Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Environmental impacts”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Contrasting Community Assembly Forces Drive Microbial Structural and Potential Functional Responses to Precipitation in an Incipient Soil System

Microbial communities in incipient soil systems serve as the only biotic force shaping landscape evolution. However, the underlying ecological forces shaping microbial community structure and function are inadequately understood. We used amplicon sequencing to determine microbial taxonomic assembly and metagenome sequencing to evaluate microbial functional assembly in incipient basaltic soil subjected to precipitation. Community composition was stratified with soil depth in the pre-precipitation samples, with surficial communities maintaining their distinct structure and diversity after precipitation, while the deeper soil samples appeared to become more uniform. The structural community assembly remained deterministic in pre- and post-precipitation periods, with homogenous selection being dominant. Metagenome analysis revealed that carbon and nitrogen functional potential was assembled stochastically. Sub-populations putatively involved in the nitrogen cycle and carbon fixation experienced counteracting assembly pressures at the deepest depths, suggesting the communities may functionally assemble to respond to short-term environmental fluctuations and impact the landscape-scale response to perturbations. We propose that contrasting assembly forces impact microbial structure and potential function in an incipient landscape; in situ landscape characteristics (here homogenous parent material) drive community structure assembly, while short-term environmental fluctuations (here precipitation) shape environmental variations that are random in the soil depth profile and drive stochastic sub-population functional dynamics.

16S amplicon sequencing↗
DOE OSTI2021

Cost Efficiency of Environmental DNA as Compared to Conventional Methods for Biodiversity Monitoring Purposes at Marine Energy Sites

The installation of marine energy systems may affect marine environments, and by extension, marine fish communities. Therefore, biomonitoring is an integral part of assessing impacts on species. Environmental DNA (eDNA) provides a noninvasive alternative to conventional monitoring surveys and the possibility of a more accurate assessment of species richness. Yet, its cost efficiency compared to traditional methods of monitoring is relatively unknown, especially when applied to monitoring around tidal, wave, and offshore wind energy installations. For this study, 202 peer-reviewed journal articles were dissected to inventory the diversity of supplies used for collecting and processing eDNA samples and to compile the average cost of eDNA surveys. Information collected included the type, volume, and brand of containers used in sampling; material, size, and brand of filters; and extraction methods. Cost information was gathered for the most common supplies, and a total cost was estimated for a hypothetical eDNA survey in Sequim Bay, WA, to compare with traditional methods of surveying such as beach seining and scuba surveys. The results showed a higher-than-expected diversity of supplies to collect and process eDNA samples. The most common supplies were 1 L Nalgene bottles at an average cost of 7.96 USD for collecting samples, 0.45 µm glass fiber Merck Millipore filters at an average cost of 1.51 USD for filtering samples, and the Qiagen DNeasy Blood and Tissue kit at 3.54 USD per sample for extracting DNA. When compared to beach seine and scuba surveys, eDNA surveys undertaken by senior researchers are less expensive for both initial surveys with all new materials as well as for follow-up surveys reusing some of the supplies. However, when surveys are done solely by students, eDNA surveys are more expensive than scuba surveys when no prior supplies are available and more than both beach seine and scuba surveys for follow-up surveys reusing supplies. In a professional sphere, where surveys are less often conducted by teams of students only, eDNA surveys are an effective and less-costly alternative to conventional methods. We anticipate that the development and refinement of eDNA methodology will continue to decrease surveying costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980–2019 using a machine learning approach

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM 2.5 (particulate matter less than 2.5 µm in diameter) concentrations. This study constructs a gridded near-surface PM 2.5 concentration dataset across China covering 1980–2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM2.5 concentrations are in excellent agreement with ground measurements during 2015–2019, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, both emissions and meteorological conditions are also essential factors affecting PM2.5 predictions. From 1980 to 2014, the model-predicted PM2.5 concentrations increased constantly with the maximum growth rate of 5–10 µg/m3/decade over eastern China. Due to the clean air actions, PM 2.5 concentrations have decreased effectively at a rate over 50 µg/m 3 /decade in the North China Plain and 20–50 µg/m 3 /decade over eastern China during 2014–2019. The newly generated dataset of 1-degree gridded PM 2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Accurate determination of genotypic variance of cell wall characteristics of a Populus trichocarpa pedigree using high-throughput pyrolysis-molecular beam mass spectrometry

