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Journal articles on the topic 'Algal blooms'
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1
Ma, Jinge, Feng He, Tianci Qi, Zhe Sun, Ming Shen, Zhigang Cao, Di Meng, Hongtao Duan, and Juhua Luo. "Thirty-Four-Year Record (1987–2021) of the Spatiotemporal Dynamics of Algal Blooms in Lake Dianchi from Multi-Source Remote Sensing Insights." Remote Sensing 14, no. 16 (August 17, 2022): 4000. http://dx.doi.org/10.3390/rs14164000.
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Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake’s trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development.
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Jing, Yuanyuan, Yuchao Zhang, Minqi Hu, Qiao Chu, and Ronghua Ma. "MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China." Remote Sensing 11, no. 21 (November 4, 2019): 2582. http://dx.doi.org/10.3390/rs11212582.
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Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi is so far insufficient. Therefore, the algae pixel-growing algorithm (APA) was used to accurately identify algal bloom areas at the sub-pixel level on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2018. The results showed that algal blooms were observed all year round, with a reduced frequency in winter–spring and an increased frequency in summer–autumn, which lasted a long time for about 310–350 days. The outbreak areas were concentrated in 20–80 km2 and the top three largest areas were observed in 2002, 2008, and 2017, reaching 168.80 km2, 126.51 km2, and 156.34 km2, respectively. After deriving the temporal-spatial distribution of algal blooms, principal component analysis (PCA) and redundancy analysis (RDA) were applied to explore the effects of meteorological, water quality and human activities. Of the variables analyzed, mean temperature (Tmean) and wind speed (WS) were the main drivers of daily algal bloom areas and spatial distribution. The precipitation (P), pH, and water temperature (WT) had a strong positive correlation, while WS and sunshine hours (SH) had a negative correlation with monthly maximum algal bloom areas and frequency. Total nitrogen (TN) and dissolved oxygen (DO) were the main influencing factors of annual frequency, initiation, and duration of algal blooms. Also, the discharge of wastewater and the southwest and southeast monsoons may contribute to the distribution of algal blooms mainly in the north of the lake. However, different regions of the lake show substantial variations, so further zoning and quantitative joint studies of influencing factors are required to more accurately understand the true mechanisms of algae in Lake Dianchi.
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Yi, Hye-Suk, Sunghwa Choi, Dong-Kyun Kim, and Hojoon Kim. "Improvement of Algal Bloom Identification Using Satellite Images by the Algal Spatial Monitoring and Machine Learning Analysis in a New Dam Reservoir." GEO DATA 5, no. 3 (September 30, 2023): 126–36. http://dx.doi.org/10.22761/gd.2023.0021.
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Algal blooms are major issues and an ongoing cause of water quality problems in inland waters globally. In the case of harmful algal blooms, the water temperature rises after nitrogen and phosphorus inflow, which occurs in the summer, is the main cause of the algae bloom. In South Korea, algae monitoring methods have been performed by collecting water in point monitoring stations. Recently, in order to overcome the limitations of these existing monitoring methods, spatial monitoring methods using hyperspectral images and satellite images has been researched. We used satellite images for analysis of the spatial algal variation. The accuracy of algal identification is imperative for effective spatial monitoring of algal blooms in the context of ecological health and assessment. In this study, we generated algal big-data with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement and predicted chlorophyll-a concentrations using 13- band satellite images derived from Sentinel-2. In order to validate the values from the satellite images, we compared them with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement. The goal of this study is to improve the accuracy of predictions induced from satellite images. The analytical techniques were comparatively evaluated. The results showed that Artificial Neural Networks exhibited the best performance among them, improving more than 30% accuracy compared to that of multiple linear regression. Furthermore, the accuracy of identifying algal blooms has been shown to increase at high algal concentrations. In the end, it was successful to create algal bloom maps using a new algorithm to analyze algal bloom management.
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Wang, Shu Hang, Wen Wen Wang, and Xia Jiang. "The Process of Algal Bloom Formation and the Effects of Wind - An Enclosure Experiment and In Situ Investigation in a Large Hyper-Eutrophic Shallow Lake in China." Advanced Materials Research 518-523 (May 2012): 4303–14. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4303.
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An enclosure experiment was conducted to evaluate the processes involved in algal bloom formation at different trophic levels in Chaohu Lake, which is a large hyper-eutrophic shallow lake in China. In situ investigations were conducted concurrently to quantitatively describe the effects of wind on the horizontal transference and vertical hybridization of algal blooms. The results revealed that certain nutrient trophic levels played a crucial role in the formation of algal blooms. Specifically, sediments were identified as an important source of nutrients and algal seeds that are necessary to maintain the trophic level and growth of algal blooms. In addition, the disturbance caused by wind accelerated the multiplication of algae, as well as their sedimentation and suspension at the water-sediment interface. Furthermore, when the wind speed was less than 3m•s-1, algal clusters drifted on the water surface at a speed that was exponentially related to the wind velocity with a coefficient of 0.9052. When the wind speed was greater than 3m•s-1, the algae mixed together vertically and then sank. The algal bloom distribution in the lake was dominated by leading wind directions. Moreover, the algal biomass of the surface lake water in leeward areas was 8.8 times greater than the biomass in the windward areas during the study period.
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Lathrop, Richard C. "Evaluation of Whole-Lake Nitrogen Fertilization for Controlling Blue-Green Algal Blooms in a Hypereutrophic Lake." Canadian Journal of Fisheries and Aquatic Sciences 45, no. 12 (December 1, 1988): 2061–75. http://dx.doi.org/10.1139/f88-240.
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Indian Lake, a shallow hypereutrophic lake in southern Wisconsin, was treated with ammonium nitrate to test whether high dissolved inorganic nitrogen (DIN) concentrations or high total nitrogen to phosphorus (N:P) ratios could prevent the development of summer blue-green algal blooms (predominately Microcystis aeruginosa). The fertilizer was applied weekly from late April to early July 1981–82 in annual amounts of 14 and 23 g N/m2, respectively. In 1981, a dense summer blue-green algal bloom developed, although both dissolved inorganic phosphorus (DIP) and algal P (particulate P/chlorophyll a) were lower than in summers without fertilizer treatments. In 1982, unusually clear water in May allowed aquatic macrophytes and associated filamentous algae to become very dense in June. The increase in water clarity and macrophytes was probably a response to a fishkill the previous winter. Later in the summer the macrophyte community disappeared and a blue-green algal bloom developed. After each fertilization in both treatment years, the NH4+ and NO3− decreased rapidly, suggesting nitrification/denitrification in the lake sediments. Whole-lake N fertilization did not prevent the summer blue-green algal blooms in Indian Lake. Low DIN apparently does not trigger the bloom development or cause the vernal nonblue-green algae to decline. Also, Sow total N:P ratios (<11 by weight) during the blooms occur partly because of high DIP and because of high algal P levels resulting from luxury P consumption. The Indian Lake data suggest that low total N:P ratios are only predictive of (or resultant from) blue-green algal blooms rather than causative.
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Sidabutar, Tumpak, Endang Sunarwati Srimariana, Hendrik Cappenberg, and Sam Wouthuyzen. "Comprehensive analysis of harmful algal blooms in indonesia: from occurrence to impact." BIO Web of Conferences 87 (2024): 02003. http://dx.doi.org/10.1051/bioconf/20248702003.
