Bibliometric network analysis has revealed that the widespread distribution of microplastics (MPs) has detrimental effects on marine organisms; however, the combined effects of MPs and climate change (e.g., warming) is not well understood. In this study, Prorocentrum donghaiense, a typical red tide species in the East China Sea, was exposed to different MP concentrations (0, 1, 5, and 10 mg L⁻¹) and temperatures (16, 22, and 28 °C) for 7 days to investigate the combined effects of MPs and simulated ocean warming by measuring different physiological parameters, such as cell growth, pigment contents (chlorophyll a and carotenoid), relative electron transfer rate (rETR), reactive oxygen species (ROS), superoxide dismutase (SOD), malondialdehyde (MDA), and adenosine triphosphate (ATP). The results demonstrated that MPs significantly decreased cell growth, pigment contents, and rETRₘₐₓ, but increased the MDA, ROS, and SOD levels for all MP treatments at low temperature (16 °C). However, high temperatures (22 and 28 °C) increased the pigment contents and rETRₘₐₓ, but decreased the SOD and MDA levels. Positive and negative effects of high temperatures (22 or 28 °C) were observed at low (1 and 5 mg L⁻¹) and high MP (10 mg L⁻¹) concentrations, respectively, indicating the antagonistic and synergistic effects of combined warming and MP pollution. These results imply that the effects of MPs on microalgae will likely not be substantial in future warming scenarios if MP concentrations are controlled at a certain level. These findings expand the current knowledge of microalgae in response to increasing MP pollution in future warming scenarios.

Freshwater ecosystems play a key role in global greenhouse gas estimations and carbon budgets, and algal blooms are widespread owing to intensified anthropological activities. However, little is known about greenhouse gas dynamics in freshwater experiencing frequent algal blooms. Therefore, to explore the spatial and temporal variations in methane (CH₄) and carbon dioxide (CO₂), seasonal field investigations were performed in the Northwest Bay of Lake Chaohu (China), where there are frequent algal blooms. From the highest site in the nearshore to the pelagic zones, the CH₄ concentration in water decreased by at least 80%, and this dynamic was most obvious in warm seasons when algal blooms occurred. CH₄ was 2–3 orders of magnitude higher than the saturated concentration, with the highest in spring, which makes this bay a constant source of CH₄. However, unlike CH₄, CO₂ did not change substantially, and river mouths acted as hotspots for CO₂ in most situations. The highest CO₂ concentration appeared in winter and was saturated, whereas at other times, CO₂ was unsaturated and acted as a sink. The intensive photosynthesis of rich algae decreased the CO₂ in the water and increased dissolved oxygen and pH. The increase in CH₄ in the bay was attributed to the mineralization of autochthonous organic carbon. These findings suggest that frequent algal blooms will greatly absorb more CO₂ from atmosphere and increasingly release CH₄, therefore, the contribution of the bay to the lake's CH₄ emissions and carbon budget will be major even though it is small. The results of this study will be the same to other shallow lakes with frequent algal bloom, making lakes a more important part of the carbon budget and greenhouse gases emission.

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Around the world, green tides are happening with increasing frequency because of the dual effects of increasingly intense human activity and climate change; this leads to significant impacts on marine ecology and economies. In the last decade, the world's largest green tide, which is formed by Ulva/Enteromorpha porifera, has become a recurrent phenomenon every year in the southern Yellow Sea (China), and it has been getting worse. To alleviate the impacts of such green tide outbreaks, multiple measures need to be developed. Among these approaches, biotechnology plays important roles in revealing the outbreak mechanism (e.g., molecular identification technology for algal genotypes), controlling and preventing outbreaks at the origin sites (e.g., technology to inhibit propagation), and utilizing valuable algal biomass. This review focuses on the various previously used biotechnological approaches that may be applicable to worldwide seaweed blooms that result from global climate change and environmental degradation.

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle–lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 μm(PM₁₀), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O₃), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH₄⁺ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.

