Despite green tides (or macroalgal blooms) having multiple negative effects, it is thought that they have a positive effect on carbon sequestration, although this aspect is rarely studied. Here, during the world’s largest green tide (caused by Ulva prolifera) in the Yellow Sea, the concentration of dissolved organic carbon (DOC) increased by 20–37% in intensive macroalgal areas, and thousands of new molecular formulas rich in CHNO and CHOS were introduced. The DOC molecular species derived from U. prolifera constituted ∼18% of the total DOC molecular species in the seawater of bloom area, indicating the profound effect that green tides have on shaping coastal DOC. In addition, 46% of the macroalgae-derived DOC was labile DOC (LDOC), which had only a short residence time due to rapid microbial utilization. The remaining 54% was recalcitrant DOC (RDOC) rich in humic-like substances, polycyclic aromatics, and highly aromatic compounds that resisted microbial degradation and therefore have the potential to play a role in long-term carbon sequestration. Notably, source analysis showed that in addition to the microbial carbon pump, macroalgae are also an important source of RDOC. The number of RDOC molecular species contributed by macroalgae even exceed (77 vs 23%) that contributed by microorganisms.

Dissolved organic matter (DOM) sustains a substantial part of the organic matter transported seaward, where photochemical reactions significantly affect its transformation and fate. The irradiation experiments can provide valuable information on the photochemical reactivity (photolabile, photoresistant, and photoproduct) of molecules. However, the inconsistency of the fate of irradiated molecules among different experiments curtailed our understanding of the roles the photochemical reactions have played, which cannot be properly addressed by traditional approaches. Here, we conducted irradiation experiments for samples from two large estuaries in China. Molecules that occurred in irradiation experiments were characterized by the Fourier transform ion cyclotron resonance mass spectrometry and assigned probabilistic labels to define their photochemical reactivity. These molecules with probabilistic labels were used to construct a learning database for establishing a suitable machine learning (ML) model. We further applied our well-trained ML model to "unmatched" (i.e., not detected in our irradiation experiments) molecules from five estuaries worldwide, to predict their photochemical reactivity. Results showed that numerous molecules with strong photolability can be captured solely by the ML model. Moreover, comparing DOM photochemical reactivity in five estuaries revealed that the riverine DOM chemistry largely determines their subsequent photochemical transformation. We offer an expandable and renewable approach based on ML to compatibly integrate existing irradiation experiments and shed insight into DOM transformation and degradation processes.

Dissolved organic matter (DOM) is a complex mixture of molecules that constitutes one of the largest reservoirs of organic matter on Earth. While stable carbon isotope values (δ 13 C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ 13 C. To address this, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the molecular composition of DOM in 510 samples from the China Coastal Environments, with 320 samples having δ 13 C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ 13 C values with a mean absolute error (MAE) of 0.30‰ on the training data set, surpassing traditional linear regression methods (MAE 0.85‰). Our findings suggest that degradation processes, microbial activities, and primary production regulate DOM from rivers to the ocean continuum. Additionally, the machine learning model accurately predicted δ 13 C values in samples without known δ 13 C values and in other published data sets, reflecting the δ 13 C trend along the land to ocean continuum. This study demonstrates the potential of machine learning to capture the complex relationships between DOM composition and bulk parameters, particularly with larger learning data sets and increasing molecular research in the future.

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