The influence of physical and physiological processes on the spatial heterogeneity of a Microcystis bloom in a stratified reservoir

Cyanopeptides occurrence and diversity in a Brazilian tropical reservoir: Exploring relationships with water quality

Microcystins (MCs) are a class of toxic secondary metabolites produced by some cyanobacteria strains that endanger aquatic and terrestrial organisms in various freshwater systems. Although patterns in MC occurrence are being recognized, divergences in the global data still hamper our ability to predict the toxicity of cyanobacterial blooms. This study aimed (i) to determine the dynamics of MCs and other cyanopeptides in a tropical reservoir, (ii) to investigate the correlation between peptides and potential cyanotoxin producers (iii) identifying the possible abiotic factors that influence the peptides. We analyzed, monthly, eight MC variants (MC-RR, -LA, -LF, -LR, -LW, -YR, [D-Asp3]-RR and [D-Asp3]-LR) and other peptides in 47 water samples collected monthly, all season long, from two sampling sites in a tropical eutrophic freshwater reservoir, in southeastern Brazil. The cyanopeptides were assessed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The biomass of potential cyanobacterial producers and water quality variables were measured. MCs were detected in both sampling sites year-round; the total MC concentration varied from 0.21 to 4.04 μg L-1, and three MC variants were identified and quantified (MC-RR, [D-Asp3]-RR, -LR). Additionally, we identified 28 compounds belonging to three other cyanopeptide classes: aeruginosin, microginin, and cyanopeptolin. As potential MC producers, Microcystis spp. and Dolichospermum circinalis were dominant during the study, representing up to 75% of the total phytoplankton. Correlational and redundancy analysis suggested positive effects of dissolved oxygen, nitrate, and total phosphorus on MC and microginins concentration, while water temperature appeared to favor aeruginosins. A comparison between our results and historical data showed a reduction in total phosphorus and cyanobacteria, suggesting increased water quality in the reservoir. However, the current MC concentrations indicate a rise in cyanobacterial toxicity over the last eight years. Moreover, our study underscores the pressing need to explore cyanopeptides other than MCs in tropical aquatic systems.

Causal impact analysis of weir opening on cyanobacterial blooms and water quality in the Yeongsan River, Korea: A bayesian structural time-series analysis and median difference test

The construction of weirs in Korea's Four Major Rivers Project has led to an increase in cyanobacterial blooms, posing environmental challenges. To address this, the government began opening weirs in 2017. However, interpreting experimental results has proven to be complex due to the multifaceted nature of blooms. This study aimed to assess the impact of opening the Juksan Weir on cyanobacterial blooms and water quality in the Yeongsan River. Using a median difference test (MDT) and causal impact analysis (CIA) with Bayesian structural time-series (BSTS) models, changes in cyanobacterial cell density (Cyano) and chlorophyll a concentration (Chl-a) before (January 2013 to June 2017) and after (July 2017 to December 2021) the weir-opening event were analyzed. The MDT revealed no significant change in Cyano post-weir opening (p = 0.267), but Chl-a significantly increased by 48.1 % (p < 0.01). As a result of CIA, Cyano decreased, albeit statistically insignificantly (p = 0.454), while Chl-a increased by 59.0 % (p < 0.01). These findings contradict the expectation that Cyano decrease due to the increased flow velocity resulting from weir opening. The absence of changes in Cyano and the increase in Chl-a can be attributed to several factors, including the constrained and inadequate duration of full weir opening combined with conducive conditions for the proliferation of other algae such as diatoms and green algae. These findings suggest that the effectiveness of weir opening in controlling Cyano may have been compromised by factors influencing the overall aquatic ecosystem dynamics. Further analysis revealed that factors such as elevated water temperatures (≥ 30 °C) and reduced flow rates (< 37 m3/s) contributed to the flourishing of cyanobacteria, whose concentrations exceeded 10,000 cells/mL. In analyzing causal relationships in environmental management, especially when there are complex causal interactions, the application of MDT and CIA provides valuable insights.

