DOCTORAL T H E SIS Simon Pontér Isotope Ratio and Trace Element Measurements Using Inductively Coupled Plasma – Mass Spectrometry

Department of Civil, Environmental and Natural Resources Engineering Division of Geosciences and Environmental Engineering

ISSN 1402-1544 Isotope Ratio and Trace Element ISBN 978-91-7790-787-9 (print) ISBN 978-91-7790-788-6 (pdf) Measurements Using Inductively Luleå University of Technology 2021 Coupled Plasma – Mass Spectrometry Method Development and Applications in Environmental Forensics

Simon Pontér Tryck: Lenanders Grafiska, 135581

Applied Geochemistry

135581 LTU_ Pontér.indd Alla sidor 2021-03-30 11:30

Isotope Ratio and Trace Element Measurements Using Inductively Coupled Plasma – Mass Spectrometry:

Method Development and Applications in Environmental Forensics

Simon Pontér

Applied Geochemistry

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Isotope Ratio and Trace Element Measurements Using Inductively Coupled Plasma – Mass Spectrometry:

Method Development and Applications in Environmental Forensics

SIMON PONTÉR Luleå, April 2021

Department of Civil, Environmental and Natural Resources Engineering Division of Geosciences and Environmental Engineering Luleå University of Technology SE – 971 87 LULEÅ www.ltu.se/sbn

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Printed by Lenanders Grafiska AB, Göteborg, 2021 ISSN 1402-1544 ISBN 978-91-7790-787-9 (print) ISBN 978-91-7790-788-6 (electronic)

5 Abstract

Environmental Forensics is a scientific methodology developed for identifying sources, the timing of release, and transport pathways for potentially hazardous environmental contaminants. It combines a variety of analytical methods with principles derived from disciplines such as chemistry, geology, geochemistry, hydrogeology, and statistics, with the purpose to provide objective scientific and legal conclusions on the source and/or time of a contaminant release. Instrumental development and refining separation schemes have allowed higher quality data to be obtained and played a major role in the recent progress of the field. The use of modern techniques such as inductively coupled plasma sector field mass spectrometry (ICP-SFMS) and multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) for trace and ultra-trace element concentrations and isotope ratio measurements provides Environmental Forensics with new opportunities. The work described in the present thesis has been focused on closing knowledge gaps in the field of Environmental Forensics, including analytical method development as well as processes- and source(s)t-tracing using multiple isotopes in environmental studies.

Paper I is dedicated to the assessment of performance of double-focusing, sector field mass spectrometry (ICP-SFMS) for determination of analytes (including technology critical elements (TCE)) at ultra-trace levels in complex matrixes, with a special emphasis on the determination of Au, Ag, Ir, Os, Pd, Pt, Re, Rh, Ru, Sb, and Te. Instrumentation development was performed by optimization and testing different configurations of the ICP-SFMS introduction system as well as various sample preparations, pre-concentration, and matrix separation methods. Factors affecting instrumental sensitivity, contamination risks, reagent purity, spectral interferences, matrix effects, and analyte recovery are discussed. Optimized matrix specific methods were applied to a range of reference and control materials (riverine and brackish waters, seawater, whole blood, serum, and urine). Samples included brackish water and seawater from the Laptev Sea, venous blood, tap water, and snow samples collected in Luleå, northern Sweden.

In Paper II an analytical procedure was developed, consisting of high pressure/temperature acid digestion using an UltraCLAVE system followed by a one pass, single column matrix separation allowing the first Cr isotope study in epiphytic lichens and mosses, as well as analysis of chromite and soils by ICP-SFMS and MC-ICP-MS. The overall reproducibility of the method, which was found to be ±0.11‰ (2σ), was assessed by replicate preparation and Cr isotope ratio measurements performed by different operators in multiple analytical sessions over a few months. Results indicated no correlation between soil concentrations and isotopic compositions (R2=0.2), while a strong negative correlation (R2=0.7) between Cr concentrations in lichens and mosses and δ53Cr signatures indicates airborne Cr contribution from local anthropogenic source(s) depleted in heavy isotopes. Chromium isotope data obtained for lichens and mosses indicate the potential of using this approach for tracing and quantifying airborne Cr pollution caused by stainless steel foundries.

Paper III evaluates heavy metal contamination in brackish water, groundwater, and sediments collected close to a deposit facility at the Rönnskär Cu–Pb–Zn smelter in Skellefteå, northern Sweden. This study investigates the ranges of isotopic compositions of four elements (Cd, Cu, Pb, and Zn) in smelter process materials (ores, products, and waste), as well as in polluted groundwater and sediments of the affected area. The study’s objective was to evaluate the isotope variability of the polluting source and identify possible isotope fractionation between a source and a sink. This study further assesses the viability of using isotopic information to identify the source of the pollutant in various matrices. Isotope composition data were used as a compliment to multi-element screening analysis and multivariate

6 statistical techniques. Expanding the number of elements utilized in isotope tracing empowers our abilities to decipher the source(s) and the extent of environmental exposure from contamination events related to mining and refining operations.

Results show clusters of elements with elevated concentrations and significant inter-element correlations that can be traced practically in all matrices tested (from dust samples to sediments), confirming a link between the source and the polluted environmental compartments. Differences in the relative mobility in the environment for different elements (shown in the example of Re and Mo distribution in sediments) may however affect the usefulness of the elemental ratios in reconstructing the extent and timing of pollution events.

Among the isotopes evaluated in this study, radiogenic Pb and stable Zn isotope systems offer the most promising source identification in the area close to the smelter. However, temporal variability in the isotopic composition of the source adds complexity for the Pb isotopes. Numerous post-deposition fractionating processes alter the original source ratios for Cu, Zn, and to a lesser extent, Cd. At larger distances from the source, additional fractionation during element migration and dilution of source- specific signatures with background components makes source tracing more challenging. To fully realize the great promise offered by expanding the number of elements utilized in isotope tracing as a powerful way to decipher sources and fate of environmental exposure, a comprehensive evaluation of both source(s) and background variability, as well as post-depositional fractionation, needs to be an integral part of any Environmental Forensics investigation.

Paper IV combines uranium (U) and other trace element concentrations with iron and uranium isotope measurements as a proxy to reconstruct historical changes of U release and accumulation in one tailings pond and two lakes (Mettä-Rakkurijärvi and Rakkurijärvi) receiving deep mine waters in northern Sweden, Kiruna. Uranium is deposited in lake sediments downstream of the mine, with elevated U concentrations in the surface sediments exceeding 55 mg kg-1, a >20-fold increase from the pre- industrial years. The distribution of anthropogenic U between the lakes does not follow the distribution of other contaminants reaching the system with mine waters, with a higher relative proportion of U accumulating in sediments of the second lake. Vertical concentration profiles for redox-sensitive elements as well as Fe isotopic composition were used to re-construct past redox-conditions potentially controlling early diagenesis of U in surface sediments. The isotopic signature of U in surface sediments (activity ratio AR=2.5) is far from that of secular equilibrium. These signatures are a function of time and weathering-induced fractionation, used here as a source signature of U originating in the deep groundwater in the mine. Linear regressions of inverse U concentration in water (dissolved, particulate, and total) versus AR reaffirms a simplified mixing situation with two isotopically distinct sources: 1) a natural source (low U concentration, AR 2.64), and 2) an anthropogenic source (high U concentration, AR ≈1.95). After mixing with mine water from the Rakkurijoki system, the AR of receiving Kalix River water decreases from 2.66 to 2.24. Monitoring data on the surface waters demonstrate the effects of the tributary waters of the Rakkurijoki systems as it discharges into the Kalix River, where the U concentration of the river downstream is more than doubled.

Keywords: Ultra-trace; TCE; Screening analysis; ICP-MS; ICP-SFMS; MC-ICP-MC; Multi- tracer studies; Isotope ratio measurements; Fractionation; Environmental Forensics; Tracing

7 Acknowledgements

First, I would like to express my deepest gratitude to my supervisors Prof. Ilia Rodushkin, Associate prof. Anders Widerlund, and Adjunct prof. Emma Engström who have time spent time meticulously reviewing all my written works. Thank you for answering my questions, the constructive criticisms, and the constant encouragement while supervising my work during these years. A special thank you to Ilia, who has spent countless hours in the lab teaching me everything I know about theoretical and practical mass spectrometry.

I would also like the thank Prof. Johan Ingri and all the colleagues at the Division of Geosciences and Environmental Engineering at Luleå University of Technology for all the inspiring geochemical discussions.

ALS Scandinavia AB and the staff in Luleå are gratefully acknowledged for generously sharing their workspace and for all the technical support during the laboratory work. I also want to acknowledge Pasi Peltola and Boliden Mineral AB as well as Elsa Peinerud and LKAB for permitting access to the study sites.

This project was funded by The Swedish Agency for Economic and Regional Growth, and Region Norrbotten.

And lastly, I would like to thank my family for providing me with the tools of curiosity to explore and seek knowledge, and to Malin for lightening my days.

8 List of papers This thesis is based on the following papers hereafter referred to by their Roman numerals.

I. Application of double-focusing sector field ICP-MS for determination of ultra-trace constituents in samples characterized by complex composition of the matrix Ilia Rodushkin, Cora Paulukat, Simon Pontér, Emma Engström, Douglas C. Baxter, Dieke Sörlin, Nicola Pallavicini and Katerina Rodushkina Science of the Total Environment., 2018, 622-623, 203-213

II. Chromium isotope ratio measurements in environmental matrices by MC-ICP-MS Simon Pontér, Nicola Pallavicini, Emma Engström, Douglas C. Baxter and Ilia Rodushkin Journal of Analytical Atomic Spectrometry., 2016, 31, 1464-1471

III. Evaluation of a multi-isotope approach as a complement to concentration data within Environmental Forensics Simon Pontér, Stacy Sutliff-Johansson, Emma Engström, Anders Widerlund, Anna Mäki, Katerina Rodushkina, Cora Paulukat and Ilia Rodushkin Minerals., 2021, 11(1), 37

IV. Early diagenesis of anthropogenic uranium in lakes receiving deep groundwater from the Kiruna mine, northern Sweden Simon Pontér, Ilia Rodushkin, Emma Engström, Katerina Rodushkina, Cora Paulukat, Elsa Peinerud, and Anders Widerlund Submitted, Manuscript 2021

9 Other publications written within the degree but not included in this thesis: i Stacy Sutliff-Johansson, S. Pontér, A. Mäki, E. Engström, I. Rodushkin, P. Peltola & A. Widerlund, (2020) Groundwater environmental forensic investigation combining multivariate statistical techniques and screening analyses. Environmental Forensics, 1-15, https://doi.org/10.1080/15275922.2020.1850571 ii Stacy Sutliff-Johansson, S. Pontér, E. Engström, I. Rodushkin, P. Peltola & A. Widerlund, (2021) Tracing anthropogenic sources of Tantalum and Niobium in Bothnian Bay sediments, Sweden. Journal of Soils and Sediments, 21(3), 1488-1503. iii Stacy Sutliff-Johansson, S. Pontér, A. Mäki, E. Engström, I. Rodushkin, P. Peltola & A. Widerlund, (2021) Environmental Monitoring of Technology Critical Elements in contaminated Sediments in the Bothnian Bay, Northern Sweden. Manuscript.

