Chromophoric Dissolved Organic Matter Research Articles

Retrieval of Total Suspended Matter Concentration Based on the Iterative Analysis of Multiple Equations: A Case Study of a Lake Taihu Image from the First Sustainable Development Goals Science Satellite’s Multispectral Imager for Inshore

Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration (Ctsm). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate Ctsm. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations B6/3 and B6/5 for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving Ctsm was formed, which was successfully verified by using the modeling data (R2 = 0.97, RMSE = 4.89 g/m3, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R2 = 0.87, RMSE = 3.92 g/m3, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring Ctsm was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of Ctsm, as well as monitoring and improving the water environment.

Measurement of light absorption by chromophoric dissolved organic matter (CDOM) using a type-II liquid capillary waveguide: assessment of an achievable accuracy.

Measurement of light absorption by chromophoric dissolved organic matter (CDOM) using a type-II liquid capillary waveguide: assessment of an achievable accuracy.

Drought Offsets the Controls on Colored Dissolved Organic Matter in Lakes

The concentration of colored dissolved organic matter (CDOM) in lakes is strongly influenced by climate, land cover, and topographic settings, but it is not known how drought may affect the relative importance of these controls. Here, we evaluate the controls of CDOM during two summers with strongly contrasting values of the Palmer drought index (PDI), indicating wet vs. dry conditions. We hypothesized that lake CDOM during a wet summer season is regulated mainly by the surrounding land cover to which the lakes are hydrologically connected, while, during drought, the lakes are disconnected from the catchment and CDOM is regulated by climatic and morphometric factors that govern the internal turnover of CDOM in the lakes. A suite of climate, land cover, and morphometric variables was assembled and used to explain remotely sensed CDOM values for 255 boreal lakes distributed across broad environmental and geographic gradients in Sweden and Norway. We found that PDI explained the variability in CDOM among lakes in a dry year, but not in a wet year, and that severe drought strongly decreased CDOM during the dry year. Large lakes, especially, with a presumed high degree of catchment uncoupling, showed low CDOM during the dry year. However, in disagreement with our hypothesis, climate, land cover, and morphometry all showed a stronger impact on lake CDOM in wet vs. dry years. Thus, drought systematically weakened the predictability of CDOM variations at the same time as CDOM was offset toward lower values. Our results show that drought not only has a direct effect on CDOM, but also acts indirectly by changing the spatial regulation of CDOM in boreal lakes.

Assessing the water quality dynamics in the coastal waters of Kollam (Kerala, India) using Sentinel images

Coastal waters are complex, dynamic, and sensitive, and any change in the system impacts the marine environment and life. Coastal water quality has been decreasing due to the incursion of anthropogenic derived waste and toxins into the ocean. This study investigates water quality along the Kollam coast of Kerala State, India, using Sentinel-2 Multispectral Imager (MSI) data for the period of 2019–2022. Four key water quality parameters, chlorophyll (Chl-a), total suspended matter (TSM), turbidity, and coloured dissolved organic matter (CDOM), were analysed for seasonal variations and driving factors. The study highlights the potential of web-based platforms like Google Earth Engine for facilitating large-scale water quality assessments. The results reveal a distinct seasonal pattern in all parameters, primarily influenced by monsoonal riverine discharge and anthropogenic activities as contributing factors to water quality degradation. Overall, the study emphasises the need for comprehensive monitoring and management strategies to ensure the long-term sustainability of the coastal ecosystem.

Note on volume and distribution of fresh water in the Amazon River plume under low discharge conditions

In this research communication, we report the results of a field survey conducted in a part of the plume of the Amazon River between 0° and 5°N and offshore of the 28 m isobaths in November of 2022, during the low river discharge season. By comparing the observed vertical salinity profiles ‘disturbed’ by continental discharges within the plume with the virtually ‘undisturbed’ ones outside the plume, we estimated the total content of fresh water in the area covered by the measurements as 203 ± 22 km3, which equals to less than 3% of the average annual Amazon River discharge. Furthermore, we argue that the river-borne continental water was not confined to the upper mixed layer and show that about 37%, or 76 km3, of its volume was entrained into the plume-underlying layer between the mixed layer and the salinity maximum. This point is additionally supported by analysis of chromophoric dissolved organic matter (CDOM) fluorescence in water samples, demonstrating significant concentrations of terrigenous CDOM to depths up to 140 m. We also observed that there was a significant direct correlation between the volume of freshwater accumulated in the affected layer and background stratification (expressed as buoyancy frequency) in the unaffected layer below it.

