Cody Millar


DOI bib
On the urgent need for standardization in isotope‐based ecohydrological investigations
Cody Millar, Kim Janzen, Magali F. Nehemy, Geoff Koehler, Pedro Hervé‐Fernández, Hongxiu Wang, Natalie Orlowski, Adrià Barbeta, Jeffrey J. McDonnell
Hydrological Processes, Volume 36, Issue 10

Abstract Ecohydrological investigations commonly use the stable isotopes of water (hydrogen and oxygen) as conservative ecosystem tracers. This approach requires accessing and analysing water from plant and soil matrices. Generally, there are six steps involved to retrieve hydrogen and oxygen isotope values from these matrices: (1) sampling, (2) sample storage and transport, (3) extraction, (4) pre‐analysis processing, (5) isotopic analysis, and (6) post‐processing and correction. At each step, cumulative errors can be introduced which sum to non‐trivial magnitudes. These can impact subsequent interpretations about water cycling and partitioning through the soil–plant‐atmosphere continuum. At each of these steps, there are multiple possible options to select from resulting in tens of thousands of possible combinations used by researchers to go from plant and soil samples to isotopic data. In a newly emerging field, so many options can create interpretive confusion and major issues with data comparability. This points to the need for development of shared standardized approaches. Here we critically examine the state of the process chain, reflecting on the issues associated with each step, and provide suggestions to move our community towards standardization. Assessing this shared ‘process chain’ will help us see the problem in its entirety and facilitate community action towards agreed upon standardized approaches.


DOI bib
Organic contamination detection for isotopic analysis of water by laser spectroscopy
Cody Millar, Kim Janzen, Magali F. Nehemy, Geoff Koehler, Pedro Hervé‐Fernández, Jeffrey J. McDonnell
Rapid Communications in Mass Spectrometry, Volume 35, Issue 15

Rationale Hydrogen and oxygen stable isotope ratios (δ2H, δ17O, and δ18O values) are commonly used tracers of water. These ratios can be measured by isotope ratio infrared spectroscopy (IRIS). However, IRIS approaches are prone to errors induced by organic compounds present in plant, soil, and natural water samples. A novel approach using 17O-excess values has shown promise for flagging spectrally contaminated plant samples during IRIS analysis. A systematic assessment of this flagging system is needed to prove it useful. Methods Errors induced by methanol and ethanol water mixtures on measured IRIS and isotope ratio mass spectrometry (IRMS) results were evaluated. For IRIS analyses both liquid- and vapour-mode (via direct vapour equilibration) methods are used. The δ2H, δ17O, and δ18O values were measured and compared with known reference values to determine the errors induced by methanol and ethanol contamination. In addition, the 17O-excess contamination detection approach was tested. This is a post-processing detection tool for both liquid and vapour IRIS triple-isotope analyses, utilizing calculated 17O-excess values to flag contaminated samples. Results Organic contamination induced significant errors in IRIS results, not seen in IRMS results. Methanol caused larger errors than ethanol. Results from vapour-IRIS analyses had larger errors than those from liquid-IRIS analyses. The 17O-excess approach identified methanol driven error in liquid- and vapour-mode IRIS samples at levels where isotope results became unacceptably erroneous. For ethanol contaminated samples, a mix of erroneous and correct flagging occurred with the 17O-excess method. Our results indicate that methanol is the more problematic contaminant for data corruption. The 17O-excess method was therefore useful for data quality control. Conclusions Organic contamination caused significant errors in IRIS stable isotope results. These errors were larger during vapour analyses than during liquid IRIS analyses, and larger for methanol than ethanol contamination. The 17O-excess method is highly sensitive for detecting narrowband (methanol) contamination error in vapour and liquid analysis modes in IRIS.