Surface Scum Research Articles

Rapid flotation of Microcystis wesenbergii mediated by high light exposure: implications for surface scum formation and cyanobacterial species succession.

Increasing occurrences of Microcystis surface scum have been observed in the context of global climate change and the increase in anthropogenic pollution, causing deteriorating water quality in aquatic ecosystems. Previous studies on scum formation mainly focus on the buoyancy-driven floating process of larger Microcystis colonies, neglecting other potential mechanisms. To study the non-buoyancy-driven rapid flotation of Microcystis, we here investigate the floating processes of two strains of single-cell species (Microcystis aeruginosa and Microcystis wesenbergii), which are typically buoyant, under light conditions (150 μmol photons s-1 m-2). Our results showed that M. wesenbergii exhibited fast upward migration and formed surface scum within 4 hours, while M. aeruginosa did not form visible scum throughout the experiments. To further explore the underlying mechanism of these processes, we compared the dissolved oxygen (DO), extracellular polymeric substance (EPS) content, and colony size of Microcystis in different treatments. We found supersaturated DO and the formation of micro-bubbles (50-200 µm in diameter) in M. wesenbergii treatments. M. aeruginosa produces bubbles in small quantities and small sizes. Additionally, M. wesenbergii produced more EPS and tended to aggregate into larger colonies. M. wesenbergii had much more derived-soluble extracellular proteins and polysaccharides compared to M. aeruginosa. At the same time, M. wesenbergii contains abundant functional groups, which was beneficial to the formation of agglomerates. The surface scum observed in M. wesenbergii is likely due to micro-bubbles attaching to the surface of cell aggregates or becoming trapped within the colony. Our study reveals a species-specific mechanism for the rapid floatation of Microcystis, providing novel insights into surface scum formation as well as succession of cyanobacterial species.

Dynamics of Microcystis surface scum formation under different wind conditions: the role of hydrodynamic processes at the air-water interface.

Due to climate change, Microcystis blooms occur at increasing frequencies in aquatic ecosystems worldwide. Wind-generated turbulence is a crucial environmental stressor that can vertically disperse the Microcystis surface scum, reducing its light availability. Yet, the interactions of Microcystis scum with the wind-generated hydrodynamic processes, particularly those at the air-water interface, remain poorly understood. Here, we explore the response of Microcystis (including colony size and migration dynamics) to varying magnitudes and durations of intermittent wind disturbances in a mesocosm system. The flow velocities, size of Microcystis colonies, and the areal coverage of the water surface by scum were measured through video observations. Our results demonstrate that low wind speeds increase colony size by providing a stable condition where Microcystis forms a scum layer and aggregates into large colonies at the air-water interface. In contrast, wind disturbances disperse scum and generate turbulence, resulting in smaller colonies with higher magnitudes of wind disturbance. We observed that surface scum can form rapidly following a long period (6h) of high-magnitude (4.5m s-1) wind disturbance. Furthermore, our results indicate reduced water surface tension caused by the presence of Microcystis, which can decrease surface flow velocity and counteract wind-driven mixing. The reduced surface tension may also drive lateral convection at the water surface. These findings suggest that Microcystis reduces surface tension, likely by releasing surface-active materials, as an adaptive response to various wind conditions. This could result in an increased rate of surface scum re-formation under wind conditions and potentially facilitate the lateral expansion of scum patches during weak wind periods. This study reveals new insights into how Microcystis copes with different wind conditions and highlights the importance of the air-water interface for Microcystis scum dynamics.

Modeling Physical and Physiological Processes Reveals the Role of Turbulence in the Prerequisites for Microcystis Blooms

AbstractHarmful algal blooms of Microcystis have become a global problem. Turbulence, a determining factor affecting blooms, not only disperses surface scum but also controls the growth of Microcystis. Numerous studies have analyzed the effects of turbulence on the growth and colony size of Microcystis in laboratories, but the turbulence thresholds for Microcystis growth and colony disaggregation in the field are difficult to determine due to the complex environment. In addition, the quantitative contribution of turbulence‐driven blooms and the intrinsic mechanisms of the spatial distribution responding to turbulence are unclear. In this study, a fully integrated filed scale computational model focusing on turbulence‐driven blooms was developed, which incorporates physical processes (turbulence‐induced vertical mixing, VMT) and physiological processes such as buoyancy‐controlling transport (BCT), turbulence‐induced colony size variation (CSV), and growth rate variation (GRV). We performed model sensitivity analysis and evaluated the effects of turbulence intensity and duration on the biomass and vertical distribution of Microcystis. The results show that the optimal turbulence dissipation rate for Microcystis growth in the field is 1.0 × 10−5 m2/s3 and the critical turbulence dissipation rate for aggregation distribution is 3.81 × 10−6 m2/s3 in shallow lakes. A quantitative comparison of the effects of physical/physiological processes on blooms shows that physiological processes (CSV, GRV, and BCT) are critical for biomass enrichment, and the accumulation of Microcystis at the water surface is dominated by physical processes (VMT). This study reveals the mechanisms of turbulence‐driven Microcystis blooms and provides new insights for algal bloom prediction and control.

