Open Access Article
This Open Access Article is licensed under a Creative Commons Attribution-Non Commercial 3.0 Unported Licence

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, SK17 9JN, UK. E-mail: owen.butler@hsl.gsi.gov.uk
bCNR-IDPA, Universita Ca' Foscari, 30123 Venezia, Italy
cBritish Geological Survey, Keyworth, Nottingham, NG12 5GG, UK
dUniversity of Strathclyde, Cathedral Street, Glasgow, G1 1XL, UK

Received 11th November 2015

First published on 8th December 2015


Abstract

This is the 31st annual review of the application of atomic spectrometry to the chemical analysis of environmental samples. This update refers to papers published approximately between August 2014 and July 2015 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 laser fluorescence instrument for the ultratrace determination of mercury vapour; single particle ICP-MS studies and the coupling of elemental analysers to mass spectrometers for the improved characterisation of carbonaceous aerosols. In the arena of water analysis, methods continue to be developed: for the extraction and preconcentration of elements, As, Cr, Hg and Sb species and determination of elemental constituents in colloidal and NP fractions. Emerging elements of interest include Gd derived from MRI agents discharged at low level from medical facilities in water courses. 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 period covered by this update several articles have explored the analysis of soil extracts for geochemical prospecting. There has been widening interest in the use of CS-AAS and in the application of techniques capable of direct sample analysis such as slurry sampling ETAAS and ETV-ICP-AES. Portable XRF instrumentation is now being used in many disciplines to quantify trace elements in soils – bringing a need for better transfer of analytical knowledge to non-specialist users – and the growing use of portable XRF in proximal sensing is also noteworthy. Recent research indicates that geological applications still drive many of the instrumental and methodological advances in LA-ICP-MS. Fundamental studies continued to shed light on the processes involved and hence ways of improving the analysis of laser-produced aerosols and to minimise matrix and fractionation effects. A new technique LA-DOF-MS (distance of flight) was described. The utility of LIBS and portable XRF for in situ survey work continues to show promise but issues such as appropriate calibration regimes and data processing protocols will still need to be addressed.


1 Air analysis

1.1 Review papers

Useful review papers were published that provide a summary of the current and emerging technologies for the characterization of the elemental component in atmospheric particles7 (22 references); brown carbon aerosols8 (377 references) airborne nanoparticles9 (91 references) and in nanomaterials (57 references).10 Given the overlap in measurement science and the fact that airborne particles can deposit into the seas, then this review11 (142 references) on the analysis of major and trace elements in marine particles is also recommended. Particles and fumes from smoking can contribute to the degradation of air quality and here so it was interesting to note a review of the development of standardised methods to test cigarette smoke12 (70 references) and a review of analytical methods to test the chemical components of e-cigarette cartridges and refill fluids13 (47 references). Researchers in the UK have considered potential methods14 (9 references) for the determination of total halogenated compounds in H2 for use in membrane fuel cells and have proposed that ICP-MS provides the best currently available analytical solution.

1.2 Sampling techniques

Evaluation of existing personal air sampler systems to monitor workers' exposure to airborne particles continues to be of growing interest. A US research group15 evaluated whether the relative orientation of the widely used 37 mm closed faced cassette sampler when worn by a worker had an influence on the sample mass that was collected and found that it did not. Sampling pumps attempt to maintain a constant, known airflow during sampling exercises otherwise sampling volume will be unknown and hence worker concentration exposure indeterminate. Despite integrating pulsation dampers within pump designs, nevertheless, periodic pulsations remain, for example due to reciprocating motion of a piston stroke. The EN standard 1232 recommends that the amplitude of pulsations be <10% of the mean flow rate and provides a way of assessing such deviations. Researchers at NIOSH16 have now developed an alternative assessment approach which they consider to be more representative of real-world pump performance and have noted that, for a range of pumps tested, pulsation measurements that were 1.5–2.2 times higher than those measured using the codified EN approach. Findings from this study will be reported to the consensus committees to be considered when this EN standard and other similar EN/ISO standards are up for revision. Deposit of particles to the internal surfaces of the widely used 37 mm closed faced cassette sampler can result in measurement results that are biased low if only the filter itself is analysed. For this reason, recent guidelines recommend that these surfaces be wiped and the wipe material employed be analysed alongside the filter. This is a cumbersome procedure and in response acid digestible cellulose acetate cassette inserts have been developed that fit inside samplers that are bonded to the filter. In summary they act as a liner thus preventing particle losses to walls. Researchers at NIOSH17 have undertaken a study, through parallel measurements of airborne levels of Pb and Sn in a solder manufacturing plant, to show that using these new inserts gave comparable results to the current practice of analyzing filters with their corresponding sampler surface wipe.

Exploiting new developments in sampler technologies for the improved assessment of worker exposure to airborne pollutants is of interest to a number of research groups. Elemental carbon is typically used as a marker for worker exposure to diesel fumes but analytically it is not possible, with combustion-based laboratory techniques, to differentiate between carbon in diesel fume samples from carbon derived from coal dust samples. Aerosols in a coal mine environment, where diesel powdered equipment is used, exhibit an aerodynamic bimodal particle distribution with a distinct accumulation mode peak at ∼0.12 μm, attributable primarily to diesel fume emissions and a coarse mode peak at ∼6 μm attributable primarily to coal particles with a minimum between modes at ∼0.8 μm. Work commissioned by MSHA18 in the USA exploited this biomodal distribution through the design and validation of two new sharp cut cyclones operating at a nominal 0.8 μm cut size that would allow the preferential sampling of diesel fume over coal dust. NIOSH researchers19 established procedures (extraction methods and IC/ICP-MS analysis) to estimate the respiratory deposition of the nano fraction of selected metals in welding fume using a recently developed nanoparticle respiratory deposition (NRD) sampler. It was noted that a large fraction of the sample mass, 30–60% of the Cr, Mn and Ni content, lay within this nanoparticle fraction of <300 nm. Canadian researchers20 have evaluated the capabilities of a recently commercialized aerosol-to-liquid particle extraction sampler coupled to ICP-MS for the monitoring of trace metals in indoor air. This sampler, working on an electrostatic precipitation process, charges particles as the pass through the sampler, focuses them onto an electrode which is constantly washed using water resulting in sample being collected in a suspension reservoir which can then be taken away for digestion and analysed. The key advantage of this approach over a more conventional filter-based sampler is that higher air flow rates are achievable i.e. 300 L min−1vs. 1–20 L min−1. This is due to the absence of back pressure in the system that would be present in filter based systems and hence load on the air pumping system. Further refinements are required such as inlets to enable this sampler to sample specific particle size ranges and to assess sampler efficiencies over different particle size ranges.

New ambient air samplers continue to be developed. A high volume aerosol-into-liquid sampler at 200 L min−1 was developed to preconcentrate coarse particulate matter (PM2.5–10) into a slurry sample.21,22 Copper was chosen as an element of interest for system evaluation given the reported association between this metal and ROS activity of PM and its potential to contribute therefore to overall PM induced toxicity. The soluble Cu concentration in these slurry samples were measured in situ with ISE and results obtained compared favourably with off-line measurements by ICP-MS. This monitoring system achieved near-continuous measurements, at 2–4 h intervals, for up to 7 days and application of this system to measure other water soluble metallic species by deploying other ISEs is envisaged. Working on a similar principle but at a lower flow rate of 16 L min−1, a near-continuous – at 60 min intervals – PM10 sampling system was deployed by Korean researchers23 to study the elemental composition of Asian dust particles. Here 16 elements (Al, As, Ca, Cd, Co, Cr, Cu, Fe, K, Mn, Ni, Pb, Se, Ti, V and Zn) were determined off-line in the collected slurry samples by ICP-MS. A PM1.0/2.5/10 trichotomous sampler has been developed24 to determine if airborne particles in the saddle point between the coarse and fine particle modes are derived from coarse or fine particles. This sampler consisted of a standard high volume sampler with two virtual impactors – one with a cut size of 2.5 μm and the second with a cut size of 1.0 μm – inserted between the PM10 inlet and the sample filter. Filter portions were analysed: following leaching, by IC to determine SO42−, a species primarily found in the fine aerosol mode and analysed directly by PIXE to determine Ca, Fe, Si, and S species which are normally associated with coarse mode aerosols i.e. resuspended soil particles. Application of this sampler to Phoenix, Arizona, representing an arid region, showed that particles in this saddle point – specifically the 1 to 2.5 μm particle size range – consisted of ∼75% of particles from the coarse mode and ∼25% from the fine mode. The Hg isotopic signature may provide insight into tracking the sources and pathways of both airborne particulate matter and HgP in the atmosphere. A new sampler has been developed25 that thermally liberates Hg from particulate matter sampled onto a quartz filter and collects the effluent in an acid-trapping solution. An aliquot of BrCl was then added to ensure that the collected Hg is in an ionic Hg2+ form. Total Hg concentrations were determined using CVAFS and isotopic ratios determined using CV-MC-ICP-MS. System verification was undertaken by spiking precleaned blank quartz sample filters with aliquots of aqueous Hg standard (NIST 3133) or powdered soil CRMs (CRM 021, 024 from Sigma Aldrich and IERM GBW07405) which were subsequently analysed. The average Hg recovery was determined to 99 ± 6% (2SD, n = 90). The corresponding Hg isotopic ratio determined in this solid matrix samples using this thermal release approach were found to be in good agreement with those obtained with a standard addition protocol – solid test samples doped with various aqueous aliquots of NIST 3133 as the isotopic dopant – indicating the absence of matrix interferences. Initial field studies with this new system indicated that different Hg isotopic compositions do exist in the atmosphere and that further studies are therefore warranted.

Validation of samplers commonly used for volatile gaseous species is of interest to many. A European research group26 has evaluated the performance of the Raschig-tube (RT) and the Drechsel impinger (DI) sampler against the well-established filter pack (FP) technique for the sampling of acidic gaseous species (CO2, HBr, HCl, HF, HI and SO2) emitted from volcanos. Analytically IC (for anionic Br, Cl, F and S species), ICP-MS (for Br and I species) and titrations (for dissolved CO2) were used to perform measurements. The RT sampler approach is based upon the creation of a large interaction surface for sampling gaseous species through the continuous dosing of many little glass rings with a 1 M NaOH solution achieved by rotating this bundle of glass rings within the body of the sampler. The mass per unit time collection efficiency of this sampler was far superior to the DI sampler – 13-fold – not only due to the larger surface area available for absorption but also to higher sampling flow rates that were achievable. The fact that a smaller absorption solution volume was required was an additional bonus resulting in a more concentrated solution for analysis. That said, since both RT and DI samplers use caustic solutions for trapping purposes, the FP is logistically the more practical to use since handling corrosive reagents is probably the last thing one would want to do at the rim of an active volcano! Biomethylation and volatilisation of trace elements is a process that requires more study so as to gain a fuller understanding of biogeochemical cycle of certain metals. A paper has been published which presents a robust and versatile gas trapping method using classical impinger samplers filled with nitric acid that preserved gaseous As, Se and S species once trapped.27 The novel value of this work lay in the extensive trapping efficiency studies conducted wherein volatile As (DMA, MMA and TMA), Se (DMSe and DMDSe) and S (DMS, DMDS) species were generated, either by a reductive hydride generation process or via the use of purchased gas standards, and subsequently passed through three impinger samplers connected in series. Trapping efficiencies were calculated as the ratio between total elemental amounts of analyte in the impinger traps and the total elemental amounts of introduced volatile analyte as determined using ICP techniques. In the case of the As species the injected quantity was calculated as the difference between starting and residual quantities left in the hydride generation chamber. In the case of the Se and S species, direct inject of gas standards into the ICP was performed. Speciation analysis of trapped species in the nitric acid filled impingers was undertaken using HPLC-HR-ICP-MS with identification by retention-time matching in conjunction with ESI-MS/MS analysis. The reported trapping efficiencies were DMA (110 ± 4%), MMA (104 ± 12%), TMA (89 ± 6%), DMSe (96 ± 2%), DMDSe (50 ± 11%), DMS (101 ± 5%) and DMDS (74 ± 8%). Furthermore it was ascertained that volatile Se and S species, one trapped, transformed into stable and specific non-volatile forms thus preserving the speciation information. Researchers28 in the US examined, in the field, the performance of the widely used KCl-coated denuder for trapping GOM by challenging them to a known concentration of HgBr2. It was noted that such denuders had 95% collection efficiency for HgBr2 in zero air (challenge gas standard supplied in air prescubbed of Hg0 and O3) but that capture efficiencies dropped drastically to 20–54% when ambient air was used. Subsequent tests carried out back in a laboratory setting revealed that O3 and absolute humidity facilitated the re-release of a portion of the trapped HgBr2 as Hg0 thus explaining the low recoveries obtained in the field. Based upon these findings the authors concluded that atmospheric GOM measurements to date are probably underestimated and that the check system they have used should be used on other Hg sampling and monitoring systems deployed elsewhere.

Sampling using passive samplers is another approach. Two review papers considered the use of plants as biomonitors. In the first concise review (15 references),29 passive sampling of the air by lichens, mosses and the leaves and bark of trees were considered. The second review (123 references)30 was more specific and considered the use of conifer needles as passive samplers of inorganic pollutants. A French research consortium31 have designed a new continuous dust deposition sampler for unattended operation over extended sampling periods. In summary, an inlet funnel is connected to a 25-place filter carousel that can be rotated so that at periodic planned intervals, a fresh filter can be place underneath this inlet. An automated combined funnel wall vibration and wash system is used to ensure that particles deposited from the atmosphere end upon the filter. Filtration is then carried out under gravity leaving insoluble particles on the filters for subsequent collection and analysis. The autonomy of the instrument can range from 25 days for a 1 day sampling interval to ∼1 year for a bi-weekly sampling interval. The collection efficiencies32 of twelve widely used dust deposition samplers were tested for large particles in the size range 0.5–1 mm in a controlled wind tunnel facility at wind velocities over the range 1 to 5.5 m s−1. The best eight samplers demonstrated collection efficiencies in the range 60–80% whilst the four worst performers could only demonstrate efficiencies in the range 5–40%. The authors concluded that better design considerations of inlet and outlet flow geometries are therefore required. In a similar study33 three passive dust samplers were tested for their collection efficiencies for particles in the range 250–4140 nm. The evaluation was carried out in the field in parallel with a wide range particle spectrometer used to provide highly temporally resolved particle number and size distribution reference data. Two of the samplers sets demonstrated collection efficiencies of 91.5 ± 13.7% and 103 ± 15.5% but the third sampler demonstrated poorer performance at an efficiency of 54 ± 8.0%. Chinese researchers34 have developed a new passive sampler for Hg0 using a pulverised S-impregnated carbon material. Both laboratory and field testing was carried out. In the laboratory a stable Hg0 atmosphere in the range 5–10 ng m−3 was generated using a permeation tube system and verified using an online analyser (Lumex RA-915). Five replicate samplers could be tested in parallel over a range of environmental conditions – relative humidity (25–90%), temperature (−10 to 35 °C) and wind velocity (0.5 to 5.0 m s−1). Sorbent beds (0.3 g) were analysed for their Hg content using a combustion-based analyser (Milestone DMA-80) in accordance with USEPA method 7473. Sorbent blanks were 0.58 ± 0.09 and 0.71 ± 0.11 ng g−1 from laboratory and field studies. The method LOD was determined to be 0.18 ng m−3 for an 8 day deployment in the laboratory chamber dropping to 0.05 ng m−3 for a 30 day field deployment. The average RSD of replicate samplers was 11% (laboratory) and 9% (field). An average uptake rate of 0.225 ± 0.022 m3 d−1 g−1 was determined in field trials and results correlated favourably from active measurements made in parallel (R2 = 0.509, slope = 0.991, 95% CI range of 0.895–1.086). Temperature or humidity did not have significant influence on the sampling rate but wind correction was important when comparing passive sampling data between sites with large variations in wind speeds.

1.3 Reference materials and calibrants

The international database for certified reference materials, COMAR, developed in the late 1970s to assist analytical laboratories to find appropriate RMs and a useful overview article has now been published35 (16 references).

Whilst the determination of metals in airborne particles using plasma-based instrumentation is now well established and codified in a number of standardised methods, biased results can be obtained if a suitable dissolution procedure is incorrectly used. The extent of this bias can be assessed if matrix matched RMs were to be used and it is therefore welcome now to see three new RMs added to the existing rather limited portfolio of materials. For the analysis of metals in ambient air particles, French researchers36 prepared a new CRM by simply pressing known masses of a well homogenized fly ash material (nominal 15 mg) into a fibre filter. The certification was based upon a single laboratory approach using ID-ICP-MS for Cd, Ni and Pb and a standard addition calibration approach for As. A microwave assisted digestion procedure involving a HNO3/H2O2 acid mixture was used and this material is aimed at laboratories working to procedures set out EN 14902. In the reviewer's eyes this material could also be potentially useful for those undertaking stack air analysis to requirements set out in EN 13528. However given that the starting material was a fly ash derived from an incineration process, it would be informative to obtain elemental data following the addition of HF acid to the digestion mixture. British researchers37 have described the preparation and certification of two bulk welding fume RMs for use in laboratories undertaking the analysis of metals in workplace air to procedures set out in ISO 15202. These materials were certified at a nominal 10 mg test portion using an alternative interlaboratory certification approach. Here various hotplate, hotblock and microwave assisted dissolution procedures, codified in this ISO standard and deemed suitable for the dissolution of such materials, were used alongside an ICP-AES finish. Both certification exercises followed data handling and statistical requirements codified in ISO guide 35. The IRMM CRM BCR-723 (Pd, Pt and Rh in road dust) is widely used in supporting measurements in assessing the extent of PGE emissions from catalytic converters and new compositional data can be found in this paper.38

Nanoparticle RMs are now required to support measurement initiatives in this growing arena. An Anglo-American university consortium39 has described the synthesis and characterization of isotopically-labelled Ag NPs for future tracer studies. In summary monodispersive suspensions of citrate-stabilised Ag NPs, with target sizes of 17, 20 and 30 nm were synthesized by the controlled reduction of AgNO3 solutions with NaBH4. Both natural (107Ag[thin space (1/6-em)]:[thin space (1/6-em)]109Ag = 52%[thin space (1/6-em)]:[thin space (1/6-em)]48%) and enriched isotopic (107Ag = 99.2%) Ag NPs were prepared. Characterisation techniques used included DLS, TEM and A4F and the particle size distributions showed good reproducibility between the laboratories and stability over 12 months of storage. In order to produce spherical monodispersive Ag NPs with diameters up to 100 nm, a seeded growth process has been advocated.40 Here Au seed particles produced with a spark discharge generator were carried by N2 through a three-zone tube furnace. In the first zone, Ag was evaporated and particle growth and shaping took place in the subsequent zones. An on-line SMPS system was used to monitor the generated aerosol and variables such as furnace temperature, Au seed particle size and concentration and carrier gas flow rates were examined. Off-line techniques such as AFM and TEM were also used to characterize the morphology of the generated Ag NPs. The mobility diameter of the monodispersive aerosol could be varied in the range of 50 to 115 nm by changing the furnace temperature or the Au seed particle size. A European research group41 has synthesized isotopically enriched 29Si NPs for use as potential ID spikes for the quantification of natural silica NPs. Initially 29SiCl4 was prepared by heating 29Si in a Cl2 gas stream and a 29Si tetraethyl orthosilicate intermediate obtained following a careful cooling and dilution procedure. A basic amino acid catalysis route was then used to form the NPs which were characterized using DLS, SAXS and TEM. The research group led by Detlef Günther42 have fabricated custom engineered and compacted nanoparticles as potential calibration materials for quantification using LA-ICP-MS. Here a flame spray technique was used to produce a nanomaterial with a customised elemental composition. Liquid organic precursors of Al, Ca, Fe, Mg, Si and Ti in a concentration similar to the matrix of the well-known NIST SRM 610 glass standard were mixed with a selection of REE (Ce, Gd, Ho and Tb); precious metals (Ag, Au, Pt, Rh and Ru) and Pb at concentrations of ∼400–500 mg kg−1. This precursor mixture was sprayed, collected as a nanopowder, compacted to pellets and characterised by LA and solution-based ICP-MS. In summary they demonstrated that this flame spray approach allows production of customised doped calibration materials for micro-analytical techniques.

An advanced gravimetric system was reported for the production of gaseous CRMs of mixtures of He isotopes.43 Three artificial He isotope reference mixtures were prepared with absolute isotope ratios R (4He/3He) of 18.905 ± 0.036; 98.78 ± 0.21 and 209.82 ± 0.44 (k = 1). The internal consistency between the mixtures was verified using a high precision single-focus magnetic sector MS system with a dynamic inlet system. Chemiluminescent analysers used in situ for measuring NO and NO2 in ambient air are generally calibrated with certified gas standards of NO in a balance of N2. Degradation of this NO standard, via oxidation to NO2, has now been reported.44 If left uncorrected the authors suggest that a systematic under-reporting of NO2, a pollutant of concern in many urban locations, of up to 20% is possible.

1.4 Sample preparation

Automobile catalyst emissions can release not only Pd, Pt and Rh particles into the environment but also Ir, Os and Ru. All these trace elements can be challenging to measure thus the publication of improved methods is most welcome. Chinese researchers38 report a method that involves both the use of ID-ICP-MS and N-TIMS (for Os determination) and applied it for the comprehensive characterization of both CRM BCR-723 (Pd, Pt and Rh in road dust) and road dust samples. Samples were digested with inverse aqua regia in a Carius tube. A solvent extraction step with CCl4 then separated Os from the other elements prior to the use of an ion exchange process to remove interferents (Cd, Hf, Mo and Zr). Analysis of BCR-723 yielded reproducible results and well-defined average Ir; Os; Pd; Pt; Re and Ru of 0.23 ± 0.12; 0.37 ± 0.04; 4.6 ± 0.8; 79.8 ± 6.0; 6.5 ± 0.1 and 1.1 ± 0.13 (ng g−1, 95% CI, n = 10). The 187Os/188Os ratio was determined to be 0.537 ± 0.022 (95% CI, n = 10). In a similar vein, European researchers45 using ID-SF-ICP-MS and a combination of both cation and anion exchange chemistry validated their procedure using IRMM CRM BCR-723 and IAEA CRM 450 (algae). For the SI-traceable results complete uncertainty budgets were calculated that yielded expanded uncertainties (k = 2) of ∼1% for analyte masses in the ng range and the reported LOD were 12 pg for Pd and 7 pg for Pt.

Sample preparation procedures for emerging nanomaterials have been developed. A SWCNT material produced by the laser ablation of a renewable biochar in the presence of Co and Ni catalysts was characterized for residual catalyst as well as trace metal impurity content (Cr, Fe, Hg, Mo and Pb) using ID-ICP-MS following sample digestion.46 Several digestion procedures were examined including a multi-step microwave-assisted acid digestion, dry ashing at 450 °C and microwave-induced combustion with oxygen. Results were benchmarked against those derived from both NAA and SS-CS-AAS. The multi-step microwave-assisted acid provided to be the most reliable although Cr results were biased low mostly likely due to the formation of a refractory Cr carbide. A similar comparative study has been published looking at procedures to dissolve Ti NPs.47 Here the performance of a HNO3/HF microwave-assisted acid digestion approach was compared against that using a KOH fusion in a muffle furnace and it was found that the fusion approach provided the more consistent results. Conventional FFF technique for particle size separation use narrow bore channels hence the sample mass that can be injected is typically <1 mg to avoid system overload. A variation of this technique, sedimentation field flow fractionation in a rotating coiled column,48,49 enabled up to 1 g of street dust to be processed and it was shown that the metals of a predominately anthropogenic origin – Cd, Cr, Cu, Ni, Pb, Sn and Zn – were found to concentrate mainly in the <1 μm size fraction. French researchers50 investigated the elemental recoveries for metal oxide NPs – Al2O3, CeO2, TiO2, SiO2 and ZnO – analysed by direct injection ICP-MS and the influence of particle size, agglomeration state and sample matrix. They noted for the NPs tested that an alkaline sodium hydroxide matrix presented the best results; that method performance was satisfactory for primary particles <50 nm but that recoveries decreased when particles were >80 nm especially those particles of a more refractory phase such as TiO2 anatase. A generic sample preparation scheme for inorganic engineered NPs for subsequent detection, characterization and quantification by A4F coupled to DLS and ICP-MS techniques has been published.51

Physiologically-based extraction test52 with two simulated lung fluids, Gamble's and Hatch's solution, and simulated gastric and pancreatic solutions were applied to determine the bioaccessibility of As, Cd, Cr, Hg, Mn, Ni, Pb and Zn in urban particulate matter with analysis undertake using both ICP-AES AND ICP-MS. The effect of the extractant agent employed, extraction time, sample to extractant ratio, sample particle size and elemental properties were evaluated. The authors concluded that the extractable portions are affected not only by their mobility in the test material itself but also by the sample preparation procedure itself. Iron is an essential micronutrient for phytoplankton growth and is supplied to the remote areas of the oceans mainly through atmospheric dust. The amount of soluble Fe content is a major source of uncertainty in modelled dissolution and deposition models coupled with a wide variety of published leach methods. The authors of a new continuous flow leach procedure53 claim that this method can be run with low sample mass, <10 mg, compared to the standard method, based upon BCR protocols, which uses up to 1 g of material. This is very useful given the limited mass of sample that is usually collected. Reproducibility studies were conducted on two well characterized sedimentation dust and ash and ranged between 8–22% as compared to 6–19% for the standard method. Sonication is often used to remove particles trapped on air filter samples for subsequent chemical and toxicological studies. The energy of an applied ultrasonic wave to a sample can cause localized hot spots at elevated pressures, production of free radicals and potential formation of artifact compounds. For those readers that employ ultrasonication in their studies, a read of this paper,54 that details the operation of this technique and possible consequences of its use, is therefore recommended. American researchers55 compared multi-solvent extraction (MSE) and spin-down extraction (SDE) procedures on PM2.5 particulate matter collected on filter and noted remarkable compositional variance between the respective PM extracts for all the components tested including metals, water soluble ions and elemental/organic carbon. Mass closure was greater than >90% for MSE but was lower for SDE due to a process-based loss of sample mass. Lessons learnt included the importance of standardizing filter extraction objectives. A microwave assisted extraction procedure56 used a Na2CO3–NaOH leachate to extract total CrVI from airborne particles collected on a Teflon filter prior to analysis via the widely used 1,5-diphenylcarbazide adduct at 540 nm. Recoveries from NIST SRM 2700 (hexavalent Cr in contaminated soil) were 106 ± 17%; total CrVI in NIST 1648a (urban particulate matter) was determined to be 26 ± 3 mg kg−1 and the method LOD was 0.33 ng m−3. The total CrVI concentrations measured in New Jersey urban air was 1–1.6 ng m−3. The same research group57 undertook a study to ascertain whether the use of an enriched CrIII ID spike, advocated in EPA method 6800, was useful in tracking the extent of CrIII to CrVI interconversion during extraction of air samples and rather predictably noted that this ID protocol could only be applied to the soluble fraction of Cr species.

Improvements in sample preparation have been advocated. A method based upon pyrohydrolysis of airborne particulate matter collected on glass fibre filters for the subsequent determination of Br and I has been reported.58 Sample filters were ground using an agate mortar, homogenized and placed on an alumina platform, mixed with a solid V2O5 powdered catalyst, fired at 950 °C and the resultant effluent trapped in water prior to analysis by ICP-MS. Quantitative recoveries of 104% (Br) and 95% (I) were obtained when NIST SRMs 1633b (Constituent Elements in Coal Fly Ash) and 2709 (San Joaquin Soil) were analysed and the method LoQ was 0.05 μg g−1 for Br and 0.006 μg g−1 for I. The authors concluded that their elegant approach was suitable for routine analysis and provided a clean solution for subsequent ICP-MS analysis. An improved method59 for the 14C analysis of water-soluble organic carbon in aerosol samples employed a water extraction step followed by a freeze drying preconcentration step prior to AMS analysis. Reference test samples with known 14C content were prepared from NIST SRM 4990 (oxalic acid) and IAEA CRM C6 (sucrose). Accurate results were now possible using only 10 μg C containing samples, typical of low-loaded air filter samples, and an improvement on current methods which typically required >250 μg C samples.

1.5 Instrumental analysis

1.5.1 Atomic absorption and fluorescence spectrometry. A tutorial review paper60 (105 references) considered the development of the HR-CS-ETAAS technique for the direct analysis of solid samples and complex materials including the analysis of atmospheric particles. The authors concluded that the advent of this technology has enabled the direct determination of non-metals and that with solid state detectors all spectral events taking place in the regional around the analyte signal can be directly observed. This greatly assists with method optimization and allows chemometric approaches to be used to handle spectral interferents. Looking forward they see a need for detector systems that would enable the monitoring of a much wider spectral window thus facilitating the potential of the technique for multi-element applications. The absorption of CaF molecule at a characteristic line of 606.440 nm has been employed in a study for the determination of fluorine in coal.61 The LOD and M0 were 0.3 and 0.1 ng F. Recoveries from NIST SRM 1635 (trace elements in coal) with a certified F value and from the following materials with indicative F values – SARM CRM 18 (coal-Witbank) and CRM 20 (coal-Sasolburg); IRMM BCR CRM 40 (coal) and 180 (gas coal) – ranged between 90 and 104%. The determination of Pd, Pt and Rh in a challenging automobile catalyst matrix62 required the addition of a chemical modifier (NH4F·HF), an elevated temperature program and the use of a solid standard IRMM ERM EB504 (PGE in used automobile catalyst) to obtain accurate results. Method precision was determined to <10%, which was most favourable, given that test sample sizes were as low as 0.05 mg and the LOD achieved were 6.5 μg g−1 for Pd, 8.3 μg g−1 for Pt and 9.3 μg g−1 for Rh.

The development of a new laser-based fluorescence system for the ultra-trace determination of Hg(0)g has been reported.63 The system utilizes sequential two-photon laser excitation with detection of blue-shifted laser-induced fluorescence to provide a highly specific detection system that precludes detection of any other species other than atomic mercury. This includes gas-phase oxidized Hg, so called reactive gaseous Hg (RGM). Currently the achievable detection capability is 15 pg m−3 at a sampling rate of 0.1 Hz i.e. averaging 100 measurements with a 10 Hz laser system, without any need for sample trapping and preconcentration. Further improvements are planned including the addition of a pyrolysis chamber for the measurement of total gaseous Hg and RGM by difference with good sensitivity and time resolution.

1.5.2 Atomic emission spectrometry. Japanese workers64 employed an ETV unit with a tungsten boat furnace sample cuvette and ICP optical emission spectrometry for the direct determination of chlorine in metallic nanopowders and fine powder samples. A modifier of either aqueous or alcoholic KOH was used together with an external calibration approach using aqueous calibration standards. A LOD of 170 ng g−1 was achieved and sample throughput was ∼30 per hour. Precision was determined to be 8.7% following analysis of 16 replicates of a 100 ng chlorine containing sample. Various miniaturized OES systems based upon dielectric barrier discharge micro-plasma have recently been developed which have been used for the determination of elemental species in gaseous samples. A review65 discusses progress to date and offers future perspectives for trace elemental analysis. A miniaturized OES system was developed that uses a needle-plate electrode discharge as the light source.66 Using a Pt/CNT fluorine-doped tin oxide electrode, this system was used to measure H2 at room temperature using the 656 nm emission line. Under optimum conditions, this emission intensity was linearly correlated to the H2 concentration in the range 0.17–4% (v/v). Testing has now been extended to include other species such as O2 and Na.

The LIBS technique is now seen as a useful technique for in situ measurements for process monitoring. Carbon capture and storage relies upon the long-term isolation of CO2 from the atmosphere and here attendant technologies will be required for the reliable measurement, monitoring and verification of the integrity of compression, distribution and storage systems. American researchers67 detail laboratory scale work that they have carried out in assessing the potential of LIBS for this application and they also discuss its potential for measurements in high pressure and temperature conditions. Chinese researchers68 have explored the potential of using LIBS for the rapid measurement of unburned carbon in fly ash in coal-fired power plants using characteristic emission from the CN molecule. This approach overcame the spectral interference between Fe 247.98 nm and C 247.86 nm and the diminishing intensity of C 193.09 nm line when measured in air. An LOD of 0.16% (m/m) was achieved with precision <5% thus meeting requirements of this power generation sector. Both direct and indirect LIBS have been deployed at a French research centre69 to measure Cu emissions in the exhaust duct of their casting operations. In the direct mode, a representative portion of the duct effluent was sampled isokinetically and passed through an analytical cell upon which the laser was focused whilst in the indirect mode this particulate effluent, once passed the analytical cell, was then trapped on a quartz filter and then interrogated by the laser. A second sampling line was employed to take parallel filter samples which were analysed off-line by ICP-AES following digestion. These sampling lines conformed to specifications set out in EN 14385, the standardised European method for stack air monitoring. Using this parallel sampling approach it was then possible to calibrate both laser-based approaches against a standard reference method using real-world matrix samples. The LOD was determined to be ∼20 μg m−3 and future efforts will examine the potential to measure other metals that are regulated under emission directives.

Fundamental developments in the LIBS technique are also being investigated. The quantitative analysis of gaseous samples, by LIBS is limited due to its relatively lower signal reproducibility compared to that of solid samples. The research group at Malaga University70 have devised a sequential process consisting of optical catapulting (OC), optical trapping (OT) followed by LIBS as a means of increasing instrumental sensitivity. The OC stage serves to put the particulate material under inspection in an aerosol form, the OT stage permits the isolation and manipulation of individual particles from this aerosol which are subsequently analysed by LIBS. In one study conducted the LOD for the analysis of alumina particles was calculated to be 200 ag Al. Fluorine and chlorine do not produce atomic or ionic line spectra of sufficient intensity to permit their detection by LIBS. They do however combine with alkali-earth elements to form molecular species with more sensitive characteristic spectral lines.71 In this study72 the spatial confinement i.e. the analytical zone was optimised which enabled the pulse-to-pulse RSDs to be reduced to <4% for the determination of nitrogen and oxygen in a gas stream, comparable to RSDs encountered in the analysis of solid materials. For more information on fundamental developments in atomic spectrometry readers are directed to our companion review.3

1.5.3 Mass spectrometry.
1.5.3.1 Inductively coupled plasma mass spectrometry. New methods based upon the use of ICP-MS are being reported. Atmospheric dust has a substantial impact on climate and knowing its composition helps to determine dust sources and climate processes. Here rapid elemental screening on small test samples would be advantageous. German researchers73 have determined up to 46 major and trace elements in dust samples, using in situ 200 nm femtosecond laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS). This method was used on very small test portions of 4–7 μg and was sufficiently sensitive thus enabling LODs down to fg and ng g−1, to be achieved. The technique was found to be especially useful for measurements of small amounts of dust on filters, and for 2D-distribution maps of selected elements to identify minerals or contamination. A novel gas-to-particle conversion gas exchange technique for the direct analysis of metal carbonyl gas by ICP-MS has been proposed.74 Here particles are generated in a conversion chamber from Cr(CO)6, Mo(CO)6 and W(CO)6 gaseous species following oxidation with O3 or agglomeration of metal oxide with NH4NO3 which is formed by the in situ reaction of NH3 with O3. A gas exchange device was then used to remove residual NH3 and O3 and to provide an Ar carrier gas to the plasma. The achieved detection limits were 0.07–0.3 ng m−3 which were 4–5 orders of magnitude lower than those of conventional techniques, such as GC-ECD and FT-IR, used to measure carbonyl species and comparable or slightly better than those obtained with an GC-ICP-MS approach. This new technique holds promise for monitoring of gas quality in the semiconductor industry and in gases from engines and waste incineration.

The use of ICP-MS for isotopic measurements continues to be investigated. Review articles on the performance of ICP-based system to determine Cd75 (47 references) and Pb76 (70 references) have been published. The Pb isotopic analysis of Antarctic snow using MC-ICP-MS with a torch-integrated sample introduction system (TISIS) has been reported.77 With this instrumental setup, accurate and precise determination of isotope ratios was possible at Pb concentrations as low as 0.5 ng mL−1 and sample volumes of 0.2 mL (equating to 100 pg Pb). The repeatability of the 207Pb/206Pb ratio was 0.16‰ for a nominal 10 ng mL−1 Pb solution. With a freeze drying technique it was possible to preconcentrate samples 100-fold starting with a 20 g snow sample. Procedural blanks were 0.5 ± 0.3 pg g−1 enabling the determination of isotope ratio in snow samples containing Pb as low as 5 pg g−1. Isotopic fingerprinting of single particles is of increasing value in nuclear auditing and safeguarding processes. In this study, sp-MC-ICP-MS was proposed for the precise determination of individual particles in suspension using Er oxide as surrogate for U oxide.78 This was a suitable surrogate as the U isotope ratios, at different enrichment levels, could be covered by a corresponding Er isotopic range. From the histogram of the pulse intensities, the particle diameter was estimated to ∼226 nm with a Er mass of 7 × 10−16 g. Two data processing strategies, point by point (PBP) and linear regression slope (LRS) were employed in the data analytics. Results showed that precisions of the 170Er/166Er, 168Er/166Er and 167Er/166Er determined by the PBP method were 5.5%, 4.6% and 3.9% respectively. Due to the weak signal intensity for 164Er the precision and accuracy of 164Er/166Er ratio measurements were substantially poorer. The relative errors of these ratios measurements after mass bias correction were 0.2–0.4%. By the LRS method, measured ratio precisions improved to <0.3% and allowed 164Er/166Er and 162Er/166Er measurements to be performed. The authors concluded that this proposed technique was suitable for the assay of particles in the size range of 130–3000 nm and combines fast screening, sensitive detection and isotopic identification of an individual particle.

The characterisation of nanoparticles by ICP-MS remains a fertile topic for research. Two reviews,79,80 (35 and 52 references) presented the historical development of FFF-ICP-MS techniques, their current status and future prospects. The research group led by Detlef Gunther81 has investigated the potential of a high temporal resolution ICP-TOF-MS in conjunction with a microdroplet generator for the simultaneous mass quantification of different NPs in a mixture. Here calibration with monodisperse droplets consisting of standard solutions allowed for the quantification of NPs without the need for NP reference materials. On the basis of this mass quantification, the sizing of the calibrant NPs was simultaneously determined with an accuracy of 7–12%. In an another study82 two different sample introduction systems for the analysis of NP suspensions were investigated. For accurate sp-ICP-MS analysis it is crucial to ascertain the transport efficiency of nebulized sample to the plasma. Here, using the waste collection method, efficiencies were 11–14% for Ag NP suspensions and 9–11% for Au NP suspensions by pneumatic nebulisation. In contrast the transport efficiency using a microdroplet generation was 100%. British researchers83 modified a HDC-ICP-MS system to facilitate the injection of NIST-traceable standards into the post-column effluent so that quantification by standard addition could be carried out. Combining the simultaneously acquired particle sizing data and mass concentration data allowed accurate quantification of the particle number concentration to be made in a single analytical run. A US based consortium84 has developed a method that estimated the size detection limit – the Dmin value – for various NPs when sp-ICP-MS is used. There was the assumption that each NP sample consists of only one element and the lowest Dmin values of <10 nm were obtained for elements such as Ce, Ir and U; Dmin values of 11–20 nm for elements such as Ag, Au, Cd and Pb; Dmin of 21–90 nm for elements such as Co, Ni, Ti and Sn and Dmin values > 200 nm for elements such as Ca, Se and Si. Parameters that influence these Dmin values include instrument sensitivity, nanoparticle density and instrumental noise and that Dmin values can be reduced by analyte isotope selection and the use of DRC/CCT to facilitate the removal of isobaric interferences. A validation exercise then compared estimated Dmin values against Dmin values experimentally derived by sp-ICP-MS for the following three nanoparticles in a range of aqueous matrices: Ag (13 nm vs. 21 ± 4 nm); CeO2 (10 nm vs. 19 ± 8 nm) and TiO2 (91 nm vs. 130 ± 28 nm) which showed reasonable agreement. Other work has been reported that included: application85 of a k-means clustering algorithm to signal processing of raw sp-ICP-MS data that improved discrimination of particle signals from background signals; improving86 the detection and characterization of engineered NPs by sp-ICP-MS using short dwell times; investigations87 into calibration approaches and non-linear detector responses and development88 of a data evaluation tool for the calculation of particle size, concentration and size distribution from raw sp-ICP-MS data.


