Algal Biomass Research Articles

Elucidation of Domain Function of a Novel Multifunctional Glycoside Hydrolase and Its Use in Efficient Preparation of Oligosaccharides from Kelp Powder.

Kelp contained laminarin, cellulose, and alginate as major polysaccharides and could be utilized as functional oligosaccharides. A new multifunctional glycoside hydrolase CelA was identified and characterized for the efficient degradation of kelp powder. It displayed cellulase (2308.38 U/mg), alginate lyase (578.68 U/mg), and laminarinase (720.97 U/mg) activities. It exhibited maximal activity on both sodium alginate and laminarin at 50 °C and pH 8.0, while it could degrade sodium carboxymethylcellulose (CMC-Na) by maximal activity at 40 °C and pH 7.0. The action mode analysis by thin layer chromatography and electrospray ionization mass spectrometry indicated that CelA adopted an endolytic manner to degrade CMC-Na, sodium alginate, and laminarin releasing oligosaccharides with degrees of polymerization (Dps) of 2-5. According to domain analysis, CelA contained a GH5 module and a PL6 module, and both of them exhibited glycoside hydrolase and polysaccharide lyase activity. The docking results revealed that Glu163 and Glu250 are essential in cellulose and laminarin degradation. As to the degradation of alginate, Asn376, Lys436, Arg464, Asp496, and Asn551 could bind alginate and Tyr492 and Lys529 acted as catalytic sites. CelA displayed high hydrolysis efficiency for cellulose, β-glucan, laminarin, alginate, and kelp powder. Thus, it has strong potential in food and feed industries as a catalyst for bioconversion of algal biomass into value-added products oligosaccharides.

Environmental concentrations of the fungicide tebuconazole alter microbial biodiversity and trigger biofilm-released transformation products

Freshwater microbial communities are integral components of riverine biodiversity. The ecological effects of toxic chemical pollutants, such as fungicides (e.g., tebuconazole), on microbial abundance and diversity are needed for risk assessment and regulation. The emergence of RNA metabarcoding approaches allow us to describe at unprecedented resolution the microbial diversity of the active part of a microbial community. Our study assesses the ecotoxicological impact of chronic and acute tebuconazole exposures on fungal, bacterial, and algal biomass and biodiversity of aquatic fungi and bacteria in stream biofilms using an RNA metabarcoding approach. In addition, the study uses HPLC-MS to evaluate the capability of biofilms to metabolize tebuconazole. Natural biofilm communities from a Swedish stream were exposed chronically (24 days) and acutely (96 h) to environmental concentrations of tebuconazole (10 and 100 μg/L) in microcosms conditions. The diversity and community structure of fungi and bacteria was assessed by ITS2 and 16S cDNA amplicon-sequencing, respectively. Biofilms chronically exposed to tebuconazole produced and released unidentified transformation products into the water column, suggesting a biotransformation capability following 24 days of uninterrupted exposure. The fungal biomass markedly decreased by a biomass loss of 40% when chronically exposed to 10 μg/L, and 60% when chronically exposed to 100 μg/L. Bacterial and algal biomass remained comparable with the controls in all tebuconazole treatments. Fungal and bacterial alpha diversity metrics were not significantly impacted, although a decreasing trend in fungal richness was observed with the treatments. However, beta diversity was significantly impacted in both fungal and bacterial compartments. Chronic exposures resulted in a shift in community composition, where taxa potentially more tolerant to tebuconazole (i.e. Lecanoromycetes) replaced more sensitive taxa (i.e. Malasseziomycetes). This study indicates that tebuconazole at environmental concentrations might pose a risk to freshwater systems, mainly due to its high toxicity to fungi.

