Algal Bloom Occurrence and Effects in Russia

Biogeographic Analysis Suggests Two Types of Planktonic Prokaryote Communities in the Barents Sea

The Barents Sea is one of the most rapidly changing Arctic regions, with an unprecedented sea ice decline and increase in water temperature and salinity. We have studied the diversity of prokaryotic communities using 16S metabarcoding in the western and northeastern parts of the Barents Sea along the Kola Section and the section from Novaya Zemlya to Franz Joseph Land. The hypothesis-independent clustering method revealed the existence of two distinct types of communities. The most common prokaryotic taxa were shared between two types of communities, but their relative abundance was different. It was found that the geographic location of the sampling sites explained more than 30% of the difference between communities, while no statistically significant correlation between environmental parameters and community composition was found. The representatives of the Psychrobacter, Sulfitobacter and Polaribacter genera were dominant in samples from both types of communities. The first type of community was also dominated by members of Halomonas, Pseudoalteromonas, Planococcaceae and an unclassified representative of the Alteromonadaceae family. The second type of community also had a significant proportion of Nitrincolaceae, SAR92, SAR11 Clade I, NS9, Cryomorphaceae and SUP05 representatives. The origin of these communities can be explained by the influence of environmental factors or by the different origins of water masses. This research highlights the importance of studying biogeographic patterns in the Barents Sea in comparison with those in the North Atlantic and Arctic Ocean prokaryote communities.

Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality

The study goal was to determine spatiotemporal variations in chlorophyll-a (Chl-a) concentration using models that combine hydroclimatic and nutrient variables in 150 tropical reservoirs in Brazil. The investigation of seasonal variability indicated that Chl-a varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Therefore, an empirical model for Chl-a prediction based on the product of TN, TP, and normalized functions of V and P was proposed, but their individual exponents as well as a general multiplicative factor were adjusted by linear regression for each reservoir. The fitted relationships were capable of representing algal temporal dynamics and blooms, with an average coefficient of determination of R2 = 0.70. The results revealed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. Chl-a blooms presented seasonal and interannual variability, being more frequent in periods of high precipitation and low volume. The equations demonstrate different Chl-a responses to the parameters. In general, Chl-a was positively related to TN and/or TP. However, in some cases (22%), high nutrient concentrations reduced Chl-a, which was attributed to limited phytoplankton growth driven by light deficiency due to increased turbidity. In 49% of the models, precipitation intensified Chl-a levels, which was related to increases in the nutrient concentration from external sources in rural watersheds. Contrastingly, 51% of the reservoirs faced a decrease in Chl-a with precipitation, which can be explained by the opposite effect of dilution of nutrient concentration at the reservoir inlet in urban watersheds. In terms of volume, in 67% of the reservoirs, water level reduction promoted an increase in Chl-a as a response to higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destratification of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, applying the model to the two largest studied reservoirs showed greater sensitivity of Chl-a to changes in water use classes regarding variations in TN, followed by TP, V, and P.

A framework for exploring environmental risk of the longest inter-basin water diversion project under the influence of multiple factors: A case study in China

Multi-factor risk assessment is an important prerequisite for water quality protection and the safe operation of mega hydro-projects. As the largest long-distance inter-basin water diversion project in the world, the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC) has been in operation for 8 years and has benefited 79 million people along the canal. However, concerns have been raised in recent years about the potential negative effects of abnormal algal proliferation in the MRSNWDPC. It is very important for the safety of water supply to carry out relevant risk analysis and formulate regulatory management. In order to quantitatively evaluate the risk of algal proliferation in the MRSNWDPC under the influence of multiple factors, a multivariate risk assessment method based on Vine Copula theory and Monte Carlo simulation was proposed. Five key factors (water temperature, flow velocity, flow rate, algal cell density, and dissolved oxygen) were used and multiple dependency models in each section of the MRSNWDPC from January 2016 to January 2019 were established to study the risk of algal proliferation under multiple scenarios. The results demonstrate that water temperature can be used as an appropriate early-warning indicator of algal proliferation. The early-warning interval (unit: °C) of water temperature in the upper, middle, and lower reaches are 26-29°C, 23-26°C, and 21-23°C, respectively. Unlike bivariate analysis, the multiple dependency model describes the relationship between variables more accurately and enriches the scenarios of multiple conditional probabilities. When the water temperature fluctuates in the early-warning interval, regulating the upstream, midstream, and downstream flow velocity to be higher than 0.6 m/s, 0.5 m/s, and 0.6 m/s, respectively, can effectively reduce the risk of algal proliferation. This research not only provides a reference for the ecological control of algae in the MRSNWDPC and similar mega hydro-projects but also enriches the application of the Vine Copula theory coupled with the random sampling method for multi-variable risk analysis.

