O)], refer to Sect. 6.3.1.

The soil sampling frequency should be adapted to the pesticide application and the expected degradation kinetics. Transects should be selected to account for soil type, heterogeneity, and the variability of moisture conditions, drainage characteristics and to maximize the number of plots where the pesticide is applied. In addition, a digital elevation model (DEM) may be used to determine local slopes and to estimate the topographical wetness index (TWI) [-]. TWI mainly quantifies the impact of topography on soil moisture. Soil crust development should be characterized across the catchment after a precipitation event as a function of mm of cumulative rainfall. This should allow us to evaluate the reduction in the soil infiltration capacity due to crusting, and to interpret the temporal evolution of TWI along with rainfall-runoff data.

<table><caption>**Table 4.1** Typical sampling scheme for water and soil samples for implementing pesticide CSIA on the catchment scale</caption><thead><tr><th></th><th>Prior to sampling</th><th>Measurements and analyses</th><th>Sampling</th></tr></thead><tbody><tr><td>Water</td><td><em>Hydrological and climatic data</em><ul><li>Daily precipitation and temperature data</li><li>Contribution of surface, subsurface, and base-flows</li><li>Concentration time of the catchment</li></ul></td><td><ul><li>Discharge measurements</li><li>Multiparameter probes in situ</li><li>Hydrochemical analysis</li></ul></td><td>Flow proportional sampling with refrigerated automatic sampler with glass vials<br>Applied commercial pesticide formulations</td></tr><tr><td>Soil</td><td><em>Pedological and surface soil data</em><ul><li>Pedological characterization</li><li>Digital elevation model</li><li>Agricultural plots (owners, limits, etc.)</li><li>Typical agricultural scheme</li></ul></td><td><ul><li>Farmers survey for the period of pesticide application</li><li>Plant growth and coverage</li><li>Soil moisture characteristics</li><li>Soil physicochemical characterization</li><li>Erosion budget</li></ul></td><td>Topsoil (1 cm) and/or discrete subsurface soil (in different layers from 0 to 20 cm or deeper) across several transects<br>Applied commercial pesticide formulations</td></tr></tbody></table>
<figure><img src="image_27.png" /><figcaption>**Fig. 4.3** Relationship between the necessary S-Metolachlor concentrations in water and collected water volume for reliable carbon or nitrogen CSIA and concentration analysis (Alvarez-Zaldivar et al. 2018; Torrentó et al. 2019). The solid black line represents feasibility of carbon CSIA, solid grey line represents feasibility of carbon and nitrogen CSIA, dashed line represents feasibility of concentration analysis. Note a different scale for the concentration analysis. Note that this relationship is site-specific</figcaption></figure>

During sampling, the water discharge at the catchment outlet should be continuously monitored to evaluate hydrological functioning and to establish water and pesticide mass balances. After sampling, the sub-samples of water may be pooled into composite samples according to hydrograph characteristics and pesticide concentrations, yielding one or more samples weekly with the chosen water volume. This volume should be selected to ensure that the concentrations of each pesticide are sufficient for CSIA analysis. An example of the sample size and the potential for pesticide and isotope analysis is illustrated in Fig. 4.3.

## 4.4.2 Water and Soil Sample Processing and Conservation

Water samples should be collected using a refrigerated autosampler in the field, stored in the dark at 4 °C during collection (to avoid photolysis and limit further biodegradation), and placed on ice during transportation to the laboratory for immediate filtering (on 0.7 or 0.45 µm glass fibre). Water samples should be kept at 4 °C from the collection up to the extraction and should be preferably further filtered and analysed (using SPE, see Sect. 4.4.3) within 24 h. Preliminary tests on the effect of water sample sampling, transport, and storage should be done prior to the study. Water samples may also be frozen for longer-term conservation, but preliminary tests of the effect of freezing on both extraction yield and stable isotope fraction are required for each targeted compound.
After collection, soil samples should be kept in an ice box during transport to the laboratory and kept frozen at -20 °C until analysis. Soils should be homogenized, quartered before sieving (e.g., according to NF X 31,100 standard), and sieved at 2 mm. Water content, pH, organic content, CEC, and other parameters can be measured to characterize soil samples and help in data interpretation.

### 4.4.3 Extraction Methods for CSIA from Water and Soil

To expand the use of pesticide CSIA to agricultural catchments, it is crucial to use an appropriate extraction method. Extraction methods for pesticide residues from environment matrices for ME-CSIA should: (i) provide sufficient analyte mass for reliable isotope analysis, (ii) cause no isotope fractionation [Δ(^HE)], (iii) be applicable to a wide range of pesticides and matrices, (iv) limit matrix co-enrichment to avoid co-elution during chromatographic separation.

#### 4.4.3.1 Extraction Methods from Water

To date, SPE has been the most common method used to extract water samples for pesticide CSIA although liquid-liquid extraction has been used for small amounts of water (from 0.5 mL) in laboratory experiments (Chevallier et al. 2018; Knossow et al. 2020). The extraction methods should be tested within the expected concentration range and target environmental matrix to determine the feasibility of pesticide CSIA from water samples. SPE has been tested previously in combination with CSIA of atrazine, acetochlor, S-Metolachlor, metalaxyl, butachlor, alachlor, terbutryn, chlordizon, bentazone, dichlorvos, dimethoate, omethoate and several of their metabolites (Schreglmann et al. 2013; Elsayed et al. 2014; Wu et al. 2014; Schürner et al. 2016; Masbou et al. 2018a; Torrentó et al. 2019; Drouin 2021; Droz 2021; Pérez-Rodríguez et al. 2021). To ensure maximum recovery, the type and quantity of sorbents and extraction eluents should be adjusted to the physical properties of the analytes and matrix. As the physicochemical properties of parent compounds and their TPs may differ significantly, different sorbents or sorbent combinations may be used to pre-concentrate the parent compounds and the TPs (Torrentó et al. 2019).

The majority of pesticide CSIAs studied did not show isotope fractionation using SPE (Schreglmann et al. 2013; Elsayed et al. 2014; Wu et al. 2014; Masbou et al. 2018a; Torrentó et al. 2019; Droz 2021; Pérez-Rodríguez et al. 2021). A sample size of up to 10 L is more frequently used for SPE and pesticide CSIA, as increasing the volume > 10 L could change the carbon isotope ratios δ(^C) of pesticides (Melsbach et al. 2021). However, this change may not be correlated with the SPE procedure but rather with the matrix effect on the measurement, decreasing oxidation capacity, and increasing background levels and instrument maintenance issues. Therefore, prior
to the pesticide measurements, clean-up strategies (discussed below) are typically applied to minimize the matrix effect. Nevertheless, current SPE methods allowed carbon and nitrogen pesticide CSIA in the ng/L to µg/L of pesticide concentration range (Schreglmann et al. 2013; Alvarez-Zaldivar et al. 2018; Torrentó et al. 2019). This underscores the feasibility of carbon and nitrogen CSIA from water samples in agricultural settings throughout the agricultural season.

### 4.4.3.2 Extraction Methods from Soil and River Sediment

When choosing an extraction method and solvent, one should take into account the physicochemical properties of pesticides, including their hydrophobicity and acid dissociation constant, as well as soil properties like pH, organic matter content, and moisture levels. For optimal results, extraction tests must be carried out with the studied soil or sediment to ensure high recovery and non-significant isotope fractionation and evaluate the matrix effect due to the co-extraction of soil organic matter. Modifications to existing methods and protocols, such as increasing the sample volume from 5 to 20 g or sequential use of extraction solvents, should also be evaluated.

Pesticide extraction methods from soil and sediment for reliable pesticide CSIA have been already used in both laboratory and field studies (Alvarez-Zaldivar et al. 2018; Masbou et al. 2018b; Wu et al. 2019b; Pérez-Rodríguez et al. 2021). Ivdra et al. (2014) proposed a modified ultrasonic-assisted extraction (MUSE) without carbon-isotope fractionation associated with extraction [Δ(<sup>13</sup>C) ≤ 0.4) for hexachlorocyclohexanes (HCHs). A modified MUSE method (Ivdra et al. 2014) was also tested with ethylacetate (Alvarez-Zaldivar et al. 2018; Masbou et al. 2018b) or dichloromethane:pentane (Droz et al. 2021; Pérez-Rodríguez et al. 2021) as the extraction solvent. Another study used accelerated solvent extraction (ASE) for the extraction of HCHs from soil and plants, which enabled stable C, H, and Cl isotope analysis (Wu et al. 2019b; Liu et al. 2020, 2021). The application of QuEChERS for the extraction of metolachlor from two agricultural soils led to an insignificant (< 1 ‰) isotope fractionation for carbon CSIA (Torrentó et al. 2021).

