Predicting Dissolved Black Carbon Concentration From Chromophoric Dissolved Organic Matter Along the Land‐Ocean Continuum
Year: 2025
Type: article
Abstract: Dissolved black carbon (DBC) plays a key role in global carbon cycle and pollutant transport. However, the time‐consuming and labor‐intensive chemical analysis limits its spatiotemporal resolution. Here, we developed models to predict DBC from chromophoric dissolved organic matter (CDOM) measurements across the land‐to‐ocean continuum. We found that the mean ratio of DBC to light absorbance at 254 nm ( a 254 ) changed <20% among different environments. However, a single‐wavelength model is inadequate for precise prediction due to microbial production of CDOM. Incorporating longer wavelengths using multiple linear regression improves model performance. Random Forest Regression using the full spectral range performed even better at all environments, including the open ocean, achieving a root mean square logarithmic error of <0.15, median symmetric accuracy of <10%, and R 2 of >0.85. This study demonstrates the feasibility of using CDOM to predict DBC concentrations and highlights the potential for in situ monitoring and remote sensing applications.
Source: Geophysical Research Letters
DOI: 10.1029/2025gl119009
Institutions: Xiamen University, State Key Laboratory of Marine Environmental Science, Hong Kong University of Science and Technology, Carl von Ossietzky Universität Oldenburg, Institute for Chemistry and Biology of the Marine Environment, Carnegie Institution for Science, Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), City University of Hong Kong, State Key Laboratory of Marine Pollution, Helmholtz Institute for Functional Marine Biodiversity, Hainan University, State Key Laboratory of Marine Resource Utilization in South China Sea
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $3,190
OpenAlex ID: W4416411405