Classification of optical water groups in the subarctic Pacific and adjacent seas using satellite-derived light absorption spectra of chromophoric dissolved organic matter

The characteristics of the water masses that contribute to high biological production in the subarctic Pacific and adjacent seas (SPA) could change because of recent climate change. This study reports on a method to classify water in the SPA into distinct optical water groups using the light absorption coefficient of chromophoric dissolved organic matter (CDOM), aCDOM(λ), captured using an ocean color satellite. In situ samples obtained from ship surveys between 2006 and 2021 were classified into five optical group numbers (OGN1–OGN5) based on aCDOM parameters in the ultraviolet (UV) region: aCDOM(λ) at 350 nm (aCDOM(350)) and the spectral slopes at 275–295 nm (S275–295) and at 350–400 nm (S350–400). We were also able to identify OGN with a new method using machine learning technique developed in this study that adopted satellite-derived aCDOM(λ) in the visible (VIS) region. The distribution and characteristics of OGN classified using the in situ aCDOM parameters in the UV region supplement the interpretation of the origin and mixing of the water masses classified by temperature and salinity. Relative to in situ samples, the accuracy of the OGN estimation from the ocean color satellites was 83.3%. The satellite-derived OGN were able to distinguish high chlorophyll-a areas where high phytoplankton productivity is expected. In addition, identifying the distribution of OGN from satellites supports improved understanding of the bloom process. This method has potential to help to understand the impact of phenomena from accelerating ocean warming (e.g., sea ice decline, enhancement of stratification and increase in riverine input) on water masses structure and the consequent changes in the phytoplankton productivity in the SPA.

Prediction of sea-surface salinity using MODIS and Sentinel-3 data

ABSTRACT Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions.

Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of hyperspectral imagery with precisely collocated in situ data. We showcase the capabilities of this team using data collected in a northern Texas pond across three days in 2020. Machine learning models for 13 variables are trained using the dataset of paired in situ measurements and coincident reflectance spectra. These models successfully estimate physical variables including temperature, conductivity, pH, and turbidity as well as the concentrations of blue–green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners, and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. Maps generated by applying the models to the collected images reveal small-scale spatial variability within the pond. This study highlights the value of combining real-time, in situ measurements together with hyperspectral imaging for the rapid characterization of water composition.

Indirect photodegradation of zinc pyrithione: The effect of chromophoric dissolved organic matter components and seawater factors

Chromophoric dissolved organic matter (CDOM) is the main photosensitizer in seawater. CDOM can absorb solar radiation extensively and generate reactive intermediates (RIs), including singlet oxygen (1O2), hydroxyl radicals (HO·), and CDOM triplet states (3CDOM*), that are favorable for indirect photodegradation of organic pollutants. In the current study, the effect and mechanism of CDOM components on zinc pyrithione (ZnPT) indirect photodegradation were studied. Among the three CDOM isolates (humic acid (JKHA), Suwannee River humic acid (SRHA), and Suwannee River natural organic matter (SRNOM)), JKHA had the highest fluorescence intensity per gram of carbon and the fastest indirect photodegradation rate for ZnPT. Furthermore, the fluorescent components were divided into four terrestrial humic-like components: C1, C2, C3, and C4, by using excitation–emission matrix spectroscopy combined with parallel factor analysis. A notable relationship was found between the four fluorescent components of CDOM and ZnPT indirect photodegradation. The indirect photodegradation rate of ZnPT varied in different types of CDOM solutions, which may be attributed to the different compositions of CDOM. The relative contents of C1 and C4 were significantly higher in JKHA than in the other two CDOMs, and they were the major contributors to the production of 3CDOM* and 1O2, which promoted ZnPT indirect photodegradation. The impact of seawater factors on ZnPT indirect photodegradation was also investigated. The ZnPT indirect photodegradation rate initially decreased and then increased as the pH increased from 5.00 to 11.00. The lowest rate of indirect photodegradation of ZnPT was observed at pH = 9.00. As the salinity was <25‰, in salinity the ZnPT indirect photodegradation rate increased concomitantly with an increase, and pronounced inhibition of ZnPT indirect photodegradation was observed when the salinity was >25‰. Nitrate had a promoting influence on ZnPT indirect photodegradation, whereas HCO3−, halogen ions, and metal ions all inhibited it.

