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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership

The 2005 Intergovernmental Panel on Climate Change (IPCC) Special Report on CCS raised the profile of CO2 capture and storage (CCS) as an important technology for reducing greenhouse gas (GHG) emissions. CCS is now recognized as a key component of most climate change mitigation scenarios. Since publication of that report the international research, development, and deployment (RD&D) community has advanced key technical aspects, clarified regulatory requirements, explored value chain and infrastructure solutions, and developed incentive paradigms to enable and promote large-scale deployment of CCS. These efforts have included research to better characterize geologic storage resources, to improve injection performance and storage efficiency, to assess and manage subsurface environmental risks, and to advance monitoring technologies to assure system conformance. These efforts have helped to build confidence in the viability of geologic carbon storage (GCS), but stakeholder concerns about long-term risks and liability associated with GCS remain a hurdle to broad acceptance and large-scale deployment of CCS. Since 2010, the U.S. DOE’s National Risk Assessment Partnership (NRAP) – a research collaboration between five contributing national laboratories – has worked to establish and demonstrate methods and tools to quantify and manage the subsurface environmental risks associated with GCS, amidst uncertainty. This work supports the Office of Fossil Energy and Carbon Management Carbon Transport and Storage Program’s goal of advancing safe and secure commercial-scale GCS deployment. To address the technical challenge of simulating the physical response of the GCS site to large-scale CO2 injection, NRAP has adopted an approach that relies on coupling computationally efficient reduced-order and/or data-driven proxy models of important system components (i.e., storage reservoir, sealing caprock, leakage pathways, intermediate formations, overlying groundwater aquifers, and the atmosphere) in integrated assessment framework. That integrated model of the physical system is complemented with fit-for purpose functionality to support site characterization and risk-related decisions. The recently released NRAP Phase II toolset includes the Open-Source Integrated Assessment Model (NRAP-Open-IAM) for evaluation of trends in leakage risk and potential impact, tools to support monitoring design optimization (Designs for Risk Evaluation and Management – DREAM v3.0 and Passive Seismic Monitoring Tool - PSMT), and tools for state of stress evaluation (State-of-Stress Analysis Tool - SOSAT) and forecasting induced seismicity risk. The NRAP team has also released a pair of reports describing conceptual workflows to incorporate physics-based, quantitative risk assessment into many of the design, planning, operation, and closure decisions for GCS projects. An online catalogue highlights published studies where these tools and methods are demonstrated. In this presentation, the utility of these products to assess risks and address key stakeholder questions will be highlighted through examples, and related insights about the safety and security of geologic carbon storage in qualified storage sites will be discussed. The prospect of rapid, large-scale deployment of GCS technology to aggressively reduce anthropogenic CO2 emissions requires careful consideration of interference between multiple commercial-scale storage projects within a basin. Going forward, NRAP is expanding and adapting site-scale risk quantification tools and methods to enable assessment of risks and inform management decisions for basin-scale deployment. Increasingly, this work will leverage next-generation approaches for surrogate modelling, fast prediction, and advanced visualization enabled by machine learning and artificial intelligence to promote virtual learning, scenario evaluation, and augment risk-based decision making.

quantitative risk assessment, geologic carbon stor↗
DOE OSTI2017

Characterizing Risk for Cumulative Risk Assessments: Characterizing Risk for Cumulative Risk Assessments

In assessing environmental health risks, the risk characterization step synthesizes information gathered in evaluating exposures to stressors together with dose-response relationships, characteristics of the exposed population, and external environmental conditions. This article summarizes key steps of a cumulative risk assessment (CRA) followed by a discussion of considerations for characterizing cumulative risks. Cumulative risk characterizations differ considerably from single chemical- or single source-based risk characterization. CRAs typically focus on a specific population instead of a pollutant or pollutant source and should include an evaluation of all relevant sources contributing to the exposures in the population and other factors that influence dose-response relationships. Second, CRAs may include influential environmental and population-specific conditions, involving multiple chemical and nonchemical stressors. Third, a CRA could examine multiple health effects, reflecting joint toxicity and the potential for toxicological interactions. Fourth, the complexities often necessitate simplifying methods, including judgment-based and semi-quantitative indices that collapse disparate data into numerical scores. Fifth, because of the higher dimensionality and potentially large number of interactions, information needed to quantify risk is typically incomplete, necessitating an uncertainty analysis. Three approaches that could be used for characterizing risks in a CRA are presented: the multiroute hazard index, stressor grouping by exposure and toxicity, and indices for screening multiple factors and conditions. Other key roles of the risk characterization in CRAs are also described, mainly the translational aspect of including a characterization summary for lay readers (in addition to the technical analysis), and placing the results in the context of the likely risk-based decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Quantitative Risk Management - Risk Informed Decision Making on High-Risk Waste Remediation Projects - 19022