Current methods allow for carbon and nitrogen CSIA in soil samples in the range of ng/g to µg/g range for carbon and several µg/g for nitrogen CSIA (Alvarez-Zaldivar et al. 2018; Masbou et al. 2018b; Droz 2021). This currently restricts the application of carbon and nitrogen CSIA of pesticides to source areas and laboratory studies, respectively. In order to reduce concentration ranges for ME-CSIA, there is a need for simple and fractionation-free purification techniques that can be applied to a wide spectrum of environmental soils and pesticides.
#### 4.4.3.3 Clean-Up Procedures

A number of clean-up procedures can be applied to address the issue of co-enrichment to maximize the analytical performance of pesticide CSIA extraction from environmental matrices without altering the isotope ratio of the target compounds. These include: (i) the addition of a sorbent, such as primary secondary amine or graphitized carbon black, to remove pigments, such as chlorophyll (Anastassiades et al. 2003; Wilkowska and Biziuk 2011), (ii) chromatography HPLC separation or column chromatography (Schreglmann et al. 2013; Mogusu 2016), and (iii) the use of molecularly imprinted polymers (MIP) (Bakkour et al. 2018). MIP is likely the most effective clean-up method for CSIA. However, MIP is not commercially available for all classes of compounds and therefore must be specifically synthesized prior to clean-up. Furthermore, samples should only be processed in glass when using organic solvents, especially dichloromethane, to reduce matrix interferences from the extraction procedure.

Carbon and nitrogen CSIA in complex matrices can also benefit from two-dimensional gas chromatography (GC × GC)-IRMS. This approach has been applied to polychlorinated biphenyls, chloronaphthalenes, and chlorofluorocarbons (Horii et al. 2005; Horst et al. 2015). In GC × GC-IRMS, the system needs to be equipped with a column-switching device, such as moving capillary stream switching (MCSS), or with a 6-port valve. By column-switching parts of the effluent from the first column are cut and transferred to a second column, where separation of compounds of interest can be enhanced. In addition, the use of (GC × GC)-IRMS can improve sensitivity by eightfold (Horst et al. 2015) by enhancing chromatographic separation. Therefore, this approach has potential for pesticide CSIA application.

Whenever possible, one or several clean-up strategies, depending on the complexity of the matrix, should be applied during pesticide CSIA. Along with improving the background of pesticide CSIA, it will reduce the need for increased maintenance of the GC-IRMS instrument, including oven and column replacement as well as blockage of capillaries. It is also possible to cut the first 10–20 cm of the GC column if some small chromatographic interferences are observed in blanks preventing retention of the entire column (be aware of the change in retention time and the measurement windows). Complementarily with clean-up procedures, it may be possible to extend the temperature programme to improve the level of background and chromatographic separation when the background compromises the GC-IRMS measurements. However, this will increase the measurement times.

It is worth mentioning that pesticide CSIA must be preceded by quantitative evaluation of the pesticides in the extracts to optimize injections of the target analytes. Prior CSIA, rigorous quality assurance practices and referencing strategies must be established to ensure that isotope measurements are accurate and reproducible.
### 4.4.4 Optimal GC-IRMS Conditions for Pesticide CSIA

#### 4.4.4.1 Referencing Strategies

An important component of quality assurance is the use of analytical standards of pesticides calibrated on international isotope scales. The referencing strategy of the "identical treatment principle" (Werner and Brand 2001) should be implemented: (i) to measure external standards before and after the sample to correct for an offset, and (ii) to use a reference material that is identical to the target substance. To obtain in-house isotopic standards, a mass spectrometer equipped with an elemental analyzer and isotope ratio mass spectrometer (e.g., Flash EA IsoLinkTM CN IRMS, Thermo Fisher Scientific, Bremen, Germany) or offline conversion followed by dual-inlet-IRMS are typically used. The $δ(^{13}C)$ and $δ(^{15}N)$ values should be normalized to VPDB and Air scales, respectively, using a minimum of two international reference materials with the range of isotope $δ$ values, that would encompass all the measured samples, are chosen as an anchor point for the regression line, e.g., IAEA600 [$δ(^{13}C) = -27.77‰$] and USGS41 [$δ(^{13}C) = +37.63‰$] (Coplen et al. 2006). Then, the third standard, which has the value between the chosen anchors is treated as an unknown sample for quality control (QC), and is used to evaluate combined analytical uncertainty (Coplen 1988). As a part of inter-laboratory comparison, it is recommended to measure the same standards at other lab facilities using another EA-IRMS.

Unless otherwise specified, the uncertainties for pesticide CSIA are reported as standard deviation ($1σ$) calculated from replicate measurements. The combined analytical uncertainty of $δ(^{13}C)$ and $δ(^{15}N)$ values should not differ ($≤ 0.5‰$) from values obtained by EA-IRMS.

The standard injection frequency depends on the pesticides, the element measured, the matrix, and the number of pesticides analysed. For relatively simple environmental matrices (e.g., groundwater), it is recommended to measure an in-house pesticide mix standard with known isotope composition at least every six samples to control the retention time of the target compounds and assess the instrument performance (e.g., conversion efficiency). The standard mixtures are injected every three samples, however, for simultaneous $δ(^{15}N)$ analysis of several pesticides for complex environmental matrix (e.g., soil). Peak amplitudes of injected standards should closely match those of analysed samples (Sherwood Lollar et al. 2007). To conserve the combustion capacity of the oven, the measurement window (effluent mode) should be kept to a minimum: opened before the targeted peak elution and closed shortly thereafter.

As part of CSIA method validation, the linearity range should be determined for each pesticide. The linearity range specifies the range of measurements with sufficient precision and accuracy, indicating, within an acceptable range [e.g., $± 0.5‰$ for $δ(^{13}C)$], that the stable isotope composition is independent of the amount of compound injected (Jochmann et al. 2006). The method detection limit (MDL) is the point with the lowest (or highest) concentration within a $± 0.5‰$ linear interval of the
<figure><img src="image_28.png" /><figcaption>**Fig. 4.4** Concentration and A—δ(^13C) measurements and amplitude of the mass 44 for tebuconazole B—δ(^15N) measurements and amplitude of the mass 28 for terbutryn. Circles represent stable carbon- and nitrogen-isotope compositions. Triangles indicate the amplitude of mass 44 and 28 peaks. The solid line represents the calculated mean δ(^13C) value (–29.9 ± 0.2‰, n = 51) and δ(^15N) value (–2.3 ± 0.2‰, n = 30); dashed lines indicate the ± 0.5‰ interval. Measurements were performed in triplicate; the standard deviation of each point is indicated by error bars. The dotted line represents the δ(^13C) value of tebuconazole (–29.7 ± 0.1‰, n = 3) and δ(^15N) value of terbutryn (–2.8 ± 0.1‰, n = 3) measured by EA-IRMS. Values outside the linear range-filled circles are excluded from the mean δ(^13C) and δ(^15N) value calculation due to either being outside ± 0.5‰ interval from the mean value or due to low reproducibility of triplicate measurement (> 0.5‰). MDL—method detection limit. The major principles illustrated in this figure are described in (Jochmann et al. 2006)</figcaption></figure>

mean value for the standard measured with GC-IRMS and with good reproducibility in triplicate measurements (< 0.5‰). In Fig. 4.4a shows the linearity for δ(^13C) for pesticide tebuconazole, with three data points (filled circles) outside ± 0.5‰ linear interval from the mean value or with low reproducibility of triplicate measurements (> 0.5‰). Such measurements are not taken into consideration and are considered to be outside the linear range. Figure 4.4b shows the linearity for δ(^15N) for terbutryn, with MDL corresponding to a significantly higher concentration of the compound in the sample.

#### 4.4.4.2 Conversion to gas

Organic compounds are converted to a simple measuring gas CO₂ for carbon CSIA and N₂ for nitrogen CSIA. For both carbon and nitrogen CSIA, the conversion can be achieved in a combustion reactor comprised of, for example, NiO tube, CuO, NiO, and Pt wires and operated at 1000 °C (P/N 1255321, GC IsoLink II IRMS System, Thermo Fisher Scientific). Within the combustion oven, two reactions occur for nitrogen (some setups use two separate units for oxidation and reduction) (Eq. 4.3).

$$R - NH_2 \rightarrow \text{Oxidation} \quad N_2 \xleftarrow{\text{Reduction}} \text{N}_x\text{O}_y \quad (4.3)$$
During nitrogen CSIA, the produced CO₂ is trapped downstream using a liquid nitrogen trap to reduce measurement interference. Pesticides containing heteroatoms, such as N and Cl, and more complex chemical structures pose challenges to the combustion process, resulting in incomplete conversion and lower sensitivity. CSIA of nitrogen is particularly challenging, since the heavier isotope ¹⁵N has a lower natural abundance (0.04%) than carbon heavier isotope ¹³C (1.1%), and fewer heavy nitrogen atoms are present in organic substances. Moreover, two N atoms are required to form N₂, and N₂ has a lower ionization efficiency. To obtain a similar precision to that of δ(¹³C) analysis, theoretically a 50 times higher sample amount is required for δ(¹⁵N) analysis, leading to high substance loads that can affect conversion efficiency (Reinnicke et al. 2012). To enhance combustion during pesticide CSIA, different modifications were proposed, combining changes in reactor design, reactor temperature, and oxidation pattern. The variation of these parameters can be optimized if a large offset or low reproducibility is observed during carbon or nitrogen pesticide CSIA. A self-made Ni/NiO reactor operated at 1150 °C (Meyer et al. 2008), a NiO tube/CuO–NiO reactor operated at 940 °C (Reinnicke et al. 2012), a Ni/Ni/Pt reactor operated at 1000 °C (Spahr et al. 2013) have been used to enhance conversion of different pesticides during carbon and nitrogen CSIA.

