Search NASA⌕ Search

SEARCH · Search NASA

Results for “Resource Assessment”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Geothermal Power Systems Analysis: Outcome of Industry Stakeholders Workshop: Preprint

Geothermal cost and performance evaluation implemented via technoeconomic assessment (TEA) modeling is critical for the Department of Energy (DOE) and other geothermal industry stakeholders in assessing the current state of geothermal technologies and to identify existing hurdles to commercially viable geothermal development. The Geothermal Electricity Technology Evaluation Model (GETEM) is a major TEA tool used in estimating the economic feasibility and levelized cost of energy (LCOE) of conventional hydrothermal systems and enhanced geothermal systems (EGS). Since 2021, GETEM has been transitioning from an intricate spreadsheet model to a user-friendly tool within the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory (NREL). Apart from enabling an expanded visibility of the geothermal model among other renewable resources, having GETEM in SAM has the advantage of simulation automation, better usability, updates tracking, active user inputs/feedback, and extended financial modeling. GETEM is used in developing supply curves for the Annual Technology Baseline (ATB). The ATB data are inputs to the Renewable Energy Potential (reV) and the Regional Energy Deployment System (ReEDS) models. The geothermal module in NREL’s reV model assesses the geothermal energy potential in the conterminous United States by defining the geospatial intersection of geothermal resources with existing grid infrastructure within the constraint of land use characteristics. The ReEDS model is a capacity expansion model used for simulating the long-term build-out and operation of the US generation and transmission system based on current energy costs and policies. To ensure enhanced representation of current industry trends in our model transitions and development, we organized a two-day virtual workshop to elicit geothermal industry stakeholder input and recommendations on our current approaches and assumptions on technoeconomic, resource assessment, and deployment scenarios modeling of geothermal technologies. Participants included developers, operators, investors, regulatory agencies, system modelers, national laboratory researchers, consultants, and other stakeholders. In this workshop, we gained stakeholder insights on current geothermal plant performance (i.e., capacity factors), updated drilling costs and learning curves, and next generation technologies such as closed loop and superhot rock geothermal. Other outcomes from this workshop and its impact on future geothermal development feasibility, resource availability, and capacity expansion studies are compiled and discussed.

Annual Technology Baseline↗
DOE OSTI2025

A life cycle assessment of e-hydrogen production using proton-exchange membrane water electrolysis coupled with desalination in Saudi Arabia

Hydrogen, considered a crucial element in the transition towards a sustainable energy future, offers the potential to mitigate greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. Here, this study explores the viability of hydrogen production using proton exchange membrane water electrolysis (PEMWE) as a key driver of decarbonization within the Vision 2030 framework in the Kingdom of Saudi Arabia. A first-of-a-kind life cycle assessment (LCA) of electrolytic hydrogen (e-hydrogen) production using PEMWE in the Kingdom is performed. As the hydrogen will be produced in a freshwater scarce region, the inclusion of water desalination processes adds an important dimension to the assessment, reflecting the local context and resource availability. Two main renewable energy scenarios are assessed: solar energy through photovoltaics (PV) and wind energy through onshore turbines. The global warming potential (GWP) results indicate a GHG emissions reduction of up to 95 % compared to the state-of-the-art steam methane reforming process if the electrolysis process is powered exclusively by renewable electricity. The scenarios powered by solar and wind energy result in 3.66 and 0.76 kg CO 2 eq/kg H 2 , respectively. The metal depletion is assessed to consider the requirement of rare materials, with a 7.19 × 10 −2 kg Cu eq/kg H 2 for the solar scenario and 2.82 × 10 −2 kg Cu eq/kg H 2 for the wind scenario. A contribution analysis reveals that the majority of emissions in both scenarios originate from the electricity used for electrolysis, with the electrolyser itself contributing minimally. The absolute impact of the water desalination process is the same in both scenarios; however, it appears more prominent in the wind-powered case due to the significantly lower overall emissions in that scenario. The findings underscore the importance of renewable energy integration and process optimization in minimizing environmental impacts and advancing the sustainability of e-hydrogen production.

