Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images

Transcriptome Analysis of the Harmful Dinoflagellate Heterocapsa bohaiensis Under Varied Nutrient Stress Conditions

The increasing prevalence of harmful algal blooms (HABs) driven by eutrophication, particularly in China's nearshore waters, is a growing concern. Dinoflagellate Heterocapsa bohaiensis blooms have caused significant ecological and economic damage, as well as mass mortality, in cultivated species. Nutrients are one of the primary inducers of H. bohaiensis blooms. However, the transcriptomic studies of H. bohaiensis remain sparse, and its metabolic pathways are unknown. This study analyzed the transcriptome of H. bohaiensis under varying nutrient conditions (nitrogen at 128, 512, and 880 μM; phosphate at 8, 6, and 32 μM), focusing on differential gene expression. The results indicated that deviations in nutrient conditions (higher or lower N:P ratios) led to a higher number of differentially expressed genes compared to the control (N:P ratios = 27.5), thereby underscoring their pivotal role in growth. Gene Ontology (GO) enrichment analyses showed that nutrient limitation upregulated the biosynthesis and catabolism processes while downregulating the cell cycle and division functions. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that, under nitrogen limitation, the proteasome pathways were upregulated, while photosynthesis and carbon fixation were downregulated; under phosphorus limitation, the proteasome pathways were upregulated and nitrogen metabolism was downregulated. These findings suggest that H. bohaiensis adapts to nutrient stress by adjusting its metabolic processes.

Study on the applicability of FAI linear fitting model in the extraction of cyanobacterial blooms

Currently, more and more lakes around the world are experiencing outbreaks of cyanobacterial blooms, and high-precision and rapid monitoring of the spatial distribution of algae in water bodies is an important task. Remote sensing technology is one of the effective means for monitoring algae in water bodies. Studies have shown that the Floating Algae Index (FAI) is superior to methods such as the Standardized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in monitoring cyanobacterial blooms. However, compared to the NDVI method, the FAI method has difficulty in determining the threshold, and how to choose the threshold with the highest classification accuracy is challenging. In this study, FAI linear fitting model (FAI-L) is selected to solve the problem that FAI threshold is difficult to determine. Innovatively combine FAI index and NDVI index, and use NDVI index to find the threshold of FAI index. In order to analyze the applicability of FAI-L to extract cyanobacterial blooms, this paper selected multi-temporal Landsat8, HJ-1B, and Sentinel-2 remote sensing images as data sources, and took Chaohu Lake and Taihu Lake in China as research areas to extract cyanobacterial blooms. The results show that (1) the accuracy of extracting cyanobacterial bloom by FAI-L method is generally higher than that by NDVI and FAI. Under different data sources and different research areas, the average accuracy of extracting cyanobacterial blooms by FAI-L method is 95.13%, which is 6.98% and 18.43% higher than that by NDVI and FAI respectively. (2) The average accuracy of FAI-L method for extracting cyanobacterial blooms varies from 84.09 to 99.03%, with a standard deviation of 4.04, which is highly stable and applicable. (3) For simultaneous multi-source image data, the FAI-L method has the highest average accuracy in extracting cyanobacterial blooms, at 95.93%, which is 6.77% and 13.26% higher than NDVI and FAI methods, respectively. In this paper, it is found that FAI-L method shows high accuracy and stability in extracting cyanobacterial blooms, and it can extract the spatial distribution of cyanobacterial blooms well, which can provide a new method for monitoring cyanobacterial blooms.

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

Harmful algal blooms in inland waters

Harmful algal blooms can produce toxins that pose threats to aquatic ecosystems and human health. In this Review, we outline the global trends in harmful algal bloom occurrence and explore the drivers, future trajectories and potential mitigation strategies. Globally, harmful algal bloom occurrence has risen since the 1980s, including a 44% increase from the 2000s to 2010s, especially in Asia and Africa. Enhanced nutrient pollution owing to urbanization, wastewater discharge and agricultural expansion are key drivers of these increases. In contrast, changes have been less substantial in high-income regions such as North America, Europe and Oceania, where policies to mitigate nutrient pollution have stabilized bloom occurrences since the 1970s. However, since the 1990s, climate warming and legacy nutrient pollution have driven a resurgence in toxic algal blooms in some US and European lakes, highlighting the inherent challenges in mitigating harmful blooms in a warming climate. Indeed, advancing research on harmful algal bloom dynamics and projections largely depends on effectively using data from multiple sources to understand environmental interactions and enhance modelling techniques. Integrated monitoring networks across various spatiotemporal scales and data-sharing frameworks are essential for improving harmful algal bloom forecasting and mitigation.

