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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dynamic CCS-EJ-SJ Database and Web Application - What's New

At the 2024 FECM/NETL Carbon Management Research Project Review Meeting, within the Carbon Transport and Storage Breakout Session 3, the presentation "Dynamic CCS-EJ-SJ Database and Web Application - What's New" highlights the critical tool designed to integrate environmental and social justice considerations into Carbon Capture and Storage (CCS) projects. Key features include an interactive dashboard for data access and visualization, which supports stakeholders in making informed decisions regarding CCS implementation, and updated data layers. The latest version enhances data integration and usability, providing a comprehensive resource for assessing the social and environmental impacts of CCS projects. There are 7 categories in the CCS EJSJ v2 database (released 03/31/2024): environmental justice, energy justice, economic justice, social justice, ecosystem assets, clean energy, and infrastructure. Most of the layers within each category have been updated in this version. As compared to the old database, there are 3 new categories in the v2 database: ecosystem assets, clean energy, and infrastructure.

Sharma, Maneesh↗
DOE OSTI2024

Preferred Practices Through a Project Template

In the realm of scientific software development, adherence to best practices is often advocated. However, implementing these can be challenging due to differing opinions. Certain aspects, such as software licenses and naming conventions, are typically left to the discretion of the development team. Our team has established a set of preferred practices, informed by, but not limited to, widely accepted best practices. These preferred practices are derived from our understanding of the specific contexts and user needs we cater to. To facilitate the dissemination of these practices among our team and foster standardization with collaborating domain scientists, we have created a project template for Python projects. This template serves as a platform for discussing the implementation of various decisions. This paper will succinctly delineate the components that constitute an effective project template and elucidate the advantages of consolidating preferred practices in such a manner.

Zhang, Chen↗
DOE OSTI2023

Immersive Digital Twins and Decision Making

This work focuses on developing and implementing immersive digital twins to support decision-making processes in construction automation and energy simulation analysis. The use of digital twins enables users to explore and interact with virtual replicas of physical systems, providing a unique and valuable perspective for problem-solving. In the context of construction automation, we present two case studies that integrate immersive visualization and worker-machine interaction. In the context of energy simulation analysis using digital twins, we describe our digital twin framework that combines interactive visualization with machine learning techniques to approximate computationally expensive simulations, allowing for faster and more efficient analysis of complex energy systems. Through these research endeavors, we demonstrate the potential of immersive digital twins in decision-making across various domains.

digital twins↗
DOE OSTI2022

Predicting the Impact of Utility Lighting Rebate Programs on Promoting Industrial Energy Efficiency: A Machine Learning Approach

Implementation costs are a major factor in manufacturers’ decisions to invest in energy-efficient technologies. Emerging technologies in lighting systems, however, typically require small investment costs and offer short, simple payback periods, due, in part, to federal, state, and utility incentive programs. Recently, however, certain state and federal mandates have reduced the support for and efficacy of electricity utility incentivizing programs. To determine the impact of such support programs, this study examined historical data regarding lighting retrofit savings, implementation costs, and utility rebates gathered from 13 years of industrial energy audits by a U.S. Department of Energy Industrial Assessment Center in a midwestern state. It uses a machine learning approach to evaluate the industrial energy and cost-saving opportunities that may have been lost due to decisions attributable to legislative mandates, utility policies, and manufacturers’ calculations and to evaluate the potential effect of lighting rebates on manufacturers’ decisions to implement industrial energy-efficient lighting retrofits. The results indicate that the decision not to implement lighting energy efficiency recommendations resulted in a loss of more than USD800,000 in potential rebates by industries during the study period and that the implementation of lighting energy assessment recommendations could have increased by about 50% if electric utility rebates had been available. These findings can help industries evaluate the benefits of implementing lighting efficiency improvements, and help utilities determine feasible lighting retrofit rebate values for incentivizing such changes by the industries they serve.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Making of a Metric: Co-Producing Decision-Relevant Climate Science