During the oxidation procedure, oxygen is pumped into the reactor in backflash mode, replenishing the oxygen required for conversion. For nitrogen, excess oxygen might lead to incomplete reduction of NₓOᵧ species into N₂. The oxidation time should be adjusted to achieve a balance between sufficient oxygen for the quantitate conversion but not too much, so that reduction is not hindered.

## 4.5 Towards Field Studies Using Pesticide CSIA and Interpretation of Stable Isotope Signatures

Integrative strategies relying on isotope data have been conducted for more than three decades in the case of nitrate and have significantly contributed to the development of water management policies (Nestler et al. 2011; Lutz et al. 2013; Moschet et al. 2013). A similar strategy has been developed for legacy pollutants (Hunkeler et al. 2008) and may be now developed for pesticides in agricultural catchments, as recently described (Alvarez-Zaldivar et al. 2018).

Prior to the sampling campaign, which would include pesticide CSIA information about applied pesticides, application period, published ε and Λ values for pesticides used, the isotope signature of the pesticide formulation should be gathered.
**4.5.1 Insights from Pesticide CSIA**

**4.5.1.1 Occurrence of in Situ Degradation and Pathway Identification**

When $\Delta(^{13}C)$ or $\Delta(^{15}N) > 2‰$ (assuming combined analytical uncertainty of $\pm 0.5‰$) of pesticide molecules is observed across space, time, or source (i.e., compared to the isotope signatures of pesticides in applied commercial formulations), pesticide degradation is likely occurring in situ. Further, the degradation pathways may be identified based on the determined $\varepsilon$ and $\Delta$ values. For example, in the biotic hydrolysis of atrazine by *Arthrobacter aurescens* TC1, an unusual trend towards more negative $\delta(^{15}N)$ values is observed. Protonation of nitrogen atoms during the reaction makes $^{14}N$ react more rapidly while $^{15}N$ accumulates in the remaining atrazine (inverse isotope effect) (Meyer et al. 2009). In contrast, oxidative dealkylation by *Rhodococcus* sp. strain NI86/21 would result in more positive $\delta(^{15}N)$ values. As mentioned in Sect. 4.3, the use of dual element isotope plots is preferred, especially when multiple sources are present.

**4.5.1.2 Quantitative Assessment of in Situ Degradation**

When the degradation pathway is identified in the field, then Eq. 4.2 can be used to calculate the extent of biodegradation. For pesticides, these calculations typically require a large extent of degradation to fulfil $\Delta(^{13}C) > 2‰$ requirement. For example, for S-Metolachlor (using $\varepsilon_C = -1.5 \pm 0.5‰$ for biodegradation in soil (Alvarez-Zaldivar et al. 2018; Droz et al. 2021), the extent of degradation of the applied pesticide should be higher than 74% to apply carbon CSIA to identify and quantify degradation in situ. However, owing to the multitude of processes influencing pesticide residues in agricultural fields, such as contamination pulses during runoff events, these estimates of the extent of biodegradation are likely to be conservative. Therefore, a subsurface-surface reactive transport model incorporating CSIA isotope source ratios, $\varepsilon$ values for different elements can help examine sources and dissipation of pulses of diffuse contaminants (Van Breukelen 2007; Lutz et al. 2017).

**4.5.2 Pesticide Database for Source Identification and Apportionment**

The initial stable isotope composition of organic chemicals depends on the conditions and the pathways used to synthesize the compound and thus depends on the manufacturers and the time frame of production. This variation may serve to identify chemical sources or trace the time of contaminant release in the environment. The range of the initial isotopic ratios of these compounds from various manufacturers is thus needed to trace pesticide origin and dissipation.
<table><caption>**Table 4.2** Range of δ(^13C) of S-Metolachlor for a standard (PESTANAL®) and four different pesticide formulation measured by GC-IRMS</caption><thead><tr><th></th><th>PESTANAL®</th><th>Dual Gold<br>SAFENEUR®</th><th>CAMIX<br>440®</th><th>MERCANTOR<br>GOLD®</th><th>S-Metolastar®</th></tr></thead><tbody><tr><td>δ(^13C) ± uncertainty<br>(‰)</td><td>-31.0 ± 0.5</td><td>-31.9 ± 0.2</td><td>-31.7 ±<br>0.2</td><td>-32.6 ± 0.5</td><td>-32.2 ± 0.2</td></tr></tbody></table>

Therefore, prior to applying pesticide CSIA in the field, the isotopic composition of commonly or formerly used pesticides produced by different manufacturers from different regions should be determined for different elements. Relevant pesticide commercial formulations (1–5 mL in clean glass vials stored at 4 °C or at –20 °C until analysis) can be collected directly at the farms or following official requests to regional and national pesticide providers and manufacturers. The active compounds of the different pesticide formulations can be isolated by either liquid–liquid extraction or measured directly by GC-IRMS. For example, Table 4.2. shows the narrow range of δ(^13C) of S-Metolachlor for four different pesticide formulations (from –31.7 ± 0.2 ‰ to –32.6 ± 0.5 ‰) and slightly different composition of an analytical standard (–31.0 ± 0.5 ‰). Based on that range, if the δ(^13C) of S-Metolachlor measure in situ is around –29.0 ‰, it may indicate degradation.

The ISOTOPEST database (https://ites.unistra.fr/isotopest) was established to address the need for a comprehensive repository of stable isotope signatures of pesticides in commercial formulations. Its primary purpose is to enable seamless inter-laboratory comparisons and foster the utilization of stable isotope data in forthcoming research endeavors concerning pesticide transformation and source tracking in the environment.

### 4.5.3 Identifying Isotope Fractionation During Degradation in Microcosm Experiments

Degradation laboratory experiments under controlled conditions must clearly demonstrate the relationship between concentrations decrease and changes of stable isotope composition, and identify the prevailing degradation pathway in specific environmental compartments (Hunkeler et al. 2008). Several studies have determined the range of ε and Λ values for various pesticides (Masbou et al. 2018a; Cui et al. 2021; Drouin et al. 2021; Torrentó et al. 2021; Won et al. 2021). However, with the increasing number of commercial formulations, it is likely that specific information concerning isotope fractionation during degradation may not be available for most pesticides. Additionally, field-specific information is preferred for the most accurate identification and quantification of the degradation process. Reference hydrolysis, photolytic, or biodegradation experiments under laboratory-controlled conditions can be carried out to derive field-specific ε and Λ values for pesticides from water, sediment, or soil samples. It is possible that photodegradation may be a relevant
process in the first soil mm or water surface (Cui et al. 2021; Drouin et al. 2021). Reference degradation experiments may ideally be carried out with an isolated strain degrading metabolically the targeted pesticide, together with the identification of TPs and targeted microbial assessment (i.e., specific functional genes and/or taxa associated with degradation) to characterize degradation in the field using the multi-line-of-evidence approach.

### 4.5.4 Outlook

The requirements, bottlenecks, and possible solutions at various scales and levels of complexity to apply pesticide CSIA to field-based studies are summarized in Table 4.3. In most cases, field applications have focused on point source scenarios of persistent pesticides, which typically have higher concentrations and therefore require smaller sample sizes and present fewer measurement challenges (Hunkeler et al. 2008). Degradation is typically assessed by comparing the source isotope signature over time and space. Such applications can be combined with ME-CSIA, ESIA, and microbial analysis to identify the prevailing degradation processes (Bashir et al. 2015; Chartrand et al. 2015; Liu et al. 2017, 2021; Wu et al. 2018, 2019a; Qian et al. 2019). Additionally, the implementation of high-resolution CSIA approaches can provide further constraints on characterizing the dynamics of active degradation zones and compartments (e.g., sediment-water interfaces or soil surface) (Gilevska et al. 2021).