Analyzing eutrophication and harmful algal bloom dynamics in a deep Mediterranean hypereutrophic reservoir

Excessive point and non-point nutrient loadings accompanied with elevated temperatures have increased the prevalence of harmful algal bloom (HAB). HABs pose significant environmental and public health concerns, particularly for inland freshwater systems. In this study, the eutrophication and HAB dynamics in the Qaraoun Reservoir, a hypereutrophic deep monomictic reservoir suffering from poor water quality, were assessed. The reservoir was mostly phosphorus limited, and large algal particulates dominated light attenuation in the water column. During bloom events, surface chlorophyll-a concentrations increased up to 961.3 µg/L, while surface concentrations of ammonia and ortho-phosphate were rapidly depleted; surface dissolved oxygen reached supersaturation levels and surface pH levels were up to 3 units higher than those measured in the hypolimnion. Meanwhile, measured Microcystin-LR toxin concentrations in the reservoir exceeded the World Health Organization 1 μg/L provisional guideline 45% of the times. Yet, the results showed that most of the toxins were intra-cellular, suggesting that they decayed rapidly when released into the reservoir. Results from a random forests ensemble model indicated that tracking the changes in surface dissolved oxygen levels, ammonium, ortho-phosphate, and pH can be an effective program towards predicting the reservoir's trophic state and algae blooms.

A theoretical modeling framework for motile and colonial harmful algae

Climate change is leading to an increase in severity, frequency, and distribution of harmful algal blooms across the globe. For many harmful algae species in eutrophic lakes, the formation of such blooms is controlled by three factors: the lake hydrodynamics, the vertical motility of the algae organisms, and the ability of the organisms to form colonies. Here, using the common cyanobacterium Microcystis aeruginosa as an example, we develop a model that accounts for both vertical transport and colony dynamics. At the core of this treatment is a model for aggregation. For this, we used Smoluchowski dynamics containing parameters related to Brownian motion, turbulent shear, differential setting, and cell‐to‐cell adhesion. To arrive at a complete description of bloom formation, we place the Smoluchowski treatment as a reaction term in a set of one‐dimensional advection‐diffusion equations, which account for the vertical motion of the algal cells through molecular and turbulent diffusion and self‐regulating buoyant motion. Results indicate that Smoluchowski aggregation qualitatively describes the colony dynamics of M. aeruginosa. Further, the model demonstrates wind‐induced mixing is the dominant aggregation process, and the rate of aggregation is inversely proportional to algal concentration. Because blooms of Microcystis typically consist of large colonies, both of these findings have direct consequences to harmful algal bloom formation. While the theoretical framework outlined in this manuscript was derived for M. aeruginosa, both motility and colony formation are common among bloom‐forming algae. As such, this coupling of vertical transport and colony dynamics is a useful step for improving forecasts of surface harmful algal blooms.

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CI_cyano). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CI_cyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CI_cyano was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.

Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province

Study of socioeconomic factors can play an important role in the spatial distribution of breast cancer by leading to a better understanding of its spatial pattern and assist breast cancer screening and early diagnosis. Taking Nanchang, a major city in central China, as an example, spatial autocorrelation and stratified heterogeneity were applied using a 10 10 km grid division to analyse breast cancer risk and socioeconomic factors. The research results showed that the median incidence rate of female breast cancer in Nanchang from 2016 to 2018 was 6.6/100,000 with a standard deviation of 12.3/100,000. Areas with higher incidence rates were mainly located in the central urban area and the major county towns. Spatial regression analysis showed that there was a statistically significant correlation between the spatial patterns of breast cancer incidence on the one hand, and on the other socioeconomic factors, such as total gross domestic product (GDP), per capita GDP and density of places of social and economic activities, i.e. points of interest. In addition, the normalized difference vegetation index also played a part in this respect. This research could serve as a reference for regional public health policy formulation and breast cancer screening.

