Monitoring Of Harmful Algal Blooms Research Articles

Biodiversity and Hemolytic Toxicity of the Genus Heterocapsa (Dinophyceae) in the Beibu Gulf, China

The dinoflagellate genus Heterocapsa includes several widely distributed and potentially toxic species associated with Harmful Algal Blooms (HABs), particularly affecting the Western Pacific Ocean. To reveal the biodiversity of Heterocapsa in Beibu Gulf, six strains were morphologically characterized using light and scanning electron microscopy, while large subunit rDNA (LSU rDNA) and internal transcribed spacer (ITS) were sequenced for phylogenetic analysis through maximum likelihood and Bayesian inferences. Two strains (BGERL169, BGERL170) were identified as Heterocapsa philippinensis ribotype I, three (BGERL171-BGERL173) as a new Heterocapsa philippinensis ribotype II, and one strain (BGERL174) as Heterocapsa pseudotriquetra. Cells of H. philippinensis were ovoid to spherical, yellowish-brown, with reticulate chloroplasts, and had a sausage-shaped nucleus positioned longitudinally along the dorsal side of the cell, and the theca was arranged in Po, cp, X, 5′, 3a, 7″, 6c, 5s, 5‴, 2⁗. Additionally, BGERL169 and BGERL171 showed no hemolytic toxicity in rabbit erythrocyte lysis assays. To the best of our knowledge, this research provides the first morphological and phylogenetic analysis of H. philippinensis, including the identification of a new ribotype, as well as the discovery of H. pseudotriquetra in Chinese waters. The findings contribute to the understanding of Heterocapsa species biogeography and toxicity in Chinese waters, offering valuable data for future HAB monitoring in Beibu Gulf.

Expert insights on managing harmful algal blooms

Freshwater harmful algal blooms (HABs) are a global environmental, economic, social, and public health hazard. While there is an increasing understanding of the ecological considerations of HABs, there is limited understanding of human dimensions and management needs. We conducted semi-structured key informant interviews with 28 water managers and researchers to better understand how they perceive current management and to identify future management priorities in regard to HABs. For this study, we interviewed 31 key informants from three regions of the United States (New England, Ohio, and the Mountain West). We solicited insights across aspects of HABs management, including prevention, forecasting, monitoring, response, and communication. Nutrient management was the main consideration for the prevention of HAB events. Key informants noted that forecasting has the potential to be a valuable tool in the future but is not yet accurate enough at a local scale for widespread use. Monitoring was found to be implemented in varying ways across and even within the states and regions, with a need for more funding and standardization. HAB event responses vary greatly from chemical and physical treatments that suppress toxins to no-swim advisories, all with a mix of strengths and weaknesses. Finally, an increase in and improvement of communication efforts was identified as critical for reducing public health risks. These findings provide perceptions of current management practices and future plans while including opportunities to improve current freshwater HAB management efforts.

Effects of Harmful Algal Blooms on Amphibians and Reptiles are Under-Reported and Under-Represented.

Harmful algal blooms (HABs) are a persistent and increasing problem globally, yet we still have limited knowledge about how they affect wildlife. Although semi-aquatic and aquatic amphibians and reptiles have experienced large declines and occupy environments where HABs are increasingly problematic, their vulnerability to HABs remains unclear. To inform monitoring, management, and future research, we conducted a literature review, synthesized the studies, and report on the mortality events describing effects of cyanotoxins from HABs on freshwater herpetofauna. Our review identified 37 unique studies and 71 endpoints (no-observed-effect and lowest-observed-effect concentrations) involving 11 amphibian and 3 reptile species worldwide. Responses varied widely among studies, species, and exposure concentrations used in experiments. Concentrations causing lethal and sublethal effects in laboratory experiments were generally 1 to 100 µg/L, which contains the mean value of reported HAB events but is 70 times less than the maximum cyanotoxin concentrations reported in the environment. However, one species of amphibian was tolerant to concentrations of 10,000 µg/L, demonstrating potentially immense differences in sensitivities. Most studies focused on microcystin-LR (MC-LR), which can increase systemic inflammation and harm the digestive system, reproductive organs, liver, kidneys, and development. The few studies on other cyanotoxins illustrated that effects resembled those of MC-LR at similar concentrations, but more research is needed to describe effects of other cyanotoxins and mixtures of cyanotoxins that commonly occur in the environment. All experimental studies were on larval and adult amphibians; there were no such studies on reptiles. Experimental work with reptiles and adult amphibians is needed to clarify thresholds of tolerance. Only nine mortality events were reported, mostly for reptiles. Given that amphibians likely decay faster than reptiles, which have tissues that resist decomposition, mass amphibian mortality events from HABs have likely been under-reported. We propose that future efforts should be focused on seven major areas, to enhance our understanding of effects and monitoring of HABs on herpetofauna that fill important roles in freshwater and terrestrial environments. Environ Toxicol Chem 2024;43:1936-1949. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Geomorphological controls on estuary hydrodynamics with implications for diatom blooms in deglaciated coastal areas

