Photodynamic Controls of Harmful Algal Blooms by an Ultra-efficient and Degradable AIEgen-based Photosensitizer

Harmful <a></a><a>algal blooms</a> (HAB) have severe impacts on human health, aquatic ecosystems, and economy. There is still a lack of effective means to control the algal blooms. Herein, a positively charged photosensitizer with aggregation induced emission (AIE) characteristics, namely TVP-A, is reported for its super-efficient, cost-effective, and eco-friendly governance of HAB. TVP-A possesses a characteristically high quantum yield of harvesting white light into reactive oxygen species (ROS). Attributed to its positive charges, TVP-A has good water solubility and can quickly adsorb onto algal cells floating on the water surface. It effectively triggers cell death through oxidative destruction of the nuclei and chloroplasts of algae. The extremely low effective concentration of TVP-A and the short irradiation time by natural light in removing algal blooms ensure its application at large scales under most weather conditions, without affecting other existing organisms. The slow but consistent self-degradation of TVP-A during the photodynamic controls of algal blooms avoids generating any environmental residues or secondary pollution to environmental systems. TVP-A thereby serves as an excellent candidate for the green governance of HAB, and this work represents a new paradigm for the development of efficient and degradable AIEgens for future environmental applications.

Download Full-text

Photodynamic Controls of Harmful Algal Blooms by an Ultra-efficient and Degradable AIEgen-based Photosensitizer

10.26434/chemrxiv.12471557.v1 ◽

2020 ◽

Author(s):

Qiang Yue ◽

Xuewen He ◽

Neng Yan ◽

sidan tian ◽

Chenchen LIU ◽

...

Keyword(s):

Harmful Algal Blooms ◽

Algal Blooms ◽

Water Solubility ◽

Irradiation Time ◽

Effective Means ◽

Cost Effective ◽

Weather Conditions ◽

New Paradigm ◽

Green Governance ◽

Positively Charged

Download Full-text

Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

Frontiers in Marine Science ◽

10.3389/fmars.2021.729954 ◽

2021 ◽

Vol 8 ◽

Author(s):

Sang-Soo Baek ◽

JongCheol Pyo ◽

Yong Sung Kwon ◽

Seong-Jun Chun ◽

Seung Ho Baek ◽

...

Keyword(s):

Deep Learning ◽

Harmful Algal Blooms ◽

Algal Blooms ◽

Effective Means ◽

Ocean Model ◽

Alexandrium Catenella ◽

The Public ◽

Global Issue ◽

Simulated Distribution ◽

Classification And Regression

In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L–1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.

Download Full-text

sxtA-Based Quantitative Molecular Assay To Identify Saxitoxin-Producing Harmful Algal Blooms in Marine Waters

Applied and Environmental Microbiology ◽

10.1128/aem.05308-11 ◽

2011 ◽

Vol 77 (19) ◽

pp. 7050-7057 ◽

Cited By ~ 67

Author(s):

Shauna A. Murray ◽

Maria Wiese ◽

Anke Stüken ◽

Steve Brett ◽

Ralf Kellmann ◽

...

Keyword(s):

Harmful Algal Blooms ◽

Algal Blooms ◽

Harmful Algal Bloom ◽

Effective Means ◽

Molecular Genetic ◽

Alexandrium Tamarense ◽

Alexandrium Catenella ◽

Content Type ◽

Eukaryotic Phytoplankton ◽

Novel Method

ABSTRACTThe recent identification of genes involved in the production of the potent neurotoxin and keystone metabolite saxitoxin (STX) in marine eukaryotic phytoplankton has allowed us for the first time to develop molecular genetic methods to investigate the chemical ecology of harmful algal bloomsin situ. We present a novel method for detecting and quantifying the potential for STX production in marine environmental samples. Our assay detects a domain of the genesxtAthat encodes a unique enzyme putatively involved in thesxtpathway in marine dinoflagellates,sxtA4. A product of the correct size was recovered from nine strains of four species of STX-producingAlexandriumandGymnodinium catenatumand was not detected in the non-STX-producingAlexandriumspecies, other dinoflagellate cultures, or an environmental sample that did not contain known STX-producing species. However,sxtA4was also detected in the non-STX-producing strain ofAlexandrium tamarense, Tasmanian ribotype. We investigated the copy number ofsxtA4in three strains ofAlexandrium catenellaand found it to be relatively constant among strains. Using our novel method, we detected and quantifiedsxtA4in three environmental blooms ofAlexandrium catenellathat led to STX uptake in oysters. We conclude that this method shows promise as an accurate, fast, and cost-effective means of quantifying the potential for STX production in marine samples and will be useful for biological oceanographic research and harmful algal bloom monitoring.

