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Composite indicators (CIs) are being utilized more frequently to assess and monitor environmental systems. The revised leachate pollution index (r-LPI) is one such composite indicator used to quantify the pollution potential of landfill leachate on a scale of 5–100. The development of CIs involves several steps, and each of these steps has various methodological choices, each of which could lead to different results. Thereby, the reliability of the quantified pollution potential of leachate may be questioned. This study investigated the techniques for developing the r-LPI, examining decisions related to parameter selection, normalization technique, weighting approach, sub-indicator weights, and their aggregation. As the index developer made the decisions, each of these stages was fraught with uncertainty. The uncertainty in the various stages of the development of r-LPI was quantified using the Monte Carlo-based uncertainty analysis and the sensitivity analysis approach. Uncertainty analysis is a helpful but seldom-used step of index development that identifies the model's most dependable sections. Sensitivity analysis was carried out to ascertain the degree of impact the input parameters have on the r-LPI values. The combined use of sensitivity and uncertainty analysis in this study for the formulation of r-LPI affirmed the transparency, credibility, and accuracy of the index. [ABSTRACT FROM AUTHOR]

CO2 conversion is gradually seen as a better way for society to effectively use carbon sources and avoid climate crisis associated with fossil CO2 emissions. And the decision to deploy CO2 technology scale should be relied on its environmental impact. In this work, life cycle assessment model evaluates the environmental performance of CO2 conversion by photocatalytic reaction process with two different catalysts (NiAl-LDH and Co-ZIF-9). Six impact categories considered in this analysis, including climate change, acidification potential, depletion of abiotic resources, eutrophication potential, ozone layer depletion potential, and photochemical oxidation potential. Results indicated that CO2 conversion with Co-ZIF-9 photocatalyst has a better environmental impact than the NiAl-LDH photocatalyst route. Moreover, the Co-ZIF-9 catalyst scenario also has a lower total environmental burden than the conventional CO production route. Sensitivity analysis shows that recycle performance of the catalyst is highly sensitive to the production process in two scenarios. This study could provide a framework for robust decisions in CO2 conversion by photocatalytic reaction, which is useful for policymakers to decide the feasibility of industrialization. [ABSTRACT FROM AUTHOR]

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production. [ABSTRACT FROM AUTHOR]

While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the nonspecialist. Here we propose a sensitivity analysis approach based on the concept of discrepancy that is as easy to understand as the visual inspection of input-output scatterplots. First, we show that some discrepancy measures are able to rank the most influential parameters of a model almost as accurately as the variance-based total sensitivity index. We then introduce an ersatz-discrepancy whose performance as a sensitivity measure is similar that of the best-performing discrepancy algorithms, is simple to implement, easier to interpret and orders of magnitude faster. [ABSTRACT FROM AUTHOR]

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output. KEY POLICY HIGHLIGHTS: Spatially explicit uncertainty and sensitivity analysis extends the geodiversity assessment methods and comprises the next step in improving the accuracy of geodiversity assessments for land management decisions. An integrated uncertainty and sensitivity analysis helps focus on high-priority areas (high sensitivity and low uncertainty) to improve assessment reliability. The uncertainty and sensitivity analysis in models with spatially explicit output results depicted on maps can help reduce field research expenses by identifying a particular area or a specific natural component that warrants better-quality data. Bivariate maps employed to visualize the results of spatially explicit uncertainty and sensitivity analysis can potentially reduce the cognitive effort of visually switching between multiple single-variable (theme) maps. [ABSTRACT FROM AUTHOR]

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors' findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources. [ABSTRACT FROM AUTHOR]

This paper optimizes a ground replenishment strategy for large-scale LEO satellite constellations, aiming for ensuring high economic efficiency and performance requirements. Firstly, the uncertainties of influential elements are analyzed and satellite lifetime distributions are estimated to determine satellite operating statuses. Secondly, satellite statuses are used for obtaining real-time constellation status and simulating data package transmission paths. Performance degradation function is then derived by selecting time delay as the key performance. To confront with the performance degradation, an optimization model of ground replenishment strategy is established with the objective of minimizing the overall replenishment cost under the time delay and loading capacity constraints. The model is solved by a Dichotomous-domain algorithm. Finally, a case study of a large-scale STARLINK constellation is adopted to validate the effectiveness of the model and sensitivity analyses are conducted to verify its robustness. [ABSTRACT FROM AUTHOR]