For every $1 billion invested in projects, $97 million is wasted due to poor project performance. That means 9.7% of money spent on projects is wasted due to poor risk and uncertainty management. High-risk projects, similar to those in waste remediation, have the most to lose. A solid quantitative risk management program is one of the keys to increasing the likelihood that a project will succeed. A quantitative risk program solves two major problems that challenge the effective management of risk and uncertainty: 1. Risks arise at all levels of an organization; however, not all organizational levels have an effective way to identify and resolve them. This can lead to risks that, even if identified, are not reported. 2. Decision-makers are often forced to operate solely on intuition when implementing mitigation strategies, not fully understanding the effects that certain decisions have on the program's cost and schedule. A quantitative risk program uses a risk register and builds it into the project/program integrated schedule to assess the impact of these risks, typically at the 80% confidence contingency level for specific milestones and/or project completion. Implementing a quantitative risk program drives a forward-thinking, proactive mentality to problem solving into all levels of the organization. A healthy risk program for regular reporting and communication ensures that all members of an organization are thinking about risks and using their communication channels to resolve them. It gives a method of communication and incentive for all levels in the organization and helps to ensure that risks will be reported and resolved. Pacific Northwest National Laboratory (PNNL) has developed tools and methods that provide a level of realism for analyzing different scenarios and estimating contingency that does not exist in the standard industry risk tool kit. These tools and methods provide decision-makers with actionable, quantitatively based information to guide their decision making. The first tool is a probabilistic branching risk model for mitigation strategies. Current quantitative risk analysis tools in the industry simulate two cases: fully mitigated and un-mitigated. In the fully mitigated case, the user assumes that every planned mitigation activity is successful. In the un-mitigated case, the user assumes that none are successful. The truth lies between these two extremes. The PNNL team has developed a probabilistic risk model for mitigation scenarios that includes a probability that a mitigation activity will succeed and a probability that a mitigation action will be funded. These probabilities are elicited from subject matter experts and ensure that the contingency calculations at the specified confidence are approaching reality. The second tool developed at PNNL is the Risk Box, which is a tool that integrates with different scenarios in the risk register. For example, say the user is faced with two different and mutually exclusive mitigation actions to reduce a risk. The Risk Box methodology performs the contingency calculations and then displays them on a time-phased graph that shows how each scenario reduces the risk contingency needed on the project/program. This graph then provides quantitative information to show decision-makers which mitigation action provides the greatest reduction (cost and schedule) in risk contingency for the program. Implementing these and other tools within a structured risk program increases the likelihood that a project will come in on schedule and budget, ensures that all personnel are proactively thinking about problems and resolutions, creates communication channels for all members to submit and resolve potential problems, produces a prioritized list of risk rankings to focus leadership on issues that may cause the biggest problems, and implements processes that drive updates to the risk program as the risk profile changes. PNNL has been developing these methods and using them on projects for over 20 years. They have been successfully applied on waste management projects, high-tech research and development projects, and construction projects, and they continue to be used on a number of U.S. Department of Energy projects today. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Progress in Monitoring Strategies for Risk Reduction in Geologic CO2 Storage

