Monitoring Of Blooms Research Articles

Real-time monitoring of cyanobacterial harmful algal blooms by graphene field-effect transistor

Demands for clean water are increasing due to factors such as population growth, global warming, and environmental degradation. One of the main obstacles to achieving this purpose is the cyanobacterial blooms, which are the overgrowth of cyanobacteria well known as blue–green algae. Since many cyanobacteria strains are reported to produce geosmin (GSM) or 2-methylisoborneol (MIB) as metabolites, monitoring the level of those two odor compounds is available to the detection of cyanobacterial bloom. The limit of detection (LOD) of the pre-developed equipment is high to detect the initial proliferation of cyanobacteria. To overcome the limitation, we fabricated graphene field-effect transistors (GFETs) based bioelectronic nose which use human olfactory receptors (hORs) 51S1 and 3A4 as bioprobe. The hOR51S1 and 3A4 were reconstituted to nanodiscs (NDs) due to their stable, sensitive, and selective interaction with GSM and MIB, respectively. The LOD (10 fM) of the fabricated device was the lowest among previously developed detection platforms. In addition, the device demonstrated high selectivity for GSM and MIB with other odor compounds, such as IBMP and IPMP. To evaluate sensing properties of the bioelectronic nose, the real samples from river water containing GSM and MIB were prepared and monitored by bioelectronics nose to measure its sensitivity and selectivity. Furthermore, the developed platform, a bioelectronic nose mimicking the human sense of smell, can be applied to detect other harmful compounds in environmental areas by using various bioprobes.

Remote sensing for mapping algal blooms in freshwater lakes: a review.

A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.

Sea Surface Temperature and Phytoplankton Abundance as Crucial Proxies for Green Noctiluca Bloom Monitoring in the Northeastern Arabian Sea: A Case Study

Sea Surface Temperature and Phytoplankton Abundance as Crucial Proxies for Green Noctiluca Bloom Monitoring in the Northeastern Arabian Sea: A Case Study

Detection and identification of a mixed cyanobacteria and microalgae culture using derivative spectrophotometry.

Early detection and monitoring of algal blooms and potentially toxic cyanobacteria in source waters are becoming increasingly important with rising climate change and industrialization. There is a growing need to measure the mixed microalgae cultures sensitively and accurately, as multiple algae species are present in natural source waters. This study investigated the detection of an equal concentration, mixed-culture of cyanobacteria (Microcystis aeruginosa) and a common green algae (Chlorella vulgaris) in water using UV-Vis spectrophotometry while employing longer pathlengths and derivative spectrophotometry to improve the detection limit. A strong linear relationship (R2>0.99) was found between the concentration and absorbance of the mixed-culture at 682nm using 50 and 100mm pathlengths. This study showed that the cyanobacterial (phycocyanin) peak could be separately identified in mixed-culture setting, while the chlorophyll peaks of both algae overlapped each other. The lowest detection limit of the mixed algal culture using traditional spectrophotometry and derivative spectrophotometry was calculated to be 25,997 cells/mL and 5505 cells/mL using a 100mm cuvette pathlength. Lastly, the performance of mixed-culture and individual algal cultures were compared, and analyses were carried out to evaluate differences in slopes which can be used for quantification purposes. The results indicate that derivative spectrophotometry significantly improved the detection limit making the method potentially viable for the early detection of mixed algal cultures.

A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems

Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine Learning (ML) models can potentially leverage large volumes of IoT data to aid in near real-time predictions. ML models have evolved as efficient tools for understanding patterns and relationships between water quality parameters and HAB expansion. This review describes ML models currently used for predicting and forecasting HABs in freshwater ecosystems and presents model structures and their application for predicting algal parameters and related toxins. The review revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the most frequently used models for HABs monitoring. This review shows ML models' prowess in identifying significant variables influencing algal growth, HAB drivers, and multistep HAB prediction. Hybrid models also improve the prediction of algal-related parameters through improved optimization techniques and variable selection algorithms. While ML models often focus on algal biomass prediction, few studies apply ML models for toxin monitoring and prediction. This limitation can be associated with a lack of high-frequency toxin datasets for model development, and exploring this domain is encouraged. This review serves as a guide for policymakers and researchers to implement ML models for HAB prediction and reveals the potential of ML models for decision support and early prediction for HAB management. Keywords: Cyanobacteria, Freshwater, Harmful algal blooms, Machine learning, Water quality.

