Algal Blooms Research Articles

Photosynthetic Toxicological Effects of Organic Extracts from Zanthoxylum bungeanum Leaves on Controlling the Microcystis aeruginosa Blooms.

In recent years, the frequent occurrence of algal blooms has posed continuous threats to aquatic ecosystems and social safety. Environmentally friendly algae-inhibiting methods utilizing allelopathic substances offer advantages such as convenient application and low costs, presenting a bright application prospect in the fields of water and ecological restoration. This study aimed to investigatethe procedure for extracting total flavonoids from Zanthoxylum bungeanum leaves and assess allelopathic mechanism of Z. bungeanum leaf extracts on Microcystis aeruginosa. The optimal extraction conditions were determined as follows: solvent-to-material ratio = 10mL/g; extraction time = 55min; ethanol volume fraction = 80%; and ultrasonic temperature = 80°C. Antioxidant activity tests revealed that Z. bungeanum leaf extracts exhibit superior capacity compared to Butylated hydroxytoluene (BHT) but inferior to Vitamin C (VC). Moreover, high-concentration Z. bungeanum leaf extracts can disrupt the structures of oxygen-evolving complexes at the donor sides of photosystem II (PSII) reaction center and influence energy distribution at PSII reaction centers, thereby inhibiting electron transmission activities at both donor and receptor sides of the PSII reaction center. The elucidated allelopathic mechanism of Z. bungeanum leaf extracts provides theoretical evidence for the algal-bloom control and the development of allelopathic algae-inhibiting agents, offering a reference for the further comprehensive development and utilization of Z. bungeanum.

Friends and foes: symbiotic and Algicidal bacterial influence on Karenia brevis blooms

Abstract Harmful Algal Blooms (HABs) of the toxigenic dinoflagellate Karenia brevis are pivotal in structuring the ecosystem of the Gulf of Mexico (GoM), decimating coastal ecology, local economies and human health. Bacterial communities associated with toxigenic phytoplankton species play an important role in influencing toxin production in the laboratory, supplying essential factors to phytoplankton and even killing blooming species. However, our knowledge of the prevalence of these mechanisms during HAB events is limited, especially for Kophosoma brevis blooms. Here, we introduced native microbial communities from the GoM, collected during two phases of a Karenia bloom, into K. brevis laboratory cultures. Using bacterial isolation, physiological experiments, and shotgun metagenomic sequencing, we identified both putative enhancers and mitigators of K. brevis blooms. Metagenome-assembled genomes (MAGs) from the Roseobacter clade showed strong correlations with K. brevis populations during HABs, akin to symbionts. A bacterial isolate from this group of MAGs, Mameliella alba, alleviated vitamin limitations of K. brevis by providing it with vitamins B1, B7 and B12. Conversely, bacterial isolates belonging to Bacteroidetes and Gammaproteobacteria, Croceibacter atlanticus and Pseudoalteromonas spongiae, respectively, exhibited strong algicidal properties against K. brevis. We identified a serine protease homolog in P. spongiae that putatively drives the algicidal activity in this isolate. While the algicidal mechanism in C. atlanticus is unknown, we demonstrated the efficiency of C. atlanticus to mitigate K. brevis growth in blooms from the GoM. Our results highlight the importance of specific bacteria in influencing the dynamics of HABs and suggest strategies for future HAB management.

Exchange flow in a highly stratified fjord in drought conditions

Fjords are known for their biodiversity and abundant aquaculture resources. However, climate and anthropogenic pressures are altering fjord biological, physical, and chemical processes that will undoubtedly change the ecosystem as a whole. To investigate the impact of climate change on fjord functioning, this study examines the impacts of drought conditions on the physical dynamics and salinity variations in a fjord known for its bolstering aquaculture industry in Northern Chilean Patagonia, the Reloncaví Fjord (41.5° S). Using a high-resolution hydrodynamic model and the Total Exchange Flow (TEF) framework, we analyzed the impacts of river discharge, tides, and wind during a dry year (2016) and a typical year (2018). In 2016, reduced freshwater input decreased exchange flow and increased salinity compared to 2018. In 2018, river discharge dominated TEF variability (74%), while tides and wind contributed 17% and 9%, respectively. In summer 2016, tidal and wind influences rose to 21% and 16%, highlighting their role under low freshwater conditions. Increased wind facilitated destratification, mixing high-salinity subsurface waters with fresh surface layers, affecting ecosystem dynamics. From these results we developed a method to predict long-term stratification variability (1980–2021), identifying critical ecological shifts. Logistic regression models showed significant links between stratification levels and harmful algal blooms (HABs) of Pseudchatonella spp. and Alexandrium catenella. Lower stratification was linked to higher Pseudchatonella spp. HABs in summer, while higher stratification correlated with Alexandrium catenella blooms in spring, tied to increased river discharge. These results suggest that severe HAB events in Northern Patagonia may become more frequent with climate change, underscoring the need to consider local environmental dynamics and stratification in HAB studies.

