A study on the relationship between metabolism of Cyanobacteria and chemical oxygen demand in Dianchi Lake, China

Unveiling the trifecta of cyanobacterial quorum sensing: LuxI, LuxR and LuxS as the intricate machinery for harmful algal bloom formation in freshwater ecosystems

Harmful algal blooms (HABs) are causing significant disruptions in freshwater ecosystems, primarily due to the proliferation of cyanobacteria. These blooms have a widespread impact on various lakes globally, leading to profound environmental and health consequences. Cyanobacteria, with their ability to produce diverse toxins, pose a particular concern as they negatively affect the well-being of humans and animals, exacerbating the situation. Notably, cyanobacteria utilize quorum sensing (QS) as a complex communication mechanism that facilitates coordinated growth and toxin production. QS plays a critical role in regulating the dynamics of HABs. However, recent advances in control and mitigation strategies have shown promising results in effectively managing and reducing the occurrence of HABs. This comprehensive review explores the intricate aspects of cyanobacteria development in freshwater ecosystems, explicitly focusing on deciphering the signaling molecules associated with QS and their corresponding genes. Furthermore, a concise overview of diverse measures implemented to efficiently control and mitigate the spread of these bacteria will be provided, shedding light on the ongoing global efforts to address this urgent environmental issue. By deepening our understanding of the mechanisms driving cyanobacteria growth and developing targeted control strategies, we hope to safeguard freshwater ecosystems and protect the health of humans and animals from the detrimental impacts of HABs.

Phytoplankton Community Structure Dynamics in Relation to Water Environmental Factors in Zhalong Wetland

Phytoplankton, as the primary producer of the wetland water ecosystem's food chain, are very sensitive to environmental changes. In order to explore the significance of phytoplankton in protecting ecosystem integrity, the wetland ecosystem in Zhalong wetland, one of the most important international wetlands, was selected as the research area. For the study, 81 sampling sites were set up in the whole wetland, and phytoplankton samples and water quality environmental factors were measured in spring, summer, and autumn of 2019. The phytoplankton community structure and water environmental factors were evaluated by canonical correspondence analysis (CCA). The main research findings are as follows: a total of 292 species and variants of phytoplankton belonging to 8 phyla and 110 genera were identified within Zhalong wetland in spring, summer, and autumn 2019. The total phytoplankton abundance and biomass in summer were higher than in spring and autumn, and Cyclotella meneghiniana was the most dominant species in three seasons and three areas. The results of random forest are generally consistent with the results of CCA in spring, when the main environmental factors affecting phytoplankton were NTU and WT; the result in summer and autumn agreed with those of CCA, which awaits further study. In addition, the phytoplankton is mainly affected by WT, depth, and DO in the lake area, TP, DO, and NTU in the river area, and WT in the wetland area.

Autochthonous sources and drought conditions drive anomalous oxygen-consuming pollution increase in a sluice-controlled reservoir in eastern China

Freshwater reservoirs are an important type of inland waterbody. However, they can suffer from oxygen-consuming pollution, which can seriously threaten drinking water safety and negatively impact the health of aquatic ecosystems. Oxygen-consuming pollutants originate from both allochthonous and autochthonous sources, and have temporally and spatially heterogeneous drivers. Datanggang Reservoir, China, is located in a small agricultural watershed; it is controlled by multiple sluice gates. Anomalously high oxygen consumption indicators were observed in this reservoir in March 2021. Here, it was hypothesized that autochthonous sources were the primary drivers of oxygen-consuming pollution in the reservoir under drought conditions. Datasets of water quality, precipitation, primary productivity, and sediment were used to analyze water quality trends in the reservoir and inflow rivers, demonstrating the effects of allochthonous inputs and autochthonous pollution. No correlation was found between reservoir oxygen consumption indicators and allochthonous inputs; reservoir oxygen consumption indicators and chlorophyll-a concentration were significantly positively correlated (p < 0.05). Substantially lower precipitation and higher water temperature and pH (compared to historical levels) were also observed before the pollution event. Therefore, during this period the hydrological conditions, water temperature, pH, and other variables caused by short-term drought conditions may have facilitated phytoplankton growth in the reservoir. This contributed to a large increase in autochthonous oxygen-consuming pollutants, as reflected by the abnormally high indicators. Sediments contaminated with organic matter may also have been an important contributor. As the effects of environmental management and pollution control continue to emerge, exogenous pollutants imported from the land to reservoirs are currently effectively controlled. However, endogenous pollutants driven by a variety of factors, such as meteorology and hydrology, will likely become the main drivers of short-term changes in oxygen-consuming pollution in freshwater reservoirs in the foreseeable future.