Selection of photosynthetic traits by turbulent mixing governs formation of cyanobacterial blooms in shallow eutrophic lakes.

Abstract Prediction of the complex cyanobacteria-environment interactions is vital for understanding harmful bloom formation. Most previous studies on these interactions considered specific properties of cyanobacterial cells as representative for the entire population (e.g. growth rate, mortality, and photosynthetic capacity (Pmax)), and assumed that they remained spatiotemporally unchanged. Although, at the population level, the alteration of such traits can be driven by intraspecific competition, little is known about how traits and their plasticity change in response to environmental conditions and affect the bloom formation. Here we test the hypothesis that intraspecific variations in Pmax of cyanobacteria (Microcystis spp.) play an important role in its population dynamics. We coupled a one-dimensional hydrodynamic model with a trait-based phytoplankton model to simulate the effects of physical drivers (turbulence and turbidity) on the Pmax of Microcystis populations for a range of dynamic conditions typical for shallow eutrophic lakes. Our results revealed that turbulence acts as a directional selective driver for changes in Pmax. Depending on the intensity of daily-periodic turbulence, representing wind-driven mixing, a shift in population-averaged phenotypes occurred toward either low Pmax, allowing the population to capture additional light in the upper layers, or high Pmax, enhancing the efficiency of light utilization. Moreover, we observed that a high intraspecific diversity in Pmax accelerated the formation of surface scum by up to more than four times compared to a lower diversity. This study offers insights into mechanisms by which cyanobacteria populations respond to turbulence and underscores the significance of intraspecific variations in cyanobacterial bloom formation. Highlights

Harmful Algal Blooms: A Prolific Issue in Urban Stormwater Ponds

Nutrient-driven cyanobacteria blooms are an increasingly common issue in freshwater environments, particularly in anthropogenically altered landscapes. As stormwater runoff is one of the largest sources of nutrients for freshwater bodies, stormwater retention ponds in urban and suburban areas are likely environments for harmful cyanobacteria blooms and were thus targeted for an in-depth investigation assessing taxonomic composition, bloom morphological composition, toxicity, and impact of nutrients and other environmental drivers. Eighty-seven algal blooms were sampled from 2019 to 2022 in the greater Wilmington, North Carolina, area. Physicochemical parameters were recorded, and blooms were classified by type (defined as surface mat, surface scum, water column distribution, or benthic mat) and dominant taxa. Blooms of potentially toxic cyanobacteria genera in the water column of stormwater retention ponds were most prevalent. Dissolved inorganic phosphorus was significantly related to chlorophyll-α, Microcystis bloom formation, and the production of microcystin. Seventeen potentially toxic cyanobacteria genera were identified in retention ponds, some of whose blooms demonstrated detectable microcystin. Monoclonal cultures isolated from some blooms were found to produce anabaenopeptin and saxitoxin. The results demonstrate a higher incidence of potentially toxic cyanobacteria over other bloom-forming taxa (chlorophytes, euglenoids, chrysophytes, dinoflagellates, and diatoms) in the 39 water bodies sampled. The frequency of blooms occurring in stormwater ponds and the diversity of potentially toxic cyanobacteria identified suggest such harmful blooms are likely widespread in similar freshwater environments across multiple urbanizing areas. The blooms sampled in this study were all within residential, commercial, or recreational areas easily accessible to people, presenting serious hazards to both environmental and public health.