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

CO2 conversion is gradually seen as a better way for society to effectively use carbon sources and avoid climate crisis associated with fossil CO2 emissions. And the decision to deploy CO2 technology scale should be relied on its environmental impact. In this work, life cycle assessment model evaluates the environmental performance of CO2 conversion by photocatalytic reaction process with two different catalysts (NiAl-LDH and Co-ZIF-9). Six impact categories considered in this analysis, including climate change, acidification potential, depletion of abiotic resources, eutrophication potential, ozone layer depletion potential, and photochemical oxidation potential. Results indicated that CO2 conversion with Co-ZIF-9 photocatalyst has a better environmental impact than the NiAl-LDH photocatalyst route. Moreover, the Co-ZIF-9 catalyst scenario also has a lower total environmental burden than the conventional CO production route. Sensitivity analysis shows that recycle performance of the catalyst is highly sensitive to the production process in two scenarios. This study could provide a framework for robust decisions in CO2 conversion by photocatalytic reaction, which is useful for policymakers to decide the feasibility of industrialization.

Since the level 2 PSA of OPR-1000 was the requirement for regulatory purposes, Cs-137 release estimation was contained as the Nuclear Safety Act of ROK in which the Cs-137 release frequency exceeding 100 TBq was determined to happen less than 1.0E-6 per year after the Fukushima Daiichi Accident. However, Cs-137 release estimation from the conventional level 2 PSA of OPR-1000 provided uncertainty due to dominant accident sequence consideration. Thus, this study aimed to develop systematic methods through the overall framework to quantify realistic uncertainty concerns of radioactive material release using sensitivity and uncertainty analysis methods and apply them to OPR-1000. This framework helped to quantify confidential value for the Cs-137 release under the BEPU approach using both parametric and non-parametric methods to cover both realistic and conservative points. Uncertainty propagation analysis showed the unexpected uncertainty increase of Cs-137 release exceeding 100 TBq. The non-parametric uncertainty analysis provided higher conservative concerns for safety than the realistic concerns in terms of economics when compared with the parametric uncertainty analysis. Wilks’ uncertainty analysis showed the importance to consider conservative Cs-137 release in order to reach the higher safety need. Sensitivity analysis showed reasonable relationships between engineering safety parameters with the Cs-137 release.

Since random uncertainty may cause severe aerodynamic performance fluctuations for the wing-mounted aircraft, the Gaussian process regression (GPR) surrogate model method based on was proposed. The strategy of adding sample points by active-learning method can effectively reduce model uncertainty and improve the accuracy of uncertainty prediction. Focusing on incoming flow with uncertainty, the polynomial chaos expansion (PCE) method based on Smolyak sparse grid and the GPR surrogate model method based on the strategy of adding sample points by active-learning method were used to analyze the uncertainty of the wing-body-nacelle-pylon geometry combined with the Sobol sensitivity analysis method. Results show that the uncertainty of angle of attack and Mach number will cause dramatic fluctuation in lift coefficient and drag coefficient of the wing-mounted aircraft under transonic condition. The fluctuation of lift coefficient is affected by angle of attack and Mach number, and the fluctuation of drag coefficient is mainly determined by Mach number.

Sensitivity and uncertainty analysis serves as a tool for modeling physical phenomenon and evaluating the model validity. It finds applications in simulating the dynamical behavior of aerosols in different contexts. Hygroscopic growth of aerosol particles is one such domain where it can optimize the accuracy of model output greatly. Accurate prediction of hygroscopic growth characteristics depends on the intrinsic parameters and the size-dependent evolution of growth factors. In reactor accident scenarios, growth characteristics of fission/activation product aerosols play a significant role in determining the airborne or deposited fraction. Köhler theory is fundamentally used to predict the growth behavior of aerosols with a detailed physical explanation of critical size and supersaturation characteristics. Alternative approaches including modified Köhler theories and semi-empirical models rely on the linkage of physical and chemical composition-based parameters. The variation of involved parameters and the choice of working range influence the outcome of the theory greatly. This study is focused on sensitivity analysis of the modified Köhler theory given by Brechtel and Kreidenweis and the evaluation of sensitivity and associated uncertainty in the parameters. Monte Carlo approach has been employed, and sensitivity indices were obtained. Identified sensitive parameters were dry particle diameter, solute density, molecular weight, and chemical composition dependent factor (b0). These parameters require careful attention while designing experimental measurements and/or preprocessing model inputs. All sensitive parameters were subjected to uncertainty analysis and the overall output of the aerosol growth code thus obtained has been observed to follow log-normal law with median size at 335.2 nm with a geometric standard deviation of 1.54. This finds applications in the case of studies aiming to predict the growth of aerosols in the reactor component systems during accident conditions. In such conditions, there lies no choice of selection of input parameters and one cannot be sure about their variation range. This provides a conservative estimate for such a random scenario as uncertainty analysis performed in this study covers all the random combinations of input parameters and the overall most probable grown droplet size is estimated.