Developing decision-relevant science for adaptation requires the identification of climatic parameters that are both actionable for practitioners as well as tractable for modelers. In many sectors, these decision-relevant climatic metrics and the approaches that enable their identification remain largely unknown. “Co-production” of science with scientists and decision-makers is one potential way to identify these metrics, but there is little research describing specific and successful co-production approaches. This paper examines the negotiations and outcomes from Project Hyperion, wherein scientists and water managers jointly developed decision-relevant climatic metrics for adaptive water management. We identify successful co-production strategies by analyzing the project’s numerous back-and-forth engagements and tracing the evolution of the science during these engagements. We found that effective mediation between scientists and managers needed dedicated “boundary spanners” with significant modeling expertise. Translating practitioners’ information needs into tractable climatic metrics required direct and indirect methods of eliciting knowledge. We identified four indirect methods that were particularly salient for extracting tacitly held knowledge and enabling shared learning: developing a hierarchical framework linking management issues with metrics, starting discussions from the planning challenges, collaboratively exploring the planning relevance of new scientific capabilities, and using analogies of other “good” metrics. The decision-relevant metrics we developed provide insights into advancing adaptation-relevant climate science in the water sector. The co-production strategies we identified can be used to design and implement productive scientist–decision-maker interactions. Overall, the approaches and metrics we developed can help climate science to expand in new and more use-inspired directions.

Planning↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2022

Demonstrating Advanced Nuclear Energy Solutions for Net Zero

Background/Objectives. The aggressive goals being set by nation states, communities, and private industry for decarbonization of grid electricity, industrial heat sources, and transportation around the world are imperative to mitigating the devastating effects that we are seeing from climate change. Although many of these goals focus on accomplishments by 2035 or 2050, the decisions that we make today won’t just impact the landscape of energy systems for the next 20 or 30 years—they will shape the world’s environment for centuries to come. That means that we can’t just focus on technologies that will get us to 2050, but technologies that will withstand our energy demands over that long-ranging future. Success will require us to utilize all of the clean energy resources that we have available to meet demands for electricity, heat, and steam, and we will need energy carriers such as hydrogen that do not emit additional greenhouse gases at the point of use. Nuclear energy, ranging from technologies in service today to advanced, higher temperature and modular systems that will be in service this decade, will provide a robust complement to renewable energy resources that operate variably. Researchers across the U.S. Department of Energy laboratory complex are working to advance multiple aspects of these clean energy solutions, with many focusing on integrated energy system solutions that leverage all available clean energy assets to meet wide-ranging energy demands. Approach/Activities. Nuclear energy is a proven, zero-emission option during operation that can provide consistent, dispatchable power to meet electricity demands while also providing high-quality heat that can meet energy demands beyond the electricity sector. Energy system design should seek to maximize these assets. As a dispatchable energy source with a small land utilization footprint, nuclear energy can be collocated with renewable resources, and the smaller systems that will be deployed this decade (ranging from a few megawatts to hundreds of megawatts) can be installed right where that energy is needed. Integrated nuclear and renewable systems will enhance power grid reliability and resilience, and they will help stabilize the grid through their increasingly flexible operation. Licensing, installation, and broad adoption of these advanced nuclear energy systems are expected to progress significantly in the 2020s, but this may be longer than desired by some stakeholders wishing to implement impactful clean energy decisions today. However, one must recall that nuclear energy systems will operate for 80 or more years, as is being demonstrated by current fleet nuclear systems. The nuclear community is extremely thorough in reviewing these systems with regard to safety and security; these efforts ensure that the deployed systems will continue to provide reliable, resilient energy over that operational lifetime. That investment of time up front will ensure that we can support energy demands over the centuries to come. While advanced nuclear technologies move through this process, communities and private industry may choose to install renewable generation systems that can later be coupled to the complementary nuclear systems as they become available—thus moving closer to the net zero goals in the near term. Choosing technologies and deployment configurations that allow small modular nuclear powerhouses to be added to these “energy parks” as they become available will ensure that advanced technologies can be readily adopted to support growing demands for clean energy. Results/Lessons Learned. The primary focus of integrated energy systems (IES) research is to assess the technical and economic potential of novel multi-input, multioutput solutions that are expected to enhance energy system flexibility, reliability, and resilience as we pursue a clean energy transition. Various energy applications and product streams beyond electricity are being evaluated, ranging from generation of potable water to production of hydrogen, fertilizers, synthetic fuels, and various chemicals. In early FY23 Idaho National Laboratory (INL) will commission thermal energy generation systems that emulate nuclear fission energy input using electric heating and will allow for integrated system testing with thermal energy storage, hydrogen production via high temperature electrolysis (HTE), and power systems hardware to demonstrate operation of a clean energy park within a microgrid or larger grid infrastructure, supporting up to 450 kW of heat input via electric heating and demonstrating operation of HTE systems at the multi-hundred kW scale. This presentation will highlight the wide array of RD&D being conducted at INL and partner laboratories to develop and deploy nuclear and renewable-based IES that will be key to achieving our net zero goals, including both computational and experimental demonstrations. By working with key collaborators in industry, analytical st