Innovative Remote Sensing Identification of Cyanobacterial Blooms Inspired from Pseudo Water Color

The identification and monitoring of cyanobacterial blooms (CBs) is critical for ensuring water security. However, traditional methods are time-consuming and labor-intensive and are not ideal for large-scale monitoring. In operational monitoring, the existing remote sensing methods are also not ideal due to complex surface features, unstable models, and poor robustness thresholds. Here, a novel algorithm, the pseudo-Forel-Ule index (P-FUI), is developed and validated to identify cyanobacterial blooms based on Terra MODIS, Landsat-8 OLI, Sentinel-2 MSI, and Sentinel-3 OLCI sensors. First, three parameters of P-FUI, that is, brightness Y, saturation s, and hue angle α, were calculated based on remote sensing reflectance. Then, the robustness thresholds of the parameters were determined by statistical analysis for a frequency distribution histogram. We validated the accuracy of our approach using high-spatial-resolution satellite data with the aid of field investigations. Considerable results were obtained by using water color differences directly. The overall classification accuracy is more than 93.76%, and the user’s accuracy and producer’s accuracy are more than 94.60% and 94.00%, respectively, with a kappa coefficient of 0.91. The identified cyanobacterial blooms’ spatial distribution with high, medium, and low intensity produced consistent results compared to those based on satellite data. Impact factors were also discussed, and the algorithm was shown to be tolerant of perturbations by clouds and high turbidity. This new approach enables operational monitoring of cyanobacterial blooms in eutrophic lakes.

Landsat-Satellite-Based Analysis of Long-Term Temporal Spatial Dynamics of Cyanobacterial Blooms: A Case Study in Taihu Lake

Cyanobacterial blooms in large and shallow freshwater lakes have become one of the most severe ecological problems threatening the environment and public health. Although great progress has been made in Taihu Lake in cyanobacterial bloom monitoring, most previous studies have used MODIS images with a resolution greater than 250 m, available after 2000, while the fine-scale studies on its long-term spatio-temporal dynamics to date are insufficient. This study monitored the spatiotemporal distribution of cyanobacterial blooms in Taihu Lake between 1984 and 2021 using Landsat images of 30 m resolution on the cloud computation platform Google Earth Engine and calculated the cyanobacterial blooms’ area percentage and the cyanobacterial bloom frequency index. Then, we investigated the influence of water quality and meteorological factors on area and frequency using Spearman correlation and principal component analysis. The results show that cyanobacterial blooms spread from the northern to the central, western, and eastern parts of Taihu Lake from 1984 to 2021. With the exception of East Lake, the area and frequency of cyanobacterial blooms increased significantly. Hypereutrophic water conditions, high temperatures, abundant sunshine hours, and low wind velocities all favor cyanobacteria blooms in Taihu Lake, and the key influencing factors of dynamics in cyanobacterial blooms are the comprehensive trophic level index, annual sunshine hours, and annual maximum wind speed. This study can serve as a reference for lake eutrophication monitoring and water resource management and protection.

Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles

Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles.

Accurate Monitoring of Algal Blooms in Key Nearshore Zones of Lakes and Reservoirs Using Binocular Video Surveillance System

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.

Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning

Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011–2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.

Lake Erie field trials to advance autonomous monitoring of cyanobacterial harmful algal blooms

Biomolecular analyses are used to investigate the dynamics of cyanobacterial harmful algal blooms (cyanoHABs), with samples collected during monitoring often analyzed by qPCR and sometimes amplicon and metagenomic sequencing. However, cyanoHAB research and monitoring programs face operational constraints due to the reliance on human resources for sample collections. To address this impediment, a third-generation Environmental Sample Processor (3G ESP) integrated with a long-range autonomous underwater vehicle (LRAUV) was tested during seasonal blooms of Microcystis in western Lake Erie (WLE) in 2018 and 2019. The LRAUV-3G ESP successfully performed flexible, autonomous sampling across a wide range of cyanoHAB conditions, and results indicated equivalency between autonomous and manual methods. No significant differences were found between LRAUV-3G ESP and manual sample collection and handling methods in the 12 parameters tested. Analyzed parameters included concentrations of total cyanobacteria and microcystin toxin gene via qPCR; relative abundances of bacterial amplicon sequence variants (ASVs) from 16S rRNA gene amplicon sequencing; and community diversity measures from both 16S amplicon and metagenomic sequencing. The LRAUV-3G ESP provided additional sampling capacity and revealed differences between field seasons for bacterial taxa and concentrations of total cyanobacteria and microcystin toxin gene. Metagenomic analysis of multiple microcystin toxin genes corroborated the use of the mcyE gene as a proxy for the genomic potential of WLE cyanoHABs to produce microcystin. Overall, this study provides support for the use of autonomous ‘omics capability in WLE to help expand the spatial and temporal coverage of cyanoHAB monitoring operations.

