Atomic spectrometry update – a review of advances in environmental analysis

Jeffrey R. Bacon a, Owen T. Butler b, Warren R. L. Cairns c, Olga Cavoura d, Jennifer M. Cook e, Christine M. Davidson f and Regina Mertz-Kraus g
a59 Arnhall Drive, Westhill, Aberdeenshire, AB32 6TZ, UK. E-mail: bacon-j2@sky.com
bHealth and Safety Executive, Harpur Hill, Buxton, SK17 9JN, UK
cCNR-ISP, Universita Ca’ Foscari, Via Torino 155, 30123 Venezia, Italy
dSchool of Public Health, University of West Attica, Leof Alexandras 196, 115 21 Athens, Greece
eBritish Geological Survey, Keyworth, Nottingham, NG12 5GG, UK
fDepartment of Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow, G1 1XL, UK
gInstitut für Geowissenschaften, Johannes Gutenberg-Universität, Becher-Weg 21, 55099 Mainz, Germany

Received 11th October 2021

First published on 10th November 2021


Abstract

In the field of air analysis, highlights within this review period included: a review paper of developments in new personal air samplers for workplace air measurements; a new passive air sampler for Hg0; new filter-based RMs; a new field-deployable carbon analyser for speciation measurements of carbonaceous aerosols and advances in the measurement of RCS using vibrational spectroscopic techniques. The maturity of AAS methods for the analysis of waters was highlighted this year by the lack of significant developments. All the novelty was in sample preparation as reflected in Tables 1 and 2. The tables also reflect the large interest in the use of graphene oxide as a solid phase for preconcentration and the continued use of magnetic nanoparticles. There are now enough methods and application to warrant the inclusion of the analysis of nanoparticles as a separate section rather than as a subsection in speciation. This trend is set to continue as instrumental improvements allow lower size LODs and the determination of lower particle numbers. Notable advances in the analysis of soils and plants included work on the preparation of new RMs as alternatives to NIST soil SRMs that are now in short supply. There was increased interest in the development of miniaturised AES instruments with the potential for field deployment. In the analysis of soil by LIBS, more studies included some form of external validation e.g. comparison with results of an established analytical method, which is most welcome. The availability of well-characterised matrix-matched reference materials continues to be a limitation in the production of high-quality geoanalytical data, particularly when exploring the fine structure of geological materials by microanalytical techniques such as LA-ICP-MS and SIMS. It is heartening to see that many of the researchers working in this area appreciate the importance of making new RMs available to the wider geochemical community. Although the development of portable LIBS systems continues to attract much interest, the current generation of instruments do not appear to be able to match the capabilities of techniques like pXRFS, particularly for the quantification of minor and trace elements in geological settings. Some of the LIBS calibration issues highlighted are reminiscent of those encountered in the early days of pXRFS and should be overcome in time. Many of the reported improvements to in situ measurements of isotope ratios were in support of geochronological studies. Likely future trends include the increased use of imaging in U–Pb geochronology given that LA-ICP-QMS mapping is now capable of acquiring all the required compositional and U–Pb age information at high-spatial resolution.


1 Introduction

This is the 37th annual review of the application of atomic spectrometry to the chemical analysis of environmental samples. This Update refers to papers published approximately between August 2020 and June 2021 and continues the series of Atomic Spectrometry Updates (ASUs) in Environmental Analysis1 that should be read in conjunction with other related ASUs in the series, namely: clinical and biological materials, foods and beverages;2 advances in atomic spectrometry and related techniques;3 elemental speciation;4 X-ray spectrometry;5 and metals, chemicals and functional materials.6 This review is not intended to be a comprehensive overview but selective with the aim of providing a critical insight into developments in instrumentation, methodologies and data handling that represent significant advances in the use of atomic spectrometry in the environmental sciences.

All the ASU reviews adhere to a number of conventions. An italicised word or phrase close to the beginning of each paragraph highlights the subject area of that individual paragraph. A list of abbreviations used in this review appears at the end. It is a convention of ASUs that information given in the paper being reported on is presented in the past tense whereas the views of the ASU reviewers are presented in the present tense.

2 Air analysis

2.1 Sampling techniques

Highlights in a review7 (102 references) of developments in new personal air samplers for workplace air measurements were a new disposable device for sampling inhalable-sized particles and a device that collected a <300 nm particle size fraction. There are challenges in comparing exposure datasets because different designs of personal samplers can possess different sampling efficiencies. A review (54 references) of studies published between 2004 and 2020 compared8 different samplers but, alas, found no discernible trends. The authors recommended that a standardised testing protocol be adopted to aid future comparisons. Following a review (29 references) of procedures for measuring surface contamination in workplaces, it was concluded9 that, whilst undertaking quantitative measurements remains problematical because of challenges in sample collection, such measurements nevertheless were useful in assessing contamination routes and in validating efficacy of cleaning procedures. In a review (79 references) of methods for measuring reactive Hg in air, it was concluded10 that future research should focus on developing both better sampling substrates for collecting Hg species and new MS-based methods for their subsequent identification. Methods for the collection of ambient air particles, characterising their physicochemical properties and ascertaining their in vitro toxicities were summarised11 (243 references).

A new ambient air sampler12 made it possible to collect sufficient size-segregated ambient air particulate matter in 1 h for improved time-resolved speciation analysis to be carried out. Complementary to this was the development of an agitator sampler for re-suspending and collecting representative road dust samples, thereby enabling both direct (airborne) and indirect (re-suspension) sources of emissions from vehicles to be assessed. A new online integrated PM2.5 measurement system developed for improved chemical mass-closure and source apportionment studies consisted13 of: an aerosol MS for determining molecular species; an XRF system for determining elemental species; an aethalometer for black carbon measurements and a combined nephelometer with beta attenuation for mass measurements. Key to the success of this instrument was the development of a single isokinetic sampler inlet that, under conditions of laminar flow, ensured particles were transported to these various analyser components with a transmission efficiency of close to 100%.

Modification to an air-sampling cassette facilitated14 the rapid workplace analysis of RCS by portable FTIR spectroscopy for timely exposure intervention studies. Particles of <300 nm size can penetrate deep into the lung so use of a new deposition sampler that collects these small particles was proposed15 to provide better estimates of biologically relevant exposures to welding fume than achievable with traditional penetration-based samplers.

A new passive air sampler (PAS) for Hg0 consisted16 of a sulfur-impregnated activated-carbon sorbent contained in the body of a Radello® radial diffusive sampler. It had a sampling uptake rate of 0.07 m3 per day and operated over a useful 1–10[thin space (1/6-em)]000 ng m−3 concentration range so reliable time-weighted exposure averages could be determined in either ambient, residential or occupational air settings. Now commercially available, this sampler was used17 in a study to determine airborne Hg0 concentrations in and around residential settings. The indoor levels (2–10.8 ng m−3) were higher than those (1.8–2.5 ng m−3) measured outdoors so it was concluded that emissions from the households in this study were a net contributor of Hg to the wider atmosphere.

Sampling gases into cylinders or flasks for subsequent compositional and/or isotopic analysis is well established but measurement challenges still exist: these include the need for large air volumes and for dealing with condensable interferences such as water vapour. In a new field-deployable system18 that employed a Nafion™ air drier for sampling CO2, the requisite dry purge-air was usefully provided by initially feeding the spent-exhaust flow from the flask sampling-system through a molecular-sieve cartridge. Sampling requirements for 14CO measurements were reduced19 from 500–1000 L to ca. 90 L at STP by using air compression in conjunction with a small-volume electro-polished stainless-steel cylinder for sample collection. Embedded in this system was a CO scrubber that facilitated the production of a procedural blank for assessing the potential for extraneous 14CO sample contamination that could occur during transportation of samples back to the laboratory. Sources of this contamination could be ingress of air due to leakage or in situ14CO formation due to bombardment of samples with cosmic radiation.

2.2 Reference materials, calibrants and interlaboratory comparisons

The development of RMs for air analysis is to be encouraged given that the availability of such materials is poorer than that for other environmental matrices. Newly available single-element-on-PTFE-filters (Al, Ca, Ce, Cl, Cr, Cu, Fe, Na, Pb, S, Si, Ti, V and Zn at mass loadings of 0.2–70 μg cm−2, uncertainties < 10%) met20 the need for appropriate filter media at suitable mass loading values and so will benefit future aerosol measurements by XRFS. A procedure for the repeatable spiking of reference filters with fly-ash was reported.21 Individual filters were spiked sequentially and a target mass-loading of 2.5 mg achieved by the judicious selection of the volume of fly-ash-laden air sampled through each filter. This volume was calculated from real-time aerosol concentrations derived from continuous TEOM readings and from sampling-flow-rate data derived from continuous MFC readings. Candidate filters so produced were distributed to three laboratories for analysis by ICP-MS in accordance with procedures stipulated in EN 14902. The RSD was <20% for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn. Reference filters containing a carbonaceous matrix that mimics particles derived from combustion-derived processes are desirable. Relative differences of 1–17% between measured and certified elemental values were obtained22 when sample masses of 1 mg rather than the recommended 10 mg of either HSL RM MSWF-1 (elements in mild steel welding fume) or RM SSWF-1 (elements in stainless steel welding fume) were analysed. New data were provided on the elemental solubility in a phosphate buffer, an extractant often used in procedures to estimate metal bioaccessibility, and the oxidative potential of these RMs, as determined using a dithiothreitol-based assay, given that exposure to transitional metals in such materials can induce the formation of reactive oxygen species in human cells. Development of μm-sized U-containing reference particles for nuclear safeguarding purposes involved23 generation of an aerosol using a VOAG and a drying step that produced spherical particles homogeneous with respect to their size, volume and U-content. The U content (3.56 ± 0.08 pg, k = 1) determined using ID-MC-ICP-MS, a technique widely used in nuclear facilities, agreed with certified values (3.58 ± 0.31 pg, k = 1) by the RM producer, who used ID-TIMS.

Performance testing of calibration systems for generating Hg gas standards was reported in three publications. A system developed at VSL for production of Hg0 gas standards traceable to the SI was based24 on a diffusion process described in ISO 6145-8. Standards could be prepared with a relative expanded uncertainty of 3% (k = 2) for concentrations between 0.1 and 2.1 μg m−3 and 1.8% for concentrations between 5 and 100 μg m−3. The so-called “bell-jar” calibration, which involves the analysis of a saturated head space of Hg0 vapour kept at a defined temperature, is widely used but a discrepancy of 8% was noted25 between the calibrant gas output from this approach and the output from the new SI-traceable approach. This indicated a problem with the former that is a concern because of its wide use. It was planned to measure Hg0 with a new laser-absorption spectroscopic method (see Section 2.4.5) and to use transfer standards, prepared by dosing sorbent tubes with defined amounts of Hg0, in order to calibrate instruments previously calibrated using the “bell-jar” approach. The output from two generators that produced an oxidised Hg calibrant gas was analysed26 online by ICP-MS. Use of a customised PTFE connector inserted into the ICP-MS torch to ca. 1 cm before the end of the quartz injector improved analyte transfer and so enabled standards of up to 10[thin space (1/6-em)]000 ng m−3 to be measured in <5 min without significant memory effects.

Diesel engine emissions are best mimicked by soot particles with a high EC/TC ratio and a small size. A new prototype particle generator with a novel premixed-flame mode generated27 such particles with an EC/TC ratio of 0.85 and mobility diameter of 50 nm without recourse to a thermal after-treatment process that had previously been required for generation using a diffusion flame. A new laboratory-based facility28 for producing particles that mimic those found in ambient air employed: a generator for production of a carbonaceous particle fraction; a smog chamber to coat these particles with organic compounds and thereby simulate aged particles; a nebuliser to produce an inorganic-salt-particle fraction; and a rotating brush generator to produce a mineral-dust-particle fraction. It is anticipated that this facility will be used for: performance testing of new air sensors; measurement intercomparison studies; and health-related aerosol studies.

In an interlaboratory comparison (ILC) organised29 by JRC Ispra, PM2.5 filter samples that contained elements regulated under the EU air quality directive (As, Cd, Ni and Pb) were sent to laboratories for analysis by either ICP-MS (using EN 14902) or EDXRFS. Whereas there was good agreement amongst the ICP-MS results, it was concluded, unsurprisingly, that EDXRFS analysis was not sufficiently sensitive for the determination of As or Cd at concentrations typically encountered in such particles in ambient air. Reliable EDXRFS Ni and Pb measurements that met data quality objectives prescribed in the EU directive were possible if the concentrations were >25 and >8 ng m−3, respectively. Results from an ILC carried out in 2014 and then repeated in 2019 for the determination of metals (using EN 14385) and Hg (using EN 13211) in stack gas emissions showed30 that participants’ performance had improved but that better consistency and more transparency were still required in the calculation and reporting of LOQs and associated MU values. Blank Hg concentrations in sorbents can sometimes cause inaccuracies. Following a field trial of three different Hg0 passive sampler designs, it was concluded31 that the MerPAS® sampler performed better than the CNR-PAS and IVL-PAS alternatives because it possessed a superior sampling uptake of Hg0 and gave a better S/N.

2.3 Sample preparation

The BCR sequential extraction protocol was automated32 using a new FI-based ICP-MS system so that leachates from 5 mg test samples could be analysed for 17 elements in 80 min. The summed elemental recoveries for a road dust sample of only 72% of those obtained using the established but manually operated protocol was a reflection on the lower extractant concentrations and shorter extraction times used in the new automated approach. The measurement repeatability of 1–48% (RSD) is influenced, no doubt, by the heterogeneity arising from the small sample masses.

Measuring PGEs and Re at trace levels in road dust samples remains problematical because of severe isobaric and polyatomic interferences that can arise in ICP-MS analysis. In a new procedure, dust samples were digested33 in inverse aqua regia in a HPA and the PGEs separated from the matrix by CEC for ID-ICP-MS/MS analysis. The use of a mass filter to select only ions of interest in conjunction with the use of ion-reaction chemistry meant that spectral interferences derived from residual elements remaining after clean-up could be avoided. Analysis of IRMM BCR CRM 723 (road dust) gave values that were 71% (Ir), 91% (Os), 100% (Pd), 100% (Pt), 98% (Re), 81% (Rh) and 79% (Ru) of the certified values.

2.4 Instrumental analysis

2.4.1 Atomic absorption and emission spectrometries. Direct solid-analysis of PM10atmospheric particles by CS-AAS involved:34 quartering of glass-fibre filters; homogenisation by ball-milling; and analysis of 0.08–0.6 mg sample aliquots. Impressive LODs of 0.001, 0.001 and 0.004 ng m−3 for Be, Cd and Pb, respectively, could be achieved. An aqueous calibration was deemed sufficient as the calibration curves obtained were close to those obtained when a matrix-matched calibration that involved spiking of 0.30 mg aliquots of powdered glass-fibre was employed. Results for five air samples were in statistical agreement with results obtained using a solution-based assay that involved MAD of 100 mg sample aliquots in a HNO3–HCl–H2O2 (4 + 1 + 1) acid mixture and analysis by CS-AAS and by ICP-MS. Although, undoubtedly, this assay is very sensitive and so has potential applications in studies in which masses are limited, one suspects that the preparatory step involving ball-milling is too time-consuming for routine applications. Use of water as a solvent to determine both soluble S (by ICP-AES) and SO42− (by IC) in PM2.5 filter extracts made35 the rapid, cost-effective screening of air samples for water-soluble organic atmospheric-S-species possible. These are important indicators of the formation of secondary organic aerosols for which complex and expensive analytical tools such as field-based aerosol-MS or laboratory-based LC-MS were previously required.

Evaluation of a new Cyrotrap-GC-AED system for the determination of atmospheric trace-organic gas species at pptv levels focused36 on calibration linearities, LODs and the generation of compound-specific response factors for 64 VOCs. The emission lines of Br (163 nm), C (197 nm), I (178 nm), N (174 nm) and S (181 nm) were used. Although quantification with VOC gas standards was preferable, the authors suggested that these new response factors made semi-quantitative analysis to an accuracy of ca. 30–40% possible for those compounds present in a chromatogram but not present in the gas standard. Furthermore, they suggested that this new instrument with its simultaneous CCD detection had potential for detecting other volatile species such as organo-metallic species emitted from volcanoes.

2.4.2 Mass spectrometry.
2.4.2.1 Inductively coupled plasma mass spectrometry. A summary of progress in the determination of elements in PM2.5by ICP-MS discussed37 (91 references) advantages and disadvantages of various filter media and of digestion procedures as well as developments in isotopic and speciation analysis. A bespoke sample-aerosol introduction-device in which sampled air was replaced with argon was used38 with a desolvating HEN system for the online ICP-MS determination of 239Pu in the air at a nuclear fuel reprocessing plant. The LOD was 2.24 × 10−5 Bq m−3 and the LOQ 7.45 × 10−5 Bq m−3 when 0.8 L of air was sampled. Rapid discrimination39 of gunshot residue containing Ba, Pb and Sb from other particles such as those from brake pads and fireworks that can contain the same elements was possible by LA-ICP-MS analysis and interrogation of the resultant elemental distribution patterns using data-visualisation software. Samples were collected by swabbing hands with carbon adhesive tape. The analysis time of as little as 33 min was considerably less than that required for SEM-EDS analysis. Measurement of 194Pt/195Pt ratios in individual particles was possible by sp-MC-ICP-MS analysis40 with a newly developed fast data-acquisition system for capturing transient signal events. The measurement precision (2SD) for 30, 50 and 70 nm-sized particles of ca. 40, 20 and 10%, respectively, were comparable to those estimated from counting statistics.
2.4.2.2 Other mass spectrometry techniques. The soot-particle aerosol-mass-spectrometer is an instrument that combines the attributes of HR-aerosol-TOFMS with those of a soot particle photometer (SP2) for unrivalled measurements of both the chemical and size compositions of aerosols at high time-resolution. Studies to date using this technique were summarised41 (89 references) and potential applications proposed. Latest versions of the instrument employ both a resistively-heated tungsten filament for vaporising non-refractory PM and an intracavity laser for vaporising refractory particles such as black carbon and metal particles. Increases in non-refractory PM ion signals observed when a dual-ionisation mode was used instead of a single-ionisation mode were42 attributed to differences in either particle collection efficiencies or in the relative ionisation efficiencies for the same species vaporised from these two sources rather than to the laser heating.

The aerosol chemical speciation monitor (ACSM) is a field-deployable mass spectrometer for measuring the mass and chemical composition of non-refractory sub-μm PM in real-time. In order to validate ACSM measurements, data from laboratory measurements of aerosol PM1 and PM2.5 samples were compared43 with results for samples collected at the same time on filters. Data for particle number and size distributions and black carbon concentrations taken at the same time were also used in the comparison. Systematic deviations between the results for a PM1 fraction obtained using ACSM and those obtained from the offline weighing of filters were attributed to a lower transmission efficiency of the particle-focusing inlet lens for smaller particles than for larger particles. Better correlations were obtained for SO42− (slope 0.96, R2 0.77) and for total particle mass (slope 1.02, R2 0.90). Studies like this are valuable for QA purposes and for identifying potential biases between old and new measuring approaches. Particulate chloride is an important PM component in marine air masses. A revised calibration procedure for improved chloride quantification by ACSM involved44 modifying the standard ion fragmentation data held within the software and adopting a specific calibration for NH4Cl.

The single-particle aerosol-mass-spectrometry of samples from pristine environments with low concentrations of aerosol particles was made45 possible by using a new aerodynamic lens system inlet in conjunction with use of a delayed-ion-extraction protocol. Detection efficiencies of 50% for 230–3240 nm-sized particles and 95% for 350–2000 nm-sized particles were possible for PSL and NaCl test particles. Use of this procedure improved the ion-yield from the ablation, ionisation and extraction processes and resulted in a 7-fold enhancement in signal intensities for cationic species.

2.4.3 X-Ray spectrometry. The relative merits of sampling onto either polycarbonate or quartz-fibre filters were explored46 for the measurement of metals in PM10particles by TXRFS. The direct analysis of flat filters such as polycarbonate was demonstrated by the determination of elements in NIST SRM 2783 (air particulate on filter media), for which measured values were within 8% of certified values. The procedure involved: punching out a 0.5 cm2 area of filter; spiking with ISs (Ga and Y) together with 5 μL of HNO3; and evaporating on a hotplate for 10 min at 80 °C. The MDLs for many elements were ca. 1 ng cm−2 but were higher for Ca and Zn because these were contaminants in the polymer material. The use of MAD with a HNO3–HCl mixture was preferable for quartz filters because particles were embedded in the media: the MDLs were similar to those achieved with the flat filter but Ba, Ca, K and Ti were noticeable contaminants. Adoption of a procedure widely employed in the PLM analysis of asbestos fibres ensured47 that the requisite thin-layer sample was presented for TXRFS analysis by placing a segment of a cellulose-based air filter sample directly on a quartz reflector and dissolving it with a few drops of acetone. As remarked earlier, whilst there is sometimes merit in using sensitive techniques such as TXRFS or SS-AAS for the ultra-trace analysis of aerosol samples, routine use remains hampered by the often laborious and time-consuming sample preparation involved. Developments in automating filter preparation would therefore be welcome.
2.4.4 Combustion-based techniques. Carbonaceous aerosols captured on filters are classified by their organic (OC), elemental (EC) and, by summation, their total (TC) carbon content. Whereas there is, in general, good agreement in TC results from the different combustion protocols used for this classification, results for EC can vary between protocols because OC can be misclassified as EC due to the formation of so-called pyrolysed carbon that can sometimes arise from OC charring during the combustion process. Reference filter materials with certified OC and EC contents are therefore desirable but their production remains elusive. Therefore, practitioners typically use spiked simulant filters for calibration, method performance checks and QC purposes. One such approach involved48 the preparation of two starting materials: a 1 mg mL−1 suspension of Cab-o-Jet 200 (19.92% mass fraction) in water as an EC surrogate; and a 4.0 mg mL−1 solution of sucrose as an OC surrogate. Binary working mixtures were prepared from these starting materials so that filters could be spiked with defined EC[thin space (1/6-em)]:[thin space (1/6-em)]OC mass ratios. Use of a new field-deployable carbon analyser enabled49 speciation measurements of carbonaceous aerosols to be performed at high time resolution. The TC content of an aerosol sample collected on a quartz-fibre filter was determined as CO2 following combustion. The LOD was 0.3 μg m−3 C when hourly samples were collected at a 16.7 L min−1 flow rate. The system comprised of two identical sampling channels so continuous operation was possible with one channel used for sampling whilst the other was used for analysis. Equivalent black carbon concentrations were concurrently measured using an aethalometer so the OC aerosol content could be calculated by difference. The advantages of this instrumental approach were: carbon speciation at a high time resolution; avoidance of pyrolytic effects prevalent with conventional OC/EC combustion-based analysers; and elimination of the need for speciality gases because only filtered-air was required for combustion. Carbonaceous aerosols are typically measured either as EC or as equivalent black carbon mass concentrations but, increasingly, determination of a refractory black carbon component using instruments such as a SP2 are becoming more commonplace. In an informative study of colocation data from five European air sampling sites, the median ratio of refractory black carbon to EC ranged50 from 0.53 to 1.29 with a GSD of 1.5. Whilst there were deviations of up to a factor of 2 in individual paired values, the correlation between refractory black carbon and EC (determined using the EUSAAR-2 TOT protocol) fitted a linear relationship with a constant ratio indicating that both methods were measuring the same carbonaceous aerosol component.
2.4.5 Other techniques. There is growing interest in assessing the contribution of windblown mineral dusts to air pollution because of their role in climatic radiative forcing and their impact at ground level on air quality. A rapid, inexpensive and non-destructive semi-quantitative method involved51 use of UV-VIS DRS to measure the colour of collected air filter samples. The finding that a redness in filters was due to the presence of hematite, a major mineral in Saharan dust, correlated well with Fe-on-filter measurements by PIXE. Measurements in real-time are preferable but can be challenging. In the aethalometer analysis of mineral dusts in Crete, determining the optical absorption was52 difficult because small black-carbon particles such as diesel fumes possess a much larger mass absorption cross-section and so can obscure the much smaller absorption from larger mineral particles. The larger mineral dust particles could be preferentially sampled by using a high-volume virtual impactor. A mass absorption cross section value of 0.24 ± 0.01 m2 g−1 at 370 nm for a PM10−2.5 particle size fraction was established by determining the mineral dust concentrations in filter samples taken concurrently. These dust concentrations were calculated from the measured Ca content and by assuming an average Ca concentration (12%, m/m) in the mineral dust previously established for this particular sampling site.

Advances in vibrational spectroscopic techniques offer new potential for measuring RCS at low airborne concentrations within occupational settings. Emissions from the cutting and polishing of natural and artificial stones, a workplace activity that can give rise to substantial inhalation exposures if uncontrolled, were measured53 using a new Raman-based procedure. An established XRD-based assay was used in parallel. Results correlated well for both respirable-sized (R2 values of 0.95–0.98, slope of 1.11) and thoracic-sized (R2 values of 0.95–0.98, slope of 1.07) RCS particles tested. The LOD of ca. 0.2 μg was an order of magnitude better than that achievable by XRD analysis. An alternative, QCL-based, IR method involved:54 collection of aerosol on a filter; filter treatment using a low-temperature O2-plasma asher to remove the matrix; redeposition of the RCS as a dried 1 mm2 diameter spot on a secondary filter for direct-on-filter analysis; and measurement of α-quartz at its characteristic 798 cm−1 peak. The LOD was 0.12 μg and results for samples taken from workplaces agreed to within 3–28% with those determined using a reference XRD method.

The reliability of monitoring mercury in stacks is constrained by the availability of accurate and metrologically traceable calibrant Hg0 gas standards which are typically generated on demand using either controlled gas diffusion or permeation processes. Verifying the output from generators is therefore important. Researchers at NIST compared55 results obtained using a previously reported56 high-resolution laser-absorption spectroscopic instrument with those obtained by sampling defined volumes of Hg0 onto sorbent tubes and analysing following desorption using an accurate and precise ID-CV-ICP-MS method.57 The output from a calibrator unit was split so that the two methods could be performed simultaneously. Agreement between the two techniques was within 1–1.8% for Hg0 concentrations of 41–287 μg m−3 thereby confirming the suitability of this new approach as an alternative primary measurement method.

3 Water analysis

A review (144 references) of metals in waste waters associated with advanced technologies included58 consideration of current analytical methods for determining Gd, PGE and REEs in waste waters. Whereas Gd came mainly from MRI contrast agents and personal care products, the PGEs and REEs probably came from materials used in modern technologies.

Two interesting papers on the release of trace elements into water have been published. The first looked59 at the release of Ag from two types of PET water bottles doped with Ag: one with porous glass containing Ag3PO4 and the other with Ag NPs. The PET bottles (500 mL) doped with 300 mg kg−1 of Ag (as Ag3PO4 glass) released enough Ag after 10 days to reach a concentration of 2 μg L−1 when stored at 21 °C. This increased to 72 μg L−1 when stored at 43 °C, thereby exceeding the European Food Safety Authority migration limit of 0.05 mg L−1 for 10 day storage at >40 °C. In contrast, the bottles doped with Ag NPs at a higher concentration (400 mg kg−1) released hardly any Ag: the test solutions concentrations were below the ICP-MS LOD (0.1 μg L−1) under all conditions. In an extremely relevant article, given the current Covid-19 pandemic, the leaching of trace elements into water from disposable face masks was measured.60 Seven types of face masks were immersed in 250 mL of deionised water for 24 h. The test solutions were then acidified with 1 mL of 1 M HNO3 and analysed by ICP-MS. It was thought that the Cd, Co, Cu, Pb, Sb and Ti found in some of the solutions came from additives in fibres used to manufacture the masks.

3.1 Certification of reference materials and metrological investigations

A candidate seawater CRM from the Turkish National Metrology Institute was analysed61 by ID-ICP-MS/MS to determine the Cd, Cr, Cu, Ni, Pb and Zn concentrations after trimethylamine-assisted Mg(OH)2 co-precipitation. The results were validated by analysis of the NRCC CRM CASS 6 (nearshore seawater) and the NMIA CRM MX014 (trace elements in sea water).

3.2 Sample preconcentration

The affinity of DGT samplers for PGE in aged samples was determined.62 Whereas DGT samplers loaded with the Purolite S914, S920 and IONQUEST® MPX-317 chelating resins had an unchanged quantitative affinity for Pt in spiked waters, even after ageing for 17 days, the sampling efficiency for Rh decreased by 70% because Rh was hydrolysed to Rh(OH)3(s) which is not taken up by the resin. Fortunately though, this effect was much less pronounced when sea water was sampled.

Other significant advances in the SPE of trace elements from water samples are summarised in Table 1 and those in the use of LPE in Table 2.