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The occurrence and frequency of harmful algal blooms have become a significant problem in Indonesia's coastal waters since 1991. This article aims to thoroughly analyze the diversity of algal species involved in these harmful algal blooms (HABs). It highlights that some algae, such as toxic species, can produce dangerous toxins, while others, such as non-toxic algae, remain harmless. This article reviews research studies that describe the occurrence, frequency, and causative types of bloom events in several contaminated coastal regions of Indonesia. The blooms had many consequences for fisheries, the aquatic environment, the economy, and public health. Among the identified species, Pyrodinium bahamense var. compressum is known for its high toxicity. The most common taxa contributing to bloom tragedy are Chaetoceros, Noctiluca, and Skeletonema. The study recognizes that human-induced nutrient enrichment is a major and significant factor in triggering the bloom phenomena. The paper recommends various management strategies and further research initiatives to prevent and reduce the impacts of HABs in Indonesia.
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Yun, Hongwon. "Prediction model of algal blooms using logistic regression and confusion matrix." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (June 1, 2021): 2407. http://dx.doi.org/10.11591/ijece.v11i3.pp2407-2413.
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Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis.
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Zhang, Yuchao, Steven Loiselle, Kun Shi, Tao Han, Min Zhang, Minqi Hu, Yuanyuan Jing, Lai Lai, and Pengfei Zhan. "Wind Effects for Floating Algae Dynamics in Eutrophic Lakes." Remote Sensing 13, no. 4 (February 22, 2021): 800. http://dx.doi.org/10.3390/rs13040800.
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Wind-speed decline is an important impact of climate change on the eastern Asian atmospheric circulation. Although wind does not determine algae biomass in eutrophic lakes, it is a decisive factor in the formation and severity of algae blooms. Based on 2000–2018 MODIS images, this study compared the effects of wind speed on algal blooms in three typical eutrophic lakes in China: Lake Taihu, Lake Chaohu and Lake Dianchi. The results indicate that climate change has different effects on the wind speed of the three lakes, but a common effect on the vertical distribution of algae. A wind speed of 3.0 m/s was identified as the critical threshold in the vertical distribution of chlorophyll-a concentrations in the three study lakes. The basic characteristics of the periodic variation of wind speed were different, but there was a significant negative correlation between wind speed and floating algal bloom area in all three lakes. In addition, considering lake bathymetry, wind direction could be used to identify locations that were particularly susceptible to algae blooms. We estimated that algal bloom conditions will worsen in the coming decades due to the continuous decline of wind, especially in Lake Taihu, even though the provincial and national governments have made major efforts to reduce eutrophication drivers and restore lake conditions. These results suggest that early warning systems should include a wind-speed threshold of 3.0 m/s to improve control and mitigation of algal blooms on these intensively utilized lakes.
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Zhao, Ziyue, Xuemei Liu, Yanfeng Wu, Guangxin Zhang, Changlei Dai, Guoli Qiao, and Yinghui Ma. "A Review on the Driving Mechanism of the Spring Algal Bloom in Lakes Using Freezing and Thawing Processes." Water 16, no. 2 (January 11, 2024): 257. http://dx.doi.org/10.3390/w16020257.
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Spring algal blooms in mid–high-latitude lakes are facing serious challenges such as earlier outbreaks, longer duration, and increasing frequency under the dual pressure of climate warming and human activities, which threaten the health of freshwater ecosystems and water security. At present, the freeze-thaw processes is the key to distinguishing spring algal blooms in mid- to high-latitude lakes from low-latitude lakes. Based on the visualization and an analysis of the literature in the WOS database during 2007–2023, we clarified the driving mechanism of the freeze-thaw process (freeze-thaw, freeze-up, and thawing) on spring algal bloom in lakes by describing the evolution of the freeze-thaw processes on the nutrient migration and transformation, water temperature, lake transparency and dissolved oxygen, and physiological characteristics of algae between shallow lakes and deep lakes. We found that the complex phosphorus transformation process during the frozen period can better explain the spring-algal-bloom phenomenon compared to nitrogen. The dominant species of lake algae also undergo transformation during the freeze-thaw process. On this basis, the response mechanism of spring algal blooms in lakes to future climate change has been sorted out. The general framework of “principles analysis, model construction, simulation and prediction, assessment and management” and the prevention strategy for dealing with spring algal bloom in lakes have been proposed, for which we would like to provide scientific support and reference for the comprehensive prevention and control of spring algal bloom in lakes under the freezing and thawing processes.
10
Schleyer, Guy, and Assaf Vardi. "Algal blooms." Current Biology 30, no. 19 (October 2020): R1116—R1118. http://dx.doi.org/10.1016/j.cub.2020.07.011.
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Li, Gege. "Algal blooms." New Scientist 258, no. 3439 (May 2023): 30–31. http://dx.doi.org/10.1016/s0262-4079(23)00911-9.
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Li, Yachun, Shihua Zhu, Xin Hang, Liangxiao Sun, Xinyi Li, Xiaochun Luo, and Xiuzhen Han. "Variation of Local Wind Fields under the Background of Climate Change and Its Impact on Algal Blooms in Lake Taihu, China." Water 15, no. 24 (December 12, 2023): 4258. http://dx.doi.org/10.3390/w15244258.
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Global climate change can greatly promote the continuing expansion of algal blooms in eutrophic inland lakes. Wind fields, an important climate factor, provide an external driving force for the movement of algal blooms. Based on algal bloom satellite imageries and wind observation data from 2003 to 2022, this study explored a quantitative assessment of the variations in surface wind fields and their impacts on the algal blooms in Lake Taihu, China. The results indicate that the mean wind speed at different time scales in the Lake Taihu area presents a continuous descending tendency in recent decades, which is the probable cause for the increasing frequency and severity of algal blooms in the lake. Wind fields affect the formation, location, and severity of algal blooms in diverse and complex ways. The area and frequency of algal blooms in Lake Taihu increase with the decrease in wind speed. The 6 h mean wind speed before 12:00 LT (Local Time) on the day of the algal bloom occurrence generally follows a Gaussian distribution, with a wind speed range of (0.6 m/s, 3.4 m/s) at the 95.5% confidence level. Accordingly, the wind speeds of 0.6 m/s and 3.4 m/s are identified to be the lower and upper critical wind speed indicators suitable for the formation of algal blooms, respectively. Another meaningful finding is that the outbreak of large-scale algal blooms requires stricter wind speed conditions, with a significantly lower wind speed threshold of around 2 m/s. Our study also demonstrates that the dominant wind direction of southeast in the region may be an important cause of the continuous water-quality decline and the high frequency and severity of algal blooms in the northwest waters of the lake. These findings will contribute to further studies on the dynamic mechanism of algal blooms and provide support for water environment management and algal bloom prevention and control.
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Liu, Jia, Chunlin Xia, Hui Xie, Xiaodong Wang, and Yinguo Qiu. "Accurate Monitoring of Algal Blooms in Key Nearshore Zones of Lakes and Reservoirs Using Binocular Video Surveillance System." Water 14, no. 22 (November 17, 2022): 3728. http://dx.doi.org/10.3390/w14223728.