Reservoirs are an important type of drinking water source for megacities, while lots of reservoirs are threatened by odor problems during certain seasons. The influencing factors of odor compounds in reservoirs are still unclear. During August 2019, a nationwide survey investigating the distribution of odor compounds in reservoirs used as drinking water sources was conducted on seven reservoirs. 2-methylisoborneol (2-MIB) and geosmin were detected in almost every reservoir, and some odor compound concentrations even exceeded the odor threshold concentration. The average concentration of 2-MIB was 2.68 ng/L, and geosmin was 3.63 ng/L. The average chlorophyll a concentration was 8.25 μg/L. The dominant genera of phytoplankton in these reservoirs belonged to cyanobacteria and diatom. Statistical analysis showed that odor compound concentration was significantly related to the chlorophyll a concentration and indicated that the odor compounds mainly came from phytoplankton. The concentration of odor compounds in the euphotic zone was significantly related to phytoplankton species and biomass. Therefore, the odor compound concentrations in the subsurface chlorophyll maxima layer was generally higher than in the surface layer. However, the odor compounds in the hypolimnion layer were related to the density current. This research suggests that both phytoplankton proliferation events and heavy storm events are important risk factors increasing odor compounds in reservoirs. Control of algal bloom, in-situ profile monitoring system and depth-adjustable pumping system will greatly reduce the risk of odor problems in reservoirs using as water supplies for large cities.

It is well-accepted that phosphorus, particularly orthophosphate, is a determinant factor in aquatic eutrophication. However, numerous kinds of phosphorus sources exist in real world scenario, and limited studies have characterized the pairwise relationships among abundant different phosphorus sources and the physiological behaviour of algae. The present study developed a high-throughput assay to investigate the effects of 59 different phosphorus sources (equal initial concentration of total phosphorus) on the growth and alkaline phosphatase (AKP) activities of Microcystis aeruginosa, a model cyanobacteria whose predominance holds sway in lake eutrophication. M. aeruginosa cultivated with nucleoside monophosphates (NMPs) had higher growth, relative AKP activities and residual orthophosphate, which were positively intercorrelated. Oppositely, non-NMPs cultivation of M. aeruginosa led to negative relationships between the relative AKP activities and their growth or residual orthophosphate. These results indicated distinct mechanisms for M. aeruginosa to utilize different phosphorus sources in real-world scenario, and both phosphorus source and content are determinant factors on the growth and physiological behaviour of M. aeruginosa. Given the complicated and vast phosphorus pool in the natural environment, phosphorus resources might significantly alter the abundance and physiological behaviour of M. aeruginosa and other bloom-forming algae, then influence the phytoplanktonic community structure and affect the possibility and intensity of algal bloom. Our work hints the underestimation of the restriction factors in lake eutrophication and provides a new tool to study the driven forces of phytoplanktonic community dynamics as phosphorus from both internal and external sources.

High phosphorus (P) load and consequent algal bloom are critical issues because of their harmful effects to aquatic ecosystems. The organic phosphorus (Po) cycling and hydrolyzation pathway in the sediments of a hypereutrophic lake area with high algae biomass were investigated using stable isotopes (δ¹³C and δ¹⁵N) along with C/N ratios, a sequential extraction procedure, ³¹P NMR spectrum, and alkaline phosphatase activity (APA) was measured simultaneously. C/N ratios lower than 10 combined with lighter δ¹³C (−23.5 to −25.2‰) and δ¹⁵N values (3.7–9.5‰) indicated that endogenous algal debris contributed to the predominant proportions of P-containing organic matter in the sediments. Sequential extraction results showed that Po fractions decreased as nonlabile Po > moderately labile Po > biomass-Po. Decreasing humic-associated Po (HA-Po) in sediments downward suggested the degradation of high-molecular-weight Po compounds on the geological time scale to low-molecular-weight Po including fulvic-associated Po (FA-Po), which is an important source of labile Po in the sediment. An analysis of the solution ³¹P NMR spectrum analysis showed that important Po compound groups decreased in the order of orthophosphate monoesters > DNA-Po > phospholipids. The significant correlation indicated that orthophosphate monoesters were the predominant components of HA-Po. Rapid hydrolysis of labile orthophosphate diesters further facilitated the accumulation of orthophosphate monoesters in the sediments. Additionally, the simultaneously upward increasing trend demonstrated that APA accelerated the mineralization of Po into dissolved reactive phosphorus (DRP), which might feed back to eutrophication in algae-dominant lakes. The significantly low half-life time (T₁/₂) for important Po compound groups indicated faster metabolism processes, including hydrolysis and mineralization, in hypereutrophic lakes with high algae biomass. These findings provided improved insights for better understanding of the origin and cycling processes as well as management of Po in hypereutrophic lakes.