Remote sensing techniques for monitoring algal blooms in the area between Jeddah and Rabigh on the Red Sea Coast

The Red Sea is known to host and facilitate the growth and reproduction of several species of phytoplankton and algae. The degree of density and distribution of these organisms differ with region. However, very few studies have been conducted on algal blooms in this region. Hence, this study aimed to identify the effectiveness of remote sensing techniques in monitoring and controlling algal blooms in the area extending between the cities of Jeddah and Rabigh on the Red Sea coast. A series of satellite images were used to study algae from January 2020 to March 2021. All study data were subjected to processing and analysis using spectral indices [chlorophyll a (Chl-a); Concentration and Sea Surface Temperature (SST); Normalized Difference Turbidity Index (NDTI); Surface Scum Index (SSI); Surface Algal Bloom Index (SABI); Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)], and the classification, overlay, and buffer methods. We found that the rate of chlorophyll a (Chl-a) increased in February 2021 and decreased in May 2020, with the algal bloom dwindling with increasing distance from the shores of the Red Sea. Furthermore, despite the varying effectiveness of the spectral indices in estimating the distribution and density of algae in the Red Sea, Landsat SSI was the most effective. One of the primary results of the study was that algal blooms more frequently occur in the winter (February). The study produced a digital map of algal blooms in the study area. Algal blooms exhibited medium inverse correlations with temperature and specific humidity, and strong direct correlation with wind speed. Valley estuaries, close to the beaches, were found to be the most suitable area for the concentration and growth of algae. The study's findings emphasize the need for close monitoring and controlling changes in the components of the marine environment using remote sensing techniques, in order to track the changes that marine plants (algae) are exposed to in the Red Sea.

Learning and inferring the diurnal variability of cyanobacterial blooms from high-frequency time-series satellite-based observations

Observational evidences have suggested that the surface scums of cyanobacterial harmful blooms (CyanoHABs) are highly patchy, and their spatial patterns can vary significantly within hours. This stresses the need for the capacity to monitor and predict their occurrence with better spatiotemporal continuity, in order to understand and mitigate their causes and impacts. Although polar-orbiting satellites have long been used to monitor CyanoHABs, these sensors cannot be used to capture the diurnal variability of the bloom patchiness due to their long revisit periods. In this study, we use the Himawari-8 geostationary satellite to generate high-frequency time-series observations of CyanoHABs on a sub-daily basis not possible from previous satellites. On top of that, we introduce a spatiotemporal deep learning method (ConvLSTM) to predict the dynamics of bloom patchiness at a lead time of 10 min. Our results show that the bloom scums were highly patchy and dynamic, and the diurnal variability was assumed to be largely associated with the migratory behavior of cyanobacteria. We also show that, ConvLSTM displayed fairly satisfactory performance with promising predictive capability, with Root Mean Square Error (RMSE) and determination coefficient (R2) varying between 0.66∼1.84 μg/L and 0.71∼0.94, respectively. This suggests that, by adequately capturing spatiotemporal features, the diurnal variability of CyanoHABs can be well learned and inferred by ConvLSTM. These results may have important practical implications, because they suggest that spatiotemporal deep learning integrated with high-frequency satellite observations could provide a new methodological paradigm in nowcasting of CyanoHABs.

Pollen in the Baltic Sea as viewed from space

Multi-sensor satellite images often show recurrent image slicks in the Baltic Sea. Based on historical reports, laboratory experiments, and spectral diagnostics, these image slicks are determined to be caused by pine (Pinus sylvestris) pollen aggregations as opposed to surface scums of cyanobacteria or other floating matters. This attribution is because the reflectance spectral shapes of these image slicks resemble those of pollen grains, which are all different from other floating matters. They all show rapid reflectance increase from ∼400 to ∼500 nm, beyond which the reflectance shape appears to plateau. During the 22-year period of 2000–2021, satellite images indicated large amounts of pollen grains in 14 years, with the earliest day being May 10 (in 2000) and the latest day being June 16 (in 2006). The longest duration in a single year is 22 days, from May 12 to June 2 of 2018. The waters containing pollen grains have expanded significantly in recent years, encompassing nearly the entire Baltic Sea. Because pollen grains contain a significant amount of carbon and have distinctive optical properties from other particles, these findings have significant implications on carbon sequestration, marine ecology, bio-optics, ocean color calibration/validation, and remote sensing of marine debris.

Retracing cyanobacteria blooms in the Baltic Sea

In late summer, massive blooms and surface scums of cyanobacteria emerge regularly in the Baltic Sea. The bacteria can produce toxins and add bioavailable nitrogen fixed from atmospheric nitrogen to an already over-fertilized system. This counteracts management efforts targeted at improving water quality. Despite their critical role, the controls on cyanobacteria blooms are not comprehensively understood yet. This limits the usability of models-based bloom forecasts and projections into our warming future. Here we add to the discussion by combining, for the first time, satellite estimates of cyanobacteria blooms with output of a high-resolution general ocean circulation model and in-situ nutrient observations. We retrace bloom origins and conditions by calculating the trajectories of respective water parcels backwards in time. In an attempt to identify drivers of bloom development, we find that blooms originate and manifest themselves predominantly offshore where conditions are more nutrient-depleted compared to more coastal environments.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density.

Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach.

Hyperspectral remote sensing has tremendous potential for monitoring land cover and water bodies from the rich spatial and spectral information contained in the images. It is a time and resource consuming task to obtain groundtruth data for these images by field sampling. A semi-supervised method for labeling and classification of hyperspectral images is presented. The unsupervised stage consists of image enhancement by feature extraction, followed by clustering for labeling and generating the groundtruth image. The supervised stage for classification consists of a preprocessing stage involving normalization, computation of principal components, and feature extraction. An ensemble of machine learning models takes the extracted features and groundtruth data from the unsupervised stage as input and a decision block then combines the output of the machines to label the image based on majority voting. The ensemble of machine learning methods includes support vector machines, gradient boosting, Gaussian classifier, and linear perceptron. Overall, the gradient boosting method gives the best performance for supervised classification of hyperspectral images. The presented ensemble method is useful for generating labeled data for hyperspectral images that do not have groundtruth information. It gives an overall accuracy of 93.74% for the Jasper hyperspectral image, 100% accuracy for the HSI2 Lake Erie images, and 99.92% for the classification of cyanobacteria or harmful algal blooms and surface scum. The method distinguishes well between blue green algae and surface scum. The full pipeline ensemble method for classifying Lake Erie images in a cloud server runs 24 times faster than a workstation.

BENTUK, FUNGSI DAN MAKN A REGISTER KOMUNITAS PECINTA AQUASCAPE DI ROKAN HULU

The diversity of language use in a heterogeneous society gives rise to language variations, including registers. The Aquascape Lovers Community in Rokan Hulu uses a separate register. This study aims to describe the form, function, and meaning of the limited and open circle register in the Aquascape Lovers Community in Rokan Hulu. This research is quantitative research with a descriptive method. Listening and recording methods were used in data collection. The informant is a Community of Aquascape Lovers in Rokan Hulu. The data were analyzed qualitatively by referring to the concept of form, function, and meaning of closed registers and closed registers proposed by Halliday. The results of the study are as follows. First, the limited circle register of the Aquascape Lovers Community in Rokan Hulu at the level of words and phrases in the form of abbreviations and the use of foreign words such as WC, kebo, and sticky rice with interactional and personal functions. Each word and phrase has a specific meaning. Second, the open circle register of the aquascape lover community in Rokan Hulu at the word and phrase-level in the form of abbreviations and the use of foreign words such as PH, lily pipe, and surface scum with interactional, heuristic, and regulatory functions. Each word and phrase has a specific meaning. It can be concluded that some of the registers used by this community can only be understood by community members and some can also be understood by people outside the community.

Sea Snots in the Marmara Sea as Observed From Medium-Resolution Satellites

Multi-sensor medium-resolution satellite images from MODIS, VIIRS, OLCI, and MERIS are used to study spatial and temporal distributions of sea snot features in the Marmara Sea between 2000 and 2021. Suspicious image slicks are identified in most years, and spectral diagnostics indicate sea snot features in 2007, 2008, and 2021, with the record-high sea snot event occurring in spring-summer 2021. In other years when similar image slicks are found, they appear to be from surface scums of red Noctiluca scintillans, a heterotrophic dinoflagellate responsible for red tides. Based on the medium-resolution images, the 2021 sea snot event started from March 14 and ended on June 27, with its peak time around May 4 when the sea snot features are found in the entire Marmara Sea covering an area of 1,160 km2. When all sea snots are aggregated together, the estimated areal coverage during the peak time is 50 km2, suggesting significant patchiness in the surface scums.

Subtropical Freshwater Cyanobacterial Blooms as Hydrogen Peroxide Hot Spots

In aquatic environments, chemical and biological processes represent major sources of reactive oxygen species (ROS) such as hydrogen peroxide. Diurnal patterns of hydrogen peroxide in surface water may suggest photochemical processes affect ROS dynamics. We hypothesized that thin surface hydrogen peroxide maxima could be detected through millimeter-scale depth profiling. These maxima were previously overlooked because they cannot be detected using standard surface water sampling techniques. Combined field and laboratory experiments demonstrated that cyanobacterial blooms could create extremely high concentrations of hydrogen peroxide in a thin layer of surface water (5–60 mm). Hydrogen peroxide concentrations were higher within surface scums of Microcystis aeruginosa than in areas without scum. The cyanobacterial scum increased the temperature of the ambient water, and hydrogen peroxide concentrations were higher at locations exposed to sunlight that at locations in the shade. We found that scum samples directly collected from the field could release hydrogen peroxide to the surrounding water when the biomass was exposed by the light, while 20 laboratory cultures tested did not. The production of hydrogen peroxide from cyanobacteria may depend on the physiological condition of cells. Overall, we identified thin surface water layers during subtropical freshwater cyanobacterial blooms as hydrogen peroxide hot spots.