Abstract: This geophysical monitoring review is intended to lay the ground work for an approach to integrating monitoring strategies with risk assessment and risk reduction and to summarize work conducted by U.S. Department of Energy's National Risk Assessment Partnership (NRAP) participants that addresses key questions related to monitoring strategies. Commercial, demonstration and pilot geologic CO2 storage (GCS) projects have provided a wealth of practical experience in monitoring technologies. In this manuscript we review a few key projects, specific geophysical methods, well-based methods, geophysical workflows and some suggested best practices. These practices are often driven and governed by regulatory requirements (e.g. U.S. Environmental Protection Agency [US-EPA] Class VI underground injection regulation) and the need for effective reservoir management of pressures and volumes as well as the site-specific risks to containment, plume migration, and underground sources of drinking water. Reducing uncertainty in monitoring is likely to lead to reducing uncertainty in risk. One of our conclusions is the need for field experiment testing that is risk-driven. It is notable that GCS pilot projects have been designed for safe storage, and to date they have been successful. Technical challenges at all geologic CO2 storage sites include assuring injectivity and capacity predictions are reliable, the fate and subsurface topology of the CO2 plume is acceptable, regulatory permitting and compliance are accomplished in a timely manner, needed business assurances are supported by quantitative technical data, and the storage operation is acceptable to stakeholders. There is risk associated with all of these challenges necessitating a monitoring program utilizing both direct and indirect monitoring methods. Because of the direct association of risk and monitoring in GCS, geophysical monitoring is viewed as a key component in approaching risk assessment. To address monitoring in the context of risk assessment the U.S. Department of Energy's National Risk Assessment Partnership (NRAP) has undertaken a review of work related to monitoring storage sites. In this review we provide a framework for monitoring and then present some specific monitoring tools that may be useful for regulatory compliance, as well as technologies that are applicable to leakage detection.

Harbert, William↗
DOE OSTI2015

Recent advances in risk assessment and risk management of geologic CO 2 storage

This article gives an overview of the advances made in the field of risk assessment and risk management of geologic CO 2 storage (GCS), since the publication of the IPCC Special Report on Carbon Capture and Storage in 2005. Development and operation of a wide range of demonstration projects coupled with development of new regulations for safe injection and storage of CO 2 have led to development and deployment of a range of risk assessment approaches. New methods and tools have been developed for quantitative and qualitative risk assessment. These methods have been integrated effectively with monitoring and mitigation techniques and deployed in the field for small-scale field tests as well as large-scale commercial projects. An important development has been improved definition of risks, which can be broadly classed as site performance risks, long-term containment risks, public perception risks and market risks. Considerable experience has now been gained on understanding and managing site performance risks. Targeted research on containment risks and induced seismicity risks has led to improved understanding of parameters and processes influencing these risks as well as identifying key uncertainties that need to be targeted. Finally, significant progress has been made to effectively integrate communication strategies with risk management approaches to increase stakeholder confidence in effectiveness of deployed risk management approaches to manage risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Risk analysis of heat recovery steam generator with semi quantitative risk based inspection API 581

Corrosion is a major problem that most often occurs in the power plant. Heat recovery steam generator (HRSG) is an equipment that has a high risk to the power plant. The impact of corrosion damage causing HRSG power plant stops operating. Furthermore, it could be threaten the safety of employees. The Risk Based Inspection (RBI) guidelines by the American Petroleum Institute (API) 58 has been used to risk analysis in the HRSG 1. By using this methodology, the risk that caused by unexpected failure as a function of the probability and consequence of failure can be estimated. This paper presented a case study relating to the risk analysis in the HRSG, starting with a summary of the basic principles and procedures of risk assessment and applying corrosion RBI for process industries. The risk level of each HRSG equipment were analyzed: HP superheater has a medium high risk (4C), HP evaporator has a medium-high risk (4C), and the HP economizer has a medium risk (3C). The results of the risk assessment using semi-quantitative method of standard API 581 based on the existing equipment at medium risk. In the fact, there is no critical problem in the equipment components. Damage mechanisms were prominent throughout the equipment is thinning mechanism. The evaluation of the risk approach was done with the aim of reducing risk by optimizing the risk assessment activities.

