Unraveling the Distribution of Black Carbon in Chinese Forest Soils Using Machine Learning Approaches

Abstract Black carbon (BC) is a highly persistent yet poorly understood component of forest soil carbon reservoirs, while its inventory, distribution, and determining factors in forest soils on a large geographic scale remain unclear. Here, we characterized soil BC across 68 Chinese forest sites using benzene polycarboxylic acid method and developed machine learning (ML) models to predict and interpret potential impacts of soil organic matter (SOM) properties, soil physiochemical properties, meteorological conditions, wildfire history, and microbial diversity on BC. Results revealed that SOM properties were the most critical in predicting BC, complemented by the negative impact of mean annual temperature and alkaline mineral composition. The superior prediction accuracy for BC with higher condensed aromaticity (more benzene hexa‐ and penta‐carboxylic acid monomers) likely results from its simpler sources and greater resistance to transformation. This study introduces an effective ML model for predicting and interpreting soil BC inventory to better understand BC cycling.