1.5.3.2 Other mass spectrometry techniques. Developments in IRMS techniques for the isotopic analysis of gaseous species have been published. A technique for compound specific analysis of stable carbon isotope ratios of ambient VOCs was developed.89 It was based upon the selective sampling of VOCs onto sorbent cartridges which are recovered and separated by a TD-GC-MS followed by on-line oxidation in a combustion interface prior to IRMS. The method allowed measurements of isotope ratios of VOCs present in ambient air at low pptv to ppbv concentrations with an accuracy of typically better than 0.5‰. An on-line IRMS system90 for analysis of single substituted isotopologues (12C16O17O, 12C16O18O, 13C16O16O) in nanomolar CO2 extracted from stratospheric air samples has been developed. The Δ17O excess was derived from isotope measurements on two different CO2 aliquots: unmodified CO2 and modified CO2 after complete oxygen isotope exchange with CeO2 at 700 °C. This exchange and measurement process took 15 minutes, a major improvement over previously off-line methods that took up to 3 hours. Other IRMS related papers of interest included: the development91 of a high temperature interface to convert organic Cl to HCl prior to MS/IRMS analysis; a device to extraction trapped ancient air from large ice samples;92 separation and purification of Ar/O2 mixtures by cryogenic means rather than the conventional chromatographic approaches;93 an investigation into the matrix effects of gases other than He have on measured 13C and 18O isotopes ratios of CO2;94 a bracketing calibration protocol that makes reference gas measurement potentially redundant thus saving cost and time95 and an elemental analyser-IRMS instrument96 that provided high-precision, rapid simultaneous measurements of 13C/12C, 15N/14N and 34S/32S ratios as well as elemental concentrations. Studies have shown that nitrous acid (HONO) chemistry can play an important role in O3 production but that the detailed chemical mechanism of HONO production and identification of its sources are poorly characterised. A novel approach97 for the ambient measurement of HONO using ion drift chemical ionization MS employed SF6, generated in situ, to form the F·HONO analytical adduct by an F transfer reaction. The typical sensitivity achieved was 200–300 cps per ppb resulting in a LOD of 10–20 ppt HONO for a 1 s integration. This instrumental sensitivity coupled with the high temporal resolution will assist in the future mechanistic studies that are required.

There have been developments in MS techniques for analysis of airborne particulates. The hypenation of a thermal-optical carbon combustion analyser to a PI-TOF-MS allowed deeper insight into the organic composition of primary particles and the formation mechanism of secondary organic aerosols.98 Here the combination of PI-TOF-MS was beneficial as the data offered a more detailed insight into the molecular composition of evolved gaseous species following determination of bulk carbon parameters in the preceding combustion step. A new particle trap laser-induced incandescence mass spectrometer for the examination of the black carbon mixing state within aerosols has been developed.99 Total aerosol composition was analysed with the laser stitched off and the aerosol particles externally mixed with black carbon can be analysed with the laser switched on. By difference the chemical composition of particles internally mixed with black carbon could be measured. Entrained flow gasification is a promising technique where biomass is converted to a syngas such as CO and H2 which can then be used as building blocks, via further catalysis, to biofuels of chemicals. To better understand the mechanisms involved, on-line measurements100 involved soot particle aerosol mass spectrometry and the main advantage compared to other techniques was that particle composition – ash-forming elements, organics, PAH and soot – was obtainable at high time resolution. Improvements in particle sample introduction into online mass spectrometers included: a new aerodynamic lens inlet system101 designed to transmit and allow the analysis of individual particles in the 4–10 μm size range with a peak detection efficiency of 74 ± 9% for 6 μm particle.

Characterisation of individual particles released from nuclear facilities and processes can be a useful tool for auditing and safeguarding purposes. The direct isotope ratio analysis102 in individual U–Pu mixed particles with various U/Pu atomic ratios were analysed without prior chemical separation by TIMS. In the analysis, peaks of Am, Pu and U ion signals were successfully separated by continuously increasing the evaporation filament current when applied to model particles mixtures consisting of μm-sized RM particles with U/Pu ratios of 1, 5, 10, 18 and 70. The use of TIMS combined with a FT technique can be an efficient method for determining the isotope ratio of individual U particles but erroneous conclusions can be derived from averaged ratio results if a number of particles are perceived to be a single particle – so called particle mixing. In a refinement of this approach,103 prior microsampling using SEM was deployed to identify and thus isolate individual particles thus minimise the potential for this particle mixing effect. The efficacy of this approach was proven by the successful analysis of a mixture of NIST SRM 950a (U3O8) and NBL CRM U100 (Uranium Isotopic Standard) particles. The contribution104 of three traffic-related components – abrasion, exhaust and resuspension – to kerbside and urban background PM10 and PM1 levels was quantified based upon the analysis of individual particles collected on B-substrates using a two stage cascade impactor (0.1–1 μm and 1–10 μm) by SEM-EDS. In total 160 urban air samples were collected, and 111[thin space (1/6-em)]003 individual particles examined and classified into 14 particle classes as to their origins! Results showed that traffic-related contribution to PM10 load was 27% at an urban background sampling site away from roads rising to 48% at kerbside. For PM1 load the values were 15% and 38%. The relative contribution of the different traffic components to PM10 was 15% from abrasion processes, 27% from exhaust and 58% from resuspension and for PM1 these figures were 8%, 38% and 54%. Compared to previous work, the authors observed significantly lower portion of exhaust particles, undoubtedly due to increasingly stringent emission legislation, but the high abundance of resuspension particles highlights the need for more health effects and mitigation studies.

1.5.4 X-ray spectrometry. The use of multifunctional systems involving X-ray techniques has been reported for the analysis of airborne particulate matter. Both X-ray diffraction and scattering on a single platform have been used to examine the structure, composition and thermal properties of TiO2 particles in the 1 to 10 nm size range.105 A new system106 has been deployed, that combined SWAXS with a single stage multi-orifice low-pressure impactor (SS-MOLPI), as a process analytical tool for the quasi-online analysis of NPs generated by electrical discharge in an inert gas. The SS-MOLPI allowed quick sampling of aerosol particle directly from the process gas stream onto an adhesive tape substrate which could then be transferred to the SWAXS for rapid analysis. The entire process took only a few minutes and provided information on primary particle size, aggregation state, morphology and crystalline properties in a single run. Information provided was thus comparable to that obtained by SEM/TEM and XRD but was more affordable and quicker. A new apparatus for the simultaneous application of PIXE and XRF for the elemental analysis of atmospheric aerosol samples has been described.107 Here the relatives strength of PIXE to interrogate light elements (Al to Zn) and XRF to interrogate higher elements (Fe and above) neatly compliment each other. Improved elemental analysis methods for the characterization of atmospheric particulate matter on filters have been reported.108 These included: a revised microwave assisted acid digestion protocol that used less than 2 mL acid mixture with a FIA-SF-ICP-MS method that measured 44 elements with a daily throughput of up to 30 samples; and an EDXRF method that could quantify 15 elements per filter at a rate of 4 samples per hour. The EDXRF method employed a FP calibration protocol and daily calibration checks were carried out with NIST SRM 2783 (Air Particulate on Filter Media) with recoveries in the range 85–115%. As an additional validation check, in a study involving the determination of metals in diesel fume samples, filters were analysed initially by EDXRF and then digested and analysed by ICP-MS. The EDXRF/ICP-MS ratios, obtained for elements amenable for analysis by both techniques, were Ca (0.8 ± 0.2); Ce (1.1 ± 0.0); Cu (0.9 ± 0.2); Fe (1.1 ± 0.4); Mg (1.2 ± 0.3); Ni (1.0 ± 0.3); P (0.7 ± 0.2); Pt (1.1 ± 0.2); S (1.2 ± 0.3) and Zn (1.2 ± 0.2).

Speciation of airborne particles by X-ray techniques continues to be investigated. Iron is often the most abundant metal in airborne particles and can act as a catalyst in the formation of radicals and in the generation of reactive oxygen species, hence it is an element of interest in respiratory health effects studies. XANES was used to study the local order and valance state of Fe in urban PM2.5 collected on filters.109 Based upon a comparison with model compounds, fayalite (Fe2+) and ferrihydrite (Fe3+), Fe in samples was prevalent as Fe3+ whereas EAFS data attributed this valent state to a nanocrystalline phase of ferrihydrite. The authors noted that ferrihydrite has never been shown to be associated with suspended urban dusts in previous literature or specific studies to investigate its toxicology. Aerosol deposition of P to the oceans is a key source of nutrient for biological productivity. To study its potential bioavailability in the Mediterranean, NEXAFS was used to examine aerosol samples in both European and North African air masses.110 It was found that European derived aerosols delivered on average 3.5 times more soluble P than aerosols originating from North Africa. This soluble P source was dominated by organic P compounds derived it was concluded from a bacterial source. The composition of Se in atmospheric urban particle was investigated by XANES.111 It was noted that SeIV was the dominant oxidation state in ambient aerosols followed by Se0, SeII and SeVI. The SeIV oxidation state was only observed in particles derived from gasoline, diesel and coal fly ash whilst a combination of Se0, SeII and SeIV was found in particles originating from biomass burning. Understanding the speciation of metals in workplace aerosols is required for better exposure assessment and health effects studies. The Zatka sequential extraction method has been developed to speciate Ni aerosol samples in refineries using model Ni compounds but its performance on real-world dust samples is somewhat unknown. In this study,112 XANES was used to examine samples prior to extraction and it was noted that the Zatka method can overestimate the soluble Ni fraction and it may underestimate the sulfidic and metallic fractions in some samples. Importantly, XANES was able to identify component sulfidic nickel species which should assist in more accurate exposure assessments and more refined epidemiological analysis of respiratory cancers in workers processing such ores. A Canadian study113 investigated the impact of humid indoor air conditions on the bioaccessibility of Zn in dust and the transformation of Zn species during weathering. House dust samples were subjected to an oxygenated humid environment in a sealed chamber for up to 5 months. Bulk and μXAS was used to interrogate the Zn species and test samples were subjected to a gastric acid extraction procedure, before and after, each storage experiment to determine Zn bioaccessibility. It was noted that under these humid conditions that there was a significant increase in Zn bioaccessibility with redistribution from inorganic Zn to Zn adsorbed on humates. The results helped explain the greater bioaccessibility of certain metals in house dust compared to soil samples. Interested readers are invited to read our companion XRF review.5

1.5.5 Other spectrometric techniques. There is growing interest in laser-based techniques for gas monitoring because they can be portable and can offer a non-invasive or stand-off monitoring capability with high sensitivity, selectivity and high time resolution. The application of absorption spectroscopy using quantum cascade lasers (QCLs) has been reviewed (150 references)114 and covered applications such as molecular gas spectroscopy, industrial process control, combustion diagnostics and medical breath analysis. Two pulsed mid-IR QCLs were deployed to make in-line simultaneous measurements of NO and NO2 in the cooling stack of a power plant.115 Despite the harsh environment, water content up to 235 g m−3 and average particle loads of 15.8 mg m−3, in the flue gas, LODs of 219 ppb for NO and 164 ppb NO2 were achieved. The authors employed116 the same open path technology at the kerbside and achieved a LOD of <1 μg m−3 for both species for a 1 minute integration and using a path length of up to 428 m. The results obtained correlated well with those obtained using the reference chemiluminescence method and the fast response time allowing emissions from different vehicle classes to be distinguished was most advantageous. The development and demonstration of a ground based mobile differential adsorption lidar (DIAL) system for monitoring CO2 emission plumes from industrial sources has been reported for the first time117 but further improvements in detection limits will be required to quantify smaller fugitive emissions. A newly developed isotope ratio laser spectrometer for CO2 analysis has been tested during a tracer experiment at a German pilot plant for CO2 storage.118 In this experiment 500 tonnes of CO2, with a δ13C value significantly different from background value, was injected in a supercritical state into a reservoir in the ground. This new instrument was then used to monitor the breakthrough of the isotope tracer through a stainless steel riser tube installed in an observation well. Measurements compared favourably with those derived from IRMS measurements and it is believed that this new capability, with its high temporal resolution, can contribute to future CO2 capture and storage projects by assisting in the assessment of the long-term integrity of reservoirs.

Measuring carbonaceous particles using thermal-optical analysis, that thermally fractionates the content of such particles in an oven into organic and elemental carbon, is widely as a reference method. Oven temperature accuracy is therefore rather important and it has now been demonstrated that temperatures measured by oven thermocouples can vary by as much as 50 °C from instrument to instrument. A new temperature calibration kit has now been evaluated119 and the same device was then used to calibrate different instruments as part of an interlaboratory trial120 resulting in significant improvement in measurement precisions. A thermal-optical carbon analyser has been modified121 to now include a seven-wavelength light source/detector system covering the range 405–980 nm so that carbonaceous material can be examined optically as it undergoes combustion. It is hoped that this new modification will assist in a better understanding of this fractionation process leading to more accurate carbon measurements and hence more value to aerosol monitoring programs.

The aethalometer, an instrument which collects airborne particulate matter onto a filter while continuously measuring its light transmission, has been widely deployed to measure black carbon in high time resolution. Here the measurement relies on ideally a linear relationship between attenuation and black carbon on the filter. At higher filter loadings this response becomes non linear and correction procedures such as an algorithm build into the instrument calibration have been used to compensate. An improved system, the “dual-spot” aethalometer has now been developed to undertake this correction in real-time.122 This was achieved by measuring concurrently the attenuation of light on two sample spots on the filter which are sampled at different flow rates. This resultant continuous difference in sample loading is then used in a new algorithm to develop this time correction model. The AE51 μ-aethalometer was designed as a smaller wearable version and researchers123 have examined the potential for saving the filters for subsequent off-line SO42− measurements by IC for personal exposure assessment purposes. The estimated LOD for a 24 hour time integrated sample was estimated to 1.4 μg m−3 and results compared favourably with those obtained by collocated filterpack sampling (R2 = 0.87, slope = 1.02). Comparing optical based methods, such as the aethalometer, against thermal-based reference methods has been the focus of two reported studies.124,125

A round up of new instrumental techniques and interesting applications included a demonstration126 of the first continuous measurements of δ18O–CO2 in air by FTIR which showed good measurement stability of <0.1‰ (over a day) and <0.25‰ (over 9 minute averages). Whilst this precision is not as good as that obtainable by IRMS, the ability to perform highly time resolved measurements could be exploited to explore the dynamics of atmospheric gas processes. A study127 aimed at determining the FeIII/II ratio of the Fe-bearing minerals contained in the Harmattan dust, a northeasterly Saharan winter wind that blows over the West African subregion, by 57Fe Mössbauer spectroscopy, determined that this dust was seriously deficient in soluble FeII needed in the soil for healthy crops and plants. Silver NPs are the most commonly used nanoparticles in consumer products. A studied investigated128 the feasibility of SERS as a method for the detection and quantification of Ag NPs. By using ferbam, ferric dimethyl-dithiocarbamate, as an indicator molecule that binds strongly onto NPs, detection and discrimination was based upon a signature SERS response of Ag-ferbam NP complexes. Particles in the 20–200 nm range were detectable with highest signal intensity for particles in the range 60–100 nm and a linear relationship was noted between the Raman intensity and Ag NPs concentration over the range 0–20 mg L−1. The authors concluded that SERS could be a promising analytical tool in the future not only for the determination of Ag NPs in consumer products but in other environmentally relevant matrices.

2 Water analysis

2.1 Sample preconcentration and extraction

The majority of review articles focussed on specific methodologies rather than groups of analytes or matrices, reflecting the proliferation of microextraction techniques now available. Al-Saidi and Emara129 (74 references) covered recent developments in DLLME for the determination of inorganic analytes whilst Hagarova130 (84 references) (in Slovakian) covered the use of supramolecular solvents for the extraction of metals. A very similar theme, the use of ionic liquids in microextraction techniques131 (112 references) was reviewed for the preconcentration of metals. Although the majority of papers considered in the timely review of nm-sized materials for the SPE of trace elements132 (265 references) covered water analysis, virtually every possible matrix from human hair to crude oil had also been analysed. A review133 (84 references) of microextraction techniques for the determination of organomercury and organotin compounds in environmental samples reflected how laborious current standard methods are. Great strides have been made in the elimination of toxic organic solvents from preconcentration methods, as evidenced by the latest approaches for “green” chemistry preconcentration methods134 (46 references) specifically applied to the determination of trace elements in seawater. This author appreciates not having to use MIBK or CCl4 anymore, but does wonder just how environmentally friendly ionic liquids, 1-undecanol, and other “green” reagents really are. 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
Al, Cd, Co, Cu, Fe, Ni, Mn, Pb, Zn Seawater NOBIAS chelate-PA1 None ICP-SF-MS LOD, 0.9 (Pb) pmol kg−1 to 0.3 (Al) nmol kg−1 120 g seawater sample NRCC CRMs CASS-5 (nearshore seawater) and NASS-5 (seawater), GEOTRACES RMs GS (surface seawater), and GD (deep water seawater) 135
AsV, CrVI, SeVI River, rain and lake water MWCNT 3-(2-Aminoethylamino)-propyltrimethoxysilane ICP-MS LOD 15 (As) to 38 (Cr) 4 mL sample (Environmental waters) IERM CRMs GSBZ50009-88 GSBZ50027-94 and NRCCRM CRM GBW3209 and GBW3210 136
As, Sb, Se Tap and lake water Iron oxide silicon nanoparticles CeO2 coating to trap in situ generated hydrides ICP-AES LOQ 0.26 (Sb) to 0.44 (As) 50 mL sample ERM CA021a (soft drinking water) 137
Be Seawater Silica gel Interference removal with EDTA solution ICP-MS LOD 0.2 pmol kg−1 250 mL sample NRCC CRM SLRS-5 (river water) 138
Cd, Cu Environmental water samples Glass beads with a nano-TiO2 surface 1-Octyl-3-methylimidazolium hexafluorophosphate and trapping of chelates with 2-[(5-bromo-2-pyridyl)azo-5-(diethyl-amino)phenol] FAAS LOD 0.1 (Cd) and 0.3 (Cu), 100 mL sample Spike recovery 139
Cd, Co, Ni, Pb Environmental water samples Graphene Dithiazone WD-XRF LOD 1.1 (Co) to 6.1 (Pb), 100 mL sample Spike recovery and comparison with ICP-MS results 140
Cd, Co, Cu, Mn, Ni, Pb Water Poly(methyl methacrylate) lab on a chip Cl ICP-MS LOD 3.48 ng L−1 (Pb) to 20.68 ng L−1 (Mn), 50 μL sample Spike recovery and NIST SRM 1643e (artificial saline water) 141
Co, Cu, Pb Environmental water samples Co-precipitation of a zinc hydroxide suspension Metal complexes with 1-(2-pyridylazo)-2-naphthol FAAS LOD 1.5 (Cu) to 2.4 (Co), 40 mL sample Spike recovery 142
Cu, Pb, REEs Water, seawater and sediments Graphene oxide TiO2 ICP-MS LOD 0.13 (Eu) to 2.64 (Pb), 7 mL sample Spike recovery (waters), NRCCRM CRM GBW07301a (stream sediment) 143
Fe, Zn Seawater, lake, mine and tap water MWCNT HR-CS-ETAAS LOD 0.5 ng L−1 (Zn) and 4 ng L−1 (Fe), 150 mL sample NIST SRM 1643e (trace elements in water), ERM-CA011b (hard drinking water) 144
Hg Seawater, ground water Poly(acrylamide)-grafted poly(propylene) sheet Ag nanoparticles ED-XRF and CV-AAS LOD 6 (CV-AAS) and 30 (ED-XRF), 25 mL sample Spike recovery 145
Hg Water Polydimethylsiloxane microfluidic chip Au nanoparticles ICP-MS LOD 0.07, 20 μL sample NIST SRM1641d, (mercury in water) 146
Mo River, thermal, mine and tap water Carbon nanotubes L-Tyrosine ICP-AES LOD 40 ng L−1, 2 mL sample NIST SRM 1643e (trace elements in water) 147
Mo, Nb, Ti, V, W, Zr Seawater SeaFAST column Ethylenediamine triacetic acid and iminodiacetic acid ICP-MS LOD 1.27 pmol kg−1 (Nb) to 4970 pmol kg−1 (V) 50 mL sample NRCC CRM NASS-6 (seawater) 148
REEs Tap and seawater Amberlite XAD-4 resin 2,6-Pyridine dicarboxaldehyde ICP-AES LOD 0.006 (Yb) to 0.15 (Nd), 50 mL sample SPS RMSW2 Batch 127 (elements in surface waters) 149
REEs Seawater and water samples Amberlite XAD-4 resin 8-Hydroxy-2-quinoline carboxaldehyde ICP-AES LOD 0.01 (Yb) to 0.42 (Pr), 25 mL sample SPS RM SW2 Batch 127 (elements in surface waters) 150
99Tc Water TEVA®resin ICP-MS LOD 0.005 ng L−1, 100 mL sample Spike recovery 151


Table 2 Preconcentration methods using liquid phase extraction for the analysis of water
Analytes Matrix Method Reagents Detector Figures of merit Method validation References
Ag, Au Water and ore samples Magnetic DLLME 1,3-(Propyl-1,3-diyl)bis-(3-methylimidazolium)bis-(tetrachloroferrate (III)) and 4,4′-bis(dimethylamino)thiobenzophenone (chelating agent) ETAAS 3.2 (Au) and 7.3 (Ag) ng L−1, 25 mL sample Spike recovery (96–104%) 152
AsIII and AsV (by difference) Water SPE-DLLME-SFOD Diethyldithiophosphate (chelating agent), acetone and 1-undecanol ETAAS LOD 2.5 ng L−1 (AsIII), 100 mL sample NIST SRM 1643e (trace elements in water) and spike recovery 153
AsV (AsIII by difference) Water LLME Arsenomolybdate complexes, and tetradecyl(trihexyl)phosphonium dicyanamide ETAAS LOD 1.9 ng L−1, 5 mL sample NIST SRM 1643e (trace elements in water) and spike recovery 154
Au Water and hair DLLME Benzyldimethyltetradecyl ammonium chloride dehydrate (ion pairing agent) and 1-hexyl-3-methylimidazolium hexafluorophosphate ETAAS LOD 2.0 ng L−1, 20 mL sample Spike recovery, NRC CRM MA-1b (gold ore) 155
Cd Water, seawater, beer, wine CPE Ag nanoparticles, APDC and Triton™ X-114 ETAAS LOD 1 ng L−1, 20 mL sample NIST SRM 1640a (trace elements in natural water), NRCC CRM NASS-6 (sea water), spectrapure standards RM SPS-SW2 batch 125 (surface water) 156
Hg Water, seawater and fish Single drop ME lab in syringe Pd(NO3)2, L-ascorbic acid, SnCl2 VG-ETAAS LOD 0.48 μg L−1, 3.5 mL sample Spike recovery (water) and ERM CE278 (mussel tissue) and IAEA-CRM 350 (tuna homogenized) 157
Ho, Yb Water Emulsification ME 1-(2-Pyridylazo)-2-naphthol (chelating agent), C2Cl4 ICP-AES LOD 0.364 μg L−1 (Ho) and 0.252 μg L−1 (Yb), 10 mL sample Spike recovery 158
REEs Tap, river and seawater DLLME Acetone and CCl4 ICP-MS LOD 0.68 (Tb) to 26.6 (Ce) ng L−1, 5 mL sample Spike recovery 159
Si Water Ion associated complexation Hexaammonium heptamolybdate tetrahydrate, N-[4-[bis[4-(dimethylamino)phenyl]methylene]-2,5-cyclohexadien-1-ylidene]-n-methyl, chloride (crystal violet) ED-XRF LOD 9 ng mL−1, linear range 0.02–1.0 μg mL−1, 5 mL sample Spike recovery and comparison with ICP-AES 160


2.2 Speciation and fractionation analysis

This year has been particularly blessed with review articles; authoritative reviews on the determination of dissolved Fe in seawater161 (138 references) and on coupled techniques for As speciation in food and water162 (243 references) are of particular interest. Other review articles covered As, Cr, Sb and Tl speciation in water and sediments by LC-ICP-MS163 (103 references) and speciation analysis of Cr by HPLC-ICP-MS since 2000 with particular emphasis on traceability and metrology164 (98 references). The onward march of nanomaterials and their characterisation is underlined by the reviews on FFF coupled to ICP-MS80 (51 references) and the application of NPs and nanostructured materials to elemental speciation165 (62 references).

By coupling IC with an HG-AFS instrument for the on-site determination of arsenic species in sulfidic waters,166 it was possible to separate in just 25 minutes AsIII and thioarsenite from AsV and its monothio-, dithio-, trithio- and tetrathio-forms (7 species in total). Separation was achieved on an IonPac® AS 16 analytical column using a KOH mobile phase gradient at a flow rate of 1 mL min−1. Peak identification was based upon the retention times observed for standard solutions of the target analytes. Calibration was carried out against an AsV stock solution since all peaks were reduced to arsine gas and the dissolution of the standards typically resulted in the formation of more than one species. Because samples were stable only for <2 h, use of a mobile laboratory was necessary. There was good agreement between the total As concentration and the sum of the individual species concentrations. Using this method the LOD was 1–3 μg L−1 depending on the daily baseline response. Strangely, the authors managed to publish their results without reporting any significant method validation. A screening method167 for the selective trapping and determination of AsIII and AsV with WDXRF detection involved first passing a 50 mL sample (pH 5–9) through an Empore™ chelating disk to remove Pb since the PbLα line can interfere with the AsKα line. The pH was adjusted to 2–3 and 1 mL of 0.06 M APDC added to form a complex with AsIII. The resulting solution was passed at 12.5 mL min−1 through a PTFE disk placed on top of an Empore™ Cation-SR cation-exchange disk to trap chelated AsIII on the top disk and the AsV on the bottom disk. Both disks were washed with 0.5 mL of a pH 3 HCl solution, separated and dried at 100 °C for 15 minutes prior to WDXRF analysis. The LODs were 0.8 and 0.6 μg L−1 for AsIII and AsV, respectively. Recovery of AsIII and AsV spikes from local mineral waters was 98–104% for both species. In an interesting method168 that did not use chromatography, labile inorganic and organic As species were trapped in a ferrihydrite gel layer of a DGT unit for direct determination using XANES spectroscopy. They did not recommend extraction into 1 M HNO3 and analysis by HPLC-HG-AFS because 20% of the arsenate was converted to arsenite and a significant amount of DMA into MMA or AsV during extraction. Even worse results were obtained for extraction into 1 M NaOH. This reviewer is surprised that they did not try extraction solutions such as their mobile phase (20 mM phosphate buffer at pH 6.5) or a nitrate buffer with added EDTA in which As species are stable.

Methods for the determination of arsenic plus other elemental species are always welcome. A method169 for the separation and determination of AsIII, AsV, DMA, MMA, CdII, CrIII and CrVI in one chromatographic run involved addition of 3 mL of concentrated ammonium phosphate buffer (pH 7) containing 10 mM EDTA to a 7 mL sample before incubation at 70 °C for 15 minutes to ensure the formation of anionic EDTA complexes of CrIII and CdII. A 50 μL aliquot was injected onto a Hamilton PRP-X100 anion-exchange column and the species separated using a mobile phase of 40 mM NH4NO3 (pH 8.6) at a flow rate of 1.0 mL min−1. With ICP-MS detection, the LODs for the individual species ranged from 0.07 (CrVI) to 0.12 (CdII) μg L−1 and spike recoveries (n = 9) from 91–97% (CrIII) to 105–116% (CdII). A slightly different philosophy was used to determine AsIII, AsV, SbIII, SbV, SeIV and SeVI in water samples.170 The same analytical column was used but EDTA solution was the mobile phase rather than being added to the sample. All the species were separated within 11 minutes using a mobile phase with a constant pH of 4.7 (adjusted with formic acid) and organic modifier content (97[thin space (1/6-em)]:[thin space (1/6-em)]3 EDTA[thin space (1/6-em)]:[thin space (1/6-em)]CH3OH). The EDTA concentration was held at 5 mM for the first 4.5 minutes, stepped up to 30 mM EDTA in 1 minute and then held at 30 mM for 11 minutes. The flow rate was 1.5 mL min−1 and the sample injection volume 50 μL. With SF-ICP-MS detection, the LODs ranged from 0.02 μg L−1 for AsIII and SbV to 0.4 μg L−1 for SeVI. The sum of the species concentrations measured in NIST SRM 1643e (trace elements in water) and the HPS CRM-SW (elements in seawater) agreed well with the certified data for total concentrations. Spike recoveries from pure water and two hot spring samples ranged from 94% (SbV) to 123% (SbIII) (n = 3). The fluxes of volatile species of As, S and Se above a peat bog were quantified27 by trapping in concentrated HNO3 in 3 sequential liquid traps attached to a 60 L h−1 air pump for 24 h. Trap solutions as well as surface water samples were analysed for As, S and Se species by HPLC-ICP-SF-MS. The air trap samples were diluted 1 + 49 with ultrapure water whereas the water samples were analysed directly to prevent speciation changes during sample manipulation. Slightly different HPLC separations were required for the two sample matrices; both methods used a 250 × 4 mm id OmniPac® PAX-500 mixed mode column with an eluent A of 30 mM NH4NO3 pH 7.5 containing 1% v/v CH3OH. The differences lay in eluent B (50 mM Na2CO3–NaHCO3 pH 8.5 containing 25% v/v CH3OH for air trap samples and H20 adjusted to pH 8.4 for water samples) and the flow rates (0.5 and 1 mL min−1, respectively). The instrumental LODs ranged from 0.17 (methane selenic acid) to 13 μg L−1 (dimethyl sulfone) for air trap samples and from 0.10 (SeVI) to 22 (SVI) μg L−1 in water samples. The method was validated by measuring spike recoveries from the two matrices. Total elemental values determined by ICP-MS or ICP-AES agreed with the sum of the species concentrations.

A study171 of the fractionation of Gadolinium in Japanese river waters used sequential SPE. Humic and fulvic acid complexes of Gd were trapped on a strong anion-exchange column (QAE-Sephadex A-25) together with the MRI contrast agent diethylenetriamine-N,N,N′,N′′,N′′-penta acetato aquo gadolinium(III) (Gd-DTPA). Free GdIII was not retained and was trapped on a separate iminodiaceticacid-type chelating resin (Empore™). After the Gd-DTPA complex had been eluted with 4.0 mL of a 50 mM tetramethyl ammoniumsulfate solution, the Gd humic and fulvic acid complexes were dissociated with 2 mL of 1 M HNO3 and the Gd desorbed. Free GdIII was eluted from the iminodiaceticacid-type chelating resin with 5.0 mL of 3 M HNO3 and all eluates analysed for Gd by ICP-MS. The method LOD was in the low ng L−1 range for a 500 mL sample and its accuracy verified in spike recovery experiments. Whereas no free Gd or contrast agents were detected in a sample from a remote area, an urban sample contained 0.82 ± 0.09 ng (n = 4) Gd in the contrast agent fraction. In both rivers, the humic and fulvic acid fraction contained 3.6 ± 0.2 ng Gd (n = 4), taken to be the Japanese background level.

The determination of mercury species in aquatic samples continues to be of interest. A method for the determination of thimerosal (sodium ethylmercurythiosalicyclate) and its breakdown products (EtHg and Hg2+) in pharmaceutical effluents discharged to river waters in Argentina172 involved separation on a Zorbax SB-Aq C18-RP (1.6 mm × 150 mm, 5 μm particle size) analytical column. An eluent system of 0.1% formic acid followed by 0.1% formic acid + 0.1% 2-mercaptoethanol at a flow rate of 1.0 mL min−1 was used. The Hg species concentrations were determined by CV-AFS after UV photo-oxidation of the HPLC eluent. The LODs ranged from 0.07 μg L−1 for Hg2+ to 0.09 μg L−1 for thimerosal and EtHg, sufficient to detect EtHg and Hg2+ in the river water and industrial effluents. Thimerosal, however, was not detected. The accuracy of the method was validated by comparing the speciation results with total Hg measurements. A highly sensitive and automated HPLC-AFS method173 for the determination of MeHg and Hg2+ in waters involved preconcentration onto a column packed with thiourea- and thiol-functionalised silica and separation on an EclipseXDB C8 (4.6 × 150 mm, 5 μm) analytical column with a mobile phase of 1.5 mM APDC in a 75% (v/v) methanol[thin space (1/6-em)]:[thin space (1/6-em)]water solution at a flow rate of 1 mL min−1. An LOD of 40 pg L−1 (MeHg) using UV oxidation CV-AFS was obtained for a 200 mL sample. Accuracy was determined by spiking a number of water samples (sewage, river and seawater) with MeHg and determining MeHg in the CRMs NIES no. 13 (human hair) and IAEA-085 (human hair).

A method for the determination of iodine and selenium species in groundwater174 used anion-exchange chromatography coupled to ICP-MS. A 10 minute separation of I, IO3, SeIV and SeVI was achieved using a Hamilton PRP X100 anion-exchange column and a aqueous mobile phase of 3 mM ammonium citrate and 30 mM ammonium perchlorate at pH 8.5 with 2% v/v CH3OH as an organic modifier. The LODs ranged from 4.3 ng L−1 for I to 23 ng L−1 for SeVI. Method validation was carried out using spike recoveries from a groundwater. The method was deemed suitable for baseline monitoring of candidate sites for a Chinese high-level radioactive waste repository.

The determination of nanoparticles in environmental samples is becoming increasingly important as NPs are now finding uses in all walks of life. Although most methods rely on coupled techniques for their quantification and characterisation, there is still scope for simple fractionation methods to separate the particulate phase from the ionic phase. Although CPE is extensively used to preconcentrate trace elements, it has also been employed175 to separate silver NPs from dissolved AgI species. Both silver NPs and AgI were extracted from a 20 mL sample acidified with 0.1 mL of 0.1 M HNO3 into the Triton-X114 micellular phase. To extract only the silver NPs, 0.2 mL of 1 M ammonium thiocyanate was added to complex the AgI to prevent its extraction into the surfactant phase. The AgI concentration was calculated by difference. With ETAAS detection, the LOD was 2 ng L−1. The accuracy of the method was tested by carrying out spike recovery experiments in a range of waters including NIST SRM 1640a (trace elements in natural water). The use of HDC to resolve engineered NPs in water samples was demonstrated176 through use of a PL-PSDA cartridge, type 1 column (80 cm length and 7.5 mm id) with a separation range of 5 to 300 nm. The mobile phase was 1 mM NaNO3 containing 0.0013% (w/w) SDS and 0.0013% (w/w) Triton™ X-100 at pH 7.5. Under these conditions a mixture of polystyrene, silver and gold NPs were detected on-line with DLS to determine the retention times of the different NPs. These results and those obtained by AUC with off-line fraction collection, DLS and sp-ICP-MS agreed well with the manufacturer's declared size for the NPs. In the sp-ICP-MS analysis, 20 μL of a 100 ng L−1 solution of NPs was injected onto the column. The 75 μL fractions were collected and analysed off-line after a further 1 + 99 dilution to ensure single particle conditions. Under these conditions, 10 nm gold NPs were below the LOD but 20 nm silver NPs were detectable. To further show proof of concept, the column was connected directly to an ICP-MS instrument to detect a 4 μg L−1 spike of 20 nm radius silver NPs in river water. Asymmetric flow FFF was coupled with ICP-MS for the determination of engineered NPs in tap and domestic waste water.177 Samples were spiked with various quantities of gold (20 and 60 nm) and silver NPs (10, 60 and 70 nm) capped with either citrate or polyvinylpyrrolidone to reduce the formation of agglomerates or aggregates. This also had the side effect of reducing solubility of the NPs thus maintaining their particle size. The FFF device was fitted with a 10 kDa cutoff membrane with a spacer/channel height of 350 μm. The mobile phase was 0.005% (w/v) NaHCO3 with a flow rate of 0.8 mL min−1 at the detector. The capped NPs were stable in tap water for up to 2 h but domestic waste water had to be diluted at least fivefold to reduce matrix effects. Using an injection volume of 100 μL, the LODs of 30 for Ag and 15 μg L−1 for Au were not low enough to determine the current suspected levels of NPs in tap or domestic waste water. Spike recoveries were ∼100% for all the different types of NP but the measurement uncertainty seemed to depend on particle size, with 70 nm silver NPs having the worst expanded uncertainty of 51% for a 0.13 mg L−1 spike based on 4 replicate measurements.

The formation of thiomolybdate species in geothermal and euxinic saline lake bottom waters178 was studied using ion pair chromatography coupled with ICP-MS. The mono, di, tri and tetra thiomolybdate species as well as molybdate were separated on a RP polymeric column (Dionex, IonPac NS1, 10 μm, 250 × 4 mm) thermostated at 30 °C. The mobile phase (1 mL min−1) contained an aqueous component (2 mM tetrabutylammonium hydroxide as an ion pairing agent and 1 mM sodium carbonate) and an organic solvent (2-propanol) the proportion of which was increased from 10 to 25% in 10 minutes. The LOD was 10 nM of molybdate. To investigate thiomolybdate formation, a lake water sample was spiked to 250 nM of ammonium molybdate in the field and subsamples taken after 60 minutes and flash frozen with liquid N2 to preserve the species. Thiomolybdate formation was dependent on sulfide availability and pH but all the species were formed with tetrathiomolybdate the most dominant one. To see if these species were formed in nature, 47 samples of geothermal waters from Yellowstone National Park were taken and preserved in the same way. Dithiomolybdate, trithiomolybdate or tetrathiomolybdate were detected in almost half of these waters and constituted 12–38% of the total Mo measured in acidified sub-samples.

Although most methods for the speciation analysis of tin in waters focus on the determination of butyltin compounds, procedures for other species are being developed. A method179 for the determination of SnII and SnIV was based on the on-column complexation of ionic Sn with DTPA. The Sn–DTPA complexes were retained on a porous polymethacrylate strong anion-exchange resin and eluted with a mobile phase containing 20 mM NH4NO3 and 3 mM DTPA at pH 6.0. Complete separation was achieved within 5 minutes at a flow rate of 1 mL min−1. The LODs for a 50 μL injection and ICP-MS detection were 0.3 μg L−1 for SnII and 0.1 μg L−1 for SnIV. The method was sufficiently sensitive for the detection of these analytes in contaminated water from an industrial site. Recoveries for a mixed 20 μg L−1 spike in water samples were 96.8–103%. A rapid derivatisation-free HPLC-ICP-MS method180 for the detection of MBT, DBT and TBT in water, sea water and sediments used stir bar sorptive extraction preconcentration. All the analytes were separated in <8 min using a CN bonded silica analytical column with a mobile phase of ethanol–formic acid–water (16[thin space (1/6-em)]:[thin space (1/6-em)]8[thin space (1/6-em)]:[thin space (1/6-em)]76, v/v) containing 5 mM mercaptoacetic acid at pH 2.5. When a C18-coated stir bar was used in the preconcentration procedure, enrichment factors of up to 127 could be achieved, resulting in LODs ranging from 15.6 (TBT) to 29.4 (MBT) ng L−1. The accuracy of the method was assessed by analysis of the NRCC CRM PACS-2 (marine sediment) as well as by spike recovery from lake, river and seawaters. A method181 for the determination of TBT in surface water at the European framework directive limit of 0.2 ng L−1 was based on GC-ICP-MS. Response curves were constructed for TBT extraction using SPE with a C18 stationary phase. To determine TBT at the required level, preconcentration from a sample volume of between 250 and 1000 mL was necessary. The analyte could be eluted with CH2Cl2, ethylacetate, THF or MeOH. Ethylation of TBT should take place at pH 5, so that it could be carried out either before or after preconcentration. A LOQ of ca. 0.06 ng L−1 was achieved when carrying out the analysis under these constraints.

2.3 Instrumental analysis

2.3.1 Atomic absorption spectrometry. One of the main innovations in AAS is the development of continuum source instruments that do not rely on hollow cathode lamps and so can operate in multi-elemental mode much more easily. A fast sequential method182 for the determination of Ca, Cd, Cu, Fe, K, Mg, Mn, Ni, Na, Pb and Zn in environmental samples and drinking water used a high resolution continuum source instrument coupled with FI, which reduced the sample uptake to <1 mL per element for triplicate measurements. The linear range was expanded through use of multiple lines, elemental multiplets (such as that for Fe) and side pixel registration (for Mg). The resultant linear working range of 0–120 mg L−1 for Mg meant that sample dilutions were not required. Concentrations of Cd, Cu, Fe, Mn, Ni, Pb, and Zn in river, sea, mineral, bottled and tap waters were below the instrumental LOD but those of Ca, K, Mg and Na were in good agreement with the levels given on the labels of the mineral and bottled waters. Recoveries of spikes from water samples ranged from 92% (Cd) to 110% (Pb). Accuracy at higher concentrations was checked by analysing NIST SRM 1575a (pine needles); the results were in good agreement with the certified values.
2.3.2 Laser-based spectroscopy. The use of laser-induced luminescence spectroscopy for the determination of U in groundwater has been reviewed183 (47 references). The two main detection methods for UVI were TRLFS, and the measurement of the ratio between the UVI luminescence and the Raman scattering intensity of water as a kind of internal standard to correct for variations in laser pulse energy and drift in the voltage to the PMT. Results were generally in good agreement with those obtained by ICP-MS and radiochemical spectrometry with LODs as low as 0.03 μg L−1 when an excitation wavelength of 266 nm was used.