Download Full-text

Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms: A new paradigm in water quality monitoring

Journal of Great Lakes Research ◽

10.1016/j.jglr.2019.03.006 ◽

2019 ◽

Vol 45 (3) ◽

pp. 444-453 ◽

Cited By ~ 10

Author(s):

Richard H. Becker ◽

Michael Sayers ◽

Dustin Dehm ◽

Robert Shuchman ◽

Kaydian Quintero ◽

...

Keyword(s):

Water Quality ◽

Harmful Algal Blooms ◽

Algal Blooms ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Unmanned Aerial System ◽

New Paradigm

Download Full-text

Nutrient Retention in Ecologically Functional Floodplains: A Review

Water ◽

10.3390/w12102762 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2762

Author(s):

Brad A. Gordon ◽

Olivia Dorothy ◽

Christian F. Lenhart

Keyword(s):

Harmful Algal Blooms ◽

Algal Blooms ◽

Effective Means ◽

Nutrient Retention ◽

Field Studies ◽

Nutrient Concentrations ◽

Nutrient Loads ◽

Nutrient Deposition ◽

Hydraulic Load ◽

The Many

Nutrient loads in fresh and coastal waters continue to lead to harmful algal blooms across the globe. Historically, floodplains—low-lying areas adjacent to streams and rivers that become inundated during high-flow events—would have been nutrient deposition and/or removal sites within riparian corridors, but many floodplains have been developed and/or disconnected. This review synthesizes literature and data available from field studies quantifying nitrogen (N) and phosphorus (P) removal within floodplains across North America and Europe to determine how effective floodplain restoration is at removing nutrients. The mean removal of nitrate-N (NO3−-N), the primary form of N in floodplain studies, was 200 (SD = 198) kg-N ha−1 year−1, and of total or particulate P was 21.0 (SD = 31.4) kg-P ha−1 year−1. Based on the literature, more effective designs of restored floodplains should include optimal hydraulic load, permanent wetlands, geomorphic diversity, and dense vegetation. Floodplain restorations along waterways with higher nutrient concentrations could lead to a more effective investment for nutrient removal. Overall, restoring and reconnecting floodplains throughout watersheds is a viable and effective means of removing nutrients while also restoring the many other benefits that floodplains provide.

Download Full-text

Knowledge Spillover and Positive Environmental Externality in Agricultural Decision Making under Performance-Based Payment Programs

Agricultural and Resource Economics Review ◽

10.1017/age.2020.18 ◽

2020 ◽

Vol 49 (2) ◽

pp. 270-290

Author(s):

Hongxing Liu ◽

Christopher S. Ruebeck

Keyword(s):

Harmful Algal Blooms ◽

Algal Blooms ◽

Management Practice ◽

Profit Maximization ◽

Spillover Effect ◽

Cost Effective ◽

Knowledge Spillover ◽

Program Participation ◽

Policy Makers ◽

World Government

Agricultural activities have imposed significant impacts on water resources, leading to hypoxic zones and harmful algal blooms all over the world. Government agencies, nongovernmental organizations, and individuals have been making various efforts to reduce this non-point source pollution. Among those efforts, even the more cost-effective examples of performance-based environmental payment programs generally have low participation rates. We investigate the effects of externalities in farmers’ decisions on neighboring farms, incorporating both a knowledge spillover effect and a positive environmental outcome externality of farmers’ best-management practice (BMP) adoption decisions. Our focus is on how these effects may influence the outcome of performance-based payment programs and how policy makers might recognize these effects in the design of cost-effective policies to promote program participation and BMP adoption. Rather than imposing an assumption of profit-maximization or forward-looking behavior, we allow outcomes to emerge from interactions among neighboring farmers. We recommend cost-effective policies across communities depending on their composition. It is more cost-effective to target communities with fewer innovators and/or target the programs towards the least-innovative individuals.