Properties of Biofilm Prokaryotic and Eukaryotic Communities in a Representative Hypereutrophic Urban River

ABSTRACT Biofilms play an important role in nutrient and food web dynamics of shallow aquatic ecosystems. Multiple prokaryotic and eukaryotic microorganisms within biofilms interact with each other to shape the community structure and their functional attributes. However, there is no clear understanding of varied patterns of biofilm prokaryotic versus eukaryotic microbial abundances, diversities and communities, which limit our understanding of how biofilm communities and functions in hypereutrophic urban river ecosystems are changing. To elucidate the properties of biofilm communities and controls on biofilm communities in a hypereutrophic urban river, we conducted a one‐year study to investigate the seasonal and water‐depth variations on the abundance/biomass, diversity and structure of biofilm prokaryotic and eukaryotic communities. The structure and dynamics of biofilm prokaryotic and eukaryotic communities were determined by high‐throughput sequencing based on the 16S and 18S rRNA gene. Sequencing revealed that Proteobacteria, Bacteroidota and Cyanobacteria were the three dominant phyla in biofilm prokaryotic communities, and Rotifera, Chlorophyta, Annelida and Bacillariophyta were the four dominant phyla in biofilm eukaryotic communities. Biofilm bacterial abundance depended mainly on the water temperature, whereas biofilm algal biomass correlated with rotifer grazing and light levels. Prokaryotic communities had higher species richness and diversity than eukaryotic communities. Species richness and diversity displayed significant seasonal variations with minima for prokaryotic communities in winter and eukaryotic communities in summer, which were linked to water temperature and rotifer grazing, respectively. Variations in biofilm prokaryotic and eukaryotic community composition were mainly related to ammonia concentration and water temperature, respectively. The co‐occurrence network analysis suggested that rotifer grazing could considerably decrease the complexity of the biofilm network in summer, and the algal groups, especially for Chlorophyta and Bacillariophyta, were the key to the formation of stable biofilm networks. There were significant differences in seasonal and water‐depth heterogeneity of biofilm prokaryotic and eukaryotic community composition. Our findings indicate that variations in water temperature, light level, rotifer grazing and nutrients (especially ammonia) appreciably contribute to changes in the abundance, diversity and composition of biofilm prokaryotic versus eukaryotic communities in the hypereutrophic urban river. These findings increase our understanding of biofilm community characteristics in hypereutrophic urban rivers and provide insights for water pollution remediation strategies.

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.

Simultaneous nutrient-abundant hydroponic wastewater treatment, direct carbon capture, and bioenergy harvesting using microalgae–microbial fuel cells

Hydroponics has increasingly been recognized as an important agricultural method due to its stable crop yields under rapidly changing environmental conditions. However, the efficient treatment of nutrient-rich hydroponic wastewater remains a major challenge. This study investigates the effect of anodic pH on the performance of microalgae–microbial fuel cells (mMFCs), focusing on bioelectricity generation, photosynthetic oxygen supply, nutrient removal and recovery, and carbon capture. The mMFC system achieved a maximum power density of 122.5mW/m², a chemical oxygen demand removal efficiency of 93.7%, and an anode-side total nitrogen removal efficiency of 27.5% at an acidic anodic pH. In addition, the cathode chamber had a total ammonium nitrogen removal efficiency of 22.6%, which was ascribed to a combination of ammonium migration and subsequent nitrogen assimilation, and a phosphate removal efficiency of 100%, likely due to microalgal uptake and adsorption. The mMFC also effectively captured CO2 with an algal biomass yield of 0.01379g·L-1·d-1 and a CO₂ fixation rate of 0.02528g·L-1·d-1. These findings provide insights into the optimization of mMFCs as a sustainable solution for managing nutrient-rich hydroponic wastewater, contributing to energy-efficient and resource-recovering wastewater treatment technologies.

Algal Classification and Chlorophyll-a Concentration Determination Using Convolutional Neural Networks and Three-Dimensional Fluorescence Data Matrices

In recent years, the frequency of harmful algal blooms has increased, leading to the release of large quantities of toxins and compounds that cause unpleasant odors and tastes, significantly compromising drinking water quality. Chlorophyll-a (Chl-a) is commonly used as a proxy for algal biomass. However, current methods for measuring Chl-a concentration face challenges in accurately quantifying algae by categories and effectively adapting to natural aquatic environments. This study combined convolutional neural networks (CNNs) and three-dimensional fluorescence data matrices to address these challenges. The algal classification model achieved over 99.5% accuracy in identifying thirteen types of algal samples, with class activation maps showing that the model primarily focused on algal pigment regions. In determining Chl-a concentrations of each algal species in mixed algae solutions (Microcystis aeruginosa, Cyclotella, and Chlorella), the models demonstrated Mean Absolute Percentage Errors (MAPEs) ranging from 6.55% to 10.56% in the ultrapure water background, 11.57% to 14.12% in the Qingcaosha Reservoir raw water background, and 21.46% to 123.37% in the Lake Taihu raw water background. After calibration, the models were significantly improved, achieving MAPEs ranging from 11.86% to 14.18% in the Lake Taihu raw water background. Discrepancies in determination performance indicated that the intensity and locations of characteristic algal pigment fluorescence peaks greatly influenced the models' accuracy. This research introduces a novel approach for algal classification and Chl-a concentration determination in water bodies, with significant potential for practical applications.

