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Harmful algal blooms (HABs), mainly formed by dinoflagellates, have detrimental effects on marine ecosystems and public health. Therefore, detecting HABs is crucial for early warning and prevention of HABs as well as the mitigation of their adverse effects. Although various methods, such as light microscopy, electron microscopy, real-time PCR, andmicroarrays, have already been established for the detection of HABs, they are still cumbersome to be exploited in the field. Therefore, rapid nucleic detection methods such as recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP)-lateral flow dipstick (LFD) have been developed for monitoring bloom-forming algae. However, the CRISPR/Cas-based detection of HABs has yet to be applied to this field. In this study, we developed a method for detecting Karenia mikimotoi (K. mikimotoi), a typical ichthyotoxic dinoflagellate responsible for global blooms. Our method utilized Cas12a from Lachnospiraceae bacterium ND2006 (LbCas12a) to target and cleave the internal transcribed spacer (ITS) of K.mikimotoi, guided by RNA. We leveraged the target-activated non-specific single-stranded deoxyribonuclease cleavage activity of LbCas12a to generate signals that can be detected using fluorescence-read machines or LFDs. By combining RPA and LbCas12a with reporters, we significantly enhanced the sensitivity, enabling the detection of ITS-harboring plasmids at concentrations as low as 9.8 aM and genomic DNA of K. mikimotoi at levels as low as 3.6 × 10-5 ng/µl. Moreover, we simplified the genomic DNA extraction method using cellulose filter paper (CFP) by directly eluting the DNA into RPA reactions, reducing the extraction time to <30 s. The entire process, from genomic DNA extraction to result reporting, takes less than an hour, enabling the identification of nearly a single cell. In conclusion, our method provided an easy, specific, and sensitive approach for detecting K. mikimotoi, offering the potential for efficient monitoring and management of K. mikimotoi blooms. [ABSTRACT FROM AUTHOR]

Timely and accurate observations of harmful algal blooms dynamics help to coordinate coastal protection and reduce the damage in advance. To date, predicting changes in the spatial distribution of algal blooms has been challenging due to the lack of suitable tools. The paper proposes that the development and disappearance of algal bloom can be monitored by satellite remote sensing in a large area from the diurnal variation of chlorophyll a. In this paper, 32 pairs of observed data in 2011–2020 showed that it was most appropriate to outline the areas where the diurnal variation (the standard deviation calculated from the daily chlorophyll a) in chlorophyll a was more than 2.2 mg/m3 . Among them, 30 pairs of data showed that the high chlorophyll a diurnal variation could predict the growth of the algal bloom in the next days. In these events, the median area difference between the two spatial distributions was -0.08%. When there was a high diurnal variation in chlorophyll a in the area adjacent to where algal bloom was occurred, a new algal bloom region was likely to spread in subsequent days. Continuous multiday time series showed that the diurnal variation in chlorophyll a can reflect the algal bloom’s overall growth condition. [ABSTRACT FROM AUTHOR]

Biotoxins and harmful algal blooms (HABs) are damaging to aquaculture operations. Occurrences lead to disrupted operations, fish kills, and significant risks to human health. The conditions leading to blooms are driven by known, but complex processes. Heuristics exist about the drivers but the nonlinearity and opaqueness of relationships make it difficult to resolve using traditional rule-based mathematical models. An alternative approach leverages machine learning to uncover the conditions that lead to the closure of farms. This paper presents a comprehensive framework that combines semi-automated machine learning with ensemble classification approaches to predict site closures. Performance is evaluated on 7 years of site closure data from a shellfish farm in Southwest Portugal, together with publicly available environmental data. The model reports an accuracy of 83% across a challenging forecasting task. The proposed framework provides a pragmatic, scalable, site-specific decision tool to help aquaculture stakeholders mitigate the impacts of HABs. [ABSTRACT FROM AUTHOR]

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases. [ABSTRACT FROM AUTHOR]

Oil spillages on a sea's or an ocean's surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan's territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan's Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. [ABSTRACT FROM AUTHOR]

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs. [ABSTRACT FROM AUTHOR]

Reconstructing the field of currents using remote sensing methods is a traditional task of oceanology. Satellite instruments such as altimeters, Doppler radars, and optical sensors are routinely used to solve oceanological problems for waters with large dimensions (open areas of the seas and oceans). The latter work well for reservoirs with significant water temperature gradients when applying the method of processing successive satellite images with varying time delays. Less commonly, areas of intense phytoplankton blooms, which are usual for productive waters, are used as markers for image matching. Such waters can be monitored with satellite ocean color sensors that provide high spatial resolution. The goal of the present paper is to investigate the possibility of reconstructing the flow field in the Gorki Reservoir, as an example of a medium-sized eutrophic reservoir, from sequent images of two different high-resolution ocean color sensors with a short time delay between images. This paper describes the field experiment under the satellite overpasses and presents the results of applying the maximum correlation method to two satellite images for the retrieval of the current field in comparison with shipboard data. It is shown that the proposed method has prospects for development. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the escalating challenge of nutrient enrichment in aquatic ecosystems, spotlighting the dire ecological threats posed by harmful algal blooms (HABs) and excessive particulate organic matter (POM). Investigating recent advancements in water treatment technologies and management strategies, the study emphasizes the critical need for a multifaceted approach that incorporates physical, chemical, and biological methods to effectively address these issues. By conducting detailed comparative analyses across diverse aquatic environments, it highlights the complexities of mitigating HABs and underscores the importance of environment-specific strategies. The paper advocates for sustainable, innovative solutions and international cooperation to enhance global water quality and ecosystem health. It calls for ongoing advancement, regular monitoring, and comprehensive research to adapt to emerging challenges, thus ensuring the preservation of aquatic biodiversity and the protection of communities reliant on these vital resources. The necessity of integrating technological innovation, ecological understanding, and global cooperation to safeguard aquatic ecosystems for future generations is paramount. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite Systems (GNSS), including the US's GPS, China's BDS, the European Union's Galileo, and Russia's GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth's surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring. [ABSTRACT FROM AUTHOR]

