Atomic spectrometry update – a review of advances in environmental analysis

Owen T. Butler *a, Warren R. L. Cairns b, Jennifer M. Cook c and Christine M. Davidson d
aHealth and Safety Laboratory, Harpur Hill, Buxton, UK SK17 9JN. E-mail: owen.butler@hsl.gsi.gov.uk
bCNR-IDPA, Universita Ca' Foscari, 30123 Venezia, Italy
cBritish Geological Survey, Keyworth, Nottingham, UK NG12 5GG
dUniversity of Strathclyde, Cathedral Street, Glasgow, UK G1 1XL

Received 22nd November 2016

First published on 7th December 2016


Abstract

This is the 32nd annual review of the application of atomic spectrometry to the chemical analysis of environmental samples. This update refers to papers published approximately between August 2015 and June 2016 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 In the field of air analysis, highlights within this review period included the development of a new prototype fluorescence instrument for the ultratrace determination of oxidised mercury species, and coupling of elemental analysers to CRDS alongside the development of FTIR and Raman techniques for the improved characterisation of carbonaceous aerosols. In the arena of water analysis, methods continued to be reported for the speciation of As, Cr and Hg species and, following on from last year, Gd species derived from MRI agents discharged at low level from medical facilities into water courses. Improved methods for the determination of legacy compounds such as organoleads and tins made use of plasma techniques that nowadays are more tolerant of organic solvents. Instrumental developments reported included the use of MC-ICP-MS for isotopic tracer studies and a review of TXRF techniques and associated preconcentration procedures for trace element analysis. In the field of plant and soil analysis, there is a welcome trend in that more workers appear to be optimising their analytical methods (or at least checking their performance, e.g. by analysis of CRMs) even if the main purpose of their study is environmental application rather than fundamental spectroscopy. On-going challenges include: the fact that most speciation methods reported are still too complicated, costly or time consuming, for routine use; the need for more and a wider range of CRMs, especially for speciation analysis and for use with laser-based techniques; and the lack of harmonised analytical methodology, which hinders international environmental regulatory monitoring efforts. In geological applications, a variety of techniques have been employed in the drive towards high resolution multi-elemental imaging of complex solid samples. Recent developments in cell design, aerosol transport and data acquisition for LA-ICP-MS, combined with improvements in ICP mass spectrometer design, provided evidence of its potential for very rapid quantitative 3D imaging. Elemental and isotope imaging by NanoSIMS enabled accurate U–Pb dating of mineral domains too small for reliable measurements by LA-ICP-MS. Although megapixel synchrotron XRFS is still in its infancy, it too should open up new horizons in the study of trace and major element distributions and speciation in geological materials and offer a complementary method to other imaging techniques. The deployment of ICP-MS/MS technology has resulted in successful method development to overcome several intractable isobaric interferences in the analysis of geological materials by single quadrupole ICP-MS with LA and solution sample introduction. Many more environmental applications using this approach are likely to be reported in future ASUs.


1 Air analysis

1.1 Review papers

Review papers summarised current and emerging technologies for the detection, characterisation and quantification of inorganic engineered-nanomaterials in complex samples7 (217 references) and, upon their release, into the wider environment8 (80 references). An interesting review of laser-based techniques9 (180 references) covered the in situ characterisation of tailored nanomaterials, synthesised from gas-phase precursors. Progress in the analysis of nanomaterials for toxicological purposes was reported10 (91 references), as was the suitability of methods to measure solubility11 (116 references), an important physiochemical parameter within emerging nanoregulation. In a thought-provoking review12 (53 references), the question “do ICP-MS based methods fulfil the EU monitoring requirements for the determination of elements in our environment?” was answered in the affirmative but it was considered that challenges such as sample contamination, robust implementation of suitable QA/QC programmes and lack of harmonisation in the reporting of data remained. Other useful review papers summarised new environmental applications of ICP-MS/MS13 (54 references), progress in PIXE for the analysis of aerosol samples14 (24 references), analytical approaches for the determination of As in air15 (139 references), emerging applications for a new SEM-EDX/Raman spectroscopic system within environmental, life and material sciences16 (45 references) and a review on field-based measurements17 (110 references) which discussed the advantages and limitations in the use of portable instruments for environmental analysis.

1.2 Sampling techniques

Particle-collection efficiency is an important consideration in selecting suitable filter media for workplace air monitoring. New data for commonly used filters confirmed18 that MCE, PTFE and PVC filters have relatively high collection efficiencies for particles much smaller than their nominal pore size and are considerably more efficient than polycarbonate and Ag-membrane filters. Personal air samplers designed to collect NPs (nanodeposition samplers) often use nylon meshes to trap small particles but porous polyurethane foam was considered19 a suitable alternative with low elemental impurities and good collection efficiencies. Although large particles (30–100 μm) found in workplace air can be inhaled, commonly used size-resolved samplers, such as cascade impactors, are generally limited to handling particles sizes of <20 μm. Two new prototype samplers capable of collecting larger particles were based20 upon the principles of a vertical elutriator and it will be interesting to watch their future development.

Evaluation of the performance of impactor samplers continued to be reported. Two ISO methods for the in-stack sampling of both PM2.5 and PM10 employing both conventional and virtual impactors were compared21 by use both in the laboratory and in the field at a coal-fired plant. The conventional impactor performed worse as it overestimated PM2.5 concentrations due to particle bounce and re-entrainment even when an adhesive coating was applied to the impaction plates. Collecting sufficient sample mass for detailed chemical characterisation in supporting health effects studies requires air samplers operating at substantially higher flow rates than the 1–2 m3 h−1 typically used currently. The design and validation of two new high volume PM2.5 impactors operating at 57 and 66 m3 h−1 has been reported22,23 as has a new impactor design24 that can sample either PM1 or PM2.5 at a nominal 10.5 m3 h−1 flow rate.

Continuous analytical systems are proving useful for the time-resolved measurements of aerosol chemical composition which are needed to elucidate a greater understanding of atmospheric processes and reactions. With the objective of unattended continuous long-term weekly sampling of size segregated ambient particulate matter, a sampling system25 consisting of a modified 3-stage rotating drum impactor in series with a sequential filter sampler was used to collect <0.36 μm, 0.36–1.0 μm, 1.0–2.4 μm and 2.4–10.0 μm particle size fractions. Accumulated sample deposits were subsequently analysed either by thermal desorption GC-TOF-MS (organic species) or by XRFS (elemental species). The sequential spot sampler is a design that uses a water-based condensation growth technique to grow fine particles into μm-sized droplets which can subsequently be impacted as dry spots. In one particular design,26 impaction of a droplet resulted in a sample spot of ∼1 mm diameter within a well of a 96 place collection plate. Subsequent droplets were deposited sequentially in clean wells thereby facilitating the collection of time-resolved air samples. In one application of this new system, a multi-well plate recovered from the field was processed in the laboratory wherein each spot was extracted with water and analysed by IC for its nitrate and sulfate content. This multi-well plate approach has good potential as the plates could potentially be incorporated in a range of instrumental autosampler systems thereby facilitating automation of extraction and analysis. The semi-automatic measurement27 of soluble Cu and Pb in atmospheric samples was achieved by coupling a deposition sampler to an ASV detection system that employed screen-printed electrodes. In a one-month field study, this approach proved reliable with low ng L−1 LODs. Successful validation involved analysis of water CRMs and comparison with data obtained by ICP-MS analysis. The fate of anthropogenic Hg emissions in the atmosphere is influenced by the exchange of Hg0 with the earth surface but the accurate determination of Hg0 fluxes has proved technically challenging as airborne concentration differences between up-draughts and down-draughts can be very small (<0.5 ng m−3). An improved REA system28 built around a single AFS detector system had twin-inlets and pairs of Au preconcentration cartridges for the concurrent sampling and analysis of Hg0 in both up and down-draughts. This sophisticated system possessed a Hg0 reference gas calibration generator that enabled instrumental drift to be monitored and, if necessary, re-calibrations to be undertaken.

Interesting new biosampler systems have been proposed. After a gun is fired, gun shot residue deposited on a shooter's hand disappears gradually through washing or contact with surfaces so detection on skin is limited by the need to sample within eight hours of the firing. Particles trapped within nasal mucus however had29 potentially longer residence times. Swabbing with an EDTA-wetted cotton bud and digestion in acid was all that was needed to prepare samples. Particle concentrations were lower than those found in hand swab samples but this was not an issue if a sensitive technique such as ETAAS were employed. Progress continued30 in the LA-ICP-MS measurement of the isotopic composition and concentration of Pb in the dentine and enamel of deciduous teeth which gave a record of historical UK Pb exposure during fetal development and early childhood. Children born in 2000, after the withdrawal of leaded petrol in 1999, had lower dentine Pb concentrations than children born in 1997 and an isotopic ratio fingerprint that correlated very closely with modern day Western European industrial PM2.5/10 aerosols. In contrast, for those born in 1997, the isotopic ratio fingerprint was a binary mixture of industrial aerosols and leaded petrol emissions. Exhaled breath condensate (EBC), the condensate from exhaled breath during regular tidal breathing, has been proposed31 as a useful medium which, when used alongside established urine biomonitoring, can give a more comprehensive picture of worker exposure to CrVI. Collection used a portable sampler similar to a breathalyser with a Peltier cooler unit for condensation of the exhaled breath. Single-use mouthpiece, plumbing and clean test tubes were used for each sample taken. The EBC was diluted ten-fold with an EDTA solution and analysed by microbore LC-ICP-MS. The Cr speciation profile in spiked EBC samples could be maintained for up to 6 weeks if stored at 4 °C but not if samples were frozen.

1.3 Reference materials and calibrants

Reference materials (thin film standards) available for calibration of XRFS do not necessarily mimic real-world filters collected in air quality monitoring programmes. New Pb reference filters were generated32 by mounting air samplers, with the appropriate filter substrate, within an enclosed aerosol chamber and challenging them with Pb-containing aerosols produced from ICP-grade standards using a desolvating nebuliser. Filters were prepared to mimic mass loadings typically found in surveys and equivalent to airborne concentrations of between 0.0125 and 0.70 μg m−3. Extension of this work in preparing filters with other elements is now underway. Methods for the generation of test Pb or PbO NPs involved33 either the thermal decomposition and oxidation of lead bis(2,2,6,6-tetramethyl-3,5-heptanedionate) or the evaporation and condensation of metallic Pb. The latter approach was deemed to be more suitable due to its simplicity, high production rate and the well-defined composition of the NP formed. A novel porous tube reactor33 facilitated the production of NPs from the gas phase and offered a controlled process for the synthesis of ultrafine metal particles with subsequent oxidation and dilution steps. Magnetic Fe and maghemite were synthesised using Fe pentacarbonyl as a gas-phase precursor and NPs with primary particle sizes of 24 and 29 nm and geometric mean diameters of 110 nm and 150 nm produced. Data agreed well with those derived from modelling which, for Fe NPs, predicted a primary particle size of 36 nm and an agglomerate size of 134 nm.

The generation and testing of gas standards is of widespread interest. High purity nitrogen or air, often referred to as “zero gas”, is essential as a blank standard for calibrating instruments used in air quality monitoring. Providing traceable and accurate quantification of impurities in such gases is challenging as the LODs of analytical techniques required are often similar to the concentrations of the measurands in question. A useful review paper34 (21 references) described the status of the measurement science and available data on the performance of a selection of zero air generators and purifiers. Although gas standards in pressurised metal cylinders are popular, there is potential for selective adsorption onto the metal surfaces. In a new study35 on the reversible adsorption process between trace species – CH4, CO, CO2 and H2O – and cylinder surfaces such as aluminium and steel, the authors recommended that for highly precise trace gas analysis aluminium cylinders should be used, temperature fluctuations should be minimised to limit desorption and diffusion effects and cylinder usage should be restricted to units pressurised above 30 bar.

1.4 Sample preparation

In a microwave-assisted extraction procedure36 for the speciation of SbIII and SbV in PM10 airborne particles collected on quartz fibre filters, leaching with 0.05 M hydroxylammonium chlorohydrate solution was recommended. This new approach recovered spikes quantitatively and extracted more Sb from samples than the hither-to used ultrasonic extraction procedure. Optimal digestion conditions37 for the dissolution of TiO2 NPs collected on air filter samples involved the use a H2SO4[thin space (1/6-em)]:[thin space (1/6-em)]HNO3 acid mixture (2[thin space (1/6-em)]:[thin space (1/6-em)]1 v/v) heated to 210 °C.

An operationally defined sequential leach procedure38 for Mn speciation in welding fume involved four-steps: a 0.1 M ammonium acetate leachate for soluble Mn components; a 25% (v/v) acetic acid leachate to dissolve Mn0/II species; a 0.5% (w/v) hydroxylamine hydrochloride in 25% (v/v) acetic acid leachate to dissolve MnIII/IV species and a final HCl–HNO3 acid mix to digest the residue. Recoveries for test samples consisting of pure Mn compounds (Mn nitrate solution, Mn powder, MnII/III oxide) were in the range from 88 to 103%. A SiMn alloy and two certified welding fume RMs were subsequently tested but in these cases total Mn recoveries were only 68–75% suggesting, in this reviewer's opinion, that the final acid digestion step was not aggressive enough. Analysis of fumes derived from flux welding demonstrated that the dominant forms were Mn0/II and insoluble Mn. For fume derived from an arc weld process, the dominant form was the MnII/IV fraction. Interested readers are referred to a review39 (112 references) on Mn speciation.

New approaches for the preparation of particulate samples for subsequent instrumental analysis included tangential flow filtration used40 to preconcentrate black carbon particles from ice-water, remove matrix salts and limit particle aggregation, prior to TEM analysis. The continuous flow of sample solution tangentially across a filter membrane not only minimised particle clogging but also facilitated the filtration of unwanted dissolved matrix salts. The interrogation of aerosol samples is often challenging due to the limited sample quantity available. The use of an automated graphitisation equipment enabled41 small quantities of carbon-containing particulates, collected on quartz filters, to be converted effectively into a graphite target for subsequent AMS analysis. Recoveries were >80% and reproducible C14 values were obtained for sample masses in the range 50–300 μg. Strategies for the preparation of samples for LIBS have been reviewed42 (145 references). A new micromanipulator system43 facilitated a better handling of radioactive fall-out particles found in sediment samples prior to analysis using SEM and SR techniques.

1.5 Instrumental analysis

1.5.1 Atomic absorption, emission and fluorescence spectrometry. The direct analysis of particles remains attractive as onerous sample preparatory steps can be minimised or even eliminated. The determination44 of Cl in pulverised coal samples using solid sampling HR-CS-AAS exploited the characteristic molecular absorption of the SrCl molecule at 635.862 nm. Under optimised conditions of pyrolysis at 700 °C and atomisation at 2100 °C, the LOD and M0 were 0.85 and 0.24 ng, respectively. Results for five, well homogenised, coal CRMs (BCR 180, 181, 182 and NIST SRM 1630a and 1632b) agreed with certified values. Refreshingly, the authors concluded however that similar analytical performance may not be possible for coarser-grained real-world coal samples given that the proposed method consumed a sample mass of only ∼0.15 mg. They suggested that one possible option would be to increase the sample mass taken for analysis in conjunction with the selection of a less sensitive molecular transition line. In a fast screening method involving ETV-ICP-AES,45 P, S and Si impurities in Ag NPs were determined at a rate of 35 samples per hour. The important point in this proposed method was that the entire sample could be vaporised thereby enabling simultaneous measurement of the emission from both the impurity elements and the Ag matrix. No tedious weighing procedure was therefore required. The LODs for P, S and Si in a dry powder Ag matrix, were 4.2, 62 and 15 μg g−1, respectively.

A commercially available AFS analyser was modified46 to undertake airborne measurements of atmospheric Hg as part of the ongoing CARIBIC project. Salient features included the use of: two Au cartridges to achieve continuous sampling (while one was sampling the other was being desorbed); a pressure-stabilised AFS detector cell to ensure a stable detector response; and a molecular sieve to remove the 0.25% (v/v) CO2 from the argon carrier gas as this would otherwise have quenched the AFS signal. In an attempt to minimise the number of calibrant gases taken on board, this gas supply was also used to calibrate the onboard CO2 gas analyser.

Developing LIBS as a quantitative technique is a goal that is shared by a number of research groups. Ideally the measurement requirements are that the sample be completely dissociated and diffused within the plasma on time-scales conducive with analysis thereby resulting in analyte emission at the bulk plasma temperature with a signal that is linear with mass concentration. Following experiments involving the interrogation of multi-elemental test aerosols, it was concluded47 that local perturbations of plasma properties can occur so significant analyte-in-plasma residence times (tens of μs) were therefore necessary. Another study48 concluded that the goal of achieving accurate compositional measurements without the use of calibrants was only possible if the delay between the laser pulse and the detector gate remained short, i.e. <1 μs. Investigations into the use of on-line LIBS for the elemental analysis of powered coals have been reported.49,50 In the first paper,49 a tapered sampling tube was useful both for enriching the coal particles within the laser focus spot (another design goal when applying LIBS to the analysis of aerosol samples) and to reducing the influence of air entrainment and fluctuations in plasma conditions. In the second paper,50 on the influence of omnipresent moisture, it was concluded that part of the laser energy could indeed be expended on ionising the surrounding water vapour. This resulted in less coal mass being ablated and consequently in lower emission intensities. For more information on fundamental developments in atomic spectrometry readers are directed to our companion ASU.3

1.5.2 Mass spectrometry.
1.5.2.1 Inductively coupled plasma mass spectrometry. The advent of a new ICP-MS/MS instrument has encouraged development of new applications. In one,51 three cell modes: single quadrupole (Be, Pb and U); MS/MS with NH3–He (Co, Cr) and MS/MS with O2 (As, Cd, Mn, Ni and Se) were used for quantification in cigar smoke. The elimination of unwanted interfering isobaric ions was achieved using a shifted analyte masses mode (via ammonical clusters or oxides) which gave better LODs than those obtained with a single-quadrupole ICP-MS instrument. For example, the LOD for Mn was reduced from 13 μg g−1 to <3 μg g−1 and that for Se from 0.7 μg g−1 to <0.02 μg g−1. In a somewhat unusual study,52 ICP-MS/MS was used to study the abiotic methylation reaction of inorganic Hg with VOCs. Several VOCs (acetic acid, ethyl acetate, methyl benzene and methyl iodide) reacted with Hg to form methyl Hg at a conversation rate of 1–2%. One is left to ponder whether ion chemistry within an ICP-MS system can be truly representative of atmospheric processes but also whether this rather innovative approach involving an alternative use of an ICP-MS system could be useful for studying other gaseous reactions. A useful tutorial review13 (55 references) describing this new instrument has been published.

Speciation applications involving the use of HPLC-ICP-MS included53 the coupling of AEC to ICP-MS for the simultaneous speciation of chromate, molybdate, tungstate and vanadate in alkaline extracts of welding fume. At the high alkalinity conditions employed, the CrO42−, MoO42− and WO42− species gave single sharp chromatographic peaks but the peak for VO43− was slightly broader. The LODs ranged from 0.02 ng mL−1 for CrO42− to ca. 0.1 ng mL−1 for the other measurands. Method accuracy was checked using either IRMM CRM 545 (CrVI in welding fume loaded on a filter) or, for the other analytes, spiked samples. Results for Cr were within the certification range and spike recoveries were 98–101%. Five As species (AsIII, AsV, MA, DMA and TMAO) in water extracts from air filter samples were determined54 by HPLC-HG-ICP-MS. The total extractable As content was 0.03–0.7 ng m−3 and the relative abundance in the sequence AsV > TMAO > DMA > AsIII > MA. There were no discernable seasonality effects although TMAO concentrations were higher in winter samples than in summer samples. In a similar study55 on the extraction of As species, up to 54% of an AsIII spike added to extracts was oxidised to AsV. This finding emphasised the challenge of converting laboratory-based speciation science into real-world applications where such transformations can occur readily.

The LA-ICP-MS technique enables swift interrogation of particles with minimal sample preparation but further work is required to develop calibration strategies for quantitation. One proposed approach,56 involving the use of MC-ICP-MS, offered a rapid, accurate and precise method for the determination of isotopic ratios in U-containing particles. The methodology involved the use of adhesive-tape-sampling to fix particles, SSB to correct for mass fractionation effects and repeat analysis of suitable CRMs such as NBL CRM 124-1 (U3O8 24 element impurity standard) and NRCCRM GBW 04234/04236 (U isotopic abundance in UF6). The relative uncertainties in 235U/238U, 234U/235U and 236U/238U measurements were <0.05, 1.7 and 1.8%, respectively, and the isotopic ratios determined were in good agreement with certified values. A new procedure57 for the determination of the trace element content in powdered environmental samples did not require matrix-matched CRMs. Powdered samples were mixed with an AgO internal standard and a Na2B4O7 binder and pelletised. Powdered CRMs with varying matrix composition and analyte content were prepared and analysed in the same way for quantification. Applicability of the procedure was demonstrated by the successful quantification of As, Cu, Ni and Zn in four different matrix CRMs: NIST SRM 1648a (urban particulate matter); NIST SRM 2709 (San Joaquin soil); IRMM CRM 144 (sewage sludge) and IRMM CRM 723 (road dust). Three of these materials were used as calibrants and the fourth analysed as an unknown sample.

Using an ICP-MS instrument as detector for the on-line measurement of particles is a fertile, interesting but challenging research area. Researchers in Austria described58,59 a system for measurement of the time-resolved release of Cl, K, Na, Pb, S and Zn from single particles during biomass combustion. Researchers in Switzerland developed60 a SMPS-ICP-MS system coupled with a rotating-drum device for the simultaneous determination of both the size distribution and elemental composition of NPs. Meanwhile in the Czech Republic, researchers used61 substrate-assisted laser desorption to introduce Au NPs from a plastic surface into an ICP-MS instrument. A 61% transport efficiency was achieved using 56 nm-sized reference NPs. In a more fundamental study,62 particles (Al2O3, Ag, Au, CeO2 and Y2O3) in the 100–1000 nm size range were injected into an ICP-MS system in order to calculate relative detector response factors. The response factors ranged between 10−5 and 10−11.


1.5.2.2 Other mass spectrometry techniques. Developments in other MS techniques for gaseous analysis included a new analyser63 for the speciation of trace levels of atmospheric oxidised Hg compounds, required to gain a better understanding of the biogeochemical cycle of Hg. The system consisted of an ambient air collection device (either nylon membrane or quartz wool substrate), a TD module, a cryofocusing system and a GC-MS analytical system. A permeation-based calibration system with an associated AFS detector provided stable and quantifiable amounts of gas-phase Hg0, HgBr2, HgCl2, Hg(NO3)2 and HgO calibrants. In a laboratory setting, this instrument could be used to speciate HgX2 compounds at an instrumental LOD of 90 pg but it was not possible to ascribe unequivocally mass spectra to either Hg(NO3)2 or HgO species. In field use, the LOD was 10–18 pg m−3 but no oxidised Hg species could be detected when air samples were analysed. It was concluded that either a lower LOD was required or that species transformation during sampling occurred. Future work in this most challenging field will include the testing of more inert sample collection substrates and the use of alternative MS detectors. A GC-MS method64 achieved LODs of 3.3 × 10−8 (v/v) and 2.6 × 10−9 (v/v) for atmospheric Kr and Xe gases, respectively, with a relative standard uncertainty of ca. 3%.

Improvements in isotope ratio-MS included a fully automated system65 for the determination of Δ13C and Δ18O in atmospheric CO samples which used Schutze reagent (I2O5 on silica gel) to convert extracted CO to CO2. Use of high-purity He to flush continuously the instrument system resulted in low but constant system blank signals that were <1–3% of typical sample signals. The measurement repeatability was <0.2% and a single measurement took 18 minutes. A commercial GC-isotope ratio-MS system modified66 for on-line carbon ID used a constant flow of CO2, enriched in 13C and diluted in He, added via the flow splitter located within the chromatography oven. The precision for isotopic ratio measurements was ca. 0.05% RSD (n = 50). The relative abundances of N2O isotopocules (molecules that have the same chemical constitution and configuration and only differ in isotopic composition) are potentially useful tracers for understanding the atmospheric production pathways, sinks and decomposition reactions of N2O, an ozone-depleting gas. A new automated sample preparation system67 able to accommodate flask samples that previous systems could not handle consisted of a sample injection unit, a cryogenic concentration unit, a purification unit and a cryofocusing unit, all mounted on a compact mobile trolley that could be wheeled into place and connected to the IRMS instrument. A sample could be processed in 40 minutes. The precision values of <0.1‰ for Δ15N and <0.2‰ for Δ18O were comparable to those obtained with other automated but less mobile systems and better than those obtained using manual off-line preparatory systems.

Developments in MS techniques for analysis of airborne particulates included a newly developed LA-TOF-AMS system68 that consisted of two 405 nm scattering lasers for particle sizing, a 193 nm excimer laser for ablation/ionisation of particles and a TOF-MS detection system with a mass resolution of MM > 600. Laboratory tests gave a maximum detection efficiency of 2.5% for particles with a nominal diameter of 450 nm.

A particle trap laser desorption mass spectrometer69 for the quantification of SO42− aerosols gave results highly correlated (r2 = 0.96) with but consistently lower than those obtained using a more conventional thermal decomposition/oxidiser system coupled to a SO2 gas sensor. These discrepancies were explained by differences in the respective sampling inlets and differences in the vaporisation efficiencies of particles since the laser desorption MS system was operated at ∼500 °C whereas the thermal decomposition analyser ran at 1000 °C.

The Aerodyne aerosol mass spectrometer is a commercially available and frequently used instrument for the on-line measurement of sub-μm ambient aerosols. Two papers described work undertaken to understand better the performance of this instrument. In the first,70 an instrument was challenged with test aerosols ranging from NH4NO3 (non-refractory) to ZnI2 (semi-refractory) in order to gain a better understanding of how well particles vaporised at ∼600 °C. It was concluded that the W vaporiser unit did not always behave inertly towards particles, that no sharp separation between non-refractory and refractory species was possible and that, as a result, measurements of semi-refractory aerosols could indeed be biased. The second paper71 addressed errors inherent in the fitting and integration of ion peaks that could be an appreciable source of potential measurement imprecision. Coupling of the Aerodyne aerosol mass spectrometer with a Nd:YAG laser (from a single particle soot photometer) to produce an instrument known as the soot-particle aerosol mass spectrometer which could be used to measure atmospheric particles including refractory black carbon (rBC) species. A method72 for the detection and quantification of the trace metal contents of soot particles involved preparing synthetic calibration standards by dosing suspensions of carbon black particles with various concentrations of aqueous metal spikes. The resultant standards were then nebulised, dried and directed through a differential mobility analyser to generate a monodispersive (300 nm) test aerosol (i.e. dried carbon particles coated with trace metals) for soot particle-aerosol MS. In an initial field trial conducted in the vicinity of an oil fired power station, qualitative mass spectra data revealed evidence for metallic oxide and sulfate species. The Ba, Fe and V data agreed, within a factor of 2, to those obtained using the ICP-MS of filter samples taken at the same time.

1.5.3 X-ray spectrometry. The analysis of particles on filters by XRFS is now well established but new approaches are always welcome. One feasibility study73 investigated whether it would be possible to analyse particles collected using the Streaker™ sampler by EDXRFS rather than by the more conventional PIXE approach. In this ambient air sampler, a filter is rotated at a constant rate under an incoming stream of particle-laden air thus forming a continuous streak which provides time-resolved elemental air concentration data. A customised XRF instrument with a focused but small collimated beam provided data as good as those obtained by PIXE analysis. Irregular dust depositions on 25 mm diameter filters mounted in the widely used IOM inhalable workplace dust sampler can pose difficulties when attempting elemental quantification using pXRFS instruments which, by their design, have intrinsically small X-ray beams. Averaging four filter readings, obtained by manual rotation of filters by quarter turns, yielded74 Pb results that were within −28% and +38% of results obtained previously using a laboratory-based WDXRF system. The latter possessed a wider X-ray beam that could illuminate the whole filter and an automatic sample spinner to average out heterogeneities in dust deposits on filters. Measurement of Pu fall-out particles in soil matrices is of interest to those working in nuclear safeguarding, forensics and remediation activities. In a powerful demonstration75 of advances in analytical capabilities, the elemental composition of two Pu-contaminated soil samples was characterised using both high resolution μXRFS and 3D confocal XRFS. The LOD was <15 pg for samples with a nominal 30 μm grain size. Complimentary morphologic and sizing information was available using X-ray transmission microscopy and micro X-ray tomography.

The solid state speciation of airborne particles provides powerful new information on the composition of individual particles. The analysis of PM10 and PM2.5 by XANES and XRD confirmed55 the presence of Ca3Sr2(AsO4)2.5(PO4)0.5(OH), As2O3 and As2O5 species. An understanding of Cs speciation in dust emissions from either municipal solid waste incineration (MSWI) or sewage sludge incineration (SSI) is important when considering disposal options of waste which may be contaminated with low levels of radionuclides. Analysis by μXAS confirmed76 that Cs speciation in MSWI dust was best described as a potentially soluble CsCl2 species but that in SSI dust it was best described as an insoluble pollucite material, a zeolitic structure with a typical composition of Cs2Al2Si4O12·2H2O. Mercury can be associated with fly ash in emissions from coal-fired power stations. The μXAS analysis of a simulated flue gas showed77 that Hg was associated with Br and Cl, could be bound to Fe oxides and could also occur as a cinnabar (HgS) species. This information would be most useful for those tasked with the safe disposal of Hg-containing fly ash. Nuclear forensics makes use of tools such as XAS but reference spectroscopic signatures for a range of U compounds in the soft X-ray spectral region are required. A new study78 compiled suitable reference spectra into a useful searchable database for a variety of common uranyl-bearing minerals including carbonates, oxyhydroxides, phosphates and silicates.

Interested readers are invited to read our companion XRF ASU5 to learn more about instrumental developments and potential applications.

1.5.4 Other analytical techniques. Commercially available field-based IC-based systems that measure, in near real-time, water soluble airborne ionic species are useful in gaining a better insight that such species play within atmospheric processes. In these systems, particles are sampled, hydrated in a steam generator and the resultant water-soluble ions extracted and analysed using IC. It is also possible to separate gas-phase ionic species from particles that contain ionic species by using denuder technology. There is now a need to compare data generated using these new systems with data generated using more established laboratory-based IC methods to ensure continuity in monitoring data sets. One study,79 conducted at an urban location, compared hourly in situ data with data derived from 24 h filter samples returned and analysed back in the laboratory. Overall, data correlated well for Cl, Mg2+, NH4+, NO3 and SO42− (r2 > 0.83) but less so for Ca2+, Na+, K+ (r2 < 0.5). On average, the in-field approach gave substantially higher concentrations for K+, Na+ and NH4+ than those measured in the laboratory. In a second study,80 conducted at a rural location, online measurement of NH4+ concentrations compared favourably with off-line measurements (r2 > 0.83, mean differences < 6%). The SO42− concentrations determined online correlated well with off-line measurements (r2 > 0.84) but with mean differences of up to 35%. In the case of NO3, the correlation was poor (r2 < 0.1) and the mean difference could be as great as 520%. Performance differences could be attributed to a number of factors including: differences in the particle size selectivity of the respective sampler inlets; collection efficiencies and volatility losses within the steam-jet aerosol collector; instrument saturation effects; sampling artifacts (both positive and negative) in the off-line filter sampling method; and challenges and uncertainties in measuring low airborne concentrations of species such as K+. Nevertheless, such studies are most informative as the air monitoring community slowly transitions from laboratory-based to field-based measurements. Modification of a particle-into-liquid sampler coupled with IC led81 to a dramatic increase in performance. Twin ion exchange preconcentration cartridges (one for cationic and one for anionic species) were inserted so that one sample could be enriched while the preceding one underwent chromatographic separation and analysis. This gave a 10- to 15-fold improvement in LOD and, importantly, a 24-fold increase in live time coverage from 2 to 48 minutes in every hour.

A TD carbon analyser82 used with a cavity ring-down spectroscopy system enabled isotope ratio measurements to be performed on carbonaceous particulate matter. The data were in reasonable agreement with values previously reported in the literature. The precision was <1.0‰. This study demonstrated the potential of the new system as an alternative to the established IRMS measurement approach. Assessing the containment performance of storage wells in carbon capture schemes requires time-resolved measurements taken at locations around potentially large sites together with the use of isotopic CO2 tracers. Use of IRMS is not feasible but the use of a CRDS system equipped with a gas sampling manifold system has been advocated.83 A H2S interference84 which biased 12CO2 measurements high and 13CO2 measurements low was overcome by installing a scrubber packed with Cu filings to remove H2S selectively as samples entered the instrument. A CRDS system, modified for use in flight, was used85 to make NO2 measurements over the eastern seaboard of the USA. Instrumental calibrations were linear up to 150 nM. The LOD was 80 pM. The remarkably consistent airborne concentrations (∼3 × 1015 molecules per cm2) from ground level up to an altitude of 2.5 km indicated that NO2 was widely but uniformly distributed in the air over the eastern USA.

There is growing interest in the use of quantum cascade lasers for gas monitoring applications as these systems can be portable, sensitive and selective and provide rapid analysis. A preconcentration unit, in which electrical cooling rather than the more conventional liquid N2 cooling was used, trapped86 CH4 but not other major components (e.g. N2 and O2) or interferents (e.g. CO2 and N2O). The preconcentration factors of up to 500 resulted in an analytical precision of 0.1‰ for Δ13C and 0.5‰ for ΔD-CH4 based upon a nominal 10 minute instrumental integration. The average differences in results obtained by this new approach and the currently used approach of Dewar sampling and IRMS were within the WMO compatibility goals of 0.2‰ for Δ13C and 5.0‰ for ΔD-CH4. Use of a new pressure-corrected calibration protocol reduced uncertainties in the airborne measurement87 of CH4 and N2O to ±2.47 and ±0.54 ppb (2σ), respectively.

The use of thermal-optical analysis for measuring the carbonaceous content in atmospheric particles is well established. Addition88 of a multi-wavelength capability to an existing instrument, made possible by recent advances in laser diode technology, should provide better optical interrogation of filter samples in the furnace as they undergo combustion and thereby provide improved identification of the source of carbon. A method89 for calculating equivalent black carbon concentrations from elemental carbon data derived from thermo-optical analysis will make it easier to compare data derived from combustion-based and optical-based measurement systems. Determination of the organic carbon content of atmospheric particles, measured using a thermal-optical approach,90 made use of an empirically-derived organic carbon volatility model. Data for this model were obtained from paired samples: quartz fibre filters that collected all organic carbon species; and quartz fibre filters mounted behind Teflon filters that collected volatile organic species but not particulate-bound organic carbon species.