For pesticide CSIA application to event-based scenarios, a detailed sampling strategy must be developed based on possible sources, hydrology, and land use (see Sect. 4.4.1). Additionally, isotope data can be interpreted within a framework that includes physicochemical and hydrological tracer data to evaluate and quantify pollution sources and the processes that the pesticide has undergone during its retention and transport in a catchment (Elsner and Imfeld 2016). In order to monitor the degradation of pesticides effectively, it is imperative to conduct sampling prior to

<table><caption>**Table 4.3** Requirements, bottlenecks, and possible solution of pesticide CSIA application for different scenarios</caption><thead><tr><td>Scenario</td><td>Requirements</td><td>Bottlenecks</td><td>Possible solutions</td><td>References</td></tr></thead><tbody><tr><td>Point source</td><td><ul><li>ε and Λ values for different degradation processes</li><li>Hydro-climatic conditions</li><li>Isotope signature at the source area</li></ul></td><td><ul><li>Multiplicity of degradation processes</li></ul></td><td><ul><li>Application of ME—CSIA</li><li>Combination of pesticide CSIA with ESIA, concentration analysis of parent compound and TPs, microbial analysis</li></ul></td><td>(Bashir et al. 2015; Chartrand et al. 2015; Liu et al. 2017, 2021; Wu et al. 2018, 2019a; Qian et al. 2019)</td></tr></tbody></table>

(continued)
<table><caption>**Table 4.3 (continued)**</caption><thead><tr><th>Scenario</th><th>Requirements</th><th>Bottlenecks</th><th>Possible solutions</th><th>References</th></tr></thead><tbody><tr><td>Event base</td><td><ul><li>ε and Λ values for different degradation processes</li><li>δ(<sup>13</sup>C) and δ(<sup>15</sup>N) of the applied pesticides</li><li>Hydro-climatic conditions</li><li>Survey of pesticide application periods</li></ul></td><td><ul><li>Low concentration of pesticides</li><li>Short contamination event can be missed</li><li>Lack of published ε and Λ values</li><li>Multiplicity of degradation processes</li></ul></td><td><ul><li>Sampling campaign should begin before the expected application period to measure background concentration and isotope signatures of remaining pesticides, during the application, and after to follow the degradation of pesticides</li><li>Large sample volumes should be collected in case of low environmental concentration (e.g., up to 10 L for water samples)</li><li>Automatic proportional sampling or application of POCIS would capture short contamination events</li><li>Field-specific ε and Λ experiments</li><li>Combination of pesticide CSIA with ESIA, concentration analysis of parent compound and TPs, microbial analysis, and modelling approaches</li></ul></td><td>(Schreglmann et al. 2013; Lutz et al. 2017; Alvarez-Zaldivar et al. 2018)</td></tr><tr><td>Large scale</td><td><ul><li>ε and Λ values for different degradation processes</li><li>Database of different source signatures</li><li>Hydro-climatic conditions of catchment(s)</li><li>Survey of pesticide application periods</li></ul></td><td><ul><li>Complexity of multiple pesticide sources, application events, and variability of site-specific characteristics</li><li>Lack of published ε and Λ values</li><li>Low concentration of pesticides</li></ul></td><td><ul><li>Pesticide isotope data should be interpreted together with physicochemical and hydrological tracer data to evaluate the pollution sources and transport in a catchment</li><li>Combination of pesticide CSIA with ESIA, concentration analysis of parent compound and TPs, microbial analysis, and modelling approaches</li><li>Several field-specific ε and Λ experiments</li></ul></td><td>(Niu et al. 2016)</td></tr></tbody></table>
the pesticide application period. This initial sampling serves the crucial purpose of quantifying background concentrations and isotopic signatures of residual pesticides. Establishing this accurate baseline is essential as it provides a reference point for the assessment of both short-term and long-term impacts following pesticide application. Given that diffuse pesticide pollution typically leads to low environmental concentrations, the collection of substantial and representative volumes of water and soil becomes imperative for the reliable implementation of pesticide CSIA. This need for comprehensive sampling is particularly significant in cases where nitrogen isotopic analysis is involved. In addition, a monitoring strategy incorporating this complementary analysis requires narrower sampling time frames to ensure sufficient sampling resolution to capture short-duration high concentration episodes, such as large runoff events. In future, integrative sampling with passive samplers (e.g., POCIS) can be implemented to ensure that such events will be sampled at sufficiently high resolution. It can be anticipated that the continued improvement of analytical methods for clean-up and extraction for pesticide CSIA will prove invaluable in this application.

The application of CSIA to extensive case studies presents a significant challenge, primarily due to the presence of multiple sources of pesticides, diverse application events, and the unique characteristics of individual sites. To confront this challenge effectively, the catchment areas under investigation must be subdivided in a manner that aligns with their specific land use and water flow directions. This approach enables the utilization of a series of mixing models tailored to the respective subareas, providing a more nuanced and accurate understanding of pesticide dynamics within these complex systems (see Chap. 2). However, in the case of persistent and well-known pesticides, CSIA may offer a unique opportunity to study their long-term degradation, e.g., DDTs (Niu et al. 2016) or chlordecone. Similarly, CSIA holds substantial promise in the investigation of persistent pesticides, like atrazine, which continue to be detected in environmental field sites even decades after their prohibition in Europe (Elsner and Imfeld 2016).

Pesticide application and subsequent pollution remain a major and long-lasting environmental issue that will continue throughout the twenty-first century. The application of pesticide CSIA presents a unique opportunity to identify and quantify pesticide degradation in situ in agricultural catchments, although it requires a careful analysis of its feasibility, a robust sampling design, and a joint interpretation with hydro-climatic, geochemical, and microbial conditions and pesticide concentrations.

## References

* Alder L, Greulich K, Kempe G, Vieth B (2006) Residue analysis of 500 high priority pesticides: better by GC-MS or LC-MS/MS? Mass Spectrom Rev 25:838-865

* Alvarez-Zaldivar P, Payraudeau S, Meite F et al (2018) Pesticide degradation and export losses at the catchment scale: Insights from compound-specific isotope analysis (CSIA). Water Res 139:197-207
Anastassiades M, Lehotay SJ, Štajnbaher D, Schenck FJ (2003) Fast and easy multiresidue method employing acetonitrile extraction/partitioning and "dispersive solid-phase extraction" for the determination of pesticide residues in produce. J AOAC Int 86:412-431

Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142:47-69

Bakkour R, Bolotin J, Sellergren B, Hofstetter TB (2018) Molecularly imprinted polymers for compound-specific isotope analysis of polar organic micropollutants in aquatic environments. Anal Chem 90:7292-7301

Bashir S, Hitzfeld KL, Gehre M et al (2015) Evaluating degradation of hexachlorcyclohexane (HCH) isomers within a contaminated aquifer using compound-specific stable carbon isotope analysis (CSIA). Water Res 71:187-196

Bonansea RI, Amé MV, Wunderlin DA (2013) Determination of priority pesticides in water samples combining SPE and SPME coupled to GC-MS. A case study: Suquía River basin (Argentina). Chemosphere 90:1860-1869

Chartrand M, Passeport E, Rose C et al (2015) Compound specific isotope analysis of hexachlorocyclohexane isomers: a method for source fingerprinting and field investigation of in situ biodegradation. Rapid Commun Mass Spectrom 29:505-514

Chevallier ML, Cooper M, Kümmel S et al (2018) Distinct carbon isotope fractionation signatures during biotic and abiotic reductive transformation of chlordecone. Environ Sci Technol 52:3615-3624

Coplen TB (1988) Normalization of oxygen and hydrogen isotope data. Chem Geol Isot Geosci Sect 72:293-297

Coplen TB, Brand WA, Gehre M et al (2006) New guidelines for delta13C measurements. Anal Chem 78:2439-2441

Cui G, Lartey-Young G, Chen C, Ma L (2021) Photodegradation of pesticides using compound-specific isotope analysis (CSIA): a review. RSC Adv 11:25122-25140

Drouin G (2021) Micropollutant dissipation at the sediment-water interface by coupling modelling and compound-specific isotope analysis. University of Strasbourg

Drouin G, Droz B, Leresche F et al (2021) Direct and indirect photodegradation of atrazine and S-metolachlor in agriculturally impacted surface water and associated C and N isotope fractionation. Environ Sci Process Impacts 23:1791-1802

Droz B (2021) Pesticide dissipation at the sediment-water interface: insights from compound-specific isotope analysis (CSIA). University of Strasbourg

Droz B, Drouin G, Maurer L et al (2021) Phase transfer and biodegradation of pesticides in water-sediment systems explored by compound-specific isotope analysis and conceptual modeling. Environ Sci Technol 55:4720-4728

Dubus IG, Brown CD, Beulke S (2003) Sources of uncertainty in pesticide fate modelling. Sci Total Environ 317:53-72

Ehrl BN, Kundu K, Gharasoo M et al (2019) Rate-limiting mass transfer in micropollutant degradation revealed by isotope fractionation in chemostat. Environ Sci Technol 53:1197-1205

Elsayed OF, Maillard E, Vuilleumier S et al (2014) Using compound-specific isotope analysis to assess the degradation of chloroacetanilide herbicides in lab-scale wetlands. Chemosphere 99:89-95

Elsner M, Imfeld G (2016) Compound-specific isotope analysis (CSIA) of micropollutants in the environment—current developments and future challenges. Curr Opin Biotechnol 41:60-72

Fenner K, Canonica S, Wackett LP, Elsner M (2013) Evaluating pesticide degradation in the environment: blind spots and emerging opportunities. Science 341:752-758

