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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sustainable Supply Chains by Integrating Life Cycle Modeling and Techno-Ecological Synergy with Application to Mitigation of Harmful Algal Blooms

Harmful algal blooms (HABs) are a challenge in water bodies across the planet and are often caused by fertilizer runoff from intensively farmed lands. This chapter describes a framework by which producers of biomass-based products could manage their supply chain to encourage mitigation of HABs and their life cycle environmental impacts, while also considering economic implications. The emphasis is on corn-based products such as ethanol in the region around Lake Erie in Northwest Ohio. It shows how wetland ecosystems could be designed synergistically with farming practices, transportation of the corn, and conversion and use of ethanol. The resulting techno-ecologically synergistic supply chains can be environmentally and economically superior to conventional techno-centric supply chains. The resulting designs can help ethanol manufacturers choose between farms, encourage farmers to adopt wetland ecosystems for intercepting and mitigating nutrient runoff, and determine the location of biorefineries for optimizing environmental and economic objectives.

air quality regulation↗
DOE OSTI2024

Linking Spatiotemporal Biological Data to Predict Harmful Algal Blooms

Cyanobacterial Harmful Algal Blooms (cHABs) have significant impacts on an affected region’s economy, ecology, and human health. The blooms can release toxins that kill fish and poison water for people and animals. The global adverse effects of cHABs are exacerbated by the consequences of climate change and increased pollution. Though the phenomena are well documented, scientists’ efforts to mitigate the damage are hampered by insufficient predictive models and incomplete granular knowledge of cHAB community structure. With a goal of leveraging bioinformatics and machine learning tools to better understand and predict cHABs, we are first exploring water sample data sets. Using nearly four thousand samples from the National Center for Biotechnology Information Sequence Read Archive (NCBI-SRA) across 16 years with latitude and longitude embedded in the metadata, we mapped the location of the samples onto a Lake Erie shape file. We combined information about location, date, and community taxa in the NCBI samples to discover factors that determine cHAB features. The data are separated into three distinct zones, with the majority pooled at the southwest end of the lake and occurring in 2017. The samples are rich in biological data; our next steps are to carry out whole genome sequence analysis and use the community profiles as part of our predictive machine learning model.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A critical review of operational strategies for the management of harmful algal blooms (HABs) in inland reservoirs

Occurrences of freshwater harmful algal blooms (HABs) are increasing on a global scale, largely in part due to increased nutrient input and changing climate patterns. While reservoir management strategies that can influence phytoplankton are known, there is no published guideline or protocol for the management of harmful algal blooms. There is a need to establish what factors are the predominant drivers of blooms, and how common reservoir management strategies specifically influence each factor. The following literature review seeks to establish the benefits and drawbacks of operational management strategies that currently exist. The main focus is altering hydrodynamic conditions (hypolimnetic withdrawals, surface flushing, pulsed inflow, artificial mixing), in order to induce environmental changes within the reservoir itself. Since excess nutrients are one of the biggest contributors to worsening bloom conditions, internal nutrient dynamics and reduction are also discussed. Additionally, we review the predominant seasonal factors (stratification, light, temperature, and wind) that affect likelihood of bloom occurrence and duration. In conclusion, the ultimate objective of this review is to increase understanding of the relationships between HAB drivers and reservoir operations in order to inform the development of data, modeling, and management strategies for the prevention and mitigation of blooms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mineral-hydrogel composites for mitigating harmful algal bloom and supplying phosphorous for photo-biorefineries

Harmful algal blooms (HAB) are a major environmental concern in eutrophic aquatic systems. To mitigate HABs and recover the phosphorus that drives algal growth, this study developed hydrogel composites seeded with calcium phosphate and wollastonite particles, which first adsorb phosphate (P) and then precipitate it as calcium phosphate. Using a fast-growing cyanobacterium, Synechococcus elongatus 2973, as a model microalga, we found that the mineral-hydrogel composites reduced dissolved P in BG11 media from 5.1 mg/L to 0.31 mg/L, initially reducing the biomass growth rate by up to 73 % and ultimately reducing the total biomass concentration by 75 %. When applied to municipal wastewater and agricultural run-off, the composites removed 96 % and 91 % of the dissolved P, respectively. Moreover, when the recovered P-enriched composites were reused as a slow-release bio-compatible fertilizer in a photobioreactor, they effectively supported algal growth without blocking light and interfering with photosynthesis. The P-enriched composites could tune the P concentration in the culture medium and significantly promote algal lipid accumulation. Finally, this study demonstrates the mineral-hydrogel composites' potential to treat point sources of P pollution and subsequently facilitate photoautotrophic biofuel production as a nutrient, effectively recycling the captured P.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Aerosolized Harmful Algal Bloom Toxin Microcystin-LR Induces Type 1/Type 17 Inflammation of Murine Airways