Conference contributions Luleå University of Technology: Waterface workshop 2017 Isotope Tracers at an Industrial Site

2018 Kategorisering av Grundvatten en Förstudie: Gäddviksvattentäkt

2019 Uran och Spårämnestransport vid Kirunagruvan

2021 Spårämnesmobilitet vid Kirunagruvan

Peer-reviewed abstracts in international conference proceedings

I Simon Pontér, Ilia Rodushkin, Emma Engström, and Anders Widerlund., (2019). Uranium and trace metal fate in lakes receiving mine waters in Northern Sweden. Poster presentation at the Goldschmidt Conference in Barcelona, Spain II Simon Pontér, Ilia Rodushkin, Emma Engström, and Anders Widerlund., (2020). Trace element distribution in redox-controlled lake sediments. Oral presentation at the ContaSed in Bern, Switzerland (Covid-19 cancelled).

10 Contents 1.0 Introduction 13 1.1 Research objectives and scope of the thesis 15 1.2 Ultra-trace analyses 16 1.3 Isotope analyses 17 1.4 Origin of isotope variations (fractionation) 17 1.5 Major principles of the ICP Instrumentation 18 1.5.1 High-resolution single-collector, inductively coupled plasma 19 double-focusing sector-field mass spectrometer (ICP-SFMS) 1.5.2 Multi-collector, inductively coupled plasma mass spectrometer (MC-ICPMS) 1.6 Avoiding contamination: clean room 21 1.7 Quantitative recovery 21 1.8 Spectral interferences 22 1.9 Instrumental mass bias 23 2.0 Quality control 23 3.0 Summary of results 24 3.1 Determination of ultra-trace concentrations of TCEs in complex matrices 25 3.1.1 Instrumental blanks 25 3.1.2 Pre-concentration, separation, and interference 25 3.1.3 Accuracy and reproducibility 25 3.1.4 Concentrations: Snow 26 3.1.5 Concentrations: Water 28 3.1.6 Concentrations: Clinical 29 3.2 Chromium isotope ratio measurements in environmental matrices 30 3.2.1 Separation efficiency, accuracy, and reproducibility 30 3.2.2 Cr concentrations and isotope ratios: tracer application 30 3.3 Multi-isotope approach: spatial and temporal variability in anthropogenically 32 affected matrices 3.3.1 Concentration: Water 32 3.3.2 Concentrations: Sediments 34 3.3.3 Isotopic composition 35 3.3.4 Isotope tracing application: Pb and Zn 37 3.4 Early diagenesis of anthropogenic uranium in lakes receiving deep groundwater 40 3.4.1 Concentrations: Water 41 3.4.2 Isotope ratios: Uranium 44 3.4.3 Sediments: Element distribution and isotope ratios 46 4.0 Overall conclusions 49 5.0 Future studies 51 6.0 References 53

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12 1.0 Introduction

The public awareness of our effects on the environment has grown in recent decades, reflecting the increased anthropogenic load since the start of the industrial revolution. This development is also seen in the great number of studies published every year with a focus on pollution sources, transport, distribution, and remediation (De Vleeschouwer et al., 2007; Miller 2013; Shotyk et al., 1998; Tchounwou et al., 2012; Vörösmarty et al., 2010; Weiss et al., 2003). Major, trace, and ultra-trace elements may enter the environment through numerous pathways, where a combination of individual element characteristics, prevailing physicochemical conditions, and speciation govern their geochemical behavior, bioavailability, and toxicity.

As the demand for raw materials changes over time due to socio-economic factors and technological advances, new contaminants emerge. One such group of emerging contaminants is the Technologically Critical Elements (TCE) (Barbante et al., 2001; Rauch et al., 2005; Tseren-Ochir et al., 2011). Production of these low abundance elements has increased dramatically over the last few decades due to new emerging application areas, most notably in advanced electronics and the introduction of automobile catalysts worldwide (Johnson- Matthey, 2003, 2004). The general pathways and behavior as well as potential negative health effects of these elements are poorly-documented and their low abundances in environmental and clinical matrices present an analytical challenge even for modern techniques. Environmental Forensics is a scientific methodology developed for identifying sources, the timing of the release, and transport pathways for contaminants. It combines a variety of analytical methods with principles derived from disciplines such as chemistry, geology, geochemistry, hydrogeology, and statistics, with the purpose to provide objective scientific and legal conclusions on the source and/or time of a contaminant release (Murphy & Morrison 2014; Förstner & Wittmann 2012: Miller 2013).

Over the last few decades, isotope ratio analysis has become an important tool within the Environmental Forensics’ toolbox. For example, isotope fingerprinting techniques for anthropogenic source tracing of lead have been accomplished in numerous studies in a variety of matrices (Cheng et al., 2010; Cloquet et al., 2006; Søndergaard et al., 2010; Baroni et al., 2015; Cheng et al., 2010; Cloquet et al., 2006; Hissler et al., 2008; von Blanckenburg et al., 2013)

13 The term fingerprint is often used synonymously with tracer. A tracer, however, is more strictly defined as a parameter that not only exhibits the characteristics of a fingerprint that is distinguishable from other materials but can be used to track the movement and cycling in the natural environment, such as post-depositional changes.

The number of parameters utilized in tracing studies can be expanded, providing a more powerful way to decipher sources and the fate of environmental exposure, by adding degrees of freedom to the process. For example, improvements in instrumental measurement precision and accuracy, as well as refined separation methods, have widened the field making measurements of heavier, “non-traditional” stable and radiogenic isotopes from almost the entire periodic table possible.

Multi-elemental and multi-isotope approaches in Environmental Forensics studies have become more viable as the optimization of analytical protocols with a focus on reliability and overall efficiency of methods combined with developments in instrumentation broaden the list of isotopes to be used and matrices to be studied (Álvarez et al., 2012; Pallavicini et al., 2018; Rabinowitz et al., 1995; Sangster et al., 2010; Rodushkin et al., 2013; Rodushkin et al., 2016; Ripperger et al., 2007; Wombacker et al., 2003).

The use of modern techniques such as inductively coupled plasma sector field mass spectrometry (ICP-SFMS) and multi-collector inductively coupled plasma mass spectrometry (MC-ICPMS) for trace and ultra-trace element concentrations and isotope ratio measurements has opened new opportunities in Environmental Forensics studies. High precision isotopic measurements have emerged as essential tools in environmental sciences, instrumental development and refining separation schemes have allowed higher quality data to be obtained and played a major role in the recent progress of the field. The work described in the present thesis has been focused on closing knowledge gaps in many parts of the field of Environmental Forensics, from analytical method development to process- as well as contaminant-tracing using multiple isotopes in environmental studies.

14 1.1 Research objectives and scope of the thesis

The motivations for this work have been the following:

(I) Further enhance the capabilities of modern ICP-SFMS instrumentation through optimization of sample introduction set-ups and sample preparation/analyte separation, with special emphasis on factors affecting detection capabilities and accuracy of data produced at ultra-trace levels for a wide range of analytes including emerging contaminants.

(II) Develop and optimize analytical protocols for separation and accurate measurement of isotope ratios in various environmental and geological matrices.

(III) To assess the extent of the spatial and temporal isotopic variability in anthropogenically affected environments.

(IV) Apply the aforementioned analytical progress within the emerging scientific field of Environmental Forensics

15 1.2 Ultra-trace analyses

Since its introduction in the 1970s, inductively coupled plasma (ICP) has become the most versatile atomization/ionization source in a range of analytical techniques, making ICP optical/atomic emission spectrometry (ICP-O/AES) and ICP mass spectrometry (ICP-MS) leading techniques for multi-elemental analysis. However, the moderate sensitivity of ICP- O/AES for certain elements has been the main obstacle for a wide coverage of trace and ultra- trace elements (Miekeley et al., 1998; Rodushkin et al., 1999a; Rodushkin et al., 1999b).

Though the introduction of ICP-MS in the 1980s has offered significantly improved detection capabilities, the accuracy of results obtained by low resolution, quadrupole-based ICP-QMS was still severely hampered by spectral interferences and matrix effects (Vanhoe et al 1994). Modern high-resolution sector field ICP-MS (HR-ICP-SMS or ICP-SFMS) instruments provide improved instrumental sensitivity (due to higher ion transmission) and lower dark noise (due to curved ion path between the source and the detector) and thus are capable of reaching sub-ng L-1 detection limits for almost all elements of the periodic table, except for H, O, N, C, and the noble gases (Becker 2002; Becker 2007; Jakubowski 1998; Jakubowski 2013). More importantly, the technique also possesses the ability to resolve many spectral interferences improving the accuracy of results for trace analytes in challenging matrixes. Moreover, flat- topped peaks in low resolution mode allow notable gains in isotope ratio precision compared with that offered by conventional ICP-QMS instruments (Becker 2007).

Despite the significant progress in instrumental and method developments making a reliable determination of analytes at µg L−1 in liquids and mg kg−1 in solids a matter of routine, the analytical challenges grow exponentially at or below ng L−1 in liquids and μg kg−1 in solids. High instrumental sensitivity is one of the mandatory factors for ultra-trace analysis, which, together with the level and stability of instrumental blanks, defines the detection capability of the technique (instrumental limits of detection, LOD). Contamination of the introduction system and samples, impurities of reagents used in sample preparation, spectral interferences, and the need for dilutions to minimize matrix effects effectively increase practically achievable method LOD, for some elements by several orders of magnitude. Accurate ultra-trace analysis requires not only the use of extremely sensitive and selective techniques but often also a combination of matrix separation and/or analyte pre-concentration procedures. Potential limitations of these time-consuming and delicate pre-concentration procedures include the need for quantitative analyte recoveries, the need for rather large sample volumes, and the increased sample contamination risks during excessive handling.

16 1.3 Isotope analyses

All isotopes of a given element have the same number of protons but different numbers of neutrons, therefore having different masses. The Greek term isotope, meaning "equal place”, refers to different isotopes of a single element occupying the same position in the periodic table of elements (Soddy, 1923). Isotopes of similar mass behave similarly in most chemical reactions, which are governed by the nuclear charge. However, mass differences between isotopes cause slight differences in reactivity and physicochemical properties. Only 21 elements in the periodic table consist of only one stable isotope, all other elements consist of mixtures of two or more stable isotopes (up to 10). Isotope abundance measurements are based on the fact that the sum of the abundances of all the isotopes belonging to the same element is 100% (Becker, 2007). Isotope ratio measurements may be divided into two groups, absolute ratios, and relative values compared to an isotopic standard. Absolute determinations of isotope ratios require standards of known isotopic composition. Achievement of this can be done by a double- spiking method, where known quantities of highly enriched isotopes are added.

Data produced in environmental studies (mainly for stable isotope systems) have traditionally been reported as relative values, referring to internationally recognized isotopic standards using the delta (δ) notation. The isotopic composition relative to the selected reference material is expressed as δ-values by normalizing isotope ratios in samples to the ratio of the isotope standard (Eq 1)

x y x y where M and M correspond to the two studied isotopes of each element, the ( M/ M)sample value x y refers to the measured sample isotope ratio and ( M/ M)0δstandar is the isotope ratio of the corresponding isotope standard. In the isotope ratio xM/ yM, the heavy isotope is in the numerator, and the light isotope in the denominator position. The factor 1000 is used to convert the δ-values to per mil notation. Using Eq 1 to describe the isotopic composition, a positive δ- value corresponds to an enrichment of the heavier isotope. However, it must always be clarified which isotope standard is used for comparison as reference materials used by laboratories may change over time.

1.4 Origin of isotope variations (fractionation)

Fractionation is defined as an alteration of the isotope ratio of an element, because of a physicochemical process (natural or anthropogenic). There are two major categories of isotopic variation in environmental matrices, radiogenic and stable isotope fractionation. Radiogenic isotopes are formed when a nuclide decays, thus changing the isotopic budget in the product material. Such variations in isotopic compositions of Pb, Sr, Nd, Os, Th, U, etc. have been widely used in geochronology and source tracing (Cheng et al., 2010; Søndergaard et al., 2010).