Understanding local hydraulic conditions is imperative to coastal harmful algal bloom (HAB) monitoring. The research summarized herein describes how the locations and tidal phases selected for coastal hazard sampling can influence measurement results used to guide management decisions for HABs. Our study was conducted in Frenchman Bay, Maine, known for its complex deglaciated coastline, strong tidal influence, and shellfishing activities that are susceptible to problematic HABs such as those produced by some species (spp.) of the diatom genus Pseudo-nitzschia. In-situ measurements of current velocity, density, and turbulence collected over a semidiurnal tidal cycle and a companion numerical model simulation of the study area provide concurrent evidence of two adjacent counter-rotating sub-mesoscale eddies (2–4 km diameter) that persist in the depth-averaged residual circulation. The eddies are generated in the wake of several islands in an area with abrupt bathymetric gradients, both legacy conditions partly derived from deglaciation ∼15 kya. Increased concentrations of Pseudo-nitzschia spp. measured during the semidiurnal survey follow a trend of elevated turbulent dissipation rates near the water surface, indicating that surface sampling alone might not adequately indicate species abundance. Additional measurements of Pseudo-nitzschia spp. from two years of weekly sampling in the region show that algal cell abundance is highest where residual eddies form. These findings provide incentive to examine current practices of HAB monitoring and management by linking coastal geomorphology to hydraulic conditions influencing HAB sampling outcomes, coastal morphometric features to material accumulation hotspots, and millennial time scales to modern hydraulic conditions.

An investigation of cyanobacteria, cyanotoxins and environmental variables in selected drinking water treatment plants in New Jersey

Harmful Algal Blooms (HAB) have the potential to impact human health primarily through their possible cyanotoxins production. While conventional water treatments can result in the removal of unlysed cyanobacterial cells and low levels of cyanotoxins, during severe HAB events, cyanotoxins can break through and can be present in the treated water due to a lack of adequate toxin treatment. The objectives of this study were to assess the HAB conditions in drinking water sources in New Jersey and investigate relationships between environmental variables and cyanobacterial communities in these drinking water sources. Source water samples were collected monthly from May to October 2019 and analyzed for phytoplankton and cyanobacterial cell densities, microcystins, cylindrospermopsin, Microcystis 16S rRNA gene, microcystin-producing mcyB gene, Raphidiopsis raciborskii-specific rpoC1 gene, and cylindrospermopsin-producing pks gene. Water quality parameters included water temperature, pH, dissolved oxygen, specific conductance, fluorescence of phycocyanin and chlorophyll, chlorophyll-a, total suspended solids, total dissolved solids, dissolved organic carbon, total nitrogen, ammonia, and total phosphorus. In addition to source waters, microcystins and cylindrospermopsin were analyzed for treated waters. The results showed all five selected New Jersey source waters had high total phosphorus concentrations that exceeded the established New Jersey Surface Water Quality Standards for lakes and rivers. Commonly found cyanobacteria were identified, such as Microcystis and Dolichospermum. Site E was the site most susceptible to HABs with significantly greater HAB variables, such as extracted phycocyanin, fluorescence of phycocyanin, cyanobacterial cell density, microcystins, and Microcystis 16S rRNA gene. All treated waters were undetected with microcystins, indicating treatment processes were effective at removing toxins from source waters. Results also showed that phycocyanin values had a significantly positive relationship with microcystin concentration, copies of Microcystis 16S rRNA and microcystin-producing mcyB genes, suggesting these values can be used as a proxy for HAB monitoring. This study suggests that drinking water sources in New Jersey are vulnerable to forthcoming HAB. Monitoring and management of source waters is crucial to help safeguard public health.