Harmful algal blooms are increasing globally and pose serious health concerns releasing cyanotoxins. Microcystin-LR (MC-LR), one of the most frequently produced cyanotoxins, has recently been detected in aerosols generated by the normal motions of affected bodies of water. MC-LR aerosol exposure has been linked to a pro-inflammatory influence on the airways of mice; however, little is understood about the underlying mechanism or the potential consequences. This study aimed to investigate the pro-inflammatory effects of aerosolized MC-LR on murine airways. C57BL/6 and BALB/c mice were exposed to MC-LR aerosols, as these strains are predisposed to type 1/type 17 and type 2 immune responses, respectively. Exposure to MC-LR induced granulocytic inflammation in C57BL/6 but not BALB/c mice, as observed by increased expression of cytokines MIP-1α, CXCL1, CCL2, and GM-CSF compared with their respective vehicle controls. Furthermore, the upregulation of interleukins IL-17A and IL-12 is consistent with Th1- and Th17-driven type 1/type 17 inflammation. Histological analysis confirmed inflammation in the C57BL/6 lungs, with elevated neutrophils and macrophages in the bronchoalveolar lavage fluid and increased pro-inflammatory and pro-resolving oxidized lipids. In contrast, BALB/c mice showed no significant airway inflammation. These results highlight the ability of aerosolized MC-LR to trigger harmful airway inflammation, requiring further research, particularly into populations with predispositions to type 1/type 17 inflammation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A family of tubular pili from harmful algal bloom forming cyanobacterium Microcystis aeruginosa

Cyanobacteria are vital photosynthetic prokaryotes, but some form harmful algal blooms (cyanoHABs) that disrupt ecosystems and produce toxins. The mechanisms by which these blooms form have yet to be fully understood, particularly the role of extracellular components. Here, we present a 2.4 Å cryo-EM structure of a pilus, termed the cyanobacterial tubular (CT) pilus, found in the cyanoHAB-forming Microcystis aeruginosa. The pilin exhibits a unique protein fold, forming a tubular pilus structure with tight, double-layer anti-parallel β-sheet interactions. We show that CT pili are essential for buoyancy by facilitating the formation of micro-colonies, which increases drag force and prevents sinking. The CT pilus surface is heavily glycosylated with ten monosaccharide modifications per pilin. Furthermore, CT pili can enrich microcystin, potentially enhancing cellular resilience, and co-localize with iron-enriched extracellular matrix components. Thus, we propose that this pilus plays an important role in the proliferation of cyanoHABs. This just discovered pilus family appears to be widely distributed across several cyanobacterial orders. Our structural and functional characterization of CT pili provide insights into cyanobacterial cell morphology, physiology, and toxin interactions, and identify potential targets for disrupting bloom formation.

Cryoelectron microscopy↗
DOE OSTI2024

Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐ a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States

Abstract Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐aconcentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐aacross the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐aconcentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐afor ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐aconcentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐ahot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.

Environmental Sciences & Ecology↗
DOE OSTI2023

Analytical population dynamics underlying harmful algal blooms triggered by prey avoidance

A modified version of the NPZD ecosystem model is used to analytically examine the effects of predation avoidance, a possible mechanism for triggering harmful algal blooms (HAB). To resolve HAB development caused by predation avoidance, an additional phytoplankton functional group is considered, one that has slower nutrient uptake and better predation avoidance characteristics than the non-harmful phytoplankton group used in traditional NPZD models. Because the two phytoplankton groups (one non-harmful and one HAB) compete for only one resource within the same system, steady state (equilibrium) conditions cannot occur without the presence of zooplankton; only the non-harmful phytoplankton group, which defeats the HAB group in the resource competition, can survive in the equilibrium. The presence of sufficient zooplankton effectively acts to replenish the nutrient pool by consuming the non-harmful phytoplankton. When this occurs, two equilibrium states are found: one with both phytoplankton groups coexisting, and one that only includes the HAB group. Further, the condition required for equilibrium is that the total nitrogen within the system should be larger than a threshold determined by model coefficients. The threshold and feasibility of the equilibrium are sensitive to the relative HAB predation avoidance coefficient. If the coefficient is larger than the ratio of net growth rates between the HAB and non-harmful phytoplankton group, the threshold becomes infinite, and an equilibrium is not feasible. The time scale for the system to reach an equilibrium state that includes a HAB group is determined asymptotically. The dependence of a threshold condition as a controlling factor may explain the regime shift of dominant species causing HABs. The ecosystem model is fully implemented into the Regional Ocean Modeling System and applied to an idealized coastal embayment (with depths and geometry taken from San Francisco Bay) to show numerically the dominance of prey avoidance dynamics in a natural shallow water environment that includes advection and diffusion. The analytical results improve strategies for HAB modeling and provide guidance for setting model coefficients necessary to resolve a HAB event.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