Abstract Background Pyrolysis-molecular beam mass spectrometry (py-MBMS) analysis of a pedigree of Populus trichocarpa was performed to study the phenotypic plasticity and heritability of lignin content and lignin monomer composition. Instrumental and microspatial environmental variability were observed in the spectral features and corrected to reveal underlying genetic variance of biomass composition. Results Lignin-derived ions (including m/z 124, 154, 168, 194, 210 and others) were highly impacted by microspatial environmental variation which demonstrates phenotypic plasticity of lignin composition in Populus trichocarpa biomass. Broad-sense heritability of lignin composition after correcting for microspatial and instrumental variation was determined to be H 2 = 0.56 based on py-MBMS ions known to derive from lignin. Heritability of lignin monomeric syringyl/guaiacyl ratio ( S / G ) was H 2 = 0.81. Broad-sense heritability was also high (up to H 2 = 0.79) for ions derived from other components of the biomass including phenolics (e.g., salicylates) and C5 sugars (e.g., xylose). Lignin and phenolic ion abundances were primarily driven by maternal effects, and paternal effects were either similar or stronger for the most heritable carbohydrate-derived ions. Conclusions We have shown that many biopolymer-derived ions from py-MBMS show substantial phenotypic plasticity in response to microenvironmental variation in plantations. Nevertheless, broad-sense heritability for biomass composition can be quite high after correcting for spatial environmental variation. This work outlines the importance in accounting for instrumental and microspatial environmental variation in biomass composition data for applications in heritability measurements and genomic selection for breeding poplar for renewable fuels and materials.

09 BIOMASS FUELS↗
DOE OSTI2022

Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic

Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumulus cloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using the ground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. The ACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloud condensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vapor environment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL) conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effect of enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal component analysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the vertical component of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects on ACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lower and higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained by the fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties and the below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud droplet size spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensational growth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increased ACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraints to future model evaluations of aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Linking the Dynamic Response of the Carbon Dioxide-Concentrating Mechanism to Carbon Assimilation Behavior in Fremyella diplosiphon

ABSTRACT Cyanobacteria use a carbon dioxide (CO 2 )-concentrating mechanism (CCM) that enhances their carbon fixation efficiency and is regulated by many environmental factors that impact photosynthesis, including carbon availability, light levels, and nutrient access. Efforts to connect the regulation of the CCM by these factors to functional effects on carbon assimilation rates have been complicated by the aqueous nature of cyanobacteria. Here, we describe the use of cyanobacteria in a semiwet state on glass fiber filtration discs—cyanobacterial discs—to establish dynamic carbon assimilation behavior using gas exchange analysis. In combination with quantitative PCR (qPCR) and transmission electron microscopy (TEM) analyses, we linked the regulation of CCM components to corresponding carbon assimilation behavior in the freshwater, filamentous cyanobacterium Fremyella diplosiphon . Inorganic carbon (C i ) levels, light quantity, and light quality have all been shown to influence carbon assimilation behavior in F. diplosiphon . Our results suggest a biphasic model of cyanobacterial carbon fixation. While behavior at low levels of CO 2 is driven mainly by the C i uptake ability of the cyanobacterium, at higher CO 2 levels, carbon assimilation behavior is multifaceted and depends on C i availability, carboxysome morphology, linear electron flow, and cell shape. Carbon response curves (CRCs) generated via gas exchange analysis enable rapid examination of CO 2 assimilation behavior in cyanobacteria and can be used for cells grown under distinct conditions to provide insight into how CO 2 assimilation correlates with the regulation of critical cellular functions, such as the environmental control of the CCM and downstream photosynthetic capacity. IMPORTANCE Environmental regulation of photosynthesis in cyanobacteria enhances organismal fitness, light capture, and associated carbon fixation under dynamic conditions. Concentration of carbon dioxide (CO 2 ) near the carbon-fixing enzyme RubisCO occurs via the CO 2 -concentrating mechanism (CCM). The CCM is also tuned in response to carbon availability, light quality or levels, or nutrient access—cues that also impact photosynthesis. We adapted dynamic gas exchange methods generally used with plants to investigate environmental regulation of the CCM and carbon fixation capacity using glass fiber-filtered cells of the cyanobacterium Fremyella diplosiphon . We describe a breakthrough in measuring real-time carbon uptake and associated assimilation capacity for cells grown in distinct conditions (i.e., light quality, light quantity, or carbon status). These measurements demonstrate that the CCM modulates carbon uptake and assimilation under low-C i conditions and that light-dependent regulation of pigmentation, cell shape, and downstream stages of carbon fixation are critical for tuning carbon uptake and assimilation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Environmental Thermodynamic Effects on Future Changes of Mesoscale Convective Systems and Isolated Deep Convection Over the US: A Case Study

