Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids (BPCA)
Fire-derived, pyrogenic carbon (PyC), sometimes called black carbon (BC), is the carbonaceous solid residue of biomass and fossil fuel combustion, such as char and soot. PyC is ubiquitous in the environment due to its long persistence, and its abundance might even increase with the projected increase in global wildfire activity and the continued burning of fossil fuel. PyC is also increasingly produced from the industrial pyrolysis of organic wastes, which yields charred soil amendments (biochar). Moreover, the emergence of nanotechnology may also result in the release of PyC-like compounds to the environment. It is thus a high priority to reliably detect, characterize and quantify these charred materials in order to investigate their environmental properties and to understand their role in the carbon cycle.
Here, we present the benzene polycarboxylic acid (BPCA) method, which allows the simultaneous assessment of PyC's characteristics, quantity and isotopic composition (13C and 14C) on a molecular level. The method is applicable to a very wide range of environmental sample materials and detects PyC over a broad range of the combustion continuum, i.e., it is sensitive to slightly charred biomass as well as high temperature chars and soot. The BPCA protocol presented here is simple to employ, highly reproducible, as well as easily extendable and modifiable to specific requirements. It thus provides a versatile tool for the investigation of PyC in various disciplines, ranging from archeology and environmental forensics to biochar and carbon cycling research.
Digital Object Identifier (DOI)
Published in Journal of Visualized Experiments, Volume 111, 2016.
Wiedemeier, D., Lang, S., Gierga, M., Abiven, S., Bernasconi, S., & Früh-Green, G. et al. (2016). Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids (BPCA). Journal Of Visualized Experiments, (111). doi: 10.3791/53922