Management Of Algal Blooms Research Articles

Control of Vibrio vulnificus proliferation in the Baltic Sea through eutrophication and algal bloom management

Due to climate change the pathogenic bacterium Vibrio vulnificus proliferates along brackish coastlines, posing risks to public health, tourism, and aquaculture. Here we investigated previously suggested regulation measures to reduce the prevalence of V. vulnificus, locally through seagrass and regionally through the reduction of eutrophication and consequential formation of algal blooms. Field samples collected in the summer of 2021 covered the salinity and eutrophication gradients of the Baltic Sea, one of the largest brackish areas worldwide. Physico-, biological- and hydrochemical parameters were measured and variables explaining V. vulnificus occurrence were identified by machine learning. The best V. vulnificus predictors were eutrophication-related features, such as particulate organic carbon and nitrogen, as well as occurrence of potential phytoplankton blooms and associated species. V. vulnificus abundance did not vary significantly between vegetated and non-vegetated areas. Thus, reducing nutrient inputs could be an effective method to control V. vulnificus populations in eutrophied brackish coasts.

Cyanobacterial bloom affects structural and functional succession of microbial communities in urban wetland sediments in the Qinghai Plateau

Abstract Algal blooms significantly affect microbial communities in wetland ecosystems. However, little is known about the succession of sediment microbial communities during algal blooms. This study aimed to investigate the temporal patterns of sediment bacterial community structure and function succession during algal blooms (March to May 2022) with high-throughput sequencing technology. To this end, algal blooms were divided into the bloom stage (BS), decomposition stage (DS) and end stage (ES). The results showed that: (i) The algal blooms were dominated by Microcystis species within Cyanobacteria. Both phytoplankton abundance and biomass reached the peak in the BS, with 45.78 × 105 cells/L and 5.97 mg/L, respectively. (ii) The alpha diversity indices of the sediment bacterial community showed a monotonically increasing trend, with the lowest value in the BS. Cyanobacterial blooms reduced the relative abundance of Actinobacteria and Chloroflexi, and raised that of Firmicutes, Proteobacteria and Bacteroidetes. (iii) Cyanobacterial blooms remarkably decreased the complexity of the co-occurrence network of the sediment bacterial community, which resisted extreme environmental conditions in the BS and DS by increasing positive relationships. (iv) Mantel test showed that phytoplankton biomass, T, dissolved oxygen and organic carbon had significant effects on the sediment bacterial community. The results of this study are important for the ecological management of algal blooms through microorganisms.

Automatic Group Decision-Making for Algal Bloom Management Based on Information Self-Learning

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.

Algae development in rivers with artificially constructed weirs: Dominant influence of discharge over temperature

Algal blooms contribute to water quality degradation, unpleasant odors, taste issues, and the presence of harmful substances in artificially constructed weirs. Mitigating these adverse effects through effective algal bloom management requires identifying the contributing factors and predicting algal concentrations. This study focused on the upstream region of the Seungchon Weir in Korea, which is characterized by elevated levels of total nitrogen and phosphorus due to a significant influx of water from a sewage treatment plant. We employed four distinct machine learning models to predict chlorophyll-a (Chl-a) concentrations and identified the influential variables linked to local algal bloom events. The gradient boosting model enabled an in-depth exploration of the intricate relationships between algal occurrence and water quality parameters, enabling accurate identification of the causal factors. The models identified the discharge flow rate (D-Flow) and water temperature as the primary determinants of Chl-a levels, with feature importance values of 0.236 and 0.212, respectively. Enhanced model precision was achieved by utilizing daily average D-Flow values, with model accuracy and significance of the D-Flow amplifying as the temporal span of daily averaging increased. Elevated Chl-a concentrations correlated with diminished D-Flow and temperature, highlighting the pivotal role of D-Flow in regulating Chl-a concentration. This trend can be attributed to the constrained discharge of the Seungchon Weir during winter. Calculating the requisite D-Flow to maintain a desirable Chl-a concentration of up to 20 mg/m3 across varying temperatures revealed an escalating demand for D-Flow with rising temperatures. Specific D-Flow ranges, corresponding to each season and temperature condition, were identified as particularly influential on Chl-a concentration. Thus, optimizing Chl-a reduction can be achieved by strategically increasing D-Flow within these specified ranges for each season and temperature variation. This study highlights the importance of maintaining sufficient D-Flow levels to mitigate algal proliferation within river systems featuring weirs.

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies.

Improvement of Algal Bloom Identification Using Satellite Images by the Algal Spatial Monitoring and Machine Learning Analysis in a New Dam Reservoir

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.

Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model

The timeliness of monitoring is essential to algal bloom management. However, acquiring algal bio-indicators can be time-consuming and laborious, and bloom biomass data often contain a large proportion of extreme values limiting the predictive models. Therefore, to predict algal blooms from readily water quality parameters (i.e. dissolved oxygen, pH, etc), and to provide a novel solution to the modeling challenges raised by the extremely distributed biomass data, a Bayesian scale-mixture of skew-normal (SMSN) model was proposed. In this study, our SMSN model accurately predicted over-dispersed biomass variations with skewed distributions in both rivers and lakes (in-sample and out-of-sample prediction R2 ranged from 0.533 to 0.706 and 0.412 to 0.742, respectively). Moreover, we successfully achieve a probabilistic assessment of algal blooms with the Bayesian framework (accuracy >0.77 and macro-F 1 score >0.72), which robustly decreased the classic point-prediction-based inaccuracy by up to 34%. This work presented a promising Bayesian SMSN modeling technique, allowing for real-time prediction of algal biomass variations and in-situ probabilistic assessment of algal bloom.

Co-occurrence of pectenotoxins and Dinophysis miles in an Indonesian semi-enclosed bay

The study aims to unravel the variability of Dinophysis spp. and their alleged toxins in conjunction with environmental drivers in Ambon Bay. Phytoplankton samples, lipophilic toxins and physiochemical water properties were analysed during a 1.5-year period. Three Dinophysis species (D. miles, D. caudata, and D. acuminata) were found in plankton samples, of which D. miles was the most abundant and persistently occurring species. Pectenotoxin-2 (PTX2) and its secoacid (PTX2sa) were detected throughout, and PTX2sa levels strongly correlated with D. miles cell abundance. The toxin showed a positive correlation with temperature, which may suggest that D. miles cells contain rather constant PTX2sa during warmer months. Dissolved nitrate concentrations were found to play a major role in regulating cell abundances and toxin levels. This study adds adequate information regarding marine biotoxins and potentially toxic species for future Harmful Algal Bloom management in Ambon and Indonesia at large.

Effects of ultrasound on the physiological characteristics and competitive growth between Microcystis aeruginosa and Chlorella pyrenoidosa

Microcystis aeruginosa is a common bloom-forming cyanobacterium, which generally coexists and competes with Chlorella pyrenoidosa in lakes. Sonication can be used for emergency management of algal blooms. Ultrasound influences algal growth and physiological parameters, as well as interspecific competition in algal community. To explore the effects of ultrasonic stress (35 kHz, 0.035 W·cm-3) on physiological characteristics and interspecific competition of algae, M. aeruginosa and C. pyrenoidosa were sonicated in mono- and co-cultures (1:1 mixture, according to cell concentration). Results showed that M. aeruginosa was more sensitive to ultrasonic stress. After the sonication for 600 s, both photosynthetic activity (Fv/Fm) and esterase activity of M. aeruginosa showed significant changes, with Fv/Fm values in mono- and co-cultures being decreased by 51.8% and 64.7%, respectively. In comparison, Fv/Fm values of C. pyrenoidosa changed slightly. M. aeruginosa released more chromophoric dissolved organic matter (CDOM, including tryptophan-, tyrosine-, and fulvic-like substances) than C. pyrenoidosa. The cell concentration of C. pyrenoidosa showed little changes regardless of sonication time, while the cell concentration of M. aeruginosa decreased at different degrees. The cell concentration of M. aeruginosa in co-cultures decreased by 42.6% after sonication for 600 s, which might be responsible for the dominance of C. pyrenoidosa during 8 days after sonication. M. aeruginosa inhibited C. pyrenoidosa in other treatments, but mutual inhibition appeared in the 600 s sonication treatment. After ultrasonic treatment, the activity of M. aeruginosa could recover gradually. The treatment should be conducted again within a week to improve the persistence of algal control.

Thirty-Four-Year Record (1987–2021) of the Spatiotemporal Dynamics of Algal Blooms in Lake Dianchi from Multi-Source Remote Sensing Insights

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.