Ecological niche and in-situ control of MIB producers in source water

Odor problems in source water caused by 2-methylisoborneol (MIB) have been a common issue in China recently, posing a high risk to drinking water safety. The earthy-musty odorant MIB has an extremely low odor threshold (4–16 ng/L) and is hard to remove via conventional processes in drinking water plants (DWP), and therefore could easily provoke complaints from consumers. This compound is produced by a group of filamentous cyanobacteria, mainly belonging to Oscillatoriales. Different from the well-studied surface-blooming Microcystis, filamentous cyanobacteria have specific niche characteristics that allow them to stay at a subsurface or deep layer in the water column. The underwater bloom of these MIB producers is therefore passively determined by the underwater light availability, which is governed by the cell density of surface scum. This suggests that drinking water reservoirs with relatively low nutrient contents are not able to support surface blooms, but are a fairly good fit to the specialized ecological niche of filamentous cyanobacteria; this could explain the widespread odor problems in source water. At present, MIB is mainly treated in DWP using advanced treatment processes and/or activated carbon, but these post-treatment methods have high cost, and not able to deal with water containing high MIB concentrations. Thus, in situ control of MIB producers in source water is an effective complement and is desirable. Lowering the underwater light availability is a possible measure to control MIB producers according to their niche characteristics, which can be obtained by either changing the water level or other measures.

Feedback regulation of surface scum formation and persistence by self-shading of Microcystis colonies: Numerical simulations and laboratory experiments

Light availability is an important driver of algal growth and for the formation of surface blooms. The formation of Microcystis surface scum decreases the transparency of the water column and influences the vertical distribution of light intensity. Only few studies analysed the interactions between the dynamics of surface blooms and the light distribution in the water column. Particularly the effect of light attenuation caused by Microcystis colonies (self-shading) on the formation of surface scum has not been explored. In the present study, we simulate the effect of variable cell concentration of Microcystis colonies on the vertical distribution of light in the water column based on experimental estimates of the extinction coefficient of Microcystis colonies. The laboratory observations indicated that higher cell concentration of Microcystis enhance the light attenuation in water column and promotes surface scum formation. We extended an existing model for the light-driven migration of Microcystis by introducing the effect of self-shading and simulated the dynamics of vertical migration for different cell concentrations and different colonial morphologies. The simulation results show that high cell concentrations of Microcystis promote surface scum formation, as well as its persistence throughout diel photoperiods. Large and tight Microcystis colonies facilitate scum formation, while small and loose colonies increase scum stability and persistence. This study reveals a positive feedback regulation of Microcystis surface scum formation and stability by self-shading and provides novel insights into the underlying mechanisms.

Occurrence of Botryococcus terribilis Komárek & Marvan in a small sandpit lake: The first report from Serbia

Alkaline saline ponds and lakes are habitats with unique biodiversity. The green alga of the genus Botryococcus was accidentally discovered during a micro-algal survey of these habitats in Serbia. Species B. terribilis was found and identified for the first time in Serbia in the small sandpit lake of the Rusanda Nature Park. The first sampling was incomplete due to the unknown origin of the orange-red surface scum. After the identity of the species had been confirmed, three additional samplings were conducted aiming to collect phytoplankton and water samples for physical and chemical analyses. Major anions and cations were analysed in order to characterize the chemical type of sandpit lake, as well as nutrient content. Sandpit lake is an alkaline water body that belongs to sodium bicarbonate chemical type. Morphometric analysis of B. terribilis was performed using light and transmission electron microscopy. B. terribilis was found to be numerous in the phytoplankton community in late autumn and winter. Species from the genus Botryococcus are known as a rich source of different lipids and this discovery can be the first step in the further biotechnological application of this species in sustainable biofuel production.