42 ENGINEERING↗
DOE OSTI2018

Introducing a risk aggregation rationale for mapping risks to aquifers from point- and diffuse-sources–proof-of-concept using contamination data from industrial lagoons

Highlights: • Aquifer risk mapping is introduced for contaminants acting as point/diffuse-sources. • Innovations are framed by risk cells based on Source-Pathway-Receptor-Source (SPRC). • Point-source (Risk Cell 1) and diffuse-source (Risk Cell 2) use sets of algorithms. • Algorithms include MODPATH, DRASTIC, GWPI and are integrated on a modular basis. • Risk Cells 1/2 are aggregated giving fit-for-purpose results as proof-of-concept. - Abstract: Proof-of-concept for a methodology is presented on mapping risks to aquifers impacted from point- and diffuse-sources, where mapping or indexing refers to relative but not absolute values. The methodology is generic but tested by investigating impacts of a risk exposure from industrial wastewater lagoons. The methodology is innovative for using the qualitative Source-Pathways-Receptors-Consequences (SPRC) framework to aggregate risks from both point-sources and diffuse-sources through breaking down a study area into SPRC risk cells. In this paper, two risk cells are required as: (i) Cell 1 is directly impacted from a point-source; and (ii) Cell 2 is impacted by diffuse-sources to slowly contaminate the aquifer by infiltration over a large area. Indexing both types of risk cells generically comprise three tiered processes: (i) binary indexing establishes if a grid cell is at a potential risk or not; (ii) graded indexing measures the strength of the risk pathways from source to receptors; and (iii) local indexing measures intrinsic potentials at the grid cell to propagate the risk. These three processes apply to both point- and diffuse-sources but with different mathematical formulations. The proof-of-concept for the methodology of risk aggregation using the SPRC framework is supported by results of a study area, in which a set of performance metrics are used by comparing with the measurements. The results are found to be fit-for-purpose for serving as proactive management tools and to provide a deeper insight into potential impacts of adverse effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Risk Analysis for Remote Operation of Microreactors

Microreactors are a subset of advanced nuclear reactors that can be factory fabricated, transportable, and self-regulating. They have the potential to be used in microgrids, rural and remote areas, or emergency response applications, replacing fossil fuel sources like diesel generators and enabling sustainable energy generation. In order to make microreactor operation cost-effective, it is likely that remote communications will be needed to reduce the number of personnel required to be on site. While remote operation of energy generation and other industrial control systems is common in other industries, it is not yet adopted in the nuclear community and has many perceived and actual risks. In this paper, the severity of the risks introduced by remote operations for microreactors are explored. The primary changes in the operations involve the addition of a remote communications network and a certification system for data and controls. These changes lend themselves to considerations of cyber risks, whether unintentional or adversarial, but the assessment considers not just cyber risks introduced, but also how physical and human factors-based risks will impact the remote operations system and change the overall risk profile. This initial assessment indicates that there are standard cyber and mitigation measures that can be put in place so the risk of doing remote operations does not dramatically increase compared to local operations. This evaluation is a critical step in the process of evaluating if remote operations of microreactors is a suitable solution to meet future sustainable grid needs

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

An Improved Risk Priority Measure for the Management of Project Risk

Monte Carlo-based project risk models are essential tools in project risk management, particularly for large, complex projects in which it is difficult to glean intuitively the importance of individual risks to the overall project. Conventional correlation-based methods for measuring the importance of contributing risks in such models are argued here to be both statistically flawed and unintuitive in the importance metrics they produce. Drawing on and adapting methods more traditionally associated with engineering risk models, Risk Reduction Worth (RRW) is introduced as an importance measure. RRW is shown to be statistically robust and provide an easily interpretable, quantitative measure of the importance of a risk to the overall performance of a project; specifically, measuring the extent to which removal of a risk would improve confidence in project outcomes. To ensure practicality of the method, an approach is described in which RRWs can be produced for all contributing risks based on a single Monte Carlo sample. This is demonstrated through application to a large, complex project risk model from which intuitive risk importance insights provide a robust, transparent basis for the allocation of risk management resources.