17 Stable isotopes do not undergo measurable radioactive decay. The relative mass difference between stable isotopes is the major factor in fractionation processes (Bullen 2012). The relative mass differences vary significantly throughout the periodic table. For example, the relative mass difference between 6Li and 7Li is almost 15%, decreasing to approximately 3% between 64Zn and 66Zn is 2%, and further to approximately 1% difference between 203Tl and 205Tl. Therefore, significantly more pronounced mass dependent fractionation is expected for Li than for U isotopes. The trend is clearly visible through the periodic table, where Figure 1 shows the extent of observed natural stable isotope variations for different elements from an IUPAC compilation made in 2002. The existence of natural isotopic fractionation of heavier transition elements is a relatively recent discovery. The main reasons for this being the low abundances of these elements and the assumption that the isotopic composition of elements heavier than Ca was constant (except for radiogenic isotopes) or exceeding small between all terrestrial materials (Vanhaecke & Heumann 2004; Russ III 1989).

Figure 1 The extent of natural stable isotope variations of chemical elements plotted against the atomic number. “Traditional” stable isotopes are marked with a red border and elements usually occurring only in one oxidation state in natural systems are marked with yellow symbols (modified from Wiederhold, 2015).

1.5 Major principles of the ICP Instrumentation

There is a plethora of instrumental types, designs, geometries, and hardware configurations available to the modern ICP-MS. However, most instruments share several common "building blocks” and analyte flow can be simplified as follows; the sample is introduced as a liquid through a nebulizer creating a fine mist of aerosol particles. After filtering off the major part of the larger aerosol fraction in into a spray chamber the particles are then introduced to the ionization source (an Ar-plasma generated by a high radio-frequency current operated at 1-1.5 kW). In the plasma particles desolvate, following by vaporization, atomization, and ionization of the content. The analytical zone of plasma with the highest ion densities is continuously

18 sampled through the continuously pumped interface into a high vacuum part of MS. The positively charged ions are accelerated towards the mass analyzer where separation of ions by the mass-to-charge ratio (m/z) takes place. After separation, ions reach the detector(s), and signals proportional to ion beam density are amplified and recorded by a detection system either simultaneously (multi-collector) or sequentially (single collector set up).

1.5.1 High-resolution single-collector, inductively coupled plasma double- focusing sector-field mass spectrometer (ICP-SFMS)

In the present study, determinations of element concentrations were performed using the Element XR (ThermoScientific, Bremen, Germany). The instrument can physically resolve analyte signals from many spectrally interfering species such as molecular and multiply charged ions in medium- and high-resolution modes, due to a combination of narrow entrance and exit slits and the double-focusing of the ion beam. Separation and focusing are done by a combination of a magnetic and an electric sector, hence “double-focusing”. A magnetic analyzer separates ions according to their momentum-to-charge ratio. However, the energy distribution of the ions produced in the highly energetic ion source is too broad to achieve high mass resolution. To compensate for this effect, an electric sector analyzer (ESA) is used to separate ions by their kinetic energy (Jakubowski et al., 1998). The ESA focuses ions of the same mass with varying kinetic energies towards the exit slit and detector. Several geometries can be used for combining magnetic and electric sector fields, the Element XR uses the so- called reverse Nier-Johnson geometry (magnetic analyzer before ESA Figure 2a).

The Element XR is equipped with a detector system consisting of discrete dynode as well as with a Faraday cup extending a linear dynamic range to >1012. There is generally no requirement for matrix separation for either liquid or solid samples as dilution is often sufficient to overcome severe matrix effects on the sample introduction system, plasma, and MC interface. These capabilities made it a very versatile, ’omnivore’ instrument used in the present thesis to measure element concentrations in water samples, sample digests, and purified fractions after column separation using a combination of internal standardization and external calibration.

1.5.2 Multi-collector, inductively coupled plasma mass spectrometer (MC- ICPMS)

Analytical developments of Multi-collector instrumentation (MC-ICPMS) over the last three decades have made high-precision stable isotope analyses of almost the entire periodic table possible, with stable isotope geochemistry applications a fast-growing field (Miller 2013; Wiederhold 2015). The Neptune PLUS (ThermoScientific, Bremen, Germany) MC-ICPMS was used for isotope ratio measurements throughout the papers of this thesis. Isotope ratio measurements using single-collector instruments are possible, by sequentially measuring each isotope of interest in the single detector. However, instability of the plasma ion source (flicker noise) combined with the sequential detection, limits measurement precision significantly (Jakubowski et al., 2011).

19 To overcome this effect of instability of an ion source, the Neptune PLUS has nine detectors (Faraday cups), allowing (with few exceptions) the simultaneous detection of all isotopes of interest in static mode. This produces data of high quality with the precision of isotopic measurements (expressed as relative standard deviation) better than 0.01% for heavier elements. Eight of the nine Faraday cups (L4, L3, L2, L1, H1, H2, H3, and H4) can freely be positioned along the focal plane of ion beams with µm precision, with a fixed center detector (C). The maximum relative mass range for the outermost detectors, L4 and H4, is 17%, which limits the range of isotopes for simultaneous measurement somewhat. Table 1 displays all cup configurations used throughout this thesis.

An additional requirement for high precision isotope ratio measurements is wide flat-topped peaks with sharp rising flanks since small fluctuations in the mass calibration would otherwise severely lower the precision. Wide flat-topped peaks are achieved in high resolution multi- collector instruments using a narrow entrance slit and a wider exit slit.

The Neptune PLUS has a forward Nier-Johnson geometry (the magnetic analyzer is positioned after the ESA, Figure 2b). More detailed descriptions of the Neptune are given elsewhere (Weyer and Schwieters, 2003; Wieser and Schwieters, 2005).

It should be noted that the resolving power (R = m/Δm), has different definitions on the single and multi-collector. In the conventional approach, for the single collector, the mass difference Δm is defined as the full width of the peak at 5% of its height (Montaser, 1998). Since the multi- collector has wide flat-topped peaks the conventional approach cannot be applied. The alternative approach used is called pseudo-resolution, where conventional Δm is replaced by the mass difference between 5% and 95% of the plateau height (Weyer and Schwieters (2003).

Figure 2 Schematics of ICP instruments. a) Reverse Nier-Johnson geometry (magnetic analyzer before ESA) of the Element XR. b) Forward Nier-Johnson geometry (ESA before magnetic analyzer) used in the Neptune PLUS.

20 Table 1 MC-ICP-MS parameters and cup configurations used for isotope ratio measurements in this thesis.

Resolutio Integration Blocks / Elements Cup configuration n mode time (s) Integrations L4 L3 L2 L1 C H1 H2 H3 H3 Paper

108Cd 110Cd 112Cd 114Cd 116Cd Cd/Ag* a Low 0.524 9/5 107Ag 109Pd 111Cd 117Sn III 108Pd 110Pd 112Sn 114Sn 116Sn 54Cr Cr Main High 1.262 9/3 - 52Cr 53Cr - 56Fe - - - II 54Fe Cr/Ni*a Sub. High 1.262 9/3 - - - - - 60Ni 61Ni 62Ni - 58Fe Fe/Ni* Medium 0.262 9/3 54Fe - 56Fe 57Fe 60Ni 61Ni 62Ni - IV 58Ni 204Pb Pb/Tl*a Low 0.524 9/5 - 202Hg 203Tl 205Tl 206Pb 207Pb 208Pb - III 204Hg

U c Low 0.524 9/5 - - - 234U 235U 236U 238U - - IV

64Zn Zn/Cu*b Medium 0.262 9/5 - 63Cu 65Cu 66Zn 67Zn 68Zn 70Zn - III 64Cu * internal standard. a introduction system: Aridus/Apex desolvating systems, self-aspirating microconcentric PFA nebulizer, X-type skimmer cone. b introduction system: Pumped Micromist nebulizer, double spray chamber, H-type skimmer cone. c Bracketing standard only.

1.6 Avoiding contaminations: clean room

All qualitative analytical procedures rely on proper planning of representative sampling and meticulous sample handling considering contamination sources. While all sample modifications should strive to be contamination-free, throughout the analytical chain, the requirements are strongly dependent on the sample matrix and the analyte of interest (Irrgeher and Prohaska, 2015). In particular, for trace and ultra-trace studies where element concentrations often are below ng L−1 levels, contamination control at all stages of sampling, transport, sample manipulation, and analysis cannot be overemphasized (Boutron, 1990; Planchon et al., 2001; Rodushkin et al., 2010). All the experiments and sample manipulations performed in this thesis were conducted in clean laboratory areas (Class 10.000) by personnel wearing clean room gear and following all general precautions to reduce contaminations (Rodushkin et al., 2010).

1.7 Quantitative recovery

Ultra-trace analysis often requires a combination of matrix separation and/or an analyte pre- concentration procedure. Since many measurements are performed in samples with low analyte concentrations and with challenging matrixes, quantitative retaining of the analyte through the entire procedure is of paramount importance. Significant losses result in underestimation of the target analyte content and may introduce isotope artificial mass fractionation. In order to accurately estimate overall method recovery, separation and preconcentration of a certified reference material (CRM) should be performed. However, CRMs for ultra-trace elements are very scarce. Recovery evaluation can therefore be performed by sample spiking with the elements of interest prior to evaporation and separation steps. For example, an aliquot

21 of a brackish water sample can be analyzed directly on the ICP-SFMS and compared with a spiked matrix-matched aliquot from the same sample after matrix separation and/or an analyte pre-concentration procedure. For isotope ratio measurements matrix separation is almost always a must due to spectral interference. Recovery of 100% of the analyte of interest is required from the ion-exchange column to ensure that no artificial isotopic fractionation is occurring during the analyte to column interactions. Significant∼ column fractionation has been demonstrated in sequentially eluted fractions of Cu, the progressive effect being as large as 9.7‰ between the first and the last fraction, with the lighter isotope preferentially retained by the column resin (Maréchal et al., 1999; Maréchal & Albarède 2002). Clearly, the more times a sample is processed, transported between sample vessels, and passed through multiple columns the less is a likelihood to achieve 100% overall recovery. Therefore, the analytical procedure should be optimized to lower the sample manipulation as much as possible and the effectiveness of matrix separation should not be achieved by sacrificing analyte recovery. During the studies present in this thesis, this was accomplished by multi-elemental analysis of all fractions by ICP-SFMS. providing (I) direct assessment of analyte recovery, (II) information on separation efficiency from matrix elements, (III) information on analyte concentration and, (IV) information on the potential presence of spectrally interfering elements and isobars either from the sample matrix or from contamination during sample preparation.