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In recent years, algal blooms break out frequently and often accumulate in nearshore zones of eutrophic lakes and reservoirs, which seriously threaten regional water supply security. It is of great significance to grasp the status of algal blooms in key nearshore zones timely for the emergency prevention and control of algal blooms. A video surveillance system provides a new method for achieving this goal. The results of algal-bloom monitoring in current research, however, are usually interfered by onshore vegetation for their similar textural features. Accordingly, there are great limitations in current works in terms of decision support for emergency prevention and control of algal blooms. To solve this problem, a binocular video surveillance system based an accurate monitoring method of algal blooms is proposed in this paper. Binocular images of monitoring areas are obtained periodically by exploiting the binocular video surveillance system, which is performed by a stereoscopic 3D reconstruction method to obtain the 3D point cloud data of monitoring areas. Afterward, water regions and non-water regions are intelligently discriminated according to the elevation characteristics of point clouds, and only the image data of the water regions are finally adopted for algal-bloom extraction. Thus, the influence of onshore vegetation on the extraction of algal blooms can be eliminated. The system was implemented and applied, and the experimental results show that the proposed method can eliminate effectively the interference of onshore vegetation on the extraction of algal blooms and improve significantly the accuracy of existing methods for algal-bloom monitoring based on video surveillance system.
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Gong, Xingrui, Chao Ma, Beili Sun, and Junyi Zhang. "An Efficient Self-Organized Detection System for Algae." Sensors 23, no. 3 (February 1, 2023): 1609. http://dx.doi.org/10.3390/s23031609.
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Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μm/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms.
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Sidabutar, Tumpak, Endang Sumarwati S Srimariana, Hendrik Cappenberg, and Sam Wouthuyzen. "EARLY WARNING SYSTEM (EWS) FOR ALGAL BLOOMS USING SATELLITE IMAGERY IN JAKARTA BAY." Jurnal Ilmu dan Teknologi Kelautan Tropis 15, no. 3 (December 31, 2023): 369–88. http://dx.doi.org/10.29244/jitkt.v15i3.52627.
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Jakarta Bay is experiencing eutrophication, primarily due to nutrient inflows from agriculture, industry, and urban sources. This abundance of nutrients has led to significant algae blooms. A study using Terra and Aqua MODIS satellite data from 2004 to 2007 monitored these blooms by measuring chlorophyll-a levels. During this period, large-scale fish kills were observed directly related to the algal blooms, as evidenced by high chlorophyll-a concentrations and blooms covering more than a quarter of the bay. Interestingly, not all intense blooms resulted in massive fish kills. The study suggests that this mortality is primarily due to oxygen depletion after peak bloom periods, compounded by poor water circulation in the bay. Using satellite imagery to monitor algal blooms is a practical tool for implementing an early warning system (EWS) in Jakarta Bay. Satellite imagery has proven effective in monitoring these blooms and could help develop an early warning system in Jakarta Bay despite limitations such as cloud cover.
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Zhang, Tiantian, Hong Hu, Xiaoshuang Ma, and Yaobo Zhang. "Long-Term Spatiotemporal Variation and Environmental Driving Forces Analyses of Algal Blooms in Taihu Lake Based on Multi-Source Satellite and Land Observations." Water 12, no. 4 (April 5, 2020): 1035. http://dx.doi.org/10.3390/w12041035.
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The algal blooms caused by the eutrophication of lakes is a major environmental problem. In this study, we took China’s Taihu Lake as the research area, using multi-source satellite imagery data to monitor the information of algal blooms from 2008 to 2017. Following the analyses of the temporal and spatial variation trends of the blooms, water quality and meteorological data from land observation stations were employed to investigate the main environmental driving forces of the algal bloom outbreaks. The results show that, over the decade, the blooms with medium and higher hazard degrees mainly occurred in summer and autumn, and especially in autumn. From 2008 to 2016, the algal blooms outbreak degree was relatively stable, but, in 2017, it was severe, and the Northwest Lake area and the northern bays had heavier blooms than the other lake areas. From the analyses of the environmental driving forces, the variation trend of total nitrogen (TN) and total phosphorus (TP) concentrations in Taihu Lake from 2008 to 2017 was moderate, and the minimum concentrations of TN and TP both exceeded the threshold for algal bloom outbreaks. It was also found that the algal bloom area had notable correlations with the sunshine duration, wind speed and direction, precipitation, and air pressure. The research results of this paper will provide a theoretical basis for the scientific prediction of the occurrence of algal blooms in Taihu Lake.
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Son, Geunsoo, Dongsu Kim, Young Do Kim, Siwan Lyu, and Soojeong Kim. "A Forecasting Method for Harmful Algal Bloom(HAB)-Prone Regions Allowing Preemptive Countermeasures Based only on Acoustic Doppler Current Profiler Measurements in a Large River." Water 12, no. 12 (December 11, 2020): 3488. http://dx.doi.org/10.3390/w12123488.
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Harmful algal blooms (HABs) have been recognized as a serious problem for aquatic ecosystems and a threat to drinking water systems. The proposed method aimed to develop a practical and rapid countermeasure, enabling preemptive responses to massive algal blooms, through which prior to the algal bloom season we can identify HAB-prone regions based on estimations of where harmful algae initiates and develops significantly. The HAB-prone regions were derived from temperature, depth, flow velocity, and sediment concentration data based only on acoustic Doppler current profilers (ADCPs) without relying further on supplementary data collection, such as the water quality. For HAB-prone regions, we employed hot-spot analysis using K-means clustering and the Getis-Ord G*, in conjunction with the spatial autocorrelation of Moran’s I and the local index of spatial association (LISA). The validation of the derived HAB-prone regions was conducted for ADCP measurements located at the downstream of Nam and Nakdong River confluence, South Korea, which preceded three months of algal bloom season monitored by unmanned aerial vehicles (UAVs). The visual inspection demonstrated that the comparison resulted in an acceptable range of agreement and consistency between the predicted HAB-prone regions and actual UAV-based observations of actual algal blooms.
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Sidabutar, T., E. S. Srimariana, H. Cappenberg, and S. Wouthuyzen. "An overview of harmful algal blooms and eutrophication in Jakarta Bay, Indonesia." IOP Conference Series: Earth and Environmental Science 869, no. 1 (November 1, 2021): 012039. http://dx.doi.org/10.1088/1755-1315/869/1/012039.
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Abstract Algal blooms have been occurring in Jakarta Bay for twenty years. However, recently the occurrence of algal blooms, their harmful effects, and their duration have been intensified. Algal blooms have devastated the marine environment, caused fish mortality, and been detrimental to local tourism, local fishing, and other industries along the coast. It comes to speculation that the increase of anthropogenic activity from surrounding areas is taking a toll on the environment. So, this research aimed to study the recent rise of algal blooms in Jakarta Bay and the possible anthropogenic links, mainly through cultural eutrophication, to the increasing occurrence of red tides and their impact. Observation has been conducted to study the dynamic of algal blooms concerning eutrophication and the existing seasons. Collecting samples were performed using a canonical plankton net from 2008 until 2015. The results showed that the abundance of phytoplankton ranged from 40.90 x 106 up to 1699.10 x 106 cells.m−3. The highest quantity of cells was observed in May 2010 between rainy to dry seasons. There is evidence that the reported increase in frequency and magnitude of algal bloom events in Jakarta Bay is linked to cultural eutrophication. The recent exponential growth of the city may be a contributing factor in the increasing intensity of algal blooms. The cultural eutrophication of coastal waters increased, leading to the intensity and frequency of algal bloom.
