Seeking excellence: An evaluation of 235 international laboratories conducting water isotope analyses by isotope-ratio and laser-absorption spectrometry

作者: L.I. Wassenaar , S. Terzer-Wassmuth , C. Douence , L. Araguas-Araguas , P.K. Aggarwal

DOI: 10.1002/RCM.8052

关键词: SpectrometerMass spectrometryLaser absorption spectrometryStable isotope ratioChemistryInstrumentationEnvironmental chemistryIsotopeCore sampleContamination

摘要: RATIONALE Water stable isotope ratios (δ2 H and δ18 O values) are widely used tracers in environmental studies; hence, accurate precise assays required for providing sound scientific information. We tested the analytical performance of 235 international laboratories conducting water analyses using dual-inlet continuous-flow ratio mass spectrometers laser through a inter-comparison test. METHODS Eight test samples were distributed by IAEA to laboratories. These consisted core set five spanning common δ-range natural waters, three optional (highly depleted, enriched, saline). The fifth sample contained unrevealed trace methanol assess analyst vigilance impact organic contamination on isotopic measurements made all instrument technologies. RESULTS For ~73 % gave acceptable results within 0.2 ‰ 1.5 reference values δ2 H, respectively; ~27 produced unacceptable results. Top was dominated IRMS laboratories; top led spectrometer Continuous-flow instruments yielded comparatively intermediate Trace resulted extreme outlier δ-values instruments, but also affected reactor-based systems; however, unaffected. CONCLUSIONS Analysis laboratory their metadata suggested inaccurate or imprecise stemmed mainly from skill- knowledge-based errors including: calculation mistakes, inappropriate compromised calibration standards, poorly performing instrumentation, lack contamination, inattention unreasonable outcomes. To counteract errors, we recommend that include 1-2 'known' control standards autoruns; should screen each autorun spectral contamination; evaluate whether derived d-excess realistic when both measured. Combined, these data evaluation strategies immediately inform about fundamental mistakes samples.

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