Other instrumental developments and applications included an INAA method91 for measurement of 37 elements in particles trapped in ice core samples. Reduction in background instrumental noise resulted in a 1–3 order of magnitude improvement in LOD, equivalent to absolute LODs in the range 10−13 to 10−6 g. Raman spectroscopy92 was the basis of a new continuous soot monitoring system used to provide the first diesel fume measurements in a controlled environmental chamber. Future work will include the use of multivariate data analytics to interrogate spectral information as well as the optimisation of instrumental hardware to improve sensitivity. The potential of the EBS and PESA techniques for measuring low Z-elements such as C, H, N and O collected on PTFE filters was evaluated.93 Direct measurement of organic or elemental C was not possible but it was suggested that H could be used as a proxy for organic C and that the elemental C fraction could then be calculated as the difference between total C and this organic C fraction. A non-destructive, fast and inexpensive FTIR approach94,95 could be used to predict the levels of organic and elemental C in particulate matter collected on PTFE filters. The FT-IR spectra were calibrated, via PLSR, using OC/EC data obtained from the thermal combustion analysis of particulate matter sampled in a similar way but on quartz filters. Automated particle screening software,96 developed for SIMS analysis, enabled those few U particles with irregular isotopic composition to be identified and to be isolated for further TIMS analysis.

1.5.5 Intercomparisons and data analytics. Laboratory intercomparison exercises can be most useful in assessing the performance of new methodologies and instrumentation. A study97 evaluated how well laboratories performed in dissolving new acid-soluble cellulose-based air sampling capsules designed to sample metals in workplace air. Capsules were spiked, at three loadings, with 33 elements in the range 2–100 μg per sample and triplicates sent to each of eight laboratories. A variety of hotblock, hotplate and microwave-assisted digestion protocols were used to prepare the test samples for analysis by ICP-AES. For 30 of the 33 elements the NIOSH accuracy criterion of results not deviating by >25% from spiked value was achieved. The elements that presented difficulties were Ag (potential for precipitation in chloride-based solutions), In (low instrumental sensitivity) and Sn (passivation in oxidising acids). Data from this study supported the development of the new NIOSH 7306 method. Laboratories employed98 EDXRF (using both external calibration and FP approaches) and PIXE methodologies in a comparative study of the measurement of elemental loadings on PM10 filter samples. The NIST SRM 2783 (air particulate on filter media) was analysed by all laboratories to provide data for comparison. Further data were obtained by digesting representative filters in HF using a microwave procedure for ICP-MS analysis. The data for a range of elements (Br, Cu, Fe, K, Mn, Pb, S, Sr and Ti) were consistently within 20% of each other. Data were also comparable with those obtained by ICP-MS except for those for Fe and Zn. Cross-contamination was a possible explanation for these discrepancies. In summary, the authors concluded that it was possible for laboratories with different instruments, setups and calibration approaches to make comparable measurements on filter samples.

Undertaking instrumental intercomparisons in the field can be both time consuming and logistically challenging! In a comprehensive exercise,99 47 CRDS instruments were tested to assess their performances for measuring atmospheric CH4, CO, CO2 and H2O species. Only 15 instruments were actually tested in the field following an initial screening in the laboratory. As might be expected, newer models performed better than older ones and the overall recommendations included: instrument performance should be verified in the laboratory using a standardised protocol before deployment in the field; instruments should be stabilised for 10 minutes prior to undertaking measurement; in the field calibrations should be performed initially every 2 weeks for the first 6 months and subsequently after every instrument restart. The first ever large scale intercomparison100 of aerosol mass spectrometers, carried out at a field station outside Paris, took 3 weeks to complete. The first week was dedicated to instrumental set-up, tuning and calibration and then comparative studies took place in the second and third weeks. Chemical species (ammonium, chloride, nitrate, organic matter and sulfate) in the non-refractory sub PM1 fraction were measured using 13 different instruments. Taking the median as a reference value, correlations were strong (r2 > 0.9) for all systems across all measurands except chloride for which correlation was poorer. It was suggested that this was due to instrumental sensitivity issues when attempting to measure low atmospheric concentrations. Recommendations included guidance on how best to perform calibrations and standardised protocols for data processing.

In two interesting studies, elemental ratio data have been used to track potential emissions from specific industrial point sources. In the first,101 La/Ce ratios were determined in PM2.5 filter samples taken from the vicinity of petroleum refineries as these elements are characteristic emission tracers from fluidised-bed catalytic cracker (FCC) columns. The use of a high-throughput hot-block digestion for rapid ICP-MS analysis of 64 filter samples was verified (80–90% elemental recoveries) using NIST SRM 1648a (urban particulate matter) and SRM 2783 (air particulate on filter media). Subsequent modelling could not reliably apportion measured PM2.5 to FCC emissions suggesting that the impact of refinery particulate emissions on local air quality was minimal. In the second study,102 Cd/Cu, Cd/Pb, Cr/Pb and Cu/Pb ratios were determined in filters collected from the chimney stacks of six municipal waste incinerators and at locations 10 km downwind. The stack samples, taken on quartz fibre filters, were analysed by ICP-MS following a HNO3–HF microwave-assisted digestion using the EN 14385 method. The ambient air PM10 samples, collected on cellulose filters, were analysed by ICP-MS following a HNO3–H2O2 microwave-assisted digestion using the EN 14902 protocol. Method performance checks used BCR CRM-038 (fly ash from pulverised coal) and NIST SRM 1648A (urban particulate matter). There was no evidence of emissions impacting upon local air quality around four installations and at the other two installations the influence of emissions was minimal.

2 Water analysis

2.1 Sample preparation and storage

Two papers on oceanographic studies compared on-board sample preparation with return of samples to the laboratory for processing. The first103 studied the partitioning of As, Ba, Cd, Cu, Fe, Li, Mg, Mn, Pb, U, V and Zn between the dissolved and particulate fractions in water samples from oceanic hydrothermal vents. When the samples were filtered on board, results for dissolved fraction were higher than if the samples were sent back to the laboratory for processing. As a consequence, results for samples taken back to the laboratory overestimated the particulate fraction for all the elements studied. In the case of Fe, the underestimation of the dissolved fraction was up to 96%. Although the measurement bias for Li, Mg, Mn and U for the dissolved fraction of ≤3% was deemed acceptable, for all other elements in situ filtration was necessary. The authors concluded that filtration after freezing should not be used for deep sea elemental fractionation studies. In the second study104 on the Hg isotopic composition of Arctic seawater, samples were collected and either preconcentrated on board immediately or stored in the dark and preconcentrated in the laboratory. Samples preconcentrated in the laboratory had more positive δ202Hg values than those prepared on board, probably due to abiotic reduction of Hg in the dark by organic matter during storage and shipment. A fractionation factor of 1.49 ± 0.12‰ for δ202Hg was applied to correct for this effect.

The stability and degradation of elemental species under storage has been of interest for a long time. In a study105 of the degradation of butyl tin compounds in surface waters, where isotopically labelled DBT, MBT and TBT samples were stored in glass, polypropylene or PTFE containers, both biodegradation and photolytic degradation were mechanisms for species interconversion. Dealkylation was higher for samples stored in polypropylene bottles than for those stored in glass or PTFE bottles. Storage in amber glass bottles in the dark at −18 °C resulted in little dealkylation after two weeks but after four months 19% of the DBT spike was converted to MBT. No degradation of TBT was observed, however. Pillay and Kindness106 re-confirmed that addition of EDTA to water samples helped to preserve As species in the presence of up to 50 mg L−1 Fe and Mn but not in the presence of the same amount of sulfide. Simulated pore water was spiked with 50 μg L−1 AsIII, AsV, DMAV and MMAV and 100 μg L−1 monothioarsenateV and tetrathioarsenateV. Following addition of EDTA to a final concentration of 0.025 M, the samples were aliquoted into plastic vials and stored at −20 °C. If only Fe and Mn were present, the species were preserved for up to 2 months but in the presence of S2− some of the As species degraded almost immediately.

2.2 Sample preconcentration and extraction

Reflecting the maturity of this field, review articles are written every year on various aspects of sample preconcentration. This year was no different. Deng et al.107 (135 references) provided a comprehensive review on the application of preconcentration and separation techniques in AFS, covering not only preconcentration but also separation techniques such as CVG in various solid and liquid matrices. A review108 (76 references) on the use of biosorbents for SPE of toxic elements in waters covered the use of algae, bacteria, fungi and yeast as new absorbents. Hagarova and Urik109 (60 references) reviewed new approaches to CPE. They focused on either speeding up or improving the selectivity of this popular method for the determination of trace metals.

The most significant developments in analyte preconcentration for water analysis are summarised in Tables 1 and 2.

Table 1 Preconcentration methods using solid phase extraction for the analysis of water
Analytes Matrix Substrate Coating or modifying agent Detector Figures of merit (μg L−1 unless otherwise stated) Method validation Reference
Total As (sum AsIII and AsV), MMA, DMA River water and sediment pore water Acrylamide gel ZnFe2O4 ICP-MS Passive DGT sampler LOD not reported Comparison with HPIC-ICP-MS results 146
As, Cd, Cr, Cu, V, Zn Environmental waters MWCNT Aminopropyl modified silica ICP-AES 0.11 (Cd) to 0.91 (Cu), 6 mL sample IERM GSBZ, 50009-88 and GSBZ 50029-94 (environmental water) 147
Au Water and waste water Graphene oxide Precipitated FeII, FeIII mix FAAS 4 ng L−1, 50 mL sample NRCC CRM CCU-1b (copper concentrate) 148
Bi River and sea water MWCNT L-Proline FI-HG-AAS 0.7 ng L−1, 5 mL sample NIST SRM 1643e (trace elements in water) 149
Cd, Cu, Pb Water, cigarette and fertiliser samples MWCNT Triethylenetetramine FAAS 0.3 (Cd) to 3.7 (Pb), 100 mL sample NWRI CRM TMDA-53.3 (fortified water) and TMDA-64.2 (Lake Ontario water) 150
Cd, Pb Water MWCNT None CS-ETAAS 0.001 (Cd) and 0.03 (Pb), 20 mL sample NIST SRM 1643e (trace elements in water) and ERM-CA011b (hard drinking water) 151
Cd, Co, Cu, Fe, Mn, Ni, Pb Environmental waters Pr(OH)3 co-precipitate None FAAS 0.71 (Co) to 5.18 (Pb), 450 mL sample NWRI CRM TMDA-54.4 (fortified water) and NIST SRM 1570a (spinach leaves) 152
Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn Water Membrane filter 1-(2-Pyridylazo)-2-naphthol and Y EDXRFS 0.3 (Cr) to 2.0 (Pb), 50 mL sample Spike recovery and comparison with ETAAS results 153
Co, Cr, Cu, Ni, Pb, Zn Water Dispersed graphene oxide NPs 2-(5-Bromo-2-pyridylazo)-5-diethylaminophenol EDXRFS 0.07 (Ni) to 0.25 (Cr), 50 mL sample Spike recovery 154
CrVI Sea and lake water Graphene membrane None TXRFS 0.08, 50 mL sample NRCC CRM CASS-4 (seawater) and NWRI EC CRM NWTM-27.2 (lake water) 155
Hg Natural water Silica monolith Au NPs TD-AFS 1.31 ng L−1, 4 mL sample Spike recovery from seawater and analysis by CV-AFS using U.S. EPA method 1631 156
Hg Water Silver NPs None CS-ETAAS 0.005, 10 mL sample Spike recovery 157
I Water Activated carbon disks None WDXRFS 29, 20 mL sample Spike recovery and comparison with ICP-MS 158
Mo, Sb, V Environmental water samples Ultrasound-dispersed Al2O3 NPs Fe3O4 ICP-AES 0.16 ng L−1 (Sb) to 0.18 (V) ng L−1, 50 mL sample HPS CRM CWW-TM-B (certified waste water) and CWW-TM-A (waste water standard) and other, CRMs (source and material unreported) 159


Table 2 Preconcentration methods using liquid phase extraction for the analysis of water
Analytes Matrix Method Reagents Detector Figures of merit (μg L−1 unless otherwise stated) Method validation Reference
Ag NPs Water DLLME 1-Octyl-3-methylimidazolium hexafluorophosphate, methanol ICP-MS 0.01, 9 mL sample Spike recovery 160
Ag, Cd, Co, Cr, Cu, Fe, Ga, In, Mn, Ni, Pb, Zn Water Dual CPE 8-Hydroxyquinoline, Triton™ X-114 ICP-MS 0.012 (Cu) to 0.36 (Ga), 50 mL sample Spike recovery 161
Ag, Cd, Co, Ni, Pb Water and waste water CPE APDC, Triton™ X-114, ethanol FAAS 0.42 (Ag) to 1.42 (Pb), 10 mL sample NIST SRM 1643e (trace elements in water) 162
AsIII, SeIV, TeIV Water DLLME DDTC, bromobenzene, methanol ETV-ICP-MS 0.56 (Te) to 8.6 (Se) ng L−1, 5 mL sample IERM GSB Z50004-88, GBW (E) 080395 and GBW (E) 080548 (environmental water samples) 163
Co, Cu, Ni, Zn Water DLLME DDTC, methanol, 1,2-dibromoethane, DMSO ETAAS 0.89 (Zn) to 1.82 ng L−1 (Cu), 35 mL sample NRCC CRM SLRS-4 (riverine water) 164
CrVI Water DLLME DDTC, 1-undecanol and ethanol LIBS 3.1, 1 mL sample LGC ERM CA011a (hard drinking water) 165


2.3 Speciation and fractionation analysis

2.3.1 Review papers. Most reviews of speciation analysis covered several matrices, including waters, but that110 (77 references) on Tl speciation was specific to water analysis. Recent advances in the separation and quantification of metallic and ionic NPs were reviewed8 (80 references), as was the use of NPs and nanoscale sorbents for the speciation of trace elements in the environment111 (103 references). Mercury is always of interest and two reviews covered sample preparation and quantification112 (90 references) and advances in separation and detection techniques since 2013113 (157 references). Other reviews are included in the soils and plants section of this ASU. For a broader overview of speciation analysis, the reader is referred to our companion ASU114 (215 references).
2.3.2 Elemental speciation. Faster separation was achieved115 for redox As species in river sediment pore waters by operating an HPIC system at a flow rate of 400 μL min−1. Separation occurred within 4 min but an although an additional 4 min was required for effective column reconditioning. The LODs with ICP-MS detection ranged from 0.05 (AsV) to 0.25 (MMAV). The accuracy of the method was checked against the NRCC CRMs SLRS-4 (river water) and SLEW-3 (estuarine water) and the sum of AsIII and AsV concentrations agreed with the certified total As value. This method was considered to be suitable for the analysis of pore waters from “poorly contaminated” sediment samples.

Methods for multi-elemental speciation are quite rare due to the compromise conditions required. A Polish research group developed116 two chromatographic methods for the separation of AsIII, AsV and CrVI in water, using a Hamilton PRP X-100 4.6 × 150 mm normal bore anion-exchange column. Although both methods used isocratic elution at a constant pH of 9.2 and a flow rate of 1.4 mL min−1, one method employed a mobile phase of 22 mM (NH4)2HPO4 and 25 mM NH4NO3 and the other 22 mM (NH4)2HPO4 and 65 mM NH4NO3. The first mobile phase gave higher signal and a shorter analysis time (<3 min for elution of the analytes) whereas the second gave an improved separation resulting from the longer elution time of 6 min. The LODs with ICP-MS detection in reaction mode ranged from 0.090 (AsV) to 0.16 (AsIII) μg L−1 for the first method and 0.062 (AsV) to 0.15 (CrVI) for the second. Method validation involved the spiking of real samples at three concentration levels but, strangely, different concentrations were used to evaluate the two methods, namely 0.5, 3.0 ad 9.0 μg L−1 for the first and 5, 25 and 50 μg L−1 for the second. All recoveries were close to 100%. The same authors117 used the same column and HPLC-ICP-MS instrumentation to separate AsIII, AsV, CrVI, SbIII and SbV within 15 min. A binary elution system of 3 mM Na2EDTA at pH 4.6 and 36 mM NH4NO3 at pH 9.0 at a flow rate of 1.5 mL min−1 and injection volume 100 μL were used. The LODs ranged from 0.038 (SbV) to 0.098 (CrVI) μg L−1 and spike recoveries from 93% (AsV) to 110% (AsIII) for a 0.5 μg L−1 spike in drinking water samples.

The speciation analysis of Cr usually involves determination of the concentration of just one species and calculation of the other as a difference from the total concentration. A non-chromatographic chromium speciation method was developed118 to preconcentrate selectively and thereby separate both CrIII and CrVI. Mesoporous amino-functionalised Fe3O4–SiO2 magnetic NPs were used to extract CrVI from a 45 mL sample at pH 5.0. The remaining CrIII was then extracted as a complex with 4-(2-thiazolylazo)resorcinol using CPE. The CrVI was extracted into 0.5 mL of 2.5 M HCl from the magnetically-recovered NPs whereas the CrIII cloud point phase was diluted with 600 μL of 0.1 M HNO3. The Cr content of both phases was determined by FAAS. The LODs were 3.2 μg L−1 for CrIII and 1.1 μg L−1 for CrVI. Recoveries from spiked tap, mineral and lake water samples were 91–103% for both species at a 45 μg L−1 spike concentration.

Investigations into the presence of Gd contrast agents in the waters around Munster University and in the Ruhr valley continued.119 The sensitivity of a HILIC-ICP-MS procedure reported previously was improved by changing the column to a diol-functionalised HILIC column with USN sample introduction. A binary eluent of 25% 50 mM ammonium formate at pH 3.7 and 75% acetonitrile was used to elute the analytes isocratically at a flow rate of 800 μL min−1. A 5 μL sample loop and ICP-SF-MS detection provided a LOD of 0.6 pM for total Gd, sufficient to detect the contrast agents in various stages of the water treatment process and to show that species transformation products such as ionic Gd were not formed during normal municipal water treatment processes.

An interesting non-chromatographic method120 for the determination of mercury species in water and edible oils involved the use of magnetic Fe3O4 NPs functionalised either with silver and then sodium 2-mercaptoethane-sulfonate to make them specific for Hg2+ or with L-cysteine to make them specific for inorganic mercury and organo-mercury species (i.e. total mercury). The authors used AgNO3 as a modifier in measurements by ETAAS because the large amounts of iodine introduced during sample preparation would otherwise have made Hg more volatile during the ashing cycle. The LOD for Hg with a preconcentration factor of 196 was 0.01 μg L−1. It seems a pity that the authors did not take the opportunity to extract sequentially organo-mercury species from the sample. Although this method was not sufficiently sensitive for the analysis of uncontaminated waters, it was successfully applied to waters from a mining site where, unsurprisingly, all the mercury was present as Hg2+. In a CE-ICP-MS method121 for the determination of MeHg, EtHg and Hg2+ in waters, the sensitivity was improved by up to 100-fold. This was achieved by combining extraction and preconcentration of the analytes from 500 mL samples using dispersive SPE with field-amplified sample stacking injection, in which an amplified electric field applied at the injection point of the capillary column enriched the analytes. Using ICP-MS detection with a microconcentric nebuliser, the LOQs were 0.37, 0.45 and 0.26 pg mL−1 for MeHg, EtHg and Hg2+, respectively. For 2 pg mL−1 spikes of tap water, the recoveries ranged from 92% for EtHg to 108% for MeHg and the RSD (n = 3) ranged from 5–6%. Results for the Chinese CRM GBW08603 (water) agreed well with the certified value for Hg2+.

Methods continue to be published for legacy pollutants such as organolead or organotin compounds even though their use is banned. A rapid HPLC-ICP-MS method122 for the speciation of Pb in water used a column packed with 5 μm C18 bonded-silica stationary phase and sodium 1-pentanesulfonate as an ion pairing agent. This is essentially a procedure first used in the early 1990s and improved through use of modern instrumentation which is more tolerant to organic solvents. All the Pb species were separated in <5 min using a binary gradient programme consisting of 5 mg L−1 sodium 1-pentanesulfonate solution buffered to pH 5 as an ion pairing agent and methanol. The proportion of methanol was increased from 5 to 90% in 1 min at a flow rate of 1.2 mL min−1. Under these conditions, the LOD was 0.01 μg L−1 for Pb2+ and 0.02 μg L−1 for triethyl, trimethyl and triphenyl lead. The calibration was linear over 0.1–10 μg Pb per L for 20 μL sample injections. Spike recoveries from seawaters were 92% (trimethyl lead) to 104% (triphenyl lead). In a rapid HPLC-ID-ICP-MS method123 for quantification of organotin compounds in water and sediment samples, six organotin species were eluted from a high-throughput Zorbax XDB Eclipse C18 bonded-silica in <7 min using a binary gradient programme. Mobile phase A consisted of 0.0625% tropolone, 0.1% triethylamine and 6% glacial acetic acid (v/v) in LC-grade H2O whereas mobile phase B was 100% acetonitrile. The mobile phase composition increased from 45% B to 55% B in 0–5 s following injection. Bond Elut SPE cartridges were used to preconcentrate the analytes in 250 mL water samples and to remove the matrix. In contrast to the experience of other researchers, the authors reported that the mobile phase caused no plasma instability or baseline drift. The method LODs ranged from 1.5 ng L−1 (MBT) to 25.6 ng L−1 (TPhT) but spike recoveries using external calibration were poor (33% for TPhT to 68% for DPhT). Therefore ID was necessary to compensate for these recoveries. This improved the LODs and recoveries to 0.5 ng L−1 and 72%, respectively, for MBT and 1.2 ng L−1 and 114%, respectively, for TBT. This made HPLC-ICP-MS with IDA a viable alternative to GC-ICP-MS.

2.3.3 Characterisation and determination of nanomaterials. The separation of CdSe–ZnS and InP–ZnS quantum dots124 from their dissolved ionic species was achieved using a SEC column packed with a 5 μm particle size stationary phase with 12.5 nm pore size. The mobile phase (1 mL min−1) consisted of a 20 mM citrate buffer to prevent agglomeration of the quantum dots, 5 mM EDTA as a complexing ligand to ensure elution of the ions, 4 mM ammonium lauryl sulfate as a surfactant to reduce particle interactions with the column and 20 mg L−1 formaldehyde as a biocide. The quantum dots and ions were detected by ICP-MS with a linear range from 10 to 200 μg L−1. Recoveries of known quantities injected onto the column were 97% (Cd) and 102% (Zn) for quantum dots and between 87% (Zn) and 108% (Cd) for their ions. These good column recoveries resulted in LODs for the quantum dots of 3.0 (Cd) to 10.0 (Zn) μg L−1. The method was therefore suitable for following the dissolution kinetics of quantum dots in waste waters. These results compared very well with those obtained by centrifuge ultrafiltration of the samples.

The separation of Ag ions from Ag NPs was a hot topic this year. A research group in Taiwan used125 a 3D printer to create a 768 turn knotted-coil reactor capable of separating dissolved Ag+ from the NPs. During method development, municipal waste waters were spiked and the two species separated using xanthan/phosphate-buffered saline as a dispersion medium that also stabilised the two Ag species. The ICP-MS LODs of 0.86 (Ag+) and 0.52 (Ag NPs) ng L−1 were low enough to detect Ag ions and NPs at concentrations expected in samples from waste water treatment plants although, in the samples analysed, the concentrations (n = 5) of Ag NPs (311.9 ± 21.8 ng L−1) and Ag+ (18.8 ± 2.1 ng L−1) were surprisingly high. Samples had to be analysed within 12 h of collection as the proportion of silver present in the ionic form rose from 5.3% at sampling to 66.9% after 48 h due to NP dissolution. The proposal126 to use asymmetric flow FFF-ICP-MS as an alternative to CPE coupled with ICP-MS or ETAAS for separation and detection of Ag NPs and Ag+ might seem strange as CPE was originally used as an alternative to asymmetric flow FFF but has poor extraction efficiencies for hydrophilic NPs such as those with an organic coating. To avoid loss in the FFF system, the Ag+ ions were complexed with penicillamine. With a 5 mL sample loop and using the membrane both to preconcentrate and separate the analytes, the LOD was 4 ng kg−1 for Ag NPs. Although originally developed for biological samples, the method was adopted successfully for the determination of NPs in river waters with varying humic acid contents. An alternative approach127 was the use of hollow fibre FFF together with a minicolumn packed with Amberlite IR120 cation-exchange resin to trap Ag+ in the radial flow. It was possible to separate and quantify Ag NPs with nominal diameters of 1.4, 10, 20, 40 and 60 nm in surface water samples with a LOD of ca. 3 μg L−1. Silver ions were eluted from the minicolumn with 5 mM Na2S2O3 at a flow rate of 1 mL min−1. The LOD was 1.6 μg L−1. It is debatable whether better results could have been obtained if dilute HNO3 had been used as in most applications of this column. Recoveries of 10 μg L−1 spikes from lake water ranged from 108% for Ag+ to 77.9% for 60 nm Ag NPs.

2.4 Instrumental analysis

2.4.1 Atomic absorption spectrometry. The main innovations continued to be the development of new methods that make use of high resolution continuum source AAS. This technique was used128 in a novel approach for determining Cl isotope ratios in mineral waters by monitoring the molecular vibrational transitions at 262.238 nm for Al35Cl and 262.222 nm for Al37Cl. When 10 mg of Al was added as an in-tube reactant and 20 mg of Pd as a modifier before injection of 10 μL of sample, AlCl was formed in situ in the ETAAS furnace. Accuracy was checked using NIST SRM 975a (isotopic standard for chlorine). The precision of 2% RSD (n = 20), obtained for a 200 ng spike of this SRM in water, was insufficient for discriminating natural variations in Cl isotope ratios but suitable for tracer experiments or IDA measurements. The same instrument in FAAS mode was used129 to determine Cd, Cu, Fe, Ni, Mn, Pb and Zn sequentially in 1 + 1 diluted seawater after standard additions. Forty spectra for each element were collected over a 3 s read time and the signal summed over 5 analytical pixels for all the elements except Mn, which had an optimum of 3 pixels. The LODs with an air-acetylene flame ranged from 6.6 (Cu) to 142 (Pb) μg L−1. Spike recoveries from seawater ranged from 94.7% for Fe (0.25 mg L−1 spike) to 107.8% for Pb (0.5 mg L−1 spike). Results for the Spectrapure Standards CRM SPS-WW2 (wastewater) agreed well with the certified values.
2.4.2 Inductively coupled plasma atomic emission spectrometry. The renaissance of the ultrasonic nebuliser continued. One was attached130 to an axial view ICP-AES instrument for the determination of trace levels of HF, Th and U in various matrices including water. The USN gave slightly lower LODs than pneumatic nebulisation with desolvation. Results for the NIST CRM 1640 (trace elements in natural water) were in good agreement with the certified values. An USN improved131 the sensitivity of ICP-AES for the determination of trace elements in surface waters by about an order magnitude compared to pneumatic nebulisation. The LODs of 0.024 (Cd) to 0.05 (Cu) μg L−1 were sufficient for the monitoring of Danube river water.
2.4.3 Inductively coupled plasma mass spectrometry. A review of the determination of Pu in seawater by ICP-MS132 (99 references) covered matrix separation, sample preparation (coprecipitation, valence adjustment, chemical separation) and purification procedures.

The determination of δ11B isotope ratios by MC-ICP-MS was speeded up133 simply by using matrix-matched standards instead of matrix separation in the analysis of seawater and porewaters. The determination of Br isotope ratios was simplified134 by removing the major ions on Dowex® 50WX8 cation-exchange resin and evaporating the resulting solution at 90 °C to preconcentrate the Br without causing fractionation. The δ81Br values measured in the IRMM CRM BCR-403 (seawater) were consistent with those reported in the literature. This approach was also used135 to simplify measurement of Cl isotope ratios in seawater. Operating a MC-ICP-MS instrument at edge-mass resolution (i.e. removing interference peaks by “aiming” the analyte peak at the edge of the detector) allowed136 the direct measurement of 34S/32S in sulfate from environmental samples. The expanded uncertainty U (k = 2) was as low as ±0.3‰ (for a single measurement).

The ultratrace determination of REEs in saline ground waters was achieved137 by combining Fe(OH)3 co-precipitation with an aerosol dilution system. The coprecipitation removed 93% of the matrix and preconcentrated the REEs 15-fold and the aerosol dilution reduced residual matrix effects such as oxide formation by a factor of 10. The LODs using ICP-MS ranged from 0.05 ng L−1 for Lu to 0.6 ng L−1 for Nd. Results for the NRCC CRM NASS-6 (seawater) agreed with values reported in the literature.

2.4.4 Laser induced breakdown spectroscopy. The applicability of LIBS to water analysis is slowly being improved by adapting ideas from other atomic spectroscopy methodologies. In the determination138 of Cu, Fe, Mg, Mn, Na, Pb and Zn spikes in water samples, probing the droplet cloud generated by an USN with the laser improved S/N and gave LODs of 0.00596 (Na) to 21.7 (Pb) mg L−1, sufficient for the measurement of all these elements except Pb in natural waters. A preconcentration method typically used in XRFS was applied139 to LIBS. Drying a sample drop onto a solid substrate improved LODs such that Cu and Mn (but not Cd and Pb) could be determined in the High Purity Standards CRM (trace metals in drinking water) with results in good agreement with the certified values.
2.4.5 Vapour generation techniques. One of the advantages of photochemical vapour generation is that all chemical species have a similar generation yield, as demonstrated by Gao et al.140 who used multivariate optimisation to determine total As in seawater by PVG-ICP-MS. Signal suppression by the matrix was eliminated through use of a mixture of 20% (v/v) formic and 20% acetic acid (v/v) in water as the photochemical reductants. The fact that the vapour generation yields for AsIII, AsV, MMA and DMA were the same meant that a sample prereduction step was unnecessary. The LOD of 3 pg g−1 represented a 15-fold improvement over that obtained using direct solution nebulisation and was comparable to that obtained using conventional HG-ICP-MS. Results for the NRCC CRMs NASS-6 (seawater) and CASS-5 (nearshore seawater) agreed with the certified values. This work was replicated141 in the determination of Sb in water and seawater. In this study, the photochemical reductants (5% formic and 15% acetic acids (v/v)) were used after irradiation of the samples with a deep UV (185 nm) lamp. The LODs were 0.0006 ng g−1 for external calibration and 0.0002 ng g−1 with ID calibration. The recoveries of spikes from the NRCC CRMs NASS-6 (seawater) and CASS-5 (nearshore seawater) were quantitative. Results for NIST SRM 1640a (trace elements in water) and NRCC CRM SLRS-6 (river water) agreed with the certified values. Mercury in high salinity petroleum production water was determined142 by PVG-ICP-AES using a 17 W UV grid lamp with tandem gas liquid separators to reduce the amount of aerosol reaching the plasma. The sample was processed on-line in a continuous flow of 1.63 M formic acid at pH 1.5 with an irradiation time of 30 s to give a LOD of 1.2 μg L−1. Recoveries of spikes from real samples varied from 79 to 121% using standard addition.

Given the effort involved, development of new multi-elemental chemical vapour generation methods is always welcome. In one paper,143 SPE with magnetic NPs functionalised with [1,5-bis(2-pyridyl)-3-sulfophenyl methylene]thiocarbonohydrazide was used together with a CVG-ICP-AES system fitted with a commercial combined cyclonic spray chamber and gas–liquid separator to determine As, Bi, Cd, Co, Cr, Cu, Hg, Mn, Pd, Pt, Sb, Se, Sn and Zn in natural waters. The NPs were loaded onto a microcolumn in a FI system and NaBH4 used as the reductant. The calibration graphs were linear from 0.5–200 μg L−1 and the LODs ranged from 0.01 (As) to 5.11 (Sn) μg L−1 for vapour-forming elements and from 3.16 (Mn) to 11.3 (Zn) μg L−1 for those elements conventionally nebulised, i.e. Co, Cu, Cr, Mn and Zn. The method was validated against CRMs NRCC SLRS-4 (river water), NWRI TMDA 54.4 (fortified lake water), Spectra Pure Standards SW2 Batch 125 (surface water) and NIST SRM 1643e (trace elements in water).

2.4.6 X-ray spectrometry. Rainwater was analysed144 for Co, Cr, Cu, Fe, K, Mn, Ni, Pb, S, Sr, V and Zn by synchrotron radiation TXRFS after collection on an acrylic reflector and addition of Ga as internal standard. The LODs from 0.08 (Ca) to 0.85 (Pb) μg L−1 were achieved with a sample volume of 5 mL and a read time of 200 s.

At the opposite end of the instrument scale, Cd and Pb concentrations in water were determined using a portable EDXRF instrument in the field.145 The elements were preconcentrated from a 1 L sample onto filter paper coated with immobilised TiO2. The linear range was 1.0 to 50 μg L−1 for both elements and the method LODs 0.69 (Cd) and 0.51 (Pb) μg L−1. The method was validated by analysis of the Chinese CRMs GBW(E)080401 (Cd in natural water) and GBW(E)080398 (Pb in natural water) and comparison of the results with those obtained by ICP-MS.

3 Analysis of soils, plants and related materials

3.1 Review papers

Element-specific reviews focused on As speciation in environmental media166 (a book chapter with 127 references); As speciation in environmental, biological and food samples by HPLC-ICP-MS and HPLC-HG-AFS167 (479 references); methods for the measurement of Cs isotopes168 (122 references); Hg speciation in water, sediment and soil112 (90 references); Mn speciation by coupled techniques169 (63 references) and Te in environmental samples170 (72 references). A review171 (73 references) of analytical strategies for the determination of As in rice concluded that approaches currently available to obtain quantitative information on As species are too complicated for routine use in human health risk assessment. The authors of a review172 (146 references) on Se supplementation, bioavailability and determination highlighted the need for improved analytical methods, together with new CRMs, for Se speciation analysis.

Two review articles concerning the analysis of plants were of interest. The first173 (119 references) provided a comprehensive overview of metal and metalloid speciation, covering sampling and sample pre-treatment, direct and indirect analytical techniques, and recent studies on As, Cd, Mn, Ni, Pb, Sb and Se. The second174 (81 references) discussed analytical approaches for the study of metal binding by phytochelatins, a topic of importance for the development of appropriate strategies for the phytoremediation of contaminated land.

A review175 (110 references) on the growth in application of non-destructive spectroscopy with chemometric data processing to environmental samples covered vibrational techniques (both IR and Raman) and XRFS, and included numerous examples of studies involving soil and plant analysis.

Rabajczyk176 (113 references) provided a useful overview of analytical approaches for the measurement of metal NPs in solid environmental samples with particular emphasis on soil and sediments.