A variational optimization algorithm for Secchi disk depth based on Multi-satellite data

A Secchi depth is a basic parameter to describe the optical properties of water, which is related to the composition and content of chlorophyll, suspended solids and yellow substances in water, and is also closely related to solar radiation on the surface of water, physical and chemical properties of water and meteorological conditions. The most direct way to obtain the spatiotemporal distribution of water Secchi depth is to use ships to regularly measure the Secchi depth of the base-stations, but this method can only obtain the Secchi depth of the measurement station state, and it is impossible to obtain the Secchi depth characteristics of seawater with large spatiotemporal distribution. As a brand-new observation method, remote sensing technology can obtain the distribution characteristics of ocean parameters in large time and space. In recent years, with the rapid development of remote sensing technology, especially the development of water-colored remote sensors and the improvement of the accuracy of inversion algorithms, many remote sensing products for water Secchi depth have been provided. However, due to the difference in orbit operation and observation parameters of different remote sensing loads, water Secchi depth products are affected by clouds and meteorological climate environment, and there is no same standard for verification and evaluation, which limit the promotion and application of water Secchi depth products. At the same time, data integrated technology has been widely recognized by many disciplines and has been greatly developed in recent years, and pixel-level, feature-level, decision-level integrated technology and development are also popular research directions in the world. Therefore, based on an improved variational optimization algorithm, this article integrates SDD retrieval products on multiple satellite data t such as SNPP, MODIS, and MERIS, and verifies them with ship survey data. The correlation is better than 0.9, proving that this method can improve the accuracy of SDD inversion products.

Characterization of microplastic-derived dissolved organic matter in freshwater: Effects of light irradiation and polymer types

This study investigated the impacts of light irradiation and polymer types on the leaching behavior of dissolved organic matter (DOM) from microplastics (MPs) in freshwater. Polypropylene had the highest leaching capacity of DOM after photoaging, followed by polystyrene (PS), polyamide (PA) and polyethylene terephthalate (PET). While similarly low levels of DOM were observed in the remaining 5 MP suspensions under UV irradiation and in almost all MP suspensions (except PA) under darkness. These suggest that the photooxidation of some buoyant plastics may influence the carbon cycling of nature waters. Among 9 MP-derived leachates, PET leachates had the highest chromophoric DOM concentration and aromaticity, probably owing to the special benzene rings and carbonyl groups in PET structures and its fast degradation rate. Protein-like substances were the primary fluorescent DOM in MP suspensions (except PS), especially in darkness no other fluorescent substances were found. Considering the bio-labile properties of proteins together, MPs regardless of floating or suspended in an aquatic environment may have prevalent long-term effects on microbial activities. Besides, from monomers to hexamers with newly formed chemical bonds were identified in UV-irradiated MP suspensions. These results will contribute to a deep insight into the potential ecological effects related to MP degradation.