Algal blooms caused by eutrophication are a major global problem, and the monitoring and prediction of algal densities in lakes are important indicators of eutrophication management. However, the reliability of the commonly used chlorophyll-a (Chl-a) to characterize phytoplankton density in lake environments needs to be further investigated. In this paper, we sampled and analyzed 365 samples from nine plateau lakes in Yunnan Province during the dry and rainy seasons. The Chl-a data measured by the laboratory spectrophotometric method and the portable YSI multi-parameter water quality meter (YSI) directly used in the field were compared, and regression analysis and correlation analysis with phytoplankton density were performed. Most of the Chl-a values measured by the laboratory instrument were greater than those measured by the YSI, and the correlation between the two methods was weak (0.492, p < 0.001). The correlation between Chl-a and phytoplankton density measured by the YSI reached 0.67 (p < 0.001) in the dry season, while the laboratory methods used to measure Chl-a to characterize phytoplankton density may have led to an overestimation of phytoplankton density due to nonspecific sources of Chl-a. However, both methods are relatively inaccurate for characterizing phytoplankton density. For different trophic states of lakes, nutrient concentration changes affect the Chl-a concentration of phytoplankton. During different seasons, changes in the fluorescence intensity of phytoplankton in response to environmental conditions prevent the YSI results from reflecting the authentic phytoplankton density. Furthermore, high species diversity can lead to inconsistent changes in Chl-a and phytoplankton because the content of Chl-a in individual cells of different phytoplankton is different. The relationship between Chl-a and phytoplankton density was species specific. Therefore, when applying Chl-a to characterize phytoplankton density in lakes, it is necessary to consider environmental conditions, phytoplankton community structure and other practical conditions. In addition, laboratory analytical methods and instrumental techniques and instruments need to be improved. [ABSTRACT FROM AUTHOR]

This document summarizes a collection of research papers on the effects of anthropogenic activities and climate change on harmful algal blooms (HABs) in marine and freshwater ecosystems. The papers discuss topics such as the discharge of nutrients into water systems, eutrophication, and the increase in frequency and severity of HABs worldwide. They also explore the diversity, growth, toxins, and toxicity of harmful algae, as well as mitigation strategies. The studies provide valuable insights into the impact of human activities on HAB-forming species and the need for further research and mitigation efforts. [Extracted from the article]

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms. [ABSTRACT FROM AUTHOR]

Harmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifying hazards and preventing or at least minimizing their impact. Traditional monitoring approaches often fail to provide the real-time, high-resolution data needed for effective early warnings. The integration of Internet of Things (IoT) technologies offers a promising avenue to address these limitations by creating a network of interconnected sensors capable of continuously collecting and transmitting data from various aquatic environments. In this paper, we present DEVS-BLOOM-WebUI, an advanced web application that extends the capabilities of the DEVS-BLOOM framework, providing a user-friendly interface that supports different user roles. The application includes an interface to manage users and permissions, dashboards to inspect data (from sensors, Unmanned Surface Vehicles (USVs), weather stations, etc.), train AI models, explore their predictions, and facilitate decision-making through notification of early warnings. A key feature of DEVS-BLOOM-WebUI is the Scenario Configuration Editor (SCE). This interactive tool allows for users to design and configure the deployment of monitoring infrastructure within a water body, enhancing the system's adaptability to user-defined simulation scenarios. This paper also investigates the practical implementation of an IoT-based EWS, discussing design considerations, sensor technologies, and communication protocols essential for seamless data integration and effective operation of the EWS. [ABSTRACT FROM AUTHOR]

Phytoplankton plays an essential role in the biogeochemical cycle because it is at the top of the food chain and is a source of oxygen. Eutrophication causes coastal areas to deteriorate as industrialization accelerates, leading to harmful algal blooms (HABs), severely affecting human and ecological health. The frequency and extent of HAB events potentially may increase due to climate change. HAB outbreaks have led to substantial losses for major coastal economies globally, and therefore have emerged as a critical research focus in environmental sciences. However, the lack of an overview of diverse factors influencing HABs complicates the cause identification and the effective countermeasure development for HAB occurrence, thereby impeding the formulation of targeted strategies for prediction and mitigation. Therefore, this review summarizes the influential factors affecting HABs in coastal areas, including water quality factors (nutrients, salinity, stratification, and biological factors) and climatological factors (temperature, pH and pCO2 , and irradiance and light). Recent work with several harmful algae species suggested that warmer temperatures combined with nutrient variation, stronger stratification, and ocean acidification may increase the growth of some toxic dinoflagellate species. Although the effects of factors vary for different species and locations, the intensification of anthropogenic activities and climate change likely will increase the frequency, outbreak scale, and severity of most coastal HABs. Because predicting HABs is crucial for understanding the factors and synergy affecting their growth and minimizing losses for decision makers and stakeholders, we reviewed models for predicting HABs, including process-based models, traditional statistical-empirical models, and data-driven machine learning models. Predicting HABs becomes more challenging as the spatial distribution of harmful algae is influenced by future climate patterns. This review paper presents a comprehensive overview of the various factors impacting HABs in coastal areas, serving as a valuable resource for decision makers and researchers to design targeted research and mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper concerns a discrete-time phytoplankton–zooplankton model in which the effects of toxin produced by phytoplankton and refuges provided for phytoplankton on the interactions between phytoplankton and zooplankton are considered. We first discuss the existence and stability of fixed points. When two parameters change, it is shown that there exist some codimension-two bifurcations, including fold-flip bifurcation and strong resonance bifurcations. Moreover, we give the corresponding bifurcation diagrams, phase diagrams and maximum Lyapunov exponent diagrams. The results show that phytoplankton toxin and refuges play important roles in the occurrence and termination of algal blooms in freshwater lakes. [ABSTRACT FROM AUTHOR]

Algal bloom has been a persistent problem for both fresh and marine water, with no exception for a coastal reservoir (CR). Among the algal bloom mitigations for a CR, shape optimisation to reduce algal bloom occurrence has been frequently mentioned. However, there was no literature found on the actual shape optimisation study or process for CR. Thus, this research was done to bridge this gap, particularly for the second-generation CR. Hydrodynamic model of MIKE 21 has been used, with secondary data obtained from published papers and Google Earth. The secondary data of critical velocity corresponding to algal growth were only available for cyanobacteria, chlorella, filamentous algae and phytoplankton. Hence, only these algae species were considered in the algal mapping. All models were simulated for eight idealised cases of second-generation CR at the Yangtze Estuary. These different geometric shapes were analysed and compared, considering several factors including the average velocity in the reservoir, presence of stagnant water, percentage of occurrence for each algae species and so on. From the results, the reservoir model with the shape of "shorter piano key" ranked the highest among all the shapes in terms of proneness to algal bloom, based on the flow velocity within the reservoir. From the findings, further shape optimisation was done on second-generation CRs. From shape optimisation process, the optimised shape of the "little dinosaur" and "little pencil" showed excellent reduction in algal blooming. However, "little dinosaur" was preferred as its location for algal bloom is small and controllable due to the presence of "piano key" structure. Lastly, all the findings were applied to an existing CR at Qingcaosha to see if shape optimisation based on the analysis can reduce areas prone to algal blooming. The optimised model of Qingcaosha showed great reduction on area prone to algal blooming compared to its original shape but the addition of "piano key" structure did not have significant impact on the reduction of algal bloom occurrence since the shape of Qingcaosha is highly dependent on its natural topography. From the study, it was concluded that shape optimisation for topographic-dependent CR should be done on case-by-case basis, following the flow direction in the reservoir. As for second-generation CR without topographical constraints, the shape optimisation can be done by placing inlet perpendicular to the flow direction, minimising corners, implementing piano-key-like structure, optimising shape based on flow direction and refer "little dinosaur" or "little pencil" for the overall shape optimisation design. [ABSTRACT FROM AUTHOR]