Robust prediction of chlorophyll-A from nitrogen and phosphorus content in Philippine and global lakes using fine-tuned, explainable machine learning

Chlorophyll-a (Chl-a) content in waterbodies is a primary indicator of algal biomass and is used to detect impending harmful algal blooms. This paper presents a methodology using 8 popular machine learning (ML) models for estimating Chl-a concentration from nutrient content in lakes. Different from previous works, we introduce 3 novel steps: (i) the use of Bayesian optimization for fine-tuning ML hyper-parameters to improve performance; (ii) the use of explainability methods to understand the most influential inputs to Chl-a prediction; and (iii) the use of robustness analysis to assess how models are affected by measurement noise. Two case studies were used to test our approach: Laguna Lake, Philippines, and various lakes from Japan, the United States of America, Canada, and Uganda. We found that fine-tuned Kernel Ridge Regression and Gaussian Process Regression are consistently the most accurate (>80%) and robust models in both case studies. In Laguna Lake, Shapley explanations revealed that phosphate and nitrate ions are the most important predictors of Chl-a, while total phosphorus is that for global lakes. Hence, these parameters are suggested to be monitored more closely for detecting algal blooms. By making our codes accessible, we hope that our methods can serve as a benchmark for the data-driven modeling of Chl-a content in lakes, and aid in their management through model deployment.

Data-driven models for forecasting algal biomass in a large and deep reservoir

Early warning of algal biomass is important for the preservation and management of drinking water. However, accurate prediction of algal biomass in large and deep reservoirs remains a challenge. Here, we used six years of high-frequency observations (30 min/time) to train long short-term memory (LSTM) models for forecasting chlorophyll-a concentration (CChla) and column-integrated CChla (CIC) for a large and deep Chinese reservoir (Xin'anjiang Reservoir). Five LSTM-based algal biomass forecasting models were developed, including four CChla models for various forecasting scales (1-hour, 3-hour, 6-hour, and 24-hour) and a CIC model (forecasting scale: 1-day). The results showed that the trained LSTM-based models can accurately predict CChla and CIC at reservoir scale and the root mean square error (RSME) values are less than 1.1 and 14.9 μg/L, respectively. The proposed CChla LSTM model outperformed the MLP, CNN, CNN-LSTM, and RNN models, with the RMSE decreasing by 2.6%, 4.8%, 5.3%, and 9.3%, respectively. Similarly, the proposed CIC LSTM model surpassed the MLP, CNN, CNN-LSTM, and RNN models, resulting in a RMSE reduction of 36.1%, 46%, 50.3%, and 52.8%, respectively. With the time lag increase, the performance of the multistep-ahead forecasting model exhibits initial improvement followed by deterioration. The best performance of the multistep-ahead forecasting model was observed when the input time length is 6–8 times the forecasting time length. Spatially, the proposed models perform better at the sites with small variations in algal biomass. On the other hand, water temperature is the most important influential factor for predicting algal biomass. Our work provides an effective tool for managers to develop preemptive measures to control algal blooms.