Table 1 Preconcentration methods using solid-phase extraction for the analysis of waters
Analytes Matrix Substrate Coating or modifier Detector LOD in μg L−1 (unless stated otherwise) Validation Reference
AgI, CdII, and PbII Drinking, swamp and waste waters GO–Fe2O3 nanocomposite 3-Mercaptopropyl trimethoxysilane FAAS 3.2 (Cd) to 47.3 (Pb) Sample spike recovery 279
AsIII River, tap and waste waters Magnetic GO nanocomposite Glucamine ICP-MS 0.05 Sample spike recovery 280
AsV Natural water TiO2 nanofibers Graphitic carbon nitride ICP-MS 0.6 ng L−1 Institute for Reference Materials of SEPA, CRM GSBZ50004-88 (water) 281
AsV Water Quartz support N-Methyl-D-glucamine TXRFS 0.05 Sample spike recovery 282
AsV, SeVI Water Cellulose filters TiO2 EDXRFS 0.25 (As) 0.4 (Se) Sample spike recovery 283
Au Environmental waters, sediments, soils, coal, ores Fe3O4 NPs Poly(1-vinylimidazole) IIP ICP-MS 0.002 Sample spike recovery (water samples); National Research Center for Certified Reference Materials (China) CRM ZBK338 (ore) 284
Be Waters, alloy samples Fe3O4 NPs MIL-53(Fe) type MOF FAAS 0.07 Sample spike recovery 285
Cd Drinking and river waters, fruit, meat Magnetic GO 2-(3-Hydroxy-1-methylbut-2-enylideneamino)pyridine-3-ol (acacapyrH2) FI-FAAS 0.06 Sample spike recovery 286
Cd, Co, Cu, Ni, Pb Water, food Cellulose nitrate membrane filter Quinalizarin FAAS 0.1–0.7 Sample spike recovery; analysis of CRMs 287
Cd, Co, Cu, Ni, Pb, Zn Spring and sea waters Cellulose membranes Mixed GO and carbon CNTs TXRFS 0.08 (Cd) −0.21 (Co) Sample spike recovery; NIST SRM 1640a (trace elements in natural water) and Supelco CRM QC3163 (trace metals 1 in seawater) 288
Cd, Cu, Ni Estuarine and sea waters PVC and TBP polymeric membrane Pyridine-2-acetaldehyde benzoylhydrazone FAAS 0.33 (Cu)–0.75 (Ni) LGC CRM LGC6019 (Thames water); seawater spike recovery 289
Cd, Cu, Pb Ground, river, and waste waters GO membrane Poly-aminophosphonic acid ICP-AES 1.1 ng L−1 (all elements) NIST SRM 1572b (citrus leaves), NIES CRM 10C (rice flour); spike recovery 290
Cd, Co, Cu, Fe, Mo, Ni, Pb, Ti, V Ground and waste waters Methacrylate resin Carboxymethylated polyallylamine ICP-AES 4 (Co)–220 (Zn) ng L−1 SCP Science CRM Enviromat EU-L-1 (wastewater low) 291
Cd, Co, Cu, Mn, Ni, Pb, Zn Ground, river and sea waters 3D printed SPE cartridges with Lay-Fomm 40 porous filaments ICP-MS 0.3 (Pb) to 6.7 (Zn) ng L−1 NRCC CRMs CASS-4 (coastal seawater), SLEW-3 (estuarine water), NIST SRMs 1640a (trace elements in natural water) and 1643f (trace elements in water) 292
Cd, Hg, Pb Environmental water Fe3O4 NPs coated with SiO2 Poly (ethylene glycol dimethacrylate-co-glycidyl methacrylate ICP-MS 0.21 (Hg) to 2.9 (Pb) ng L−1 Chinese GBW CRMs GSB 07-1185-2000 (water quality Cd), GSB 07-1183-2000 (water quality Pb) and GSB 07-3173-2014 (water quality Hg) 293
Cd, Ni Water Fe3O4 NPs Polydopamine IIP FAAS 25 (Cd) to 60 (Ni) ng L−1 NIST SRM 1643e (trace elements in water) 294
Cd, Ni, Pb Water Granular activated carbon Quercetin FAAS 0.78 (Cd) to 8.1 (Ni) NIST SRM 1643e (trace elements in water) 295
CrIII Drinking, spring and waste waters Amberlite® CG-120 resin FAAS 0.3 Environment Canada CRM TMDA-70.2 (high level fortified sample for trace elements of Ontario lake water) 296
CrVI Water Magnetic MWCNTs 3-Aminopropyltrimethoxysilane FAAS 3.2 Spike recovery; comparison with ETAAS results 297
CrVI Drinking and sea waters GO MoS2 EDXRFS 0.05 Supelco CRMs QC1453 (chromium VI in drinking water) and QC3015 (chromium VI in seawater) 298
Hg Water Filter paper Au NPs Head space portable Zeeman AAS 0.09 Spike recovery 299
Hg2+, MeHg+ and EtHg+ Ground, surface and sea waters; fish Fe3O4 NPs coated with covalent organic frameworks 1,2-Ethanedithiol HPLC-ICP-MS 0.96 (Hg2+), 0.17 (MeHg+) and 0.47 (EtHg+) Spike recovery (water); NRCC CRM DORM-4 (fish protein) 300
Hg Spring and river waters; artificial seawater Bi2S3 NPs EDXRFS 0.06 Supelco CRM QC3163 (trace metals 1 in seawater) 301
In Water Activated carbon Sodium dodecyl sulfate ETAAS 0.2 ng L−1 Spike recovery 302
Pb Drinking, lake and spring waters Amberlite® CG-120 resin SQT-FAAS 0.23 Environment Canada CRM TMDA-70.2 (high level fortified sample for trace elements in Ontario lake water) 303
Pb Contaminated water MWCNTs Phenylenediamine FAAS 1.5 NIST SRM 1640a (trace elements in natural water) 304
Ra226 River water AG® 50W-X8 resin ICP-SFMS 0.46 fg L−1 (10 mBq L−1) Spike recovery 305
REE and Y Sediment pore water Nobias PA1® resin ICP-MS 0.06 (Tm) to 1.47 (Y) pM Spike recovery and comparison with literature values found in NRCC CRMs CASS-5 (coastal seawater) and NASS-6 (seawater) 306
Sb Ground, surface and waste waters Electrodeposition on microporous glassy carbon electrode HG-HR-CS-AAS 0.1 NIST SRM 1640 (trace elements in natural water) 307
SbIII and TeIV Lake, river, and environmental waters Poly(glycidyl methacrylate–ethylene dimethacrylate) monolithic capillary Cystamine ICP-MS 3.9 (Sb) and 5.9 (Te) ng L−1 GBW CRMs GSB 07-1376-2001 (water quality antimony) and GBW(E)080548 (tellurium in water) 308
Sr90 Lake, ground, sea and radioactive waters Eichrom DGA-B and Sr resins ICP-MS 0.47 Bq L−1 (0.09 pg L−1), Spike recovery and IAEA 2018 proficiency test exercise water 309
Th, U Tap, river and lake waters Fe3O4 NPs Anoxybacillus flavithermus SO-15 ICP-AES 0.014 (Th) and 0.015 (U) NWRI EC RM, NWTM-15 (fortified lake water) 310
V Water Fe3O4 NPs and MWCNTs 4-(2-Pyridylazo)resorcinol ICP-MS 1.5 ng L−1 NWRI EC CRM TMDA 51.3 (fortified lake water) 311


Table 2 Preconcentration methods using liquid-phase extraction for the analysis of water
Analytes Matrix Method Reagents Detector LOD in μg L−1 (unless stated otherwise) Method validation Reference
AsIII, AsV Water, soil, sediment DLLME APDC (chelating agent) and trihexyl(tetradecyl)phosphonium hexachlorodisprosiate (AsIII) and trihexyl(tetradecyl)phosphonium tetrachloroferrate (AsV) ETAAS 17 (AsV) and 20 (AsIII) ng L−1 Spike recovery 312
AsIII, AsV, MMA, DMA, AB Drinking, bottled, sparkling and sea waters DLLME Sequential extraction with the ionic liquid (IL) 1-octyl-3-methylimidazolium bis((trifluoromethane)sulfonyl)imide (DMA and MMA). ETAAS 0.02 (all species) NRCC CRM NASS-6 (seawater), NIST SRM 1640a (trace elements in natural water) and SPS CRM SPS-SW2 (elements in surface waters) 313
Addition of APDC for AsIII, reduction of AsV to AsIII with sodium thiosulphate.
Separate extraction of AB with sodium mercaptoethane sulphonate solution
Cd Water, vegetables, hair UA-DLLME 1-Hexyl-3-methylimidazolium tris(pentafluoroethyl)trifluorophosphate (IL) and 2-(6-methylbenzothiazolylazo)-6-nitrophenol (complexing agent) FAAS 0.1 Environment Canada CRM TMDA-51.3 (fortified water) 314
Cd Water, urban wastewater, molluscs UA-LLME 1,2-Dichloroethane and trichloroethylene (acceptor phase) and 2-(2-bromo-5-pyridylazo)-5(diethylamino)phenol (chelating agent) FAAS 0.39 IRMM CRM BCR-713 (effluent wastewater) and NIST SRM 1566b (oyster tissue) 315
Cd, Co, Cu, Ni, Pb, Zn Waste and surface waters LLE Tetrabutylammonium bromide, (NH4)2SO4 and with 4-(2-pyridylazo)-resorcinol ICP-AES 0.012 (Cd) to 0.4 (Zn) Spike recovery; SPS RMs SPS-SW2 (surface water) and SPS-WW1 (wastewater) 316
Cd, Cu, Ni, Pb Food, water Carrier-free coprecipitation 3-Benzyl-4-p-nitrobenzylidenamino-4,5-dihydro-1,2,4-triazole-5-thiol FAAS 0.3–0.8 317
Co, Ni Food, water UA-DLLME with SFOD Decanoic acid and DL-menthol (eutectic solvents) and 2-(5-bromo-2-pyridylazo)-5-(diethylamino) phenol (chelating agent) FAAS 0.3 (Ni)–0.4 (Co) 318
CrVI Water, tea UA-LLME Choline chloride and phenylethanol (eutectic solvent) and APDC (complexing agent) FAAS 0.8 Spike recovery 319
Cu Water, dialysis solution, sweat, urine Microextraction THF and decanoic acid (supra molecular solvent) and 1-(2-pyridylazo)-2-naphthol (chelating agent) FAAS 7.3 Environment Canada CRMs TMDA 53.3 (fortified lake water) and TMDA 64.2 (fortified lake water) 320
FeIII Water, wastewater LLME N,N-Dimethyl-n-octylamine (solvent) and 2-(5-Bromo-2-pyridylazo)-5-(diethylamino) phenol (complexing agent) FAAS 0.015 SPS RM SPSWW1 (wastewater) 321
Mn River, ground, well and tap waters LLME Methanol and toluene (extraction solvent) (1-(2-pyridylazo)-2-naphthol (ligand) ICP-MS 0.1 ng L−1 Spike recovery 322
Pb Tap, lake and river waters; salted peanuts, chickpeas, roasted yellow corn, pistachios, almonds LLME Alpha-benzoin oxime and iron(III) chloride dissolved in phenol FAAS 8 ng L−1 Environment Canada CRMs TMDA 64.2 (lake Ontario water), TMDA 53.3 (fortified lake water), and NACIS CRM NCSDC-73349 (trace elements in bush branches and leaves) 323
Pd Stream, tap and sea waters LLME N-(3-Chloro-4-fluorophenyl)-N-phenylthiourea FAAS 2.3 Spike recovery 324
SbIII Tap, dam, mineral, wetland, underground, rain and river waters DLLME Trihexyl(tetradecyl)phosphonium tetrachloroferrate (magnetic ionic liquid extractant) and ammonium diethyldithiophosphate (complexing agent) ETAAS 0.02 Spike recovery 325
Se and Te Soils and sediments; sea, river, underground and tap waters FI-LLME 1-Octyl-3-methylimidazolium chloride and KPF6 (ionic liquid), APDC (chelating agent) HG-AFS 1.8 (Te) and 2.6 (Se) ng L−1 Spike recovery; IRMM CRM BCR 402 (white clover) 146
Th Wastewater; phosphate rocks CPE 2-(2,4-Dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one (complexing agent) and Triton X-114 and KI (cloud point agents) ICP-AES 0.11 Spike recovery 326


3.3 Speciation analysis

The reaction kinetics of the transformations of the arsenic species AsV, AB and AC in aqueous solutions during ultrasonic extraction were followed63 in detail in order to obtain the optimum extraction conditions. A previous study by the same authors had shown that arsenic species were unstable. The transformation followed 1st-order reaction kinetics at different ultrasonic water bath temperatures (0–80 °C) and powers (150–750 W). Reasonable extraction efficiencies with relatively low interspecies transformation rates were considered possible if the ultrasonic bath conditions were 150–300 W and 40–60 °C with a 5–10 min extraction time. To separate AsIII from AsV in Iranian mine waters, an AsV-specific IIP was prepared64 and applied to an etched TLC plate to form a set of μTLC plates for subsequent LA-ICP-MS detection. Optimum separation for a 10 μL sample volume was obtained within 10 min with a 1[thin space (1/6-em)]:[thin space (1/6-em)]1 acetonitrile[thin space (1/6-em)]:[thin space (1/6-em)]water mobile phase. The LOD for As in the μTLC plates was 0.06 μg L−1 for both species. Results for mine water samples compared well with those obtained using the standard HG-AAS method. Although this method is time-consuming and so provides little advantage for the analysis of waters, it would have an advantage for the analysis of crude oil samples because organic solvents cannot be transported to the plasma source during μTLC-LA-ICP-MS analysis.

The wider availability of ICP-MS/MS has made the speciation of organo-halogen or halogen-tagged molecules more accessible. For instance, a mix of chlorinated and brominated acetic acids was successfully separated65 on a mid-bore C18 HPLC column and detected by HPLC-MS/MS. With an injection volume of 50 μL, the LODs of 0.8 and 1.4 μg L−1 for Br and Cl, respectively, were well below the US EPA maximum contaminant level of 60 μg L−1 for these compounds. The method was validated by analysis of spiked samples and the Sigma Aldrich RM EPA 552.2 (haloacetic acids mix). Mono- and di-iodoacetic acids were separated66 by AEC and ICP-MS detection. No preconcentration was necessary. The results compared favourably with those from 5 control laboratories using Chinese standard methods for the analysis of raw waters with high I concentrations after disinfection with Cl and UV irradiation. The LODs of 0.06 and 0.04 μg L−1 for the mono- and di-iodoacetic acids, respectively, were comparable to those obtained by the control laboratories which used a sample preconcentration step before analysis.

Although not strictly atomic spectrometry, the determination of Fe2+by ESI-MS is of interest.67 Complexation with 1,10-phenanthroline resulted in peaks such as that at m/z 208 ([Fe (phen)2]2+) which could be used to determine Fe2+ concentrations in river waters. Results compared well with those obtained using a standard colorimetric method. Of the total Fe present in Japanese rivers, 40% was present as Fe2+. Unfortunately, the authors did not report an LOD for comparison with current colorimetric and chemiluminescence methods.

The determination of Gd-based contrast agents in waters remains of interest because these agents can enter the environment from WWTPs. Gadolinium-containing compounds in seawater were successfully separated68 from the matrix on micro SPE cartridges packed with graphitic carbon for analysis by HILIC-ICP-MS. A four-fold preconcentration factor was obtained by using a sample volume of 1 mL and an elution volume of 0.25 mL. It was possible to determine Gadoterate Meglumine (Gd-DOTA) and Gadobutrol (Gd-BT-DO3A) at concentrations of 180 and 140 ng L−1, respectively, in Australian coastal seawater. As HILIC chromatography can be considered similar to normal-phase chromatography, it is not generally considered to be compatible with ICP-MS analysis. To overcome this problem, researchers developed69 an aqueous mobile phase capable of separating the main 6 commercially available Gd-based contrast agents on a HILIC column before ICP-MS detection. The LODs were between 3.7 ng L−1 (Magnevist® Gd-DTPA) and 22 ng L−1 (ProHance® Gd-HP-DO3A) and so were sufficient for identifying these compounds in waters near the outfall from a WWTP. The method also allowed the preliminary quantification of unknown Gd compounds (suspected transformation products) in river water.

To widen the applicability of mercury speciation methods in water, a SPE method was adapted70 to preconcentrate Hg and Pb species simultaneously for detection by HPLC-ICP-MS. A C18 SPE cartridge conditioned with 2-mercaptoethanol was used to separate species from coexisting ions in water with preconcentration factors ranging from 459 for Pb2+ to 2400 for Hg2+ for 10 mL samples. The LODs were 0.001 and 0.011 ng L−1 for Hg and Pb, respectively, in freshwater samples. The results were validated by analysis of the Chinese RMs GBW08603 (mercury in water) and GBW08601 (lead in water). A two-step CPE method was developed71 to prevent peak overlap after extraction and thereby to improve the LOD of an HPLC-CV-AFS method for mercury species in water and soils. In the first step, the mercury species were preconcentrated into Triton™ X-114 micelles as DDTC chelates. To make the extract compatible with HPLC, this solution was then back extracted into a 0.1% m/v L-cysteine solution. The LOD of 4 ng L−1 for inorganic mercury and 16 ng L−1 for methylmercury were probably sufficient for the quantification of Hg species in soil samples but insufficient for the analysis of uncontaminated waters.

As chromatographic methods for elemental speciation mature, focus is shifting to preconcentration methods for elemental species. Methods developed for one specific oxidation state are included in Tables 1 and 2 whereas methods for one or more organometallic species are included in this section. Graphene oxide bound to silica NPs was modified72 with sodium dodecyl benzene sulfonate to preconcentrate tetramethyl and tetraethyl lead from river waters. Enrichment factors of >1300 were obtained for both species in 10 mL samples. By using fairly standard HPLC conditions (buffered ion pairing agents) and ICP-MS detection, the method LODs were 0.018 for (tetramethyl lead) and 0.023 (tetraethyl lead) ng L−1. Cytostatic Pt compounds used in chemotherapy were isolated73 from a WWTP effluent using strong anion exchange cartridges. The ionic forms of Pt were adsorbed on the cartridge at pH 3 and then eluted at high pH with 5–10% v/v NH3 solutions. Despite extensive optimisation, the adsorption and desorption efficiencies were quite low (44–68%). No HPLC-ICP-MS analysis was undertaken so the actual species being preconcentrated was not confirmed. The extensive body of literature on the separation and retention of this class of compounds on ion-pair-modified C18 silica phases contains the information needed for improving this method.

3.4 Analysis of nanomaterials

The analysis of nanomaterials in waters is becoming widespread and even routine, yet advances and challenges remain to be overcome as highlighted by a number of reviews. An article74 (68 references) on the trends in sample preparation and analysis of nanomaterials in environmental samples covered: the sources of nanomaterials; their transformation products in aquatic environments; sampling; and analytical techniques. A helpful summary was provided with the aim of avoiding potential pitfalls. A more specific review75 (159 references) covered the challenges in determining Ti NPs in aqueous samples. Focussing on the analysis of TiO2 NPs from sunscreens, the authors reviewed the use of sp-ICP-MS and highlighted the need for improved sample pretreatment and better modelling of the fate of these materials. A review (186 references) on the use of asymmetric FFF in the analysis of engineered and natural NPs as well as colloids in aquatic systems covered76 advances in this technique and the various challenges faced. Deviations from theoretical results were typically caused by sample losses, membrane–analyte interactions and overloading of the analytical system.

The performance of single particle ICP-MS continues to be improved. A total consumption nebuliser was proposed77 as a means to avoid particle loss during nebulisation. As all the sample was nebulised at a liquid flow rate of 8 μL min−1, there was no need for determining the transport and nebulisation efficiencies. Under these conditions, the size distributions of gold, platinum and silver NPs in known standards were determined successfully. The method was then applied to the determination of gold NPs in surface waters after dissolved-standard calibration to determine the NPs’ average diameter. In the ICP-SFMS determination of CeO2 NPs in Canadian waterways, the size LOD was improved78 to <4 nm by using a 50 μs dwell time and gold and silver NPs as size RMs. The NP number and size were then successfully determined in samples of urban rainwater and river water. Up to 36% of the Ce-containing NPs in Montreal rain were engineered NPs. As mentioned in previous ASUs, sizing can be improved by eliminating the dissolved fraction signal. Cloud point extraction of dissolved Ag has once again been confirmed79 as a suitable method for this task. By removing the dissolved Ag from the sample and then preconcentrating the silver NPs present, the size LOD was reduced from 20 to 4.5 nm.

The coupling of asymmetric field-flow-fractionation with ICP-MS continues to be a reference method for the sizing of particles within complex matrices. In a study of coated platinum-NPs in water samples, quantitative membrane recoveries for a 30 min run time allowed80 5, 30 and 50 nm NPs added to natural and artificial (humic acid added) surface water samples to be separated. As found in previous studies, complexation by OM in the water was a critical factor.

The concentrations and metal loads of nanoplastics in the environment are becoming of increasing concern. A method to detect microplastics by ICP-MS involved81 functionalising polystyrene microspheres with gold NPs and detection with sp-ICP-MS. The functionalisation with gold NPs made it possible to count individual particles. The quantification limit was 8.4 × 105 particles per L and the response was linear up to 3.5 × 108 particles per L for particles of up to 1 μm diameter. The size LOD was 135 nm. The LA-ICP-MS depth profiling and mapping of marine plastics was used82 to discriminate between surface adsorbed metals (As, Cd, Cu, Fe, Sb, Sn, Pb, U and Zn) and additives (Cu, Pb or Sb) in the bulk polymer. Mapping of the plastics made it possible to estimate diffusion of the metals from the surface to the subsurface during weathering.

3.5 Instrumental analysis

3.5.1 Atomic emission spectrometry. A review (177 references) of analytical applications of a commercially available MIP-AES instrument found83 that preconcentration and matrix removal were necessary for the analysis of seawater because this technique has a sensitivity somewhere between those of FAAS and ICP-AES. Some elements (Al, As, Ba, Ca, Cr, Cu, Fe, K, Mn, Ni, Pb, Zn) were present at concentrations that could be determined directly in drinking water, effluent and wastewater. Better performance could be achieved by improving sample transport with HG or ultrasonic nebulisation.

The use of heated spray chambers was investigated84 to improve the sensitivity of ICP-AES instruments at low flow-rates. An instrument operating with different heated spray chambers and nebulisers at sample flow rates between 50–75 μL min−1 had a sensitivity similar to that obtained using a normal cyclonic spray chamber with a nebuliser operating at 1 mL min−1.

The development of low-power discharges for water analysis continues to be of interest and useful LODs are now being achieved. A solution cathode-glow-discharge was used85 as an excitation source for the determination of Ga and In in water samples. Whereas few interferences were present in the determination of In, even when matrix components were present in a 20-fold excess over the analyte concentration, the determination of Ga suffered severely from interferences. As a consequence, the LOD for Ga (0.1 μg L−1) was much higher than that for In (0.016 μg L−1). The same type of excitation source was successfully coupled86 with HG to determine Sb in water samples. Standard HG conditions for the generation of stibine gas gave an LOD of 0.36 μg L−1, good spike recoveries and results for real samples comparable with those obtained by ICP-MS. The use of low power means instruments can be made portable. An instrument with a DBD to generate a microplasma was used87 in the field to determine As and Sb in environmental water samples. The solid reagent sulfamic acid was used instead of HCl when possible in order to reduce liquid waste. The LODs of 0.5 and 0.2 μg L−1 for As and Sb, respectively, were impressive for an instrument that weighed only 15 kg in total and operated at <60 W total power consumption.

3.5.2 Vapour generation. The vapour generation of Cd remains a key method for improving instrument sensitivity. Use of an electrochemical cell to generate volatile forms of Cd using graphite electrodes and a DC power supply removed88 the need for an ion exchange membrane to separate the electrolyte solutions. An AFS LOD of 0.05 μg L−1 was achieved and good results were obtained for the analysis of the Chinese IERM CRMs, GBW0806, GSBO7-3186-2014 and GSB07-1185-200 (environmental water). The PVG-SQT-AAS LOD for Cd in waters of 2.5 μg L−1 was89 significantly better than the 0.14 mg L−1 achieved when using AAS alone. Use of PVG improved analyte transport to the flame where atoms were trapped in the quartz tube with a lean flame for 6 min before introduction of H2 to release the trapped atoms.

The chemical vapour generation of CrVI was enhanced90 by the addition of sodium diethylaminodithioformate to the reaction mix of KBH4 and HCl. The total Cr concentration was determined after oxidation of the sample with KMnO4 so the CrIII concentration could be calculated. The chemical VG efficiency of 28% resulted in an LOD of 0.2 μg L−1 for CrVI when using ICP-MS detection. The method was validated by spike recovery from tap water and analysis of the Chinese CRM GEW(E)080197.

A gold trap typically employed in the determination of Hg was used91 for the preconcentration of Se after HG. The AAS LOD was improved from 0.23 to 0.013 μg L−1 by trapping at of 450 °C for 300 s and desorbing at 970 °C. This device could not be used with ICP-MS because of losses of volatile Se species by condensation onto the cold tube walls between the amalgamation unit and the plasma torch. In an offline preconcentration procedure,92 H2Se generated by direct addition of the reagents to the sample was trapped in an aqueous ammonium solution that was then analysed directly by AAS. Recoveries of spikes from tap water samples were 99 ± 3% and the LOD was 1.3 μg L−1.

The dielectric barrier discharge, an up-and-coming addition to VG systems, was successfully coupled93 with ICP-MS for the detection of PGEs in river, lake and rain waters. The sample was nebulised into the DBD in the presence of formic acid to improve the generation of volatile PGE species. The LOD was reduced 10-fold when compared to normal nebulised sample introduction and the spike recoveries from the water matrices ranged from 80% (Pd in rainwater) to 110% (Pt in lake water). The typical use of DBDs in AAS and AFS is as atomisers and atom traps for hydrides, as exemplified94 by the determination of Sb in bottled water. An LOD of 5 ng L−1 was achieved with a 50 s accumulation time followed by atomisation and AFS detection. The instrumental response was linear up to 50 μg L−1. A seven-fold improvement in sensitivity over that achievable by standard HG-AFS was obtained and the recoveries of spikes from bottled tap water samples were quantitative.

3.5.3 Inductively coupled plasma-mass spectrometry. An extensive review (126 references) of the determination of REEs in waters by ICP-MS covered95 sample introduction systems and the reduction or elimination of interferences in the analysis of surface, ground and rain waters and Antarctic ice. Tables of assigned or published values were presented for many available water CRMs.

Sample stabilisation before the determination of mercury by ICP-MS is always important. A mixture of thiourea and HCl stabilised96 Hg in humic-acid-rich waters before direct analysis without the need for further sample preparation. The LOD of 0.4 ng L−1 indicated that the researchers had managed to maintain low Hg blank levels. The method was validated by analysis of the IRMM CRM ERM®-CA615 (groundwater) and by recoveries of spikes from waters with artificially enriched humic-acid contents.

Matrix separation remains an important strategy for ICP-MS analysis of samples such as seawater that have challenging matrices. An online desalination device removed97 Ca, Mg and Na from seawater samples during analysis. A helical coil packed with an element-specific ion-imprinted-polymer removed the matrix ions but did not retain the analytes (As, Cd, Cu, Mn, Ni, Pb, Sb, Tl). The desalination device was effective for at least 9 h. Removal of the matrix improved the LODs for all analytes. Trueness and repeatability of the method were determined to be <3 and <5%, respectively, by analysis of the NRCC CRM CASS-5 (nearshore seawater) during the analytical run. Because analyte preconcentration from WWTP effluents was impeded by the presence of EDTA in samples, it was destroyed98 by digestion with a mix of HNO3 and H2O2 prior to preconcentration of Fe, Cu and REEs on a chelating resin. The method was validated against the JSAC CRM JSAC 0302-3c (river water) with ICP-MS detection.

It has been demonstrated99 through analysis of the NMIJ CRM 7202-c (river water) that Ne can be used successfully as a collision gas for ICP-MS analysis of river water. Helium was considered100 the collision gas of choice for the quantification of REEs in fresh and slightly saline waters and digested sediments but the interference of Ba on Eu and the difficulties in determining Sc remained. The method was validated by the recovery of spikes from 5 commercial mineral waters and by the analysis of the NRCC CRMs SLRS-6 (river water), SLEW-3 (estuarine water) and AQUA-1 (drinking water). Oxygen was used101 as reaction gas in the ICP-MS/MS determination of REEs in order to form REE monoxide ions and thereby displace the analyte peaks from those of the interferences. The LOD for the direct analysis of waters were from 0.004 (Er) to 0.26 (Nd) ng L−1. The method was validated against the NRCC CRM SLRS-4 (river water). The REE concentrations in various Japanese lake and river water samples were determined.

Addition of methane to the plasma improved102 the detection limit for P in water as determined by ICP-SFMS. The addition of a flow of 6 mL min−1 resulted in a 3-fold sensitivity increase and a LOD of 7 ng L−1. That methane did not generate any spectral interferences was verified by scans in medium resolution mode. The method, validated by analysis of the Chinese RM GBW(E)081215 (phosphate in plant root water), was considered sensitive enough to determine P in Arctic glacial meltwaters and lake, river, rain and sea waters.

The determination of226Ra was simplified103 by replacing the standard R = 10[thin space (1/6-em)]000 medium resolution slit of an ICP-SFMS instrument with a R = 2000 slit to ensure spectral peak separation from known interferents whilst maintaining a flat topped peak for good isotope ratio determinations. As the concentrations in seawater were expected to be between 0.03 and 0.1 fg g−1, the 226Ra was initially preconcentrated from 1 L of seawater by coprecipitation with MnO2 and then isolated using a 2 column clean-up procedure (strong CEC followed by extraction chromatography). The extraction efficiency was 90–99% and the method LOD 0.033 fg g−1. The results for samples taken at one of the cross reference stations used in the GEOTRACES cruises compared well with those obtained by alternative methods.

The isotope ratios of Sr (87Sr/86Sr and δ88/86Sr) in sediment pore waters were determined104 by MC-ICP-MS following a single miniaturised extraction protocol that used 30 μL of Sr Spec resin. The matrix was removed with three 1 mL aliquots of 3 M HNO3 and the Sr by three volumes (0.25, 1.25 and 1.5 mL) of deionised water. Indicative values measured for the NIST SRM 1640a (trace elements in natural water) were 87Sr/86Sr = 0.708790 ± 0.000062 and δ88/86Sr = 0.07 ± 0.10‰ (2SD, n = 6). All of the various analytical methods compared105 for the MC-ICP-MS determination of Pb isotopes in seawater involved Pb separation by either SPE with the Nobias Chelate PA-1® resin or coprecipitation with Mg(OH)2. Mass bias was corrected using either a Pb double-spike or external normalisation to Tl. For the analysis of seawater with Pb concentrations of 1–7 ng, all the ICP-MS methods gave results comparable to those obtained by TIMS analysis but with a higher sample throughput. The use of resin extraction gave the highest analytical throughput and was most suitable for high-silicate waters but the low cost of precipitation made it an attractive alternative for waters with low concentrations of silicates.

3.5.4 Laser methods. The detection of molecular species in water samples is now generally undertaken by LIBS rather than AAS. To determine Cl and F in water, 100 μL samples of water were deposited106 onto a calcite absorbent. The LODs of 0.38 mg L−1 for F and 1.03 mg L−1 for Cl were obtained using the molecular emission spectra of CaCl and CaF. The method was verified against results obtained by an IC standard method and was deemed sufficiently sensitive as a screening method for WHO action levels of Cl and F in drinking water. The dissolution of isotopically enriched B powders in water followed by deposition onto a Cu sputtering plate demonstrated107 that LIBS can distinguish between B isotopes by detection of BO molecular species. The measurement uncertainty of ca. 10% and mediocre accuracy when carrying out IDA analysis at concentrations of 1000 mg L−1 demonstrated that further method development is required before the technique becomes the method of choice.