Sediment organic matter properties facilitate understanding nitrogen transformation potentials in East African lakes

East African lakes include the most productive and alkaline lake group in the world. Yet, they generally receive fewer nutrient inputs than the densely populated subtropical and temperate lakes in the northern hemisphere. In these lakes with insufficient supplies of inorganic nitrogen, the mineralization of benthic organic matter can play an important role in driving the nutrient cycle and nitrogen loss. Using a suite of stable ¹⁵N isotope dilution and tracer techniques, we examined five main processes of the sediment nitrogen cycle in 16 lakes and reservoirs of Tanzania and Kenya, East Africa: gross nitrogen mineralization, ammonium immobilization, dissimilatory nitrate reduction to ammonium (DNRA), and the dinitrogen (N₂) production via denitrification and anaerobic ammonium oxidation (anammox). Gross nitrogen mineralization and ammonium immobilization showed the maximum values of 9.84 and 12.39 μmol N kg^-1 h^-1, respectively. Potential DNRA rates ranged from 0.22 to 8.15 μmol N kg^-1 h^-1 and accounted for 10 %-74 % (average 25 %) of the total dissimilatory nitrate reduction. Potential nitrate reduction rates in most lakes were dominated by denitrification with a contribution of 26 %-85 % and a mean of 65 %. We further found that the sediment nitrogen transformations were driven mainly by benthic organic matter properties and water column phosphate concentrations, reflecting microbial metabolic responses to the changing carbon and nutrients availability. For instance, autochthonous production of protein-like organic matter attributed to active sediment nitrogen mineralization, DNRA, and denitrification. In contrast, the high degree of humification caused by the inputs of terrestrial humic-like substances slowed down the sediment nitrogen transformations. The contribution of DNRA to total dissimilatory nitrate reduction was significantly positively correlated to sediment C: N ratios. These results indicate that predictions of sediment N supply and loss in East African lakes can be improved by incorporating sediment organic matter properties.

Evidence of the Anthropic Impact on a Crustacean Zooplankton Community in Two North Patagonian Lakes

Lately, agriculture, livestock, forestry, and aquaculture activities have been greatly developed in Chilean North Patagonia, negatively impacting the balance of the environmental conditions in lakes and affecting the development and survival of several native species. The aim of this study was to assess the anthropic impact on a zooplankton community in two North Patagonian lakes. We collected samples from four sites belonging to Lake Icalma and Lake Llanquihue, including four replicates per site. Water samples were analyzed for physicochemical characteristics and zooplankton communities. We focused on the presence of Daphnia pulex, a species of zooplanktonic crustacean that performs a key role in capturing energy from primary producers to deliver it to final consumers such as fish. We found that Llanquihue showed higher total phosphorus, nitrogen, copper, iron, manganese, total dissolved solids (TDS), and conductivity (EC) than Icalma. Furthermore, ecological variables were greatly decreased due to total P, total N, manganese, copper, total dissolved solids, and conductivity, which changed the species dominance of the zooplankton community in Llanquihue, indicating some degree of anthropization. This study provides fundamental information on the anthropogenic impact on water quality, as well as on zooplankton diversity, highlighting the importance of monitoring the health of these North Patagonia freshwater ecosystems.

Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea

The increasing release of nutrients to aquatic environments has led to great concern regarding eutrophication and the risk of unwanted algal blooms. Based on observational data of 20 water quality parameters measured on a monthly basis at 40 stations from 2011 to 2020, this study applied different Machine Learning (ML) algorithms to suggest the best option for algal bloom prediction in the Han River, a large river in South Korea. Eight different ML algorithms were categorized into several groups of statistical learning, regression family, and deep learning, and were then compared for their suitability to predict the chlorophyll-derived trophic index (TSI-Chla). ML algorithms helped identify the most important water quality parameters contributing to algal bloom prediction. The ML results confirmed that eutrophication and algal proliferation were governed by the complex interplay between nutrients (nitrogen and phosphorus), organic contaminants, and environmental factors. Of the models tested, the adaptive neuro-fuzzy inference system (ANFIS) exhibited the best performance owing to its consistent and outperforming prediction both quantitatively (i.e., via regression) and qualitatively (i.e., via classification), which was evidenced by the lowest value of mean absolute error (MAE) of 0.09, and the highest F1-score, Recall and Precision of 0.97, 0.98 and 0.96, respectively. In a further step, a representative web application was constructed to assist common users to predict the trophic status of the Han River. This study demonstrated that ML techniques are not only promising for highly accurate water quality modeling of urban rivers, but also reduce time and labor intensity for experiments, which decreases the number of monitored water quality parameters, providing further insights into the driving factors of water quality deterioration. They ultimately help devise proactive strategies for sustainable water management.

Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.

Isolation and Identification of Two Algae-Lysing Bacteria against Microcystis aeruginosa

Algae blooms present an environmental problem worldwide. In response to the outbreak of harmful algal blooms in cyanobacteria, the role of biological control has drawn wide attention, particularly for algicidal bacteria. The mechanism underlying algicidal activity was determined in our study. Algae-lysing bacteria used were separated from water and sediment collected from the Fenhe scenic spot of Taiyuan. Genetic and molecular identification was conducted by polymerase chain reaction amplification based on 16S rDNA gene. These bacterial strains were identified as Raoultella planticola and Aeromonas sp. The algae-lysing characteristics were evaluated on Microcystis aeruginosa. For the two algicidal bacteria, the high inoculation ratio (>8%) of bacteria strains contributed to the lytic effect. M. aeruginosa could be completely removed by these strains at different cell ages. However, the time used decreased with an increase in cell age. The removal rate was increased while M. aeruginosa was in the lag and logarithmic phases. The earlier bacteria strains could be inoculated, the sooner all algae could be removed. Both algicidal substances were protein, which could destroy the photosynthetic systems and break the cell of M. aeruginosa. The algicidal bacteria strain has important theoretical and practical significance for economic and feasible algae removal and provides good germplasm resources and technical support for the control of cyanobacterial bloom.