3.2 Sample preparation

3.2.1 Sample dissolution and extraction. The more noteworthy examples of the many sample digestion and analyte extraction methods published are discussed in the following section. Further examples are summarised in Table 3.
Table 3 Digestion and extraction methods used in the analysis of soils, plants and related materials
Analyte(s) Sample Type of digestion or extraction Procedural notes Detector Validation Reference
Al, B, Ca, Cu, Fe, K, Mg, Mn, Mo, P, S, Si and Zn Plants Two stage MAE (required for effective solubilisation of Si) 0.1 g sample, stage 1: 5 mL 1 M HNO3 + 5 mL 30% (v/v) H2O2, 28 min, stage 2: 5 mL 1.5 M NaOH, 25 min ICP-AES NIST SRM 1515 (apple leaves), NIST SRM 1573 (tomato leaves), IRMM BCR 679 (white cabbage powder), NCS DC 73349 (bush branches and leaves) 271
Al, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sr, V, Zn Tea, wheat Closed PTFE vessel MAE 0.5 g sample, 2 mL H2O2 + 3 mL HNO3 + 2 mL H2O, 21 min ICP-AES NIST SRM 1567a (wheat flour), NRCCRM GBW 07602 (bush branches and leaves), NRCCRM GBW 08505 (tea), and spike recovery 272
As species Rice Water bath with shaking 1 g sample, water, 60 min, 85 °C HPLC-ICP-MS NRCCRM GBW 10043 (rice flour) and GBW 10045 (rice) 273
As, Ba, Be, Bi, Co, Cr, Cu, Ga, Li, Mo, Ni, Pb, Sn, Sr, V, Tl, U, Zn Soil MAE 0.3 g sample, 10% (v/v) HNO3 + 7% (v/v) HF ICP-MS NRCCRM GBW 07409 (soil) 274
As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Se, Zn Plants UAE 0.5 g sample, 10 mL HNO3 + 3 mL HCl + 3 mL H2O2, 90 min, 40 °C ICP-AES IRMM BCR 062 (olive leaves) 275
As, Cd, Hg, Pb Medicinal plants Closed vessel MAE 0.5 g sample, 4 M HNO3, 1 h ICP-MS (As, Cd, Pb), CVG-ICP-MS (Hg) NIST SRM 1547 (peach leaves), IRMM BCR 062 (olive leaves), and spike recovery 276
As, Cd, Cr, Pb, Se Tea Open vessel on hot plate 1 g sample, 10 mL HNO3, overnight at room T then heated to semi-dryness ETAAS Spike recovery 277
As, Cr, Cu Wood Closed flask in oven HNO3 + H2O2, 24 h, 95 °C ICP-MS NIST SRM 1575a (pine needles) 278
Au, Pd, Pt Sediment, soil Closed PTFE vessel Aqua regia ETAAS (Au), ICP-MS (Pd, Pt) Soil and stream sediment CRMs 279
Ba, Cd, Co, Cu, Fe, Mn, Mo, Ni, Pb, Sb, Se, V and Zn Mushroom MAE ICP-AES NIST SRM 1515 (apple leaves) 280
Ca, Cu, Mg, Mn Oilseed crops UAE 0.3 g sample, 10 mL 1.4 M HNO3, 10 min, 25 °C FAAS Brazilian soybean RM PIATV 2/2020 and 5/2010 281
Cd, Cr, Cu, Pb, Zn Sediment UAE FAAS, ETAAS CRM and spike recovery 282
Co, Cr, Cu, Fe, Mn, Ni, Se, and Zn Soil, vegetable, nuts, grain MAE ICP-AES NIST SRM 7001 (light sandy soil), NIST SRM 1515 (apple leaves) 283
Hg Sediment, soil Water bath 0.5 g sample, 1 mL 40% (v/v) Universol®, 30 min, 60 °C CV-AAS IRMM ERM CC580 (estuarine sediment) 284
V Sediment Open vessel on hotplate 0.25 g sample, 25 mL 0.1 M Na2CO3, 10 min ETAAS NRCC PACS-2 and MESS-3 (sediments) and spike recovery 285


Methods applicable to soil or sediment analysis included a purge-and-trap procedure177 for determination of acid-volatile S in river sediment which involved acidification of the sample with HCl, trapping of the released H2S in 0.1 M NaOH and determination of S in the trapping solution by ICP-AES. An ashing temperature of <500 °C was recommended178 in the determination of Pu in soil by SF-ICP-MS because refractory silicates formed if a higher temperature were used during sample pre-treatment. These could not subsequently be dissolved in HNO3 and led to underestimation of the Pu content. Underestimation of analyte content due to the presence of recalcitrant minerals was also found179 in a comparison of EDXRFS and ICP-MS for the determination of Cr and Zr in soils. Even though hot HF–HClO4 digestion was used in the ICP-MS method, incomplete dissolution of CrFe2O4 and ZrSiO4 probably occurred.

Methods applicable to plants included a procedure180 for Sb speciation that used a silica-based, hydrophilic, strong-anion-exchange cartridge to separate SbIII and SbV in citric acid extracts prior to quantification by ETAAS with PdNO3 matrix modifier. The efficiency of the separation was verified by HPLC-ICP-MS. A rapid method181 for extraction and purification of B in bark, wood and leaf samples for measurement of concentration by ID-ICP-MS and δ11B by MC-ICP-MS involved three stages: MAE of 100 mg sample in 10 mL of 1 M HCl–HNO3 (1 + 2); cation removal using BioRad AG50W-X12 resin and removal of DOC and Si by microsublimation. An extraction protocol182 was notable in that it allowed multiple isotope systems to be studied simultaneously and so allowed the natural variability in B, Cd, Cu, Fe, Pb, Sr, Tl and Zn concentrations and isotope ratios to be measured, by SF-ICP-MS and MC-ICP-MS, respectively, in leaves, needles and mushrooms. The procedure involved high pressure acid digestion (0.5 g sample + 5 mL HNO3) followed by a two-stage chromatographic matrix separation using BioRad AG MP-1M and Eichrom Sr-Spec columns. A set of ‘δ-zero’ standards with concentrations typical of birch leaves was processed to ensure that the procedure did not artificially introduce isotope fractionation.

A closed-vessel microwave-assisted extraction method183 for the determination of B and Si used NH4F as an alternative to hazardous HF. The optimised conditions, obtained using fractional factorial design, involved the extraction of 50 mg samples with 5 mL of 100 g L−1 NH4F at 180 °C for 15 min. The procedure gave good recoveries (96–108% for B and 84–101% for Si) from several CRMs including IRMM BCR 060 (aquatic plant), NCS DC 73350 (poplar leaves), NRCCRM GBW 07602 and 07603 (both bush branches and leaves). A research team in China184 built a novel dynamic MAE system in which soil samples were packed into columns and irradiated in a microwave oven at 375 W whilst 20 mL of 20% (w/w) HNO3 was pumped through. Leachates were collected, centrifuged and diluted prior to analysis by ICP-AES. Results for the analysis of soils NRCCRM GBW 07401 and GBW 08303 showed close to quantitative recoveries for Cu, Mn, Pb and Zn (95–112%), but low recoveries for Co, Cr and Ni (60–79%). Other workers185 took the approach of grinding their samples, slurrying the powder with acid (usually 6 M HNO3 but dependent on sample type) and pumping the slurry at 5 mL min−1 through a specially designed high pressure coiled perfluoroalkoxy tube reactor within a 500 W microwave cavity. Results for ICP-AES analysis of NIST SRM 1515 (apple leaves) were reasonable. Although, both of these flow digestion systems were operated off-line, there is scope for further development leading to on-line analysis.

The procedure186 chosen following a comparison of methods for the MAE of sediment for the determination of Nd and Sr isotope ratios by HR-ICP-MS involved overnight leaching of 50 mg samples in 2 mL HNO3 + 1 mL HCl followed by addition of 1 mL HF prior to microwave heating. The use187 of 1% HNO3 instead of enzymatic extraction with cellulase gave better recoveries for the determination of As in NIES CRM 106 (unpolished rice flour) and NRCCRM GBW 10015 (spinach), better mass balance with respect to total As concentrations and maintained the integrity of the As species for determination by HPLC-ICP-MS.

Species stability during sample extraction is a perennial challenge in speciation analysis. Difficulties encountered in the measurement188 of Tl by HPLC-ICP-MS included the reduction of TlIII to TlI in contact with plant extracts and speciation changes on freezing. A thorough re-evaluation of a MAE method previously used successfully for analysis of NIST SRM 2701 (hexavalent chromium in contaminated soil) by ID-HPLC-ICP-MS was necessary189 when an unexpected and complete reduction of CrVI to CrIII occurred during analysis of contaminated soil from Lombardia, Italy. A milder, single-step digestion (5 min at 80 °C) replaced the earlier two-stage process (5 min at 90 °C followed by 5 min at 110 °C). Numerous parameters190 such as digestion conditions (open vs. closed vessels), digestions reagents, and even the nature of the sample itself were all found to affect the redox speciation of Te during sample preparation, and hence results obtained by HG-AFS. The need for standard analytical protocols was noted.

A useful study191 combined X-ray analysis with a sequential extraction procedure to investigate the binding forms of Cu in eight Japanese soil and sediment RMs. The XANES analysis of the residue remaining at each stage of the BCR sequential extraction revealed that CuSO4 and elemental Cu accounted for much of the analyte isolated in the acid-soluble fraction and not Cu associated with exchange sites on clay minerals and CaCO3, which are the nominal target phases. The Cu in the reducible fraction originated from Fe/Mn(O)OH, and Cu in the oxidisable fraction from organic matter and sulfides, as was expected. A new four-step procedure192 specially adapted for the study of REE mobilisation in soil and mine tailings involved sequential extraction into 0.05 M CaNO3, 0.1 M citric acid, 0.05 M NH2OH·HCl and 1.4 M HNO3. A key advantage was the ability to target soluble REE phosphates separately. These are often abundant, especially in tailings, but would be included with other minerals in the residual fraction by the BCR protocol. A rapid variant193 on the BCR extraction used sonication to reduce the total time required for extraction of Zn from soil samples from 48 h to 27 min. The operational nature of sequential extraction procedures was highlighted in a study194 of P in soil. This showed that manipulation of sample extracts after isolation – even by filtration or dilution – affected results obtained by ICP-AES.

3.2.2 Sample preconcentration. Numerous preconcentration procedures for specific analytes have been reported. 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 Tables 4–6. A notable development was the marked rise in use of switchable polarity solvents in LPME.
Table 4 Preconcentration methods involving coprecipitation used in the analysis of soils, plants and related materials
Analyte(s) Matrix Carrier Detector LOD (μg L−1) CRMs or other validation Reference
Cd, Co, Cu, Fe, Mn, Ni, Pb Spinach, water Praseodymium hydroxide FAAS 0.7–5.2 NIST SRM 1570a (spinach leaves), NWRI TM DA-54.4 (fortified water) 152
Co, Cu, Fe, Mn, Ni, Pb Spinach, water Ytterbium hydroxide FAAS 2.1–8.2 NIST SRM 1570a (spinach leaves), SPS WW2 (waste water) 286


Table 5 Preconcentration methods involving liquid-phase microextraction used in the analysis of soils, plants and related materials
Analyte(s) Sample matrix Method Reagent(s) Detector LOD (μg L−1) CRMs or other validation Reference
a 143/145Nd, 147/149Sm and 151/153Eu. b Inorganic Hg, MeHg, EtHg.
Au, Pd Sediment, ore, water, DLLME N-(6-Morpholin-4-ylpyridine-3-yl)-N′-phenylthiourea FAAS 1.75 for Au, 1.65 for Pd HPS CRM SA-C (sandy soil C) 287
Cd Fruit, vegetables, tobacco, water LLME APDC, switchable polarity solvent FAAS 0.16 NWRI TMDA 51.3 and 53.3 (fortified water), SPS WW2 (waste water), NIST SRM 1573a (tomato leaves), INCT-OBTL-5 (tobacco leaves) 288
Cd Fruit, vegetables, water LLME 1-(2-Pyridylazo)-2-naphthol, “switchable water” ETAAS 0.0004 NRCC SLRS-4 (riverine water), NIST SRM 1515 (apple leaves) 289
Cd, Cu, Ni Sediment CPE 2-(5-Bromo-2-pyridylazo)-5-(diethylamino)-phenol, Triton X-114 ICP-AES 0.066 for Cd, 0.15 for Cu, 0.19 for Ni NIST SRM 1646a (estuarine sediment), NIST SRM 2702 (marine sediment) 290
Cd, Pb Hair, soil, water LLME Deep eutectic solvent modified magnetic NPs FAAS 0.1 for Cd, 0.4 for Pb Spike recovery, comparison with ETAAS 291
Co Cereal, fruit, lentils LLME Diethyldithiocarbamate, supramolecular solvent (1-decanol/THF) FAAS 1.89 NWRI TMDA 53.3 and 64.2 (water), SPS WW2 (waste water), INCT-OBTL-5 (tobacco leaves), NCS ZC73033 (scallions) 292
Cu Hair, fruit, vegetables, spices, water LLME 1-(2-Pyridylazo)-2-naphthol, switchable polarity solvent FAAS 1.8 NWRI TMDA 51.3, 53.3 and 64.2 (water), NIST SRM 1573a (tomato leaves), INCT-OBTL-5 (tobacco leaves), NCS ZC8100 2b (human hair) 293
Eu, Nd, Sma Sediment, soil CPE N,N,N′,N′-Tetraisopropyl diglycolamide, Triton X-114 ICP-MS/MS Isotope ratio measurement NIST SRM 2709a (San Joaquin soil) 294
Hgb Sediment IL-VALLME Dithizone, 1-hexyl-3-methylimidazolium hexafluorophosphate HPLC-CV-AFS 0.037–0.061 μg kg−1 IAEA-405 (estuarine sediment), IRRM ERM CC580 (estuarine sediment) 295
Mo Plants DLLME 8-Hydroxyquinoline, 1-undecanol ICP-AES 0.2 NIST 1568a (rice flour), NIST-8433 (corn bran) and NIST-1515 (apple leaves) 296
Pb Plants, water LLME APDC, dicationic ionic liquid, magnetic NPs FAAS 0.7 Spike recovery 297
Se Beer, eggs, fruit, honey, milk, wine UA-IL-DLLME 1-Phenylthiosemicarbazide, 1-hexyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide ETAAS 0.012 LGC 6010 (water), NIST SRM 1573a (tomato leaves) 298


Table 6 Preconcentration methods involving solid phase extraction used in the analysis of soils, plants and related materials
Analyte(s) Matrix Substrate Substrate coating/modifying agent or analyte complexing agent Detector LOD (μg L−1) CRMs or other validation Reference
a CrIII, then reduction, measurement of Cr, and estimation of CrVI by difference.
Ag Fish, fruit, water Benzophenone Methanol, Eriochrome Cyanine R FAAS 0.8 NCS DC73323 (soil) 299
Ag Soil, water Magnetic halloysite nanotubes 5-(p-Dimethylaminobenzylidene) rhodanine FAAS 1.6 Spike recovery 300
Au Anodic slime, soil, water MWCNTs None FAAS 1.71 Spike recovery 301
Au Anodic slime, soil, water Diaion SP 207 2-Aminobenzothiazole FAAS 3.8 CDN-GS-3D (gold ore) 302
Cd Tea, tobacco, water Y-Zeolite L-Cystine FAAS 0.04 Spike recovery, comparison with ICP-AES 303
Cd, Co, Cu Chicken, honey, water Silica gel 2-(N,N′-Bis(2,3-dihydroxybenzaldimin))aminoethylamine FAAS 0.65 for Cd, 1.42 for Co, 0.52 for Cu INCT CTA-VLT-2 (tobacco leaves), NWRI NWTM-15.2 (water) 304
Cd, Co, Cu, Ni, Pb Black pepper, chilli, hemp, water Magnetic graphene oxide–poly(vinylacetate-co-divinylbenzene) Allylamine FAAS 0.37–2.39 NIST SRM 1573a (tomato leaves), INCT-OBTL-5 (tobacco leaves), SPS WW2 (waste water), and TMDA 64.2 (water) 305
Cd, Cu Soil, water Magnetic zeolite 2-(3,4-Dihydroxyphenyl)-1,3-dithiane FAAS Not reported Spike recovery, comparison with ETAAS 306
Cd, Cu, Ni Mushroom, water Amberlite XAD-4 Thermophilic bacterial strain KG9 FAAS 0.42 for Cd, 0.54 for Cu, 1.24 for Ni NRCC-SLRS-4 (riverine water), NIST SRM 1570a (spinach leaves) 307
Cd, Cu, Ni Fruit, sugar Magnetic NPs 2-Aminobenzothiazole FAAS 0.03 for Cd, 0.009 for Cu, 0.1 for Ni LGC RM 6010 (drinking water), NIST SRM 1515 (apple leaves) 308
Cd, Cu, Ni, Zn Tea, water Nanoporous pumpellyite zeolite 2-Phenyl-4-(1-(2-thienyl)methylidene)-5-oxazolone FAAS 1.1–3.2 Tea leaf CRM 309
Cd, Cu, Ni, Pb, Zn Cabbage, rice, tomato, water MWCNTs 2-(2-Benzothiazolylazo)orcinol FAAS 0.7–2.6 NIST SRM 1570a (spinach leaves) 310
Cd, Ni, Zn Plants, tea, water Polyurethane foam 2-Aminothiazole FAAS 3.0 for Cd, 2.0 for Ni, 3.0 for Zn Spike recovery 311
Cd, Pb Fruit, water Benzophenone Dithizone FAAS 0.2 for Cd, 1.2 for Pb NCS DC73323 (soil) for Cd, Zidarovo-PMZrZ CRM206 BG 326 (polymetallic gold ore) for Pb 312
Cd, Pb Honey, lentils, nuts, potato, rice, tea, water Magnetic NPs Thermophilic bacteria Geobacillus galactosidasius ICP-AES 0.06 for Cd, 0.07 for Pb NWRI NWTM-15 (water), SCP science EU-L-2 (wastewater), NCS DC73350 (poplar leaves), NIST SRM 1643e (simulated fresh water) 313
Cd, Pb Hair, fruit, vegetables, spices, tobacco, water Bovine serum albumin-CuII hybrid nanoflowers None SS-FAAS 0.37 for Cd, 8.8 for Pb NWRI TMDA 53.3 and 70 (water), SPS WW2 (waste water), NCS DC73349 (bush branches and leaves) 314
Cr speciesa Tea, water Cellulose acetate membrane filter Cochineal red A FAAS 1.4 NWRI TMDA 54.4 (lake water) and TM 25.3 (water), NIST SRM 2710 (Montana soil), IRMM BCR 144R (sewage sludge) 315
Cr, Cu, Pb Liver, water Magnetite Shell 1: poly(3-(trimethoxysilyl)propyl methacrylate), shell 2: dithizone grafted onto poly(allyl chloride) ICP-AES 0.029 for Cr, 0.079 for Cu, 0.87 for Pb NRCCRM GBW 080001 (tea leaves) 316
Co, Ni, Pb Fertiliser, water Activated carbon cloth EDTA FAAS 0.99 for Co, 0.91 for Ni, 4.4 for Pb SRS WW2 (waste water), IRMM BCR 146R (sewage sludge amended soil) 317
Cu, Pb Cumin, lentils, lettuce, potato, strawberry Nanosized spongelike Mn3O4 None FAAS 2.6 for Cu, 3.0 for Pb IRMM BCR 482 (lichen), NIST SRM 1573a (tomato leaves) 318
Ni Plants, water Ion imprinted polymer None ICP-AES 0.38 NCS ZC73036 (tea) 319
Ni Plants, soil, tobacco, water Alumina coated magnetite NPs Dimethylglyoxim/SDS FAAS 4.6 Spike recovery 320
Ni Water SiO2/Al2O3/Sb2O5 sol–gel None FAAS 0.48 NRCC MESS-3 (marine sediment) 321
Pb Rice Mercapto-grafted silica polymer None ETAAS 0.94 Spike recovery 322
Pb Plants, serum, water Nanocarbon black particles 3-Mercaptopropyltrimethoxysilane FAAS 1.33 NRCC MESS-3 (marine sediment) 323
Pt Soil, water Fe3O4/graphene/polypyrrole nanocomposite None FAAS 16 NIST SRM 2556 (used auto catalyst pellets), spike recovery 324
U Soil, water Titanium oxide nanotubes CuFe2O4 quantum dots ICP-AES 0.12 Spike recovery 325


3.3 Instrumental analysis

3.3.1 Atomic absorption spectrometry. Response surface methodology was used195 to determine that a solution containing 0.0674 g tartaric acid + 0.1950 g citric acid + 0.0273 g sucrose, diluted to 10 mL with 0.5% (v/v) HNO3, represented the optimal chemical modifier for the determination of Pb in plants and water by ETAAS. Results for NIST SRM 151 (apple leaves) and NIST SRM 1570a (spinach leaves) were similar to certified values. The LOD was 0.14 μg L−1. The characteristic mass was comparable to that obtained with classical Pd–Mg or Pd–NH4NO3 modifiers.

Interest continues in the use of TD-AAS for the rapid determination of Hg in solid samples, with workers in the Czech Republic196 and Brazil197 exploring cheaper alternatives to the use of commercial direct Hg analysers. Both groups designed their own furnace/quartz tube atom cell assemblies and used them with (elderly) atomic spectrometers interfaced to digital converters for signal output in the analysis of local soil samples.

Developments in HG-AAS included the use of gold– or silver–mercury amalgamated cathodes198 for the electrochemical generation of stibine prior to determination by FAAS. Method performance was comparable with the best previously reported, with a LOD of 0.007 μg L−1. The measured Sb concentration in NRCC PACS-2 (marine sediment) was not significantly different from the certified value, based on a student t-test at 95% CI. A combination199 of alkaline extraction in 0.5 M NaOH and HG-ETAAS with Ir permanent modifier was used successfully to measure SeIV in soil, with determination of total Se by complementary techniques and estimation of SeVI by difference.

Research teams in Brazil continued to champion CS-AAS, reporting several analytical methods based on SS-HR-CS-ETAAS. These included procedures for the determination of: Pb in (plant-derived) biomass;200 Cr in infant formulas (verified with NIST SRM 1563a (tomato leaves) and hence applicable to plants);201 Cd, Ni and V in spices (also validated by means of plant CRMs);202 Cu and Hg in phosphate fertilisers;203 Si in plants;204 Mn, Ni, Rb and Sr in catuaba, coffee, ginger, ginseng, guarana and mate;205 and Mo and Ni in plant-derived materials including straw and bagasse.206 A feature of the latter study was the successful use of Co as an internal standard to correct for matrix interference in the determination of Ni.

3.3.2 Atomic emission spectrometry. Developments in radiation sources for the determination of Hg included a novel CV-APGD system207 optimised using Design of Experiments and applied to 5 M HCl extracts of mosses to generate a map of Hg concentrations in the urban area of Wrocklaw, Poland. The LOD was 0.07 μg L−1 and the RSD ≤ 5% (n = 3). Other workers208 coupled GC with a DBD for Hg speciation in rice. Samples were digested in methanolic KOH and then the analytes preconcentrated by headspace SPME on a porous carbon-coated fibre for introduction to the instrument. The LODs were 0.5 μg kg−1 for inorganic Hg, 0.75 μg kg−1 for MeHg, and 1.0 μg kg−1 for EtHg. In the absence of a rice CRM certified for Hg species, the method was validated by spiking experiments (recoveries 90–105%) and comparison with HPLC-ICP-MS data.

Work on tungsten coil AES continues, with a new method reported209 for determination of Mn in sludge, alloy and soil. The LOD was reduced from ca. 0.6 to <0.2 mg L−1 and the accuracy improved by combining the signals for the 403.07, 403.31 and 403.45 nm lines.

A study130 on the determination of HF, Th and U by axially viewed ICP-AES with USN compared results obtained for the analyses of NIST SRM 694 (phosphate rock), NIST SRM 2710 (Montana soil) and NCS DC70319 (Tibet sediment) at several wavelengths following MAE using three different acid mixtures. Optimal digestion conditions and spectral lines for each analyte, in each type of sample, were recommended.

3.3.3 Atomic fluorescence spectrometry. Advances in CVG-AFS included an optimised method210 for direct determination of As in soil. A 5–10 mg test portion was placed in a glass tube in a furnace and 0.2 mL 30% (m/v) sodium formate, 0.05 mL 10% thiourea and 0.05 mL 1[thin space (1/6-em)]:[thin space (1/6-em)]1 HCl were added. The mixture was then pyrolysed at 500 °C and the evolved gases swept into a H2–Ar flame. Results for NRCCRM GBW07453 and GBW07450 (both soils) were almost identical to certified values. The use211 of LiAlH4 as a solid reductant for the CVG of As, Hg and Sb was demonstrated for the first time. Analytes were extracted from solid samples using an ionic liquid. Aliquots (10 μL) of the extracts were manually pipetted onto excess reductant in a reactor, and the resulting AsH3, Hg0 and SbH3 carried by a stream of Ar into a standard commercial AFS instrument for detection. The LODs were 0.2, 0.5 and 0.1 μg L−1 for As, Hg and Sb, respectively.

A HPLC-CV-AFS method for the determination of MeHg in rice 212 was adapted from an earlier procedure applied to water, sediment and marine biota. Modifications included the use of a larger test portion (300 mg), treatment with HCl to reduce the extract viscosity and the introduction of a circular plastic ‘foam breaker’ to the gas–liquid separator. Since no CRM exists for this analysis, the procedure was validated by use of spike recovery tests and comparison with species-specific ID-GC-ICP-MS data.

3.3.4 Inductively coupled plasma mass spectrometry. Tirez et al.213 discussed the fitness-for-purpose of ICP-MS for fulfilling EU environmental monitoring requirements. They identified sample contamination, limited implementation of QC programmes and lack of harmonisation between member states as key challenges that are yet to be fully addressed.

The first report214 of the coupling of a commercial direct mercury analyser to ICP-MS represents a useful advance in the analysis of solids. The sample was combusted, Hg species converted to Hg0, then Hg0 collected on gold-coated sand, as carried out in conventional procedures. However, instead of desorbing the analyte into the analyser's single beam spectrometer for determination by AAS, the Hg0 was flushed into a SF-ICP-MS instrument. Calibration was achieved by introduction of Hg into the analyser. The LOD was 1.2 ng kg−1, based on analysis of a 250 mg sediment sample. Results for NIST SRM 2709 (sediment), NIST SRM 1547 (peach leaves) and NRCC DORM-3 (fish muscle) were 94–108% of target values when external calibration was used, and 98–101% when IDA was used. A new FI-CVG-ICP-MS method215 for the determination of As, Hg and Pb also provided excellent recoveries for analysis of CRMs, NIST SRM 1547 (peach leaves) and SRM 1573a (tomato leaves), when plant samples were introduced into the instrument in the form of a slurry containing 0.5% (m/v) powdered sample, 0.1% (m/v) citric acid and 2% (v/v) HCl.

Advances in quantitative analysis by LA-ICP-MS was the topic of a detailed review216 (174 references) that covered not only environmental but also biological and medical applications. Approaches for calibration were compared critically and the need for more CRMs highlighted – especially CRMs that are homogeneous on a smaller scale than is typically the case with those currently available. Two of the review's authors57 further explored these issues in their study on the use of non-matrix-matched CRMs for quantification of Cd, Cu, Ni and Zn. Using sodium tetraborate binder and silver oxide internal standard, along with collision/reaction cell technology, it was shown that any three of the CRMs NIST SRM 1648a (urban particulate matter), NIST SRM 2709 (San Joaquin soil), IRMM BCR 144 (sewage sludge) and IRMM BCR 723 (road dust) could serve as calibrants for analysis of the fourth, giving results within 5% of certified values. Another study217 – the focus of which was mainly sample preparation – also used CRMs as calibrants, but with GeO2 internal standard. Sediment was slurried with ethanol and finely ground (d50 < 3 μm) in a mill for 40 min, before drying at 40 °C and pressing into pellets. Other workers218 proposed the use of aqueous standards dried onto filter paper as calibrants for the LA-ICP-MS analysis of plants, and use of the 13C in the paper as an internal standard (because of its similar concentration in paper and botanical tissue). More than 80% of results obtained for determination of 11 elements agreed with certified values in NIST SRM 1515 (apple leaves), NIST SRM 1575 (pine needles), IRMM BCR 060 (aquatic plant) and IRMM BCR 062 (olive leaves).

In the measurement of isotope ratios by MC-ICP-MS, Georg and Newman219 discovered a potential limitation in the use of Tl to correct for mass bias in the determination of Hg in sediment using an instrument fitted with high sensitivity cones. The occurrence of isobaric Hg hydrides at the Tl isotope masses could readily be overcome by using standard cones but highlighted the need to check for concentration-dependent hydride formation when carrying out any analyses in which the analyte and internal standard have isotopes overlapping in mass. In an optimised analytical procedure for the determination of Sr isotopes in plants,220 the average 87Sr/86Sr ratio measured in NIST SRM 1515 (apple leaves) was 0.71398 ± 0.00004. A procedure221 for spatially resolved Pu isotope measurement was applied successfully to nuclear fuel particles deposited in soil around the Chernobyl Nuclear Power Plant. The use of D2 in place of H2 in the collision cell of a MC-ICP-MS instrument improved222 measurement of 41K/39K isotope ratios in a variety of samples, including plants. A method223 for isotope ratio measurement of MeHg extracted from sediment avoided the isotopic fractionation that can occur when a transient peak produced by chromatographic separation is introduced directly into a MC-ICP-MS instrument through use of an off-line preconcentration procedure.

Simplifications in sample preparation made possible by the availability of ICP-MS/MS included a single-column chromatography procedure224 for determination of 135Cs concentrations and 135Cs/137Cs isotope ratios in soil and sediment, and a fast method225 for measuring 90Sr, 137Cs, 238Pu, 239Pu and 240Pu in soil digests. In a study226 of the quantitative determination of C, it was suggested that routine measurement of residual C content, along with more conventional analytes, in plant digests by ICP-MS/MS could allow the efficiency of sample digestion to be assessed.

Analytical methods featuring chromatographic separation coupled with ICP-MS included an ionic-liquid-enhanced HPLC-ICP-MS method227 for As and Se speciation applicable to rice; a HPLC-VG-ICP-MS method228 for HgII and MeHg in plants in which incorporation of a VG unit built in-house enhanced sensitivity relative to HPLC-ICP-MS by at least an order of magnitude; and a ID-HPLC-ICP-MS method123 that could be used to separate MBT, DBT, TBT, MPhT, DPhT and TPhT isolated from sediment extracts in 6.5 min, yielding LOD values in the range 0.5–1.2 ng L−1. The latter method was proposed as an alternative to the more commonly used GC-ICP-MS approach, with the advantage that there is no need for analyte derivatisation prior to separation. A procedure229 for measurement of six polybrominated diphenyl ethers in sewage sludge by GC-ICP-MS was validated by spike addition (recoveries 95–104%) and comparison with ID-GC-ICP-MS data.

Continuing interest in the environmental behaviour and fate of NPs has prompted the development of new methods. The concentrations of Ag NPs adsorbed into the structure of Arabidopsis thaliana plants were determined230 by SP-ICP-MS following an enzymatic digestion with Macerozyme R-10. The concentrations of Cu NPs in colloidal extracts of soil were also determined231 by SP-ICP-MS. Another study232 used FFF-SF-ICP-MS and stable isotope labelling to distinguish 57Fe@SiO2 engineered NPs (iron oxide NPs isotopically enriched in 57Fe and coated with a SiO2 shell) spiked into a resuspended river sediment slurry from the natural colloidal Fe background on the basis of their distinct isotopic fingerprints.

3.3.5 Accelerator mass spectrometry. Recent technological developments at AMS facilities should enhance capacity for analysis of soils, plants and related material. For example, the 1 MV AMS system at the Centro Nacional de Aceleradores, Seville, Spain233 can now be used to measure 236U routinely at environmental levels. Improvements at the Micro Analysis Laboratory Tandem Accelerator, University of Tokyo, Japan234 have been made for the measurement of 129I (much of it arising in the aftermath of the Fukushima Daiichi Nuclear Power Plant accident) and 236U.

There have also been improvements in sample preparation for AMS. Satou et al.235 reported an improved approach that chemically separated Sr from the sample matrix then used a mixed (1[thin space (1/6-em)]:[thin space (1/6-em)]4) SrF2 + PbF2 target to produce intense beam currents for the measurement of 90Sr in soil. Liu et al.236 described a method for 129I measurement that required minimal sample preparation and so could be very useful in emergency situations in which large numbers of samples need to be analysed rapidly for contamination assessment. Stable 127I carrier was added to Nb matrix powder, mixed with dried seaweed or sediment powder, and finally pressed directly into a target holder.

3.3.6 Thermal ionisation mass spectrometry. Methods for isotope ratio measurement have been reported for various elements. Application237 of a method for the extraction of Pu and U from soils to samples from around the Fukushima Daiichi Nuclear Power Plant provided evidence that nuclear fuel had been released to the atmosphere during the accident. In another study,238 Fe3O4@SiO2 NPs were embedded in porous poly(ethersulfone) resin functionalised with ammonium and phosphate for measurement of Pu in soil by a single-bead TIMS method. The careful measurement of the recovery at each stage of a procedure239 for extraction of B from plants showed that the heavier isotope 11B was more enriched in leaves and flowers than in other tissues. A new method240 for extracting Cs from soil and plants with use of a novel ammonium molybdophosphate–polyacrylonitrile column gave the highest precision (2–4% at 2σ) 135Cs/137Cs ratios reported to date by TIMS for soil samples containing Cs at the fg level.
3.3.7 Laser induced breakdown spectroscopy. A review article provided an excellent overview241 (145 references) of sample treatment and preparation, including approaches for solid, liquid and biological specimens. The authors considered lack of attention to the development of rigorous sample preparation protocols one of the reasons that LIBS is not yet consider a mature technique. Another review242 (298 references) discussed the determination of trace elements in environmental samples, including soils, ores and water, whilst a third243 (75 references), in Chinese with English abstract, described achievements and research trends in the application of LIBS to soil.

Developments in the application of LIBS to soils included a hemispherical plasma confinement device244 that increased the intensity of lines for Cd, Cu, Ni, Pb and Zn two to three-fold, thereby giving LOD values of <10 mg kg−1. Signal enhancement was also achieved245 for several analytes by carefully optimising the delay time between the two laser pulses in double pulse LIBS. Spatially resolved measurements246 revealed a difference between the spectral intensity distributions of major elements (K, Na) and those of trace elements (Cu, Pb) in plasma derived from soil NRCCRM GBW 070008. This difference was attributed to greater self-absorption occurring for the major elements. Analysis could be improved by judicious selection of the signal-collecting zone in the plasma. Background removal using a wavelet transform algorithm markedly improved247 the accuracy for measurement of Pb, reducing the RMSEP from >303 to 26 mg kg−1.

Amongst the numerous LIBS methods published were single-element procedures for the determination of Be,248 Cr,249 Mn250 and Pb251 in soil and of Cr252 and Pb251 in plants. A multi-analyte method253 for the determination of Al, Ca, K, Mg, Mn and Na in tea was reported. Microsampling strategies for LIBS analysis of dried sugar cane leaves were evaluated254 and a standard protocol recommended for the determination of B, Ca, Cu, Fe, K, Mg, Mn, P, Si and Zn. This involved rastering three equally spaced sampling lines perpendicular to the leaf midrib, with 48 accumulated laser pulses per line. Results obtained for Ca, Fe, K, Mn, P and Si correlated well with EDXRF data.