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Guo, Yansen, Wenrui Fu, Nan Xiong, Jian He, and Zheng Zheng. "Phosphorus Threshold for the Growth of Microcystis wesenbergii, Microcystis aeruginosa, and Chlorella vulgaris Based on the Monod Formula." Water 15, no. 24 (December 12, 2023): 4249. http://dx.doi.org/10.3390/w15244249.
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The outbreak of algae in freshwater bodies poses an important threat to aquatic ecosystems, making finding an effective method for controlling algal blooms imperative. Numerous key factors influence algal bloom outbreaks, with nutrient levels in the water body being the decisive factor. Current research regarding the effect of nutrient levels on algal growth shows that phosphorus is a nutrient that influences algal blooms. Herein, we propose the concept of a modified Monod model for the relationship between algal specific growth rate and phosphorus concentration. Through this improved Monod model, we inferred that the phosphorus concentration at a specific growth rate of zero is the lower threshold of phosphorus concentration that limits algal growth and can effectively control algal outbreaks. This lower threshold is denoted as S′. On the basis of this concept, we designed algal growth experiments. Our results provided an equation that effectively describes the relationship between algal growth and nutrient concentration. When three algal species grow under phosphorus-limited conditions, the corresponding phosphorus concentrations at which they maintain a growth rate of 0 are 0.0565, 0.0386, and 0.0205 mg/L as reflected by the following order of their S′ values: Microcystis wesenbergii S′ < Microcystis aeruginosa S′ < Chlorella vulgaris S′. Furthermore, with the increase in phosphorus concentration, the growth of M. aeruginosa becomes faster than that of M. wesenbergii and C. vulgaris. Consequently, M. aeruginosa becomes the dominant population in the water, leading to its predominance in algal blooms. This situation explains the common occurrence of cyanobacterial blooms. Our findings provide a theoretical basis for regulating the concentration of phosphorus to control algal outbreaks. Therefore, our study is of great importance for controlling the eutrophication of water bodies.
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Havens, Karl. "The Future of Harmful Algal Blooms in Florida Inland and Coastal Waters." EDIS 2018, no. 1 (February 26, 2018): 4. http://dx.doi.org/10.32473/edis-sg153-2018.
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Microscopic algae in oceans and inland waters sometimes grow to excessive levels called “blooms.” Warmer water temperatures and increased nutrient levels exacerbate blooms, and when nutrients are high, temperature increases of just a few degrees cause exponential increases of algae and blooms. This 4-page fact sheet written by Karl Havens and published by the Florida Sea Grant College Program and UF/IFAS Extension explains why algal blooms can be harmful and provides advice for communities seeking to reduce nutrient levels in their lakes, streams, and other bodies of water. In a warmer future, harmful algal blooms will be much more challenging to control than they are today. http://edis.ifas.ufl.edu/sg153
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Al-Ghelani, H. M., A. Y. A. AlKindi, S. Amer, and Y. K. Al-Akhzami. "Harmful Algal Blooms: Physiology, Behavior, Population Dynamics and Global Impacts- A Review." Sultan Qaboos University Journal for Science [SQUJS] 10 (June 1, 2005): 1. http://dx.doi.org/10.24200/squjs.vol10iss0pp1-30.
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Harmful, toxic algae are now considered as one of the important players in the newly emerging environmental risk factors. The apparent global increase in harmful algal blooms (HABs) is becoming a serious problem in both aquaculture and fisheries populations. Not only has the magnitude and intensity of public health and economic impacts of these blooms increased in recent years, but the number of geographic locations experiencing toxic algal blooms has also increased dramatically. There are two primary factors causing HABs outbreaks. The natural processes such as upwelling and relaxation, and the anthropogenic loading resulting in eutrophication. However, the influence of global climate changes on algal bloom phenomenon cannot be ignored. The problem warrants development of effective strategies for the management and mitigation of HABs. Progress made in the routine coastal monitoring programs, development of methods for detection of algal species and toxins and coastal modeling activities for predicting HABs reflect the international concerns regarding the impacts of HABs. Innovative techniques using molecular probes will hopefully result in development of rapid, reliable screening methods for phycotoxins and the causative organisms.
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Namsaraev, Zorigto, Anna Melnikova, Anastasia Komova, Vasily Ivanov, Anastasia Rudenko, and Evgenii Ivanov. "Algal Bloom Occurrence and Effects in Russia." Water 12, no. 1 (January 18, 2020): 285. http://dx.doi.org/10.3390/w12010285.
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Eutrophication caused by the entry of nutrients into a water body may lead to algal bloom. Russia possesses the world’s second highest supply of renewable freshwater resources and has faced the problem of eutrophication for many years. Nevertheless, as far as we know, no general analysis of Russia’s algal bloom situation has been before carried out. We have analyzed mass media and scientific reports about algal outbreaks from 2016 to 2018, which allowed us to determine the geographical distribution of algal blooms in Russia, as well as the major effects of eutrophication. As a result, we showed that algal blooms happened in all major climate zones and all federal districts. Cyanobacteria are the most frequently reported photosynthetic organisms comprising algal blooms in freshwater reservoirs located in the continental part of Russia and in the Baltic Sea. Dinoflagellate dominated blooms are more characteristic for the coastal parts of the northeastern Pacific Ocean. The largest number of reports comes from the south of the European part of Russia. However, we did not find significant correlations between state statistics data on factors possibly affecting eutrophication (e.g., population, arable land area, fertilizers, livestock, air temperature, etc.) and the number of algal outbreaks in the regions. Mass media analysis showed that algal blooms attract considerable public attention in Russia, which requires the scientific community to actively participate in solving the problem.
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Dembowska, Ewa. "Cyanobacterial blooms in shallow lakes of the Iławskie Lake District." Limnological Review 11, no. 2 (January 1, 2011): 69–79. http://dx.doi.org/10.2478/v10194-011-0028-y.
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Cyanobacterial blooms in shallow lakes of the Iławskie Lake DistrictThe dominance of blue-green algae observed in many lakes is related to a high trophic level. Shallow eutrophic lakes are particularly often abundant in blue-green algae. The research on phytoplankton, the results of which are presented in this paper, was carried out between 2002 and 2005 in six lakes. These lakes differed considerably in their size and management methods applied in the catchment (drainage) area. A few types of water blooms were distinguished, which is related to the catchment area management, the intensity of mixing and the trophic level. Algal blooms of the Planktothrix type appeared in lakes situated in an open area of agricultural catchment basins. Algal blooms of the Limnothrix type were characteristic of lakes with a forest-agricultural catchment area but surrounded by high shores, which reduced the wind influence on the mixing. Sporadic mixed algal blooms were typical of lakes situated in forest catchment areas.
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EPSTEIN, P. "Harmful algal blooms." Lancet 342, no. 8879 (October 1993): 1108. http://dx.doi.org/10.1016/0140-6736(93)92085-8.
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Lihepanyama, Deogratias Ladislaus, Patrick Alois Ndakidemi, and Anna Christina Treydte. "Spatio–Temporal Water Quality Determines Algal Bloom Occurrence and Possibly Lesser Flamingo (Phoeniconaias minor) Presence in Momella Lakes, Tanzania." Water 14, no. 21 (November 3, 2022): 3532. http://dx.doi.org/10.3390/w14213532.
