Analysis of Environmental Samples Using Microwave-Assisted Acid Digestion and Inductively Coupled Plasma Mass Spectrometry: Maximizing Total Element Recoveries View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-01

AUTHORS

Nouri M. Hassan, Pat E. Rasmussen, Ewa Dabek-Zlotorzynska, Valbona Celo, Heidi Chen

ABSTRACT

For the routine determination of metals in environmental samples, we require microwave-assisted digestion methods that yield ‘total’ or ‘near-total’ recoveries while avoiding the use of HF acid. As inductively coupled plasma mass spectrometry (ICP-MS) is the method of detection, it is desirable to minimize the use of HCl to avoid spectral interferences caused by high Cl– concentrations. Using certified reference materials, we performed a series of modifications to the US EPA method 3051 which included: increasing the temperature and durations of microwave digestion, varying the ratio of sample mass to acid volume, and alterations to the compositions of the acid digestion mixture. The experiments were conducted using urban particulate matter (NIST-1648), coal fly ash (NBS-1633) and six CANMET certified reference materials (Till-2, Till-3, Till-4, LKSD-1, LKSD-2 and LKSD-4), in two laboratories (Health Canada and Environment Canada) using different microwave digestion systems and different ICP-MS instruments. Our modified microwave-assisted nitric acid digestion method improved recoveries for Pb, Zn, V, Fe and Cu approaching ‘total’ recoveries in the same matrices determined using X-ray fluorescence (XRF) and instrumental neutron activation analysis (INAA) as reported in the certificates of analysis. Recoveries for other elements such as Cr and Ni compared well with ‘near-total’ recoveries yielded by traditional (non-assisted) acid digestion methods. More... »

PAGES

323-334

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11270-006-9201-3

DOI

http://dx.doi.org/10.1007/s11270-006-9201-3

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https://app.dimensions.ai/details/publication/pub.1021300573


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