A review184 (86 references) of the use of LIBS for water analysis noted that although LIBS should not require much sample pretreatment, an improvement in LODs and stability is necessary to meet water monitoring requirements. The LODs for LIBS were improved by combining it with DLLME.185 Chromium, Cu, Mn, Ni and Zn were extracted as APDC complexes and then deposited onto an aluminium sample holder. By extracting a 9 mL sample into 100 μL of tetrachloromethane, followed by deposition of a 10 μL drop, LODs of between 107 (Ni) and 19 (Cr) μg kg−1 were achieved. This presented a 3.7–5.6 times improvement over those obtained with no preconcentration. In an alternative approach,186 Zn2+ was deposited onto a Cu electrode in situ and the Zn then measured by underwater LIBS. Emission was detectable at the 5 mg L−1 concentration level and the calibration was linear up to 50 mg L−1.

2.3.3 Inductively coupled plasma mass spectrometry. In a survey187 of the niobium content of mineral and freshwaters by ICP-SF-MS it was possible to obtain a LOD of 0.02 ng L−1 in low resolution mode (R = 500) if the instrumental sensitivity was greater than 1.2 × 106 cps per ppb of 115In. Under these conditions, replicate measurements of a 1 ng L−1 standard (n = 20) gave an RSD of 3.8%. Accuracy was checked against a 5 ng L−1 in-house standard. Measured Nb concentrations ranged from 0.37 ng L−1 for a bottled mineral water to 417 ng L−1 in a geothermal water. An indicative value of 2.8 ng L−1 was obtained for the NRCC CRM SLRS-5 (river water).

Vertical distribution profiles of dissolved platinum in the Sea of Japan were obtained188 using SPE followed by IDA-ICP-MS. On board ship, 12 L seawater samples were filtered and acidified to 0.024 M HCl. In the laboratory, a 1 L aliquot was acidified to 0.5 M HCl and equilibrated with a 192Pt-enriched standard for 24 h. The Pt was retained on a minicolumn packed with Biorad AG1 X-8 anion exchange resin. Following removal of the matrix with 6 mL of 0.05 M HCl and 6 mL of ultrapure water, the Pt was eluted with 25 mL of 5 M HNO3 and 5 M HClO4, the eluent evaporated to <0.1 mL and then redissolved in 1.5 mL of 5% HCl for quantification by ICP-MS. The LOD was 0.015 pmol L−1 and the precision (n = 3) 0.23 ± 0.026 pmol L−1. The authors not that the levels of Pt in ocean water are similar to those reported 20 years ago suggesting that Pt has a conservative behaviour in the ocean and that the anthropogenic Pt effect seen on land has yet to reach the Sea of Japan.

In the multi-element analysis of drinking water by ICP-MS, an excess of HCl or other agents containing Cl is often added to the sample to preserve any Hg2+ in solution but this has the drawback of increasing Cl-based polyatomic interferences at the As, Cr and V masses. In an ICP-MS-MS procedure,189 oxygen was introduced into the octopole reaction cell thereby shifting the analyte masses by 16 amu. Mercury did not react in the cell so continued to be determined at m/z 202. The instrumental LODs ranged from 1.6 (As) to 38 (Hg) ng L−1 and results for the NIST SRM 1643e (trace elements in water) were in agreement with the certified values.

The uncertainty contributions associated with the determination of trace elements in seawater by ICP-MS after matrix separation and preconcentration have been investigated190 by using Toyopearl AF-Chelate-650 resin to preconcentrate Co, Fe, Pb and V and ICP-MS to determine their concentrations in the NRCC CRM NASS-6 (seawater) and two GEOTRACES RMs. The experimental design allowed a thorough investigation of the uncertainty contribution for each parameter to the overall expanded uncertainty of the measurement. The largest contributions were the uncertainties from the precision of the peak area measurement (for NASS-6 from 10% for Fe to 88% for Pb) and those associated with the slope of the calibration curve (again for NASS-6 from 10% for Pb to 81% for Fe). The results obtained were in good agreement with the certified or consensus values, demonstrating the suitability of the analytical method used for the investigation.

Most advances in IR analysis revolve round strategies for analyte preconcentration or separation from matrix elements to reduce fractionation and mass bias effects. The Mg coprecipitation method for Si in natural waters is widely used for seawater analysis but the addition191 of a 1 M MgSO4 solution to the sample in the ratio of 20[thin space (1/6-em)]:[thin space (1/6-em)]1 sample[thin space (1/6-em)]:[thin space (1/6-em)]MgSO4 made it applicable also to river and estuarine waters. An improved recovery of 98.0% of Si from low silicate waters was achieved by using NH4OH (approximately 7 M) instead of 1 M NaOH (80% recovery) with a coprecipitation time of 6 h, this meant that only one extraction was required. The direct coupling of IEC with MC-ICP-MS promises to be a faster method192 as demonstrated through use of an ICE-AS1 IEC column (9 mm id × 250 mm) with a mobile phase of 0.01% HCl at a flow rate of 0.8 mL min−1. The Si peak was separated from other matrix components in 10 minutes. The mass-independent bias was corrected by using a bracketing calibration solution in nutrient-free seawater. To demonstrate the applicability of this method, the Si isotope ratios of NRCC CRMs MOOS-3 (seawater) and SLRS-5 (river water) were successfully determined.

In a procedure for the preconcentration of mercury adapted for measurement of Hg isotope ratios in seawater,193 filtered seawater was acidified with 10 mL of concentrated HNO3 per L of sample. The sample was then purged with N2 overnight to remove I2, acidified to a concentration of 0.1 M with HCl and treated with BrCl to release the organically-bound Hg. The BrCl was then neutralised with NH2OH·HCl and Hg retained on a Biorad AG1-X4 anion-exchange resin. For a 10 L sample, the resin was washed with 40 mL of 0.1 M HCl followed by 80 mL of water and the Hg eluted with 10 mL of a 0.05% L-cysteine solution in 1% sodium citrate. Mercury was detected using MC-ICP-MS with CVG as the sample introduction system. The preconcentration recovery was 98 ± 6%. It was suggested that Hg isotope ratios may be used to distinguish between anthropogenic mercury sources in seawater. In an alternative purge-and-trap method,194 Hg in seawater was purged as Hg0 from the sample and then trapped in three amalgamation tubes filled with gold-coated glass beads. The trapped Hg was thermally desorbed and retrapped in a KMnO4 solution, before analysis by CV-MC-ICP-MS to determine the Hg isotope ratio. During measurement of the NIST SRM 3133 (Mercury (Hg) Standard Solution), the external precision for δ202Hg was 0.06‰ (2SD, n = 310).

2.3.4 Vapour generation techniques. The advantages of vapour generation for trace element analysis are well known but the technique is applicable to only a small number of elements. An alternative approach is photochemical vapour generation in which a UV reactor is used to catalyse the generation of volatile species. Such a technique195 for the production of CH3Br used a photochemical generator fitted with a 19 W low pressure mercury vapour lamp. Use of a solution of 2% v/v acetic acid containing 3000 μg mL−1 NH4Cl gave a VG efficiency of 95% and a LOD at m/z 79 of 0.14 ng mL−1, which was superior to that (2.4 ng mL−1) obtained using solution nebulisation ICP-TOF-MS. However, the analytical gains to be made by extending this method to elements that are known to undergo hydride or CVG are more dubious. For example, CVG has been applied to Sn196 with ICP-MS detection and Hg197 with ETAAS detection to obtain LODs (LOQ for Sn 0.02 μg L−1, LOD Hg 0.02 μg L−1) similar to those obtainable by traditional VG. One advantage of CVG was that more stable reagents could be used to volatilise the target analyte, e.g. stannic instead of stannous chloride for Sn and HCOOH for Hg. However, this was at the cost of increased analysis time such as the 32 s irradiation time required for Sn with CVG compared to the quasi instantaneous determination of traditional VG.

The hydride generation of thallium198 with NaBH4 was enhanced by the addition of rhodamine B (3.5 × 10−5 M) and KI (0.018 to 0.024 M) to the sample. These enhancing/catalysing agents resulted in a 7-fold signal enhancement. Under optimum conditions a LOD of 1.5 μg L−1 was obtained with AAS detection. The method was validated by spike recovery experiments in tap and river water samples, good agreement was obtained with the certified values for NIST SRM 1643e (trace elements in water).

2.3.5 X-ray spectrometry. The use of T-XRF for water analysis was reviewed199 (175 references) with special attention paid to sample preparation, instrumentation, matrix effects, potential errors and the figures of merit. Readers are directed to our companion ASU review5 on advances in X-ray fluorescence spectrometry for more specific information on XRF in all its guises.

3 Analysis of soils, plants and related materials

3.1 Review papers

Multi-technique reviews have highlighted the growing contributions200 of X-ray spectroscopy and ICP-MS to the understanding of P cycling in soil (397 references); the application201 of AAS, AES and AFS in the analysis of conifers and conifer-derived products (106 references); and laser-based approaches for algal biomass analysis202 with particular emphasis on LIBS and, to a lesser extent, LA-ICP-MS (136 references). In a review203 (112 references) of the advantages and limitations of atomic spectrometric techniques for authentication of organically grown food, it was concluded that chemometric analysis of multi-element and stable isotope data generated by several complementary techniques represented the best option for classifying the agricultural origin of plant products.

Country-specific reviews included a description of the substantial contribution made by Brazilian researchers204 (217 references) to the field of flow analysis and a summary of Turkish studies205 (146 references) that applied atomic spectrometry to investigate the uptake of potentially toxic elements by plants.

3.2 Sample preparation

3.2.1 Sample storage and pre-treatment. The collection, preservation and pre-treatment of samples can have a marked influence on results obtained by atomic spectrometry. Amaral et al.206 used HPLC-ICP-MS to investigate the effects of four storage regimes on As speciation in tissue of the tropical grass Brachiaria brizantha. Portions were: (a) freeze dried, milled in liquid N2 and then stored at room temperature; (b) freeze-dried and stored at 4 °C; (c) stored at −18 °C; or (d) stored at −80 °C. None of the samples showed a marked change in As speciation over a 12 month period, but highest recoveries were always obtained following storage regime (a) and this was the only portion from which DMA could be extracted. Loppi et al.207 showed that painstaking removal of extraneous particles from lichen using nylon tweezers under a binocular microscope – a process that took 3–4 h to prepare a 200 mg sample – almost halved the measured concentrations of the lithogenic elements Al and Fe, presumably due to the removal of adhering soil-derived material. In forensic science,208 double-sided tape could be used to mount small samples of soil (10 ± 4 mg) typically recovered in criminal investigations for analysis by either LA-ICP-MS or LIBS without introducing significant blank signals from either the adhesive or the tape. Similarly, Arnoldussen and van Os209 showed that portable XRF could be used to study lacquer-peel soil sections from archaeological sites without interference from the fixing and mounting agents used.

The suitability of ferrihydrite gel DGT samplers168 combined with extraction procedures and solution techniques such as HPLC-HG-AFS for determination of As species in soil solution has been questioned. Use of XANES to measure As species directly in the gel showed that the species distribution changed significantly during extraction, e.g. about 20% of arsenate was converted to arsenite.

3.2.2 Sample dissolution and extraction. Sample preparation methods used in the determination and speciation of arsenic in rice have been reviewed210 (76 references) and a new method211 proposed based on enzyme-assisted microwave extraction prior to CE-ICP-MS. This gave excellent separation of DMA, MMA, AsIII and AsV, and LODs in the range 0.15–0.27 ng g−1.

An optimised pyrohydrolysis method212 was designed for the extraction of I from organic-rich samples such as seaweed prior to ICP-MS or AMS analysis. Use of 125I as a yield tracer was recommended.

A closed-vessel conductively heated digestion system 213 gave ICP-AES results close to certified values (recoveries 87–104%) for Al, B, Ca, Cu, Fe, K, Mg, Mn, P, S and Zn in NIST SRMs 1515 (apple leaves), 1575a (pine needles) and IRMM BCR 679 (white cabbage). When applied to sugarcane leaf – a potentially important source of bioethanol – the method gave results similar to those obtained using microwave-assisted digestion.

Several comparisons of sample extraction methods have been published. Amongst the most interesting were a study214 featuring the determination of MeHg in IAEA CRM 405 (estuarine sediment).

405 (estuarine sediment) by GC-AFS following: (a) microwave-assisted extraction with 0.5% (v/v) 2-mercaptoethanol in 5% (v/v) methanol; (b) acid leaching with 3 M HNO3/1 M CuSO4 at room temperature, dichloromethane extraction and back extraction into water by evaporation; (c) alkaline digestion with 25% (w/w) KOH in methanol at 75 °C, dichloromethane extraction and back extraction into water by evaporation; or (d) acid leaching with 3 M HNO3/1 M CuSO4 at room temperature, dichloromethane extraction and back extraction into 1 mM Na2S2O3. Recoveries of >90% were obtained with procedures (b) and (d) but (d) was preferred because it took less time to perform (95 vs. 180 minutes). Other workers215 compared four methods for extraction of As species from freeze-dried roots of the hyperaccumulator plant Pteris vittata. The methods were: sonication with 2 mM NaH2PO4 + 0.2 mM Na2EDTA at pH 6.0; acid digestion with 1% HNO3; sonication with 1 + 1 methanol–water; and sonication with 1 + 1 ethanol–water. Analysis of the extracts by HPLC-ICP-MS indicated that ethanol–water was the most efficient extractant. Following further optimisation, the final recommended procedure was sonication of 0.05 g sample for 0.5 h (fronds) or 2 h (roots) in 10 mL 1 + 3 ethanol–water mixture. In a study216 of various combinations of HNO3 and HCl for determination of Cd, Cr, Cu, Pb and Zn in herbal plants by FAAS, oven-dried leaves were digested either following dry ashing, using a hotplate, or with microwave-assistance. Highest recoveries were obtained with dry ashing followed by 4 + 1 HNO3–HCl (v/v).

Improvements in microwave-assisted extraction procedures included217 generation of UV irradiation in situ in the digestion vessel to reduce acid consumption without compromising digestion efficiency in the analysis of seaweed by ICP-MS. Results obtained for As, Cd, and Pb when 0.7 g samples were treated with 2 M HNO3 were comparable to those obtained using conventional microwave digestion with concentrated HNO3 and to certified concentrations for aquatic plant IRMM CRMs BCR 670 and BCR 060. The efficiency of acid digestion of biological samples, including plants, with 2 M HNO3 was also improved218 by performing the digestion under 7.5 bar of O2 and cooling the vessel with an external air flow. Results obtained for Ca, Cu, Fe, K, Mg, Mn, Mo, Na and Zn in NIST SRMs 1515 (apple leaves), 1577 (bovine liver) and 8435 (whole milk powder) were statistically similar to certified values. The optimal extractant in a MAME procedure219 that did away with acid altogether was a mixture of 1.25% (w/v) SDS and 0.1% (v/v) Triton X-100. The method could be used in the determination of pseudototal Cd, Cr, Cu, Ni and Pb in sediment by ETAAS without the need to add chelating agents to improve extraction efficiency. New MAE methods have also appeared. That for Cr species220 in soil and sediment used 0.1 M EDTA, 1% tetrabutyl ammonium bromide and HF, followed by ion-exchange separation and quantification by ICP-AES. A MAE method for As species in ornithogenic sediments221 used 1.0 M orthophosphoric acid and 0.1 M ascorbic acid, with separation and quantification by HPLC-HG-AFS.

Developments in ultrasound-assisted extraction procedures included the application of a rapid, small-scale ultrasonic probe-based method222 previously optimised for dust, to extract Hg from industrial soil (20 mg soil, 1 mL 8 M HCl, 3.2 min sonication), and a new ultrasonic bath-based method223 for the extraction of ten trace elements from estuarine sediment (100 mg sediment, 1 mL 7.5 M HF + 1 mL 3.5 M HNO3, sonication time 15 min). An ultrasonic probe-based method224 for extraction of Hg species from fish and plant tissue (0.5 g sample, 8 mL 2% tetramethylammonium hydroxide, sonication time 5 min) gave quantitative recoveries for IRMM CRMs BCR 060 (aquatic plant) and BCR 482 (lichen).

Research continues to improve methods for estimation of the available trace element fraction in soils. Duval et al.225 found a relationship between Mo extracted into 0.3 M ammonium oxalate and foliar uptake for the N2-fixing legume Galactia elliottii but not for oak (Quercus myrtifolia). Workers in Brazil226 used the radionuclide 109Cd as a tracer to show that extraction with a mixture of 1 M acetic, 0.72 M citric, 0.49 M lactic and 0.12 M oxalic acids gave a better estimation of Cd available to rocket (Eruca sativa) than extraction into DTPA, Mehlich-1 or Mehlich-3 reagents. An ammonium phosphate method227 was optimised for extraction of labile As species from soil for determination by HPLC-ICP-MS. A two-level Plackett–Burman factorial design was used to identify variables affecting extraction efficiency, and then these were each optimised in turn using a univariate approach to give the final recommended method in which 0.1 g soil was extracted with 30 mL of 40 mM NH4H2PO4, at 70 °C for 120 min. Ultrasound and microwave-assisted versions228 of standard soil extraction methods using 0.01 M CaCl2, 0.43 M acetic acid and 0.05 M EDTA were developed using IRMM CRMs BCR 483 (sewage sludge amended soil) and BCR 700 (organic rich soil) as test substrates. The CaCl2 and EDTA extractions could each be performed in 2 min by UAE or 5 min by MAE (cf. 3 h and 1 h by conventional shaking), and the acetic acid extraction in 15 min with either approach (cf. 16 h).

There has been interest in partial soil extractions for geochemical prospecting. Of 11 extraction procedures229 applied to the determination of 30–55 elements in 15 soils overlying the Talbot VMS Cu–Zn prospect in Canada, weaker leachates such as deionised water detected anomalies with greater contrast than strong acid digests. A similar conclusion was reached by workers in China230 who found that whereas extraction with 2% HCl and determination of Bi, Cu, Pb and Zn in leachates by ICP-MS gave a good indication of the location of the Jiaolongzhang polymetallic deposit beneath ca. 100 m of soil overburden, total digestion did not.

Advances in human bioaccessibility tests have continued with the development of an automated in-line dynamic extraction system231 used in the determination of bioaccessible Cr, Cu, Ni, Pb and Zn in forest and garden soil. To validate the method, the residue remaining after extraction was digested with aqua regia and the sum of the bioaccessible and residual fractions compared with the result following a separate MAE on whole soil. Recoveries were 96–110%. Growing interest in extraction tests that estimate bioaccessibility of potentially toxic elements following inhalation was reflected in two studies. Boisa et al.232 used a novel simulated epithelial lung fluid to extract Pb from the <10 μm diameter particle size fraction of urban soil and mining waste. Sysalova et al.52 applied established bioaccessibility tests – Hatch's solution and the PBET – to the determination of bioaccessible As, Cd, Cr, Hg, Mn, Ni, Pb and Zn in various size fractions of urban particulate matter.

In a sequential extraction procedure for P in volcanic soil233 modified to include an additional fraction, ‘recalcitrant organic P’, both air-dried subsamples and subsamples that had been heated at 300 °C were analysed. The ‘recalcitrant organic P’ fraction was estimated from the difference between results for the two subsamples. The sum of the steps of the modified method corresponded better with total P concentrations than those of the original procedure, which accounted for <40% of analyte present.

Two automated flow-through fractionation procedures for As have been described. A modified (3-step) BCR sequential extraction234 and HG-AFS system with on-line UV photo-oxidation for degradation of organic As species and reduction of AsV to AsIII was optimised using an As-enriched soil and then applied to four agricultural soils impacted by mining activities. Results were generally within 10% of those obtained by conventional batch extraction. A four step sequential extraction procedure235 involving 0.05 M ammonium sulfate, 0.05 M ammonium dihydrogen phosphate, 0.2 M ammonium oxalate and finally 0.2 M ammonium oxalate + 0.1 M ascorbic acid at 96 °C, was combined with HG-ICP-AES for on-line speciation of AsIII and AsV in soil. Results similar to batch extraction were obtained and the overall As recovery (sum of the 4 steps plus off-line aqua regia digestion of the residue) obtained for NIST SRM 2710a (Montana soil) was excellent.

3.2.3 Preconcentration procedures. Numerous preconcentration procedures for specific analytes continued to be 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 3–5.

Table 3 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 References
Cd, Co, Cu, Fe, Mn, Ni, Pb Herbal plants Lutetium hydroxide FAAS 1.7–7.2 NIST SRM 1570a (spinach leaves), NWRI CRM TMDA-70 (fortified lake water) 311
Cr Drinking water, soil extracts, wastewater APDC/CeIII FAAS 2.1 NRCCRM CRM GBW 07309 (stream sediment) 312
Cr, Cu, Fe, Pb, Zn Foods, water 2,9-Dimethyl-4,7-diphenyl-1,10-phenanthroline FAAS 0.28–3.1 NIST SRM 1515 (apple leaves), NRCCRM CRM GBW 07605 (tea) 313
Cr, Cu, Pb Tea, tobacco, water 2-{4-[2-(1H-indol-3-yl)ethyl]-3-(4-chlorobenzyl)-5-oxo-4,5-dihydro-1H-1,2,4-triazol-1-yl}-N-aryl methylidene acetohydrazide FAAS 2.1 for Cr 0.56 for Cu 0.86 for Pb HPS CRM-SA-C (sandy soil C) 314


Table 4 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 CrIII then reduction, measurement of Cr, and estimation of CrVI by difference. b HgII, MeHg, EtHg, PhHg.
As Rice CPE Ammonium molybdate, Triton X-114 ETAAS 0.01 IRMM CRM 804 (rice flour) 315
Cd Hair, herbs, spices IL-UAEME (4-(2-Pyridylazo)resorcinol), 1-butyl-3-methylimidazolium hexafluorophosphate FAAS 0.21 NIST SRM 1570a (spinach leaves) 316
Cd Fruit, vegetables IL-DMME Pyrrolidine dithiocarbamate, 1-butyl-3-methylimidazolium hexafluorophosphate, iron oxide nanoparticles FAAS 0.32 NIST SRM 1570a (spinach leaves) 317
Cd, Co, Cu, Ni, Pb Sediment CPE 1-(2-Thiazolylazo)-2-naphthol, Triton X-114 FAAS 0.77–2.8 318
Cd, Cr, Pb Herbal medicines UAEME APDC, toluene ETAAS 0.002–0.03 Spike recovery 319
Cd, Ni Artificial saliva extracts of tobacco IL-UADLLME APDC, 1-butyl-3-methylimidazolium hexafluorophosphate ETAAS 0.05 for Cd, 0.14 for Ni INCT CRM CTA-VTL-2 (virginia tobacco leaves) 320
Cd, Pb Rice CPE 8-Quinolinol, Triton X-45 ETAAS 0.018 for Cd, 0.043 for Pb Spike recovery 321
Co Rice, water UAEME Sodium dodecyl sulfate, chloroform ETAAS 0.016 NRCCRM CRMs GBW 07605 (tea leaves), GBW 10015 (spinach) 322
Cra Soil solution, water CPE Alizarin-3-methyliminodiacetic acid, OP-5 surfactant FAAS 1.0 Spike recovery 323
Cu Sediment, soil IL-DLLME 1-Hexyl-3-methylimidazolium bis-[(trifluoromethyl)sulfonyl]imide ETAAS 1.8 NIST SRMs 2709 (San Joaquin soil), 2711 (Montana soil), 2704 (Buffalo river sediment), IAEA CRM 433 (marine sediment) 324
Cu Water CPE 7-Iodo-8-hydroxyquinoline-5-sulfonic acid, alkylphenol polyoxyethylene FAAS 0.6 NRCCRM CRM GBW 08301 (river sediment) 325
Cu, Ni, Pb Fruit, spices, vegetables IL-UADLLME Carbon tetrachloride, 1-butyl-3-methylimidazolium hexafluorophosphate ionic liquid FAAS 0.17 for Cu, 0.49 for Ni, 0.95 for Pb NIST SRM 1515 (apple leaves) 326
Ga, In, Tl Mobile phone LCDs, sediment CPE Gallic acid, Triton X-114 FAAS 3.5 for Ga, 1.2 for In, 0.92 for Tl Spike recovery 327
Hg Rice, water, wheat DLLME Displacement of copper from copper pyrrolidine dithiocarbamate, carbontetrachloride, methanol ETAAS 0.019 NRCCRM CRM GBW 08508 (rice flour) 328
Hg speciesb Atmospheric particles, fish, plant, sediment, water Hollow fibre-LLLME Na2S2O3 solution, toluene, 1-(2-pyridylazo)-2-naphthol HPLC-ICP-MS 0.003–0.006 NRCC CRM DORM-2 (dogfish muscle) 329
Mo, V Drugs, soil, water DLLME 8-Hydroxyquinoline, 1-undecanol LIBS 30 for Mo 5 for V NCS CRM ZC 85005 (beef liver) 330
Pb Medicinal plants Hollow fibre-LLLME EDTA, 1-hexyl-3-methylimidazolium hexafluorophosphate ionic liquid, dicyclohexyl-18-crown-6 ETAAS 0.008 Spike recovery 331
Pb Fruit, herbs, macaroni, plants, tea, vegetable, water DLLME 5-(4-Dimethylaminobenzylidine) rhodamine FAAS 1.1 SPS RM WW2 (wastewater), NIST SRM 1515 (apple leaves), NWRI CRM TMDA-51.3 (fortified water) 332
Pb Soil, tea CPE Displacement of zinc from zinc diethyldithiocarbamate TS-FF-AAS 0.5 333
Pd Soil, vegetables, water IL-DLLME 1-(2-Pyridylazo)2-naphthol, 1-hexyl-3-methylimidazolium hexafluorophosphate FAAS 3.2 334
REE Soil CPE Diglycolamide, Triton X-114 ICP-MS 0.0002–0.03 NIST SRM 2709a (San Joaquin soil), IAEA CRM 384 (sediment) 335
Se Tea SFODME APDC, 1-undecanol ETV-ICP-MS 0.0002 for SeIV, 0.0003 for SeVI NRCCRM CRM GBW 07605 (tea leaves) 336
V Milk, vegetables, water, wine IL-DLLME 4-(2-Pyridylazo) resorcinol, 1-butyl-3-methylimidazolium hexafluorophosphate ETAAS 0.018 NRCC CRM SLRS-4 (riverine water), NIST SRM 1515 (apple leaves) 337


Table 5 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 MBT, DBT, TBT (as the chlorides).
Cd Soil, rice, tea, vegetables, water, wheat Alumina 1-(5-Nitrofuran-2-yl) thiosemicarbazide ICP-AES 0.025 338
Cd Fish, water Cation exchange fibre None (non-selective sorption then selective elution with KI) CV-AAS 0.0006 NRCC CRM DOLT 4 (dogfish liver), NRC CRM AGAL-10B (river sediment) 339
Cd, Co, Ni Sediment Polystyrene–divinylbenzene 2-Hydroxyacetophenone FAAS 0.1 for Cd 0.8 for Co 0.6 for Ni NIST SRM 2702 (marine sediment) 340
Cd, Cu, Ni Food, water Silica gel Pentaethylene hexamine FAAS 0.19 for Cd, 0.73 for Cu 0.91 for Ni INCT CRM CTA-VTL-2 (virginia tobacco leaves), NWRI CRM NWTM-15.2 (water) 341
Cd, Ni, Pb Sewage sludge, soil, water Activated carbon cloth 1-(2-Pyridylazo)-2-naphthol FAAS 0.1–2.8 NWRI CRM TMDA-64.2 (fortified lake water), IRMM CRM BCR 146R (sewage sludge amended soil) 342
Cd, Pb Fish, sediment, soil, water Fe3O4@SiO2 NPs Phenyl isothiocyanate FAAS 0.05 for Cd 0.9 for Pb NRC CRM LKSD-4 (lake sediment) 343
Cd, Pb Fish, soil, water Mesoporous silica 1-(2-Pyridylazo)-2-naphthol FAAS 0.04 for Cd 0.9 for Pb NCS CRM DC 73323 (soil) 344
Cd, Pb Coffee, tea, water Obsidian Cetyltrimethylammonium bromide FAAS 0.37 for Cd 0.89 for Pb HPS CRM-SA-C (sandy soil C) 345
Cd, Pb Fruit, walnut Magnetite NPs 3-Aminopropyltriethoxysilane-2,4-bis(3,5-dimethylpyrazol)-triazine FAAS 0.01 for Cd 0.7 for Pb NIST SRMs 1571 (orchard leaves), 1572 (citrus leaves) 346
Cd, Pb Herbs, meat products, spices, plants, tea, water Dowex Marathon C None FAAS 0.13 for Cd 0.18 for Pb SPS RM WW2 batch 108 (wastewater level 2), INCT CRM TL-1 (tea leaves) 347
Ce, Cu, Dy, Eu, La, Pb, Yb Sediment, water Graphene oxide–titanium dioxide composite None ICP-AES 0.13–2.6 NRCCRM CRM GBW 07301a (stream sediment) 143
Co, Cu, Ni Plants, water Fe3O4@MCM-41 NPs N-(4-Methoxysalicylidene)-4,5-dinitro-1,2-phenylenediamine FAAS 0.03 for Co, 0.03 for Cu 0.04 for Ni Spike recovery 348
Co, Cr, Cu, Fe, Ni, Pb, Zn Fish, mango, mint, water Amberlite XAD-16 p-Aminobenzene sulfonic acid FAAS 4.0–6.6 Spike recovery 349
Co, Pb Fruit Diethylamine phosphorus containing polymer 1-(2-Thazolylazo)-2-naphthol FAAS 4.4 for Co, 1.0 for Pb NIST SRM 1515 (apple leaves) 350
Cs (135Cs, 137Cs) Sediment Amberchrom CG-71 Calix[4]arene-bis(tert-octylbenzo-crown-6) in octan-1-ol SF-ICP-MS <0.001 351
Cu Food, water Silica gel Bis(3-aminopropyl)amine FAAS 0.12 INCT CRM TL-1 (tea leaves), NRCC CRM DORM-3 (fish protein) 352
Cu Plant, water Naphthalene 2-(5-Bromo-2-pyridylazo)-5-diethylaminophenol ETAAS 0.0015 CRMs 353
Cu, Fe, Mn, Zn Beans, fish, leaves, water Natural cellulose (almond bark) Rhizopus oryzae fungus HR-CS-FAAS 1.2–2.8 NIST SRM 1547 (peach leaves), IAEA CRM 407 (fish tissue) 354
Cu, Fe, Pb Cosmetics, food, soil, water Polyhydroxybutyrate-b-polydimethyl siloxane 2-(5-Bromo-2-pyridylazo)-5-diethylamino-phenol FAAS 1.9 for Cu, 2.2 for Fe, 2.5 for Pb NIST SRM 1577b (bovine liver), IAEA-336 (lichen), NWRI CRM TMDA-51.3 (fortified lake water) 355
Cu, Ni Soil, vegetables, water Amberlite XAD-16 Bacillus subtilis ICP-AES 0.21 for Cu, 0.33 for Ni NCS CRM ZC 73014 (tea) 356
Hg Water PTFE APDC CVAAS 0.02 IRMM CRM BCR 060 (aquatic plant) 357
Ni Sediment, water Ethylvinylacetate None FAAS 3.8 IJS TRAP-LRM (lake sediment) 358
Pb Lipstick, pine leaves, water MWCNTs Thiourea functionalised ionic liquid ETAAS 0.13 Spike recovery 359
Pb Fish, fruit MWCNTs Poly(N-phenylethanolamine)/ FAAS 0.8 NIST SRM 1515 (apple leaves), IAEA CRM 336 (lichen) 360
Pb Water Cellulose nitrate membrane filters Brilliant black BN FAAS 1.5 SPS RM WW2 (wastewater), NRCCRM CRM GBW 07424 (soil) 361
Pd, Rh Catalytic converter, street dust, rock, water Nanosponge Mn2O3 None FAAS 1.0 for Pd, 0.37 for Rh NIST SRM 2556 (used auto catalyst pellets) 362
Sn speciesa Sediment, water Stir bar C18 HPLC-ICP-MS 0.016–0.029 NRCC CRM PACS-2 (sediment) 180
Tc (99Tc) Soil, water TEVA® None ICP-MS 0.00005 Spike recovery 151
Tc (99Tc) Seaweed, solidified cement TEVA® None ICP-MS 0.0024 NIST SRM 4359 (seaweed) 363


3.3 Instrumental analysis

3.3.1 Atomic absorption spectrometry. In simultaneous multi-element ETAAS, the use of permanent chemical modification236 (200 μg Zr + 20 μg Ir) and Te internal standard improved the LODs for As from 1.48 to 0.59 μg L−1 and for Se from 1.96 to 0.35 μg L−1. The method was applied successfully to IRMM CRM BCR 402 (white clover) and river sediment samples.

New procedures involving slurry sampling ETAAS included a matrix-modifier-free method237 for the measurement of As in soils; a procedure238 for determination of Bi in sediments using platform atomisation, NbC permanent modifier and Ba(NO3)2 chemical modifier; and a method239 for the determination of As, Cd, Cr, Cu, Ni and Pb in moss. The latter involved sonication of pulverised moss (1–50 mg) with 0.2 M HNO3 and 0.01% Triton X-100 for 25 seconds prior to injection into the furnace. Chemical modifiers used were: 3 μg Mg(NO3)2 + 5 μg Pd for As and Cu; 70 μg NH4H2PO4 + 4 μg Mg(NO3)2 for Cd and Pb, and 20 μg Mg(NO3)2 for Cr. No modifier was required for Ni determination.

A comprehensive tutorial review60 (105 references) of HR-CS-ETAAS summarised progress to date and provided guidelines for future method development. Researchers at IAEA, Monaco, optimised slurry sampling HR-CS-ETAAS methods for the determination of Hg in sediment and fish240 and for As, Cd, Co, Cr, Cu and Ni in sediment.241 In both cases calibration based on solid CRMs rather than liquid standard solutions improved accuracy, yielding results similar to certified values for other CRMs.

A fast sequential HR-CS-FAAS method182 for determination of Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Pb and Zn in soil and water used secondary lines and side-pixel registration to extend the calibration range for elements present at high concentration. Results obtained for NIST SRM 1575a (pine needles) were in agreement with certified values at 95% CI for all the elements except Cd, Ni and Pb for which the certified concentrations were below the LODs of 1.1 (Cd), 2.0 (Ni) and 6.2 mg kg−1 (Pb) for a 250 mg sample.

Coufalik and Komarek242 provided an overview (48 references) of the use of TD-AAS for mercury speciation analysis in solid samples. Particularly useful was their inclusion of a summary of desorption temperatures reported for key mercury species.

3.3.2 Atomic emission spectrometry. Articles purporting to compare the performance of ICP-AES and X-ray techniques – but actually comparing sample pre-treatment procedures – have been published for determination of trace elements in mosses243 and soil.244 The moss study243 used HNO3–HCl–HF to digest samples for ICP-AES but HNO3–HCl–H2O2 for TXRF spectrometry because the sample digest was dried and analysed in a quartz reflector and this was damaged if HF was used. Lower values obtained for Cr and Fe by the X-ray technique likely resulted from their presence in refractory forms that only HF could dissolve. In contrast, the soil study244 obtained higher concentrations for most elements when XRF spectrometry was applied directly to solid samples than for ICP-AES analysis of sample digests. This can be readily explained by the inappropriate choice of aqua regia extractant for the ICP-AES procedure – a reagent well-known to give only incomplete digestion of soils.

A fundamental study of the effect of adding N2to the plasma in ICP-AES included soil and sediment CRMs in performance assessment.245 The excitation temperature increased when 20 mL min−1 of N2 was introduced to the central channel and matrix interference effects were reduced. The authors recommended this approach be used with, in particular, ETV and LA sample introduction systems.

Notable amongst improvements in solid sampling ETV-ICP-AES was the modification246 of the sample introduction system to incorporate a heated sheath between the spray chamber and the torch to generate hot aerosol. A switching valve placed between the ETV system and the sheath device allowed pyrolysis products generated during the ashing step to be directed to waste before introduction of the analytes generated during the vaporisation step into the plasma. In comparison to conventional systems in which the ETV module is connected directly to the torch, the new apparatus allowed soil samples as large as 13 mg to be analysed thereby increasing sensitivity for a suite of over 30 analytes by up to 5-fold and improving LODs by up to an order of magnitude. A conventional solid sampling ETV-ICP-AES system247 was used to measure the pathfinder elements Ag, Al, Ba, Cu, Hg, P, Pb, S and Zn in soil profiles for mineral exploration in Canada. This allowed the underlying ore body to be readily located. A procedure for the analysis of rice,248 which included cooling steps both before and after the sample vaporisation step and also point-by-point internal standardisation to Ar, gave results statistically similar to those obtained by ICP-MS analysis of rice digests (demonstrated by F-tests at 95% CI). The total analysis time of just 5 min per sample included time for grinding and weighing. In what must surely count as a labour of love, Masson249 demonstrated for the first time that ETV-ICP-AES can be used for imaging trace elements in biological tissue. Using a ceramic knife, tobacco leaves were subdivided into >100 individual segments which were cut out and weighed. Portions of each segment were analysed to generate elemental maps for Cd and Mg. The concept may have been proven but the fact that the analysis of a single leaf took more than a day to complete, at a resolution of about 0.5 cm2, suggests that the approach is unlikely to become a major rival to established imaging techniques such as LA-ICP-MS!

The use of 750 mg L−1 MgNO3 as a chemical modifier improved250 the LOD for determination of Mn in plants by tungsten coil AES from 0.86 to 0.05 mg L−1. Results for analysis of NIST SRMs 1547 (peach leaves) and 1573 (tomato leaves) were statistically similar to certified values at 95% CI whereas, without modifier, results approximately twice the certified values were obtained.

The performance of MP-AES with nitrogen as plasma gas was assessed for determination of four major and 13 trace elements in HNO3 digests of sunflowers.251 Results were similar to those obtained by ICP-MS, except for As, Co and Mo whose concentrations were below the LODs of the new technique. As the plasma temperature was lower than that of an Ar ICP, it was recommended that atom (as opposed to ion) lines should be used for analysis, together with an ionisation suppressor such as CsNO3. In an environmental survey252 of an industrially contaminated area in Hyderabad, India, MP-AES gave results broadly similar to certified values for NRC CRM SO-1 (soil) and data comparable to those obtained by ICP-MS for soils, sediments, and water samples.

Several developments in capacitively coupled plasma microtorch AES have been reported by a research group in Romania. Use of a coiled Rh filament253 for ETV sample introduction allowed determination of Ag, Cd, Cu, Pb and Zn in six soil and two sediment reference materials with mean recoveries of 95–108%. The LODs were in the range 0.5–20 μg L−1. Mercury was successfully determined254 in soil, sediment and fish by CCP-AES following sonically-induced CVG in 0.2 M formic acid. Simultaneous determination255 of As and Sb in soil using HG-CCP-AES gave LODs for both analytes of 0.02 mg kg−1 for the most sensitive emission line. Results obtained for CRMs were close to certified values (recoveries 96–104%) and for soil samples were similar to those obtained by HG-ICP-AES.

A solution-cathode glow discharge AES method256 for determination of Hg incorporated on-line SPE using L-cysteine-modified mesoporous silica for analyte preconcentration. A Hg concentration of 0.24 ± 0.04 mg kg−1 (certified value 0.28 ± 0.03 mg kg−1) was obtained for NRCCRM CRM GBW 07310 (stream sediment). The LOD was 0.75 μg L−1.

3.3.3 Atomic fluorescence spectrometry. A direct on-line HPLC-CV-AFS method 257 for the determination of MeHg in water was shown also to be applicable to urine, sediment and biological samples. The method was adapted for each sample matrix to address the different challenges posed. For example, a selective extraction of MgHg into DCM was used for sediments to separate it from the large (typically three orders of magnitude) excess of inorganic Hg present and to avoid contamination of the preconcentration column.