Organic petrology and geochemistry of the late Neogene Shizigou Formation in the Qaidam Basin, China: Characteristics of a prospective microbial gas source rock

The discovery of natural gas trapped in the late Neogene Shizigou Formation in the Yikeyawuru anticline indicates the potential for additional microbial gas reservoirs outside of the primary exploration targets for microbial gas in the younger, i.e., the Pleistocene sediments of the Qaidam Basin. In this study, a detailed investigation is presented on the bulk geochemistry and organic petrography of the potential microbial source rocks as well as on molecular organic geochemistry of the solvent extracts obtained from the late Neogene Shizigou Formation of the Yiliping Depression. The objectives are to elucidate i) the depositional environment, ii) biological sources of organic matter (OM), and iii) biodegradation levels in these microbial gas source rocks.The samples from the well situated at the center of the Yiliping Depression (the H 1 well) exhibit minor variations in total organic carbon (TOC) and total sulfur contents, whereas the samples from the well located at the margin of the depression (the Y 3 well) show large variations in these values. All these samples are presently thermally immature. The kerogen of the TOC-rich Y 3 well samples is mainly composed of mixed types II–III kerogen and characterized by a complex maceral composition (i.e., a mixture of large fragments of huminite, semifusinite, fusinite, resinite/fluorinite, lamalginite, and liptodetrinite). In contrast, samples from the H 1 well contain typically type III kerogen with a less complex maceral composition consisting of huminite, lamalginite, and liptodetrinite. The molecular data illustrates that the OM is predominantly derived from bacterial and algal biomass as well as aquatic higher plants (primarily in the Y 3 well samples), while angiosperms are the primary source of the subordinate terrestrial OM in the samples. The marginal area is characterized by salinity levels akin to normal marine conditions with bottom-water paleoredox conditions ranging from dyoxic (samples with high TOC content) to oxic, whereas the central area developed a mesosaline environment with oxic bottom-water conditions prevailing. In contrast to the primary microbial gas producing layer, the Pleistocene Qigequan Formation, the late Neogene Shizigou Formation exhibits a higher contribution of emergent macrophytes but a reduced abundance of lower aquatic organisms in the OM as well as a higher salinity level in the water column. Despite the late Neogene Shizigou Formation demonstrating a lower potential for hydrocarbon generation and a lower degree of biodegradation of OM than the Qigequan Formation, it still shows generally favorable geological and geochemical conditions that are conducive to the development of microbial gas reservoirs, which is underscored by the biodegradation levels between 3 and 4 for the studied samples.

The Role of Spring-Neap Phasing of Intermittent Lateral Exchange in the Ecosystem of a Channel-Shoal Estuary

Lateral variability is a fundamental feature of channel-shoal estuaries, and exchanges between the channel and shoal can play an important role in the dynamics of the ecosystem in each region. This lateral exchange of biomass interacts with vertical structure and variability, particularly in the channel, to define algal biomass accumulation in the estuary. In this paper, we investigate how time-variable lateral exchange affects phytoplankton dynamics with a biophysical model that links two water columns via intermittent exchange. We find that time variability in the exchange influences biomass by increasing concentrations in the shoals and decreasing them in the channel when the time variability happens on a timescale greater than the timescales of biological processes, and the strength of the effect increases with the period of the intermittency. At timescales of variability comparable to the spring-neap cycle, however, the interplay between lateral exchange and the ecosystem response is complicated by the fortnightly development of stratification in the channel and the role that channel-shoal interaction plays in defining that stratification. As a result, for lateral exchange variability with periods of 7 and 14 days, the influence of the shoal ecosystem on the channel ecosystem is sensitive to the phasing of exchange relative to the spring-neap cycle, due to the fact that neap tide exchanges can create stratification events that are larger in magnitude and duration than would occur in the absence of lateral exchange, causing the channel to transition into net positive growth conditions. We conclude that lateral exchange influences the estuarine ecosystem both directly, through the exchange of biomass between shoals with net positive growth and adjoining channels and indirectly through its role in defining stratification events that allow the channel itself to have net positive growth.

