Results for “algal blooms” — Page 3

DOE OSTI2023

Designing an Observing System to Study the Surface Biology and Geology (SBG) of the Earth in the 2020s

Abstract Observations of planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of Earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multispectral thermal infrared imager to meet a range of needs for studying Surface Biology and Geology (SBG). SBG will be the premier integrated observatory for observing the emerging impacts of climate change by characterizing the diversity of plant life and resolving chemical and physiological signatures. It will address wildfire risk, behavior, and recovery as well as responses to hazards such as oil spills, toxic minerals in minelands, harmful algal blooms, landslides, and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal, and spectral resolutions, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. We present an overview of the Research and Applications trade‐study analysis of algorithms, calibration and validation needs, and societal applications with specifics of substudies detailed in other articles in this special collection. We provide a value framework to converge from hundreds down to three candidate architectures recommended for development. The analysis identified valuable opportunities for international collaboration to increase the revisit frequency, adding value for all partners, leading to a clear measurement strategy for an observing system architecture.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Light-independent phytoplankton degradation and detoxification of methylmercury in water

Phytoplankton serves as a key entry point for the trophic transfer and bioaccumulation of the neurotoxin methylmercury (MeHg) in aquatic food webs. However, it is unclear whether and how phytoplankton itself may degrade and metabolize MeHg in the dark. Here, using several strains of the freshwater alga Chlorella vulgaris, the marine diatom Chaetoceros gracilis and two cyanobacteria (or blue-green algae), we report a light-independent pathway of MeHg degradation in water by phytoplankton, rather than its associated bacteria. About 36–85% of MeHg could be degraded intracellularly to inorganic Hg(II) and/or Hg(0) via dark reactions. Furthermore, endogenic reactive oxygen species, particularly singlet oxygen, were identified as the main driver of MeHg demethylation. Given the increasing incidence of algal blooms in lakes and marine systems globally, these findings underscore the potential roles of phytoplankton demethylation and detoxification of MeHg in aquatic ecosystems and call for improved modelling and assessment of MeHg bioaccumulation and environmental risks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Phenylpropanoid methyl esterase unlocks catabolism of aromatic biological nitrification inhibitors

Microbial nitrification of fertilizers represents is a significant global source of greenhouse gas emissions. This process increases emissions, fosters toxic algal blooms, and raises crop production costs. Some plants naturally release biological nitrification inhibitors to suppress ammonium-oxidizing microbes and reduce nitrification. Engineering nitrification inhibitor production into food and bioenergy crops via synthetic biology offers a promising mitigation strategy, but its success depends on addressing gaps in our understanding of inhibitor degradation in soil. This study begins to fill this gap by identifying a previously unknown microbial pathway for degrading phenylpropanoid methyl esters, a key class of aromatic nitrification inhibitors. Using transcriptomics and high-throughput functional genomics, we discovered genes essential for phenylpropanoid methyl ester degradation. Genetic and biochemical analyses revealed two novel enzymes, including a newly identified phenylpropanoid methyl esterase, that direct phenylpropanoid methyl esters into known metabolic pathways. Importantly, transferring these genes into bacteria capable of metabolizing other phenylpropanoids enabled them to use the methyl esters as a carbon source. This work provides critical insights into microbial nitrification inhibitor degradation, a poorly understood element of the nitrification cycle.

Genetic Engineering↗

DOE OSTI2024

Dominant nitrogen metabolisms of a warm, seasonally anoxic freshwater ecosystem revealed using genome resolved metatranscriptomics

In aquatic ecosystems where primary productivity is limited by nitrogen (N), whether continuously, seasonally, or in concert with additional nutrient limitations, increased inorganic N availability can reshape ecosystem structure and function, potentially resulting in eutrophication and even harmful algal blooms. Whereas microbial metabolic processes such as mineralization and dissimilatory nitrate reduction to ammonium increase inorganic N availability, denitrification removes bioavailable N from the ecosystem. Therefore, understanding these key microbial mechanisms is critical to the sustainable management and environmental stewardship of inland freshwater resources. This study identifies and characterizes these crucial metabolisms in a warm, seasonally anoxic ecosystem. Results are contextualized by an ecological understanding of the study system derived from a multi-year continuous monitoring effort. This unique data set is the first of its kind in this largely understudied ecosystem (tropical lakes) and also provides insight into microbiome function and associated taxa in warm, anoxic freshwaters.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