A new cell258 was constructed for the determination of Mg and Mn in water and plants by continuum source tungsten coil AFS. It contained a tungsten coil atomiser and three quartz windows arranged in a T-shape to allow entry of light from a 300 W Xe arc lamp and collection of the fluorescence radiation by a CCD. Following optimisation of the optical alignment, cell purge gas flow rate (1.0 L min−1 Ar/H2) and atomisation current (4.5 A) the LODs were 0.06 mg L−1 for Mg and 0.1 mg L−1 for Mn. Results for Mg in NIST SRMs 1547 (peach leaves) and 1643e (trace elements in water) were similar to the certified values at 95% CI and Mn recoveries from a series of spiking experiments were 90–105%.

3.3.4 Inductively coupled plasma mass spectrometry. New methods for specific analytes included a procedure259 for estimation of Hf by SF-ICP-MS. Acid digestion (even with HF) was unable to fully recover Hf and Zr from a suite of 60 rock, soil and sediment RMs. However, the recoveries for the two elements were always similar. Therefore, given a known Zr content and the measured Zr/Hf ratio, the Hf concentration could be calculated. An ICP-TOF-MS method260 for determination of Pt in algae and water allowed the impact of hospital waste to be estimated. Simultaneous measurement261 of 237Np, 239Pu and 249Pu was achieved by using 242Pu as a yield tracer for both elements. Results obtained for NIST SRM 4357 (ocean sediment powder) were consistent with those reported in the literature. A novel approach262 for measuring Cr species in soil involved SSID and mass balance. An IC-ICP-MS instrument was used to determine CrIII and CrIV in soil extracts and ICP-MS to measure Cr in acid digests of extract residues. Species interconversion during sample processing was tracked by using SSID. The sum of detected species, corrected for interconversion, agreed well with certified total Cr concentrations in NIST SRMs 2709a (San Joaquin soil) and 2711a (Montana soil).

Careful optimisation of reaction cell conditions in ICP-MS/MS successfully removed doubly charged interferences from Gd, Nd and Sm in the determination of As and Se in plants.263 Introduction of 3.5 mL min−1 O2–H2 to convert 75As+ to 91AsO+ and 78Se+ to 94SeO+ gave excellent results for NIST SRMs 1515 (apple leaves) and 1547 (peach leaves) without the need for mathematical correction based on simultaneous monitoring of the abundances of REEs in the sample. A proposed alternative method264 for quantification of As and Se used CH3F–He reaction gas and measurement of AsCH2+ and SeCH2+. Results obtained for a suite of plant, marine biota and sediment CRMs were in agreement with certified values at 95% CI.

Transition-metal-ion assisted PVG265 was coupled with MC-ICP-MS and optimised for the determination of Pb using sediment and water CRMs. A 22-fold enhancement in sensitivity was obtained with PVG relative to direct solution nebulisation, yielding a LOD of 0.005 μg kg−1. Two research groups optimised MC-ICP-MS methods for Cd. A group in Sweden75 developed a method applicable to a wide range of sample types – soil, sediment, plant and animal tissues – that was used to study Cd isotope variability in birch leaves during the growing season. A group in China266 studied four species of plant – two grown in soil and two hydroponically. Both methods achieved overall reproducibility better than 0.1‰ for δ114Cd/110Cd measurement.

In a valuable study to establish lead isotope ratios in biological CRMs,267 indicative values were reported for NIST SRMs 1515 (apple leaves), 1566b (oyster tissue), 1570a (spinach), 1573a (tomato leaves), 1575a (pine needles); IRMM CRMs BCR 100 (beech leaves), BCR 101 (spruce needles), BCR 670 (aquatic plant); and IAEA CRM 359 (cabbage). Data were obtained by three ICP-based techniques – conventional quadrupole ICP-MS, SF-ICP-MS, and MC-ICP-MS. The precision of the quadrupole instrument was poorest but all three instruments obtained similar 208Pb/206Pb and 206Pb/207Pb values for the same sample. Uncertainties arising from use of different methods of mass fractionation correction in Pb isotope ratio measurement by MC-ICP-MS and TIMS were evaluated76 based on data acquired for rock, soil and metal samples in a single laboratory over an 18 year period. It was found that precision of measurement had improved by an order of magnitude. An optimised ICP-MS method268 was used to discriminate successfully cigarettes according to their geographic origin. Data for 208Pb/206Pb and 207Pb/206Pb in INCT CRM CTA-VTL-1 (Virginia tobacco leaves) were reported for the first time.

Two new ICP-MS methods for determination of135Cs/137Cs isotope ratios were applied to the analysis of soils and plants. Both involved selective adsorption by ammonium molybdophosphate followed by ion-exchange chromatography to remove interfering elements such as Ba. The LODs achievable using SF-ICP-MS269 were markedly superior (e.g. 0.16 vs. 40 Bq kg−1 for 137Cs) to those achievable using ICP-MS/MS.270

Two reviews addressed the use of LA-ICP-MS for analysis of biological samples and provide a comprehensive overview and introduction to the field. Becker et al.271 (127 references) focussed particularly on bioimaging, combining comments on analytical aspects with discussion of numerous applications in both clinical and environmental science. Pozebon et al.272 (219 references) placed more emphasised on analyte quantification and IDA, though also covered some bioimaging applications. The development273 of a LA-ICP-MS method for quantification and mapping of Fe in soybean leaves revealed that differences in analyte distribution were present between transgenic and non-transgenic plants.

A novel DGT probe274 for studying the remobilisation of sulfide and trace metals in anoxic sediments comprised two polyacrylamide gel layers overlain with a 0.45 μm pore size filter membrane. The first gel layer contained AgI to trap sulfide for determination by densitometry and the second a chelating resin to trap metals for LA-ICP-MS. Depth resolution of 100 μm was achieved. A consideration of the uncertainty budget275 associated with LA-ICP-MS imagining of DGT samplers concluded that the relatively low signal stability arising from the LA process was the major contributing factor.

Although the combination of chromatographic separation with ICP-MS is becoming relatively routine, new methods continue to be published. An IC-ICP-MS method276 used an IonPac® AS7 anion-exchange column and HNO3 gradient elution in the speciation analysis of orthophosphate and myo-inositol hexakisphosphate, a stable phosphate monoester formed by degradation of plant litter, in soil solution and plant extracts. An HPLC-ICP-MS procedure277 employed a Purospher® RP-8e column and a mobile phase containing 0.02 M CH3COONH4 + 0.2% (v/v) 2-mercaptoethanol + 1% (v/v) CH3OH for the determination of MeHg, HgII and EtHg in leafy vegetables. An ID-GC-ICP-MS procedure278 for Hg speciation in peat soils was used to demonstrate that N2-assisted distillation could be used successfully to isolate MeHg from relatively unpolluted soils in which this species represented at least 1% of the total Hg present. A review163 (103 references) of hyphenated HPLC-ICP-MS methods for the speciation analysis of As, Cr, Sb and Tl in water and sediment samples placed emphasis on the need to preserve species integrity during sample preparation.

As in many other fields, there is interest in the use of ICP-MS to determine NPs in soil and plants. Koopmans et al.279 developed a asymmetric flow FFF method with on-line UV-vis spectroscopy and off-line HR-ICP-MS to characterise silver NPs in soil water extracts that could be useful in future studies of soil-NP interactions. Another group280 used macerozyme R-10 enzyme digestion and SP-ICP-MS to study the uptake of gold NPs by tomato plants. Particle size, particle concentration and dissolved Au concentration could all be measured accurately. Tomato plants grown hydroponically could take up 40 nm gold NPs intact and translocate them from roots to shoots.

3.3.5 Laser induced breakdown spectroscopy. A detailed technical review281 with 111 references highlighted good practice in LIBS analysis. Advice was included on identification and classification of samples, and on concentration measurement.

A study282 of the influence of sample particle size distribution on the analysis of plants and related materials by LIBS found that emission intensities increased as particle size decreased in pressed pellets of boldo (Peumus boldus Molina) leaves. However, the magnitude of the increase depended on laser fluence. Workers in New Zealand283 used LIBS to measure major and minor elements in pressed pellets of 100 pasture samples. Results correlated well with those obtained by ICP-AES following microwave digestion for B, Ca, Na, Fe, K, Mg, Mn and P (R2 > 0.8), less well for Zn (R2 = 0.64) and poorly for S (R2 < 0.4) and Cu (R2 < 0.1). The provenance284 of the herbal medicine Blumea balsamifera from different regions of China was determined successfully by application of PLS-DA models to either complete LIBS spectra or selected spectral lines.

Applications of LIBS in soil analysis included285 the use of a series of ANNs in the determination of Pb. Improved prediction was obtained when different ANN models were optimised for Pb concentrations either above or below a 1% (m/m) threshold and when the type of soil matrix (which could also be estimated by ANN modelling) was taken into account. As LIBS could be used to measure Cu, Mo, Pb concentrations in soils down to typical crustal abundances, the technique was considered286 to have potential application for detecting geochemically anomalous concentrations indicative of underlying ore bodies. The technique has been applied287 for the first time to the determination of the degree of humification of bulk soil organic matter, knowledge of which provides an estimate of the capacity of soil to sequestrate carbon from the atmosphere under different soil management regimes and thereby make an important contribution to climate change mitigation.

3.3.6 X-ray spectrometry. Advances in pXRF were reviewed288 (140 references) in a volume of Elsevier's ‘Advances in Agronomy’ series. An overview of the development, theory and uses of the technique was presented, along with information on its strengths and limitations.

Brand and Brand289 carried out a very revealing inter- and intra-instrument comparison of pXRF instrumentation using six Olympus Innov-X Delta Premium and three Thermo-Niton XL3t GOLD+ systems. Each individual instrument performed differently – a conclusion also reached by Hall et al.290 in their study of 41 rock, soil, sediment and ore CRMs – but the performance of all deteriorated over a relatively short period (two-three months). Frequent recalibration by the manufacturer was recommended, which is costly and inconvenient.

Application of pXRF to soil and sediment analysis included a study by Shand and Wendler291 on the suitability of the method for analysis of organic-rich soils. They analysed a suite of CRMs and 183 Scottish topsoils with variable organic matter content and demonstrated the need for matrix-specific calibration models. Arne et al.292 showed that even with little or no sample preparation or instrument calibration it was possible to obtain results for the pathfinder elements As, Cu and Pb that were fit-for-purpose in gold exploration. In the on-site analysis of samples of dredged waterway sediment, Lemiere et al.293 showed that partial removal of water using a simple hand press significantly improved correlations with results of laboratory-based analysis.

Investigations into the use of pXRF in archaeological studies have highlighted the need to transfer good practice from analytical geochemistry294 and the requirement for more appropriate calibration models for archaeological sediments and ceramics.295

There is growing interest in use of pXRF data for proximal sensing either alone or combined with other techniques. Field spectroscopic methods and chemometrics296 were used to predict soil quality characteristics normally measured in the laboratory, offering a huge saving in time and labour. Portable XRF equipment was used to estimate soil pH,297 soil organic matter content,298 cation exchange capacity299 and soil salinity.300 Together, VIS-NIR DRS and pXRF data could be used to predict total C and total N content301 and to quantify total petroleum hydrocarbon contamination302 in soils. Soil salinity could be mapped using pXRF and VIS-NIR DRS data and Landsat imagery.303 The scope and enormous potential benefits of integrating field spectroscopy and spatial analysis for assessing soil contamination were summarised by Horta et al.304 (240 references).

A μXRF system for the analysis of plants305 was used to measure uptake of Co in vivo from the leaves of the aquatic plant common duckweed (Lemna minor) floating in dilute CoCl2 solution. Both benchtop and handheld EDXRF systems306 used for the determination of Ca, Fe, K, Mn, P, S and Si in pressed pellets of sugarcane leaves had similar performance in terms of correlation coefficients for calibration graphs and LODs.

There is continuing research into the best method for preparing biological tissue for PIXE analysis. Significant differences were revealed in a comparison307 between element distributions obtained by μPIXE analysis of freeze-dried and frozen-hydrated plant and animal samples. Morphological changes such as cell shrinkage were induced by water removal but artefacts could be introduced during cryo-fracturing and mounting of shock-frozen samples.

A TXRF method308 involving room temperature trapping of bismuthine and stibine onto quartz substrates coated with palladium NPs gave LODs of 0.20 μg L−1 for Bi and 0.50 μg L−1 for Sb. Although developed primarily for the analysis of foodstuffs such as milk, the procedure was applied to NIST SRMs 2711 (Montana soil) and 2702 (marine sediment) proving its applicability to these matrices.

3.4 Analytical quality assurance

An international proficiency test309 to evaluate mercury determination in environmental samples included soil and sediment, together with fish and human hair. Of the 29 participant laboratories, 74% performed satisfactorily for measurement of total Hg concentration (|z score| ≤ 2) but 18% required action (|z-score| > 3). The inter-comparison sought also to include measurement of available Hg in soil and organoHg in soil, sediment and fish, but too few results were returned to permit conclusions to be drawn. Mercury was measured310 by TD-AAS in 116 different environmental RMs from ten international suppliers. Each material was analysed at least in triplicate and comparison between different bottles was performed for half of the RMs. Significant mercury heterogeneity was found for some RMs and a list provided of those the authors recommended should be used – and those that should be avoided.

4 Analysis of geological materials

4.1 Reference materials and data quality

New geological reference materials included three samples of Ni ore (0.1–1.0% m/m) and two Ni concentrates (6.0–9.0% m/m) prepared by the Chinese Institute of Geophysical and Geochemical Exploration.364 Certified values for 23 components in the Ni ores and 20 in the concentrate were established following analysis in 19 laboratories. Nickel was mainly determined by volumetric and gravimetric methods, SiO2 and H2O+ by gravimetry and trace elements by AFS, FAAS, ICP-AES and ICP-MS. Because of the high S content of these materials, storage in sealed containers at low temperatures was recommended. Five Chinese clay CRMs,365 originally certified for only major elements in the late 1980s, were reanalysed for 10 major and 29 minor and trace elements by INAA and EDXRF and WDXRF spectrometries, for which there was good agreement. These methods had better precision and lower LODs than those used for the original certification so these new data should make these clay CRMs much more useful.

The lack of reference materials for microanalysis continues to be a major concern. A large Ti-rich natural quartz crystal366 from Shandong province China was characterised for use as a RM in titanium-in-quartz thermobarometry. Pieces of the crystal were analysed by seven different laboratories worldwide using EPMA, LA-ICP-MS and solution ICP-MS to give well-characterised working values for Al, Fe, Ge, Li, Mn and Ti. Approximate concentrations of B, Be, Cu, Na, Pb, Sn and Zr were also determined but those for Ag, Au, Ba, Ca, K, Mo, Sb and Sr were below the LODs of all three techniques. The RM, with a mean Ti concentration of 57 ± 4 μg g−1, was considered to be perfectly suitable for the intended purpose. However, because some of the trace elements, particularly Al and Mn, were not homogeneously distributed, practical use of this quartz as a RM required at least 10 points to be measured along a transect through the entire middle growth zone.

A recent IUPAC Technical Report367 provided a comprehensive overview of international RMs for isotope ratio analysis, including materials for which the supply was exhausted or superseded by newer materials. The number of primary isotopic RMs for anchoring stable isotope delta scales now exceeds 30, covering more than 25 elements. There are also over 150 secondary isotopic RMs with a specified delta value, although half of these were produced for isotope measurements of one of seven elements: B, C, H, Li, N, O and S. Tables of relevant RMs for every element for which a zero-scale material has been produced or proposed, listed delta values with uncertainties as given in the original reference. The current recommended delta value was highlighted where there was more than one entry for a particular RM.

New high resolution SIMS U–Pb determinations have confirmed the age homogeneity of zircon RM Temora 2 and its value as a geochronological RM.368 The geological processes that lead to the formation of isotopically homogeneous Temora zircons within the host rock, the Middledale Gabbroic Diorite in New South Wales, Australia, were elucidated from a combination of petrographic observations, EPMA major element data and LA-ICP-MS trace element data. It was concluded that sources of future zircons appropriate for use as RMs may lie in igneous suites where Zr saturation is not achieved until late in the crystallisation history. Eocene gem zircons369 from Glen Innes, Australia were assessed for isotopic and chemical homogeneity by ID-TIMS, LA-ICP-MS, SIMS and WDXRF spectrometry. Twelve U–Pb determinations by ID-TIMS of separate 200–400 μm chips measured in two laboratories showed that one of the zircons (Aus22) had remarkable isotopic homogeneity at the sub-per mil level, with a 206Pb–238U age of 38.8963 ± 0.0044 Ma (2σ). This zircon was also very homogeneous for the REEs and other trace elements at the analytical volumes sampled by these techniques and thus suitable as a RM for U–Pb isotope and trace element microanalysis.

In the 40Ar/39Ar dating technique, neutron fluence monitors (standards) are required for age calculations. For Precambrian samples there are few standards with sufficient grain-to-grain reproducibility and an appropriate age for acceptable argon isotope ratio measurements. Jourdan et al.370 presented data for a new muscovite standard WA1ms with an age of 2614.2 ± 1.5 Ma, an age closer to those of Proterozoic and Archaean samples than those of previously used standards. As muscovite does not contain Ca, it was possible to analyse the samples soon after irradiation because there was no need to wait for 37Ar to decay. In addition, corrections for Ca-related interferences were not required. Its high K content (∼10%) resulted in a large Ar signal even for the minute amounts analysed and so provided better precision for individual measurements.

In an inter-laboratory comparison371 organised by the IAEA, six laboratories were asked to quantify 69 impurities in two uranium ore concentrates. The main technique employed was ICP-MS with matrix-matched external calibration. As well as establishing consensus values for the two materials, the exercise also served to identify the current state of practice for analysis of this type of matrix. Lessons learnt included issues related to sample dissolution, blank correction and calibration, previously unaccounted polyatomic interferences, and the challenge of estimating measurement uncertainties. Most laboratories significantly underestimated their uncertainties. These ore concentrates will go some way to making more uranium RMs available for identification of the origins of uranium ore concentrates, currently a hot topic in nuclear forensics. In a novel approach to tracing the origin of unknown uranium ore concentrates, Lin et al.372 established a database of published REE data of uranium ores from different countries and then transformed the data by a two-step pretreatment to show stronger geographical and geological indications. Of the different multidimensional statistical pattern recognition techniques tested, orthogonal partial least squares discriminant analysis was shown to the most effective in revealing the fractionations of the HREEs and LREEs. This made it possible to delineate uranium deposits by country and then by deposit type within a country. An unknown uranium ore concentrate could then be attributed to one of ten different countries using a decision tree based on the REE data of uranium ores.

4.2 Solid sample introduction

4.2.1 Laser ablation inductively coupled plasma mass spectrometry. Despite the indisputable progress in laser technology over the last decade, LA-ICP-MS (and other microbeam techniques) still suffers from the limited availability of suitable calibration standards. A review373 (112 references) of common strategies for the calibration of LA-ICP-MS strongly recommended the use of matrix-matched standards and considered various methods that have been employed to prepare such RMs using powdered RMs, synthetic standards or spiked materials. Where matrix-matched standards were not available for whatever reason, solution-based calibration or the use of standard reference glasses were adopted. The utility and limitations of all these approaches were very dependent on the matrix and experimental conditions, as illustrated by examples of their application. One area of topical interest is the development of RMs for the determination of PGEs in sulfide minerals by LA-ICP-MS. Ke et al.374 developed a series of synthetic sulfide calibration standards by spiking a mixture of nanoparticulate grains of CuS, FeS2, NiS and ZnS with a standard solution containing Pd, Pt, Rh and Ru, remilling and fusing the powder in a homemade high temperature furnace designed specifically for this purpose. Argon gas was introduced into the furnace to avoid oxidation of the sulfide mixture during fusion. The differences in results for PGEs in these materials by solution ICP-MS and LA-ICP-MS were generally <11%. Homogeneity, assessed by the signal intensity of LA-ICP-MS line scans, was <10% RSD. Unfortunately, the lack of a recognised sulfide standard made it difficult to verify independently the accuracy of these standards.

Quantification using non-matrix-matched calibration standards is limited by the occurrence of elemental fractionation, which represents the sum of all non-stoichiometric effects occurring during the ablation process, transport and ionisation. In order to separate the relative contributions of processes in the laser plume and ICP, Luo et al.375 studied the fractionation behaviour of 63 elements using a modified single volume LA cell. The cell modifications enabled the distance between the gas outlet, a needle with a nozzle tip (id 0.6 mm), and the ablation site to be varied between 1 and 10 mm. This distance was found to be positively correlated with the size of the LA-produced aerosol particles. A high carrier gas flow rate at the ablation site facilitated the production of small aerosol agglomerates or particles. Larger aerosol particles produced in zones of the cell with low helium flow rates were not completely vaporised in the ICP, particularly under conditions of high mass loading. To reduce the ICP-induced fractionation, hot plasma conditions and high carrier gas velocity at the ablation site were required. These results also demonstrated that the position of the sample within a standard LA cell affected the size of the resulting laser aerosol particles.

The use of fs laser radiation is one of the most promising approaches for minimising elemental fractionation and matrix effects. Jochum et al.376 used a new 200 nm fs LA system with spot sizes of 10–55 μm to analyse RMs with different matrices in an investigation of matrix-related effects. The experiments were also undertaken with 193 nm excimer and 213 nm Nd:YAG laser systems. The short laser pulse duration of the fs LA system resulted in reduced fractionation and matrix effects, especially for volatile, siderophile and chalcophile elements. Any matrix effects were smaller than the analytical uncertainty of the measurements and therefore considered to be negligible. Consequently, 22 RMs with varying matrices could be analysed with a precision of 1–3% and an accuracy of ±10% when a non-matrix-matched calibration based on NIST SRM 610 (trace elements in glass) was used. Although the precision of ns LA-ICP-MS was comparable with that of fs LA-ICP-MS, the overall analytical uncertainty increased to 15–30% for volatile, siderophile and chalcophile elements. Similar conclusions were reached by Li et al.377 who ablated silicate reference glasses with either a 193 nm ArF excimer ns laser or a 257 nm fs LA system. The significantly different fractionation behaviours of Co, Cr, Cs, Cu, Fe, K, Li, Mn, Na, Ni, Rb, Si, U and V for NIST SRM 610 and USGS CRM GSE-1G (basalt) observed in 193 nm ns LA-ICP-MS were eliminated using fs LA-ICP-MS at high spatial resolution. The use of fs LA also reduced mass loading effects. Values determined for a range of MPI-DING, NIST and USGS glasses using fs LA-ICP-MS with a laser spot size of 24 μm agreed within ±10% for most elements. The NIST SRM 610 was considered to be unsuitable as an external RM for the analysis of natural silicates using ns LA unless matrix effects were minimised by using Si as an internal standard. The CRM GSE-1G standard was recommended as a better choice.

The influence of ablation cell geometry and laser wavelength on aerosols produced by fs LA was evaluated by d’Abzac et al.378 by monitoring 56Fe/54Fe ratios of particles generated from magnetite, pyrite, haematite and siderite. There was no discernible difference in the ablation mechanisms at the laser wavelengths investigated (198 and 266 nm), indicating that the laser interactions were independent of the sample's optical absorption properties. The HelEx two-volume cell produced smaller particles with a larger range of 56Fe/54Fe ratios than particles from the Frames single-volume cell, but the composition of the bulk aerosol matched that of the sample, demonstrating stoichiometric fs-LA sampling. The faster washout of the HelEx cell gave a more constant stream of small particles to the ICP, thereby producing a more stable Fe ion signal (0.7% versus 1.5% RSE for 56Fe in a 40-cycle single analysis) and consistent instrumental mass bias. The measurement was therefore more precise. Oeser et al.379 demonstrated that in situ determinations of δ56Fe and δ26Mg in silicates by fs-LA-MC-ICP-MS could be performed with a measurement reproducibility of better than 0.13‰ (2s) at a spatial resolution of about 50 μm. The fs-LA-MC-ICP-MS determinations (at a wavelength of 194 nm) were largely matrix-independent and agreed, within analytical uncertainties, with data obtained by solution MC-ICP-MS. All the MPI-DING and USGS reference glasses analysed were homogeneous with respect to their Fe and Mg isotopic signatures making them suitable RMs for future in situ Fe–Mg isotope studies.

A second-generation custom-built fs LA system380 operating at 196 nm and coupled to MC-ICP-MS was used to assess optimum analytical conditions for the measurement of stable Si isotope ratios for a wide range of geological materials. Wet plasma conditions were preferred to provide stable and reproducible ICP conditions. Although precise δ29Si and δ30Si values of better than ±0.23‰ (2SD) were obtained if the ablated area was at least 50 × 50 μm, the best δ30Si precision that could be achieved for single spots of <30 μm diameter was about ±0.6‰ (2SD). The laser beam was focussed below the sample surface with energy densities of 1–3.8 J cm−2. Using NIST 8546 (pure quartz) as the measurement standard for calibration in a standard-sample-bracketing protocol resulted in accurate and precise data for a range of international RMs. No composition-dependent matrix effects were discernible within the uncertainties of the method.

Recent hardware developments included a novel device381 for signal smoothing and removal of Hg during the measurement of Pb isotopes by LA-ICP-MS. The device consisted of a stainless steel cylinder with a total volume of 94 cm3 filled with nine smoothing elements, each comprising seven intersecting corrugated plates 30 mm long. The internal surfaces were coated with a ∼10 μm thick layer of gold to trap any Hg in the carrier gas. The device did not affect the aerosol transport efficiency significantly and no oscillation of the signal intensity occurred at a laser repetition rate of 1 Hz. Incorporation of this device reduced the Hg background by an order of magnitude and, more importantly, the signal intensity of 202Hg was reduced from 256 to 0.7 mV when ablating the USGS RM MASS-1 (synthetic polymetal sulfide). Feng and Wang382 evaluated four different designs of mixing devices for on-line ID-LA-ICP-MS for accurate quantification of Pb in NIST SRM 610 (trace elements in glass). The spike solution was introduced either before or after the ablation cell and the mixing efficiencies assessed from the signal sensitivity, plus the accuracy and precision of the 207Pb/208Pb ratio. The best performance was given by a ball-shaped device with a volume of 35 cm3 and inserted prior to the ablation cell. The practicalities of on-line double ID for the analysis of solids by LA-ICP-MS was also evaluated by Fernandez et al.383 A liquid aerosol containing an isotopically-enriched spike solution was mixed with the laser-generated aerosol in a Y-piece placed just before the ICP torch, a design rejected by Feng and Wang.381 The double ID strategy required the sequential analysis of a certified natural-abundance standard placed alongside the sample in the ablation cell so that the mass fraction of the analyte in the sample could be directly referenced to the certified standard without prior knowledge of the composition of the spike solution. To validate the procedure, Pb, Rb and Sr were determined in silicate glass RMs prepared as fused glass beads and pressed powder pellets. Precisions were 6–21% RSD (n = 3) for pressed pellets and 3–21% RSD (n = 3) for fused beads. Given that bulk analysis of pellets and beads by LA-ICP-MS (without ID) is a well-known technique, these precisions seem surprisingly poor. As this procedure did not remove the need for matrix-matched standards for accurate data, it is difficult to see the advantages of this approach.

An instrumental development that could have a significant influence on the analysis of solid samples by LA is distance-of-flight mass spectrometry (DOF-MS). In this technology, batches of ions are spatially separated across a field-free region according to their m/z-dependent velocities and then directed onto a spatially selective ion detector. Like TOF-MS, DOF-MS is well suited for the analysis of laser-generated aerosols because it offers rapid simultaneous multi-element detection over the whole mass range. Initial results384 from the first coupling of LA to an ICP-DOF-MS instrument equipped with a microchannel plate-based imaging detector provided LODs of 1 μg g−1 for steady state LA signals. The system was also capable of performing time-resolved single-pulse LA analysis with a LOD of 200 fg for U. Shot-to-shot reproducibility was 6% RSD and isotope ratios were measured with a precision of 0.3% with a 10 s integration time. It is expected that future developments in high-speed ion-detection arrays will facilitate the growth of DOF-MS for the measurement of transient signals.

Although the majority of U–Pb dating applications are performed by MC-ICP-MS, much useful data can be acquired using quadrupole instruments. Columbite-tantalite (Coltan) is an ideal mineral for U–Pb dating of Nb–Ta mineralisation because of its high U and low common Pb contents. To obtain accurate dates from samples of this mineral in pegmatites and granites from China, Che et al.385 found that it was necessary to use a coltan standard from Namibia for external standardisation rather than the 91[thin space (1/6-em)]500 zircon RM as there was a marked difference in ablation characteristics between these two minerals when using a ArF excimer system at 193 nm. This finding raises questions about the use of zircon RMs as external standards in U–Pb age determinations of baddeleyite (ZrO2), another mineral that incorporates relatively high amounts of U in its crystal lattice. Wohlgemuth-Ueberwasser et al.386 evaluated the use of baddeleyite from the Duluth Gabbro FC-4b as an external standard in the analysis of baddeleyites from South Africa and Finland. This matrix-matched standard made it possible to correct for downhole and laser-induced fractionation and provide dates that were both accurate (within the error of published U–Pb ages obtained by ID-TIMS) and precise (0.3–0.7%, 2σ, on the concordia ages). The lack of zoning, as shown by CL imaging, and the homogeneous age distribution of the FC-4b baddeleyite crystals made it suitable for use as a RM. Another accessory mineral, allanite, can provide reliable chronological information on metamorphic and magmatic events but has rarely been used successfully for dating hydrothermal ore deposits because of high levels of common Pb and the resultant degradation in precision resulting from the corrections required. However, Deng et al.387 measured trace elements and U–Th–Pb isotopes by LA-ICP-MS in spot ablations of allanite grains from a Chinese iron skarn deposit. Although the allanite grains exhibited optical and chemical zoning, they had low Th and U contents and did not contain common Pb so were comparable to zircon RMs 91[thin space (1/6-em)]500 and GJ-1. Consequently, a matrix-matched external standard was not required to obtain a high precision U–Pb age of 136 ± 1 Ma, indistinguishable from a less precise Th–Pb age of 139 ± 2 Ma and consistent with a U–Pb age of 137.1 ± 1.5 Ma obtained for a zircon from a nearby ore-related diorite intrusion.

To improve the sensitivity of a MC-ICP-MS instrument for the measurement of B isotope ratios by LA of geological materials with low B content, three different combinations of sample and skimmer cones were investigated together with the addition of nitrogen gas to the central channel.388 Compared to the standard arrangement (H skimmer cone and standard sample cone), the use of the new X skimmer cone and Jet sample cone improved the 11B signal intensity by a factor of 3.8. The addition of nitrogen at 4 mL min−1 improved the stability of the instrumental mass bias although it had little effect on the B signal intensity. Boron ratios measured in nine different RMs, including reference minerals as well as MPI-DING, NIST and USGS reference glasses, were in good agreement with literature values. Results for DD-1 (an in-house natural tourmaline), GSD-1G and GP-4 (USGS reference glasses) were very consistent, indicating that they would be good candidate RMs for B isotope measurements.

Although TIMS is still regarded as the benchmark method for the determination of Nd and Sr isotope ratios because of its inherently high precision, MC-ICP-MS has gained in popularity for these measurements because of its higher sample throughput and, when combined with LA, the ability to perform in situ analysis. However, isobaric interferences hamper the precise determination of Nd and Sr isotope ratios by LA-MC-ICP-MS so Yang et al.389 assessed 11 apatite RMs commonly used in U–Th–Pb geochronology as potential Nd or Sr isotope RMs. The Nd and Sr isotope ratios they obtained were consistent with values from solution-based methods by MC-ICP-MS or TIMS, indicating the reliability of their analytical and data reduction protocol. It proved impossible to obtain reliable 87Sr/86Sr data by LA-MC-ICP-MS for samples with both Er/Sr or Yb/Sr ratios greater than 0.1 and low Sr contents. Eight of the apatites tested were relatively homogeneous in terms of their Sr isotopic compositions while six were relatively uniform in terms of their Sm–Nd isotopic compositions. However, the UWA-1 sample, a fluorapatite from Bancroft Ontario, was suitable for neither Nd nor Sr isotopic analyses and Durango apatite was unsuitable for Sm–Nd determinations despite yielding homogenous 143Nd/144Nd data. To minimise the isobaric interference of CaPO+ on 87Sr+ in the determination of 87Sr/86Sr in bioapatites by LA-MC-ICP-MS, Lewis et al.390 developed a customised plasma interface through which they could introduce helium gas. By adopting this interface and tuning the mass spectrometer for low oxide production, this oxide interference was reduced to such an extent that the accuracy of the 87Sr/86Sr measurements was within the measured precision of ±50 ppm (2σ) without any need for further mathematical correction. In a system devised by Huang et al.391 to measure simultaneously all the relevant isotopes in studies of Rb–Sr and Sm–Nd systematics in natural minerals, a 193 nm excimer LA system was connected to two MC-ICP-MS instruments. The aerosol from the ablation cell was split into two gas streams; one was introduced into a Neptune instrument for Sr determinations and the other into a Neptune Plus instrument for Nd measurements. Varying the proportions of the ablated material received by each instrument did not introduce any bias for the Sr–Nd isotopes measured on apatite and loparite, indicating that no fractionation occurred during aerosol transport. This system was also used to measure Sm–Nd and Lu–Hf isotopes simultaneously from a single sampling site. The data obtained for five natural minerals were identical to the reference values within analytical errors.

In an excellent review of techniques for U–Pb dating of accessory minerals Schaltegger et al.392 (164 references) detailed the current status, advantages and limitations of CA-ID-TIMS, LA-ICP-MS and SIMS and the interpretational differences that still need to be resolved. They argued that the choice of technique should be governed by the scientific question posed and demonstrated that comprehensive sample characterisation using a full range of textural and compositional analyses was necessary for the highest quality geochronological data. Inter-laboratory and methodological differences limited the accuracy of LA-ICP-MS data to ∼2% (2σ) for 206Pb/238U and approximately 1% (2σ) for 207Pb/206Pb but recently recommendations for better practice have been developed (see http://www.Plasmage.org). It was recognised that more consistent and better documented practices were required and that any paper submitted for review should contain full information on the parameters affecting data acquisition and manipulation as well as proof of accuracy and laboratory reproducibility. A growth in studies employing detrital zircon geochronology has been stimulated by technical advances in LA-ICP-MS that make it possible to obtain U–Pb ages from individual crystals both rapidly and reliably. The analytical methodology, a range of applications and future directions of this technique were critically assessed in an authoritative review393 (128 references). It was noted that many of the applications would benefit from improvements in the precision and accuracy of the measured data. The latter depended on the availability of better RMs used for sample-standard bracketing. One of the most critical areas for future development was software for manipulating, displaying and archiving detrital zircon geochronological data so that researchers fully understood the strengths and weaknesses of the data being generated.

Chemical abrasion, which involves thermal annealing followed by relatively low-temperature partial dissolution in HF, was specifically developed to minimise or eliminate loss of Pb from zircons prior to analysis by TIMS for U–Pb dating and thereby improve precision. Crowley et al.394 evaluated the application of CA to LA-ICP-MS by analysing untreated and chemically abraded zircon RMs. The extent to which a zircon was visibly affected by CA seemed to depend primarily on its U concentration and the presence of physical defects, but all U–Pb data from treated zircons showed a substantial improvement in concordance compared with data from untreated samples. Comparison of downhole fractionation of 206Pb/238U in untreated and abraded portions of the 91[thin space (1/6-em)]500 zircon RM showed that it was important to expose both RMs and unknown samples to CA in the same way. In a similar study by von Quadt et al.,395 the precision of U–Pb age determinations of CA-treated grains was 0.1–0.2% for ID-TIMS analysis but only ∼1.5% by LA-ICP-MS. The U–Pb ages determined by the two analytical techniques overlapped within analytical uncertainty. Differences of between 4 and 6% were found between U–Pb ages obtained from untreated and CA-treated zircons. However, both studies cautioned that results from the CA technique may need careful interpretation in some cases, particularly for very young samples (<1 Ma) for which Pb-loss in the radiation-damaged area is unimportant and for very old zircons (Archean) with elevated U concentrations.

A logical development in U–Pb dating of zircon and monazite has been the use of fs LA combined with MC-ICP-MS instruments equipped with high gain amplifiers for increased sensitivity.396 By this technique, it was possible to achieve accurate U–Pb dating with an internal precision comparable to that obtainable by SHRIMP, although the sample volume required for a single spot by LA-MC-ICP-MS was ten times greater. In a study of zircons with high U content from granite-hosted uranium ore deposits in South China,397 SHRIMP analyses were unreliable due to a matrix-effect from the high U concentrations, whereas by LA-ICP-MS matrix effects were insignificant even in rims with U concentrations of up to 26[thin space (1/6-em)]000 ppm. However, to achieve accurate data it was important to check the cross-calibration between the different modes of ion collection on the quadrupole ICP-MS pulse-analogue detector. Accurate U–Pb dating of very young zircons (<1 Ma) is challenging partly because of the very small amount of radiogenic Pb formed they contain. In a new method398 of Th–U dating such zircons by LA-MC-ICP-MS, the formation of previously unreported complex polyatomic species during ablation of different zircons was minimised by increasing the plasma temperature and thus the residence time of ions in the ICP. Careful instrumental optimisation was a compromise between maximum sensitivity and minimum U–Th fractionation and was fundamental to precise and accurate in situ dating. As might be expected for these old zircons, repeat analyses of 230Th/238U in the 91[thin space (1/6-em)]500 zircon RM and an in-house zircon RM over eight months were indistinguishable from secular equilibrium. The calculated ages were in agreement with other quaternary dating methods such as 14C, 40Ar/39Ar and 36Cl.

Another area where LA-ICP-MS has had a significant impact is in the imaging of mineral crystals, of particular value to the mineral exploration industry. However, the analysis of multiple mineral phases presents many difficulties, not least in the choice of internal standard and RMs. Paul et al.399 devised a method of extending single grain mapping to multi-phase materials by adapting some of the concepts behind automated mineralogy to LA. The sample was prepared as a standard 30 μm polished petrographic thin-section and elemental images obtained by quadrupole ICP-MS using a 193 nm ArF excimer laser with a spot size of 33 μm, repetition rate of 10 Hz and stage translation rate of 60 μm s−1. Successive rasters were spaced 32 μm from their neighbours and it took about 5 h to cover an area of 2.8 × 5.3 mm. Initial images were created using the CellSpace module within the Iolite software. The use of PCA greatly improved the sensitivity of identifying the mineral phases. Once the individual phases were identified, it was a relatively simple but computationally intensive task to perform IS and RM normalisation. The image was then processed pixel-by-pixel and phase-by-phase to build up maps for each element. A fruitful area of future study could be the integration of SEM elemental maps to provide IS values for subsequent deconvolution of LA maps. Hennekam et al.400 investigated the accuracy and precision of LA-ICP-MS for line-scan measurements of varved (laminated) sediments for the purpose of accessing high resolution paleoclimatic records within the sediments. Fluid-displacement resin-embedding was used to preserve the laminated structure of these sediments, which are often unconsolidated and difficult to sample. A series of resin-embedded homogenised pellet standards with matrices of calcite, quartz and clay spiked with powdered oxides of Al, Ba, Mo and V were prepared. In addition, two synthetic laminated sediment standards in resin were made, the first with a sequence of laminations with the same matrix (calcite) but different spike concentrations and the second with a sequence of calcite and quartz laminations with similar spike concentrations. Deviations from the reference values were generally <5% and precisions measured as repeated analyses on the same sample <5% RSD. Although the LA-ICP-MS line scanning closely recorded the alternating geochemical profiles of the artificial laminations, the Ca signal showed a clear tailing at the transition from a calcite layer to a quartz layer. Potential artefacts introduced from the use of the non-matrix-matched calibration standard NIST SRM 610 glass were overcome by calibrating the LA-ICP-MS data against calibrations constructed from a parallel series of discrete sample analyses by conventional techniques such as solution ICP-AES, solution ICP-MS and XRF spectrometry.