Monitoring of high biomass Phaeocystis globosa blooms in the Southern North Sea by in situ and future spaceborne hyperspectral radiometry

Phaeocystis globosa (P. globosa hereafter) is a phytoplankton species which commonly blooms at high biomass in April–May in the Southern North Sea and forms undesirable foam which accumulates on the beaches. Monitoring of this species is required by EU directives. Measurement of phytoplankton species composition has historically been made by pigment or microscopic analysis of water samples, which is spatially sparse and temporally infrequent e.g. weekly/monthly. In-water instruments such as flow cytometers can provide very high frequency data but at high acquisition and maintenance cost. Automated in situ above water radiometry has the potential to provide very high frequency data at single locations but requires very careful design of processing algorithms in turbid waters with high non-algal absorption. Spaceborne radiometry could provide both very good spatial coverage and moderate/high frequency of data, e.g. daily/weekly, but accurate determination of phytoplankton species composition is considerably more difficult in turbid waters than in open ocean waters. Prior studies based on a limited number of shipborne reflectance measurements suggested feasibility of P. globosa detection in turbid waters from hyperspectral radiometry. The availability of a new autonomous above water hyperspectral radiometer system has enabled further refinement and intensive testing of these techniques. From a time-series of 4356 water reflectance spectra measured near Ostend harbour in Belgian coastal waters from 2020/04/01 to 2020/08/18, two existing algorithms for P. globosa detection were successfully applied. Results show a high biomass P. globosa bloom occurring in late-April/early-May as found every year in water sample analyses for Belgian coastal waters. The high temporal resolution of the radiometric data allows to capture the evolution of the bloom at time scales sufficiently short (hourly and daily) compared to growth/decay and tidal processes.The challenges of extending the methods to future spaceborne instruments are also tested by simulating the impact of errors in sensor inter-band calibration, atmospheric correction and radiometric noise. Results show that because of their spectral coherence, atmospheric correction errors impact only slightly P. globosa detection whereas inaccuracy in inter-band calibration and radiometric noise are much more problematic as they affect each spectral band independently. Because radiometric noise should be reduced in the new generation of hyperspectral sensors and can always be reduced by spatial binning, the inter-band relative calibration uncertainty appears to be the main challenge for spaceborne mission design. Indeed, it was demonstrated that inter-band calibration error should be <0.25% and ideally 0.1% at top of the atmosphere highlighting the need for particular attention to inter-band calibration in sensor design and post processing treatments including vicarious calibration.

Lake algal bloom monitoring via remote sensing with biomimetic and computational intelligence

Traditional supervised classifications for remote sensing-based water quality monitoring count on a set of classifiers to retrieve features and improve their prediction accuracies based on ground truth samples. However, many existing feature extraction methods in remote sensing are unable to exhibit multiple-instance nonlinear spatial pattern recognition at scales via ensemble learning. This paper designed for lake algal bloom monitoring presents intelligent feature extraction for harmonizing local and global features via tensor flow-based ensemble learning with integrated biomimetic and computational intelligence. To explore such complexity, an Integrated Biomimetic and Ensemble Learning Algorithm (IBELA) was developed to synthesize the contribution from different classifiers associated with the biomimetic philosophy of integrated bands. It leads to strengthened multiple-instance spatial pattern recognition in lake algal bloom monitoring via image fusion at the decision level. With the implementation of IBELA, a case study of a eutrophic freshwater lake, Lake Managua, for water quality monitoring leads to demonstrate six input visual senses showing different impacts on retrieving Chl-a concentrations in the dry and wet season, respectively. The input of total nitrogen from the watershed plays the most important role in water quality variations in both seasons in a watershed-based food–water nexus. Although ultraviolet and microwave bands are important in the dry season, Secchi disk depth is critical in the wet season for water quality monitoring.

