A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery

Algal blooms are a major environmental issue in many freshwater environments. While traditional in-situ measurements remain indispensable to monitor algal dynamics, they offer only limited spatiotemporal coverage, especially when dealing with large water bodies. Satellite remote sensing can help overcome this limitation. Here, a semi-empirical model for retrieving surface water Chlorophyll-a (Chl-a) concentrations, a proxy of phytoplankton biomass, was developed for the western basin of Lake Ontario, one of the Laurentian Great Lakes. ACOLITE-corrected Landsat 8 and 9 imagery between 2013 and 2023 was calibrated and verified with local in-situ Chl-a measurements. The nearshore areas of Western Lake Ontario, including the semi-enclosed Hamilton Harbour, are prone to algal blooms, while oligotrophic conditions prevail in the offshore areas. Three bloom indicators-intensity, extent, and severity-were used to characterize the variability and seasonality of algal blooms in different areas of the lake. Time-series analyses revealed contrasting temporal trends in Chl-a concentrations of the nearshore and offshore waters over the eleven-year period of observation. Analysis of external factors impacting algal blooms in Western Lake Ontario and Hamilton Harbour revealed temperature, wind speed, and cloud cover as the most influential, with around 80 % of blooms occurring under moderate conditions (temperature 4-26 °C and wind speed 2.5-5. m s-1). Overall, our research underlines the great potential for cost-effective monitoring of algal dynamics in large lakes, utilizing publicly available satellite imagery, in order to support eutrophication management.

The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential

Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven by the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, particularly those associated with the Internet of Things (IoT). AI is pivotal in enabling IoT and DT by enhancing decision-making, automating processes, and delivering actionable insights. The intersection of DT and AI in the context of ABs presents a promising new area for research exploration. Digital twins, which serve as virtual replicas of physical entities, systems, or processes, offer significant potential when combined with AI technologies, paving the way for novel research avenues in algal management (AM). This literature review examines digital twins' challenges and applications within AM. It also comprehensively analyzes the current state of IoT-based applications developed using AI and DT. The review further explores the tools for implementing DT systems and surveys existing AI techniques incorporating DTs. Additionally, it discusses the opportunities and challenges associated with creating various IoT-based applications by integrating AI and DT. The review concludes by identifying unexplored research avenues in this emerging field, underscoring the potential for future advancements in Artificial Intelligence of Things (AIoT) within AM.

Determining chlorophyll-a thresholds for characterizing algal bloom conditions: An ocean colour remote sensing approach

The Indian coast has been experiencing an increase in algal bloom events over the decades. Owing to the regional and seasonal dynamics of algal biomass (proxy: chlorophyll-a, hereafter chl-a), a multitude of thresholds of chl-a has been defined for different parts of the global seas to determine algal bloom conditions. However, no such clear definition is given for the Indian coastal waters. The current study defined chl-a thresholds to demarcate algal bloom conditions for the coastal waters of India, accounting for the variability at hotspots (in accordance with reported events: secondary data), causative species, and satellite-retrieved long-term trends. The secondary data analysis identified nine bloom hotspots along the east and west coasts of India. Among the blooms, diatoms prevailed the most, compared to dinoflagellates and cyanobacteria. Quartile analysis was employed on satellite-retrieved daily chl-a anomaly to determine specific bloom thresholds. Consequently, these quartile thresholds were utilized to classify various bloom phases, such as the 25th percentile (Likely to Bloom), 50th percentile (Bloom), 75th percentile (Intense Bloom), and 90th percentile (Extreme Bloom). As per this percentile-based classification, the 'Bloom' category corresponds to a chl-a range of 0.89-0.94 mg m-3 and 0.76-2.87 mg m-3, for the identified hotspots along the east and west coasts, respectively. Likewise, during the 'Intense Bloom' phase, the chl-a concentration exceeds 0.99-1.47 and 1.12-4.46 mg m-3 at the hotspots along the east and west coasts, respectively. The seasonality of blooms revealed an increasing trend during the pre-southwest monsoon period on the central east coast of India. On the west coast, significant upwelling during the southwest monsoon period has been substantiated to be conducive for blooms. This study provides an avenue towards utilization of the threshold criteria in detecting different phases of bloom using satellite data in coastal waters where field observations are limited due to various factors.

Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data

Since water is an essential resource in various fields, it requires constant monitoring. Chlorophyll-a concentration is a crucial indicator of water quality and can be used to monitor water quality. In this study, we developed methods to forecast chlorophyll-a concentrations in real-time using hyperspectral data on IoT platform and various machine learning algorithms. Compared to regular cameras that record information only in the three broad color bands of red, green, and blue, the hyperspectral images of drinking water sources record the data in dozens or even hundreds of distinct small wavelength bands, providing each pixel in an image with a full spectrum. Different machine learning algorithms have been developed using hyperspectral data and field observations of water quality and weather conditions. Previous studies have predicted chlorophyll concentrations using either partial least squares (PLS), which is a dimensionality reduction method, or machine learning. In contrast, our study employed the PLS technique as a preprocessing step to diminish the dimensionality of the hyperspectral data, followed by the application of the machine learning techniques with optimized hyperparameters to improve the precision of the predictions, thereby introducing a real-time mechanism for chlorophyll-a prediction. Consequently, a machine learning algorithm with R2 values of 0.9 or above and sufficiently small RMSE was developed for real-time chlorophyll-a forecasting. Real-time chlorophyll-a forecasting using LightGBM has the best performance, with a mean R2 of 0.963 and a mean RMSE of 2.679. This paper is expected to have applications in algal bloom early detection on monitoring systems.

Recent advances in algal bloom detection and prediction technology using machine learning

Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.

Assessing the global distribution and risk of harmful microalgae: A focus on three toxic Alexandrium dinoflagellates

Harmful dinoflagellates and their resulting blooms pose a threat to marine life and human health. However, to date, global maps of marine life often overlook harmful microorganisms. As harmful algal blooms (HABs) increase in frequency, severity, and extent, understanding the distribution of harmful dinoflagellates and their drivers is crucial for their management. We used MaxEnt, random forest, and ensemble models to map the habitats of the representative HABs species in the genus Alexandrium, including A. catenella, A. minutum, and A. pacificum. Since species occurrence records used in previous studies were solely morphology-based, potentially leading to misidentifications, we corrected these species' distribution records using molecular criteria. The results showed that the key environmental drivers included the distance to the coastline, bathymetry, sea surface temperature (SST), and dissolved oxygen. Alexandrium catenella thrives in temperate to cold zones and is driven by low SST and high oxygen levels. Alexandrium pacificum mainly inhabits the Temperate Northern Pacific and prefers warmer SST and lower oxygen levels. Alexandrium minutum thrives universally and adapts widely to SST and oxygen. By analyzing the habitat suitability of locations with recorded HAB occurrences, we found that high habitat suitability could serve as a reference indicator for bloom risk. Therefore, we have proposed a qualitative method to spatially assess the harmful algae risk according to the habitat suitability. On the global risk map, coastal temperate seas, such as the Mediterranean, Northwest Pacific, and Southern Australia, faced higher risks. Although HABs currently have restricted geographic distributions, our study found these harmful algae possess high environmental tolerance and can thrive across diverse habitats. HAB impacts could increase if climate changes or ocean conditions became more favorable. Marine transportation may also spread the harmful algae to new unaffected ecosystems. This study has pioneered the assessment of harmful algal risk based on habitat suitability.

Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic

Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022-2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 μg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 μg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.

Screen and Optimization of an Aptamer for Alexandrium tamarense-A Common Toxin-Producing Harmful Alga

Among all the paralytic shellfish toxins (PSTs)-producing algae, Alexandrium tamarense is one of the most widespread harmful species posing a serious threat to marine resources and human health. Therefore, it is extremely important to establish a rapid and accurate monitoring method for A. tamarense that can provide early warnings of harmful algal blooms (HABs) caused by this alga and limit the contamination due to PSTs. In this study, an ssDNA library was first obtained by whole cell systematic evolution of ligands by exponential enrichment after 18 consecutive rounds of iterative screening. After sequencing in combination with subsequent multiple alignment of sequences and secondary structure simulation, the library could be classified into 2 families, namely, Family1 and Family2, according to sequence similarity. Flow cytometry was used to test the affinity and cross-reactivity of Ata19, Ata6, Ata25 and Ata29 belonging to Family2. Ata19 was selected to be modified by truncation, through which a new resultant aptamer named as Ata19-1-1 was obtained. Ata19-1-1 with a KD of 75.16 ± 11.10 nM displayed a much higher affinity than Ata19. The specificity test showed that Ata19-1-1 has the same discrimination ability as Ata19 and can at least distinguish the target microalga from other microalgae. The observation under a fluorescence microscopy showed that the A. tamarense cells labeled with Ata19-1-1 are exhibiting bright green fluorescence and could be easily identified, factually confirming the binding of the aptamer with target cells. In summary, the aptamer Ata19-1-1 produced in this study may serve as an ideal molecular recognition element for A. tamarense, which has the potential to be developed into a novel detection method for this harmful alga in the future.

Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review

The increasing frequency and intensity of extreme climate events are among the most expected and recognized consequences of climate change. Prediction of water quality parameters becomes more challenging with these extremes since water quality is strongly related to hydro-meteorological conditions and is particularly sensitive to climate change. The evidence linking the influence of hydro-meteorological factors on water quality provides insights into future climatic extremes. Despite recent breakthroughs in water quality modeling and evaluations of climate change's impact on water quality, climate extreme informed water quality modeling methodologies remain restricted. This review aims to summarize the causal mechanisms across climate extremes considering water quality parameters and Asian water quality modeling methods associated with climate extremes, such as floods and droughts. In this review, we (1) identify current scientific approaches to water quality modeling and prediction in the context of flood and drought assessment, (2) discuss the challenges and impediments, and (3) propose potential solutions to these challenges to improve understanding of the impact of climate extremes on water quality and mitigate their negative impacts. This study emphasizes that one crucial step toward enhancing our aquatic ecosystems is by comprehending the connections between climate extreme events and water quality through collective efforts. The connections between the climate indices and water quality indicators were demonstrated to better understand the link between climate extremes and water quality for a selected watershed basin.

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.

Microcystis sp. AE03 strain in Dal Lake harbors cylindrospermopsin and microcystin synthetase gene cluster

Cyanobacterial harmful algal blooms (CHABs) are increasing at an alarming rate in different water bodies worldwide. In India, CHAB events in water bodies such as Dal Lake have been sporadically reported with no study done to characterize the cyanobacterial species and their associated toxins. We hypothesized that this Lake is contaminated with toxic cyanobacterial species with the possibility of the presence of cyanotoxin biosynthetic genes. We, therefore, used some of the molecular tools such as 16S ribosomal DNA, PCR, and phylogenetic analysis to explore cyanobacterial species and their associated toxins. A 3-year (2018–2020) survey was conducted at three different sampling sites of Dal Lake namely, Grand Palace Gath (S1), Nigeen basin (S2), and Gagribal basin (S3). Two strains of Dolichospermum sp. AE01 and AE02 (S3 and S1 site) and one strain of Microcystis sp. AE03 (S2 site) was isolated, cultured, and characterized phylogenetically by 16S ribosomal DNA sequencing. The presence of cyanotoxin genes from the isolates was evaluated by PCR of microcystins (mcyB), anatoxins (anaC), and cylindrospermopsins (pks) biosynthesis genes. Results revealed the presence of both mcyB and pks gene in Microcystis sp. AE03, and only anaC gene in Dolichospermum sp. AE02 strain. However, Dolichospermum sp. AE01 strain was not found to harbor any such genes. Our findings, for the first time, reported the coexistence of pks and mcyB in a Microcystis AE03 strain. This study has opened a new door to further characterize the unexplored cyanobacterial species, their associated cyanotoxin biosynthetic genes, and the intervention of high-end proteomic techniques to characterize the cyanotoxins.

Mapping Benthic Algae and Cyanobacteria in River Channels from Aerial Photographs and Satellite Images: A Proof-of-Concept Investigation on the Buffalo National River, AR, USA

Although rivers are of immense practical, aesthetic, and recreational value, these aquatic habitats are particularly sensitive to environmental changes. Increasingly, changes in streamflow and water quality are resulting in blooms of bottom-attached (benthic) algae, also known as periphyton, which have become widespread in many water bodies of US national parks. Because these blooms degrade visitor experiences and threaten human and ecosystem health, improved methods of characterizing benthic algae are needed. This study evaluated the potential utility of remote sensing techniques for mapping variations in algal density in shallow, clear-flowing rivers. As part of an initial proof-of-concept investigation, field measurements of water depth and percent cover of benthic algae were collected from two reaches of the Buffalo National River along with aerial photographs and multispectral satellite images. Applying a band ratio algorithm to these data yielded reliable depth estimates, although a shallow bias and moderate level of precision were observed. Spectral distinctions among algal percent cover values ranging from 0 to 100% were subtle and became only slightly more pronounced when the data were aggregated to four ordinal levels. A bagged trees machine learning model trained using the original spectral bands and image-derived depth estimates as predictor variables was used to produce classified maps of algal density. The spatial and temporal patterns depicted in these maps were reasonable but overall classification accuracies were modest, up to 64.6%, due to a lack of spectral detail. To further advance remote sensing of benthic algae and other periphyton, future studies could adopt hyperspectral approaches and more quantitative, continuous metrics such as biomass.