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College of Forest Resources and Environmental Science


Quantifying historical patterns of fire regimes in peatlands can help contextualise current fire behaviour and aid in planning on ecosystem and landscape scales. However, current methods for detecting the evidence of past fires in peat soils are laborious or expensive. Our goal was to develop an effective and inexpensive method for detecting pyrogenic carbon (PyC) concentration in peat which could be used to estimate the occurrence of fires by analysis of discrete soil samples. We correlated diffuse reflectance Fourier-transform infrared spectrometry (FTIR) measurements of peat, and admixtures of peat and PyC, with nuclear magnetic resonance spectrometry (NMR) estimates of PyC concentrations. We compared two methods for modelling PyC concentration based on FTIR data, namely peak fitting and partial least squares regression. Peak fitting analyses of FTIR spectra isolated 15 unique spectral features within the peat matrices, of which five were statistically relevant to PyC detection. Peak-fitting and partial least squares regression modelling both reliably predicted peat sample PyC concentrations, though partial least squares regression needs additional work before a general model can be developed. Therefore, FTIR spectrometry could be used to detect the presence of past fire events within peat soil profiles with relatively low cost and time investment.

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© 2022. Publisher’s version of record:

Publication Title

Mires and Peat

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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