1.8 Spectral interferences

Spectral interferences have long been recognized as one of the major obstacles for accurate ICP-MS measurements. Accurate trace-element and isotope analyses require the efficient removal of polyatomic and isobaric interferences appearing at both high- and low-mass sides of the isotopes of interest. The argon plasma itself, combined with water, acid, and the sample matrix can produce/give rise to a wide range of polyatomic ion species on the same nominal mass as the analyte ion and thus result in inaccurate measurements, skewing analyte concentrations towards overestimations compared to the real ones (Becker 2002; Becker 2007; Jakubowski et al., 1998; Jakubowski et al., 2011; Lum and Sze-Yin Leung, 2016; Vanhoe et al., 1994;). The interference from these polyatomic ions is most effectively reduced using a higher resolution mode. However, there are a number of spectral interferences, especially in the mass range 90–120 amu, which cannot be resolved with currently available instrumentation. Moreover, significant reduction of sensitivity while increasing resolution (almost by a factor of 100 between low resolution (LR) and high-resolution (HR)) affects detection capabilities, limiting the versatility of this approach when handling samples with low concentrations. Apart from polyatomic interferences isotopes are also subject to interference from isobars, i.e. isotopes from other elements with the same nominal mass, which are impossible to separate with physical resolution (such as 54Fe+ on 54Cr+). However, isobars can effectively be removed prior to instrument introduction using a matrix separation scheme(s). The effect of residual elements causing isobaric interferences on isotope ratios of interest can be minimized by using

22 mathematical corrections and the efficiency of the latter should be evaluated by spiking purified fractions with increasing amounts of interfering elements,

1.9 Instrumental mass bias

Instrumental fractionation termed a mass bias originates from plasma/sampling/extraction/transport processes favoring the transmission of heavier ions. For example, lighter isotopes are to a larger extent lost from the ion beam, mainly because of repulsive forces between the positively charged ions and collisions with heavier ions. The monitoring and correction of analytical mass bias in MC-ICPMS measurements are particularly important due to the magnitude of the effect, often several % per atomic mass unit (>10%, 2%, and <1% is observed for Li, Fe, and U, respectively (Andrén et al., 2004; Weyer and Schwieters, 2003). However, the magnitude is dependent on the configuration of the introduction system, daily operating parameters/conditions and on the sample matrix, Therefore, mass bias needs to be continually monitored during each instrumental session and efficiently corrected for. While instrumental mass bias is a phenomenon affecting isotope ratios measured by all forms of mass spectrometry, there is currently no consensus on the best approach to compensate for it. One strategy, originally proposed by Longerich et al. (1987) is the use of an internal standard element (or ratio of stable isotopes) to correct mass bias by external normalization, a technique receiving significant praise and improvements by contributions from among others Baxter et al., (2006), Maréchal (1999), and Woodhead (2002). Since differences in matrices affect the ionization efficiency and may consequently modify the instrumental mass bias, internal standard and double spike techniques are also used. To minimize these effects all samples should be closely matrix- and concentration-matched to cancel out possible matrix effects. All isotope ratio measurements on the MC-ICP MS except U ratios in paper IV, were performed by a combination of internal standardization and a bracketing technique where δ-values were calculated against a δ-zero solution following the revised exponential correction model by Baxter et al. (2006) to correct for instrumental mass-bias. Three samples were analyzed between two standards, together forming a block (block: standard 1 – sample 1 – sample 2 – sample 3 – standard 2). The mean value of the two consequent measurements of the sample ratio was calculated against ratios for standards in each block. Assuming a linear change in mass bias, ratios for samples 1 and 3 were calculated relative to those for standards 1 and 3, respectively, while sample 2 was calculated against the mean ratio for both standards. Results from the two measurements were used to calculate mean δ-values and σ for each sample.

2.0 Quality control

Assessment of method performance included frequent precision, reproducibility, and accuracy controls. In-run instrumental repeatability (precision) was estimated as twice the standard deviation (SD) of duplicate consecutive measurements of all samples (single sample preparation/separation). Overall reproducibility of the method was evaluated by replicate preparation, separation, and multiple analyses of a set of CRMs and samples from each preparation batch. The set of samples

23 used in Paper I and II was representative for the matrices studied, and prepared, and analyzed in different analytical sessions to ensure long-term reproducibility. The accuracy of concentration data for most of the analytes was verified by analyses of various CRMs and comparison with certified values in Paper II, III, and IV. However, there are no CRMs with certified values for ultra-trace concentration for TCEs or PGEs, therefore the results in Paper I was compared to the very limited published values on available CRMs. The reference materials utilized in papers are not certified for isotopic composition. Therefore, the presented isotope ratio data’s accuracy was evaluated by comparing it with published isotope data on available CRMs where such published values could be found.

3.0 Summary of results

This section provides a summary of the results obtained through the analytical method development and application of Environmental Forensics techniques. For this purpose, individual subsections will be dedicated to an overall description of the main experimental procedures followed by the most interesting findings. Paper I and II describes analytical method developments and to a lesser extent application of optimized methods to real word samples. Paper II, III, and IV are application focused.

24 3.1 Paper I: Determination of ultra-trace concentrations of TCEs in complex matrices

Paper I describes the development and optimization of analytical protocols for ultra-trace concentration measurements, with an emphasis on the determination of Au, Ag, Ir, Os, Pd, Pt, Re, Rh, Ru, Sb, and Te.

3.1.1 Instrumental blanks

Measurements were performed on two different ICP-SFMS instruments, one dedicated to the analysis of clinical samples and one reserved for purity testing of reagents used by the laboratory. To reduce instrumental blanks further, introduction systems were cleaned in the following sequence: hot concentrated HCl, hot concentrated HNO3, overnight leach by 1.4 M HNO3 followed by multiple rinses with Milli-Q water. Sample probe and nebulizers were cleaned off-line by passing diluted HCl + HNO3 + thiourea mixture overnight.

For the two ICP-SFMS instruments used, background equivalent concentrations (BEC) in 0.2 M HCl were typically at single pg L−1 level for Ir and Re, and in the range of 10–30 pg L−1 for Os, Pd, Pt, Rh, and Ru. Typical BECs for Ag and Au were 200 pg L−1 and 500 pg L−1, respectively. Higher BEC for Ag and Au were due to memory effects from regular analyses of serum/urine test samples spiked with hundreds of μg L−1 of Au during regular contract analyses on the instrument.

3.1.2 Pre-concentration, separation, and interference

Evaporation pre-concentrated almost all elements present in samples indiscriminately. However, this step alone provides limited improvement to the detection capabilities for elements severely affected by spectral interferences. The combination of evaporation and ion- exchange provided enrichment factors >50 for melted snow and freshwaters while allowing analysis of seawaters and body fluids with no or very minor dilution. However, modest (if any) improvements in method limit of detection (MLOD) after pre-concentration compared to MLOD for direct analysis reflects contamination from beakers, acids, laboratory environment, and handling contamination because of long evaporation times and extensive manipulation of the sample as well as column blanks.

3.1.3 Accuracy and reproducibility

Evaluation of the accuracy of the method by applying matrix-matched CRMs was not possible since no such CRMs are commercially available for the ultra-trace elements studied. Hence, data generated were compared to the very limited number of ultra-trace studies published on natural river water CRM SLRS-4.

Antimony, Pt, and Ir concentrations found were in close agreement with values published by Shimamura et al. (2007) and Soyol-Erdene et al. (2011). There are several publications reporting Ag in SLRS-4 with a wide spread of results, ranging from 0.5 to 5 ng L−1 (Dressler et al., 2001; Krachler et al., 2005, 2004; Shimamura et al., 2007), with values obtained in this

25 study supporting the low end of the range. Antimony was the only analyte measured with a certified value (230±40 ng L−1), in good agreement with values obtained 255±22 ng L−1.

The reproducibility of the method was assessed by replicating the entire procedure for several water samples as well as for body fluid control materials (each batch was prepared and analyzed in triplicates). Reproducibility was better than 10% RSD for analytes with concentrations above 100 ng L−1 but increasing towards 30–50% RSD for sub ng L−1 concentrations.

3.1.4 Concentrations: Snow

Gold, Ag, Ir, Pd, Pt, Re, Rh, Ru, Sb, and Te were successfully measured in all snow samples; fresh snow, snow column, and roadside snow, comfortably over the method detection limit. Concentrations for all elements measured can be seen in Table 2. These results are to our knowledge, the most extensive measurements of TCE/PGE in snow.

Palladium, Pt, and Rh concentrations found in fresh snow from Luleå are similar to those reported for remote snow/ice samples from the European Alps, Greenland, rain from the suburbs of Tokyo as well as snow deposited in Greenland and Antarctica (1969–1995).

The similarity in concentrations found is somewhat surprising given the spatial differences and that the higher current anthropogenic load of these elements should be reflected in higher concentrations. Studies of airborne dust (Alt et al., 1993) demonstrated that only 30–43% of Pt in airborne dust is soluble. This was confirmed our finding, since the addition of aqua regia to the dry residue before re-dissolution significantly increases measured concentrations of many elements (e.g. Ir, Pt, Pd, Au) while not affecting preparation blanks notably, indicating that a significant part of these analytes is particle-bound.

Table 2 Concentrations in snow samples

MLOD Direct MLOD Pre- Fresh snow, Snow column, Roadside Element analysis ng L−1 concentration Mean (SD) ng L−1, Mean ng L−1, snow, ng L−1, ng L−1 n=14 n=4 n= 8 Ag 0.2 0.05 3.3(2.9) 1.7 2,8 Ala 0.2 1.4 25(8) 30 710 As 1 0.5 130(22) 50 210 Au 0.3 0.05 0.07(0.04) 0.09 0.9 B 50 3 40(20) 70 920 Ba 2 6 620(210) 1300 13 000 Be 0.01 0.01 0.52(0.33) 2.4 34 Bi 0.5 0.05 6(3) 6 21 Caa 0.3 2 90(30) 140 1600 Cd 0.05 0.02 12(3) 11 13 Ce 0.02 0.1 46(16) 80 2900 Co 0.05 0.5 34(14) 150 4300 Cr 0.3 3 220(70) 240 3200 Cs 0.07 0.01 4(1) 7 120 Cu 50 30 1900(600 3100 6100 Dy 0.01 0.02 3(1) 5 86 Er 0.01 0.005 0.6(0.2) 1.72 45 Eu 0.01 0.007 0.5(0.2) 0.9 18

26 Fea 0.05 0.9 110(45) 84 970 Co 0.05 0.5 34(14) 150 4300 Cr 0.3 3 220(70) 240 3200 Ga 0.1 0.2 15(6) 15 330 Gd 0.01 0.02 1.8(0.6) 3 100 Hf 0.02 0.05 1.9(0.9) 2 37 Ho 0.005 0.002 0.20(0.07) 0.5 18 Ir 0.007 0.0004 0.005(0.003) 0.009 0.06 La 0.03 0.06 16(5) 50 1700 Ga 0.1 0.2 15(6) 15 330 Gd 0.01 0.02 1.8(0.6) 3 100 Hf 0.02 0.05 1.9(0.9) 2 37 Li 0.1 1 40(18) 70 1400 Lu 0.002 0.001 0.11(0.04) 0.25 6 Mga 0.06 0.7 19(5) 50 540 Mn 1 4 6000(2000) 2400 25 000 Mo 0.1 0.6 36(12) 32 150 Naa 0.3 3 95(27) 600 6300 Li 0.1 1 40(18) 70 1400 Nb 0.05 0.01 69(27) 13 140 Nd 0.04 0.03 14(5) 36 1200 Ni 1 2 120(25) 93 1700 Osb 0.01 NA <0.01 <0.01 0.04 P 500 10 1900(500) 3500 38 000 Pb 0.1 2 400(150) 280 780 Pd 0.1 0.01 0.13(0.06) 0.19 5.4 Pr 0.01 0.02 3.9(1.4) 12 340 Pt 0.04 0.01 0.12(0.05) 0.09 0.36 Rb 2 5 63(21) 140 3100 Re 0.005 0.001 0.11(0.05) 0.1 0.12 Rh 0.04 0.003 0.014(0.006) 0.03 0.15 Ru 0.04 0.003 0.017(0.008) 0.024 0.17 Sa 4 0.2 140(20) 180 210 Sb 0.3 0.1 54(31) 100 360 Sc 0.04 0.02 2.3(0.9) 5 110 Se 5 0.8 25(9) 17 33 Sm 0.03 0.01 2.0(0.7) 5 170 Sn 8 1 150(60) 70 320 Sr 7 3 140(50) 700 11,000 Ta 0.01 0.001 0.13(0.07) 0.8 51 Tb 0.005 0.002 0.23(0.07) 0.6 15 Te 3 0.07 1.5(0.8) 1.9 2.1 Th 0.02 0.03 4.4(2.0) 10 470 Tia 0.01 0.08 1.4(0.6) 1.6 51 Tl 0.03 0.005 2.6(0.8) 1.6 22 Tm 0.005 0.001 0.089(0.035) 0.3 6.2 U 0.01 0.02 2.3(0.6) 6 160 W 0.03 0.05 93(44) 100 2700 V 0.1 0.3 990(400) 480 2500 Y 0.1 0.03 8.2(2.9) 17 550 Yb 0.01 0.006 0.59(0.22) 1 42 Zna 0.3 0.1 2.7(1.2) 4.7 28 Zr 0.06 0.1 59(27) 30 700 a Concentrations in mg L-1 .