Autonomous UAV-Mounted LoRaWAN System for Real-Time Monitoring of Harmful Algal Blooms (HABs) and Water Quality

Autonomous UAV-Mounted LoRaWAN System for Real-Time Monitoring of Harmful Algal Blooms (HABs) and Water Quality

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

Flow Imaging Microscopy for Harmful Algal Bloom Monitoring

Key TakeawaysFlow imaging microscopy (FIM) combines a flow cytometer's fluidic architecture with a microscope's spatial resolution to allow morphometric analysis of objects in suspensions such as water.FIM's real‐time analysis and identification of potential cyanotoxin producers help utilities understand population dynamics to maintain healthier source waters and reduce response times when a bloom is indicated.Case studies show FIM's advantages and limitations in harmful algal bloom monitoring programs for reservoir management and water treatment.

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Contamination Status and Acute Dietary Exposure Assessment of Paralytic Shellfish Toxins in Shellfish in the Dalian Area of the Yellow-Bohai Sea, China

The Yellow-Bohai Sea is an important semi-enclosed continental shelf marginal seas with an intensive aquaculture industry in China. The current study analyzed the contamination status and the time variations of paralytic shellfish toxins (PSTs) in shellfish between 2019 and 2020 from the Yellow-Bohai Sea in the Dalian area and estimated the acute health risks to consumers in China. A total of 199 shellfish samples (including 34 Pacific oysters, 25 Mediterranean blue mussels, 34 Manila clams, 36 bay scallops, 34 veined rapa whelks and 36 bloody clams) were analyzed from four representative aquaculture zones around the Yellow-Bohai Sea in Dalian. Among the samples, scallops and blood clams were the shellfish species with the highest detection rate of PSTs (94.4%), and the highest level of PSTs was detected in scallops with 3953.5 μg STX.2HCl eq./kg (μg STX.2HCL equivalents per kg shellfish tissue), followed by blood clams with 993.4 μg STX.2HCl eq./kg. The contents of PSTs in shellfish showed a time variation trend, and autumn was the season of concern for PST contamination in Dalian. For general Chinese consumers, the probability of acute health risks to shellfish consumers from dietary exposure to PSTs was around 13%. For typical consumers in coastal areas of China, especially those with higher shellfish intake, there was an acute health risk associated with exposure to PSTs through shellfish consumption during the occurrence of harmful algal blooms. It is suggested that the government continue to strengthen the monitoring of the source of PSTs and the monitoring of harmful algal blooms and give reasonable advice on shellfish consumption for consumers in coastal areas, such as not eating scallop viscera.

Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models

Highlights The monitoring of HABs can be improved using ML models for chlorophyll-a prediction. ML model selection for HABs monitoring depends on target objectives. Random forest model predicts chlorophyll-a better when the temporal dimension is not considered. The LSTM model is essential for making time-dependent chlorophyll-a predictions for HABs monitoring. Abstract. The complex dynamics of freshwater harmful algal blooms (HABs) necessitate proactive monitoring approaches to mitigate their impacts. The rapid breakthrough in computing prowess and statistical advances is triggering the development of data-driven techniques such as machine learning (ML) models, which have been shown in different fields to be instrumental in finding patterns for explaining relationships in observed data. This study assesses the ability of ML models for HABs monitoring in a lake using chlorophyll-a concentration as the index. The selected models for this study were regression tree, random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent unit (GRU) models, with the last two models able to consider the temporal sequence of obtained water quality datasets. The results showed that the RF model with R2, mean absolute error (MAE), and root mean square error (RMSE) of 0.87 µgL-1, 0.97 µgL-1, and 3.53 µgL-1, respectively, outperformed the SVR, MLP, and regression tree models. LSTM model with MAE and RMSE of 2.39 µgL-1 and 3.29 µgL-1, respectively, predicted temporal dynamics of chlorophyll-a better than GRU, although with more runtime, and showed the potential for developing real-time HAB monitoring and early warning systems. The findings reveal the robustness of the chosen ML models, thereby shedding light on crucial factors that necessitate careful deliberation by researchers and policymakers in determining the most suitable approaches for monitoring HABs. Keywords: Cyanobacteria, Early warning systems, Freshwater, HABs, Machine learning models.