Ghafoor A, Jarvis NJ, Thierfelder T, Stenström J (2011) Measurements and modeling of pesticide persistence in soil at the catchment scale. Sci Total Environ 409:1900-1908

Gilevska T, Gehre M, Richnow HH (2014) Performance of the wet oxidation unit of the HPLC isotope ratio mass spectrometry system for halogenated compounds. Anal Chem 86:7252-7257
Gilevska T, Masbou J, Baumlin B, Imfeld G (2020) Passive samples (POCIS) in ponds and wetlands to evaluate pesticide degradation using compound-specific isotope analysis. In: 5th international electronic conference on water sciences. MDPI

Gilevska T, Ojeda AS, Kümmel S et al (2021) Multi-element isotopic evidence for monochlorobenzene and benzene degradation under anaerobic conditions in contaminated sediments. Water Res 207:117809

Gilevska T, Masbou J, Baumlin B, Chaumet B, Chaumont C, Payraveau S, Tournebize J, Probst A, Luc Probst J, Imfeld G (2022) Do pesticides degrade in surface water receiving runoff from agricultural catchments? Combining passive samplers (POCIS) and compound-specific isotope analysis. Sci Total Environ 842:156735. https://doi.org/10.1016/j.scitotenv.2022.156735

Goli O, Górecki T, Mugammar HT et al (2017) Evaluation of the suitability of the waterloo membrane sampler for sample preconcentration before compound-specific isotope analysis. Environ Technol Innov 7:141–151

Goumenou M, Renieri EA, Petrakis D et al (2021) Methods for environmental monitoring of pesticide exposure. In: Exposure and risk assessment of pesticide use in agriculture. Elsevier, pp 347–387

He P, Aga DS (2019) Comparison of GC-MS/MS and LC-MS/MS for the analysis of hormones and pesticides in surface waters: advantages and pitfalls. Anal Methods 11:1436–1448

Höhener P, Imfeld G (2021) Quantification of Lambda (A) in multi-elemental compound-specific isotope analysis. Chemosphere 267:129232

Horii Y, Kannan K, Petrick G et al (2005) Congener-specific carbon isotopic analysis of technical PCB and PCN mixtures using two-dimensional gas chromatography—Isotope ratio mass spectrometry. Environ Sci Technol 39:4206–4212

Horst A, Lacrampe-Couloume G, Sherwood Lollar B (2015) Compound-specific stable carbon isotope analysis of chlorofluorocarbons in groundwater. Anal Chem 87:10498–10504

Hunkeler D, Höhener P, Bernasconi S, Zeyer J (1999) Engineered in situ bioremediation of a petroleum hydrocarbon-contaminated aquifer: assessment of mineralization based on alkalinity, inorganic carbon and stable carbon isotope balances. J Contam Hydrol 37:201–223

Hunkeler D, Meckenstock RU, Sherwood Lollar B et al (2008) A guide for assessing biodegradation and source identification of organic ground water contaminants using compound specific isotope analysis (CSIA). USEPA Publ 600/R–08/1:1–82

Imfeld G, Payraveau S, Tournebize J et al (2021) The role of ponds in pesticide dissipation at the agricultural catchment scale: a critical review. Water (switzerland) 13:1202

Imfeld G, Vuilleumier S (2012) Measuring the effects of pesticides on bacterial communities in soil: a critical review. Eur J Soil Biol 49:22–30

Ivdra N, Herrero-Martín S, Fischer A (2014) Validation of user-and environmentally friendly extraction and clean-up methods for compound-specific stable carbon isotope analysis of organochlorine pesticides and their metabolites in soils. J Chromatogr A 1355:36–45

Jeffries TC, Rayu S, Nielsen UN et al (2018) Metagenomic functional potential predicts degradation rates of a model organophosphorus xenobiotic in pesticide contaminated soils. Front Microbiol 9:147

Jin B, Rolle M (2016) Joint interpretation of enantiomer and stable isotope fractionation for chiral pesticides degradation. Water Res 105:178–186

Jochmann MA, Blessing M, Haderlein SB, Schmidt TC (2006) A new approach to determine method detection limits for compound-specific isotope analysis of volatile organic compounds. Rapid Commun Mass Spectrom 20:3639–3648

Karlsson AS, Weihermüller L, Tappe W et al (2016) Field scale boscalid residues and dissipation half-life estimation in a sandy soil. Chemosphere 145:163–173

Knossow N, Siebner H, Bernstein A (2020) Isotope analysis method for the herbicide bromoxynil and its application to study photo-degradation processes. J Hazard Mater 388:122036

Kuntze K, Eisenmann H, Richnow HH, Fischer A (2020) Compound-Specific Stable Isotope Analysis (CSIA) for evaluating degradation of organic pollutants: an overview of field case studies.
In: Boll, M. (eds) Anaerobic utilization of hydrocarbons, oils, and lipids. Handbook of hydrocarbon and lipid microbiology. Springer, Cham. https://doi.org/10.1007/978-3-319-50391-2_23

Lindahl AML, Söderström M, Jarvis N (2008) Influence of input uncertainty on prediction of within-field pesticide leaching risks. J Contam Hydrol 98:106–114

Liu X, Wu L, Kümmel S et al (2020) Compound-specific isotope analysis and enantiomer fractionation to characterize the transformation of hexachlorocyclohexane isomers in a soil-wheat pot system. Environ Sci Technol 54:8690–8698

Liu X, Wu L, Kümmel S, Richnow HH (2021) Characterizing the biotransformation of hexachlorocyclohexanes in wheat using compound-specific stable isotope analysis and enantiomer fraction analysis. J Hazard Mater 406:124301

Liu Y, Bashir S, Stollberg R et al (2017) Compound specific and enantioselective stable isotope analysis as tools to monitor transformation of hexachlorocyclohexane (HCH) in a complex aquifer system. Environ Sci Technol 51:8909–8916

Lutz SR, Van Meerveld HJ, Broers HP, van Breukelen BM (2013) A model-based assessment of the potential use of compound-specific stable isotope analysis in river monitoring of diffuse pesticide pollution. Hydrol Earth Syst Sci 17:4505–4524

Lutz SR, van der Velde Y, Elsayed OF et al (2017) Pesticide fate on catchment scale: conceptual modelling of stream CSIA data. Hydrol Earth Syst Sci 21:5243–5261

Maia AS, Ribeiro AR, Castro PML, Tiritan ME (2017) Chiral analysis of pesticides and drugs of environmental concern: Biodegradation and enantiomeric fraction. Symmetry (basel) 9:196

Malla MA, Dubey A, Raj A et al (2022) Emerging frontiers in microbe mediated pesticide reme-diation: Unveiling role of omics and In silico approaches in engineered environment. Environ Pollut 299:118851

Mancini SA, Hirschorn SK, Elsner M et al (2006) Effects of trace element concentration on enzyme controlled stable isotope fractionation during aerobic biodegradation of toluene. Environ Sci Technol 40:7675–7681

Masbou J, Drouin G, Payraveau S, Imfeld G (2018a) Carbon and nitrogen stable isotope fractionation during abiotic hydrolysis of pesticides. Chemosphere 213:368–376

Masbou J, Meite F, Guyot B, Imfeld G (2018b) Enantiomer-specific stable carbon isotope analysis (ESIA) to evaluate degradation of the chiral fungicide Metalaxyl in soils. J Hazard Mater 353:99–107

Masbou J, Payraveau S, Guyot B, Imfeld G (2023) Dimethomorph degradation in vineyards examined by isomeric and isotopic fractionation. Chemosphere 313:137341. https://doi.org/10.1016/j.chemosphere.2022.137341

Meckenstock RU, Morasch B, Warthmann R et al (1999) ¹³C/¹²C isotope fractionation of aromatic hydrocarbons during microbial degradation. Environ Microbiol 1:409–414

Melsbach A, Pittois D, Bayerle M et al (2021) Isotope fractionation of micropollutants during large-volume extraction: heads-up from a critical method evaluation for atrazine, desethylatrazine and 2, 6-dichlorobenzamide at low ng/L concentrations in groundwater. Isotopes Environ Health Stud 57:35–52

Melsbach A, Ponsin V, Torrentó C et al (2019) ¹³C-and ¹⁵N-isotope analysis of desphenylchloridazon by liquid chromatography-isotope-ratio mass spectrometry and derivatization gas chromatography-isotope-ratio mass spectrometry. Anal Chem 91:3412–3420

Menger F, Boström G, Jonsson O et al (2021) Identification of pesticide transformation products in surface water using suspect screening combined with national monitoring data. Environ Sci Technol 55:10343–10353

Meyer AH, Penning H, Elsner M (2009) C and N isotope fractionation suggests similar mechanisms of microbial atrazine transformation despite involvement of different enzymes (AtzA and TrzN). Environ Sci Technol 43:8079–8085