"Red tide" is the popular name for a harmful algal bloom that occurs frequently in Florida waters and has significant impacts on the environment, the economy, and human and animal health. This paper addresses the limited understanding of public attitudes surrounding red tide and the potential for message framing to increase support for mitigation action. A sample of Florida residents (n = 498) was randomly shown a digital article framed around different red tide impacts (economic loss, human health risks, ecosystem damage, or harm to charismatic species) or a control article not framed around a specific red tide impact. Participants were then queried about their emotional responses, support for red tide mitigation policies, and intentions to change their behavior to mitigate impacts. Participants reported higher levels of negative emotions after viewing the harm to charismatic megafauna framing, indicating that messaging may play a significant role in eliciting environmental emotions. Message frames did not significantly impact other policy attitudes. Results demonstrate barriers to shifting support for mitigation where public support for action is already high. [ABSTRACT FROM AUTHOR]

Water exchange in lake basins is a very important process in regulating the health of the aquatic environment, e.g., by shaping algal blooms. Thus, knowledge of the process is also required to develop management strategies. The paper presents a dynamic of water renewal time in the Uściwierz chain of lakes, in which the natural hydrological connectivity of the catchment areas has been altered due to human impact. Calculations of water renewal were limited to the part of the lake basin corresponding to the active (dynamic) retention layer. A comparative analysis of the rate of potential water renewal, based on the structure difference index, was used as an indicator of the degree of anthropogenic transformation of water distribution in the lake catchments. The smallest differences in the structure of the water renewal rate between the neighboring lakes in the chain system were observed in the cold period, and the largest differences were observed in the warm period. The results showed that the shorter the timescale (5-day period), the higher the similarity in the structure of the water renewal rate between the lakes. Very large differences between the structure of the potential water renewal rate in Lakes Uściwierz and Bikcze indicated a significant transformation of the water cycle down the lake chain. The water renewal rate proved to be a good indicator of the degree of anthropogenic transformation in the catchments located in close proximity. [ABSTRACT FROM AUTHOR]

Most of the microscopic images of Harmful Algae Blooms (HABs) acquired in marine engineering are subject to blurred cell textures and poor overall clarity due to the effects of seawater impurities, unknown suspended particle deposits and high-speed cell motions. To solve the above problems, a microscopic image enhancement method based on the recursive-overlapped contrast limited adaptive histogram specification (CLAHS) and dual-image wavelet fusion (RO-CLAHS and DIWF) is proposed in this paper. It combines three main steps: homomorphic filtering, empirical modal feature map extraction, and dual-image wavelet fusion. This method firstly adopts homomorphic filtering to strengthen the illumination uniformity of the entire HABs image, and then obtains an empirical modal feature map of algal cells by an improved empirical modal decomposition method, which highlights the detailed features of algal cells due to histogram stretching, clip limit and grey-level mapping. Finally, this paper proposes a dual-image wavelet fusion method adapted for HAB microscopic images, which integrates the images processed by contrast limited adaptive histogram equalization (CLAHE) with an empirical modal feature map and then achieves the image enhancement effect. The comparison experimental results show that the enhanced microscopic images of HABs by this method presents the best texture clarity and global contrast and improves the accuracy of algae image edge detection significantly. [ABSTRACT FROM AUTHOR]

Humans have always lived with and around algae. At times, algae enable life, and at others, render life difficult. This article examines two sites suffering from atypical—and potentially harmful—algae blooms: Lake Erie in Ohio (USA) and the Riviera Maya (Mexico). Referring to ethnographic fieldwork, as well as to newspaper articles, policy papers, and online fora, we demonstrate how the narratives around algae have changed over time and shed light on how changes in these narratives opened the discussion of wetland repair and coastal integrity. We argue that conceptualizing algae as the "unwanted" unifies people, brings them together, and makes the treatment of lake eutrophication and coastal protection actionable. [ABSTRACT FROM AUTHOR]

Using a three-dimensional coupled physical–biological model, this paper explores the effect that short-lived wind events lasting a few days in duration have on the creation of phytoplankton blooms in island wakes. Findings show that wind-induced coastal upwelling creates initial nutrient enrichment and phytoplankton growth near the island, whereas an oscillating flow, typical of island wakes, expels patches of upwelled water, including its nutrient and phytoplankton loads, into the ambient ocean. Dependent on the wind direction, a short-lived wind event can create one or more plankton patches with diameters of the order of the island diameter. Phytoplankton continues to grow within floating patches, each forming an individual marine ecosystem. While the ecological features of island wakes are well documented, this study is the first that describes the significance of short-lived, transient wind-driven upwelling in the process. [ABSTRACT FROM AUTHOR]

Harmful algae and cyanobacteria blooms are increasing in frequency and intensity in freshwater systems due to anthropogenic impacts such as nutrient loading in watersheds and engineered alterations of natural waterways. There are multiple physical factors that affect the conditions in a freshwater system that contribute to optimal habitats for harmful algae and toxin-producing cyanobacteria. A growing body of research shows that climate change stressors also are impacting water-body conditions that favor harmful algae and cyanobacteria species over other phytoplankton. The overgrowth of these organisms, or a "bloom," increases the opportunity for exposure to toxins by humans, companion animals, livestock, and wildlife. As waters warm and precipitation patterns change over time, exposure to these blooms is projected to increase. Hence, it is important that states and tribes develop monitoring and reporting strategies as well as align governmental policies to protect their citizens and ecosystems within their jurisdiction. Currently, the policies and approaches taken to monitor and report on harmful algae and cyanobacteria blooms vary widely among states, and it is undetermined if any tribes have specific policies on harmful algae blooms. This paper synthesizes research on algal blooms in inland freshwater systems of the United States. This review examines how climate change contributes to trends in bloom frequency or severity and outlines approaches that states and tribes may use to monitor, report, and respond to harmful algae and cyanobacteria blooms. Significance Statement: Inland bodies of freshwater supply drinking water for humans and animals, water for irrigating crops, habitats for aquatic species, places of cultural significance for Indigenous peoples, and other important functions. Many of these bodies of water have been polluted with runoff from industry, including agriculture, and already support harmful algal blooms during warm conditions. Hot extremes associated with climate change are expected to increase the occurrence and duration of harmful algal blooms, and in some places, initiate blooms where none have been recorded previously. These toxic blooms are harmful to people, companion animals, livestock, and wildlife. It is important to review the interconnections among biological, climate, and water systems to monitor blooms and alert the public about their toxin production. [ABSTRACT FROM AUTHOR]