Download Full-text

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Remote Sensing ◽

10.3390/rs10101656 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1656 ◽

Cited By ~ 5

Author(s):

Sita Karki ◽

Mohamed Sultan ◽

Racha Elkadiri ◽

Tamer Elbayoumi

Keyword(s):

Remote Sensing ◽

Harmful Algal Blooms ◽

Multivariate Regression ◽

Algal Blooms ◽

Wildlife Conservation ◽

River Mouth ◽

Warning System ◽

Remote Sensing Data ◽

Cost Effective ◽

Color Processing

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Download Full-text

Use of biosensors for the detection of marine toxins

Essays in Biochemistry ◽

10.1042/ebc20150006 ◽

2016 ◽

Vol 60 (1) ◽

pp. 49-58 ◽

Cited By ~ 13

Author(s):

Daniel A. McPartlin ◽

Michael J. Lochhead ◽

Laurie B. Connell ◽

Gregory J. Doucette ◽

Richard J. O'Kennedy

Keyword(s):

Climate Change ◽

Economic Impact ◽

Harmful Algal Blooms ◽

Algal Blooms ◽

Cost Effective ◽

Detection Methods ◽

Global Effect ◽

Economic Implications ◽

Marine Biotoxins ◽

Exciting Area

Increasing occurrences of harmful algal blooms (HABs) in the ocean are a major concern for countries around the globe, and with strong links between HABs and climate change and eutrophication, the occurrences are only set to increase. Of particular concern with regard to HABs is the presence of toxin-producing algae. Six major marine biotoxin groups are associated with HABs. Ingestion of such toxins via contaminated shellfish, fish, or other potential vectors, can lead to intoxication syndromes with moderate to severe symptoms, including death in extreme cases. There are also major economic implications associated with the diverse effects of marine biotoxins and HABs. Thus, effective monitoring programmes are required to manage and mitigate their detrimental global effect. However, currently legislated detection methods are labour-intensive, expensive and relatively slow. The growing field of biosensor diagnostic devices is an exciting area that has the potential to produce robust, easy-to-use, cost-effective, rapid and accurate detection methods for marine biotoxins and HABs. This review discusses recently developed biosensor assays that target marine biotoxins and their microbial producers, both in harvested fish/shellfish samples and in the open ocean. The effective deployment of such biosensor platforms could address the pressing need for improved monitoring of HABs and marine biotoxins, and could help to reduce their global economic impact.

Download Full-text

Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

10.20944/preprints201809.0038.v1 ◽

2018 ◽

Author(s):

Sita Karki ◽

Mohamed Sultan ◽

Racha Elkadiri ◽

Tamer Elbayoumi

Keyword(s):

Remote Sensing ◽

Harmful Algal Blooms ◽

Multivariate Regression ◽

Algal Blooms ◽

Wildlife Conservation ◽

River Mouth ◽

Warning System ◽

Remote Sensing Data ◽

Cost Effective ◽

Color Processing

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Download Full-text

Plasticity and Multiplicity of Trophic Modes in the Dinoflagellate Karlodinium and Their Pertinence to Population Maintenance and Bloom Dynamics

Journal of Marine Science and Engineering ◽

10.3390/jmse9010051 ◽

2021 ◽

Vol 9 (1) ◽

pp. 51

Author(s):

Huijiao Yang ◽

Zhangxi Hu ◽

Ying Zhong Tang

Keyword(s):

Harmful Algal Blooms ◽

Algal Blooms ◽

Signal Molecules ◽

New Paradigm ◽

Dominant Group ◽

Toxic Species ◽

Plankton Ecology ◽

Multiple Characteristics ◽

Population Maintenance ◽

Molecular Genes

As the number of mixotrophic protists has been increasingly documented, “mixoplankton”, a third category separated from the traditional categorization of plankton into “phytoplankton” and “zooplankton”, has become a new paradigm and research hotspot in aquatic plankton ecology. While species of dinoflagellates are a dominant group among all recorded members of mixoplankton, the trophic modes of Karlodinium, a genus constituted of cosmopolitan toxic species, were reviewed due to their representative features as mixoplankton and harmful algal blooms (HABs)-causing dinoflagellates. Among at least 15 reported species in the genus, three have been intensively studied for their trophic modes, and all found to be phagotrophic. Their phagotrophy exhibits multiple characteristics: (1) omnivority, i.e., they can ingest a variety of preys in many forms; (2) flexibility in phagotrophic mechanisms, i.e., they can ingest small preys by direct engulfment and much bigger preys by myzocytosis using a peduncle; (3) cannibalism, i.e., species including at least K. veneficum can ingest the dead cells of their own species. However, for some recently described and barely studied species, their tropical modes still need to be investigated further regarding all of the above-mentioned aspects. Mixotrophy of Karlodinium plays a significant role in the population dynamics and the formation of HABs in many ways, which thus deserves further investigation in the aspects of physiological ecology, environmental triggers (e.g., levels of inorganic nutrients and/or presence of preys), energetics, molecular (genes and gene expression regulations) and biochemical (e.g., relevant enzymes and signal molecules) bases, origins, and evaluation of the advantages of being a phagotroph.

Download Full-text