[en] The determination of naturally occurring and anthropogenic radionuclides in the environment by inductively coupled plasma mass spectrometry has gained recognition over the last fifteen years, relative to radiometric techniques, as the result of improvement in instrumental performance, sample introduction equipment, and sample preparation. With the increase in instrumental sensitivity, it is now possible to measure ultratrace levels (fg range) of many radioisotopes, including those with half-lives between 1 and 1000 years, without requiring very complex sample pre-concentration schemes. However, the identification and quantification of radioisotopes in environmental matrices is still hampered by a variety of analytical issues such as spectral (both atomic and molecular ions) and non-spectral (matrix effect) interferences and instrumental limitations (e.g., abundance sensitivity). The scope of this review is to highlight recent analytical progress and issues associated with the determination of radionuclides by inductively coupled plasma mass spectrometry. The impact of interferences, instrumental limitations (e.g., degree of ionization, abundance sensitivity, detection limits) and low sample-to-plasma transfer efficiency on the measurement of radionuclides by inductively coupled plasma mass spectrometry will be described. Solutions that overcome these issues will be discussed, highlighting their pros and cons and assessing their impact on the measurement of environmental radioactivity. Among the solutions proposed, mass and chemical resolution through the use of sector-field instruments and chemical reactions/collisions in a pressurized cell, respectively, will be described. Other methods, such as unique sample introduction equipment (e.g., laser ablation, electrothermal vaporisation, high efficiency nebulization) and instrumental modifications/optimizations (e.g., instrumental vacuum, radiofrequency power, guard electrode) that improve sensitivity and performance will also be examined

[en] Biogeochemical conditions and landscape can have strong influences on mercury bioaccumulation in fish, but these effects across regional scales and between sites with and without point sources of contamination are not well understood. Normal means clustering, a type of unsupervised machine learning, was used to analyze relationships between forage fish (Fundulus heteroclitus and Menidia menidia) mercury (Hg) concentrations and sediment and water column Hg and methylmercury (MeHg) concentrations, ancillary variables, and land classifications within the sub-watershed. The analysis utilized data from 38 sites in 8 estuarine systems in the Northeast US, collected over five years. A large range of mercury concentrations and land use proportions were observed across sites. The cluster correlations indicated that for Fundulus, benthic and pelagic Hg and MeHg concentrations were most related to tissue concentrations, while Menidia Hg was most related to water column MeHg, reflecting differing feeding modes between the species. For both species, dissolved MeHg was most related to tissue concentrations, with sediment Hg concentrations influential at contaminated sites. The models considering only uncontaminated sites showed reduced influence of bulk sediment MeHg for both species, but Fundulus retained sediment drivers at some sites, with dissolved MeHg still highly correlated for both. Dissolved organic carbon (DOC), chlorophyll, land use, and other ancillary variables were of lesser importance in driving bioaccumulation, though DOC was strongly related within some clusters, likely in relation to dissolved Hg. Land use, though not of primary importance, showed relationships opposite to those observed in freshwater, with development positively correlated and forests and agriculture negatively correlated with tissue concentrations across clusters and species. Clusters were composed of sites from geographically distinct systems, indicating the greater importance of small scale drivers of MeHg formation and uptake into the food web over system or region-wide influences.