Responses of fisheries ecosystems to marine heatwaves and other extreme events.

Marine ecosystems and their living marine resources (LMRs) continue to respond to the effects of global change, with environmental factors impacting marine fisheries biomass, distribution, harvest, and associated economic performance. Extreme events such as high-category hurricanes, harmful algal blooms, marine heatwaves, and large-scale hypoxia affect major regions and subregions of United States waters, with their frequency expected to increase over the next decades. The impacts of extreme events on fisheries biomass, harvest, and economic performance have not been examined as closely as a system (i.e., cumulatively), or in terms of their differential effects on particular functional groups of a given system. Among several U.S. subregions, we examined responses of fisheries biomass, landings, and revenue for particular functional groups to large-scale environmental perturbations (i.e., marine heatwaves, Hurricane Katrina, Deepwater Horizon oil spill). Distinct negative short-term consequences to annual fisheries biomass, landings, and revenue were observed in all regions, including at the system-level scale for several ecosystems which have higher proportions of pelagic species composition and variable shellfish-based revenue. In addition, shifts in species composition often were associated with environmental perturbations. Recovery to pre-perturbation levels (both in the immediate years following the event and over the post-event period of study) and resilience at the system level was observed in several cases, although post-event declines in biomass and landings occurred in the California ecosystem. Certain extreme events are expected to become more common in marine environments, with resulting perturbations throughout multiple components of U.S. socioecological systems. The recognition and understanding of the consequences of extreme events throughout marine ecosystems is necessary for effective, holistic, and sustainable management practices.

Factors associated with disease in farmed and wild salmonids caused by Tenacibaculum maritimum: a scoping review

IntroductionYellow mouth disease, caused by Tenacibaculum maritimum, is an important disease of farmed salmonids. Disease management currently necessitates the use of antimicrobials, raising concerns about antimicrobial resistance (AMR) in aquatic and potentially terrestrial environments. Identifying management, production, environmental, and other factors associated with the development of yellow mouth in salmonids will help to elucidate disease control strategies and decrease the economic and environmental burden of its treatment. The objective of this scoping review was to synthesize the available literature to identify factors associated with disease in farmed and wild salmonids from T. maritimum.MethodsThe scoping review followed the framework outlined in the Joanna Briggs Institute Reviewer’s Manual and PRSIMA-ScR reporting guidelines. The protocol was developed a priori in consultation with a librarian and was used to search Environment Complete®, Earth, Atmospheric, and Aquatic Science®, Scopus®, and Web of Science™ databases on July 21, 2022, and again on April 27, 2023. Articles were included if they focused on T. maritimum infection in salmonids and discussed factors (environmental, management, or other) that impacted the disease and/or organism of interest.ResultsTwenty-five articles were included for review. Over half were published within the last five years (n=14/25). The included articles revealed a complex interplay of salmonid (host)-specific factors (age/size), management practices (vaccination, marine transfer, stocking density, gill/body abrasion), environmental conditions (water temperature, oxygenation, salinity, algal blooms, vectors), and microbial dynamics (load, co-infections, strain, biofilms, microbiome) influencing T. maritimum infections. Only one study conducted multivariable analysis to understand this complex interplay between the diverse factors that impact infection with T. maritimum.DiscussionThe review highlights the complex, multifactorial nature of T. maritimum infections, including the interplay of host biology, environmental factors, and pathogen characteristics. A comprehensive approach incorporating both management and environmental components is essential to mitigate T. maritimum infections in salmonid production.

