Quantitative Assessment Of Uncertainty Research Articles

Assessing uncertainty in building material emissions using scenario-aware Monte Carlo simulation

Global greenhouse gas emissions from the built environment remain high, driving innovative approaches to develop and adopt building materials that can mitigate some of those emissions. However, life-cycle assessment (LCA) practices still lack standardized quantitative uncertainty assessment frameworks, which are urgently needed to robustly assess mitigation efforts. Previous works emphasize the importance of accounting for the three types of uncertainties that may exist within any quantitative assessment: parameter, scenario, and model uncertainty. Herein, we develop a quantitative uncertainty assessment framework that distinguishes between different types of uncertainties and suggest how these uncertainties could be handled systematically through a scenario-aware Monte Carlo simulation (MCS). We demonstrate the framework’s decision-informing power through a case study of two multilevel ordinary Portland cement (OPC) manufacturing scenarios. The MCS utilizes a first-principles-based OPC life-cycle inventory, which mitigates some of the model uncertainty that may exist in other empirical-based cement models. Remaining uncertainties are handled by scenario specification or sampling from developed probability distribution functions. We also suggest a standardized method for fitting distributions to parameter data by enumerating through and implementing distributions based on the Kolmogorov–Smirnov test. The level of detail brought by the high-resolution parameter breakdown of the model allows for developing emission distributions for each process of OPC manufacturing. This approach highlights how specific parameters, along with scenario framing, can impact overall OPC emissions. Another key takeaway includes relating the uncertainty of each process to its contributions to total OPC emissions, which can guide LCA modelers in allocating data collection and refinement efforts to processes with the highest contribution to cumulative uncertainty. Ultimately, the aim of this work is to provide a standardized framework that can provide robust estimates of building material emissions and be readily integrated within any uncertainty assessment.

Quantifying uncertainty in carbon emission estimation: Metrics and methodologies

Carbon emissions are a significant driver of global climate change in today's world. A central concern in discussing carbon emissions is the level of uncertainty associated with them. This study aims to assess the feasibility of using the Mean Squared Error (MSE) as a point estimation measure and confidence interval (CI) as an interval estimation measure to quantify the uncertainty surrounding carbon emissions. To achieve this goal, the bootstrap and Markov Chain Monte Carlo (MCMC) sampling methods were used, utilizing both classical and Bayesian inference methods to uncover the true parameter value.In the context of Bayesian inference, a 2-chain MCMC method proved to be the optimal choice for generating posterior distributions and accurately estimating the true parameter of the distribution θ. The analysis also shows that, while a CI is valuable as an evaluative measure, it does not inherently provide a quantified form of uncertainty and should not be used for quantitative uncertainty assessment. Instead, the Relative Standard Error (RSE) emerges as a promising quantitative measure for capturing the uncertainty of θ, while the percentage uncertainty offers a qualitative perspective. These combined measures provide a comprehensive toolkit for computing and communicating the uncertainty in carbon emission and emission factor values. This reinforces a move towards incorporating transparent uncertainty metrics, ensuring that stakeholders have access to reliable and accurate carbon emission values.

Quantitative assessment method of new energy output uncertainty based on the prediction error

With a high percentage of distributed new energy sources connected to the power system, the power grid needs to reserve a larger margin to deal with the uncertainty of renewable energy outputs, leading to an increase in the cost of controlling the margins for the safe operation of the power grid. In order to reduce costs and increase efficiency, a quantitative assessment of new energy output uncertainty is needed. In this paper, a quantitative assessment method of new energy output uncertainty based on the prediction error is proposed, which makes use of a graph database to efficiently obtain massive new energy historical data, uses the clustering in quest (CLIQUE) algorithm to cluster the new energy historical data, and calculates the renewable energy real power confidence interval based on a given new energy power prediction, taking account of the impact of prediction errors caused by the new energy uncertainty and realizing the quantitative description of new energy output uncertainty. Finally, the method is calculated and analyzed together with the actual example data to verify the practical effect of the method.