Mesoscale convective systems (MCSs) and isolated deep convection (IDC) significantly influence local weather conditions and the hydrological cycle across the United States. Their future changes under warming is critical to the society. However, the exact impacts of thermodynamic environmental changes on them are difficult to resolve using climate model simulations which integrate both dynamic and thermodynamic factors. This study explores a theoretical modeling approach to isolate environmental thermodynamic effects on their future changes through a case study. For a 50-day period during the 2020 summer, a pair of convection-permitting model simulations indicate contrasting changes in MCSs and IDC between inland and coastal regions under pseudo global warming. Driven by the thermodynamic environments of these simulations, a single-column parcel model indicates a decrease in the frequency of IDC occurrences, along with increases in duration and precipitation amount under warming, attributable to rising most unstable convective available potential energy (MUCAPE), convective inhibition (MUCIN), and precipitable water (PW). A multi-column parcel model reveals contrasting changes in the frequency and mean area of MCSs between inland and coastal regions, underscoring the increase in mean MUCIN over inland regions and increases in mean MUCAPE and PW over coastal regions. The increase in mean MCS area over inland regions is linked to the interplay between accelerated gust fronts and enhanced subsidence strength, which is further traced to the unchanged mean MUCIN but increased mean MUCAPE in large-scale environments. In conclusion, these case study results suggest a major role of thermodynamic environmental changes in controlling characteristics of MCSs and IDC under warming.

Mesoscale convective system↗
DOE OSTI2022

Strategies for achieving circular economy goals in the lighting industry through design for disassembly- based methodologies

The significance of circular economy and its integration into core strategies for smart, sustainable, and inclusive growth, is increasingly being recognized by individuals, organisations, and nations in recent times. The term Design for Disassembly (DfD) may conjure up images about the end-of-life of a product or its disposal. However, DfD connects to numerous circularity and sustainability goals over the lifetime of a product or project and can have a large impact on the carbon footprint of the product. The DfD is a product development methodology which is in line with the vision of a circular economy, and supports increase in material efficiency, extends product lifetimes and improves recycling efficiency. Reduction of the imposed environmental risks and impact on the climate, by utilising circularity approaches such as remanufacturing, are tied to lowering of carbon footprint. This paper describes specific and actionable approaches that can be applied by luminaire manufacturers, specifiers, and other players in the lighting industry. The readers will learn about current tools and methodologies that can be used to improve iterative design, as well as measure, assess, and compare products or materials.

design for disassembly, dfd, circularity↗
DOE OSTI2024

How will future climate change impact prescribed fire across the contiguous United States?

