Will various interpretation strategies of the same ultra-high-resolution mass spectrometry data tell different biogeochemical stories? A first assessment based on natural aquatic DOM

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Optical Properties of Dissolved Organic Matter and Controlling Factors in Dianchi Lake Waters

Characterization of dissolved organic matter (DOM) is useful in understanding environment quality and carbon cycling in the lake system. In this study, the fluorescence of DOM, major ions, and nutrients in water were investigated to understand the sources and the transformation of DOM in Dianchi Lake, the sixth largest freshwater lake in China. The dissolved organic carbon content in water above the deposition layer was higher than 5 mg C∙L−1 but lower than that in pore water. Two primary components of humic (C1) and protein-like components (C2) were identified using parallel factor (PARAFAC) modeling on sample fluorescence spectra. Organic components were related to mineral structures, and encapsulation of bacterial or algal cells into particulates could be disintegrated to release DOM. The aromaticity and the hydrophobicity of optical properties were regulated by percentages of chromophores (CDOM) of DOM in surface water, whereas by percentages of fluorophores (FDOM) in DOM in pore water, the underlying water layer was defined as a belt of transition. The molecular weight enhanced with percentages of C1 in CDOM increased in water above the sediment layer and the pore water at the northern lake site, but molecular weight attenuated with percentages decreased in pore water at the southern lake site. DOM not only originated from particulate decomposition but also derived from internal transformation among different, dissolved organic molecules. Small molecules were aggregated into larger ones, and, conversely, large molecules decomposed into small sizes. Another speculation is that dissolved molecules adsorbed or were encapsulated into particulates or were degraded and released into dissolved phases. The precise factors regulated composition, structure, and spectral properties of dissolved organic matter in the Dianchi Lake. This study highlights that sources of DOM and transformation mechanisms in the lake water could be correlated with nutrients and primary geochemical factors for mobility and distribution in different water compartments

Research progress on the dissolved and particulate carbon of reservoirs in karst areas of Southwest China

Southwest China is the largest concentrated karst landscape distribution area in the world, with dense river networks and abundant hydroelectric resources in the area, which is an important area for the development of hydroelectric power generation in China. To elucidate the impact of karst reservoirs on the carbon cycle of the river system, this work summarizes the research progress of damming in karst watersheds on different forms of carbon transport transformation and the environment in recent years. Through the study of dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), particulate inorganic carbon (PIC) and particulate organic carbon (POC) transport and transformation and their control mechanisms in the river-reservoir system in the karst region by spectroscopy, stability and radioisotopes, this work found that the carbon cycle in karst reservoirs exhibits obvious seasonal distribution characteristics, as well as cascade reservoirs, which may further amplify a single reservoir's environmental influence. These results not only contribute to the understanding of the reservoir carbon cycle but also help to explore the "missing" carbon sinks in the river carbon cycle and to more accurately assess the role of karst reservoirs in the global river carbon cycle. In general, karst reservoirs are likely to be more responsive to increased anthropogenic activities than nonkarst reservoirs, which implies that the role of karst reservoirs in the global warming trend needs to be more accurately assessed, and in future research, a systematic characterization of the carbon transport and transformation of different forms from microscopic to macroscopic levels by different analytical tools will more accurately answer this question

Seasonal Variations of Dissolved Organic Matter by Fluorescent Analysis in a Typical River Catchment in Northern China

Fluorescence (excitation-emission matrices, EEMs) spectroscopy coupled with PARAFAC (parallel factor) modelling and UV-Vis (ultraviolet visible) spectra were used to ascertain the sources, distribution and biogeochemical transformation of dissolved organic matter (DOM) in the Duliujian River catchment. Dissolved organic carbon (DOC), chromophoric dissolved organic matter (a335) (CDOM), and hydrophobic components (a260) were higher in summer than in other seasons with 53.3 m−1, while aromaticity (SUVA254) was higher in spring. Four fluorescent components, namely terrestrial humic acid (HA)-like (A/C), terrestrial fulvic acid (FA)-like (A/M), autochthonous fulvic acid (FA)-like (A/M), and protein-like substances (Tuv/T), were identified using EEM-PARAFAC modelling in this river catchment. The results demonstrated that terrestrial HA-like substances enhance its contents in summer ARE compared with BRE, whilst terrestrial FA-like substances were newly input in summer ARE, which was entirely absent upstream and downstream, suggesting that rain events could significantly input the terrestrial soil-derived DOM in the ambient downward catchments. Autochthonous FA-like substances in summer BRE could derive from phytoplankton in the downstream waters. The results also showed that DOM from wetland exhibited lower fluorescent intensity of humic-like peak A/C and fulvic-like peak A/M, molecular weight (SR) and humification index (HIX) during the low-flow season. Built-up land, cropland, and unused land displayed higher a335 (CDOM). A higher proportion of forest and industrial land in the SCs showed higher SUVA254 values. Humic-like moiety, molecular weight and aromaticity were more responsive to land use during stormflow in summer. Rainfall could increase the export of soil DOM from cropland and unused land, which influences the spatial variation of HIX. The results in this study highlighted that terrestrial DOM has a significant influence on the biogeochemical alterations of DOM compositions and thus water quality in the downward watershed catchments, which might significantly vary according to the land-use types and their alterations by human activities

Unraveling the Linkages between Molecular Abundance and Stable Carbon Isotope Ratio in Dissolved Organic Matter Using Machine Learning

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 (δ13C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ13C. 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 δ13C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ13C 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 δ13C values in samples without known δ13C values and in other published data sets, reflecting the δ13C 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

Unraveling the Linkages between Molecular Abundance and Stable Carbon Isotope Ratio in Dissolved Organic Matter Using Machine Learning

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 (δ13C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ13C. 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 δ13C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ13C 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 δ13C values in samples without known δ13C values and in other published data sets, reflecting the δ13C 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

Supporting data for "Deciphering sources and processing of dissolved black carbon (DBC) in coastal seas"

These two datasets are supporting data for the manuscript "Deciphering sources and processing of dissolved black carbon (DBC) in coastal seas". Dataset 01 includes the concentration of DBCBPCA and the molecular composition of DBCFT. Dataset 02 includes the detailed molecular composition of DBCFT in each sample.</p

Supporting Dataset for "Beyond Hydrology: Exploring Factors Influencing the Seasonal Variation in the Molecular Composition of Riverine Dissolved Organic Matter"

This is the supplementary file for the manuscript "Beyond Hydrology: Exploring Factors Influencing the Seasonal Variation in the Molecular Composition of Riverine Dissolved Organic Matter". The file containing the relative intensity of each molecular formulae data of each sample.</p