Header

UZH-Logo

Maintenance Infos

Quantification of pyrogenic carbon in the environment: An integration of analytical approaches


Cotrufo, M Francesca; Boot, Claudia; Abiven, Samuel; Foster, Erika J; Haddix, Michelle; Reisser, Moritz; Wurster, Christopher M; Bird, Michael I; Schmidt, Michael W I (2016). Quantification of pyrogenic carbon in the environment: An integration of analytical approaches. Organic Geochemistry, 100:42-50.

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 (R2 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.

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 (R2 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.

Statistics

Citations

3 citations in Web of Science®
2 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

1 download since deposited on 07 Nov 2016
1 download since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2016
Deposited On:07 Nov 2016 10:28
Last Modified:07 Nov 2016 10:28
Publisher:Elsevier
ISSN:0146-6380
Publisher DOI:https://doi.org/10.1016/j.orggeochem.2016.07.007

Download