Stochastic velocity modeling for assessment of imaging uncertainty during seismic migration: Application to salt bodies

Variations in the migration velocity model directly affect the position of the imaged reflectors in the subsurface, leading to structural imaging uncertainties. These uncertainties are not explicitly addressed when trying to deterministically build an adequate velocity model. This paper presents a new stochastic geology-controlled velocity modeling method that handles the possible presence of a salt weld. This permits the generation of a large set of geologic scenarios and associated velocity models. Each model is used to remigrate the seismic data. Then, a statistical analysis of the resulting seismic images is performed to quantify the local variability of the seismic responses. The approach is applied to the imaging of salt diapirs in an iterative scheme (migrate, pick, and update). The results indicate that similar to stacking common-midpoint gathers, the statistical analysis preferentially preserves recurrent features from one image to another. In particular, this analysis permits the distinction between the connected and detached diapirs without prior knowledge about their connectivity, highlighting the potential of the method to resolve important aspects of the basin and reservoir architecture. More generally, it provides quantitative information on the parts of the seismic image most sensitive to migration velocity variations, which opens an interesting perspective to quantitative interpretation uncertainty assessment. Finally, the presented application also suggests that it is possible to significantly improve the quality of the generated seismic images by sampling many possible geologic scenarios.

Assessing the uncertainties in modeling water temperatures during river cooling and freeze-up periods

Accurate simulation of water temperatures is crucial to the simulation of ice processes in river ice models. This study presents a quantitative assessment of uncertainties in simulating the water temperatures during river cooling and freeze-up periods. In particular, the selections of the heat transfer model and the proximity of the weather station to the study reach are investigated using the University of Alberta's River1D Ice Process model. Results show that using local versus remote weather data only had a significant impact on the performance of the full energy budget model during the river cooling period. This was mainly attributed to the large difference in the locally and remotely measured wind speeds. The full energy budget model during the freeze-up period and the linear heat transfer model were relatively insensitive to the source of weather data. The full energy budget model using local weather data was the most accurate in simulating water temperatures. When using remote weather data, the full energy budget model was generally more accurate than the linear heat transfer model in the calibration year and the two validation years, except during the cooling period of the calibration year. Neither model was able to consistently and accurately simulate the timing and magnitude of observed supercooling events. It is recommended that the full energy budget model be used whenever comprehensive weather data are available due to its overall better accuracy and less calibrated parameters than the linear heat transfer model.

Revisiting the 1899 earthquake series using integrative geophysical analysis in Yakutat Bay, Alaska, USA

Abstract A series of large earthquakes in 1899 affected southeastern Alaska near Yakutat and Disenchantment Bays. The largest of the series, a MW 8.2 event on 10 September 1899, generated an ~12-m-high tsunami and as much as 14.4 m of coseismic uplift in Yakutat Bay, the largest coseismic uplift ever measured. Several complex fault systems in the area are associated with the Yakutat terrane collision with North America and the termination of the Fairweather strike-slip system, but because faults local to Yakutat Bay have been incompletely or poorly mapped, it is unclear which fault system(s) ruptured during the 10 September 1899 event. Using marine geophysical data collected in August 2012, we provide an improved tectonic framework for the Yakutat area, which advances our understanding of earthquake hazards. We combined 153 line km of 2012 high-resolution multichannel seismic (MCS) reflection data with compressed high-intensity radar pulse (Chirp) profiles, basin-scale MCS data, 2018 seafloor bathymetry, published geodetic models and thermochronology data, and previous measurements of coseismic uplift to better constrain fault geometry and subsurface structure in the Yakutat Bay area. We did not observe any active or concealed faults crossing Yakutat Bay in our high-resolution data, requiring faults to be located entirely onshore or nearshore. We interpreted onshore faults east of Yakutat Bay to be associated with the transpressional termination of the Fairweather fault system, forming a series of splay faults that exhibit a horsetail geometry. Thrust and reverse faults on the west side of the bay are related to Yakutat terrane underthrusting and collision with North America. Our results include an updated fault map, structural model of Yakutat Bay, and quantitative assessment of uncertainties for legacy geologic coseismic uplift measurements. Additionally, our results indicate the 10 September 1899 rupture was possibly related to stress loading from the earlier Yakutat terrane underthrusting event of 4 September 1899, with the majority of 10 September coseismic slip occurring on the Esker Creek system on the northwest side of Yakutat Bay. Limited (~2 m) coseismic or postseismic slip associated with the 1899 events occurred on faults located east of Yakutat Bay.