Kelly et al.401 investigated the effectiveness of depth profiling using laser ablation as an alternative to SIMS for measuring changes in mineral chemistry or age as a function of depth. Use of a single-pulse approach rather than continuous ablation made it possible to reduce the volume of material analysed by LA-ICP-MS to levels required for SIMS analysis. Profiles of U–Pb ages in unpolished, naturally zoned zircon grains from a Neoarchaean metasediment were obtained by ablation with a 193 nm laser using a 48 μm beam diameter at a repetition rate of 2.5 Hz, the slowest rate capable of being smoothed into a steady signal by the “SQUID” smoothing device employed. Rare earth element profiles were also acquired but with a smaller spot size of 17 μm at a repetition rate of 3 Hz. It was possible to identify isotopically distinct rims <3 μm thick and to demonstrate that REE alteration had penetrated no more than 10 μm into the crystal. Ages provided by LA-ICP-MS analysis fell within the 2s uncertainties of 207Pb/206Pb ages measured by SIMS on the same zircon population. Although the smaller SIMS spot size was less subject to lateral mixing, the LA-ICP-MS analysis was much more rapid. It was possible to identify rock-fluid alterations of significance in studies on the timing of metamorphic or hydrothermal fluid events in relation to metallic ore deposits.

4.2.2 Laser induced breakdown spectroscopy. The increasing popularity of LIBS for the analysis of geological materials was highlighted in several recent reviews. A treatise (215 references) by Senesi402 covered basic theory and practice, conventional laboratory and portable configurations, the main methodologies of LIBS measurements together with the advantages and limitations of the technique. The review also provided a comprehensive overview of the application of LIBS to the analysis of minerals and rocks, including the remarkable progress made in the last decade on the use of LIBS on robotic vehicles for extra-terrestrial studies. A shorter review (123 references) by Qiao et al.403 concentrated on the qualitative and quantitative aspects of the analysis of geological materials by LIBS. Recent developments were illustrated by examples from the analysis of ores, extra-terrestrial materials, speleothems, marine sediments and fluid inclusions. Currently most studies remain laboratory-based but improved field-portable systems, together with better data processing methods make LIBS a potentially significant tool in geological applications. McMillan et al.404 discussed three case studies to illustrate different ways in which LIBS can be employed in a geological context: (i) to screen samples in the field for small changes in composition prior to sampling for conventional chemical analysis; (ii) provenancing and correlation studies, such as identifying the origin of gemstones from different deposits; and (iii) geochemical mapping, using a sample of copper ore as an example.

The ability of LIBS to differentiate ores rapidly in situ with minimum sample preparation makes it a suitable tool in the mineral industry for QC purposes. Applications included combination with PLSR in the determination Al2O3, CaO, MgO and SiO2 in iron ores405 to derive the sample acidity from a ratio involving these four oxides. Different grades of iron ore were classified406 using LIBS combined with Random Forest, a new classification algorithm based on multiple classifiers. A method407 for the determination of fluorite (CaF2) in powdered ore samples was based on the measurement of the emission from CaF molecular bands as an alternative to the use of atomic F emission lines. In the determination of Cu in mineral ores,408 the samples were first classified by rock type using PCA of their LIBS spectra. This division into individual classes allowed matrix-specific calibrations for each rock type to be used and resulted in improved accuracy and precision. A fast classification of bricks,409 based on their LIBS spectra and a combination of PCA and linear discriminant analysis, was demonstrated by analysis of 29 samples from seven different locations.

The utility of LIBS for geochemical prospecting was assessed286 through the direct determination of Ag, Cu, Mo and Pb present at concentrations close to their crustal abundance in ores and soils from four different types of mineralisation. Prior to measurement, the powdered samples were pressed into 12 mm diameter pellets under a pressure of 750 MPa. Selection of an appropriate IS compensated for the large matrix effects observed for Mo but no matrix effects were observed for the other elements. Although the LODs for Cu, Mo and Pb (0.6, 0.3 and 8 ppm, respectively) were sufficient to determine these elements at their crustal abundances, that for Ag (0.3 ppm) was insufficient. One drawback to the technique was the relatively narrow linear range of 1–100 ppm for the resonance lines selected. With the increased use of pXRF equipment for mineral prospecting, it will be interesting to see whether LIBS makes a significant impact in this field. Although currently LIBS could probably provide better LODs, a comparison of the figures of merit for LIBS and pXRF on the same samples would be informative.

The LIBS technique has been used to detect subtle differences in rock compositions. In a novel method410 for analysing individual carbonate grains, sand grains were mounted on a slide with acrylic adhesive, stained and viewed with a high magnification camera. Mounting of 125–150 grains on each slide provided >100 carbonate grains, sufficient to identify groups of carbonate populations. Non-carbonate grains were excluded from the dataset using hierarchal cluster analysis and sets of training data constructed using PCA. Final modelling was carried out using a Soft Independent Modelling of Class Analogy method, a supervised classification method of pattern recognition based on PCA. It was acknowledged that sourcing matrix-matched standards for LIBS calibration remained a challenge and that, when applied to individual sand grains, the technique was best suited to pattern recognition and classification. It revealed that carbonate grains from sand dunes in Oman were comprised of several sub-populations and originated from multiple sources. Roux et al.411 determined 12 major and minor elements in situ to evaluate a homemade portable LIBS system for classification of volcanic rocks into various magmatic series. A LIBS classification model, based on ICP-AES analyses of these rocks, was constructed and used to discriminate rocks in the field with 90–100% success.

The recent explosion of interest in the use of LIBS instruments has been stimulated in part by development of instruments for planetary exploration missions. In the year after the Mars Curiosity rover landed in August 2012, the ChemCam instrument generated more than 75[thin space (1/6-em)]000 LIBS spectra and some data interpretation is now available. Specially designed calibration targets412 on board the Curiosity rover included homogeneous glass and fine-grained glass-ceramics used to construct calibrations for each element of interest prior to LIBS measurements of Martian samples. The in situ univariate calibrations involved identifying the most stable emission lines and optimising the auto-focussing system and laser energy. Comparisons with APXS data for selected targets showed good agreement for most major elements, although estimates of SiO2 were not possible using univariate calibration and the K2O and Na2O contents were probably underestimated because of the compositions of the on-board calibration targets. Trace element calibration curves for Li, Mn and Sr down to several ppm were used for rapid identification of noteworthy rocks and soils along the traverse.

Work has already started on the next generation of instruments for the analysis of solids on planetary bodies. Following the success of the ChemCam instrument, Gasda et al.413 reported a prototype design based on a combination of remote LIBS and Raman spectroscopy. The Q-switched laser-induced time-resolved spectroscopy used was approximately 70[thin space (1/6-em)]000 times more efficient at recording signals than a commercially-available LIBS instrument, due to the development of a directly coupled system, the use of an intensified CCD image detector and a pulsed laser that allowed time-resolved measurements. With an improved S/N of at least an order of magnitude, the dual instrument provided enhanced quantitative analysis of the LIBS spectrum with a spatial resolution of 200–300 μm when data were collected 7 m from the target. The Raman instrument was capable of 1 mm spatial resolution at a distance of 3 m from the target and bioorganic fluorescence detection at greater distances. This performance fulfilled all of NASA's expectations for such instruments. Other laboratory prototype instruments designed for space research included a microscope camera system for high resolution optical imagery and a miniature LA ionisation mass spectrometer414 to improve the sensitivity and spatial resolution of in situ chemical analysis of extra-terrestrial materials. These instruments formed part of a miniature analytical suite proposed for ESA's MarcoPolo-R mission to an asteroid. The mass spectrometer had an effective dynamic range of at least eight orders of magnitude and, in addition to major element compositions, trace elements at concentrations below 1 ppm could be measured. Isotope ratio analysis was performed with accuracies at the ‰ level.

The relatively new technique of determining K–Ar ages in rocks and minerals by LIBS is based on the simultaneous extraction of Ar and K by LA with detection of K by LIBS, using the light emitted in the ablation plume, and Ar measurement by noble gas MS. The methodology, first proposed in 2008, relied on the fact that the only quantity required to calculate a K–Ar age, the 40Ar*/40K ratio (where 40Ar* is radiogenic argon), was preserved in the ablated aerosol. Solé415 described a fully developed in situ protocol, known as micro-K–Ar, in which each analysis consisted of 1 to 12 laser runs on the same crater and each run was a burst of 30–50 laser pulses. The number of pulses was optimised to obtain an adequate LIBS signal for K and the total volume ablated chosen to obtain sufficient Ar signal. If an uncertainty of ≤5% could be tolerated for a particular application, this method had the advantages of very fast analytical times and analysis of unirradiated samples when compared with the classical K–Ar and the laser microprobe Ar–Ar dating techniques. In addition, LIBS provided chemical information. Similar methods were proposed for the remote exploration of Mars (e.g. Cho et al.,416 Cohen et al.417) but, unlike the APXS instrumentation used on the Curiosity rover to measure whole-rock K–Ar ages, the LIBS measurements needed to be conducted in a vacuum chamber to allow for simultaneous Ar determinations. In a detailed assessment of figures of merit for K measurements under high vacuum conditions (<10−2 Pa), Cho et al.416 used a Nd:YAG laser and a compact Czerny-Turner type spectrometer equipped with a CCD detector to measure 23 geological RMs by LIBS. The LOD was estimated to be 300 ppm and the relative uncertainty (1σ) of the K calibration was 20% for 1 wt% K2O. They calculated that if the Ar content was measured with a 15% error for 3500 Ma year-old rocks containing 1 and 0.3 wt% K2O, then the K–Ar ages would be determined with 10% and 20% (1σ) errors, a considerable improvement on current Martian chronology, in which the uncertainty is a factor of 2 to 4. The K–Ar laser experiment417 (KArLE) at NASA, destined to make in situ noble gas geochronology measurements aboard planetary robotic missions, reported a measurement uncertainty of 10% or better for the determination of whole-rock K–Ar ages for rocks older than 2600 Ma. In this experiment the relative K contents determined using LIBS were related to the absolute Ar abundance by sample masses (determined by optical measurement of the ablated volume) and bulk densities (using sample mineralogy). All the analytical components had been flight-proven and did not require further technical development.

A significant development in underwater LIBS was a device called ChemiCam418 capable of performing in situ multi-element chemical analysis of liquids and mineral deposits in the ocean at depths of up to 3 km. This system, which consisted of a long-pulse (∼150 ns) laser, a spectrometer and a high-speed camera, was successfully deployed from a remotely operated vehicle which provided the power supply, instrument control and signal telemetry through a tether. The construction of a compact and reliable long-pulse laser was considered as key for the success of this project. The sensitivity was sufficient to detect Ca, K, Li, Mg and Na simultaneously over a range of concentrations appropriate for seawater analysis. The lowest LOD achieved was 25 μmol kg−1 for Li. For mineral deposits, reliable detection of elements at >1.0 wt% was possible and methods to determine the relative abundance of Cu, Pb and Zn in hydrothermal deposits were developed.

Calibrations that relate laboratory standards to unknown samples are key to interpreting any type of LIBS data. Boucher et al.419 examined ten linear and non-linear regression methods for modelling and interpreting the chemical abundances from LIBS spectra of geological samples. Although model performances differed, all techniques except k-nearest neighbour produced statistically-indistinguishable results when the probability function (p) was set at 0.05.

4.3 Sample dissolution, separation and preconcentration

Although there is no shortage of methods for the extraction and determination of gold, there is continuing demand for a rapid and accurate procedure with low LODs particularly for the mining industry. Apart from the difficulty of complete extraction of Au from the host rock, a strategy is required to overcome memory effects in the glass sample introduction systems used in AAS, ICP-AES or ICP-MS. To overcome these problems Wang and Brindle420 treated milled powders with HNO3–HBr acids in a digestion device known as the ColdBlock™, in which samples were heated by IR radiation. A solution of 1% (m/v) L-cysteine and 1% (v/v) HCl was effective in stabilising solutions containing Au and eliminating the Au memory effect in ICP-MS. The procedure was validated by analysing six CRMs certified for Au, including one sulfide-rich copper concentrate. Taking 0.5–2.0 g of sample and digestion times of 10–12 minutes, 94–100% recoveries of Au were achieved using a total acid volume of 6–9 mL with a between-run precision of 2–4%.

Dedicated instruments based on thermal decomposition, catalytic conversion, amalgamation and AAS are commonly used to determine the Hg content of geological materials. To improve low Hg recoveries from phosphate and apatite rocks, D'Agostino et al.421 performed an alkaline fusion directly inside the furnace of the instrument. The flux consisted of a mixture of Na2CO3, K2CO3 and Li2CO3, which melted at about 400 °C and decomposed phosphorite matrices at 700 °C by transforming the crystal lattice into a carbonate form. Recovery of Hg from IRMM CRM BCR 32 (Moroccan phosphate rock) was close to 100%, far superior to the 40% recovery achievable when alkaline fusion was not used. Analysis of a range of geological CRMs and RMs demonstrated that Hg recoveries for apatite and phosphate rocks were greatly improved with the alkaline fusion while recoveries for other rock types were unaffected.

Geological materials with high iron contents may require special conditions for their dissolution. Sampaio and Enzweiler422 assessed three procedures for complete dissolution of five iron formation RMs. Two of the methods employed either open-vessel or closed-bomb digestions involving HF and the third sintering with Na2O2. Unusually, full digestion of the RMs with the bomb procedure required the addition of a small amount of water to the acids. In general the REE recoveries from the hot plate digestion were slightly higher than those from the bomb digestion but those for the heavy REEs in two of the RMs were up to 30% lower than the published values. The results from sintering tended to be lower than those obtained from the bomb digestion, which was considered to provide the most accurate data. In a study423 of REEs at ng g−1 concentrations in magnetite samples from banded iron formations both cation- and anion-exchange chromatographic procedures were evaluated. Samples were dissolved in HF–HBr acids in a Teflon capsule by heating at 130 °C on a hot plate for 48 h and the digests cleaned up using either AG50W-X12 or AG1-X8 resin prior to HR-ICP-MS analysis. Although the results for both methods were in agreement within experimental limits, the anion method was considered to be more suitable because it was more rapid and consumed smaller volumes of reagents, resulting in lower blanks. Unfortunately, some of the iron formation RMs used in these studies are no longer commercially available, highlighting the paucity of well-characterised iron formation RMs.

Digestion procedures for the measurement of PGE concentrations and Os and Re isotope ratios in geological materials have been reassessed. After intensive tests, Ishikawa et al.424 recommended inverse aqua regia attack of 1–2 g of sample in Carius tubes heated to 240 °C for 72 h, CCl4 solvent extraction of Os and desilicification with HF. Whereas the method resulted in significantly improved recoveries from basaltic RMs, particularly for Ru, mainly due to the use of HF in the desilicification step, for ultramafic and sedimentary RMs the recoveries were largely independent of the use of HF. Sample heterogeneity revealed in CANMET CRM TDB-1 (diabase) was thought to relate to minor minerals – probably sulfides – enriched in Ir, Os, Pt and Ru. In contrast, the ID-ICP-MS analysis of USGS RM BIR-1 (basalt) gave excellent reproducibilities (RSDs, n = 9) of 5.1, 1.5, 5.1, 0.7 and 2.0% for Ir, Pd, Pt, Re and Ru, respectively. The NTIMS determination of Os in the same sample had reproducibilities (RSD, n = 9) of 6.9% for the Os concentration and 0.6% for the 187Os/188Os ratio. Li et al.425 improved the extraction of Os and Re from basaltic and andesitic rocks by employing HF desilicification prior to Carius tube digestion. Both these studies indicated that the addition of HF in the analytical scheme can be beneficial when some of the elements of interest are structurally bound or occur as inclusions in the silicate minerals. In a modification of established protocols for the chemical separation of the PGEs and Re from matrix elements in geological materials, necessary to avoid complex interferences on the PGEs, Chu et al.426 first separated Ir, Pd, Pt, Re and Ru by anion-exchange chromatography into Re–Ru, Ir–Pt and Pd subgroups, each of which were then further purified. The secondary purification of Pd and Ir–Pt on Eichrom®-LN columns was deemed particularly important to minimise any interferences from ZrO and HfO in the determination of Ir, Pd and Pt by ID-MC-ICP-MS. The procedural blanks were generally comparable to recently published blank data. The accuracy of the procedure was confirmed using a peridotite and several mafic rock RMs. Results for USGS RM BIR-1a (basalt), in particular, confirmed its suitability as a mafic RM for the measurement of Re–Os isotopes and the PGEs.

4.4 Instrumental analysis

4.4.1 Atomic absorption and atomic emission spectrometry. Although analytical techniques based on detection by AAS and ICP-AES are routinely employed in geoanalytical laboratories, there are increasingly few novel developments to report. After assessing a variety of digestion procedures, Evdokimova et al.427 opted for one based on sintering with MgO in the presence of NaNO3 (or K2S2O7) for the determination of Re in copper and molybdenum ores and concentrates by ICP-AES. Rhenium measurements were made at 197.248 nm using Gd as an internal standard. Sodium and potassium were added to the calibration solutions to compensate for non-spectroscopic matrix effects. Recoveries were 100–111% with precisions of 1.1–3.2% RSD (n = 5) for four relevant RMs. A simple method for the separation of the REEs, Th and Y from uranium, niobium and tantalum-rich mineral samples prior to their measurement by ICP-AES was reported by Krishnakumar et al.428 After fusion with KHSO4, the elements of interest were precipitated as their oxalates, thereby separating them from matrix elements like Nb and Ta which remained in solution at pH 2 or less. The presence of KHSO4 enhanced the precipitation of the HREE oxalates and resulted in virtually complete recoveries and separation from interfering elements. The method precision was 1–3% RSD at typical analyte concentrations. The quest for a fast multi-element analytical method with simplified sample preparation prompted development of an ETV-ICP-AES procedure429 for the determination of trace elements in coal. The procedure had precisions of <10% and LODs in the sub-ppm range and was validated against a range of coal RMs. It was amenable to automation and considered to be a suitable alternative to traditional more labour intensive methods.
4.4.2 Inductively coupled plasma mass spectrometry. Instrumental developments in ICP-MS are covered in our sister update3 on advances in atomic spectrometry and related techniques. The robustness of a Ar–N2–H2 mixed-gas plasma430 was assessed through the quadrupole ICP-MS analysis of seawater CRMs and in-house Pd–Pt ore RMs using only a simple external calibration. The addition of 23% (v/v) N2 to the outer plasma gas and 0.50% (v/v) H2 to the central channel as a sheath around the nebuliser gas flow resulted in a dramatic reduction (by over an order of magnitude) in oxide levels and background signals from ArO+ and Ar+, although background signals from NO+ and ArN+ increased by a similar amount. Levels of doubly-charged ions also increased and LODs were generally 5–15 fold poorer than for an argon plasma for a matrix-free solution. The LODs for some elements were improved in a 0.1 M sodium matrix. Nevertheless, the inherent robustness of the Ar–N2–H2 mixed-gas plasma was demonstrated through quantitative multi-element analysis of ore RMs and the determination of Cd and Mo in seawater without matrix-matching or IS. Chen et al.431 assessed the effect of the addition of ethanol on the performance of a shielded torch in the analysis of geological RMs. Although the shielded torch increased the sensitivity by a factor of 17–58 for 39 elements, it also increased levels of oxide formation. The addition of 4% ethanol enhanced the signal intensities for As, Au, Sb, Se and Te but suppressed intensities for other elements. In addition, the formation of CeO+ and Ce2+, as well as Kr and Xe background signals, were suppressed. The combination of a shielded torch and introduction of ethanol allowed the direct determination of ng levels of Te with a LOD of ca. 0.5 ng kg−1. Liezers et al.432 demonstrated for the first time that quadrupole ICP-MS instruments could be modified to generate ng–μg quantities of a single isotope of an element with extremely high purity (>99.99%). A standard instrument detector was replaced with a modified collector assembly machined from PEEK with a copper foil target attached to the end. This arrangement allowed collection of 151Eu from a Eu standard solution while monitoring the incident ion current. Potential applications of this development could be considerable and included the preparation of highly-enriched isotope spikes using widely available equipment.

The precision and accuracy of isotope ratio data are heavily dependent on various corrections applied to measured ion signals. A review76 (70 references) of Pb isotope measurements by MC-ICP-MS and TIMS explored how measurement precisions had improved by an order of magnitude over the last 20 years. Much of this improvement lay in the techniques used for correcting for mass fractionation. A statistical comparison of the analytical uncertainty of each technique based on data acquired in the analysis of rocks, soils and metals was used to choose the most appropriate correction method. On a related topic, a simple procedure433 was proposed to assist analysts in selecting the most appropriate model for mass discrimination correction in ID-ICP-MS. Doherty434 investigated the origins of drift and proportional errors in Pb isotope ratio data corrected for mass bias using Tl as the IS. Comparison of results obtained using a model developed in this study with measured drift trends confirmed that multiple correction factors should be applied to isotope measurements obtained by MC-ICP-MS in the order mass bias correction, drift correction and a proportional error correction. In a companion paper, Doherty et al.435 demonstrated that neither of the two general methods of isotope ratio drift correction, polynominal interpolation and internal standardisation, was always reliable. They advocated the use of the range-based merit function ωm to identify the optimal correction factor for isotope measurements by MC-ICP-MS on a run-by-run basis. The ωm test could be applied to a variety of isotopic systems provided enough reference signals were measured to perform the calculations. The time lag436 between signals from different Faraday detectors was found to be a major source of drift in isotope ratios during acquisitions from transient signals, such as those generated by chromatography coupled to MC-ICP-MS. A strategy was proposed to correct for this observed drift based on synchronisation of the raw isotope signals and the precise calculation of the time lag between different amplifier responses. Application of this drift correction to Pb isotope ratios from transient signals generated by flow injection and GC introduction systems resulted in a 14 to 20-fold improvement in measurement uncertainty.

A summary of newly published methods for the determination of isotope ratios by MC-ICP-MS is given in Table 6.

Table 6 Methods used in the determination of isotope ratios in geological materials by ICP-MS and TIMS
Element Matrix Sample treatment Technique Comments Reference
B Silicate glass, Ca-rich water, seawater, spinach (i) Chromatographic separation of 0.1 mL sample digest on AG 50W-X8 cation-exchange resin. Eluted B fraction spiked with HF and loaded onto an Eichrom® strong anion-exchange resin, matrix removed with 0.5 M HF + 2 M HCl and B eluted with 6 M HCl; or (ii) sublimation of 50 μL sample digest in 1.4 M HNO3 at 110 °C for 24 h MC-ICP-MS Comparison of 2 separation methods: ion chromatography and microsublimation. The latter better in terms of efficiency of matrix removal, lower procedural blank, precision of δ11B values, time and consumable costs (exception was spinach where fractionation occurred during sublimation) 437
B Silicate rocks Alkali fusion followed by purification on Amberlite™ IRA 743 B-specific resin, B absorbed on resin at pH > 8 and eluted with 0.1 M HNO3 or HCl (pH ∼1) MC-ICP-MS Observed bias in δ11B values attributed to selective adsorption of metasilicate species to Amberlite IRA 743 resin under different pH conditions 455
B Silicate rock, carbonates, waters Silicate samples digested by K2CO3 fusion, cations removed using Dowex™ AG 50W-X8 cation-exchange resin and then B purified on Amberlite™ IRA 743 columns MC-ICP-MS An automated direct injection nebuliser employed. Precision of δ11B in pure boric acid solutions 0.1‰ but 0.02–0.5‰ for natural samples after chemical separation. δ11B of 7.25 ± 0.47‰ (2SD, n = 8) in GSJ CRM JB2 (basalt) 438
B Marine carbonates, waters Sublimation (micro-distillation) of 50 μL sample digest (<pH 2) at 95 °C for 15–18 h before addition of 0.5 mL of 0.3 M HF HR-ICP-MS Precision of δ11B measurements ≤0.5‰ (2σ) with low B mass consumption (<3.0 ng B for quintuplicate analysis). HF-based matrix and platinum injector for improved washout; jet interface improved instrument sensitivity 5-fold 439
B Marine carbonates Samples dissolved in dilute HNO3, major cations removed by cation-exchange, then B fraction passed through anion-exchange column MC-ICP-MS Analysed solutions contained 50–500 ppb B, equivalent to 2–10 mg coral samples. δ11B of 24.3 ± 0.34‰ (2SD) in carbonate RM JCp-1 456
B Marine carbonates None LA-MC-ICP-MS, ICP-AES Simultaneous measurement of B isotopic composition and B/Ca ratio on ng masses. Light transmitted to ICP-AES instrument via optical fibre from MC-ICP-MS torch. For foraminifers δ11B was measured with a precision of 0.52‰ for a mean B content of 53 ± 7 μg g−1 440
Cr Silicate rocks 4-step chromatographic procedure for Cr purification: (i) AEC on AG1-X4 resin using 6 M HCl to remove Fe; Cr converted to CrIII before (ii) matrix removal on AG50W-X8 resin; (iii) separation from Ti on Eichrom® TODGA resin in conc. HNO3; (iv) separation from V on TODGA resin in 8 M HCl MC-ICP-MS Total blank few ng Cr (insignificant). Nitride and oxide interferences minimised. Precisions better than 2.5 ppm for 53Cr and 5.8 ppm 54Cr (2SD), which represents a 2-fold improvement on previous studies with higher throughput than TIMS 458
Cs Marine sediments Lithium borate fusion, followed by column separation using ammonium molybdophosphate and cation-exchange chromatography on AG 50W-X8 resin, with a final clean up on Eichrom® Sr resin to separate Ba from Cs SF-ICP-MS LOD of 0.05 ng kg−1 achieved for 135Cs and 137Cs in sediments. Main issue is removal of barium to eliminate isobaric interferences arising from 135Ba and 137Ba. 135Cs/137Cs can be used in nuclear forensic work 269
Cu Geological samples After dissolution in HF-based acid mixtures, AEC on AG1-X8 was used to separate Cu, Fe and Zn. After removing matrix elements, Cu was eluted with 6 M HCl, Fe with 0.5 M HCl and Zn with 3 M HNO3. Column yield 100% MC-ICP-MS Ni spike used to correct for mass bias. Precisions were ±0.04‰ (2s) for δ65Cu, ±0.03‰ for δ57Fe and ±0.06‰ for δ66Zn. Data for 15 geological RMs reported 457
Fe Geological samples See Cu, ref. 457 MC-ICP-MS Ni spike used to correct for mass bias 457
Hf Silicate rocks See Sr, ref. 459 MC-ICP-MS Proposed separation scheme to replace the traditional 3-step separation scheme for Sr-Nd-Hf. Blanks 55–65 pg Hf 459
Mg Rock RMs HF–HNO3 digestion ending up in 1 mL 2 M HNO3. Separation on AG 50W-X12 cation-exchange resin. Inclusion of HNO3–HF step to improve separation from remove matrix elements such as Al, Fe, Li, Na, Ti MC-ICP-MS Method designed for rocks with low Mg content (MgO < 1 wt%). Long-term reproducibility for δ26Mg better than ±0.05‰ (2σ). Mg isotope data reported for a range of 16 rock RMs 460
Mo Geological samples A 97Mo–100Mo double spike added to digested sample before Mo purification by ion-exchange chromatography on BioRad AG MP-1 M resin, with the Mo fraction collected in 8 M HF + 2 M HCl Validation of 97Mo–100Mo double spike protocol. Procedural blanks 3–12 ng. Long-term reproducibility of <0.05‰ (2s) for δ98Mo/95Mo measurements of standards. Spike:sample molar ratios need to be between 0.4 and 0.8 for accurate results. Accuracy confirmed by measurements of USGS CRMs BCR-2 (basalt) and SDO-1 (shale) 444
Mo Geological samples A 97Mo–100Mo double spike was added to 40–70 mg of sample powder before digestion in HF–HNO3–HCl. Chemical separation of matrix and interfering elements performed on a BPHA resin column before Mo eluted with 6 M HF + 1 M HCl, with ca. 100% recovery MC-ICP-MS Procedural blanks 0.14–0.21 ng – relatively low due to comparatively small volumes of resin and acids used. A disadvantage is the relatively large volumes of HF required. Measurement reproducibility of <0.09‰ (2s) for δ98Mo/95Mo. Mean value for USGS CRM BHVO-2 (basalt) similar to values reported elsewhere 461
Nd Silicate rocks See Sr, ref. 459 TIMS Proposed separation scheme to replace the traditional 3-step separation scheme for Sr-Nd-Hf. Blanks 70–80 pg Nd 459
Nd Geological materials See Sr, ref. 462 TIMS and MC-ICP-MS Proposed separation scheme for Sr-Nd-Pb. Analyses normalised to 146Nd/144Nd using an exponential law. Recoveries > 90% Nd; procedure blank < 50 pg Nd. External precision by MC-ICP-MS (12 to 32 ppm) worse by a factor of 3–5 compared to that of TIMS 462
Nd Uranium ores and concentrates After digestion, the lanthanide content of the sample was separated by selective retention on Eichrom® TRU resin and eluted in 4 M HCl. In a second step, Eichrom® Ln resin was used to separate Nd from the rest of the lanthanides. After separation the Nd[thin space (1/6-em)]:[thin space (1/6-em)]Sm ratio was <0.01 MC-ICP-MS As no certified Nd isotope standard with uranium matrix available, method was validated by measuring 143Nd/144Nd in USGS CRM BCR-2 (basalt) and GJS CRM JB-2 (basalt). Nd isotopes considered a promising diagnostic for the source of uranium ores 463
Nd Geological materials Sample digested with HF–HNO3–HClO4 and Nd separated in 3 steps; (i) cation-exchange chromatography on AG 50W-X12 to separate REEs from matrix elements; (ii) Ce in eluate oxidised to CeIV and then removed by SPME using mini HEHEHP tandem column; (iii) Sm and Nd separation on HEHEHP column TIMS Total procedure blanks 65–90 pg Nd; yield > 92%. Virtually all Ce removed as well as elimination of Na and Sm but corrections still performed for isobaric interferences from Ce and Sm on Nd isobars. Proposed protocol validated by measurements of nine USGS and GSJ RMs 464
Nd Melt inclusions See Sr, ref. 447 TIMS See Sr, ref. 447 447
Os Geological materials Carius tube digestion with inverse aqua regia (3 + 1 HNO3/HCl) followed by sparging with argon gas and transfer to the ICP-MS instrument fitted with enhanced-sensitivity ICP interface and 1012 Ω high-gain amplifiers on the Faraday collectors MC-ICP-MS Precision of 0.02% (2SD) obtained on 2 ng Os was comparable with those by other methods including NTIMS. Method applicable to most rock samples containing 15–4000 pg Os 465
Pb Geological materials See Sr, ref. 462 MC-ICP-MS Proposed separation scheme for Sr-Nd-Pb. Pb content determined independently to calculate 203Tl/205Tl spike required. After Tl-normalisation and an exponential function, data renormalised to recommended values of NIST SRM 981 (common Pb isotopic standard). Recoveries > 90%; procedure blank < 10 pg Pb 462
S Marine sediments 2–5 g frozen sediment dried under vacuum, free lipids obtained by microwave extraction at 100 °C for 15 min in 9 + 1 (v/v) dichloromethane–MeOH. Organosulfur compounds separated on a silica gel column by sequential elution GC-MC-ICP-MS δ 34S measurements of organosulfur compounds from marine sediments ranged from −43.6‰ to −18.7‰, similar to coexisting pyrite but more 34S-depleted than total extractable and residual organic S 466
Se Black shales Samples digested in HF–HNO3–H2O2 at <80 °C to avoid Se loss. Double spike (74Se–78Se or 78Se–82Se) added, reduced in 4 M HCl and Se purified on thioglycolic cotton fibre. Solutions doped with high purity Mg before desolvation nebulisation MC-ICP-MS Two different Se double spikes trialled in this study. Aluminium cones used to reduce NiO contribution. Mg-doping and desolvation enhanced Se sensitivity 2-fold compared to HG introduction to ICP. Data for Se isotope ratios in two USGS CRM shales (SGR-1 and SCo-1) 467
Sr Geological materials After digestion with HF–HNO3, ascorbic acid added to reduce FeIII to FeII. Sample passed through two columns in series. The upper Eichrom® Sr column extracted Sr and Pb while the lower Eichrom® TRU column extracted the LREEs. Sr eluted from Sr column in 0.05 M HNO3 and then Pb eluted with 6 M HCl. The LREEs were eluted directly onto Ln Spec resin to obtain Nd fraction by sequential elution with 0.25 M HCl TIMS Proposed separation scheme for Sr-Nd-Pb, which has low acid consumption and can be completed in 6 h. TIMS used for Sr measurements as MC-ICP-MS did not offer any advantage. Analyses normalised to 86Sr/88Sr using an exponential law. Procedure blank < 100 pg Sr 462
Sr Silicate rocks Sample digests from a HF–HNO3–HClO4 attack passed through a two-layered mixed-resin column: upper layer contained AG 50W-X12 resin and bottom layer Eichrom® Ln resin. Most matrix elements eluted first followed by Sr, Nd and Hf in turn TIMS and MC-ICP-MS Proposed separation scheme to replace the traditional 3-step separation scheme for Sr-Nd-Hf. Blanks 170–200 pg Sr. The method was validated using USGS CRMs BCR-2 (basalt), W-2 (diabase), BHVO-2 (basalt) and GSJ andesite CRMs JA-1 and JA-3 459
Sr Melt inclusions Samples digested in HF–HNO3 before separation on two columns in series, the upper containing Eichrom® Sr resin and the lower TRU resin. After loading, column separated to retrieve Sr from Sr resin and LEEs eluted from TRU resin onto Ln Spec resin to obtain Nd fraction by sequential elution with HCl TIMS Sr isotopes measured using default 1011 amplifiers whereas Nd determined using new 1013 amplifiers. Method validated using USGS CRMs AGV-1 (andesite) and BHVO-2 (basalt) and samples as small as 2 ng Sr and 30 pg Nd successfully analysed. Advantage over LA-ICP-MS is that method not limited by amount of Rb in sample 447
Ti Basalts 50–100 mg sample digested with HF–HNO3–HClO4. A 47Ti–49Ti double spike (50% of each isotope) added to an aliquot and Ti separated from matrix using an Eichrom® TODGA column. Ti purified further on AG1-X8 resin, particularly to remove Mo, and collected in 5 mL of 9 M HCl + 0.01 M HF MC-ICP-MS Insignificant procedural blank of 10–15 ng. External precision of ca. 0.020‰ (2SD). New data for δ49Ti for 5 basalt RMs. All data reported relative to newly created RM prepared from very pure Ti metal rod 468
W Geological samples After dissolution in HF–HNO3–HCl, sample digests in 6 M HCl were treated with more HF and W washed from any fluoride precipitates. AEC on AG1-X8 resin to separate W from matrix elements, with W eluted in 4 M HNO3 + 0.5 M HF in the final cut. 100% W recovery was achieved with Mo[thin space (1/6-em)]:[thin space (1/6-em)]W ratios < 10−3 MC-ICP-MS Procedure blank 50–100 pg W. Long-term reproducibility for 183W/184W was 80 ppm (2σ). 178Hf/179Hf ratio used to correct for instrumental mass bias. Protocol demonstrated to be suitable for a range of geological matrices 443
W Iron meteorites W in sample digest purified in a two-step AEC procedure. An aliquot of the resulting solution loaded onto a rhenium filament, dried at 100–120 °C and then inserted into the ETV chamber and W evaporated at a filament temperature of >1000 °C in a stream of helium ETV-MC-ICP-MS Mass fractionation during evaporation of W from the filament was corrected using the Rayleigh fractionation law. Precision of 182W/183W and 183W/184W ratios on 6 ng W was comparable to those from 30–50 ng W by conventional solution nebulisation 469
Zn Geochemical and cosmochemical rocks 67Zn spike added to sample powders before digestion in mixture of HF–HNO3–HClO4. Two-step chemical separation on aliquot of digest using solvent extraction in diisopropyl ether (to remove Fe) and AEC to separate Zn from matrix elements including Ba. Either 66Zn/67Zn or 68Zn/67Zn ratio could be used for ID calculation ID-ICP-MS High procedural blank due to impurities in the reagents resulted in a Zn LOQ after matrix separation of 3.6 μg g−1 compared to 0.087 μg g−1 with no separation. However, separation gave best accuracy and precision for range of geological RMs 470
Zn Geological samples See Cu, ref. 457 MC-ICP-MS Cu spike used to correct for mass bias 457


There is much interest in the determination of B isotopes, particularly in marine biocarbonates for paleoclimatic reconstructions such as past seawater pH. A comparison437 of ion-exchange chromatography and microsublimation for isolation of B from a range of matrices concluded that the latter offered more efficient matrix removal, lower blanks and better δ11B precisions and was also more rapid and economical. An automated sample introduction system438 for B isotope measurements by MC-ICP-MS employing a demountable DIHEN not only increased throughput but also promoted greater stability as a result of the rapid washout. Precisions were sub-0.1‰ for δ11B in pure boric acid. Even though the main limitation to achieving this precision for natural samples, including silicate rocks and carbonates, was the chemical separation, the achievable precisions of 0.2–0.4‰ (2SD) were sufficient for most geochemical applications. The B isotope analysis of marine biocarbonates is challenging because of the low B contents present. Misra et al.439 summarised δ11B methods and their precisions for the analysis of mainly carbonates and waters by MC-ICP-MS or TIMS. They reported a new method based on microsublimation prior to analysis by HR-ICP-MS in an HF matrix to improve the wash out characteristics. A jet interface increased instrumental sensitivity 5-fold. Precisions of ≤0.5‰ (2σ) for δ11B measurements were achieved with low B mass consumption (<3.0 ng B for quintuplicate analysis). Replicate separations indicated that laboratory contamination of samples was still the biggest challenge in δ11B determination procedures, especially for small sample masses. This problem may be minimised in a novel LA approach440 involving the simultaneous measurement of B isotopic composition and B/Ca ratio on ng masses of foraminifera and corals. In this technique, an optical fibre connected the torch of the MC-ICP-MS system to an optical spectrometer, so that an aerosol generated by ablating single foraminiferal shells could be analysed by both instruments. Data on the B[thin space (1/6-em)]:[thin space (1/6-em)]Ca ratio was used to correct for different ablation rates. The δ11B of foraminifera within a size range of 380 to 520 μm and a mean B concentration of 53 μg g−1 were measured with an internal precision of 0.52‰.

Reviews of isotope systems and their geological applications can provide valuable perspectives. Chakrabarti441 (112 references) compared current analytical capabilities with the observed variation in Si isotope compositions in wide range of natural materials including bulk meteorites and lunar rocks, terrestrial igneous rocks, waters and biota. A review442 (84 references) of protocols for the determination of Mg isotope ratios by MC-ICP-MS provided a detailed description of the measurement process and the pitfalls in high precision isotope analysis as well as the behaviour of Mg during geological processes. Tungsten isotope ratios are useful in understanding the first stages in the history of the solar system and planetary formation but their measurement requires the high precision achieved by Breton and Quitté.443 With a long term reproducibility of 80 ppm for 183W/184W, they were able to resolve W-isotope variations in terrestrial and extraterrestrial materials from −0.05 to +0.36‰ per mass unit.

A practical guide444 to the design and implementation of the double-spike technique for precise determinations of Mo isotope composition may also be applicable to double-spike procedures for other elements. In a related paper, Malinovsky et al.445 presented a methodology for determining Mo isotope ratios by MC-ICP-MS using calibration with synthetic isotope mixtures.

Although the double-spike approach for the correction of instrumental mass bias in mass spectrometry is well established, more consistency in the data reduction of such data is desirable. With this in mind, Creech and Paul446 produced IsoSpike, a generalised computer procedure designed to process double-spike mass spectrometry data and constructed as an add-on for the Iolite data reduction package. The integration-by-integration approach was faster and more consistent than commonly employed methods. In addition, data could be visualised and exported. The procedure was applicable to any double-spike system and is freely available (http://www.isospike.org).

4.4.3 Other mass spectrometric techniques.
4.4.3.1 Thermal ionisation mass spectrometry. The improvement of detector systems has been a fruitful area of research in addressing the challenge of producing more precise isotope ratio measurements on smaller sample masses by TIMS. Mounting prototype 1013 Ω resistors in the feedback loop of Faraday cup amplifiers resulted447 in a 10-fold improvement of S/N and more precise Nd and Sr isotope data when analysing small ion beams (<20 mV). Sarkar et al.448 compared the performances of Faraday collector amplifiers fitted with 1012 Ω resistors and MICs for Pb isotope determinations of pg-size samples. The Faraday array performed better for sample sizes down to 10 pg Pb, producing data 4–5 times more precise than static MICs (0.05%, 2SD, vs. 0.18–0.23% for 207Pb/206Pb). Sample sizes <10 pg were only measured in MIC mode as the 204Pb signal was below the LOD in Faraday mode. Thus, the MIC system offered considerable promise for tracer work in the <10 pg Pb range where data with an accuracy and precision of a few percent are useful, provided blank contributions were kept below 0.1 pg. An overview of advances in TIMS ion detection over the last few years can be found in Wiedenbeck et al.449

Sources of NTIMS systematic bias and random error associated with high precision 186Os/188Os measurements were examined in detail.450 The largest contribution to random error, total amplifier noise on baseline integrations, was reduced by increasing the acquisition time for baseline measurements. Although it was important to determine the in-run oxygen isotopic composition to correct for isobaric interferences involving 17O and 18O, it was considered sufficient to take oxygen isotope measurements before and after the run and not waste time making line-by-line O isotope measurements within the run. The importance of monitoring and correcting for PtO2 interferences was emphasised as these could mask other potential interferences on 186Os/188Os measurements. Within-run measurement of oxygen isotope composition for oxide corrections was however recommended by Chu et al.451 to obtain high precision Nd isotope data by NTIMS. Small ion signals of 150Nd17O+ and 150Nd18O+ were measured with amplifiers equipped with 1012 Ω resistors and the Nd16O+ beams with 1011 Ω amplifiers. The 143Nd/144Nd ratios of several geological RMs determined on 4 ng Nd loads were consistent within analytical error with previously reported values and had an external precision of better than 30 ppm (2RSD).