Impacts of Hydrology and Extreme Events on Dissolved Organic Carbon Dynamics in a Heavily Urbanized Estuary and Its Major Tributaries: A View From Space

AbstractDissolved organic matter and its colored component, Colored Dissolved Organic Matter (CDOM), play a major role in global carbon budgets, and their fluxes provide an essential link between terrestrial and aquatic biogeochemical cycles. Satellite observations can uniquely capture the hydro‐biogeochemical connectivity of terrestrial and aquatic landscapes, across scales. Yet, accurate satellite retrievals of CDOM and dissolved organic carbon (DOC) dynamics remain challenging in urbanized estuaries and coasts. Here, we present an advanced unified algorithm for space‐based retrieval of coastal CDOM and DOC dynamics and its application in Long Island Sound—one of the world's most heavily urbanized estuaries that is becoming increasingly vulnerable to climate change stressors. A rich bio‐optical data set, encompassing a wide range of environmental conditions, was integrated into the algorithm training to retrieve DOC concentrations and CDOM spectral shape (i.e., spectral slope S275–295)—a proxy for DOC quality. The new algorithms were applied to full‐resolution satellite imagery from the Sentinel‐3 Ocean and Land Color Instrument (OLCI) after thoroughly evaluating the performance of six ocean color atmospheric correction approaches (ACOLITE, BAC, C2RCC, MUMM, l2gen, and Polymer). Evaluation of the algorithms yielded mean absolute percent differences of 28%, 12%, and 10% for aCDOM(300), S275–295, and DOC, respectively. Application of the algorithms to multi‐year satellite OLCI imagery captured, for the first time, the coupled impact of seasonal transitions, wind regimes, freshwater inputs, anthropogenic disturbances, and hydrological extremes (both intense precipitation and droughts) on DOC fluxes and CDOM quality at the ecosystem scale. Results have important implications for improved predictions of coastal biogeochemical fluxes in complex urban−estuary systems.

Dynamic distribution and photochemical-microbial coupling degradation of dissolved organic matter in a large river-Influenced Bay

A large river-influenced offshore area is the junction where land and open sea meet, which is important in the biogeochemical processes of dissolved organic matter (DOM). Because hydrodynamic and biogeochemical processes are complex, processes such as the photochemical and microbial degradation of DOM are poorly understood. In this study, to further understand the degradation mechanisms of DOM from different sources, water was sampled from three cruises during autumn 2018 and spring and summer 2019 in Laizhou Bay (LZB), as this bay is greatly influenced by the Yellow River. Field incubation experiments examining the riverine, mixed, and marine DOM photochemical and microbial degradation processes alone or in combination were conducted onboard. Due to the dual influence of the Yellow River input and phytoplankton autogenous production, dissolved organic carbon (DOC) and chromophoric DOM (CDOM) show relatively conservative mixing behaviour with maximum values in summer, and the S275–295 gradually increases from the river to nearshore. In addition, it is indicated that higher primary productivity from large nutrient inputs in areas with moderate salinity during the summer is often accompanied by higher bioavailability DOM based on field investigation results. Various sources of DOM have different sensitivities to photochemical and microbial degradation due to their components and properties. Riverine DOM is susceptible to photochemical degradation, while marine DOM is more susceptible to microbial degradation. Moreover, the low-molecular-weight organic matter produced by photochemical degradation effectively promotes the microbial degradation of DOM from riverine sources, whereas photodegradation from mixed and marine sources do not significantly increase the biodegradation efficiency due to the low content of aromatic substances. Furthermore, photoammonification and bioammonification of autochthonous and allochthonous DOM may be the potential processes driving ammonia regeneration.

Spatial gradients and molecular transformations of DOM, DON and DOS in human-impacted estuarine sediments