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Eutrophication and algal blooms have sparked worldwide concern because of their widespread effects on water-dependent species. Harmful algal blooms can cause fatal effects to lesser flamingos (Phoeniconaias minor), obligatory filter feeders and vital bio-indicators in soda lakes. Thus, early detection of algal blooms and potential indicators in water quality is critical, but general tools are lacking in eastern African soda lakes. We monitored algal biomass changes and related water physico–chemical variables for 12 consecutive months in the lakes Big Momella and Rishateni in northern Tanzania. We used chlorophyll-a to measure algal biomass and quantified water physico–chemical variables that might influence algae growth. We also monitored lesser flamingo numbers to understand trends across the year and according to algal bloom occurrence. Algal biomass was strongly related to water nitrogen (r = 0.867; p < 0.001) and phosphorus (r = 0.832; p < 0.001). Monthly patterns showed significant differences in water quality and algal biomass (F = 277, p < 0.001) but not across sampling sites (F = 0.029, p = 0.971). Lesser flamingo numbers seemed to be related to algal biomass at Lake Big Momella (r = 0.828; p < 0.001) and shortly after algal biomass peaked high (i.e., March and April 2021), flamingo numbers declined. Lake Rishateni showed similar patterns. Our findings can provide a basis towards understanding the factors contributing to temporal changes in lesser flamingo abundance due to spatio–temporal water quality variations, which is important for optimising conservation efforts for the species in these unique Momella lakes.
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Rostam, Nur Aqilah Paskhal, Nurul Hashimah Ahamed Hassain Malim, Nur Afzalina Azmee, Renato J. Figueiredo, Mohd Azam Osman, and Rosni Abdullah. "Harmful algal blooms (HAB) open issues: A review of ecological data challenges, factor analysis and prediction approaches using data-driven method." Computing and Artificial Intelligence 1, no. 1 (November 17, 2023): 100. http://dx.doi.org/10.59400/cai.v1i1.100.
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Ongoing research on the temporal and spatial distribution of algae ecological data has caused intricacies entailing incomprehensible data, model overfit, and inaccurate algal bloom prediction. Relevant scholars have integrated past historical data with machine learning (ML) and deep learning (DL) approaches to forecast the advent of harmful algal blooms (HAB) following successful data-driven techniques. As potential HAB outbreaks could be predicted through time-series forecasting (TSF) to gauge future events of interest, this research aimed to holistically review field-based complexities, influencing factors, and algal growth prediction trends and analyses with or without the time-series approach. It is deemed pivotal to examine algal growth factors for useful insights into the growth of algal blooms. Multiple open issues concerning indicator types and numbers, feature selection (FS) methods, ML and DL forms, and the time series-DL integration were duly highlighted. This algal growth prediction review corresponded to various (chronologically-sequenced) past studies with the algal ecology domain established as a reference directory. As a valuable resource for beginners to internalize the algae ecological informatics research patterns and scholars to optimize current prediction techniques, this study outlined the (i) aforementioned open issues with an end-to-end (E2E) evaluation process ranging from FS to predictive model performance and (ii) potential alternatives to bridge the literature gaps.
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Krimsky, Lisa, Elizabeth Staugler, Dail Laughinghouse IV, and Joy Hazell. "Florida Sea Grant Symposia Promote Collaboration Among Harmful Algal Bloom Stakeholders." Oceanography 37, no. 1 (2024): 70–74. http://dx.doi.org/10.5670/oceanog.2024.209.
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Algal blooms are a pervasive problem for Florida, and successful management decisions must rely on the best available science. In 2019, Florida Sea Grant convened a forum of harmful algal bloom (HAB) scientists for the first Harmful Algal Bloom State of the Science Symposium. The goals of the two-day forum were to develop consensus statements identifying the current state of the science regarding what we know and what we think we know, data gaps and areas of uncertainty, and research priorities, with a focus on Karenia brevis red tides and Microcystis aeruginosa cyanobacterial blooms. In 2023, Florida Sea Grant convened a second symposium at the request of the state. This symposium focused specifically on cyanobacteria and assessed progress made over the four-year period between symposia. Consensus statements summarizing what we’ve learned, new research priorities, and best practices for cyanobacterial HAB research and management efforts were developed. The symposia consensus reports are used to inform Florida’s Harmful Algal Bloom and Blue-Green Algae Task Forces by aligning and prioritizing the management and research needs of the agencies and scientific institutions and to facilitate cohesive public outreach.
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Zhu, Shengyuan, Yinglei Wu, and Xiaoshuang Ma. "Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example." Sustainability 15, no. 5 (March 3, 2023): 4545. http://dx.doi.org/10.3390/su15054545.
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Rapid and accurate monitoring of algal blooms using remote sensing techniques is an effective means for the prevention and control of algal blooms. Traditional methods often have difficulty achieving the balance between interpretative accuracy and efficiency. The advantages of a deep learning method bring new possibilities to the rapid and precise identification of algal blooms using images. In this paper, taking Chaohu Lake as the study area, a dual U-Net model (including a U-Net network for spring and winter and a U-Net network for summer and autumn) is proposed for the identification of algal blooms using remote sensing images according to the different traits of the algae in different seasons. First, the spectral reflection characteristics of the algae in Chaohu Lake in different seasons are analyzed, and sufficient samples are selected for the training of the proposed model. Then, by adding an attention gate architecture to the classical U-Net framework, which can enhance the capability of the network on feature extraction, the dual U-Net model is constructed and trained for the identification of algal blooms in different seasons. Finally, the identification results are obtained by inputting remote sensing data into the model. The experimental results show that the interpretation accuracy of the proposed deep learning model is higher than 90% in most cases with the fastest processing time being less than 10 s, which achieves much better performance than the traditional supervised classification method and also outperforms the single U-Net model using data of whole year as the training samples. Furthermore, the profiles of algal blooms are well-captured.
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Al-Shehhi, Maryam R., and Yarjan Abdul Samad. "Identifying Algal Bloom ‘Hotspots’ in Marginal Productive Seas: A Review and Geospatial Analysis." Remote Sensing 14, no. 10 (May 20, 2022): 2457. http://dx.doi.org/10.3390/rs14102457.
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Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity.
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Sun, Longfei, Leixiang Wu, Xiaobo Liu, Wei Huang, Dayu Zhu, Zhuowei Wang, Ronghao Guan, and Xingchen Liu. "Reducing the Risk of Benthic Algae Outbreaks by Regulating the Flow Velocity in a Simulated South–North Water Diversion Open Channel." International Journal of Environmental Research and Public Health 20, no. 4 (February 17, 2023): 3564. http://dx.doi.org/10.3390/ijerph20043564.