To improve the accuracy and precision of U–Pb geochronology and facilitate better inter-laboratory comparisons within the U–Pb community generally, two mixed U–Pb tracers were prepared and calibrated.452 The U/Pb ratio of the tracer had an uncertainty of <0.05% (95% CL) and was fully traceable to SI units. Thus comparison of data generated in different laboratories using this tracer solution would not require propagation of uncertainty in the U/Pb ratio of the tracer, effectively eliminating a major source of inter-laboratory bias. A companion paper453 outlined the algorithms required for the transformation of the tracer calibration inputs and their associated uncertainties into the parameters required for U–Pb ID-TIMS data reduction.

In an investigation454 of the whole-grain evaporation technique for the determination of Pb isotopes in zircon grains, hand-picked grains were initially heated to 1450 °C to remove common Pb and then two approaches for the evaporation and condensation of Pb assessed. Grains loaded onto an evaporation filament were heated at 1600–1700 °C for 1–2 h and radiogenic Pb, together with a small amount of silica, condensed either onto an extra wide rhenium filament placed 1–2 mm away (the filament condensation approach) or onto the interior surfaces of a 3 mL Savillex FEP vial (the vial condensation approach). The condensed Pb was subsequently recovered in HF and, following addition of a 202Pb–205Pb double spike, dried down for loading onto a conventional rhenium filament. Both methods achieved good accuracy and precision because of the ability to add the Pb double spike, and allowed Precambrian rocks containing <200 ppm U to be dated with a precision of ±0.5 Ma or better. Although blank levels were higher and more erratic for the vial condensation method, this procedure was more efficient and easier to apply and possible improvements were suggested.

Other newly published methods for the determination of isotope ratios by TIMS are included in Table 6.


4.4.3.2 Secondary ion mass spectrometry. A review449 of recent advances in SIMS as applied to geochemical applications noted that state-of-the-art instrumentation can routinely produce data sets for isotopic studies with precisions of better than ±0.2‰ (1s) for major elements on a few 100 pg of material. It was concluded that the quality of available RMs rather than SIMS instrument technology was the limiting factor in defining analytical uncertainty and that any direct comparison between SIMS laboratories would only be meaningful if the same calibrant was used or the chain of traceability was kept very short. In a novel method471 for synthesising homogeneous RMs, nanocrystals of calcite (20–40 μm) were sintered at 1000 °C and 1 GPa for 24 h to produce coarse-grained crystals (100–500 μm). In situ determination of 18O/16O ratios by SIMS showed no detectable variation in the synthetic calcite within an analytical uncertainty of 0.1‰ (1SD). Trace elements P and Sr were also homogeneously distributed. Modification472 of the optical microscope system in a Cameca IMS 1280 instrument to incorporate a UV light source improved the optical resolution from 3.5 to 1.3 μm, making it easier to view samples at the analytical scale of 1–10 μm. New software for sample imaging also enhanced the accuracy of positioning and efficiency of instrument operation.

The use of SIMS to determine water contents in minerals is gaining in popularity because it has the advantage of being able to measure hydrogen isotope ratios as well. The water content and D/H ratio in apatite and silicate glasses were measured473 by nanoSIMS using three different detector configurations. An LOD of <10 ppm water content was achieved by improving the vacuum in the analysis chamber, using a high primary beam current and applying a blanking technique to reduce the H background. The three configurations had different applications. The peak jump isotope mode was preferred for determining F, S and 35Cl/37Cl but the multicollection isotope mode gave comparable precisions more quickly in the determination of water contents and H isotopes. The multicollection element mode had higher sensitivity than the two isotope modes but could not be used for the measurement of water content because of matrix effects. A procedure474 for measuring Cu and H isotopes in turquoise by SIMS was developed as a tool for the provenancing of turquoise and other Cu-bearing gem minerals. Variations in instrumental mass fractionation were correlated with the water and iron content of the samples. The correction models proposed relied on the availability of suitably characterised standards so turquoise samples with a range of compositions were selected as standards and analysed independently by gas source MS (D/H) and MC-ICP-MS (63Cu/65Cu). When two or three of these turquoise standards were included in every SIMS analytical session to bracket the unknown samples, accuracies of ±5‰ for D/H and ±0.5‰ for 63Cu/65Cu were obtained.

Current capabilities475 of SIMS for the determination of B isotopes in natural volcanic glasses were assessed using three MPI-DING glasses (GOR128-G, GOR132-G and StHs6/80-G) and RM B6, a natural obsidian glass that was characterised in a B isotope inter-laboratory study in the early 2000s and distributed by the IAEA. The enhanced transmission and stability of the instrumental setup resulted in an improvement in analytical uncertainty by a factor of 2–4 and a reduction in analysis time by a factor of three compared to previous studies. Although the measurement repeatability was 0.5‰ (2RSE) when B concentrations were >20 μg g−1, five analyses of homogenous basaltic glass containing only 1 μg g−1 B were required to achieve a precision of better than ±1.5‰ (2RSE). No significant differences in instrumental mass fractionation were observed for the range of glass compositions investigated and drift during a day was <1.8‰. Single analyses with a spatial resolution of 30 μm × 30 μm were completed within 32 minutes. Similar issues were investigated in a SIMS study476 of B isotopes in palagonite, a basalt glass that has undergone hydrolytic alteration. Illite IMt-1 from Silver Hill, Montana was a suitable calibration material for this purpose as it had a similar, relatively high, B content and was easy to mount for SIMS analysis. Measurements made before and after treatment of the samples with NH4Cl to remove exchangeable B provided information on the structure of the palagonite samples.

An overview477 of the protocols and pitfalls in the in situ measurement of oxygen isotope ratios in monazite by SHRIMP identified the sourcing of suitable RMs as a common problem in many applications of SIMS to geological samples. Three monazites, two of which are recognised U–Th–Pb standards, were characterised and analysed independently for O isotopes by laser fluorination techniques. Two of these, USGS-44069 and Itambé monazite, were proposed as O isotope standards with δ18O reference values of 7.67 ± 0.26‰ and 0.46 ± 0.20‰, respectively. The reproducibility of 0.4–0.6‰ (95% CL) obtained at a standard spatial resolution of 20–25 μm was similar to that obtained for homogeneous glasses by SHRIMP. Additional corrections were required for matrix effects, particularly when the Th content of the sample was significantly different from that of the standards (2.1–6.4 wt% Th). To evaluate the influence of surface relief, analyses of zircon crystal rims were compared478 with those made close to the centre of the crystal. Within a single grain, the topographical effect was more prominent in the horizontal direction of the stage than in the vertical direction. The poorer precision, attributed to lateral dispersion of the secondary ions caused by the surface topography changing the ion position in the plane of the entrance slit, could be significantly improved by increasing the magnification of the transfer optics.

Ways of improving the precision and spatial resolution of sulfur isotope analysis by nanoSIMS was explored479 using three different arrays of Faraday cup (FC) and electron multiplier (EM) detectors. Corrections for the effects of EM aging and quasi-simultaneous arrival of the ion beam at the detector were applied before instrumental mass fractionation corrections from standard-sample bracketing. A lateral resolution of ca. 5 μm in samples of pyrite and sphalerite gave an analytical precision of better than 0.3, 0.3 and 0.7‰ (1SD) for δ33S, δ34S and δ36S, respectively, when the 32S, 33S and 34S beams were measured with FCs and the 36S beam with the EM. The best lateral resolution of 0.5 μm was obtained when the 32S, 33S and 34S beams were all counted with EMs, in which case the analytical uncertainty was <1.5‰ (1SD) for both δ33S and δ34S. On the other hand, Ushikubo et al.480 employed a FC detector to measure the 36S beam because the relative efficiency between two FC detectors (compared to that between FC and EM detectors, as used in the previous study) was more stable. The restricted count rate for an EM prevented the use of a strong primary beam current, thus reducing the analytical precision attainable for the other S isotopes. The pyrite standard UWPy-1, whose S isotopes ratios were determined independently by gas source MS, was used as a running standard during SIMS analysis. Typical reproducibilities of spot-to-spot analyses of UWPy-1 with a primary beam diameter of 20 μm were 0.23, 0.05 and 0.86‰ (2SD) for δ34S, Δ33S and Δ36S, respectively.

The U–Th–Pb dating of geological materials, especially zircons, continues to be a major activity in SIMS laboratories.449 Liu et al.481 proposed a hybrid method of acquiring data in the dating of zircons in which the 207Pb/206Pb ratio was measured with high precision in the static multi-collector mode without compromising the precision of the 238U/206Pb ratio measurement made in the peak hopping single collector mode. Four zircon RMs (91[thin space (1/6-em)]500, M257, Temora and Plešovice) were analysed to demonstrate that this new analytical protocol could simultaneously obtain 207Pb/206Pb and 238U/206Pb ages with comparable quality and thus effectively evaluate the concordance of the U–Pb system on zircons as young as 500 Ma.


4.4.3.3 Accelerator mass spectrometry. This technique is mainly used to detect long-lived radionuclides and provides the lowest LODs of all MS methods, reaching atomic abundances as low as 10−16. Recent advances in accelerator-based methods and the wide range of geochemical applications employing AMS were covered in the biennial review449 (188 references) of geoanalytical techniques published in Geostandards and Geoanalytical Research. Not only is 129I the focus of much research as a result of the Fukushima incident but also in environmental and geological applications involving the measurement of naturally produced 129I. Liu et al.482 separated I by co-precipitation of AgI with AgCl in the measurement of very low levels of 129I in carrier-free AgI–AgCl sputter targets. Copper sample holders were preferred over aluminium ones because the latter reacted with the sputter targets. A conducting matrix of niobium powder was mixed with the AgI–AgCl powder in the proportion of 5 + 1 (m/m) to obtain a stable and high I ion current intensity and to reduce memory effects. A typical current of 5–100 nA 127I5+ was obtained using AgI–AgCl targets containing 5–80 μm I. Although the method appears to be promising, data from the analysis of real samples have yet to be presented.
4.4.3.4 Isotope ratio mass spectrometry. The carbon and oxygen isotope compositions of carbonate minerals are typically measured using automated systems to digest and purify the evolved CO2 prior to measurement by IRMS because of their efficiency in handling small sample sizes. However, a note of caution was sounded483 when three different instruments (Kiel IV, GasBench II and a dual inlet IRMS) were used to analyse loess and lake sediments because large variations occurred in the δ13C and δ18O values obtained. The observed maximum differences of between −0.4‰ to 0.3‰ for δ13C and −0.5‰ to +0.6‰ for δ18O, relative to the mean values of the three methods, may have been related to the organic matter in the samples. However, vacuum roasting of the samples prior to measurement only increased the variability of the data in some cases. Data from one of the automated methods were generally lower than those from the other two. The use of clumped isotope compositions of carbonate, i.e. isotopologues such as 13C18O16O, to elucidate paleotemperatures is a relatively new field of research and requires the determination of Δ47, defined as the excess abundance of CO2 of mass 47 relative to the theoretical random distribution. However, comparison of Δ47 data generated by different laboratories using IRMS methods remains a problem, as exemplified by a recent inter-laboratory calibration exercise484 in which differences of up to 0.07‰ (equivalent to a temperature difference of 15 °C) were reported. The extraction potential of the IRMS ion source was an important factor influencing the accuracy and precision of CO2 clumped isotope measurements. At an optimum extraction potential of 90%, reproducible and accurate Δ47 values for IAEA CRM NBS-19 (TS-limestone) were maintained for over one year.

A recognised method for determining stable sulfur isotopes32S/34S alongside 13C/12C and 15N/14N is the coupling of an EA to an IRMS instrument. A new purge and trap EA system96 generated high quality SO2 peaks even for samples with low S concentrations (<1% m/m). The CO2 and SO2 generated by the conventional EA were trapped separately and desorbed at 220 and 850 °C, respectively. The N2 passed straight through the trap assembly. Although the measurement of 32S/34S ratios in CRMs BCR 32 (Moroccan phosphate) and NIST 120c (Florida phosphate rock) containing 0.4–0.7% S m/m were accurate,485 relatively large aliquots (5–8 mg) had to be analysed to ensure satisfactory precision (0.4‰, 1SE). These materials were proposed as potential isotope RMs for future studies of S isotopes in biogenic apatites.

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 update on XRF spectrometry5 (426 references).

Although portable XRF instrumentation has been commercially available for well over a decade, it has only had a major impact since the introduction of silicon drift detectors, which are capable of better resolution than previous detectors. The acceptance of pXRF technology has not been assisted by the fact that the accuracy and precision of these instruments has not lived up to manufacturers' claims when applied to geological materials.290 In 2012 Hall et al. (http://www.appliedgeochemists.org) undertook a detailed examination of the performance of portable XRF technology on behalf of the Canadian Mining Industry Research Organisation. Their report was a salutary assessment of the state-of-the-art, demonstrating inconsistent results between instruments even from the same manufacturer and their inability to handle spectral interferences. They concluded that no instrument of the five tested (three handheld and two portable benchtop instruments) stood out as being superior as their performance depended on the element and matrix.

A resurgence of interest in pXRF was marked by a recent issue of Geochemistry: Exploration Environment Analysis (August 2014, vol 14) devoted to this technique. A number of instrumental comparative studies within geological applications supplemented the observations of Hall et al.290 Brand and Brand289 tested multiple units of the same model of handheld equipment from two manufacturers under standardised conditions in periods of five hours of continuous operation. Data were generally precise (<5% RSD) but very inaccurate and performance varied significantly between individual instruments from the same manufacturer. Instrument performance deteriorated measurably over two to three months and operators were advised to record the battery serial numbers, as replacing the lithium-ion battery pack during a run was seen to have a measurable effect on the precision and accuracy of some data. This deterioration together with inconsistent factory calibrations and variable battery power meant that every pXRF instrument should be considered unique and that raw data from several instruments should not be combined without appropriate post-processing or recalibration. Ross et al.486 confirmed that pXRF instruments produce precise (<5% RSD) but not particularly accurate data in a test of three Olympus machines for analysing unprepared exploration drill cores using factory calibrations. Empirical correction factors unique to each instrument and each project were required to account for systematic biases. Within 20 cm long cores, the effect of mineralogical heterogeneity was much greater than the instrument precision.

Measurements of whole-rock powders 487 with a benchtop Olympus Innov-X X5000 pXRF spectrometer were within ±20% of values previously obtained by conventional laboratory-based methods for many major and minor elements except when at concentrations approaching the LOD. The data for Cr, Ni, V, MgO and P2O5 were poorer and more variable. A single-point external calibration using a RM with a similar matrix and composition was applied to the raw values. It was concluded that pXRF spectrometry was fit-for-purpose as a preliminary screening tool to discriminate lithogeochemical variations prior to sample selection but was not a substitute for conventional laboratory-based chemical analysis, particularly when important economic decisions were to be made.

Portable XRF instrumentation has been widely adopted by the mining industry to collect large amounts of multi-element data rapidly in the field at relatively low cost and is now regarded by some exploration geologists as the modern equivalent of the geological hammer. The capability to make in situ measurements of exploration drill cores at high resolution, down to centimetres if necessary, resulted in improved geological logs and maps on which to base decisions. Thus, pXRF data from half-cut diamond drill core surfaces were used to characterise dolerite dykes intruded into an area of gold mineralisation in Western Australia.488 Plots of Ti versus Zr combined with PCA revealed four distinct geochemical groupings of dolerite dykes. This new geological interpretation resulted in a significant amount of rock previously modelled as dolerite being reclassified as potential host rock for gold exploration. Similarly, Ross et al.489 distinguished between two visually similar and variably altered rhyolites in a Canadian massive sulfide deposit on the basis of plots of Ti/Zr against Al/Zr obtained by pXRF analysis of core surfaces, thereby confirming that the correct stratigraphic target had been reached.

However, not all operators of pXRF equipment appreciate the principles behind the acquisition of quality assured data and the lack of a standardised approach for pXRF data collection often makes it difficult to compare datasets collected by different users at different times, particularly when used on uncrushed rock. Fisher et al.490 stressed the need for quality assurance and control systems as robust as those employed in conventional assaying. Key considerations when establishing a workflow for the acquisition of pXRF data for exploration and mining applications included the development and documentation of sampling protocols, instrument set-up, QA/QC checks, calibration to standards of known concentration, drift monitoring, collection of blanks and data management. The crucial steps in a rapid pXRF assessment of komatiite-hosted nickel sulfide deposits491 in Western Australia were the development of a strict calibration process as well as numerous data quality checks. Comparison of pXRF data determined on manually sawn half-cores with those obtained by conventional laboratory XRF spectrometry demonstrated that for typical abundances of Cr, Ni, Ti and Zr in komatiites the quality of the analytical data was good enough to evaluate sulfide segregation and to define the nickel sulfide prospectivity of the komatiitic units.

Several Canadian studies examined the suitability of pXRF spectrometry for assessment of REE-enriched deposits. Simandl et al.492 evaluated the technical merits and limitations of portable XRF technology for mineral exploration of rare metals such as Nb, Ta and REEs by using data acquired over nearly two years from the analysis of three powdered geochemical standards and a silica blank. Under relatively ideal conditions, data with precisions of <4% RSD and accuracies of better than ±17% were obtained for Ce, La, Nd, Pr and Y at concentrations >0.1%. Although it was not possible to detect any of the HREEs, the elements Ce, La and Y were considered to be good pathfinder elements in the search for REE deposits. In particular, Y was suitable because of its lower LOD and similar chemical characteristics to the HREEs. The same model of pXRF instrument was assessed for its suitability for the exploration493 of carbonatite-related deposits for Nb and REEs. From an exploration geologist's point of view, in most cases useful field determinations of Al, Ba, Ce, K, La, P, Sr, Zn, Zr and Y could be made on crushed samples after recalibration to correct for bias. Spot analyses performed directly on 10–15 cm core sections were severely affected by the coarse nature of some carbonatite units. Although taking an average of several spot measurements improved the precision to a level where potentially economically significant zones could be identified, manual scanning of core sections could rapidly distinguish between areas of Nb mineralisation and barren zones and so provide useful information for immediate decision making when drilling exploration cores. Sedimentary phosphate rocks can also be potential sources of F and REEs as well as fertiliser. An orientation survey494 of sedimentary phosphate rocks in SE British Columbia concluded that pXRF spectrometry could be used to identify zones enriched in REEs if the determinations were performed on finely ground and homogenised material and biases observed using the factory-set calibrations were corrected by recalibrating the instrument using ICP-MS data. The particular instrument tested operated on four different beams in order to obtain measurements on a wide range of elements (33 were evaluated) and was one of the first capable of determining Nd and Pr without using a radioactive source.

In conclusion, it is evident that the demand for in situ geochemical analysis from regional surveys to mapping of individual mineral grains will continue to be a driver for many of the current developments in analytical geochemistry generally, and issues such as appropriate calibration regimes and data processing protocols will still need to be addressed.

5 Glossary of terms

2DTwo-dimensional
A4FAsymmetrical flow field flow fractionation
AASAtomic absorption spectrometry
AECAnion exchange chromatography
AESAtomic emission spectrometry
AFMAtomic force microscopy
AFSAtomic fluorescence spectrometry
AGALAustralian Government Analytical Laboratories
AMSAccelerator mass spectrometry
ANNArtificial neural network
APDCAmmonium pyrrolidine dithiocarbamate
APXSAlpha particle X-ray spectrometry
ASUAtomic spectrometry update
AUCAnalytical ultracentrifugation
BCRCommunity Bureau of Reference (of the European Community) now IRMM
BPHA N-Benzoyl-N-phenylhydroxylamine
C18Octadecyl bonded silica
CAChemical abrasion
CANMETCanadian Certified Reference Materials Project (of National Resources Canada)
CCDCharge coupled detector
CCPCapacitively coupled plasma
CCTCollision cell technology
CECapillary electrophoresis
CIConfidence interval
CLCathode luminescence
CNTCarbon nanotube
CPECloud point extraction
CRMCertified reference material
CSContinuum source
CVCold vapour
CVAASCold vapour atomic absorption spectrometry
CVAFSCold vapour atomic fluorescence spectrometry
CVGChemical vapour generation
DBTDibutyl tin
DCMDichloromethane
DGTDiffusion gradient in thin films
DIHENDirect injection high efficiency nebuliser
DLLMEDispersive liquid liquid microextraction
DLSDynamic light scattering
DMADimethylarsenic acid
DMDSDimethyl disulfide
DMDSeDimethyl diselenide
DMMEDual magnetic microextraction
DMSDimethyl sulfide
DMSeDimethyl selenide
DOFDistance of flight
DOMDissolved organic matter
DRCDynamic reaction cell
DRSDiffuse reflectance spectrometry
DTPADiethylenetriaminepentaacetic acid
EAElemental analyser
ECDElectrochemical detector
EDEnergy dispersive
EDSEnergy dispersive spectrometry
EDTAEthyldiaminetetraacetic acid
EDXRFEnergy dispersive X-ray fluorescence
EGTAEthylene glycol tetra-acetic acid
ENEuropean Committee for Standardisation
EMElectron multiplier
EPAEnvironmental Protection Agency (USA)
EPMAElectron probe microanalysis
ERMEuropean reference material
ESAEuropean Space Agency
ESI-MSElectrospray ionisation mass spectrometry
ETAASElectrothermal atomic absorption spectrometry
EtHgEthyl mercury
ETVElectrothermal vaporisation
FAASFlame atomic absorption spectrometry
FCFaraday cup
FEPFluorinated ethylene propylene
FFFField flow fractionation
FIAFlow injection analysis
FPFundamental parameter
fsFempto second
FTFission track
FTIRFourier transform infrared
GBWCRMs of the National Research Centre for Certified Reference Materials (China)
GCGas chromatography
GEMOCThe Australian Research Council National Key Centre for Geochemical Evolution and Metallogeny of Continents
GEOTRACESAn International Study of the Marine Biogeochemical Cycles of Trace Elements and their Isotopes (France)
GOMGaseous oxidized mercury
GSBCRMs of the Institute for Environmental Reference Materials (of Ministry of Environmental Protection, China)
GSJGeological Survey of Japan
HDCHydrodynamic chromatography
HEHEHP2-Ethylhexyl phosphoric mono-2-ethyhexyl ester
HGHydride generation
HgPParticulate phase mercury
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
idInternal diameter
IDIsotope dilution
IDAIsotope dilution analysis
IECIon exchange chromatography
IERMInstitute for Environmental Reference Materials (of Ministry of Environmental Protection, China)
IJSInstitut Jozef Stefan (Slovenia)
ILIonic liquid
IL-MEIonic liquid microextraction
INAAInstrumental neutron activation analysis
INCTInstitute of Nuclear Chemistry and Technology (Poland)
IRInfrared
IRMMInstitute for Reference Materials and Measurements
IRMSIsotope ratio mass spectrometry
ISInternal standard
ISEIon selective electrode
ISOInternational Organisation for Standardisation
IUPACInternational Union of Pure and Applied Chemistry
LALaser ablation
LCLiquid chromatography
LCDLiquid crystal display
LIBSLaser induced breakdown spectroscopy
LLMELiquid liquid microextraction
LNEGNational Laboratory of Energy and Geology (Portugal)
LODLimit of detection
LOQLimit of quantification
LREELight rare earth element
LRMLaboratory reference material
MaMillion years
MAEMicrowave assisted extraction
MALDI-MSMatrix assisted laser desorption ionization mass spectrometry
MAMEMicrowave assisted micellar extraction
MBTMonobutyl tin
MCMulticollector
MEMicroextraction
MeHgMethyl mercury
MIBKMethyl isobutyl ketone
MICMultiple ion counter
MIPMicrowave induced plasma
M 0 Characteristic mass
MMAMonomethylarsenic acid
MPMicrowave plasma
MPIMax Planck Institute (Germany)
MRIMagnetic resonance imaging
MSMass spectrometry
MSHAMine Safety and Health Administration (USA)
MWCNTMultiwalled carbon nanotube
m/zMass to charge ratio
NAANeutron activation analysis
NASANational Aeronautics and Space Administration (USA)
NBLNew Brunswick Laboratories (USA)
NBSNational Bureau of Standards (USA) now known as NIST
NCSCRMs of the China National Analysis Centre for Iron and Steel
Nd:YAGNeodymium doped:yttrium aluminum garnet
NEXFASNear edge X-ray absorption fine structure
NIESNational Institute for Environmental Studies (Japan)
NIOSHNational Institute of Occupational Safety and Health (USA)
NIRNear infrared
NISTNational Institute of Standards and Technology (USA)
NPNanoparticle
NRCNational Resources Canada
NRCCNational Research Council (of Canada)
NRCCRMNational Research Centre for Certified Reference Materials (China)
NTIMSNegative thermal ionisation mass spectrometry
NWRINational Water Research Institute (Canada)
OESOptical emission spectrometry
PAHPolyaromatic hydrocarbons
PBETPhysiologically based extraction test
PCAPrincipal component analysis
PEEKPerfluoroalkyl
PhHgPhenyl mercury
PFAPerfluoroalkyl
PGEPlatinum group element
PIPhoto ionisation
PIXEParticle induced X-ray emission
PLSPartial least squares
PLS-DAPartial least squares discriminant analysis
PLSRPartial least squares regression
PMParticle matter
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)
PMTPhotomultiplier tube
ppbPart per billion
ppmPart per million
PTFEPoly(tetrafluoroethylene)
PVGPhotochemical vapour generation
pXRFPortable X-ray fluorescence
QCQuality control
REERare earth element
RMReference material
ROSReactive oxygen species
RPReversed phase
RSDRelative standard deviation
RSERelative standard error
sStandard deviation of sample
SARMSouth African producers of Metallurgical and Geological Certified Reference Materials
SAXSSmall angle X-ray scattering
SDStandard deviation
SDSSodium dodecyl sulfate
SEStandard error
SEMScanning electron microscopy
SERSSurface enhanced Raman spectroscopy
SFSector field
SFODSolidification of floating organic drop
SFODMESolidification of floating organic drop microextraction
SHRIMPSensitive high resolution ion microprobe
SILe Système International d'Unités (International System of Units)
SIDSpeciated isotope dilution
SIMSSecondary ion mass spectrometry
SMPSScanning mobility particle sizer
S/NSignal-to-noise ratio
spSingle particle
SPESolid phase extraction
SPMESolid phase microextraction
SPSSpectrapure standards (Norway)
SRMStandard reference material
SSSolid sampling
SSIDSpecies specific isotope dilution
SWAXSSmall and wide angle X-ray scattering
SWCNTSingle wall carbon nanotube
TBTTributyl tin
TDThermal desorption
TEMTransmission electron microscopy
TIMSThermal ionisation mass spectrometry
TMATrimethylarsenic acid
TOFTime of flight
TRLFSTime resolved laser fluorescence spectroscopy
TS-FF-AASThermospray flame furnace atomic absorption spectrometry
TXRFTotal reflection X-ray fluorescence
UADLLMEUltrasound assisted dispersive liquid liquid microextraction
UAEUltrasonic extraction
UAEMEUltrasonic extraction microextraction
USGSUnited States Geological Survey
USEPAUnited States Environmental Protection Agency
UVUltra violet
UV-VISUltra violet-visible spectrophotometry
VGVapour generation
VISVisible
VOCVolatile organic carbon
WDXRFWavelength dispersive X-ray fluorescence
XANESX-ray absorption near edge structure
XASX-ray absorption spectrometry
XRDX-ray diffraction
XRFX-ray fluorescence