This description outlines a Geographic Information System (GIS)-based rainfall-runoff model that simulates the flow of water in a river basin. The model operates on a daily time step and consists of 4 non-linear storage components: Interception, soil moisture, channel, and groundwater. It employs (SCS) Unit Hydrograph model to determine unit hydrograph ordinates. The model replicates the movement and storage of water in various parts of the basin, including vegetation, the soil surface, the soil profile, and groundwater layers. To address uncertainty, a Monte Carlo simulation feature is integrated into the model. Monte Carlo Simulation involves predicting outcomes by generating numerous iterations using estimated ranges of values for variables with inherent uncertainty. This feature generates required number of sample sets with random parameter values. The model is run for all these realizations during a calibration period, and performance metrics like NSE are calculated for each calibration year to assess prediction uncertainty, model parameter weights are computed by normalizing the corresponding likelihood values. These weights sum up to one and represent the probabilistic distribution of predicted variables, illustrating the impact of structural and parameter errors on model predictions. A sensitivity analysis reveals that the Muskingum constants K and X have the greatest influence on model performance, while parameters Фgw, Фsw, Фfc, and Фpc have a minimal effect on the model's performance. The outcomes presented in the findings indicate that the Soil moisture accounting Model successfully forecasted peak discharge by leveraging the existing historical data. The accuracy of both volume and timing in the predictions suggests the model's appropriateness for the examined catchments. [ABSTRACT FROM AUTHOR]

This study delineates the intricate dynamics of gas hydrate production in the UBGH2-6 reservoir, located in the Ulleung Basin, by deploying a comprehensive simulation model. By integrating a sensitivity analysis with Latin hypercube sampling-based Monte Carlo simulations, we evaluated the influences on gas and water production and explored the underlying uncertainties within this gas hydrate reservoir. The simulation model revealed significant findings, including the production of approximately 440 t of gas and 34,240 t of water, facilitated by a depressurization strategy at 9 MPa for a year. This highlights the pivotal roles of porosity, permeability, and thermal properties in enhancing production rates and influencing hydrate dissociation processes. Sensitivity analysis of 19 parameters provides insights into their impact on production, identifying the key drivers of increased production rates. Furthermore, uncertainty analysis examined 300 reservoir models, utilizing statistical percentiles to quantify uncertainties, projecting a median gas production of approximately 455 t. This study identifies critical factors affecting gas hydrate production and offers valuable insights for future exploration and exploitation strategies, making a significant contribution to the field of gas hydrate research. [ABSTRACT FROM AUTHOR]

This paper examines the uncertainty of greenhouse gas (GHG) emissions during monorail construction. Firstly, a deterministic analysis is conducted. Subsequently, the obtained data are evaluated using the data quality indicator (DQI), and a Markov chain Monte Carlo (MCMC) simulation method is employed to assume different parameter distributions. The results of the deterministic calculation indicate that the calculated emissions per unit area of the station amount to 1.97 ton CO2e/m2, while the calculated emissions per unit section length reach 7.55 ton CO2e/m2. To simulate parameter distribution, we utilize a Beta distribution with good shape applicability. Furthermore, we establish scenarios involving system boundary reduction, low-emission factors, and reduced material and energy inputs in order to analyze scenario uncertainties. Regarding model uncertainty, this paper assumes that the material and energy quantity data conform to the normal, log-normal, uniform, and triangular distributions, respectively, subsequently analyzing the uncertainty distributions. This paper analyzes the GHG emission uncertainty evaluation of 16 monorail stations and sections during the construction period, which is divided into parameter, scenario, and model uncertainty. We provide a concrete framework for studying uncertainties related to GHG emissions at stations and sections during the monorail construction period. The scenario analysis results will help to make decisions about the choice of parameters, system boundaries, and other settings. It provides new guidance for emission reduction policies, such as reducing the use of steel-related products or using alternative environmentally friendly materials, considering emission reduction factors more comprehensively and setting emission reduction factors according to uniform distribution principle as far as possible. [ABSTRACT FROM AUTHOR]

The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship between CO2 emission and multivariate vehicle driving data, in which the vehicle specific power (VSP) is identified as the most significant factor on the CO2 emission factor, followed by speed. Additionally, acceleration and exhaust temperature had the least impact. It is inferred that the changes in data sampling transform the establishment of subspace matrices, affecting the calculation of eigenvector components and the fitting of the final quadratic response surface, so that the emission sensitivity and final fitting accuracy are impressionable by the data distribution form. For the VSP, the best fitting result can be obtained when the VSP conforms to a uniform distribution. Moreover, the Bayesian linear regression method accounts for fitting parameters between the VSP and CO2 emission factor with uncertainties derived from heteroscedastic measurement errors, and the values and distributions of the intercept and slope α and β are obtained. In general, the high-resolution inventory of the carbon emission factor of the tested vehicle is set up via systematically analyzing it, which brings a bright view of data processing in further counting the carbon footprint. [ABSTRACT FROM AUTHOR]