Meyer AH, Penning H, Lowag H, Elsner M (2008) Precise and accurate compound specific carbon and nitrogen isotope analysis of atrazine: critical role of combustion oven conditions. Environ Sci Technol 42:7757–7763
Milosevic N, Qiu S, Elsner M et al (2013) Combined isotope and enantiomer analysis to assess the fate of phenoxy acids in a heterogeneous geologic setting at an old landfill. Water Res 47:637–649

Mogusu E (2016) Compound specific isotope analysis to investigate sources and degradation of glyphosate. Universität Tübingen

Mogusu EO, Wolbert JB, Kujawinski DM et al (2015) Dual element (¹⁵N/¹⁴N, ¹³C/¹²C) isotope analysis of glyphosate and AMPA by derivatization-gas chromatography isotope ratio mass spectrometry (GC/IRMS) combined with LC/IRMS. Anal Bioanal Chem 407:5249–5260

Monard C, Martin-Laurent F, Lima O et al (2013) Estimating the biodegradation of pesticide in soils by monitoring pesticide-degrading gene expression. Biodegradation 24:203–213

Moschet C, Götz C, Longrée P et al (2013) Multi-level approach for the integrated assessment of polar organic micropollutants in an international lake catchment: the example of Lake Constance. Environ Sci Technol 47:7028–7036

Mottes C, Lesueur-Jannoyer M, Le Bail M, Malézieux E (2014) Pesticide transfer models in crop and watershed systems: a review. Agron Sustain Dev 34:229–250

Nestler A, Berglund M, Accoe F et al (2011) Isotopes for improved management of nitrate pollution in aqueous resources: review of surface water field studies. Environ Sci Pollut Res 18:519–533

Niu L, Xu C, Zhu S et al (2016) Enantiomer signature and carbon isotope evidence for the migration and transformation of DDTs in arable soils across China. Sci Rep 6:1–10

Ojeda A, Phillips E, Mancini S, Sherwood Lollar B (2019) Sources of uncertainty in biotransformation mechanistic interpretations and remediation studies using CSIA. Anal Chem 91:9147–9153

Pérez-Rodríguez P, Schmitt A-D, Gangloff S et al (2021) Plants affect the dissipation and leaching of anilide pesticides in soil mesocosms: insights from compound-specific isotope analysis (CSIA). Agric Ecosyst Environ 308:107257

Perini M, Bontempo L (2021) Liquid chromatography coupled to isotope ratio mass spectrometry (LC/IRMS): a review. TrAC Trends Anal Chem 47:116515

Qian Y, Chen K, Liu Y, Li J (2019) Assessment of hexachlorocyclohexane biodegradation in contaminated soil by compound-specific stable isotope analysis. Environ Pollut 254:113008

Reinnicke S, Bernstein A, Elsner M (2010) Small and reproducible isotope effects during methylation with trimethylsulfonium hydroxide (TMSH): a convenient derivatization method for isotope analysis of negatively charged molecules. Anal Chem 82:2013–2019

Reinnicke S, Juchelka D, Steinbeiss S et al (2012) Gas chromatography/isotope ratio mass spectrometry of recalcitrant target compounds: performance of different combustion reactors and strategies for standardization. Rapid Commun Mass Spectrom 26:1053–1060

Renpenning J, Rapp I, Nijenhuis I (2015) Substrate hydrophobicity and cell composition influence the extent of rate limitation and masking of isotope fractionation during microbial reductive dehalogenation of chlorinated ethenes. Environ Sci Technol 49:4293–4301

Riml J, Wörman A, Kunkel U, Radke M (2013) Evaluating the fate of six common pharmaceuticals using a reactive transport model: Insights from a stream tracer test. Sci Total Environ 458:344–354

Rodríguez A, Castrejón-Godínez ML, Salazar-Bustamante E et al (2020) Omics approaches to pesticide biodegradation. Curr Microbiol 77:545–563

Schreglmann K, Hoeche M, Steinbeiss S et al (2013) Carbon and nitrogen isotope analysis of atrazine and desethylatrazine at sub-microgram per liter concentrations in groundwater. Anal Bioanal Chem 405:2857–2867

Schürner HKV, Maier MP, Eckert D et al (2016) Compound-specific stable isotope fractionation of pesticides and pharmaceuticals in a mesoscale aquifer model. Environ Sci Technol 50:5729–5739

Scott KM, Lu X, Cavanaugh CM, Liu JS (2004) Optimal methods for estimating kinetic isotope effects from different forms of the Rayleigh distillation equation. Geochim Cosmochim Acta 68:433–442
Sherwood Lollar B, Hirschorn S, Mundle SOC et al (2010) Insights into enzyme kinetics of chloroethane biodegradation using compound specific stable isotopes. Environ Sci Technol 44:7498-7503

Sherwood Lollar B, Hirschorn SK, Chartrand MM, Lacrampe-Couloume G (2007) An approach for assessing total instrumental uncertainty in compound-specific carbon isotope analysis: implications for environmental remediation studies. *Anal Chem* 79:3469-3475

Sherwood Lollar B, Slater GF, Ahad J et al (1999) Contrasting carbon isotope fractionation during biodegradation of trichloroethylene and toluene: implications for intrinsic bioremediation. *Org Geochem* 30:813-820

Silva V, Mol HGJ, Zomer P, Tienstra M, Ritsema CJ, Geissen V (2019) Pesticide residues in European agricultural soils—A hidden reality unfolded. *Sci Total Environ* 653:1532–1545. https://doi.org/10.1016/j.scitotenv.2018.10.441. Epub 2018 Nov 6. PMID: 30759587

Spahr S, Huntscha S, Bolotin J et al (2013) Compound-specific isotope analysis of benzotriazole and its derivatives. *Anal Bioanal Chem* 405:2843-2856

Torrentó C, Bakkour R, Glauser G et al (2019) Solid-phase extraction method for stable isotope analysis of pesticides from large volume environmental water samples. *Analyst* 144:2898-2908

Torrentó C, Ponsin V, Lihl C et al (2021) Triple-element compound-specific stable isotope analysis (3D-CSIA): added value of Cl isotope ratios to assess herbicide degradation. *Environ Sci Technol* 55:13891-13901

Van Breukelen BM (2007) Quantifying the degradation and dilution contribution to natural attenuation of contaminants by means of an open system Rayleigh equation. *Environ Sci Technol* 41:4980-4985

Wang R, Yuan Y, Yen H et al (2019) A review of pesticide fate and transport simulation at watershed level using SWAT: current status and research concerns. *Sci Total Environ* 669:512-526

Werner RA, Brand WA (2001) Referencing strategies and techniques in stable isotope ratio analysis. *Rapid Commun Mass Spectrom* 15:501-519

Wilkowska A, Biziuk M (2011) Determination of pesticide residues in food matrices using the QuEChERS methodology. *Food Chem* 125:803-812

Won E-J, Yun H-Y, Lee D-H, Shin K-H (2021) Application of compound-specific isotope analysis in environmental forensic and strategic management avenue for pesticide residues. *Molecules* 26:4412

Wu L, Liu Y, Liu X et al (2019a) Isotope fractionation approach to characterize the reactive transport processes governing the fate of hexachlorocyclohexanes at a contaminated site in India. *Environ Int* 132:105036

Wu L, Moses S, Liu Y et al (2019b) A concept for studying the transformation reaction of hexachlorocyclohexanes in food webs using multi-element compound-specific isotope analysis. *Anal Chim Acta* 1064:56-64

Wu L, Verma D, Bondgaard M et al (2018) Carbon and hydrogen isotope analysis of parathion for characterizing its natural attenuation by hydrolysis at a contaminated site. *Water Res* 143:146-154

Wu L, Yao J, Trebse P et al (2014) Compound specific isotope analysis of organophosphorus pesticides. *Chemosphere* 111:458-463

**T. Gilevska** 's research focuses on stable isotope analysis (CSIA) and contaminant hydrogeology. She has researched new CSIA methods and their applicability for polar organic contaminants during her doctoral studies. Furthermore, she broadened her expertise in understanding the attenuation of toxic legacy chemicals derived from anthropogenic activities in groundwater, and lastly, she has been working on developing and applying new methods to understand in situ processes influencing the fate of pesticides in freshwater systems. All of this research was under the umbrella of applying advanced multi-element carbon, hydrogen, chlorine, and nitrogen CSIA to environmental samples. Tetyana has worked extensively in three leading laboratories (Department of Isotope Biogeochemistry at Helmholtz Centre for Environmental Research—UFZ in Germany, the
Sherwood Lollar Research Group at the University of Toronto in Canada, and the Earth and Envi-
ronment Institute of Strasbourg at the University of Strasbourg, France), all internationally recog-
nized for isotope analysis and technique development around the world. She is a co-author of a
book chapter ("Influence of extraction methods on stability of polyphenols." Polyphenols. Haup-
page, NY, USA: Nova Science Publishers, Inc (2014)) and 18 peer-reviewed journal papers. She
has been awarded Marie Curie and EUTOPIA-SIF-Post-Doctoral Fellowships.