A perspective on the algae-derived dissolved organic matter and its dynamic influence on the aggregation of nanoplastics in eutrophic waters

The aggregation behavior of nanoplastics (NPs) is crucial in determining their fate in aquatic environments. Dissolved organic matter (DOM), characterized by its complex molecular structure and diverse functional groups, can spontaneously absorb on the surface of NPs, thus altering their colloidal stability. In eutrophic waters, DOM primarily originates from metabolic byproducts released by phytoplankton, and its molecular composition and hydrophilic properties change dynamically as the progression of algal blooms. This perspective aims to summarize the heterogeneity of DOM during the initiation, outbreak and recession of algal blooms. And we investigate the influence of molecular-level variations in DOM composition on the aggregation behavior of NPs. Additionally, this study provides insights into the underlying mechanisms relating to the interactions between DOM and NPs. Ultimately, it tackles the challenges and future directions, highlighting the necessity for comprehensive studies to understand the fate of NPs in eutrophic waters.

Mapping hazards to the global food system

Environmental hazards associated with the global food system threaten societal integrity. Yet, there is a major data gap in the global understanding of how the prevalence of hazards is changing over time, how different classes of hazard are distributed, and whether the combined literature represents hazard prevalence equitably across research, policy and legislation, and news. Here, we explore this data gap, leveraging global research, policy, and news databases. We reveal increasing attention on food system hazards over time, in line with major geopolitical events. Coverage on environmental hazards is not distributed equally geographically, and media attention does not match research and policy evidence focus. Climate change and water scarcity in particular receive substantial attention across all source types, whilst, for example biodiversity loss, genetic erosion, or harmful algal blooms receive much less. Environmental, financial and food systems sustainability damage due to hazard neglect should be avoided and a first step is to understand, map, and quantify biases in focus.

Bioaccumulation of environmental pollutants and marine toxins in bivalve molluscs: a review

Seafood is both nutritionally and economically significant, with bivalve molluscs being particularly valuable for monitoring environmental pollutants due to their filter-feeding nature and ability to bioaccumulate pollutants. While not often linked to food poisoning, these molluscs can occasionally introduce health risks, highlighting the need for vigilant monitoring. This review provides a thorough analysis of pollutants—including persistent and emerging pollutants, as well as marine toxins—found in bivalve molluscs between 2019 and 2024. Among the studied pollutants, plasticizers and alkaloids are the most frequently analyzed, with liquid and gas chromatography (GC) tandem mass spectrometry (MS) the predominant methods, although novel approaches to determine these compounds, such as sensors, have also emerged in recent years. However, many studies are focused on establishing pollutant content without addressing bioaccumulation (BA) factors, and a lack of standardization in species and sampling locations complicates comparisons between the different published works. Despite some studies linking human activity and algal blooms to BA dynamics, more comprehensive research is needed. Additionally, limited data on the depuration capacity of molluscs underscores the need for further investigation. Although pollutant levels generally remain within legal limits, many substances remain unregulated. Environmental factors also play a critical role in influencing BA, emphasizing the need for future studies to focus on BA factors to better understand these complex dynamics.

Influence of regulated water discharges on phytoplankton composition and biomass in a subtropical canal

Phytoplankton composition and biomass were investigated in the C-43 Canal in southwest Florida during a period of shifting discharges from water control structures. The canal receives regulated discharges from eutrophic Lake Okeechobee via the S77 structure. During periods of high S77 discharge in spring and early summer, cyanobacteria biomass dominated the phytoplankton community, including blooms of the harmful algal bloom (HAB) species Raphidiopsis raciborskii, Limnothrix redekei and Microcystis aeruginosa. During periods of low discharges from the lake, in mid-summer and autumn, water inputs to the canal came primarily from tributaries in the watershed surrounding the C-43. Phytoplankton biomass decreased, but the relative importance of dinoflagellates increased, including a bloom in July. The dinoflagellate community included Ceratium, Durinskia baltica, Glochidinium penardiforme, Gymnodinium fuscum, Parvodinium goslaviense, Parvodinium umbonatum/inconspicuum complex, Peridiniopsis quadridens, Woloszynskia reticulata, and an unidentified thecate and athecate species. D. baltica and P. goslaviense were recorded for the first time in Florida. Data was also obtained on water temperature, conductivity, fluorescent dissolved organic matter, chlorophyll a, total nitrogen, dissolved inorganic nitrogen, total phosphorus, PO4, discharge rates from water control structures, and water residence times. Results show that temporal shifts in the sources and rates of water inputs to the C-43 influence the character of environmental conditions that define phytoplankton composition and biomass in the canal. This suggests that management of discharges can play a role in mitigating HABs in the canal and downstream coastal environments receiving water from the canal.

