Stable polycyclic aromatic carbon (SPAC) formation in wildfire chars and engineered biochars

Abstract

Pyrogenic carbon (PyC) is an important component of wildfire chars and engineered biochars due to its potential environmental longevity, the most environmentally stable fraction of which is called stable polycyclic aromatic carbon (SPAC) and is projected to persist in global environments for >1000 yr. Rigorous characterization of SPAC, whether formed in wildfires or engineered, is essential for accurate global carbon cycle models. However, the quantification of SPAC remains challenging and methods for its direct characterization are often inaccessible and/or highly specialized. Additionally, these methods often rely on SPAC formation measured in laboratory biochars produced in inert environments, which have been shown to correlate poorly with wildfire chars and/or engineered biochars manufactured in oxidative environments. The present study investigated the relationship between SPAC formation and physicochemical metrics - mass loss and molar H:C and O:C ratios - that capture the influences of multiple formation variables, including gas environment temperature, O2 availability, and pyrolysis duration, and negates the need for these variables to be directly measured. SPAC content is measured in this study using hydrogen pyrolysis (HyPy), which is an established accurate method for characterizing that most environmentally stable PyC fraction. Results show that SPAC formation and elemental ratios correlate linearly with increased mass loss, which is reflective of increased pyrolysis severity. The relationship between these char characteristics allows for SPAC prediction based on measurement of mass loss during char formation, as well as the standardized elemental analysis method. In this study, wildfire chars exhibited relatively low SPAC contents of <30 wt% on a dry, ash-free basis, indicating that a significant fraction of PyC formed in these chars remains labile or semi-labile, while engineered biochars had a range of SPAC contents up to approximately 75 wt%. The predictive SPAC models developed in this work can improve global carbon accounting models.