'Omics and Big Data in Harmful Algal Bloom Research

Phytoplankton, a group including eukaryotic microalgae and cyanobacteria, play a crucial climate role converting CO 2 into organic carbon through global primary production. They support a wide range of life, both freshwater and marine, from zooplankton to fish and mammals. While they are essential in nutrient cycles, certain phytoplankton species can proliferate excessively under favorable conditions, leading to harmful algal blooms (HABs) that pose significant threats to human and ecosystem health through the toxins they produce.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Novel Toxin Biosynthetic Gene Cluster in Harmful Algal Bloom-Causing Heteroscytonema crispum : Insights into the Origins of Paralytic Shellfish Toxins

Caused by both eukaryotic dinoflagellates and prokaryotic cyanobacteria, harmful algal blooms are events of severe ecological, economic, and public health consequence, and their incidence has become more common of late. Despite coordinated research efforts to identify and characterize the genomes of harmful algal bloom-causing organisms, the genomic basis and evolutionary origins of paralytic shellfish toxins produced by harmful algal blooms remain at best incomplete. The paralytic shellfish toxin saxitoxin has an especially complex genomic architecture and enigmatic phylogenetic distribution, spanning dinoflagellates and multiple cyanobacterial genera. Using filtration and extraction techniques to target the desired cyanobacteria from nonaxenic culture, coupled with a combination of short- and long-read sequencing, we generated a reference-quality hybrid genome assembly for Heteroscytonema crispum UTEX LB 1556, a freshwater, paralytic shellfish toxin-producing cyanobacterium thought to have the largest known genome in its phylum. We report a complete, novel biosynthetic gene cluster for the paralytic shellfish toxin saxitoxin. Leveraging this biosynthetic gene cluster, we find support for the hypothesis that paralytic shellfish toxin production has appeared in divergent Cyanobacteria lineages through widespread and repeated horizontal gene transfer. This work demonstrates the utility of long-read sequencing and metagenomic assembly toward advancing our understanding of paralytic shellfish toxin biosynthetic gene cluster diversity and suggests a mechanism for the origin of paralytic shellfish toxin biosynthetic genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms

Abstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sandwich Hybridization Assay for In Situ Real-Time Cyanobacterial Detection and Monitoring: A Review

As cyanobacterial harmful algal bloom (cHAB) events increase in scale, severity, frequency, and duration around the world, rapid and accurate monitoring and characterization tools have become critically essential for regulatory and management decision-making. The composition of cHAB-forming cyanobacteria community can change significantly over time and space and be altered by sample preservation and transportation, making in situ monitoring necessary to obtain real-time and localized information. Sandwich hybridization assay (SHA) utilizes capture oligonucleotide probes for sensitive detection of target-specific nucleic acid sequences. As an amplification-free molecular biology technology, SHA can be adapted for in-situ, real-time or near real-time detection and qualitatively or semi-quantitatively monitoring of cHAB-forming cyanobacteria, owing to its characteristics such as being rapid, portable, inexpensive, and amenable to automation, high sensitivity, specificity and robustness, and multiplexing (i.e., detecting multiple targets simultaneously). Despite its successful application in the monitoring of marine and freshwater phytoplankton, there is still room for improvement. The ability to identify a cHAB community rapidly would decrease delays in cyanotoxin analyses, reduce costs, and increase sample throughput, allowing for timely actions to improve environmental and human health and the understanding of short- and long-term bloom dynamics. Real-time detection and quantitation of HAB-forming cyanobacteria is essential for improving environmental and public health and reducing associated costs. We review and propose to apply SHA for in situ cHABs monitoring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Opportunities for Utilization of Low-Cost Algae Resources: Techno-Economic Analysis Screening for Near-Term Deployment (Parts 1 and 2)