Identification of the Driving Factors to Algal Biomass in Lake Dianchi: Implications for Eutrophication Control

Accurate analysis of spatiotemporal variations in algal biomass and their underlying causes is crucial for controlling algal blooms and enhancing aquatic ecological quality. The present study, spanning 2011 to 2020, was conducted across 10 sites in Lake Dianchi, where peak algal biomass levels occur from May to September, with higher concentrations in the lake’s northern zones compared to other areas. Employing Spearman’s correlation analysis, generalized additive models (GAMs), and random forest (RF) techniques, the relationships between algal biomass and water quality indicators were investigated. Spearman’s correlation analysis revealed a positive relationship between chlorophyll a (Chla) and total phosphorus (TP) across various spatial scales. RF analysis identified TP as the most influential factor on a lake-wide scale; while in localized RF models, organic pollution-related indicators (COD, CODMn, and BOD5) and TP consistently emerged as the primary predictors of Chla at most sites. GAM results indicated spatially variable and nonlinear responses of algal biomass to predictors, reinforcing TP’s significance lake-wide and at many localized scales. This comprehensive analytical approach provides valuable insights into the role of water quality factors and nonlinear dynamics, thereby advancing our understanding of the relationships between algal biomass and environmental conditions. These findings are pivotal for the development of scientifically informed strategies for lake management and conservation.

Shifting phenology as a key driver of shelf zooplankton population variability

AbstractThe timing of biological events, known as phenology, plays a key role in shaping ecosystem dynamics, and climate change can significantly alter these timings. The Gulf of Maine on the Northeast U.S. Shelf is vulnerable to warming temperatures and other climate impacts, which could affect the distribution and production of plankton species sensitive to phenological shifts. In this study, we apply a novel data‐driven modeling approach to long‐term datasets to understand the population variability of Calanus finmarchicus, a lipid‐rich copepod that is fundamental to the Gulf of Maine food web. Our results reveal how phenology impacts the complex intermingling of top‐down and bottom‐up controls. We find that early initiation of the annual phytoplankton bloom prompts an early start to the reproductive season for populations of C. finmarchicus in the inner Gulf of Maine, resulting in high spring abundance. This spring condition appears to be conducive to enhanced predation pressure later in the season, consequently resulting in overall low C. finmarchicus abundance in the fall. These biologically controlled dynamics are less pronounced in the outer Gulf of Maine, where water exchanges near the boundary have a greater influence. Our analysis augments existing hypotheses in fisheries oceanography and classical ecological theory by considering unique plankton life‐history characteristics and shelf sea dynamics, offering new insights into the biological factors driving C. finmarchicus variability.

Are more data always better? – Machine learning forecasting of algae based on long-term observations

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool. However, the performance of ML models is heavily dependent on the availability of appropriate training data. Consequently, it is essential to determine the volume of data necessary to develop reliable ML forecasts. Understanding this will guide future monitoring strategies, optimize resource allocation, and set realistic expectations for management outcomes. In this study, we used 30 years of fortnightly measurements of 13 different parameters from a lake in the English Lake District (UK) to examine the impact of training data duration on the performance of ML models for forecasting chlorophyll-a two weeks in advance. Once training data availability exceeded four years, a Random Forest model was found to consistently outperform naive benchmarks (mean absolute percentage error 16.4 % lower than the best-performing benchmark). With more than 5 years of training data, model performance generally continued to improve, but with diminishing returns. Furthermore, it was found that equivalent and, in some cases, better performance could be achieved by only using a subset of the most important input features. Additionally, it was found that reducing the sampling frequency had negative impacts on performance, both due to the reduced number of training observations available, and increased forecast horizon. Our findings demonstrate that for lakes ecologically similar to the study site, a consistent and regular sampling programme focused on monitoring a limited number of key parameters can provide sufficient observations for generating short-term algae forecasts after approximately five years of data collection. Importantly, this result provides justification for the initiation of new monitoring programmes for sites where algal blooms are a concern, and suggests that there are likely many pre-existing monitoring datasets which would be suitable for training algae forecast models.