Predicting Chlorophyll-a Concentration and Harmful Algal Blooms in Lake Okeechobee Using Time-Series MODIS Satellite Imagery and Long Short-Term Memory

Highlights A 10-year dataset of time-series MODIS imagery and in situ Chl-a concentration were curated for Lake Okeechobee. LSTM significantly outperformed KNN, SVR, and RF for Chl-a prediction and subsequent HAB detection. The optimal window length was found to be 13 days with a 4-day temporal resolution for the LSTM model. KNN, SVR, and RF models were not effective at utilizing the temporal dynamics of the input features. Abstract. Harmful algal blooms (HABs) in inland waterbodies are a global concern due to their negative impact on human, animal, and ecosystem health. Chlorophyll-a (Chl-a) concentration is an important water quality parameter for monitoring HABs. While statistical and machine learning (ML) models have been widely studied to predict Chl-a concentration and HABs based on single-time-point satellite data, this work assessed whether long short-term memory (LSTM) can improve both tasks by leveraging temporal features in time-series MODIS satellite images compared to three classical ML models, including k-nearest neighbor (KNN), support vector regression (SVR), and random forest (RF). A dataset of daily MODIS images and monthly in situ Chl-a concentration measurements from 2011 to 2020 was curated for Lake Okeechobee, Florida. A window size of 13 days with a temporal resolution of four days was found to produce the optimal performance for LSTM, which significantly outperformed KNN, SVR, and RF for Chl-a prediction with a root mean square error of 11.95 µg/L, a mean absolute error of 8.55 µg/L, and a R2 value of 0.43. The superior performance of LSTM for Chl-a prediction was likely due to its ability to leverage the temporal dynamics in the features associated with HAB development. The Chl-a predictions were further used to determine HAB events, showing better accuracy and a significantly higher F1 score for LSTM over the other models. The study suggested that combining LSTM with high-temporal-resolution time-series data should be preferred over applying common ML models on time-series or single-time-point remote sensing data for Chl-a and HAB monitoring. Keywords: Cyanobacteria, LSTM, Machine Learning, Remote Sensing, Water Quality.

A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms.

Harmful algal blooms (HABs) caused by lake eutrophication and climate change have become one of the most serious problems for the global water environment. Timely and comprehensive data on HABs are essential for their scientific management, a need unmet by traditional methods. This study constructed a novel digital twin lake framework (DTLF) aiming to integrate, represent and analyze multi-source monitoring data on HABs and water quality, so as to support the prevention and control of HABs. In this framework, different from traditional research, browser-based front ends were used to execute the video-based HAB monitoring process, and real-time monitoring in the real sense was realized. On this basis, multi-source monitored results of HABs and water quality were integrated and displayed in the constructed DTLF, and information on HABs and water quality can be grasped comprehensively, visualized realistically and analyzed precisely. Experimental results demonstrate the satisfying frequency of video-based HAB monitoring (once per second) and the valuable results of multi-source data integration and analysis for HAB management. This study demonstrated the high value of the constructed DTLF in accurate monitoring and scientific management of HABs in lakes.