Growth Inhibition of Algae in Aquaculture Fishponds Using Banana Peel Powder: A Mesocosm Experiment

Cyanobacterial blooms and algal problems in aquaculture fish-ponds have become a significant concern for fish farmers due to the resultant decline in fish production. While numerous studies have been conducted globally on cyanobacterial blooms in oceans and lakes, relatively few have focused on the inhibition of algal biomass affecting aquaculture fish ponds. This study evaluated the potential of banana peel ashes and potassium sulphate to inhibit algal growth in fish ponds through a six-week mesocosm experiment using varying ash concentrations (i.e., 2, 4, 6, 8, 10 g·L−1). Differences among treatments in nutrient variables (nitrates, ammonium, and phosphates) were analyzed at the conclusion of the experiment. Factorial repeated measures ANOVA was employed to assess significant experimental differences in physicochemical variables across the study weeks (i.e., 1–6 weeks), treatments (three levels, including controls), and ash concentrations. Additionally, algal growth inhibition was assessed by measuring chlorophyll-a concentration over the six-week period. Banana peel ashes exhibited significantly greater efficacy in inhibiting algal growth compared to potassium sulphate, with the exception of the controls. However, no clear patterns were observed on nutrient dynamics throughout the experiment. The study demonstrated that banana peel ashes were more effective in inhibiting algal biomass compared to potassium sulphate, while physicochemical variables remained largely unchanged from the start to the end of the experiment. Given the efficacy of banana peel ashes as an inhibitor in this study, further research is recommended to investigate their potential impacts on fish within the ponds.

Total ammonia removal from anaerobic digestion effluents of municipal sewage sludge using Nordic microalgae

The treatment of organic waste using anaerobic digestion is a promising and well-matured organic waste management method. However, the effluent from anaerobic digestion has a significant discharge risk due to its high ammonium content. Microalgae could be a valuable solution to remove this nitrogen. This work aimed at evaluating the growth of three Nordic microalgae strains (Chlorella vulgaris, Chlorococcum sp. and Coelastrella sp.) in different concentrations of effluent from anaerobic digestion of municipal sewage sludge. None of the strains was able to grow in effluent diluted two times (X2) or three times (X3) due to the high ammonium content (600 and 400 mg L−1, respectively). While Chlorococcum sp. showed a lag phase of 7 and 11-days in 5 times (X5) and 7 times (X7) diluted effluent, respectively, this strain demonstrated 53 % and 86 % total ammonia nitrogen (TAN) removal efficiency after 15 days; in X10 its TAN removal was 100 %. Without any lag phase Coelastrella sp. showed the same TAN removal efficiencies in X5 and X7 as Chlorococcum sp. However, C. vulgaris had the highest TAN removal in X5 (90%) and X7 (90%). Furthermore, this strain showed the highest amount of biomass dry weight production in all media (1.1 g L−1 in X5). Therefore, C. vulgaris and Chlorococcum sp. are promising candidates for nitrogen removal and sustainable algae biomass production, resulting in mitigating the environmental issues of anaerobic digestion effluents in Nordic countries through the conversion of waste streams into resources.

Agarase cocktail from agarolytic Alteromonas sp. Aga1552 converts homogenized Gelidium amansii into monosaccharide

Marine algae biomass utilization has attracted considerable attention, however, the preparation of monosaccharides from raw algae is still hindered by many technical barriers. In this study, three genes, aga1365, aga1364, and aga1360, encoding key enzymes constituting a complete agar decomposition pathway were expressed and characterized. Recombinant Aga1365, Aga1364, and Aga1360 exhibited high optimal reaction temperatures and excellent thermal stability. Moreover, enzyme cocktail was proved to have higher synergistic effect to prepare monosaccharide from raw seaweed. The enzyme cocktail of Aga1360 (GH117) with Aga1365 (GH16) and enzyme cocktail of Aga1360 with both Aga1365 and 1364 (GH50) were used to synergistically degrade homogenized Gelidium amansii, maximum monosaccharide production of 21.47 mg/g and 39.28 mg/g could be achieved, respectively. This study presents an environment-friendly, time saving and efficient way to prepare monosaccharides from raw seaweed, which also provide a potential strategy to effectively convert algae biomass for biofuel and biochemical production by utilizing the synergistic effects of enzyme cocktail.