Dissolved organic matter (DOM) constitutes the most active fraction in global carbon pools, with estuarine sediments serving as significant repositories, where DOM is susceptible to dynamic transformations. Anthropogenic nitrogen (N) and sulfur (S) inputs further complicate DOM by creating N-bearing DOM (DON) and S-bearing DOM (DOS). This study delves into the spatial gradients and transformation mechanisms of DOM, DON, and DOS in Pearl River Estuary (PRE) sediments, China, using combined techniques of UV–visible spectroscopy, Excitation–emission matrix (EEM) fluorescence spectroscopy, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and microbial high-throughput sequencing. Results uncovered a distinct spatial gradient in DOM concentration, aromaticity (SUVA254), hydrophobicity (SUVA260), the content of substituent groups including carboxyl, carbonyl, hydroxyl and ester groups (A253/A203) of chromophoric DOM (CDOM), and the abundances of tyrosine/tryptophan-like protein and humic-like substances in fluorophoric DOM (FDOM). These all decreased from upper to lower PRE, accompanied by a decrease in O3S and O5S components, indicating seaward reduction in the contribution of terrestrial OM, especially anthropogenic inputs. Additionally, sediments exhibited a reduction in molecular diversity (number of formulas) of DOM, DON, and DOS from upper to lower PRE, with molecules tending towards a lower nominal oxidation state of carbon (NOSC) and higher bio-reactivity (MLBL), molecular weight (m/z) and saturation (H/C). While molecular composition of DOM remained similar in PRE sediments, the relative abundance of lignin-like substances decreased, with a concurrent increase in protein-like and lipid-like substances in DON and DOS from upper to lower PRE. Mechanistic analysis identified the joint influence of terrestrial OM, anthropogenic N/S inputs, and microbial processes in shaping the spatial gradients of DOM, DON, and DOS in PRE estuarine sediments. This study contributes valuable insights into the intricate spatial gradients and transformations of DOM, DON, and DOS within human-impacted estuarine sediments.

Driving factors of colored dissolved organic matter dynamics across a complex urbanized estuary

The role of estuaries in sourcing and transforming dissolved organic matter - the largest reservoir of organic carbon in the ocean - still presents many unknowns for coastal biogeochemical cycles, and is further complicated by increasing human pressures and a changing climate. Here, we examined the major drivers of colored dissolved organic matter (CDOM) dynamics in Long Island Sound (LIS), a heavily urbanized estuary of National Significance with a storied water quality past. A comprehensive new optical dataset, including measurements of CDOM absorption and fluorescence signatures, was integrated with biological and hydrological measurements to capture the spatiotemporal heterogeneities of LIS, including its urban-to-rural gradient, dynamic river mouths, and blue carbon ecosystems across seasons, following episodic storm events, and over five years. Results reveal longitudinal gradients in both DOM amount and quality. While carbon-rich and humic terrigenous DOM was dominant in the heavily riverine-influenced Central to Eastern LIS, an uncoupling between CDOM absorption (aCDOM) and dissolved organic carbon (DOC) concentration in Western LIS, and a stronger correlation with Chlorophyll-a, indicated increased autochthonous CDOM production. Closer to the New York City urban core, aCDOM was highly correlated to turbidity, consistent with increased wastewater influences. Fluorescence PARAFAC analysis provided strong evidence for seasonal processing of CDOM in LIS, related to increased summertime photochemical degradation of humic-like components and shoulder-season microbial processing. Riverine CDOM export was influenced by discharge amount, residence time, and coastal wetlands acting as additional sources of strongly humic and aromatic organic matter. These measurements allowed us to assess how hydrologic, biological, and anthropogenic processes impact DOM dynamics and, subsequently, biogeochemical variability and trophic status in this complex urbanized estuary, with implications for water quality management and policy. Results discussed here are applicable beyond LIS, as urbanized estuaries globally face similar hydrological and anthropogenic forcings.

Probing the optical and molecular properties of sedimentary dissolved organic matter in the laminated diatom mats from the southern Mariana Trench

