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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Lidar-based remote infrared gas sensor for monitoring anthropogenic pollution: a proof of concept

We propose using wavelength-modulation laser absorption spectroscopy in combination with quadrature detection of scattered light for remote industrial pollution monitoring in the atmosphere with a compact lidar-based gas sensor, which can be mounted on board an unmanned aerial vehicle. The instrument can be used for detecting leaks in product pipe lines; monitoring toxic gases near landfill sites, waste incineration plants, and other hazardous man-made facilities; analysing the gas atmosphere in industrial buildings and structures; and monitoring engineering processes at a sensitivity level of tens of ppm m in gas concentration measurements at characteristic distances of tens of metres. (laser applications and other issues in quantum electronics)

42 ENGINEERING↗
DOE OSTI2023

Long-term, large-scale experiment reveals the effects of seed limitation, climate, and anthropogenic disturbance on restoration of plant communities in a biodiversity hotspot

Ecological restoration is essential for maintaining biodiversity in the face of dynamic, global changes in climate, human land use, and disturbance regimes. Effective restoration requires understanding bottlenecks in plant community recovery that exist today, while recognizing that these bottlenecks may relate to complex histories of environmental change. Such understanding has been a challenge because few long-term, well-replicated experiments exist to decipher the demographic processes influencing recovery for numerous species against the backdrop of multiyear variation in climate and management. Here we address this challenge through a long-term and geographically expansive experiment in longleaf pine savannas, an imperiled ecosystem and biodiversity hotspot in the southeastern United States. Using 48 sites at three locations spanning 480 km, the 8-y experiment manipulated initial seed arrival for 24 herbaceous plant species and presence of competitors to evaluate the impacts of climate variability and management actions (e.g., prescribed burning) on plant establishment and persistence. Adding seeds increased plant establishment of many species. Cool and wet climatic conditions, low tree density, and reduced litter depth also promoted establishment. Once established, most species persisted for the duration of the 8-y experiment. Plant traits were most predictive when tightly coupled to the process of establishment. Our results illustrate how seed additions can restore plant diversity and how interannual climatic variation affects the dynamics of plant communities across a large region. The significant effects of temperature and precipitation inform how future climate may affect restoration and conservation via large-scale changes in the fundamental processes of establishment and persistence.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Anthropogenic aerosols prolong fog lifetime in China

Investigation of aerosol effects on fog with long-term measurements has generally focused on fog occurrence frequency and intensity; here we examine the effects on fog lifetime, fog formation, and fog dissipation. From analysis of 52 years (1960–2011) of data collected at 404 stations in China, it is found that fog lifetime exhibits a clear increasing trend with time, and the increased lifetime is mainly attributable to delayed fog dissipation. Increased aerosol levels and global warming affect fog lifetime in opposite ways; increased aerosol levels serve to prolong fog lifetime by primarily delaying fog dissipation, whereas warming decreases fog lifetime by primarily delaying fog formation. The overall aerosol effect on fog lifetime in China is shown to predominate, especially in the highly polluted region of Eastern China. The observational findings are confirmed by a suite of WRF-Chem simulations that reveal the influences of both increased aerosol levels and temperatures through a complex chain of interactions among microphysical, dynamical, thermodynamic, and radiative processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Anthropogenic uranium signatures in turtles, tortoises, and sea turtles from nuclear sites

Chelonians (turtles, tortoises, and sea turtles) grow scute keratin in sequential layers over time. Once formed, scute keratin acts as an inert reservoir of environmental information. For chelonians inhabiting areas with legacy or modern nuclear activities, their scute has the potential to act as a time-stamped record of radionuclide contamination in the environment. Here, we measure bulk (i.e., homogenized scute) and sequential samples of chelonian scute from the Republic of the Marshall Islands and throughout the United States of America, including at the Barry M. Goldwater Air Force Range, southwestern Utah, the Savannah River Site, and the Oak Ridge Reservation. We identify legacy uranium ( 235 U and 236 U) contamination in bulk and sequential chelonian scute that matches known nuclear histories at these locations during the 20th century. Our results confirm that chelonians bioaccumulate uranium radionuclides and do so sequentially over time. This technique provides both a time series approach for reconstructing nuclear histories from significant past and present contexts throughout the world and the ability to use chelonians for long-term environmental monitoring programs (e.g., sea turtles at Enewetok and Bikini Atolls in the Republic of the Marshall Islands and in Japan near the Fukushima Daiichi reactors).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects

Abstract Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and nonunique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here, we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof of concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity. Significance Statement Predicting the climate response for a given climate forcing is a direct problem, while inferring the forcing for a given desired climate response is often an inverse, ill-posed, problem, posing a new challenge to the climate community. This study makes the first attempt to infer the radiative forcing for a given target pattern of global surface temperature response using a deep learning approach. The resulting deeply trained convolutional neural network inversion model shows promise in capturing the forcing pattern corresponding to a given surface temperature response, with a significant implication on the design of an optimal solar radiation management strategy for curbing global warming. This study also highlights the technical challenges that future research should prioritize in seeking feasible solutions to the inverse climate problem.

Ren, Huiying↗
DOE OSTI2021

Anthropogenic Influence on Hurricane Dorian’s Extreme Rainfall

Hurricane Dorian formed on 24 August 2019 from a tropical wave and developed into a Category 5 hurricane on 1 September 2019 before making landfall in the Bahamas (Avila et al. 2020). The impacts on the Bahamas were extreme, including rainfall totals over 0.5 m in the region (Avila et al. 2020). This was on the heels of the recent damaging North Atlantic hurricanes of 2017 and 2018, which impacted various regions with different combinations of hazards (Klotzbach et al. 2018a; Avila 2019).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Anthropogenic Influences on Tornadic Storms

The impact of climate change on severe storms and tornadoes remains uncertain, largely owing to inconsistencies in observational data and limitations of climate models. Here, we performed ensembles of convection-permitting climate model simulations to examine how three tornadic storms would change if similar events were to occur in pre-industrial and future climates. The choice of events includes winter, nocturnal, and spring tornadic storms to provide insight into how the timing and seasonality of storms may affect their response to climate change. Updraft helicity (UH), convective available potential energy (CAPE), storm relative helicity (SRH), and convective inhibition (CIN) were used to determine the favorability for the three tornadic storm events in the different climate states. We found that from the pre-industrial to present, the potential for tornadic storms decreased in the winter event and increased in the nocturnal and spring events. With future climate change, the potential for tornadic storms increased in the winter and nocturnal events in association with increased CAPE, and decreased in the spring event despite greater CAPE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reactive Transport Modeling of Anthropogenic Carbon Mineralization in Stacked Columbia River Basalt Reservoirs

Numerical simulation of CO2 storage in basalts and related reactive lithologies requires modeling complex, coupled hydrologic and chemical processes, including multi-phase flow and transport, partitioning of CO2 into the aqueous phase, and chemical interactions with aqueous fluids and rock minerals. We conducted reactive transport simulations of the Wallula pilot-scale CO2 injection into the flow tops of the Grande Ronde Basalt using our PNNL STOMP-CO2 simulator with the ECKEChem reactive module. Our mineralization simulation of the ~1,000 tons of injected CO2 into the interflow zones was based on the hydrologic transport model we previously developed. For this work, the simulations considered geochemical reactions involving the basalt components, precipitates, formation brine, and injected CO2. In our benchmark case, carbonate minerals precipitated, resulting in ~20% of the CO2 being mineralized in 10 years. Increasing the reaction rate of a single primary mineral phase (clinopyroxene) by an order of magnitude resulted in a carbon mineralization reaction extent of ~90% over the same time interval. Based on these initial sensitivity analysis results, it is clear that a thorough understanding of primary mineral dissolution rates is required for accurately predicting long-term fate and transport of injected CO2 into basalt formations. Our reactive transport numerical simulations will be key components of commercial-scale CO2 storage operation permitting, de-risking, and optimization in mafic and ultramafic reservoirs.