In common with XRFS, LIBS is being increasingly applied not only to trace element measurements but also to the determination of bulk soil properties by proxy analysis. A PLS model255 for prediction of soil pH was constructed based on selected spectral lines of 50 soil samples (the calibration set) and subsequently used to predict the pH of 10 additional samples (the validation set). Results were within 0.4 pH units of those obtained by conventional methods for all but one sample. Use of similar modelling approaches256 allowed the estimation of the relative proportions of sand, silt and clay in the same 60 soil samples. The potential of PLS-DA and SVM algorithms257 for use in classification of different types of rocks and soil was demonstrated in a study of Chinese CRMs.

3.3.8 X-ray spectrometry. Review articles included a comprehensive overview258 (108 references) of the application of TXRFS in food analysis – including the analysis of fruit, vegetables and cereals – and a well-written, critical and accessible introduction259 (84 references) to the use of synchrotron radiation in plant research.

Optimisation of calibration strategies for XRFS was addressed in several articles. One group of researchers260 established that spiking solid matrices – NIST SRM 1571 (orchard leaves), 1633b (coal fly ash) and 4357 (ocean sediment) – with multi-element standard solutions allowed estimation of not only the added elements but also others by interpolation. In contrast, another team261 found that addition of liquid standards was unsuccessful for the determination of S in soil and plants, at least at the relatively high concentrations likely to be encountered following a chemical spill. It was recommended that solid CaSO4 be added directly to a well-characterised natural soil for soil analysis and to cellulose powder for plant analysis.

In the detection and characterisation of Pu in soil particles by multiple complementary X-ray techniques, conventional μEDXRFS highlighted75 the location of the analyte and possible inter-element associations, high-resolution XRF images were obtained of Pu hotspots, and 3D confocal μEDXRFS was used to confirm whether the Pu was part of a surface feature or incorporated within the soil matrix.

Advances in the analysis of plants included the first simultaneous microchemical mapping using μEDXRFS262 of As and P in ferns grown hydroponically in As-enriched nutrient solution, and a modelling study263 of factors contributing to the XRF background spectrum in samples composed principally of light elements. A detailed evaluation264 of limitations associated with the determination of F in solid materials by WDXRFS used rice contaminated with F as a test sample. A flow chart was presented of analytical strategies to improve sensitivity and accuracy in different situations.

Wider adoption of pXRFS for elemental analysis of soil or plants has continued although, unfortunately, not all new users appear aware of previous developments in the field, resulting in a degree of ‘reinvention of the wheel’ (i.e. repetition of well-established knowledge in newly published articles). Amongst the more useful contributions was a study265 of the effect of moisture on results obtained by pXRFS for Ba, Ca, Cr, Cu, Fe, Mn, Pb, Rb, Sn, Sr and Zn in a set of 215 soil samples, which showed that the decrease in signal with increasing water content could be modelled using the Beer–Lambert Law. Other workers266 used pXRFS to measure Al, Ca, Fe, K, Mn, Si, Ti and Zr concentrations down the walls of trial pits in Australia and then calculated geochemical index values that shed light on pedogenic pathways occurring at the site. A critical comparison267 of the performance of two pXRF instruments (a XOS prototype and a Thermo Niton XL3t) concluded that neither instrument was able to determine PTEs at concentrations < 15 mg kg−1 with good accuracy in tea and ethnic herbal medicine products. Highly recommended for those new to the field, was an accessible introduction268 to the measurement of plant nutrients by pXRFS.

As with LIBS, the use of XRFS together with other non-destructive types of spectrometry for proxy analysis is increasing. For example, in the analysis of 700 samples from across Sub-Saharan Africa, diffuse reflectance FT mid-IR spectroscopy and TXRFS were successfully used269 to predict soil parameters related to nutrient binding capacity such as pH, organic matter content and some exchangeable bases. Another study270 combined Vis-NIR DRS and pXRF data from 116 arid soils in Spain to predict salinity, gypsum content, Ca content and CaCO3 equivalent.

4 Analysis of geological materials

4.1 Reference materials and data quality

With the rapid increase in isotopic analytical facilities worldwide, various approaches have been taken to satisfy the current demand for well-characterised isotopic RMs. One popular strategy has been to take RMs originally prepared for elemental analysis and assess their suitability as matrix-matched isotope RMs. Examples included the analysis of 24 geological RMs for Mg isotopes,326 three Chinese igneous rock RMs for Fe and HF isotopes327 and a variety of RMs including 20 igneous and sedimentary rocks for Mo isotope ratios.328 Fourny et al.329 provided HF–Nd–Pb–Sr isotope data on 11 rock RMs covering a wide range of geological compositions, while another study330 published long term QC data for the HF–Nd–Pb–Sr isotopic composition of USGS RM BCR-2 (Columbia River Basalt). Unfortunately, BCR-2 was not included in the compilation by Fourny et al.,329 although both papers noted a need to leach basaltic RMs prior to dissolution to obtain reproducible Pb isotope ratios. In these examples, care was taken to control the accuracy and precision of the analytical data as far as possible and the resulting data were compared with other published values where available. While all these efforts are highly commendable, significant progress in developing well-characterised RMs for the geological isotope community will only be achieved when reliable consensus values are available, rather than just another set of data that generally agrees with previous published values. This goal will require much more rigorous metrological examination of data acquired in a planned exercise to avoid inter-laboratory biases. As an interim measure, Jochum et al.331 calculated new reference values and their uncertainties at the 95% confidence level for 19 of the most popular geological RMs accessed in the GeoReM database. They took the most reliable published data sets available and followed ISO guidelines as closely as possible. These new reference values, for a wide range of major, minor and trace elements, will be known as the GeoReM preferred values.

A paucity of suitable matrix-matched calibration standards for the microanalysis of manganese nodules prompted Jochum et al.332 to prepare a synthetic RM suitable for the analysis of material from a wide range of different Mn–Fe deposits. This new RM, FeMnOx-1, was examined with three LA systems (200 nm fs, and 193 nm and 213 nm ns lasers) using different spot sizes and fluence. It was homogeneous in the nm to μm range and therefore well suited to microanalytical applications. Repeated measurements of test portion masses of 5–100 ng had an RSD of 2–3%, comparable to those of reference glasses such as NIST SRM 610. Seven laboratories using five different bulk and microanalytical techniques were involved in the characterisation of this RM. A pressed powder pellet and fused bead produced from ultrafine chalcopyrite and pyrite RM powders333 (China National Research Centre for Analysis) were homogeneous enough to act as in-house standards for the measurement of Os isotope ratios in sulfides by LA-MC-ICP-MS. The pyrite fused bead showed a greater degree of phase separation than that prepared from chalcopyrite, implying that the Fe–S system may be more suitable than the Cu–Fe–S system for making sulfide fused beads.

With the increased sensitivity of modern analytical instrumentation, the mass of test portion required for quantification has decreased. However, accurate determinations can be compromised by microheterogeneities within a sample or RM. This is often referred to as the ‘nugget effect’, especially when precious metals such as Au, Ir, Pd, Pt and Ru are the target elements. In a novel approach, Bedard et al.334 estimated the minimum mass required for a representative sample by mapping the distribution of minerals containing precious metals in relevant RMs, mainly sulfides, by μ-XRFS. The importance of treating RMs and unknowns in exactly the same manner was emphasised.

Several new mineral standards have been characterised and are available on request. A new titanite standard,335 MKED1, from Queensland, Australia, with relatively high concentrations of REEs, Th and U, was shown to be largely free of inclusions and have a high degree of elemental and isotopic homogeneity, including very low levels of common Pb. Comprehensive bulk sample and in situ microanalysis demonstrated its suitability as an RM for U–Pb dating and the Nd–Sm isotope composition of titanites. Over 1400 EPMA and 700 ion probe measurements336 confirmed homogeneous distributions of Li and its isotopes in 11 ultramafic mineral separates of olivine, orthopyroxene and clinopyroxene from Cenozoic basalts in northern China. These separates were deemed to be suitable for use as RMs for calibration and measurement by SIMS and LA-MC-ICP-MS. A gem garnet,337 GA1 from Sri Lanka, characterised for major and trace elements using LA-ICP-MS with a spot size of 35 μm, was homogeneous for all the elements determined except V. Reference values for 40 elements were reported.

Fission track dating of apatites by direct measurement of U concentrations using LA-ICP-MS depends on the availability of homogeneous RMs for accurate calibration. Until recently, no suitable matrix-matched RMs were available. However, Soares et al.338 identified two large natural apatite crystals as potential RMs for this purpose: a 1 cm3 fraction of a Durango crystal (7.5 μg g−1 U) and a 1 cm3 Mud Tank crystal (6.9 μg g−1 U). Major element compositions were determined by EPMA, and a combination of ID-ICP-MS and LA-ICP-MS was used to confirm the homogenous distribution of U in the samples. The overall uncertainties on the mean U concentrations of ≤1.5% RSD were relatively small compared to the overall precision of the LA-ICP-MS measurements of ca. 4%. These results represented an important step in establishing in situ dating routines for fission track analysis by LA-ICP-MS and these materials will be shared with the fission-track community.

4.2 Solid sample introduction

4.2.1 Laser ablation inductively coupled plasma mass spectrometry. Current thinking on elemental fractionation and matrix effects in laser-based sampling techniques and ways to minimise them were the subject of a tutorial review339 (277 references). A series of experiments340 using six geological RMs helped to distinguish the influence of the plasma on elemental fractionation from processes occurring at the ablation site and in the mass spectrometer. The thermal state of the plasma was estimated from the 38Ar+/40Ar2+ ratio. Element-specific behaviour dominated under cool conditions but vanished under hot plasma conditions. The more robust operating conditions obtained by tuning the ICP for hot plasma conditions improved matrix tolerance, sample decomposition and the degree of ionisation, while also reducing the formation of polyatomic ions. Guidance was given for the rigorous control of low sample and auxiliary gas flows while at the same time maintaining efficient extraction and focusing of the mass spectrometer.

Currently there is much interest in the use of LA-ICP-MS for quantitative elemental imaging of solid samples as it offers several advantages over other techniques. For example, LA-ICP-MS has better LODs than electron beam techniques, it is considerably cheaper than SIMS to operate, and, because the LA is performed at atmospheric pressure, it can handle samples with high moisture content in contrast with other techniques where samples are placed in a high vacuum. However, there are many factors to consider when optimising a LA-ICP-MS system for this purpose, as exemplified in experiments341 performed to optimise LA-ICP-MS mapping of trace element concentrations in igneous minerals. Maps of clinopyroxene and amphibole macrocrysts were produced using a 193 nm excimer laser system with a two-volume ablation cell coupled to a quadrupole ICP-MS instrument. Evenly ablated lines were generated from overlapping square laser beam spots of either 12 or 24 μm edge length using a square-shaped laser aperture. The final ablated area was either square or rectangular in shape, with side lengths of 200 μm to 2 mm, to facilitate production of trace element maps using different data reduction approaches. Because of the excellent reproducibility of the laser stage movement and the limited depth of ablation (<1 μm), it was possible to re-ablate the same area many times using different instrument parameters or element menus. Spatial variations in samples that appeared to be petrographically homogeneous were resolved at a smaller scale than the beam diameter, e.g. 7–10 μm discontinuities using a 12 μm laser beam. A method for quantitative imaging of elements in ferromanganese nodules342 by LA-ICP-MS was developed using a 213 nm Nd:YAG laser system with Mg as an internal standard. To overcome any artefacts from elemental fractionation, several matrix-matched calibration standards were prepared from GSJ RM JMn1 diluted with high-purity MnO2 powder. An area of 5 × 20 mm was analysed as a series of lines at a scan speed of 100 μm s−1 and a total of 5000 peak intensities were obtained for each element. The validity of the imaged data was confirmed by comparison with elemental concentrations obtained by ICP-MS after dissolution of representative chips from the nodules. A 2D plotting system for displaying the images was established in which the colours corresponding to elemental concentration ranges could be easily changed to provide better contrast. These applications demonstrate that LA-ICP-MS imaging is likely to provide new information and insights about many geological processes in the future.

When performing line scans, the crucial parameters are the laser repetition rate and scan speed, both of which will influence the quality of the image of the resulting maps. Bonta et al.343 devised a novel method of evaluating the image quality of chemical maps based on squares with edges between 200 and 400 μm printed on paper. Copper in the blue ink was mapped by LA-ICP-MS and the elemental distributions compared to optical images taken before ablation. This approach allowed quantitative determination of the image quality under various measurement conditions and was used for method optimisation to obtain a reasonable compromise between image quality and acquisition time.

Although many of the frontier advances in LA-ICP-MS mapping are currently in its application to the imaging of biological materials, some of the recent developments in cell design, aerosol transport and data acquisition reviewed by Van Malderen et al.344 (165 references) are relevant to geochemical imaging. An example is a novel two-volume LA cell and integrated ICP torch designed to minimise aerosol dispersion for fast efficient sample transport.345 Its design incorporated a direct concentric injector consisting of a short, single-diameter fused-silica conduit from the point of ablation, through the ICP torch into the base of the plasma. When NIST SRM glass 612 was ablated with a 5 μm spot at a fluence of 22.1 J cm−2, resolved single-shot peak widths of 1.4, 2.9 and 4.3 ms were achieved at 50%, 10% and 1% maximum full width, respectively. The absolute sensitivity was improved 8- to 14-fold compared to that possible with a single-volume ablation cell. This design is subject to a patent and has recently been adopted by a major instrument manufacturer. Another authoritative review216 (175 references) considered recent advances in quantitative analysis by LA-ICP-MS. Although this article was directed at the life sciences and environmental research, many of the concepts discussed are equally applicable to the analysis of geological materials, not least the concluding remarks about the need for sufficiently homogeneous RMs to enable results to be compared.

Although low dispersion cells are expected to have impact in many fields, it was noted344 that their adoption may be hampered by the absence of affordable, fast, sensitive, simultaneous mass spectrometer systems. However, the high speed and quasi-simultaneous detection capabilities of time of flight mass spectrometry (TOF-MS) are well suited to the measurement of short transient LA signals. Gundlach-Graham et al.346 demonstrated the performance characteristics of a low dispersion LA cell coupled to a prototype ICP-TOF-MS instrument for 2D imaging of geological materials. An excimer LA system operating at 193 nm was used to ablate a pyrite-rich region of an Opalinus clay sample and a polished section of a named meteorite. A laser frequency of 20 Hz was chosen to prevent pulse-to-pulse mixing and minimise image acquisition times. As well as an improved ability to separate HF laser-generated signals, fast-washout LA-ICP-TOF-MS provided better sensitivity because all the ions were contained in a shorter time window, allowing imaging of major and minor elements down to a spot size of 1.5 μm. The lowest LODs were single digit ppm for a single-shot LA signal from a 10 μm diameter spot. A 3 × 1.5 mm, 45[thin space (1/6-em)]600 pixel, multi-elemental image of the meteorite was acquired in 50 min, compared to over 12 h using a conventional LA-ICP-MS system operating with an ablation cell washout of 1 s and ablation rate of 1 Hz. In a further development,347 the potential of this technology for rapid, high resolution, quantitative 3D multi-elemental imaging was demonstrated. Quantification of elements ablated from each individual laser pulse was carried out by 100% mass normalisation. Ablating heterogeneous samples, such as the Opalinus clay, resulted in different ablation yields depending on the target phase. This created a complex surface morphology and posed a problem for adequate 3D representation of the data. Nevertheless, this study demonstrated the potential of this technique for acquiring 3D multi-element images with high spatial resolution more rapidly than previously reported. The analysis of fluid inclusions is another application that is likely to benefit from the development of LA-ICP-TOF-MS, with its advantages of rapid, quasi-simultaneous acquisition for all isotopes from 6Li to 238U in a very short cycle time down to 30 ms. This exciting prospect was confirmed in an authoritative comparison348 of the capabilities SF-ICP-MS and ICP-TOF-MS with those of quadrupole ICP-MS for the analysis of fluid inclusions by LA. Although SF-ICP-MS provided improved LODs over quadrupole ICP-MS, its longer acquisition times limited the number of measurable elements and the precision attained. When ICP-TOF-MS was coupled to a fast washout cell, marked improvements in the figures of merit for the analysis of small (<10 μm) and multiphase fluid inclusions were observed, making it possible to discriminate signals from different phases (liquids and solids) as well as detect a larger number of isotopes.

It is acknowledged that the use of femtosecond LA minimises elemental fractionation compared to ablation with ns lasers because of the shorter interaction with the sample, decreased thermal effects and the production of very small aerosol particles. In a study349 to examine whether fractionation occurs during fs LA, several different sulfide minerals were ablated with three different LA systems (213 nm ns solid state, 193 nm ns excimer and 200 nm fs) coupled to quadrupole or SF-ICP-MS instruments. No melting of any of the sulfides was observed with the fs laser, in contrast to the use of ns LA which produced large amounts of melt at both laser wavelengths. In spite of these different melting characteristics, no downhole fractionation was observed for any of the LA systems even at the highest melt production. Sulfur proved to be an appropriate internal standard for ns LA-ICP-MS of sulfides, as long as the instrument was tuned for low oxide production. However, Fe was the recommended internal standard for the 200 ns fs LA system because of variations in S sensitivity when ablating different minerals with this system. Although matrix effects using fs LA were shown to be negligible, the case was made for more sulfide RMs because these are still required for the best measurement accuracy and precision.

Femtosecond LA has been used for analysis of a variety of isotope systems. Lazarov and Horn350 demonstrated that by using a low fluence it was possible to measure Cu isotope ratios in native copper and Cu-bearing sulfides, carbonate and oxides by fs LA (194 nm) MC-ICP-MS with a precision of better than 0.1‰ (2SD) without using a matrix-matched standard. A new analytical protocol351 for determining stable Cl isotopes in halite and igneous rocks employed a fs laser operating at 266 nm coupled to MC-ICP-MS. Chlorine was extracted from igneous rocks by pyrohydrolysis and then precipitated as AgCl from which pellets were prepared. Argon isotopes (36Ar/38Ar) were used to correct for mass fractionation. Complex theoretical overlap calculations were performed to correct for isobaric interferences from ArH+, K+ and S+ on Ar and Cl isotopes. The external δ37Cl reproducibilities of ±0.18‰ (2SD) for halite and ±0.05‰ (2SD) for the AgCl precipitates compared favourably with the precision obtainable by gas source IRMS. The use of fs LA was important in this study because ablation with a ns 193 nm excimer laser partially melted the AgCl pellets, releasing Cl in the process. A fs laser was also chosen to minimise any fractionation in the determination352 of Mg isotopes in basalt glasses by LA-MC-ICP-MS. The results revealed that the Mg isotopic ratios were affected by changes in the mass discrimination caused by different mass loadings in the plasma. The amount of ablated aerosol was greatly influenced by LA parameters such as spot size, laser frequency and scanning speed. To obtain reliable Mg isotope data it was important that the difference in Mg concentrations between the sample and standard was no more than a factor of 3. The measurement precision of δ26Mg was better than ±0.17‰ (2SD).

Several studies of the use of fs LA for the bulk analysis of fused rock powders have been reported. Kon and Hirata353 determined ten major and 34 trace elements in 34 GSJ geological RMs by fs LA ICP-MS. The rock powders were prepared as fused glass beads using a lithium tetraborate flux and 6Li was monitored as an internal standard. Precisions were better than 5% for most elements. Bao et al.354 fused powdered-rock RMs without a flux in a small (450 mm3) sealed boron nitride crucible at 1400 °C for 1 min before rapid immersion in liquid N2; the resulting glasses were mounted in resin and polished to obtain an even surface before analysis. Loss of volatile elements was negligible due to the rapid and hermetic nature of the melting process. Data were generally within 15% of the preferred values for the geological RMs. Kimura et al.355 determined Pb isotopes by fs LA-MC-ICP-MS using multiple Faraday cups equipped with state-of-the-art high gain 1013 Ω amplifiers. Because of the slow response of these amplifiers, a correction based on a linear correlation between the rate of change of the signal intensity and that of the Pb isotope ratios was applied. This improved the intermediate precisions and repeatability of the measurements, which were approximately 2–3 times better than those obtained using MICs or FCs with 1012 Ω amplifiers. The increased sensitivity of the system with 1013 Ω amplifiers resulted in analytical performance comparable with that of SIMS (2–0.5%, 2SE) from the same sample mass. In spite of claims to the contrary, it is difficult to envisage the rapid adoption of bulk analysis by fs LA given the cost of such systems and it could be considered inappropriate use of such facilities.

A primary application of laser ablation split-stream (LASS) ICP-MS is zircon and monazite petrochronology. In this technique the laser aerosol is split between two ICP-MS instruments to obtain U–Pb ages (or other geochronometers) and trace element compositions simultaneously from very small sample volumes. Single shot LASS-ICP-MS depth profiling356 was used to sample thin metamorphic zircon overgrowths at a spatial resolution of <1 μm per analysis. Ages of RMs measured by this technique were accurate to within 1.5% of published values. The analysis only took 3–6 min per grain allowing the collection of the large datasets required to resolve short-duration (<106 years) zircon growth events with confidence. Thus the high spatial and temporal resolution of single shot LASS shows great promise as a tool to decipher petrochronological events on this timescale. The LASS depth profiling technique was also used to obtain a continuous rim-to-core record357 of U–Pb ages and trace element composition preserved within variably recrystallised zircon from different rock types within a well-studied granulite domain in Canada. Distinct homogeneous domains and heterogeneous intervening zones could be correlated with textural features observed by CL. The response of monazite and its host rocks during the subduction of continental crust to mantle depths was studied358 using U–Pb isotope ratios and elemental abundances measured simultaneously by LASS ICP-MS in rocks from the Scandinavian Caledonides. All the samples were analysed in thin section so that the U–Pb dates and trace element data could be tied to metamorphic textures and parageneses. A tectonic history of the region was inferred from the analysis of 69 samples.

Several different strategies have been deployed to avoid or mitigate isobaric interference from 87Rb and other spectral interferences during the measurement of Sr isotope ratios by LA-ICP-MS. Bolea-Fernandez et al.359 explored the capabilities of an ICP-MS/MS instrument for obtaining direct isotopic information from solid samples with high (>0.2) Rb/Sr ratios. This instrument has an additional quadrupole located before the octopole collision-reaction cell, providing greater control over the ions entering the cell. A mixture of CH3F–He (1 + 9) in the collision-reaction cell converted Sr+ ions to the corresponding SrF+ ions, whereas Rb+ ions did not react. Instrumental mass bias was corrected by a combination of internal correction using the Russell law, followed by external correction in a SSB approach. Reference glass NIST SRM 610 was used as the external standard for the seven silicate rock RMs analysed. No closer matrix-matching or additional correction was required. Under wet plasma conditions, precisions of 0.02–0.05% RSD for 87Sr/86Sr ratios were obtained irrespective of the matrix composition or Rb/Sr ratio of the materials examined. Zack and Hogmalm360 also employed LA-ICP-MS/MS for on-line separation of Rb and Sr but used O2 as the reaction gas. The precision for 87Sr/86Sr was <0.5% for a single spot. The procedure was applied to in situ dating of micas and feldspars. Systematic investigations361 of factors affecting the accuracy of Sr isotope determinations in apatite by LA-MC-ICP-MS found no evidence of significant polyatomic interferences from Ca argides or dimers or any significant interference on m/z 87 from 40Ca31P16O+. However, fractionation of elemental Rb/Sr during ablation of apatite with a 193 nm excimer laser was shown to be significant (ca. 15%) and the accuracy of the 87Rb correction was called into question. In the revised procedure, aliquots of concentrated Ca–P solutions with varying 85Rb/86Sr ratios were used to improve the accuracy of the 87Rb correction and matrix-matched phosphate glasses were incorporated to correct for Rb/Sr fractionation.

Other novel approaches to the analysis of geological materials by LA-ICP-MS included an unusual method362 for the determination of trace amounts of Os. Samples were digested using aqua regia in Carius tubes and the Os converted to OsO4, which was distilled and trapped in 2 mL of HBr solution to convert the Os to a non-volatile Br species. The purified Os was dissolved in 10 μL of a 0.02% sucrose–0.005% H3PO4 solution and evaporated on a small piece of PFA film, resulting in the formation of a tiny residue. When analysed by LA-ICP-MS, the residue provided Os signals at least 100 times greater than those from solution ICP-MS while successfully avoiding any memory effect. The procedural blank was 3.0 pg and the LOD 1.8 pg Os when 1 g of sample was processed by this method; the precision was 0.6 to 9.4% RSD (n = 5) depending on the RM analysed.

Instrumental developments in ICP-MS are covered in our sister ASU3 on advances in atomic spectrometry and related techniques. It will be interesting to see whether zoom TOF-MS363 and distance-of-flight mass spectrometry364 techniques, which are in their early stages of development, will advance sufficiently to impact on LA measurements of geological materials in future.

4.2.2 Laser induced breakdown spectroscopy and related techniques. An exciting new development is Laser Ablation Molecular Isotopic Spectrometry (LAMIS) which utilises optical emission from laser-induced plasmas for isotopic analysis. An excellent tutorial review365 (49 references) is invaluable for those unfamiliar with the technique. The advantages of fs ablation over ns ablation in LAMIS, such as significantly stronger molecular emissions with lower backgrounds and fewer matrix effects because of the lower energies of fs laser pulses, result in improved precision and accuracy. In spite of this, the majority of published LAMIS studies used ns lasers operating at a wavelength of 1064 nm. Both LIBS and LAMIS probe the optical spectra produced in ablation plumes, so potentially it should be possible to build a portable device incorporating both techniques for direct measurements in the field. So far, studies of LAMIS have been limited to a few elements and isotopes, i.e. B, C, Cl, H, N, O, Sr and Zr, of which B and C have been examined in the greatest detail. The current sensitivity and precision of LAMIS requires further improvement for most practical applications and several possibilities for such improvements were discussed.

Several recent applications of LIBS to geological materials involved the assessment of hydrocarbon source rocks. In a multi-elemental surface mapping of carbonaceous shale rocks366 by LIBS, a drill core was sliced in half vertically, one half polished for LIBS scanning and the other divided into small sections that were reduced to a powder for analysis by XRFS and ICP-AES to validate the measurements. Local thermal equilibrium conditions were verified for the induced plasma for a point-by-point line profile. Matrix effects were negligible. Concentration maps for Al, Ca, Fe, K, Mg, Na and Si were constructed with a 2D spatial resolution of 300 × 300 μm2. A new approach367 to measuring kerogen H[thin space (1/6-em)]:[thin space (1/6-em)]C elemental ratios in shales and mudrocks by LIBS provided a rapid means of assessing kerogen quality and thermal maturity with minimal sample processing. Predicted kerogen H[thin space (1/6-em)]:[thin space (1/6-em)]C ratio from the LIBS measurements of whole rock samples were well correlated (r2 = 0.99) with values determined from the elemental analysis of kerogen isolates. A study368 of different methods of processing data acquired from LIBS measurements of sedimentary rocks concluded that better accuracy and precision were obtained using algorithms based on support vector regression compared to PLSR. A selection of sandstones, limestones and mudstones were analysed for Al, Ca, Fe, Mg and Si. Mapping369 the 2D distribution of Li and other light elements (Z < 10) in pegmatite minerals by LIBS was carried out using a grid of laser spots of 125 μm diameter, spaced at 200 μm intervals, on the surface of a core sample cut with a diamond saw blade without any additional preparation. The Li maps effectively discriminated between the ore mineral spodumene (LiAlSi2O6), its alteration mineralogy and matrix silicate minerals. However, full quantification of the results using Li-doped borosilicate glasses as standards was limited by self-absorption effects that were evident when the Li2O content was >2 wt% and especially at values > 6 wt%. This demonstrated that LIBS could be a useful complementary mapping technique to others, such as EDXRFS, because of its ability to measure the light elements effectively. A pilot study370 showed that LIBS has potential in the provenancing of gem stones. Nearly 570 ruby and sapphire specimens from 21 localities in 11 countries were analysed for the main database. The method utilised PLSR to build a series of models to compare spectra. Each specimen was analysed at 30 spots and averaged to create one spectrum per sample. Several Al peaks were removed from the spectra to allow multivariate analysis to focus on trace element distributions. The overall success rate of identifying the correct deposit of origin for unknown samples was >95%.

Many of the developments in LIBS over the last few years have been driven by the requirements of space research. A comprehensive review371 (107 references) of the scientific achievements of ChemCam on Mars highlighted the lessons learned from the first use of LIBS in space. New tools were developed for data processing and element detection to take account of the unique nature of the Mars data. ChemCam was shown to be a very versatile instrument and its ability to survey quickly the geochemistry of several targets to facilitate rapid understanding of an environment was a great boon for efficient use of mission time. The experience gained on this mission will be incorporated in the instrumentation that is scheduled to fly on board the NASA Mars 2020 rover. A prototype multispectral instrument372 based on Raman, laser-induced fluorescence, LIBS and a lidar system has already been designed and tested by NASA scientists in relation to missions to the icy moons of Mars and Jupiter. Its ability to identify water, water-ice, dry ice and samples of hydrous minerals at distances from 1 to 50 m were demonstrated. It could also characterise chemicals and minerals up to 15 m away and conduct atmospheric aerosol and cloud profiling from up to 10 km distance. The ultimate aim was to use this instrument to detect chemical signatures of life through biogenic molecules (biomarkers) and small bio-organic precursors of life.

A related innovation is a miniature LA ionisation TOF mass spectrometer developed at the University of Bern for quantitative measurements of the elemental and mineralogical composition of planetary surfaces. Its capability for quantitative measurements of geological materials was assessed373 using four rock RMs (andesite, shale, clay and quartz latite) to determine the RSFs for the instrument. These factors were close to 1 for all elements determined and their consistency between matrices provided a sound basis for in situ standardless measurements. Another study374 investigated the instrument's ability to determine stable isotope abundances and produce chemical maps of rock samples at the μm scale. Test samples consisted of μm-size filaments of fossilised micro-organisms embedded in aragonite veins from a harburgite. Results indicated that accurate chemical mapping of heterogeneous rock samples could be achieved but that the isotope ratios were not sufficiently accurate for biomarker identification. Improvements to the S/N were required.

4.3 Sample dissolution, separation and preconcentration

All geoanalysts need to be mindful of potential sources of contamination from equipment used to produce rock powders of a suitable size fraction for dissolution. In a recent study375 of primary and cross contamination, quartz crystals were crushed using conventional steel alloy and tungsten carbide jaw crushers, followed by milling in agate, tungsten carbide or chromium steel ring planetary ring mills. Semi-quantitative ICP-MS data for 75 elements confirmed the accepted wisdom that for many applications a combination of crushing with a steel jaw crusher, followed by milling in agate afforded the least contamination.

The literature is littered with investigations into suitable methods of effecting a complete dissolution of geological materials prior to analysis. In a recent example,186 four different acid attacks were assessed for the dissolution of geological RMs prior to the determination of Nd and Sr isotope ratios by MC-ICP-MS and trace elements by HR-ICP-MS. A method based on microwave-assisted digestion using a mixture of HCl, HNO3 and HF followed by evaporation of excess HF provided the best figures of merit. It should be noted, however, that the assessment of accuracy was based on recoveries from two Chinese stream sediments and USGS RM BCR-2 (basalt) but no values for Zr were reported. Basaltic rocks are relatively easy to dissolve and the Zr contents, which are often associated with the presence of resistant minerals, can be a good indicator of the effectiveness of dissolution. The digestion of bauxite, with Al2O3 as its major component, is particularly prone to the formation of insoluble AlF3 precipitates when a mixed HF–HNO3 attack is used. For this reason, Zhang et al.376 investigated four alternative approaches and recommended open vessel methods based on NH4HF2 or NH4F for multi-elemental analysis of bauxite samples by ICP-MS.

In spite of the wealth of information available on appropriate digestion methods, there was evidence from the GeoPT proficiency testing scheme377 that a significant number of geochemical laboratories using acid attack dissolutions reported low results for elements such as Y, Yb (representing the HREEs) and Zr. This was not observed in results obtained using a fusion or sintering approach, XRFS or INAA. A detailed evaluation of the acid attack procedures used by laboratories indicated that quantitative recoveries could be obtained using 2 mL HF per 100 g of test material, heated to 180 °C for 48 hour in a pressure bomb. It was likely that the less rigorous conditions of acid attack used by many laboratories on a routine basis resulted in an incomplete dissolution of resistant minerals such as zircon. It was therefore incumbent on laboratories to identify test materials that fall outside the scope of validation of their dissolution procedure and take appropriate action.

Several methods for the production of glasses for LA by flux-free fusion techniques have been advocated. An iridium-strip heater was used to melt rock powders directly.378 The temperature was controlled manually by monitoring the current; the samples melted within 30 s and were then air cooled. The homogeneity of fused glasses prepared from 11 USGS and GSJ RMs was <5% RSD (1σ, n = 3) for most elements. However, in common with previous studies employing this fusion technique, Pb was lost on heating, Cr was less homogeneously distributed than the other elements, particularly when present at >150 μg g−1, and HF, Lu and Ta were occasionally enriched by contamination. Retention of volatile elements and reduced contamination were achieved379 by melting rock powders without a flux in a molybdenum capsule sealed in a graphite tube in a temperature-controlled furnace. After water cooling, the quenched glasses were mounted in epoxy resin and polished for microscopic examination and LA-ICP-MS analysis. Major and trace element data for a series of USGS RMs containing 47 to 73% SiO2 (m/m) were mostly within 5–10% of recommended values with a precision generally better than 5–10%. Any heterogeneity of Cr and Ni caused by the fusion process was smaller than the analytical uncertainties of LA-ICP-MS. This approach was similar to that of Bao et al.354 in which 500 mg of silicate rock powders was fused in a small boron nitride vessel before measurement of 34 trace elements and Pb isotope ratios by fs LA-MC-ICP-MS (see Section 4.2.2). The sealed melting process ensured that Pb volatilisation was negligible so that in situ determinations of Pb isotope ratios for geological RMs with a range of compositions were in good agreement (within 2s) with published data or values obtained by solution MC-ICP-MS.

Aqua regia is often preferred as the digestion medium in the determination of Ag and Au in geological samples even though low recoveries of Au have been reported by several researchers. An experiment380 to monitor the effect of varying the ratios (v/v) from the usual HCl[thin space (1/6-em)]:[thin space (1/6-em)]HNO3 ratio of 3[thin space (1/6-em)]:[thin space (1/6-em)]1 to 1[thin space (1/6-em)]:[thin space (1/6-em)]3 (reverse aqua regia) revealed that the recoveries of Ag and Au exhibited inverse trends. Complete recovery of Au with excellent reproducibility was achieved with a HCl[thin space (1/6-em)]:[thin space (1/6-em)]HNO3 ratio of 1[thin space (1/6-em)]:[thin space (1/6-em)]3. The low recoveries for samples with high Ag contents were improved by the addition of a small volume of concentrated HCl to the cooled digests to resolubilise the AgCl precipitate. Based on these observations, a rapid (10–15 min) method using reverse aqua regia digestion with IR heating was proposed for determinations by ICP-MS. The strong memory effect of Au was overcome by preparing standards and samples in a solution of L-cysteine in 1% (v/v) HCl.