Unsaturated diacylglycerols are a class of antioxidant compounds with the potential to positively impact human health. Their ability to combat oxidative stress through radical scavenger activity underscores their significance in the context of preventive and therapeutic strategies. In this paper we highlight the role of Anabaena flos-aquae as a producer of unsaturated mono and diacylglycerols, and then demonstrate the antioxidant activity of its methanolic extract, which has as its main components a variety of acylglycerol analogues. This finding was revealed using a sustainable strategy in which the One Strain Many Compounds (OSMAC) cultivation in microscale was coupled with a bioinformatic approach to analyze a large dataset of mass spectrometry data using the molecular networking analyses. This strategy reduces time and costs, avoiding long and expensive steps of purification and obtaining informative data on the metabolic composition of the extracts. This study highlights the role of Anabaena as a sustainable and green source of novel bioactive compounds. [ABSTRACT FROM AUTHOR]

Populations are generally censused daily, weekly, monthly or annually. In this paper, we introduce a discrete-time nutrients-phytoplankton-oysters (NPO) model that describes the interactions of nutrients, phytoplankton and oysters in a bay ecosystem. We compute the threshold parameter $ \mathcal {R}_N $ R N for persistence of phytoplankton with or without oysters. When $ \mathcal {R}_{N} \lt 1 $ R N < 1 , then both phytoplankton and oysters populations go extinct. However, when $ \mathcal {R}_N \gt 1 $ R N > 1 , we show that the model may exhibit two scenarios: (1) a locally asymptotically stable equilibrium with positive values of nutrients and phytoplankton with oysters missing, and (2) a locally asymptotically stable interior equilibrium with positive values of nutrients, phytoplankton and oysters. We use sensitivity analysis to study the impact of human and environmental factors on the model. We use examples to illustrate that some human activities and environmental factors can force the interior equilibrium to undergo a Neimark–Sacker bifurcation which generates phytoplankton blooms with oscillations in oysters population and nutrients level. [ABSTRACT FROM AUTHOR]

This paper reports data on the structural and production characteristics of bacterioplankton in the western Kara Sea at the beginning and middle of summer. On the slope of the St. Anna Trough, the average prokaryote abundance in the water column was 594–708 × 103 cells mL−1 (26.4–36.5 mgC m–3) in June and 247–517 × 103 cells mL−1 (12–28 mgC m–3) at the beginning of August. On the transect along the Novaya Zemlya archipelago, the average bacterioplankton abundance in the water column was 186–554 × 103 cells mL−1 (8.5–30 mgC m–3) within a week after the seasonal ice retreat and 169–443 × 103 cells mL−1 (8–21 mgC m–3) in midsummer. The specific bacterial growth rate did not exceed 1.28 day–1; high values were observed in the upper warm water layer, above the halocline, and also in near-bottom water. At the beginning of summer, the bacterioplankton production tended to decrease in the northeast direction. The prokaryote abundance distribution was associated with water temperature and oxygen saturation possibly as an indirect indicator of the past phytoplankton bloom. [ABSTRACT FROM AUTHOR]

In this paper, we use remotely sensed imagery to identify the location and size of animal feeding operations in the Maumee River Watershed, a key drainage area to Lake Erie's Western Basin, which has recently experienced severe harmful algal blooms. We then estimate the relationship between the intensity of animal feeding operations in the watershed and surface water body concentrations of dissolved reactive phosphorus (DRP), the pollutant most responsible for algal growth. We find that stream reaches with relatively larger increases in upstream animal feeding exposure experience significantly higher increases in concentrations of DRP. The average marginal upstream animal feeding operation in the watershed increases downstream DRP concentrations by between 10% and 15%. In contrast, when restricting the analysis to include only permitted operations, coefficient estimates are practically zero and statistically insignificant. Our work presents evidence that the increasing intensity of animal feeding operations contributes to water quality problems. Permitting and identification of animal feeding operations is therefore important for managing runoff and correctly attributing the causes of excess nutrients in surface water bodies. [ABSTRACT FROM AUTHOR]

Whole lake residence time has been associated with various water quality parameters, including harmful algal blooms. Despite observations of spatial variability in commonly measured lake water quality parameters, little attention is given to the spatial variability of residence time in lakes. In this paper we use water age as a surrogate for residence time and we examine its spatial and temporal distribution in 10 bays of varying size in Lake George, New York (USA). Using a validated hydrodynamic model against observations of water temperature and water currents, and using simulated water age, we show that the average residence time in most of the bays is less than 3 days. Timeseries of bay‐average water age shows that it can sharply decrease within 1 day due to a strong wind event. The average spatial distribution is shown to be non‐uniform, with only a small section of the bottom layer of the bays having a substantially greater age, which may be more than 1 week in certain bays. Snapshots of water age transects indicate that strong wind events substantially change the vertical distribution of water age in some bays, even to the extent of inverting the distribution. The substantial decreases of water age in the bays were associated with the shallowing and deepening of the thermocline. Our results highlight how variations in water residence times within lakes could introduce substantial variation in water quality attributes. Whole lake residence times may serve as a poor proxy to understand the dynamics of water masses, especially in large and morphologically complex waterbodies. Key Points: Simulation of water age with a validated hydrodynamic model against temperature and currentsAverage water age (surrogate to residence time) in bays is smaller than 6 daysSpatial distribution of water age within bays is not uniform and strong wind events lead to lower water age within 1 day [ABSTRACT FROM AUTHOR]