Laminated diatom mats (LDMs) deposition, formed by the aggregation of giant diatoms (e.g., Ethmodiscus rex), are recognized as a crucial driver of the global carbon and silicon cycles due to their substantial contribution to the flux of organic carbon and biogenic silica. The biogenic silica-rich sediment is one of the typical sediment types in the deep-sea setting of the tropical Pacific Ocean. Here, we analyzed the concentrations of pore-water dissolved organic carbon (DOC) and phosphate (PO43−), the optical properties, and molecular compositions of dissolved organic matter (DOM) in the two LDMs cores and one non-LDMs core retrieved from the southern Mariana Trench. Our results showed that the LDMs sediments with higher total organic carbon (TOC) contents have lower concentrations of DOC and chromophoric dissolved organic matter (CDOM), compared to the non-LDMs core. Meanwhile, the LDMs samples exhibited lower relative intensity of fluorescent proteins-like, molecular lability boundary (MLBL%) and higher values for humification index (HIX) and aromaticity modification (AImod) compared to the non-LDM core, indicating a higher level of stability and recalcitrance of DOM likely resulting from microbial degradation of relatively labile DOM compounds. Furthermore, the LDMs samples were characterized by a high abundance of CHO-containing DOM components and a low abundance of nitrogen- and sulfur-containing components, potentially linked to microbial degradation of hydrocarbon compounds that were enriched in the LDMs sediments. Additionally, the unique formulas in the LDMs samples demonstrated higher degree of recalcitrance suggested by higher aromaticity and fraction of CRAM, which may be attributed to microbial transformation of DOM. These findings add to our limited knowledge of the optical properties and molecular composition of DOM in the deep-sea opal-rich sediments, and have implications for understanding DOM cycling in the deep oceans.

Primary production and microbial heterotrophy in the Siberian arctic seas, Bering Strait, and Gulf of Anadyr, Bering Sea

The data obtained during the recent decades indicate large-scale climatic changes in the Arctic Ocean, especially in the Siberian shelf seas. The impact of shrinking of the Arctic ice cover on marine net primary production (PP) was mostly studied using satellite-derived estimates of chlorophyll-a (Chl a), sea surface temperature, and sea ice concentrations, as well as various modeling approaches. However, no data are available on the quantitative relations between phytoplankton production and heterotrophic bacterioplankton activity in the broadest and shallowest shelf of the World Ocean - the East Siberian Arctic seas (ESAS). In this paper we present a full set of in situ measurements of main PP and microbial heterotrophy characteristics required for better understanding and further validation of the satellite and modeling approaches. In September–October 2011, during the 57th cruise of RV Akademik M.A. Lavrent'ev, PP, bacterioplankton abundance (BA) and bacterioplankton production (BP), as well the rates of dark CO2 assimilation (DCA) were determined in the Chukchi, East Siberian, and Laptev seas and in the Gulf of Anadyr of the Bering Sea (a total of 26 stations) by in situ measurements using radiolabeled bicarbonate. All three important production indicators exhibited significant variance within the studied water areas of the Arctic (РР from 0.98 to 54.7 μg C L−1 day−1), (ВР from 1.1 to 17.1 μg C L−1 day−1), and (DCA from 0.23 to 8.7 μg C L−1 day−1). The state of phyto- and bacterioplankton was determined by analysis of the BP/PP and DCA/PP ratios. Active photosynthesis with efficient incorporation of inorganic carbon into the biomass was observed in the Chukchi Sea coastal eastern waters and in the Gulf of Anadyr. On the contrary, the phytoplankton primary production was extremely low in oligotrophic basins of the East Siberian and Laptev seas, while the activity of heterotrophic bacterioplankton consuming organic matter of phytoplankton origin was relatively high. The data of direct measurements can be used for further adjustment of the indirect assessments of production obtained by satellite observations. Glossary East Siberian Arctic seas (ESAS), net primary production (PP), bacterioplankton abundance (BA), bacterial biomass (BB), bacterial production (BP), Arctic Ocean PP (AO-PP), organic matter (OM), dissolved organic matter (DOM), colored dissolved organic matter (CDOM), light CO2 assimilation (LCA), dark (heterotrophic + chemotrophic) CO2 assimilation (DCA), production indicators of the surface water layer (PPsurf, DCAsurf, BPsurf), estimated production indicators for the entire depth of the sea (PPint, DCAint, BPint; mg C m−2 day−1).

Photolysis of Dissolved Organic Matter over Hematite Nanoplatelets.