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The reduction in open-channel flow velocity due to China’s South-to-North Water Diversion Project (SNP) increases the risk of benthic algal community blooms resulting in drinking water safety issues. Consequently, it has attracted attention from all walks of life. However, regulatory measures to mitigate the risk of algal blooms and the main risk-causing factors are unclear. This study simulated the river ecosystem of the SNP channel through water diversion. Simulated gradient-increasing river flow velocity affects environmental factors and benthic algal alterations, and can be used to explore the feasibility of regulating the flow velocity to reduce the risk of algal blooms. We found that the algal biomasses in the velocity environments of 0.211 and 0.418 m/s decreased by 30.19% and 39.88%, respectively. Community structure alterations from diatoms to filamentous green algae were 75.56% and 87.53%, respectively. We observed significant differences in biodiversity, especially in terms of richness and evenness. The α diversity index of a species is influenced by physical and chemical environmental factors (especially flow velocity). Our study revealed that flow velocity is the main factor affecting the growth and outbreak of benthic algae. The risk of algal blooms in open channels can be effectively mitigated by regulating the flow velocity. This provides a theoretical basis for ensuring the water safety of large-scale water conservancy projects.
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Guan, Di, Da Wen Gao, Nan Qi Ren, and Yi Fan Li. "Viewpoints of Dominant Environmental Factors Influencing Algal Blooms." Applied Mechanics and Materials 66-68 (July 2011): 155–59. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.155.
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Harmful algal blooms (HABs) are generally known as excessive phytoplankton growth or rapidly concentrate to high biomass. This study summarized the situation of HABs in China, and discussed possible dominant factors stimulating algal blooms by analyzing several actual HABs cases. It was manifested nutrients may affect algae concentration principally, but such impact tended to decease with degradation of background water. Meanwhile the hydrological and meteorological factors expressed greater correlation to chlorophyll concentration under multiple coupling effects of complex environmental factors. For the complex mechanisms, the determination of principle factors which stimulate excessive algal blooms effectively still need further researches, which are suggested to conduct under overall considerations on 3 scales: macro dimension, medium dimension and micro dimension.
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Pu, Jing, Kaishan Song, Yunfeng Lv, Ge Liu, Chong Fang, Junbin Hou, and Zhidan Wen. "Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image." Remote Sensing 14, no. 9 (April 21, 2022): 1988. http://dx.doi.org/10.3390/rs14091988.
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Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments.
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Patiño, Reynaldo, Victoria G. Christensen, Jennifer L. Graham, Jane S. Rogosch, and Barry H. Rosen. "Toxic Algae in Inland Waters of the Conterminous United States—A Review and Synthesis." Water 15, no. 15 (August 3, 2023): 2808. http://dx.doi.org/10.3390/w15152808.
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Cyanobacteria are the most common toxigenic algae in inland waters. Their toxins can affect the health of aquatic and terrestrial organisms, including humans. Other algal groups, such as haptophytes (e.g., Prymnesium parvum) and euglenoids (e.g., Euglena sanguinea), can also form harmful algal blooms (HABs) whose toxins cause injury to aquatic biota but currently have no known effects on human health. Prymnesium parvum, however, is responsible for some of the worst HAB-related ecological disasters recorded in inland waters. Here, we provide an overview of the primary toxigenic algae found in U.S. inland waters: cyanobacteria (planktonic forms), P. parvum, and E. sanguinea with the objective of describing their similarities and differences in the areas of HAB ecology, algal toxins, and the potential for future range expansion of HABs. A detailed account of bloom habitats and their known associations with land cover and use is provided from the perspective of water quality. This review revealed that salinity may have an influence on inland cyanobacterial blooms and cyanotoxins that had not been fully recognized previously.
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Park, Jungsu, Hyunho Lee, Cheol Young Park, Samiul Hasan, Tae-Young Heo, and Woo Hyoung Lee. "Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network." Water 11, no. 7 (June 28, 2019): 1338. http://dx.doi.org/10.3390/w11071338.
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An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.
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Wang, Haojiong, Elroy Galbraith, and Matteo Convertino. "Algal Bloom Ties: Spreading Network Inference and Extreme Eco-Environmental Feedback." Entropy 25, no. 4 (April 10, 2023): 636. http://dx.doi.org/10.3390/e25040636.
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Coastal marine ecosystems worldwide are increasingly affected by tide alterations and anthropogenic disturbances affecting the water quality and leading to frequent algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impacts on ecological processes and services, such as carbon cycling and sequestration. The exploration of eco-environmental feedback and algal bloom patterns remains challenging and poorly investigated, mostly due to the paucity of data and lack of model-free approaches to infer universal bloom dynamics. Florida Bay, taken as an epitome for biodiversity and blooms, has long experienced algal blooms in its central and western regions, and, in 2006, an unprecedented bloom occurred in the eastern habitats rich in corals and vulnerable habitats. With global aims, we analyze the occurrence of blooms in Florida Bay from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoint the source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to assess the environmental impacts of ecological imbalances and target the prevention and control of algal blooms; and (3) the topological co-evolution of biogeochemical and spreading networks to quantify ecosystem stability and the likelihood of ecological shifts toward endemic blooms in the long term. Here, we propose the transfer entropy (TE) difference to infer salient dynamical inter actions between the spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spread as well as environmental effects. A Pareto principle, defining the top 20% of areal interactions, is found to identify bloom spreading and the salient eco-environmental interactions of CHLa associated with endemic and epidemic regimes. We quantify the spatial dynamics of algal blooms and, thus, obtain areas in critical need for ecological monitoring and potential bloom control. The results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factors, in particular, about how blooms affect temperature locally. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrient overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently, the occurrence of blooms increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable habitats, such as tidal flats, salt-marshes and mangroves. The probabilistic distribution of CHLa is found to be indicative of endemic and epidemic regimes, where the former sets the system to higher energy dissipation, larger instability and lower predictability. Algal blooms are important ecosystem regulators of nutrient cycles; however, chlorophyll-a outbreaks cause vast ecosystem impacts, such as aquatic species mortality and carbon flux alteration due to their effects on water turbidity, nutrient cycling (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising the local water quality, other socio-ecological services are also compromised at large scales, including carbon sequestration, which affects climate regulation from local to global environments. Yet, ecological assessment models, such as the one presented, inferring bloom regions and their stability to pinpoint risks, are in need of application in aquatic ecosystems, such as subtropical and tropical bays, to assess optimal preventive controls.
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TAS, S., and I. N. YILMAZ. "Potentially harmful microalgae and algal blooms in a eutrophic estuary in Turkey." Mediterranean Marine Science 16, no. 2 (July 17, 2015): 432. http://dx.doi.org/10.12681/mms.1042.
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Distribution of potentially harmful microalgae and algal blooms were investigated at monthly and weekly time scales between October 2009 and September 2010 in the Golden Horn, a eutrophic estuary in the Sea of Marmara (Turkey). Several physical and chemical parameters were analysed together with phytoplankton composition and abundance. A total number of 23 potentially harmful and/or bloom-forming microalgae (14 dinoflagellates, 4 diatoms and 5 phytoflagellates) were identified throughout this study period, of which nine taxa have been confirmed to be toxic elsewhere in the world. Most harmful species and algal blooms were observed in late spring and summer particularly in the middle and upper estuaries, and nine taxa formed dense and successive algal blooms causing water discoloration. Nutrient concentrations increased significantly from the lower to the upper estuary. Additionally, high organic matter loads in the upper estuary could also have benefited by mixotrophic species. The increasing number of potentially harmful and bloom-forming species and algal blooms indicated that the GHE is a potential risk area for future HABs.