The abnormal reduction of kelp production occurred in Rongcheng area of the Yellow Sea in 2021, which is closely related to the red tide. However, the relationship between the red tide event and the hydrodynamic environment remains unclear. In response to these issues, this study established a coupled physical-biological model which coupling the ROMS dynamic model with the RED_TIDE biological model for the Yellow Sea and Bohai Sea region, and discussed driving factors of this red tide. Based on this coupled model, various factors influencing the development of red tide were investigated, with a focus on analyzing the key factors contributing to the occurrence of the red tide event in the northern sea area of the Shandong Peninsula in 2021. The aggregation of dinoflagellate cells triggers red tide events. The distribution characteristics and concentration changes of dinoflagellate cells were studied by designing simulation experiments under different conditions to reflect the contributions of various influencing factors to red tide. According to observation data and simulation experiments, this study explored the effects of factors such as light conditions, tides, Yellow River runoff and wind field on the distribution and concentration of dinoflagellate cells. The variation of wind field can promote the proliferation and aggregation of dinoflagellate cells, serving as key factors in triggering red tide. The occurrence of red tide is a complex ecological phenomenon influenced by multiple factors, necessitating the comprehensive consideration of these factors to more accurately predict and prevent red tide events. [ABSTRACT FROM AUTHOR]

Viruses of eukaryotic algae have become an important research focus due to their role(s) in nutrient cycling and top-down control of algal blooms. Omics-based studies have identified a boom of genomic and transcriptional potential among the Nucleocytoviricota, a phylum of large dsDNA viruses which have been shown to infect algal and non-algal eukaryotes. However, little is understood regarding the infection cycle of these viruses, particularly in how they take over a metabolically active host and convert it into a virocell state. Of particular interest are the roles light and the diel cycle play in virocell development. Yet despite such a large proportion of Nucleocytoviricota infecting phototrophs, little work has been done to tie infection dynamics to the presence, and absence, of light. Here, we examined the role of the diel cycle on the physiological and transcriptional state of the pelagophyte Aureococcus anophagefferens while undergoing infection by Kratosvirus quantuckense strain AaV through flow cytometry and differential expression analyses. Our observations demonstrate how infection by the virus interrupts the diel growth and division of this cell strain, and that infection further complicates the system by enhancing export of cell biomass. Furthermore, these analyses reinforce the expectation that viral activity is heavily associated with the diel cycle. [ABSTRACT FROM AUTHOR]

Human activities have resulted in the eutrophication of rivers, leading to heightened concerns regarding the occurrence of filamentous algal blooms. With the increasing utilization of rivers by humans, the occurrence of these nuisance filamentous algae is expected to increase in frequency in the future. Blooms primarily occur due to energy congestion at the trophic level of primary producers, resulting from inefficient energy flow in both the bottom-up and top-down pathways. To investigate the mechanism underlying the outbreak of filamentous algae, two streams in the southern Taihang catchment with different nutrient conditions were selected for this study. The objective of this study was to understand the effects of nutrient levels and other potential factors on the distribution and succession of filamentous algae. Our findings revealed a positive correlation between nutrient conditions and the biomass of filamentous algae. Cladophora and Spirogyra were identified as the dominant species among filamentous algae, each exhibiting unique distribution patterns in the two streams. Spirogyra thrived predominantly in the Baligou stream, where lower nutrient levels and warmer temperatures prevailed. In contrast, Cladophora flourished in the nutrient-rich Nanping stream at colder temperatures. Results from the generalized linear model indicated that the biomass of Cladophora was influenced by nutrient concentration, water depth, water temperature, and macrobenthic biomass. The biomass of Spirogyra, on the other hand, was primarily determined by water temperature, nutrient concentrations, water depth, and velocity. The positive correlation between Cladophora and macrobenthos revealed a possible mutually beneficial relationship, suggesting that macrobenthos may promote the growth of Cladophora by inhibiting periphytic diatoms. In return, the macrobenthos benefit from a secure refuge and an environment conducive to foraging and reproduction. This study suggested that to alleviate energy flow congestion in the benthic food chain, it is advisable to address this issue by either reducing nutrient loadings in rivers or enhancing the presence of benthivorous fishes in streams. [ABSTRACT FROM AUTHOR]

The Gulf of St. Lawrence (GSL), along with the St. Lawrence Estuary, is the largest estuarine system in North America. It is a biologically productive sea and an important fishing ground in Canada. The objectives of this study are to determine how climate changes will affect primary production in the GSL and on the eastern Scotian Shelf, and to determine the drivers of the changes. We use a regional ocean model forced with downscaled output of the Max-Planck Institute Earth System Model to study the changes in net primary production under the climate change scenario of Representative Concentration Pathway 8.5 (RCP8.5). Results reveal a projected 13.4% decrease in annual primary production across the GSL system over the next 70 years. This decline primarily stems from reduced nutrient concentrations in the upper layer, despite the increase in nutrients supplied by rivers. Enhanced freshwater influx and ocean surface warming contribute to heightened stratification, that in turn reduce the vertical nutrient fluxes from deeper layers. This mechanism affects the upwelling of nutrient-rich water at the head of the Laurentian Channel in the Lower St. Lawrence Estuary, leading to a 22% reduction in the vertical nitrate flux in the future. Additionally, nutrient concentrations in the water masses entering the GSL at the Strait of Belle Isle and at Cabot Strait are also reduced. Due to declining sea-ice concentration, the phytoplankton bloom is expected to occur between 9 and 23 days earlier under a warmer climate, and last for up to 24 days longer, depending on the GSL subregions, compared to the historical period (2001-2020). Biological productivity at higher trophic levels, and thus fisheries productivity, could be negatively impacted under the RCP8.5 climate change scenario. [ABSTRACT FROM AUTHOR]

Hydrothermal activity is widespread in sedimentary basins, but its dual effects (chemistry and temperature) on shale reservoirs are rarely discussed. In this research, we systematically collected 33 well core samples from Q1 to Q9 units in Gulong Sag, Songliao Basin, and analyzed them using a variety of analytical techniques, including a field emission scanning electron microscopy (FE-SEM), an energy-dispersive spectrometer (EDS), X-ray diffraction (XRD), and stable C-O isotopes. Combined with the collected vitrinite reflectance (Ro), total organic carbon (TOC), and soluble hydrocarbon content data, which is the sum of free oil (pyrolysis S1) and sorb oil content (pyrolysis S2), the results show that (1) Q4 and Q8 units have large amounts of hydrothermal minerals, and its C-O isotope obviously shifts to negative, which implied those units are the main hydrothermal fluid influence area; (2) the hydrothermal activity occurred in the late depositional period of Q1–Q9 units such that its geochemistry has little effect on the proliferation of algae blooms, but its high temperature calculated by δ18O temperature formulas (around 208 °C) promoted the organic matter maturation process around Q4 and Q8; and (3) the overpressure caused by hydrothermal activity protected the shale reservoir and minimized the decrease in mineral reservoir brittleness index caused by hydrothermal fluid influence. We suggest that the shale reservoir affected by hydrothermal fluid will become a good geology "dessert", and its upper and/or lower bounds can form an engineering "dessert" due to the precipitation of large amounts of brittle carbonate minerals. [ABSTRACT FROM AUTHOR]

