Quantification of pyrogenic carbon in the environment: An integration of analytical approaches. (October 2016)

• Record Type: Journal Article
• Authors: Cotrufo, M. Francesca; Boot, Claudia; Abiven, Samuel; Foster, Erika J.; Haddix, Michelle; Reisser, Moritz; Wurster, Christopher M.; Bird, Michael I.; Schmidt, Michael W.I.
• Is Part Of: Organic geochemistry. Volume 100(2016:Nov.)
• Page Start: 42
• Page End: 50
• Publication Date: 2016-10
• Subjects: Pyrogenic carbon -- Black carbon -- Benzene Polycarboxylic Acids -- Hydrogen pyrolysis -- Mid Infrared Spectroscopy -- Fire -- Sediments -- Soils; Organic geochemistry -- Periodicals; Biogeochemistry -- Periodicals; Géochimie organique -- Périodiques; 553.205
• DOI: 10.1016/j.orggeochem.2016.07.007
• Languages: English
• ISSNs: 0146-6380

Abstract

Highlights: BPCA, hydrogen pyrolysis and MIR used to quantify PyC in a range of matrices. PyCBPCA yield varied with matrix, and correlated with degree of condensation. MIR can be used to predict PyC as measured from BPCA or hydrogen pyrolysis. Abstract: Pyrogenic carbon (PyC), the product of incomplete combustion of biomass during fire, is now recognized as a significant component of the global carbon cycle. However, quantitative determination of PyC is challenging, in particular at a large scale. We conducted a comparison of three methods for PyC analysis: benzene polycarboxylic acid (BPCA) method, hydrogen pyrolysis (hypy) and mid infrared spectroscopy (MIR) to identify a suitable approach for the determination of PyC at large geographical scales and across different environmental matrices. We analyzed samples ( n = 165) derived from a variety of matrices (i.e. forest floor, soils, sediments and char), most of which were collected in the natural environment after fire. BPCA and hypy PyC estimates correlated linearly ( R 2 between 0.74 and 0.92), thus suggesting that they can be merged in larger scale PyC syntheses. However, the slope of the regression varied among different matrices, ranging between 0.1 and 0.44, likely due to differences in the degree of aromatic condensation. MIR coupled with partial least-squares regression (MIR-PLSR) was demonstrated to be a powerful tool for estimating PyC across a variety of environmental matrices, with high throughput and low analytical cost in comparison with the other two PyC analytical methods. Furthermore, we obtained accurate calibrations for MIR-PLSR from the hypy and the BPCA method, the latter in particular for soil samples. We thus conclude that PyC estimates at large geographical scales and across different environmental matrices can be obtained from MIR-PLSR, previous calibration with hypy or BPCA, for matrices for which the PyC yields are known.