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

Environmental Research, Journal Year: 2023, Volume and Issue: 225, P. 115617 - 115617

Published: March 4, 2023

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

Language: Английский

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Spatio-temporal distribution of harmful algal blooms and their correlations with marine hydrological elements in offshore areas, China

Ocean & Coastal Management, Journal Year: 2023, Volume and Issue: 238, P. 106554 - 106554

Published: March 10, 2023

Language: Английский

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A review on monitoring, forecasting, and early warning of harmful algal bloom

Aquaculture, Journal Year: 2024, Volume and Issue: 593, P. 741351 - 741351

Published: July 16, 2024

Language: Английский

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Harmful Algal Blooms in Eutrophic Marine Environments: Causes, Monitoring, and Treatment

Water, Journal Year: 2024, Volume and Issue: 16(17), P. 2525 - 2525

Published: Sept. 5, 2024

Marine eutrophication, primarily driven by nutrient over input from agricultural runoff, wastewater discharge, and atmospheric deposition, leads to harmful algal blooms (HABs) that pose a severe threat marine ecosystems. This review explores the causes, monitoring methods, control strategies for eutrophication in environments. Monitoring techniques include remote sensing, automated situ sensors, modeling, forecasting, metagenomics. Remote sensing provides large-scale temporal spatial data, while sensors offer real-time, high-resolution monitoring. Modeling forecasting use historical data environmental variables predict blooms, metagenomics insights into microbial community dynamics. Control treatments encompass physical, chemical, biological treatments, as well advanced technologies like nanotechnology, electrocoagulation, ultrasonic treatment. Physical such aeration mixing, are effective but costly energy-intensive. Chemical including phosphorus precipitation, quickly reduce levels may have ecological side effects. Biological biomanipulation bioaugmentation, sustainable require careful management of interactions. Advanced innovative solutions with varying costs sustainability profiles. Comparing these methods highlights trade-offs between efficacy, cost, impact, emphasizing need integrated approaches tailored specific conditions. underscores importance combining mitigate adverse effects on

Language: Английский

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

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 938, P. 173546 - 173546

Published: May 27, 2024

Language: Английский

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Advancing harmful algal bloom detection with hyperspectral imaging: Correlation of algal organic matter and fouling indices based on deep learning

Desalination, Journal Year: 2025, Volume and Issue: unknown, P. 118505 - 118505

Published: Jan. 1, 2025

Language: Английский

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Recent Developments in Artificial Intelligence in Oceanography

Ocean-Land-Atmosphere Research, Journal Year: 2022, Volume and Issue: 2022

Published: Jan. 1, 2022

With the availability of petabytes oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety applications. In this paper, these applications reviewed from perspectives identifying, forecasting, parameterizing ocean phenomena. Specifically, usage AI algorithms for identification mesoscale eddies, internal waves, oil spills, sea ice, marine algae discussed paper. Additionally, AI-based forecasting surface El Niño Southern Oscillation, storm surges is discussed. This followed by discussion on schemes to parameterize oceanic turbulence atmospheric moist physics. Moreover, physics-informed deep learning neural networks within an context, further with digital twins physics-constrained described. review meant introduce beginners experts sciences methodologies stimulate future research toward causality-adherent Fourier oceanography.

Language: Английский

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Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 279, P. 113089 - 113089

Published: June 12, 2022

Algal blooms around the world are increasing in frequency and severity, often with possibility of adverse effects on human ecosystem health. The health economic impacts associated harmful algal blooms, or HABs, provide compelling rationale for developing new methods monitoring these events via remote sensing. Although concentrations chlorophyll-a key pigments like phycocyanin routinely estimated from satellite images used to infer cyanobacterial cell counts, current unable information taxonomic composition a bloom. This study introduced approach capable differentiating among genera based their reflectance characteristics: Spectral Mixture Analysis Surveillance SMASH. foundation SMASH is multiple endmember spectral mixture analysis (MESMA) algorithm that takes library cyanobacteria endmembers hyperspectral image as input estimates fractional abundance each genus, plus water, per-pixel basis. Importantly, we assume water column consists only pure cyanobacteria, implying our linear unmixing models do not account other optically active constituents such suspended sediment colored dissolved organic matter (CDOM). We spectra 12 measured under microscope populate an applied workflow four waterbodies across United States. Normalized separability scores indicated were distinct one another MESMA reproduced known fractions simulated mixtures included all pairwise combinations water. Upper Klamath Lake example illustrate data products generated SMASH: maps normalized difference chlorophyll index index, MESMA-based classification genera, fraction endmember, root mean square error (RMSE) summarizes uncertainty. For Lake, outputs highlighted complex bloom featuring several primarily Aphanizomenon, intricate spatial patterns gyres. maximum RMSE constraint imposed provided means avoiding false positive detection present waterbody but must be set so low leave much unclassified cases where present. Comparison relative biovolumes calculated field samples was consistent observations. example, successfully identified Microcystis Owasco avoided misclassifying Asterionella, genus yet library, Detroit Lake. proof-of-concept investigation demonstrates potential enhance understanding particularly respect temporal dynamics.

Language: Английский

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

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(8), P. 19602 - 19616

Published: Jan. 16, 2023

Language: Английский

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Comprehensive Insights into Harmful Algal Blooms: A Review of Chemical, Physical, Biological, and Climatological Influencers with Predictive Modeling Approaches

Journal of Environmental Engineering, Journal Year: 2024, Volume and Issue: 150(4)

Published: Feb. 9, 2024

Phytoplankton plays an essential role in the biogeochemical cycle because it is at top of food chain and a source oxygen. Eutrophication causes coastal areas to deteriorate as industrialization accelerates, leading harmful algal blooms (HABs), severely affecting human ecological health. The frequency extent HAB events potentially may increase due climate change. outbreaks have led substantial losses for major economies globally, therefore emerged critical research focus environmental sciences. However, lack overview diverse factors influencing HABs complicates cause identification effective countermeasure development occurrence, thereby impeding formulation targeted strategies prediction mitigation. Therefore, this review summarizes influential areas, including water quality (nutrients, salinity, stratification, biological factors) climatological (temperature, pH pCO2, irradiance light). Recent work with several algae species suggested that warmer temperatures combined nutrient variation, stronger ocean acidification growth some toxic dinoflagellate species. Although effects vary different locations, intensification anthropogenic activities change likely will frequency, outbreak scale, severity most HABs. Because predicting crucial understanding synergy their minimizing decision makers stakeholders, we reviewed models HABs, process-based models, traditional statistical-empirical data-driven machine learning models. Predicting becomes more challenging spatial distribution influenced by future patterns. This paper presents comprehensive various impacting serving valuable resource researchers design mitigation strategies.

Language: Английский

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