Mapping of coastal surface chlorophyll-a concentration by multispectral reflectance measurement from unmanned aerial vehicles

In subtropical coastal waters, the explosive growth of phytoplankton under favorable conditions can lead to water discolouration and massive fish kills. Manual field sampling and laboratory analysis of chlorophyll-a concentration (Chl-a) as an indicator to algal biomass, is resources intensive and time consuming, delaying responses to disastrous harmful algal blooms. Cloudy weather often precludes the use of satellite images for water quality and algal bloom monitoring. This study aims at developing an estimator algorithm for quantitative mapping of surface Chl-a for coastal waters, based on surface reflectance measurement from an Unmanned Aerial Vehicle (UAV) with a five-band multispectral camera. The surface reflectance is obtained from calibrated multispectral images which are radiometric-corrected against incoming solar radiation. It is found that Chl-a has an inverse correlation with the Normalized Green-Red Difference Index (NGRDI). A regression estimator model for Chl-a from NGRDI is developed, showing excellent performance for fish farms in coastal waters with different characteristics. The technology is demonstrated for mapping the spatial and temporal variation of Chl-a during an algal bloom, offering a useful complement to traditional field monitoring for fisheries management and emergency response.

A simple method for estimating macroalgae area under clouds on MODIS imagery

The presence of clouds interferes with optical remote sensing monitoring of macroalgae blooms. To solve this problem, we propose a simple method for estimating macroalgae area under clouds (Area_cloud_GT) on MODIS imagery using the principle behind the lowpass filter. The method is based on a rectangle with clouds and eight identical adjacent rectangles surrounding it that contain macroalgae. The cloud rectangle is a central ‘pixel’ (Cloud) and the eight adjacent rectangles are ‘pixels’ GT1–GT8. The core operation is to calculate the central ‘pixel’ value, i.e., the macroalgae coverage rate in the Cloud rectangle. The macroalgae area detected by semi-simultaneous fine resolution images in the same region was taken as the ‘real’ value. A comparison of the estimation results and the ‘real’ value shown that the mean relative difference between them (MRD) was 30.09% when the time interval of the images within 10 minutes. When the time interval was over 3 hours, the MRD was more than 60%. The MRD increased significantly with increasing time interval because of the constant movement of the macroalgae and the limitations of the remote sensing image. The results indicate that this simple method is effective to a certain extent. These results can provide a reference for the quantitative analysis of green tide.

Karenia brevis bloom patterns on the west Florida shelf between 2003 and 2019: Integration of field and satellite observations

Harmful algal blooms of the toxic dinoflagellate Karenia brevis occur almost annually on the West Florida Shelf (WFS) of the eastern Gulf of Mexico. To date, however, comprehensive assessments of K. brevis bloom spatial extent and temporal occurrence are lacking due to limitations in the two primary bloom monitoring techniques: microscopy evaluation of field-collected water samples and satellite remote sensing of ocean color. This is despite community efforts in expanding sampling coverage statewide and developing remote sensing algorithms to interpret color changes of surface waters. In this work, an approach is developed to combine the strengths of both techniques to estimate mean bloom occurrence frequency and bloom intensity as well as bloom extent at weekly, bi-weekly, monthly, and annual intervals between 2003 and 2019. Here, due to technical constraints on ocean color remote sensing, a bloom is defined as waters with K. brevis concentrations greater than 1.5 × 105 cells L−1. While microscopy examination of surface water samples provides K. brevis cell concentrations to help delineate bloom locations from Moderate Resolution Imaging Spectrometer on Aqua (MODIS/A) images, the imagery provides far more synoptic and frequent observations to make the bloom characterization statistically meaningful. Such derived bloom statistics often show bloom patterns that are not always known previously or at the time of the event, and in some years, they also differ from those determined from microscopic taxonomy data alone. For example, in terms of bloom size, two major bloom periods are observed in 2005 – 2007 and 2014 – 2018, respectively, when annual cumulative bloom size exceeded ∼50,000 km2. While preliminary in nature, the approach and results from this work may represent a first step to integrate water sample analysis and satellite remote sensing towards an improved characterization of K. brevis blooms on the WFS.