08 HYDROGEN↗
DOE OSTI2025

Using Boosted Decision Trees to Select High Quality Measurements in the Mu2e Experiment at Fermilab

This thesis presents the implementation and evaluation of a Boosted Decision Tree (BDT) model to improve the selection of high-quality track measurements in the Mu2e experiment at Fermilab. The Mu2e experiment is a high-energy physics experiments seeking to observe a rare theoretical physics process known as Charged Lepton Flavor Violation. A significant challenge faced by the Mu2e experiment are so-called background events, which are events whose data mimics that of the rare physics process the experiment seeks to observe. Without a mechanism to reduce background, it would be impossible to know whether Charged Lepton Flavor Violation occurred or not. To this end, high-quality track measurements must be distinguished from low-quality track measurements. A track can be conceived of as the reconstructed path of a particle that traveled through the Mu2e detector. In addition to other data, data about such tracks is stored using a C++-based framework, specific to the domain of high-energy physics, known as ROOT. A boosted decision tree model was trained using ROOT’s Toolkit For Multivariate Analysis by leveraging variables ancillary to track quality. In evaluation, the BDT achieves a ROC-AUC of 0.927 in discriminating good-quality tracks from poor-quality tracks. Such a score is indicative of both strong discrimination and strong generalization. Subsequently, it is shown that applying a BDT-based quality cut to the distribution of particle momenta significantly enhances the signal-to-background distinction for signal electrons, paving the way for improved sensitivity to Charged Lepton Flavor Violation.

Mullany, Brendan T. [Drew U.] (ORCID:0009000818888↗
DOE OSTI2022

Integrating terrestrial scavenging ecology into contemporary wildlife conservation and management

Scavenging plays a vital role in maintaining ecosystem health and contributing to ecological functions; however, research in this sub-discipline of ecology is underutilized in developing and implementing wildlife conservation and management strategies. We provide an examination of the literature and recommend priorities for research where improved understanding of scavenging dynamics can facilitate the development and refinement of applied wildlife conservation and management strategies. Due to the application of scavenging research broadly within ecology, scavenging studies should be implemented for informing management decisions. In particular, a more direct link should be established between scavenging dynamics and applied management programs related to informing pharmaceutical delivery and population control through bait uptake for scavenging species, prevention of unintentional poisoning of nontarget scavenging species, the epidemiological role that scavenging species play in disease dynamics, estimating wildlife mortalities, nutrient transfer facilitated by scavenging activity, and conservation of imperiled facultative scavenging species. This commentary is intended to provide information on the paucity of data in scavenging research and present recommendations for further studies that can inform decisions in wildlife conservation and management. Additionally, we provide a framework for decision making when determining how to apply scavenging ecology research for management practices and policies. Due to the implications that scavenging species have on ecosystem health, and their overall global decline as a result of anthropic activities, it is imperative to advance studies in the field of scavenging ecology that can inform applied conservation and management programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EIC Software Overview

Development of the EIC project detector "ePIC" is now well underway and this includes the "single software stack" used for simulation and reconstruction. The stack combines several non-experiment-specific packages including ACTS, DD4hep, JANA2, and PODIO. The software stack aims to be forward looking in the era of AI/ML and heterogeneous hardware. A formal decision making process was implemented to choose the components that involved everyone in the collaboration that was interested. This talk will present an overview of the software stack currently used for development of the ePIC detector and on which we expect to execute the experiment.