The oxidation state speciation of Fe using LIBS detection was achieved108 by preferentially trapping Fe2+ and Fe3+ on an iminodiacetic acid functional group resin at different pH values. After species-specific trapping, the resin was ground and tabletted before LIBS measurement of the Fe content. The LODs were 0.120 (Fe2+) and 0.142 (Fe3+) mg L−1. The method was validated by spike recovery and comparison with ICP-AES results.

3.5.5 X-Ray fluorescence spectrometry. Depositing a 200 μL single water drop onto a hydrophobic support that was then agitated prevented109 the formation of a coffee-ring type drying effect and so resulted in a more uniform deposit for the direct TXRFS analysis of mineral waters. The potential of this method was demonstrated by the improvement in emission intensity for V in a mineral water sample from 1005 counts to 3709 counts. Direct determination of Se in a single drop of mine water110 by TXRFS was possible in unfiltered and unacidified samples when a 600 s counting time was used. Increasing this time to 1800 s allowed Se detection in all the samples down to the ng g−1 concentration range. The method was verified by comparison with results obtained by ICP-MS analyses of the same samples and of NIST SRM 1640a (trace elements in natural water). The detection of trace elements at the μg L−1 level in a 5 μL drop by EDXRFS was made possible111 by modifying the instrument so that the entire Bremsstrahlung spectrum emitted from the tube was focussed into a 35 μm spot using polycapillary optics. The LODs of between 0.38 (Cr) and 4.11 (Bi) μg L−1 were obtained without the need for any preconcentration or complicated sample preparation. The results were validated using the NIST SRM 1640 (trace elements in water).

4 Analysis of soils, plants and related materials

4.1 Review papers

A review (132 references) on methods for the separation and characterisation of environmental nano- and micro-particles included112 useful information on the typical range of particle sizes than can be investigated using different separation methods and on the scope and limitations of techniques such as FFF-ICP-MS.

4.2 Reference materials

The preparation and characterisation of new RMs was the goal of a collaboration113 between the IAG and the USGS. Sediments and soils from six locations in the Western USA were combined to produce three RMs with the same elemental compositions as NIST SRM 2709a (San Joaquin soil), 2710a (Montana I soil) and 2711a (Montana II soil). Analysis in more than 80 laboratories of the candidate RMs, SdAR-H1 (metalliferous sediment), SdAR-M2 (metal rich sediment) and SdAR L2 (blended sediment) as part of the GeoPT proficiency testing scheme, produced reference values for 41, 48 and 49 elements, respectively. The new RMs, whose elemental compositions are close to (correlation coefficients > 0.85) those of the existing NIST SRMs, are alternatives for use in monitoring studies.

A 111Cd–113Cd double-spike ICP-MS procedure was used114 to determine Cd isotope ratios in 34 RMs that covered a wide range of environmental matrices. The method precision of better than ± 0.074‰ (2SD) was determined from analysis of RMs using an optimum mixing ratio of 111Cdspike/112Cdsample of 2.0. The δ114/110Cd values of −0.001 to +0.287‰ for rock and −0.806 to +0.142‰ for biological samples provided a useful database for monitoring studies. The Cd mass fractions and isotope compositions in NIST SRMs 1566b (oyster tissue), 1573a (tomato leaves) and 1575a (pine needles), IRMM CRM BCR 482 (lichen) and ERM CE278k (mussel tissue) were determined115 by TIMS using a 116Cd–106Cd double-spike. Based on analysis of NIST SRM 3108 (Cd standard solution), the precision of the procedure was high at δ114/110Cd = −0.005 ± 0.029‰ (2SD, n = 47). Variations in δ114/110Cd ranged from −0.52‰ ± 0.03 (2SD, n = 5) for mussel to +0.24‰ ± 0.04 (2SD, n = 6) for pine needles. This study highlighted the potential for using natural mass-dependent Cd fractionation in biogeochemical studies.

Isotopic ratios for Mg were determined116 following digestion in HNO3 + H2O2 for a range of RMs: IRMM CRMs BCR-383 (green beans) and BCR-380R (whole milk); NIST SRM 1577c (bovine liver); ERM-CE464 (tuna fish); and NRCC CRMs DORM-2 and DORM-4 (fish protein) and TORT-3 (lobster hepatopancreas). The separation of Mg from the sample matrices involved a three-column procedure: the first column contained AG1-X8 anion-exchange resin for removal of Fe; the second contained AG50W-X12 cation-exchange resin, which was eluted with 1 M HCl for removal of Ca, Na, P and S; and the third was a another AG50W-X12 column, which was eluted with 0.4 M HCl for removal of other matrix elements, in particular K. The isotopic analysis was performed in two laboratories on three MC-ICP-MS instruments. The measured δ26Mg values for three USGS basalt RMs and IAPSO (standard sea water) were not significantly different from published values. Measured relative to the value for the RM DSM-3 (nitrate solution), δ26Mg ranged from 0.52‰ ± 0.29 (2SD, n = 7) for the bovine liver to −1.45‰ ± 0.2 (2SD, n = 5) for the tuna fish with a precision of 0.03‰ (2SD, n = 85). Accurate Mg isotopic determinations would be of value not only in environmental but also in human health studies as variations have recently been reported in patients with type 1 diabetes.

4.3 Sample preparation

4.3.1 Sample dissolution and extraction. Alternative digestion procedures were investigated with the aim of avoiding the use of HF. These included117 the use of HBF4 as a HF substitute in a method that involved MAD (room temperature to 60 °C in 9 min; 60–125 °C in 25 min; 125–160 °C in 12 min; 160–240 °C in 14 min; 20 min hold) using 200 mg of sample with double distilled HNO3 (3 mL) and incremental volumes (0, 0.1, 0.5, 1.0, 1.5, and 2 mL) of HBF4, followed by ICP-MS. For IAEA CRMs Soil-7 (soil) and SL-1 (lake sediment), HNO3 alone gave extraction efficiencies of 87–120% for Cd, Co, Cr, Fe, Mn, Pb, V and Zn. Addition of HBF4 was necessary to recover Ba, Sb, and Sr adequately; the highest recovery (>85%) was achieved on addition of 1.5 mL HBF4. The elements Th, Ti, Y and Zr could not be recovered by this procedure so HF would still be needed.

A protoype IR sample digestor developed118 as a simple and inexpensive means for digesting soils consisted of a wooden box that housed two commercial IR lamps and had a metal lid with 18 holes to accommodate 50 mL PTFE microwave-digestion vessels. For digestion, HNO3 (2 mL) was added to the sample (150 mg) and heated to dryness, followed by addition of H2O2 (2 mL), also heated to dryness. The total heating time was 30 min. Triplicate MIP-AES determinations of Ca, Cd, Cu, Fe, K, Mg, Mn, P, Pb and Zn in NIST SRM 2711 (Montana soil) and EMBRAPA RM Agro E2002a (tropical soil) gave extraction efficiencies of 80% (Mg) to 99% (Pb) and of 83% (Ca) to 99% (Mg), respectively.

Various digestion methods for the determination of Cd isotope ratios in a range of soil and plant materials were studied.119 High pressure bomb digestion at 195 °C for 72 h with a 1 + 3 mixture of HNO3 and HF did not lead to Cd loss and δ114/110Cd values for the four soil and two plant RMs tested gave reproducible results within reported values. On the other hand, dry ashing, performed at 550 °C for between 12 and 24 h with 25–700 mg sample, followed by heating at 120 °C for 24 h with a 1 + 3 v/v mixture of HNO3 and HF, resulted in Cd loss and perturbation of Cd isotope ratios to varying degrees in all sample types. That dry ashing was unsuitable for Cd isotope ratio measurements was demonstrated by the 2.69‰ deviation in δ114/110Cd obtained for NIST SRM 1570a (spinach leaves). The values of −0.07 ± 0.09‰ and 0.26 ± 0.05‰ for δ114/110Cd in NIST SRM 1573a (tomato leaves) and NRCCRM RM GSD-30 (drainage sediment), respectively, were the first that had been reported.

The drive for improved reagents for extraction led120 to the synthesis of three stable ILs derived from substituted benzothiophenes. The relatively time-consuming process involved thiophene metalation, performed at −30 °C, followed by α substitution with an ethyl, butyl or dodecyl group to produce the 2-alkylbenzothiophene. This was reacted with silver tetrafluoroborate (0.44 mM) and methyl iodide (2.8 mM) for 12 h and cooled to −18 °C overnight to obtain the final salt. The products were characterised spectroscopically and by DSC. The 1,2-dimethylbenzo[b]thiophenium tetrafluoroborate salt (10 mM) for the MAE (250 W, 120 °C) of metals in spiked estuarine sediments (pH = 2) extracted the same amounts of Cd, Cu, Cr, Ni, Pb and Zn (paired t-test, tcalc. < tcrit.) as the first two fractions of the BCR protocol together. However, the reaction time of only 30 s was much shorter than the 32 h required for the BCR procedure. Based on simulations of solvent efficiency, nine natural DESs were prepared121 and water content, reaction time and temperature optimised for the extraction of Cu, Mn, Mo and Zn from powdered barley grass (Hordeum vulgare L.). Metal concentrations were determined by ICP-MS/MS. Maximum extraction efficiency was obtained when the water content was above 50% for all solvents. Optimum extraction durations and temperatures were DES-specific. The Cu, Mn and Mo contents determined in ERM CD281 (rye grass) were accurate but there was an overestimation of the Zn content that was attributed to contamination. A methanol-based solvent-extraction-procedure was proposed122 to overcome the problem of changing particle size distribution observed in enzymatic extraction protocols for NP analysis. It involved sonification of homogenised NP-treated leaves, addition of 50% v/v MeOH + deionised water and shaking of the resultant solution (150 rpm, 1 h). Tween 80® (1% v/v) was added for NP stabilisation prior to filtration and dilution for sp-ICP-MS or sp-ICP-TOFMS analysis. The method was tested on corn, lettuce and kale samples spiked with Au, CuO and ZnO NPs. The total metal recoveries of 70–100% were influenced by both the NP and the leaf type. The procedure has potential for the monitoring of nanoparticulate agrichemicals.

A HNO3-based leaching method for the determination of radionuclides of Am and Pu in soils and sediments employed123 several clean-up steps to remove interfering elements and to reduce matrix effects. The method combined several traditional steps: sample ashing; HNO3 leaching; coprecipitation with Fe(OH)3 for the removal of alkali and alkaline metals; a two-stage chromatographic separation, firstly with UTEVA and DGA resins for separation of Am and Pu and removal of interfering elements (Bi, Hf, Hg, Pb, Pt, Th, Tl and U) and then with TEVA® resins for further purification; and ICP-SFMS quantification. Concentrations of Am and Pu measured in IAEA CRMs 447 (moss-soil), 384 (Fangataufa lagoon sediment) and 385 (Irish Sea sediment) and NIST SRM 4357 (ocean sediment) ranged from 83.6 ± 8.0% to 88.4 ± 2.4% and 70.6 ± 6.4% to 91.1 ± 0.5% of the certified values, the LODs for 2 g samples of 0.039 mBq g−1 (0.31 fg g−1), 0.94 μBq g−1 (0.41 fg g−1), 2.33 μBq g−1 (0.28 fg g−1) and 1.35 mBq g−1 (0.35 fg g−1) for 241Am, 239Pu, 240Pu and 241Pu, respectively, made the method suitable for simultaneous determination of ultratrace quantities of the nuclides from global fallout or nuclear accidents.

A novel online protocol for sequential extraction from small samples such as fine dusts was based32 on the BCR and Tessier schemes but with a more relevant reduced sample-size of 5 mg. Traditional solvents were diluted with 3% HNO3 to enable direct online introduction of extracts for ICP-SFMS analysis. Optimal pump-flow-rate was 0.3 mL min−1 with a transfer-line diameter of 0.25 mm. Mobility patterns were determined in the multi-element (Al, Ba, Ca, Cd Co, Cr, Cu, K, Mg, Mn, Na, Ni, Pb, Rb, Sr, V and Zn) characterisation of soils, coals and mine- and road-dusts. The total mass recovery was 72% of that obtained by conventional offline extraction because lower extractant concentrations and shorter extraction times were used. To determine the physico-chemical partitioning of PTEs in airborne PM, Sagagi et al.124 developed two non-specific flow-through sequential extraction procedures with chemometric identification of substrates and element distribution (CISED) methodology. One method was based on reduction of sample requirement (2 g of sample and 10 mL of extractant) for a standard CISED extraction to 0.1 g of sample and 3 mL extractant. The other method employed an in-line quasi-flow-through system in which the sample was supported on a TX40 filter in a 47 mm polycarbonate filter holder. When applied to IRMM BCR CRM 701 (lake sediment), the extractions gave recoveries comparable to those of the well-established BCR extraction steps 1–3. The CISED data processing identified seven distinct geochemical components including carbonates, oxyhydroxides and species dominated by Fe and Si. The flow-through method is potentially useful for identification of the geochemical components of urban air pollution and the assessment of associated health risks.

In the element-specific extraction of Hg from surface sediments, de Jesus et al.125 demonstrated that Hg could be extracted without digestion by using dithiothreitol as a chelating agent. The relatively simple procedure involved sonication (250 mg sample, 30 min) with dithiothreitol solution (4 mL, 0.1 M, pH 9) and CV-AAS analysis. Method LODs and LOQs were 6 and 19 ng g−1, respectively. Results obtained for analysis of NRCC marine sediment CRMs MESS-3 and PACS-2 were 0.112 ± 0.014 and 3.11 ± 0.36 mg kg−1 (certified values 0.091 ± 0.009 and 3.04 ± 0.20 mg kg−1). A method for the microcolumn-based leaching of bioaccessible Pb for online ICP-AES analysis allowed126 near-real-time monitoring of Finnish shooting-range soils under various physicochemical conditions. The custom-made 5 × 0.8 cm column consisted of a Teflon cylinder with three cotton buds positioned at the column inlet and outlet for eluate filtration. The sample mass of 2.5 g, larger than that usually packed in microcolumns, gave more representative sampling. The column was positioned upright and an eluent flow rate of 0.3 mL min−1 used to deliver eluate to the ICP-AES instrument through polyurethane tubing. A range of solutions was tested. For soil samples containing ca. 500 mg kg−1 Pb, extractant efficiency was 55% for 0.11 M acetic acid, 58% for 1 M MgCl2, 61% for 0.1 M NH2OH·HCl, 93% for 0.1 M citric acid and 96% for 0.1 M HCl. Analysis time was under 5 h. A slurry sampling procedure followed by HR-CS-ETAAS analysis, proposed for the determination of Yb in road dust samples, involved127 ultrasonic homogenisation for 34 min with 0.24 M HNO3. The analysis of NIST SRM 2586 (trace elements in soil containing lead from paint), NCS DC 73301 (rock) and CCRMP TILL-1 (geochemical soil) using pyrolysis and atomisation temperatures of 1200 °C and 2700 °C, respectively, gave accuracies of 99 ± 4 to 104 ± 2%.

4.3.2 Analyte separation and preconcentration. Methods for the separation of specific elements from soils and plants continue to be developed. The treatment of PUF by boiling with 5% NaOH, washing and immersion in 10% HCl improved128 Au adsorption and minimised adsorption of Ca, Fe, K, Mg, Na and Si in comparison to the use of untreated foam. Following hotplate digestion of soil containing between 0.5 and 1000 ng g−1 Au, adsorption was 92.6–98% for treated foam but only 77.1–86% for the untreated sorbent. Although the LOD of 0.02 ng g−1 was better than that (0.04 ng g−1) achievable using MAD, the latter was considered a more attractive option for routine trace level determination because of the shorter extraction time. A single-step procedure for the separation and purification of Sb in soils and plants used129 a thiol-functionalised mesoporous silica powder. Following sample digestion (microwave with various acid mixtures depending on sample) and Sb reduction, samples were loaded onto SPE columns containing 0.2 g of the porous sorbent. Sorbed elements were released with increasing concentrations of HCl, Sb being eluted with 6 M HCl. Quantification was by HG-MC-ICP-MS. The method was tested by the analysis of 14 CRMs. Interfering elements were removed with >97% efficiency and Sb recovery after purification was 100 ± 7% of the Sb concentration determined without purification. A two-step purification process for the separation of Cd involved130 AG-MP-1M and TRU resins. The method was validated by the analysis of NIST SRM 3108 (Cd standard solution) and secondary RM BGEG-Cd. The δ114/110Cd measured for a range of geological standards were in good agreement with reported values. Common water hyacinth (Eichhornia crassipesas) can be used131 as a biosorbent for Pb. By applying factorial design, SPE conditions were optimised at 0.02 g of dried and milled leaves as sorbent, with sample inlet and eluent (0.1 M HCl) flows of 5.6 and 6.8 mL min−1, respectively. This low-cost and environmentally friendly method was validated with NIST SRM 1515 (apple leaves). The mean Pb value determined by FAAS was not statistically different from the certified value.

Other methods for the analysis of soils, plants or related materials, or those developed for other sample matrices that used soil or plant CRMs for validation, are summarised in Table 3 (liquid-phase extraction methods) and 4 (solid-phase extraction methods).

Table 3 Preconcentration methods involving liquid-phase microextraction used in the analysis of soils, plants and related materials
Analytes Sample matrix Method Reagent(s) Detector LOD (μg L−1, unless otherwise stated) RMs or other validation Reference
Ag Sediment, water CPE n-Octanol, Triton X-100 (cloud point reagents), rhodanine chelating agent FAAS 0.18 NRCCRM GBW 07301 (stream sediment), GBW 07311 (stream sediment); GBW 07312 (stream sediment) 327
AsIII, AsV Sediment, soil, water MIL LLME For AsIII, APDC chelating agent and trihexyl(tetradecyl)phosphonium hexachlorodisprosiate MIL extraction solvent; For AsV trihexyl(tetradecyl)phosphonium tetrachloroferrate and magnetic rod separation ETAAS 0.13 ng g−1, 0.11 ng g−1, 17 ng L−1. For AsIII; 0.14 ng g−1, 0.13 ng g−1, 20 ng L−1 for AsV in sediment, soil and water respectively. Spike recovery 312
CrVI Organic fertilisers CPE 2% m/v NaCl, 0.3% v/v Triton X-100, and 0.05% (m/v) 1,5 diphenylcarbazide FAAS or UV-Vis 0.55 μg g−1 for FAAS, 0.41 μg g−1 for UV-Vis NIST SRMs 2701, (hexavalent chromium in contaminated soil), and 695 (trace elements in multi-nutrient fertilizer), spike recovery 328
iHg, MeHg Soil, water Dual CPE Sodium DDTC and Triton X-114 followed by displacement into L-Cys HPLC-HG-AFS 0.004 for iHg and 0.016 for MeHg Spike recovery 71
Mn Soil Switchable solvent LLME N,N-Dimethylbenzylamine with deionised water switchable solvent; ((E)-2,4-dibromo-6-(((3-hydroxyphenyl)imino)methyl)phenol complexing agent SQT FAAS 0.71 Spike recovery 329
Pd Catalytic converter, road dust, water (tap, sea), wastewater LLME DES based on phenyl salicylate and menthol ETAAS 0.03 Spike recovery 330
Se Food, soil, water VAME APDC complexation,1-butyl-3-methylimidazolium hexafluorophosphate IL, Triton X-114 ETAAS 0.07 NRCCRM GBW 07309 (stream sediment); NCS ZC73032 (celery) 331


Table 4 Preconcentration methods involving solid-phase (micro) extraction used in the analysis of soils, plants and related materials
Analytes Matrix Substrate Substrate coating Detector LOD (μg L−1) RMs or other validation Reference
AsIII, As Rice Tetra-n-butylammonium bromide HG-AAS 0.01 μg g−1 IRMM 804 (rice flour) 332
Cd, Co, Cu, Ni, Zn Parsley, radish (black), quince, soil, water Fe3O4 MNP Polythiophene Microsample injection system- FAAS 1.1 for Cd, 3.2 for Co, 1.4 for Cu, 9.6 for Ni, 1.2 for Zn IRMM BCR 715 (industrial effluent waste water); SPS WW2 Batch 114 (wastewater); NCS DC 78302 (Tibet soil); LGC7162 (strawberry leaves) 333
Cd, Zn Soil, water Chemically-modified activated carbon with L-arginine FAAS 1.6 for Cd, 2.4 for Zn Spike recovery 334
Co, Cu Soil, vegetable, water Amberlite® XAD-4 resin Tricholoma populinum fungal biosorbent ICP-AES 0.019 for Co, 0.034 for Cu CNRC DORM-2 (dogfish muscle); NCS ZC73014 (powdered tea); NWRI NWTM-15 (fortified water); NIST SRM 1643e (trace elements in water) 335
Cu, Pb Herbs Magnetic MWCNTs 1-(2-Pyridylazo)2-naphthol FAAS 18.9 for Cu, 16.6 for Pb SPS WW2 (wastewater); NCS DC73349 (bush branches and leaves) 336


4.4 Instrumental analysis

4.4.1 Atomic absorption spectrometry. Optimised ETAAS methods were reported for the determination of As, Cd and Pb in hemp products;132 Cd and Pb in boron minerals and boron-rich waste;133 and Cr in cigarette tobacco, filters and ash.134 All of these methods were validated by analysis of soil, sediment or plant CRMs.

Interest in the use of HR-CS-ETAAS and its variant HR-CS-ETMAS continued. An optimised method for the quantification of Yb in road dust involved127 sonication of the sample in 0.24 M HNO3 for 34 min, injection of 20 μL of slurry into a graphite furnace pre-conditioned with 250 μg of W permanent chemical modifier, pyrolysis at 1200 °C and atomisation at 2700 °C. The LOD was 22 ng g−1 and results for analyses of three CRMs were not significantly different from the certified values according to a Student’s t-test at 95% CI. Germanium could be measured135 directly in 0.1–0.2 mg solid plant and soil samples by using a mixed-matrix modifier (0.1% w/w Mg(NO3)2 + 0.2% w/w Pd(NO3)2 + 0.1% Triton X-100) and pyrolysis and atomisation temperatures of 1500 and 2550 °C, respectively. The LOD of 0.5 mg kg−1 made it possible to quantify Ge in tissue samples from a range of plants but some discrepancies were noted with respect to results obtained by ICP-MS following acid digestion. A method for quantification of P in solid samples based on the PO molecular species involved136 pyrolysis at 1500 °C and vaporisation at 2200 °C. The preferred chemical modifier was 10 μg of Au which promoted conversion of P to PO. The method was validated by the analysis of NIST SRMs 1547 (peach leaves), 1573a (tomato leaves) and 1570a (spinach leaves) which contain 1371 ± 82, 2161 ± 28 and 5187 ± 67 mg kg−1 of P, respectively.

4.4.2 Atomic emission spectrometry. Novel developments in sample introduction included137 the first use of ETV-ICP-AES for the determination of F in solid samples of plant materials. The optimised temperature program (pyrolysis at 250 °C and vaporisation at 2200 °C) took just 70 s to complete. Calibration involved increasing amounts of either NRCC CRM DUWF-1 (durum wheat flour) or F standard solutions. A previously reported multimode sample introduction system that interfaced two HPLC instruments to an ICP-AES system was adapted138 to allow simultaneous separation and detection of both hydride-forming (anion-exchange column and in-spray chamber HG) and non-hydride-forming elements (cation-exchange column and conventional pneumatic nebulisation).

Aerosol dilution was demonstrated139 to improve the tolerance of MIP-AES to samples with high total-dissolved-solids content. The signal suppression that occurred at NaCl concentrations > 0.9 g L−1 when using conventional nebulisation was less severe with aerosol dilution: indeed, the emission intensity for some lines was greater in solutions containing 5 g L−1 NaCl than in those containing 0.1 g L−1. A PVG-SPME-TD manifold was optimised140 for the determination of Hg by MIP-AES. Sample solution containing 5% v/v formic acid was pumped through a quartz reactor where it was exposed to UV radiation (100–280 nm) to generate Hg vapour. This vapour was preconcentrated onto a SPME fibre in the gas–liquid separator under stopped-flow conditions and the fibre then transferred to the TD port of the MIP-AES instrument. The results of 133 ± 5 mg kg−1 obtained for CRM ERM CC580 (estuarine sediment) was in agreement with the certified value of 132 ± 3 mg kg−1.

There has been considerable interest in developing miniaturised atomic emission spectrometers as alternatives to pXRFS and LIBS instruments for field analysis. Different approaches have been chosen to improve analytical performance. He et al.141 developed a novel cross double-point discharge incorporating four electrodes that produced a larger microplasma than previous single-point discharge devices, thereby ensuring that a higher proportion of analyte (introduced as the hydride) was excited. The LODs of 2.4, 0.15 and 1.9 μg L−1 for As, Hg and Pb, respectively, were about a third of those previously reported. Yuan et al.142 optimised a GD-AES method using a HG-solution anode for determination of Hg with an LOD of 0.03 μg L−1. Deng et al.143 designed a compact device integrating a tungsten coil ETV atomiser and a conventional (two electrode) point-discharge microplasma in a single quartz tube. The unusually close proximity of the atomiser and plasma (optimal distance 12.5 mm) had improved transport efficiency and, hence, better LODs than devices that incorporate an interface. Results for Ag, As, Bi, Cd, Cu, In, Pb, Sb and Zn in a selection of 14 environmental CRMs were not significantly different from certified values at 95% CI. Liu et al.144 explored the use of an electromagnetically-heated tungsten boat for direct analysis of solid samples as an alternative to the use of a tungsten coil atomiser and liquid sample introduction. The excitation source was a DBD. The LODs for analysis of a 5 mg sample were 0.018, 0.008 and 3.5 mg kg−1 for Cd, Hg and Pb, respectively. Accurate analysis of 10 soil CRMs was demonstrated (t-test at 95% CI).

The optimum conditions in the first demonstration145 of LA-APGD-AES for direct determination of Cd, Pb and Zn in soil were: 28 mA discharge current, 14 mm discharge distance, 140 mL min−1 gas flow rate (3% H2 + 97% He), 60 mJ laser energy and 10 Hz laser repetition rate. Samples were presented as pressed pellets without binder. Calibration curves were obtained by analysis of CRMs covering a range of concentrations. The LODs were 0.31, 2.7 and 0.68 mg kg−1 for Cd, Pb and Zn, respectively. Results for two soil samples from different parts of China were similar to those obtained by ICP-AES but no statistical comparison was reported.

4.4.3 Atomic fluorescence spectrometry. A novel method for simultaneous preconcentration and speciation of Se and Te involved146 online IL-formation, microextraction and HG-AFS. Mixing of the IL [C8mim]Cl and KPF6 to yield non-soluble [C8mim]KPF6 extracted SeIV and TeIV from soil and sediment extracts as their APDC complexes at pH 4. These were retained on a cotton microcolumn before elution with 5 M HNO3 in methanol into a miniaturised external hydride generator. Pre-reduction of analytes allowed total Se and Te concentrations to be determined so that SeVI and TeVI concentrations could then be calculated by difference. Acceptable (p < 0.01) accuracy was demonstrated by analysis of IRMM CRM BCR 402 (white clover) containing 6.7 ± 0.25 mg kg−1 Se. Recoveries of spikes from soil and sediment samples were 96–102% for Se species and 95–108% for Te species at a spike concentration of 0.5 mg kg−1.
4.4.4 Inductively coupled plasma mass spectrometry. The sources and types of interference effects commonly encountered in geochemical analysis by quadrupole ICP-MS were discussed147 in an excellent tutorial review (178 references) along with means by which these can be overcome. Although not specifically for the analysis of soil and plants, the publication148 of a searchable database of ions that can cause spectral overlap in ICP-MS was a valuable contribution likely to be of interest to analytical geochemists.

A comparison of calibration strategies for the determination of Cd, Co, Cu, Fe, Mn, Ni and Pb in soil by ICP-SFMS concluded149 that there was generally little difference between results obtained using IDA, gravimetric external calibration with gravimetric internal standardisation, and gravimetric standard addition with gravimetric internal standardisation but noted that the volumetric preparation of standards could lead to loss of accuracy.

A flow-injection slurry-sampling ICP-MS method used150 a mixed-gas plasma to achieve a high-throughput determination of Al, Cd, Cu, Fe, K, Mo and Zn in wheat flour. Sample slurry (1% m/m) in a surfactant solution (Aerosol-OT) was stable enough to be introduced without continuous agitation during sampling. The plasma gas was made up of 462 mL min−1 N2 + 18.5 L min−1 Ar and the sheath gas was 4 mL min−1 H2. Results for analysis of NIST SRM 8437 (wheat flour) were statistically similar to certified values (95% CI) for all elements except Al, which was overestimated due to polyatomic ion interferences, in particular 1H12C14N+, caused by addition of the H2 and N2. The coupling of chromatographic separation with ICP-MS continues to be of interest because of the insight it can provide into trace element speciation. Of particular note for As were two HPLC-ICP-MS methods. The first was used151 not only for speciation analysis in different tissues of rice plants but also to explore associations between As and DNA fractions. The second was applied152 together with UHPLC/ESI-MS/MS to identify As species in marine algae and algae-based foodstuffs. Another HPLC-ICP-MS method could resolve153 CrIII and CrVI in rice extracts in <4.5 min and had a LOD of 0.012 ng mL−1 for both species. An unknown Cr compound was detected that required further investigation. A novel online microdialysis-HPLC-ICP-MS system was developed154 for continuous sampling and measurement of CrVI in soil solution, thereby minimising Cr species interconversion which is the bane of conventional extraction procedures. Because the HgI species is also prone to redox instability, it is difficult to explore its role in biogeochemical processes. A new method based on 2-mercaptoethanol extraction and HPLC-ICP-MS was used successfully to detect155 HgI in plant and soil samples. The application of SEC-ICP-MS and AEC-ICP-MS aided156 the identification of new Ni-containing proteins in water extracts of Arabidopsis root.