08 HYDROGEN↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
DOE OSTI2026

Impact of hydropower availability on resource adequacy of the United States western interconnection

Hydropower is a key energy source in the western interconnection of the United States, comprising about 25% of the annual installed nameplate capacity in 2020. However, its generation is increasingly impacted by changing hydrological conditions, operational constraints, environmental factors, water availability, and aging dam infrastructure. Assessing hydropower availability and resource adequacy is valuable for shaping energy policies and infrastructure requirements. In this study we evaluate the sensitivity of resource adequacy in the Western Interconnection to the unavailability of hydropower plants, under a wide-ranging set of 16,384 hydropower loss scenarios derived from a combination of 14 major hydrologic regions. Although a complete loss of hydropower capacity in any region is unlikely, studying such scenarios helps in identifying hydrologic regions most critical for maintaining resource adequacy. So, we identify key hydrologic regions with disproportionately large adequacy impacts relative to their installed hydropower capacity and study compounding effects between regions using classification and regression trees. Even after controlling for total installed capacity, we find that hydropower resources in the Pacific Northwest contribute the most to interconnection-wide adequacy outcomes, with resources in Northern California and the Desert Southwest providing more moderate incremental contributions.

13 HYDRO ENERGY↗
DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2026

Energy Baseline Assessment and Project Considerations for the Wai'anae Moku Community Energy Plan

This report was prepared as a supplement to the Waianae moku community's energy plan and includes results of two activities conducted by the National Laboratory of the Rockies in support of the planning process: (1) an energy baseline assessment and (2) considerations and resources for possible energy projects prioritized by the community in its energy plan. Results of the energy assessment describe the energy landscape in the state of Hawaii, on the island of Oahu, and in the Waianae moku at the time the plan was developed, with the intention of supporting informed community participation in the planning process. Considerations and resources align with each of the energy project ideas identified in the community's energy plan, and are intended to describe the current context and activities related to each, along with possible next steps toward implementation, and additional resources. The report concludes with takeaways for the community to consider as they pursue their energy goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

STARFIRE – Science and Technology Accelerated Research for Fusion Innovation and Reactor Engineering progress report

The STARFIRE hub leverages foundational science and technology (S&T) research and integrated plant modeling tools to advance Inertial Fusion Energy (IFE). S&T breakthroughs achieved in each thrust are incorporated into an integrated modeling tool to assess and prioritize remaining gaps to bridge for IFE to become viable. Scientific highlights span high-gain target design, diode-pumped solid-state lasers, and innovative target manufacturing. Key highlights include the investigation of shock propagation through various foam structures among other limiting factors of compression of IFE target, exploration of novel 3D printing techniques such as light-sheet additive manufacturing for 2-photon polymerization foams, and diode-pumped solid-state laser architectures required for several IFE approaches. In addition, the STARFIRE team has established two public-private working groups to address laser diode supply chain issues and test advanced laser diode configurations. Finally, a lite version of the integrated modeling tool has been developed to facilitate outreach and engage with private companies. STARFIRE’s management structure has been designed to ensure agile resource allocation, robust performance assessment, and alignment with the broader fusion energy mission. STARFIRE’s breakthroughs are disseminated to the community via reports, presentations, and publications, while agreements with private companies are set in place to protect proprietary information.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model

Near-real-time (NRT) streamflow data are critical importance for timely water resources management. Here, we developed an open-source tool, FlowStats, for NRT streamflow analysis and visualization in Germany, based on NRT meteorological data from the German Weather Service and simulated streamflow from a long short-term memory neural network (LSTM). The LSTM model achieved very good overall performance, median NSE of 0.80 for the test period across 1,479 catchments. FlowStats provides options for deriving various streamflow statistics, from normal and abnormal streamflow detection to drought and flood analyses. An example analysis from FlowStats revealed widespread below-normal to extreme low-flow conditions across Germany from March to May 2025, which weakened from June to September 2025. Drought analysis for September 2025 highlighted severe to extreme drought conditions in northwestern Germany, while flood classifications indicated that high-flow events occurred in southwestern Germany. FlowStats can be used for various hydrological assessments to support water resources management.

Hydrological modeling↗
DOE OSTI2025

Cement and concrete as carbon sinks: Transforming a climate challenge into a carbon storage opportunity

Cement and concrete, while traditionally recognized as the main contributors to anthropogenic CO 2 emissions, also have untapped capacity to serve as substantial and scalable carbon sinks. This perspective examines how engineered mineral carbonation can transform cement-based materials into functional carbon storage systems, generating both environmental and economic value. We review the fundamental mechanisms of CO 2 uptake in cementitious systems, highlighting current limitations in reaction kinetics, phase control, and durability under varying environmental conditions. Emphasis is placed on the utilization of alkaline industrial residues and emerging magnesium-based cements, which offer synergistic pathways for carbon sequestration and circular resource use. We further assess the performance trade-offs associated with CO 2 uptake and the feasibility of deploying these technologies on industrial scales. A strategic roadmap is proposed that integrates scientific innovation, regulatory alignment, and carbon accounting in the life cycle to accelerate the adoption of carbon-storing concrete. This perspective provides a comprehensive framework to advance cement and concrete as engineered carbon sinks and supports the transition to a climate-positive construction industry.