Single-variable method for predicting trends in chlorophyll a concentration based on the similarity of time series

Algal blooms are increasing in global water bodies, which threatens social development. Predicting the chlorophyll a (Chla) concentration is useful for water environmental management. Most previous studies of Chla prediction required multiple variables and considerable computation. Therefore, a similarity-based prediction algorithm (SPA) was developed for this prediction. The mean relative error (MRE) and dynamic time warping (DTW) algorithms are similarity measures used to create SPAs (SPA-MRE, SPA-DTW and SPA-MRE&DTW).Model validation was performed at the Yuqiao Reservoir north of Tianjin, China. The Chla concentration dataset of the Yuqiao Reservoir contains hourly data for 745 days from 2017 to 2020. According to the model validation results, SPA methods performed better than traditional time-series analysis methods (ARIMA (−9.7%) and Holt-Winters (−21.2%)). The MRE predicted by the SPA method at 24 h was 0.187.These three SPA methods showed different characteristics and applicability. SPA-MRE was suitable for predicting low Chla concentrations. SPA-DTW was suitable for predicting high Chla concentrations with obvious fluctuations. SPA-MRE combined with DTW exhibited the best comprehensive performance. Moreover, the SPA maintained high robustness and reliability even in the absence of 9% of the data.SPA is a single-variable prediction method that is more cost-effective than traditional regression-based and mechanism-based methods. SPA can be used with adjacent data for forecasting as in traditional time-series analysis models, and time series similar to the current series can be retrieved from historical data and used for predictions based on similarity. This study provides a robust and reliable prediction method for algal bloom management. SPA can be used to deeply mine characteristic information from historical data.

Water quality, phytoplankton composition and microcystin concentrations in Kisumu Bay (Kenya) of Lake Victoria after a prolonged water hyacinth infestation period

AbstractThe production of phytoplankton (algal) toxins and their control is of concern because of the need to reduce their negative impacts on water quality and facilitate effective management of algal blooms. The present study was conducted between September 2017 to May 2018, focusing on Kisumu Bay in the Kenyan portion of Lake Victoria, in order to establish the magnitude of potential impacts on phytoplankton composition and microcystin following a prolonged presence of water hyacinth coverage between 2013 and 2018 within the gulf, with an estimated coverage range varying between 644 and 1224 ha. Triplicate samples of physico‐chemical parameters, nutrients, phytoplankton, chlorophyll‐a and algal toxins (N = 88) were collected at eleven sampling sites to determine their spatio‐temporal variability. The main identified algal taxa comprised Cyanophyceae, Bacillariophyceae, Chlorophyceae, Euglenophyceae, Zygnematophyceae and Dinophyceae. The most dominant algal species were Microcystis aeruginosa (25%), Merismopedia spp. (23%) and Anabaena flos‐aquae (16%). Enzyme‐linked immunosorbent assay (ELISA) technique was used to determine microcystin (MC) toxins in the water. Mean MC‐LR and MC‐YR concentrations were significantly correlated (R2 = 0.972), exceeding WHO standards at three sampling sites (Coca Cola, 2.84 ± 4.76; Kisumu pier, 1.78 ± 1.87; Midpoint, 1.44 ± 2.71 μg/L MC–LR). There were significant temporal variations (p < .05) in the SRP, TN, NO3‐N, NO2‐N, NH4‐N, SiO2‐Si, MC‐LR, MC‐YR, dissolved oxygen (DO), total dissolved and suspended solids (TDS; TSS), turbidity, electrical conductivity, Secchi depth, temperature and pH levels. The water depth, TP and DO also vary spatially. The nitrogen to phosphorus concentration ratios differed from the expected N:P ratio of 16:1, indicating a highly eutrophic status. The disproportionate ratio of total phosphorus and total nitrogen in the bay may be responsible for the enhanced cyanobacterial blooms it exhibits. The results of the present study provide useful information and data for formulating regulations for water quality management.

Hydrological management strategies for the control of algal blooms in regulated lowland rivers

AbstractReservoirs of lowland floodplain rivers with eutrophic backgrounds cause variations in the hydrological and hydraulic conditions of estuaries and low‐dam reservoir areas, which can promote planktonic algae to proliferate and algal bloom outbreaks. Understanding the ecological effects of variations in hydrological and hydraulic processes in lowland rivers is important for algal bloom control. In this study, the middle and lower reaches of the Han River, China, a typical regulated lowland river with a eutrophic background, are selected. Based on the effect of hydrological and hydraulic variability on algal blooms, a hydrological management strategy for river algal bloom control is proposed. The results showed that (a) differences in river morphology and background nutrient levels cause significant differences in the critical threshold flow velocities for algal bloom outbreaks between natural river and low‐dam reservoir sections; there is no uniform threshold flow velocity for algal bloom control. (b) There are significant differences in the river hydrological/hydraulic conditions between years with and without algal blooms. The average river flow, water level and velocity in years with algal blooms are significantly lower than those in years without algal blooms. (c) For different river sections where algal blooms occur and to meet the threshold flow velocities, the joint operation of cascade reservoirs and diversion projects is an effective method to prevent and control algal blooms in regulated lowland rivers. This study is expected to deepen our understanding of the ecological significance of special hydrological processes and guide algal bloom management in regulated lowland rivers.