A manifold learning perspective on surrogate modeling of nitrate concentration in the Kansas River

Abstract A non-linear surrogate model of nitrate concentration in the Kansas River (USA) is described. The model is an (almost) Piece-wise Linear response surface that provides a mean field approximation to the dynamics of the measured data for nitrate plus nitrite (target product) correlations to turbidity and chlorophyll-a concentrations (input variables). The method extends the United States Geological Survey’s linear procedures for surrogate data modeling allowing for better approximations for river systems exhibiting algal blooms due to nutrient-rich source waters. The model and visualization procedures illustrated in the Kansas River example should be generally applicable to many medium-size rivers in agricultural regions.

Tufillaro, Nicholas (ORCID:0009000628968832)↗

DOE OSTI2021

Trustworthy AI for Extreme Event Prediction and Understanding

Our transformational science question is: can we revolutionize both the prediction and understanding of extreme events through trustworthy AI? Our use-cases include extreme weather such as tornadoes and hail as well as water-based events including extreme precipitation, compound flooding, harmful algal blooms, and sea turtle cold stunnings and nest inundations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Decarbonization of Wastewater Treatment with Microalgae Processes.

Microalgae can contribute to the decarbonization of wastewater treatment by municipalities, industries and agriculture, by substituting sunlight for energy intensive conventional treatment processes, while capturing nutrients and carbon in the algal biomass. The carbon fixed into the algal biomass can be converted to renewable natural gas (RNG) using anaerobic digestion or into renewable diesel and sustainable aviation fuel (SAF) using hydrothermal liquefaction (HTL), with greatly reduce the carbon footprints compared to conventional fuels. Bioplastics, biofertilizers and other bioproducts from microalgae biomass also provide potential benefits in greenhouse gas (GHG) reduction and domestic supply chains. Microalgae technologies can counter eutrophication caused by harmful algal blooms by recovering nutrients, N and P, from wastewaters, and even from very low nutrient concentrations found in surface waters. CO2 is required in microalgae cultivation and wastewater treatment to support maximal rates of the photosynthesis, with the CO2 obtained from the wastes themselves, other local sources or even atmospheric CO2. Greenhouse gas mitigation with microalgae processes is based on life cycle assessments, comparing such green processes with current energy intensive wastewater treatment, and nutrient reduction technologies. Microalgae technologies are specifically relevant to smaller, often disadvantaged, communities, where currently about 5,000 algae wastewater treatment ponds are operated by public utilities with many more are operated by industries. However, many, if not most, of these pond facilities require urgent technology upgrades to achieve the potential and goals of low cost GHG mitigation and efficient nutrient recycling. MicroBio Engineering Inc. is developing and has demonstrated several technologies that combine innovative carbon mitigation and decarbonization technologies for a circular economy, with longer-term potential for large-scale biofuels and biofertilizer production.

09 BIOMASS FUELS↗

DOE OSTI2023

Decarbonization of Wastewater Treatment with Microalgae Processes.

Microalgae can contribute to the decarbonization of wastewater treatment by municipalities, industries and agriculture, by substituting sunlight for energy intensive conventional treatment processes, while capturing nutrients and carbon in the algal biomass. The carbon fixed into the algal biomass can be converted to renewable natural gas (RNG) using anaerobic digestion or into renewable diesel and sustainable aviation fuel (SAF) using hydrothermal liquefaction (HTL), with greatly reduce the carbon footprints compared to conventional fuels. Bioplastics, biofertilizers and other bioproducts from microalgae biomass also provide potential benefits in greenhouse gas (GHG) reduction and domestic supply chains. Microalgae technologies can counter eutrophication caused by harmful algal blooms by recovering nutrients, N and P, from wastewaters, and even from very low nutrient concentrations found in surface waters. CO2 is required in microalgae cultivation and wastewater treatment to support maximal rates of the photosynthesis, with the CO2 obtained from the wastes themselves, other local sources or even atmospheric CO2. Greenhouse gas mitigation with microalgae processes is based on life cycle assessments, comparing such green processes with current energy intensive wastewater treatment, and nutrient reduction technologies. Microalgae technologies are specifically relevant to smaller, often disadvantaged, communities, where currently about 5,000 algae wastewater treatment ponds are operated by public utilities with many more are operated by industries. However, many, if not most, of these pond facilities require urgent technology upgrades to achieve the potential and goals of low cost GHG mitigation and efficient nutrient recycling. MicroBio Engineering Inc. is developing and has demonstrated several technologies that combine innovative carbon mitigation and decarbonization technologies for a circular economy, with longer-term potential for large-scale biofuels and biofertilizer production.