Advances in FFF-ICP-MS included157 the first successful use of a preparative rather than an analytical asymmetric FFF instrument, thereby increasing the injection volume and sensitivity for the detection of particulate P in soil extracts. The capabilities of FFF-ICP-TOFMS and FFF-sp-ICP-TOFMS for analysis of soil spiked with engineered NPs (CeO2, Fe2O3 and TiO2 at concentrations of 0.05, 0.5 and 5 mg kg−1) were explored.158 An increase in Ce, Fe and Ti signals with increasing spike concentrations was observed when using FFF-ICP-TOFMS. However, the magnitude of the increase did not correspond to the amounts of NPs added and the signals for a range of other elements also increased. The explanation proposed was enhanced extraction of natural particles caused by introduction of residual surfactants left over from NP synthesis. It was not possible to distinguish the NPs, at least at the concentrations added, from the high background of soil particles containing Ce, Fe and Ti. However, the study demonstrates the importance of multi-element screening to avoid misinterpretation of results.

Other developments in sp-ICP-MS included159 a method for the analysis of CeO2 NPs extracted from soil with 2.5 mM tetrasodium pyrophosphate that had LODs of 15 nm particle size and 194 NPs mL−1. An assessment of aging on NP in soils found that no marked change occurred in particle size, size distribution or particle concentration over a period of one month. A LA-sp-ICP-MS study initially ablated160 Au NPs deposited on a flat polyether sulfone membrane in order to check particle size preservation and linearity of response. Subsequently, NP-doped soils were analysed. A key finding was that particle recovery was affected by the nature of the substrate on which the particles were presented to the laser. In a procedure for the sp-ICP-MS/MS analysis of TiO2 NP in plant digests, O2 was added161 to the collision/reaction cell to overcome spectral interferences on 48Ti+. Quantification of Ti was based on the 48Ti16O+ ion at m/z = 64. The size LOD was 15 nm. The method was used to study NP uptake in radish.

Studies involving elemental imaging by LA-ICP-MS included162 a cold plasma method for studying the distribution of Na in roots of Arabidopsis thaliana grown in a cultivation medium containing 0.05 nM Na. Plasma cooling was achieved by reducing the plasma power to 670 W and delivering an additional wet aerosol (dilute HNO3) to the ICP. This improved the LOD for Na two-fold. When a laser spot size of 4 μm was employed, it could be seen that Na was located in a narrow band of cells. This finding supported the hypothesis that the root epidermis may act as a diffusion barrier. The first application of LA-ICP-MS to the study of the spatial distribution of major (Ca, K, Mg and P) and trace (Co, Fe, Mn, Na and Zn) elements in the rhizosphere through analysis of soils cores (0–15 cm depth) provided163 useful information on the effects of different soil amendments and agronomic practices.

An investigation of the utility of CaF2as a dominating matrix for quantitative analysis by LA-ICP-MS used164 NIST SRM 2719a (Montana soil) as its test substrate. A mixture of 0.3 g NH4HF2 + 0.65 g CaCl2 + 0.05 g soil was heated in an electric furnace, ground and pressed into a pellet for analysis. Although promising results were obtained for Al, Fe, Mg and Mn using 44Ca+ as IS, there is a need to prove this approach for analytes present at lower concentrations.

New methods for the precise measurement of isotope ratios by MC-ICP-MS included165 a procedure that used a single cation-exchange column to separate K from sample matrix elements with a 99.5 ± 0.6% yield. That K isotopes may have greater potential as environmental tracers than previously thought was highlighted by the analysis of 23 geological and biological RMs. Whereas the δ41K values for rocks were similar to each other and consistent with published data, those for soil, sediment and plants were more varied. A single-step purification method for Sb based on a thiol-functionalised mesoporous silica powder was used129 to determine the δ123Sb values in a wide variety of CRMs with complex matrices. A double-spike MC-ICP-MS method for Cd provided130δ114/110Cd values in four soil, nine sediment and two manganese nodule CRMs that fell within the range of published data. A value (−0.08 ± 0.04 (n = 6)) for δ114/110Cd in IGGE GSD-3a (sediment) was reported for the first time.

Radionuclides in soil were a focus of ICP-MS/MS research. In a method for the determination of 236U, 0.6 mL min−1 CO2 and 7 mL min−1 He were introduced166 to the collision/reaction cell to remove interferences, in particular by converting 235U1H+ to 235U16O+. This allowed determination of 236U/238U ratios as low as 3 × 10−10, an order-of-magnitude better than previously achievable. A study conducted using NH3 and He cell gases demonstrated167 that the determination of Pu was relatively insensitive to the sample preparation method used. However, removal of Pb and U was more challenging following lithium metaborate fusion than following digestion in either aqua regia or 8 M HNO3. A procedure for the measurement of 241Am, also using He–NH3 collision–reaction gas, had168 a LOD of 0.091 fg g−1, three times better than that achievable using other types of ICP-MS. The results for the analysis of the IAEA CRMs 375 (soil) and Soil-6 of 0.758 ± 0.078 and 0.528 ± 0.055 mBq g−1 were not significantly different from the certified values of 0.71 ± 0.16 and 0.48 ± 0.10 mBq g−1, respectively. The method was applied to seven surface soils from China and a sediment from Denmark. A key step33 in a ID-ICP-MS/MS method for simultaneous determination of PGEs and Re was removal by CEC of the majority of elements likely to form isobaric or polyatomic ion interferences. The reaction gas in the collision cell was 10% NH3 in He.

4.4.5 Laser-induced breakdown spectroscopy. A competition organised under the auspices of the 10th Euro-Mediterranean Symposium on LIBS involved169processing of soil spectral data with the aim of correctly classifying the highest possible number of unknown spectra (the test set) using a training set of labelled spectra. The classification approaches employed by the five most successful teams were discussed in detail. This type of exercise is very much to be encouraged as it leads to sharing of knowledge and good practice across the community.

Efforts have continued with the aim of improving the performance of LIBS for soil analysis. A 532 nm monomode and a 1064 nm multimode laser were compared170 using the same spectrometer system and laser fluence (460 J cm−1). A larger mass was ablated at 532 nm but the plasma temperatures and electron densities were similar for the two lasers. The superior S/N obtained for C, Fe, Mg and K in a test soil meant that use of the 1064 nm laser was recommended. The importance of optimising the energy ratio and inter-pulse delay time in DP-LIBS was demonstrated171 and the enhancement in emission line intensity, and hence sensitivity, that can be achieved by plasma spatial confinement (discussed in last year’s ASU1) was confirmed.172 The beneficial effects of using a heated target in soil analysis were demonstrated173 for the first time. When a sample spiked with Pb powder (80 ppm) was analysed at 100 and 200 °C, emission intensities for matrix elements such as Al, Ca, Fe and Si increased and spectral lines of the Pb became visible. None was visible at room temperature. The LOD for measurement at 200 °C was 3.8 ppm.

In LIBS-LIF, a second, tuneable laser is used to pump analyte atoms in the primary laser ablation plume to excited states from which emission can occur, thereby increasing emission intensities and S/N. When a LIBS-LIF method for micronutrients in soil was applied174 to a suite of 12 GBW CRMs, a linear response was obtained with LODs of 0.36, 0.48, 23, 1.2 and 1.2 ppm for B, Cu, Fe, Mn and Zn, respectively. The approach showed promise but application to CRMs not used in calibration would have been desirable.

Numerous LIBS methods for the determination of specific elements in soil have been reported and it is pleasing to note that several researchers evaluated performance by comparison with results obtained by other techniques. Noteworthy was a procedure for the determination of Cd with an LOD of 7.8 mg kg−1 that gave175 a relative error of <6.1% for analysis of soil samples previously tested by the Chinese Institute of Environmental References Materials or the Ministry of Environmental Protection. A method for estimating available-K involved176 immersing a cation-exchange membrane in a soil suspension in deionised water, shaking for 10 min, recovery of the membrane, removal of adhering soil particles by sonication, drying at 40 °C and LIBS analysis. Results agreed within 2.6% with ammonium-acetate-extractable K values. A study combined177 LIBS and VIS-NIR data from various wavelength regions using different models to estimate P concentration in 147 Danish agricultural soils. Results were compared with data for water-extractable P; available-P as defined by the method of Olsen; oxalate-extractable P; and total P obtained by wet chemical methods. The combined LIBS and VIS-NIR models with variable selection generally produced the results most similar to the reference method for all four P pools. In an improved method for the determination178 of soil C, overestimation of the analyte at 247.8561 nm due to interference from the Fe II line at 247.8572 was overcome by use of a high resolution spectrometer and a LS interference-correction method.

A design of experiments approach was used179 to optimise a multi-element LIBS method for analysis of river sediments. Both single-pulse LIBS at 1064 nm and DP-LIBS using 532 and 1064 nm lasers were studied. Parameters considered were laser energy, delay time, gate width and number of pulses accumulated (plus inter-pulse delay for DP-LIBS). The selection of optimal conditions was based on S/N contour graphs. Single- and DP-LIBS methods for determination of P were developed180 and used to analyse samples taken from sediment cores collected in two rivers in Brazil. Although results sometimes differed from those obtained by ICP-AES following HNO3 digestion, overall trends in P concentration with depth were similar.

Research on elemental analysis of plants by LIBS is generally less advanced than the analysis of soils. Although the field is advancing rapidly, method performance assessment still tends to focus on model development and internal validation using training and test sets rather than on comparison with results obtained by established methods. Procedures have been reported for the determination of: Cr and Cu in mulberry leaves;181 Al, B, Ca, Fe, K, Mg, Mn, N and P in vegetables182 and Al, Ca, Cu, Fe, K, Mg, Na, P, Si and Zn in different parts of the rice plant.183

A method for the determination of Pb in rhododendron leaves by LIBS-LIF involved184 direct analysis of solid samples following grinding, spiking with different amounts of Pb(NO3)2 solution (for standard addition calibration), drying and mounting on adhesive tape. An alternative sample preparation method, described as “solid–liquid–solid transformation” (SLST) had an intriguing name but was in fact a conventional UA digestion in 0.03 M HCl followed by spiking with the Pb(NO3)2 and drying onto glass slides. Results were compared with those obtained by ICP-AES following MAD. Unsurprisingly, the two methods involving total sample digestion gave similar results! However, the conclusion that this proves the superiority of SLST-LIBS-LIF over ICP-AES in terms of overall speed and efficiency is based on an unfair comparison, since the extracts prepared for LIBS analysis could easily have been analysed by ICP-AES without the need for MAD. It is also unclear why, given that one of the key strengths of LIBS is the ability to analyse solid sampled directly, the introduction of sample digestion and extraction steps to analytical protocols should be desirable.

In spark discharge-LIBS, electrodes are positioned just above the sample surface and held at a voltage below the spark-gap self-breakdown potential. When the laser is fired at the sample, the ablation plume expands in the gap and triggers the spark discharge which reheats the plasma and increases signal intensity. A further potential advantage is the ability to calibrate the analysis by using a single solid standard with a matrix composition similar to that of the samples and by varying the voltage applied to the electrodes, thereby avoiding the need to produce a set of solid standards with different analyte concentrations. Multi-voltage calibration was investigated185 for the determination of Al in plants and P in fertilisers, together with two other strategies that involved just one standard: (a) multi-line calibration and (b) slope ratio calibration based on accumulation of multiple laser shots. Accurate results were obtained at 95% CI for Al in NIST SRMs 1570a (spinach leaves), 1515 (apple leaves) and 1573a (tomato leaves) using all three approaches. Whereas results for P in fertilisers based on multi-voltage and slope-ratio calibration were in agreement with those obtained by HR-CS-FAAS, those based on multi-line calibration were not.

4.4.6 Thermal ionisation mass spectrometry. A novel extraction method allowed186 quantitative isolation of water-soluble Cl from soil for measurement of δ37Cl values by TIMS. Soil was dried, sieved, ground, treated with H2O2 to decompose OM and then extracted with high purity water. The extract was purified using a four-step ion-exchange-chromatography procedure. Although soil samples from Weifang, China were affected by seawater intrusion and were highly contaminated with Cl (up to 531 mg kg−1), δ37Cl values (−2.40 to 1.16) indicated the presence of Cl from multiple anthropogenic sources in addition to that from seawater.
4.4.7 X-Ray spectrometry. A review (100 references) (in Chinese with English abstract) focused187 on recent advances and limitations in the use of X-ray spectrometry for the study of element translation in plants. A review by Feng et al.188 (76 references) on the application and limitation of XRFS studies of plants and soils included assessments of food safety and nutrition and concluded that development in detectors, beamline capability and scanning systems would improve performance and application.

Developments in the XRFS analysis of soils focused on sample preparation and spectral interpretation. Croffie et al.189 compared sample presentation using loose powder, pressed pellet and pressed pellet with wax binder for the EDXRFS determination of 13 elements in four international soil exchange RMs ISE 952 and 961 (clay), 992 (sandy clay), and 995 (sand). Recoveries were highly dependent on both the element and the preparation method and ranged from 87% for Al in clay prepared with binder to 710% for Si in sand with pressed pellet preparation. A general overestimation (>20%) of recoveries was attributed to large particle sizes in Si-containing samples and peak overlap. Wu et al.190 corrected the overlap of As and Pb peaks by applying a Gaussian mixture statistical model and chaotic-particle-swarm optimisation, thereby providing a mechanism for more accurate element quantification. In a paper in Chinese, spectra obtained in the field in the determination of Pb were corrected191 for noise by applying harmonic analysis at different Pb concentrations and smoothed pseudo Wigner–Ville distribution.

A measuring chamber developed192 for fluorescence kinetic measurements was modified193 for the μXRFS imaging of trace elements in living plants. The measuring chamber provided the humidity and nutrients needed for continued plant growth during analysis so that the health of the pepper plant could be maintained during 20 h of irradiation with a dwell time of up to 600 ms per spot. The customised bench-top XRFS instrument was modified with a multilayer mask on the detectors to minimise spurious counts and result in cleaner spectra. The system was used for whole-leaf mapping (rather than spot measurements) of Zn in the hyperaccumulator Noccaea caerulescens and, as a result of the long irradiation time possible, was also successfully used to study the distribution of Cu, Fe, Ni and Zn in pepper leaves and soy bean roots.

5 Analysis of geological materials

5.1 Review articles

In a review (63 references) of the advances in ICP-MS technology and their impact on the mineral exploration sector, the ability of multi-element ICP-MS data to reveal significant ore bodies was demonstrated194 in a series of case studies. However, not all the developments in ICP-MS have been widely implemented by exploration geochemists and reasons for this were examined. A review (87 references) on advances in exploration geochemistry between 2007 and 2017, particularly in relation to ore deposits under relatively young cover, considered195 four key areas: understanding metal mobility and mechanisms; rapid geochemical analyses; data access, integration and interoperability; and innovation in laboratory-based methods. It was noted that a common problem in the geosciences was the poor availability of suitably-trained geochemists for industry, although this had started to be addressed by recent initiatives at various universities at graduate level. A short paper (41 references) on advances in the use of isotopes in geochemical exploration focussed196 on developments in instrumentation and the application of isotopes for the understanding of geochemical processes. Although it was felt that isotopic compositions were unlikely to become a standard tool in exploration, they were considered important for tracing the origin of key elements and as indicators of dispersion processes.

Photo-induced force microscopy is a cutting-edge technique that combines the advantages of AFM with IR spectroscopy to acquire simultaneously 3D topographic data and molecular chemical information at high spatial (ca. 5 nm) and spectral (ca. 1 cm−1) resolution. A comprehensive review197 (80 references) of the technical aspects of this technique and its application to geosciences through nanoscale chemical imaging had the aim of introducing this new analytical development to a broader geochemical audience. Examples of its application included the visualisation of growth zonation in zircons and the identification of fluid speciation in high-pressure experimental samples and of nanoscale organic phases in biominerals.

An annual bibliographic survey provided198 a summary of RMs used in geochemistry and related fields, such as paleoclimate and environmental research, that featured in papers published in 2019. This review examined 6850 papers from more than 20 scientific journals and used data from nearly 700 articles. All the RM data are freely available in the GeoReM database (http://georem.mpch-mainz.gwdg.de) and all the articles are listed in an appendix. A review in Chinese (115 references) discussed199 progress in the production of geological RMs with ultrafine particle sizes in China since 2003 and their role in improving analytical accuracy.

Field-portable geochemical techniques have seen rapid development over the past two decades, so a review200 (61 references) of handheld and site-portable instruments used for mineral exploration is timely. It covered a range of techniques including core scanners, XRFS, XRD, LIBS, IR, FTIR, μRaman and gamma spectroscopies and discussed the benefits, challenges and pitfalls of field measurements as well as the balance between data quality and quantity. A comprehensive review (121 references) of field-portable and handheld LIBS provided201 an historical overview of the technique and a comparison with its main competitor, pXRFS. A detailed description of the current status of LIBS instrumentation and a wide variety of applications was presented. It was concluded that although the future for this technique was very promising, important technological advances were still required to maximise its potential. The limited availability of matrix-matched calibrants was also a major impediment to environmental applications.

Those engaged in research on mineral-hosted melt inclusions are directed to a series of recommendations202 (279 references) for sample preparation, analysis and data presentation. The paper was dedicated, by the participants and organisers of the melt-inclusion workshop held in 2018 at Woods Hole Oceanographic Institution, to Erik Hauri in recognition of his contributions to the in situ analysis of melt inclusions. The intention of these guidelines was to bring some uniformity to the characterisation of melt inclusions and to systematise data collection and reporting, thereby facilitating the comparison and evaluation of published data.

A review (250 references) of the analysis of geological materials by a wide variety of techniques provided203 a basic introduction to sample decomposition and analysis. The merits and limitations of 11 commonly used techniques for geochemical applications were discussed and some practical examples given. The same author summarised147 (178 references) the technical advances in ICP-MS made over the last three decades aimed at reducing the different types of interferences encountered. Developments discussed included the use of cool plasmas, collision/reaction cell technology, HR-ICP-MS and ICP-MS/MS. The contribution of approaches such as internal standardisation, ID, standard addition and matrix-matching calibrations to the improvement of data quality were also discussed.

Advances in laser-assisted techniques such as LA-ICP-MS and LIBS were the focus of several review papers. Recent developments related to isotopic fractionation, matrix effects and isobaric interferences in LA-MC-ICP-MS were considered204 (149 references) as well as methods for their mitigation. It was emphasised that the selection of appropriate correction methods was critical to obtain accurate and precise isotope ratios. There was, however, no universal solution and each isotope system required its own procedure dependent on the hardware available and the samples of interest. A review205 (123 references) on recent developments in LIBS analysis combined with machine learning for geochemical and environmental resources exploration, covered instrumentation, sample preparation, spectral fusion technology, field-portable and remote LIBS, machine learning methods and applications to various geological and environmental materials. The accuracy, precision and LODs of LIBS analysis constrained current applications, and substantial improvements were required to achieve potential applications.

5.2 Reference materials and data quality

There is an ongoing interest in the synthesis and characterisation of microanalytical RMs for in situ microbeam technologies such as SIMS, LA-ICP-MS, LIBS and EMPA. One study described206 the production of PGE RMs from sub-μm-size PGE-doped silica particles. These doped particles were used as feedstock materials for electrophoretic deposition followed by thermal processing to fabricate two cm-sized materials with PGE concentrations between 0.5 to 3 μg g−1. Excellent homogeneities of 2–5% RSD were confirmed by LA-ICP-MS, provided that the rims of the materials (outer 1 mm) were not used. These materials could represent some of the most complete and homogeneous PGE RMs synthesised with a silicate matrix. The process of ‘additive manufacturing’ clearly has great potential in the production of matrix-matched RMs. The incorporation of Pb, REEs and U into calcite involved207 heat- and pressure-induced crystallisation from amorphous calcium carbonate that had been precipitated from an element-doped reagent solution. The doped elements in the resulting calcite RMs were homogenously distributed (<12%, 2RSD). As the 207Pb/206Pb ratio varied by <1% and the 238U/206Pb ratio by 3–24%, depending on element mass fractions, these synthetic RMs were considered to be a promising alternative to natural calcite RMs for U–Pb geochronology. Courtney-Davies et al.208 used ID-TIMS and LA-ICP-MS to characterise cm-sized chips of a synthetic haematite RM called MR-HFO. A comparison of this MR-HFO and a zircon RM (GJ-1) for the calibration of LA-ICP-MS analyses of natural haematite samples, ranging from Cenozoic to Proterozoic in age, indicated that MR-HFO yielded more concordant U–Pb ratios that were in better agreement with independently acquired ID-TIMS data from the same natural haematite grains. It was concluded that MR-HFO was a suitable RM for LA-ICP-MS haematite U–Pb geochronology despite there being some heterogeneity in U/Pb ratios detected by ID-TIMS. Four iron sulfide RMs for LA-ICP-MS analysis were prepared209 by hydrothermal synthesis, in which doped elements in solution entered the synthetic sulfide lattice as isomorphs during crystal growth. The resulting nanosulfide pressed-powder-pellets had a typical grain size of 500–600 nm and their excellent cohesion allowed them to be prepared without the addition of a binder. Good homogeneity was demonstrated for most trace elements (<3% RSD for PGEs, Au and Pb) and S isotope compositions (<0.23‰).

Several studies examined natural minerals as RMs for in situ isotope ratio analysis. After screening a large number of apatite specimens from several mineral collections, Wudarska et al.210 focussed on a set of six apatite RMs with Cl mass fractions spanning a range within the apatite solid solution series. Their Cl isotope homogeneity was evaluated using SIMS and δ37Cl measured by IRMS. All six apatite RMs were deemed suitable for the calibration of in situ δ37Cl measurements. In addition, major and key trace element compositions as determined by EPMA were reported. In a related study, three Harvard tourmaline RMs (schorl 112566, dravite 108796 and elbaite 98144), already used for the calibration of in situ B isotope measurements, were characterised211 for their Li and O isotope compositions by SIMS. The Li mass fractions of these tourmalines vary over three orders of magnitude. Homogeneity tests by SIMS demonstrated that the 7Li/6Li and 18O/16O isotope ratios were constant within ±2.2 and ±0.2‰ (1SD), respectively, at sub-ng test portion masses. Reference values reported for δ18O, Δ17O and δ7Li in these tourmaline RMs were based on results for bulk mineral analyses undertaken at several independent laboratories. Any inter-laboratory bias was assessed. A new set of six tourmalines presented212 as potential RMs for in situ B isotope ratio measurements by SIMS covered an extended compositional range of tourmaline supergroup minerals, including Fe-, Li- and Mg-rich end-members. Bulk MC-ICP-MS analysis yielded B isotopic compositions ranging from −15.5‰ to −9.2‰ with uncertainties of 0.1–0.7‰ (2SD). Four existing tourmaline RMs were also re-evaluated for their major element and B isotopic compositions. For two of the RMs, a discrepancy of ca. 1.5‰ in δ11B values was observed between the previously published reference values and those determined in this study. It was concluded that a multi-analytical comparison study between different laboratories was required to elucidate fully the reasons for the differences in these results. Two materials, a natural crystallised quartz (Qinghu-Qtz) and a fused quartz (Glass-Qtz), were described213 as new RMs for in situ O isotope analysis by SIMS. Variations in δ18O of 0.50‰ (n = 1083, 2SD) for Qinghu-Qtz and 0.22‰ (n = 283, 2SD) for Glass-Qtz, indicated that both materials were homogeneous at the scale of ca. 20 μm. In comparison, NBS 28 quartz RM, a well-established quartz O-isotope RM designed for bulk analysis, was less homogeneous at this scale. Two new natural iron sulfide RMs – pyrrhotite JC-Po and pentlandite JC-Pn – were characterised214 for their Fe and S isotope compositions. Homogeneity was demonstrated by EMPA (major elements), SIMS (S isotopes) and LA-MC-ICP-MS (Fe isotopes). Recommended values for δ56Fe were based on solution MC-ICP-MS analysis whereas δ33S and δ34S were determined by IRMS. Li215 proposed a natural chalcopyrite (HTS4-6) as a RM for in situ S-isotope-ratio microanalysis after examination by SIMS and LA-MC-ICP-MS. The measurement precision for 34S/32S from both techniques was <0.39‰ (2SD). The recommended δ34S value determined by IRMS was +0.63 ± 0.16‰ (2SD, n = 23). Another chalcopyrite RM (TC1725) was described216 for in situ Cu isotope ratio measurements by LA-MC-ICP-MS. Homogeneity was evaluated by solution MC-ICP-MS analysis of bulk material and single grains as well as by fs LA-MC-ICP-MS. Results from all analytical methods agreed within uncertainties. It was concluded that chalcopyrite TC1725 was texturally and chemically sufficiently homogenous to be used as a Cu isotope RM. The recommended mean δ65Cu value for TC1725 was given as −0.06 ± 0.03‰ (2SD, n = 132) as determined by bulk solution MC-ICP-MS. It is pleasing to note that all the authors of the papers cited in this section appreciated the importance of making these new RMs available to the wider geochemical community.

New reference minerals for U–Pb geochronology studies are of continuing interest. More than 100 grains of a bastnaesite K-9 were analysed217 by EMPA, LA-ICP-MS, SIMS, ID-TIMS and ID-MC-ICP-MS to establish its credentials as a RM for element concentration and U–Th–Pb dating measurements. The homogeneity of major elements (<5 wt% RSD) was demonstrated by EPMA and that of the U–Pb and Th–Pb systems (<2% RSD) by SIMS. The recommended age data (a 206Pb/238U age of 116.80 ± 0.13 Ma and a 208Pb/232Th age of 116.59 ± 0.11 Ma) were determined by ID techniques. A natural zircon megacryst SA02 was studied218 as a potential RM for micro-beam U–Pb geochronology and Hf–O isotopic analysis. A mean ID-TIMS 206Pb/238U age of 533.7 ± 3.7 Ma (2SD, n = 8) was recommended as the working value when using untreated grains for calibration. Results obtained by SIMS and LA-ICP-MS were in agreement with the ID-TIMS data within the uncertainties of these techniques. The recommended reference value for 176Hf/177Hf was 0.282287 ± 0.000016 (2SD, n = 31) based on solution data measured by MC-ICP-MS. Homogeneity was assessed by LA-MC-ICP-MS. A mean δ18O value of 6.03 ± 0.28‰ (2SD, n = 13) determined by laser fluorination was in agreement with data obtained by SIMS.

New isotope RMs for solution-based analyses have been developed by several laboratories, particularly with the aim of comparing data for non-traditional stable isotopes. For example, the isotope RMs often used as zero-δ scale for Fe (IRMM-014) and Mg (DSM3) are either out of stock or not readily available so RM IRMM-524A (Fe solution) and ERM-AE143 (Mg solution) have been produced as alternatives by IRMM and BAM, respectively. The Fe and Mg isotope ratios for these two new materials, determined by MC-ICP-MS, were reported by de Vega et al.219 to be δ56FeIRMM-014 = −0.004 ± 0.014‰ (2SD) and δ57FeIRMM-014 = +0.005 ± 0.024‰ (2SD) for IRMM-524A (Fe), and δ26MgDSM3 = −3.295 ± 0.040‰ (2SD), and δ25MgDSM3 = −1.666 ± 0.043‰ (2SD) for ERM-AE143 (Mg). Based on these values, Fe and Mg isotopic compositions for 21 geological RMs were determined. In a parallel study, Bao et al.220 characterised GSB-Mg, a large volume (9.5 L) of a pure, concentrated and homogeneous Mg standard solution for use as a secondary RM for Mg isotope analysis. Its Mg isotope composition was determined by MC-ICP-MS at five different laboratories and quoted relative to DSM3 (a 0.3 M HNO3 solution of Mg). The reference values for the GSB-Mg standard solution were given as δ26MgDSM3 = −2.049‰ and δ25MgDSM3 = −1.056‰, with a combined expanded (k = 2) uncertainty of 0.049‰ and 0.028‰, respectively. Three new Mo isotope RMs have been developed221 by the National Institute of Metrology (China). One of these, GBW 04504, has natural isotope abundances whereas the other two are enriched in either 97Mo (GBW 04505) or 100Mo (GBW 04506). The mass bias of the MC-ICP-MS measurements was accurately corrected using five gravimetric isotope mixtures. It was claimed that these three RMs are the first with absolute Mo isotope amount ratios traceable to SI units. Another contribution222 described the development, uncertainty evaluation and certification of a set of eleven new Zn isotope RMs (GBW04465-04475) with 64Zn/66Zn ratios ranging from 0.02 to 13. They were gravimetrically prepared from enriched isotopic materials whose composition had been accurately determined by total evaporation TIMS. Traceability to the mole SI unit was claimed and reference values were validated by a between-laboratory comparison.

It is difficult to obtain marine carbonate RMs such as JCp-1 (Porites coral) and JCt-1 (giant clam) because of their animal origins so NIST prepared223 RM 8301 (boron isotopes in marine carbonate (simulated coral and foraminifera solutions)). This consists of two synthetic solutions produced to imitate typical coral and foraminifera in terms of their δ11BSRM951 values and trace element contents. Seven leading research laboratories contributed B isotope data to the characterisation of this new SRM. The assigned NIST reference values for δ11BSRM951 were 24.17 ± 0.18‰ (NIST RM 8301 (coral)) and 14.51 ± 0.17‰ (NIST RM 8301 (foram)). Use of these new solution RMs should assist the palaeoceanographic community in evaluating the quality of their analyses. A new high-purity CaCO3 isotopic RM, designated USGS44, was developed224 as a secondary isotope RM to normalise stable-C-isotope measurements to the δ13CVPDB-LSVEC scale. The δ13CVPDB-LSVEC value was 42.21 ± 0.05‰ measured by both EA and DI IRMS. Although δ18O values were reported, this new RM was not considered suitable for δ18O measurements because its fine grain size (<63 μm) may result in exchange with atmospheric water.

The development of new RMs is important for mineral exploration and environmental contamination studies. Three sediment-based RMs (SdAR-L2 (blended sediment); SdAR-M2 (metal-rich sediment); SdAR-H1 (metalliferous sediment)) described by Wilson et al.113 were designed to mimic NIST SRMs 2709-2711 for selected element mass fractions while retaining a natural mineralogy essential for matching matrix and dissolution characteristics. The reference values for over 40 elements in each material were determined as part of the IAG GeoPT proficiency testing programme.