The Changjiang (Yangtze River) River estuary (CRE) and its adjacent coastal waters is a notable region for nutrient pollution, which results in severe problems of coastal eutrophication and harmful algal blooms (HABs). The occurrence of HABs, particularly those of dinoflagellate Alexandrium spp. capable of producing paralytic shellfish toxins (PSTs), has an increasing risk of contaminating seafood and poisoning human-beings. The investigation of PSTs, however, is often hampered by the relatively low abundance of Alexandrium spp. present in seawater. In this study, a monitoring strategy of PSTs using net-concentrated phytoplankton from a large volume of seawater was employed to examine spatiotemporal variations of PSTs in the CRE and its adjacent waters every month from February to September in 2015. Toxins in concentrated phytoplankton samples were analyzed using high-performance liquid chromatography coupled with a fluorescence detector (HPLC-FLD). The results showed that PSTs could be detected in phytoplankton samples during the sampling stage in the CRE and its adjacent waters. Toxin content increased gradually from February to May, reached the peak in June, and then decreased rapidly from July to September. The maximum value of PST content was 215 nmol m⁻³ in June. Low-potency toxins N-sulfocarbamoyl toxins 1/2 (C1/2) were the most dominant components of PST in phytoplankton samples from February to June in 2015, while high-potency gonyautoxin 4 (GTX4) became the dominant component from July to September. Toxins were mainly detected from three regions, the sea area north to the CRE, the sea area east to the CRE, and sea area near Zhoushan Island south to the CRE. Based on the results of this study, it can be inferred that the three regions around the CRE in May and June is of high risk for PST contamination and seafood poisoning.

Most aquatic systems show characteristic seasonal fluctuations in the total nutrient pool supporting primary productivity. The nutrient dynamics essentially exacerbate critical demand for the counterpart micronutrients towards achieving ecosystem equilibrium. Herein, the phytoplankton demand for iron (Fe) uptake under high concentration of nitrate-nitrogen during spring in Xiangxi Bay, China, was studied. Our result confirmed that significant Fe concentrations (P = 0.01) in both autumn (0.62 ± 0.02 mgL⁻¹) and winter (0.06 ± 0.03 mgL⁻¹) relative to spring (0.004 ± 0.01 mgL⁻¹) are linked to the low NO₃⁻N paradigms during autumn and winter. As NO₃⁻N showed a sharp increase in spring, a dramatic reduction in the Fe pool was observed in the entire tributary, driving the system to a critical Fe limited condition. Bioassay study involving Fe additions both alone and in combinations led to maximum growth stimulation with biomass as chla (16.44 ± 0.82 μgL⁻¹) and phytoplankton cell density (6.75 × 10⁶ cellsL⁻¹) which differed significantly (P = 0.03) with the control. Further, the study demonstrated that Fe additions triggered biomass productions which increased linearly with cell densities. The P alone addition caused biomass production (15.26 ± 2.51 μgL⁻¹) greater than both NO₃⁻N (9.15 ± 0.66 μgL⁻¹) and NH₄⁺N (13.65 ± 1.68 μgL⁻¹) separate additions but reported a low aggregate cell density (3.18 × 10⁶ cellsL⁻¹). This indicates that nutrient and taxonomic characteristics e.g., high cell pigment contents rather than just the cell bio-volume also determine biomass. The Bacilliarophyta, Chlorophyta, and Cryptophyta with the total extinction of Cyanophyta characterized the bloom in spring. The anthropogenic NO₃⁻N input into XXB would have driven to higher NO₃⁻N than NH₄⁺N situation, and incapacitated the Cyanophyta that preferentially utilize NH₄⁺N. Our study provides a useful report for incorporation into the monitoring programs for prudent management of phytoplankton bloom and pollution across the eutrophic systems.