The rapid growth of phytoplankton and microalgae has presented considerable environmental and societal challenges to the sustainable development of human society. Given the inherent limitations of satellite-based algal bloom detection techniques that rely on chlorophyll and fluorescence methods, this study proposes a method that employs hyperspectral data to calculate water chromatic indices (WCIs), including hue, saturation (S), dominant wavelength (λd), and integrated apparent visual wavelength (IAVW), to identify algal blooms. A global in situ hyperspectral dataset was constructed, comprising 13,110 entries, of which 9595 were for normal waters and 3515 for algal bloom waters. The findings of our investigation indicate statistically significant discrepancies in chromaticity parameters between normal and algal bloom waters, with a p-value of 0.05. It has been demonstrated that different algal blooms exhibit distinct chromatic characteristics. For algae of the same type, the chromaticity parameters increase exponentially with chlorophyll concentration for hue and λd, while S shows low correlation and IAVW displays a good linear relationship with chlorophyll concentration. The application of this method to the Bohai Sea (coastal) and Taihu Lake (inland water) for the extraction of algal blooms revealed a clear separation in chromaticity parameters between normal and algal bloom waters. Moreover, the method can be applied to satellite data, offering an alternative approach for the detection of algal blooms based on satellite data. The indices can serve as ground truth values for colorimetric indices and provide a benchmark for the validation of satellite chromatic products. [ABSTRACT FROM AUTHOR]

Algae, an important foundation of aquatic ecosystems, can become a nuisance or harmful when it grows in excess. Many government agencies have a role in monitoring, responding to, and confirming a harmful algal bloom (HAB). HAB scientists have important information to share, however, given the complexities of HABs, which often involve decoupled drivers from observed impacts, presents challenges to outreach and engagement. Understanding key audience information needs can help scientists prioritize key science communication and engagement opportunities to maximize the impact of such efforts. Scientists may need additional science communication training or support for scientist‐community partnerships. This will be evermore important into the future with the likely range expansion of HABs due to climate change. [ABSTRACT FROM AUTHOR]

Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐a hot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals. Plain Language Summary: When lakes receive large amounts of nutrients from the surrounding landscape due to fertilizer runoff or other sources of nutrient pollution, they can develop algal blooms. Algal blooms are harmful to the lake ecosystem and sometimes produce toxins which are dangerous to humans and animals. To assess this issue, lake chlorophyll‐a, a measure of algal presence, is monitored. This monitoring is limited in reach due to the expense of in‐lake sampling and the limited resolution of satellite technology. However, there is a wealth of climate, nutrient, landscape, and in‐lake characteristic data for the conterminous US (CONUS) which explains much of what contributes to nutrient pollution and algal growth. Here, we use this data in a machine learning model to predict nutrient (total nitrogen and total phosphorus) and chlorophyll‐a concentrations in about 112,000 lakes in the CONUS. We found that high chlorophyll‐a concentrations are more likely in the Midwest where agriculture is prevalent, but other areas with high lake chlorophyll‐a concentrations are present across the CONUS in nutrient pollution hot spots. These predictions of lake nutrient and chlorophyll‐a concentrations can help managers identify areas of concern, prioritize at‐risk lakes for testing, and target management to protect human health and the environment. Key Points: Chlorophyll‐a and nutrient concentrations in ∼112,000 lakes were predicted using widely available national data sets and machine learningNutrients far outweigh other environmental predictors in driving chlorophyll‐a concentrations in lakesWith more information about likely chlorophyll‐a concentrations, managers can prioritize lakes at risk for harmful algal bloom production [ABSTRACT FROM AUTHOR]

Phytoplankton and bacteria form the foundation of marine food webs. While most studies on phytoplankton bloom influence on bacteria dynamics focus on diatom‐dominated blooms due to their global ecological significance, it is unclear if similar patterns extend to other species that compete with diatoms like Phaeocystis spp. This study aimed to contribute to the understanding of associations between phytoplankton and bacteria in a temperate ecosystem. For this, we studied the dynamics of phytoplankton and bacteria, combining 16S metabarcoding, microscopy, and flow cytometry over 4 years (282 samples). Phytoplankton and bacterial communities were studied throughout the year, particularly during contrasting phytoplankton blooms dominated by the Haptophyte Phaeocystis globosa or diatoms. We applied extended local similarity analysis (eLSA) to construct networks during blooming and non‐blooming periods. Overall, the importance of seasonal and species‐specific interactions between phytoplankton and bacteria is highlighted. In winter, mixed diatom communities were interconnected with bacteria, indicating a synergistic degradation of diverse phytoplankton‐derived substrates. In spring, despite the intensity variations of P. globosa blooms, the composition of bacterial communities remained consistent over several years, suggesting establishing a stable‐state environment for bacterial communities. Specific associations between monospecific diatom blooms and bacteria were evidenced in summer. [ABSTRACT FROM AUTHOR]

Cyanobacterial blooms are increasingly common during winters, especially when they are mild. The goal of this study was to determine the summer and winter phytoplankton community structure, cyanotoxin presence, and toxigenicity in a eutrophic lake susceptible to cyanobacterial blooms throughout the year, using classical microscopy, an analysis of toxic cyanometabolites, and an analysis of genes involved in biosynthesis of cyanotoxins. We also assessed whether cyanobacterial diversity in the studied lake has changed compared to what was reported in previous reports conducted several years ago. Moreover, the bloom-forming cyanobacterial strains were isolated from the lake and screened for cyanotoxin presence and toxigenicity. Cyanobacteria were the main component of the phytoplankton community in both sampling times, and, in particular, Oscillatoriales were predominant in both summer (Planktothrix/Limnothrix) and winter (Limnothrix) sampling. Compared to the winter community, the summer community was denser; richer in species; and contained alien and invasive Nostocales, including Sphaerospermopsis aphanizomenoides, Raphidiopsis raciborskii, and Raphidiopsis mediterranea. In both sampling times, the blooms contained toxigenic species with genetic determinants for the production of cylindrospermopsin and microcystins. Toxicological screening revealed the presence of microcystins in the lake in summer but no cyanotoxins in the winter period of sampling. However, several cyanobacterial strains isolated from the lake during winter and summer produced anabaenopeptins and microcystins. This study indicates that summer and winter blooms of cyanobacteria in the temperate zone can differ in biomass, structure, and toxicity, and that the toxic hazards associated with cyanobacterial blooms may potentially exist during winter. [ABSTRACT FROM AUTHOR]