Development of a collaborative framework for quantitative monitoring and accumulation prediction of harmful algal blooms in nearshore areas of lakes

Harmful algal blooms (HABs) in lakes have been a salient problem of global water environment due to their harmful impacts. Present situation and future tendency of HABs in nearshore areas are more important than those in other areas of lakes, but traditional researches generally ignored this point. This study developed a novel framework aiming to timely and accurately grasp both present conditions and accumulation risks of HABs in nearshore areas of lakes. By designing a quantitative monitoring method, land-based video monitoring devices around lakes were used to obtain present situation of HABs in nearshore areas rapidly, periodically and automatically. On this basis, a coupled simulation model was established to predict accumulation risks of HABs in nearshore areas using five indexes, i.e., shoreline index, chlorophyll-a index, algal bloom index, wind direction index and dynamic index. Experimental results indicated that the accuracy of HAB monitoring in nearshore areas is quite satisfying (85.20%) and the prediction results of HABs accumulation in nearshore areas is reasonable and credible. This study demonstrated a high value of the developed collaborative framework in emergency prevention and control of HABs in lakes.

Detection of Karenia brevis red tides on the West Florida Shelf using VIIRS observations: Accounting for spatial coherence with artificial intelligence

Harmful algal blooms (HABs) of the toxic dinoflagellate Karenia brevis (K. brevis) occur annually on the West Florida Shelf (WFS). Detection of these blooms using satellite observations often suffers from two problems: lack of accurate algorithms to identify phytoplankton blooms in optically complex waters and patchiness (i.e., heterogeneity) of K. brevis during blooms. Here, using data collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) between 2017 and 2019, we develop a practical approach to overcome these difficulties despite the lack of a chlorophyll-a fluorescence band on VIIRS. The approach is based on artificial intelligence (specifically, a deep-learning (DL) convolutional neural network model), which uses spatial coherence of bloom patches to account for the patchiness of K. brevis concentrations. After proper training, the overall performance (i.e., F1 score) of the deep learning model is 89%. Extracted K. brevis patches were consistent with those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, which has a fluorescence band. Furthermore, the wider swath of VIIRS over MODIS (3040-km versus 2330-km) led to more valid observations of bloom extent, enabling improved near-real-time applications. The results not only demonstrate the capacity of VIIRS in HABs monitoring, but also show the value of the DL model in extracting K. brevis bloom patches for both near real-time applications and retrospective analysis.

The relationship between salmon (Salmo salar) farming and cell abundance of harmful algal taxa

The effects of nutrient effluents from fish cage aquaculture are an important eutrophication concern. It has been proposed that marine fish farm derived nutrients have the potential to increase phytoplankton abundance and lead to intensification of Harmful Algal Blooms (HABs), and that these blooms may negatively impact both the finfish and the shellfish industry. This study addressed this hypothesis using farmed salmon biomass in Scottish marine waters (as a proxy for nutrient load added to the water column as a consequence of fish farming) cell abundance of HAB taxa that most frequently impact shellfish farms and human health in the region (Dinophysis spp., Alexandrium spp. and Pseudo-nitzschia spp.), and cell abundance of one phytoplankton species of particular concern to the salmon farming industry (Karenia mikimotoi). Data from a 15-year weekly HAB monitoring programme and parallel national monitoring data relating to salmon farm stocking biomass were summarised in 5 km per 5 km aggregation boxes. Linear regression models were used to assess (i) inter-annual variation in cell abundance and total annual farmed salmon biomass; (ii) intra-annual (monthly) variation in harmful phytoplankton cell abundance and salmon biomass; (iii) a further analysis included seasonal effects within the intra-annual analysis. Farmed salmon biomass alone had a non-significant effect on cell abundance of any of the studied phytoplankton taxa. In contrast, a significant effect on cell abundance was found when using location, month or season as the predictive variable. Despite the non-significant impact of fish biomass on phytoplankton counts, the relationship varied seasonally, with a different response of Dinophysis spp. indicating a taxa specific interaction. A possible explanation for the lack of a significant relationship between farmed salmon and harmful phytoplankton cell abundance is that aquaculture farms are generally located in hydrodynamically energetic locations where recurrent flushing likely allows efficient dilution of nutrients. Overall, the analyses suggest that current levels of salmon farming activities do not markedly impact the abundance of routinely monitored biotoxin producing or fish killing phytoplankton taxa in Scottish waters.