Risk↗
DOE OSTI2016

Influence of Comorbidity on the Risk of Mortality in Men With Unfavorable-Risk Prostate Cancer Undergoing High-Dose Radiation Therapy Alone

Purpose: To explore whether a subgroup of men with unfavorable-risk prostate cancer (PC) exists in whom high-dose radiation therapy (RT) alone is sufficient to avoid excess PC death due to competing risk from cardiometabolic comorbidity. Methods and Materials: This was a cohort study of 7399 men in whom comorbidity (including congestive heart failure, diabetes mellitus, or myocardial infarction) was assessed and recorded with T1-3NxM0 PC treated with brachytherapy with or without neoadjuvant RT, October 1997 to May 2013 at a single providing institution. Cox and competing risks regression analyses were used to assess whether men with unfavorable–intermediate/high-risk versus favorable–intermediate/low-risk PC were at increased risk of PC-specific, all-cause, or other-cause mortality (PCSM, ACM, OCM), adjusting for number of comorbidities, age at and year of brachytherapy, RT use, and an RT treatment propensity score. Results: After a median follow-up of 7.7 years, 935 men died: 80 of PC and 855 of other causes. Among men with no comorbidity, PCSM risk (adjusted hazard ratio [AHR] 2.74 [95% confidence interval (CI) 1.49-5.06], P=.001) and ACM risk (AHR 1.30 [95% CI 1.07-1.58], P=.007) were significantly increased in men with unfavorable–intermediate/high-risk PC versus favorable–intermediate/low-risk PC, with no difference in OCM (P=.07). Although PCSM risk was increased in men with 1 comorbidity (AHR 2.87 [95% CI 1.11-7.40], P=.029), ACM risk was not (AHR 1.03 [95% CI 0.78-1.36], P=.84). Neither PCSM risk (AHR 4.39 [95% CI 0.37-51.98], P=.24) or ACM risk (AHR 1.43 [95% CI 0.83-2.45], P=.20) was increased in men with 2 comorbidities. Conclusions: To minimize death from PC, high-dose RT alone may be sufficient treatment in men with 2 or more cardiometabolic comorbidities and unfavorable–intermediate- and high-risk PC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Risk Ratio and Risk Difference Estimation in Case-cohort Studies

Background: In case-cohort studies with binary outcomes, ordinary logistic regression analyses have been widely used because of their computational simplicity. However, the resultant odds ratio estimates cannot be interpreted as relative risk measures unless the event rate is low. The risk ratio and risk difference are more favorable outcome measures that are directly interpreted as effect measures without the rare disease assumption. Methods: We provide pseudo-Poisson and pseudo-normal linear regression methods for estimating risk ratios and risk differences in analyses of case-cohort studies. These multivariate regression models are fitted by weighting the inverses of sampling probabilities. Also, the precisions of the risk ratio and risk difference estimators can be improved using auxiliary variable information, specifically by adapting the calibrated or estimated weights, which are readily measured on all samples from the whole cohort. Finally, we provide computational code in R (R Foundation for Statistical Computing, Vienna, Austria) that can easily perform these methods. Results: Through numerical analyses of artificially simulated data and the National Wilms Tumor Study data, accurate risk ratio and risk difference estimates were obtained using the pseudo-Poisson and pseudo-normal linear regression methods. Also, using the auxiliary variable information from the whole cohort, precisions of these estimators were markedly improved. Conclusion: The ordinary logistic regression analyses may provide uninterpretable effect measure estimates, and the risk ratio and risk difference estimation methods are effective alternative approaches for case-cohort studies. These methods are especially recommended under situations in which the event rate is not low.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

New risk metrics and mathematical tools for risk analysis: Current and future challenges

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares, (e.g., Seafood Spoilage Predictor) have evolved the use of information systems in the food safety management. Such tools are updateable with new food-pathogen specific models containing cardinal parameters and multiple dependent variables, including plate counts, concentration of metabolic products, or even expression levels of certain genes. Then, these tools may further serve as decision-support tools which may assist in product logistics, based on their scientifically-based and “momentary” expressed spoilage and safety level.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Chemoprevention Uptake for Breast Cancer Risk Reduction Varies by Risk Factor

