Issue 4, 2016

Ferric ion induced enhancement of ultraviolet vapour generation coupled with atomic fluorescence spectrometry for the determination of ultratrace inorganic arsenic in surface water

Abstract

A novel method of ultraviolet vapour generation (UVG) coupled with atomic fluorescence spectrometry (AFS) was developed for the determination of ultratrace inorganic arsenic (iAs) in surface water. In this work, different ferric species were utilised for the first time as an enhancement reagent for the ultraviolet vapour generation of As(III), and their UVG efficiencies for volatile species of arsenic were investigated. 15 mg L−1 of ferric chloride provided the greatest enhancement of approximately 10-fold, using 20% acetic acid combined with 4% formic acid with 30 s ultraviolet irradiation at 200 mL min−1 Ar/H2 flow rate. Under the optimised conditions, the linear range was 1.0 μg L−1–100.0 μg L−1, and the spiked recoveries were 92%–98%. The limit of detection was 0.05 μg L−1 for iAs, and the relative standard deviation (RSD) value of the repeated measurements was 2.0% (n = 11). This method was successfully applied to the determination of ultratrace iAs in tap water, river water, and lake water samples using 0.2% H2SO4 (v : v) as the sample preserver. The obtained values for the water samples of certified reference materials (CRMs) including GSB-Z50004-200431, GBW08605 and GBW(E)080390 were all within the certified ranges.

Graphical abstract: Ferric ion induced enhancement of ultraviolet vapour generation coupled with atomic fluorescence spectrometry for the determination of ultratrace inorganic arsenic in surface water

Article information

Article type
Paper
Submitted
04 Dec 2015
Accepted
27 Dec 2015
First published
04 Jan 2016

Analyst, 2016,141, 1530-1536

Author version available

Ferric ion induced enhancement of ultraviolet vapour generation coupled with atomic fluorescence spectrometry for the determination of ultratrace inorganic arsenic in surface water

Y. Wang, L. Lin, J. Liu, X. Mao, J. Wang and D. Qin, Analyst, 2016, 141, 1530 DOI: 10.1039/C5AN02489G

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