27 3.1.5 Concentrations: Water

There are few elements that can be detected in distilled, de-ionized water (DDIW) filled into plastic bags for snow collection and analyzed after 60-fold pre-concentration (Table 3). However, the spread of the results is rather high, reflecting high uncertainty in results close to MLOD for the studied elements.

Tap water has significantly higher levels of all analytes than snow but Os showed concentrations just above MLOD. Concentrations of Pt in tap water are close to the discharge- weighted mean for dissolved Pt in major rivers of East Asia (~0.07 ng L−1, Soyol-Erdene and Huh, 2012). Except for Re, all other elements have similar concentrations in tap water and snow samples (Tables 2 and 3).

Table 3 Concentrations in DDIW, tap water and spike recovery tests for melted snow samples

Element DDIW Tap water Recovery Recovery Recovery ng L− 1 ng L− 1 10 ng L− 1% 1 ng L− 1% 0.1 ng L− 1% n = 6 n = 10 Ag 0.09(0.06) 8.5(3.5) 97 95 87 Au < 0.05 0.41(0.36) 75 73 58b Ir < 0.0004 0.004(0.003) 95 92 85 Osa < 0.01 < 0.01 22 18 14 Pd < 0.01 0.12(0.06) 106 105 93 Pt 0.016(0.004) 0.15(0.13) 106 100 87 Re < 0.003 2.6(0.6) 98 95 96 Rh < 0.003 0.019(0.012) 101 103 99 Ru < 0.003 0.021(0.008) 104 100 94 Sb 0.16(0.04) 25(8) 89 NA NA Te < 0.07 0.9(0.4) 88 87 77b

Concentrations measured in water CRMs as well as in Laptev seawater samples are presented in Table 4, where only Ag, Au, Pt, Re, Sb, and Te were detected in most samples. Mean Pt concentrations found in Laptev seawater are in agreement with these estimates, although the majority of results in our study were below MLOD, as was the case for all Pd, Rh, and Ir results. Measured seawater concentrations of Ag, Re, and Sb are consistent with those compiled by (Bruland and Lohan, (2006). However, much lower Te concentrations have been reported (0.064–0.153 ng L−1), i.e., two to three orders of magnitude lower than found in this study. The reason for significantly lower mean Au and Pt concentrations in Laptev seawaters compared to seawater CRMs (Table 4) is unclear.

28 Table 4 Concentrations in snow samples

Laptev sea Recovery SLRS-4 SLEW2 SLEW3 NASS-4 NASS-6 CASS-5 Element samples 10 ng L− 1% ng L− 1 ng L− 1 ng L− 1 ng L− 1 ng L− 1 ng L− 1 ng L− 1 n = 12 n = 6 n = 6 n = 6 n = 6 n = 6 n = 40 Ag 94 0.5(0.2) 2.6(0.4) 2.2(0.6) 14(2) 13(3) 16(3) 5.1(3.3) Au 71 0.8(0.2) < 0.3 1.7(0.3) 113(9) 1.4(0.3) 11(2) < 0.3 Ir 99 0.011(0.005) < 0.007 < 0.007 < 0.007 < 0.007 < 0.007 < 0.007 Os 55 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 Pd 92 < 0.1 < 0.1 < 0.1 0.10(0.03) 0.7(0.2) 0.7(0.2) < 0.1 Pt 96 1.31(0.09) 5.4(0.3) 4.2(0.3) 3.4(0.2) 2.5(0.3) 3.6(0.4) 0.06(0.04) Re 107 6.9(0.4) 4.6(0.3) 5.5(0.4) 7.3(0.4) 7.6(0.5) 7.4(0.4) 4.7(2.2) Rh 97 < 0.07 < 0.07 < 0.07 < 0.07 < 0.07 < 0.07 < 0.07 Ru 96 < 0.04 < 0.04 < 0.04 < 0.04 < 0.04 < 0.04 < 0.04 Sb Naa 255(22) 388(25) 250(14) 250(22) 280(35) 490(30) 220(48) Te 85 4.4(1.5) 9(3) 11(3) 46(5) 48(5) 45(6) 29(21) a Too low spike compared to native Sb concentration in sample.

3.1.6 Concentrations: Clinical

Comparing whole blood concentrations for two volunteers to pooled controls, there is a close agreement apart from slightly lower Ag, Sb, and Te in the former. Volunteer A has no known exposure to PGEs while volunteer B has actively participated in performing the analytical work for the present study. Differences in Ir, Pt, Rh, and Ru can be observed between the blood concentrations of the two. This might be due to the handling of concentrated PGE standards. (Table 5). Concentrations of Pd, Rh, and Ir are lower than previously reported (Begerow et al., 1997; Krachler et al., 1998), sometimes by two orders of magnitude. This emphasizes the importance of preventing contamination and/or efficient elimination of spectral interferences. To the best of our knowledge, no reliable Ru or Re concentrations in body fluids have been published previously.

Table 5 Concentrations in clinical samples. aserum contains >200 μgL−1 Au added during production. Pooled whole LOD Recovery Whole Whole Pooled serum, Pooled urine, blood, Element ng 10 ng blood A blood B Mean(min-max) Mean(min-max) Mean(min-max) L−1 L−1% ng L−1 ng L−1 ng L−1 n=4 ng L−1 n=10 ng L−1 n=8

Ag 2 104 28 43 136(97–166) 237(163–290) 23(10–52) Au 0.7 72 20 37 12(2−30 Naa 3.2(0.7–8.3) Ir 0.02 102 <0.02 0.13 0.023(<0.02–0.04) 0.19(0.14–0.26) 0.015(b0.02–0.04) Pd 0.2 104 <0.2 0.24 0.20(<0.2–0.42) 0.19(<0.2–0.37) 0.94(<0.2–2.5) Pt 0.1 90 0.2 1.2 1.7(0.2–4.1) 1.4(1.2–1.6) 2.1(0.3–5.7) Re 0.02 94 1.5 2.7 1.7(0.6–2.5) 1.7(0.8–2.4) 48(23–75) Rh 0.07 104 <0.07 0.24 0.31(<0.07–0.8) 0.12(<0.07–0.16) 0.20(<0.07–0.4) Ru 0.07 99 <0.07 0.35 0.06(<0.07–0.07) 0.09(0.07–0.12) 0.12(<0.07–0.21) 12,000 (1000– 15,000 (7000– Sb 4 86 650 1600 20,000) 31,000) 1050 (340–1600) Te 3 87 4 7 35(20–50) 9(7–14) 32(20–45)

29 3.2 Paper II: Chromium isotope ratio measurements in environmental matrices

Paper II is based on the development and optimization of an analytical protocol for matrix separation, isotope ratio measurements, and application of Cr-isotopes as a tracer.

3.2.1 Separation efficiency, accuracy, and reproducibility

Chromium occurs as either cationic Cr(III) or anionic Cr(VI) species, and the separation scheme was developed around this charge dichotomy. Separation protocols are therefore adapted accordingly to either 1) separate Cr(III) and Cr(VI) individually and later merging them prior to analysis or 2) converting all Cr to the chosen valence state and adapting the separation to either the cationic or anionic properties. While both approaches have their benefits, this protocol was developed to simplify the separation processes, by providing high recoveries using a single column pass. The matrix separation procedure was adapted from previously proposed protocols (Ball and Bassett, 2000; Schoenberg et al., 2008). To achieve this all Cr was converted to Cr(VI) using a strong oxidizing agent (NH4)2S2O8 coupled with small amounts of NH4 before loading onto the column. Cr(VI) forms oxyanions in weak hydrochloric acid matrix which is retained in the column while matrix elements are washed out. The separation protocol was evaluated on a wide range of environmental matrices; Cr-bearing minerals, soils, lichens, and mosses with Cr-concentrations varying several orders of magnitude, providing consistent recoveries exceeding 93%. However, trace levels of interfering elements such as Fe leave room for separation improvements to be made. In-run instrumental repeatability was, as a rule, better than 0.04‰ with a traditional solution nebulizer, and better than 0.09‰ using the Apex introduction system. Method reproducibility assessed by including in-house soil control samples corresponded to a 2σ of 0.11‰. No matrix-matched reference material with a certified Cr isotopic composition exists, hampering the possibilities to evaluate the accuracy. The δ53Cr measured for a basalt CRM, JB- 1, (-0.091± 0.052‰) is in between two published by Ellis et al. 2002 (-0.04‰, with no stated uncertainty) and Schoenberg et al. 2008 (-0.178±0.048‰).

3.2.2 Cr concentrations and isotope ratios: tracer application

Lichen and moss samples, as well as soil samples from six vertical soil profiles, were obtained from two preceding studies (Pallavicini et al., 2016; Rodushkin et al., 2010). Soil samples were collected in the suburbs of Luleå, and biological samples were collected along a transect stretching from the Swedish/Finnish border in the northeast to Luleå in the southwest. The δ53Cr values in lichens and mosses vs inverse Cr concentrations show a relatively strong correlation (R2 = 0.7, Figure 3). Lichens with the highest Cr concentration, sampled at approximately 2 km from the steelworks located close to the Swedish/Finnish border in Torneå, have mainly negative δ53Cr values. The predominantly light composition of lichens and mosses

30 is likely a result of smelting and refining processes producing airborne light Cr. Further away from the smelter a heavier δ53Cr composition was observed, the signature found in lichens and mosses in Luleå is heavier than in the local soils (+0.4‰). This result can be explained as a predominantly wet deposition contribution. In this work, the first-ever Cr isotope data for lichens and mosses suggest that these species can serve as bio-indicators for tracing air-borne Cr pollution emitted from stainless steel smelters.

Figure 3 δ53Cr in chromites and environmental samples as a function of inverse Cr concentration, error bars are 2σ

31 3.3 Paper III: Multi-isotope approach: spatial and temporal variability in anthropogenically affected matrices

Heavy metal contamination was identified in groundwater monitoring wells surrounding a waste deposit facility at the Rönnskär Cu–Pb–Zn smelter in Skellefteå, northern Sweden, as well as in brackish water and sediments from the nearby harbor. Paper III studies the surrounding area, brackish water from the Baltic Sea and sediments from a nearby harbor were also determined to be contaminated. This paper focuses on the ranges of isotopic compositions of four elements (Cd, Cu, Pb, and Zn) in smelter materials (ores, products, waste, and waste- leachate) and polluted groundwater sediments of the affected area (Figure 4).

Figure 4 Map of the Rönnskär smelter – Bothnian Bay area in northern Sweden. Sampling locations of transect sediment cores are marked A-E. Brackish water sampled in the Skelleftehamn harbor basin (Site A), and Bothnian Bay “clean” reference water at the site marked *.

3.3.1 Concentration: Water

Mean concentrations of selected elements in unpolluted surface water from the Bothnian Bay and brackish waters from the harbor basin are shown in Table 6. A more than tenfold increase in mean concentrations was found in harbor water for As, Cd, Re, and Zn. These elements were also present at high levels in groundwaters at a waste deposit site at the Rönnskär smelter, described in a preceding study (Sutliff-Johansson et al., 2020). Concentrations of trace elements in the harbor water are, however, several orders of magnitude lower than in the groundwater surrounding the waste deposit area. This difference in concentration is due to dilution by open Bothnian Bay water.