A Julia software package for transdimensional Bayesian inversion of electromagnetic data over horizontally stratified media

A quantitative assessment of model parameter uncertainty is vital for a reliable interpretation of electromagnetic (EM) data due to the nonuniqueness inherent to EM inverse problems. Conventional gradient-based inversion approaches typically produce a single preferred model with limited information about parameter uncertainty. The inverse problems can be alternatively postulated into a sampling-based Bayesian inference framework where the solution is represented by a posterior probability distribution of model parameters, which can provide an effective way to rigorously estimate parameter uncertainty related to the recovered solution. We have implemented a Bayesian inversion framework for probabilistic inversion of EM data, which is an open-source software package implemented in the Julia programming language. The key feature of the framework is that it allows the model complexity to be adaptively adjusted to an appropriate level compatible with the data by implementing a reversible jump Markov chain Monte Carlo algorithm, thus allowing the data to infer the appropriate level of model complexity and associated parameter uncertainty. The authors have elaborated the structure of the package with a focus on code modularity and extensibility. Finally, the authors determine the capacity and versatility of the software package through three synthetic examples that simulate different EM scenarios. The inversion results demonstrate the performance and inherent resolving abilities of different EM surveys for conductive and/or resistive structures. In addition, the model ensemble produced by the transdimensional Bayesian inversion conveys a wealth of information. Many important quantities for data interpretation such as parameter uncertainty and correlation between model parameters of interest can be measured by exploring the model ensemble.

A quantitative assessment of uncertainty in the measurement of violin impact response

It is a commonly stated belief among violin players and luthiers that new violins require a period of “playing in” for the tone to develop. Several studies have worked towards an answer to this question, such as measuring the change in tone according to the human ear or the vibrational response of stimulated wood. As the effects of sustained excitation on the mechanical response of violins will likely be subtle, it is necessary to create a consistent method of measurement and to quantify the expected range of deviation among repeated measurements. We measure admittance (velocity/force) by tapping the bridge with a small modal impact hammer and recording the velocity response of the top plate near the opposite side of the bridge using a laser doppler vibrometer. The acoustic response is also measured in an anechoic chamber. Since measurements of the same violin on different days do not produce identical response curves, several methods of characterizing the deviation were developed and compared. These uncertainty metrics will be used in the second phase of the experiment to determine the significance of the results, and ultimately work towards a better understanding of the effects of breaking in violins.

Quantitative Assessment of Uncertainties and Sensitivities in the Estimation of Life Loss Due to the Instantaneous Break of a Hypothetical Dam in Switzerland

High safety standards of operators and regulators for dams in Switzerland require periodic assessments of risk mitigation measures at dams. Therefore, risk assessments need to include the estimation of life loss (LL) due to a potential dam break. This study demonstrated the benefits of applying the HEC-LIFESim software for modelling LL due to the instantaneous break of a hypothetical dam in Switzerland. HEC-LIFESim overcomes limitations of empirical methods by modelling evacuation and warning processes. Furthermore, for credible LL estimates, metamodelling was used to quantify uncertainty in model parameters. Polynomial chaos expansion (PCE) was applied to approximate the LL model of HEC-LIFESim using only 550 runs. Uncertainty in the model inputs was propagated through the metamodel to quantify uncertainty in the LL estimates. Finally, a global sensitivity analysis was performed by calculating Sobol’ and Borgonovo indices. The results demonstrate that the three-parameter population in a locality within all considered localities, fatality rate in the chance zone, and warning issuance delay contributed most to the variability of the LL estimates. The application of the proposed methodology can support risk management by providing detailed and accurate risk measures and helping in prioritizing safety measures to be considered and implemented.

A Monte Carlo Method for Analyzing Systematic and Random Uncertainty in Quantitative Nuclear Magnetic Resonance Measurements.