Background/Objective: The efficacy of chemoprevention for breast cancer risk reduction has been demonstrated in randomized controlled trials; however, use remains low. We sought to determine whether uptake differed by risk factors, and to identify reasons for refusal and termination. Methods: Women seen in a high-risk clinic from October 2014 to June 2017 considered eligible for chemoprevention (history of lobular carcinoma in situ, atypia, family history of breast/ovarian cancer, genetic mutation, or history of chest wall radiation) were retrospectively identified. Breast cancer risk factors were compared among those with and without chemoprevention use, and compliance was noted. Results: Overall, 1506 women were identified, 24% with prior/current chemoprevention use. Women ≥ 50 years of age were more likely to use chemoprevention than women < 50 years of age (28% vs. 11%, p < 0.001). Chemoprevention use by risk factor ranged from 7 to 40%. Having multiple risk factors did not increase use. Significant variation by risk factor was present among women ≥ 50 years of age (p < 0.001), but not among women < 50 years of age (p = 0.1). Among women with a documented discussion regarding chemoprevention (575/1141), fear of adverse effects was the most common refusal reason (57/156; 36%). The majority of women (61%) who initiated chemoprevention completed 5 years. Conclusion: Chemoprevention use among women at increased risk for breast cancer remains low, with more frequent use among women ≥ 50 years of age. These data highlight the need for ongoing educational efforts and counseling, as the majority who begin therapy complete 5 years of use. Given the fear of adverse effects as well as low uptake, particularly among women < 50 years of age, alternative risk-reducing strategies are needed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Task 12 PV Sustainability - Human Health Risk Assessment Methods for PV, Part 3: Module Disposal Risks

End-of-life management is important for addressing large future photovoltaic (PV) waste volumes and conserving raw materials for use in new PV modules. In regions without regulatory mandates for PV recycling, end-of-life PV modules can be disposed in accordance with general waste laws. Given the use of various metals as raw materials in semiconductor compounds and electrical contacts in commercial PV modules, some stakeholders have raised concerns regarding potential environmental impacts if PV modules are subject to improper disposal instead of being recycled or disposed in sanitary landfills, as is required in most world regions. To evaluate these concerns, screening-level risk assessment methods are developed herein that evaluate potential human health risks from groundwater and surface (air, soil, surface water) exposure pathways. The methods estimate potential impacts from disposal of end-of-life (EOL) PV modules in non-sanitary landfills under the following worst-case conditions: no leachate collection or groundwater monitoring, no liner for preventing leachate migration, uncovered waste, and lack of stormwater management. Examining worst-case conditions allows the exploration of maximum potential risk to attempt to ensure disposal does not increase health risk above regulatory thresholds. Specifically, this report presents an analysis of potential human health risks associated with non-sanitary landfill disposal for three PV technologies, focusing on release of the highest-prioritized chemical element for each: lead (Pb) in crystalline-silicon (c-Si) PV modules, cadmium (Cd) in thin film cadmium telluride (CdTe) PV modules, and selenium (Se) in thin film copper indium selenide (CIS) PV modules. The prioritization of these chemical elements for analysis is based on stakeholder interest. Because the methodology is chemical-specific, the risk assessment results for these chemicals cannot be directly generalized to other chemicals, although the risk assessment methodology can be applied to other chemicals. If the chemicals chosen are indeed the ones presenting greatest risk, then the results herein should represent the upper bound of health risk from exposure to a single constituent.

14 SOLAR ENERGY↗
DOE OSTI2023

Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals

A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population that ultimately develops lung cancer. Previously, we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at risk for lung cancer. The purpose of this study is to validate, in an independent cohort, a selected list of 55 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM). Bronchial brushings collected from individuals at low and high risk for developing lung cancer as well as patients with lung cancer, from both a subset of the original cohort (batch 1: n = 10 per group) and an independent cohort of 149 individuals (batch 2: low risk (n = 32), high risk (n = 34), and lung cancer (n = 83)), were analyzed using multiplexed SRM assays. ALDH3A1 and AKR1B10 were found to be consistently overexpressed in the high-risk group in both batch 1 and batch 2 brushing specimens as well as in the biopsies of batch 1. Validation of highly discriminatory proteins and metabolic enzymes by SRM in a larger independent cohort supported their use to identify patients at high risk for developing lung cancer.

60 APPLIED LIFE SCIENCES↗