Further characterisation of existing RMs can be helpful when there is a paucity of suitable RMs for analyses. For instance, Druce et al.225 reported δ66Zn values for 18 geological and biological RMs, some for the first time. Similarly, N mass fraction and δ15N data were published226 for 14 silicate RMs chosen to span the range of crustal signatures. The δ15N values of these RMs ranged from −0.5 to +19.8‰. Of these, USGS RM BHVO-2 (basalt) and CPRG RMs UB-N (serpentine), FK-N (feldspar) and Biotite-Fe (biotite) were considered to the most appropriate for use as QC materials as they were most reproducible in terms of N mass fraction (<10% relative error) and isotopic composition (<0.6‰, 1SD). New δ114Cd/110Cd values were reported114 for 34 geological and biological RMs. Analyses were performed with a precision of <0.074‰ (2SD) using a 111Cd–113Cd double spike technique and MC-ICP-MS.

5.3 Sample preparation, dissolution, separation and preconcentration

Preconcentration methods for the determination of noble metals in geological samples included227 the use of alumina as a sorbent to preconcentrate Ag, Au, Ir, Os, Pd, Pt, Re, Rh and Ru prior to their simultaneous determination by ICP-MS. After loading the target elements as chloro-complexes onto alumina columns, most determinands could be recovered by using 2 M HCl as eluent. However, for the determination of Au all HNO3 had to be removed from the sample digests and the eluent needed to contain 3% thiourea. Tin powder was used228 as an alternative to SnO2 as the collector in a fire assay procedure for the determination of Ir, Pd, Pt and Rh. This enabled the melting temperature to be reduced from 1250 to 1050 °C. The tin bead produced was dissolved in HCl and the insoluble PtSn4, PdSn4, RhSn4 and Ir3Sn7 compounds separated by filtration before microwave-assisted digestion with aqua regia. The ICP-MS LODs were 0.003–0.057 ng g−1. The accuracy of this procedure was confirmed by the analysis of 11 Chinese geological CRMs, including two chromites.

New sample purification procedures for the determination of REEs in geological materials continue to be reported. Liu et al.229 developed a simple method using polyurethane foam to determine ultra-low amounts of REEs in iron-rich mineral samples by HR-ICP-MS. About 10–20 mg of sample powder were dissolved and then reacted with 0.14 g of foam by shaking vigorously for three hours. Under optimised conditions, over 98% of the iron was removed while recoveries for REEs and Y were between 98% and 104%. Analysis of two CCRMP RMs (iron formation FER-1 and FER-2) and one from NRCGA (GBW07267 (pyrite)) yielded results that agreed within uncertainties with previously reported values. In a chemical separation procedure for the determination of REEs and Y mass fractions in carbonates by ICP-MS, significant amounts of Ba and other alkaline earth elements were removed230 using DGA resin (TODGA) after addition of a Tm spike. Three carbonate RMs (BEAN, CAL-S, JLs-1) were used to validate the method. This procedure could be applied to other rock types or organic-rich matrices for which quantitative removal of Ba is necessary.

5.4 Instrumental analysis

5.4.1 Laser-induced breakdown spectroscopy. As in previous years, various regression models were proposed for the processing of data generated by LIBS. These included a hybrid random-forest algorithm231 combined with wavelet transformation applied to the quantitative determination of the K content in potash, and automatic preprocessing232 of LIBS signals using machine-learning techniques.

A comprehensive review (193 references) on elemental imaging using LIBS covered233 the technical requirements and data processing involved for successful LIBS imaging measurements. It also provided an overview of potential applications in life sciences, geosciences, cultural heritage studies and materials science. Janovszky et al.234 demonstrated the potential of LIBS mapping for classifying minerals such as biotite, feldspar, quartz and amphibole and for assessing the distribution of Be and Li within these minerals in granitic rock samples. The three statistical approaches evaluated were: classification tree based on indicator elements; linear discriminant analysis; and random forest. The results indicated that biotite and amphibole were good targets when mining for Be and Li in granitic rocks. The presentation of a method for rapid and qualitative geochemical imaging of rocks and minerals using handheld LIBS included235 an open source workflow for processing LIBS data to merge multiple raster grids and so produce cm-scale geochemical images.

The accuracy and detection limits of elemental rock analysis by LIBS are heavily dependent on the availability of robust calibration models. This is particularly true for pLIBS. Ytsma et al.236 assessed the quality of data produced by a commercial pLIBS instrument using various calibration strategies which included univariate calibration, multivariate regression modelling, the default pLIBS model predictions supplied with the instrument and an in-house calibration method based on rock RMs. Quantification of trace and minor elements with multivariate regression yielded more accurate results than with univariate models. Calibration using the new in-house method had lower prediction errors than the default models. Whereas the accuracies for major elements based on measurements of over 2000 geological RMs were satisfactory, the authors concluded that another spectroscopic technique, such as XRFS, may be preferable for in situ quantification of minor and trace elements in rocks since the errors for all calibration strategies were often greater than the average concentration of these elements in the test materials.

The potential of vacuum UV-LIBS for the quantification of S on planetary bodies without an atmosphere such as the Moon was investigated.237 Experiments were conducted in high vacuum (10−3 Pa) with a moderate laser pulse energy (25 mJ), conditions realistically achievable under the mass, size and power constraints of space flight instrumentation. An LOD of 0.5 at% S was an improvement over that achievable for lunar rocks by LIBS in the more commonly used UV/VIS/NIR spectral range.

Accurate determination of S content plays a vital role in the evaluation of coal quality. Double-pulse LIBS in a helium atmosphere enhanced238 the intensity of S spectral lines and reduced the interference from oxygen emissions. The best LOD of 0.038 wt% represented an improvement of two orders of magnitude compared to previous literature values so it was concluded that this technique had an enormous potential for online detection of S in the coal industry. Zhan et al.239 developed a simple and fast method of sample preparation for online analysis of coal by LIBS. Pulverised coal was confined in a closed cavity in the form of a “coal pillar” with a narrow opening in the side of the device through which the plasma could target the sample. The flatness of the sample surface and stability of the ablation provided good spectral quality with respect to signal intensity, RSD and S/N.

5.4.2 Dating techniques. Although a number of improvements related to in situ measurements for the U–Pb geochronology of zircons were reported, not many were particularly novel. Lin et al.240 investigated Pb/U downhole fractionation effects at different laser (193 nm ArF excimer) settings in LA-MC-ICP-MS using small spot sizes (≤10 μm) and their effect on accuracy and precision of U–Pb age determinations in zircons. The aim of this study was to develop a method for high-spatial-resolution U–Pb dating because natural zircons often contain μm-scale heterogeneities, inclusions, cracks and narrow growth zones. Applying Iolite software to correct for downhole fractionation yielded accurate and precise U–Pb ages for four zircon RMs (Plešovice, 91500, GJ-1 and Temora-2). Another study investigated241 the effect of the laser focus position on the accuracy and precision of routine U–Pb isotope analysis of several reference zircons, titanite and rutile by LA-ICP-MS. Varying the focus of an excimer laser (193 nm) by 30 μm could result in a systematic 4–6% shift in the 206Pb/238U ratio in zircons. This effect was smaller for larger diameter-to-depth ratios. It was concluded that, if the laser focus was accurate to within 5 μm, the influence of the focus offset was smaller than the analytical uncertainty and repeatability was improved. The importance of checking the laser focus during setup was stressed and users were encouraged to record focus positions in their laser log file as a matter of routine. Wu et al.242 improved the sensitivity of in situ zircon dating measurements by LA-ICP-SFMS at high spatial resolution (5–16 μm) by replacing the standard cones in the ICP-MS instrument interface with a Jet sample (1.2 mm orifice diameter compared to 0.8 mm) and X skimmer cones, and adding small amounts of N2 to the plasma. Downhole fractionation was corrected by normalisation to zircon 91500. The overall accuracy of the age determinations was 1.5%. A method for the analysis of detrital zircons243 demonstrated rapid sample throughputs of 120, 300, 600 and 1200 h−1, equivalent to 30, 12, 6 and 3 s per analysis. These were achieved largely through modifications in data acquisition and reduction software and only minor adjustments to the LA-MC-ICP-MS hardware. In a comparison244 of the analytical performances of IR (1030 nm) and UV (257 nm) lasers for U–Pb zircon dating by fs-LA-ICP-MS, the measured ages were in agreement with the reference ages at both wavelengths but the IR laser produced larger uncertainties. Imaging by HR synchrotron X-ray micro-tomography showed that there was a link between the evolution of crater morphology and size with the analytical performance of each laser. Although the IR fs laser was capable of producing accurate U–Pb ages, it required higher fluence, had a greater sample consumption and yielded poorer statistics in comparison with use of the UV fs laser. Thompson et al.245 assessed the potential benefits of ICP-TOFMS for LA zircon geochronology by comparing data for various zircon RMs with those acquired by ICP-QMS. Some of the data were acquired in a split stream configuration. In general, the two techniques produced similar levels of precision and accuracy for most U–Pb ages despite the significantly lower sensitivity of ICP-TOFMS. However, the simultaneous detection capability of TOFMS made significantly longer integration times possible without compromising the range of elements determined, thereby providing greater geological context for interpretation of the U–Pb isotopic ages and better identification of mineral inclusions. Reduced accuracy due to the limited linear dynamic range of the TOF detector at higher count rates was mitigated by the use of 235U instead of 238U.

A method for the 238U–206Pb dating of young zircons by SIMS in studies of the pre-eruptive history of magma reservoirs included246 corrections for common Pb and initial 230Th disequilibrium based on theoretical considerations. An evaluation of the advantages and limitations of 238U–206Pb age determinations compared to the widely-used 238U–230Th dating of young zircons concluded that the 238U–206Pb method was more appropriate for zircons older than 150 ka but required high U and low common Pb contents. On the other hand, the 238U–230Th method was more suited to samples younger than 150 ka but it relied on a large Th/U fractionation between the zircon and magma and a high U content. A combined geochronological and geochemical study employed247 SIMS to determine the U–Pb age and REE compositions of zircons simultaneously with a spatial resolution of <10 μm. Whereas previous methods used energy filtering at relatively low mass resolution, in this procedure the SIMS instrument was operated at a high mass resolution of ca. 15[thin space (1/6-em)]000 to separate the target isotopes from complex isobaric interferences. Operation in a dynamic MC mode reduced the measurement time to 18 min per analysis. The analysis of six well-characterised zircon RMs gave U–Pb ages within ca. 1% of the recommended values and REE data consistent with those determined by SIMS and LA-ICP-MS.

Investigations of in situ dating techniques for minerals other than zircon included a study248 of the U–Pb geochronology of a collection of 11 wolframite minerals by LA-ICP-SFMS. Significant interference effects from tungsten-oxide polyatomic ions on Hg and Pb made it inappropriate to use the 204Pb correction procedure for common Pb contribution so alternative procedures using 207Pb and 208Pb were recommended. Specimens with a relatively high U content and negligible common Pb content typically gave a 206Pb/238U age precision of around 1%. Thompson et al.249 developed a LA-ICP-MS methodology for the analysis of the mineral ilmenite for major, minor and trace element concentrations and U–Pb isotopic compositions. Modifications to a typical LA-ICP-MS geochronology protocol used for other mineral phases were required because of the challenges presented by low U (tens of ng g−1) and low radiogenic Pb (<10 ng g−1 for 206Pb) contents. Results obtained using a rutile RM for calibration yielded ages comparable to ages published for other minerals from the same sample. Mercury interferences need to be taken into account when correcting for common Pb in minerals such as apatite and titanite. Gilbert and Glorie250 utilised LA-ICP-MS/MS to demonstrate that the 204Hg interference on 204Pb can be removed effectively using NH3 gas to induce a charge transfer reaction in the reaction cell, thereby making additional corrections for this interference unnecessary. Another paper on detrital apatite geochronology promoted251 the use of (U–Th–Sm)/He dating by LA-ICP-MS as a possible solution to some of the problems associated with bulk measurements of whole grains of apatite in such studies. As well as having high sample throughput and increased analytical efficiency, the spatially resolved He extraction method removed the sampling bias inherent in whole-grain methods and allowed the dating of imperfect grains with inclusions, fractures and unusual morphologies. A LASS-ICP-MS method for the rapid in situ Sm–Nd characterisation of scheelite grains acquired252 the isotope and trace element (Mo, REEs, Sr) data simultaneously. Despite some large error bars, it was concluded that this technique could be used as a rapid reconnaissance tool to constrain the age of mineral deposits when no other suitable minerals were present to characterise pathways of ore-forming fluids. Monazites often occur as minute crystals with fine textures that limit the use of analytical techniques. In the APT of nanoscale domains of monazite (0.0007 μm3 analytical volume), application of a new protocol to correct for fractionation between 232ThO2+ and 208Pb2+ resulted253 in analytical uncertainties of 15–20% on the corrected 208Pb/232Th age. This was considered to be sufficiently precise for addressing geological questions on an unprecedented small scale.

Although the Rb–Sr isotopic system has been widely employed in geochronology for decades, the advent of ICP-MS/MS instruments now makes it possible to achieve reliable in situ dating using quadrupole instruments by removing the spectral interference of 87Rb on 87Sr. Gorojovsky and Alard254 assessed laser and mass spectrometer parameters associated with in situ Rb/Sr dating by LA-ICP-MS/MS and concluded that the most accurate and reproducible results were obtained using N2O as the reaction gas in conjunction with shorter laser wavelengths (193 nm) and lower frequencies (5 Hz). Matrix-matched RMs were also recommended for obtaining accurate Rb/Sr ages. Redda et al.255 assessed matrix effects and downhole fractionation in the in situ Rb–Sr dating by LA-ICP-MS/MS of phlogopite minerals using 193 and 213 nm laser systems. They too recognised that the general lack of matrix-matched RMs was a major obstacle that affected both precision and accuracy, so they prepared a nano-powder pellet from a phlogopite RM (Mica-Mg). However, this Mica-Mg nano pellet was not ideal for use as a primary calibrant because of differences in ablation properties between it and the natural phlogopite mineral. Ages with an accuracy of 3% or better could be achieved through regular analysis of a secondary, matrix-matched, standard to assess the accuracy of the in situ Rb–Sr ages. A novel mass spectrometer with a quadrupole mass-filter and collision cell fitted to the front-end of a MC-ICP-MS/MS instrument had256 better ion transmission than that achievable with a single collector ICP-MS/MS instrument for Rb–Sr dating. The simultaneous collection of all Sr isotopes on FCs provided more precise 87Sr/86Sr ratios. The importance of mass-filtering before the collision cell for LA 87Sr/86Sr measurements was emphasised. Because only atomic ions with m/z from 82 to 92 were transmitted, accurate 87Sr/86Sr measurements could be made without any corrections for atomic or polyatomic isobaric interferences.

Developments in Re–Os isotope dating methods included257 an improved alkaline fusion procedure for the decomposition of molybdenite in a study of hydrothermal ore deposits. Powdered samples were mixed with a 50% NaOH solution, carbonised flour and an enriched Re–Os isotope spike before digestion in a Zr crucible at 700 °C. Less NaOH was required than in previous fusion methods so procedural blanks were lower at 2 and 62 pg for Os and Re, respectively. The Os and Re isotopes were measured by ICP-MS following separation of Re by absorption on an anion-exchange resin and purification of Os by distillation. This simplified procedure yielded Os blanks comparable to those of methods involving Carius tube and high-pressure ashing and had the advantages of being simpler, quicker and safer. Another paper reported258 modifications to the Carius tube digestion scheme for the NTIMS determination of the Re–Os geochronology in organic-rich sedimentary rocks. It was essential to maintain a low Re blank for samples with low Re and Os mass fractions, so the new method employed H2SO4–Na2CrO4 rather than H2SO4–CrO3 for the digestion because the purification of Na2CrO4 using an acetone extraction method was easier. The procedural blanks for Os (0.6–1 pg) and Re (1–2 pg) were an order of magnitude lower than with H2SO4–CrO3 digestion and comparable to those obtained with an inverse aqua regia digestion. Results obtained for black shale samples were consistent with those obtained following digestion with inverse aqua regia.

5.4.3 Inductively coupled plasma mass spectrometry. An Excel-based searchable and filterable database for identifying potential spectral overlaps from elemental, doubly charged and polyatomic ions was compiled148 from a literature search (with citations) combined with experimental determinations on 74 single-element solutions. More than 50 ions had not been identified previously. However, no information was provided regarding the relative severity of the spectral overlaps. The database was accessible as an appendix in the ESI to the published paper.

High signal intensities and minimised spectral interferences are necessary for precise and accurate measurements of REEs in geological samples at ultra-trace concentrations if preconcentration procedures are to be avoided. A novel method for the direct determination of REEs at very low concentrations in carbonates and peridotites involved259 the construction of an in-house heating-condensing system. This was based on a Scott double-pass spray chamber heated with an IR lamp inside an aluminium box and connected to another double-pass spray chamber covered with a Peltier-cooler device to prevent too much solvent vapour from entering the plasma. At a temperature of 180 °C, signal intensities were improved 5–8 or 6–24 times depending on the type of skimmer cone employed. The geometry of the skimmer cone was crucial to achieving higher sensitivities and the addition of N2 to the central channel of the plasma reduced oxide formation rates considerably. Wu et al.260 used a multi-REE spike and ICP-MS analysis to generate high-quality REE data that were claimed to be comparable to those obtained using high-precision ID-TIMS but within a few minutes rather than a couple of days. To facilitate calculation of the final data, a “REE Calculation Workbook” based on Microsoft Excel was made available. For the spike elements, concentrations were calculated using ID equations. Concentrations of the four monoisotopic REEs and the almost monoisotopic REEs La and Lu were calculated by comparing their intensities with intensities of unenriched isotopes of ID elements.

The challenges and limitations in the analysis of apatite by LA-ICP-MS for in situ determinations of Nd isotope ratios were summarised by Doucelance et al.261 who evaluated the influence of laser parameters such as spot size, fluence, frequency and He/N2 gas flows on measured 143Nd/144Nd, 145Nd/144Nd and 147Sm/144Nd ratios. The laser parameters had no influence on the 143Nd/144Nd and 145Nd/144Nd ratios and some cone geometries provided more precise data by MC-ICP-MS than others. For a laser spot of 40 μm, a precision of 125–150 ppm was obtained for the 143Nd/144Nd ratio. A systematic decrease in 147Sm/144Nd ratios during an analytical session resulted from a systematic instrumental drift so it was essential to normalise measured ratios to a matrix-matched RM measured under the same analytical conditions. Qian and Zhang262 reported the simultaneous in situ determination of REE concentrations (by ICP-SFMS) and Nd isotope ratio measurements (by MC-ICP-MS) in apatite by LASS-ICP-MS analysis. The measured Nd isotopic compositions for two apatite RMs (Durango and Otter Lake) agreed with their recommended values within analytical uncertainties as did the measured REE concentrations. A new microanalytical protocol was developed263 for the determination of Br, Cl and I in apatite, scapolite and silicate glass RMs by LA-ICP-MS. Ablations were performed with a range of square spot sizes (30–80 μm) using high repetition rates (25 Hz) and extended dwell times (up to 250 ms) to improve signal intensity and stability. The method was rapid (1 min per analysis) with minimal sample preparation and straightforward offline data reduction. The calculated LOQs were 8, 360 and 0.75 μg g−1 for Br, Cl and I, respectively. Results obtained on test minerals were comparable to EPMA and SIMS data on the same materials. Well-characterised matrix-matched RMs spanning a broad range of concentrations were required to identify spurious contributions to measured signals.

Imaging by LA-ICP-MS is a rapidly expanding field in the geosciences because of the wealth of spatial information it provides on geological processes. In an invited review article, Chew et al.264 (162 references) discussed advances in LA-ICP-MS methodology for this purpose and included a practical guide for setting up LA-ICP-MS multi-elemental mapping experiments. Consideration of the latest innovations in data reduction and image processing packages placed particular emphasis on quadrupole systems. Future trends included the increased use of imaging in U–Pb geochronology given that LA-ICP-QMS mapping is now capable of acquiring all the required compositional and U–Pb age information at high-spatial resolution. The LASS approach with both MC and single-collector detection showed great promise for obtaining high precision U–Pb isotope ratios and trace element data simultaneously. To support the great interest in elemental mapping by LA-ICP-MS an online app was developed265 to assist the user in fine-tuning operational conditions to avoid aliasing, minimise blur and maximise the S/N. The app generated a list of optimised conditions from which the user may choose the most appropriate set based on requirements for single- or multiple-pulse LA-ICP-QMS mapping. Neff et al.266 presented a control system for flexible LA-ICP-TOFMS imaging which synchronised movements of the translational stage, laser operation and data acquisition. The system could perform either in single pulse mode with high scanning speeds or hole drilling mode that provided lower LODs. Due to the synchronised and selective triggering in both modes, data were stored for each pixel individually to assist analysis data processing and image generation. As defined areas of interest could be ablated, imaging times shorter than those previously possible were achieved. That flexible adjustments of ablation modes could be made for specific analytical tasks was considered to be a significant advance towards fully automated element imaging.

The considerable research effort focussed on isotope ratio determinations by MC-ICP-MS and other techniques is reflected in Table 5. Because the range of elemental isotope ratios now being measured in geological materials is so diverse, a table is provided as a starting point for readers to explore the systems of most relevance to them. In general, it is difficult to discern any major breakthroughs as many of the studies provided modest improvements to existing separation procedures or analytical protocols.

Table 5 Methods used in the determination of isotope ratios in geological materials by solution ICP-MS and TIMS
Analyte Sample matrix Separation and purification Detector RMs and figures of merit Ref.
Ca, Fe Geological materials Matrix removal on single TODGA resin column MC-ICP-MS, TIMS Procedure validated with USGS RMs AGV-2 andesite), BCR-2 (basalt) and BHVO-2 (basalt) 337
Ca, K, Sr Silicate rocks; calcium carbonate 1-step separation of Ca-K-Sr based on CEC with AG50W-X12 resin MC-ICP-MS External reproducibility on USGS RM BCR-2 (basalt): ±0.05‰ (2SD, n = 9) for δ41K/39K, ±0.04‰ (2SD, n = 9) for δ44Ca/42Ca, and 0.000012 (2SD, n = 4) for 87Sr/86Sr 338
Cd Geological materials 2-step Cd purification using AG-MP-1M resin and TRU resin MC-ICP-MS Long term repeatability of 0.00 ± 0.04‰ (2SD, n = 45) for δ114/110Cd in NIST SRM 3108 (Cd isotope solution) 130
Ce Geological RMs Separation on AG 50W-X12 resin and purification using Ln resin. MC-ICP-MS Typical reproducibility ca. ±0.030‰ for δ142/140Ce in NIST SRM 3110 (Ce isotope solution) 339
Ce Rock RMs 4-column ion chromatography using resins AG1-X8 (Fe removal), AG50W-X8 (REE separation), HDEHP (Ce separation) and G50W-X8 (Na removal) MC-ICP-MS Reproducibility of 0.03‰ (2SD) for δ142/140Ce obtained for Ce Ames metal standard 340
Cu Cu minerals No chromatography after digestion in 4M HNO3 MC-ICP-MS NWU-Cu-A and NWU-Cu-B Cu solutions prepared from pure Cu used as RMs; results reported relative to NIST SRM 976 (Cu isotopes) 341
Cu, Pb, Zn Geological RMs AG MP1 resin for Cu and Zn separation and Cu purification, Eichrom Pb resin for Pb separation MC-ICP-MS Measurement precision <±0.03‰ (2SD) for δ65/63Cu and δ64/66Zn. Validation using 5 USGS RMs for Cu and Zn, NIST SRM 981 (Pb isotopes) for Pb 342
Dy, Er, Yb Geological materials CEC on AG50W-X8 resin to collect REE followed by 2-column separation and purification on Eichrom Ln resin TIMS Validation using GSJ RMs JB-2 (basalt) and JG-2 (granite) 343
Fe Geological RMs Glasses prepared from geological RMs Fs-LA-MC-ICP-MS Precision <0.15‰ (2SD) for δ56/54Fe. USGS and MPI-DING reference glasses 344
K Geological and environmental RMs Single column CEC with AG50W-X12 resin MC-ICP-MS Precision <±0.06‰ (2SD) for δ41/39K. Range of geological RMs 165
K Terrestrial and lunar rocks 2-step CEC on AG50-X8 resin MC-ICP-MS Precision ca. 0.05‰ (2SD) for δ41/39K. Range of geological RMs 345
Mg Carbonates Single step chromatography on Eichrom DGA resin MC-ICP-MS Precision ca. 0.13‰ for δ26/24Mg. Validation using GSJ RM JDo-1 (dolomite) 346
Ni Igneous rocks 2-step AEC on AG1-X8 resin MC-ICP-MS Precision ±0.032‰ (2SD) for δ60/58Ni. Range of geological RMs; data reported relative to NIST SRM 986 (Zn isotopes) 347
Os Os RM solution No sample preparation N-TIMS Intermediate measurement precision of 85 ppm for 186Os/188Os (2SD) for IAG DROsS RM (Os isotope solution) 348
Pb Geological RMs AEC on AG 1-X8 resin MC-ICP-MS GSJ RMs JA-1 (andesite) and JMn-1 (manganese nodule). Reproducibility <±0.0032‰ (2SD) for 206Pb/204Pb and 207Pb/204Pb, <±0.0069‰ (2SD) for 208Pb/204Pb 349
S Calcium sulfate and carbonates S converted to sulfate and purified by AEC on AG1-X8 resin MC-ICP-MS Precision of 0.2‰ (2SD) for δ34S; method validation assessed by comparison with EA-IRMS values on a variety of materials 350
Sb Geological materials 2-step chromatography on AG 1-X4 and AG 50W-X8 resins MC-ICP-MS Reproducibility <0.04‰ for δ123/121Sb; data reported relative to NIST SRM 3102a (Sb isotopes). Standard addition with in-house synthetic solution for validation 351
Si Silicate rocks Laser fusion of rock powders to produce fused glasses fs-LA-MC-ICP-MS Measurement precisions of 0.06–0.11‰ for δ29/28Si and 0.10–0.17‰ for δ30/28Si. Five USGS RMs for method assessment 352
U Zircon and titanite RMs RMs mounted in resin and polished to obtain flat surface LA-MC-ICP-MS Typical precisions were 1‰ (2SD) for 235U/238U and 7‰ (2SD) for 234U/238U. Zircon RMs for validation 353
W Terrestrial rocks, mainly basalts Organic solvent extraction with 4-methyl-2-pentanone and purification by CEC (AG50W-X8) and AEC (AG 1-X8) MC-ICP-MS Reproducibility of 6–9 ppm (2SD) for μ182/184W. GSJ RM JB-2 (basalt) for validation 354
Zn Geological and cosmochemical samples 2-step AEC using Eichrom AG1-X8 resin MC-ICP-MS Analytical precision of 0.02‰ (2SD) for 66Zn/64Zn in JMC-Lyon (Zn solution). Four USGS and one GSJ RM used to assess accuracy 355
Zn Geological and biological RMs 2-step AEC using AG-MP1 resin MC-ICP-MS Measurement precision <0.02‰ for 66Zn/64Zn. IRMM-3702 used as the primary RM and values reported relative to JMC-Lyon (Zn solution) 225
Zr Zoned zircons Two-column purification procedure, Eichrom Ln-Spec and AG1-X8 resins MC-ICP-MS External reproducibility ±28 ppm for μ94/90Zr; RM zircon 91500 for validation 356
Zr Zr standard solutions Dilution of stock solutions in 1% HNO3 MC-ICP-MS Assessment of new Zr isotope RM (NRCC ZIRC-1) using the regression method with 2 NIST isotope SRMs 984 (Rb) and 987 (Sr) 357


Isotope ratio measurements in biogenic carbonates is of great relevance to studies of climate change and geological processes. To assess the intercomparability of B isotope data generated in different laboratories, a group of researchers set up267 the “Boron Isotope Intercomparison Project” in which two GSJ marine carbonate CRMs JCp-1 (Porites sp.) and JCt-1 (giant clam, Tridacna gigas) were analysed by 10 laboratories, mainly by MC-ICP-MS. The study included a comparison of different sample handling and mass spectrometric approaches. More consistent B isotope results were obtained if the carbonate CRMs were exposed to moderate oxidative treatment prior to sample dissolution. Overall, there was encouragingly good agreement of δ11B values obtained by participating laboratories, with the distribution of laboratory mean values being close to commonly reported in-house intermediate precisions. A new analytical approach to the measurement of Mg isotopes in biogenic carbonates using LA-MC-ICP-MS demonstrated268 that accurate δ26Mg data could be achieved by avoiding scattered Na ions and removing interferences arising from the carbonate matrix. Analytical biases of up to 3‰ were observed when analysing carbonates with significant Fe or Mn content (ca. 1%) but this could be limited to 0.2‰ in carbonates with low trace metal contents typical in many marine calcifiers. The method was applied in an assessment of the sub-μm diurnal variability of δ26Mg in planktonic foraminifera and could be adapted for the analysis of other inorganic carbonates such as speleothems and carbonatites.

An investigation of instrumental mass bias resulting from oxide formation in the measurement of Nd isotope ratios by MC-ICP-MS looked269 at several instrumental set-ups, including wet and dry plasmas, different introduction methods, the addition of N2 and various sampler and skimmer cone geometries. A mathematical model was developed from this extensive dataset to describe the behaviour of Nd isotopic ratios for a range of oxide formation rates and different instrument settings. A qualitative model predicted the correction to Nd isotope measurements required to account for contributions from the major sources of mass bias. In addition, a series of recommendations was given for improving the data quality of Nd isotope ratio determinations by MC-ICP-MS.

Simultaneous determination of S isotope ratios and trace element chemistry by fs-LASS-ICP-MS was reported270 for a range of sulfide and sulfate samples. Trace element quantification was performed by ICP-QMS and stable S isotope ratio measurements by MC-ICP-MS. The use of fs LA allowed matrix-independent calibration of the stable S isotope ratios to be made using IAEA-S-1 (silver sulfide) as the bracketing standard. However, the accuracy and precision of the trace element analysis were restricted by the limited availability of homogeneous RMs. Quantification of major and trace elements at only the μg g−1 level was considered to be acceptable given the additional information on S isotope composition that could be obtained simultaneously.

New free software called Iso-Compass was developed271 for data reduction of isotope data measurements by LA-MC-ICP-MS. Features highlighted included: a simple user-friendly interface for data input; manually defined selection of laser and background signals; corrections for background, interferences and mass bias; visual data presentation; and formatted data output for easier and more efficient processing of LA-MC-ICP-MS data. The flexible nature of the software enabled Iso-Compass to be used for various isotope systems.