Over the years, a wide range of absorbants and ion-exchangers have been proposed for the separation and preconcentration of Au. Xue et al.381 synthesised a modified polyurethane foam incorporating a cytosine ligand to extract Au from aqua regia digests of geological samples prior to measurement by ICP-AES. They reported a LOD of 6 ng L−1 (3σ, n = 6) with an enrichment factor of 480. Other procedures for SPE of Au from environmental samples involved the use of MWCNTs301 or extraction of its 2-aminobenzothiazole complex302 on Diaion SP-207 resin, a brominated styrene–divinylbenzene polymer, prior to FAAS detection. Preconcentration factors and LODs were 150 and 1.71 μg L−1, respectively, for the MWCNT method and 250 and 3.8 μg L−1, respectively, using Diaion SP-207 resin.

4.4 Instrumental analysis

4.4.1 Atomic absorption and atomic emission spectrometry. For determinations of trace amounts of Cd in complex matrices, ETAAS offers several advantages over ICP-MS including relatively few spectral interferences, good LODs and low cost. In a procedure382 for the determination of Cd in geological materials, samples were digested in HF–HNO3 in a sealed bomb and Pd(NO3)2 solution added as a matrix modifier to increase the thermal stability of Cd and allow higher pyrolysis temperatures. A LOQ of 0.75 ng g−1 and characteristic mass of 0.8 ± 0.1 pg for Cd demonstrated sufficient sensitivity for the analysis of soil, sediment and rock samples for which the Cd content was 0.02–0.5 μg g−1. Method accuracy was assessed by the determination of Cd in 51 geological RMs. To improve throughput, Cui et al.383 developed a method using slurry sample introduction for trace Cd determinations by ETAAS. Solid samples were ground to a particle size of 62 μm and a slurry prepared in 0.5% (v/v) HNO3 containing 0.6% (v/v) Triton X-100. After the addition of Pd(NO3)2 as a chemical modifier, the slurry was homogenised in an ultrasonic bath before delivery to a graphite tube for analysis.

A novel adsorbant384 composed of cellulose fibre, activated carbon and Dowex 1X8 anion-exchange resin was devised for the separation and preconcentration of Au, Pd and Pt prior to determination by GF-AAS. Geological CRMs were digested in aqua regia and transferred to a column containing a homogenised mixture of 5.0 g Dowex 1X8 resin, 3.5 g activated carbon and 100 mL of cellulose fibre pulp. The precious metals were eluted with 0.25 M HCl and the resulting solution digested in a microwave oven to liberate the analytes and reduce the sample volume to 5 mL. The LODs were 0.008 ng mL−1 (Au), 0.017 ng mL−1 (Pd) and 0.014 ng mL−1 (Pt) and maximum throughput was 30 samples every 8 h.

Two new optical methods were assessed385 for the detection of Au NPs in soils and vegetation in the vicinity of gold deposits. The first was based on localised SPR, in which electrons on the surface of the metal are excited by photons, resulting in a peak in the absorption spectrum of a sample. The second property explored was the ability of Au NPs to catalyse the conversion of the non-fluorescent compound I-BODIPY to its fluorescent derivative H-BODIPY. For both methods, the LOQ was dependent on the size of the NPs, i.e. 71 ppb for 5 nm and 24.5 ppb for 50 nm NPs using the absorption method, and 74 ppb for 5 nm and 1200 ppb for 50 nm NPs with the fluorescence procedure. These LOQs demonstrated a potential for deployment in the field, although the size of the target NPs would need to be taken into account as fluorescence would be the better option for the analysis of small NPs whereas the absorption measurements would give lower LOQs for larger NPs.

4.4.2 Inductively coupled plasma mass spectrometry. The relatively recent introduction of an ICP-MS/MS instrument onto the market has spawned research papers on its application in many fields. The main feature of this instrument (often referred to – incorrectly – as a triple quadrupole system) is the insertion of an additional quadrupole before the collision-reaction cell. This acts as a mass filter allowing ions with only one m/z ratio to enter the cell, providing better control over reactions taking place in the cell and thus offering new approaches to avoiding isobaric overlaps. An excellent tutorial review13 (55 references) describing the operating principles and procedures available for advanced method development is essential reading for those new to this technique. Whitty-Léveillé et al.386 explored the potential of this instrument to determine Sc (monoisotopic at m/z 45) at low concentrations in silicate minerals. Discrimination from Si-based ions was achieved using O2 as the reaction gas and monitoring ScO+ ions at m/z 61. The LOD of 3 ng L−1 in solution was sufficient to provide accurate results in the low mg kg−1 Sc range for a variety of RMs. Fernández et al.387 advocated ICP-MS/MS for the determination of very low B[thin space (1/6-em)]:[thin space (1/6-em)]Ca ratios in biogenic carbonates. By introducing O2 into the collision-reaction cell, P, S and Ti were converted to their corresponding oxides while 11B and 46Ca, which did not react with O2, were monitored as elemental ions. In addition, some of the carbon present reacted with the O2 enabling resolution of the large 12C peak from the 11B signal. The ability to make interference-free measurements of 46Ca+, which is only 0.004% abundant, meant that 11B and 46Ca could both be measured in counting mode, thereby improving the ratio precision. As noted in Section 4.2.1, ICP-MS/MS coupled to LA has been exploited for the determination of Sr isotope ratios.359,360 Bolea-Fernandez et al.388 demonstrated an external precision of 0.05% when determining 87Sr/86Sr in digested rock RMs using a similar methodology, i.e. a mixture of CH3F–He (1 + 9) as the reaction gas and monitoring the SrF+ reaction products. Corrections for mass discrimination using a combination of Russell's law and SSB with NIST SRM 987 (SrCO3) were applied. Because matrix elements were removed before they entered the reaction cell, matrix-matched standards were not required to correct for mass bias. It is likely that many more relevant applications will be published in the near future when the benefits of ICP-MS/MS are realised.

Additional data for established and relatively new geological RMs have emerged from several studies. The performances389 of three ICP-MS instruments (two SF and one quadrupole system) were assessed through the trace element characterisation of six USGS RMs. The agreement between data from this study and previously published values was best for the RMs of mafic to intermediate compositions, i.e. BHVO-2 (basalt), BCR-2 (basalt) and AGV-2 (andesite) for which there is a large volume of comparative data. Published data were relatively sparse for the felsic RMs, RGM-2 (rhyolite) and G-3 (granite), and completely unavailable for STM-2 (syenite), making the data from this study valuable for a more complete characterisation of these RMs. A method390 for the determination of trace amounts of Cd in geological samples used argon aerosol dilution ICP-MS to overcome polyatomic interferences from Mo and Zr oxides and hydroxides. In this technique, the sample aerosol from the spray chamber was diluted with argon before it reached the plasma, resulting in less water in the plasma and reduced oxide formation. Over 90% of the sample Zr content was eliminated during extraction of Cd by boiling with inverse aqua regia. Aerosol dilution reduced the residual interfering oxides and hydroxides by up to 90% compared to conventional ICP-MS without argon addition. The LOD for 111Cd was 1.6 ng g−1 and the proposed method was applied to the determination of Cd in 81 soil, sediment and rock RMs. Boron concentrations in nine geochemical RMs were measured391 on three different ICP-MS instruments after extraction by a modified pyrohydrolysis technique. Blanks for the whole procedure were 14 ± 5 ng B and precisions were <10% for samples containing >2 μg g−1 B. A procedure392 for the determination of chalcophile and siderophile elements in crustal rocks using SF-ICP-MS with standard addition reported new values for Bi, Cd, Ga, Ge, In, Mo, Sb, Sn, Tl and W abundances in six USGS RMs (AGV-2, BHVO-2, BIR-1, G-2, GSP-1 and W-2). Poor precisions for Cd and Mo concentrations in GSP-1 were ascribed to powder heterogeneity at the test portion size caused by a sulfide nugget effect whereas the RSDs of >10% for Mo and W in several RMs were attributed to high analytical blanks.

A summary of newly published methods for the determination of isotope ratios by MC-ICP-MS is given in Table 7. Many of these involve new or modified separation schemes, including the incorporation of a low pressure, fully automated fluoropolymer chromatography system (prepFAST™) capable of purifying Ca and Sr for isotope analysis.393 The equipment was able to process over 200 samples on the same column at a rate of over 30 samples per day for a wide range of sample matrices (rocks, bone ash, seawater). Automated chromatographic methods based on this system are under development for a range of other isotope systems and it will be interesting to see whether it has an impact on future applications.

Table 7 Methods used in the determination of isotope ratios in geological materials by ICP-MS and TIMS
Analyte Matrix Sample treatment Technique Analysis and figures of merit Ref.
Ba Geological materials Decomposition in HNO3–HF, take up in HCl. Two-column purification with AG50W-X12 cation-exchange resin MC-ICP-MS SSB using NIST SRM3104a (Ba(NO3)2 solution). Long-term precision for δ137Ba/134Ba better than ±0.05‰ (2SD); new data for 8 USGS and GSJ geological RMs 434
Ca Basalt and bone Purification using a low pressure, fully automated fluoropolymer chromatography system (prepFAST MC) with a proprietary Sr–Ca column designed for wide range of sample types MC-ICP-MS Ca isotopes measured in medium resolution mode, with SSB using in-house Ca solution and conversion to SRM 915a scale. Typical precision δ44Ca/42Ca 0.06‰ (2σ) 393
Cd FeMn nodule Dissolution in 6 M HCl, addition of Cd double spike before anion-exchange chromatography with Eichrom TRU resin. Liquid–liquid extraction with n-heptane used to remove organic resin residues eluted with Cd MC-ICP-MS Changes in instrumental mass bias calculated from 111Cd–114Cd double spike. Typical procedural blanks contained <20 pg Cd. Repeat measurements of Cd isotope RM BAM-I012 gave ε114Cd/111Cd of −13.2 ± 0.7 (2SD, n = 15), cf. literature value of −13.3 ± 0.4 435
Cr Lunar basalts Dissolution in HNO3–HF–HCl, addition of 50Cr–54Cr double spike and separation from matrix via a two column chemistry utilising AG50X8 resin MC-ICP-MS Medium resolution mode, Cr double spike used to correct for mass bias. Accuracy confirmed with NIST SRM 979 (hydrated chromium nitrate) 436
Cr Meteorites For silicate samples, preparation was same as in ref. 437. For Fe-rich samples, an extra column containing AG1X8 resin was employed to remove Fe from sample before continuing as for silicates MC-TIMS Mass-independent and mass-dependent Cr isotope compositions reported. Two methodologies used to correct for mass fractionation because assumptions made for terrestrial samples do not hold for extra-terrestrial samples 437
Cu, Zn Geological materials Digestion in HF–HNO3–HClO4. Simplified purification procedure using BioRad AG MP-1 resin reduced handling time, amount of HCl used and eliminated need for treatment with H2O2 MC-ICP-MS Procedure blanks of 2 ng Cu and 23 ng Zn, with yield of nearly 100%. Potential polyatomic interferences from ArN2+ and ArNO+ studied, use of desolvating nebuliser recommended to enhance ionisation of Zn 438
Cu Geological RMs Dissolution in HNO3–HF and take up in HCl. Separation on anion-exchange resin BioRad AG MP-1M. Purified Cu fraction spiked with Ga prior to analysis. Blank 2 ng Cu MC-ICP-MS Mass bias correction using combined SSB and Ga internal standard resulting in a 5-fold improvement in precision of δ65Cu compared with SSB alone 439
Fe Geological RMs Digestion in HF–HNO3–HCl–HClO4 and purified using a 2 column procedure with anion-exchange resin AG1X8. Blanks <10 ng Fe HR-MC-ICP-MS Medium to high resolution mode. SSB with in-house high purity Fe solution; data reported relative to IRMM-014. Long-term reproducibility and accuracy of <0.03‰ (2s) for 56Fe/54Fe. Data reported for 22 geological RMs 440
Fe Ultramafic minerals Digestion in HF–HNO3, take up with HCl and separation on AG1X4 column with quantitative recoveries. Addition of 57Fe–58Fe double spike, dried down and redissolved in HNO3. Blanks <10 ng MC-ICP-MS Mass bias corrections using Fe double spike. 60Ni monitored and used to correct for 58Ni interference on 58Fe. Ion counter rather than Faraday detector for 60Ni signal improved δ56Fe reproducibility from ±0.145‰ to ±0.052‰ 441
HF Zircon, baddeleyite Dissolution of ng quantities of material but no separation from matrix elements. Method can be used for minerals as small as ca. 24 ng MC-ICP-MS Study of isobaric interferences on 176HF/177HF and methods proposed to account for them. SSB approach with matrix-matched standards. LOQ < 2 ng g−1 HF 442
HF Terrestrial rocks and meteorites Basalt samples digested in HF–HNO3, chondrites in HF–HNO3–HClO4 (HF–HNO3 only for some types of chondrite) and iron meteorites in aqua regia. Two-stage chemistry using Eichrom™ Ln resin to separate HF from matrix elements and then AG 1-X8 anion-exchange resin to minimise interference from 174Yb. Blanks were <30 pg HF MC-ICP-MS For isotope ratios involving low abundance isotopes 174HF, 180W and 190Pt, uncertainties of <100 ppm were typically achieved, by use of Faraday amplifiers with 1012 Ω resistors and improved separation procedures 443
HF Rocks and mineral grains Modified procedure suitable for Lu–HF, Sm–Nd and Rb–Sr geochronology. Mixed spikes of 176Lu–180HF, (also 149Sm–150Nd and 87Rb–84Sr if required) added before digestion with HF–HNO3 and take up in HCl. To improve the purification of HF, HFSEs were separated from REEs and then purified on Ln Spec column. Sm–Nd and Rb–Sr separations could be added easily. Blank <10 pg HF MC-ICP-MS Instrument equipped with three FCs fitted with 1012 Ω resistors for improved precision and an X-skimmer and jet sample cone to enhance sensitivity. 176HF/177HF measurements on geological RMs gave precisions of 5–20 ppm for solutions containing 40 ppb HF and 50–180 ppm for 1 ppb solutions 444
K Igneous rocks Digestion with HNO3–HF and take-up in HCl. Separation from matrix elements in two-column procedure using BioRad AG50W-X12 and then BioRad AG50W-X* resin to achieve >99% recovery with blanks 3–8 ng K MC-ICP-MS Single focusing instrument with hexapole collision cell used to suppress ArH+ isobaric interferences. Better precision K isotope ratios achieved using D2 rather than H2 as the reaction gas. External reproducibility of better than ±0.21‰ (2SD) for 41K/39K 222
Li Geological RMs Digestion with HNO3–HF, final solution in 0.67 M HNO3/30% methanol (v/v). Separation on AG50W-X8 cation-exchange resin achieved full recovery MC-ICP-MS 5% NaCl rinse solution used to reduce Li instrumental background and memory effect. With NaCl washing, no significant mass bias observed when measuring Li isotope ratios making it unnecessary for strict matching of Li concentrations in samples and standards. External precision ±0.25‰ (2SD) for δ7Li 445
Li Carbonates and clays Fine tuning of different digestion and column separation procedures for carbonates and clay matrices. Separation on BioRad AG 50W-X8 cation-exchange resin gave >99% recovery of 10–20 ng Li MC-ICP-MS SSB approach for correcting instrumental mass bias, with same Li concentrations in all standard and sample solutions. External precision ±0.2‰ (2SE, n = 15) for δ7Li 446
Mg Geological RMs 12 geological RMs analysed in 5 labs using various sample dissolution and chromatography schemes MC-ICP-MS Comparison of 26Mg/24Mg data and interlab mass bias using 3 types of MC-ICP-MS instrument. Mass bias determined by SSB using a pure Mg metal standard with matched Mg concentrations 396
Mo Geological (and U-rich samples) Digestion in HF–HNO3–HCl–HClO4 with take up in HCl. Addition of Mo double spike before three-stage ion chromatography procedure based on AG1X8, TRU Spec and another AG1X8 column to obtain high degree of purification; recoveries 42–80% MC-ICP-MS Instrument equipped with jet cones; solutions analysed contained ca. 30 ng mL−1 Mo. Chemical and instrumental mass bias corrected using 97Mo–100Mo double spike. Precision 0.02‰ (2SE, n = 8) for δ98Mo 447
Mo Rocks and iron meteorites Silicates digested in HF–HNO3 and Fe meteorites in HNO3–HCl. Mo was separated from the sample solution using a two-stage HF–W chemical separation technique and then further purified by two-step anion-exchange chromatography on Eichrom AG1X8 with HF–HCl and HF–HNO3. Blank ca. 1 ng NTIMS For most efficient ionisation to MoO3, sample loaded onto Re filament and covered with La(NO3)2. Mo trioxide ions at masses 149 and 150 used to correct for oxide interferences and mass dependent fractionation. Precisions (2SD) for 92Mo/96Mo, 94Mo/96Mo, 95Mo/96Mo, 97Mo/96Mo and 100Mo/96Mo were 47, 16, 10, 13 and 33 ppm respectively 448
Ni Iron meteorites and geological RMs Aqua regia digestion for meteorites, HF–HNO3 attack followed by aqua regia digestion for silicate samples. Three-step ion-exchange chromatographic purification: Dowex 50WX4 resin to separate Ni from matrix followed by two columns containing Eichrom 1X8 resin to separate Ni from Fe, Ti and Zn MC-ICP-MS Medium resolution mode employed. Instrumental mass bias corrected using 63Cu/65Cu internal normalisation or 62Ni/58Ni of sample and SSB used to correct for instrumental drift. Mathematical correction for 56Fe and 66Zn isobars a critical aspect of method. Precisions for four Ni isotope ratios equal or better than other reported methods 449
Nd Geological materials Digestion in HF–HNO3 and take up in HCl. Improved four-step separation and purification scheme: (i) REE separation on AG50W-X8; (ii) Ce clean up on Ln Spec; (iii) automated separation of LREE and Sm on Ln Spec; and (iv) AG50W-X8 to remove any organics and phosphates discharged from previous columns. Nd recovery > 99% MC-ICP-MS 148Nd/144Nd used for internal normalisation plus SSB with GSJ RM JNdi-1 (Nd oxide) as the reference standard. Iolite software package used for all data reduction off-line. External precision of 0.20 on ε145Nd was comparable to that obtained by double spike techniques 450
Nd, Th and U Carbonates Combined separation procedure for Nd, Th and U. Sample dissolution in HNO3 with addition of 236U–229Th spike. Trace metals coprecipitated with FeCl3, redissolved in 8 M HNO3 before anion chromatography to obtain separate fractions of REE, Th and U, followed by a two-stage separation and purification of Nd. Average blank 11 pg Nd TIMS Nd measured as NdO+ with GSJ RM JNdi-1 (Nd oxide) used to monitor instrumental offset and correct for mass bias. Accuracy of 143Nd/144Nd ratios confirmed using USGS RM BCR-2 (basalt) and in-house coral RM 451
Os Geological RMs Digestion in Carius tubes with reverse aqua regia, Os extracted into CCl4 and back-extracted into HBr before purification by microdistillation. Blanks were 0–0.3 pg Os TIMS Static collection involving in-run measurement of oxygen isotope ratios to correct for isobaric oxide interferences on 186Os/188Os and 187Os/188Os. Main Os16O3 ion beam collected with FCs fitted with 1011 Ω amplifiers and 192Os16O217O and 192Os16O218O ion beams with FCs fitted with 1012 Ω amplifiers 452
Pt Terrestrial rocks and meteorites Basalt samples digested in HF–HNO3, chondrites in HF–HNO3–HClO4 (HF–HNO3 only for some types of chondrite) and iron meteorites in aqua regia. One column purification procedure based on BioRad AG1X8 resin. Blanks were <370 pg Pt MC-ICP-MS See HF ref. 443 443
S Gypsum Dissolution in water at 40 °C (maximum solubility at this temperature) and dilution to S concentration of 0.30 mM MC-ICP-MS Evaluation of Ca matrix effects on S isotope ratio measurements. Medium mass resolution mode with SSB using Vienna Canon Diablo Troilite. Ca matrix effects were found to depend on absolute Ca concentration rather Ca[thin space (1/6-em)]:[thin space (1/6-em)]S ratio and were more significant under dry compared to wet plasma conditions 399
S Sulfates and sulfides Powdered samples either pressed into a powder without binder or leached in 2% HNO3 to give a final sulfate concentration of ca. 3 mg L−1 MC-ICP-MS and LA-MC-ICP-MS Because of large range of δ34S in nature, SSB with a single isotope standard not able to provide accurate corrections for mass bias. Likely cause was thought to be fractionation in ICP associated with valence state. Proposed procedure for mass bias correction based on external calibration provided accurate data for LA and solution analysis 398
Sr Basalt and bone See Ca, ref. 393 MC-ICP-MS Radiogenic and stable Sr isotopes measured in low resolution mode, with SSB using NIST SRM 987 (SrCO3) and Zr doping. Typical precisions 87Sr/42Sr 0.00001 (2σ) and δ88Sr/86Sr 0.04‰ (2σ) 393
Th Silicate RMs Rock powders digested in HF–HNO3 and dissolved in 0.5 M HNO3 prior to a 2-step purification using TRU-Spec resin followed by AG1X8. Median blank 9 pg Th; recoveries ranged from 66 to 100% MC-ICP-MS Two-step wash procedure after every sample to reduce Th backgrounds to acceptable levels. Measurement of 232Th tail and its contribution at mass 230 subtracted. SSB procedure with in-house Th standard solution to correct for mass bias. Intermediate precisions for 230Th/232Th in rock samples (0.24–0.49%, 2RSD) were similar to those achieved for synthetic solutions 453
V Terrestrial rocks Samples digested in HF–HNO3 for 3–4 days followed by aqua regia, HCl and HNO3 sequentially to remove any remaining fluorides. Modified 3-step separation procedure coupling cation- and anion-exchange chromatography to avoid the use of expensive TRU Spec resin. Blanks <1.5 ng V MC-ICP-MS Instrument run in medium mass resolution mode with SSB protocol using in-house V solution as standard. Cup configuration and instrument set up provided improved instrument sensitivity and so reduced amount of V required for 51V/50V measurements. V isotope ratios of 12 RMs reported, with long term precision of ±0.1‰ (2SD) 401
W Terrestrial rocks and meteorites Basalt samples digested in HF–HNO3, chondrites in HF–HNO3–HClO4 (HF–HNO3 only for some types of chondrite) and iron meteorites in aqua regia. Two stage purification based on BioRad AG 1-X8 resin to separate W from matrix followed by clean-up using Eichrom™ TEVA resin. Blanks were <100 pg W MC-ICP-MS See HF ref. 443 443
W Geological RMs Tungsten double spike added prior to sample dissolution in HF–HNO3–H2O2. Three-step anion-exchange chromatography on AG10X8 resin to purify samples. Yield typically 50–80%; blanks 100–500 pg W MC-ICP-MS Double spike of 180W–183W used to correct for mass bias and fractionation effects. External precision of ±0.05‰ (2SD, n = 171). 186W/184W ratios for 5 USGS RMs reported 454


A review394 (177 references) examined the application of stable isotope systems such as Cu, Li and Zn, often referred to as non-traditional isotopes, to the emerging field of analytical ecogeochemistry. It stressed the importance of metrologically sound analytical protocols, data-processing strategies and uncertainty considerations for the successful detection and interpretation of small isotopic shifts. Mass spectrometric techniques for determining 135Cs/137Cs in environmental samples were reviewed168 (123 references). Critical issues affecting the accuracy and LODs were the effectiveness of procedures to remove isobaric Ba interferences and eliminate peak tailing from 133Cs on 135Cs. A state-of-the-art review395 (97 references) of isotope ratio measurements by solution MC-ICP-MS presented guidelines for data reduction strategies and uncertainty assessments using Sr isotope ratios as an example. Although the data set presented was based on the analysis of wood cores from trees in Austria, many of the principles are applicable to geochemical samples.

In an interlaboratory comparison396 of Mg isotopic data, 12 Chinese rock RMs were analysed in five laboratories using various sample dissolution and chromatography schemes and three types of MC-ICP-MS instrument. Although 25Mg/24Mg and 26Mg/24Mg compositions from all laboratories were in agreement within quoted uncertainties for most rocks, there were some significant differences of up to 0.3‰ in 26Mg/24Mg for some mafic samples. The source of these discrepancies was thought most likely to arise from the column chemistry employed, although incomplete sample dissolution may have been another factor. It was concluded that well characterised RMs with a range of matrices were required to reduce such interlaboratory mass bias. In their quest for a new absolute Mg isotope RM, Vogl et al.397 characterised three candidate solutions by sending them to three partner laboratories for analysis, together with calibration solutions prepared from isotopically enriched and purified Mg materials. The project's target uncertainty of <0.5‰ relative (k = 2) was achieved and a set of Mg isotope RMs, including ERM-AE143, which is nearly identical to NIST SRM 980 in terms of its Mg isotopic composition, will be made available.

Differences in sulfur isotope ratios were found398 in well-characterised sulfate RMs when determined by both solution and LA-MC-ICP-MS but not in sulfide samples when analysed by LA-MC-ICP-MS. The interference of 16O2+ on 32S+ and complex matrix effects were investigated but the cause was actually vaporisation-induced plasma fractionation associated with the S valence state. Accurate data were obtained in both solution and LA modes by application of an external isotope calibration, constructed using in-house and NIST S isotope RMs, in combination with SSB. Matrix effects from Ca during the measurement of S isotopes in gypsum399 were found to depend on the absolute Ca concentration rather than the Ca[thin space (1/6-em)]:[thin space (1/6-em)]S ratio. Gypsum samples were dissolved in water at 40 °C, diluted to 0.30 mM S and measured directly by MC-ICP-MS using a SSB procedure. An ammonium sulfate solution of known S isotopic composition and matched S concentration was employed as the standard.

In a new protocol400 for the measurement of V isotopes by MC-ICP-MS, the instrument was operated in medium mass resolution mode (ΔM/M ca. 4000) to separate Cr, Ti and V isotopes from polyatomic ions of Ar, C, Cl, N, O and S. The method, based on the analysis of synthetic solutions, achieved a precision of ±0.12‰ (comparable to that of low resolution methods) while consuming as little as 260 ng V. When combined with ion-exchange chromatographic separation procedures, this approach should facilitate the determination of V isotope ratios in samples with low V contents, such as depleted peridotites, iron meteorites and carbonates. Wu et al.401 used a similar FC configuration in medium mass resolution mode (ΔM/M > 5500) for V isotope measurements of 12 geological RMs including igneous rocks and manganese nodules. Based on replicate analysis of solution and rocks standards, the long-term external reproducibility for δ51V was better than ±0.1‰ (2SD).

High precision Br isotope measurements by MC-ICP-MS represent a considerable challenge because of isobaric interferences from 40Ar38ArH+ and 40Ar40ArH+. Wei et al.402 reported that it was possible to resolve these argides from 79Br and 81Br by using high mass resolution and appropriate settings of the instrument's Zoom Optics. The external precision of 81Br/79Br in selected RMs ranged from ±0.03 to ±0.14‰. Solutions of NIST SRM 977 (bromine isotope solution) prepared in the NaBr-form produced larger signals and better precisions than solutions prepared as HBr, reflecting loss of HBr in the nebuliser and potential diffusive isotope fractionations in the plasma.

4.4.3 Other mass spectrometric techniques.
4.4.3.1 Thermal ionisation mass spectrometry. This is still a popular technique for determining Sr isotope ratios, especially in samples with low Sr contents. A useful review403 (79 references) summarised methods of obtaining very low blanks and high precision Sr isotope measurements on ng samples. A new system for microsampling404 by laser cutting followed by conventional Rb–Sr isotopic analysis of μg-sized samples by TIMS was used for extracting calcite and white-mica domains from samples of granitic mylonites. The automated cutting process minimised loss of material and the risk of handling errors while facilitating sampling of complex shapes of almost any size.

Several studies sought to improve the double-spike technique for determining Ca isotope ratios by MC-TIMS. Feng et al.405 investigated three double spike pairs, 42Ca–48Ca, 43Ca–48Ca and 44Ca–48Ca, using the Monte Carlo simulation technique to predict the internal precision in peak jumping mode. Theoretical precisions were confirmed by repeat measurements of NIST SRMs 915a and 915b (calcium carbonate) for δ44Ca, thus validating the simulation as an effective method of predicting optimal FC configurations, ratio combinations and integration times. However, the observed external precisions were 8–9 times poorer than the internal precisions; these were ascribed to an additional, yet unknown, source of uncertainty. Lehn et al.406 also used a Monte Carlo error model to optimise a 43Ca–48Ca double spike method for measuring δ44Ca/40Ca and δ44Ca/42Ca. Whilst the measured internal precisions generally agreed with model predictions, external reproducibility for a range of RMs including NIST SRMs 915a and 915b (calcium carbonate) was much worse than expected. This was attributed to filament reservoir effects causing deviation from ideal exponential mass fractionation during ionisation. They concluded that a 42Ca–43Ca double spike should provide the most precise δ44Ca/40Ca values because the average mass difference between the spike pair of isotopes and the measured isotopes is only 0.5 amu. In contrast to these peak jumping methodologies, Naumenko-Dèzes et al.407 measured all Ca isotopes simultaneously using a MC-TIMS instrument with a specially developed collector geometry. Sample masses were kept to <1 μg Ca and measurement uncertainties were 0.06‰ for 40Ca/44Ca and 0.12‰ for 48Ca/40Ca. Deficiencies in the exponential law used to correct instrumental mass fractionation were highlighted and the accuracy of an improved exponential law confirmed with NIST SRMs 915a and 915b (calcium carbonate). The laser microsampling technique described previously404 was employed to extract μg fragments of calcite and apatite for Ca isotopic analysis.408 Samples were dissolved in HNO3 with the addition of a 42Ca–44Ca spike and loaded onto a Re filament using a parafilm dam technique to minimise in-run fractionation. Measurements of 40Ca, 42Ca, 43Ca, 44Ca and 48Ca by TIMS and MC-ICP-MS were reported after mass fractionation correction using the double-spike and a Matlab model. Accurate TIMS data were achieved without chemical purification whereas analysis by MC-ICP-MS was challenging without separation from matrix elements.

The determination of Pb isotope ratios in ng-size samples by TIMS is hampered by the low abundance of 204Pb. This limitation was overcome409 by the use of an FC with a 1013 Ω resistor in the amplifier feedback loop for the collection of 204Pb. This resulted in a 10-fold improvement in the S/N but necessitated an external gain correction using a secondary standard and careful monitoring of the ion beam stability. Using a 207Pb–204Pb spike to correct for instrumental mass fractionation, results for 5 ng aliquots of NIST SRM 982 (Pb isotopic standard) had a reproducibility of 90 ppm (2SD) for 206Pb/204Pb. Similar precision was achieved for 5 ng portions of USGS RMs AGV-1 (andesite) and BCR-1 (basalt) indicating that the ion-exchange procedure had no adverse effect on data quality and the blank contribution was negligible. A different strategy was adopted by von Quadt et al.410 for high precision zircon U–Pb geochronology by ID-TIMS in samples containing small amounts of radiogenic Pb (<1–700 pg). The instrument was configured with FCs fitted with 1013 Ω resistors for static collection of all the Pb isotopes except 204Pb, which was measured with the axial secondary electron multiplier. Gain calibration factors for the 1013 Ω resistors determined using the GSJ Nd standard JNdi-1 were crucial for the accuracy of subsequent isotope ratio determinations. Accuracy was demonstrated by analysis of synthetic and natural U–Pb standards and by comparison with conventional dynamic ion counting data. Although the static FC measurements were more reproducible by a factor of 2–5, the uncertainties on the final U–Pb ratios and derived U–Pb dates were only slightly reduced due to external sources of uncertainty. These would need to be eliminated to fully realise the benefit of the improved precision of the FC measurements.

Other newly published methods for the determination of isotope ratios by TIMS are included in Table 7.


4.4.3.2 Secondary ion mass spectrometry. Detailed mapping by SIMS, in combination with other techniques, has been employed to elucidate the origins of accessory minerals. Elemental and isotope ratio imaging of monazite411 by NanoSIMS provided high quality sub-μm scale images that revealed chemical domains not distinguishable by EPMA mapping, especially for Pb and U. It also enabled accurate dating of domains that were too small for reliable measurements by LA-ICP-MS. Maps of 208Pb/232Th offered the opportunity to correlate ages with distinct chemical domains. Trace element data obtained by LA-ICP-MS provided important evidence in reconstructing the petrological history of the monazite. Three zircon RMs (Plešovice, Qinghu and Temora) were mapped for their Li abundance and isotopic composition by SIMS in a study412 designed to understand the behaviour of Li in zircon. All the RMs had rims 5–20 μm wide in which the Li concentration was 5 to 20 times higher than in the zircon core. The Li contents and δ7Li values were very variable in the rims but relatively homogenous in the cores. From rim to core, the Li concentrations decreased rapidly while δ7Li values increased, suggesting that the large Li isotopic variation in the zircons could be caused by diffusion. A tutorial review413 (215 references) on the use of isotope ratios in cosmochemistry compared the performance of MC-ICP-MS, SIMS and TIMS for this purpose. Because of its high spatial resolution and sensitivity for many key elements, SIMS has proved to be indispensable for the in situ characterisation of extraterrestial materials.

Other examples of geological applications of NanoSIMS included the measurement of stable C and O isotope compositions of methane-derived carbonates414 in rocks from Poland. The fine spatial resolution (5 μm) of the NanoSIMS analyses revealed a very high variability in δ13C, even in individual crystals, from very negative (−54‰) to positive (+7‰). This indicated that these carbonates were predominately formed by the anaerobic oxidation of biogenic methane. The C and O stable isotope compositions of these microcrystalline cements could be used to reconstruct the diagenetic evolution of porewaters in this region. Hauri et al.415 demonstrated that NanoSIMS was capable of high precision S isotope measurements (32S, 33S and 34S) with a precision capable of resolving variations in Δ33S of ca. 0.4‰ (2σ) with a spatial resolution of 15 μm. Pyrite grains from metasedimentary rocks in Ontario, Canada had δ34S values of between −9.6 and +6.3‰ and corresponding Δ33S values of between −0.8 and +1.5‰. These results indicated that microbial sulfate reduction was widespread in the Neoarchean era. Figures of merit obtained in this study by NanoSIMS were comparable to those of large radius SIMS instruments, indicating the potential of these smaller instruments.