Phytoplankton and zooplankton production in the Bonney Coast upwelling, Australia: A coupled physical-biological model investigation

The Bonney Coast Upwelling is a prominent seasonal coastal upwelling region that develops during November–April on Australia's southeastern continental shelf. This study couples a three-dimensional hydrodynamic model with a nitrogen-phytoplankton-zooplankton-detritus (NPZD) model to explore the plankton dynamics during the upwelling season. Findings suggest that, while the physical response to upwelling-favorable winds occurs rapidly on a timescale of 5–10 days, phytoplankton blooms develop only slowly on time scales of ∼30–60 days. To this end, the region of high zooplankton levels is predicted to form slowly on timescales of 2–3 months. As expected, the zooplankton maximum develops downstream from the upwelling center in the shallow waters of an adjacent bay (i.e., Long Bay) over an alongshore distance of 200–300 km. Unexpectedly, high zooplankton levels also develop on the inner shelf adjacent to the upwelling plume on spatial scales of 20–30 km, mainly due to the onshore transport of phytoplankton via lateral turbulent diffusion. Overall, remineralization by detritus contributes significantly (>50%) as nitrogen source to the photosynthetic phytoplankton production on the continental shelf.

Untangling the coupling effect of water quality and quantity on lake algal blooms in Lake Hulun from a dual perspective of remote sensing and sediment cores

Algal blooms and sediment diatoms are crucial indicators of lake water ecology, influenced by water quantity and quality. However, the coupled effects of water quality and quantity changes on algal blooms are still unclear, especially for lakes in cold and arid regions. This study assessed the long-term variations in algal blooms in Hulun Lake using a novel approach combining remote sensing and sediment core samples for diatom analysis. Two mutation points from the structural change test were identified in approximately 2000 and 2010 for algal bloom area (MBE) and sediment diatom richness, indicating asynchronous algal blooms. A structural equation model (SEM) demonstrated that water level (WL) changes were the dominant influencing factor, co-driving the variations in algal blooms and sediment diatoms in conjunction with total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). The results revealed nonlinear relationships between the lake WL, TN, Chla, and COD. The water level of 543 m emerged as a critical threshold affecting the relationship between water quality and quantity. Distinct differences in this relationship were observed when water levels were above or below this threshold. These variations became particularly pronounced during periods of high and low water levels. The results provide novel insights into the dynamics of algal blooms and can further support lake ecosystem conservation and management.

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir

The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal blooms following their occurrence. In this study, we aimed to develop a proactive prediction model for cyanobacterial alert levels to enable efficient decision-making in management practices. We utilized 11 years of water quality, hydrodynamic, and meteorological data from a reservoir that experiences frequent harmful cyanobacterial blooms in summer. We used these data to construct a deep-learning model, specifically a 1D convolution neural network (1D-CNN) model, to predict cyanobacterial alert levels one week in advance. However, the collected distribution of algal alert levels was imbalanced, leading to the biased training of data-driven models and performance degradation in model predictions. Therefore, an adaptive synthetic sampling method was applied to address the imbalance in the minority class data and improve the predictive performance of the 1D-CNN. The adaptive synthetic sampling method resolved the imbalance in the data during the training phase by incorporating an additional 156 and 196 data points for the caution and warning levels, respectively. The selected optimal 1D-CNN model with a filter size of 5 and comprising 16 filters achieved training and testing prediction accuracies of 97.3% and 85.0%, respectively. During the test phase, the prediction accuracies for each algal alert level (L-0, L-1, and L-2) were 89.9%, 79.2%, and 71.4%, respectively, indicating reasonably consistent predictive results for all three alert levels. Therefore, the use of synthetic data addressed data imbalances and enhanced the predictive performance of the data-driven model. The reliable forecasts produced by the improved model can support the development of management strategies to mitigate harmful algal blooms in reservoirs and can aid in building an early warning system to facilitate effective responses.

Recovering NDVI over lake surfaces: Initial insights from CYGNSS data enhanced by ERA-5 inputs

The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous water bodies, such as Taihu Lake. Sensors operating in the microwave band can effectively mitigate this issue, particularly the emerging Global Navigation Satellite System Reflectometry (GNSS-R), which offers high temporal resolution and cost-effectiveness. In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. We also examine the effective range of GNSS-R data within this application scenario. Meanwhile, the Weighted Linear Regression-Laplacian Prior Regulation Method (WLR-LPRM) image gap-filling algorithm is used as a benchmark to evaluate recovery accuracy. The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. Compared to the previous work on GNSS-R algal bloom detection with overall accuracy of 0.82, this work shows significant improvement in both accuracy and utility. The recovery of lake surface NDVI provides detailed insights into algal blooms, including quantifiable metrics such as the amount and spatial distribution, which are crucial for effective monitoring and management. Additionally, the recovered image textures exhibit high clarity and closely resemble the reference NDVI images. Experimental evaluation using simulated and actual cloud blocks indicates the model’s robustness to recover NDVI under varying cloud cover conditions. In summary, this study demonstrates the capability of GNSS-R aided by supplementary data for recovering missing NDVI values on lake surfaces when optical observations are absent for the first time.