As of 2023, the use of prescribed fire to manage ecosystems accounts for more than 50% of area burned annually across the United States. Prescribed fire is carried out when meteorological conditions, including temperature, humidity, and wind speed are appropriate for its safe and effective application. However, changes in these meteorological variables associated with future climate change may impact future opportunities to conduct prescribed fire. In this study, we combine climate projections with information on prescribed burning windows for ecoregions across the contiguous United States (CONUS) to compute the number of days when meteorological conditions allow for the safe and effective application of prescribed fire under present-day (2006–2015) and future climate (2051–2060) conditions. The resulting projections, which cover 57% of all vegetated area across the CONUS, indicate fewer days with conditions suitable for prescribed burning across ecoregions of the eastern United States due to rising maximum daily temperatures, but opportunities increase in the northern and northwestern United States, driven primarily by rising minimum temperatures and declining wind speeds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2023

Genetic determinants of switchgrass-root-associated microbiota in field sites spanning its natural range

A fundamental goal in plant microbiome research is to determine the relative impacts of host and environmental effects on root microbiota composition, particularly how host genotype impacts bacterial community composition. Most studies characterizing the effect of plant genotype on root microbiota undersample host genetic diversity and grow plants outside of their native ranges, making the associations between host and microbes difficult to interpret. Here, we characterized the root microbiota of a large diversity panel of switchgrass, a North American native C4 bioenergy crop, in three field locations spanning its native range. Our data, composed of 1,961 samples, suggest that field location is the primary determinant of microbiome composition; however, substantial heritable variation is widespread across bacterial taxa, especially those in the Sphingomonadaceae family. Despite diverse compositions, relatively few highly prevalent taxa make up the majority of the switchgrass root microbiota, a large fraction of which is shared across sites. Local genotypes preferentially recruit/filter for local microbes, supporting the idea of affinity between local plants and their microbiota. Using genome-wide association, we identified loci impacting the abundance of >400 microbial strains and found an enrichment of genes involved in immune responses, signaling pathways, and secondary metabolism. We found loci associated with over half of the core microbiota (i.e., microbes in >80% of samples), regardless of field location. Finally, we show a genetic relationship between a basal plant immunity pathway and relative abundances of root microbiota. Furthermore, this study brings us closer to harnessing and manipulating beneficial microbial associations via host genetics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Contextualizing Non-Powered Dam Site Selection for Archimedes Screw Turbines: A Methodology for Responsible Archimedes Screw Turbine Conversion at Existing Dams

Non-powered dams represent 97% of dams in the United States and their energy generation potential has not been fully realized. The use of an Archimedes screw turbine to generate power at non-powered dams offers a dual benefit; producing electricity, and acting as downstream fish passage, helping to reconnect previously separated ecosystems. In this study, we assess the technical, environmental, social, and economic feasibility of generating power at non-powered U.S. dam sites using Archimedes screw turbines by integrating mechanical constraints, social impact metrics, proximity to infrastructure, and environmental sensitivity data. Results account for future precipitation predictions and show, between 2024 and 2050, the number of sites where Archimedes screw turbines are viable decreases by one site, but overall generation capacity increases due to increased flow rates across persisting locations. Our analysis identified 82 non-powered dam sites with a mean generation capacity of 49 kW that meet the mechanical requirements for Archimedes screw turbine technology in 2024. Our analysis presents a framework for considering social, environmental, and economic impacts of specific turbine technologies to convert non-powered dams to generate power.

Archimedes screw turbine↗
DOE OSTI2024

Abstract for CRADA between Mati Car bon PBC and NETL

The National Energy Technology Laboratory (NETL) will assist Mati Carbon PBC (participant) in the design and execution of their community benefits plan (CBP) for scaling Enhanced Rock Weathering (ERW) carbon dioxide removal (CDR) in Arkansas. ERW involves spreading finely crushed rocks in agricultural fields to speed up the dissolution and release of certain minerals that amend soil acidity, replenish soil nutrients, and simultaneously converting CO 2 from the atmosphere to Dissolved Inorganic Carbon (DIC). ERW not only removes CO 2 from the atmosphere but also has the potential to generate co-benefits for famers through cost reductions and increased crop yields. The collaboration between NETL and Mati Carbon, PBC (participant) will entail a detailed review of the economic, environmental and community impact of ERW in agricultural settings. This review will guide all project related decisions. It will provide a better understanding of the scale of these potential co-benefits and quantify community needs that ERW can address.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗
DOE OSTI2020