Lawrence, David↗
DOE OSTI2021

Nuclear Safeguards: Feature Extraction for Machine Learning Enrichment Analysis of UF 6 Cylinders

With the increasing international interest in using nuclear material as sources of energy, comes the growing concern that we may see an increase in proliferation threats. Uranium hexafluoride (UF 6 ) is used in the nuclear fuel cycle for uranium enrichment. Inspectors from the International Atomic Energy Agency (IAEA) monitor the enrichment levels of UF 6 stored in transportation cylinders, to ensure the enrichments match that of a facility operator’s declarations. However, only a characteristic subset of UF 6 cylinders can be measured by inspectors from the IAEA during these inspections. In turn, the inspector may not identify a cylinder whose enrichment levels do not match the facility declarations. Therefore, our team attempts to develop a machine learning network that could determine the enrichment percentage of cylinders as they enter and exit facilities. The machine learning model must be robust against spectral variations due to factors that are internal and external to the UF 6 cylinder. Such factors include but are not limited to the speed and distance of the moving vehicle, cylinder type, cylinder orientation, and fill level. Many features in the spectral continuum can be used to identify and correct for some of these variations. As a consequence, the model must extract numerous features in the continuum. We investigate new approaches in continuum subtraction to verify enrichment percentage: interpolation and extrapolation of lines at the notable characteristic spectral peaks. It was determined that there was no clear answer on which method proved superior, therefore the decision was made to implement both new methods into the current codebase. We currently rely on synthetic training and testing data to analyze the results and fine tune our models but hope to test the system on measured data soon.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Practices for Demonstrating Energy Savings from Commercial PACE Projects

Nearly three-fourths of U.S. states have authorized local governments to use voluntary special assessments on commercial properties to finance energy improvements that boost economic development, create jobs, increase property values and advance energy goals. Commercial Property Assessed Clean Energy (C-PACE) financing allows building owners to repay the borrowed capital — from private or public sources — over time using their property as security. Berkeley Lab is supporting the Department of Energy’s Commercial PACE Working Group by developing a series of C-PACE issue briefs. The second brief in this series, Practices for Demonstrating Energy Savings from Commercial PACE Projects, looks at common practices for demonstrating energy savings to support state and local governments that sponsor C-PACE programs and want to track their energy impacts. This brief reviews: -The value proposition and trade-offs of conducting energy impact assessments for C-PACE programs; -Methods to quantify energy savings impacts from energy efficiency building improvements; and -Available resources and tools to support energy impact assessments. C-PACE programs may benefit from energy impact assessments for many reasons, including: -Validating the public benefits of the programs -Demonstrating that C-PACE can deliver participant benefits -Illustrating program impacts on public policy goals -Generating data to help improve program performance Many C-PACE programs are collecting data on project energy savings impacts, and these data can be leveraged to further support decision making and program implementation. Potential drawbacks to energy impact assessments may include added cost and burdens on property owners (e.g., the need to collect building energy consumption data). Where these burdens are considerable, they might slow program uptake. State and local governments can balance the benefits of energy impact assessments with the range of costs and accuracy inherent to available assessment methodologies. Additionally, depending on the policy context in the state or local government, a C-PACE program may be able to leverage existing efforts (e.g., building energy benchmarking programs) to reduce impact assessment costs, align with building owner practices and expectations, and efficiently assess program impact.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

EIC Software Overview

Development of the EIC project detector "ePIC" is now well underway and this includes the "single software stack" used for simulation and reconstruction. The stack combines several non-experiment-specific packages including ACTS, DD4hep, JANA2, and PODIO. The software stack aims to be forward looking in the era of AI/ML and heterogeneous hardware. A formal decision making process was implemented to choose the components that involved everyone in the collaboration that was interested. This talk will present an overview of the software stack currently used for development of the ePIC detector and on which we expect to execute the experiment.