Quantitative nuclear magnetic resonance (qNMR) is a powerful analytical technology that is capable of quantifying the concentration of any analyte with exquisite accuracy and precision so long as it contains at least one nonlabile nuclear magnetic resonance (NMR)-active nucleus. Unlike with traditional analytical technologies, the concentrations of analytes do not directly influence the uncertainty in the quantification of NMR signals because an ideal NMR response depends only on the nature and amount of the nucleus being observed. Rather, in the absence of spectral artifacts and under favorable experimental conditions, the measurement uncertainty may be influenced by the following factors: (1) spectroscopic parameters such as the spectral width, number of time domain points, and acquisition time; (2) postacquisition data processing, such as apodization and zero-filling; (3) the signal-to-noise ratios (SNRs) and lineshapes of the two signals being used in a qNMR measurement; and (4) the method of signal quantification employed, such as numerical integration or lineshape fitting (LF). Here, a general Monte Carlo (MC) method that considers these factors is presented, with which the random and systematic contributions to qNMR measurement uncertainty may be calculated. Autocorrelation analysis of synthetic and experimental noise is used in a fingerprint-like approach to demonstrate the validity of the simulations. The MC method allows for a general quantitative assessment of measurement uncertainty without the need to acquire spectral replicates and without reference to the molecular structures and concentrations of analytes. Representative examples of qNMR measurement uncertainty simulations are provided in which the metrological performances of integration and LF are contrasted for signal pairs obtained using various acquisition and processing schemes in the low-SNR regime-an area where application of the proposed MC method may prove to be particularly salient.

Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime

AbstractAerosols are an important component of the climate system affecting visibility, clouds, and climate. They are also transported over short and long distances with consequences for continental‐scale nutrient availability. Processes that remove aerosols from the atmosphere are difficult to evaluate with observations in isolation from aerosol sources. As a result, a quantitative assessment of uncertainties in our understanding of aerosols is limited and narrowing of those uncertainties is difficult to demonstrate. In this study, we use satellite observations of sulfur dioxide (SO2) and aerosols downwind of the Kīlauea volcano during May–September 2008. We derive the SO2 emission rate, the loss rate of SO2 that is indicative of the production rate of sulfate aerosols, and the subsequent removal rate of the formed sulfate. The result is the first determination of volcanic sulfate aerosol lifetime, 40–64 h, directly from satellite observations. The derived sulfate lifetime is significantly lower than the global average (3–5.5 days) and reflects the rapid wet scavenging of this plume.

Uncertainty assessment of spatial domain models in early stage mining projects – A review

• Performance of feasibility studies in mining projects is poor which could be due to omission of quantitative uncertainty assessments in public reporting. • JORC 2012 standards require quantitative uncertainty assessment for geological and spatial domain models. • Current methodologies of quantitative uncertainty assessment and communication exist and can be used in early-stage mining projects. • Professional development and incorporating software and process workflows could be the way to go to improve current reporting practice. Sustainability of mining projects is linked to informed investment decisions based on public reporting of exploration and mineral resource estimation results. In Australia, guidelines for public reporting are established by the Joint Ore Reserves Committee reporting code through the JORC Code (2012). Although assessment of uncertainty in the results reported is a requirement, this is often communicated in a qualitative manner and subjectively evaluated. This can become a liability if not communicated effectively, particularly in early stages of mining projects when spatial domains of geological interpretation and mineralization envelopes influence reliability of resource estimates. This review describes methodologies for quantitative uncertainty assessment and communication, and explores how they could be applied in public reporting practice. The complexity, cost and additional work of doing a quantitative assessment could hinder a straightforward implementation. This could be overcome if mining companies budget for quantitative uncertainty assessment and associated professional development. A compulsory requirement for inclusion of uncertainty assessments in public reporting or use of standardized subjective language would improve industry practice.

ПРИНЯТИЕ ИНВЕСТИЦИОННЫХ РЕШЕНИЙ НА ПРОМЫШЛЕННОМ ПРЕДПРИЯТИИ В УСЛОВИЯХ НЕОПРЕДЕЛЕННОСТИ

The article is devoted to the issue of selecting investment projects for the modernization of an industrial enterprise in the conditions of uncertainty. Uncertainty in this work is understood as the lack of data on the likelihood of the implementation of scenarios of alternative investment projects. As a methodological base, a set of generally accepted quantitative criteria for reducing uncertainty is proposed, which is characterized by different attitudes towards the probability of future events (pessimistic, optimistic, neutral, etc.). Additionally, a criterion is selected that combines expert and quantitative assessment of uncertainty. Practical calculations are made according to five criteria for three scenarios of five investment projects of an industrial enterprise. The aim of this work is to assess the difficulty of making unambiguous investment decisions under conditions of uncertainty. As a result, each of the four quantitative criteria has pointed to a different project. The fifth criterion has made it possible to limit the range of choice to two projects. This has confirmed the conclusions that making investment decisions requires the responsible person to use not only quantitative indicators, but also existing experience and intuition. The reliability of the findings has been confirmed by the use of generally accepted models and methods, as well as the practical implementation of the results. It is recommended to use these results in the scientific community when conducting subsequent methodological studies aimed at reducing uncertainty, as well as to business owners and investors when making strategic decisions.

Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling.

ABSTRACTThe adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data‐demanding systems biology models. Here, we propose a less data‐demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, “Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition,” using a small experimental data set from exposure of Lemna minor to the pesticide 3,5‐dichlorophenol. The AOP‐BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose–response and response–response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose–response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP‐BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP‐BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP‐BN is limited by the small data set, our study demonstrates a proof‐of‐concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147–164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

Uncertainty in Detection of Volcanic Activity Using Infrasound Arrays: Examples From Mt. Etna, Italy

The injection of gas and pyroclastic material from volcanic vents into the atmosphere is a prolific source of acoustic waves. Infrasound arrays offer efficient, cost-effective and near real-time solutions to track the rate and intensity of surface activity at volcanoes. Here, we present a simple framework for the analysis of acoustic array data, based on least-squares beamforming, that allows to evaluate the direction and speed of propagation of acoustic waves between source and array; we also propose a new scheme, based on error propagation theory, for the quantitative assessment of uncertainty on these measurements. We the apply the algorithms to data collected by two 6-element infrasound arrays deployed at Mt. Etna during the period July-August 2019. Our results demonstrate that the use of two infrasound arrays allowed detecting and tracking acoustic sources from multiple craters and active vents associated with degassing and ash-rich explosions, vigorous and frequent Strombolian activity, opening of new eruptive fractures and emplacement of lava flows. Finally, we discuss the potential use of metrics based on infrasound array analyses to inform eruption monitoring operations and early warning at volcanoes characterized by episodic intensification of activity.

Water scarcity in Brazil: part 2—uncertainty assessment in regionalized characterization factors

Despite recommendations, uncertainty results are rarely incorporated in Life Cycle Assessment (LCA) studies, especially regarding characterization factors (CF). Part 1 of this study conducted AWARE CF regionalization for Brazil, concluding that the Semiarid region had maximum scarcity values. The goal of this study is to evaluate the uncertainties of regionalized AWARE CF in the Semiarid region. Data used to obtain the AWARE BR CF for Brazil were qualitatively and quantitatively assessed. An adapted Pedigree Matrix was adopted to assess qualitative uncertainties. Classical statistical analysis was used for quantitative uncertainty assessment, and 10,000 Monte Carlo simulations were computed for uncertainty propagation. Qualitative results indicated that the natural flow’s parameter was very uncertain due to poor spatial correlation and low reliability, as it is based on empirical models. Quantitative results showed that water availability data, which had large temporal variability, typical of the Brazilian Semiarid region, was the main responsible for uncertainties in input data. Area uncertainty had a good performance in both qualitative and quantitative assessments. Regarding output data, moderate CF were found to be more uncertain, while more extreme CF exhibited lower variation, corroborating with previous analyses. Moreover, the adoption of shorter datasets led to a reduction in average and standard deviation values for CF. Findings from this study showed two important reasons why the quantitative and qualitative assessments should be conducted simultaneously. The first one was to avoid bias, as availability data and natural flow performed differently in each evaluation. The second one was to confirm results, as the area proved to be very little uncertain in both assessments. An adaptation of Pedigree Matrix and a penalty factor for missing data could be used as a base for quantitative uncertainty parameters for LCIA. Generating SD and k-factor was very positive in terms of results for AWARE method and comparison with other methods. Both indicators had similar results and led to a common conclusion: uncertainties are mainly low and very low for AWARE BR CF in the Semiarid region.

Quantitative assessment of uncertainties in modeled crater retention ages on Mars