Response of Rice Algal Assemblage to Fertilizer and Chemical Application: Implications for Early Algal Bloom Management

California water-seeded rice is challenged with the rapid growth of nuisance algae at the beginning of the season. Rice seedlings entangled in the algal mat may not be established, causing empty rice patches in the field. Two separate studies were conducted to (1) evaluate the effect of nutrients (nitrogen and phosphorous) on algae growth, and (2) test various chemical options to control algae. Both studies utilized 19 L buckets inoculated with algae collected from a rice field. In the nutrient evaluation study, 36 nutrient treatments obtained from a combination of nitrogen (0, 60, 120, 180, 240 and 300 kg ha−1) and phosphorous (0, 20, 40, 60, 80 and 100 kg ha−1) rates were applied into the buckets, while eight chemicals with potential for algae control (two Protox inhibitor herbicides, four copper-based compounds, hydrogen peroxide, Zinc sulfate) were tested in the second experiment. In addition, a yeast extract-based surfactant (AMP activator) was tested in combination with a chelated copper formulation (Algimycin) and hydrogen peroxide. The studies had a completely randomized design with three replicates and each study was repeated two times. The result from the nutrient evaluation study showed that nitrogen and phosphorus can independently cause change in algae growth as reflected in the fresh and dry biomass. Moreover, low rates of either nitrogen or phosphorus resulted in a rapid increase in algae biomass and water chlorophyll a content, whereas the growth of algae declined at higher rates of applied fertilizer. Among tested chemicals in the second experiment, the chelated formulation of copper (Algimycin PWF) controlled algae (85%) better than the ethanolamine formulations [Cutrine-Ultra (70%) and Cutrine-Plus (52%)] and elemental copper (crystalline copper sulfate) (75%). Protox herbicides (oxyfluorfen and oxadiazon) were able to control algae by up to 70%. Hydrogen peroxide controlled algae by 50% and its efficacy dropped dramatically five days after treatment (DAT). Zinc sulfate was only effective at 1 DAT and algae recovered rapidly after that. Combining fertilizer management practices with chemical options will enable us toward an integrative approach for early algal bloom in the rice cropping system.

Magnetic Fe3O4 nanoparticles enhance cyanobactericidal effect of allelopathic p-hydroxybenzoic acid on Microcystis aeruginosa by enhancing hydroxyl radical production

Recently, considerable progress has been made in the environmental application of nanotechnology. However, little is known about how nanomaterials might affect the cyanobacterial suppression potential of allelochemicals. In this study, a microcosm was employed to simulate and verify the effect of magnetic Fe3O4 nanoparticles (MFN) on the inhibitory influence of allelopathic hydroxybenzoic acid (p-Ha) on bloom-forming Microcystis aeruginosa. MFN had a hormetic effect on cyanobacterial growth. At a neutral concentration of 182 mg/L, MFN enhanced the algal suppression by p-Ha and decreased the IC50 by half, which was significantly and positively associated with the amount of OH. Furthermore, adding MFN induced a stronger physiological response than treatment with only p-Ha. The cellular integrity was severely disrupted for the cyanobacterium M. aeruginosa. The total protein content decreased rapidly to inactivate the algae by limiting the amounts of extracellular microcystin and polysaccharide released. The modification of the effect of p-Ha by MFN was reflected by the intracellular NO content of M. aeruginosa. In addition, the typical radical scavengers ascorbic acid and 5,5-dimethyl-1-pyrroline N-oxide decreased OH production to weaken algal suppression under the combined treatment with p-Ha and MFN. By contrast, the addition of Fe3+ and increasing the light intensity triggered the generation of OH and strong cyanobacterial suppression. Thus, MFN could enhance the cyanobacterial control efficiency of p-Ha and decrease the input of allelochemicals in the field. These findings suggest a novel mode of allelochemical modification by nanomaterials as a promising cyanobactericide for harmful algal bloom management.

Screening of Chilean fish-killing microalgae using a gill cell-based assay

Fish-killing algal species are responsible for important global economic losses to the finfish industry. Chile is the world’s second-largest salmon exporter, and fish-killing algal blooms have widely impacted its production in the last decades. The lack of standardized analytical methods to quantify and characterize the so-called “ichthyotoxins” has hindered our understanding of the underlying ichthyotoxic modes of action. The novel application of a highly sensitive and reproducible fish RTgill-W1 cell line-based assay has allowed significant progress in the field. In this study, the ichthyotoxic potency of the main microalgae species, which has been reported in fish-killing events in the historical Chilean monitoring programs, was assessed. The dinoflagellate Karenia selliformis was the most ichthyotoxic species against the RTgill-W1 (cell viability down to 8%), representing the major threat for the local salmon industry. In comparison, the raphidophyte Heterosigma akashiwo and the dinoflagellate Prorocentrum micans were the least toxic (gill cell viability down to 81 and 89%, respectively). Importantly, ichthyotoxic flagellates were more toxic to fish gill cells upon rupture. These results have important implications for the mitigation and management of algal blooms by the salmon industry.