Solar photoexcitation of chromophoric groups in dissolved organic matter (DOM), when coupled to photoreduction of ubiquitous Fe(III)-oxide nanoparticles, can significantly accelerate DOM degradation in near-surface terrestrial systems, but the mechanisms of these reactions remain elusive. We examined the photolysis of chromophoric soil DOM coated onto hematite nanoplatelets featuring (001) exposed facets using a combination of molecular spectroscopies and density functional theory (DFT) computations. Reactive oxygen species (ROS) probed by electron paramagnetic resonance (EPR) spectroscopy revealed that both singlet oxygen and superoxide are the predominant ROS responsible for DOM degradation. DFT calculations confirmed that Fe(II) on the hematite (001) surface, created by interfacial electron transfer from photoexcited chromophores in DOM, can reduce dioxygen molecules to superoxide radicals (•O2-) through a one-electron transfer process. 1H nuclear magnetic resonance (NMR) and electrospray ionization Fourier-transform ion cyclotron resonance mass spectrometry (ESI-FTICR-MS) spectroscopies show that the association of DOM with hematite enhances the cleavage of aromatic groups during photodegradation. The findings point to a pivotal role for organic matter at the interface that guides specific ROS generation and the subsequent photodegradation process, as well as the prospect of using ROS signatures as a forensic tool to help interpret more complicated field-relevant systems.

Assessing and predicting the influence of chromophoric dissolved organic matter on light absorption by phytoplankton in boreal lakes

AbstractMany boreal lakes are colored brown due to strong light absorption by chromophoric dissolved organic matter (CDOM), which reduces light penetration into the water column. However, the influence of CDOM on the fraction of photosynthetically utilizable radiation (PUR) absorbed by phytoplankton from the photosynthetically active radiation (PAR) entering the lake (i.e., PUR/PAR) remains largely unknown. Here, we (1) quantified PUR/PAR values and examined the major water quality parameters determining PUR/PAR from 128 sampled boreal lakes, (2) predicted PUR/PAR values for 2250 reference boreal lakes, and (3) estimated the response of PUR/PAR to typical browning trends reported in earlier studies. The PUR/PAR values ranged from 0.4% to 17% in the sampled lakes, and a logarithmic model including CDOM and chlorophyll a (Chl a) concentration was the most parsimonious for predicting PUR/PAR values. Applying the model to the reference lakes, PUR/PAR values ranged from 0.5% to 20% (median 3%). In the model, an increase in CDOM content decreases the PUR/PAR value, but a concurrent increase in Chl a concentration with the CDOM partly compensates the negative effect of CDOM on the PUR/PAR values. Assuming that browning increases both CDOM and Chl a contents, as found for our reference lakes, our model suggests that the decrease in light absorption by phytoplankton in response to a typical degree of browning is only moderate. The moderate response of the PUR/PAR to browning may be explained by photoacclimation of phytoplankton to lowered light availability, and/or an increased loading of nutrients to lakes both leading to higher Chl a concentration.

Estimation of Dissolved Organic Carbon Using Sentinel-2 in the Eutrophic Lake Ebinur, China

Dissolved organic carbon (DOC) in lakes, as a regulatory agent and light-absorbing compound, is a key component of the global carbon cycling in lacustrine ecosystems. Hence, continuous monitoring of the DOC concentration in arid regions is extremely important. This study utilizes the QAA-CDOM semi-analytical model, which has good accuracy in retrieving the CDOM (colored dissolved organic matter) concentration of Lake Ebinur. We chose to invert the CDOM time-series data from May to October during the 2018–2022 period. A DOC estimation model was then established using the linear regression approach based on the CDOM inversion data and the field DOC measurements. In general, the DOC concentration in Lake Ebinur exhibited an increasing trend from 2018 to 2022, typically lower in May and higher in June. When comparing the average values of DOC in Lake Ebinur for the same months across different years, it can be observed that the month of September exhibits the greatest variability, whereas June shows the least variability. In sum, this study successfully retrieved CDOM concentrations for a saline lake within an arid region and developed a DOC estimation model, thereby providing a reference for investigating carbon cycling in typical lakes of arid areas.