Applying a coupled model framework to assess global climate change impacts on the river-type algal blooms in the middle and lower reaches of the Hanjiang River, China

Global climate change (GCC), characterized by warming, affects the hydrological conditions at the basin scale and whether harmful algal blooms (HABs) occur at the scale of river ecological systems. Research on HABs mainly focuses on oceans and lakes, and there is still less research on the effects of GCC on river-type HABs that differ from oceans and lakes in hydrodynamic, water temperature, and nutrient conditions. This study constructed a coupled model framework that includes the GCC model, downscaling model, hydrological model, hydrodynamic model, and eutrophication model, analyzing and exploring the effect of changes in the aquatic ecological environment caused by GCC on river-type HABs in the middle and lower reaches of the Hanjiang River (MLHR). Firstly, based on the three GCC models and statistical downscaling model in CMIP6, high-precision meteorological factors such as future precipitation and temperature were obtained. Secondly, a coupled model based on SWAT and MIKE21-ECOLab was used with the digital elevation model (DEM), land use, soil, meteorological, pollution source, and measured terrain data in the MLHR Basin, which was validated by observed data. Thirdly, there has not been a significant increase in Chl-a, and the impact of GCC has not fundamentally changed the temporal and spatial distribution of HABs. Fourthly, this study proposes to use 0.2 m/s (Corresponding discharge 1160 m3/s) as the hydrodynamic condition for preventing and controlling HABs in the Shayang section.

Performance of gravity-driven membrane filtration for treatment of reservoir water with algal bloom

Performance of gravity-driven membrane filtration for treatment of reservoir water with algal bloom

Cascade reservoirs affected chemical compositions of dissolved organic matter and greenhouse gas dynamics in the Lancang River.

Dissolved organic matter (DOM) is an important component in aquatic systems. There has been much debate about the potential effects of cascade reservoirs on the transport and transformation of DOM. Here, through a survey of source to leave-boundary section of Lancang River (LCR) in June and November of 2017-2018, our results revealed that weak spatiotemporal variations were observed for DOC content, whereas DOM parameters were significantly different between natural and reservoir reaches. And DOM showed higher humification degree from allochthonous sources with increasing autochthonous matter in reservoir reach, may due to high particulate organic matter and releasing autochthonous DOM from phytoplankton blooms in the LCR, which can be evidenced by depleted DIC, enriched δ13CDIC and higher BIX. A unique fluorescent fraction (C5) appeared in the reservoir reach and increased along water flow, which was strongly associated with dissolved CO2 and N2O. Meanwhile, BIX value decreased with increasing dam height, hydraulic residence time (HRT), and reservoir capacity, which may promote CH4 production, highlighting variation of DOM compositions in understanding the effect of greenhouse gas (GHG) dynamics in the LCR. The findings were essential for comprehending the influences of cascade reservoirs on carbon cycle, and informed policy development for the sustainable management of transboundary water resources like the LCR.

Cyanobacterial hydrothermal carbonization carbon as photocatalyst for selective aerobic oxidation of cyclohexane

The exploration of catalyst preparation techniques that incorporate environmental-friendly methodologies has consistently been the focus of scientific inquiry within the field of green synthesis of oxygen-containing organic chemicals. Herein, a metal-free hydrothermal carbonation carbon (HTCC) has been prepared utilizing cyanobacterial biomass produced during a water bloom in a freshwater lake. The HTCC produced is capable of performing photocatalytic selective oxidation of cyclohexane to cyclohexanol and cyclohexanone (KA oil) under ambient conditions. The use of cyanobacterial biomass allows for the efficient preparation of HTCC, which exhibits a conversion rate of cyclohexane that is 4.1 times of HTCC prepared with alternative carbon sources (glucose). The selectivity to KA oil over the samples are almost 100%. Characterizations and analysis reveal that cyanobacteria can lead to self-doping of nitrogen and hydrophobicity of the resulting HTCC, while sulfuric acid etching endow it with more photoactive sp2 hybridized structure units that contribute to a higher photogenerated carrier separation efficiency. The suitable band structure enables the cyanobacterial HTCC to produce superoxide radicals to oxidize cyclohexane. These findings are of great significance for the development of eco-friendly photocatalysts and the utilization of biomass resources.