5.4.4 Secondary ion mass spectrometry. The Sr isotope analysis of apatites using large geometry SIMS instruments is technically challenging because of the molecular isobars on the Sr isotopes. For this reason, Jeon and Whitehouse272 evaluated all the theoretically possible interferences on masses 82 to 88. By carefully controlling the peak shape and applying precise mass centring at an intermediate mass resolution of about 4500, the only significant interferences were Ca dimers and 40Ca31P16O, both of which could be corrected for. Precisions of between 0.001 and 0.0002 (1 SE) were achieved for apatites containing <100 to 1500 μg g−1 Sr. A SIMS method developed by Gillespie et al.273 specifically for the analysis of small apatite inclusions in zircon was capable of producing 87Sr/86Sr isotopic data with 1‰ precision at a spatial resolution of 15 μm or better. The several strategies investigated for overcoming the Ca dimer interference problem all involved peak-stripping algorithms, each tailored to different mass-resolution settings. The most accurate and precise 87Sr/86Sr ratios were achieved at a mass resolution of 3000 with appropriate energy filtering and use of a cold trap.
5.4.5 Thermal ionisation mass spectrometry. Several developments of TIMS instrumentation have been reported. The cavity thermal ionisation source is a specific type of thermal source with high ionisation efficiency that has been employed in nuclear physics for decades and is capable of producing 10–40 times more ions from a given amount of analyte than conventional flat filaments. Preliminary results for Nd isotope measurements demonstrated274 that cavity ion sources may be the key to achieving isotope ratios precisions at the sub-ppm level by reducing the limits imposed by counting statistics. The key characteristics of new ATONA amplifiers fitted to FCs on a TIMS instrument were275 their low and stable noise levels similar to those of 1012 to 1013 Ω resistors, a response time of <0.5 s, an exceptional gain stability of <1 ppm and a vast dynamic range which theoretically would allow quantification of signals from 10−18 to 10−9 A. The U–Pb analysis of a set of Pb RMs, synthetic U–Pb solutions and natural zircons clearly demonstrated the advantages of using ATONA-FC detection over the use of Daly ion counting for ion currents of >10−14 A. The authors concluded that ATONA-Faraday collection had the potential to increase both sample throughput and measurement precision for this type of application.

Table 5 includes a number of recent papers on isotope ratio determinations by TIMS listed by element as a starting point for readers to explore the systems of most relevance to them.

5.4.6 X-ray spectrometry and related techniques. In an intercomparison276 of XRFS core scanning data designed to resolve some of the myriad of complexities of the technique, the same set of seven marine sediment sections (1.5 m each in length) was circulated to seven XRFS facilities around the world. Each laboratory was asked to use the element menus and instrument parameters they typically employed. Although the results expressed as raw element cps varied substantially between laboratories, there was better agreement for element ratios. Four of the laboratories also scanned a set of homogenised sediment pellets with compositions previously determined by ICP-AES and ICP-MS. A log-ratio calibration rather than a linear one proved to be an effective way to convert XRF scans to quantitative values and significantly improved the match between the profiles from different laboratories.

A method for the preparation of fused glass discs in the determination of major and minor elements by WDXRFS needed277 only 30 mg of sample with a sample to flux ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]100 to produce normal-sized glass discs ca. 30 mm in diameter that could be measured in most laboratories. The fused discs were prepared using an automatic fluxer and their homogeneity evaluated using μ-XRFS mapping. Although the agreement with certified values was satisfactory for major elements, the 100-fold dilution factor resulted in unreliable data for many minor elements. A useful comparison of the advantages and drawbacks of this method with previously published fusion methods for small sample masses was presented.

A review (70 references) of the latest available pXRF technologies focussed278 on the determination of light elements, in particular Al, Mg, Na and Si. Quantification through numerical modelling of the variables that attenuate X-ray energies identified the distance between the sample and the detector as the key limiting factor. The use of a helium flush significantly improved the X-ray transmission effectiveness for both Mg and Na. The combination of helium flush with a new graphene detector window assembly in place of a traditional beryllium one significantly reduced scanning times without compromising precision.

6 Glossary of abbreviations

2Dtwo dimensional
3Dthree dimensional
AASatomic absorption spectrometry
ABarsenobetaine
ACarsenocholine
ACGIHAmerican Conference of Governmental Industrial Hygienists
ACSMaerosol chemical speciation monitor
AECanion exchange chromatography
AEDatomic emission detector
AESatomic emission spectrometry
AFatomic fluorescence
AFSatomic fluorescence spectrometry
AMSaccelerator mass spectrometry
ANNartificial neural networks
APDCammonium pyrrolidine dithiocarbamate
APGDatmospheric pressure glow discharge
ASUatomic spectrometry update
BARGEBioaccessibility Research Group of Europe
APTatom probe tomography
BAMBundesamt für Materialforschung und Prüfung (Germany)
BASBureau of Analysed Samples
BCRCommunity Bureau of Reference (of the Commission of the European Communities)
BMEMCBeijing Municipal Environmental Protection Monitoring Center
BPNNback-propagation neural network
C18octyldecylsilane
CAchemical abrasion
CABMCanadian Aerosol Baseline Measurement
CAPMoNCanadian Air and Precipitation Monitoring Network
CASChemical Abstracts Service
CCcollision cell
CCDcharged coupled device
CCPcapacitively coupled plasma
CCRMCanadian Certified Reference Material
CEcation exchange
CECcation exchange chromatography
CENEuropean Committee for Standardisation
CFcontinuous flow
CFAcontinuous flow analysis
CIconfidence interval
CISEDchemometric identification of substrates and element distribution
CNTcarbon nanotube
CPEcloud point extraction
cpscounts per second
CRMcertified reference material
CRPGCentre de Recherches Pétrographiques et Géochimiques (France)
CScontinuum source
CTcomputer tomography
CVcold vapour
CVGcold vapour generation
Cyscysteine
DAdiscriminant analysis
DBDdielectric barrier discharge
DDTCdiethyldithiocarbamate
dDIHENdemountable direct injection high efficiency nebuliser
DDTPdiethyldithiophosphoric acid
DESdeep eutectic solvent
DGAdiglycolamide
DGTdiffusive gradients in thin films
DIdual inlet
DLSdynamic light scattering
DLLMEdispersive liquid liquid microextraction
DMAdimethylarsinic acid
DNAdeoxyribonucleic acid
DPdouble pulse
DPMdiesel particulate matter
DRSdifferential reflectance spectroscopy
DSCdifferential scanning calorimetry
DTPAdiethylenetriamine pentaacetate
EAelemental analyser
ECelemental carbon
EDenergy dispersive
EDSenergy dispersive (X-ray) spectrometry
EDTAethylenediaminetetraacetic acid
EDXRFSenergy dispersive X-ray fluorescence spectrometry
EMelectron multiplier
EMBRAPAEmpresa Brasileira de Pesquisa Agropecuária (Brazilian Agricultural Research Association)
EMPAelectron microprobe analysis
EMPIREuropean Metrology Programme for Innovation and Research
ENEuropean Committee for Standardisation
EPMAelectron probe microanalysis
ERMEuropean Reference Material
ESIelectrospray ionisation
ETAASelectrothermal atomic absorption spectrometry
EtHgethylmercury
ETMASelectrothermal molecular absorption spectrometry
ETVelectrothermal vaporisation
EUEuropean Union
EUSAAREuropean Supersites for Atmospheric Aerosol Research
FAASflame atomic absorption spectrometry
FCFaraday cup
FFFfield flow fractionation
FIflow injection
FIAflow injection analysis
FTIRFourier transform infrared
GCgas chromatography
GDglow discharge
GFgraphite furnace
GLMgeneralised linear model
GOgraphene oxide
GSBZInstitute for Environmental Reference Materials, Ministry of Environmental Protection of China, Beijing, China
GSDgeometric standard deviation
GSJGeological Survey of Japan
HDEHPbis(2-ethylhexyl)phosphoric acid
HENhigh efficiency nebuliser
HFSEhigh field strength element
HGhydride generation
HILIChydrophilic interaction liquid chromatography
HPAhigh pressure asher
HPLChigh performance liquid chromatography
HPSHigh Purity Standards (USA)
HRhigh resolution
HSLHealth and Safety Laboratory (Health and Safety Executive, UK)
iHginorganic mercury
IAEAInternational Atomic Energy Authority
IAGInternational Association of Geoanalysts
IAPSOInternational Association for the Physical Sciences of the Ocean
ICion chromatography
ICPinductively coupled plasma
IDisotope dilution
IDAisotope dilution analysis
IECInternational Electrotechnical Commission
IERMInstitute for Environmental Reference Materials (Ministry of Environmental Protection of China, Beijing, China)
IIPion imprinted polymer
IGGEInstitute of Geophysical and Geochemical Exploration, China
IGGEInstitute of Geophysical and Geochemical Prospecting, China
ILionic liquid
ILCinterlaboratory comparison
IMFinstrumental mass fractionation
IMPROVEInteragency Monitoring of Protected Visual Environments
INAAinstrumental neutron activation analysis
INCTInstitute of Nuclear Chemistry and Technology (Poland)
IRinfra-red
IRMMInstitute for Reference Materials and Measurements
IRMSisotope ratio mass spectrometry
ISinternal standard
ISEion selective electrode
ISOInternational Organisation for Standardization
JRCJoint Research Centre (of the European Union)
JMCJohnson Matthey Corporation
JSACJapanese Society for Analytical Chemistry
k coverage factor
KEDkinetic energy discrimination
LAlaser ablation
LASSlaser ablation split stream
LCliquid chromatography
LDAlinear discriminant analysis
LDPElow density polyethylene
LEAFlaser excited atomic fluorescence
LGCLaboratory of the Government Chemist (UK)
LIBSlaser induced breakdown spectroscopy
LIFlaser-induced fluorescence
LLEliquid liquid extraction
LLMEliquid liquid microextraction
LODlimit of detection
LOQlimit of quantification
LPMEliquid phase microextraction
LREElight rare earth element
LSleast squares
MADmicrowave-assisted digestion
MAEmicrowave-assisted extraction
MALDImatrix-assisted laser desorption and ionisation
MCmulticollector
MDLmethod detection limit
MFCmass flow controller
MeHgmethylmercury
MILmagnetic ionic liquid
MIPmicrowave-induced plasma
MIRmid infrared
MMAmonomethylarsonic acid
MOFmetal organic framework
MPI-DINGMax Planck Institute
MRImagnetic resonance imaging
MSmass spectrometry
MS/MStandem mass spectrometry
MTZMud Tank zircon
MUmeasurement uncertainty
MWCNTmultiwalled carbon nanotube
m/zmass to charge ratio
NACISNational Analysis Centre for Iron and Steel (China)
NBSNational Bureau of Standards (USA)
NCSNCS Testing Co., Ltd (China)
NDIRnon dispersive infra red
NIESNational Institute for Environmental Studies
NIOSHNational Institute of Occupational Safety and Health
NIRnear infra-red
NISTNational Institute of Standards and Technology
NMIANational Measurement Institute of Australia
NMIJNational Metrology Institute of Japan
NPnanoparticle
NRCCNational Research Council of Canada
NRCCRMNational Research Centre for Certified Reference Materials (China)
NRCGANational Research Centre for Geoanalysis (China)
NTIMSnegative thermal ionisation mass spectrometry
NWRINational Water Research Institute
OCorganic carbon
PCAprincipal component analysis
PDOprotected designation of origin
PETpolyethylene terephthalate
PFAperfluoroalkoxy alkane
PGEplatinum group element
PIXEparticle-induced X-ray emission
pLIBSportable laser induced breakdown spectroscopy
PLMpolarised light microscopy
PLSpartial least squares
PLSRpartial least square regression
PMparticulate matter
PM0.1particulate matter (with an aerodynamic diameter of up to 0.1 μm)
PM0.5particulate matter (with an aerodynamic diameter of up to 0.5 μm)
PM1particulate matter (with an aerodynamic diameter of up to 1.0 μm)
PM2.5particulate matter (with an aerodynamic diameter of up to 2.5 μm)
PM10particulate matter (with an aerodynamic diameter of up to 10 μm)
pptvparts per trillion (by volume)
PSLpolystryrene latex (particles)
PTEpotentially toxic element
PTFEpolytetrafluoroethylene
PUFpolyurethane foam
PVGphotochemical vapour generation
pXRFSportable X-ray fluorescence spectrometry
Qquadrupole
QAquality assurance
QCquality control
QCLquantum cascade laser
QMSquadrupole mass spectrometry
RCCrotating coiled column
RCSrespirable crystalline silica
REErare earth element
REPrelative error of prediction
RMreference material
RMSEroot mean square error
RNAribonucleic acid
RPDrelative percentage difference
RSDrelative standard deviation
RSFrelative sensitivity factor
SARMService d’Analyses des Roches et des Minéraux (France)
SAXstrong anion exchange
SCXstrong cation exchange
SDstandard deviation
SECsize exclusion chromatography
SEMscanning electron microscopy
SFsector field
SFMSsector field mass spectrometry
SFODsolidification of floating organic drop
SFODMEsolidified floating organic drop microextraction
SHRIMPsensitive high resolution ion microprobe
SISystème International (d’unités)
SIMSsecondary ion mass spectrometry
S/Nsignal to noise ratio
SOMsoil organic matter
spsingle particle
SPEsolid-phase extraction
SPMEsolid-phase microextraction
SQTslotted quartz tube
SRMstandard reference material
SSsolid sampling
SSBsample standard bracketing
SVMsupport vector machine
SVRsupport vector regression
TCtotal carbon
TDthermal desorption
TEtotal evaporation
TEOMtapered element oscillating microbalance
TEMtransmission electron microscopy
THFtetrahydrofuran
TLCthin layer chromatography
TIMSthermal ionisation mass spectrometry
TOFtime-of-flight
TOFMStime-of-flight mass spectrometry
TOTthermal optical transmission
TXRFtotal reflection X-ray fluorescence
TXRFStotal reflection X-ray fluorescence spectrometry
UAultrasound-assisted
UBMunified bioaccessibility method
UHPLCultra high performance liquid chromatography
UNCUniversity of North Carolina
US EPAUnited States Environmental Protection Agency
USGSUnited States Geological Survey
UTEVAuranium and tetravalent actinides
UVultraviolet
VAMEvortex-assisted microextraction
VISvisible
VOAGvibrating orifice mondisperse aerosol generator
VOCvolatile organic carbon
VPDBVienna Pee Bee Belemnite
VSLDutch Metrology Institute
WDXRFSwavelength dispersive X-ray fluorescence spectrometry
WWTPwaste water treatment plant
XAFSX-ray absorption fine structure
XANESX-ray absorption near edge structure
XASX-ray absorption spectroscopy
XFMX-ray fluorescence microscopy
XRDX-ray diffraction
XRFX-ray fluorescence
XRFSX-ray fluorescence spectrometry
Z atomic number

7 Conflicts of interest

There are no conflicts to declare.