Assessing the environmental footprint of microalgae biofuel production: A comparative analysis of cultivation and harvesting scenarios

This study investigated the impact of fixed combinations of three flocculants and four buoy-beads under two cultivation systems on the harvesting efficiency, as well as the environmental performance in different scenarios. Results showed that the harvesting efficiency exhibited a tendency of initially increasing and then decreasing with rising concentrations of buoy-beads and flocculants, with an optimal harvesting efficiency of 98.03 %. Life cycle assessment (LCA) compared the environmental performance of five scenarios. Ecotoxicity Soil Chronic (ESC) and Aquatic Eutrophication EP(P) (AEP(P)) were major environmental impacts. The scenario employing re-frying oil emulsion (RFOE) and aluminum sulfate flocculation (R+A) contributed significantly to Human Toxicity Water (HTW) and Aquatic Eutrophication EP(N) (AEP(N)) with normalized values of 0.0137 and 0.0147, respectively. In the assessment of Global Warming Potential (GWP), R+A was responsible for a high amount of Greenhouse Gas (GHG) emissions (2.826 kg CO2 eq/100 g of dry algal biomass in photobioreactor (PBR) and 2.917 kg CO2 eq/ 100 g of dry algal biomass in open raceway ponds (ORP)). Notably, sodium alginate microspheres (SAMs) and aluminum sulfate flocculation (S+A) was considered a more environmentally favorable option, 0.773 kg CO2 eq and 0.864 kg CO2 eq GHG emissions of PBR and ORP, respectively. Furthermore, the less GHG emissions of PBR than ORP, making it a more effective solution for reducing emissions and mitigating global warming trends.

Evaluating poultry excreta leachate for algal biomass and biodiesel production for resource recovery and circular economy

Evaluating poultry excreta leachate for algal biomass and biodiesel production for resource recovery and circular economy

Efficient and sustainable approach of heavy metal adsorption from wastewater using algal biomass of Hypnea valentiae (Turner) Montagne, 1841

The biosorption of heavy metals especially, Cd2+, Pb2+, and Zn2+ from wastewater were evaluated using the biomass of macroalgae, Hypnea valentiae found in coastal waters of Rameshwaram, Tamil Nadu, India. The probable functional groups involved in the biosorption process was analyzed by Fourier transform infrared spectroscopy. The specific surface area, pore volume and pore diameter of prepared biosorbent was determined by Brunauer–Emmett–Teller (BET) analysis and it was found to be 59.993 m2 g−1, 0.292 cc g−1 and 1.429 nm, respectively. The scanning electron microscopy images after the biosorption revealed that the heavy metals have been adsorbed on to the surface of biosorbent. The optimization study was performed using Box–Behnken design and the optimum parameters used were initial concentration of metal ions (10–500 mg L−1), biomass dose (5–25 g L−1), contact time (10–120 min), temperature (25–45 °C), agitation speed (60–120 rpm) and pH (2–14). The heavy metal uptake (biosorption) was assessed by isotherm models including Dubinin–Radushkevich (D–R), Freundlich and Langmuir models. The maximum sorption of metal ions was observed in Langmuir isotherm model (R L 0.1773 and K F 2.2030 L mg−1) with an R 2 value close to 0.99 clearly indicating the chemical mode of metal ion adsorption. Also, the sorption of Cd2+, Pb2+, and Zn2+ on to the surface of algal biomass followed pseudo-second-order kinetics with a rate constant, k2 being 9.627 × 10−3 g (mg min) −1 and qe being 93.98496 mg g−1, strongly supporting the chemisorption mechanism. The computed thermodynamic parameters (ΔG°, ΔH, and ΔS) demonstrated an endothermic, spontaneous biosorption that was feasible. The maximum removal efficiency was achieved for Pb (92.86%) followed by Cd (91.48%) and Zn (87.32%). The biosorbent can be reused and regenerated up to four cycles efficiently. There was a significant reduction in the biological oxygen demand, chemical oxygen demand, total dissolved solids, and pH of wastewater after treatment with the biosorbent. From this study, it is assessed that the biomass from red algae can be utilized for the biosorption of heavy metals from polluted water which is cost-effective and eco-friendly.

Exploring the Remarkable Potential of Algal Biomass for the Production of Nutraceutical Compounds and Their Applications

Exploring the Remarkable Potential of Algal Biomass for the Production of Nutraceutical Compounds and Their Applications

Review of recent advances in utilising aquaculture wastewater for algae cultivation and microalgae-based bioproduct recovery.

