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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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DOE OSTI2022

Fragile Earth: AI for Climate Mitigation, Adaptation, and Environmental Justice

The Fragile EarthWorkshop is a recurring event that gathers the research community to find and explore howdata science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).Fragile Earth 2022: AI for Climate Mitigation, Adaptation, and Environmental Justice is a workshop taking place as part of the ACM's KDD 2022 Conference on research in knowledge discovery and data mining and their applications. The dates for the Conference are August 14-18, 2022.

Abe, Naoki↗
DOE OSTI2025

Global Precipitation Experiment—A New World Climate Research Programme Lighthouse Activity

The future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2–3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

Climate change↗
DOE OSTI2026

Advancing representations of equity and justice in climate mitigation futures

THIS PAPER WAS PRIMARILY COMPLETED PRIOR TO THE AUTHOR JOINING PNNL AND NO DOE FUNDING WAS USED FOR THIS PAPER. In this work, we review how equity and justice issues in global climate mitigation scenarios are addressed within Integrated Assessment Models (IAMs) and propose a new research agenda to strengthen their integration in model development and application. We begin by examining prominent concerns at the science-policy interface. We introduce a typology of equity and justice limitations in climate mitigation scenarios, distinguishing among structural, methodological, and epistemological biases that shape what integrated assessment models can reveal at policy-relevant scales. Reflecting on these concerns, we propose a research agenda that describes new avenues of work and draws together distinct emerging initiatives. This agenda is based on the feasibility and depth of required interventions, from incremental improvements to structural reforms and alternative participatory approaches. Drawing on reflexive insights from integrated assessment practitioners, it addresses the operational challenges of translating justice concepts into metrics, including risks of reductionism, tokenism, and narrow definitions. Underlying this research agenda is a recognition that modeling communities must engage more critically with implicit assumptions in model design and use that have equity and justice implications. Achieving equitable climate futures will require transformative actions that integrate diverse justice concerns, advance sustainable development goals, and confront systemic inequities across both human and ecological dimensions. Although models will never capture all these aspects, they can be significantly enhanced to support more informed discussion and practical application. Our contribution proposes a way forward to achieving this goal.

Pachauri, Shonali↗
DOE OSTI2022

Increasing Access to Grid-Tied Distributed Photovoltaics for Low-Income Populations: Considerations for Developing Countries

Governments around the world are under immense pressure to promote inclusive economic growth, reduce budget deficits, and promote sustainable development goals. Such goals may potentially be addressed through public policy approaches to encourage the use of distributed photovoltaic (DPV) systems in developing countries, particularly among low-income electricity customers. At the same time, low-income customers face numerous barriers to DPV deployment including a lack of access to capital and financing, a lack of awareness about the technology, lack of homeownership, and distorted price signals via lower retail tariffs. As a result of these factors, among others, they are often the least likely customers in developed and developing countries alike to deploy solar. However, under the right set of conditions, low-income grid tied DPV programs can offer beneficial outcomes for governments, customers, utilities, and the environment. This brief informs decision makers in developing countries as they explore ways to promote equitable access to solar energy in their communities.

14 SOLAR ENERGY↗
DOE OSTI2022

Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Chapter 11

The AR5 was published in 2014. The Paris Agreement and the 17 Sustainable Development Goals (SDGs) were adopted in 2015. An increasing number of countries have since announced ambitions to be carbon neutral by 2045–2060. The COVID-19 pandemic shocked the global economy in 2020 and motivated economic stimulus with demands for green recovery and concerns for economic security. All this has created a new context and a growing recognition that all industry, including the energy and emissions intensive industries, need to reach net zero GHG emissions. There is an ongoing mind shift around the opportunities to do so, with electrification and hydrogen emerging among key mitigation options as a result of renewable electricity costs falling rapidly. On the demand side there has been renewed attention to end-use demand, material efficiency, and more and better-quality recycling measures. This chapter takes its starting point in this new context and emphasizes the need for deploying innovative processes and practices in order to limit the global warming to 1.5°C or 2°C (IPCC 2018a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Citizen-Led Community Innovation for Food Energy Water Nexus Resilience

Food-energy-water (FEW) resources are necessary for the function of multiple socio-natural systems. Understanding the synergies and trade-offs in the FEW nexus, and how these interconnections impact earth’s systems, is critical to ensure adequate access to these resources in the future; an essential component for achieving the Sustainable Development Goals (Scanlon et al., 2017). Although, over the last decade, the identification of FEW nexus complexities has increased at a global (IPCC, 2018; D’Orodico et al., 2018), national (Lant et al., 2019), and city scale (Rushforth and Ruddell, 2018), these findings are yet to be adequately translated into ‘on the ground’ action due a lack of technical and political capacity (Weitz et al., 2017). Specifically, local FEW systems have been overlooked in these analyses (Scanlon et al., 2017; Lant et al., 2019), thus leaving small and medium towns vulnerable due to a lack of data and inadequate FEW system management. Building on three years of field-tested FEW nexus research in the Ruddell Lab, we argue that participatory citizen science projects, such as our FEWSION for Community Resilience initiative, can bridge the data-policy gaps that exist within local FEW system management by: 1) providing last mile data on the FEW system, and 2) translating local data into evidence-based solutions at a grassroots level. Thus, we present a broadly applicable framework and call to action for local scale participatory citizen science to solve complex FEW nexus issues at a local, regional, and national scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications

Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving bioeconomic growth. Trichoderma species are well-studied model fungal organisms used for their biocontrol properties with great potential to alleviate the use of agrochemicals in agriculture. However, identifying and characterizing effective natural products in novel species or strains as biological control products remains a meticulous process with many known challenges to be navigated. Integration of recent advancements in various “omics” technologies, next generation biodesign, machine learning, and artificial intelligence approaches could greatly advance bioprospecting goals. Herein, we propose a roadmap for assessing the potential impact of already known or newly discovered Trichoderma species for biocontrol applications. By screening publicly available Trichoderma genome sequences, we first highlight the prevalence of putative biosynthetic gene clusters and antimicrobial peptides among genomes as an initial step toward predicting which organisms could increase the diversity of natural products. Next, we discuss high-throughput methods for screening organisms to discover and characterize natural products and how these findings impact both fundamental and applied research fields.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Unveiling the Occupational Exposure to Microbial Contamination in Conservation–Restoration Settings

Assuring a proper environment for the fulfillment of professional activities is one of the Sustainable Development Goals and is contemplated in the One Health approach assumed by the World Health Organization. This particular study is applied to an often neglected sector of our society—the conservators/restorers—despite the many health issues reported by these professionals. Three different specialties (textiles, paintings and wood sculpture) and locations were selected for evaluation by placement of electrostatic dust cloths. After treatment of the samples, bacterial and fungal contamination were assessed, as well as mycotoxin determination, the presence of azole-resistant strains and cytotoxicity of the microorganisms encountered. Bacteria were only present in one of medias used and showed relatively low numbers. The highest level of contamination by fungi was identified in one of the textiles settings. The textile area also showed the highest variability for fungi. Aspergillus sp. are one indicator of possible environmental issues, and A. sections Fumigati and Circumdati were particularly relevant in two of the settings and identified in all of them. No mycotoxins were detected and the large majority of the fungi identified were non-cytotoxic. Overall, these can be considered low-contaminated environments but attention should be given to the Aspergillus sp. contamination. Additional studies are needed not only to make these results more robust, but also to test if the environmental sampling alone is the best approach in a setting where there is very little movement and dust displacement and where professionals are in very close proximity to the artefacts being treated, which may suggest the existence of a micro-atmosphere worth evaluating and comparing to the obtained results.

Viegas, Carla (ORCID:0000000215456479)↗
NASA NTRS2018

Bayesian Geostatistical Modelling of PM10 and PM2.5 Surface Level Concentrations in Europe Using High-Resolution Satellite-Derived Products

Air quality monitoring across Europe is mainly based on in situ ground stations which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive modelsthat estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite derived products to improve particulate matter (PM) estimates. Bayesiangeostatistical models addressing confounding between the spatial distribution of pollutants and remotely sensed predictors were developed to estimate yearly averages of both, fine (PM2.5) and coarse (PM10) surface PM concentrations at 1 sq.km spatial resolution over 46 European countries and were compared to geostatistical, geographically weighted and land-use regression formulations. Rigorous model selection identified the Earth observation data which contribute most to pollutants' estimation. Geostatistical models outperformed the predictive ability of the frequently employed land-use regression. The resulting estimates of PM10 and PM2.5, which represent the main air quality indicators for the urban Sustainable Development Goal, indicate that in 2016, 66.2% of the European population was breathing air above the WHO Air Quality Guidelines thresholds. Our estimates are readily available to policy makers and scientists assessing the effects of long-term exposure to pollution on human and ecosystem health.

Beloconi, Anton↗
NASA NTRS2019

Water-Saving Agriculture Can Deliver Deep Water Cuts for China

China is working hard to reconcile growing demands for freshwater with already oversubscribed renewable water resources. However, the knowledge essential for setting and achieving the intended water consumption cuts remains limited. Here we show that on-farm water management interventions such as improved irrigation and soil management practices for maize cultivation can lead to substantial water consumption reductions, by a simulated total of 28-46% (7-14 billion m3/year) nationally, with or without the impacts of climate change. The water consumption cut is equivalent to 16-31% of the ultimate capacity of the South-North Water Transfer Project. Much of the reduction is achievable at the populous and water-stressed North China Plain and Northeast China. Meanwhile, the interventions can increase maize production by an estimated 7-15%, meeting 22-28% of demand increase projected for 2050. The water management and food production improvements obtained are crucial for achieving multiple Sustainable Development Goals (SDGs) related to water, land, and food in China and far beyond.