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

ABSTRACTInland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.

Long-term missing role of small colloids and nanoparticles on the loading and speciation of phosphorus in catfish aquaculture ponds in west Alabama

Increasing loading of phosphorus (P) into freshwater systems is deemed as one of the key drivers triggering harmful algal blooms (HABs). However, conventional water quality monitoring of P normally uses the operational cutoff (e.g., 450-nm filter membrane) to separate particulate and dissolved phases (entities passing through the 450-nm membrane are regarded as dissolved phase), which completely neglects the roles of small colloids (450–100 nm) and nanoparticles (100–1 nm). Herein, a new particle size separation approach was used to separate water samples collected from catfish aquaculture ponds in west Alabama into six size fractions: large particles (>1000 nm), large colloids (1000–450 nm), small colloids (450–100 nm), large nanoparticles (100–50 nm), small nanoparticles (50–1 nm), and the truly dissolved phase (<1 nm). The speciation and concentration of P in these six size fractions were then investigated using Hedley's sequential extraction method. The new particle size separation results showed that particle loading (mass) followed the order: >1000 nm, 450–100 nm, 1000–450 nm, 100–50 nm, and 50–1 nm. This is mainly due to the abundance of large-sized (>1000 nm) zooplankton and phytoplankton such as algae and cyanobacteria in the catfish aquaculture ponds. Importantly, the small colloid (450–100 nm) and nanoparticle (100–1 nm) size fractions, which were previously regarded as the dissolved phase using the 450-nm membrane filtration operation, accounted for ∼41.8% of the total particle mass. The Hedley's sequential extraction results showed that sodium hydroxide (NaOH)-extracted P represented the largest P pool, followed by water (H2O)- and sodium bicarbonate (NaHCO3)-extracted P pools. Smaller particles exhibited a higher loading of P due to their large surface areas. These new findings suggest that the new particle size separation approach needs to be adopted for future water quality monitoring and mitigation of HABs in freshwater ecosystems.

Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms, the spread of algae on the surface of water bodies, have adverse effects not only on aquatic ecosystems but also on human life. The adverse effects of harmful algal blooms (HABs) necessitate a convenient solution for detection and monitoring. Unmanned aerial vehicles (UAVs) have recently emerged as a tool for algal bloom detection, efficiently providing on-demand images at high spatiotemporal resolutions. This study developed an image processing method for algal bloom area estimation from the aerial images (obtained from the internet) captured using UAVs. As a remote sensing method of HAB detection, analysis, and monitoring, a combination of histogram and texture analyses was used to efficiently estimate the area of HABs. Statistical features like entropy (using the Kullback–Leibler method) were emphasized with the aid of a gray-level co-occurrence matrix. The results showed that the orthogonal images demonstrated fewer errors, and the morphological filter best detected algal blooms in real time, with a precision of 80%. This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

First Light Demonstration of Red Solar Induced Fluorescence for Harmful Algal Bloom Monitoring

AbstractThe risks harmful algal blooms (HAB) pose to aquatic ecosystems, public health, and coastal economies necessitate supplementation of current observation strategies. Herein, we explore the use of red solar induced fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), a remote sensing measurement retrievable in variable cloud conditions, for Karenia brevis detection. Along the West Florida Shelf from 2018 to 2020, we compare red SIF with normalized fluorescence line height (nFLH) from MODIS‐Aqua, a standard remote sensing HAB indicator limited to clear sky days. A strong positive linear relationship is found between nFLH and red SIF during severe bloom conditions in 2018 (N = 33,376, r‐value = 0.79, R2 = 0.63) permitting direct comparison. Red SIF provided nearly double the amount of spatiotemporal fluorescence information than nFLH. This work presents the first application of TROPOMI's red SIF for HAB monitoring and illuminates an approach for bolstering early warning systems for HABs beyond clear sky conditions.