Modeling Cyanobacteria Vertical Migration

Cyanobacteria often cause harmful algal blooms and release toxic substances that can harm humans and animals. Accurately modeling these phytoplankton is a step towards predicting, preventing, and controlling such blooms. Certain cyanobacteria species are known to migrate vertically in the water column on a daily cycle. Capturing this behavior is one aspect of modeling their dynamics. Previous studies on modeling cyanobacterial vertical migration are reviewed and summarized. Several models of cyanobacteria vertical movement are tested using data from field studies. These models are applied using both continuum and particle-tracking frameworks. Models range in complexity from simple functions of time to more complicated calculations of cyanobacteria buoyancy. Simple models were often able to predict cyanobacteria migration at low values of vertical diffusion in both types of modeling frameworks. More complicated models of buoyancy change performed better in the particle-tracking framework than in the continuum framework. Analysis of the models developed and tested provides information on the applicability of these models in more complex hydrodynamic and water quality models.

Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models

Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complexity of inland water. Therefore, this study applied a deep neural network model to monitor the vertical distribution of Chlorophyll-a (Chl-a), phycocyanin (PC), and turbidity (Turb) using drone-borne hyperspectral imagery, in-situ measurement, and meteoroidal data. The pigment concentrations were measured between depths of 0 m and 5.0 m with 0.05 m intervals. Here, four state-of-the-art data-driven model structures (ResNet-18, ResNet-101, GoogLeNet, and Inception v3) were adopted for estimating the vertical distributions of the harmful algal pigments. Among the four models, the ResNet-18 model showed the best performance, with an R² value of 0.70. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) substantially provided informative reflectance band ranges near 490 nm and 620 nm in the hyperspectral image for the vertical estimation of pigments. Therefore, this study demonstrated that the explainable deep learning model with drone-borne hyperspectral images has the potential to estimate Chl-a, PC, and Turb vertical distributions and to show influential features that contribute to describing the vertical profile phenomena.

Predicting Cyanobacterial Harmful Algal Blooms (CyanoHABs) in a Regulated River Using a Revised EFDC Model

Cyanobacterial Harmful Algal Blooms (CyanoHABs) produce toxins and odors in public water bodies and drinking water. Current process-based models predict algal blooms by modeling chlorophyll-a concentrations. However, chlorophyll-a concentrations represent all algae and hence, a method for predicting the proportion of harmful cyanobacteria is required. We proposed a technique to predict harmful cyanobacteria concentrations based on the source codes of the Environmental Fluid Dynamics Code from the National Institute of Environmental Research. A graphical user interface was developed to generate information about general water quality and algae which was subsequently used in the model to predict harmful cyanobacteria concentrations. Predictive modeling was performed for the Hapcheon-Changnyeong Weir–Changnyeong-Haman Weir section of the Nakdong River, South Korea, from May to October 2019, the season in which CyanoHABs predominantly occur. To evaluate the success rate of the proposed model, a detailed five-step classification of harmful cyanobacteria levels was proposed. The modeling results demonstrated high prediction accuracy (62%) for harmful cyanobacteria. For the management of CyanoHABs, rather than chlorophyll-a, harmful cyanobacteria should be used as the index, to allow for a direct inference of their cell densities (cells/mL). The proposed method may help improve the existing Harmful Algae Alert System in South Korea.

Characterizing Density Flow Regimes of Three Rivers with Different Physicochemical Properties in a Run-Of-The-River Reservoir

Inflow mixing affects the spatiotemporal heterogeneity of water quality in reservoirs. Reservoir water quality management requires accurate prediction of density flow regimes to understand the spatiotemporal distribution of dissolved and particulate nutrients and organics. This study aims to characterize the mixing and circulation of three rivers with different physicochemical properties in a run-of-the-river (ROR) reservoir, using high-frequency monitoring and three-dimensional (3D) hydrodynamic modeling. The Aquatic Ecosystem Model (AEM3D) was constructed for the reservoir and calibrated with high-frequency data obtained from May–June 2016, accurately reproducing the observed spatiotemporal variations of flow velocity, water temperature, and electrical conductivity (EC) in the reservoir. High-frequency data and 3D model results showed that mixing of the rivers in the ROR reservoir is governed by density flow regimes formed by influent water temperature differences. At the confluence, colder and warmer river influents formed underflows and surface buoyant overflows, respectively. The spatial arrangement of flow direction, water residence time, and EC concentration were largely controlled by the buoyancy-driven flow. Stagnant areas with long residence times corresponded with areas of observed algal blooms and hypoxia. High-frequency sensor technology, combined with 3D hydrodynamic modeling, is effective for understanding the complex flow regimes and associated water quality characteristics in ROR-type reservoirs.