Lawrence, David↗
DOE OSTI2020

Avatar Tools

Supervised machine learning is the process of using past experience to predict the future. "Ensembles" are a machine-learning meta-method that can be applied to most machine learning algorithms. Ensembles generally greatly improve accuracy, reduce or remove most of the design issues presented by machine learning, and are admirably suited to parallel and distributed computation. The Avatar Tools codes are an implementation of ensembles specifically for decision trees. Some features that distinguish Avatar Tools from other "ensembles for decision trees" codes are: (1) Does the bookkeeping necessary for out of bag (OOB) validation. (2) Can use OOB validation to automatically determine optimal ensemble size. (3) Provides an MPI-based parallel implementation, for distributed operation. (4) Provides convenient tools for cross-validation, to assess the accuracy provided by a training set. SAND2020-3858 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Siefert, Christopher↗
DOE OSTI2025

WHOLESCALE - Water & Hole Observations Leverage Effective Stress Calculations And Lessen Expenses (Final Technical Report 2020 - 2024)

The WHOLESCALE acronym stands for Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses. The goal of the WHOLESCALE project is to simulate the spatial distribution and temporal evolution of stress in the geothermal system at San Emidio in Nevada, United States. To reach this goal, the WHOLESCALE team has developed a methodology to incorporate and interpret data from four methods of measurement into a multi-physics model that couples thermal, hydrological, and mechanical (T H-M) processes. The WHOLESCALE team has applied this methodology at the San Emidio geothermal field, located ~100 km north of Reno, Nevada in the northwestern Basin and Range province. The WHOLESCALE team includes 30 individuals working at two universities, two national laboratories, and one industry partner. Two master-degree students and five post-doctoral researchers have gained professional experience and earned partial financial support via the WHOLESCALE project. The WHOLESCALE team has taken advantage of the perturbations created by changes in pumping operations during planned shutdowns in 2016, 2021, and 2022 to infer temporal changes in the state of stress in the geothermal system at San Emidio, Nevada, U.S. The WHOLESCALE results support the working hypothesis that increasing pore-fluid pressure reduces the effective normal stress acting across fault zones. During normal operations, pumping in deep production wells decreases fluid pressures and thus increases the effective normal stresses on faults, reducing microseismicity. During planned shutdowns, the cessation of production increases pore-fluid pressure and reduces effective normal stress. The WHOLESCALE products generated during the 4-year period between 2020 and 2024 include: three articles published in the open-access, peer-reviewed scientific literature, two master’s theses, 20 presentations or papers at scientific conferences, and 17 data sets available on public repositories. The WHOLESCALE project has been completed in two phases that included three performance periods separated by two Go/No-go Stage Gate Reviews. Tasks were classified by data type (i.e., Geologic Structure, Borehole, Geodesy, Hydrology, Seismology, and Modeling). The first phase of the project started July 31, 2020 and included ongoing project coordination (Task 1), a project kickoff (Task 2), analysis of existing data (Task 3), development of the initial stress model & deployment design (Task 4), and Go/No-go Decision Point #1 (Task 5). Phase II began with implementing the 2022 deployment (Task 6), followed by Go/No-go Decision Point #2 (Task 7) The remainder of Phase II consisted of analyzing data collected during deployment (Task 8), calibration of the stress model on all observations (Task 9), and the Final Review (August 23, 2024) & Reporting (Task 10).

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Optimizing Solar PV Deployment in Manufacturing: A Morphological Matrix and Fuzzy TOPSIS Approach

The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing.

Briceño, Citlaly Pérez↗
DOE OSTI2021

Supporting cost-effective watershed management strategies for Chesapeake Bay using a modeling and optimization framework

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program’s (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States’ largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for ~200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework’s current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies.

54 ENVIRONMENTAL SCIENCES↗