References

  1. O. T. Butler, W. R. L. Cairns, J. M. Cook and C. M. Davidson, J. Anal. At. Spectrom., 2015, 30(1), 21–63 RSC.
  2. A. Taylor, M. P. Day, S. Hill, J. Marshall, M. Patriarca and M. White, J. Anal. At. Spectrom., 2015, 30(3), 542–579 RSC.
  3. E. H. Evans, J. Pisonero, C. M. M. Smith and R. N. Taylor, J. Anal. At. Spectrom., 2015, 30(5), 1017–1037 RSC.
  4. C. F. Harrington, R. Clough, S. J. Hill, Y. Madrid and J. F. Tyson, J. Anal. At. Spectrom., 2015, 30(7), 1427–1468 RSC.
  5. M. West, A. T. Ellis, P. J. Potts, C. Streli, C. Vanhoof and P. Wobrauschek, J. Anal. At. Spectrom., 2015, 30(9), 1839–1889 RSC.
  6. S. Carter, A. Fisher, R. Garcia, B. Gibson, S. Lancaster, J. Marshall and I. Whiteside, J. Anal. At. Spectrom., 2015, 30(11), 2249–2294 RSC.
  7. Y. J. Wang, Z. Q. Tu, L. Zhou, Y. J. Chi and Q. Luo, Spectrosc. Spectral Anal., 2015, 35(4), 1030–1032 CAS.
  8. A. Laskin, J. Laskin and S. A. Nizkorodov, Chem. Rev., 2015, 115(10), 4335–4382 CrossRef CAS PubMed.
  9. P. Sanderson, J. M. Delgado-Saborit and R. M. Harrison, Atmos. Environ., 2014, 94, 353–365 CrossRef CAS.
  10. O. A. Sadik, N. Du, V. Kariuki, V. Okello and V. Bushlyar, ACS Sustainable Chem. Eng., 2014, 2(7), 1707–1716 CrossRef CAS.
  11. P. J. Lam, B. S. Twining, C. Jeandel, A. Roychoudhury, J. A. Resing, P. H. Santschi and R. F. Anderson, Prog. Oceanogr., 2015, 133, 32–42 CrossRef.
  12. C. Wright, TrAC, Trends Anal. Chem., 2015, 66, 118–127 CrossRef CAS.
  13. M. Famele, C. Ferranti, C. Abenavoli, L. Palleschi, R. Mancinelli and R. Draisci, Nicotine Tob. Res., 2015, 17(3), 271–279 CrossRef PubMed.
  14. A. S. Brown, A. Murugan and R. J. C. Brown, Accredit. Qual. Assur., 2015, 20(3), 223–227 CrossRef CAS.
  15. D. M. Cook, D. K. Sleeth, M. S. Thiese and R. R. Larson, J. Occup. Environ. Hyg., 2015, 12(3), 199–204 CrossRef CAS PubMed.
  16. J. C. Soo, E. G. Lee, L. A. Lee, M. L. Kashon and M. Harper, Ann. Occup. Hyg., 2014, 58(8), 1006–1017 CrossRef PubMed.
  17. E. G. Lee, W. P. Chisholm, D. A. Burns, J. H. Nelson, M. L. Kashon and M. Harper, J. Occup. Environ. Hyg., 2014, 11(12), 819–825 CrossRef CAS PubMed.
  18. E. Cauda, M. Sheehan, R. Gussman, L. Kenny and J. Volkwein, Ann. Occup. Hyg., 2014, 58(8), 995–1005 CrossRef PubMed.
  19. L. G. Cena, M. J. Keane, W. P. Chisholm, S. Stone, M. Harper and B. T. Chen, J. Occup. Environ. Hyg., 2014, 11(12), 771–780 CrossRef CAS PubMed.
  20. I. Jayawardene, P. E. Rasmussen, M. Chenier and H. D. Gardner, J. Air Waste Manage. Assoc., 2014, 64(9), 1028–1037 CAS.
  21. D. B. Wang, M. M. Shafer, J. J. Schauer and C. Sioutas, Aerosol Sci. Technol., 2014, 48(8), 864–874 CrossRef CAS.
  22. D. B. Wang, M. M. Shafer, J. J. Schauer and C. Sioutas, Environ. Pollut., 2015, 199, 227–234 CrossRef CAS PubMed.
  23. S. C. Son and S. S. Park, Environ. Sci.: Processes Impacts, 2015, 17(3), 561–569 CAS.
  24. V. Marple, D. Lundgren and B. Olson, KONA Powder Part. J., 2014, 31, 2–9 CrossRef CAS.
  25. Q. Huang, Y. L. Liu, J. B. Chen, X. B. Feng, W. L. Huang, S. L. Yuan, H. M. Cai and X. W. Fu, J. Anal. At. Spectrom., 2015, 30(4), 957–966 RSC.
  26. J. Wittmer, N. Bobrowski, M. Liotta, G. Giuffrida, S. Calabrese and U. Platt, Geochem., Geophys., Geosyst., 2014, 15(7), 2797–2820 CrossRef CAS.
  27. B. Vriens, A. A. Ammann, H. Hagendorfer, M. Lenz, M. Berg and L. H. E. Winkel, PLoS One, 2014, 9(7), 9 Search PubMed.
  28. C. D. McClure, D. A. Jaffe and E. S. Edgerton, Environ. Sci. Technol., 2014, 48(19), 11437–11444 CrossRef CAS PubMed.
  29. V. S. Lin, Environ. Sci.: Processes Impacts, 2015, 17(6), 1137–1140 CAS.
  30. G. Bertolotti and S. Gialanella, Anal. Methods, 2014, 6(16), 6208–6222 RSC.
  31. B. Laurent, R. Losno, S. Chevaillier, J. Vincent, P. Roullet, E. B. Nguyen, N. Ouboulmane, S. Triquet, M. Fornier, P. Raimbault and G. Bergametti, Atmos. Meas. Tech., 2015, 8(7), 2801–2811 Search PubMed.
  32. B. Shannak, U. Corsmeier, C. Kottmeier and T. Al-Azab, Atmos. Environ., 2014, 98, 442–453 CrossRef CAS.
  33. M. Z. Markovic, S. Prokop, R. M. Staebler, J. Liggio and T. Harner, Atmos. Environ., 2015, 112, 289–293 CrossRef CAS.
  34. H. L. Guo, H. M. Lin, W. Zhang, C. Y. Deng, H. H. Wang, Q. G. Zhang, Y. T. Shen and X. J. Wang, Atmos. Environ., 2014, 97, 310–315 CrossRef CAS.
  35. T. Steiger and R. Pradel, Accredit. Qual. Assur., 2015, 20(1), 47–52 CrossRef.
  36. C. Oster, G. Labarraque and P. Fisicaro, Anal. Bioanal. Chem., 2015, 407(11), 3035–3043 CrossRef CAS PubMed.
  37. O. Butler, D. Musgrove and P. Stacey, J. Occup. Environ. Hyg., 2014, 11(9), 604–612 CrossRef CAS PubMed.
  38. P. P. Zhao, J. Li, L. F. Zhong, S. L. Sun and J. F. Xu, Anal. Methods, 2014, 6(15), 5537–5545 RSC.
  39. A. Laycock, B. Stolpe, I. Roemer, A. Dybowska, E. Valsami-Jones, J. R. Lead and M. Rehkamper, Environ. Sci.: Nano, 2014, 1(3), 271–283 RSC.
  40. S. Zihlmann, F. Luond and J. K. Spiegel, J. Aerosol Sci., 2014, 75, 81–93 CrossRef CAS.
  41. M. Palmai, R. Szalay, D. Bartczak, Z. Varga, L. N. Nagy, C. Gollwitzer, M. Krumrey and H. Goenaga-Infante, J. Colloid Interface Sci., 2015, 445, 161–165 CrossRef CAS PubMed.
  42. D. Tabersky, N. A. Luechinger, M. Rossier, E. Reusser, K. Hametner, B. Aeschlimann, D. A. Frick, S. C. Halim, J. Thompson, L. Danyushevsky and D. Gunther, J. Anal. At. Spectrom., 2014, 29(6), 955–962 RSC.
  43. J. Tshilongo, D. Min, J. B. Lee and J. S. Kim, Bull. Korean Chem. Soc., 2015, 36(2), 591–596 CAS.
  44. B. P. Sweeney, P. G. Quincey, D. Green and G. W. Fuller, Atmos. Environ., 2015, 105, 169–172 CrossRef CAS.
  45. J. Vogl, C. Meyer, M. Koenig, D. Becker, J. Noordmann, O. Rienitz, A. Mamakos and F. Riccobono, J. Anal. At. Spectrom., 2015, 30(2), 479–486 RSC.
  46. P. Grinberg, R. E. Sturgeon, L. D. Diehl, C. A. Bizzi and E. M. M. Flores, Spectrochim. Acta, Part B, 2015, 105, 89–94 CrossRef CAS.
  47. R. G. Silva, M. N. Nadagouda, C. L. Patterson, S. Panguluri, T. P. Luxton, E. Sahle-Demessie and C. A. Impellitteri, Environ. Sci.: Nano, 2014, 1(3), 284–292 RSC.
  48. P. S. Fedotov, M. S. Ermolin, V. K. Karandashev and D. V. Ladonin, Talanta, 2014, 130, 1–7 CrossRef CAS PubMed.
  49. P. S. Fedotov, M. S. Ermolin and O. N. Katasonova, J. Chromatogr. A, 2015, 1381, 202–209 CrossRef CAS PubMed.
  50. S. Motellier, A. Guiot, S. Legros and B. Fiorentino, J. Anal. At. Spectrom., 2014, 29(12), 2294–2301 RSC.
  51. S. Wagner, S. Legros, K. Loeschner, J. Liu, J. Navratilova, R. Grombe, T. P. J. Linsinger, E. H. Larsen, F. von der Kammer and T. Hofmann, J. Anal. At. Spectrom., 2015, 30(6), 1286–1296 RSC.
  52. J. Sysalova, J. Szakova, J. Tremlova, K. Kasparovska, B. Kotlik, P. Tlustos and P. Svoboda, Biol. Trace Elem. Res., 2014, 161(2), 216–222 CrossRef CAS PubMed.
  53. L. E. Simonella, D. M. Gaiero and M. E. Palomeque, Talanta, 2014, 128, 248–253 CrossRef CAS PubMed.
  54. B. Miljevic, F. Hedayat, S. Stevanovic, K. E. Fairfull-Smith, S. E. Bottle and Z. D. Ristovski, Aerosol Sci. Technol., 2014, 48(12), 1276–1284 CrossRef CAS.
  55. K. J. Bein and A. S. Wexler, Atmos. Environ., 2015, 107, 24–34 CrossRef CAS.
  56. L. H. Huan, C. H. Yu, P. K. Hopke, P. J. Lioy, B. T. Buckley, J. Y. Shin and Z. Fan, Aerosol Air Qual. Res., 2014, 14(7), 1939–1949 Search PubMed.
  57. L. H. Huang, C. H. Yu, P. K. Hopke, J. Y. Shin and Z. H. Fan, J. Air Waste Manage. Assoc., 2014, 64(12), 1439–1445 CAS.
  58. R. S. Picoloto, S. M. Cruz, P. A. Mello, E. I. Muller, P. Smichowski and E. M. M. Flores, Microchem. J., 2014, 116, 225–229 CrossRef CAS.
  59. Y. L. Zhang, J. W. Liu, G. A. Salazar, J. Li, P. Zotter, G. Zhang, R. R. Shen, K. Schafer, J. Schnelle-Kreis, A. S. H. Prevot and S. Szidat, Atmos. Environ., 2014, 97, 1–5 CrossRef CAS.
  60. M. Resano, M. Aramendia and M. A. Belarra, J. Anal. At. Spectrom., 2014, 29(12), 2229–2250 RSC.
  61. P. M. Machado, S. Mores, E. R. Pereira, B. Welz, E. Carasek and J. B. de Andrade, Spectrochim. Acta, Part B, 2015, 105, 18–24 CrossRef CAS.
  62. M. Resano, M. D. Florez, I. Queralt and E. Margui, Spectrochim. Acta, Part B, 2015, 105, 38–46 CrossRef CAS.
  63. D. Bauer, S. Everhart, J. Remeika, C. T. Ernest and A. J. Hynes, Atmos. Meas. Tech., 2014, 7(12), 4251–4265 CrossRef CAS.
  64. K. Nakata, B. Hashimoto, H. Uchihara, Y. Okamoto, S. Ishizaka and T. Fujiwara, Talanta, 2015, 138, 279–284 CrossRef CAS PubMed.
  65. Y. L. Yu, Y. T. Zhuang and J. H. Wang, Anal. Methods, 2015, 7(5), 1660–1666 RSC.
  66. L. H. Shen, P. J. Chen, B. Yan and C. X. Zhang, Sens. Actuators, B, 2015, 215, 9–14 CrossRef CAS.
  67. J. Jain, D. McIntyre, K. Ayyalasomayajula, V. Dikshit, C. Goueguel, F. Yu-Yueh and J. Singh, Pramana, 2014, 83(2), 179–188 CrossRef CAS.
  68. S. C. Yao, Y. L. Shen, K. J. Yin, G. Pan and J. D. Lu, Energy Fuels, 2015, 29(2), 1257–1263 CAS.
  69. C. Dutouquet, G. Gallou, O. le Bihan, J. B. Sirven, A. Dermigny, B. Torralba and E. Frejafon, Talanta, 2014, 127, 75–81 CrossRef CAS PubMed.
  70. F. J. Fortes, A. Fernandez-Bravo and J. J. Laserna, Spectrochim. Acta, Part B, 2014, 100, 78–85 CrossRef CAS.
  71. M. Gaft, L. Nagli, N. Eliezer, Y. Groisman and O. Forni, Spectrochim. Acta, Part B, 2014, 98, 39–47 CrossRef CAS.
  72. H. L. Yin, Z. Y. Hou, T. B. Yuan, Z. Wang, W. D. Ni and Z. Li, J. Anal. At. Spectrom., 2015, 30(4), 922–928 RSC.
  73. D. S. Macholdt, K. P. Jochum, B. Stoll, U. Weis and M. O. Andreae, Chem. Geol., 2014, 383, 123–131 CrossRef CAS.
  74. K. Nishiguchi, K. Utani, D. Gunther and M. Ohata, Anal. Chem., 2014, 86(20), 10025–10029 CrossRef CAS PubMed.
  75. N. Pallavicini, E. Engstrom, D. C. Baxter, B. Ohlander, J. Ingri and I. Rodushkin, J. Anal. At. Spectrom., 2014, 29(9), 1570–1584 RSC.
  76. R. N. Taylor, O. Ishizuka, A. Michalik, J. A. Milton and I. W. Croudace, J. Anal. At. Spectrom., 2015, 30(1), 198–213 RSC.
  77. A. Bazzano, K. Latruwe, M. Grotti and F. Vanhaecke, J. Anal. At. Spectrom., 2015, 30(6), 1322–1328 RSC.
  78. Y. Y. Su, W. Wang, Z. M. Li, H. Deng, G. Q. Zhou, J. Xu and X. J. Ren, J. Anal. At. Spectrom., 2015, 30(5), 1184–1190 RSC.
  79. M. M. Pornwilard and A. Siripinyanond, J. Anal. At. Spectrom., 2014, 29(10), 1739–1752 RSC.
  80. B. Meermann, Anal. Bioanal. Chem., 2015, 407(10), 2665–2674 CrossRef CAS PubMed.
  81. O. Borovinskaya, S. Gschwind, B. Hattendorf, M. Tanner and D. Gunther, Anal. Chem., 2014, 86(16), 8142–8148 CrossRef CAS PubMed.
  82. S. Gschwind, M. D. A. Montes and D. Gunther, Anal. Bioanal. Chem., 2015, 407(14), 4035–4044 CrossRef CAS PubMed.
  83. D. J. Lewis, Analyst, 2015, 140(5), 1624–1628 RSC.
  84. S. Lee, X. Y. Bi, R. B. Reed, J. F. Ranville, P. Herckes and P. Westerhoff, Environ. Sci. Technol., 2014, 48(17), 10291–10300 CrossRef CAS PubMed.
  85. X. Y. Bi, S. Lee, J. F. Ranville, P. Sattigeri, A. Spanias, P. Herckes and P. Westerhoff, J. Anal. At. Spectrom., 2014, 29(9), 1630–1639 RSC.
  86. M. D. Montano, H. R. Badiei, S. Bazargan and J. F. Ranville, Environ. Sci.: Nano, 2014, 1(4), 338–346 RSC.
  87. W. W. Lee and W. T. Chan, J. Anal. At. Spectrom., 2015, 30(6), 1245–1254 RSC.
  88. R. Peters, Z. Herrera-Rivera, A. Undas, M. van der Lee, H. Marvin, H. Bouwmeester and S. Weigel, J. Anal. At. Spectrom., 2015, 30(6), 1274–1285 RSC.
  89. A. Kornilova, S. Moukhtar, M. Saccon, L. Huang, W. Zhang and J. Rudolph, Atmos. Meas. Tech., 2015, 8(6), 2301–2313 CrossRef CAS.
  90. D. J. Mrozek, C. van der Veen, M. Kliphuis, J. Kaiser, A. A. Wiegel and T. Rockmann, Atmos. Meas. Tech., 2015, 8(2), 811–822 CrossRef CAS.
  91. J. Renpenning, K. L. Hitzfeld, T. Gilevska, I. Nijenhuis, M. Gehre and H. H. Richnow, Anal. Chem., 2015, 87(5), 2832–2839 CrossRef CAS PubMed.
  92. T. K. Bauska, E. J. Brook, A. C. Mix and A. Ross, Atmos. Meas. Tech., 2014, 7(11), 3825–3837 CrossRef CAS.
  93. H. R. Keedakkadan and O. Abe, Rapid Commun. Mass Spectrom., 2015, 29(8), 775–781 CrossRef CAS PubMed.
  94. N. P. Levitt, Rapid Commun. Mass Spectrom., 2014, 28(21), 2259–2274 CrossRef CAS PubMed.
  95. L. J. Bay, S. H. Chan and T. Walczyk, J. Anal. At. Spectrom., 2015, 30(1), 310–314 RSC.
  96. F. Fourel, F. Martineau, M. Seris and C. Lecuyer, Rapid Commun. Mass Spectrom., 2014, 28(23), 2587–2594 CrossRef CAS PubMed.
  97. M. Levy, R. Y. Zhang, J. Zheng, A. L. Zhang, W. Xu, M. Gomez-Hernandez, Y. Wang and E. Olaguer, Atmos. Environ., 2014, 94, 231–240 CrossRef CAS.
  98. J. Diab, T. Streibel, F. Cavalli, S. C. Lee, H. Saathoff, A. Mamakos, J. C. Chow, L. W. A. Chen, J. G. Watson, O. Sippula and R. Zimmermann, Atmos. Meas. Tech., 2015, 8(8), 3337–3353 Search PubMed.
  99. T. Miyakawa, N. Takeda, K. Koizumi, M. Tabaru, Y. Ozawa, N. Hirayama and N. Takegawa, Aerosol Sci. Technol., 2014, 48(8), 853–863 CrossRef CAS.
  100. F. Weiland, P. T. Nilsson, H. Wiinikka, R. Gebart, A. Gudmundsson and M. Sanati, Aerosol Sci. Technol., 2014, 48(11), 1145–1155 CrossRef CAS.
  101. J. F. Cahill, T. K. Darlington, X. L. Wang, J. Mayer, M. T. Spencer, J. C. Holecek, B. E. Reed and K. A. Prather, Aerosol Sci. Technol., 2014, 48(9), 948–956 CrossRef CAS.
  102. D. Suzuki, F. Esaka, Y. Miyamoto and M. Magara, Appl. Radiat. Isot., 2015, 96, 52–56 CrossRef CAS PubMed.
  103. F. Esaka, D. Suzuki and M. Magara, Anal. Chem., 2015, 87(5), 3107–3113 CrossRef CAS PubMed.
  104. S. Weinbruch, A. Worringen, M. Ebert, D. Scheuvens, K. Kandler, U. Pfeffer and P. Bruckmann, Atmos. Environ., 2014, 99, 175–182 CrossRef CAS.
  105. M. Sommariva, M. Gateshki, J. A. Gertenbach, J. Bolze, U. Konig, B. S. Vasile and V. A. Surdu, Powder Diffr., 2014, 29, S47–S53 CrossRef CAS.
  106. X. A. Guo, M. Wagner, A. Gutsche, J. Meyer, M. Seipenbusch and H. Nirschl, J. Aerosol Sci., 2015, 85, 17–29 CrossRef CAS.
  107. J. Reyes-Herrera, J. Miranda and O. G. de Lucio, Microchem. J., 2015, 120, 40–44 CrossRef CAS.
  108. T. Okuda, J. J. Schauer and M. M. Shafer, Atmos. Environ., 2014, 97, 552–555 CrossRef CAS.
  109. F. d'Acapito, S. M. Tagliani, F. Di Benedetto and A. Gianfagna, Atmos. Environ., 2014, 99, 582–586 CrossRef.
  110. A. F. Longo, E. D. Ingall, J. M. Diaz, M. Oakes, L. E. King, A. Nenes, N. Mihalopoulos, K. Violaki, A. Avila, C. R. Benitez-Nelson, J. Brandes, I. McNulty and D. J. Vine, Geophys. Res. Lett., 2014, 41(11), 4043–4049 CrossRef CAS.
  111. A. de Santiago, A. F. Longo, E. D. Ingall, J. M. Diaz, L. E. King, B. Lai, R. J. Weber, A. G. Russell and M. Oakes, Environ. Sci. Technol., 2014, 48(16), 8988–8994 CrossRef CAS PubMed.
  112. L. L. van Loon, C. Throssell and M. D. Dutton, Environ. Sci.: Processes Impacts, 2015, 17(5), 922–931 CAS.
  113. S. Beauchemin, P. E. Rasmussen, T. MacKinnon, M. Chenier and K. Boros, Environ. Sci. Technol., 2014, 48(16), 9022–9029 CrossRef CAS PubMed.
  114. L. Z. Zhang, G. Tian, J. S. Li and B. L. Yu, Appl. Spectrosc., 2014, 68(10), 1095–1107 CrossRef CAS PubMed.
  115. C. Reidl-Leuthner, A. Viernstein, K. Wieland, W. Tomischko, L. Sass, G. Kinger, J. Ofner and B. Lendl, Anal. Chem., 2014, 86(18), 9058–9064 CrossRef CAS PubMed.
  116. C. Reidl-Leuthner, J. Ofner, W. Tomischko, H. Lohninger and B. Lendl, Atmos. Environ., 2015, 112, 189–195 CrossRef CAS.
  117. R. A. Robinson, T. D. Gardiner, F. Innocenti, A. Finlayson, P. T. Woods and J. F. M. Few, Environ. Sci.: Processes Impacts, 2014, 16(8), 1957–1966 CAS.
  118. R. van Geldern, M. E. Nowak, M. Zimmer, A. Szizybalski, A. Myrttinen, J. A. C. Barth and H. J. Jost, Anal. Chem., 2014, 86(24), 12191–12198 CrossRef CAS PubMed.
  119. J. Pavlovic, J. S. Kinsey and M. D. Hays, Atmos. Meas. Tech., 2014, 7(9), 2829–2838 Search PubMed.
  120. P. Panteliadis, T. Hafkenscheid, B. Cary, E. Diapouli, A. Fischer, O. Favez, P. Quincey, M. Viana, R. Hitzenberger, R. Vecchi, D. Saraga, J. Sciare, J. L. Jaffrezo, A. John, J. Schwarz, M. Giannoni, J. Novak, A. Karanasiou, P. Fermo and W. Maenhaut, Atmos. Meas. Tech., 2015, 8(2), 779–792 CAS.
  121. L. W. A. Chen, J. C. Chow, X. L. Wang, J. A. Robles, B. J. Sumlin, D. H. Lowenthal, R. Zimmermann and J. G. Watson, Atmos. Meas. Tech., 2015, 8(1), 451–461 CAS.
  122. L. Drinovec, G. Mocnik, P. Zotter, A. S. H. Prevot, C. Ruckstuhl, E. Coz, M. Rupakheti, J. Sciare, T. Muller, A. Wiedensohler and A. D. A. Hansen, Atmos. Meas. Tech., 2015, 8(5), 1965–1979 CAS.
  123. Q. Q. Wang, F. M. Yang, L. F. Wei, G. J. Zheng, Z. J. Fan, S. Rajagopalan, R. D. Brook, F. K. Duan, K. B. He, Y. L. Sun and J. R. Brook, Atmos. Environ., 2014, 95, 520–524 CrossRef CAS.
  124. J. A. Forder, Ann. Occup. Hyg., 2014, 58(7), 889–898 CrossRef PubMed.
  125. C. H. Yu, A. P. Patton, A. Zhang, Z.-H. Fan, C. P. Weisel and P. J. Lioy, J. Occup. Environ. Hyg., 2015, 12(9), 577–587 CrossRef CAS PubMed.
  126. S. N. Vardag, S. Hammer, M. Sabasch, D. W. T. Griffith and I. Levin, Atmos. Meas. Tech., 2015, 8(2), 579–592 CrossRef CAS.
  127. J. Adetunji, Atmos. Environ., 2014, 98, 591–598 CrossRef CAS.
  128. H. Y. Guo, Z. Y. Zhang, B. S. Xing, A. Mukherjee, C. Musante, J. C. White and L. L. He, Environ. Sci. Technol., 2015, 49(7), 4317–4324 CrossRef CAS PubMed.
  129. H. M. Al-Saidi and A. A. A. Emara, J. Saudi Chem. Soc., 2014, 18(6), 745–761 CrossRef.
  130. I. Hagarova, Chem. Listy, 2014, 108(10), 949–955 CAS.
  131. E. Stanisz, J. Werner and A. Zgola-Grzeskowiak, TrAC, Trends Anal. Chem., 2014, 61, 54–66 CrossRef CAS.
  132. B. Hu, M. He and B. B. Chen, Anal. Bioanal. Chem., 2015, 407(10), 2685–2710 CrossRef CAS PubMed.
  133. M. Rutkowska, K. Dubalska, P. Konieczka and J. Namiesnik, Molecules, 2014, 19(6), 7581–7609 CrossRef PubMed.
  134. N. S. La Colla, C. E. Domini, J. E. Marcovecchio and S. E. Botte, J. Environ. Manage., 2015, 151, 44–55 CrossRef CAS PubMed.
  135. T. Minami, W. Konagaya, L. J. Zheng, S. Takano, M. Sasaki, R. Murata, Y. Nakaguchi and Y. Sohrin, Anal. Chim. Acta, 2015, 854, 183–190 CrossRef CAS PubMed.
  136. H. Y. Peng, N. Zhang, M. He, B. B. Chen and B. Hu, Talanta, 2015, 131, 266–272 CrossRef CAS PubMed.
  137. A. Dados, E. Kartsiouli, T. Chatzimitakos, C. Papastephanou and C. D. Stalikas, Talanta, 2014, 130, 142–147 CrossRef CAS PubMed.
  138. H. Tazoe, T. Yamagata, H. Obata and H. Nagai, Anal. Chim. Acta, 2014, 852, 74–81 CrossRef CAS PubMed.
  139. J. P. Zou, X. G. Ma, Y. F. Dang and Y. Chen, J. Anal. At. Spectrom., 2014, 29(9), 1692–1697 RSC.
  140. H. T. Zheng, B. T. Jia, Z. L. Zhu, Z. Y. Tang and S. H. Hu, Anal. Methods, 2014, 6(21), 8569–8576 RSC.
  141. T. T. Shih, I. H. Hsu, S. N. Chen, P. H. Chen, M. J. Deng, Y. Chen, Y. W. Lin and Y. C. Sun, Analyst, 2015, 140(2), 600–608 RSC.
  142. L. F. Meng, C. S. Chen and Y. L. Yang, Anal. Lett., 2015, 48(3), 453–463 CrossRef CAS.
  143. Y. A. Zhang, C. Zhong, Q. Y. Zhang, B. B. Chen, M. He and B. Hu, RSC Adv., 2015, 5(8), 5996–6005 RSC.
  144. M. Krawczyk, M. Jeszka-Skowron and H. Matusiewicz, Microchem. J., 2014, 117, 138–143 CrossRef CAS.
  145. N. Thakur, S. A. Kumar, A. K. Pandey, S. D. Kumar and A. V. R. Reddy, Anal. Methods, 2014, 6(19), 7823–7830 RSC.
  146. K. C. Hsu, C. F. Lee, W. C. Tseng, Y. Y. Chao and Y. L. Huang, Talanta, 2014, 128, 408–413 CrossRef CAS PubMed.
  147. C. Bazan, R. Gil, P. Smichowski and P. Pacheco, Microchem. J., 2014, 117, 40–45 CrossRef CAS.
  148. S. Poehle, K. Schmidt and A. Koschinsky, Deep Sea Res., Part I, 2015, 98, 83–93 CrossRef CAS.
  149. C. Karadas and D. Kara, Water, Air, Soil Pollut., 2014, 225(5), 10 CrossRef.
  150. C. Karadas and D. Kara, Water, Air, Soil Pollut., 2014, 225(11), 10 Search PubMed.
  151. R. Rodriguez, L. Leal, S. Miranda, L. Ferrer, J. Avivar, A. Garcia and V. Cerda, Talanta, 2015, 133, 88–93 CrossRef CAS PubMed.
  152. A. Beiraghi, M. Shokri, S. Seidi and B. M. Godajdar, J. Chromatogr. A, 2015, 1376, 1–8 CrossRef CAS PubMed.
  153. M. Shamsipur, N. Fattahi, Y. Assadi, M. Sadeghi and K. Sharafi, Talanta, 2014, 130, 26–32 CrossRef CAS PubMed.
  154. A. C. Grijalba, L. B. Escudero and R. G. Wuilloud, Anal. Methods, 2015, 7(2), 490–499 RSC.
  155. H. Fazelirad, M. A. Taher and M. Nasiri-Majd, J. Anal. At. Spectrom., 2014, 29(12), 2343–2348 RSC.
  156. I. Lopez-Garcia, Y. Vicente-Martinez and M. Hernandez-Cordoba, J. Anal. At. Spectrom., 2015, 30(2), 375–380 RSC.
  157. C. Mitani, A. Kotzamanidou and A. N. Anthemidis, J. Anal. At. Spectrom., 2014, 29(8), 1491–1498 RSC.
  158. H. Sereshti, M. Kermani, M. Karimi and S. Samadi, Clean: Soil, Air, Water, 2014, 42(8), 1089–1097 CrossRef CAS.
  159. I. Celik, D. Kara, C. Karadas, A. Fisher and S. J. Hill, Talanta, 2015, 134, 476–481 CrossRef CAS PubMed.
  160. K. Pytlakowska, M. Dabioch and R. Sitko, Analyst, 2014, 139(16), 3911–3917 RSC.
  161. P. J. Worsfold, M. C. Lohan, S. J. Ussher and A. R. Bowie, Mar. Chem., 2014, 166, 25–35 CrossRef CAS.
  162. B. Sadee, M. E. Foulkes and S. J. Hill, J. Anal. At. Spectrom., 2015, 30(1), 102–118 RSC.
  163. M. Jablonska-Czapla, Int. J. Anal. Chem., 2015, article ID 171478 Search PubMed.
  164. B. Markiewicz, I. Komorowicz, A. Sajnog, M. Belter and D. Baralkiewicz, Talanta, 2015, 132, 814–828 CrossRef CAS PubMed.
  165. C. T. Liu and A. N. Tang, Anal. Lett., 2015, 48(7), 1031–1043 CrossRef CAS.
  166. N. S. Keller, A. Stefasson and B. Sigfuson, Talanta, 2014, 128, 466–472 CrossRef CAS PubMed.
  167. K. Hagiwara, T. Inui, Y. Koike, M. Aizawa and T. Nakamura, Talanta, 2015, 134, 739–744 CrossRef CAS PubMed.
  168. T. Huynh, H. H. Harris, H. Zhang and B. N. Noller, Environ. Chem., 2015, 12(2), 102–111 CrossRef CAS.
  169. J. Sun, Z. G. Yang, H. W. Lee and L. Wang, Anal. Methods, 2015, 7(6), 2653–2658 RSC.
  170. D. B. Wu and T. Pichler, Anal. Methods, 2014, 6(14), 5112–5119 RSC.
  171. H. Matsumiya, H. Inoue and M. Hiraide, Talanta, 2014, 128, 500–504 CrossRef CAS PubMed.
  172. G. Acosta, A. Spisso, L. P. Fernandez, L. D. Martinez, P. H. Pacheco and R. A. Gil, J. Pharm. Biomed. Anal., 2015, 106, 79–84 CrossRef CAS PubMed.
  173. C. C. Brombach, B. Chen, W. T. Corns, J. Feldmann and E. M. Krupp, Spectrochim. Acta, Part B, 2015, 105, 103–108 CrossRef CAS.
  174. Z. Nie, L. N. Zheng, W. Y. Feng and C. L. Liu, Anal. Methods, 2014, 6(20), 8380–8387 RSC.
  175. I. Lopez-Garcia, Y. Vicente-Martinez and M. Hernandez-Cordoba, Spectrochim. Acta, Part B, 2014, 101, 93–97 CrossRef CAS.
  176. K. Proulx and K. J. Wilkinson, Environ. Chem., 2014, 11(4), 392–401 CrossRef CAS.
  177. P. Krystek, P. S. Bauerlein and P. J. F. Kooij, J. Pharm. Biomed. Anal., 2015, 106, 116–123 CrossRef CAS PubMed.
  178. R. Lohmayer, G. M. S. Reithmaier, E. Bura-Nakic and B. Planer-Friedrich, Anal. Chem., 2015, 87(6), 3388–3395 CrossRef CAS PubMed.
  179. L. Huang, D. Yang, X. Q. Guo and Z. L. Chen, J. Chromatogr. A, 2014, 1368, 217–221 CrossRef CAS PubMed.
  180. X. J. Mao, W. Y. Fan, M. He, B. B. Chen and B. Hu, J. Anal. At. Spectrom., 2015, 30(1), 162–171 RSC.
  181. E. Alasonati, B. Fabbri, I. Fettig, C. Yardin, M. E. D. Busto, J. Richter, R. Philipp and P. Fisicaro, Talanta, 2015, 134, 576–586 CrossRef CAS PubMed.
  182. B. Gomez-Nieto, M. J. Gismera, M. T. Sevilla and J. R. Procopio, Anal. Chim. Acta, 2015, 854, 13–19 CrossRef CAS PubMed.
  183. E. C. Jung, H. R. Cho, W. Cha, J. H. Park and M. H. Baik, Rev. Anal. Chem., 2014, 33(4), 245–254 CAS.
  184. X. D. Yu, Y. Li, X. F. Gu, J. M. Bao, H. Z. Yang and L. Sun, Environ. Monit. Assess., 2014, 186(12), 8969–8980 CrossRef PubMed.
  185. M. A. Aguirre, E. J. Selva, M. Hidalgo and A. Canals, Talanta, 2015, 131, 348–353 CrossRef CAS PubMed.
  186. A. Matsumoto, A. Tamura, R. Koda, K. Fukami, Y. H. Ogata, N. Nishi, B. Thornton and T. Sakka, Anal. Chem., 2015, 87(3), 1655–1661 CrossRef CAS PubMed.
  187. M. Filella, D. J. Magnenat and M. Bensimon, Anal. Methods, 2014, 6(20), 8090–8093 RSC.
  188. A. Suzuki, H. Obata, A. Okubo and T. Gamo, Mar. Chem., 2014, 166, 114–121 CrossRef CAS.
  189. C. D. B. Amaral, R. S. Amais, L. L. Fialho, D. Schiavo, T. Amorim, A. R. A. Nogueira, F. R. P. Rocha and J. A. Nobrega, Anal. Methods, 2015, 7(3), 1215–1220 RSC.
  190. R. Clough, H. Sela, A. Milne, M. C. Lohan, S. Tokalioglu and P. J. Worsfold, Talanta, 2015, 133, 162–169 CrossRef CAS PubMed.
  191. A. Y. Zhang, J. Zhang, R. F. Zhang and Y. Xue, J. Anal. At. Spectrom., 2014, 29(12), 2414–2418 RSC.
  192. L. Yang, L. Zhou, Z. C. Hu and S. Gao, Anal. Chem., 2014, 86(18), 9301–9308 CrossRef CAS PubMed.
  193. M. Strok, H. Hintelmann and B. Dimock, Anal. Chim. Acta, 2014, 851, 57–63 CrossRef CAS PubMed.
  194. H. Y. Lin, D. X. Yuan, B. Y. Lu, S. Y. Huang, L. M. Sun, F. Zhang and Y. Q. Gao, J. Anal. At. Spectrom., 2015, 30(2), 353–359 RSC.
  195. R. E. Sturgeon, Anal. Chem., 2015, 87(5), 3072–3079 CrossRef CAS PubMed.
  196. H. L. Duan, Z. B. Gong and S. F. Yang, J. Anal. At. Spectrom., 2015, 30(2), 410–416 RSC.
  197. C. L. He, G. L. Cheng, C. B. Zheng, L. Wu, Y. I. Lee and X. D. Hou, Anal. Methods, 2015, 7(7), 3015–3021 RSC.
  198. D. Picon, P. Carrero, M. Valero, Y. de Pena and L. Gutierrez, Talanta, 2015, 136, 136–144 CrossRef CAS PubMed.
  199. G. V. Pashkova and A. G. Revenko, Appl. Spectrosc. Rev., 2015, 50(6), 443–472 CrossRef.
  200. J. Kruse, M. Abraham, W. Amelung, C. Baum, R. Bol, O. Kuhn, H. Lewandowski, J. Niederberger, Y. Oelmann, C. Ruger, J. Santner, M. Siebers, N. Siebers, M. Spohn, J. Vestergren, A. Vogts and P. Leinweber, J. Plant Nutr. Soil Sci., 2015, 178(1), 43–88 CrossRef CAS PubMed.
  201. D. J. Butcher, Appl. Spectrosc. Rev., 2015, 50(1), 27–45 CrossRef CAS.
  202. P. Porizka, P. Prochazkova, D. Prochazka, L. Sladkova, J. Novotny, M. Petrilak, M. Brada, O. Samek, Z. Pilat, P. Zemanek, V. Adam, R. Kizek, K. Novotny and J. Kaiser, Sensors, 2014, 14(9), 17725–17752 CrossRef CAS PubMed.
  203. K. H. Laursen, J. K. Schjoerring, S. D. Kelly and S. Husted, TrAC, Trends Anal. Chem., 2014, 59, 73–82 CrossRef CAS.
  204. A. D. Batista, M. K. Sasaki, F. R. P. Rocha and E. A. G. Zagatto, Analyst, 2014, 139(15), 3666–3682 RSC.
  205. I. Ugulu, Appl. Spectrosc. Rev., 2015, 50(2), 113–151 CrossRef.
  206. C. D. B. Amaral, J. A. Nobrega and A. R. A. Nogueira, Microchem. J., 2014, 117, 122–126 CrossRef CAS.
  207. S. Loppi, C. Faleri and L. Paoli, Bull. Environ. Contam. Toxicol., 2014, 93(3), 350–353 CrossRef CAS PubMed.
  208. S. C. Jantzi and J. R. Almirall, Appl. Spectrosc., 2014, 68(9), 963–974 CrossRef CAS PubMed.
  209. S. Arnoldussen and B. van Os, Catena, 2015, 128, 16–30 CrossRef CAS.
  210. M. Welna, A. Szymczycha-Madeja and P. Pohl, TrAC, Trends Anal. Chem., 2015, 65, 122–136 CrossRef CAS.
  211. H. O. Qu, T. K. Mudalige and S. W. Linder, J. Agric. Food Chem., 2015, 63(12), 3153–3160 CrossRef CAS PubMed.
  212. M. N. Herod, R. J. Cornett, I. D. Clark, W. E. Kieser and G. St Jean, J. Environ. Radioact., 2014, 138, 323–330 CrossRef CAS PubMed.
  213. K. Miranda, A. L. Vieira and J. A. G. Neto, Anal. Methods, 2014, 6(23), 9503–9508 RSC.
  214. L. Carrasco and E. Vassileva, Anal. Chim. Acta, 2015, 853, 167–178 CrossRef CAS PubMed.
  215. D. Zhao, H. B. Li, J. Y. Xu, J. Luo and L. Ma, Sci. Total Environ., 2015, 523, 138–145 CrossRef CAS PubMed.
  216. S. Terfi and F. Sadi, Anal. Lett., 2015, 48(7), 1190–1197 CrossRef CAS.
  217. M. F. Mesko, R. S. Picoloto, L. R. Ferreira, V. C. Costa, C. M. P. Pereira, P. Colepicolo, E. I. Muller and E. M. M. Flores, J. Anal. At. Spectrom., 2015, 30(1), 260–266 RSC.
  218. C. A. Bizzi, J. A. Nobrega, J. S. Barin, J. S. S. Oliveira, L. Schmidt, P. A. Mello and E. M. M. Flores, Anal. Chim. Acta, 2014, 837, 16–22 CrossRef CAS PubMed.
  219. A. M. Hernandez-Martinez, C. Padron-Sanz, M. E. T. Padron, Z. S. Ferrera and J. J. S. Rodriguez, J. Anal. At. Spectrom., 2015, 30(2), 435–442 RSC.
  220. P. Mamatha, G. Venkateswarlu, A. V. N. Swamy and A. C. Sahayam, Anal. Methods, 2014, 6(24), 9653–9657 RSC.
  221. C. N. Lou, W. Q. Liu and X. D. Liu, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 2014, 969, 29–34 CrossRef CAS PubMed.
  222. E. Bernalte, S. Salmanighabeshi, F. Rueda-Holgado, M. R. Palomo-Marin, C. Marin-Sanchez, F. Cereceda-Balic and E. Pinilla-Gil, Int. J. Environ. Sci. Technol., 2015, 12(3), 817–826 CrossRef.
  223. M. Frena, D. P. C. Quadros, I. N. B. Castilho, J. S. de Gois, D. L. G. Borges, B. Welz and L. A. S. Madureira, Microchem. J., 2014, 117, 1–6 CrossRef CAS.
  224. M. V. B. Krishna and D. Karunasagar, Anal. Methods, 2015, 7(5), 1997–2005 RSC.
  225. B. D. Duval, S. M. Natali and B. A. Hungate, Commun. Soil Sci. Plant Anal., 2015, 46(3), 318–326 CrossRef CAS.
  226. F. Guerra, A. R. Trevizam, R. C. Fior and T. Muraoka, Sci. Agric., 2014, 71(5), 410–415 CrossRef.
  227. J. Sun, L. Ma, Z. G. Yang, H. Lee and L. Wang, J. Sep. Sci., 2015, 38(6), 943–950 CrossRef CAS PubMed.
  228. D. Garcia-Casillas, S. Garcia-Salgado and M. A. Quijano, Anal. Methods, 2014, 6(20), 8403–8412 RSC.
  229. P. W. G. van Geffen, T. K. Kyser, C. J. Oates and C. Ihlenfeld, Geochem.: Explor., Environ., Anal., 2015, 15(1), 27–38 CrossRef.
  230. M. Q. Wang, H. Wu, Y. Liao, F. Fang, Y. Y. Gao and Y. Xu, J. Geochem. Explor., 2015, 148, 231–240 CrossRef CAS.
  231. M. Rosende, L. M. Magalhaes, M. A. Segundo and M. Miro, Anal. Chim. Acta, 2014, 842, 1–10 CrossRef CAS PubMed.
  232. N. Boisa, N. Elom, J. R. Dean, M. E. Deary, G. Bird and J. A. Entwistle, Environ. Int., 2014, 70, 132–142 CrossRef CAS PubMed.
  233. N. C. Galvan-Tejada, V. Pena-Ramirez, L. Mora-Palomino and C. Siebe, J. Plant Nutr. Soil Sci., 2014, 177(5), 792–802 CrossRef CAS.
  234. J. M. Rosas-Castor, L. Portugal, L. Ferrer, J. L. Guzman-Mar, A. Hernandez-Ramirez, V. Cerda and L. Hinojosa-Reyes, Anal. Chim. Acta, 2015, 874, 1–10 CrossRef CAS PubMed.
  235. Y. L. Zhang, M. Miro and S. D. Kolev, Environ. Sci. Technol., 2015, 49(5), 2733–2740 CrossRef CAS PubMed.
  236. K. Barkonikos, I. N. Pasias and N. S. Thomaidis, Talanta, 2014, 129, 165–170 CrossRef CAS PubMed.
  237. M. Y. Burylin, J. Anal. Chem., 2015, 70(1), 39–43 CrossRef CAS.
  238. R. Dobrowolski, J. Dobrzynska and B. Gawronska, Environ. Monit. Assess., 2015, 187(1), 8 CrossRef PubMed.
  239. M. C. B. Alonso, M. N. Pardal, M. L. Pedrouso, J. R. Aboal, J. A. Fernandez and P. Bermejo-Barrera, At. Spectrosc., 2015, 36(1), 42–48 CAS.
  240. P. Mandjukov, A. M. Orani, E. Han and E. Vassileva, Spectrochim. Acta, Part B, 2015, 103, 24–33 CrossRef.
  241. A. M. Orani, E. Han, P. Mandjukov and E. Vassileva, Spectrochim. Acta, Part B, 2015, 103, 131–143 CrossRef.
  242. P. Coufalik and J. Komarek, J. Anal. Chem., 2014, 69(12), 1123–1129 CrossRef CAS.
  243. B. Caballero-Segura, P. Avila-Perez, C. E. B. Diaz, J. J. R. Garcia, G. Zarazua, R. Soria and H. B. Ortiz-Oliveros, Int. J. Environ. Anal. Chem., 2014, 94(13), 1288–1301 CrossRef CAS.
  244. R. McIlwaine, S. F. Cox and R. Doherty, Environ. Sci. Pollut. Res., 2015, 22(8), 6364–6371 CrossRef PubMed.
  245. G. L. Scheffler and D. Pozebon, J. Anal. At. Spectrom., 2015, 30(2), 468–478 RSC.
  246. F. Kaveh and D. Beauchemin, J. Anal. At. Spectrom., 2014, 29(8), 1371–1377 RSC.
  247. F. Kaveh, C. J. Oates and D. Beauchemin, Geochem.: Explor., Environ., Anal., 2014, 14(4), 305–313 CrossRef CAS.
  248. N. Sadiq and D. Beauchemin, Anal. Chim. Acta, 2014, 851, 23–29 CrossRef CAS PubMed.