**G. Imfeld** is a research professor at the French National Centre for Scientific Research (CNRS) in environmental biogeochemistry, at the Laboratory of Hydrology and Geochemistry of Strasbourg (Ph.D. degree in 2008 at UFZ-Leipzig and University of Neuchâtel, Habilitation in 2013, University of Strasbourg). His research interests revolve around pollutant biogeochemistry, microbial transformation and ecology in continental hydrosystems, and freshwater sustainability, resilience and adaptation of socio-hydrosystems. His current research focuses on the degradation and transport of micropolluants in wetlands, soils, and aquifers, combining microbial and stable isotope approaches (CSIA). Gwenaël is leading the research team "Isotope biogeochemistry" with analytical facilities for chemical and isotopic analyses of trace elements and organic compounds. Gwenaël has published > 90 ISI publications in contaminant biogeochemistry and microbial ecology. He is Director of the Research Initiative in Sustainability and the Environment (RISE/FERED) of the University of Strasbourg/CNRS and member of the Editorial Board of *Chemo-sphere*.

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.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 3.0 IGO license (http://creativecommons.org/licenses/by/3.0/igo/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the IAEA: International Atomic Energy Agency, provide a link to the Creative Commons license and indicate if changes were made.

Any dispute related to the use of the works of the IAEA: International Atomic Energy Agency that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IAEA: International Atomic Energy Agency's name for any purpose other than for attribution, and the use of the IAEA: International Atomic Energy Agency's logo, shall be subject to a separate written license agreement between the IAEA: International Atomic Energy Agency and the user and is not authorized as part of this CC-IGO license. Note that the link provided above includes additional terms and conditions of the license.

The images or other third party material in this chapter are included in the chapter's Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter's Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.

<figure><img src="image_29.png" /></figure>
<figure><img src="image_30.png" /></figure>

Chapter 5

Stable Oxygen Isotope Composition
of Phosphates to Investigate Phosphorus
Cycling in the Soil-Plant Continuum

V. Pfahler, J. Adu-Gyamfi, Ch. Pistocchi, F. Tamburini, and S. Bauke

**Abstract** This chapter provides an overview of how the stable isotope composition of oxygen bond to phosphorus, δ(^18O)PO4, in phosphate can be used to investigate P cycling in the soil–plant continuum. In recent years, several books and articles about different aspects of P cycling have been published. This chapter provides summary information about P cycling in the soil–plant continuum focusing on the current methods in P research. It also provides an overview of the pitfalls of the δ(^18O)PO4 method, especially regarding sampling and sample handling. The chapter concludes with the way forward and prospects of the δ(^18O)PO4 method to investigate P cycling in the soil–plant continuum.

V. Pfahler
Formerly Smithsonian Tropical Research Institute, Balboa, Ancon, Panama

J. Adu-Gyamfi (✉)
Soil and Water Management and Crop Nutrition (SWMCN) Section, Joint FAO/IAEA Centre of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency (IAEA), Vienna, Austria
e-mail: j.adu-gyamfi@iaea.org

Ch. Pistocchi
Eco and Soils Research Unit—INRAE, CIRAD, IRD, Institut, Agro Montpellier, France
e-mail: Chiara.pistocchi@supagro.fr

F. Tamburini
Group of Plant Nutrition, D-USYS, ETH, Zurich, Switzerland
e-mail: federica.tamburini@usys.ethz.ch

S. Bauke
University of Bonn, Bonn, Germany
e-mail: sarabauke@uni-bonn.de
# 5.1 Overview of the Phosphorus Cycling in the Soil-Plant Continuum

Phosphorus (P) is the 11th most abundant element in the Earth's crust, which is equivalent to around 0.12%. The amount of P in the bedrocks varies (Van Kauwenbergh 2010), with the highest amounts usually found in sedimentary rocks. As bedrock is the primary source of mineral P in soils (Ruttenberg et al. 2003), the bedrock type and composition influence the total amount of P in different soil types. Total P concentrations in natural soils are usually lower than in the original bedrock material, with higher concentrations in younger than in older soils, which also differ in their relative abundance of P forms (Walker and Syers 1976). Generally, calcium-bound P dominates in younger soils, whereas occluded inorganic P and organic P forms become more abundant with progressing soil development. P concentrations and the relative proportion of various P forms vary in soils with age and have been studied extensively since the pioneering work of Walker and Syers (1976). The most recent study by Rechberger et al. (2021) examined the sorption and desorption of phosphate in volcanic soils on the Galápagos Islands. Their study sites included a climatic gradient, with annual rainfall ranging between 100 mm (dry sites) and 1600 mm (humid sites). They concluded that the main drivers for P sorption at their sites were iron (Fe) and aluminium (Al), either in amorphous phases or bound to organic matter.

The following paragraphs provide an overview of P cycling in the soil-plant continuum, for more detailed information see, for example, Arai and Sparks (2007), Raghothama and Karthikeyan (2005), White et al. (2008), Bünemann et al. (2011), Shen et al. (2011), Weihrauch and Opp (2018), and Schipanski and Bennett (2021).

## 5.1.1 Phosphorus Cycling in the Soil

Through weathering processes, P is released from the bedrock into the soil where it is transformed into the different organic and inorganic P forms and taken up by the soil biota. Weathering processes are divided into physical and (bio-) chemical weathering processes, and factors controlling weathering (such as climate, soil type, mineralogy, and other intrinsic soil properties) are highly inter-related (Scheffer and Schachtschabel 1998). Chemical weathering, for example through hydrolysis of minerals and oxidation of ions, of bedrock dominates in humid tropical regions, whereas physical weathering processes like thermal stress and frost weathering are more important in arid areas. The biochemical weathering of bedrock is favoured by processes such as exudation of organic acids by bacteria and plants and acidification due to the excretion of protons.

The importance of bedrock weathering for the P cycle in the soil-plant continuum is illustrated in the following paragraph. For more information see, for example, Formoso (2006), Derry (2009), Goll et al. (2014), and Ribeiro et al. (2020) and
<figure><img src="image_31.png" /><figcaption>**Fig. 5.1** Schematic diagram showing the complexity of soils</figcaption></figure>

references within. Soil water content is used as an example in the following paragraph to illustrate the interconnectivity of the factors controlling weathering and how it influences the cycling of other P forms in the soil-plant continuum (Arai and Sparks 2007) (Fig. 5.1).

We chose the soil water content as an example as it impacts the diffusion rate of ions in the soil which in return impacts the adsorption rate of ions like phosphate (Weihrauch and Opp 2018). It also enhances the chemical weathering of bedrock material and impacts biochemical weathering, as it directly affects soil fauna and flora. Thus, soil water content is an important factor for P cycling in the soil-plant continuum. It is impacted by factors such as precipitation and temperature (climate), soil mineralogy, and soil biotic processes. Precipitation and temperature also affect soil biotic processes (Krashevska et al. 2012; Margalef et al. 2017; Nottingham et al. 2020), the P cycle (Hou et al. 2018), and influence changes in soil mineralogy (Boero et al. 1992). Through their burrowing activities, earthworms ingest soil and via this and their excretions, they also influence soil aggregation (Blouin et al. 2013), which in return influences soil microorganisms and P sorption (Linquist et al. 1997; Six et al. 2004). Earthworms, soil fauna in general, and roots create biopores through their activities, which impact soil porosity and nutrient distribution in soils (Le Bayon et al. 2021). In return, soil fauna and flora influence bedrock weathering, as well as the P cycle. Through the exudation of enzymes by soil fauna and flora, organic P is hydrolyzed and available for plant uptake.

Similarly to weathering, chemical and physical processes (including precipitation, dissolution, adsorption, desorption) as well as biological processes (including
<figure><img src="image_32.png" /><figcaption>**Fig. 5.2** Schematic of phosphorus (P) cycling in the soil–plant continuum</figcaption></figure>

immobilization, mineralization, enzymatic hydrolysis, and the activity of soil macro-,
micro-, and mesofauna and -flora) govern the P cycle in the soil–plant continuum
(Fig. 5.2) (Arai and Sparks 2007; Vos et al. 2014).

Phosphorus occurs as inorganic and organic P forms (e.g. phosphate, phospho-
nates, and phytate) in soils, with inorganic and organic phosphates being the most
abundant (McKelvie 2005; Arai and Sparks 2007; Tiessen 2008). The bioavailability
of soil P varies greatly, and some P forms such as phytate are more recalcitrant than
others. Only a small amount (around 0.1 mg P L⁻¹; Tiessen 2008) of inorganic P
occurs as free phosphate, i.e., the readily available P, in the soil solution and can
directly be taken up by plants. Phosphate in soils is most available at a neutral pH
range but this range is recently under discussion (Barrow 2016; Simonsson et al.
2018). Most inorganic P occurs as phosphate bound to aluminium (Al) or iron (Fe)
oxyhydroxides, to soil particles like clay minerals, and to calcium (Ca) or magne-
sium (Mg) (Gérard 2016). Soil microorganisms like archaea and bacteria also partly
contain inorganic P (around 30%; Bünemann et al. (2011)), but the majority of P in
microorganisms occurs as organic P, such as phospholipids and nucleic acids. The
relative proportion of organic P forms in microorganisms is influenced, similarly to
plants (Veneklaas et al. 2012), by their P status. In P-limited aquatic bacteria, 60%
of microbial P consisted, for example, of phospholipids and nucleic acids (Vadstein
1998). Microorganisms also often store P in the form of polyphosphates in their cells
(Rao et al. 2009; Akbari et al. 2021). Depending on the soil, organic P typically makes up 30 to 65 % of total soil P (Harrison 1987; Richardson et al. 2005).