Guorui Huang↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
NASA NTRS2021

Carbon Analytics for Net-zero Emissions Sustainable Cities

Consensus on carbon accounting approaches at city-level is lacking and analytic frameworks to systematically link carbon mitigation with the Sustainable Development Goals are limited. A new accounting approach anchored upon key physical provisioning systems can help to address these knowledge gaps and facilitate urban transitions.

Anu Ramaswami↗
NASA NTRS2022

Increasing the Uptake of Ecological Model Results in Policy Decisions to Improve Biodiversity Outcomes

Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.

Biodiversity-ecosystem function relationships↗
NASA NTRS2022

Systemic Risk

Systemic risk is the name given to impacts that spread within and across systems and sectors (e.g. ecosystems, health, infrastructure and the food sector) via the movements of people, goods, capital and information within and across boundaries (e.g. regions, countries and continents). The spread of these impacts can lead to potentially existential consequences and system collapse across a range of time horizons. Globalization contributes to systemic risk affecting people worldwide. The impacts of climate change or COVID-19 show how the challenges of addressing systemic risk go beyond conventional risk management and governance. Critical system interdependencies, amplified by underlying vulnerabilities, highlight that there is a growing need to better understand cascading impacts, systemic risks and the possible political (governance) and societal responses. This includes improving our understanding of the root causes of systemic risk, both biophysical and socio-economic, and related information needs. Addressing contemporary challenges in terms of systemic risk requires integrating different systems perspectives and fostering system thinking, while implementing key intergovernmental agendas, such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals.

systemic risk↗
NASA NTRSDate not supplied

Systemic Risk From the Perspective of Climate, Environmental and Disaster Risk Science and Practice

Understanding and managing systemic risk is more important than ever due to our immense global connectivity (e.g., between sectors, such as food-health-water-energy, countries and continents, down to individuals). Despite the fact that the notion of systemic risk is several decades old, the term is used in diverse ways across different disciplines (e.g., financial systems, medicine, earth system sciences, disaster risk research and climate science). Triggered by the repercussions of the global financial crisis of the late 2000s, and more recently the COVID-19 pandemic, which are clear realization of systemic risk, the perception of systemic risk has often been focused on global and catastrophic or even existential risks. Systemic risk, however, can be seen as a feature of systems at all possible scales (e.g., global, national, regional, local) with system boundaries varying depending on the context. Addressing current societal challenges, such as climate change, in terms of systemic risk requires integrating different systems perspectives and fostering system thinking, while implementing key intergovernmental agendas, such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. Based on insights gained and knowledge collected from an expert workshop, literature review and expert elicitation, we give an integrated perspective of climate, environmental and disaster risk science and practice on systemic risk as summarized in a Briefing Note to the International Science Council. We provide an overview of concepts of systemic risk that have evolved over time and identify commonalities across terminologies and perspectives associated with systemic risk used in different contexts. Key attributes of systemic risk are outlined without prescribing a single definition, and information and data requirements are discussed that are essential for a better and more actionable understanding of the systemic nature of risk. Finally, the opportunities to connect research and policy for addressing systemic risk are highlighted.

systemic risk↗
NASA NTRS2024

Increased Atmospheric PM 2.5 Events Due to Open Waste Burning in Qaanaaq, Greenland, Summer of 2022

High levels of particulate matter (PM) are relevant to severe air pollution and can adversely impact human health. Maintaining healthy air quality for the residents of the Arctic region is essential to satisfy the no-one-left-behind policy of the Sustainable Development Goals (SDGs) by the United Nations. In this study, we installed a PM 2.5 measurement system in Qaanaaq, Greenland, and obtained the first continuous PM 2.5 measurements from 20 July to 13 August, 2022. We observed several increased PM2.5 events; relatively high PM 2.5 levels persisted from 8 August 2022. On the same day, visible black smoke emitted from the Qaanaaq dump site originated from open waste burning. By confirming less transboundary air pollution contributions from remote aerosol source regions to Qaanaaq during the measurement period using NOAA’s HYSPLIT backward trajectory analysis and NASA’s MERRA-2 aerosol re-analysis, we confirmed that the increased PM 2.5 was primarily due to local open waste burning with less contributions from transboundary air pollution. However, small contributions from biomass burning outside Greenland were plausible during the early measurement period. Additionally, NOAA’s HYSPLIT dispersion calculations suggested possible aerosol depositions from local open waste burning to nearby sea areas, such as Baffin Bay. Although the hourly mean PM 2.5 mass concentration was not alarmingly high during the measurement period, future studies should incorporate longer-term continuous PM 2.5 measurements along with other atmospheric chemical analyses to identify possible local air pollution sources in detail to ensure clean ambient air for the future in the Arctic. Our study provides quantitative evidence of the impact of open waste burning on air quality at a study site in Greenland, which could be crucial in developing air quality policies for this region in the Arctic.

aerosols↗