32 Table 6 Element concentrations in leachate, deposit well waters, groundwaters, Bothnian Bay background reference, brackish water sampled outside deposit facilities and sediments from the transect outside the Rönnskär smelter. Values displayed as mean (median) min - max.

Deposit well Bothnian Bay Brackish Element Leachate Groundwater Sediments water reference water water

n = 2 n = 10 n = 40 n = 6 n = 15 n = 20 mg/L mg/L mg/L µg/L µg/L mg/kg

As 5.67 454 0.21 0.05 1.7 655 ( - ) (0.2) (0.015) (0.05) (1.7) (244) 4.98 - 7.05 0.06 - 2610 0.0003 – 1.0 0.4 - 0.6 1.3 - 2.6 7.5 - 2850

Cd 9460 3020 73 0.02 2.2 21.5 ( - ) (41) (0.28) (0.02) (0.2) (1.0)

9220 - 9700 0.1 - 19600 <0.005 - 560 0.01 - 0.02 0.1 - 24 0.1 - 314

Cu 276 202 0.96 0.9 2.7 458 ( - ) (112) (8.4) (0.9) (2.5) (128)

269 - 282 0.2 - 799 <0.0003 – 7.9 0.7 - 1.1 1.9 - 4.9 7.7 - 2180

Mo 0.28 0.02 0.18 1.0 1.3 6.9 ( - ) (0.03) (0.23) (1.0) (1.1) (3.4)

0.27 - 0.28 <0.01 - 0.09 <0.0004 – 1.1 0.9 - 1.0 1.0 - 3.6 0.5 - 40

Pb 80,7 26.9 0.05 0.1 0.4 366 ( - ) (2.7) (<0.005) (0.1) (0.3) (102) 79.6 - 81.8 4 - 1730 <0.0005 – 0.4) 0.1 - 0.2 0.1 - 1.2 4 - 1730

Re 5,37 0.006 0.12 0.01 0.2 0.006 ( - ) (0.001) (0.22) ( 0.01 ) (0.3) (0.001)

5.36 - 5.38 <0.001 - 0.04 <0.0005 – 0.68 0.01 - 0.01 0.1 - 0.3 <0.001 - 9.2

Zn 92000 984 880 3.5 66 980 ( - ) (171) (10.1) (3.2) (12.1) (171) 91600 - 92400 26.3 - 9950 0.04 - 6500 2.4 - 5.3 8.8 - 640 26 - 9950

33 3.3.2 Concentration: Sediment

The elevated concentrations observed in the brackish water near the smelter are expected to affect the sediment composition outside the site. The extent of contamination was evaluated in several ways. One approach was to calculate an enrichment factor (EF) used based on comparing element concentrations of contaminated sediments to local sediment background concentrations. The background concentrations were represented by the mean concentrations in the 1.0–5.8 m section of a 5.8 m deep sediment core from the open Bothnian Bay (Ingri et al., 2014). This core is dated back to 5500 years BP and provides one of the longest records of sediment background concentrations in the Bothnian Bay. The EF was calculated as follows: ([Analyte]sample / [Analyte]deep core average) and was used to estimate the anthropogenic contribution to the total concentration in sediments outside of the Rönnskär site. Figure 5 shows

Figure 5. Enrichment factors for As, Cd, Cu, Mo, Pb, Re, and Zn using non-linear interpolation between the transect core samples. Cadmium concentration displayed on a log scale peak EF >1500.

34 sediments closer to the Rönnskär smelter are significantly more enriched in the elements believed to be associated with the smelter operation. The maximum enrichment for As, Cd, Cu, Pb, Re, and Zn found approximately 15 cm below the sediment surface reflects the effect of the implementation of the recent measures in pollution control.

3.3.3 Isotopic composition

Summary of isotopic information for Pb, Zn, Cd, and Cu is presented in Table 7 for topsoils, lysimetric waters, sulphide minerals, pure chemicals, K1 and F1 dust samples, leachates, deposit well water, groundwater, and sediments. Topsoils and lysimetric waters were included from a preceding study (Pallavicini et al., 2018) as they are considered as a major source of near shore sediments from relatively unpolluted areas, and therefore may provide an estimation of local background isotopic compositions. The existence of differences in mean isotopic compositions of stable elements between these two matrices would indicate fractionation during soil leaching process, a factor that must be considered when evaluating the leaching of dust samples. The isotopic composition of sulphide minerals and pure chemicals characterize potential isotopic changes in material flows entering and leaving a smelter. Sulphide minerals can also be considered as a possible direct pollution source for near shore sediments by losses during transport through and unloading in the harbor. Characterization of leachate, deposit well water, and groundwaters provides an opportunity to study potential post-deposition isotopic fractionation during dust leaching, migration of contaminated waters, and secondary mineral formation. Finally, sediments serve as a reservoir for various inputs from the area where isotopic signatures of individual sources are blended. One obvious missing link between different potential pollution sources and sediments is brackish water with no isotopic information available. Regretfully, a combination of limited volumes of brackish water sampled, relatively low analyte concentrations, and the challenging matrix have prevented reliable isotope ratio measurements from being performed in this matrix. A very simplified mixing situation with two isotopically distinct sources, natural (with a low concentration of elements studied) and anthropogenic (with high concentrations), should result in a linear regression line on a graph where the isotopic composition is plotted as a function of inverse element concentration. The intercept of the y-axis would then provide the isotopic composition of the anthropogenic source.

35 Table 7 Lead, Zn, Cd, and Cu isotopic composition in environmental, geological, and industrial samples. Matrices marked * reported from previous study (Pallavicini et al 2018).

206Pb/207Pb/208Pb/207Pb Matrix Number Mean (SD) Min. Max. Range

Topsoils* 150 1.35(0.15)/2.49(0.10) 1.05/2.30 1.77/2.61 0.72/0.31 Lysimetric waters* 15 1.17(0.03)/2.45(0.02) 1.10/2.42 1.20/2.47 0.10/0.05 Sulphide minerals 47 1.05(0.10)/2.33(0.08) 1.00/2.29 1.52/2.71 0.52/0.42 Pure chemicals 27 1.10(0.07)/2.36(0.07) 1.03/2.29 1.18/2.46 0.15/0.17 Dust K1 4 1.11(0.01)/2.38(0.02) 1.10/2.37 1.12/2.39 0.02/0.02 Dust F1 4 1.10(0.02)/2.37(0.02) 1.08/2.35 1.11/2.39 0.03/0.04 Leachate 2 1.10(0.01)/2.37(0.01) 1.10/2.37 1.11/2.38 0.01/0.01 Sediments 20 1.18(0.06)/2.41(0.03) 1.08/2.36 1.31/2.48 0.23/0.12

Matrix Number δ66Zn, ‰ Mean (SD) Min. Max. Range

Topsoils* 150 0.18(0.20) -0.47 0.69 1.18 Lysimetric waters* 15 0.15(0.13) -0.05 0.30 0.35 Sulphide minerals 56 0.08(0.12) -0.10 0.25 0.35 Pure chemicals 35 0.13(0.22) -0.25 0.57 0.82 Dust K1 2 -0.61(0.06) -0.65 -0.57 0.08 Dust F1 3 -0.67(0.10) -0.73 -0.56 0.17 Leachate 3 -1.06(0.05) -1.11 -1.00 0.11 Deposit well water 9 -1.21(0.20) -1.53 -0.95 0.58 Ground water 6 -1.40(0.16) -1.56 -1.18 0.38 Sediments 20 -0.24(0.24) -0.85 0.02 0.87

Matrix Number δ114Cd, ‰ Mean (SD) Min. Max. Range

Topsoils* 150 0.08(0.14) -0.32 0.42 0.74 Lysimetric waters* 15 0.22(0.07) 0.14 0.34 0.20 Sulphide minerals 38 -0.06(0.12) -0.17 0.08 0.25 Pure chemicals 16 -0.05(0.15) -0.58 0.02 0.60 Dust K1 2 -0.12(0.01) -0.12 -0.11 0.01 Dust F1 3 -0.13(0.05) -0.18 -0.11 0.07 Leachate 3 0.15(0.02) 0.14 0.17 0.03 Deposit well water 6 0.12(0.05) 0.06 0.21 0.15 Ground water 5 0.00(0.10) -0.13 0.09 0.20 Sediments 20 -0.16(0.14) -0.48 -0.04 0.44

Matrix Number δ65Cu, ‰ Mean (SD) Min. Max. Range

Topsoils* 150 0.06(0.21) -0.51 0.51 1.02 Lysimetric waters* 15 0.76(0.17) 0.64 0.99 0.35 Sulphide minerals 47 0.40(0.54) -0.72 1.60 2.32 Pure chemicals 18 0.38(0.78) -0.46 1.58 2.04 Dust K1 2 0.26(0.06) 0.21 0.30 0.08 Dust F1 2 0.04(0.03) 0.02 0.06 0.04 Leachate 2 0.13(0.03) 0.11 0.15 0.04 Deposit well water 3 0.98(1.23) -0.10 2.07 2.17 Ground water 1 1.66 NA NA NA Sediments 19 -0.31(0.26) -0.94 0.20 1.14

36 3.3.4 Isotope tracing application: Pb and Zn

Among elements evaluated in this study, radiogenic Pb and stable Zn isotope systems offer the most promising source identification in the area close to the smelter. Lead is the only radiogenic isotope system included in the present study. Lead isotope ratios in sediments display a clear (R2 = 0.47) correlation with inverse Pb concentrations (Figure 6) consistent with mixing between a ‘natural’ Pb with more radiogenic ratios and an anthropogenic one with less radiogenic ratios.

1.35

1.30

1.25 Pb

207 1.20 Pb/

206 1.15 y = 0,69x + 1,16 R² = 0,47 1.10

1.05 0.00 0.05 0.10 0.15 0.20 0.25 1/Pb concentration, mg kg-1

Figure 6 Correlation between radiogenic 206Pb/207Pb signatures and 1/ Pb concentration in sediments collected along the 12 km transect. Measurement error is smaller than the size of the data point markers.

However, large temporal variability of the smelter feeding material may affect Pb isotopic signatures over time and hampers the effectiveness of traditional fingerprinting (Table 7). The mixing can be further visualized in a three-isotope plot (208Pb/207Pb vs 206Pb/207Pb, Figure 7) in which Pb isotopic ratios plot in between mean value for topsoils and sulfide minerals. However, among sediment samples with the highest element concentrations, only a few have isotopic signatures identical to one found for dust samples (Figure 7). The intercept of the regression line (1.16) can probably serve as an accurate assessment for the mean 206Pb/207Pb ratio of the anthropogenic lead emitted by the smelter over recent operation history.

37 2.50 Aitik ore (1.51 - 2.69)

2.45

Pb 2.40 207

Pb/ 2.35 208

2.30

2.25 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 206Pb/207Pb Sulphide minerals Dust Sediments

Pure chemicals Top soil mean (n>150) Ore concentrates . Figure 7 Three isotope plot (208Pb/207Pb vs 206Pb/207Pb) of Swedish sulfide mineral collection, pure chemicals, ores, dusts, topsoils and sediments. One ore sample, Aitik ore is positioned outside of the figure (208Pb/207Pb=1.51; 206Pb/207Pb=2.69) indicated but the arrow. Top soil marker represents the mean for n>150 samples, previously reported (Pallavicini et al., 2018). Measurement error is smaller than the size of the data point markers.

In contrast to the wide range of Pb isotope ratios in ores, the δ66Zn of minerals and ore concentrates tested within a relatively narrow range. This indicated a uniform isotopic composition of the Rönnskär smelter feeding materials. The isotopic composition of Zn in the K1 and F1 dusts is significantly lighter than that of the ore concentrates and Zn chemicals, which is expected given that the former is affected by evaporation known to favor light isotopes (Yin et al., 2016; Mattielli et al., 2009).