09 BIOMASS FUELS↗

DOE OSTI2023

Decarbonization of Wastewater Treatment with Microalgae Processes.

Microalgae can contribute to the decarbonization of wastewater treatment by municipalities, industries and agriculture, by substituting sunlight for energy intensive conventional treatment processes, while capturing nutrients and carbon in the algal biomass. The carbon fixed into the algal biomass can be converted to renewable natural gas (RNG) using anaerobic digestion or into renewable diesel and sustainable aviation fuel (SAF) using hydrothermal liquefaction (HTL), with greatly reduce the carbon footprints compared to conventional fuels. Bioplastics, biofertilizers and other bioproducts from microalgae biomass also provide potential benefits in greenhouse gas (GHG) reduction and domestic supply chains. Microalgae technologies can counter eutrophication caused by harmful algal blooms by recovering nutrients, N and P, from wastewaters, and even from very low nutrient concentrations found in surface waters. CO2 is required in microalgae cultivation and wastewater treatment to support maximal rates of the photosynthesis, with the CO2 obtained from the wastes themselves, other local sources or even atmospheric CO2. Greenhouse gas mitigation with microalgae processes is based on life cycle assessments, comparing such green processes with current energy intensive wastewater treatment, and nutrient reduction technologies. Microalgae technologies are specifically relevant to smaller, often disadvantaged, communities, where currently about 5,000 algae wastewater treatment ponds are operated by public utilities with many more are operated by industries. However, many, if not most, of these pond facilities require urgent technology upgrades to achieve the potential and goals of low cost GHG mitigation and efficient nutrient recycling. MicroBio Engineering Inc. is developing and has demonstrated several technologies that combine innovative carbon mitigation and decarbonization technologies for a circular economy, with longer-term potential for large-scale biofuels and biofertilizer production.

09 BIOMASS FUELS↗

DOE OSTI2023

Microbiome controls on harmful algal toxin production in Lake Erie (Final Project Report)

Lawrence Livermore National Laboratory (LLNL) funded this project with the aim to understand how the microbiome influences the toxicity of harmful algal blooms in Lake Erie. Dr. Xavier Mayali from LLNL led the project. Dr. Dragan Isailovic led University of Toledo portion of the project also involving Dr. Thomas Bridgeman, and Dr. Gregory Dick led the portion of the project at the University of Michigan with Dr. Jenan Kharbush.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Quality Assurance Project Plan for the Salish Sea Model – Continuing Development of New Capabilities and Applications: European Green Crab (EGC) Larval Transport, Coupling to VELMA and Atlantis, Performance Improvements and Diagnostic Applications

This quality assurance project plan (QAPP) is for proposed FY24-FY27 efforts to (a) develop/expand a predictive European Green Crab (EGC) larval dispersal model for the Salish Sea, (b) add linkage between VELMA (Visualizing Ecosystem Land Management Assessments) watershed model and Atlantis model (ecosystem dynamics and food web) through SSM, and (c) continue SSM capability development through the addition of metals, harmful algal blooms (HABs), and FVCOM-LTRANS (Lagrangian Larval Transport) modules.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Multiplexed Toxin Activity Detection

There is a need for field forward biothreat detection methods for highly lethal marine toxins, such as conotoxins, to protect the warfighter and civilians in contested environments. These toxins arise from harmful algal blooms which impact the warfighter, pets, and civilians. The need for detection platforms that can be fielded on drones or unmanned vehicles is paramount to successful biothreat detection to serve as an early warning system for those impacted in these environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Integrated Hydro-Terrestrial Modeling: Development of a National Capability