  249. P. Masson, Spectrochim. Acta, Part B, 2014, 102, 24–27 CrossRef CAS.
  250. S. G. Silva, J. A. Nobrega, B. T. Jones and G. L. Donati, J. Anal. At. Spectrom., 2014, 29(8), 1499–1503 Search PubMed.
  251. S. Karlsson, V. Sjoberg and A. Ogar, Talanta, 2015, 135, 124–132 CrossRef CAS PubMed.
  252. C. T. Kamala, V. Balaram, V. Dharmendra, M. Satyanarayanan, K. S. V. Subramanyam and A. Krishnaiah, Environ. Monit. Assess., 2014, 186(11), 7097–7113 CrossRef CAS PubMed.
  253. T. Frentiu, E. Darvasi, S. Butaciu, M. Ponta, D. Petreus, A. I. Mihaltan and M. Frentiu, Talanta, 2014, 129, 72–78 CrossRef CAS PubMed.
  254. T. Frentiu, S. Butaciu, E. Darvasi, M. Ponta, M. Senila, E. Levei and M. Frentiu, J. Anal. At. Spectrom., 2015, 30(5), 1161–1168 RSC.
  255. T. Frentiu, S. Butaciu, M. Ponta, E. Darvasi, M. Senila, D. Petreus and M. Frentiu, J. Anal. At. Spectrom., 2014, 29(10), 1880–1888 RSC.
  256. Q. Li, Z. Zhang and Z. Wang, Anal. Chim. Acta, 2014, 845, 7–14 CrossRef CAS PubMed.
  257. C. C. Brombach, Z. Gajdosechova, B. Chen, A. Brownlow, W. T. Corns, J. Feldmann and E. M. Krupp, Anal. Bioanal. Chem., 2015, 407(3), 973–981 CrossRef CAS PubMed.
  258. S. G. Silva, J. A. Nobrega, B. T. Jones and G. L. Donati, Microchem. J., 2014, 117, 250–254 CrossRef CAS.
  259. Y. X. Liu, Q. X. Li, N. Ma, X. L. Sun, J. F. Bai and Q. Zhang, Anal. Chem., 2014, 86(23), 11570–11577 CrossRef CAS PubMed.
  260. L. Bendakovska, A. Krejcova, T. Cernohorsky and K. Zvonickova, Chem. Listy, 2014, 108, S154–S159 Search PubMed.
  261. B. S. Matteson, S. K. Hanson, J. L. Miller and W. J. Oldham, J. Environ. Radioact., 2015, 142, 62–67 CrossRef CAS PubMed.
  262. M. M. Wolle, G. M. M. Rahman, H. M. S. Kingston and M. Pamuku, J. Anal. At. Spectrom., 2014, 29(9), 1640–1647 RSC.
  263. B. P. Jackson, A. Liba and J. Nelson, J. Anal. At. Spectrom., 2015, 30(5), 1179–1183 RSC.
  264. E. Bolea-Fernandez, L. Balcaen, M. Resano and F. Vanhaecke, Anal. Bioanal. Chem., 2015, 407(3), 919–929 CrossRef CAS PubMed.
  265. Y. Gao, M. Xu, R. E. Sturgeon, Z. Mester, Z. M. Shi, R. Galea, P. Saull and L. Yang, Anal. Chem., 2015, 87(8), 4495–4502 CrossRef CAS PubMed.
  266. R. F. Wei, Q. J. Guo, H. J. Wen, J. X. Yang, M. Peters, C. W. Zhu, J. Ma, G. X. Zhu, H. Z. Zhang, L. Y. Tian, C. Y. Wang and Y. X. Wan, Anal. Methods, 2015, 7(6), 2479–2487 RSC.
  267. J. Durisova, L. Ackerman, L. Strnad, V. Chrastny and J. Borovicka, Geostand. Geoanal. Res., 2015, 39(2), 209–220 CrossRef CAS.
  268. W. Guo, S. H. Hu, Z. W. Wu, G. Y. Lan, L. L. Jin, X. G. Pang, J. C. Zhan, B. Chen and Z. Y. Tang, J. Anal. At. Spectrom., 2015, 30(4), 986–993 RSC.
  269. B. C. Russell, I. W. Croudace, P. E. Warwick and J. A. Milton, Anal. Chem., 2014, 86(17), 8719–8726 CrossRef CAS PubMed.
  270. J. Zheng, W. T. Bu, K. Tagami, Y. Shikamori, K. Nakano, S. Uchida and N. Ishii, Anal. Chem., 2014, 86(14), 7103–7110 Search PubMed.
  271. J. S. Becker, A. Matusch and B. Wu, Anal. Chim. Acta, 2014, 835, 1–18 CrossRef CAS PubMed.
  272. D. Pozebon, G. L. Scheffler, V. L. Dressler and M. A. G. Nunes, J. Anal. At. Spectrom., 2014, 29(12), 2204–2228 RSC.
  273. S. R. Oliveira and M. A. Z. Arruda, J. Anal. At. Spectrom., 2015, 30(2), 389–395 RSC.
  274. Y. Gao, S. van de Velde, P. N. Williams, W. Baeyens and H. Zhang, TrAC, Trends Anal. Chem., 2015, 66, 63–71 CrossRef CAS.
  275. A. Kreuzeder, J. Santner, H. Zhang, T. Prohaska and W. W. Wenzel, Environ. Sci. Technol., 2015, 49(3), 1594–1602 CrossRef CAS PubMed.
  276. A. Rugova, M. Puschenreiter, J. Santner, L. Fischer, S. Neubauer, G. Koellensperger and S. Hann, J. Sep. Sci., 2014, 37(14), 1711–1719 CrossRef CAS PubMed.
  277. R. Koplik, I. Klimesova, K. Malisova and O. Mestek, Czech J. Food Sci., 2014, 32(3), 249–259 CAS.
  278. H. Pietila, P. Peramaki, J. Piispanen, M. Starr, T. Nieminen, M. Kantola and L. Ukonmaanaho, Chemosphere, 2015, 124, 47–53 CrossRef CAS PubMed.
  279. G. F. Koopmans, T. Hiemstra, I. C. Regelink, B. Molleman and R. N. J. Comans, J. Chromatogr. A, 2015, 1392, 100–109 CrossRef CAS PubMed.
  280. Y. B. Dan, W. L. Zhang, R. M. Xue, X. M. Ma, C. Stephan and H. L. Shi, Environ. Sci. Technol., 2015, 49(5), 3007–3014 CrossRef CAS PubMed.
  281. J. El Haddad, L. Canioni and B. Bousquet, Spectrochim. Acta, Part B, 2014, 101, 171–182 CrossRef CAS.
  282. G. G. A. de Carvalho, D. Santos, M. D. Gomes, L. C. Nunes, M. B. B. Guerra and F. J. Krug, Spectrochim. Acta, Part B, 2015, 105, 130–135 CrossRef CAS.
  283. K. Devey, M. Mucalo, G. Rajendram and J. Lane, Commun. Soil Sci. Plant Anal., 2015, 46, 72–80 CrossRef.
  284. X. N. Liu, Q. Zhang, Z. S. Wu, X. Y. Shi, N. Zhao and Y. J. Qiao, Sensors, 2015, 15(1), 642–655 CrossRef CAS PubMed.
  285. J. El Haddad, D. Bruyere, A. Ismael, G. Gallou, V. Laperche, K. Michel, L. Canioni and B. Bousquet, Spectrochim. Acta, Part B, 2014, 97, 57–64 CrossRef CAS.
  286. A. M. Popov, T. A. Labutin, S. M. Zaytsev, I. V. Seliverstova, N. B. Zorov, I. A. Kal'ko, Y. N. Sidorina, I. A. Bugaev and Y. N. Nikolaev, J. Anal. At. Spectrom., 2014, 29(10), 1925–1933 RSC.
  287. E. C. Ferreira, E. J. Ferreira, P. R. Villas-Boas, G. S. Senesi, C. M. Carvalho, R. A. Romano, L. Martin-Neto and D. Milori, Spectrochim. Acta, Part B, 2014, 99, 76–81 CrossRef CAS.
  288. D. C. Weindorf, N. Bakr and Y. D. Zhu, in Advances in Agronomy, ed. D. L. Sparks, Elsevier Academic Press Inc, San Diego, 2014, vol. 128, pp. 1–45 Search PubMed.
  289. N. W. Brand and C. J. Brand, Geochem.: Explor., Environ., Anal., 2014, 14(2), 125–138 CrossRef CAS.
  290. G. E. M. Hall, G. F. Bonham-Carter and A. Buchar, Geochem.: Explor., Environ., Anal., 2014, 14(2), 99–123 CrossRef CAS.
  291. C. A. Shand and R. Wendler, J. Geochem. Explor., 2014, 143, 31–42 CrossRef CAS.
  292. D. C. Arne, R. A. Mackie and S. A. Jones, Geochem.: Explor., Environ., Anal., 2014, 14(3), 233–244 CrossRef CAS.
  293. B. Lemiere, V. Laperche, L. Haouche and P. Auger, Geochem.: Explor., Environ., Anal., 2014, 14(3), 257–264 CrossRef CAS.
  294. R. M. Conrey, M. Goodman-Elgar, N. Bettencourt, A. Seyfarth, A. van Hoose and J. A. Wolff, Geochem.: Explor., Environ., Anal., 2014, 14(3), 291–301 CrossRef CAS.
  295. A. M. W. Hunt and R. J. Speakman, J. Archaeol. Sci., 2015, 53, 626–638 CrossRef CAS.
  296. M. I. Kaniu and K. H. Angeyo, Geoderma, 2015, 241, 32–40 CrossRef.
  297. A. Sharma, D. C. Weindorf, T. Man, A. A. A. Aldabaa and S. Chakraborty, Geoderma, 2014, 232, 141–147 CrossRef.
  298. F. L. Melquiades and F. R. dos Santos, Spectrosc. Lett., 2015, 48(4), 286–289 CrossRef CAS.
  299. A. Sharma, D. C. Weindorf, D. D. Wang and S. Chakraborty, Geoderma, 2015, 239, 130–134 CrossRef.
  300. S. Swanhart, D. C. Weindorf, S. Chakraborty, N. Bakr, Y. D. Zhu, C. Nelson, K. Shook and A. Acree, Soil Sci., 2014, 179(9), 417–423 CrossRef CAS.
  301. D. D. Wang, S. Chakraborty, D. C. Weindorf, B. Li, A. Sharma, S. Paul and M. N. Ali, Geoderma, 2015, 243, 157–167 CrossRef.
  302. S. Chakraborty, D. C. Weindorf, B. Li, A. A. A. Aldabaa, R. K. Ghosh, S. Paul and M. N. Ali, Sci. Total Environ., 2015, 514, 399–408 CrossRef CAS PubMed.
  303. A. A. A. Aldabaa, D. C. Weindorf, S. Chakraborty, A. Sharma and B. Li, Geoderma, 2015, 239, 34–46 CrossRef.
  304. A. Horta, B. Malone, U. Stockmann, B. Minasny, T. F. A. Bishop, A. B. McBratney, R. Pallasser and L. Pozza, Geoderma, 2015, 241, 180–209 CrossRef.
  305. K. Ploykrachang, J. Hasegawa, K. Kondo, H. Fukuda and Y. Oguri, Nucl. Instrum. Methods Phys. Res., Sect. B, 2014, 331, 261–265 CrossRef CAS.
  306. M. B. B. Guerra, E. de Almeida, G. G. A. Carvalho, P. F. Souza, L. C. Nunes, D. Santos and F. J. Krug, J. Anal. At. Spectrom., 2014, 29(9), 1667–1674 RSC.
  307. P. Vavpetic, K. Vogel-Mikus, L. Jeromel, N. O. Potocnik, P. Pongrac, D. Drobne, Z. P. Tkalec, S. Novak, M. Kos, S. Koren, M. Regvar and P. Pelicon, Nucl. Instrum. Methods Phys. Res., Sect. B, 2015, 348, 147–151 CrossRef CAS.
  308. V. Romero, I. Costas-Mora, I. Lavilla and C. Bendicho, Spectrochim. Acta, Part B, 2015, 107, 125–131 CrossRef CAS.
  309. A. T. Reis, A. C. Duarte, B. Henriques, C. Coelho, C. B. Lopes, C. L. Mieiro, D. S. Tavares, L. Ahmad, J. P. Coelho, L. S. Rocha, N. Cruz, R. J. R. Monteiro, R. Rocha, S. Rodrigues and E. Pereira, TrAC, Trends Anal. Chem., 2015, 64, 137–149 CrossRef.
  310. B. Marie, L. Marin, P. Y. Martin, T. Gulon, J. Carignan and C. Cloquet, Geostand. Geoanal. Res., 2015, 39(1), 71–86 CrossRef CAS.
  311. M. Soylak and I. Murat, J. AOAC Int., 2014, 97(4), 1189–1194 CrossRef CAS PubMed.
  312. J. A. Baig, L. Elci, M. I. Khan and T. G. Kazi, J. AOAC Int., 2014, 97(5), 1421–1425 CrossRef CAS PubMed.
  313. D. Mendil, M. Karatas and M. Tuzen, Food Chem., 2015, 177, 320–324 CrossRef CAS PubMed.
  314. Z. Bahadir, V. N. Bulut, D. Ozdes, C. Duran, H. Bektas and M. Soylak, J. Ind. Eng. Chem., 2014, 20(3), 1030–1034 CrossRef CAS.
  315. B. E. D. Costa, N. M. M. Coelho and L. M. Coelho, Food Chem., 2015, 178, 89–95 CrossRef CAS PubMed.
  316. S. Khan, T. G. Kazi and M. Soylak, Anal. Lett., 2015, 48(11), 1751–1766 CrossRef CAS.
  317. M. Soylak and E. Yilmaz, Anal. Lett., 2015, 48(3), 464–476 CrossRef CAS.
  318. Z. X. Zheng and W. L. Huang, Environ. Eng. Manage. J., 2014, 13(5), 1041–1046 Search PubMed.
  319. M. Aghamohammadi, M. Faraji, P. Shahdousti, H. Kalhor and A. Saleh, Phytochem. Anal., 2015, 26(3), 209–214 CrossRef CAS PubMed.
  320. S. S. Arain, T. G. Kazi, A. J. Arain, H. I. Afridi, J. A. Baig, K. D. Brahman, Naeemullah and S. A. Arain, Spectrochim. Acta, Part A, 2015, 138, 387–394 CrossRef CAS PubMed.
  321. S. Li, M. Wang, B. Y. Yang, Y. Z. Zhong and L. Feng, PLoS One, 2014, 9(9), 9 Search PubMed.
  322. P. Liang, J. Yu, E. J. Yang and Y. J. Mo, Food Analytical Methods, 2014, 7(7), 1506–1512 CrossRef.
  323. L. M. Zhang, X. L. Li, X. Y. Wang, W. T. Wang, X. S. Wang and H. Y. Han, Anal. Methods, 2014, 6(15), 5578–5583 RSC.
  324. E. Stanisz, A. Zgola-Grzeskowiak and H. Matusiewicz, Talanta, 2014, 129, 254–262 CrossRef CAS PubMed.
  325. T. Li and J. H. Yang, J. Iran. Chem. Soc., 2015, 12(2), 367–370 CrossRef CAS.
  326. N. Jalbani and M. Soylak, Food Chem., 2015, 167, 433–437 CrossRef CAS PubMed.
  327. W. I. Mortada, I. M. Kenawy and M. M. Hassanien, Anal. Methods, 2015, 7(5), 2114–2120 RSC.
  328. P. Liang, J. Yu, E. J. Yang and Y. J. Mo, Food Analytical Methods, 2015, 8(1), 236–242 CrossRef.
  329. B. B. Chen, Y. L. Wu, X. Q. Guo, M. He and B. Hu, J. Anal. At. Spectrom., 2015, 30(4), 875–881 RSC.
  330. A. M. D. de Jesus, M. A. Aguirre, M. Hidalgo, A. Canals and E. R. Pereira, J. Anal. At. Spectrom., 2014, 29(10), 1813–1818 RSC.
  331. Y. Wang, Y. Y. Liu, J. Han, L. Wang, T. Chen and L. Ni, Anal. Methods, 2015, 7(6), 2339–2346 RSC.
  332. Z. A. Alothman, N. H. Al-Shaalan, M. A. Habila, Y. E. Unsal, M. Tuzen and M. Soylak, Environ. Monit. Assess., 2015, 187(2), 8 CrossRef PubMed.
  333. W. P. Jia, Y. Hu, F. Li and D. M. Han, At. Spectrosc., 2015, 36(2), 96–101 CAS.
  334. S. Bahar and R. Zakerian, Iran. J. Chem. Chem. Eng., 2014, 33(4), 51–58 CAS.
  335. C. Labrecque and D. Lariviere, Anal. Methods, 2014, 6(23), 9291–9298 RSC.
  336. S. Z. Chen, S. P. Zhu and D. B. Lu, Food Chem., 2015, 169, 156–161 CrossRef CAS PubMed.
  337. Naeemullah, T. G. Kazi and M. Tuzen, Food Chem., 2015, 172, 161–165 CrossRef CAS PubMed.
  338. F. Sabermahani, R. Askari, S. J. Hosseinifard and M. Saeidi, Scientia Iranica, 2014, 21(6), 2012–2020 Search PubMed.
  339. Y. L. Zhang and S. B. Adeloju, Talanta, 2015, 137, 148–155 CrossRef CAS PubMed.
  340. V. A. Lemos, G. S. do Nascimento and L. S. Nunes, Water, Air, Soil Pollut., 2015, 226(2), 10 CrossRef.
  341. S. Sivrikaya, M. Imamoglu and D. Kara, At. Spectrosc., 2014, 35(4), 168–176 CAS.
  342. Z. A. Alothman, E. Yilmaz, M. Habila and M. Soylak, Ecotoxicol. Environ. Saf., 2015, 112, 74–79 CrossRef CAS PubMed.
  343. A. Tadjarodi, A. Abbaszadeh, M. Taghizadeh, N. Shekari and A. A. Asgharinezhad, Mater. Sci. Eng., C, 2015, 49, 416–421 CrossRef CAS PubMed.
  344. H. R. Fouladian and M. Behbahani, Food Analytical Methods, 2015, 8(4), 982–993 CrossRef.
  345. D. Ozdes and C. Durans, At. Spectrosc., 2014, 35(3), 118–126 CAS.
  346. S. K. Behzad, A. Balati, M. M. Amini and M. Ghanbari, Microchim. Acta, 2014, 181(15–16), 1781–1788 CrossRef CAS.
  347. T. Dasbasi, S. Sacmaci, A. Ulgen and S. Kartal, Food Chem., 2015, 174, 591–596 CrossRef CAS PubMed.
  348. P. Azizi, M. Golshekan, S. Shariati and J. Rahchamani, Environ. Monit. Assess., 2015, 187(4), 11 CrossRef PubMed.
  349. A. Islam, A. Ahmad and M. A. Laskar, J. AOAC Int., 2015, 98(1), 165–175 CrossRef CAS PubMed.
  350. N. Jalbani, R. M. Alosmanov and M. Soylak, At. Spectrosc., 2014, 35(4), 163–167 CAS.
  351. B. C. Russell, P. E. Warwick and I. W. Croudace, Anal. Chem., 2014, 86(23), 11890–11896 CrossRef CAS PubMed.
  352. D. Cagirdi, H. Altundag, M. Imamoglu and M. Tuzen, J. AOAC Int., 2014, 97(4), 1137–1142 CrossRef CAS PubMed.
  353. F. Sabermahani and M. A. Taher, J. AOAC Int., 2014, 97(6), 1713–1718 CrossRef CAS PubMed.
  354. S. Baytak, R. Mert and A. R. Turker, Int. J. Environ. Anal. Chem., 2014, 94(10), 975–987 CrossRef CAS.
  355. Y. E. Unsal, M. Soylak, M. Tuzen and B. Hazer, Anal. Lett., 2015, 48(7), 1163–1179 CrossRef CAS.
  356. V. Okumus, S. Ozdemir, E. Kilinc, A. Dundar, U. Yuksel and Z. Baysal, Biorem. J., 2015, 19(1), 47–55 CrossRef.
  357. L. O. dos Santos and V. A. Lemos, Water, Air, Soil Pollut., 2014, 225(9), 8 CrossRef.
  358. L. A. Escudero, A. J. Blanchet, L. L. Sombra, J. A. Salonia and J. A. Gasquez, Microchem. J., 2014, 116, 92–97 CrossRef CAS.
  359. A. Saljooqi, T. Shamspur, M. Mohamadi and A. Mostafavi, J. Sep. Sci., 2014, 37(14), 1856–1861 CrossRef CAS PubMed.
  360. E. Ghorbani-Kalhor, M. Behbahani, J. Abolhasani and R. H. Khanmiri, Food Analytical Methods, 2015, 8(5), 1326–1334 CrossRef.
  361. M. Soylak and S. Yigit, At. Spectrosc., 2015, 36(1), 49–53 CAS.
  362. E. Yavuz, S. Tokalioglu, H. Sahan and S. Patat, Talanta, 2014, 128, 31–37 CrossRef CAS PubMed.
  363. H. C. Wu, T. Y. Su, T. L. Tsai, S. B. Jong, M. H. Yang and Y. C. Tyan, RSC Adv., 2014, 4(74), 39226–39230 RSC.
  364. Z. Z. Cheng, M. Liu, H. K. Huang, T. X. Gu, W. D. Yan and H. L. Wen, Geostand. Geoanal. Res., 2015, 39(2), 221–232 CrossRef CAS.
  365. A. M. W. Hunt, D. K. Dvoracek, M. D. Glascock and R. J. Speakman, J. Radioanal. Nucl. Chem., 2014, 302(1), 505–512 CrossRef CAS.
  366. A. Audetat, D. Garbe-Schonberg, A. Kronz, T. Pettke, B. Rusk, J. J. Donovan and H. A. Lowers, Geostand. Geoanal. Res., 2015, 39(2), 171–184 CrossRef CAS.
  367. W. A. Brand, T. B. Coplen, J. Vogl, M. Rosner and T. Prohaska, Pure Appl. Chem., 2014, 86(3), 425–467 CrossRef CAS.
  368. K. A. Iles, J. M. Hergt, K. N. Sircombe, J. D. Woodhead, S. Bodorkos and I. S. Williams, Chem. Geol., 2015, 402, 140–152 CrossRef CAS.
  369. A. K. Kennedy, J. F. Wotzlaw, U. Schaltegger, J. L. Crowley and M. Schmitz, Can. Mineral., 2014, 52(3), 409–421 CrossRef CAS.
  370. F. Jourdan, A. Frew, A. Joly, C. Mayers and N. J. Evans, Geochim. Cosmochim. Acta, 2014, 141, 113–126 CrossRef CAS.
  371. S. Burger, S. F. Boulyga, M. V. Penkin, D. Bostick, S. Jovanovic, R. Lindvall, G. Rasmussen and L. Riciputi, J. Radioanal. Nucl. Chem., 2014, 301(3), 711–729 CrossRef.
  372. M. Lin, Y. G. Zhao, L. F. Zhao, L. L. Li, F. Wang, L. C. Zhu, X. N. Hu and W. Ning, J. Anal. At. Spectrom., 2015, 30(2), 396–402 RSC.
  373. N. Miliszkiewicz, S. Walas and A. Tobiasz, J. Anal. At. Spectrom., 2015, 30(2), 327–338 RSC.
  374. Y. Q. Ke, X. F. Yao, S. H. Hu, W. Guo, Q. H. Hu, Z. L. Zhu and Z. C. Hu, Anal. Lett., 2015, 48(5), 830–842 CrossRef CAS.
  375. T. Luo, Y. Wang, Z. C. Hu, D. Gunther, Y. S. Liu, S. Gao, M. Li and S. H. Hu, J. Anal. At. Spectrom., 2015, 30(4), 941–949 RSC.
  376. K. P. Jochum, B. Stoll, U. Weis, D. E. Jacob, R. Mertz-Kraus and M. O. Andreae, Geostand. Geoanal. Res., 2014, 38(3), 265–292 CrossRef CAS.
  377. Z. Li, Z. C. Hu, Y. S. Liu, S. Gao, M. Li, K. Q. Zong, H. H. Chen and S. H. Hu, Chem. Geol., 2015, 400, 11–23 CrossRef CAS.
  378. F. X. d'Abzac, A. D. Czaja, B. L. Beard, J. J. Schauer and C. M. Johnson, Geostand. Geoanal. Res., 2014, 38(3), 293–309 CrossRef.
  379. M. Oeser, S. Weyer, I. Horn and S. Schuth, Geostand. Geoanal. Res., 2014, 38(3), 311–328 CrossRef CAS.
  380. J. A. Schuessler and F. von Blanckenburg, Spectrochim. Acta, Part B, 2014, 98, 1–18 CrossRef CAS.
  381. Z. C. Hu, W. Zhang, Y. S. Liu, S. Gao, M. Li, K. Q. Zong, H. H. Chen and S. H. Hu, Anal. Chem., 2015, 87(2), 1152–1157 CrossRef CAS PubMed.
  382. L. X. Feng and J. Wang, J. Anal. At. Spectrom., 2014, 29(11), 2183–2189 RSC.
  383. B. Fernandez, P. Rodriguez-Gonzalez, J. I. G. Alonso, J. Malherbe, S. Garcia-Fonseca, R. Pereiro and A. Sanz-Medel, Anal. Chim. Acta, 2014, 851, 64–71 CrossRef CAS PubMed.
  384. A. Gundlach-Graham, E. A. Dennis, S. J. Ray, C. G. Enke, C. J. Barinaga, D. W. Koppenaal and G. M. Hieftje, J. Anal. At. Spectrom., 2015, 30(1), 139–147 RSC.
  385. X. D. Che, F. Y. Wu, R. C. Wang, A. Gerdes, W. Q. Ji, Z. H. Zhao, J. H. Yang and Z. Y. Zhu, Ore Geol. Rev., 2015, 65, 979–989 CrossRef.
  386. C. C. Wohlgemuth-Ueberwasser, U. Soderlund, V. Pease and M. K. M. Nilsson, J. Anal. At. Spectrom., 2015, 30(5), 1191–1196 RSC.
  387. X. D. Deng, J. W. Li and G. Wen, Chem. Geol., 2014, 382, 95–110 CrossRef CAS.
  388. L. Lin, Z. C. Hu, L. Yang, W. Zhang, Y. S. Liu, S. Gao and S. H. Hu, Chem. Geol., 2014, 386, 22–30 CrossRef CAS.
  389. Y. H. Yang, F. Y. Wu, J. H. Yang, D. M. Chew, L. W. Xie, Z. Y. Chu, Y. B. Zhang and C. Huang, Chem. Geol., 2014, 385, 35–55 CrossRef CAS.
  390. J. Lewis, C. D. Coath and A. W. G. Pike, Chem. Geol., 2014, 390, 173–181 CrossRef CAS.
  391. C. Huang, Y. H. Yang, J. H. Yang and L. W. Xie, J. Anal. At. Spectrom., 2015, 30(4), 994–1000 RSC.
  392. U. Schaltegger, A. K. Schmitt and M. S. A. Horstwood, Chem. Geol., 2015, 402, 89–110 CrossRef CAS.
  393. G. Gehrels, Annu. Rev. Earth Planet. Sci., 2014, 42, 127–149 CrossRef CAS.
  394. Q. G. Crowley, K. Heron, N. Riggs, B. Kamber, D. Chew, B. McConnell and K. Benn, Minerals, 2014, 4(2), 503–518 CrossRef CAS.
  395. A. von Quadt, D. Gallhofer, M. Guillong, I. Peytcheva, M. Waelle and S. Sakata, J. Anal. At. Spectrom., 2014, 29(9), 1618–1629 RSC.
  396. J. I. Kimura, Q. Chang, K. Itano, T. Iizuka, B. S. Vaglarov and K. Tani, J. Anal. At. Spectrom., 2015, 30(2), 494–505 RSC.
  397. K. D. Zhao, S. Y. Jiang, H. F. Ling and M. R. Palmer, Chem. Geol., 2014, 389, 110–121 CrossRef CAS.
  398. J. P. Bernal, L. A. Solari, A. Gomez-Tuena, C. Ortega-Obregon, L. Mori, M. Vega-Gonzalez and D. G. Espinosa-Arbelaez, Quaternary Geochronology, 2014, 23, 46–55 CrossRef.
  399. B. Paul, J. D. Woodhead, C. Paton, J. M. Hergt, J. Hellstrom and C. A. Norris, Geostand. Geoanal. Res., 2014, 38(3), 253–263 CrossRef.
  400. R. Hennekam, T. Jilbert, P. R. D. Mason, G. J. de Lange and G. J. Reichart, Chem. Geol., 2015, 403, 42–51 CrossRef CAS.
  401. C. J. Kelly, C. R. M. McFarlane, D. A. Schneider and S. E. Jackson, Geostand. Geoanal. Res., 2014, 38(4), 389–407 CrossRef CAS.
  402. G. S. Senesi, Earth-Sci. Rev., 2014, 139, 231–267 CrossRef CAS.
  403. S. J. Qiao, Y. Ding, D. Tian, L. Yao and G. Yang, Appl. Spectrosc. Rev., 2015, 50(1), 1–26 CrossRef.
  404. N. J. McMillan, S. Rees, K. Kochelek and C. McManus, Geostand. Geoanal. Res., 2014, 38(3), 329–343 CrossRef CAS.
  405. Z. Q. Hao, C. M. Li, M. Shen, X. Y. Yang, K. H. Li, L. B. Guo, X. Y. Li, Y. F. Lu and X. Y. Zeng, Opt. Express, 2015, 23(6), 7795–7801 CrossRef CAS PubMed.
  406. L. W. Sheng, T. L. Zhang, G. H. Niu, K. Wang, H. S. Tang, Y. X. Duan and H. Li, J. Anal. At. Spectrom., 2015, 30(2), 453–458 RSC.
  407. C. Alvarez, J. Pisonero and N. Bordel, Spectrochim. Acta, Part B, 2014, 100, 123–128 CrossRef CAS.
  408. P. Porizka, A. Demidov, J. Kaiser, J. Keivanian, I. Gornushkin, U. Panne and J. Riedel, Spectrochim. Acta, Part B, 2014, 101, 155–163 CrossRef CAS.
  409. G. Vitkova, L. Prokes, K. Novotny, P. Porizka, J. Novotny, D. Vsiansky, L. Celko and J. Kaiser, Spectrochim. Acta, Part B, 2014, 101, 191–199 CrossRef CAS.
  410. P. Pease and V. Tchakerian, Aeolian Research, 2014, 15, 203–216 CrossRef.
  411. C. P. M. Roux, J. Rakovsky, O. Musset, F. Monna, J. F. Buoncristiani, P. Pellenard and C. Thomazo, Spectrochim. Acta, Part B, 2015, 103, 63–69 CrossRef.
  412. C. Fabre, A. Cousin, R. C. Wiens, A. Ollila, O. Gasnault, S. Maurice, V. Sautter, O. Forni, J. Lasue, R. Tokar, D. Vaniman and N. Melikechi, Spectrochim. Acta, Part B, 2014, 99, 34–51 CrossRef CAS.
  413. P. J. Gasda, T. E. Acosta-Maeda, P. G. Lucey, A. K. Misra, S. K. Sharma and G. J. Taylor, Appl. Spectrosc., 2015, 69(2), 173–192 CrossRef CAS PubMed.
  414. M. Tulej, A. Riedo, M. B. Neuland, S. Meyer, P. Wurz, N. Thomas, V. Grimaudo, P. Moreno-Garcia, P. Broekmann, A. Neubeck and M. Ivarsson, Geostand. Geoanal. Res., 2014, 38(4), 441–466 CrossRef CAS.
  415. J. Solé, Chem. Geol., 2014, 388, 9–22 CrossRef.
  416. Y. Cho, S. Sugita, S. Kameda, Y. N. Miura, K. Ishibashi, S. Ohno, S. Kamata, T. Arai, T. Morota, N. Namiki and T. Matsui, Spectrochim. Acta, Part B, 2015, 106, 28–35 CrossRef CAS.
  417. B. A. Cohen, J. S. Miller, Z. H. Li, T. D. Swindle and R. A. French, Geostand. Geoanal. Res., 2014, 38(4), 421–439 CrossRef CAS.
  418. B. Thornton, T. Takahashi, T. Sato, T. Sakka, A. Tamura, A. Matsumoto, T. Nozaki, T. Ohki and K. Ohki, Deep Sea Research, 2015, 195, 20–36 CrossRef.
  419. T. F. Boucher, M. V. Ozanne, M. L. Carmosino, M. D. Dyar, S. Mahadevan, E. A. Breves, K. H. Lepore and S. M. Clegg, Spectrochim. Acta, Part B, 2015, 107, 1–10 CrossRef CAS.
  420. Y. Wang and I. D. Brindle, J. Anal. At. Spectrom., 2014, 29(10), 1904–1911 RSC.
  421. F. D'Agostino, E. Oliveri, E. Bagnato, F. Falco, S. Mazzola and M. Sprovieri, Anal. Chim. Acta, 2014, 852, 8–12 CrossRef PubMed.
  422. G. M. S. Sampaio and J. Enzweiler, Geostand. Geoanal. Res., 2015, 39(1), 105–119 CrossRef CAS.
  423. W. J. Li, X. D. Jin, B. Y. Gao, C. L. Wang and L. C. Zhang, Anal. Methods, 2014, 6(15), 6125–6132 RSC.
  424. A. Ishikawa, R. Senda, K. Suzuki, C. W. Dale and T. Meisel, Chem. Geol., 2014, 384, 27–46 CrossRef CAS.
  425. J. Li, P. P. Zhao, J. G. Liu, X. C. Wang, A. Y. Yang, G. Q. Wang and J. F. Xu, Geostand. Geoanal. Res., 2015, 39(1), 17–30 CrossRef CAS.
  426. Z. Y. Chu, Y. Yan, Z. Chen, J. H. Guo, Y. H. Yang, C. F. Li and Y. B. Zhang, Geostand. Geoanal. Res., 2015, 39(2), 151–169 CrossRef CAS.
  427. O. Evdokimova, P. Zaitceva, N. Pechishcheva, A. Pupyshev and K. Shunyaev, Curr. Anal. Chem., 2014, 10(4), 449–456 CrossRef CAS.
  428. M. Krishnakumar, K. Satyanarayana and K. Mukkanti, At. Spectrosc., 2015, 36(2), 74–81 CAS.
  429. T. Vogt, D. Bauer, M. Neuroth and M. Otto, Fuel, 2015, 152, 96–102 CrossRef CAS.
  430. Y. Makonnen and D. Beauchemin, Spectrochim. Acta, Part B, 2014, 99, 87–93 CrossRef CAS.
  431. T. Chen, Z. C. Hu, S. H. Liu, Y. S. Liu, S. Gao, M. Li, K. Q. Zong, H. H. Chen and S. H. Hu, Spectrochim. Acta, Part B, 2015, 106, 36–44 CrossRef CAS.
  432. M. Liezers, O. T. Farmer, M. P. Dion, M. L. Thomas and G. C. Eiden, Int. J. Mass Spectrom., 2015, 376, 58–64 CrossRef CAS.
  433. J. Teran-Baamonde, J. M. Andrade, R. M. Soto-Ferreiro, A. Carlosena and D. Prada, J. Anal. At. Spectrom., 2015, 30(5), 1197–1206 RSC.
  434. W. Doherty, Spectrochim. Acta, Part B, 2015, 107, 56–60 CrossRef CAS.
  435. W. Doherty, P. C. Lightfoot and D. E. Ames, Spectrochim. Acta, Part B, 2014, 98, 28–38 CrossRef CAS.
  436. A. Gourgiotis, S. Berail, P. Louvat, H. Isnard, J. Moureau, A. Nonell, G. Manhes, J. L. Birck, J. Gaillardet, C. Pecheyran, F. Chartier and O. F. X. Donard, J. Anal. At. Spectrom., 2014, 29(9), 1607–1617 RSC.
  437. K. van Hoecke, V. Devulder, P. Claeys, P. Degryse and F. Vanhaecke, J. Anal. At. Spectrom., 2014, 29(10), 1819–1826 RSC.
  438. P. Louvat, J. Moureau, G. Paris, J. Bouchez, J. Noireaux and J. Gaillardet, J. Anal. At. Spectrom., 2014, 29(9), 1698–1707 RSC.
  439. S. Misra, R. Owen, J. Kerr, M. Greaves and H. Elderfield, Geochim. Cosmochim. Acta, 2014, 140, 531–552 CrossRef CAS.
  440. K. Kaczmarek, I. Horn, G. Nehrke and J. Bijma, Chem. Geol., 2015, 392, 32–42 CrossRef CAS.
  441. R. Chakrabarti, Curr. Sci., 2015, 108(2), 246–254 CAS.
  442. Y. J. An and F. Huang, J. Earth Sci., 2014, 25(5), 822–840 CrossRef CAS.
  443. T. Breton and G. Quitté, J. Anal. At. Spectrom., 2014, 29(12), 2284–2293 RSC.
  444. E. K. Skierszkan, M. Amini and D. Weis, Anal. Bioanal. Chem., 2015, 407(7), 1925–1935 CrossRef CAS PubMed.
  445. D. Malinovsky, P. J. H. Dunn, P. Petrov and H. Goenaga-Infante, Anal. Bioanal. Chem., 2015, 407(3), 869–882 CrossRef CAS PubMed.
  446. J. B. Creech and B. Paul, Geostand. Geoanal. Res., 2015, 39(1), 7–15 CrossRef.
  447. J. M. Koornneef, I. Nikogosian, M. J. van Bergen, R. Smeets, C. Bouman and G. R. Davies, Chem. Geol., 2015, 397, 14–23 CrossRef CAS.
  448. C. Sarkar, D. G. Pearson, L. M. Heaman and S. J. Woodland, Chem. Geol., 2015, 395, 27–40 CrossRef CAS.
  449. M. Wiedenbeck, L. P. Bedard, R. Bugoi, M. Horan, K. Linge, S. Merchel, L. F. G. Morales, D. Savard, A. K. Souders and P. Sylvester, Geostand. Geoanal. Res., 2014, 38(4), 467–512 CrossRef.
  450. R. Chatterjee and J. C. Lassiter, Chem. Geol., 2015, 396, 112–123 CrossRef CAS.
  451. Z. Y. Chu, C. F. Li, E. Hegner, Z. Chen, Y. Yan and J. H. Guo, Anal. Chem., 2014, 86(22), 11141–11150 CrossRef CAS PubMed.
  452. D. J. Condon, B. Schoene, N. M. McLean, S. A. Bowring and R. R. Parrish, Geochim. Cosmochim. Acta, 2015, 164, 464–480 CrossRef CAS.
  453. N. M. McLean, D. J. Condon, B. Schoene and S. A. Bowring, Geochim. Cosmochim. Acta, 2015, 164, 481–501 CrossRef CAS.
  454. A. Das and D. W. Davis, Chem. Geol., 2014, 385, 1–6 CrossRef CAS.
  455. H. Z. Wei, S. Y. Jiang, T. L. Yang, J. H. Yang, T. Yang, X. Yan, B. P. Ling, Q. Liu and H. P. Wu, J. Anal. At. Spectrom., 2014, 29(11), 2104–2107 RSC.
  456. M. T. McCulloch, M. Holcomb, K. Rankenburg and J. A. Trotter, Rapid Commun. Mass Spectrom., 2014, 28(24), 2704–2712 CrossRef CAS PubMed.
  457. P. A. Sossi, G. P. Halverson, O. Nebel and S. M. Eggins, Geostand. Geoanal. Res., 2015, 39(2), 129–149 CrossRef CAS.
  458. M. Schiller, E. van Kooten, J. C. Holst, M. B. Olsen and M. Bizzarro, J. Anal. At. Spectrom., 2014, 29(8), 1406–1416 RSC.
  459. C. F. Li, J. H. Guo, Y. H. Yang, Z. Y. Chu and X. C. Wang, J. Anal. At. Spectrom., 2014, 29(8), 1467–1476 RSC.
  460. Y. J. An, F. Wu, Y. X. Xiang, X. Y. Nan, X. Yu, J. H. Yang, H. M. Yu, L. W. Xie and F. Huang, Chem. Geol., 2014, 390, 9–21 CrossRef CAS.
  461. J. Li, X. R. Liang, L. F. Zhong, X. C. Wang, Z. Y. Ren, S. L. Sun, Z. F. Zhang and J. F. Xu, Geostand. Geoanal. Res., 2014, 38(3), 345–354 CrossRef CAS.
  462. C. Pin, A. Gannoun and A. Dupont, J. Anal. At. Spectrom., 2014, 29(10), 1858–1870 RSC.
  463. J. Krajko, Z. Varga, E. Yalcintas, M. Wallenius and K. Mayer, Talanta, 2014, 129, 499–504 CrossRef CAS PubMed.
  464. C. F. Li, X. C. Wang, Y. L. Li, Z. Y. Chu, J. H. Guo and X. H. Li, J. Anal. At. Spectrom., 2015, 30(4), 895–902 RSC.
  465. J. I. Kimura, T. Nozaki, R. Senda and K. Suzuki, J. Anal. At. Spectrom., 2014, 29(8), 1483–1490 Search PubMed.
  466. M. R. Raven, J. F. Adkins, J. P. Werne, T. W. Lyons and A. L. Sessions, Org. Geochem., 2015, 80, 53–59 CrossRef CAS.
  467. P. von Strandmann, C. D. Coath, D. C. Catling, S. W. Poulton and T. Elliott, J. Anal. At. Spectrom., 2014, 29(9), 1648–1659 RSC.
  468. M. A. Millet and N. Dauphas, J. Anal. At. Spectrom., 2014, 29(8), 1444–1458 Search PubMed.
  469. S. Okabayashi, S. Sakata and T. Hirata, Anal. Chim. Acta, 2015, 853, 469–476 CrossRef CAS PubMed.
  470. R. Khan, Y. Yokozuka, S. Terai, N. Shirai and M. Ebihara, J. Anal. At. Spectrom., 2015, 30(2), 506–514 RSC.
  471. Y. T. Lin, L. Feng, J. T. Hao, Y. Liu, S. Hu, J. C. Zhang and W. Yang, J. Anal. At. Spectrom., 2014, 29(9), 1686–1691 RSC.
  472. N. T. Kita, P. E. Sobol, J. R. Kern, N. E. Lord and J. W. Valley, J. Anal. At. Spectrom., 2015, 30(5), 1207–1213 RSC.
  473. S. Hu, Y. T. Lin, J. C. Zhang, J. L. Hao, W. Yang and L. W. Deng, J. Anal. At. Spectrom., 2015, 30(4), 967–978 RSC.
  474. G. Othmane, S. Hull, M. Fayek, O. Rouxel, M. L. Geagea and T. K. Kyser, Chem. Geol., 2015, 395, 41–49 CrossRef CAS.
  475. H. R. Marschall and B. D. Monteleone, Geostand. Geoanal. Res., 2015, 39(1), 31–46 CrossRef CAS.
  476. B. D. Pauly, L. B. Williams, R. L. Bervig, P. Schiffman and R. A. Zierenberg, Clays Clay Miner., 2014, 62(3–4), 224–234 CrossRef.
  477. D. Rubatto, B. Putlitz, L. Gauthiez-Putallaz, C. Crepisson, I. S. Buick and Y. F. Zheng, Chem. Geol., 2014, 380, 84–96 CrossRef CAS.
  478. G. Q. Tang, X. H. Li, Q. L. Li, Y. Liu, X. X. Ling and Q. Z. Yin, J. Anal. At. Spectrom., 2015, 30(4), 950–956 RSC.
  479. J. C. Zhang, Y. T. Lin, W. Yang, W. J. Shen, J. L. Hao, S. Hu and M. J. Cao, J. Anal. At. Spectrom., 2014, 29(10), 1934–1943 RSC.
  480. T. Ushikubo, K. H. Williford, J. Farquhar, D. T. Johnston, M. J. van Kranendonk and J. W. Valley, Chem. Geol., 2014, 383, 86–99 CrossRef CAS.
  481. Y. Liu, Q. L. Li, G. Q. Tang, X. H. Li and Q. Z. Yin, J. Anal. At. Spectrom., 2015, 30(4), 979–985 RSC.
  482. Q. Liu, X. L. Hou, W. J. Zhou and Y. C. Fu, J. Am. Soc. Mass Spectrom., 2015, 26(5), 725–733 CrossRef CAS PubMed.
  483. L. L. Cui and X. Wang, Anal. Methods, 2014, 6(22), 9173–9178 RSC.
  484. L. L. Cui and X. Wang, Int. J. Mass Spectrom., 2014, 372, 46–50 CrossRef CAS.
  485. F. Fourel, F. Martineau, M. Seris and C. Lecuyer, Geostand. Geoanal. Res., 2015, 39(1), 47–53 CrossRef CAS.
  486. P. S. Ross, A. Bourke and B. Fresia, Geochem.: Explor., Environ., Anal., 2014, 14(2), 171–185 CrossRef CAS.
  487. S. J. Piercey and M. C. Devine, Geochem.: Explor., Environ., Anal., 2014, 14(2), 139–148 CrossRef CAS.
  488. M. F. Gazley, C. M. Tutt, L. I. Brisbout, L. A. Fisher and G. Duclaux, Geochem.: Explor., Environ., Anal., 2014, 14(3), 223–231 CrossRef CAS.
  489. P. S. Ross, A. Bourke and B. Fresia, Geochem.: Explor., Environ., Anal., 2014, 14(2), 187–196 CrossRef CAS.
  490. L. Fisher, M. F. Gazley, A. Baensch, S. J. Barnes, J. Cleverley and G. Duclaux, Geochem.: Explor., Environ., Anal., 2014, 14(2), 149–159 CrossRef CAS.
  491. M. le Vaillant, S. J. Barnes, L. Fisher, M. L. Fiorentini and S. Caruso, Geochem.: Explor., Environ., Anal., 2014, 14(3), 199–209 CrossRef CAS.
  492. G. J. Simandl, R. S. Stone, S. Paradis, R. Fajber, H. M. Reid and K. Grattan, Miner. Deposita, 2014, 49(8), 999–1012 CrossRef CAS.
  493. G. J. Simandl, S. Paradis, R. S. Stone, R. Fajber, R. D. Kressall, J. Crozier and L. J. Simandi, Geochem.: Explor., Environ., Anal., 2014, 14(3), 211–221 CrossRef CAS.
  494. G. J. Simandl, R. Fajber and S. Paradis, Geochem.: Explor., Environ., Anal., 2014, 14(2), 161–169 CrossRef CAS.

This journal is © The Royal Society of Chemistry 2016