8 References

  1. J. R. Bacon, O. T. Butler, W. R. L. Cairns, O. Cavoura, J. M. Cook, C. M. Davidson and R. Mertz-Kraus, J. Anal. At. Spectrom., 2021, 36(1), 10–55 RSC.
  2. M. Patriarca, N. Barlow, A. Cross, S. Hill, A. Robson, A. Taylor and J. Tyson, J. Anal. At. Spectrom., 2021, 36(3), 452–511 RSC.
  3. E. H. Evans, J. Pisonero, C. M. M. Smith and R. N. Taylor, J. Anal. At. Spectrom., 2021, 36, 868–891 RSC.
  4. R. Clough, C. F. Harrington, S. J. Hill, Y. Madrid and J. F. Tyson, J. Anal. At. Spectrom., 2021, 36(7), 1326–1373 RSC.
  5. C. Vanhoof, J. R. Bacon, U. E. A. Fittschen and L. Vincze, J. Anal. At. Spectrom., 2021, 36, 1797–1812 RSC.
  6. S. Carter, R. Clough, A. Fisher, B. Gibson, B. Russell and I. Whiteside, J. Anal. At. Spectrom., 2020, 35(11), 2410–2474 RSC.
  7. M. Harper, Int. J. Env. Res. Public Health, 2020, 17(18) DOI:10.3390/ijerph17186820.
  8. J. Hanlon, K. S. Galea and S. Verpaele, Int. J. Env. Res. Public Health, 2021, 18(13) DOI:10.3390/ijerph18136819.
  9. D. Koster, E. Nies, J. Gerding, L. Nunemann and D. Breuer, Gefahrstoffe - Reinhalt. Luft, 2021, 81(3–4), 99–107 Search PubMed.
  10. M. S. Gustin, S. M. Dunham-Cheatham, J. Y. Huang, S. Lindberg and S. N. Lyman, Atmosphere, 2021, 12(1) DOI:10.3390/atmos12010073.
  11. P. Kumar, G. Kalaiarasan, A. E. Porter, A. Pinna, M. M. Klosowski, P. Demokritou, K. F. Chung, C. Pain, D. K. Arvind, R. Arcucci, I. M. Adcock and C. Dilliway, Sci. Total Environ., 2021, 756 DOI:10.1016/j.scitotenv.2020.143553.
  12. M. Martins, J. Lawrence, S. Ferguson, J. M. Wolfson and P. Koutrakis, J. Air Waste Manage. Assoc., 2021, 71(2), 118–128 CrossRef.
  13. C. P. Su, X. Peng, X. F. Huang, L. W. Zeng, L. M. Cao, M. X. Tang, Y. Chen, B. Zhu, Y. S. Wang and L. Y. He, Atmos. Meas. Tech., 2020, 13(10), 5407–5422 CrossRef CAS.
  14. L. G. Chubb and E. G. Cauda, J. Occup. Environ. Hyg., 2021, 18(3), 103–109 CrossRef CAS PubMed.
  15. A. Newton, B. Serdar, K. Adams, L. M. Dickinson and K. Koehler, J. Aerosol Sci., 2021, 153 DOI:10.1016/j.jaerosci.2020.105721.
  16. M. A. Snow, M. Feigis, Y. D. Lei, C. P. J. Mitchell and F. Wania, Environ. Int., 2021, 146 DOI:10.1016/j.envint.2020.106264.
  17. L. Wohlgemuth, D. McLagan, B. Fluckiger, D. Vienneau and S. Osterwalder, Environ. Sci. Technol. Lett., 2020, 7(4), 234–239 CrossRef CAS PubMed.
  18. D. Paul, H. A. Scheeren, H. G. Jansen, B. A. M. Kers, J. B. Miller, A. M. Crotwell, S. E. Michel, L. V. Gatti, L. G. Domingues, C. S. C. Correia, R. A. L. Neves, H. A. J. Meijer and W. Peters, Atmos. Meas. Tech., 2020, 13(7), 4051–4064 CrossRef CAS.
  19. V. V. Petrenko, A. M. Smith, E. M. Crosier, R. Kazemi, P. Place, A. Colton, B. Yang, Q. Hua and L. T. Murray, Atmos. Meas. Tech., 2021, 14(3), 2055–2063 CrossRef CAS.
  20. S. Yatkin, K. Trzepla, W. H. White, N. J. Spada and N. P. Hyslop, Spectrochim. Acta, Part B, 2020, 171 DOI:10.1016/j.sab.2020.105948.
  21. A. Bescond, C. Oster, P. Fisicaro, S. Goddard, P. Quincey, L. A. Tsakanika, T. Lymperopoulou and M. Ochsenkuehn-Petropoulou, Atmosphere, 2021, 12(1) DOI:10.3390/atmos12010067.
  22. M. Ghanem, E. Perdrix, L. Y. Alleman, D. Rousset and P. Coddeville, Atmosphere, 2021, 12(1) DOI:10.3390/atmos12010030.
  23. C. Venchiarutti, G. Stadelmann, R. Middendorp, Z. Macsik and A. Venzinb, J. Anal. At. Spectrom., 2021, 36(3), 548–560 RSC.
  24. I. de Krom, W. Bavius, R. Ziel, E. Efremov, D. van Meer, P. van Otterloo, I. van Andel, D. van Osselen, M. Heemskerk, A. M. H. van der Veen, M. A. Dexter, W. T. Corns and H. Ent, Measurement, 2021, 169 DOI:10.1016/j.measurement.2020.108351.
  25. I. de Krom, W. Bavius, R. Ziel, E. A. McGhee, R. J. C. Brown, I. Zivkovic, J. Gacnik, V. Fajon, J. Kotnik, M. Horvat and H. Ent, Atmos. Meas. Tech., 2021, 14(3), 2317–2326 CrossRef CAS.
  26. P. Petrov, T. Rajamaki, W. T. Corns and H. Goenaga-Infante, Atmos. Environ.: X, 2020, 8 DOI:10.1016/j.aeaoa.2020.100090.
  27. M. N. Ess, M. Berto, M. Irwin, R. L. Modini, M. Gysel-Beer and K. Vasilatou, Aerosol Sci. Technol., 2021, 55(7), 828–847 CAS.
  28. S. Horender, K. Auderset, P. Quincey, S. Seeger, S. N. Skov, K. Dirscherl, T. O. M. Smith, K. Williams, C. C. Aegerter, D. M. Kalbermatter, F. Gaie-Levrel and K. Vasilatou, Atmos. Meas. Tech., 2021, 14(2), 1225–1238 CrossRef CAS.
  29. I. Beslic, J. Burger, F. Cadoni, D. Centioli, I. Kranjc, B. Van den Bril, J. Rinkovec, K. Sega, T. Zang, S. Zuzul and D. Gladtke, Gefahrstoffe - Reinhalt. Luft, 2020, 80(6), 227–233 CrossRef.
  30. T. Pellikka and T. Kajolinna, J. Air Waste Manag. Assoc., 2020, 70(10), 990–1008 CrossRef CAS PubMed.
  31. A. Naccarato, A. Tassone, M. Martino, S. Moretti, A. Macagnano, E. Zampetti, P. Papa, J. Avossa, N. Pirrone, M. Nerentorp, J. Munthe, I. Wangberg, G. W. Stupple, C. P. J. Mitchell, A. R. Martin, A. Steffen, D. Babi, E. M. Prestbo, F. Sprovieri and F. Wania, Atmos. Meas. Tech., 2021, 14(5), 3657–3672 CrossRef CAS.
  32. N. Asante and V. Nischwitz, Spectrochim. Acta, Part B, 2020, 174 DOI:10.1016/j.sab.2020.105993.
  33. A. Mitra, I. S. Sen, C. Walkner and T. C. Meisel, Spectrochim. Acta, Part B, 2021, 177 DOI:10.1016/j.sab.2020.106052.
  34. T. S. Almeida, M. Brancher, H. D. Lisboa, D. Franco, T. A. Maranhao and D. L. G. Borges, Spectrochim. Acta, Part B, 2020, 172 DOI:10.1016/j.sab.2020.105951.
  35. T. Dombek, E. Poitras, J. Hand, B. Schichtel, J. M. Harrington and K. E. Levine, J. Environ. Qual., 2020, 49(3), 762–768 CrossRef CAS PubMed.
  36. E. Karu, M. Z. Li, L. Ernle, C. A. M. Brenninkmeijer, J. Lelieveld and J. Williams, Atmos. Meas. Tech., 2021, 14(3), 1817–1831 CrossRef CAS.
  37. X. X. Yuan, D. Y. Zhou, J. Li, X. S. Xu, L. Yong, B. Hu and T. Liu, Spectrosc. Spectral Anal., 2020, 40(8), 2373–2381 CAS.
  38. C. G. Wang, G. W. Zheng, H. C. Pang, Z. P. Luo, R. Chen, W. Hao, Y. Y. Yin, L. Chen, Z. W. Wang and R. R. Liu, J. Radiol. Prot., 2020, 40(3), 827–834 CrossRef CAS.
  39. I. M. D. Ferreira, B. F. Braz, L. da Silva, A. S. Luna and R. E. Santelli, Spectrochim. Acta, Part B, 2021, 177 DOI:10.1016/j.sab.2021.106087.
  40. S. Yamashita, M. Ishida, T. Suzuki, M. Nakazato and T. Hirata, Spectrochim. Acta, Part B, 2020, 169 DOI:10.1016/j.sab.2020.105881.
  41. Y. J. Zhang, J. F. Wang, S. J. Cui, D. D. Huang and X. L. Ge, Curr. Pollut. Rep., 2020, 6, 440–451 CrossRef CAS.
  42. A. M. Avery, L. R. Williams, E. C. Fortner, W. A. Robinson and T. B. Onasch, Aerosol Sci. Technol., 2021, 55(3), 254–267 CrossRef CAS.
  43. L. Poulain, G. Spindler, A. Gruner, T. Tuch, B. Stieger, D. van Pinxteren, J. E. Petit, O. Favez, H. Herrmann and A. Wiedensohler, Atmos. Meas. Tech., 2020, 13(9), 4973–4994 CrossRef.
  44. A. K. Tobler, A. Skiba, D. S. Wang, P. Croteau, K. Styszko, J. Necki, U. Baltensperger, J. G. Slowik and A. S. H. Prevot, Atmos. Meas. Tech., 2020, 13(10), 5293–5301 CrossRef CAS.
  45. H. C. Clemen, J. Schneider, T. Klimach, F. Helleis, F. Kollner, A. Hunig, F. Rubach, S. Mertes, H. Wex, F. Stratmann, A. Welti, R. Kohl, F. Frank and S. Borrmann, Atmos. Meas. Tech., 2020, 13(11), 5923–5953 CrossRef CAS.
  46. K. W. Fomba, N. Deabji, S. E. Barcha, I. Ouchen, E. Elbaramoussi, R. C. El Moursli, M. Harnafi, S. El Hajjaji, A. Mellouki and H. Herrmann, Atmos. Meas. Tech., 2020, 13(9), 4773–4790 CrossRef CAS.
  47. T. Matsuyama, H. Yamaguchi and K. Tsuji, J. Anal. At. Spectrom., 2021, 36(3), 570–575 RSC.
  48. C. D. Grimes, J. M. Conny and R. R. Dickerson, Atmos. Environ., 2020, 241 DOI:10.1016/j.atmosenv.2020.117647.
  49. M. Rigler, L. Drinovec, G. Lavric, A. Vlachou, A. S. H. Prevot, J. L. Jaffrezo, I. Stavroulas, J. Sciare, J. Burger, I. Kranjc, J. Tursic, A. D. A. Hansen and G. Mocnik, Atmos. Meas. Tech., 2020, 13(8), 4333–4351 CrossRef CAS.
  50. R. E. Pileci, R. L. Modini, M. Berto, J. F. Yuan, J. C. Corbin, A. Marinoni, B. Henzing, M. M. Moerman, J. P. Putaud, G. Spindler, B. Wehner, T. Muller, T. Tuch, A. Trentini, M. Zanatta, U. Baltensperger and M. Gysel-Beer, Atmos. Meas. Tech., 2021, 14(2), 1379–1403 CrossRef CAS.
  51. P. Morozzi, B. Ballarin, S. Arcozzi, E. Brattich, F. Lucarelli, S. Nava, P. J. Gomez-Cascales, J. A. G. Orza and L. Tositti, Atmos. Environ., 2021, 252 DOI:10.1016/j.atmosenv.2021.118297.
  52. L. Drinovec, J. Sciare, I. Stavroulas, S. Bezantakos, M. Pikridas, F. Unga, C. Savvides, B. Visic, M. Remskar and G. Mocnik, Atmos. Meas. Tech., 2020, 13(7), 3799–3813 CrossRef CAS.
  53. P. Stacey, S. Hall, S. Stagg, F. Clegg and C. Sammon, J. Raman Spectrosc., 2021, 52(6), 1095–1107 CrossRef CAS.
  54. S. J. Wei, P. Kulkarni, L. N. Zheng and K. Ashley, J. Aerosol Sci., 2020, 150,  DOI:10.1016/j.jaerosci.2020.105643.
  55. A. Srivastava, S. E. Long, J. E. Norris, C. E. Bryan, J. Carney and J. T. Hodges, Anal. Chem., 2021, 93(2), 1050–1058 CrossRef CAS PubMed.
  56. A. Srivastava and J. T. Hodges, Anal. Chem., 2018, 90(11), 6781–6788 CrossRef CAS.
  57. S. E. Long, J. E. Norris, J. Carney and J. V. Ryan, Atmos. Pollut. Res., 2020, 11(5), 909–919 CrossRef CAS.
  58. A. Itoh, A. Yaida and Y. B. Zhu, Anal. Sci., 2021, 37(1), 131–143 CrossRef CAS PubMed.
  59. S. Braun, V. Ilberg, U. Blum and H. C. Langowski, Food Packag. Shelf Life, 2020, 25 DOI:10.1016/j.fpsl.2020.100517.
  60. G. L. Sullivan, J. Delgado-Gallardo, T. M. Watson and S. Sarp, Water Res., 2021, 196 DOI:10.1016/j.watres.2021.117033.
  61. B. Ari and S. Bakirdere, Anal. Chim. Acta, 2020, 1140, 178–189 CrossRef CAS PubMed.
  62. E. Abdulbur-Alfakhoury and M. Leermakers, J. Anal. At. Spectrom., 2021, 36(4), 716–722 RSC.
  63. Y. F. Wang, S. Y. Chen, D. L. Fang, C. L. Song and L. Y. Zhao, Microchem. J., 2020, 157 DOI:10.1016/j.microc.2020.105068.
  64. A. S. Kisomi, T. Alizadeh, A. Shakeri, A. Nouri, M. Farsadrooh and S. N. AsliPashaki, Microchem. J., 2020, 159 DOI:10.1016/j.microc.2020.105443.
  65. B. Lajin and W. Goessler, Anal. Chem., 2020, 92(13), 9156–9163 CrossRef CAS.
  66. D. Y. Liu, S. Cheng, W. L. Ji, H. Shen, J. H. Yuan, J. Wang, W. T. Zhang, H. Liu and Y. L. Zhou, Microchem. J., 2020, 159 DOI:10.1016/j.microc.2020.105401.
  67. S. Okabe, K. Oda, M. Muto, Y. V. Sahoo and M. Tanaka, J. Mol. Liq., 2021, 329 DOI:10.1016/j.molliq.2021.115532.
  68. M. Horstmann, R. G. de Vega, D. P. Bishop, U. Karst, P. A. Doble and D. Clases, J. Anal. At. Spectrom., 2021, 36(4), 767–775 RSC.
  69. S. Okabayashi, L. Kawane, N. Y. Mrabawani, T. Iwai, T. Narukawa, M. Tsuboi and K. Chiba, Talanta, 2021, 222 DOI:10.1016/j.talanta.2020.121531.
  70. Y. H. Song, Q. F. Ma, H. Y. Cheng, J. H. Liu and Y. C. Wang, Anal. Chim. Acta, 2021, 1157 DOI:10.1016/j.aca.2021.338388.
  71. M. Y. Xie, X. T. Hao, X. Jiang, W. T. Liu, T. T. Liu, H. Zheng and M. Wang, J. Sep. Sci., 2021, 44(12), 2457–2464 CrossRef CAS PubMed.
  72. S. W. Yang, Y. H. Song, Q. F. Ma, H. Y. Cheng, Y. C. Wang and J. H. Liu, Anal. Chim. Acta, 2020, 1133, 30–38 CrossRef CAS PubMed.
  73. S. Santana-Viera, M. E. T. Padron, Z. Sosa-Ferrera and J. J. Santana-Rodriguez, Microchem. J., 2020, 157 DOI:10.1016/j.microc.2020.104862.
  74. T. A. Saleh, Trends Environ. Anal. Chem., 2020, 28 DOI:10.1016/j.teac.2020.e00101.
  75. S. Heilgeist, R. Sekine, O. Sahin and R. A. Stewart, Water, 2021, 13(5) DOI:10.3390/w13050734.
  76. Y. Wang, C. W. Cuss and W. Shotyk, J. Chromatogr. A, 2020, 1632 DOI:10.1016/j.chroma.2020.461600.
  77. M. Tharaud, P. Louvat and M. F. Benedetti, Anal. Bioanal. Chem., 2021, 413, 923–933 CrossRef CAS PubMed.
  78. I. Jreije, A. Azimzada, M. Hadioui and K. J. Wilkinson, Molecules, 2020, 25(23) DOI:10.3390/molecules25235516.
  79. A. Wimmer, A. Urstoeger, T. Hinke, M. Aust, P. J. Altmann and M. Schuster, Anal. Chim. Acta, 2021, 1150 DOI:10.1016/j.aca.2021.1001.1001.
  80. A. Sanchez-Cachero, S. Lopez-Sanz, N. R. Farinas, A. Rios and R. Martin-Doimeadios, Talanta, 2021, 222 DOI:10.1016/j.talanta.2020.121513.
  81. J. Jimenez-Lamana, L. Marigliano, J. Allouche, B. Grassl, J. Szpunar and S. Reynaud, Anal. Chem., 2020, 92(17), 11664–11672 CrossRef CAS PubMed.
  82. H. El Hadri, J. Gigault, S. Mounicou, B. Grassl and S. Reynaud, Mar. Pollut. Bull., 2020, 160 DOI:10.1016/j.marpolbul.2020.111716.
  83. V. Balaram, Microchem. J., 2020, 159 DOI:10.1016/j.microc.2020.105483.
  84. A. Al Hejami, M. J. Burgener, J. Burgener and D. Beauchemin, J. Anal. At. Spectrom., 2020, 35(6), 1125–1130 RSC.
  85. J. Yu, Y. J. Kang, Q. F. Lu, H. Luo, L. J. Cui, Z. H. Lu and J. L. Li, Spectrochim. Acta, Part B, 2020, 172 DOI:10.1016/j.sab.2020.105968.
  86. M. Y. Zhao, X. X. Peng, B. C. Yang and Z. Wang, J. Anal. At. Spectrom., 2020, 35(6), 1148–1155 RSC.
  87. Y. R. Zhang, J. Ma, X. Na, Y. B. Shao, J. X. Liu, X. F. Mao, G. Y. Chen, D. Tian and Y. Z. Qian, Talanta, 2020, 218 DOI:10.1016/j.talanta.2020.121161.
  88. S. Liu, X. C. Duan and J. S. Sun, Anal. Sci., 2020, 36(4), 419–423 CrossRef CAS PubMed.
  89. C. Buyukpinar, S. Bodur, E. Yazici, Z. Tekin, N. San, O. T. Komesli and S. Bakirdere, Measurement, 2021, 176 DOI:10.1016/j.measurement.2021.109192.
  90. W. Zou, C. H. Li, J. Hu and X. D. Hou, Talanta, 2020, 218 DOI:10.1016/j.talanta.2020.121128.
  91. A. F. de Oliveira, M. Svoboda, S. Musil, I. Machado, D. Vanek, T. Matousek, J. Dedina and J. Kratzer, J. Anal. At. Spectrom., 2020, 35(10), 2132–2141 RSC.
  92. I. Ueta, D. Kato and M. Nagao, Int. J. Environ. Anal. Chem., 2020 DOI:10.1080/03067319.03062020.01839061.
  93. Q. He, X. X. Wang, H. J. He and J. Zhang, J. Anal. At. Spectrom., 2020, 35(11), 2704–2711 RSC.
  94. M. T. Liu, L. Ding, J. X. Liu, X. Na and X. F. Mao, Anal. Lett., 2021, 54(6), 990–1004 CrossRef CAS.
  95. I. Wysocka, Talanta, 2021, 221 DOI:10.1016/j.talanta.2020.121636.
  96. S. Kulomaki, S. Peramaki and A. Vaisanen, Talanta, 2020, 218 DOI:10.1016/j.talanta.2020.121125.
  97. H. Y. Li, B. Zheng, C. F. Li, J. L. Zheng and Y. F. Zhang, J. Anal. At. Spectrom., 2020, 35(7), 1306–1315 RSC.
  98. E. Fujimori, H. Kumata and T. Umemura, Bunseki Kagaku, 2021, 70(1), 31–37 CrossRef CAS.
  99. T. Narukawa, O. Shikino and K. Chiba, J. Anal. At. Spectrom., 2020, 35(10), 2142–2147 RSC.
  100. G. Trommetter, D. Dumoulin and G. Billon, Spectrochim. Acta, Part B, 2020, 171 DOI:10.1016/j.sab.2020.105922.
  101. Y. B. Zhu, K. Nakano, Y. Shikamori and A. Itoh, Spectrochim. Acta, Part B, 2021, 179 DOI:10.1016/j.sab.2021.106100.
  102. Y. Zhang, J. G. Qu, Y. Chang and Y. Li, J. Anal. At. Spectrom., 2021, 36(2), 429–438 RSC.
  103. L. H. Vieira, W. Geibert, I. Stimac, D. Koehler and M. M. R. van der Loeff, Limnol. Oceanogr. Methods, 2021, 19(5), 356–367 CrossRef.
  104. C. Argentino, F. Lugli, A. Cipriani and G. Panieri, Limnol. Oceanogr. Methods, 2021, 19(6), 431–440 CrossRef CAS.
  105. A. Griffiths, H. Packman, Y. L. Leung, B. J. Coles, K. Kreissig, S. H. Little, T. van de Flierdt and M. Rehkamper, Anal. Chem., 2020, 92(16), 11232–11241 CrossRef CAS PubMed.
  106. Z. Y. Tang, Z. Q. Hao, R. Zhou, Q. Z. Li, K. Liu, W. Zhang, J. J. Yan, K. W. Wei and X. Y. Li, Talanta, 2021, 224 DOI:10.1016/j.talanta.2020.121784.
  107. Y. Q. Liu, G. Huang, C. J. An, X. J. Chen, P. Zhang, R. F. Feng and W. H. Xiong, J. Clean Prod., 2020, 249 DOI:10.1016/j.jclepro.2019.119352.
  108. P. C. Zheng, H. D. Zhao, J. M. Wang, R. N. Liu, N. Ding, X. F. Mao and C. H. Lai, J. Anal. At. Spectrom., 2020, 35(12), 3032–3038 RSC.
  109. T. Sugioka, H. Umeda and S. Kunimura, Anal. Sci., 2020, 36(4), 465–469 CrossRef CAS PubMed.
  110. B. Patarachao, D. D. Tyo, D. Chen and P. H. J. Mercier, Spectrochim. Acta, Part B, 2021, 177 DOI:10.1016/j.sab.2020.106053.
  111. K. Sanyal, B. Kanrar and S. Dhara, J. Anal. At. Spectrom., 2021, 36, 803–812 RSC.
  112. A. I. Ivaneev, M. S. Ermolin and P. S. Fedotov, J. Anal. Chem., 2021, 76(4), 413–429 CrossRef CAS.
  113. S. A. Wilson, P. J. Potts and P. C. Webb, Geostand. Geoanal. Res., 2021, 45(2), 359–367 CrossRef CAS.
  114. Z. Lu, J. M. Zhu, D. C. Tan, X. L. Wang and Z. B. Zheng, Geostand. Geoanal. Res., 2021, 45(3), 565–581 CrossRef CAS.
  115. J. Borovicka, L. Ackerman and J. Rejsek, Talanta, 2021, 221 DOI:10.1016/j.talanta.2020.121389.
  116. S. L. Goff, E. Albalat, A. Dosseto, J. P. Godin and V. Balter, Rapid Commun. Mass Spectrom., 2021, 35(10) DOI:10.1002/rcm.9074.
  117. M. B. Javed, I. Grant-Weaver and W. Shotyk, Can. J. Soil Sci., 2020, 100(4), 393–407 CrossRef CAS.
  118. F. C. Jofre, D. N. Larregui and M. Savio, Microchem. J., 2020, 159 DOI:10.1016/j.microc.2020.105448.
  119. W. X. Lv, H. M. Yin, M. S. Liu, F. Huang and H. M. Yu, Geostand. Geoanal. Res., 2021, 45(1), 245–256 CrossRef CAS.
  120. M. A. Schiel, C. E. Domini and G. F. Silbestri, Chemosphere, 2021, 270 DOI:10.1016/j.chemosphere.2020.128663.
  121. N. Osowska, K. Paduszynski, M. Matczuk and L. Ruzik, J. Anal. At. Spectrom., 2021, 36(5), 946–953 RSC.
  122. S. Laughton, A. Laycock, G. Bland, F. von der Kammer, T. Hofmann, E. A. Casman and G. V. Lowry, Anal. Bioanal. Chem., 2020, 413, 299–314 CrossRef PubMed.
  123. Z. T. Wang, Y. Xie, J. X. Lin, Z. J. Li and Z. Y. Tan, J. Radioanal. Nucl. Chem., 2021, 328, 137–147 CrossRef CAS.
  124. B. S. Sagagi, C. M. Davidson, M. R. Cave and J. Wragg, Talanta, 2021, 223 DOI:10.1016/j.talanta.2020.121685.
  125. R. M. de Jesus, G. C. Brandao, W. N. L. dos Santos, L. O. dos Santos and J. B. de Andrade, J. Braz. Chem. Soc., 2020, 31(12), 2553–2560 CAS.
  126. N. K. Joon, P. Ek, M. Zevenhoven, L. Hupa, M. Miro, J. Bobacka and G. Lisak, Chemosphere, 2020, 256 DOI:10.1016/j.chemosphere.2020.127022.
  127. S. S. Oliveira, V. S. Ribeiro, T. S. Almeida and R. G. O. Araujo, Spectrochim. Acta, Part B, 2020, 171 DOI:10.1016/j.sab.2020.105938.
  128. X. D. Tang, B. Li, J. L. Lu, H. Y. Liu and Y. Y. Zhao, J. Anal. Sci. Technol., 2020, 11(1) DOI:10.1186/s40543-40020-00245-40543.
  129. F. Colin, R. Eleonore, F. Remi and C. Corinne, J. Anal. At. Spectrom., 2021, 36(4), 776–785 RSC.
  130. H. Peng, D. He, R. Guo, X. Liu, N. S. Belshaw, H. T. Zheng, S. H. Hu and Z. L. Zhu, J. Anal. At. Spectrom., 2021, 36(2), 390–398 RSC.
  131. O. N. de Almeida, R. M. Menezes, L. S. Nunes, V. A. Lemos, F. H. M. Luzardo and F. G. Velasco, Environ. Technol. Innov., 2021, 21 DOI:10.1016/j.eti.2020.101336.
  132. I. Menezes, P. D. Nascimento, M. H. Gonzalez and A. Oliveira, Food Anal. Meth., 2021, 14, 1043–1053 CrossRef.
  133. A. Turker and O. Acar, J. Iran. Chem. Soc., 2021, 18, 1607–1612 CrossRef CAS.
  134. T. P. Lisboa, A. M. S. Mimura, J. C. J. da Silva and R. A. de Sousa, J. Anal. Toxicol., 2020, 44(5), 514–520 CrossRef CAS PubMed.
  135. N. Schreiter, O. Wiche, I. Aubel, Q. Roode-Gutzmer and M. Bertau, J. Geochem. Explor., 2021, 220 DOI:10.1016/j.gexplo.2020.106674.
  136. L. C. Pomarolli, M. da Veiga, M. Resano and F. V. Nakadi, J. Anal. At. Spectrom., 2020, 35(10), 2305–2314 RSC.
  137. P. Maung and D. Beauchemin, J. Anal. At. Spectrom., 2020, 35(6), 1097–1102 RSC.
  138. J. Proch and P. Niedzielski, Anal. Chim. Acta, 2021, 1147, 1–14 CrossRef CAS PubMed.
  139. A. A. Rocha, L. da Costa, C. Duyck, J. R. Barbosa and R. Lorencatto, Anal. Chim. Acta, 2020, 1140, 41–49 CrossRef CAS PubMed.
  140. M. Borowska and K. Jankowski, Talanta, 2020, 219 DOI:10.1016/j.talanta.2020.121162.
  141. L. He, P. X. Li, K. Li, T. Lin, J. Luo, X. D. Hou and X. M. Jiang, J. Anal. At. Spectrom., 2021, 36(6), 1193–1200 RSC.
  142. M. L. Yuan, X. X. Peng, F. Ge, M. Y. Zhao, Q. Li and Z. Wang, Chin. Chem. Lett., 2020, 31(10), 2814–2818 CrossRef CAS.
  143. Y. J. Deng, J. Hu, M. T. Li, L. He, K. Li, X. D. Hou and X. M. Jiang, Anal. Chim. Acta, 2021, 1163 DOI:10.1016/j.aca.2021.338502.
  144. X. Y. Liu, K. Yu, H. Zhang, X. N. Zhang, H. N. Zhang, J. Zhang, J. Gao, N. Li and J. Jiang, Talanta, 2020, 219 DOI:10.1016/j.talanta.2020.121348.
  145. F. Ge, L. Gao, X. X. Peng, Q. Li, Y. F. Zhu, J. Yu and Z. Wang, Talanta, 2020, 218 DOI:10.1016/j.talanta.2020.121119.
  146. M. Llaver, A. L. Chapana and R. G. Wuilloud, Talanta, 2021, 222 DOI:10.1016/j.talanta.2020.121460.
  147. V. Balaram, Rapid Commun. Mass Spectrom., 2021, 35(10) DOI:10.1002/rcm.9065.
  148. M. C. Lomax-Vogt, F. Liu and J. W. Olesik, Spectrochim. Acta, Part B, 2021, 179 DOI:10.1016/j.sab.2021.106098.
  149. A. Botha, Metrologia, 2020, 57(6) DOI:10.1088/1681-7575/abac1052.
  150. R. Teuma-Castelletti and D. Beauchemin, J. Anal. At. Spectrom., 2020, 35(12), 2820–2825 RSC.
  151. M. E. Foulkes, B. A. Sadee and S. J. Hill, J. Anal. At. Spectrom., 2020, 35(9), 1989–2001 RSC.
  152. W. Lorenc, D. Kruszka, P. Kachlicki, J. Kozlowska and D. Baralkiewicz, Talanta, 2020, 220 DOI:10.1016/j.talanta.2020.121384.
  153. B. H. Chen and S. J. Jiang, J. Anal. At. Spectrom., 2021, 36(1), 142–149 RSC.
  154. E. M. Hamilton, S. D. Young, E. H. Bailey, O. S. Humphrey and M. J. Watts, Environ. Sci. Technol., 2021, 55(4), 2422–2429 CrossRef CAS PubMed.
  155. Y. Wang, G. L. Liu, Y. B. Li, Y. W. Liu, Y. Y. Guo, J. B. Shi, L. G. Hu, Y. Cai, Y. G. Yin and G. B. Jiang, Environ. Sci. Technol. Lett., 2020, 7(7), 482–488 CrossRef CAS.
  156. S. Nishida, A. Takahashi, L. Negishi, M. Suzuki and N. Furuta, Soil Sci. Plant Nutr., 2021,(67) DOI:10.1080/00380768.00382020.01860645.
  157. V. Nischwitz, N. Gottselig and M. Braun, J. Chromatogr. A, 2020, 1632 DOI:10.1016/j.chroma.2020.461581.
  158. O. Meili-Borovinskaya, F. Meier, R. Drexel, M. Baalousha, L. Flamigni, A. Hegetschweiler and T. Kraus, J. Chromatogr. A, 2021, 1641 DOI:10.1016/j.chroma.2021.461981.
  159. W. Y. Liu, H. L. Shi, K. Liu, X. S. Liu, E. Sahle-Demessie and C. Stephan, J. Agric. Food Chem., 2021, 69(3), 1115–1122 CrossRef CAS PubMed.
  160. J. Tuoriniemi, T. R. Holbrook, G. Cornelis, M. Schmitt, H. J. Stark, E. Kooijman and S. Wagner, J. Anal. At. Spectrom., 2020, 35(11), 2779 RSC.
  161. J. Wojcieszek, J. Jimenez-Lamana, L. Ruzik, M. Asztemborska, M. Jarosz and J. Szpunar, Front. Environ. Sci., 2020, 8 DOI:10.3389/fenvs.2020.00100.
  162. S. Richter-Brockmann, K. Garbert, M. von Bremen-Kuhne, C. A. Wehe, O. Reifschneider, M. Sperling, L. Wallrad, I. Schmitz-Thom, J. Kudla and U. Karst, J. Anal. At. Spectrom., 2020, 35(9), 2057–2063 RSC.
  163. M. Zaeem, M. Nadeem, T. H. Pham, W. Ashiq, W. Ali, S. S. M. Gillani, E. R. Moise, H. Leier, V. Kavanagh, L. Galagedara, M. Cheema and R. Thomas, Geoderma, 2021, 385 DOI:10.1016/j.geoderma.2020.114831.
  164. Y. B. Zhu, J. J. Gonzalez, X. Y. Yang, G. C. Y. Chan, X. He, R. Kostecki, X. L. Mao, R. E. Russo and V. Zorba, Anal. Chim. Acta, 2020, 1129, 24–30 CrossRef CAS PubMed.
  165. X. Q. Li, G. L. Han, Q. Zhang and Z. Miao, J. Anal. At. Spectrom., 2020, 35(7), 1330–1339 RSC.
  166. Y. Y. Wang, X. L. Hou, W. C. Zhang, L. Y. Zhang and Y. K. Fan, Talanta, 2021, 224 DOI:10.1016/j.talanta.2020.121882.
  167. S. Xing, M. Y. Luo, N. Yuan, D. Q. Liu, Y. Yang, X. X. Dai, W. C. Zhang and N. Chen, At. Spectrosc., 2021, 42(2), 62–70 CrossRef CAS.
  168. W. C. Zhang, H. T. Zhang, S. Fang, X. L. Hou, L. Y. Zhang, H. J. Dang, X. W. Yi, S. J. Zhai, W. Wang and J. Xu, Spectrochim. Acta, Part B, 2021, 178 DOI:10.1016/j.sab.2021.106113.
  169. J. Vrabel, E. Kepes, L. Duponchel, V. Motto-Ros, C. Fabre, S. Connemann, F. Schreckenberg, P. Prasse, D. Riebe, R. Junjuri, M. K. Gundawar, X. F. Tan, P. Porizka and J. Kaiser, Spectrochim. Acta, Part B, 2020, 169 DOI:10.1016/j.sab.2020.105872.
  170. A. L. Kruger, G. Nicolodelli, P. R. Villas-Boas, A. Watanabe and D. Milori, Plasma Chem. Plasma Process., 2020, 40, 1417–1427 CrossRef CAS.
  171. G. Y. Chen, G. Yang, Z. B. Ling, Y. R. Yang, Y. Zhan and X. Y. Jin, Anal. Methods, 2021, 13(12), 1502–1510 RSC.
  172. S. Y. Zhao, X. Gao, A. M. Chen and J. Q. Lin, Appl. Phys. B, 2019, 126(1) DOI:10.1007/s00340-00019-07354-00341.
  173. Z. A. Umar, U. Liaqat, R. Ahmed and M. A. Baig, Appl. Opt., 2021, 60(2), 452–458 CrossRef CAS PubMed.
  174. R. Zhou, K. Liu, Z. Y. Tang, Z. Q. Hao, X. Y. Li and X. Y. Zeng, J. Anal. At. Spectrom., 2021, 36(3), 614–621 RSC.
  175. C. Liu, J. Q. Lu, F. Xu, X. Liu and J. M. Niu, Appl. Phys. B, 2020, 126(9) DOI:10.1007/s00340-00020-07504-00346.
  176. X. L. Fu, C. J. Zhao, S. X. Ma, H. W. Tian, D. M. Dong and G. L. Li, J. Anal. At. Spectrom., 2020, 35(11), 2697–2703 RSC.
  177. S. Sanchez-Esteva, M. Knadel, S. Kucheryavskiy, L. W. de Jonge, G. H. Rubaek, C. Hermansen and G. Heckrath, Sensors, 2020, 20(18) DOI:10.3390/s20185419.
  178. F. Ma, X. L. Mao, V. Zorba, R. E. Russo, J. Gonzalez and C. W. Du, Geoderma, 2021, 385 DOI:10.1016/j.geoderma.2020.114881.
  179. C. P. de Morais, G. Nicolodelli, M. C. Mitsuyuki, S. Mounier and D. Milori, Spectrochim. Acta, Part B, 2021, 177 DOI:10.1016/j.sab.2021.106066.
  180. C. P. de Morais, G. Nicolodelli, M. C. Mitsuyuki, K. S. G. da Silva, F. F. Mauad, S. Mounier and D. Milori, Anal. Methods, 2021, 13(1), 77–83 RSC.
  181. L. Yang, L. W. Meng, H. Q. Gao, J. Y. Wang, C. Zhao, M. M. Guo, Y. He and L. X. Huang, Food Chem., 2021, 338 DOI:10.1016/j.foodchem.2020.127886.
  182. P. Yang, X. Y. Li and Z. L. Nie, Opt. Express, 2020, 28(15), 23037–23047 CrossRef CAS PubMed.