Recovery of Microcystis surface scum following a mixing event: Insights from a tank experiment

Cyanobacteria of the genus Microcystis produces surface scum that negatively affects water quality in inland waters. This scum layer can be disintegrated and vertically dispersed by external forces (e.g., wind mixing), followed by reformation of surface scum as buoyant Microcystis colonies migrate upward. However, the recovery dynamics of Microcystis surface scum following a strong mixing event have rarely been studied. Here, we used a tank experiment to investigate the process of Microcystis surface scum recovery after a mixing event with focus on dynamics of colonies of different size classes and their contribution to that process. Microcystis colony size distribution and colony volume concentration was measured using a laser in-situ scattering and transmissometry instrument. The dynamics of Microcystis in the water column and upward colony migration velocity were strongly dependent on colony size. Larger colonies (>180 μm) with fast upward migration rates contributed the most to surface scum formation shortly after turbulence subsided. The contribution of slowly migrating smaller colonies to scum formation was observed over notably longer time. The estimated floating velocities of large colonies ranged 0.15 to 0.46 m h−1 depending on colony size and were 5–15 times higher than those of smaller colonies (~0.03 m h−1). The changes in colony size distribution of Microcystis in the water column reflect the dynamics of surface scum. Analysis of size distribution of Microcystis colonies can be used for better understanding and prediction of Microcystis surface scum development in water bodies.

Turbulence regulation of Microcystis surface scum formation and dispersion during a cyanobacteria bloom event

ABSTRACTMicrocystis blooms are a global problem adversely affecting the quality of freshwater in lakes and reservoirs. Surface scum (0–1 m) forms as a result of vertical migration by buoyant Microcystis colonies. Accurate forecasting of bloom and scum development is essential for establishing a reliable alert system and requires understanding the role that physicobiological factors play in the vertical distribution of Microcystis in natural ecosystems. We researched the role of limnological processes on the vertical distribution of Microcystis colonies in Lake Kinneret (max depth = 38 m) during a winter–spring bloom. We used measurements from a Laser In situ Scattering and Transmissometry profiler and a plankton image analyser, FlowCAM. Various hydrometeorological conditions were captured during 6 outings from 9 February to 21 March 2017. Thermal stratification and turbulence levels influenced the vertical distribution of Microcystis colonies and scum formation. During periods of weak turbulence (e.g., vertical turbulent eddy diffusivity [Kz] <1 cm2 s−1), the volume concentration of Microcystis colonies near the surface abruptly increased. High turbulence levels (e.g., windy conditions) dispersed colonies concentrated near the surface. Simulations of the vertical distribution of Microcystis colonies under different turbulence levels showed that the thickness of the surface scum layer increased and colony concentration of scum decreased as turbulence level increased. We demonstrated that Kz >3 cm2 s−1, driven by the wind and surface heating/cooling, dispersed the surface layer of concentrated Microcystis colonies. Our results revealed an underlying interdependency between environmental factors and Microcystis colony distribution that can be utilized to accurately forecast scum formation.

Effects of small-scale turbulence at the air-water interface on microcystis surface scum formation

Cyanobacterial surface scum (here defined as visible Cyanobacteria colonies accumulating at the lake surface) is a harmful phenomenon that negatively affects water quality, human and animal health. Colony-forming Microcystis is one of the most important and ubiquitous genera that can suddenly accumulate at water surfaces. Turbulent water motion, e.g., generated by wind, can vertically disperse this scum layer, which later can re-establish by upward migration of Microcystis colonies. However, the role of wind-generated turbulence in scum formation and development is still poorly understood. Here we present results from a laboratory mesocosm study where we analysed the processes of scum formation and its response to wind-generated turbulence at low wind speed (≤3.6 m s−1). Microcystis colony size and flow velocity at the water surface and in the bulk water were measured using a microscope camera and particle tracking velocimetry. The surface scum formed by aggregation of colonies at the water surface, where they formed loose clusters of increasing size. The presence of large colony aggregations or of a surface film determined the stability of the scum layer. For the largest applied wind speed, most of the aggregations were broken down to sizes <2 mm, which were dispersed to the bulk water. The surface scum recovered quickly from such disturbances after the wind speed decreased. We further observed reduced momentum transfer from wind to water with the growing scum layer. The presence of the scum increased the threshold wind speed for the onset of flow and reduced the flow velocities that were generated above that threshold. This effect was likely caused by the presence of a film of surface-active material at the water surface (surface microlayer), which is related to the presence of Microcystis. Both the small-scale turbulence and surface microlayer might play an important, yet largely unexplored role in Microcystis surface scum development in aquatic ecosystems. Improved understanding of the interplay of both processes will be instrumental for improving current mechanistic models for predicting surface bloom dynamics.