### 5.1.2 The Fate of Fertilizer P in Soils

As concentrations of bioavailable P in agricultural soils are often insufficient to maintain crop production, farmers apply P fertilizer (Schnug and De Kok 2016; Ros et al. 2020). Phosphorus fertilizers are available as mineral and organic fertilizers. Mineral P fertilizer mainly consists of inorganic P, whereas organic P fertilizers contain inorganic P but also varying amounts of organic P compounds and other organic compounds, such as humic acids. Due to their different compositions, mineral and organic P fertilizers behave differently in soils (Audette et al. 2016) and affect P cycling in the soil-plant continuum in different ways (Keller et al. 2012; Ma et al. 2020). Adding organic P fertilizers to the soil leads to an increased input of organic matter compared to mineral P fertilizers. This can result in enhanced microbial activity in organically fertilized soils (Oehl et al. 2001). Mineral P fertilization on the other hand can lead to reduced microbial and enzymatic activity in soils (Zhang et al. 2015; Chen et al. 2019).

### 5.1.3 Mineral P Fertilizers

Rock phosphate, derived from P-containing ores, is the primary raw material for mineral P fertilizers, but it can also be applied directly to agricultural fields (Zapata and Roy 2004). Mineral P fertilizers are applied in agricultural fields as triple superphosphate (TSP), (mono-/di-) ammonium phosphate, combined with other nutrients such as nitrogen (N) and potassium (K), and to a lesser degree as ammonium polyphosphate, which is, unlike the first three, applied as a liquid. Only a small amount (up to 20%; Plaxton and Tran (2011)) of inorganic P derived from mineral fertilizers is taken up by plants during a growing season (Blake et al. 2000). The majority is either adsorbed by soil particles and slowly enters the more recalcitrant P pools or is lost due to leaching into the groundwater or soil erosion. In developed countries with access to fertilizers, this process translated into the build-up over a few decades of the so-called legacy P in soils (Kamprath 1967; Menezes-Blackburn et al. 2018). Ott and Rechberger (2012) estimated that in the 15 European Union Countries as of 1 May 2004 (EU-15), the net accumulation of P in agricultural soils is 2.9 kg P yr⁻¹ per capita. This is approximately 62% of the net annual per capita consumption of P in the EU-15.

**Organic P amendments** cover a wide range of substances. Farmyard manure is probably the most common organic P amendment; other examples are biogas digestates and animal slurry. The origin of the organic P fertilizer/amendment impacts the fate
of P derived from it, as shown for three different types of animal manure (Azeez and van Averbeke 2011) and a range of organic fertilizers including digestates (Vanden Nest et al. 2015). Poultry manure, for example, reduced P fixation in soils more than goat and cattle manure, perhaps caused by a competition about the sorption sites between P and humic acid (Azeez and van Averbeke 2011). Similarly, Vanden Nest et al. (2015) observed that the different compositions of organic matter in organic fertilizers lead to different effects on soil P availability. The various organic P fertilizers also contain different organic P compounds or different amounts of organic P compounds. As organic P compounds need to be hydrolyzed before they can be used by plants (see Sect. 5.3), this also impacts the availability of P derived from organic P fertilizers. Phytate is, for example, more present in poultry manure and pig slurry, as these monogastric animals lack the enzyme phytase to hydrolyze phytate (Scholz and Wellmer 2015).

### 5.1.4 Phosphorus and Plants

The amount of total P in soils would often be sufficient to sustain plant growth, however, only a small portion of the total soil P can readily be taken up by plants or soil microorganisms. Plants take up P as a phosphate ion, mainly as $H_2PO_4^-$ or $HPO_4^{2-}$, from the soil solution, creating a P concentration gradient from the rhizosphere to the bulk soil, which leads to the desorption of phosphate from soil particles and a flux of phosphate towards the roots (Hinsinger 2001; Schnepf et al. 2008; Kreuzeder et al. 2018). Since concentrations of available P in soils are usually low compared to plant P demands, plants have developed strategies to increase their P acquisition and internal P use efficiency (Table 5.1) (Hinsinger 2001; Shen et al. 2011; Veneklaas et al. 2012; Raven et al. 2018).

**Acquisition strategies** can roughly be divided into four categories: exudation, symbiosis, expression of high-affinity P transporters, and root architecture. Plants are known to excrete organic acids, enzymes, and protons to increase the availability of soil P. Organic acids chelate ions like Fe and thus prevent the binding of P to Fe. Pigeon pea (*Cajanus cajan*) exudes piscidic acid (p-hydroxybenzyl tartaric acid), which is known to release P bound to iron (Fe) (Ae et al. 1990; Raghothama 1999; Adu-Gyamfi 2002; Ishikawa et al. 2002; Krishnappa and Hussain 2014). Enzymes such as acid phosphatase catalyze the hydrolysis of organic P into inorganic P so that plants can take it up. As mentioned in Sect. 5.1, the soil pH affects the availability of the soil P, and therefore some plants excrete protons, which leads to an acidification of the rhizosphere. Other plants express high-affinity P transporters in the roots, which leads to an increased P flux into the roots (Rausch and Bucher 2002). Changes in root architecture can also increase P acquisition. Members of the Proteaceae species form cluster roots that excrete organic acids. The formation of proteoid (or cluster) roots is, for example, a common strategy of the Proteaceae family in Australia growing on low P soils (Lambers et al. 2015a). Among the cultivated species, white lupin (*Lupinus*
<table><caption>**Table 5.1** Plant strategies to increase phosphorus (P) acquisition and internal P use efficiency</caption><thead><tr><th colspan="2">P acquisition</th></tr></thead><tbody><tr><td rowspan="3">Exudation</td><td>Organic acids</td></tr><tr><td>Enzymes</td></tr><tr><td>Protons (acidification of the rhizosphere)</td></tr><tr><td>Symbiosis</td><td>Symbiosis with mycorrhizal fungi</td></tr><tr><td colspan="2">Expression of high-affinity P transporters in the roots</td></tr><tr><td rowspan="4">Root architecture</td><td>Proteoid/cluster roots</td></tr><tr><td>Enhanced root biomass</td></tr><tr><td>Changes in root morphology and architecture (root biomass and elongation rate, number of lateral roots and root hairs, etc.)</td></tr><tr><td>Remobilization of P stored in the vacuole</td></tr><tr><td colspan="2">Re-translocation of P from older to younger leaves or reproductive organs</td></tr><tr><td colspan="2">Replacement of phospholipids by other lipids</td></tr><tr><td colspan="2">Induction of acid phosphatase</td></tr></tbody></table>

albus) is also known to form cluster roots (Lambers et al. 2015b). Phosphorus-limited plants often also invest more resources in the roots, which leads to a greater root-to-shoot ratio compared to non-limited plants. An increased number of fine roots allows plants to explore a greater soil volume. In most soils, bioavailable P concentrations are highest in the topmost soil layers. A strategy to better access this P is a change in the root architecture, with more roots growing laterally (Lynch and Brown 2001). This phenomenon is known as topsoil foraging and is increased in soybeans by a shallower root growth angle (Lynch 2011). Similarly, the symbiosis with mycorrhizal fungi greatly increases the soil volume that plants can explore and consequently have access to more bioavailable P (Schnepf et al. 2008; Smith et al. 2011). Mycorrhizal fungi, like plants and microorganisms, secrete phosphoenzymes which could increase the availability of organic P for plant nutrition (Ezawa and Saito 2018). In fact, around 90% of land plants are associated with mycorrhizal fungi (Smith and Read 2010).

Phosphorus is essential for plants as it is involved in several metabolic processes and the inorganic P concentration inside the cytoplasm is tightly regulated (Bieleski 1972; Raghothama and Karthikeyan 2005). Therefore, plants, besides their P acquisition strategies, have developed mechanisms to increase their internal P use efficiency (Plaxton and Tran 2011). More detailed information about this topic can be found in White and Hammond (2008), Rose and Wissuwa (2012), Lopez-Arredondo et al. (2014), and references therein. Inside plants, P is considered a mobile nutrient, and once it is taken up by plants, it is translocated to the different plant organs. The uptake of P and translocation to different plant organs and cell compartments involve cell membrane transporters and the xylem and phloem (Jaiwal et al. 2008; Miller et al. 2009; White 2012). Depending on the P nutrition status of a plant, most of the plant P