Evolution of Electric Sector Water Use Under Alternative Electricity Futures

Increasing competition for water resources in the United States could create future challenges for allocating and using thermal cooling water in the U.S. electric power sector. While thermal power plant retirements and the growth of wind and solar technologies can reduce national aggregate power sector cooling water use, local water constraints and growing demand for agricultural or municipal supply could create greater needs for higher-cost alternative water supplies such as groundwater or recycled wastewater. For some regions, these incentives could change future electricity planning and operational decisions. These relationships and impacts are studied here using the National Renewable Energy Laboratory (NREL) Regional Energy Deployment System (ReEDS), a national electric sector planning model that has recently been upgraded to include a highly detailed representation of thermal cooling water demand and supply. Thermal power technologies are differentiated by both cooling technology and water source type to track and constrain thermal cooling water use in a way that incorporates both physical and legal water considerations in the United States. This capability is exercised under a range of electricity sector futures with alternative technology costs, fuel prices, and water constraints to illustrate ways that U.S. electric sector water use could evolve under uncertain future electric sector drivers. In exploring changes to regional generation and transmission planning and operation, water requirements, and cost, we highlight the environmental and economic impacts of future power sector water decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Effects of dissolved cations, dissolved organic carbon, and exposure concentrations on per- and polyfluoroalkyl substances bioaccumulation in freshwater algae

Per- and polyfluoroalkyl substances (PFAS) have attracted global attention because of their persistence, toxicity, bioaccumulation potential, and associated adverse effects. As important primary producers, freshwater algae constitute the base of the food web in freshwater aquatic ecosystems. However, the effects of key environmental factors on PFAS uptake and bioaccumulation in freshwater algae have not been thoroughly studied. In this study, three bioaccumulation experiments were conducted to evaluate the influence of dissolved cations, dissolved organic carbon, and exposure concentrations on PFAS bioaccumulation in algae. Among the 14 studied PFAS, seven long-chain PFAS tended to bioaccumulate in algae. Elevated divalent cations (Ca 2+ and Mg 2+ ) and dissolved organic carbon did not significantly change the algal bioconcentration factors (BCFs) of PFAS, suggesting complexity of the interactions among PFAS, environmental factors, and biotic activities. Additionally, increasing exposure concentrations (0.5, 1, 5, and 10 μg/L of each PFAS) increased PFAS concentrations in algae but decreased the BCF values. This indicated that attention should be paid to the application of BCFs in future studies, including ecological risk assessment. Moreover, fluorotelomer sulfonic acids (FTSs) were incompletely recovered, suggesting that biotransformation occurred. Further studies should be conducted to evaluate whether algae play a role in FTSs biotransformation and to determine the mechanisms. In conclusion, studying the impacts of key environmental factors on PFAS bioaccumulation in algae is crucial for understanding the bioaccumulation processes that occur at the lowest trophic level and that eventually affect the dynamics of entire aquatic ecosystems.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Transformative Pathways for U.S. Industry: Unlocking American Innovation

The United States (U.S.) is undergoing an energy transformation that will depend on continued U.S. innovation. Although U.S. industry has been foundational to the nation’s economic growth and prosperity, it has also given rise to decades’ worth of industrial pollutants in our air and water, which acutely impact the most vulnerable communities, as well as greenhouse gas (GHG) emissions contributing to climate risk. At the same time, U.S. industry is facing growing competitive pressures. Global investors and financial regulations are increasingly focusing on emissions footprints, governments are developing emissions-based trade adjustments and procurement specifications, and downstream demand for low-carbon products is emerging. Developing cost-competitive solutions to meet these needs provides an opportunity to fundamentally transform U.S. industry and sharpen its competitive edge, while reducing the GHG emissions and adverse environmental and health impacts (see Figure ES-1). Innovation is central to this transformation. Pathways to Commercial Liftoff: Industrial Decarbonization, which provides a descriptive fact base on what is needed to reach commercial scale in the marketplace, estimates that over 60% of emissions reduction for the industrial sector will need to come from technologies that are still nascent today. This report, Transformative Pathways for U.S. Industry,3 focuses on the pathways that rely on the nascent and innovative technologies that were too early for consideration in the Pathways to Commercial Liftoff report. Targeted and sustained public and private investment in research, development, demonstration, and deployment is required to catalyze innovation and meet this moment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