  183. P. F. Zhang, C. Xu, H. P. Wang, Z. P. Xu, Y. Yang, Y. J. Wu and Q. Wang, Optik, 2020, 218 DOI:10.1016/j.ijleo.2020.164979.
  184. C. W. Zhu, Z. Y. Tang, Q. Z. Li, R. Zhou, J. X. Lv, W. Zhang, K. P. Zhan, X. Y. Li and X. Y. Zeng, Sci. Total Environ., 2020, 738 DOI:10.1016/j.scitotenv.2020.139402.
  185. A. L. Vieira, E. C. Ferreira, D. Santos, G. S. Senesi and J. A. G. Neto, At. Spectrosc., 2021, 42(1), 18–24 CAS.
  186. L. H. Liu, S. L. Mu, P. Leng, Y. L. Dong, Q. C. Xu and A. D. Sun, Chem. Geol., 2020, 558 DOI:10.1016/j.chemgeo.2020.119993.
  187. J. Liu, C. L. Lao, J. Yuan, M. H. Sun, L. Q. Luo and Y. T. Shen, Spectrosc. Spectral Anal., 2021, 41(3), 675–685 Search PubMed.
  188. X. Feng, H. H. Zhang and P. Q. Yu, Crit. Rev. Food Sci. Nutr., 2021, 61(14), 2340–2350 CrossRef CAS PubMed.
  189. M. E. T. Croffie, P. N. Williams, O. Fenton, A. Fenelon, K. Metzger and K. Daly, Agronomy, 2020, 10(9) DOI:10.3390/agronomy10091309.
  190. X. J. Wu, J. Zhang, X. L. Zhao, C. Y. Xiao, Y. X. Shi and S. H. Li, J. Chemom., 2020, 34(11) DOI:10.1002/cem.3309.
  191. P. J. Fu, K. M. Yang and P. D. Liu, Spectrosc. Spectral Anal., 2020, 40(9), 2875–2883 CAS.
  192. H. Kupper, A. Parameswaran, B. Leitenmaier, M. Trtilek and I. Setlik, New Phytol., 2007, 175(4), 655–674 CrossRef PubMed.
  193. A. Mijovilovich, F. Morina, S. N. Bokhari, T. Wolff and H. Kupper, Plant Methods, 2020, 16(1) DOI:10.1186/s13007-13020-00621-13005.
  194. J. A. Sader and S. Ryan, Geochem.: Explor. Environ., Anal., 2020, 20(2), 167–175 CrossRef CAS.
  195. P. A. Winterburn, R. R. P. Noble and D. Lawie, Geochem.: Explor. Environ., Anal., 2020, 20(2), 157–166 CrossRef CAS.
  196. T. K. Kyser, M. I. Leybourne and D. Layton-Matthews, Geochem.: Explor. Environ., Anal., 2020, 20(2), 199–204 CrossRef CAS.
  197. L. M. Otter, M. W. Forster, E. Belousova, P. O’Reilly, D. Nowak, S. Park, S. Clark, S. F. Foley and D. E. Jacob, Geostand. Geoanal. Res., 2021, 45(1), 5–27 CrossRef CAS.
  198. U. Weis, B. Stoll, J. Arns, M. W. Forster, V. Kaiser, L. M. Otter, L. Reichstein and K. P. Jochum, Geostand. Geoanal. Res., 2021, 45(1), 29–35 CrossRef.
  199. Y. Y. Wang, X. H. Gao, Y. M. Wang, S. W. Deng and S. Li, Spectrosc. Spectral Anal., 2020, 40(11), 3341–3352 CAS.
  200. B. Lemiere and Y. A. Uvarova, Geochem.: Explor. Environ., Anal., 2020, 20(2), 205–216 CrossRef CAS.
  201. G. S. Senesi, R. S. Harmon and R. R. Hark, Spectrochim. Acta, Part B, 2021, 175 DOI:10.1016/j.sab.2020.106013.
  202. E. F. Rose-Koga, A. S. Bouvier, G. A. Gaetani, P. J. Wallace, C. M. Allison, J. A. Andrys, C. A. A. de la Torre, A. Barth, R. J. Bodnar, A. Gartner, D. Butters, A. Castillejo, B. Chilson-Parks, B. R. Choudhary, N. Cluzel, M. Cole, E. Cottrell, A. Daly, L. V. Danyushevsky, C. L. DeVitre, M. J. Drignon, L. France, M. Gaborieau, M. O. Garcia, E. Gatti, F. S. Genske, M. E. Hartley, E. C. Hughes, A. A. Iveson, E. R. Johnson, M. Jones, T. Kagoshima, Y. Katzir, M. Kawaguchi, T. Kawamoto, K. A. Kelley, J. M. Koornneef, M. D. Kurz, M. Laubier, G. D. Layne, A. Lerner, K. Y. Lin, P. P. Liu, A. Lorenzo-Merino, N. Luciani, N. Magalhaes, H. R. Marschall, P. J. Michael, B. D. Monteleone, L. R. Moore, Y. Moussallam, M. Muth, M. L. Myers, D. F. Narvaez, O. Navon, M. E. Newcombe, A. R. L. Nichols, R. L. Nielsen, A. Pamukcu, T. Plank, D. J. Rasmussen, J. Roberge, F. Schiavi, D. Schwartz, K. Shimizu, J. B. Thomas, G. T. Thompson, J. M. Tucker, G. Ustunisik, C. Waelkens, Y. Zhang and T. Zhou, Chem. Geol., 2021, 570 DOI:10.1016/j.chemgeo.2021.120145.
  203. V. Balaram, Geol. J., 2021, 56(5), 2300–2359 CrossRef CAS.
  204. W. Zhang and Z. C. Hu, Spectrochim. Acta, Part B, 2020, 171 DOI:10.1016/j.sab.2020.105929.
  205. T. T. Chen, T. L. Zhang and H. Li, TrAC, Trends Anal. Chem., 2020, 133 DOI:10.1016/j.trac.2020.116113.
  206. C. K. Sio, T. Parsons-Davis, E. Lee, J. Wimpenny, A. J. Pascall, J. D. Kuntz, J. J. Goodell, K. E. Roberts, B. B. Bandong and N. R. Bennett, Rapid Commun. Mass Spectrom., 2020, 34(7) DOI:10.1002/rcm.8627.
  207. Y. Miyajima, A. Saito, H. Kagi, T. Yokoyama, Y. Takahashi and T. Hirata, Geostand. Geoanal. Res., 2021, 45(1), 189–205 CrossRef CAS.
  208. L. Courtney-Davies, S. E. Gilbert, C. L. Ciobanu, S. R. Tapster, M. W. Richardson, N. J. Cook, B. P. Wade, M. R. Verdugo-Ihl, K. Ehrig and D. J. Condon, Geostand. Geoanal. Res., 2021, 45(1), 143–159 CrossRef CAS.
  209. W. W. Yang, H. Zhao, W. Zhang, T. Luo, M. Li, K. Chen, S. H. Hu and Z. C. Hu, Spectrochim. Acta, Part B, 2021, 178 DOI:10.1016/j.sab.2021.106124.
  210. A. Wudarska, E. Slaby, M. Wiedenbeck, J. D. Barnes, M. Bonifacie, N. C. Sturchio, G. Bardoux, F. Couffignal, J. Glodny, L. Heraty, T. John, C. Kusebauch, S. Mayanna, F. D. H. Wilke and E. Deput, Geostand. Geoanal. Res., 2021, 45(1), 121–142 CrossRef CAS.
  211. M. Wiedenbeck, R. B. Trumbull, M. Rosner, A. Boyce, J. H. Fournelle, I. A. Franchi, R. Halama, C. Harris, J. H. Lacey, H. Marschall, A. Meixner, A. Pack, P. von Strandmann, M. J. Spicuzza, J. W. Valley and F. D. H. Wilke, Geostand. Geoanal. Res., 2021, 45(1), 97–119 CrossRef CAS.
  212. K. Marger, M. Harlaux, A. Rielli, L. P. Baumgartner, A. Dini, B. L. Dutrow and A. S. Bouvier, Geostand. Geoanal. Res., 2020, 44(3), 593–615 CrossRef CAS.
  213. G. Q. Tang, Y. Liu, Q. L. Li, L. J. Feng, G. J. Wei, W. Su, Y. Li, G. H. Ren and X. H. Li, At. Spectrosc., 2020, 41(5), 188–193 CrossRef CAS.
  214. L. Chen, Y. Liu, Y. Li, Q. L. Li and X. H. Li, J. Anal. At. Spectrom., 2021, 36, 1431–1440 RSC.
  215. R. C. Li, X. P. Xia, H. Y. Chen, N. P. Wu, T. P. Zhao, C. Lai, Q. Yang and Y. Q. Zhang, Geostand. Geoanal. Res., 2020, 44(3), 485–500 CrossRef CAS.
  216. Z. A. Bao, N. Lv, K. Y. Chen, Y. Luan, X. H. Sun, C. L. Zong and H. L. Yuan, Geostand. Geoanal. Res., 2021, 45(2), 401–418 CrossRef CAS.
  217. Q. L. Li, M. H. Huyskens, Y. H. Yang, X. X. Ling, Q. Z. Yin, A. V. Nikiforov and X. H. Li, At. Spectrosc., 2020, 41(6), 218–222 CAS.
  218. C. Huang, H. Wang, J. H. Yang, C. Yang, Z. X. Li, Y. H. Yang, L. J. Feng, L. W. Xie and S. T. Wu, J. Anal. At. Spectrom., 2021, 36(2), 368–374 RSC.
  219. C. G. de Vega, S. M. Chernonozhkin, R. Grigoryan, M. Costas-Rodriguez and F. Vanhaecke, J. Anal. At. Spectrom., 2020, 35(11), 2517–2529 RSC.
  220. Z. A. Bao, K. J. Huang, J. Xu, L. Deng, S. F. Yang, P. Zhang and H. L. Yuan, J. Anal. At. Spectrom., 2020, 35(6), 1080–1086 RSC.
  221. P. S. Song, J. Wang, T. X. Ren, T. Zhou, S. Wang and Y. J. Zhou, Geostand. Geoanal. Res., 2021, 45(2), 369–381 CrossRef CAS.
  222. H. Lu, J. Wang, T. X. Ren, T. Zhou, B. Wu, Y. J. Zhou, S. Wang and P. S. Song, Geostand. Geoanal. Res., 2021, 45(2), 383–399 CrossRef CAS.
  223. J. A. Stewart, S. J. Christopher, J. R. Kucklick, L. Bordier, T. B. Chalk, A. Dapoigny, E. Douville, G. L. Foster, W. R. Gray, R. Greenop, M. Gutjahr, F. Hemsing, M. J. Henehan, P. Holdship, Y. T. Hsieh, A. Kolevica, Y. P. Lin, E. M. Mawbey, J. W. B. Rae, L. F. Robinson, R. Shuttleworth, C. F. You, S. Zhang and R. D. Day, Geostand. Geoanal. Res., 2021, 45(1), 77–96 CrossRef CAS.
  224. H. P. Qi, H. Moossen, H. A. J. Meijer, T. B. Coplen, A. T. Aerts-Bijma, L. Reid, H. Geilmann, J. Richter, M. Rothe, W. A. Brand, B. Toman, J. Benefield and J. F. Helie, Rapid Commun. Mass Spectrom., 2021, 35(4) DOI:10.1002/rcm.9006.
  225. M. Druce, C. H. Stirling and J. M. Rolison, Geostand. Geoanal. Res., 2020, 44(4), 711–732 CrossRef CAS.
  226. T. J. Boocock, S. Mikhail, J. Prytulak, T. Di Rocco and E. E. Stueken, Geostand. Geoanal. Res., 2020, 44(3), 537–551 CrossRef CAS.
  227. A. Asfaw, W. R. MacFarlane and D. Beauchemin, Anal. Chim. Acta, 2020, 1136, 151–156 CrossRef CAS PubMed.
  228. W. S. Ni, X. J. Mao, H. L. Zhang, L. Liu, X. R. Guo and Q. L. Sun, Curr. Anal. Chem., 2021, 17(4), 552–563 CrossRef CAS.
  229. Y. H. Liu, D. S. Xue, W. J. Li and C. F. Li, J. Anal. At. Spectrom., 2020, 35(10), 2156–2164 RSC.
  230. J. A. Barrat, G. Bayon, X. D. Wang, S. Le Goff, M. L. Rouget, B. Gueguen and D. Ben Salem, Talanta, 2020, 219 DOI:10.1016/j.talanta.2020.121244.
  231. Y. Ding, W. Zhang, X. Q. Zhao, L. W. Zhang and F. Yan, J. Anal. At. Spectrom., 2020, 35(6), 1131–1138 RSC.
  232. E. Ewusi-Annan, D. M. Delapp, R. C. Wiens and N. Melikechi, Spectrochim. Acta, Part B, 2020, 171 DOI:10.1016/j.sab.2020.105930.
  233. A. Limbeck, L. Brunnbauer, H. Lohninger, P. Porizka, P. Modlitbova, J. Kaiser, P. Janovszky, A. Keri and G. Galbacs, Anal. Chim. Acta, 2021, 1147, 72–98 CrossRef CAS PubMed.
  234. P. Janovszky, K. Jancsek, D. J. Palasti, J. Kopniczky, B. Hopp, T. M. Toth and G. Galbacs, J. Anal. At. Spectrom., 2021, 36, 813–823 RSC.
  235. C. J. M. Lawley, A. M. Somers and B. A. Kjarsgaard, J. Geochem. Explor., 2021, 222 DOI:10.1016/j.gexplo.2020.106694.
  236. C. R. Ytsma, C. A. Knudson, M. D. Dyar, A. C. McAdam, D. D. Michaud and L. M. Rollosson, Spectrochim. Acta, Part B, 2020, 171 DOI:10.1016/j.sab.2020.105946.
  237. S. Kubitza, S. Schroder, E. Dietz, S. Frohmann, P. B. Hansen, K. Rammelkamp, D. S. Vogt, M. Gensch and H. W. Hubers, Spectrochim. Acta, Part B, 2020, 174 DOI:10.1016/j.sab.2020.105990.
  238. Y. W. Ma, W. Zhang, Z. Xiong, H. H. Cui, Q. Z. Li, R. Zhou, Y. J. Zhang, X. Y. Li, X. Y. Zeng and Q. Li, J. Anal. At. Spectrom., 2020, 35(7), 1458–1463 RSC.
  239. K. P. Zhan, J. Chen, C. He, Z. Y. Tang, Q. Z. Li, K. Liu, C. W. Zhu and X. Y. Li, J. Anal. At. Spectrom., 2021, 36, 1210–1216 RSC.
  240. M. Lin, G. B. Zhang, N. Li, H. J. Li and J. X. Wang, Geostand. Geoanal. Res., 2021, 45(2), 265–285 CrossRef CAS.
  241. H. Q. Huang, M. Guillong, Y. Hu and C. Spandler, J. Anal. At. Spectrom., 2021, 36, 836–844 RSC.
  242. S. T. Wu, M. Yang, Y. H. Yang, L. W. Xie, C. Huang, H. Wang and J. H. Yang, Int. J. Mass Spectrom., 2020, 456 DOI:10.1016/j.ijms.2020.116394.
  243. K. E. Sundell, G. E. Gehrels and M. E. Pecha, Geostand. Geoanal. Res., 2021, 45(1), 37–57 CrossRef CAS.
  244. F. X. d’Abzac, C. Noiriel, A. Marquet and S. Brichau, Spectrochim. Acta, Part B, 2020, 168 DOI:10.1016/j.sab.2020.105863.
  245. J. M. Thompson, L. V. Danyushevsky, O. Borovinskaya and M. Tanner, J. Anal. At. Spectrom., 2020, 35(10), 2282–2297 RSC.
  246. Y. S. Huang, Q. L. Li, Y. Liu, P. P. Liu, S. L. Chung and X. H. Li, J. Anal. At. Spectrom., 2021, 36, 999–1006 RSC.
  247. Y. Liu, Q. L. Li, G. Q. Tang and X. H. Li, Geostand. Geoanal. Res., 2020, 44(3), 421–437 CrossRef CAS.
  248. M. Yang, Y. H. Yang, S. T. Wu, R. L. Romer, X. D. Che, Z. F. Zhao, W. S. Li, J. H. Yang, F. Y. Wu, L. W. Xie, C. Huang, D. Zhang and Y. Zhang, J. Anal. At. Spectrom., 2020, 35(10), 2191–2203 RSC.
  249. J. M. Thompson, K. Goemann, I. Belousov, K. Jenkins, A. Kobussen, W. Powell and L. Danyushevsky, J. Anal. At. Spectrom., 2021, 36, 1244–1260 RSC.
  250. S. E. Gilbert and S. Glorie, J. Anal. At. Spectrom., 2020, 35(7), 1472–1481 RSC.
  251. J. Pickering, W. Matthews, E. Enkelmann, B. Guest, C. Sykes and B. M. Koblinger, Chem. Geol., 2020, 548 DOI:10.1016/j.chemgeo.2020.119683.
  252. M. C. Palmer, J. M. Scott, Y. Luo, C. Sakar and D. G. Pearson, J. Geochem. Explor., 2021, 224 DOI:10.1016/j.gexplo.2021.106760.
  253. D. Fougerouse, C. L. Kirkland, D. W. Saxey, A. M. Seydoux-Guillaume, M. R. Rowles, W. D. A. Rickard and S. M. Reddy, Geostand. Geoanal. Res., 2020, 44(4), 637–652 CrossRef CAS.
  254. L. Gorojovsky and O. Alard, J. Anal. At. Spectrom., 2020, 35(10), 2322–2336 RSC.
  255. A. Redaa, J. Farkas, S. Gilbert, A. S. Collins, B. Wade, S. Lohr, T. Zack and D. Garbe-Schonberg, J. Anal. At. Spectrom., 2021, 36(2), 322–344 RSC.
  256. D. Bevan, C. D. Coath, J. Lewis, J. Schwieters, N. Lloyd, G. Craig, H. Wehrs and T. Elliott, J. Anal. At. Spectrom., 2021, 36, 917–931 RSC.
  257. X. W. Huang, L. Qi, J. F. Gao, J. Hu and Y. Huang, J. Anal. At. Spectrom., 2021, 36(1), 64–69 RSC.
  258. X. W. Li, C. Li, H. X. Pei, L. M. Zhou, W. J. Qu and A. D. Du, Geostand. Geoanal. Res., 2021, 45(2), 313–323 CrossRef CAS.
  259. J. X. Yu, H. Zhao, T. He, T. Luo, W. Zhang, K. Q. Zong, J. Lin, Y. S. Liu and Z. C. Hu, J. Anal. At. Spectrom., 2021, 36(4), 723–732 RSC.
  260. Y. Wu, L. D. Pena, S. L. Goldstein, C. Basak, L. L. Bolge, K. M. Jones, D. K. McDaniel and S. R. Hemming, Geochem. Geophys. Geosyst., 2020, 21(9) DOI:10.1029/2020gc009042.
  261. R. Doucelance, E. Bruand, S. Matte, C. Bosq, D. Auclair and A. M. Gannoun, Chem. Geol., 2020, 550 DOI:10.1016/j.chemgeo.2020.119740.
  262. S. P. Qian and L. Zhang, Geochem. J., 2019, 53(5), 319–328 CrossRef CAS.
  263. J. T. Caulfield, E. L. Tomlinson, D. M. Chew, M. A. W. Marks, C. A. McKenna, T. Ubide and V. C. Smith, Chem. Geol., 2020, 557 DOI:10.1016/j.chemgeo.2020.119854.
  264. D. Chew, K. Drost, J. H. Marsh and J. A. Petrus, Chem. Geol., 2021, 559 DOI:10.1016/j.chemgeo.2020.119917.
  265. J. T. van Elteren, D. Metarapi, M. Sala, V. S. Selih and C. C. Stremtan, J. Anal. At. Spectrom., 2020, 35(11), 2494–2497 RSC.
  266. C. Neff, P. K. Schmidt, P. S. Garofalo, G. Schwarz and D. Gunther, J. Anal. At. Spectrom., 2020, 35(10), 2255–2266 RSC.
  267. M. Gutjahr, L. Bordier, E. Douville, J. Farmer, G. L. Foster, E. C. Hathorne, B. Honisch, D. Lemarchand, P. Louvat, M. McCulloch, J. Noireaux, N. Pallavicini, J. W. B. Rae, I. Rodushkin, P. Roux, J. A. Stewart, F. Thil and C. F. You, Geostand. Geoanal. Res., 2021, 45(1), 59–75 CrossRef CAS.
  268. A. Sadekov, N. S. Lloyd, S. Misra, J. P. D’Olivo and M. McCulloch, Rapid Commun. Mass Spectrom., 2020, 34(23) DOI:10.1002/rcm.8918.
  269. E. Freres, D. Weis, K. Newman, M. Amini and K. Gordon, Geostand. Geoanal. Res., 2021, 45(3), 501–523 CrossRef CAS.
  270. M. Oelze, D. A. Frick and S. A. Gleeson, J. Anal. At. Spectrom., 2021, 36, 1118–1124 RSC.
  271. W. Zhang, Z. C. Hu and Y. S. Liu, J. Anal. At. Spectrom., 2020, 35(6), 1087–1096 RSC.
  272. H. Jeon and M. J. Whitehouse, Geostand. Geoanal. Res., 2021, 45(2), 325–340 CrossRef CAS.
  273. J. Gillespie, A. A. Nemchin, P. D. Kinny, L. Martin, M. Aleshin, M. P. Roberts, T. R. Ireland, M. J. Whitehouse, H. Jeon, A. J. Cavosie and C. L. Kirkland, Chem. Geol., 2021, 559 DOI:10.1016/j.chemgeo.2020.119979.
  274. J. R. Reimink, R. W. Carlson and T. D. Mock, J. Anal. At. Spectrom., 2020, 35(10), 2337–2350 RSC.
  275. D. Szymanowski and B. Schoene, J. Anal. At. Spectrom., 2020, 35(6), 1207–1216 RSC.
  276. A. G. Dunlea, R. W. Murray, R. Tada, C. A. Alvarez-Zarikian, C. H. Anderson, A. Gilli, L. Giosan, T. Gorgas, R. Hennekam, T. Irino, M. Murayama, L. C. Peterson, G. J. Reichart, A. Seki, H. B. Zheng and M. Ziegler, Geochem. Geophys. Geosyst., 2020, 21(9) DOI:10.1029/2020gc009248.
  277. D. S. Xue, B. X. Su, D. P. Zhang, Y. H. Liu, J. J. Guo, Q. Guo, J. F. Sun and S. Y. Zhang, J. Anal. At. Spectrom., 2020, 35(12), 2826–2833 RSC.
  278. C. Adams, C. Brand, M. Dentith, M. Fiorentini, S. Caruso and M. Mehta, Geochem.: Explor. Environ., Anal., 2020, 20(3), 366–380 CrossRef CAS.
  279. N. Lamaiphan, C. Sakaew, P. Sricharoen, P. Nuengmatcha, S. Chanthai and N. Limchoowong, J. Korean Ceram. Soc., 2021, 58, 314–329 CrossRef CAS.
  280. M. A. Gabris, B. H. Jume, I. S. Amiri, R. Khanam, H. R. Nodeh and S. Shahabuddin, Int. J. Environ. Sci. Technol., 2021, 18, 1109–1118 CrossRef CAS.
  281. S. Z. Chen, C. L. Wang, J. T. Yan and D. B. Lu, Microchem. J., 2020, 158 DOI:10.1016/j.microc.2020.105211.
  282. K. Sanyal, S. Chappa, J. Bahadur, A. K. Pandey and N. L. Mishra, J. Anal. At. Spectrom., 2020, 35(11), 2770–2778 RSC.
  283. B. Zawisza, R. Sitko, I. Queralt, E. Margui and A. Gagor, Microchim. Acta, 2020, 187(8) DOI:10.1007/s00604-00020-04387-00604.
  284. Y. Hua, J. Y. Li, H. Min, X. H. Wu, X. B. Cui, Y. J. Chen, H. Z. Lian and D. Sheng, Microchem. J., 2020, 158 DOI:10.1016/j.microc.2020.105210.
  285. S. Rezabeyk and M. Manoochehri, RSC Adv., 2020, 10(60), 36897–36905 RSC.
  286. M. Karimi, S. Dadfarnia, A. M. H. Shabani and R. Vafazadeh, J. Iranian Chem. Soc., 2021, 18, 1311–1319 CrossRef CAS.
  287. A. A. Gouda, R. El Sheikh, A. O. Youssef, N. Gouda, W. Gamil and H. A. Khadrajy, Int. J. Environ. Anal. Chem., 2020 DOI:10.1080/03067319.03062020.01858070.
  288. M. Musielak, K. Kocot, B. Zawisza, E. Talik, E. Margui, I. Queralt, B. Walczak and R. Sitko, Spectrochim. Acta, Part B, 2021, 177 DOI:10.1016/j.sab.2021.106069.
  289. M. M. Lopez-Guerrero, M. D. Granado-Castro, M. Diaz-de-Alba, J. Lande-Duran and M. J. Casanueva-Marenco, Microchem. J., 2020, 157 DOI:10.1016/j.microc.2020.104980.
  290. H. Ahmad, F. M. Husain and R. A. Khan, RSC Adv., 2021, 11(20), 11889–11899 RSC.
  291. S. Kagaya, Y. Saeki, D. Morishima, T. Kajiwara, W. Kamichatani, H. Yanai, T. Katoh, M. Saito, M. Gemmei-Ide and Y. Inoue, Anal. Sci., 2020, 36(5), 583–588 CrossRef CAS PubMed.
  292. C. K. Su and J. Y. Lin, Anal. Chem., 2020, 92(14), 9640–9648 CrossRef CAS PubMed.
  293. Y. J. Chen, M. He, B. B. Chen and B. Hu, Spectrochim. Acta, Part B, 2021, 177 DOI:10.1016/j.sab.2021.106071.
  294. H. Abdolmohammad-Zadeh, E. Rahimpour and Y. Pasandideh, J. Anal. Chem., 2020, 75(8), 967–974 CrossRef.
  295. C. Ozcan and S. Akozcan, Desalin. Water Treat., 2020, 175, 219–228 CrossRef CAS.
  296. B. Kabak, Y. Arslan, D. Trak and E. Kenduzler, J. Water Chem. Technol., 2021, 43(1), 46–52 CrossRef.
  297. D. C. M. Alegre, P. M. dos Santos, P. C. de Oliveira, C. R. T. Tarley, W. R. P. Barros and M. Z. Corazza, Int. J. Environ. Anal. Chem., 2021 DOI:10.1080/03067319.03062021.01884237.
  298. K. Pytlakowska, K. Kocot, M. Pilch and M. Zubko, Microchim. Acta, 2020, 187(9) DOI:10.1007/s00604-00020-04476-00604.
  299. Y. Q. Lei, F. Zhang, P. Guan, P. R. Guo and G. H. Wang, New J. Chem., 2020, 44(33), 14299–14305 RSC.
  300. R. X. Bi, F. L. Li, J. B. Chao, H. H. Dong, X. L. Zhang, Z. H. Wang, B. Li and N. Zhao, J. Chromatogr. A, 2021, 1635 DOI:10.1016/j.chroma.2020.461712.
  301. K. Pytlakowska, E. Malicka, E. Talik and A. Gagor, J. Anal. At. Spectrom., 2021, 36(4), 786–795 RSC.
  302. M. Furukawa, I. Tateishi, H. Katsumata and S. Kaneco, Int. J. Environ. Anal. Chem., 2021, 101(5), 719–733 CrossRef CAS.
  303. E. Kenduzler, M. D. Dinc, D. Trak, B. Kabak and Y. Arslan, J. Anal. Chem., 2020, 75(10), 1264–1269 CrossRef.
  304. S. Bajaj, V. Jain, N. Sharma, S. Tiwari and R. Saxena, J. Chromatogr. A, 2021, 1638 DOI:10.1016/j.chroma.2021.461888.
  305. G. S. Yang, J. Zheng, K. Tagami, S. Uchida, J. Zhang, J. L. Wang and J. Z. Du, J. Environ. Radioact., 2020, 220 DOI:10.1016/j.jenvrad.2020.106305.
  306. H. N. Liu, L. Li, X. J. Wang, Y. J. Ren and X. F. Shi, Arch. Environ. Contam. Toxicol., 2021 DOI:10.1007/s00244-00020-00793-00240.
  307. J. Masac, L. Machynak, J. Lovic, E. Beinrohr and F. Cacho, Talanta, 2021, 223(2) DOI:10.1016/j.talanta.2020.121767.
  308. X. X. Ou, C. Wang, M. He, B. B. Chen and B. Hu, Spectrochim. Acta, Part B, 2020, 168 DOI:10.1016/j.sab.2020.105854.
  309. W. Wang, R. D. Evans, K. Newman and R. Khokhar, Talanta, 2021, 222 DOI:10.1016/j.talanta.2020.121488.
  310. E. Kilinc, S. Ozdemir, M. S. Yalcin and M. Soylak, Anal. Bioanal. Chem., 2021, 413(4), 1107–1116 CrossRef CAS PubMed.
  311. H. Filik and A. A. Avan, J. Anal. Chem., 2021, 76(2), 156–164 CrossRef.
  312. M. N. Oviedo, E. F. Fiorentini, A. A. Lemos, M. B. Botella and R. G. Wuilloud, Microchem. J., 2020, 159 DOI:10.1016/j.microc.2020.105386.
  313. Y. Vicente-Martinez, M. Caravaca and A. Soto-Meca, Microchem. J., 2020, 157 DOI:10.1016/j.microc.2020.105102.
  314. E. M. Hafez, R. El Sheikh, A. A. Sayqal, N. AlMasoud and A. A. Gouda, Curr. Anal. Chem., 2020, 16(8), 1022–1031 CrossRef CAS.
  315. V. J. Ferreira, M. S. de Jesus, M. C. dos Santos, W. N. Guedes, V. A. Lemos, C. G. Novaes, F. S. Costa, C. S. V. Pacheco, E. G. P. da Silva and F. A. C. Amorim, Anal. Methods, 2021, 13(2), 267–273 RSC.
  316. S. V. Smirnova, D. V. Ilin and I. V. Pletnev, Talanta, 2021, 221 DOI:10.1016/j.talanta.2020.121485.
  317. A. A. Gouda, R. El Sheikh, W. S. Hassan, N. Gouda and H. A. Khadrajy, Int. J. Environ. Anal. Chem., 2020 DOI:10.1080/03067319.03062020.01807971.
  318. M. Tavakoli, M. R. Jamali and A. Nezhadali, Anal. Lett., 2021 DOI:10.1080/00032719.00032021.01897990.
  319. F. Elahi, M. B. Arain, W. A. Khan, N. Shah and T. G. Kazi, Chem. Pap., 2021, 75, 717–724 CrossRef CAS.
  320. F. Uzcan and M. Soylak, Int. J. Environ. Anal. Chem. DOI:10.1080/03067319.03062020.01776865.
  321. O. A. Urucu, N. Yildiz and E. D. Aracier, Int. J. Environ. Anal. Chem., 2020 DOI:10.1080/03067319.03062020.01766037.
  322. M. Rezaee, F. Khalilian, A. Dehgan and P. Tajer-Mohammad-Ghazvini, Indian J. Chem. Section a, 2021, 60(1), 86–92 Search PubMed.
  323. M. A. Habila, N. AlMasoud, T. S. Alomar, Z. A. AlOthman, E. Yilmaz and M. Soylak, Molecules, 2020,(20), 25,  DOI:10.3390/molecules25204794.
  324. Z. Bahadir and A. Mermer, Anal. Lett., 2020 DOI:10.1080/00032719.00032020.01785484.
  325. M. N. Oviedo, E. F. Fiorentini, A. A. Lemos and R. G. Wuilloud, Anal. Methods, 2021, 13(8), 1033–1042 RSC.
  326. W. I. Mortada, A. A. Awad, M. M. El-defrawy and M. E. Khalifa, Microchem. J., 2021, 161 DOI:10.1016/j.microc.2020.105778.
  327. J. C. Xu, F. Z. Xie, X. M. Yue, Z. Y. Li, S. X. Geng, Z. L. Dai and S. Q. Xiong, J. Chem. Soc. Pak., 2020, 42(4), 542–549 CAS.
  328. I. P. de Sa, G. B. de Souza and A. R. D. Nogueira, Microchem. J., 2021, 160 DOI:10.1016/j.microc.2020.105618.
  329. B. T. Zaman, N. B. Turan, E. G. Bakirdere, S. Topal, O. Sagsoz and S. Bakirdere, Microchem. J., 2020, 157 DOI:10.1016/j.microc.2020.104981.
  330. K. Abdi, M. Ezoddin and N. Pirooznia, Microchem. J., 2020, 157 DOI:10.1016/j.microc.2020.104999.
  331. J. Ali, M. Tuzen, X. B. Feng and T. G. Kazi, Food Chem., 2021, 344 DOI:10.1016/j.foodchem.2020.128706.
  332. B. E. D. Costa and N. M. M. Coelho, J. Food Compost., Anal., 2021, 95 DOI:10.1016/j.jfca.2020.103686.
  333. N. E. Sodan, A. Hol, O. Caylak and L. Elci, Acta Chim. Slov., 2020, 67(2), 375–385 CrossRef CAS PubMed.
  334. F. Abedi and R. Shabani, Iran. J. Chem. Chem. Eng., 2019, 38(5), 239–250 CAS.
  335. S. Ozdemir, E. Kilinc, A. Poli, I. Romano, B. Nicolaus, S. D. Mustafov and F. Sen, Int. J. Environ. Sci. Technol., 2021, 18, 185–194 CrossRef CAS.
  336. D. Kamas, A. Karatepe and M. Soylak, Turkish J. Chem., 2021, 45(1), 210–218 CrossRef CAS PubMed.
  337. Q. Y. Guan, Y. L. Sun, Z. F. Zhang, X. M. Liu, Y. J. An, F. Liu and S. Q. Zhao, Geostand. Geoanal. Res., 2020, 44(4), 669–683 CrossRef CAS.
  338. X. Q. Li and G. L. Han, J. Anal. At. Spectrom., 2021, 36(3), 676–684 RSC.
  339. R. Nakada, N. Asakura and K. Nagaishi, Geochem. J., 2019, 53(5), 293–304 CrossRef CAS.
  340. H. Pourkhorsandi, V. Debaille, J. de Jong and R. M. G. Armytage, Talanta, 2021, 224 DOI:10.1016/j.talanta.2020.121877.
  341. N. Lv, Z. A. Bao, L. Chen, K. Y. Chen, Y. Zhang and H. L. Yuan, Int. J. Mass Spectrom., 2020, 457 DOI:10.1016/j.ijms.2020.116414.
  342. H. Jeong, K. Ra and J. Y. Choi, Geostand. Geoanal. Res., 2021, 45(3), 551–563 CrossRef CAS.
  343. Y. Mizutani, H. Hidaka and S. Yoneda, Geochem. J., 2020, 54(6), 381–391 CrossRef CAS.
  344. L. Xu, W. Zhang, T. Luo, J. H. Yang and Z. C. Hu, J. Anal. At. Spectrom., 2021, 36(4), 747–757 RSC.
  345. F. Moynier, Y. Hu, K. Wang, Y. Zhao, Y. Gerard, Z. B. Deng, J. Moureau, W. Q. Li, J. I. Simon and F. Z. Teng, Chem. Geol., 2021, 571 DOI:10.1016/j.chemgeo.2021.120144.
  346. C. L. Zong, Z. A. Bao, X. J. Nie, Y. Zhang, K. Y. Chen and H. L. Yuan, J. Anal. At. Spectrom., 2021, 36(2), 273–278 RSC.
  347. H. Beunon, S. M. Chernonozhkin, N. Mattielli, S. Goderis, L. S. Doucet, V. Debaille and F. Vanhaecke, J. Anal. At. Spectrom., 2020, 35(10), 2213–2223 RSC.
  348. L. Reisberg and C. Zimmermann, Geostand. Geoanal. Res., 2021, 45(2), 287–311 CrossRef CAS.
  349. K. Nagaishi, R. Nakada and T. Ishikawa, Geochem. J., 2021, 55(1), 1–9 CrossRef CAS.
  350. S. L. Schurr, F. Genske, H. Strauss and A. Stracke, Chem. Geol., 2020, 558 DOI:10.1016/j.chemgeo.2020.119869.
  351. J. F. Liu, J. B. Chen, T. Zhang, Y. N. Wang, W. Yuan, Y. C. Lang, C. L. Tu, L. Z. Liu and J. L. Birck, J. Anal. At. Spectrom., 2020, 35(7), 1360–1367 RSC.
  352. C. X. Zhang, H. Zhao, W. Zhang, T. Luo, M. Li, K. Q. Zong, Y. S. Liu and Z. C. Hu, J. Anal. At. Spectrom., 2020, 35(9), 1887–1896 RSC.
  353. K. Yamamoto, H. Asanuma, H. Takahashi and T. Hirata, J. Anal. At. Spectrom., 2021, 36(3), 668–675 RSC.
  354. A. Takamasa, K. Suzuki, Y. Fukami, T. Iizuka, M. L. G. Tejada, W. Fujisaki, Y. Orihashi and T. Matsumoto, Geochem. J., 2020, 54(3), 117–127 CrossRef CAS.
  355. Q. Amet and C. Fitoussi, Int. J. Mass Spectrom., 2020, 457 DOI:10.1016/j.ijms.2020.116413.
  356. H. G. D. Tompkins, L. J. Zieman, M. Ibanez-Mejia and F. L. H. Tissot, J. Anal. At. Spectrom., 2020, 35(6), 1167–1186 RSC.
  357. J. He, J. Meija and L. Yang, Anal. Chem., 2021, 93(12), 5107–5113 CrossRef CAS PubMed.

Footnote

Review coordinator.

This journal is © The Royal Society of Chemistry 2022