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Liao, Zihang, Shun Lv, Chenwu Zhang, Yong Zha, Suyang Wang, and Min Shao. "Analysis of Meteorological Drivers of Taihu Lake Algal Blooms over the Past Two Decades and Development of a VOCs Emission Inventory for Algal Bloom." Remote Sensing 16, no. 10 (May 9, 2024): 1680. http://dx.doi.org/10.3390/rs16101680.
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Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes.
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Lathrop, Richard C., Stephen R. Carpenter, Craig A. Stow, Patricia A. Soranno, and John C. Panuska. "Phosphorus loading reductions needed to control blue-green algal blooms in Lake Mendota." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 5 (May 1, 1998): 1169–78. http://dx.doi.org/10.1139/f97-317.
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We evaluated the reductions in P loading needed to control blue-green algal blooms in Lake Mendota, Wisconsin. After developing a 21-year loading data set, we used a P mass balance model expressed as a difference equation with an annual time step indexed from mid-April. We defined and estimated a loss parameter lambda as the proportion of the lake's April P concentration lost through sedimentation and outflow during the following year. Using the distribution of annual lambda 's and input loadings, we predicted the steady-state distribution of April P concentrations that would result from scenarios of altered inputs due to changes in management practices. These results were then linked to the probability of summer blue-green algal blooms. For no load reduction, the probability of a bloom (>2 mg algae ·L-1) on any summer day is about 60%. This probability decreases to 20% with a load reduction of 50%. Our approach illustrates how managers can consider reducing the frequency of extreme events like algal blooms, which may correspond more to the public's perception of lake water quality than average conditions.
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Sim, Bo-Ram, Hyung-Chul Kim, Chung-Sook Kim, Jin-Ho Kim, Kyung-Woo Park, Weol-Ae Lim, and Won-Chan Lee. "Seasonal Distributions of Phytoplankton and Environmental Factors Generate Algal Blooms in the Taehwa River, South Korea." Water 12, no. 12 (November 26, 2020): 3329. http://dx.doi.org/10.3390/w12123329.
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Algal blooms have occurred in the Taehwa River estuary in South Korea despite the improvement of water quality since environmental renewal projects in the 1990s. In this study, we investigated the causes of algal blooms by measuring the water retention time using a floating buoy, water quality parameters, and phytoplankton distribution data from 2012. An algal bloom did not occur in February because of phosphate limitations in the Taehwa River estuary; however, the concentration of nutrients in the water inflow from the basin triggered a significant algal bloom in the upper estuary in the month of May. In this regard, the phytoplankton population was dominated by nano- and pico-sized flagellates. In August, the freshwater inflow into the estuary greatly increased due to heavy rainfall, resulting in a shorter retention time of the water bodies, which seemed to prevent an algal bloom. In November, a bloom of Cryptophyceae occurred at one of the sites (the U2 site) due to sufficient nutrients in the water and the long retention times of the water bodies. Our results indicate that a decrease in the nutrients (N and P) supplied from the basin is required for a reduction in algal blooms in the Taehwa River estuary. Additional studies are needed to further elucidate the effects of the land-based, nutrient-rich pollutants flowing into the Taehwa River estuary on algal bloom generation considering the fact that the streams have different environmental characteristics.
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Zhang, Hong, Fayu Zhang, and Qing Huang. "Highly effective removal of malachite green from aqueous solution by hydrochar derived from phycocyanin-extracted algal bloom residues through hydrothermal carbonization." RSC Advances 7, no. 10 (2017): 5790–99. http://dx.doi.org/10.1039/c6ra27782a.
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Huge volumes of harmful algal bloom residues (ABR) are collected during emergency treatment of cyanobacteria blooms, and phycocyanin-extracted algal bloom residues (PE-ABR) are produced after extraction of phycocyanin from ABR.
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Kim, Jungwook, Hongtae Kim, Kyunghyun Kim, and Jung Min Ahn. "Research on the Development and Application of a Deep Learning Model for Effective Management and Response to Harmful Algal Blooms." Water 15, no. 12 (June 19, 2023): 2293. http://dx.doi.org/10.3390/w15122293.
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Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-National Institute of Environmental Research (EFDC-NIER) model to predict algae conditions and respond to algal blooms through the HAB alert system. This study aimed to establish an additional deep learning model to effectively respond to algal blooms. The prediction model is based on a deep neural network (DNN), which is a type of artificial neural network widely used for HAB prediction. By applying the synthetic minority over-sampling technique (SMOTE) to resolve the imbalance in the data, the DNN model showed improved performance during validation for predicting the number of cyanobacteria cells. The R-squared increased from 0.7 to 0.78, MAE decreased from 0.7 to 0.6, and RMSE decreased from 0.9 to 0.7, indicating an enhancement in the model’s performance. Furthermore, regarding the HAB alert levels, the R-squared increased from 0.18 to 0.79, MAE decreased from 0.2 to 0.1, and RMSE decreased from 0.3 to 0.2, indicating improved performance as well. According to the results, the constructed data-based model reasonably predicted algae conditions in the summer when algal bloom-induced damage occurs and accurately predicted the HAB alert levels for immediate decision-making. The main objective of this study was to develop a new technology for predicting and managing HABs in river environments, aiming for a sustainable future for the aquatic ecosystem.
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Kislik, Chippie, Iryna Dronova, and Maggi Kelly. "UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities." Drones 2, no. 4 (October 17, 2018): 35. http://dx.doi.org/10.3390/drones2040035.
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Algal blooms have become major public health and ecosystem vitality concerns globally. The prevalence of blooms has increased due to warming water and additional nutrient inputs into aquatic systems. In response, various remotely-sensed methods of detection, analysis, and forecasting have been developed. Satellite imaging has proven successful in the identification of various inland and coastal blooms at large spatial and temporal scales, and airborne platforms offer higher spatial and often spectral resolution at targeted temporal frequencies. Unmanned aerial vehicles (UAVs) have recently emerged as another tool for algal bloom detection, providing users with on-demand high spatial and temporal resolution at lower costs. However, due to the challenges of processing images of water, payload costs and limitations, and a lack of standardized methods, UAV-based algal bloom studies have not gained critical traction. This literature review explores the current state of this field, and highlights opportunities that could promote its growth. By understanding the technical parameters required to identify algal blooms with airborne platforms, and comparing these capabilities to current UAV technology, such knowledge will assist managers, researchers, and public health officials in utilizing UAVs to monitor and predict blooms at greater spatial and temporal precision, reducing exposure to potentially toxic events.
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Sidabutar, T., H. Cappenberg, E. S. Srimariana, A. Muawanah, and S. Wouthuyzen. "Harmful algal blooms and their impact on fish mortalities in Lampung Bay: an overview." IOP Conference Series: Earth and Environmental Science 944, no. 1 (December 1, 2021): 012027. http://dx.doi.org/10.1088/1755-1315/944/1/012027.