Prediction of Microcystis Occurrences and Analysis Using Machine Learning in High-Dimension, Low-Sample-Size and Imbalanced Water Quality Data

Machine learning, Deep learning, and water quality data have been used in recent years to predict the outbreak of harmful algae, especially Microcystis, and analyze outbreak causes. However, for various reasons, water quality data are often High-Dimension, Low-Sample- Size (HDLSS), meaning the sample size is lower than the number of dimensions. Moreover, imbalance problems may arise due to bias in the occurrence frequency of Microcystis. These problems make predicting the occurrence of Microcystis and analyzing its causes with machine learning difficult. In this study, a machine learning model that applies Feature Engineering (FE) and Feature Selection (FS) algorithms are used to predict outbreaks of Microcystis and analyze the outbreak factors from imbalanced HDLSS water quality data. The prediction performance was verified with binary classification to determine whether Microcystis would occur in the future by applying three machine learning models to four data patterns. The cause analysis of Microcystis occurrence was performed by visualizing the results of applying FE and FS. For the test data, the predictive performance of FE and FS methods was significantly better than that of the conventional method, with an accuracy of .108 points and an F-value of .691 points higher than the conventional method. A prediction performance increase was observed with a smaller model capacity. Data-driven analysis suggested that total nitrogen, chemical oxygen demand, chlorophyll-a, dissolved oxygen saturation, and water temperature are associated with Microcystis occurrences. The results also indicated that basic statistics of the water quality distribution (especially mean, standard deviation, and skewness) over a year, not the concentrations of water components, are related to the occurrence of Microcystis. These are new findings not found in previous studies and are expected to contribute significantly to future studies of algae. This study provides a method for analyzing water quality data with high-dimensionality and small samples, imbalance problems, or both.

Effect of Time Window on Satellite and Ground-Based Data for Estimating Chlorophyll-a in Reservoirs

Algal blooms in freshwater ecosystems can negatively impact aquatic and human health. Satellite remote sensing of chlorophyll a (Chl-a) is often used to help determine the severity of algal blooms. However, satellite revisit flyover schedules may not match the erratic nature of algal blooms. Studies have paired satellite and ground-based data that were not collected on the same day, assuming Chl-a concentrations did not change significantly by the flyover date. We determined the effects of an increasing time window between satellite overpass dates and field-based collection of Chl-a on algorithms for Landsat 5, Landsat 8, and Sentinel-2, using 14 years (2006–2020) of Chl-a data from 10 Oklahoma reservoirs. Multiple regression models were built, and selected statistics were used to rank the time windows. The Sentinel-2 results showed strong relationships between Chl-a and satellite data collected up to a ±5-day window. The strength of these relationships decreased beyond a ±3-day time window for Landsat 8 and a ±1-day time window for Landsat 5. Our results suggest that the time window between field sampling and satellite overpass can impact the use of satellite data for Chl-a monitoring in reservoirs. Furthermore, longer time windows can be used with higher resolution (spatial, spectral) satellites.

Biofilms for Turbidity Mitigation in Oil Sands End Pit Lakes

End pit lakes (EPLs) have been proposed as a method of reclaiming oil sands fluid fine tailings (FFT), which consist primarily of process-affected water and clay- and silt-sized particles. Base Mine Lake (BML) is the first full-scale demonstration EPL and contains thick deposits of FFT capped with water. Because of the fine-grained nature of FFT, turbidity generation and mitigation in BML are issues that may be detrimental to the development of an aquatic ecosystem in the water cap. Laboratory mixing experiments were conducted to investigate the effect of mudline biofilms made up of microbial communities indigenous to FFT on mitigating turbidity in EPLs. Four mixing speeds were tested (80, 120, 160, and 200 rpm), all of which are above the threshold velocity required to initiate erosion of FFT in BML. These mixing speeds were selected to evaluate (i) the effectiveness of biofilms in mitigating turbidity and (ii) the mixing speed required to 'break' the biofilms. The impact of biofilm age (10 weeks versus 20 weeks old) on turbidity mitigation was also evaluated. Diverse microbial communities in the biofilms included photoautotrophs, namely cyanobacteria and Chlorophyta (green algae), as well as a number of heterotrophs such as Gammaproteobacteria, Desulfobulbia, and Anaerolineae. Biofilms reduced surface water turbidity by up to 99%, depending on the biofilm age and mixing speed. Lifting and layering in the older biofilms resulted in weaker attachment to the FFT; as such, younger biofilms performed better than older biofilms. However, older biofilms still reduced turbidity by 69% to 95%, depending on the mixing speed. These results indicate that biostabilization is a promising mechanism for turbidity mitigation in EPLs.