With increasing high-resolution coverage of Mars' surface, crater count analysis is being used to estimate the formative ages of small depositional features (<1000 km2) to constrain timing of climate-driven events. We introduce a probabilistic cratering model that quantifies how modeled age estimates vary strongly with crater obliteration rates (β, defined as the combined rate of erosion and infilling), minimum crater diameter counted, and surface area size. Model results show that crater obliteration introduces significant uncertainty on small surfaces, where moderate obliteration rates (β ~ 25 nm a−1) require surface areas of about 10,000 km2 to date 2 to 3 Ga surfaces accurately, and strongly obliterated surfaces (β = 200 nm a−1) require surface areas up to 100,000 km2. In practice, smaller areas can nonetheless be analyzed probabilistically by estimating a range of likely obliteration rate and modeled age combinations and optimizing these values relative to observed crater count data, resulting in a distribution of possible modeled surface ages and associated β. We demonstrate this method using data from the Coprates Chasma landslide (~200 km2) and numerous fan and delta deposits in the Gale crater region. Although the deposits in and around Gale are <1000 km2, a probabilistic analysis enables us to suggest that deltaic deposits in and near Gale have a higher probability of being older (~>3 Ga) than nearby fans (~1–3 Ga). This analysis provides a simple quantitative framework for assigning probable surface age ranges for small features on Mars.

FAILURE TO ACHIEVE PLANNED ECONOMIC PERFORMANCE OF OIL AND GAS PRODUCTION PROJECTS DUE TO LACK OF RISK ASSESSMENT AND UNCERTAINTY QUANTIFICATION

Quantitative assessment of risks and uncertainty has always played the most important role in the evaluation of oil &amp; gas projects. Any estimated, measurable or evaluable magnitude contains an uncertainty, whether it concerns measuring watercut by means of a multiphase flow meter (MPFM) or to the net present value (NPV) of a major E&amp;P project. Evaluation of project economics, e.g. the calculation of net present value (NPV), internal rate of return (IRR), OPEX and CAPEX shall be probabilistic, i.e. carried out with the quantitative assessment of risks and uncertainty of estimated (predicted) values. Nevertheless probabilistic estimate takes place very rarely. Most commonly, assessment of risks and uncertainty would be intuitive or based on a number of deterministic scenarios mistakenly referred to as pessimistic, optimistic and most probable. This research simulates failure to achieve target economic efficiency of E&amp;P projects with no quantitative assessment of risks and uncertainty, and reviews previous international research conducted to identify the effect of ignoring probabilistic estimates related to the evaluation of E&amp;P projects on their final economic efficiency.

A study of the role of the PDF uncertainty on the LHC -boson mass measurement

The determination of the W-boson mass through an analysis of the decay charged-lepton transverse momentum distribution has a sizable uncertainty due to the imperfect knowledge of the relevant parton distribution functions (PDFs). In this paper, a quantitative assessment of the W-boson mass uncertainty at the LHC resulting from the PDF uncertainty is examined. We use the CT14 NNLO PDFs with a NNLL + NNLO calculation (ResBos) to simulate the W-boson production and decay kinematics. The uncertainty of the W-boson mass determination is then determined as a function of the boson and lepton kinematics. For W+ production using and (GeV) < 45, PDF uncertainties lead (at the 68% CL) to a mass uncertainty for the of MeV, MeV, and MeV, are determined at 7 TeV, 8 TeV and 13 TeV, respectively. The uncertainties of for the same cuts are found to be MeV, MeV and MeV, at 7 TeV, 8 TeV and 13 TeV, respectively.

Application of qualitative and quantitative uncertainty assessment tools in developing ranges of plausible toxicity values for 2,3,7,8-tetrachlorodibenzo-p-dioxin.

Increasing interest in characterizing risk assessment uncertainty is highlighted by recent recommendations from the National Academy of Sciences. In this paper we demonstrate the utility of applying qualitative and quantitative methods for assessing uncertainty to enhance risk‐based decision‐making for 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin. The approach involved deconstructing the reference dose (RfD) via evaluation of the different assumptions, options, models and methods associated with derivation of the value, culminating in the development of a plausible range of potential values based on such areas of uncertainty. The results demonstrate that overall RfD uncertainty was high based on limitations in the process for selection (e.g., compliance with inclusion criteria related to internal validity of the co‐critical studies, consistency with other studies), external validity (e.g., generalizing findings of acute, high‐dose exposure scenarios to the general population), and selection and classification of the point of departure using data from the individual studies (e.g., lack of statistical and clinical significance). Building on sensitivity analyses conducted by the US Environmental Protection Agency in 2012, the resulting estimates of RfD values that account for the uncertainties ranged from ~1.5 to 179 pg/kg/day. It is anticipated that the range of RfDs presented herein, along with the characterization of uncertainties, will improve risk assessments of dioxins and provide important information to risk managers, because reliance on a single toxicity value limits the information needed for making decisions and gives a false sense of precision and accuracy.