Interestingly, δ66Zn of the leachate of the deposited dust materials (representing the mobile water-soluble fraction) is even more negative (−1.06‰) (Table 7). This suggests an additional fractionation that discriminates heavy isotopes during the Zn dissolution process. No such difference was observed between the mean isotopic composition in the natural system (topsoils and lysimetric waters Table 7).

A further shift towards lighter Zn isotopic composition occurred in deposit drainage wells and then in groundwaters. The most heavily polluted harbor bay sediments at approximately 15 cm depth in the vicinity of the smelter (Table 7) have the lightest δ66Zn at −0.85‰, and a strong

38 positive correlation (R2 = 0.82) between the inverse Zn concentration and δ66Zn (Figure 8a). At larger distances from the source, additional fractionation during element migration and dilution of source-specific signatures with background components makes source tracing more challenging (Figure 8b).

0.0 a -0.2

-0.4 Zn, ‰ Zn,

66 -0.6 δ

-0.8 y = 993x - 0,943 R² = 0,82 -1.0 0.0000 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 1/Zn concentration, mg kg-1

0.2

0.0 b

-0.2

-0.4 Zn, ‰ Zn, 66 δ -0.6

-0.8 y = 10x - 0,32 R² = 0,17 -1.0 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 1/Zn concentration, mg kg-1

Figure 8 a) Correlation (R2=0.82) between δ66Zn signature and 1/ Zn concentration in a sediment core collected in the harbor bay close to the Rönnskär smelter (Site A, Figure 4). b) The correlation is significantly weaker (R2=0.17) when including all sediments samples from the 12 km transect.

39 3.4 Paper IV: Early diagenesis of anthropogenic uranium in lakes receiving deep groundwater

Paper IV examines the uranium (U) concentrations and isotopic composition of waters (dissolved and particulate operationally-defined fractions) and sediment cores downstream from an underground iron ore mine. Uranium mine water concentrations as high as 320 μg L-1 have been observed in some parts of the mine. For comparison, approximately 20 μg L-1 of U is normally found in groundwater in granite settings (Langmuir, 1997). The deep groundwater pumped to the surface has an average U concentration of 67 μg L-1. After passing through the tailings and clarification pond, the mine water is discharged, with an average concentration 14 μg L-1. The mine water effluents are discharged into the Rakkurijoki system, tributary water to the Kalix River. Discharge began around 1950, and the tailings dams were constructed in the early 1970s. Water sampling was performed from the ice in January 2021. The sampling locations for vertical water profiles were selected close to those of the sediment cores (obtained from a preceding study, Widerlund et al., 2014, Figure 9). Obtained data were also compared to data from LKABs environmental monitoring division.

Figure 9 Map of the study area (a) in northern Sweden. The catchment receives mining influenced water from the Kiruna mine site located in the Northern Norrbotten Ore District. Insets show bathymetric maps of the studied lakes Mettä Rakkurijärvi (Lake A) and Rakkurijärvi (Lake B). The sampling points of the sediment cores and water column profiles are shown in each lake. LKAB monitoring stations marked KVA were used as sampling stations for water samples.

40 3.4.1 Concentrations: Water

The characteristics of the mine effluent waters were examined using ICP-SFMS, the total concentration of all elements measured (excluding C, N, and halogens) is approximately 1550 mg L-1, with Ca (650 mg L-1), S (610 mg L-1), Na (170 mg L-1), K (75 mg L-1), and Mg (40 mg L-1) being the five major constituents. Unsurprisingly, this total concentration is higher than that in the Kalix River headwaters (10 mg L-1), approximately 150 times higher. Similar to in Paper III, the effect of mine water discharging via the Rakkurijoki system into the Kalix River was assessed using several different strategies. One was a retention factor (RF) calculated as total element concentrations in mine water normalized by the difference in total element concentrations in the river before and after the Rakkurijoki inflow. High RFs were found for several trace elements: Mo (300), U (360), Tl (670), Cd (1700), Au (3500), and W (8300). This suggests that these anthropogenically introduced elements are partly lost during water transport through the lakes. A strong (R2 = 0.92) positive correlation exists between RFs and the mean proportion of the particulate phase (calculated as % of total concentrations) for the different elements in lake waters. This suggests that sorption on settling particles is the most probable mechanism for such losses. Uranium concentrations in the lake bottom waters exceeded those in the surface waters by almost a factor of 2, displaying a well-developed gradient. Tough U was entering the system predominantly in dissolved form, during residence time in lake waters a significant part of the element was transferred into particulate phases consisting primarily of Fe-Mn hydroxides and organic matter. Dissolved U (Table 8) were strongly negatively correlated with concentrations of dissolved Fe (R2= 1.00) and Mn (R2= 0.98), while concentrations of particulate U were strongly positively correlated with concentrations of particulate Fe (R2 = 0.93) and Mn (R2 = 0.98) (Table 9). Several other elements, with elevated concentrations in mine waters, and that, are readily incorporated into freshly formed Fe and Mn hydroxides (e.g., As, Mo and Tl), exhibit behave very similar to U in both lake waters. At the headwaters of the Kalix River (KVA03 Figure 9) the average of U concentration was measured to 0.058 µg L-1 and the annual river flow of 27.8 m3s-1 (SMHI, 2021), resulting in a U transport of 44 kg y-1 upstream of the inflow of Rakkurijoki. The tributary Rakkurijoki- system (average water flow 1.39 m3s-1, SMHI, 2021 + LKAB discharge, KVA02 Figure 9) with an average U concentration of 2.3 µg L-1 adds 101 kg y-1 U to the mass balance. Theoretically this should increase the amount of transported U in the Kalix River by ≈130% to 145 kg y-1. Measured actual U concentrations downstream of the Rakkurijoki inflow (0.11 µg L-1) and a corresponding water flow of 29.2 m3s-1, however, gives a U transport of only 104 kg y-1 (KVA04 Figure 9). This relatively large discrepancy can be attributed to the uncertainties in estimates for the average annual U concentration and water flows caused by low sampling frequency. There is thus a risk of underestimating the effects from events such as spring flood, summer storms and ice coverage.

41

Table 8 Uranium concentrations and isotopic composition in water samples of Rakkurijoki system and the Kalix River. Concentrations are in µg L−1.

U Dissolved, (234U)/(238U) U Particulate, (234U)/(238U) % of particulate (234U)/(238U) δ238U, ‰, Mean (SD) dissolved, Mean (SD) particulate, U total, Mean (2SD) n=2 Mean (2SD) n=2 Mean (2SD) Mean (2SD) n=3 n=2 n=2 n=3

Mine water 19.1(0.5) 1.94(0.02) 0.0144(0.0004) 1.93(0.02) 0.08 1.947(0.002) -0.389(0.106) Lake A, depth 0.7 m 2.95(0.08) 1.93(0.02) 0.0912(0.0026) 1.95(0.02) 3.0 1.927(0.002) -0.368(0.072) Lake A, depth 1.5 m 4.44(0.12) 1.95(0.04) 0.0718(0.0022) 1.94(0.03) 1.6 1.937(0.002) -0.359(0.062) Lake A, depth 2.8 m 5.50(0.16) 1.96(0.04) 0.0561(0.0018) 1.97(0.03) 1.0 1.953(0.001) -0.543(0.098) Lake B, depth 0.9 m 0.60(0.05) 1.97(0.06) 0.0687(0.0020) 1.95(0.03) 10.3 1.93(0.02) NA Lake B, depth 2.5 m 3.39(0.11) 1.97(0.02) 0.0596(0.0017) 1.94(0.02) 1.7 1.929(0.002) -0.363(0.054) Lake B, depth 3.5 m 3.84(0.14) 1.94(0.02) 0.0596(0.0019) 1.95(0.02) 1.5 1.928(0.002) -0.376(0.102) Lake B, depth 4.5 m 3.90(0.13) 1.93(0.02) 0.0157(0.0006) 1.95(0.02) 0.4 1.927(0.002) -0.394(0.070) Lake B, depth 5.5 m 3.55(0.15) 1.93(0.03) 0.166(0.0051) 1.94(0.02) 4.5 1.926(0.002) -0.384(0.086) Kalix River 0.058(0.004) 2.66(0.21) 0.0037(0.0003) 2.67(0.40) 6.0 2.64(0.11) NA headwaters Kalix River 0.109(0.005) 2.25(0.11) 0.0065(0.0004) 2.26(0.16) 5.6 2.24(0.06) NA downstream

42

Table 9 Iron and Mn concentrations, as well as iron isotopic composition in water samples of Rakkurijoki system and Kalix river. Concentrations in µg L−1.

[Fe] [Fe] δ56Fe, ‰, δ56Fe, ‰, % of [Mn] [Mn] Dissolved, Particulate, Particulate, Total, particulate Dissolved, Particulate, Mean (SD) Mean (SD) Mean (2SD) Mean (2SD) Fe Mean (SD) Mean (SD) n=2 n=2 n=2 n=2 n=2 n=2

Mine water 2.0 61.5 -0.475(0.05) -0.519(0.06) 96.9 75.9(2.3) 3.1(0.1) Lake A, depth 0.7 m 44.9 307.5 -0.059(0.04) -0.097(0.04) 87.2 420.0(11.9) 3.5(0.1) Lake A, depth 1.5 m 35.3 278.0 -0.165(0.04) -0.188(0.05) 88.7 334.2(9.6) 5.3(0.2) Lake A, depth 2.8 m 25.5 215.8 -0.168(0.03) -0.185(0.04) 89.4 282.8(7.8) 6.1(0.2) Lake B, depth 0.9 m 393.8 319.9 -0.280(0.05) -0.303(0.04) 44.8 53.0(1.6) 2.3(0.1) Lake B, depth 2.5 m 26.2 190.6 -0.082(0.04) -0.049(0.04) 87.2 75.8(2.3) 2.3(0.2) Lake B, depth 3.5 m 22.2 118.2 -0.073(0.03) -0.053(0.04) 88.7 31.3(1.0) 1.6(0.1) Lake B, depth 4.5 m 19.9 52.1 -0.032(0.03) -0.051(0.06) 72.4 29.4(0.9) 1.3(0.1) Lake B, depth 5.5 m 25.0 359.6 -0.046(0.04) -0.057(0.05) 93.5 24.3(0.7) 48.9(1.5) Kalix River 43.4 40.1 -0.132(0.03) -0.128(0.03) 48.0 14.9(0.4) 2.4(0.02) headwaters Kalix River 56.9 39.6 -0.117(0.03) -0.139(0.03) 41.0 5.1(0.2) 1.6(0.01) downstream

43 3.4.2 Isotope ratios: Uranium

Unsurprisingly, uranium isotope ratio measurements in water samples confirm the discharged mine waters as the major pollution source for the water system, but also indicate the presence of secondary U sources (with a U isotopic composition different from that of the unpolluted Kalix River) in lakes. Figure 10 shows graphs where the AR is plotted as a function of inverse dissolved, particulate, and total U concentrations in all sampling points (a-c) and lake waters (d-f). Linear regressions with R2 = 0.98 (AR vs inverse concentration of dissolved U, Fig. 10a), R2 = 0.88 (AR vs inverse concentration of particulate U, Fig. 10b), and R2 = 0.98 (AR vs inverse concentration of total U, Fig. 10c) reaffirm a simplified mixing situation with two isotopically distinct sources: 1) a natural source (low U concentration, AR 2.64), previously reported for waters with U from Precambrian bedrock in northern Sweden (Porcelli et al., 1997; Andersson et al., 1998), and 2) an anthropogenic source (high U concentrations, AR ≈1.95). After mixing with mine water from the Rakkurijoki system, the AR of Kalix River water decreases to 2.24. When limiting input data exclusively to lake water, this results in significantly less conclusive (R2 <0.4) correlation coefficients for linear regressions for AR vs inverse concentration of dissolved U (Figure 10d) and AR vs inverse concentration of particulate U (Figure 10e). The insufficient precision of 234U/238U ratios measured by ICP-SFMS used to calculate AR values limits the usefulness of this dataset. However, AR vs inverse concentration of total U (Figure 10f), where AR was calculated from U ratios measured by MC-ICP-MS with significantly higher precision has R2 = 0.66, but a negative slope. The latter suggests the existence of additional U source(s) in the lakes with AR below 1.92, with run-off from the watersheds of the lakes being an obvious possibility. The intercept on the y-axis in all plots provides within uncertainties the isotopic composition of the mine water (Figure 10a-f). Uranium AR in water samples confirms (unsurprisingly) the mine waters as the major pollution source for the water system and may indicate the presence of secondary U sources (with U isotopic composition different from that of the unpolluted Kalix River) in lakes. Slight enrichment of 235U in near-bottom waters of Lake A may indicate a contribution from pore water U diffusing into the water column.