Analysis of Environmental Factors Associated with Cyanobacterial Dominance after River Weir Installation

Following the installation of 16 weirs in South Korea’s major rivers through the Four Rivers Project (2010–2012), the water residence time increased significantly. Accordingly, cyanobacterial blooms have occurred frequently, raising concerns regarding water use and the aquatic ecosystem health. This study analyzed the environmental factors associated with cyanobacterial dominance at four weirs on the Nakdong River through field measurements, and parametric and non-parametric data mining methods. The environmental factors related to cyanobacterial dominance were the seven-day cumulative rainfall (APRCP7), seven-day averaged flow (Q7day), water temperature (Temp), stratification strength (ΔT), electrical conductivity (EC), dissolved oxygen (DO), pH, and NO3–N, NH3–N, total nitrogen (TN), total phosphorous (TP), PO4–P, chlorophyll–a, Fe, total organic carbon (TOC), and SiO2 content, along with biological and chemical oxygen demands. The results indicate that site-specific environmental factors contributed to the cyanobacterial dominance for each weir. In general, the physical characteristics of EC, APRCP7, Q7day, Temp, and ΔT were the most important factors influencing cyanobacterial dominance. The EC was strongly associated with cyanobacterial dominance at the weirs because high EC indicated persistent low flow conditions. A minor correlation was obtained between nutrients and cyanobacterial dominance in all but one of the weirs. The results provide valuable information regarding the effective countermeasures against cyanobacterial overgrowth in rivers.

Hindcasting eutrophication and changes in temperature and storage volume in a semi-arid reservoir: a multi-decadal Landsat-based assessment

In situ monitoring of freshwater systems is often constrained by cost and accessibility, particularly in developing countries and in remote areas. Satellite remote sensing is therefore increasingly being integrated with existing in situ water quality monitoring programs. In this study, we use the Landsat TM/ETM+ image record collected between 1984 and 2015 to track temporal changes in trophic status, chlorophyll-a levels, algal bloom incidences, water clarity, water temperature, and reservoir water volume in a poorly monitored hypereutrophic semi-arid reservoir. Historical reservoir water quality data are inferred from calibrated Landsat-based empirical algorithms. The results show that, although the reservoir has existed in a eutrophic to hypereutrophic state over the past 30 years, its water quality has significantly deteriorated in the most recent decade. Mean summer chlorophyll-a concentrations were found to have increased by around 163% between 1984 and 2015, while water clarity dropped by more than 58% over the same period. Statistically significant changes in surface water temperatures were also apparent for the month of August, with a cumulative increase of 1.24 °C over the 31-year study period. The rise in temperature appears to correlate with the incidence of Microcystis blooms observed in the reservoir over the past decade. On the other hand, the water volume in the reservoir was found to have been fairly stable over time, likely as a result of adaptive reservoir management. This study demonstrates the strength of using Landsat data to hindcast and quantify changes in water quality and quantity in poorly monitored freshwater systems.

Predicting Taste and Odor Compounds in a Shallow Reservoir Using a Three–Dimensional Hydrodynamic Ecological Model

The objective of this study was to establish a technique to predict the occurrence of algal bloom and the algal-derived taste and odor compounds 2-methylisoborneol (2-MIB) and geosmin using a three-dimensional (3D) model that could reflect the complex physical properties of a shallow reservoir. Water quality, phytoplankton, and taste and odor compounds monitoring was conducted at the Jinyang Reservoir in 2016. In June, there was a potential for a high concentration of 2-MIB (maximum 80 ng/L) to occur owing to the appearance of Pseudanabaena sp.; additionally, from July to August, there was potential for a high concentration of geosmin (maximum 108 ng/L) to occur, because of the presence of Anabaena sp. A 3D hydrodynamic model was coupled with an ecological model to predict cyanobacteria bloom and the presence of taste and odor compounds. Cyanobacteria producing either 2-MIB or geosmin were distinguished to enhance the accuracy of the modeled predictions. The results showed that the simulations of taste and odor compounds spatial distribution and occurrence time were realistic; however, the concentration of geosmin was overestimated when Microcystis sp. was blooming. The model can be used as a management tool to predict the occurrence of algal taste and odor compounds in reservoir systems and to inform decision-making processes concerning dam operation and water treatment.

Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI)

Harmful algal blooms are now widely recognised as a severe threat to freshwater ecosystems, particularly in semi-fluvial environments created by river damming. Given the high spatial and temporal variability of cyanobacterial blooms, remote sensing is more suitable than conventional field surveys in monitoring blooms. However, the majority of existing algorithms cannot distinguish cyanobacterial blooms from eukaryotic algal blooms by extracting spectral features in the remote-sensing reflectance (R_rs). In this study, in situ R_rs spectra of cyanobacterial and green algal blooms in Lakes Gaoyang, Hanfeng and Changshou of the Three Gorges Reservoir (TGR) in China were recorded. Characteristic spectral indices, namely, the normalised difference peak-valley index and Cyano-Chlorophyta index, were used to develop an algorithm that can effectively distinguish cyanobacterial and green algal blooms. The proposed algorithm was also used to investigate the spatio-temporal dynamics of the two phenotypes of blooms derived from Huan Jing 1 charge-coupled device images. The resulting accuracy of 93.5% demonstrated that remote sensing technology, in conjunction with field observation, could efficiently differentiate bloom-forming species and assess the water quality in the TGR.

Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China

Harmful cyanobacterial blooms are exemplified as a major environmental concern due to producing toxin, and have generated a serious threat to public health. Knowledge on the spatial-temporal distribution of cyanobacterial blooms is therefore crucial for public health organizations and environmental agencies. In this study, field data and charge coupled device (CCD) image were collected in Lakes Gaoyang and Hanfeng of the Three Gorges Reservoir (TGR), China. We conducted the risky grade index (RGI) and coverage area index to develop a feasible estimation framework of cyanobacterial blooms. First, the close relationships between CCD reflectance spectral indices and water quality parameters were constructed based on water optical classification. Then, a regional algorithm for the RGI classification was established by density peaks. Finally, our proposed algorithm was applied to investigate dynamics of cyanobacterial blooms in the two lakes from 6-year series of CCD images. Encouraging results demonstrated that satellite remote sensing in conjunction with field observation can aid in the estimation of cyanobacterial blooms in the TGR.

Succession of phytoplankton assemblages in response to large-scale reservoir operation: a case study in a tributary of the Three Gorges Reservoir, China

The Three Gorges Dam (TGD) has greatly altered ecological and environmental conditions within the reservoir region, but it is not known how these changes affect phytoplankton structure and dynamics. Here, a bimonthly monitoring program was implemented from 2007 to 2009 to study the impact of damming on phytoplankton assemblages in the backwater area of the Pengxi River (PBA). By application of the phytoplankton functional group (C strategists, competitive species; S strategists, stress-tolerant species; R strategists, rapid propagation species), seasonal changes in phytoplankton relative to environmental variations were evaluated using ordination analysis. Seasonal patterns of phytoplankton dynamics were detected during this study, with CS/S strategists causing algal blooms from mid-spring to early summer, CS/CR strategists often observed during flood season, and CS strategists dominant during mid-autumn. CR/R groups dominated during winter and caused algal blooms in February. Our results indicated that phytoplankton assemblages were directly related to reservoir operation effects. Generally, the TGD had a low water level during flood season, resulting in a relatively short hydraulic retention time and intensive variability, which supported the cooccurrence of CS and CR species. During the winter drought season, water storage in the TGD increased the water level and the hydraulic retention time in the PBA, enabling R/CR strategists to overcome the sedimentation effect and to out-compete S/CS species in winter. As expected, these diversity patterns were significantly correlated with the hydraulic retention time and nutrient limitation pattern in the PBA. This study provides strategic insight for evaluating the impacts of reservoir operations on phytoplankton adaptation.