Water is one of our most important natural resources and is essential to our national economy and security. Multiple federal government agencies have mission elements that address national needs related to water. Each water-related agency champions a unique science and/or operational mission focused on advancing a portion of the nation’s ability to meet our water-related challenges. These diverse mission needs have engendered a rich and extensive base of water-related data and modeling capabilities. While useful for their intended purposes, these capabilities are not well integrated to address complex regional problems and overarching national problems. These major investments by a number of federal agencies, however, lay the foundation for an integrated hydro-terrestrial modeling and data infrastructure that will enhance knowledge, understanding, prediction, and management of the nation’s diverse water challenges. Creating a more seamless national hydro-terrestrial modeling and data capacity presents an enormous opportunity to advance operational as well as research capabilities leading to more effective water management. Advances are necessary not only in operational tools for forecasting but also in research to identify and resolve knowledge and data gaps that lead to unacceptable uncertainties in forecast outcomes. As such, close coordination across scientific, operational, and resource management communities is required. To this end, an interagency workshop on “Integrated Hydro-Terrestrial Modeling: Development of a National Capacity” was held at the National Science Foundation (NSF) headquarters in Alexandria, Virginia in September 2019, led jointly by the NSF, the U.S. Department of Energy (DOE) and the U.S. Geological Survey (USGS) and with broader interagency support provided through an interagency steering committee. This workshop provided a venue to bring together representatives of water-related agencies and their scientific partners (including university researchers) to initiate and refine a vision for a national Integrated Hydro-Terrestrial Modeling (IHTM) and data infrastructure and advance ideas towards its development. The workshop was designed to address three critical foci to advance the development of a national IHTM capacity: “Priority Water Challenges” around which to motivate and initiate development; Technical and methodological obstacles related to data and modeling; Organizational, structural, and cultural barriers that heretofore have impeded integration of capabilities across the federal and research landscapes. The following “Priority Water Challenge” domain areas represent targets for initiating development of the IHTM and were identified and selected in alignment with priorities of the administration’s Water Sub-Cabinet: (1) Nutrient loading, hypoxia, and harmful algal blooms; (2) Water availability in the western United States; and (3) Extreme weather-related water hazards. These water challenges span agency mission boundaries and encompass a broad range of geographies, complex system dynamics and feedbacks, and critical processes spanning hydrological, climatic, and biophysical systems as well as land-use/land-cover, agricultural, built infrastructure, societal, economic, and decisional environments. These three Priority Water Challenges cannot be fully addressed without leveraging complementary and synergistic capabilities across multiple agencies.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Long-term warming modulates diversity, vertical structuring of microbial communities, and sulfate reduction in coastal Baltic Sea sediments

Coastal waters such as those found in the Baltic Sea already suffer from anthropogenic related problems including increased algal blooming and hypoxia while ongoing and future climate change will likely worsen these effects. Microbial communities in sediments play a crucial role in the marine energy- and nutrient cycling, and how they are affected by climate change and shape the environment in the future is of great interest. The aims of this study were to investigate potential effects of prolonged warming on microbial community composition and nutrient cycling including sulfate reduction in surface (∼0.5 cm) to deeper sediments (∼ 24 cm). To investigate this, 16S rRNA gene amplicon sequencing was performed, and sulfate concentrations were measured and compared between sediments in a heated bay (which has been used as a cooling water outlet from a nearby nuclear power plant for approximately 50 years) and a nearby but unaffected control bay. The results showed variation in overall microbial diversity according to sediment depth and higher sulfate flux in the heated bay compared to the control bay. A difference in vertical community structure reflected increased relative abundances of sulfur oxidizing- and sulfate reducing bacteria along with a higher proportion of archaea, such as Bathyarchaeota, in the heated compared to the control bay. This was particularly evident closer to the sediment surface, indicating a compression of geochemical zones in the heated bay. These results corroborate findings in previous studies and additionally point to an amplified effect of prolonged warming deeper in the sediment, which could result in elevated concentrations of toxic compounds and greenhouse gases closer to the sediment surface.