An overview of management and monitoring of harmful algal blooms in the northern part of the Persian Gulf and Oman Sea (Hormuzgan Province).

Algal bloom as a common phenomenon in the Persian Gulf and the Sea of Oman had catastrophic effects on environmental, social, economic, and human health aspects from 2008 to 2009. Since 2008, the Persian Gulf and the Sea of Oman Ecological Research Institute (PGSOERI) has monitored and managed algal blooms in the Hormuzgan coast along the northern Persian Gulf and the Sea of Oman. Management strategies have included regular monitoring of chlorophyll, water quality, and remote sensing. In this regard, relevant departments and the Regional Organization for Protection of Marine Environment (ROPME) collaborated with each other to prevent and forecast algal blooms. We reviewed historical and current monitoring, mitigation, and management systems of algal blooms in the Hormuzgan coast. In addition, complications and challenges of algal bloom monitoring and management were also discussed. Documenting algal bloom monitoring and research, improving forecasting and modeling of blooms, educating the public and fishermen, developing a cooperative monitoring framework, and controlling pollution input entering the ROPME region are the main challenges of algal bloom management in the Hormuzgan coast.

Algal Morphological Identification in Watersheds for Drinking Water Supply Using Neural Architecture Search for Convolutional Neural Network

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.

Phenology of Phytoplankton Blooms in a Trophic Lake Observed from Long-Term MODIS Data

Phytoplankton phenology critically affects elements biogeochemical cycles, ecosystem structure, and productivity. However, our understanding about the phenological process and driving mechanism is still very limited due to the shortage of long-term observation data. We used all available daily MODIS-Aqua data from 2003 to 2017 to determine bloom start dates (BSDs) in a typical trophic lake (Lake Taihu) and investigate how phytoplankton BSDs respond to climate change and environmental factors. The results indicate that BSDs have advanced 29.9 days for the entire Lake Taihu from 2003 to 2017. Spatially, an earlier phytoplankton bloom was recorded in the northern bays and the littoral regions than in the center of open water. Air temperature, wind speed, and N/P ratio (N, total nitrogen; P, total phosphorus) were three important factors affecting phytoplankton phenology. Multiple linear correlation showed that air temperature, wind speed, and N/P ratio in Spring could explain 59.9% variability of BSDs for Lake Taihu. This study provides a quantitative assessment of phytoplankton phenological shifts and elucidates the inter-relationship between phenology parameters and environmental factors, thus improving our understanding on the potential impact of climate change and eutrophication on lake ecosystems. The starting earlier and lasting longer of phytoplankton are consistent with the expected effects of climate warming on aquatic ecosystem in recent decades, which will bring new challenges for algal bloom management in eutrophic Lake Taihu.

Landsat TM/ETM+ and machine-learning algorithms for limnological studies and algal bloom management of inland lakes

Accumulating remotely sensed and ground-measured data and improvements in data mining such as machine-learning techniques open new opportunities for monitoring and managing algal blooms over large spatial scales. The goal of this study was to test the accuracy of remotely sensed algal biomass determined with machine-learning algorithms and Landsat TM/ETM+ imagery. We used chlorophyll-a concentration data from the 2007 National Lake Assessment (NLA) (lake N = 1157) by the US Environmental Protection Agency to train and test Landsat TM/ETM+ algorithms. Results showed significant improvements in chlorophyll-a retrieval accuracy using machine-learning algorithms compared with traditional empirical models using linear regression. Specifically, the results from boosted regression trees and random forest explained, respectively, 45.8% and 44.5% of chlorophyll-a variation. Multiple linear regression could only explain 39.8% of chlorophyll-a variation. The chlorophyll-a concentration derived from Landsat TM/ETM+ and a simple to use Google Earth Engine application, accurately characterized a 2009 algal bloom in western Lake Erie to show the model worked well for the analysis of temporal changes in algal conditions. Compared with chlorophyll-a data from the NLA, chlorophyll-a measurements with our Landsat TM/ETM+ model had almost the same correlation with lake’s total phosphorus concentrations, especially when using multiple Landsat images. Therefore, Landsat measurements of chlorophyll-a have value for ecological assessments and managing algal problems in lakes.