The estimation of orchard blooming levels and the determination of peak blooming dates are very important because they determine the timing of orchard flower thinning and are essential for apple yield and quality. In this paper, we propose an orchard blooming level estimation method for global-level and block-level blooming level estimation of orchards. The method consists of a deep learning-based apple flower detector, a blooming level estimator, and a peak blooming day finding estimator. The YOLOv5s model is used as the apple flower detector, which is improved by adding a coordinate attention layer and a small object detection layer and by replacing the model neck with a bidirectional feature pyramid network (BiFPN) structure to improve the performance of the apple flower detector at different growth stages. The robustness of the apple flower detector under different light conditions and the generalization across years was tested using apple flower data collected in 2021–2022. The trained apple flower detector achieved a mean average precision of 77.5%. The blooming level estimator estimated the orchard blooming level based on the proportion of flowers detected at different growth stages. Statistical results show that the blooming level estimator follows the trend of orchard blooming levels. The peak blooming day finding estimator successfully positioned the peak blooming time and provided information for the flower thinning timing decision. The method described in this paper is able to provide orchardists with accurate information on apple flower growth status and is highly automated. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) pose a significant threat to aquatic ecosystems and human health due to the production of toxins. The identification and quantification of these toxins are crucial for water quality management decisions. This study used DNA analysis (PCR techniques) to identify toxin-producing strains and liquid-chromatography-tandem mass spectrometry (LC-MS/MS) to quantify microcystins in samples from Mindu and Nyumba ya Mungu Dams in Tanzania. The results showed that HABs were detected in both dams. The BLAST results revealed that the 16S gene sequences of uncultured samples were very similar to an Antarctic cyanobacterium, Leptolyngbya sp, Anabaena sp, and Microcystis aeruginosa. Sequences of the cultured samples were most similar to Nodularia spumigena, Amazoninema brasiliense, Anabaena sp, and Microcystis aeruginosa. Further analyses showed that the nucleotide sequence similarity of uncultured isolates from this study and those from the GenBank ranged from 85 to 100%. For cultured isolates from this study and others from the GenBank, nucleotide identity ranged from 81 to 100%. The molecular identification of Microcystis aeruginosa confirmed the presence of HABs in both Mindu and Nyumba ya Mungu Dams in Tanzania. At Mindu Dam, the mean concentrations (± standard deviation) of microcystin-LR, -RR, and -YR were 1.08 ± 0.749 ppm, 0.120 ± 0.0211 ppm, and 1.37 ± 0.862 ppm, respectively. Similarly, at Nyumba ya Mungu Dam, the concentrations of microcystin-LR, -RR, and -YR were 1.07 ± 0.499 ppm, 0.124 ± 0.0224 ppm, and 0.961 ± 0.408 ppm, respectively. This paper represents the first application of PCR and LC-MS/MS to study microcystins in small freshwater reservoirs in Tanzania. This study confirms the presence of toxin-producing strains of Microcystis aeruginosa in both dams and also provides evidence of the occurrence of microcystins from these strains. These findings contribute in improving the monitoring of HABs contamination and their potential impact on water quality in Tanzanian reservoirs. [ABSTRACT FROM AUTHOR]

Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world's water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real‐time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll‐a concentrations (chl‐a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real‐time monitoring of HABs. Landsat‐8 and Sentinel‐2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl‐a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R2 values of 0.75, 0.82, and 0.79 for chl‐a, turbidity, and phycocyanin, while multiple regression had R2 values of 0.29, 0.51, and 0.46 and SVR had R2 values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open‐source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality. [ABSTRACT FROM AUTHOR]

Vibrio vulnificus (Vv) and Vibrio parahaemolyticus (Vp) are water- and foodborne bacteria that can cause several distinct human diseases, collectively called vibriosis. The success of oyster aquaculture is negatively impacted by high Vibrio abundances. Myriad environmental factors affect the distribution of pathogenic Vibrio, including temperature, salinity, eutrophication, extreme weather events, and plankton loads, including harmful algal blooms. In this paper, we synthesize the current understanding of ecological drivers of Vv and Vp and provide a summary of various tools used to enumerate Vv and Vp in a variety of environments and environmental samples. We also highlight the limitations and benefits of each of the measurement tools and propose example alternative tools for more specific enumeration of pathogenic Vv and Vp. Improvement of molecular methods can tighten better predictive models that are potentially important for mitigation in more controlled environments such as aquaculture. [ABSTRACT FROM AUTHOR]

Aquaculture is an industry that has developed rapidly over the past decades. Despite several hazards in the operations, emergency preparedness is not at the same level as in the rest of the maritime industry. This paper highlights some possibilities to increase the aquaculture industry’s coastal emergency preparedness. This paper is the result of the innovation project “Coastal Emergency Preparedness” funded by the Norwegian Research Council and industry partners. The aquaculture industry must establish its own emergency preparedness. In this paper, the innovation of a fleet of emergency preparedness vessels along the coastline, which could fill the gaps in emergency preparedness, is presented. The vessels would be of varied sizes, with different equipment and response times, and could assist the aquaculture industry during local incidents or large‐scale ones, such as algae blooms or winter storms, that affect many sites at the same time. Each production zone would need its own dedicated fleet due to biosecurity regulations. The emergency preparedness vessel fleet would be led by an on‐scene commander. The fleet would deal with oil spills and tasks such as emergency towing, firefighting, rescue of people, recapturing of fish, silage making, algae detection, and diving missions. With such a vessel fleet, small and large aquaculture companies could increase their emergency preparedness with a common strategy and shared resources. [ABSTRACT FROM AUTHOR]

Management of water resources in the Murray-Darling Basin has historically focussed on water security and the allocation of water for users with competing needs. This focus was reflected in the seminal paper on multiple risks to shared water across the basin by the Commonwealth Scientific and Industrial Research Organisation 15 years ago. That paper captured key concerns that were at the forefront for decision-makers, managers and policy-makers who were, at that time, experiencing the early impacts of the Millennium Drought. Water quality, then, was secondary to the issues of water security. Across the following years, new water quality risks have emerged along with a more nuanced understanding of the complex interplay between climate, floodplain/catchment vegetation, hydrology, and water quality. Critically, this improved understanding applies to the systemic shocks of extreme events, such as the 2020 bushfires and hypoxic blackwater events, as well as the variability, duration and volumes of natural and regulated river flows. In this paper, we explore the key water quality issues that currently face the Basin, and reframe water quality as an integral rather than incidental component of the risks to shared water in the Basin, with the associated implications for policy development that this implies. [ABSTRACT FROM AUTHOR]