Aquaculture operations produce large amounts of wastewater contaminated with organic matter, nitrogenous compounds, and other emerging contaminants; when discharged into natural water bodies, it could result in ecological problems and severely threaten aquatic habitats and human health. However, using aquaculture wastewater in biorefinery systems is becoming increasingly crucial as advancements in valuable bioproduct production continue to improve economic feasibility. Research on utilisingmicroalgaeas an alternative to producing biomass and removing nutrients from aquaculture wastewater has been extensively studied over the past decades. Microalgae have the potential to use carbon dioxide (CO2) effectively and significantly reduce carbon footprint, and the harvested biomass can also be used as aquafeed. Furthermore, aquaculture wastewater enriched with phosphorus (P) is a potential resource for P recovery for the production of biofertiliser. This will reduce the P supply shortage and eliminate the environmental consequences of eutrophication. In this context, the present review aims to provide a comprehensive overview of the current state of the art in a generation, as well as the characteristics and environmental impact of aquaculture wastewater reported by the most recent research. Furthermore, the review synthesized recent developments in algal biomass cultivation using aquaculture wastewater and its utilisation as biorefinery feedstocks for producing value-added products, such as aquafeeds, bioethanol, biodiesel, biomethane, and bioenergy. This integrated process provides a sustainable method for recovering biomass and water, fully supporting the framework of a circular economy in aquaculture wastewater treatment via resource recovery.

Double effects of mitigating cyanobacterial blooms using modified clay technology: regulation and optimization of the microbial community structure.

Harmful algal blooms (HABs) are global hazards under global climate change and eutrophication conditions. Modified clay (MC) method is widely used to control HABs in Asian and American coastal waters. However, little research has been conducted on the underlying mechanisms by which MC controls blooms in freshwater environments. Herein, experiments and bioinformatics analyses were conducted for MC-based control of freshwater blooms in a closed water body with an area of approximately 240 m2 in the Fuchun River, China. Results revealed that the dominant bloom species were Microcystis, and an 87.68-97.01% removal efficiency of whole algal biomass was achieved after 3 h of MC treatment. The weaker zeta potentials of Microcystis species and hydrophilic groups such as O-H and P-O-P in the extracellular polymeric substances (EPS) surrounding Microcystis cells made them easier to be flocculated and removed by MC particles, and the relative abundance of Microcystis decreased to 29.12% and that of Cyanobium increased to 40.97%. Therefore, MC changes the cyanobacterial community structure, which is accompanied by the elimination of Microcystis sp. apical dominance and enhanced competition between Cyanobium and Microcystis in the phytoplankton community, increasing cyanobacterial community diversity. Under MC treatment, residual microorganisms, including cyanobacteria, had a high potential for DNA damage repair and were more likely to survive after being subjected to oxidative stress. In the meanwhile, the abundance of genes involved in genetic information processing, signal transduction, and photosynthesis was decreased indicating that the residual microbiome was week in proliferation and light energy harvesting. Therefore, accompanied with the destruction of Microcystis colonies, MC changes the function of cyanobacteria and phycosphere microbiome, further hindering bloom development. These findings illustrate that MC can regulate and optimize the microbial community structure through which MC controls cyanobacterial blooms in ecosystems.

Opportunities for biogas production from algal biomass

Energy security is a critical global challenge in the transition to sustainable development. Anaerobic digestion (AD) offers a promising renewable energy solution that mitigates environmental impacts. Algae, as biomass feedstock, have shown significant potential for bioenergy production; however, their complex chemical composition poses challenges to the efficiency of the AD process. To address these limitations, various pretreatment methods have been applied to enhance biogas production. In this study, we performed a comprehensive meta‐analysis to evaluate the effects of different pretreatments on methane (CH₄) yields from both microalgae and macroalgae. Our results demonstrate that biological, physical, and combined chemical–physical pretreatments significantly improve CH₄ production in microalgae, with increases of up to 141%, 125%, and 151%, respectively. For macroalgae, physical pretreatments were the most effective, leading to a 129% increase in CH₄ yield. We also estimate that utilizing just 10% of the global algal biomass production (3.6 Mt) could generate over 5.5 TWh y−1 of energy. This potential could be doubled with the application of appropriate pretreatment strategies. These findings highlight the role of algae in advancing renewable energy production and contribute to the growing body of knowledge on optimizing AD processes for cleaner energy generation.