Monitoring of toxic cyanobacterial blooms in Lalla Takerkoust reservoir by satellite imagery and microcystin transfer to surrounding farms

Cyanobacterial harmful algal blooms (CyanoHABs) threaten public health and freshwater ecosystems worldwide. In this study, our main goal was to explore the dynamics of cyanobacterial blooms and how microcystins (MCs) move from the Lalla Takerkoust reservoir to the nearby farms. We used Landsat imagery, molecular analysis, collecting and analyzing physicochemical data, and assessing toxins using HPLC. Our investigation identified two cyanobacterial species responsible for the blooms: Microcystis sp. and Synechococcus sp. Our Microcystis strain produced three MC variants (MC-RR, MC-YR, and MC-LR), with MC-RR exhibiting the highest concentrations in dissolved and intracellular toxins. In contrast, our Synechococcus strain did not produce any detectable toxins. To validate our Normalized Difference Vegetation Index (NDVI) results, we utilized limnological data, including algal cell counts, and quantified MCs in freeze-dried Microcystis bloom samples collected from the reservoir. Our study revealed patterns and trends in cyanobacterial proliferation in the reservoir over 30 years and presented a historical map of the area of cyanobacterial infestation using the NDVI method. The study found that MC-LR accumulates near the water surface due to the buoyancy of Microcystis. The maximum concentration of MC-LR in the reservoir water was 160 µg L-1. In contrast, 4 km downstream of the reservoir, the concentration decreased by a factor of 5.39 to 29.63 µgL-1, indicating a decrease in MC-LR concentration with increasing distance from the bloom source. Similarly, the MC-YR concentration decreased by a factor of 2.98 for the same distance. Interestingly, the MC distribution varied with depth, with MC-LR dominating at the water surface and MC-YR at the reservoir outlet at a water depth of 10 m. Our findings highlight the impact of nutrient concentrations, environmental factors, and transfer processes on bloom dynamics and MC distribution. We emphasize the need for effective management strategies to minimize toxin transfer and ensure public health and safety.

Microcystin Contamination in Irrigation Water and Health Risk

Microcystins (MCs), natural hepatotoxic compounds produced by cyanobacteria, pose significant risks to water quality, ecosystem stability, and the well-being of animals, plants, and humans when present in elevated concentrations. The escalating contamination of irrigation water with MCs presents a growing threat to terrestrial plants. The customary practice of irrigating crops from local water sources, including lakes and ponds hosting cyanobacterial blooms, serves as a primary conduit for transferring these toxins. Due to their high chemical stability and low molecular weight, MCs have the potential to accumulate in various parts of plants, thereby increasing health hazards for consumers of agricultural products, which serve as the foundation of the Earth's food chain. MCs can bioaccumulate, migrate, potentially biodegrade, and pose health hazards to humans within terrestrial food systems. This study highlights that MCs from irrigation water reservoirs can bioaccumulate and come into contact with plants, transferring into the food chain. Additionally, it investigates the natural mechanisms that organisms employ for conjugation and the microbial processes involved in MC degradation. To gain a comprehensive understanding of the role of MCs in the terrestrial food chain and to elucidate the specific health risks associated with consuming crops irrigated with water contaminated with these toxins, further research is necessary.

A solid phase extraction column based on SiO2@ZIF-8 for efficient analysis of domoic acid toxins in the seawater environment: experiments and DFT calculations on adsorption behaviour

Algal toxins are important metabolites of toxic harmful algal blooms (HABs), and their qualitative and qualitative detection can serve as early warning indicators for toxic HABs, complementing traditional HAB monitoring and improving the accuracy of early warning. Therefore, this work took the detection of domoic acid (DA) as an example and prepared zeolitic imidazolate framework-8 (ZIF-8) with high enrichment performance and high water stability and its core-shell composite material SiO2@ZIF-8 as an adsorbent filler. Density functional theory (DFT) calculations and interference experiments verified that Zn2+ on SiO2@ZIF-8 played a crucial role in enriching DA on SiO2@ZIF-8. By using it as a solid-phase extraction (SPE) filler, it showed excellent performance compared with other SPE columns (C18/HLB/SAX/ZIF-8). Therefore, the SiO2@ZIF-8 column was coupled to high-performance liquid chromatography-mass spectrometry (SPE-HPLC-MS/MS) to establish a highly sensitive detection method for algal toxins in seawater, which had a wide linear range (12.0-5000.0 ng L-1), good reproducibility (RSD) and low limit of detection (4.0 ng L-1), and realized the monitoring of trace DA in the Pingtan sea area of Fujian Province from 2021 to 2022. By comparing other HAB early warning indicators such as salinity and pH and combining them with the information released by the Fujian Provincial Ocean and Fisheries Bureau, the content of DA in seawater measured by the established SPE-HPLC-MS/MS method can provide reference information for HAB monitoring and early warning.