Globally, observations on marine species during marine heatwaves (MHWs) help outline the scope of the MHW's possible biological effects. In line with this effort, this paper presents a 2020 MHW that coincided with a reported "tomato jellyfish" (Crambione mastigophora Maas, 1903) bloom on 23 March 2020 in the Corong-Corong Bay of Palawan, Philippines. Detecting a moderate MHW from 21 March to 04 April 2020, the analysis of sea surface temperatures revealed that most areas surrounding the bloom site attained their peak positive anomalies on the same day as the reported bloom. Certain physical mechanisms present in the first quarter of 2020 may have played a role in the occurrence of both events: the presence of cyclonic eddies and parallel monsoonal winds alongshore can induce upwelling which promotes biological productivity in surface waters, while the observed weakening of winds have been associated with anomalous warming of the sea surface. Further studies are still highly recommended to determine the exact causes of the jellyfish bloom and what conditions make it more likely to happen during MHWs. However, if the C. mastigophora is hypothetically able to continually bloom amidst warming temperatures, the increasing trend of MHW frequency and intensity in the West Philippine Sea (where the reported bloom site is situated) may consequently yield more future co-occurrences. This paper aims to hopefully contribute to the existing knowledge of possible biological impacts associated with extreme marine events, especially in the Philippine context where both jellyfish blooms and MHWs are understudied. [ABSTRACT FROM AUTHOR]

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms. [ABSTRACT FROM AUTHOR]

This document is a preface to a special issue series in the journal Aquatic Ecosystem Health & Management, focusing on the ecology and dynamics of Lake Erie. The authors, Mohiuddin Munawar and Stuart A. Ludsin, have a long history of studying Lake Erie and its ecosystem. They discuss the history of monitoring and research efforts on Lake Erie, including the development of the Great Lakes Water Quality Agreement and the establishment of monitoring programs. The preface also mentions the recent Cooperative Science and Monitoring Initiative (CSMI) focused on Lake Erie, which aimed to understand the impacts of eutrophication and harmful algal blooms on the lake's food web. The authors express their gratitude to the contributors and editors involved in the special issue series and hope that it will contribute to a better understanding of Lake Erie's ecology. [Extracted from the article]

Cyanobacteria harmful algal blooms (cHABs) are increasingly becoming an emerging threat to aquatic life, ecotourism, and certain real estate investments. Their spontaneous yet sporadic occurrence has made mitigation measures a cumbersome task; moreover, current trends regarding anthropogenic activities, especially in agriculture and industry portend further undesirable events. Apart from the aesthetic degeneration they create in their respective habitats, they are equally capable of secreting toxins, which altogether present grave environmental and medical consequences. In this paper, we gave an update on factors that influence cHABs, cyanotoxin exposure routes, and environmental public health implications, especially impacts on fish, pets, and livestock. We discussed social economic impacts, risk assessment, and management problems for cHABs and, thereafter, assessed the extant management approaches including prevention, control, and mitigation of the proliferation of cyanobacterial blooms. In light of this, we suggest that more intensified research should be directed to the standardization of procedures for cyanotoxin analysis. Also, the provision of standardized reference material for the quantification of cyanotoxins is vital for routine monitoring as well as the development of strong in situ sensors capable of quantifying and detecting HABs cells and toxins in waterbodies to prevent the adverse impacts of cHABs. Also, more investigations into the natural and environmentally friendly approach to cyanobacteria management and the necessary and appropriate deployment of artificial intelligence are required. Finally, we wish to redirect the focus of public health authorities to protecting drinking water supply sources, agriculture products, and food sources from cyanotoxins contamination as well as to implement proper monitoring and treatment procedures to protect citizens from this potential health threat. [ABSTRACT FROM AUTHOR]

Since 2011, the Caribbean and Gulf of Mexico coasts have been receiving massive influxes of holopelagic sargasso algae composed of Sargassum natans and Sargassum fluitans. This phenomenon has been causing several negative local impacts, such as ecological disturbances and socioeconomic and health concerns of communities in impacted areas. This work aimed to assess the status of scientific knowledge related to pelagic sargasso, including trends, emphases, and gaps. A literature review was conducted on publications and reports from 2011 to 2022, of which 251 articles were collected based on an inclusion–exclusion criteria. Aspects of each article were quantified, including location, description of sargasso, the type of study, and research theme. A region-wide research emphasis on ecology, remote sensing, and valorization was observed. Areas first affected by the inundations composed a higher percentage of sargasso studies than other locations, and the distribution of studies varied among subregions. Topics requiring further investigation include sargasso's growth and mortality rates and drivers, taxonomic and physiologic differences among morphotypes, and real-time forecasting resolution at local scales both on and offshore. This research emphasized efforts from the scientific community on research and mitigation initiatives. [ABSTRACT FROM AUTHOR]

Diurnal and seasonal cycles of aeolian activity are well‐constrained for low latitude dust source regions and provide valuable insights into relationships between dust emissions and environmental drivers. Such cycles have received little systematic attention in high latitude dust source areas (≥50°N and ≥40°S), and understanding them will aid the modelling of atmospheric dust over different timescales. This paper examines the timing and drivers of atmospheric dust concentration close to source at ice‐free locations c. 6 km and c. 37 km from the Greenland Ice Sheet margin. Dust concentration, and associated environmental drivers including wind speed and velocity, air temperature and humidity, was measured from April–October 2018 and April–August 2019. Measured dust concentrations were a similar order of magnitude to source‐proximal values measured globally and varied from 0 to ≥1000 μg m−3. Diurnal cycles in the environmental drivers of dust emission were similar to those observed in low latitudes, but unlike in the low latitudes, there was no clear diurnal pattern of dust concentration. Only 3 out of 17 recorded dust events showed a significant positive relationship between wind speed and dust concentration, and factors such as wind direction, humidity and sediment availability are the over‐riding controls on dust activity at the event and seasonal scales. Whereas 7 dust events are attributed to down‐valley katabatic winds, 10 events were associated with a westerly sea breeze blowing up‐valley towards the ice sheet and high atmospheric moisture content. If deposited on the ice, this dust will alter ice albedo both directly and indirectly, (e.g. through the promotion of algal blooms) affecting cryospheric melt rates. Our results demonstrate an intricate relationship between first‐order controls and dust concentration that raises challenges for modelling the uplift and subsequent dust transport from high latitude sources, especially the appropriateness of assumptions based on emissions behaviour at low latitudes. [ABSTRACT FROM AUTHOR]