This report presents a comprehensive techno-economic analysis (TEA) for the production, collection, or procurement of several low-cost algae resources that may otherwise be considered "waste" biomass materials today, as well as the utilization of these materials through exemplary conversion processes to produce renewable fuels and chemicals. In contrast to conventional TEA models attributed to large-scale algae "farming" approaches, which may be able to produce substantially more biomass and thus fuels/products at a national scale in the future, this assessment focuses on understanding opportunities and costs for such "waste" algal biomass resources as may be available at considerably lower cost today. Economics for base case assumptions and a range of sensitivity scenarios are presented, employing conversion technologies that are simple and well understood, and thus may be deployed at smaller community scale in the near term, as a means to support and expand a nascent algae industry on the way to employing a larger commercial algae farm approach for commodity-scale production. Specifically, three algal biomass resources are considered in this assessment, as may be sourced from (1) municipal wastewater treatment (WWT) utilizing algae in place of more conventional technologies for nitrogen/phosphorus removal, (2) collection and removal of harmful algal bloom (HAB) biomass as proliferates in certain inland water bodies, and (3) procurement of residual biomass following commercial lipid extraction (EXT) operations performed at smaller scale by industry today focused on higher-value nutraceutical applications. These three resources are evaluated through two conversion pathways: (1) combined algal biomass processing (CAP) through a simple/low-complexity configuration, and (2) anaerobic digestion (AD). The CAP pathway produces liquid fuels and chemical coproducts (polymer for off-site upgrading to bioplastics), whereas the AD pathway produces biogas (specifically renewable natural gas [RNG]) and crop fertilizer coproducts. To streamline the discussion, this report is broken into two sections: Part 1 focuses on WWT-derived biomass, and Part 2 on HAB and EXT biomass.

09 BIOMASS FUELS↗
DOE OSTI2023

Synchrotron-based X-ray fluorescence microscopy mapping the ionome of a toxic freshwater cyanobacterium

Harmful algal blooms (HABs) pose a major environmental concern across the globe. In abundance, cyanobacteria, or so-called green-blue algae can produce extremely dangerous cyanotoxins that harm humans and animals. This study focused on the mapping and distribution of intracellular macro-and micronutrients of the widespread freshwater cyanobacteria Microcystis aeruginosa (M. aeruginosa). Towards a better understanding of trace metal uptake and homeostasis throughout the cell cycle, we quantitatively mapped the spatial distribution of the elements P, K, Fe, Ca, Zn, Mn, and Cu across the ultrastructure of frozen-hydrated single cells using state-of-theart X-ray nanofluorescence imaging at the Advanced Photon Source (APS) at Argonne National Laboratory. Further, bulk cellular nutrient and trace metal content correlated well with the total intracellular elemental content in individual cells obtained by quantitative synchrotron X-ray fluorescence measurements. Multi-dimensional mappings showed P and K atoms colocalized as discrete semicircular hotspots that were analyzed with respect to their stoichiometry. Elevated Cu and Ca concentrations were detected along division plane of cells. P and K were found to have similar spatial elemental distribution with about 65% and 69% of the total cellular P and K, respectively, located at the hotspots. The P and K colocalization were refined further using nanotomography, showing a K envelope surrounding the P core. Inorganic P and organic P compounds were specified using solution-state 31 P nuclear magnetic resonance (NMR) spectroscopy from M. aeruginosa. Of the total extracted P determined by 31 P NMR spectroscopy, 47% were found to be nucleotides while only 11% were polyphosphates. Multimodal X-ray imaging provides a better understanding of intracellular biochemical processes in cyanobacteria, helping us monitor and combat an emerging environmental threat.

31P NMR spectroscopy↗
DOE OSTI2024

Detection of Anatoxins in Human Urine by Liquid Chromatography Triple Quadrupole Mass Spectrometry and ELISA

Harmful cyanobacterial blooms are becoming more common and persistent around the world. When in bloom, various cyanobacterial strains can produce anatoxins in high concentrations, which, unlike other cyanobacterial toxins, may be present in clear water. Potential human and animal exposures to anatoxins occur mainly through unintentional ingestion of contaminated algal mats and water. To address this public health threat, we developed and validated an LC-MS/MS method to detect anatoxins in human urine to confirm exposures. Pooled urine was fortified with anatoxin-a and dihydroanatoxin at concentrations from 10.0 to 500 ng/mL to create calibrators and quality control samples. Samples were diluted with isotopically labeled anatoxin and solvent prior to LC-MS/MS analysis. This method can accurately quantitate anatoxin-a with inter- and intraday accuracies ranging from 98.5 to 103% and relative standard deviations < 15%, which is within analytical guidelines for mass spectrometry methods. Additionally, this method qualitatively detects a common degradation product of anatoxin, dihydroanatoxin, above 10 ng/mL. We also evaluated a commercial anatoxin-a ELISA kit for potential diagnostic use; however, numerous false positives were detected from unexposed individual human urine samples. In conclusion, we have developed a method to detect anatoxins precisely and accurately in urine samples, addressing a public health area of concern, which can be applied to future exposure events.

urine↗
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↗