Carbon storage↗
DOE OSTI2024

Endogenizing Probabilistic Resource Adequacy Risks in Deterministic Capacity Expansion Models

In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. The endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.

capacity expansion modeling↗
DOE OSTI2025

BEST USE OF BIOMASS

SF-25-046 Biomass resources are widely available in United States, however to utilize the resources we need to assess the bioenergy pathways per their economic and environmental performance. The framework combines several developments in the research field and integrates into a software easy to use and expand upon to study various bioenergy pathways.The software code is developed in Python language and works with an Excel based dashboard.

Saurajyoti, Kar [Argonne National Laboratory (ANL)↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗
DOE OSTI2026

Energy to Communities Program Short Introduction: Evaluation of Enhanced Geothermal System for Cornell District Heating [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Cornell University (Itaca, NY) community efforts in developing Enhanced Geothermal System (EGS) reservoir performance, by simulating fracture flow to estimate pressure drop, flow distribution, and heat extraction efficiency in fractured rock. Thermal drawdown modeling was used to assess long-term resource sustainability, while system optimization studies refined key parameters such as well configuration and flow rate to improve overall reservoir efficiency.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

VIC-Global Parameter Dataset Sensitivity with the Variable Infiltration Capacity Model: Evaluating the importance of dynamic land surface parameters when using the VIC-Global parameter dataset

Accurate prediction of runoff is essential to water resources management, flood risk assessment, and ecosystem protection. However, many hydrological models still have relatively substantial limitations when representing the influence of land use and land cover (LULC) on runoff generation and routing. Changes in LULC, such as deforestation, urban expansion, agricultural intensification, and wetland loss, have been shown to alter the water balance at the land surface through fundamental hydrologic processes (e.g., interception, infiltration, evapotranspiration, and soil storage). However, it remains an open question what the exact magnitude and timing of these impacts are for the spatial and temporal scales commonly used in engineering applications. In this analysis we focus on one aspect of recent LULC change for assessing human impacts, which is urbanization. Specifically we seek to determine the impacts of urbanization on the magnitude and timing of surface runoff and baseflow in HUC-12 basins in Clark County, Nevada which has experienced rapid urbanization. We use the Variable Infiltration Capacity (VIC) hydrology model with a widely used off-the-shelf dataset of land surface parameters, VIC-Global, both of which have been commonly used in the past for water and energy balance modeling for large scale hydrologic studies. We examine two scenarios where the first scenario removes all urbanized land cover and parameterizes those areas of the basins as barren or open shrubland. The second scenario tests the opposite case where all areas of the basins are classified as urban regardless of their present classification. The results from the VIC model show there is a low sensitivity for daily surface runoff between scenarios. The daily baseflow values indicate similar low sensitivity to the classification change during specific periods, but then have substantial differences during other period when large precipitation events are occurring. This is likely due to the assumed parameter values for the urban land cover classification made by the VIC-Global dataset. Using a static land cover parameterization is reasonable for large domain hydrology models that are being used for near-term planning horizons (<30 years). However, longer planning horizons where feedbacks between the atmosphere and land surface are important, especially in transient climate situations, considerations for how to update land surface parameters should be incorporated.

42 ENGINEERING↗
DOE OSTI2024

Comparison of Greenhouse Gas Emission Assessments of Solar and Energy Efficiency Improvements at Small Water Resource Recovery Facilities

Small water resource recovery facilities (WRRFs) account for the majority of centralized systems in the world and have higher energy intensities than large facilities. This study compares potential greenhouse gas emission reductions based on on-site solar energy and energy efficiency (E2) improvements made at small WRRFs. Case study data from 31 existing small WRRFs in Nebraska were collected and included 35 site-specific energy efficiency (E2) recommendations and on-site solar renewable energy systems integrated at three facilities, and the data were used to compare the benefits of on-site solar energy and E2 improvements made at small WRRFs. Improvements in E2 (e.g., improved aeration control) presented the largest reduction in emissions per dollar invested. They often exhibited shorter paybacks, with operational changes in aeration strategies showing the highest impact (up to 0.2 kg CO2eq/m3 treated water). On-site solar systems showed the largest net potential for reducing environmental footprint (0.35 kg CO2eq/m3) but often showed the smallest emissions reduction per cost. While the use of both E2 improvements and the integration of on-site solar renewable energy can significantly improve the sustainability of small WRRFs, on-site solar has advantages for small facilities in that it often requires less operational involvement, allows for greater facility resiliency, and presents less uncertainty in terms of environmental benefit.

Thompson, Matthew↗