16S rRNA gene amplicon↗

DOE OSTI2024

The use of a benign fast-growing cyanobacterial species to control microcystin synthesis from Microcystis aeruginosa

Introduction Microcystis aeruginosa(M. aeruginosa), one of the most abundant blue-green algae in aquatic environments, produces microcystin by causing harmful algal blooms (HABs). This study investigated the combined effects of nutrients and competition among cyanobacterial subpopulations on the synthesis of microcystin-LR. Methods Under varying nitrogen and phosphorus concentrations, cyanobacterial coculture, and the presence of algicidal DCMU, the growth was monitored by optical density analysis or microscopic counting, and the microcystin production was analyzed using high-performance liquid chromatography-UV. Furthermore, growth and toxin production were predicted using a kinetic model. Results and discussion First, coculture with the fast-growing cyanobacteriumSynechococcus elongatusUTEX 2973 (S. elongatus) reducedM. aeruginosabiomass and microcystin production at 30°C. Under high nitrogen and low phosphorus conditions,S. elongatuswas most effective, limitingM. aeruginosagrowth and toxin synthesis by up to 94.7% and 92.4%, respectively. Second, this biological strategy became less effective at 23°C, whereS. elongatusgrew more slowly. Third, the photosynthesis inhibitor DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) inhibitedM. aeruginosagrowth (at 0.1 mg/L) and microcystin production (at 0.02 mg/L). DCMU was also effective in controlling microcystin production inS. elongatus–M. aeruginosacocultures. Based on the experimental results, a multi-substrate, multi-species kinetic model was built to describe coculture growth and population interactions. Conclusion Microcystin from representative toxin-producingM. aeruginosacan be controlled by coculturing fast-growing benign cyanobacteria, which can be made even more efficient if appropriate algicide is applied. This study improved the understanding of the biological control of microcystin production under complex environmental conditions.

Microbiology↗

DOE OSTI2021

Toward Revealing Microcystin Distribution in Mouse Liver Tissue Using MALDI-MS Imaging

Cyanotoxins can be found in water and air during cyanobacterial harmful algal blooms (cHABs) in lakes and rivers. Therefore, it is very important to monitor their potential uptake by animals and humans as well as their health effects and distribution in affected organs. Herein, the distribution of hepatotoxic peptide microcystin-LR (MC-LR) is investigated in liver tissues of mice gavaged with this most common MC congener. Preliminary matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging experiments performed using a non-automated MALDI matrix deposition device and a MALDI-time-of-flight (TOF) mass spectrometer yielded ambiguous results in terms of MC-LR distribution in liver samples obtained from MC-LR-gavaged mice. The tissue preparation for MALDI-MS imaging was improved by using an automated sprayer for matrix deposition, and liver sections were imaged using an Nd:YAG MALDI laser coupled to a 15 Tesla Fourier-transform ion cyclotron resonance (FT-ICR)-mass spectrometer. MALDI-FT-ICR-MS imaging provided unambiguous detection of protonated MC-LR (calculated m/z 995.5560, z = +1) and the sodium adduct of MC-LR (m/z 1017.5380, z = +1) in liver sections from gavaged mice with great mass accuracy and ultra-high mass resolution. Since both covalently bound and free MC-LR can be found in liver of mice exposed to this toxin, the present results indicate that the distribution of free microcystins in tissue sections from affected organs, such as liver, can be monitored with high-resolution MALDI-MS imaging.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science

In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational models. Previous work in informative path planning, adaptive sampling, and experimental design have shown compelling results, largely in static environments, using data-driven models and information-based rewards. However, these methodologies do not trivially extend to expeditionary science in spatiotemporal environments: they generally do not make use of scientific knowledge such as equations of state dynamics, they focus on information gathering as opposed to scientific task execution, and they make use of decision-making approaches that scale poorly to large, continuous problems with long planning horizons and real-time operational constraints. In this work, we discuss these and other challenges related to probabilistic modeling and decision-making in expeditionary science, and present some of our preliminary work that addresses these gaps. We ground our results in a real expeditionary science deployment of an autonomous underwater vehicle (AUV) in the deep ocean for hydrothermal vent discovery and characterization. Our concluding thoughts highlight remaining work to be done, and the challenges that merit consideration by the reinforcement learning and decision-making community.

Preston, Victoria↗