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Dissolved organic matter (DOM) in sediment pore water plays a key role in lake water quality, complexation of the contaminants, biogeochemical cycles, and even climate. To better understand the vertical dynamics and characteristics of DOM among pore water profiles, we investigated concentrations, chemical structures, and benthic fluxes of DOM in plateau lake sediments via stoichiometric analysis, ultraviolet–visible (UV–vis) absorption, three-dimensional fluorescence matrix spectroscopy (EEMs), and parallel factor model (PARAFAC). The results revealed that dissolved organic carbon (DOC), concurrent with chromophoric DOM (CDOM) and fluorescent DOM (FDOM), tended to accumulate in the anaerobic deeper layers. These trends also demonstrated a good agreement with the ammonium nitrogen (NH 4 +-N) concentration, implying that the pathway of DOM dynamics was closely related to the redox state. The EEMs-PARAFAC model revealed that DOM in pore water mostly consisted of two terrestrial humic-like components (average contribution:> 60%) followed by microbial and tryptophan-like components, respectively. Terrestrial humic-like components were relatively stable, and their abundance was positively correlated with the FDOM level (p < 0.05). The changes of aromatic, molecular weight, and humification degree of DOM were possibly driven by microbial activities at different depths. Moreover, benthic flux estimations indicated that DOM and the total dissolved nitrogen (TDN) in inland sediments were potential sources for the overlying water and possibly affected the quality and ecological safety of lake water bodies. [ABSTRACT FROM AUTHOR]

Particulate organic carbon (POC) sources, which regulate dissolved organic carbon, sediment organic carbon, and inorganic carbon via deposition, degradation, and mineralization, play an important role in lake ecosystems. Linear or Bayesian algorithms on isotope and n-alkanes have been widely used to identify the source proportion of organic carbon. However, the applicability of these methods is ambiguous because of the unilateral advantages of each model and trace factors. To test the applicability of the various methods for identifying POC sources, we analyzed dual isotopes and n-alkanes in surface water samples of Lake Taihu, and Multi-source mixing model and Bayesian mixing model were used to distinguish between endogenous and exogenous contributions. Carbon isotope presented a clear advantage in West Taihu (-21.85 ± 0.78‰) and Southwest Taih (-22.61 ± 1.35‰); nitrogen isotope also showed high values in Meiliang Bay (9.76 ± 0.92‰). The majority of the lake was dominated by short-chain n-alkanes, except for East Taihu Lake (dominated by medium-chain n-alkanes) and areas with riverine input (dominated by long-chain n-alkanes). Different principles between the Bayesian mixing model (based on the Markov Chain Monte Carlo algorithm) and the Multi-source mixing model (based on linear estimation) caused discrepancies in the estimations of source contributions. But the fraction of chemical compounds during the migration process, and the overlap of potential sources play important role in the inconsistency of results. The estimations from the different models were consistent in indicating the dominance of endogenous organic carbon in Lake Taihu (mean of 60.18 ± 20.26%), particularly in the north and western regions (West Taihu, Meiliang Bay, and Southwest Taihu). This was likely due to algal aggregation influenced by human activities and climatic factors. [ABSTRACT FROM AUTHOR]

[Display omitted] • Combining R rs (λ) and δ13 C POC can track and monitor the source of POC. • The source of POC in surface water of Taihu Lake in summer is mainly endogenous POC. • Endogenous POC has a certain physiological relationship with water temperature and nutrient (TN, TP), while terrestrial POC was closely related to wind speed. • The a ph (λ) and a nph (λ) can effectively reflect the source of POC. The sources of particulate organic carbon (POC) determine its conversion, thereby playing an important role in the carbon cycle of lakes. Accurate estimation of the sources and dynamic characteristics of POC is important for understanding the migration and transformation of organic carbon. However, the synchronous observation of POC sources with large areas through remote sensing is still challenging because of the complex composition of POC and the optical conditions of inland lakes. In this study, a three-band (1/ R rs (689)–1/ R rs (717)) × R rs (697)) empirical algorithm of POC sources was constructed based on remote sensing reflectance (R rs (λ)) and the proportion of endogenous POC estimated from the field-measured stable isotope (δ13 C POC) values. The validation and calibration results of the three-band algorithm showed robust performance, with MAPE and RMSE of estimated values and measured values of 10% and 0.07, respectively. The three-band algorithm had good simulation results for the Ocean and Land Color Instrument (OLCI), Moderate Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), and Medium Resolution Imaging Spectrometer (MERIS) spectra. The POC sources estimated by the three-band algorithm suggest that the endogenous POC of Taihu Lake in August showed a decreasing trend from 2006 to 2019. The variation in terrestrial POC was slow and stable for both annual and monthly variations. The analysis of POC sources with total phosphorus (TP), total nitrogen (TN), water temperature, and wind speed indicated that terrestrial POC was closely related to wind speed (r = 0.33, P < 0.001), while endogenous POC was significantly associated with TP (r = 0.6, P < 0.001), TN (r = 0.56, P < 0.001), and water temperature (r = 0.49, P < 0.001). The use of remote sensing algorithms to evaluate POC from different sources is convenient and effective; furthermore, it helps to better understand the carbon cycle in lacustrine ecosystems. [ABSTRACT FROM AUTHOR]