Technical note: Spectral correction for cavity ring-down isotope analysis of plant and soil waters

Abstract

The development of laser spectroscopic analyzers has revolutionized isotope hydrology, dramatically increasing accessibility and reducing the cost of sample analysis. Despite their substantial benefits, these instruments are known to suffer from spectral interferences caused by small organic molecules that can bias measurements of some samples. Previous research has characterized this problem and tested a range of solutions for eliminating, detecting, or correcting influence in experimental or natural samples, yet interlaboratory comparisons show that affected data are still being reported. Here, we use paired spectroscopic (Picarro L2130-i; CRDS) and isotope ratio mass spectrometer (IRMS) data from a diverse suite of soil and plant xylem water samples to characterize spectral interference effects on CRDS δ2H and δ18O data. Interference is minimal for soil water but widespread in plant samples, with 13 % and 54 % of samples exhibiting biases larger than 8 ‰ for δ2H and 1 ‰ for δ18O, respectively. We develop multivariate statistical models that use analyzer-reported spectral features to correct for interference. These models account for 57 % of the observed δ2H bias and 99 % of the δ18O bias, and after correction the standard deviation of the CRDS − IRMS differences for plant samples (4.1 ‰ for δ2H and 0.4 ‰ for δ18O) was similar to that for soil samples. Applying the models to CRDS measurements of water extracted from 1176 plants and 693 soils collected across diverse ecosystems improves the correspondence between plant and source soil water values and shows strong taxonomic differences in the prevalence of spectral interference. Our results show that spectral interference remains a significant concern in ecohydrology, particularly for plant water extracted from many woody species. The success of our spectral correction models across a wide range of taxa and data generated from two different CRDS analyzers suggests that post hoc correction of these data may be a viable solution to the problem.