In a study416,417 of carbonate δ13C and δ18O records for reconstructing past climatic conditions throughout the evolution of the Earth, a suite of Ca–Fe–Mg carbonate RMs for calibrating δ13C and δ18O SIMS measurements was developed. The highly systematic, non-linear nature of SIMS instrumental bias was demonstrated for minerals with compositions along the dolomite–ankerite solid solution series.


4.4.3.3 Accelerator mass spectrometry. This technique is often used to measure cosmogenic isotopes because of its great sensitivity. Cosmogenic 10Be forms in situ when high-energy cosmic rays bombard rocks in the upper few metres of the Earth's surface. Sample preparation procedures to extract 10Be from quartz mineral separates418 were reassessed to maximise the yield of 10Be for AMS while minimising contamination and background levels of 10B. By using a beryl carrier and dedicated equipment to process samples with low Be content, the optimised method routinely achieved blanks with 10Be/9Be ratios in the mid 10−16 level, an improvement of almost two orders of magnitude compared with blanks obtained with commercial carriers. A study419 to extend the dating of sediments using the cosmogenic isotope 32Si demonstrated the potential benefits of making modest improvements in detector background levels. The design and performance of new low-background, gas-proportional beta counters to measure 32Si (via32P) were presented.
4.4.3.4 Noble gas mass spectrometry. In an interlaboratory comparison420 to determine the accuracy of cosmogenic21Ne measurements in quartz, the five participating laboratories employed their own measurement routines to analyse CREU-1, a natural quartz standard prepared from amalgamated vein casts. Although the reported analytical precision for each laboratory was as low as 2%, the 7.1% dispersion of results between laboratories was considered to be a more realistic estimate of the accuracy of the 21Ne method at the present time. During the development of a new analytical procedure421 for the determination of Ne in rocks, the contribution of isobaric ions on the three Ne isotopes were studied in detail, particularly the major interference from 40Ar2+ on 20Ne+; strategies for interference corrections were modified accordingly. Method accuracy and precision were assessed by measuring 21Ne in three aliquots of CREU-1; the average value was within 0.3% of the published value with a 2.2% uncertainty.

The main advantage of the 40Ar–39Ar method over conventional K–Ar dating is that it only relies on the ratios between five isotopes of the same element. A completely revised workflow422 for generating accurate 40Ar–39Ar ages from raw mass spectrometer data accounted for all sources of analytical uncertainty, including those associated with decay constants and the air ratio. The programme can be downloaded free of charge.

4.4.4 X-ray spectrometry. For a comprehensive review of recent advances in XRF instrumentation and geological applications, the reader is advised to consult the ASU on XRFS.5

Advances in detector technologies and associated data processing software now means that synchrotron XRF microscopy can be used for trace element mapping at the μm-scale with ppm LODs. Li et al.423 demonstrated the application of megapixel SXRFS to ore petrology by imaging six samples representative of different ore deposits related to the extraction of Cu, Ge, Pt and U. Millisecond dwell times allowed collection of maps the size of a thin section at resolutions of a few μm in just a few hours. It was particularly efficient at revealing the distribution of precious metals such as Au and Pt, which tend to occur as small inclusions of native metals and alloys, and trace contaminants that form distinct micro-minerals, as well as providing information on metal speciation. All the samples analysed revealed new features that had not been reported previously. Fisher et al.424 presented three case studies from orogenic gold deposits in which all phases in a thin section were mapped by SXRFS at high resolution (2–4 μm pixels) with LODs comparable to, or exceeding, EMPA LODs for most elements. The system employed full spectral data collection so elements did not have to be selected prior to measurement, in contrast to other imaging techniques, such as EPMA and SIMS. The first two examples examined the variation in sulfide phases round high-grade gold veins, while the third considered the fine-scale alternation of sulfides, in order to shed light on the source of and deposition mechanisms of gold in such deposits. Although megapixel SXRFS mapping is still in its infancy, it should open up new horizons in the study of trace and major element distributions and speciation in geological materials and offer a complementary method to other imaging techniques.

The performance of a laboratory μXRF system was assessed for 2D elemental mapping425 of petrographic thin sections for studies of argillaceous rocks from a potential radioactive waste repository. Different options of X-ray sources and detectors were tested to find the optimal configuration of this system in terms of sensitivities and LODs for Cs and Ni, which acted as surrogates for fission and corrosion products. Although laboratory μXRFS was an excellent tool for identifying the key minerals for the uptake of Cs, careful corrections were required for Ni because of the relatively high Ca content of the samples. The capabilities of a semi-portable μXRF instrument were demonstrated426 through the determination of Sr in speleothems. A smooth, highly-polished sample surface was required together with a strategy to account for the observed matrix effects. This type of analysis would be a suitable way of preselecting samples prior to more detailed geochemical analysis.

Over the last decade, XRF core scanning has become increasingly popular for a variety of applications. A prototype LIBS system was compared with a commercially available EDXRF core scanner for rapid detection427 of metalliferous zones in cores from a tailings deposit in a former Pb–Zn mine. Both methods suffered from matrix effects; the application of PLSR improved the results in both cases. Distributions of element concentrations obtained by the two techniques were similar and well correlated with bulk concentrations obtained by WDXRFS. Although both systems could detect metal-rich layers not visible to the naked eye up to concentrations of 2.2% Cu + Pb + Zn, the LIBS core scanner had the advantage of high spatial resolution and an ability to create 2D elemental images. A study428 of XRF core scanning to measure palaeoenvironmental markers in cores of organic-rich lake sediments and peat investigated the influence of organic matter, water content and sample porosity. The importance of careful evaluation of the data through corroborative evidence from other techniques was stressed.

Other, more unusual, examples of the application of XRF techniques to geological samples included: the determination of Mn valence state and speciation in Mn ores429 by WDXRFS; using XANES to identify the forms of Cu extracted191 from geochemical RMs by the BCR sequential extraction scheme; and the determination of Br, Cl, F and I in marine sediments430 by WDXRFS directly on pressed pellets without binder. Reported LODs for the halogens were 0.5, 5, 100, and 10 μg g−1 for Br, Cl, F and I respectively for a counting time of 100 s. Quye-Sawyer et al.431 showed that handheld EDXRF instruments were capable of rapid and quantitative determination of Al, Ba, Ca, Fe, K, Mg, Mn, Rb, Si, Sr, Ti and Zn in carbonate rocks provided appropriate corrections, based on a set of carbonate standards, were applied to the manufacturer's calibration. Grain size in powdered samples and the roughness of hand samples had no impact on measured concentrations except for Mg. However, weathering posed a significant challenge for in situ measurements of carbonate outcrops and the use of fresh rock chips hammered from the outcrop was advised for reliable quantitative results.

Traditionally, XRD devices have largely been restricted to laboratories but advances in XRD sample holders and X-ray sources have contributed to the recent development of portable XRD instruments. A study432 of the capabilities of portable XRD for mineralogical analysis of hydrothermal systems demonstrated that although laboratory-based systems delivered superior results compared to the field portable unit, there was good correlation between data from the two systems for major mineral phases; some minor and trace phases were also detectable. Portable XRD was shown to have the potential to provide the exploration geologist with a tool for the rapid acquisition of mineralogical data on which to make more informed decisions during drilling programmes. The ultimate remote field location for operating such devices is Mars, where the miniaturised XRD/XRF instrument CheMin aboard the Curiosity rover analysed four different samples.433 The primary on-board XRD standards were mixtures of beryl and quartz. By a happy coincidence, the first XRD measurements on Mars coincided with the 100th anniversary of the discovery of the technique.

Glossary of terms

2DTwo-dimensional
3DThree-dimensional
AASAtomic absorption spectrometry
AECAnion exchange chromatography
AESAtomic emission spectrometry
AFSAtomic fluorescence spectrometry
amuAtomic mass unit
AMSAccelerator mass spectrometry
APDCAmmonium pyrrolidine dithiocarbamate
APDGAtmospheric pressure glow discharge
ASUAtomic spectrometry update
ASVAnodic stripping voltammetry
BAMFederal Institute for Materials Research and Testing (Germany)
BCRCommunity Bureau of Reference (of the European Community) now IRMM
C18Octadecyl bonded silica
CARIBICCivil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrumented Container
CDNCDN Resources Laboratories Ltd (Canada)
CECapillary electrophoresis
CIConfidence interval
CLCathode luminescence
CPECloud point extraction
CRDSCavity ring-down spectroscopy
CRMCertified reference material
CSContinuum source
CVCold vapour
CVGChemical vapour generation
DADiscriminant analysis
DBDDielectric barrier discharge
DBTDibutyl tin
DCIDirect concentric injector
DDTCDiethyldithiocarbamate, sodium salt
DGTDiffusion gradient in thin films
DLLMEDispersive liquid liquid microextraction
DMADimethylarsenic acid
DMSODimethylsulfoxide
DOCDissolved organic carbon
DPDual pulse
DPhTDiphenyl tin
DRSDiffuse reflectance spectrometry
EBSElastic back scattering
EDTAEthyldiaminetetraacetic acid
EDXEnergy dispersive X-ray spectroscopy
EDXRFEnergy dispersive X-ray fluorescence
EDXRFSEnergy dispersive X-ray fluorescence spectrometry
ENEuropean Committee for Standardisation
EPAEnvironmental Protection Agency (USA)
EPMAElectron probe microanalysis
ERMEuropean Reference Material
ETAASElectrothermal atomic absorption spectrometry
EtHgEthyl mercury
ETVElectrothermal vaporisation
EUEuropean Union
FAASFlame atomic absorption spectrometry
FCFaraday cup
FFFField flow fractionation
FIFlow injection
FPFundamental parameter
fsFemtosecond
FTFourier transform
FTIRFourier transform infrared
GBWCRMs of the National Research Centre for Certified Reference Materials (China)
GCGas chromatography
GFGraphite furnace
GSBCRMs of the Institute for Environmental Reference Materials (of Ministry of Environmental Protection, China)
GSJGeological Survey of Japan
HFSEHigh field strength element
HGHydride generation
HILICHydrophobic interaction liquid chromatography
HPICHigh performance ion chromatography
HPLCHigh performance liquid chromatography
HPSHigh Purity Standards (USA)
HRHigh resolution
HREEHeavy rare earth element
IAEAInternational Atomic Energy Agency
ICIon chromatography
ICPInductively coupled plasma
ICP-AESInductively coupled plasma atomic emission spectrometry
ICP-MSInductively coupled plasma mass spectrometry
ICP-MS/MSInductively coupled plasma mass spectrometry with a quadrupole-cell-quadrupole design
IdInternal diameter
IDIsotope dilution
IDAIsotope dilution analysis
IERMInstitute for Environmental Reference Materials (of Ministry of Environmental Protection, China)
ILIonic liquid
INAAInstrumental neutron activation analysis
INCTInstitute of Nuclear Chemistry and Technology (Poland)
IOMInstitute of Occupational Medicine (Scotland)
IRInfrared
IRMMInstitute for Reference Materials and Measurements
IRMSIsotope ratio mass spectrometry
ISInternal standard
LALaser ablation
LAMISLaser ablation molecular isotopic spectrometry
LASSLaser ablation split stream
LCLiquid chromatography
LGCLaboratory of the Government Chemist (UK)
LIBSLaser induced breakdown spectroscopy
LLMELiquid liquid microextraction
LODLimit of detection
LOQLimit of quantification
LPMELiquid phase microextraction
LREELight rare earth element
MAEMicrowave assisted extraction
MBTMonobutyl tin
MCMulticollector
MCEMixed cellulose ester
MeHgMethyl mercury
MICMultiple ion counter
M 0 Characteristic mass
MMAMonomethylarsenic acid
MPhTMonophenyltin
MSMass spectrometry
MSISMultimode sample introduction system
MWCNTMultiwalled carbon nanotube
m/zMass to charge ratio
NASANational Aeronautics and Space Administration (USA)
NBLNew Brunswick Laboratories (USA)
NCSNational Analysis Centre for Iron and Steel (China)
Nd:YAGNeodymium doped:yttrium aluminum garnet
NIESNational Institute for Environmental Studies (Japan)
NIOSHNational Institute of Occupational Safety and Health
NIRNear infrared
NISTNational Institute of Standards and Technology (USA)
NPNanoparticle
NRCCNational Research Council (of Canada)
NRCCRMNational Research Centre for Certified Reference Materials (China)
nsNanosecond
NTIMSNegative thermal ionisation mass spectrometry
NWRINational Water Research Institute (Canada)
o.d.Outer diameter
PCAPrincipal component analysis
PESAProton electric scattering
PFAPerfluoroalkyl
PGEPlatinum group element
PIXEParticle induced X-ray emission
PLSPartial least squares
PLSRPartial least squares regression
PM1Particulate matter (with an aerodynamic diameter of up to 1 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)
ppbParts per billion
ppmParts per million
PTEPotentially toxic element
PTFEPoly(tetrafluoroethylene)
PVCPolyvinylchloride
PVGPhotochemical vapour generation
pXRFPortable X-ray fluorescence
pXRFSPortable X-ray fluorescence spectrometry
QAQuality assurance
QCQuality control
REARelaxed eddy accumulation
REERare earth element
RMReference material
RMSEPRoot mean square error of prediction
RSDRelative standard deviation
RSERelative standard error
RSFRelative sensitivity factor
SARMSouth African producers of Metallurgical and Geological Certified Reference Materials
SDStandard deviation
SDSSodium dodecyl sulfate
SEStandard error
SECSize exclusion chromatography
SEMScanning electron microscopy
SFSector field
SIMSSecondary ion mass spectrometry
SMPSScanning mobility particle sizer
S/NSignal-to-noise ratio
SpSingle particle
SPESolid phase extraction
SPMESolid phase microextraction
SPRSurface plasma resonance
SPSSpectrapure Standards (Norway)
SRSynchrotron radiation
SRMStandard reference material (of NIST)
SSSolid sampling
SSBSample-standard bracketing
SVMSupport vector machine
SXRFSSynchrotron X-ray fluorescence spectrometry
TBTTributyl tin
TDThermal desorption
TEMTransmission electron microscopy
THFTetrahydrofuran
TIMSThermal ionisation mass spectrometry
TMAOTrimethylarsenic oxide
TOFTime of flight
TPhTTriphenyltin
TXRFTotal reflection X-ray fluorescence
TXRFSTotal reflection X-ray fluorescence spectrometry
UAEUltrasonic extraction
USGSUnited States Geological Survey
USAUnited States of America
USNUltrasonic nebulisation
UVUltra violet
VALLMEVortex assisted liquid liquid microextraction
VGVapour generation
VISVisible
VOCVolatile organic carbon
WDXRFWavelength dispersive X-ray fluorescence
WDXRFSWavelength dispersive X-ray fluorescence spectrometry
WMOWorld Meteorological Organisation
XANESX-ray absorption near edge structure
XASX-ray absorption spectrometry
XRDX-ray diffraction
XRFX-ray fluorescence
XRFSX-ray fluorescence spectrometry