A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms

The worldwide expansion of phytoplankton blooms has severely threatened water quality, food webs, habitat stability and human health. Due to the rapidity of phytoplankton migration and reproduction, high-frequency information on phytoplankton bloom dynamics is crucial for their forecasting, treatment, and management. While several approaches involving satellites, in situ observations and automated underwater monitoring stations have been widely used in the past several decades, they cannot fully provide high-frequency and continuous observations of phytoplankton blooms at low cost and with high accuracy. Thus, we propose a novel ground-based remote sensing system (GRSS) that can monitor real-time chlorophyll a concentrations (Chla) in inland waters with a high frequency. The GRSS mainly consists of three platforms: the spectral measurement platform, the data-processing platform, and the remote access control, display and storage platform. The GRSS is capable of obtaining a remote sensing irradiance ratio (R(λ)) of 400–1000 nm at a high frequency of 20 s. Eight different Chla retrieval algorithms were calibrated and validated using a dataset of 481 pairs of GRSS R(λ) and in situ Chla measurements collected from four inland waters. The results showed that random forest regression achieved the best performance in deriving Chla (R2 = 0.95, root mean square error = 13.40 μg/L, and mean relative error = 25.7%). The GRSS successfully captured two typical phytoplankton bloom events in August 2021 with rapid changes in Chla from 20 μg/L to 325 μg/L at the minute level, highlighting the critical role that this GRSS can play in the high-frequency monitoring of phytoplankton blooms. Although the algorithm embedded into the GRSS may be limited by the size of the training dataset, the high-frequency, continuous and real-time data acquisition capabilities of the GRSS can effectively compensate for the limitations of traditional observations. The initial application demonstrated that the GRSS can capture rapid changes of phytoplankton blooms in a short time and thus will play a critical role in phytoplankton bloom management. From a broader perspective, this approach can be extended to other carriers, such as aircraft, ships and unmanned aerial vehicles, to achieve the networked monitoring of phytoplankton blooms.

Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Remote sensing technologies offer a consistent, spatiotemporal approach to assess water quality, which includes the detection, monitoring, and forecasting of cyanobacteria harmful algal blooms. In this study, a series of ex-situ mesoscale experiments were conducted to first develop and then monitor a Microcystis sp. bloom using a hyperspectral sensor mounted on an unmanned aircraft system (UAS) along with coincident ground sampling efforts including laboratory analyses and in-situ field probes. This approach allowed for the simultaneous evaluation of both bloom physiology (algal growth stages/life cycle) and data collection method on the performance of a suite of 41 spectrally-derived water quality algorithms across three water quality indicators (chlorophyll a, phycocyanin and turbidity) in a controlled environment. Results indicated a strong agreement between Lab and Field-based methods for all water quality indicators independent of growth phase, with regression R2-values above 0.73 for mean absolute percentage error (MAPE) and 0.87 for algorithm R2 values. Three of the 41 algorithms evaluated met predetermined performance criteria (MAPE and algorithm R2 values); however, in general, algal growth phase had a substantial impact on algorithm performance, especially those with blue and violet wave bands. This study highlights the importance of co-validating sensor technologies with appropriate ground monitoring methods to gain foundational knowledge before deploying new technologies in large-scale field efforts.

Development of a novel TaqMan qPCR assay for rapid detection and quantification of Gymnodinium catenatum for application to harmful algal bloom monitoring in coastal areas of Tunisia.

Gymnodinium catenatum is a dinoflagellate known to cause paralytic shellfish poisoning (PSP), commonly associated with human muscular paralysis, neurological symptoms, and, in extreme cases, death. In the present work, we developed a real-time PCR-based assay for the rapid detection of the toxic microalgal species, G. catenatum, in environmental bivalve mollusc samples as well as seawater samples. G. catenatum-specific primers and probe were designed on the ITS1-5.8S-ITS2 rDNA region. Hydrolysis probe qPCR assay was optimized. ITS1-5.8S-ITS2 rDNA region copy numbers per G. catenatum cell genome were estimated to be 122.73 ± 5.54 copies/cell, allowing cell quantification. The application of the optimized qPCR assay for G. catenatum detection and quantification in field samples has been conducted, revealing high sensitivity (detection of around 1.3105 cells/L of seawater samples. Thus, the designed hydrolysis probe qPCR assay could be considered an efficient tool for phytoplankton monitoring whilst ensuring accuracy and sensitivity and providing cost and time savings.

Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image

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.