Abstract

Wildfire greatly impacts the composition and quantity of organic carbon stocks within watersheds. Most methods used to measure the contributions of fire altered organic carbon–i.e. pyrogenic organic carbon (Py-OC) in natural samples are designed to quantify specific fractions such as black carbon or polyaromatic hydrocarbons. In contrast, the CuO oxidation procedure yields a variety of products derived from a variety of precursors, including both unaltered and thermally altered sources. Here, we test whether or not the benzene carboxylic acid and hydroxy benzoic acid (BCA) products obtained by CuO oxidation provide a robust indicator of Py-OC and compare them to non-Py-OC biomarkers of lignin. O and A horizons from microcosms were burned in the laboratory at varying levels of fire severity and subsequently incubated for 6 months. All soils were analyzed for total OC and N and were analyzed by CuO oxidation. All BCAs appeared to be preserved or created to some degree during burning while lignin phenols appeared to be altered or destroyed to varying extents dependent on fire severity. We found two specific CuO oxidation products, o-hydroxybenzoic acid (oBd) and 1,2,4-benzenetricarboxylic acid (BTC2) that responded strongly to burn severity and withstood degradation during post-burning microbial incubations. Interestingly, we found that benzene di- and tricarboxylic acids (BDC and BTC, respectively) were much more reactive than vanillyl phenols during the incubation as a possible result of physical protection of vanillyl phenols in the interior of char particles or CuO oxidation derived BCAs originating from biologically available classes of Py-OC. We found that the ability of these compounds to predict relative Py-OC content in burned samples improved when normalized by their respective BCA class (i.e. benzene monocarboxylic acids (BA) and BTC, respectively) and when BTC was normalized to total lignin yields (BTC:Lig). The major trends in BCAs imparted by burning persisted through a 6 month incubation suggesting that fire severity had first order control on BCA and lignin composition. Using original and published BCA data from soils, sediments, char, and interfering compounds we found that BTC:Lig and BTC2:BTC were able to distinguish Py-OC from compounds such as humic materials, tannins, etc. The BCAs released by the CuO oxidation procedure increase the functionality of this method in order to examine the relative contribution of Py-OC in geochemical samples.

Highlights

  • Fire can significantly reduce the amount of carbon at the ecosystem level, and leave residual organic materials, such as black carbon and poly-cyclic aromatic hydro-carbons (PAHs; Table 1)

  • Dickens et al (2007) did not report all of the same constituents we have (e.g o-hydroxybenzoic acid (oBd) was omitted from their report), so we have focused on those constituents that are common between the two studies

  • Our exploration of the response of individual and classes of CuO oxidation derived lignin and benzoic acid (BCA) has found that lignin is consumed by burning while BCAs were preferentially preserved or produced by burning

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Summary

Introduction

Fire can significantly reduce the amount of carbon at the ecosystem level, and leave residual organic materials, such as black carbon and poly-cyclic aromatic hydro-carbons (PAHs; Table 1). Black carbon is a heterogeneous, aromatic, and C-rich residue [1] and PAHs are compounds composed of several fused benzenoid rings [2]. The broad class of compounds produced as a result of incomplete combustion, including black carbon and PAHs, is referred to as pyrogenic organic carbon (Py-OC) in this paper. Pyrogenic organic carbon has been found to make up a large proportion of organic matter in soils and sediments from a variety of environments [3,4,5,6]. Because fire regimes (fire frequency and severity) have changed and will continue to change as a result of climate change and fire suppression [7, 8], it is critical to understand the role of wildfire in altering organic matter composition and carbon stocks in order to predict changes in globally-relevant Py-OC dynamics.

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