References

  1. O. T. Butler, W. R. L. Cairns, J. M. Cook and C. M. Davidson, J. Anal. At. Spectrom., 2016, 31(1), 35–89 RSC .
  2. A. Taylor, N. Barlow, M. P. Day, S. Hill, M. Patriarca and M. White, J. Anal. At. Spectrom., 2016, 31(3), 554–596 RSC .
  3. E. H. Evans, J. Pisonero, C. M. M. Smith and R. N. Taylor, J. Anal. At. Spectrom., 2016, 31(5), 1057–1077 RSC .
  4. R. Clough, C. F. Harrington, S. J. Hill, Y. Madrid and J. F. Tyson, J. Anal. At. Spectrom., 2016, 31(7), 1330–1373 RSC .
  5. M. West, A. T. Ellis, P. J. Potts, C. Streli, C. Vanhoof and P. Wobrauschek, J. Anal. At. Spectrom., 2016, 31(9), 1706–1755 RSC .
  6. S. Carter, A. Fisher, R. Garcia, B. Gibson, J. Marshall and I. Whiteside, J. Anal. At. Spectrom., 2016, 31(11), 2114–2164 RSC .
  7. F. Laborda, E. Bolea, G. Cepria, M. T. Gomez, M. S. Jimenez, J. Perez-Arantegui and J. R. Castillo, Anal. Chim. Acta, 2016, 904, 10–32 CrossRef CAS PubMed .
  8. S. M. Majedi and H. K. Lee, TrAC, Trends Anal. Chem., 2016, 75, 183–196 CrossRef CAS .
  9. T. Dreier and C. Schulz, Powder Technol., 2016, 287, 226–238 CrossRef CAS .
  10. A. Malysheva, E. Lombi and N. H. Voelcker, Nat. Nanotechnol., 2015, 10(10), 835–844 CrossRef CAS PubMed .
  11. R. Tantra, H. Bouwmeester, E. Bolea, C. Rey-Castro, C. A. David, J. M. Dogne, J. Jarman, F. Laborda, J. Laloy, K. N. Robinson, A. K. Undas and M. van der Zande, Nanotoxicology, 2016, 10(2), 173–184 CAS .
  12. K. Tirez, C. Vanhoof, J. Bronders, P. Seuntjens, N. Bleux, P. Berghmans, N. De Brucker and F. Vanhaecke, Environ. Sci.: Processes Impacts, 2015, 17(12), 2034–2050 CAS .
  13. L. Balcaen, E. Bolea-Fernandez, M. Resano and F. Vanhaecke, Anal. Chim. Acta, 2015, 894, 7–19 CrossRef CAS PubMed .
  14. W. Maenhaut, Nucl. Instrum. Methods Phys. Res., Sect. B, 2015, 363, 86–91 CrossRef CAS .
  15. D. Sanchez-Rodas, A. M. S. de la Campa and L. Alsioufi, Anal. Chim. Acta, 2015, 898, 1–18 CrossRef CAS PubMed .
  16. C. Cardell and I. Guerra, TrAC, Trends Anal. Chem., 2016, 77, 156–166 CrossRef CAS .
  17. A. Galuszka, Z. M. Migaszewski and J. Namiesnik, Environ. Res., 2015, 140, 593–603 CrossRef CAS PubMed .
  18. J. C. Soo, K. Monaghan, T. Lee, M. Kashon and M. Harper, Aerosol Sci. Technol., 2016, 50(1), 76–87 CrossRef CAS .
  19. L. W. D. Mines, J. H. Park, I. A. Mudunkotuwa, T. R. Anthony, V. H. Grassian and T. M. Peters, Aerosol Sci. Technol., 2016, 50(5), 497–506 CrossRef CAS .
  20. K. R. Anderson, D. Leith, M. Ndonga and J. Volckens, Aerosol Sci. Technol., 2015, 49(12), 1195–1209 CrossRef CAS .
  21. M. Wada, M. Tsukada, N. Namiki, W. W. Szymanski, N. Noda, H. Makino, C. Kanaoka and H. Kamiya, Aerosol Air Qual. Res., 2016, 16(1), 36–45 CrossRef CAS .
  22. A. Kumar and T. Gupta, Aerosol Air Qual. Res., 2015, 15(4), 1188–1200 Search PubMed .
  23. T. Okuda, R. Isobe, Y. Nagai, S. Okahisa, K. Funato and K. Inoue, Aerosol Air Qual. Res., 2015, 15(3), 759–767 CAS .
  24. A. Kumar and T. Gupta, Aerosol Air Qual. Res., 2015, 15(3), 768–775 Search PubMed .
  25. F. X. Li, J. Schnelle-Kreis, E. Karg, J. Cyrys, J. W. Gu, J. Orasche, G. Abbaszade, A. Peters and R. Zimmermann, Environ. Sci. Pollut. Res., 2016, 23(8), 7278–7287 CrossRef CAS PubMed .
  26. A. Hecobian, A. Evanoski-Cole, A. Eiguren-Fernandez, A. P. Sullivan, G. S. Lewis, S. V. Hering and J. L. Collett, Atmos. Meas. Tech., 2016, 9(2), 525–533 Search PubMed .
  27. F. Rueda-Holgado, L. Calvo-Blazquez, F. Cereceda-Balic and E. Pinilla-Gil, Microchem. J., 2016, 124, 20–25 CrossRef CAS .
  28. S. Osterwalder, J. Fritsche, C. Alewell, M. Schmutz, M. B. Nilsson, G. Jocher, J. Sommar, J. Rinne and K. Bishop, Atmos. Meas. Tech., 2016, 9(2), 509–524 Search PubMed .
  29. M. Aliste and L. G. Chavez, Forensic Sci. Int., 2016, 261, 14–18 CrossRef CAS PubMed .
  30. T. J. Shepherd, W. Dirks, N. M. W. Roberts, J. G. Patel, S. Hodgson, T. Pless-Mulloli, P. Walton and R. R. Parrish, Environ. Res., 2016, 146, 145–153 CrossRef CAS PubMed .
  31. E. Leese, J. Morton, P. H. E. Gardiner and V. A. Carolan, J. Anal. At. Spectrom., 2016, 31(4), 924–933 RSC .
  32. S. Yatkin, H. S. Amin, K. Trzepla and A. M. Dillner, Aerosol Sci. Technol., 2016, 50(4), 309–320 CrossRef CAS .
  33. P. Moravec, J. Smolik, J. Ondracek, P. Vodicka and R. Fajgar, Aerosol Sci. Technol., 2015, 49(8), 655–665 CrossRef CAS .
  34. M. D. Minarro, P. J. Brewer, R. J. C. Brown, S. Persijn, J. van Wijk, G. Nieuwenkamp, A. Baldan, C. Kaiser, C. Sutour, T. Mace, N. Skundric and T. Tarhan, Anal. Methods, 2016, 8(15), 3014–3022 RSC .
  35. M. C. Leuenberger, M. F. Schibig and P. Nyfeler, Atmos. Meas. Tech., 2015, 8(12), 5289–5299 CrossRef .
  36. L. AlSioufi, A. M. S. de la Campa and D. Sanchez-Rodas, Microchem. J., 2016, 124, 256–260 CrossRef CAS .
  37. I. A. Mudunkotuwa, T. R. Anthony, V. H. Grassian and T. M. Peters, J. Occup. Environ. Hyg., 2016, 13(1), 30–39 CrossRef CAS PubMed .
  38. R. N. Andrews, M. Keane, K. W. Hanley, H. A. Feng and K. Ashley, Anal. Methods, 2015, 7(15), 6403–6410 RSC .
  39. E. Grygo-Szymanko, A. Tobiasz and S. Walas, TrAC, Trends Anal. Chem., 2016, 80, 112–124 CrossRef CAS .
  40. A. Ellis, R. Edwards, M. Saunders, R. K. Chakrabarty, R. Subramanian, A. van Riessen, A. M. Smith, D. Lambrinidis, L. J. Nunes, P. Vallelonga, I. D. Goodwin, A. D. Moy, M. A. J. Curran and T. D. van Ommen, Atmos. Meas. Tech., 2015, 8(9), 3959–3969 CrossRef CAS .
  41. C. Solis, E. Chavez, M. E. Ortiz, E. Andrade, E. Ortiz, S. Szidat and L. Wacker, Nucl. Instrum. Methods Phys. Res., Sect. B, 2015, 361, 419–422 CrossRef CAS .
  42. S. C. Jantzi, V. Motto-Ros, F. Trichard, Y. Markushin, N. Melikechi and A. De Giacomo, Spectrochim. Acta, Part B, 2016, 115, 52–63 CrossRef CAS .
  43. P. G. Martin, I. Griffiths, C. P. Jones, C. A. Stitt, M. Davies-Milner, J. F. W. Mosselmans, Y. Yamashiki, D. A. Richards and T. B. Scott, Spectrochim. Acta, Part B, 2016, 117, 1–7 CrossRef CAS .
  44. E. R. Pereira, L. M. Rocha, H. R. Cadorim, V. D. Silva, B. Welz, E. Carasek and J. B. de Andrade, Spectrochim. Acta, Part B, 2015, 114, 46–50 CrossRef CAS .
  45. K. Nakata, Y. Okamoto, S. Ishizaka and T. Fujiwara, Talanta, 2016, 150, 434–439 CrossRef CAS PubMed .
  46. F. Slemr, A. Weigelt, R. Ebinghaus, H. H. Kock, J. Bodewadt, C. A. M. Brenninkmeijer, A. Rauthe-Schoch, S. Weber, M. Hermann, J. Becker, A. Zahn and B. Martinsson, Atmos. Meas. Tech., 2016, 9(5), 2291–2302 CrossRef .
  47. M. E. Asgill, S. Groh, K. Niemax and D. W. Hahn, Spectrochim. Acta, Part B, 2015, 109, 1–7 CrossRef CAS .
  48. M. Boudhib, J. Hermann and C. Dutouquet, Anal. Chem., 2016, 88(7), 4029–4035 CrossRef CAS PubMed .
  49. S. C. Yao, J. L. Xu, X. Dong, B. Zhang, J. P. Zheng and J. D. Lu, Spectrochim. Acta, Part B, 2015, 110, 146–150 CrossRef CAS .
  50. M. Y. Chen, T. B. Yuan, Z. Y. Hou, Z. Wang and Y. Wang, Spectrochim. Acta, Part B, 2015, 112, 23–33 CrossRef CAS .
  51. R. S. Pappas, N. Martone, N. Gonzalez-Jimenez, M. R. Fresquez and C. H. Watson, J. Anal. Toxicol., 2015, 39(5), 347–352 CrossRef CAS PubMed .
  52. Q. He, Z. Xing, S. C. Zhang and X. R. Zhang, J. Anal. At. Spectrom., 2015, 30(9), 1997–2002 RSC .
  53. J. Scancar, B. Berlinger, Y. Thomassen and R. Milacic, Talanta, 2015, 142, 164–169 CrossRef CAS PubMed .
  54. T. Tziaras, S. A. Pergantis and E. G. Stephanou, Environ. Sci. Technol., 2015, 49(19), 11640–11648 CrossRef CAS PubMed .
  55. K. Tirez, C. Vanhoof, J. Peters, L. Geerts, N. Bleux, E. Adriaenssens, E. Roekens, S. Smolek, A. Maderitsch, R. Steininger, J. Gottlicher, F. Meirer, C. Streli and P. Berghmans, J. Anal. At. Spectrom., 2015, 30(10), 2074–2088 RSC .
  56. W. Wang, Z. M. Li, J. Xu, G. Q. Zhou, X. P. Shen and L. H. Zhai, Chin. J. Anal. Chem., 2015, 43(5), 703–708 CAS .
  57. G. Bauer and A. Limbeck, Spectrochim. Acta, Part B, 2015, 113, 63–69 CrossRef CAS .
  58. P. Sommersacher, N. Kienzl, T. Brunner and I. Obernberger, Energy Fuels, 2015, 29(10), 6734–6746 CrossRef CAS .
  59. P. Sommersacher, N. Kienzl, T. Brunner and I. Obernberger, Energy Fuels, 2016, 30(4), 3428–3440 CrossRef CAS .
  60. F. P. Zhao, E. Repo, Y. Meng, X. T. Wang, D. L. Yin and M. Sillanpaa, J. Colloid Interface Sci., 2016, 465, 215–224 CrossRef CAS PubMed .
  61. I. Benesova, K. Dlabkova, F. Zelenak, T. Vaculovic, V. Kanicky and J. Preisler, Anal. Chem., 2016, 88(5), 2576–2582 CrossRef CAS PubMed .
  62. J. A. Hubbard and J. A. Zigmond, Spectrochim. Acta, Part B, 2016, 119, 50–64 CrossRef CAS .
  63. C. P. Jones, S. N. Lyman, D. A. Jaffe, T. Allen and T. L. O'Neil, Atmos. Meas. Tech., 2016, 9(5), 2195–2205 Search PubMed .
  64. Z. Y. Chen, S. J. Liu, J. L. Wang and Y. Z. Chang, Chin. J. Anal. Chem., 2016, 44(3), 468–473 CAS .
  65. S. L. Pathirana, C. van der Veen, M. E. Popa and T. Rockmann, Atmos. Meas. Tech., 2015, 8(12), 5315–5324 CrossRef CAS .
  66. L. A. Sobrado, M. R. Fernandez, S. C. Diaz, J. R. Encinar and J. I. G. Alonso, J. Chromatogr., 2015, 1419, 99–108 CrossRef PubMed .
  67. S. Toyoda and N. Yoshida, Atmos. Meas. Tech., 2016, 9(5), 2093–2101 CrossRef .
  68. R. Gemayel, S. Hellebust, B. Temime-Roussel, N. Hayeck, J. T. Van Elteren, H. Wortham and S. Gligorovski, Atmos. Meas. Tech., 2016, 9(4), 1947–1959 CrossRef .
  69. Y. Ozawa, N. Takeda, T. Miyakawa, M. Takei, N. Hirayama and N. Takegawa, Aerosol Sci. Technol., 2016, 50(2), 173–186 CrossRef CAS .
  70. F. Drewnick, J. M. Diesch, P. Faber and S. Borrmann, Atmos. Meas. Tech., 2015, 8(9), 3811–3830 CrossRef .
  71. J. C. Corbin, A. Othman, J. D. Allan, D. R. Worsnop, J. D. Haskins, B. Sierau, U. Lohmann and A. A. Mensah, Atmos. Meas. Tech., 2015, 8(11), 4615–4636 CrossRef .
  72. S. Carbone, T. Onasch, S. Saarikoski, H. Timonen, K. Saarnio, D. Sueper, T. Ronkko, L. Pirjola, A. Hayrinen, D. Worsnop and R. Hillamo, Atmos. Meas. Tech., 2015, 8(11), 4803–4815 CAS .
  73. M. Giannoni, G. Calzolai, M. Chiari, F. Lucarelli, A. Mazzinghi, S. Nava and C. Ruberto, X-Ray Spectrom., 2015, 44(4), 282–288 CrossRef CAS .
  74. J. P. Gorce and M. Roff, J. Occup. Environ. Hyg., 2016, 13(2), 102–111 CrossRef CAS PubMed .
  75. K. G. McIntosh, N. L. Cordes, B. M. Patterson and G. J. Havrilla, J. Anal. At. Spectrom., 2015, 30(7), 1511–1517 RSC .
  76. K. Shiota, M. Takaoka, T. Fujimori, K. Oshita and Y. Terada, Anal. Chem., 2015, 87(22), 11249–11254 CrossRef CAS PubMed .
  77. A. D. Jew, E. C. Rupp, D. L. Geatches, J. E. Jung, G. Farfan, L. Bahet, J. C. Hower, G. E. Brown and J. Wilcox, Energy Fuels, 2015, 29(9), 6025–6038 CrossRef CAS .
  78. J. D. Ward, M. Bowden, C. T. Resch, S. Smith, B. K. McNamara, E. C. Buck, G. C. Eiden and A. M. Duffin, Geostand. Geoanal. Res., 2016, 40(1), 135–148 CrossRef CAS .
  79. S. Beccaceci, E. A. McGhee, R. J. C. Brown and D. C. Green, Aerosol Sci. Technol., 2015, 49(9), 793–801 CrossRef CAS .
  80. A. Malaguti, M. Mircea, T. M. G. La Torretta, C. Telloli, E. Petralia, M. Stracquadanio and M. Berico, Aerosol Air Qual. Res., 2015, 15(7), 2641–2653 CAS .
  81. C. Perrino, M. Catrambone, C. Farao, R. Salzano, G. Esposito, M. Giusto, M. Montagnoli, A. Marini, M. Brinoni, G. Simonetti and S. Canepari, Aerosol Sci. Technol., 2015, 49(7), 521–530 CrossRef CAS .
  82. D. S. Kaul, Z. Ning, D. Westerdahl, X. J. Yin and R. A. Cary, Aerosol Air Qual. Res., 2016, 16(6), 1345–1355 CrossRef .
  83. B. Galfond, D. Riemer and P. Swart, Int. J. Greenhouse Gas Control, 2015, 42, 307–318 CrossRef CAS .
  84. K. Malowany, J. Stix, A. Van Pelt and G. Lucic, Atmos. Meas. Tech., 2015, 8(10), 4075–4082 CrossRef CAS .
  85. L. C. Brent, W. J. Thorn, M. Gupta, B. Leen, J. W. Stehr, H. He, H. L. Arkinson, A. Weinheimer, C. Garland, S. E. Pusede, P. J. Wooldridge, R. C. Cohen and R. R. Dickerson, J. Atmos. Chem., 2015, 72(3–4), 503–521 CrossRef CAS .
  86. S. Eyer, B. Tuzson, M. E. Popa, C. van der Veen, T. Rockmann, M. Rothe, W. A. Brand, R. Fisher, D. Lowry, E. G. Nisbet, M. S. Brennwald, E. Harris, C. Zellweger, L. Emmenegger, H. Fischer and J. Mohn, Atmos. Meas. Tech., 2016, 9(1), 263–280 CrossRef .
  87. J. R. Pitt, M. Le Breton, G. Allen, C. J. Percival, M. W. Gallagher, S. J. B. Bauguitte, S. J. O'Shea, J. B. A. Muller, M. S. Zahniser, J. Pyle and P. I. Palmer, Atmos. Meas. Tech., 2016, 9(1), 63–77 CrossRef CAS .
  88. J. C. Chow, X. L. Wang, B. J. Sumlin, S. B. Gronstal, L. W. A. Chen, D. L. Trimble, S. D. Kohl, S. R. Mayorga, G. Riggio, P. R. Hurbain, M. Johnson, R. Zimmermann and J. G. Watson, Aerosol Air Qual. Res., 2015, 15(4), 1145–1159 Search PubMed .
  89. N. Zikova, P. Vodicka, W. Ludwig, R. Hitzenberger and J. Schwarz, Aerosol Sci. Technol., 2016, 50(3), 284–296 CrossRef CAS .
  90. J. S. Ma, X. Li, P. S. Gu, T. R. Dallmann, A. A. Presto and N. M. Donahue, Aerosol Sci. Technol., 2016, 50(6), 638–651 CrossRef .
  91. G. Baccolo, M. Clemenza, B. Delmonte, N. Maffezzoli, M. Nastasi, E. Previtali, M. Prata, A. Salvini and V. Maggi, Anal. Chim. Acta, 2016, 922, 11–18 CrossRef CAS PubMed .
  92. M. Grafen, K. Nalpantidis, F. Platte, C. Monz and A. Ostendorf, Aerosol Sci. Technol., 2015, 49(10), 997–1008 CrossRef CAS .
  93. M. Chiari, G. Calzolai, M. Giannoni, F. Lucarelli, S. Nava and S. Becagli, J. Aerosol Sci., 2015, 89, 85–95 CrossRef CAS .
  94. A. M. Dillner and S. Takahama, Atmos. Meas. Tech., 2015, 8(10), 4013–4023 CAS .
  95. M. Reggente, A. M. Dillner and S. Takahama, Atmos. Meas. Tech., 2016, 9(2), 441–454 Search PubMed .
  96. F. Esaka, D. Suzuki, T. Yomogida and M. Magara, Anal. Methods, 2016, 8(7), 1543–1548 RSC .
  97. R. N. Andrews, H. A. Feng and K. Ashley, J. Occup. Environ. Hyg., 2016, 13(1), 40–47 CrossRef CAS PubMed .
  98. S. Yatkin, C. A. Belis, M. Gerboles, G. Calzolai, F. Lucarelli, F. Cavalli and K. Trzepla, Atmos. Environ., 2016, 125, 61–68 CrossRef CAS .
  99. C. Y. Kwok, O. Laurent, A. Guemri, C. Philippon, B. Wastine, C. W. Rella, C. Vuillemin, F. Truong, M. Delmotte, V. Kazan, M. Darding, B. Lebegue, C. Kaiser, I. Xueref-Remy and M. Ramonet, Atmos. Meas. Tech., 2015, 8(9), 3867–3892 CrossRef .
  100. V. Crenn, J. Sciare, P. L. Croteau, S. Verlhac, R. Frohlich, C. A. Belis, W. Aas, M. Äijälä, A. Alastuey, B. Artinano, D. Baisnee, N. Bonnaire, M. Bressi, M. Canagaratna, F. Canonaco, C. Carbone, F. Cavalli, E. Coz, M. J. Cubison, J. K. Esser-Gietl, D. C. Green, V. Gros, L. Heikkinen, H. Herrmann, C. Lunder, M. C. Minguillon, G. Mocnik, C. D. O'Dowd, J. Ovadnevaite, J. E. Petit, E. Petralia, L. Poulain, M. Priestman, V. Riffault, A. Ripoll, R. Sarda-Esteve, J. G. Slowik, A. Setyan, A. Wiedensohler, U. Baltensperger, A. S. H. Prevot, J. T. Jayne and O. Favez, Atmos. Meas. Tech., 2015, 8(12), 5063–5087 CAS .
  101. L. Du and J. Turner, Sci. Total Environ., 2015, 529, 65–71 CrossRef CAS PubMed .
  102. A. Font, K. de Hoogh, M. Leal-Sanchez, D. C. Ashworth, R. J. C. Brown, A. L. Hansell and G. W. Fuller, Atmos. Environ., 2015, 113, 177–186 CrossRef CAS .
  103. L. Cotte, M. Waeles, B. Pernet-Coudrier, P. M. Sarradin, C. Cathalot and R. D. Riso, Deep Sea Res., Part I, 2015, 105, 186–194 CrossRef CAS .
  104. M. Strok, P. A. Baya and H. Hintelmann, C. R. Geosci., 2015, 347(7–8), 368–376 CrossRef .
  105. A. Rodriguez-Cea, P. Rodriguez-Gonzalez and J. I. G. Alonso, Environ. Sci. Pollut. Res., 2016, 23(5), 4876–4885 CrossRef CAS PubMed .
  106. L. Pillay and A. Kindness, S. Afr. J. Chem., 2016, 69, 9–14 CrossRef CAS .
  107. D. Y. Deng, C. B. Zheng, X. D. Hou and L. Wu, Appl. Spectrosc. Rev., 2016, 50(8), 678–705 CrossRef .
  108. L. Okenicova, M. Zemberyova and S. Prochazkova, Environ. Chem. Lett., 2016, 14(1), 67–77 CrossRef CAS .
  109. I. Hagarova and M. Urik, Curr. Anal. Chem., 2016, 12(2), 87–93 CrossRef CAS .
  110. S. Sindern, J. Schwarzbauer, L. Gronen, A. Gortz, S. Heister and M. Bruchmann, Int. J. Environ. Anal. Chem., 2015, 95(9), 790–807 CAS .
  111. C. Bendicho, C. Bendicho-Lavilla and I. Lavilla, TrAC, Trends Anal. Chem., 2016, 77, 109–121 CrossRef CAS .
  112. M. Amde, Y. G. Yin, D. Zhang and J. F. Liu, Chem. Speciation Bioavailability, 2016, 28(1–4), 51–65 CrossRef .
  113. L. N. Suvarapu and S. O. Baek, J. Anal. Methods Chem., 2015, 372459 Search PubMed .
  114. C. F. Harrington, R. Clough, S. J. Hill, Y. Madrid and J. F. Tyson, J. Anal. At. Spectrom., 2015, 30(7), 1427–1468 RSC .
  115. J. Gorny, D. Dumoulin, L. Lesven, C. Noiriel, B. Made and G. Billon, J. Anal. At. Spectrom., 2015, 30(7), 1562–1570 RSC .
  116. M. Marcinkowska, I. Komorowicz and D. Baralkiewicz, Talanta, 2015, 144, 233–240 CrossRef CAS PubMed .
  117. M. Marcinkowska, I. Komorowicz and D. Baralkiewicz, Anal. Chim. Acta, 2016, 920, 102–111 CrossRef CAS PubMed .
  118. K. M. Diniz and C. R. T. Tarley, Microchem. J., 2015, 123, 185–195 CrossRef CAS .
  119. M. Birka, C. A. Wehe, O. Hachmoller, M. Sperling and U. Karst, J. Chromatogr., 2016, 1440, 105–111 CrossRef CAS PubMed .
  120. I. Lopez-Garcia, Y. Vicente-Martinez and M. Hernandez-Cordoba, J. Anal. At. Spectrom., 2015, 30(9), 1980–1987 RSC .
  121. Y. Q. Chen, X. Cheng, F. Mo, L. M. Huang, Z. J. Wu, Y. N. Wu, L. J. Xu and F. F. Fu, Electrophoresis, 2016, 37(7–8), 1055–1062 CrossRef CAS PubMed .
  122. G. J. Peng, X. Y. Zhu, J. G. Chen, X. Z. Jin, S. H. Chen and D. Y. Wei, Anal. Lett., 2015, 48(16), 2639–2649 CrossRef CAS .
  123. D. P. Bishop, D. J. Hare, A. de Grazia, F. Fryer and P. A. Doble, Anal. Methods, 2015, 7(12), 5012–5018 RSC .
  124. P. Paydary and P. Larese-Casanova, Int. J. Environ. Anal. Chem., 2015, 95(15), 1450–1470 CrossRef CAS .
  125. C. K. Su, M. H. Hsieh and Y. C. Sun, Anal. Chim. Acta, 2016, 914, 110–116 CrossRef CAS PubMed .
  126. T. K. Mudalige, H. O. Qu and S. W. Linder, Anal. Chem., 2015, 87(14), 7395–7401 CrossRef CAS PubMed .
  127. Z. Q. Tan, J. F. Liu, X. R. Guo, Y. G. Yin, S. K. Byeon, M. H. Moon and G. B. Jiang, Anal. Chem., 2015, 87(16), 8441–8447 CrossRef CAS PubMed .
  128. F. V. Nakadi, M. da Veiga, M. Aramendia, E. Garcia-Ruiz and M. Resano, J. Anal. At. Spectrom., 2015, 30(7), 1531–1540 RSC .
  129. E. Pena-Vazquez, M. C. Barciela-Alonso, C. Pita-Calvo, R. Dominguez-Gonzalez and P. Bermejo-Barrera, J. Appl. Spectrosc., 2015, 82(4), 681–686 CrossRef CAS .
  130. C. A. Martins, G. L. Scheffler and D. Pozebon, Anal. Methods, 2016, 8(3), 504–509 RSC .
  131. G. G. Vasile, N. M. Marin, J. Petre and L. V. Cruceru, J. Environ. Prot. Ecol., 2016, 17(1), 31–41 Search PubMed .
  132. L. G. Cao, W. T. Bu, J. Zheng, S. M. Pan, Z. T. Wang and S. G. Uchida, Talanta, 2016, 151, 30–41 CrossRef CAS PubMed .
  133. T. Yang, X. P. Bian, B. Zhu, S. Y. Jiang, X. Yan and H. Z. Wei, Anal. Methods, 2016, 8(7), 1721–1727 RSC .
  134. J. S. de Gois, P. Vallelonga, A. Spolaor, V. Devulder, D. L. G. Borges and F. Vanhaecke, Anal. Bioanal. Chem., 2016, 408(2), 409–416 CrossRef CAS PubMed .
  135. J. S. de Gois, M. Costas-Rodriguez, P. Vallelonga, D. L. G. Borges and F. Vanhaecke, J. Anal. At. Spectrom., 2016, 31(2), 537–542 RSC .
  136. O. Hanousek, T. W. Berger and T. Prohaska, Anal. Bioanal. Chem., 2016, 408(2), 399–407 CrossRef CAS PubMed .
  137. Y. T. Li, W. Guo, Z. W. Wu, L. L. Jin, Y. Q. Ke, Q. H. Guo and S. H. Hu, Microchem. J., 2016, 126, 194–199 CrossRef CAS .
  138. S. L. Zhong, R. E. Zheng, Y. Lu, K. Cheng and J. S. Xiu, Plasma Sci. Technol., 2015, 17(11), 979–984 CrossRef .
  139. N. Aras and S. Yalcin, Talanta, 2016, 149, 53–61 CrossRef CAS PubMed .
  140. Y. Gao, R. E. Sturgeon, Z. Mester, X. D. Hou and L. Yang, Anal. Chim. Acta, 2015, 901, 34–40 CrossRef CAS PubMed .
  141. Y. Gao, R. E. Sturgeon, Z. Mester, X. D. Hon, C. B. Zheng and L. Yang, Anal. Chem., 2015, 87(15), 7996–8004 CrossRef CAS PubMed .
  142. B. B. A. Francisco, A. A. Rocha, P. Grinberg, R. E. Sturgeon and R. J. Cassella, J. Anal. At. Spectrom., 2016, 31(3), 751–758 RSC .
  143. M. M. L. Guerrero, E. V. Alonso, J. M. C. Pavon, M. T. S. Cordero and A. G. de Torres, J. Anal. At. Spectrom., 2016, 31(4), 975–984 RSC .
  144. M. L. Lopez, S. A. Ceppi, M. L. Asar, R. E. Burgesser and E. E. Avila, Spectrochim. Acta, Part B, 2015, 113, 100–105 CrossRef CAS .
  145. X. F. Lin, S. X. Li and F. Y. Zheng, RSC Adv., 2016, 6(11), 9002–9006 RSC .
  146. J. Gorny, L. Lesven, G. Billon, D. Dumoulin, C. Noiriel, C. Pirovano and B. Made, Talanta, 2015, 144, 890–898 CrossRef CAS PubMed .
  147. H. Y. Peng, N. Zhang, M. He, B. B. Chen and B. Hu, Int. J. Environ. Anal. Chem., 2016, 96(3), 212–224 CrossRef CAS .
  148. E. Kazemi, S. Dadfarnia and A. M. H. Shabani, Talanta, 2015, 141, 273–278 CrossRef CAS PubMed .
  149. B. Parodi, A. Londonio, G. Polla and P. Smichowski, Microchem. J., 2015, 122, 70–75 CrossRef CAS .
  150. Z. A. Alothman, E. Yilmaz, M. A. Habila, I. H. Alsohaimi, A. M. Aldawsari, N. M. Al-Harbi and M. Soylak, RSC Adv., 2015, 5(129), 106905–106911 RSC .
  151. M. Krawczyk and M. Jeszka-Skowron, Microchem. J., 2016, 126, 296–301 CrossRef CAS .
  152. M. Soylak, A. Aydin and N. Kizil, J. AOAC Int., 2016, 99(1), 273–278 CrossRef CAS PubMed .
  153. S. Taguchi, M. Asaoka, E. Hirokami, N. Hata, H. Kuramitz, T. Kawakami and R. Miyatake, Anal. Methods, 2015, 7(16), 6545–6551 RSC .
  154. K. Pytlakowska, Microchim. Acta, 2016, 183(1), 91–99 CrossRef CAS .
  155. V. Romero, I. Costas-Mora, I. Lavilla and C. Bendicho, RSC Adv., 2016, 6(1), 669–676 RSC .
  156. J. Huber, L. E. Heimburger, J. E. Sonke, S. Ziller, M. Linden and K. Leopold, Anal. Chem., 2015, 87(21), 11122–11129 CrossRef CAS PubMed .
  157. M. Krawczyk and E. Stanisz, J. Anal. At. Spectrom., 2015, 30(11), 2353–2358 RSC .
  158. J. Lee, J. An, J. A. Kim and H. O. Yoon, Chemosphere, 2016, 142, 72–76 CrossRef CAS PubMed .
  159. L. Nyaba, J. M. Matong and P. N. Nomngongo, Microchim. Acta, 2016, 183(4), 1289–1297 CrossRef CAS .
  160. S. Chen, Y. J. Sun, J. B. Chao, L. P. Cheng, Y. Chen and J. F. Liu, J. Environ. Sci., 2016, 41, 211–217 CrossRef PubMed .
  161. G. L. Peng, Q. He, G. M. Zhou, Y. Li, X. X. Su, M. Z. Liu and L. L. Fan, Anal. Methods, 2015, 7(16), 6732–6739 RSC .
  162. Naeemullah, T. G. Kazi, H. I. Afridi, F. Shah, S. S. Arain, K. D. Brahman, J. Ali and M. S. Arain, Arabian J. Chem., 2016, 9(1), 105–113 CrossRef CAS .
  163. Y. Liu, M. He, B. B. Chen and B. Hu, Talanta, 2015, 142, 213–220 CrossRef CAS PubMed .
  164. S. M. Sorouraddin and S. Nouri, Anal. Methods, 2016, 8(6), 1396–1404 RSC .
  165. I. Gaubeur, M. A. Aguirre, N. Kovachev, M. Hidalgo and A. Canals, J. Anal. At. Spectrom., 2015, 30(12), 2541–2547 RSC .
  166. M. I. Leybourne, K. H. Johannesson and A. Asfaw, Arsenic, 2014, 79, 371–390 Search PubMed .
  167. W. A. Maher, M. J. Ellwood, F. Krikowa, G. Raber and S. Foster, J. Anal. At. Spectrom., 2015, 30(10), 2129–2183 RSC .
  168. B. C. Russell, I. W. Croudace and P. E. Warwick, Anal. Chim. Acta, 2016, 890, 7–20 CrossRef PubMed .
  169. M. Jablonska-Czapla, Journal of Elementology, 2015, 20(4), 1061–1075 Search PubMed .
  170. N. Belzile and Y. W. Chen, Appl. Geochem., 2015, 63, 83–92 CrossRef CAS .
  171. B. E. S. Costa, L. M. Coelho, C. S. T. Araujo, H. C. Rezende and N. M. M. Coelho, J. Chem., 2016, 11, 1427154 Search PubMed .
  172. M. Bodnar, M. Szczyglowska, P. Konieczka and J. Namiesnik, Crit. Rev. Food Sci. Nutr., 2016, 56(1), 36–55 CrossRef CAS PubMed .
  173. E. M. Kroukamp, T. Wondimu and P. B. C. Forbes, TrAC, Trends Anal. Chem., 2016, 77, 87–99 CrossRef CAS .
  174. N. Serrano, J. M. Diaz-Cruz, C. Arino and M. Esteban, TrAC, Trends Anal. Chem., 2015, 73, 129–145 CrossRef CAS .
  175. A. Gredilla, S. F. O. de Vallejuelo, N. Elejoste, A. de Diego and J. M. Madariaga, TrAC, Trends Anal. Chem., 2016, 76, 30–39 CrossRef CAS .
  176. A. Rabajczyk, Desalin. Water Treat., 2016, 57(3), 1598–1610 CrossRef .
  177. J. Cmelik, Z. Nainarova and P. Rysanek, Int. J. Environ. Anal. Chem., 2015, 95(12), 1090–1098 CrossRef CAS .
  178. Z. T. Wang, G. S. Yang, J. Zheng, L. G. Cao, H. J. Yu, Y. Zhu, K. Tagami and S. Uchida, Anal. Chem., 2015, 87(11), 5511–5515 CrossRef CAS PubMed .
  179. S. Yamasaki, A. Takeda, K. Kimura and N. Tsuchiya, Soil Sci. Plant Nutr., 2016, 62(2), 121–126 CrossRef CAS .
  180. R. Tisarum, J. H. Ren, X. L. Dong, H. Chen, J. T. Lessl and L. N. Q. Ma, Talanta, 2015, 144, 1171–1175 CrossRef CAS PubMed .
  181. P. Roux, D. Lemarchand, H. J. Hughes and M. P. Turpault, Geostand. Geoanal. Res., 2015, 39(4), 453–466 CrossRef CAS .
  182. I. Rodushkin, N. Pallavicini, E. Engstrom, D. Sorlin, B. Ohlander, J. Ingri and D. C. Baxter, J. Anal. At. Spectrom., 2016, 31(1), 220–233 RSC .
  183. L. Husakova, I. Urbanova, T. Sidova, T. Cahova, T. Faltys and J. Sramkova, Int. J. Environ. Anal. Chem., 2015, 95(10), 922–935 CrossRef CAS .
  184. J. H. Zhao, X. Yan, T. Y. Zhou, J. Wang, H. Y. Li, P. Zhang, H. Ding and L. Ding, J. Anal. At. Spectrom., 2015, 30(9), 1920–1926 RSC .
  185. T. L. Marques, H. Wiltsche, H. Motter, J. A. Nobrega and G. Knapp, J. Anal. At. Spectrom., 2015, 30(9), 1898–1905 RSC .
  186. S. Pradhan, J. Zhang, J. G. Qu and X. Yun, Anal. Lett., 2015, 48(13), 2136–2158 CrossRef CAS .
  187. B. A. Sadee, M. E. Foulkes and S. J. Hill, Food Addit. Contam., Part A, 2016, 33(3), 433–441 CrossRef CAS PubMed .
  188. M. Sadowska, E. Biadun and B. Krasnodebska-Ostrega, Chemosphere, 2016, 144, 1216–1223 CrossRef CAS PubMed .
  189. L. Guidotti, S. Q. Abad, P. Rodriguez-Gonzalez, J. I. G. Alonso and G. M. Beone, Environ. Sci. Pollut. Res., 2015, 22(22), 17569–17576 CrossRef CAS PubMed .
  190. Y. W. Chen, A. Alzahrani, T. L. Deng and N. Belzile, Anal. Chim. Acta, 2016, 905, 42–50 CrossRef CAS PubMed .
  191. A. Ohta and R. Kubota, Geostand. Geoanal. Res., 2016, 40(1), 117–134 CrossRef CAS .
  192. M. Mittermuller, J. Saatz and B. Daus, Chemosphere, 2016, 147, 155–162 CrossRef PubMed .
  193. B. Lesniewska, K. Kisielewska, J. Wiater and B. Godlewska-Zylkiewicz, Environ. Monit. Assess., 2016, 188(1), 13 CrossRef PubMed .
  194. C. A. C. do Nascimento, P. H. Pagliari, D. Schmitt, Z. Q. He and H. Waldrip, Sci. Rep., 2015, 5, 17967 CrossRef CAS PubMed .
  195. A. M. S. Lima, D. Araujo and S. L. C. Ferreira, Microchem. J., 2016, 126, 368–372 CrossRef CAS .
  196. P. Coufalik, O. Zverina and J. Komarek, Spectrochim. Acta, Part B, 2016, 118, 1–5 CrossRef CAS .
  197. L. C. Soares, F. B. Egreja, C. C. Windmoller and M. I. Yoshida, Rev. Bras. Cienc. Solo, 2015, 39(4), 1100–1111 CrossRef .
  198. J. Ordones, L. Fernandez, H. Romero, P. Carrera and J. Alvarado, Talanta, 2015, 141, 259–266 CrossRef CAS PubMed .
  199. M. Schneider, E. R. Pereira, I. N. B. Castilho, E. Carasek, B. Welz and I. B. G. Martens, Microchem. J., 2016, 125, 50–55 CrossRef CAS .
  200. A. T. Duarte, A. R. Borges, A. V. Zmozinski, M. B. Dessuy, B. Welz, J. B. de Andrade and M. G. R. Vale, Talanta, 2016, 146, 166–174 CrossRef CAS PubMed .
  201. A. S. Silva, G. C. Brandao, G. D. Matos and S. L. C. Ferreira, Talanta, 2015, 144, 39–43 CrossRef CAS PubMed .
  202. A. Virgilio, J. F. Rego, A. I. Barros and J. A. G. Neto, J. Braz. Chem. Soc., 2015, 26(10), 1988–1993 CAS .
  203. S. D. Souza, L. L. Francois, A. R. Borges, M. G. R. Vale and R. G. O. Araujo, Spectrochim. Acta, Part B, 2015, 114, 58–64 CrossRef .
  204. W. Boschetti, L. M. G. Dalagnol, M. Dullius, A. V. Zmozinski, E. M. Becker, M. G. R. Vale and J. B. de Andrade, Microchem. J., 2016, 124, 380–385 CrossRef CAS .
  205. J. S. de Gois, T. S. Almeida, R. M. de Andrade, I. M. Toaldo, M. T. Bordignon-Luiz and D. L. G. Borges, Microchem. J., 2016, 124, 283–289 CrossRef CAS .
  206. D. V. de Babos, M. A. Bechlin, A. I. Barros, E. C. Ferreira, J. A. G. Neto and S. R. de Oliveira, Talanta, 2016, 152, 457–462 CrossRef PubMed .
  207. K. Greda, K. Kurcbach, K. Ochromowicz, T. Lesniewicz, P. Jamroz and P. Pohl, J. Anal. At. Spectrom., 2015, 30(8), 1743–1751 RSC .
  208. Y. Lin, Y. Yang, Y. X. Li, L. Yang, X. D. Hou, X. B. Feng and C. B. Zheng, Environ. Sci. Technol., 2016, 50(5), 2468–2476 CrossRef CAS PubMed .
  209. M. H. Gonzalez and L. N. Santos, Anal. Lett., 2015, 48(15), 2434–2445 CrossRef CAS .
  210. X. C. Duan, J. Y. Zhang and F. L. Bu, Spectrochim. Acta, Part B, 2015, 111, 87–91 CrossRef CAS .
  211. X. D. Wen, Y. Gao, P. Wu, Z. Q. Tan, C. B. Zheng and X. D. Hou, J. Anal. At. Spectrom., 2016, 31(2), 415–422 RSC .
  212. C. C. Brombach, M. F. Ezzeldin, B. Chen, W. T. Corns, J. Feldmann and E. M. Krupp, Anal. Methods, 2015, 7(20), 8584–8589 RSC .
  213. K. Tirez, C. Vanhoof, J. Bronders, P. Seuntjens, N. Bleux, P. Berghmans, N. De Brucker and F. Vanhaecke, Environ. Sci.: Processes Impacts, 2015, 17(12), 2034–2050 CAS .
  214. D. D. Bussan, R. F. Sessums and J. V. Cizdziel, J. Anal. At. Spectrom., 2015, 30(7), 1668–1672 RSC .
  215. C. Y. Tai, S. J. Jiang and A. C. Sahayam, Food Chem., 2016, 192, 274–279 CrossRef CAS PubMed .
  216. A. Limbeck, P. Galler, M. Bonta, G. Bauer, W. Nischkauer and F. Vanhaecke, Anal. Bioanal. Chem., 2015, 407(22), 6593–6617 CrossRef CAS PubMed .
  217. G. O. Duodu, A. Goonetilleke, C. Allen and G. A. Ayoko, Anal. Chim. Acta, 2016, 898, 19–27 CrossRef PubMed .
  218. M. A. G. Nunes, M. Voss, G. Corazza, E. M. M. Flores and V. L. Dressler, Anal. Chim. Acta, 2016, 905, 51–57 CrossRef CAS PubMed .
  219. R. B. Georg and K. Newman, J. Anal. At. Spectrom., 2015, 30(9), 1935–1944 RSC .
  220. H. C. Liu, C. H. Chung, C. F. You and Y. H. Chiang, Anal. Bioanal. Chem., 2016, 408(2), 387–397 CrossRef CAS PubMed .
  221. S. Konegger-Kappel and T. Prohaska, Anal. Bioanal. Chem., 2016, 408(2), 431–440 CrossRef CAS PubMed .
  222. W. Q. Li, B. L. Beard and S. L. Li, J. Anal. At. Spectrom., 2016, 31(4), 1023–1029 RSC .
  223. S. E. Janssen, M. W. Johnson, J. D. Blum, T. Barkay and J. R. Reinfelder, Chem. Geol., 2015, 411, 19–25 CrossRef CAS .
  224. G. S. Yang, H. Tazoe and M. Yamada, Anal. Chim. Acta, 2016, 908, 177–184 CrossRef CAS PubMed .
  225. M. A. Amr, A. F. I. Helal, A. T. Al-Kinani and P. Balakrishnan, J. Environ. Radioact., 2016, 153, 73–87 CrossRef CAS PubMed .
  226. C. D. B. Amaral, R. S. Amais, L. L. Fialho, D. Schiavo, A. R. A. Nogueira and J. A. Nobrega, Microchem. J., 2015, 122, 29–32 CrossRef CAS .