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Abstract The first disaster caused by harmful algal blooms in Lampung Bay was reported in 1991, where mass mortality of cultivated shrimp occurred in the brackish water ponds due to a Trichodesmium bloom. After this incident, the phenomenon reoccured in the following years continuously. Around nine species bloom makers in this bay are namely Pyrodinium sp., Noctiluca sp., Phaeocystis sp., Dinophysis sp., Trichodesmium sp., Ceratium sp., Prorocentrum sp., Pseudonitzhia sp., and Cochlodinium sp. The most frequent causative species, such as green Noctiluca and Trichodesmium, co-occurring during blooms and causing fish mortalities in the fish farming floating nets (KJA). Two species are known as the most potentially harmful species, namely Pyrodinium sp. and Cochlodinium sp. Cochlodinium blooms happened at the end of 2012, and since then, this species has continuously reappeared in the following years. The outbreak of Cochlodinium sp. still appeared in 2017 and 2018, but no fish-killing occurred. Phytoplankton bloom events occur at specific locations, mainly at fish farming floating nets on the west side of the bay, next to Hurun Cove. This paper discusses the occurrence of algal blooms in Lampung Bay and the triggering factors for increasing phytoplankton populations that cause harmful algal blooms.
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Ma, Xin Yu, Ji Ping Xu, Xiao Yi Wang, Qing Wei Zhu, Yan Shi, Li Wang, and Hui Yan Zhang. "The Algal Blooms Prediction in the Lakes Based on Remote Sensing Information." Applied Mechanics and Materials 511-512 (February 2014): 500–505. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.500.
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The eutrophication phenomenon for lake in our country is obvious. Algal blooms have become one of the major environmental issues in the lake. In view of the traditional single point monitoring and analysis limitations, this paper proposes a bloom prediction model based on remote sensing information. Different light wavelengths has different sensitivity of chlorophyll, so extracting the reflectance values to the chlorophyll-sensitive band from satellite remote sensing information, Building water green index information and corresponding to chlorophyll values of lake-sites monitoring area by latitude and longitude geographic information , thereby build a predictive model of algal bloom based on a remote sensing, realize effective prediction of algae bloom in Taihu lake for coastal ,open ocean water and lake reservoir bloom prediction gives an idea.
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Griffin, Catherine. "Expanding toxic algal blooms." Science 356, no. 6339 (May 18, 2017): 713.3–714. http://dx.doi.org/10.1126/science.356.6339.713-c.
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Paerl, H. W., W. S. Gardner, M. J. McCarthy, B. L. Peierls, and S. W. Wilhelm. "Algal blooms: Noteworthy nitrogen." Science 346, no. 6206 (October 9, 2014): 175. http://dx.doi.org/10.1126/science.346.6206.175-a.
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Qu, M., D. D. Lefebvre, Y. Wang, Y. Qu, D. Zhu, and W. Ren. "Algal blooms: Proactive strategy." Science 346, no. 6206 (October 9, 2014): 175–76. http://dx.doi.org/10.1126/science.346.6206.175-b.
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SCHLACHER, THOMAS A., STEWART LLOYD, and AARON WIEGAND. "Use of local ecological knowledge in the management of algal blooms." Environmental Conservation 37, no. 2 (May 14, 2010): 210–21. http://dx.doi.org/10.1017/s0376892910000305.
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SUMMARYMore frequent and severe algal blooms are symptomatic of increasing ecosystem stress in coastal waters. Economic losses typically follow and local governments are forced to ‘manage’ this issue. Because many blooms are not monitored, local ecological knowledge (LEK) and oral history are the only practical tools to obtain data on bloom characteristics and identify their drivers. LEK was applied to outbreaks of brown algae on popular tourist beaches in south-east Queensland (Australia). Structured interviews with local citizens who had a close and frequent connection with the ocean provided 541 bloom records, which showed that blooms are regional (≥400 km) rather than local, and that they are a historical (≥40 years) rather than a recent phenomenon. LEK frequently cited that particular wind regimes coincided with the arrival of blooms, but this could not be verified by statistical cross-validation with empirical data. Harnessing LEK was valuable in engaging citizens, in generating testable hypotheses about plume causes, in providing a previously unrecognized historical perspective and in identifying the correct spatial scale of the issue. Multi-pronged approaches will be most effective in addressing blooms where local mitigation actions are combined with broader regional coastal environmental conservation efforts.
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Grogan, Amy E., Catharina Alves-de-Souza, Lawrence B. Cahoon, and Michael A. Mallin. "Harmful Algal Blooms: A Prolific Issue in Urban Stormwater Ponds." Water 15, no. 13 (July 1, 2023): 2436. http://dx.doi.org/10.3390/w15132436.
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Nutrient-driven cyanobacteria blooms are an increasingly common issue in freshwater environments, particularly in anthropogenically altered landscapes. As stormwater runoff is one of the largest sources of nutrients for freshwater bodies, stormwater retention ponds in urban and suburban areas are likely environments for harmful cyanobacteria blooms and were thus targeted for an in-depth investigation assessing taxonomic composition, bloom morphological composition, toxicity, and impact of nutrients and other environmental drivers. Eighty-seven algal blooms were sampled from 2019 to 2022 in the greater Wilmington, North Carolina, area. Physicochemical parameters were recorded, and blooms were classified by type (defined as surface mat, surface scum, water column distribution, or benthic mat) and dominant taxa. Blooms of potentially toxic cyanobacteria genera in the water column of stormwater retention ponds were most prevalent. Dissolved inorganic phosphorus was significantly related to chlorophyll-α, Microcystis bloom formation, and the production of microcystin. Seventeen potentially toxic cyanobacteria genera were identified in retention ponds, some of whose blooms demonstrated detectable microcystin. Monoclonal cultures isolated from some blooms were found to produce anabaenopeptin and saxitoxin. The results demonstrate a higher incidence of potentially toxic cyanobacteria over other bloom-forming taxa (chlorophytes, euglenoids, chrysophytes, dinoflagellates, and diatoms) in the 39 water bodies sampled. The frequency of blooms occurring in stormwater ponds and the diversity of potentially toxic cyanobacteria identified suggest such harmful blooms are likely widespread in similar freshwater environments across multiple urbanizing areas. The blooms sampled in this study were all within residential, commercial, or recreational areas easily accessible to people, presenting serious hazards to both environmental and public health.
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Bloetscher, Frederick, Daniel E. Meeroff, Matthew Iles, and Rohan Sethi. "Effects of Electromagnetic Hydrolysis on Algal Concentration in Warm, Stagnant Surface Water." Current Environmental Management 6, no. 3 (January 7, 2020): 220–34. http://dx.doi.org/10.2174/2212717806666191023112829.
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Background:: The study was conducted in-situ at the INCA Pond system in the City of Boynton Beach, Florida which has experienced issues with the formation of harmful algae blooms that create nuisance complaints and unhealthy conditions in the water and surrounding area. Objective:: The EMOH device is designed to supersaturate water as a means to deter harmful algal blooms. This pilot study was conducted to determine if an Electromagnetic Hydrolysis (EMOH) device can improve the health of residential surface water by adding dissolved oxygen to the water to allow the existing bacteria to remove the substrate that provides a food source for blue-green algae outbreaks when combined with naturally occurring aerobic bacteria. Methods:: Combining the EMOH device with naturally occurring aerobic bacteria demonstrated that a pond that normally contained a low DO and copious amounts of algae, would contain fewer algal blooms, that the bacteria would consume the detrital layer on the bottom of the pond that acts as nutrient source for the algae and that DO levels increase with temperature, in contrast to expectations. Four configurations were compared. Results: : The EMOH device successfully reduced the detrital layer on the bottom of the pond and experienced fewer algal blooms. The use of surface aeration permits the oxygen to escape, so having the EMOH discharge below the surface increases efficiency. Conclusion:: The EMOH device successfully accomplished its intended goals.