44

Figure 10 Correlation (R2) between (234U) /(238U) and 1/ U concentration in a) dissolved b) particulate and c) total forms from all sampling points and d) dissolved e) particulate and f) total forms from lake samples.

45 3.4.3 Sediments: Element distribution and isotope ratios

The sum of concentrations for all elements measured by ICP-SFMS re-calculated as oxides of Al, Ca, Fe, K, Mg, Mn, Na, P, Si, and Ti (excluding C, N, and halogens) was used as a measure of the inorganic fraction of sediments. To create a uniform presentation, concentrations of individual elements were first corrected for organic matter dilution using the calculated inorganic fraction in the respective layer and then normalized by dividing with the highest concentration found in both lakes (Figure 11).

Depth profiles of Mn, Fe, and S show distinct similarities between the two lakes, suggesting common redox processes (further corroborated by Fe isotope data for Lake A later).

Many trace elements such as As, Cu, and Ni are known to have high affinity with organic matter and be easily removed from the water column via primary production. Several of these elements associated to the mining operation can be seen enriched in the top sediments in Lake A with a distribution similar to that of Ni (Figure 11). Widerlund et al., (2014) demonstrated that increased accumulation of phytoplankton and macrophyte detritus from about 1950 appears to be related to the discharge of nutrient-rich mine effluents, which may increase the effectiveness of this uptake significantly resulting in immobilization in Lake A.

Several redox-sensitive are enriched in the top sediments in both lakes. For example, maximum concentrations of U, Mo, and Tl occur in the Mn-rich, oxic top sediments of both lakes. An unexpected finding was the more or less identical concentration and distribution of U. This indicates that, contrary to the behavior of the majority of the pollutants in the mine waters, a higher proportion of U reaches the sediment of Lake B (Figure 11).

The highest U concentrations in the top sediments reach 58 µg g-1, decreasing sharply downwards to significantly lower concentration found in layers deposited prior to the beginning of the LKAB discharge in the 1950s (1-5 µg g-1) (Figure 12). Thus, as a result of the Kiruna mine influence, the top sediments are significantly enriched in U, compared to average crust (2.8 µg g-1, Herring 2012).

Following the concentration behavior, the AR found in the anthropogenically affected sections of the studied lake sediments are significantly higher than those of the tailings. This again demonstrates the transport of two isotopically distinct sources; one with low AR and low U concentrations found in the larger particles settling in the tailings pond, and one with high AR and high U concentrations settling in Lake A and B. This enrichment further supports the presence of dissolved/complex-bound U species transported in the Rakkurijoki stream, with a higher AR compared to particles settling in the tailings pond (Figure 12).

46 . Figure 11 Normalized concentrations of selected elements in sediments as function of depth. The dotted red line indicates the year 1950, the year discharge to the Rakkurijoki system began, found by 210Pb age dating from a preceding study (Widerlund et al., 2014)

47 A well-defined secondary U peak occurs at depths of 11 and 10 cm in Lake A and B, respectively. This suggests a significant release event in the early years of the discharge, presumably before proper tailing dams were constructed in the 1970s. The event was big enough to affect the distribution of some elements in both lakes.

Figure 12 Uranium concentrations, (234U)/(238U) and δ 56Fe in sediments as function of depth

As mentioned previously the δ56Fe signatures confirms the present redox conditions suggested from the distribution of Mn, S, and Fe concentrations. Iron isotope signature peaks overlap with the distribution peaks observed in Figure 11, suggesting the presence of two redox- interfaces for Fe. While these two signature-shifts are, most likely, due to natural reactions within the sediments, they also may affect the distribution of anthropogenically enriched elements such as U. The ‘smoothening’ effect from early diagenetic U migration within the local sediments is likely the reason for the rounded distribution of (234U)/(238U) since such gradual source signature shift is highly unlikely (Figure 12).

.

48

4.0 Overall conclusions

The first part of this thesis focuses on the development and optimization of analytical protocols aimed at precise, reproducible, and accurate measurements of concentrations of emerging contaminants such as Technologically Critical Elements (TCEs) at ultra-trace level. The method developed was aimed at the analyzing a broad range of some of the lowest abundant elements of the periodic table in various environmental and clinical matrixes using ICP-SFMS.

Despite the very impressive features of ICP-SFMS, far from all ultra-trace elements can be determined in environmental/clinical matrices by direct analysis without analyte pre- concentration and/or matrix separation. While pre-concentration and matrix-separation techniques allow the removal of key interferences they do not offer significant improvements to the MLOD. Contaminants in anthropogenically impacted matrices (e.g. snow or freshwater samples) can be detected at low pg L−1 levels, which still make the analytical protocols described useful in pollution/exposure monitoring studies.

The dataset presented in Paper I is to our knowledge the most extensive of TCEs/PGEs measured at ultra-trace concentration in a wide range of matrices. This study shows that there is an urgent need for inter-laboratory exercises aimed at comparing results to fill a knowledge gap regarding these elements until appropriate CRMs become commercially available. The absence of commercially available CRMs, with certified ultra-trace concentrations and isotope ratios, presented a constant limiting factor for accuracy assessment throughout the method development as well as accuracy for isotope ratios reported in all papers.

The potential of isotopic data as a powerful tool, both for tracing sources of metals and processes affecting their fate in the environment, has been confirmed in the studies presented in Paper II, III, and IV.

In Paper II, a new separation scheme and cup configuration on the MC-ICPMS was successfully used to separate and determine δ53Cr in a wide range of matrices. The first-ever δ53Cr data obtained for lichens and mosses were successfully applied as a tracer, suggesting that these species can serve as bio-indicators for tracing air-borne Cr pollution emitted from stainless steel smelters.

49 Paper III studied multiple isotope systems (Cd, Cu, Pb, and Zn) in material flow at the Rönnskär sulfide ore, and scrap metal smelter. In this study, the multi-elemental dataset obtained in polluted groundwaters was extended into the Bothnian Bay sediments demonstrating the enrichment of several elements related to the smelter operation.

Among evaluated isotope systems in this study, radiogenic Pb and stable Zn isotope systems offer the most promising source identification in the area close to the smelter. However, temporal variability in the isotopic composition of the source adds complexity for the straightforward use of Pb isotope system. The stable isotope tracers suffer numerous post- depositional fractionating processes that alter the original source isotope ratios for Cu, Zn, and to a lesser extent, Cd. At larger distances from the source, additional fractionation during element migration and dilution of source-specific signatures with background signals make source tracing more challenging.

A further example of isotope tracing has been provided in Paper IV, which demonstrate the effects of the discharge of mining waters through the Rakkurijoki system into the Kalix River. The baseline uranium (U) concentration found in the river headwaters is more than doubled from the mine effluent waters. As a result, the U isotopic composition of the Kalix River clearly changes after merging with the mine effluent affected waters, even though both studied lakes effectively immobilize approximately 30% of the anthropogenic U discharged.

A combination of oxygen monitoring data, Fe isotopes, U isotopes, and elemental concentrations indicate that a lower proportion of U is immobilized in Lake A than in Lake B, compared to the majority of pollutants originating from mine waters. This is likely because of partial re-mobilization of U from sediments during anoxic conditions and increased transport of the element into Lake B in the particulate phase.

50 5.0 Future Studies

Developing a set of matrix-matched reference materials with known ultra-trace concentrations for as many elements as possible will aid a straightforward accuracy assessment for analytical methods and thus benefit method development for emerging contaminants such as the TCEs. The most realistic approach would be larger organized interlaboratory round-robin studies using the commercially available CRMs with different matrixes. Until then, larger datasets similar to Paper I are needed for a wide range of matrices and locations to describe the temporal and spatial variability of these ultra-trace elements.

Advances in analytical instrumentation, particularly the MC-ICPMS, allowed a wide range of heavier traditional stable isotopes to be used as environmental tracers. In contrast to traditional fingerprinting techniques, the isotopic composition of these elements in anthropogenic, and environmental matrices is a result of both a source(s) composition and physicochemical fractionation processes that elements undergone after deposition.

As a result, stable isotope ratios are often able to offer viable contaminant source tracing, for a given time period at a single major source settings/situation. However, in real-life application of stable isotope signatures as tracers remains challenging, especially in complex systems influenced by multiple processes and sources. Moreover, isotope ratio variability can stem from both natural and anthropogenic processes, highlighting the need for large data sets with multiple lines of argumentation to support the isotopic findings.

Future studies on sediment pore-water using multi-element analyses combined with several stable isotope systems would provide further insight into re-distribution processes taking place in the sediments. For example, such experiments might shed a light upon the fractionation dichotomy between Cd and Zn observed in Paper III, where heavier Cd isotopes favored in the leachate/lysimetric waters as compared to Zn favoring light isotopes. Moreover, detailed porewater data (including δ238U) might aid better understanding the post-depositional re- distribution of U (and other elements) in the sediment profiles.

Sequential extractions applied to soils/sediments/particulate matter combined with multi- element analyses and isotope ratio measurements may add an extra dimension to studies of element pathways in the environment. In addition, sampling larger volumes of water samples combined with ultra-filtration (preferably in-field) would provide valuable information on if

51 the contaminants, i.e whether they are truly dissolved, complexed-bound, or bound to the colloids and particulate matter.

The Environmental Forensics field has gained significant attention in the last few decades as the need to identify contaminant sources and dispersal pathways in our environment will continue to grow. To fully realize great promise offered by expanding the number of elements utilized in isotope tracing as a powerful way to decipher sources and fate of environmental exposure, a comprehensive evaluation of both source(s) and background variability, as well as post-depositional fractionation, needs to be an integral part of any Environmental Forensics investigation, thus making accumulation a welsh of information on the extent of such variability in different natural and anthropogenic settings an integral part of future studies.

52 References

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DOCTORAL T H E SIS Simon Pontér Isotope Ratio and Trace Element Measurements Using Inductively Coupled Plasma – Mass Spectrometry

Department of Civil, Environmental and Natural Resources Engineering Division of Geosciences and Environmental Engineering

ISSN 1402-1544 Isotope Ratio and Trace Element ISBN 978-91-7790-787-9 (print) ISBN 978-91-7790-788-6 (pdf) Measurements Using Inductively Luleå University of Technology 2021 Coupled Plasma – Mass Spectrometry Method Development and Applications in Environmental Forensics

Simon Pontér Tryck: Lenanders Grafiska, 135581

Applied Geochemistry

135581 LTU_ Pontér.indd Alla sidor 2021-03-30 11:30