  227. G. Z. Fang, Q. H. Lv, C. C. Liu, M. M. Huo and S. Wang, Anal. Methods, 2015, 7(20), 8617–8625 RSC .
  228. K. Malisova, R. Koplik and O. Mestek, Anal. Lett., 2015, 48(15), 2446–2462 CrossRef CAS .
  229. P. Novak, T. Zuliani, R. Milacic and J. Scancar, Anal. Chim. Acta, 2016, 915, 27–35 CrossRef CAS PubMed .
  230. D. P. Bao, Z. G. Oh and Z. Chen, Frontiers in Plant Science, 2016, 7, 32 Search PubMed .
  231. J. Navratilova, A. Praetorius, A. Gondikas, W. Fabienke, F. von der Kammer and T. Hofmann, Int. J. Environ. Res. Public Health, 2015, 12(12), 15756–15768 CrossRef CAS PubMed .
  232. B. Meermann, K. Wichmann, F. Lauer, F. Vanhaecke and T. A. Ternes, J. Anal. At. Spectrom., 2016, 31(4), 890–901 RSC .
  233. E. Chamizo, M. Christl and L. K. Fifield, Nucl. Instrum. Methods Phys. Res., Sect. B, 2015, 358, 45–51 CrossRef CAS .
  234. H. Matsuzaki, C. Nakano, Y. S. Tsuchiya, S. Ito, A. Morita, H. Kusuno, Y. Miyake, M. Honda, A. T. Bautista, M. Kawamoto and H. Tokuyama, Nucl. Instrum. Methods Phys. Res., Sect. B, 2015, 361, 63–68 CrossRef CAS .
  235. Y. Satou, K. Sueki, K. Sasa, T. Matsunaka, T. Takahashi, N. Shibayama, D. Izumi, N. Kinoshita and H. Matsuzaki, Nucl. Instrum. Methods Phys. Res., Sect. B, 2015, 361, 233–236 CrossRef CAS .
  236. Q. Liu, X. L. Zhao, X. L. Hou, G. Burr, W. J. Zhou, Y. C. Fu, N. Chen and L. Y. Zhang, Radiocarbon, 2016, 58(1), 147–156 CrossRef CAS .
  237. Y. Shibahara, T. Kubota, T. Fujii, S. Fukutani, K. Takamiya, M. Konno, S. Mizuno and H. Yamana, J. Radioanal. Nucl. Chem., 2016, 307(3), 2281–2287 CrossRef CAS .
  238. S. Paul, A. K. Pandey, R. V. Shah, D. Alamelu and S. K. Aggarwal, RSC Adv., 2016, 6(4), 3326–3334 RSC .
  239. Q. C. Xu, Y. L. Dong, H. Y. Zhu and A. D. Sun, Int. J. Anal. Chem., 2015, 6, 364242 Search PubMed .
  240. M. S. Snow, D. C. Snyder, N. R. Mann and B. M. White, Int. J. Mass Spectrom., 2015, 381, 17–24 CrossRef .
  241. S. C. Jantzi, V. Motto-Ros, F. Trichard, Y. Markushin, N. Melikechi and A. De Giacomo, Spectrochim. Acta, Part B, 2016, 115, 52–63 CrossRef CAS .
  242. N. B. Zorov, A. M. Popov, S. M. Zaytsev and T. A. Labutin, Russ. Chem. Rev., 2015, 84(10), 1021–1050 CrossRef CAS .
  243. K. Q. Yu, Y. R. Zhao, F. Liu, J. Y. Peng and Y. He, Spectrosc. Spectral Anal., 2016, 36(3), 827–833 CAS .
  244. D. S. Meng, N. J. Zhao, M. J. Ma, Y. Wang, L. Hu, Y. Yu, L. Fang and W. Q. Liu, Plasma Sci. Technol., 2015, 17(8), 632–637 CrossRef .
  245. G. Nicolodelli, G. S. Senesi, R. A. Romano, I. L. D. Perazzoli and D. Milori, Spectrochim. Acta, Part B, 2015, 111, 23–29 CrossRef CAS .
  246. R. X. Yi, L. B. Guo, C. M. Li, X. Y. Yang, J. M. Li, X. Y. Li, X. Y. Zeng and Y. F. Lu, J. Anal. At. Spectrom., 2016, 31(4), 961–967 RSC .
  247. R. X. Yi, L. B. Guo, X. H. Zou, J. M. Li, Z. Q. Hao, X. Y. Yang, X. Y. Li, X. Y. Zeng and Y. F. Lu, Opt. Express, 2016, 24(3), 2607–2618 CrossRef CAS PubMed .
  248. A. M. Popov, M. O. Kozhnov, S. M. Zaytsev, N. B. Zorov and T. A. Labutin, J. Appl. Spectrosc., 2015, 82(5), 739–743 CrossRef CAS .
  249. A. A. Ambushe, A. du Plessis and R. I. McCrindle, Bull. Chem. Soc. Ethiop., 2015, 29(3), 357–366 CrossRef CAS .
  250. B. H. Zhang, Y. C. Jiang, X. Y. Zhang and Z. F. Cui, Spectrosc. Spectral Anal., 2015, 35(6), 1715–1718 CAS .
  251. Z. Haider, J. Ali, M. Arab, Y. bin Munajat, S. Roslan, R. Kamarulzman and N. Bidin, Anal. Lett., 2016, 49(6), 808–817 CrossRef CAS .
  252. T. B. Chen, L. Huang, M. Y. Yao, H. Q. Hu, C. H. Wang and M. H. Liu, Appl. Opt., 2015, 54(25), 7807–7812 CrossRef PubMed .
  253. P. C. Zheng, M. J. Shi, J. M. Wang and H. D. Liu, Plasma Sci. Technol., 2015, 17(8), 664–670 CrossRef .
  254. M. B. B. Guerra, A. Adame, E. de Almeida, G. G. A. de Carvalho, M. A. S. Brasil, D. Santos and F. J. Krug, J. Anal. At. Spectrom., 2015, 30(7), 1646–1654 RSC .
  255. E. C. Ferreira, J. A. G. Neto, D. Milori, E. J. Ferreira and J. M. Anzano, Spectrochim. Acta, Part B, 2015, 110, 96–99 CrossRef CAS .
  256. P. R. Villas-Boas, R. A. Romano, M. A. D. Franco, E. C. Ferreira, E. J. Ferreira, S. Crestana and D. Milori, Geoderma, 2016, 263, 195–202 CrossRef CAS .
  257. G. Yang, S. J. Qiao, P. F. Chen, Y. Ding and D. Tian, Plasma Sci. Technol., 2015, 17(8), 656–663 CrossRef .
  258. L. Borgese, F. Bilo, R. Dalipi, E. Bontempi and L. E. Depero, Spectrochim. Acta, Part B, 2015, 113, 1–15 CrossRef CAS .
  259. P. Vijayan, I. R. Willick, R. Lahlali, C. Karunakaran and K. K. Tanino, Plant Cell Physiol., 2015, 56(7), 1252–1263 CrossRef CAS PubMed .
  260. N. Kallithrakas-Kontos, S. Foteinis, K. Paigniotaki and M. Papadogiannakis, Environ. Monit. Assess., 2016, 188(2), 120 CrossRef PubMed .
  261. Y. T. Kim, J. Lee, H. O. Yoon and N. C. Woo, Microchem. J., 2016, 124, 594–599 CrossRef CAS .
  262. N. V. Campos, M. B. B. Guerra, J. W. V. Mello, C. Schaefer, F. J. Krug, E. E. N. Alves and A. A. Azevedo, J. Anal. At. Spectrom., 2015, 30(12), 2375–2383 RSC .
  263. E. V. Chuparina, A. N. Smagunova and L. A. Eliseeva, J. Anal. Chem., 2015, 70(8), 949–955 CrossRef CAS .
  264. J. An, J. Lee and H. O. Yoon, Microchem. J., 2015, 122, 76–81 CrossRef CAS .
  265. A. R. Schneider, B. Cances, C. Breton, M. Ponthieu, X. Morvan, A. Conreux and B. Marin, J. Soils Sediments, 2016, 16(2), 438–448 CrossRef CAS .
  266. U. Stockmann, S. R. Cattle, B. Minasny and A. B. McBratney, Catena, 2016, 139, 220–231 CrossRef CAS .
  267. K. G. McIntosh, D. Guimaraes, M. J. Cusack, A. Vershinin, Z. W. Chen, K. Yang and P. J. Parsons, Int. J. Environ. Anal. Chem., 2016, 96(1), 15–37 CrossRef CAS .
  268. E. K. Towett, K. D. Shepherd and B. L. Drake, X-Ray Spectrom., 2016, 45(2), 117–124 CrossRef CAS .
  269. E. K. Towett, K. D. Shepherd, A. Sila, E. Aynekulu and G. Cadisch, Soil Sci. Soc. Am. J., 2015, 79(5), 1375–1385 CrossRef CAS .
  270. D. C. Weindorf, S. Chakraborty, J. Herrero, B. Li, C. Castaneda and A. Choudhury, Eur. J. Soil Sci., 2016, 67(2), 173–183 CrossRef CAS .
  271. J. Barros, P. F. de Souza, D. Schiavo and J. A. Nobrega, J. Anal. At. Spectrom., 2016, 31(1), 337–343 RSC .
  272. C. A. Martins, C. Cerveira, G. L. Scheffler and D. Pozebon, Food Anal. Methods, 2015, 8(7), 1652–1660 CrossRef .
  273. L. Ma, L. Wang, J. Tang and Z. G. Yang, Food Chem., 2016, 204, 283–288 CrossRef CAS PubMed .
  274. K. Chandrasekaran, P. R. Mamatha and D. Karunasagar, At. Spectrosc., 2015, 36(5), 202–209 CAS .
  275. G. Ahmed, D. Takuwa, I. T. Chibua, Z. Bagai, L. Morekisi, H. Shoniwa, B. Sethebe and K. Sichilongo, Commun. Soil Sci. Plant Anal., 2016, 47(4), 512–520 CAS .
  276. A. L. H. Muller, E. I. Muller, J. S. Barin and E. M. M. Flores, Anal. Methods, 2015, 7(12), 5218–5225 RSC .
  277. M. H. Rashid, Z. Fardous, M. A. Z. Chowdhury, M. K. Alam, M. L. Bari, M. Moniruzzaman and S. H. Gan, Chem. Cent. J., 2016, 10, 7 CrossRef PubMed .
  278. S. F. Ferrarini, H. S. dos Santos, L. G. Miranda, C. M. N. Azevedo, S. M. Maia, E. S. Chaves and M. J. R. Pires, At. Spectrosc., 2015, 36(5), 187–195 CAS .
  279. S. R. Choudhury, D. C. Dutta, A. Karmakar, A. Das and Y. K. Shami, At. Spectrosc., 2016, 37(2), 37–42 CAS .
  280. H. Altundag, Fresenius Environ. Bull., 2015, 24(12A), 4452–4457 CAS .
  281. V. C. D. Peronico and J. L. Raposo, Food Chem., 2016, 196, 1287–1292 CrossRef CAS PubMed .
  282. A. M. S. Mimura, M. A. L. Oliveira, V. S. T. Ciminelli and J. C. J. Silva, J. AOAC Int., 2016, 99(1), 252–259 CrossRef CAS PubMed .
  283. H. Altundag, S. Albayrak, M. S. Dundar, M. Tuzen and M. Soylak, At. Spectrosc., 2015, 36(4), 159–164 CAS .
  284. L. L. S. Almeida, M. D. R. Oliveira, J. B. B. Silva and N. M. M. Coelho, Microchem. J., 2016, 124, 326–330 CrossRef .
  285. L. A. Mampuru, N. A. Panichev, P. Ngobeni, K. L. Mandiwana and M. M. Kalumba, S. Afr. J. Chem., 2015, 68, 57–60 CrossRef CAS .
  286. M. Soylak and S. Yigit, At. Spectrosc., 2015, 36(4), 165–170 CAS .
  287. Z. Bahadir, V. N. Bulut, H. Bektas and M. Soylak, RSC Adv., 2016, 6(9), 6896–6904 RSC .
  288. E. Yilmaz and M. Soylak, Anal. Chim. Acta, 2015, 886, 75–82 CrossRef CAS PubMed .
  289. Naeemullah, M. Tuzen and T. G. Kazi, RSC Adv., 2016, 6(34), 28767–28773 RSC .
  290. L. dos Santos, Q. O. dos Santos, I. Moreno, C. G. Novaes, M. J. S. dos Santos and M. A. Bezerra, J. Braz. Chem. Soc., 2016, 27(4), 745–752 CAS .
  291. M. Karimi, A. M. H. Shabani and S. Dadfarnia, Microchim. Acta, 2016, 183(2), 563–571 CrossRef CAS .
  292. F. Aydin, E. Yilmaz and M. Soylak, RSC Adv., 2015, 5(115), 94879–94886 RSC .
  293. E. Yilmaz and M. Soylak, J. Anal. At. Spectrom., 2015, 30(7), 1629–1635 RSC .
  294. C. Labrecque, P. J. Lebed and D. Lariviere, J. Environ. Radioact., 2016, 155, 15–22 CrossRef PubMed .
  295. G. Leng, W. J. Chen and Y. Wang, J. Sep. Sci., 2015, 38(15), 2684–2691 CrossRef CAS PubMed .
  296. J. Barros, M. A. Aguirre, N. Kovachev, A. Canals and J. A. Nobrega, Anal. Methods, 2016, 8(4), 810–815 RSC .
  297. M. Shokri, A. Beiraghi and S. Seidi, Anal. Chim. Acta, 2015, 889, 123–129 CrossRef CAS PubMed .
  298. M. Tuzen and O. Z. Pekiner, Food Chem., 2015, 188, 619–624 CrossRef CAS PubMed .
  299. F. Omidi, M. Behbahani, S. J. Shahtaheri and S. Salimi, Environ. Monit. Assess., 2015, 187(6), 10 CrossRef PubMed .
  300. M. Amjadi, A. Samadi, J. L. Manzoori and N. Arsalani, Anal. Methods, 2015, 7(14), 5847–5853 RSC .
  301. A. Duran, M. Tuzen and M. Soylak, J. AOAC Int., 2015, 98(6), 1733–1738 CrossRef CAS PubMed .
  302. Y. E. Unsal, M. Tuzen and M. Soylak, J. AOAC Int., 2016, 99(2), 534–538 CrossRef CAS PubMed .
  303. S. A. Rezvani and A. Soleymanpour, J. Chromatogr., 2016, 1436, 34–41 CrossRef CAS PubMed .
  304. S. Sivrikaya, M. Imamoglu, S. Z. Yildiz and D. Kara, Anal. Lett., 2016, 49(7), 943–957 CrossRef CAS .
  305. M. Khan, E. Yilmaz, B. Sevinc, E. Sahmetlioglu, J. Shah, M. R. Jan and M. Soylak, Talanta, 2016, 146, 130–137 CrossRef CAS PubMed .
  306. M. Shirani, A. Akbari and M. Hassani, Anal. Methods, 2015, 7(14), 6012–6020 RSC .
  307. H. Alkan, R. Gul-Guven, K. Guven, S. Erdogan and M. Dogru, Pol. J. Environ. Stud., 2015, 24(5), 1903–1910 CrossRef CAS .
  308. H. Bagheri, A. A. Asgharinezhad and H. Ebrahimzadeh, Food Anal. Methods, 2016, 9(4), 876–888 CrossRef .
  309. S. Z. Mohammad, T. Shamspur, E. Shahsavani and S. Fozooni, J. AOAC Int., 2015, 98(3), 828–833 Search PubMed .
  310. A. A. Gouda and S. M. Al Ghannam, Food Chem., 2016, 202, 409–416 CrossRef CAS PubMed .
  311. N. B. Burham, S. M. Abdel-Azeem, S. R. Abdel-Hafeez and M. F. El-Shahat, Desalin. Water Treat., 2015, 56(11), 3024–3035 CAS .
  312. M. Behbahani, P. G. Hassanlou, M. M. Amini, F. Omidi, A. Esrafili, M. Farzadkia and A. Bagheri, Food Chem., 2015, 187, 82–88 CrossRef CAS PubMed .
  313. S. Ozdemir, E. Kilinc, V. Okumus, A. Poli, B. Nicolaus and I. Romano, Bioresour. Technol., 2016, 201, 269–275 CrossRef CAS PubMed .
  314. E. Yilmaz, I. Ocsoy, N. Ozdemir and M. Soylak, Anal. Chim. Acta, 2016, 906, 110–117 CrossRef CAS PubMed .
  315. M. Habila, Y. E. Unsal, Z. A. Alothman, A. Shabaka, M. Tuzen and M. Soylak, Anal. Lett., 2015, 48(14), 2258–2271 CrossRef CAS .
  316. L. Zhang, X. D. Gao, Z. C. Xiong, L. Y. Zhang, B. H. Yu, R. S. Zhang and W. B. Zhang, RSC Adv., 2015, 5(72), 58873–58879 RSC .
  317. Z. A. Alothman, E. Yilmaz, M. Habila and M. Soylak, Turk. J. Chem., 2015, 39(5), 1038–1049 CrossRef CAS .
  318. E. Yavuz, S. Tokalioglu, H. Sahan and S. Patat, Food Chem., 2016, 194, 463–469 CrossRef CAS PubMed .
  319. N. Ashouri, A. Mohammadi, M. Shekarchi, R. Hajiaghaee and H. Rastegar, Desalin. Water Treat., 2015, 56(8), 2135–2144 CrossRef CAS .
  320. M. A. Karimi and M. Kafi, Arabian J. Chem., 2015, 8(6), 812–820 CrossRef CAS .
  321. N. B. Wutke, K. M. Diniz, M. Z. Corazza, F. M. de Oliveira, E. S. Ribeiro, B. T. da Fonseca, M. G. Segatelli and C. R. T. Tarley, Anal. Lett., 2016, 49(5), 723–736 CrossRef CAS .
  322. R. H. Su, G. H. Ruan, Z. Y. Chen, F. Y. Du and J. P. Li, J. Sep. Sci., 2015, 38(24), 4262–4268 CrossRef CAS PubMed .
  323. D. M. Nanicuacua, M. G. Segatelli, M. Z. Corazzaa and C. R. T. Tarley, Anal. Methods, 2016, 8(13), 2820–2830 RSC .
  324. A. Mehdinia, M. Asiabi and A. Jabbari, Int. J. Environ. Anal. Chem., 2015, 95(12), 1099–1111 CrossRef CAS .
  325. W. I. Mortada, A. F. Moustafa, A. M. Ismail, M. M. Hassanien and A. A. Aboud, RSC Adv., 2015, 5(77), 62414–62423 RSC .
  326. F. Z. Teng, W. Y. Li, S. Ke, W. Yang, S. A. Liu, F. Sedaghatpour, S. J. Wang, K. J. Huang, Y. Hu, M. X. Ling, Y. Xiao, X. M. Liu, X. W. Li, H. O. Gu, C. K. Sio, D. A. Wallace, B. X. Su, L. Zhao, J. Chamberlin, M. Harrington and A. Brewer, Geostand. Geoanal. Res., 2015, 39(3), 329–339 CrossRef CAS .
  327. T. Cheng, O. Nebel, P. Sossi and F. K. Chen, Int. J. Mass Spectrom., 2015, 386, 61–66 CrossRef CAS .
  328. P.-P. Zhao, J. Li, L. Zhang, Z.-B. Wang, D.-X. Kong, J.-L. Ma, G.-J. Wei and J.-F. Xu, Geostand. Geoanal. Res., 2016, 40(2), 217–226 CrossRef CAS .
  329. A. Fourny, D. Weis and J. S. Scoates, Geochem., Geophys., Geosyst., 2016, 17(3), 739–773 CrossRef CAS .
  330. J. Jweda, L. Bolge, C. Class and S. L. Goldstein, Geostand. Geoanal. Res., 2016, 40(1), 101–115 CrossRef CAS .
  331. K. P. Jochum, U. Weis, B. Schwager, B. Stoll, S. A. Wilson, G. H. Haug, M. O. Andreae and J. Enzweiler, Geostand. Geoanal. Res., 2016, 40(3), 333–350 CrossRef CAS .
  332. K. P. Jochum, S. A. Wilson, H. Becker, D. Garbe-Schonberg, N. Groschopf, Y. Kadlag, D. S. Macholdt, R. Mertz-Kraus, L. M. Otter, B. Stoll, A. Stracke, U. Weis, G. H. Haug and M. O. Andreae, Chem. Geol., 2016, 432, 34–40 CrossRef CAS .
  333. L. Zhu, Y. Liu, T. Ma, J. Lin, Z. Hu and C. Wang, J. Anal. At. Spectrom., 2016, 31(7), 1414–1422 RSC .
  334. L. P. Bedard, K. H. Esbensen and S. J. Barnes, Anal. Chem., 2016, 88(7), 3504–3511 CrossRef CAS PubMed .
  335. C. Spandler, J. Hammerli, P. Sha, H. Hilbert-Wolf, Y. Hu, E. Roberts and M. Schmitz, Chem. Geol., 2016, 425, 110–126 CrossRef CAS .
  336. B. X. Su, X. Y. Gu, E. Deloule, H. F. Zhang, Q. L. Li, X. H. Li, N. Vigier, Y. J. Tang, G. Q. Tang, Y. Liu, K. N. Pang, A. Brewer, Q. Mao and Y. G. Ma, Geostand. Geoanal. Res., 2015, 39(3), 357–369 CrossRef CAS .
  337. K. Suga, T. Hirata, M. Fukuyama and M. Ogasawara, Geochem. J., 2015, 49(4), 421–424 CrossRef CAS .
  338. C. J. Soares, R. Mertz-Kraus, S. Guedes, D. F. Stockli and T. Zack, Geostand. Geoanal. Res., 2015, 39(3), 305–313 CrossRef CAS .
  339. S. D. Zhang, M. H. He, Z. B. Yin, E. Y. Zhu, W. Hang and B. L. Huang, J. Anal. At. Spectrom., 2016, 31(2), 358–382 RSC .
  340. J. Fietzke and M. Frische, J. Anal. At. Spectrom., 2016, 31(1), 234–244 RSC .
  341. T. Ubide, C. A. McKenna, D. M. Chew and B. S. Kamber, Chem. Geol., 2015, 409, 157–168 CrossRef CAS .
  342. J. Hirata, K. Takahashi, Y. V. Sahoo and M. Tanaka, Chem. Geol., 2016, 427, 65–72 CrossRef CAS .
  343. M. Bonta, A. Limbeck, C. D. Quarles Jr, D. Oropeza, R. E. Russo and J. J. Gonzalez, J. Anal. At. Spectrom., 2015, 30(8), 1809–1815 RSC .
  344. S. J. M. Van Malderen, A. J. Managh, B. L. Sharp and F. Vanhaecke, J. Anal. At. Spectrom., 2016, 31(2), 423–439 RSC .
  345. D. N. Douglas, A. J. Managh, H. J. Reid and B. L. Sharp, Anal. Chem., 2015, 87(22), 11285–11294 CrossRef CAS PubMed .
  346. A. Gundlach-Graham, M. Burger, S. Allner, G. Schwarz, H. A. O. Wang, L. Gyr, D. Grolimund, B. Hattendorf and D. Gunther, Anal. Chem., 2015, 87(16), 8250–8258 CrossRef CAS PubMed .
  347. M. Burger, A. Gundlach-Graham, S. Allner, G. Schwarz, H. A. O. Wang, L. Gyr, S. Burgener, B. Hattendorf, D. Grolimund and D. Gunther, Anal. Chem., 2015, 87(16), 8259–8267 CrossRef CAS PubMed .
  348. M. Harlaux, O. Borovinskaya, D. A. Frick, D. Tabersky, S. Gschwind, A. Richard, D. Gunther and J. Mercadier, J. Anal. At. Spectrom., 2015, 30(9), 1945–1969 RSC .
  349. C. C. Wohlgemuth-Ueberwasser and K. P. Jochum, J. Anal. At. Spectrom., 2015, 30(12), 2469–2480 RSC .
  350. M. Lazarov and I. Horn, Spectrochim. Acta, Part B, 2015, 111, 64–73 CrossRef CAS .
  351. C. Toyama, J. I. Kimura, Q. Chang, B. S. Vaglarov and J. Kuroda, J. Anal. At. Spectrom., 2015, 30(10), 2194–2207 RSC .
  352. M. N. Dai, Z. A. Bao, K. Y. Chen and H. L. Yuan, Chin. J. Anal. Chem., 2016, 44(2), 173–177 CAS .
  353. Y. Kon and T. Hirata, Geochem. J., 2015, 49(4), 351–375 CrossRef CAS .
  354. Z. A. Bao, H. L. Yuan, C. L. Zong, Y. Liu, K. Y. Chen and Y. L. Zhang, J. Anal. At. Spectrom., 2016, 31(4), 1012–1022 RSC .
  355. J. I. Kimura, Q. Chang, N. Kanazawa, S. Sasaki and B. S. Vaglarov, J. Anal. At. Spectrom., 2016, 31(3), 790–800 RSC .
  356. D. R. Viete, A. R. C. Kylander-Clark and B. R. Hacker, Chem. Geol., 2015, 415, 70–86 CrossRef CAS .
  357. J. H. Marsh and D. F. Stockli, Lithos, 2015, 239, 170–185 CrossRef CAS .
  358. B. R. Hacker, A. C. Kylander-Clark, R. Holder, T. B. Andersen, E. M. Peterman, E. O. Walsh and J. K. Munnikhuis, Chem. Geol., 2015, 409, 28–41 CrossRef CAS .
  359. E. Bolea-Fernandez, S. J. M. Van Malderen, L. Balcaen, M. Resano and F. Vanhaecke, J. Anal. At. Spectrom., 2016, 31(2), 464–472 RSC .
  360. T. Zack and K. J. Hogmalm, Chem. Geol., 2016, 437, 120–133 CrossRef CAS .
  361. W. Muller and R. Anczkiewicz, J. Anal. At. Spectrom., 2016, 31(1), 259–269 RSC .
  362. Y. L. Sun, M. H. Ren, X. P. Xia, C. Y. Li and W. D. Sun, Spectrochim. Acta, Part B, 2015, 113, 22–29 CrossRef CAS .
  363. E. A. Dennis, S. J. Ray, C. G. Enke and G. M. Hieftje, J. Am. Soc. Mass Spectrom., 2015, 27(3), 380–387 CrossRef PubMed .
  364. E. A. Dennis, S. J. Ray, C. G. Enke, A. W. Gundlach-Graham, C. J. Barinaga, D. W. Koppenaal and G. M. Hieftje, J. Am. Soc. Mass Spectrom., 2015, 27(3), 371–379 CrossRef PubMed .
  365. A. A. Bol'shakov, X. L. Mao, J. J. Gonzalez and R. E. Russo, J. Anal. At. Spectrom., 2016, 31(1), 119–134 RSC .
  366. T. Xu, J. Liu, Q. Shi, Y. He, G. H. Niu and Y. X. Duan, Spectrochim. Acta, Part B, 2016, 115, 31–39 CrossRef CAS .
  367. J. E. Birdwell and K. E. Washburn, Energy Fuels, 2015, 29(11), 6999–7004 CrossRef CAS .
  368. Q. Shi, G. H. Niu, Q. Y. Lin, T. Xu, F. J. Li and Y. X. Duan, J. Anal. At. Spectrom., 2015, 30(12), 2384–2393 RSC .
  369. M. T. Sweetapple and S. Tassios, Am. Mineral., 2015, 100(10), 2141–2151 CrossRef .
  370. K. A. Kochelek, N. J. McMillan, C. E. McManus and D. L. Daniel, Am. Mineral., 2015, 100(8–9), 1921–1931 CrossRef .
  371. S. Maurice, S. M. Clegg, R. C. Wiens, O. Gasnault, W. Rapin, O. Forni, A. Cousin, V. Sautter, N. Mangold, L. Le Deit, M. Nachon, R. B. Anderson, N. L. Lanza, C. Fabre, V. Payre, J. Lasue, P. Y. Meslin, R. J. Leveille, L. Barraclough, P. Beck, S. C. Bender, G. Berger, J. C. Bridges, N. T. Bridges, G. Dromart, M. D. Dyar, R. Francis, J. Frydenvang, B. Gondet, B. L. Ehlmann, K. E. Herkenhoff, J. R. Johnson, Y. Langevin, M. B. Madsen, N. Melikechi, J. L. Lacour, S. Le Mouelic, E. Lewin, H. E. Newsom, A. M. Ollila, P. Pinet, S. Schroder, J. B. Sirven, R. L. Tokar, M. J. Toplis, C. d'Uston, D. T. Vaniman and A. R. Vasavada, J. Anal. At. Spectrom., 2016, 31(4), 863–889 RSC .
  372. M. N. Abedin, A. T. Bradley, S. K. Sharma, A. K. Misra, P. G. Lucey, C. P. McKay, S. Ismail and S. P. Sandford, Appl. Opt., 2015, 54(25), 7598–7611 CrossRef PubMed .
  373. M. B. Neuland, V. Grimaudo, K. Mezger, P. Moreno-Garcia, A. Riedo, M. Tulej and P. Wurz, Meas. Sci. Technol., 2016, 27(3), 13 CrossRef .
  374. M. Tulej, A. Neubeck, M. Ivarsson, A. Riedo, M. B. Neuland, S. Meyer and P. Wurz, Astrobiology, 2015, 15(8), 669–682 CrossRef CAS PubMed .
  375. J. P. Sertek, S. Andrade and H. H. Ulbrich, Geostand. Geoanal. Res., 2015, 39(3), 381–397 CrossRef CAS .
  376. W. Zhang, L. Qi, Z. Hu, C. Zheng, Y. Liu, H. Chen, S. Gao and S. Hu, Geostand. Geoanal. Res., 2016, 40(2), 195–216 CrossRef CAS .
  377. P. J. Potts, P. C. Webb and M. Thompson, Geostand. Geoanal. Res., 2015, 39(3), 315–327 CrossRef CAS .
  378. A. Tamura, N. Akizawa, R. Otsuka, K. Kanayama, M. Python, T. Morishita and S. Arai, Geochem. J., 2015, 49(3), 243–258 CrossRef CAS .
  379. Z. W. He, F. Huang, H. M. Yu, Y. L. Xiao, F. Y. Wang, Q. L. Li, Y. Xia and X. C. Zhang, Geostand. Geoanal. Res., 2016, 40(1), 5–27 CrossRef CAS .
  380. Y. Wang, L. A. Baker and I. D. Brindle, Talanta, 2016, 148, 419–426 CrossRef CAS PubMed .
  381. D. S. Xue, H. Y. Wang, Y. H. Liu, P. Shen and J. F. Sun, Anal. Methods, 2016, 8(1), 29–39 RSC .
  382. C. Du, L. Luo, W. Guo, L. L. Jin, B. Chen and S. H. Hu, At. Spectrosc., 2015, 36(3), 141–145 Search PubMed .
  383. H. Cui, W. Guo, M. T. Cheng, P. Zhang, L. L. Jin, Q. H. Guo and S. H. Hu, Anal. Methods, 2015, 7(20), 8970–8976 RSC .
  384. D. S. Xue, H. Y. Wang, Y. H. Liu and P. Shen, Miner. Eng., 2015, 81, 149–151 CrossRef CAS .
  385. A. Zuber, M. Purdey, E. Schartner, C. Forbes, B. van der Hoek, D. Giles, A. Abell, T. Monro and H. Ebendorff-Heidepriem, Sens. Actuators, B, 2016, 227, 117–127 CrossRef CAS .
  386. L. Whitty-Léveillé, E. Drouin, M. Constantin, C. Bazin and D. Lariviere, Spectrochim. Acta, Part B, 2016, 118, 112–118 CrossRef .
  387. S. D. Fernández, J. R. Encinar, A. Sanz-Medel, K. Isensee and H. M. Stoll, Geochem., Geophys., Geosyst., 2015, 16(6), 2005–2014 CrossRef .
  388. E. Bolea-Fernandez, L. Balcaen, M. Resano and F. Vanhaecke, J. Anal. At. Spectrom., 2016, 31(1), 303–310 RSC .
  389. G. Schudel, V. Lai, K. Gordon and D. Weis, Chem. Geol., 2015, 410, 223–236 CrossRef CAS .
  390. Q. Xu, W. Guo, L. L. Jin, Q. H. Guo and S. H. Hu, J. Anal. At. Spectrom., 2015, 30(9), 2010–2016 RSC .
  391. A. Michel, J. Noireaux and M. Tharaud, Geostand. Geoanal. Res., 2015, 39(4), 489–495 CrossRef CAS .
  392. R. M. Gaschnig, R. L. Rudnick and W. F. McDonough, Geostand. Geoanal. Res., 2015, 39(3), 371–379 CrossRef CAS .
  393. S. J. Romaniello, M. P. Field, H. B. Smith, G. W. Gordon, M. H. Kim and A. D. Anbar, J. Anal. At. Spectrom., 2015, 30(9), 1906–1912 RSC .
  394. J. Irrgeher and T. Prohaska, Anal. Bioanal. Chem., 2016, 408(2), 369–385 CrossRef CAS PubMed .
  395. M. Horsky, J. Irrgeher and T. Prohaska, Anal. Bioanal. Chem., 2016, 408(2), 351–367 CrossRef CAS PubMed .
  396. F. Z. Teng, Q. Z. Yin, C. V. Ullmann, R. Chakrabarti, P. von Strandmann, W. Yang, W. Y. Li, S. Ke, F. Sedaghatpour, J. Wimpenny, A. Meixner, R. L. Romer, U. Wiechert and S. B. Jacobsen, Geochem., Geophys., Geosyst., 2015, 16(9), 3197–3209 CrossRef CAS .
  397. J. Vogl, B. Brandt, J. Noordmann, O. Rienitz and D. Malinovskiy, J. Anal. At. Spectrom., 2016, 31(7), 1440–1458 RSC .
  398. M. J. Pribil, W. I. Ridley and P. Emsbo, Chem. Geol., 2015, 412, 99–106 CrossRef CAS .
  399. C. H. Liu, X. P. Bian, T. Yang, A. J. Lin and S. Y. Jiang, Talanta, 2016, 151, 132–140 CrossRef CAS PubMed .
  400. S. G. Nielsen, J. D. Owens and T. J. Horner, J. Anal. At. Spectrom., 2016, 31(2), 531–536 RSC .
  401. F. Wu, Y. H. Qi, H. M. Yu, S. Y. Tian, Z. H. Hou and F. Huang, Chem. Geol., 2016, 421, 17–25 CrossRef CAS .
  402. H. Z. Wei, S. Y. Jiang, Z. Y. Zhu, T. Yang, J. H. Yang, X. Yan, H. P. Wu and T. L. Yang, Talanta, 2015, 143, 302–306 CrossRef CAS PubMed .
  403. J. Lin, Y. S. Liu, H. H. Chen, L. Zhou, Z. C. Hu and S. Gao, J. Earth Sci., 2015, 26(5), 763–774 CrossRef CAS .
  404. D. Egli, W. Muller and N. Mancktelow, Terra Nova, 2016, 28(1), 35–42 CrossRef CAS .
  405. L. P. Feng, L. Zhou, L. Yang, S. Y. Tong, Z. C. Hu and S. Gao, J. Anal. At. Spectrom., 2015, 30(12), 2403–2411 RSC .
  406. G. O. Lehn and A. D. Jacobson, J. Anal. At. Spectrom., 2015, 30(7), 1571–1581 RSC .
  407. M. O. Naumenko-Dèzes, C. Bouman, T. F. Nagler, K. Mezger and I. M. Villa, Int. J. Mass Spectrom., 2015, 387, 60–68 CrossRef .
  408. Q. Li, M. Thirlwall and W. Muller, Chem. Geol., 2016, 422, 1–12 CrossRef CAS .
  409. M. Klaver, R. J. Smeets, J. M. Koornneef, G. R. Davies and P. Z. Vroon, J. Anal. At. Spectrom., 2016, 31(1), 171–178 RSC .
  410. A. von Quadt, J. F. Wotzlaw, Y. Buret, S. J. E. Large, I. Peytcheva and A. Trinquier, J. Anal. At. Spectrom., 2016, 31(3), 658–665 RSC .
  411. A. Didier, V. Bosse, J. Bouloton, S. Mostefaoui, M. Viala, J. L. Paquette, J. L. Devidal and R. Duhamel, Contrib. Mineral. Petrol., 2015, 170(5–6), 21 Search PubMed .
  412. Y. Y. Gao, X. H. Li, W. L. Griffin, Y. J. Tang, N. J. Pearson, Y. Liu, M. F. Chu, Q. L. Li, G. Q. Tang and S. Y. O'Reilly, Sci. Rep., 2015, 5, 11 Search PubMed .
  413. S. Goderis, R. Chakrabarti, V. Debaille and J. Kodolanyi, J. Anal. At. Spectrom., 2016, 31(4), 841–862 RSC .
  414. M. J. Bojanowski, B. Baginski, C. Guillermier and I. A. Franchi, Chem. Geol., 2015, 416, 51–64 CrossRef CAS .
  415. E. H. Hauri, D. Papineau, J. H. Wang and F. Hillion, Chem. Geol., 2016, 420, 148–161 CrossRef CAS .
  416. M. G. Śliwiński, K. Kitajima, R. Kozdon, M. J. Spicuzza, J. H. Fournelle, A. Denny and J. W. Valley, Geostand. Geoanal. Res., 2016, 40(2), 157–172 CrossRef .
  417. M. G. Śliwiński, K. Kitajima, R. Kozdon, M. J. Spicuzza, J. H. Fournelle, A. Denny and J. W. Valley, Geostand. Geoanal. Res., 2016, 40(2), 173–184 CrossRef .
  418. L. B. Corbett, P. R. Bierman and D. H. Rood, Quaternary Geochronology, 2016, 33, 24–34 CrossRef .
  419. M. E. Keillor, C. E. Aalseth, L. M. Arrigo, J. M. Brandenberger, J. M. Cloutier, G. C. Eiden, J. E. Fast, Z. S. Finch, G. A. Gill, T. W. Hossbach, C. T. Overman, B. N. Seiner and J. E. Strivens, J. Radioanal. Nucl. Chem., 2016, 307(3), 2313–2319 CrossRef CAS .
  420. P. Vermeesch, G. Balco, P. H. Blard, T. J. Dunai, F. Kober, S. Niedermann, D. L. Shuster, S. Strasky, F. M. Stuart, R. Wieler and L. Zimmermann, Quaternary Geochronology, 2015, 26, 20–28 CrossRef .
  421. Y. Ma, Y. Wu, D. M. Li and D. W. Zheng, Int. J. Mass Spectrom., 2015, 380, 26–33 CrossRef CAS .
  422. P. Vermeesch, Geochim. Cosmochim. Acta, 2015, 171, 325–337 CrossRef CAS .
  423. K. Li, B. Etschmann, N. Rae, F. Reith, C. G. Ryan, R. Kirkham, D. Howard, D. R. N. Rosa, C. Zammit, A. Pring, Y. Ngothai, A. Hooker and J. Brugger, Econ. Geol., 2016, 111(2), 487–501 CrossRef .
  424. L. A. Fisher, D. Fougerouse, J. S. Cleverley, C. G. Ryan, S. Micklethwaite, A. Halfpenny, R. M. Hough, M. Gee, D. Paterson, D. L. Howard and K. Spiers, Miner. Deposita, 2015, 50(6), 665–674 CrossRef CAS .
  425. F. Gergely, J. Osan, B. K. Szabo and S. Torok, Spectrochim. Acta, Part B, 2016, 116, 75–84 CrossRef CAS .
  426. J. Buckles and H. D. Rowe, Chem. Geol., 2016, 426, 28–32 CrossRef CAS .
  427. K. Kuhn, J. A. Meima, D. Rammlmair and C. Ohlendorf, J. Geochem. Explor., 2016, 161, 72–84 CrossRef CAS .
  428. S. Chawchai, M. E. Kylander, A. Chabangborn, L. Lowemark and B. Wohlfarth, Boreas, 2015, 45(1), 180–189 CrossRef .
  429. V. Chubarov, D. Suvorova, A. Mukhetdinova and A. Finkelshtein, X-Ray Spectrom., 2015, 44(6), 436–441 CrossRef CAS .
  430. X. L. Li, Y. M. Wang and Q. Zhang, Spectrosc. Lett., 2016, 49(3), 151–154 CrossRef CAS .
  431. J. Quye-Sawyer, V. Vandeginste and K. J. Johnston, J. Anal. At. Spectrom., 2015, 30(7), 1490–1499 RSC .
  432. D. A. Burkett, I. T. Graham and C. R. Ward, Can. Mineral., 2015, 53(3), 429–454 CAS .
  433. D. Bish, D. Blake, D. Vaniman, P. Sarrazin, T. Bristow, C. Achilles, P. Dera, S. Chipera, J. Crisp, R. T. Downs, J. Farmer, M. Gailhanou, D. Ming, J. M. Morookian, R. Morris, S. Morrison, E. Rampe, A. Treiman and A. Yen, IUCrJ, 2014, 1, 514–522 CrossRef CAS PubMed .
  434. X. Y. Nan, F. Wu, Z. F. Zhang, Z. H. Hou, F. Huang and H. M. Yu, J. Anal. At. Spectrom., 2015, 30(11), 2307–2315 RSC .
  435. K. Murphy, M. Rehkamper, K. Kreissig, B. Coles and T. van de Flierdt, J. Anal. At. Spectrom., 2016, 31(1), 319–327 RSC .
  436. P. Bonnand, I. J. Parkinson and M. Anand, Geochim. Cosmochim. Acta, 2016, 175, 208–221 CrossRef CAS .
  437. P. Bonnand, H. M. Williams, I. J. Parkinson, B. J. Wood and A. N. Halliday, Earth Planet. Sci. Lett., 2016, 435, 14–21 CrossRef CAS .
  438. Z. Y. Zhu, S. Y. Jiang, T. Yang and H. Z. Wei, Int. J. Mass Spectrom., 2016, 393, 34–40 CrossRef .
  439. Q. H. Hou, L. Zhou, S. Gao, T. Zhang, L. P. Feng and L. Yang, J. Anal. At. Spectrom., 2016, 31(1), 280–287 RSC .
  440. Y. S. He, S. Ke, F. Z. Teng, T. T. Wang, H. J. Wu, Y. H. Lu and S. G. Li, Geostand. Geoanal. Res., 2015, 39(3), 341–356 CrossRef CAS .
  441. V. A. Finlayson, J. G. Konter and L. Ma, Geochem., Geophys., Geosyst., 2015, 16(12), 4209–4222 CrossRef CAS .
  442. F. X. D'Abzac, J. Davies, J. F. Wotzlaw and U. Schaltegger, Chem. Geol., 2016, 433, 12–23 CrossRef .
  443. S. T. M. Peters, C. Munker, F. Wombacher and B. M. Elfers, Chem. Geol., 2015, 413, 132–145 CrossRef CAS .
  444. R. Bast, E. E. Scherer, P. Sprung, M. Fischer-Godde, A. Stracke and K. Mezger, J. Anal. At. Spectrom., 2015, 30(11), 2323–2333 RSC .
  445. J. Lin, Y. S. Liu, Z. C. Hu, L. Yang, K. Chen, H. H. Chen, K. Q. Zong and S. Gao, J. Anal. At. Spectrom., 2016, 31(2), 390–397 RSC .
  446. K. Van Hoecke, J. Belza, T. Croymans, S. Misra, P. Claeys and F. Vanhaecke, J. Anal. At. Spectrom., 2015, 30(12), 2533–2540 RSC .
  447. V. Migeon, B. Bourdon, E. Pili and C. Fitoussi, J. Anal. At. Spectrom., 2015, 30(9), 1988–1996 RSC .
  448. Y. Nagai and T. Yokoyama, J. Anal. At. Spectrom., 2016, 31(4), 948–960 RSC .
  449. S. M. Chernonozhkin, S. Goderis, L. Lobo, P. Claeys and F. Vanhaecke, J. Anal. At. Spectrom., 2015, 30(7), 1518–1530 RSC .
  450. N. S. Saji, D. Wielandt, C. Paton and M. Bizzarro, J. Anal. At. Spectrom., 2016, 31(7), 1490–1504 RSC .
  451. T. Struve, T. van de Flierdt, L. F. Robinson, L. I. Bradtmiller, S. K. Hines, J. F. Adkins, M. Lambelet, K. C. Crocket, K. Kreissig, B. Coles and M. E. Auro, Geochem., Geophys., Geosyst., 2015, 17(1), 232–240 CrossRef .
  452. Z. Y. Chu, C. F. Li, Z. Chen, J. J. Xu, Y. K. Di and J. H. Guo, Anal. Chem., 2015, 87(17), 8765–8771 CrossRef CAS PubMed .
  453. M. Carpentier, A. Gannoun, C. Pin and O. Sigmarsson, Geostand. Geoanal. Res., 2016, 40(2), 239–256 CrossRef CAS .
  454. K. Abraham, J. Barling, C. Siebert, N. Belshaw, L. Gall and A. N. Halliday, J. Anal. At. Spectrom., 2015, 30(11), 2334–2342 RSC .

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