Abstract

A good quality control/quality assurance programme should be implemented in all environmental or health related studies on mercury and its organic compounds, particularly, for monomethylmercury (MeHg) which is the most toxic mercury compound. This can be achieved initially by analysing suitable certified reference materials (CRMs), which are available from various producers such as the National Institute of Standards and Technology (KIST) from USA, National Institute of Environmental Studies (VIES), National Research Council of Canada (NRCC), Standards, Measurements and Testing programme (SM&T) of the European Commission, and the International Atomic Energy Agency (IAEA). It is well understood that these materials are not covering present needs, as most of them are of the marine origin, while many laboratories are conducting research and monitoring in terrestrial ecosystems and fresh water environment. In addition, CRMs for human exposure assessment, such as blood, urine, and hair at several levels of concentrations are still lacking. Therefore, many other actions should be undertaken to achieve, improve and/or maintain quality of data, including participation in interlaboratory studies, proficiency testing and production of laboratory reference materials. A review of these actions has shown that MeHg compounds determination in samples such as soil, sediment and water is rather difficult and the results are also method dependent. In addition, it has been shown that some of the most frequently employed analytical methods may be a subject to spurious MeHg formation in the presence of high concentrations of inorganic mercury and organic matter. These findings have put a number of previous data on MeHg in question and consequently prompt actions were undertaken by a number of well experienced laboratories and producers of CRMs. So far, it is shown that the results obtained by various laboratories using different analytical techniques agree well with certified values in all RMs certified for MeHg. This suggests that comparability of data can be achieved, which however is not a guarantee of the true values.

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