Impacts of Urban Land Use Changes on Ecosystem Services in Dianchi Lake Basin, China

Land use change is widely regarded as a key factor altering the structure and function of ecosystems. Urbanization is a particular driver and influence in land use change and is expanding globally, especially in developing countries like China. The InVEST model was employed in this study to comprehensively assess the temporal-spatial impacts of urban land use changes (ULUC) on ecosystem services (ESs) and clarify the tradeoffs between urbanization and ecosystem services provision (ESP) in Dianchi Lake Basin (DLB), China, during 1995–2015. The results showed that DLB’s constructed land, particularly in lakeside areas, has increased substantially because of rapid urbanization (built-up area increase 51%, urbanization rate increase 91%), at the expense of agriculture, grassland, and scrubland, greatly altering ESP. Compared with 1995, carbon storage in 2015 decreased, while soil retention and nitrogen exports only fluctuated slightly. Although water yield increased, the ecological water volume of the whole DLB decreased through an increase in regional surface runoff. This resulted in more pollutants being carried into Dianchi Lake, impairing water quality and causing serious ecological and environmental issues. Thus, this study provides empirical evidence of the significant influence of ULUC on ESP. For eco-friendly urbanization in DLB and similar areas with rapid urbanization, a sustainable pattern of urbanization should be followed. The best tradeoffs between urbanization and ESP should be identified and considered in decision-making relating to urban planning, land use management, optimal natural resources management, and ecological conservation.

Effects of Malachite Green on the Microbiomes of Milkfish Culture Ponds

Intensive fish farming through aquaculture is vulnerable to infectious diseases that can increase fish mortality and damage the productivity of aquaculture farms. To prevent infectious diseases, malachite green (MG) has been applied as a veterinary drug for various microbial infections in aquaculture settings worldwide. However, little is known regarding the consequences of MG and MG-degrading bacteria (MGDB) on microbial communities in milkfish culture ponds (MCPs). In this study, small MCPs were used as a model system to determine the effects of MG on the microbial communities in MCPs. The addition of MG led to cyanobacterial blooms in the small MCP. The addition of MGDB could not completely reverse the effects of MG on microbial communities. Cyanobacterial blooms were not prevented. Microbial communities analyzed by next generation sequencing revealed that cyanobacterial blooms may be due to increase of nitrogen cycle (including nitrogen fixation, nitrate reduction and anammox) associated microbial communities, which raised the levels of ammonium in the water of the small MCP. The communities of anoxygenic phototrophic bacteria (beneficial for aquaculture and aquatic ecosystems) decreased after the addition of MG. The results of this investigation provide valuable insights into the effects of MG in aquaculture and the difficulties of bioremediation for aquatic environments polluted by MG.

Treatment and Re-Use of Raw Blackwater by Chlorella vulgaris-Based System

In this study, we examined a Chlorella vulgaris-based system as a potential solution to change liquid waste, such as blackwater, into valuable products for agriculture while protecting waters from pollution without technical demanding pre-treatment. To evaluate the possibility of nutrient removal and biomass production from raw blackwater, four blackwater dilutions were tested at lab-scale: 50%, 30%, 20%, and 10%. The results showed that even the less diluted raw blackwater was a suitable growth medium for microalgae C. vulgaris. As expected, the optimum conditions were observed in 10% blackwater with the highest growth rate (0.265 d−1) and a nutrient removal efficiency of 99.6% for ammonium and 33.7% for phosphate. However, the highest biomass productivity (5.581 mg chlorophyll-a L−1 d−1) and total biomass (332.82 mg dry weight L−1) were achieved in 50% blackwater together with the highest chemical oxygen demand removal (81%) as a result of the highest nutrient content and thus prolonged growth phase. The results suggested that the dilution factor of 0.5 followed by microalgae cultivation with a hydraulic retention time of 14 days could offer the highest biomass production for the potential use in agriculture and, in parallel, a way to treat raw blackwater from source-separation sanitation systems.