High-frequency monitoring of Secchi-disk depth in Taihu Lake using Himawari-8/AHI data

Diurnal monitoring of the Secchi-disk depth (SDD) of eutrophic lakes is the basic requirement to ensure domestic, industrial, and agricultural water use in surrounding cities. The retrieval of SDD in high frequency and longer observation period is the basic monitoring requirement to guarantee water environmental quality. Taking Lake Taihu as an example, the diurnal high-frequency observation (10 mins) data of the geostationary meteorological satellite sensor AHI/Himawari-8 were examined in this study. The results showed that the AHI normalized water-leaving radiance (L_wn) product derived by the Shortwave-infrared atmospheric correction (SWIR-AC) algorithm was consistent with the in situ data, with determination coefficient (R²) all larger than 0.86 and the mean absolute percentage deviation (MAPD) of 19.76%, 12.83%, 19.03% and 36.46% for the 460 nm, 510 nm, 640 nm and 860 nm bands, respectively. 510 nm and 640 nm bands showed more better consistency with in situ data in Lake Taihu. Therefore, an empirical SDD algorithm was established based on the AHI green (510 nm) and red (640 nm) bands. The SDD algorithm was verified by in situ data showed good performance with R² of 0.81, RMSE of 5.91 cm, and MAPD of 20.67%. Based on the AHI data and established algorithm, diurnal high-frequency variation of the SDD in the Lake Taihu was investigated and the environmental factor (wind speed, turbidity degree, and photosynthetically active radiance) corresponding to diurnal SDD variation were discussed. This study should be helpful for studying diurnal high-dynamics physical-biogeochemical processes in eutrophication lake waters.

Atmospheric correction algorithm based on the interpolation of ultraviolet and shortwave infrared bands

Traditional atmospheric correction algorithms of ocean color remote sensing are mostly based on the extrapolation of aerosol scattering radiance from a reference band (near infrared, shortwave infrared, or ultraviolet bands), which inevitably leads to the problem of extrapolation error amplification with the increase of extrapolation spectral distance. In this study, we propose a practical interpolation-based algorithm (named the UV-SWIR-AC algorithm) using three reference bands (one ultraviolet and two shortwave infrared bands) for turbid waters. According to 6SV radiative transfer simulations with 15 customized aerosol types, we establish a fitting function framework for the aerosol scattering radiance in the wavelength range of 322-1643 nm. We apply the UV-SWIR-AC algorithm to the real satellite ocean color data observed by the Second-Generation Global Imager aboard the Global Change Observation Mission-Climate (SGLI/GCOM-C) and compare the retrieved remote sensing reflectance with the in-situ data from the observation platform of Hangzhou Bay in the East China Sea and typical bays. The results show that the UV-SWIR-AC algorithm can achieve a better performance than the traditional, extrapolation-based algorithm in turbid waters. Moreover, in the typical regional analysis, this new algorithm also demonstrates a high applicability. The UV-SWIR-AC algorithm should be helpful to improve the atmospheric correction accuracy for next-generation ocean color missions (e.g., NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and China's Haiyang-1E/F (HY-1E/F) mission) with wider spectral ranges from the ultraviolet to shortwave infrared wavelengths.

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.

Removal of harmful algae in natural water by semiconductor photocatalysis- A critical review

Harmful Algal Blooms (HABs) have turned out to be a global occurrence owing to the detrimental phenomenon like eutrophication and global climate change caused by human activities. This newly emergent threat imposes a severe hazardous to public health, ecosystems and fishery-based economies. Rapid and exponential growth of certain delirious and toxic algal species shall be held causative to the formation of HABs. The potential disadvantages they pose, make it necessary the identification of efficient treatment methodologies. Photocatalysis has been identified as the most promising solution amongst all the identified and investigated methods, for the environmental and economic benefits beheld. Different treatment methodologies were evaluated and light has been thrown on the advantages beheld by photocatalysis over the other methods. Focus has been given to the different photocatalysts that have been so far put to use towards photocatalytic disinfection of HABs and algal toxins. This present study provides useful information on the application of the traditional and photocatalysis process for removal of HABs in water bodies. Moreover, the results revealed that photocatalysis method could cause potent inhibitory effect on growth of algae species and disrupted algal cells membranes to some extent. Finally, the conventional treatment techniques have been recognized to be insufficient for removal of HABs. However, the photocatalyst technology have been utilized mostly for the mineralization and neutralization of the algal pollutants without any harmful secondary pollutants.

Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations

Occurrence of cyanobacterial blooms in most lakes has dramatic changes in time and space. However, most current studies only focused on daily or seasonal scales to obtain a relatively coarse resolution result. To explore the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacterial index. Based on that, diurnal change characteristics were explored at two scales, and the environmental impacts were investigated. For that, an classification method was first designed to identify the types of diurnal change patterns of cyanobacterial blooms automatically. This method classified the patterns into four types, including the decreasing (Type1), decreasing first and then increasing (Type2), increasing (Type3), increasing first and then decreasing (Type4). Based on that, the types of diurnal change patterns of blooms in Lake Taihu (from April 1, 2011 to October 31, 2020) were identified at pixel (500 m) and synoptic scales. Results indicated that Type1 and Type3 were two hot diurnal change patterns of blooms, and lakeshore was the hotspot occurring severe diurnal changes, and autumn was the hot season occurring frequent diurnal changes. Specifically, hotspot of Type1 was lakeshore, while hotspot of Type3 was Central Regions. Environmental impacts were analyzed at two scales. At pixel scale (500 m), diurnal variation of temperature affected the regional occurence of each type ofdiurnal changes patterns of blooms, and the afternoon temperature played the most critical role (p < 0.001, N = 8316). The occurrence frequency of Type1 was positively (R = 0.41) related with the afternoon temperature, and the occurrence frequency of Type3 was negatively (R = -0.37) related with it. Diurnal variation of wind speed was another key factor impacting the occurrence of obvious diurnal blooms changes, and the wind impacts should be distinguished when the wind speed was over or below 3.5 m/s. At synoptic scale, the interaction of multi environmental factors influenced the diurnal change degree of blooms area, and the environmental contributions were 71%.Comparing with the existing manual classifying workat synoptic scale, the designed classification method can identify the types of diurnal change patterns of blooms at a higher spatial resolution (500 m). These explorations on diurnal dynamics of cyanobacterial blooms in Lake Taihu provide a new insight for advanced cyanobacteria dynamics studies and regional water management.

Dynamic variations of cyanobacterial blooms and their response to urban development and climate change in Lake Chaohu based on Landsat observations

Recurring cyanobacterial blooms have seriously hindered the sustainable development of cities. In this study, the variation trend of cyanobacterial blooms was analyzed by taking Lake Chaohu in China as the study area, and the Normalized Difference Vegetation Index (NDVI) derived from Landsat observations combined with the development index of surrounding cities from 2009 to 2019 was used to quantitatively analyze the response of cyanobacterial blooms to urban development and climate change. The results showed that the NDVI of the Northwest Lake region was significantly higher than that of other regions. Summer and autumn were the main seasons for the outbreak of cyanobacterial blooms. The NDVI of Lake Chaohu and Baohe Lake region showed a significant correlation with the gross domestic product (GDP) growth rate of Hefei city (HF), the districts and counties around the lake (DCL), Baohe District (BH), and the population (P). As the economic regions gradually focused on BH rather than on HF and DCL, there was an increasing trend correlation between the NDVI of Baohe Lake region and the GDP growth rate. However, the elimination of GDP in BH did not affect the consistency relationship between the economic growth of other regions and the NDVI of Lake Chaohu on a large scale. In addition, the results of relative importance analysis indicated that the GDP growth rate of BH and the area of Hefei except DCL (HF-DCL) accounted for important contribution to the [Formula: see text] of the regression. This study has momentous reference value for understanding the coupling relationship between urban development and lake environment.

Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea

In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms. Atmospheric correction of ocean color data is a key factor in obtaining accurate water Rrs(λ) data. Based on the QA (quality assurance) score spectral quality evaluation system, the quality of Rrs(λ) spectral of GOCI (Geostationary Ocean Color Imager) obtained from four atmospheric-correction algorithms in the Bohai Sea were evaluated and analyzed in this paper. The four atmospheric-correction algorithms are the NASA (National Aeronautics and Space Administration) standard near-infrared atmospheric-correction algorithm (denoted as Seadas—Default), MUMM (Management Unit of the North Sea Mathematical Models, denoted as Seadas—MUMM), and the standard atmospheric-correction algorithms of KOSC GOCI GDPS2.0 (denoted as GDPS2.0) and GDPS1.3 (denoted as GDPS1.3). It is shown that over 90% of the Rrs(λ) data are in good quality with a score ≥4/6 for the GDPS1.3 algorithm. The probability of Rrs(λ) with a QA score of 1 is significantly higher for the GDPS1.3 algorithm (57.36%), compared with Seadas—Default (37.91%), Seadas—MUMM (35.96%), and GDPS2.0 (33.05%). The field and MODIS measurements of Rrs(λ) were compared with simultaneous GOCI Rrs(λ), and they demonstrate that the QA score system is useful in evaluating the spectral shape of Rrs(λ). The comparison results indicate that higher QA scores have higher accuracy of the Rrs band ratio. The QA score system is helpful to develop and evaluate bio-optical algorithms based on the band ratio. The hourly variation of QA score from UTC 00:16 to 07:16 was investigated as well, and it demonstrates that the data quality of GOCI Rrs(λ) can vary in an hour scale. The GOCI data with high quality should be selected with caution when studying the hourly variation of biogeochemical properties in the Bohai Sea from GOCI measurements.