Cao, Ruoshi↗
DOE OSTI2021

Integrating AI with physics-based hydrological models and observations for insightinto changing climate and anthropogenic impacts

Focal Areas: Advanced computational methods that integrate AI, physics, and observations to provide predictive landscape hydrological modeling over large areas (regional, continental, worldwide) while incorporating increasingly available high-resolution data from drones, lidar and satellite. Science Challenge: Landscape data is available at finer scales than can be used in physics-based hydrological (PBH) models for regional or continental terrestrial water modeling. Thus, we throw away observable detail to achieve computability. We argue that integration of AI with PBH models and observed data can be used to provide upscaling for predictive models that are computable, retain physical conservation properties, and represent the fine-scale features that affect complex flow physics through both natural and urban environments. Developing such next-generation capabilities requires outside-the-box thinking that melds the different approaches of AI modeling, PBH modeling, and observation across multiple scales from local drones to satellites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Anthropogenic Markers in Alta Irrigation District Water Supply Wells

In July 2016, LLNL produced a report for the Kings River Watershed Coalition Authority (KRWCA) entitled “Groundwater Age and Nitrate Isotopic Composition in Alta Irrigation District Water Supply Wells” (Esser et al., 2016). That report summarized groundwater age and nitrate isotopic composition data produced by LLNL for Alta Irrigation District (AID) groundwater wells sampled by KRWCA in a pilot project to test the utility of advanced isotopic methods for constraining the timing and source of nitrate in groundwater at selected sites in the Kings River service area. One section of that report made recommendations for follow-on study. The first and strongest recommendation was to measure a wider suite of source tracers including co-contaminants such as herbicides, pesticides, pharmaceuticals, personal care products, and food constituents and additives. KRWCA followed up on this recommendation in Phase II of the pilot study by re-sampling the original set of wells and submitting the samples to a commercial laboratory for analysis of a limited range of co-contaminants (including pharmaceuticals, personal care products , and food constituents and additives, but not including herbicides or pesticides). They have asked that LLNL review and interpret these data in the context of previous LLNL analyses and to evaluate the utility of such co-contaminant analyses in providing additional constraints on the source of groundwater nitrate contamination in Alta Irrigation District water supply wells. The goal of the second phase of this pilot project is to evaluate the utility of co-contaminant occurrence for constraining nitrate source in groundwater at selected sites in the Kings River service area, with special reference to the use of such data in conjunction with advanced isotopic methods such as nitrate isotopic composition and groundwater age. Nitrate isotopic composition alone cannot distinguish nitrate derived from animal manure from nitrate derived from human septic waste. The presence or absence of agricultural chemicals (herbicides, pesticides, veterinary pharmaceuticals) or chemicals associated with people (human pharmaceuticals, artificial sweeteners) can help make this distinction. Co-contaminant analyses using multi-analyte LC-MS-MS protocols are now commercially available and reasonably priced. The feasibility and utility of the using co-contaminants for nitrate source attribution can be evaluated by 1) determining the degree to which co-contaminants occur in distinct and easily attributable groupings (e.g. agricultural chemicals vs personal care products and food additives); 2) determining the degree to which co-contaminant occurrence is consistent with nitrate isotopic composition and groundwater age (e.g. no co-contaminant occurrence in very old groundwater; co-contaminants consistent with animal manure or human septic waste occurring in groundwaters having nitrate isotopic composition also consistent with animal manure or human septic waste); and 3) determining the degree to which co-contaminants provide additional constraint on nitrate source over and beyond nitrate isotopic composition and groundwater age (e.g. distinguishing between animal manure and human septic waste).

58 GEOSCIENCES↗
DOE OSTI2021

Local limits of detection for anthropogenic aerosol-cloud interactions

Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.

54 ENVIRONMENTAL SCIENCES↗