The primary stage in introducing significant uncertainties into the performance-based earthquake engineering technique of the next generation is the development of fragility curves. The technique of representing epistemic and aleatory uncertainties for moment-resisting steel frames is proposed in this manuscript. Incremental dynamic analysis (IDA) utilizes for considering aleatory uncertainty and epistemic uncertainty is taken into account through mass, damping, yielding stress, and a module of elasticity that is provided in four limit states of damage. The optimized fuzzy approach is applied to address this. Epistemic and aleatory uncertainties have been included in the 3D-fragility curves; epistemic' input is fuzzy values, and fragility curves' standard deviation and mean are estimated by using the optimized fuzzy approach, which is used PSO algorithm. The IDA approach is used to create scenarios that are used to train the FCMPSO algorithm. Verification was done by applying the Monte Carlo procedure results. The approach that has been used here is beneficial regarding to both accuracy and execution time in driving the 3D-fragility curves. [ABSTRACT FROM AUTHOR]

The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels. So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction. Among the implemented models, the GPR and LSSVR models provided more reliable results based on PI width and the percentage of observed data covered by PI. According to point prediction and uncertainty analysis, it was concluded that the GPR model had a lower uncertainty and could be successfully used to predict Cd.

Tailings dam breaches (TDBs) and subsequent flows can pose significant risk to public safety, the environment, and the economy. Numerical runout models are used to simulate potential tailings flows and understand their downstream impacts. Due to the complex nature of the breach-runout processes, the mobility and downstream impacts of these types of failures are highly uncertain. We applied the first-order second-moment (FOSM) methodology to a database of 11 back-analyzed historical tailings flows to evaluate uncertainties in TDB runout modelling and conducted a sensitivity analysis to identify key factors contributing to the variability of the HEC-RAS model output, including at different locations along the runout path. The results indicate that prioritizing resources toward advancements in estimating the values of primary contributors to the sensitivity of the selected model outputs is necessary for more reliable model results. We found that the total released volume is among the top contributors to the sensitivity of modelled inundation area and maximum flow depth, while surface roughness is among the top contributors to the sensitivity of modelled maximum flow velocity and flow front arrival time. However, the primary contributors to the sensitivity of the model outputs varied depending on the case study; therefore, the selection of appropriate rheological models and consideration of site-specific conditions are crucial for accurate predictions. The study proposes and demonstrates the FOSM methodology as an approximate probabilistic approach to model-based tailings flow runout prediction, which can help improve the accuracy of risk assessments and emergency response plans. [ABSTRACT FROM AUTHOR]

Coalbed methane (CBM) pilot wells typically exhibit a short production period, necessitating evaluation of their estimated ultimate recovery (EUR) through numerical simulation. Utilizing limited geological data from the pilot areas to finish history matching and subsequent production forecasting presents substantial challenges. This paper introduces a comprehensive numerical simulation workflow for CBM pilot wells, encompassing the following steps. Initially, geological parameters are categorized into two groups based on their statistical distribution trends: trend parameters (i.e., gas content, permeability, Langmuir volume, and Langmuir pressure) and non-trend parameters (i.e., fracture porosity, gas–water relative permeability, and rock compressibility). The probability method is employed to ascertain the probable high and low limits for trend parameter distributions, while empirical or analogous methods are applied to define the boundaries for non-trend parameters. Subsequently, the parameter sensitivity analysis is conducted to understand the influence of varying parameters on cumulative gas and water production. Conclusively, experimental design algorithms generate over 100 simulation cases using the identified sensitive parameters, from which the top ten optimal cases are chosen for EUR prediction. This workflow features two technological innovations: (1) considering the most comprehensive set of reservoir parameters for uncertainty and sensitivity analyses, and (2) considering the matching accuracy of both cumulative production and dynamic production trends when selecting optimal matching cases. This approach was successfully implemented in the C pilot area of the Bowen Basin, Australia. In addition, it offers valuable insights for numerical simulation of unconventional natural gases, such as shale gas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation. [ABSTRACT FROM AUTHOR]

Building-Stock Energy Models (BSEMs) have grown in popularity, implementation, scale and complexity. Yet, BSEM quality assurance processes have lagged behind. This article proposes a scalable methodology to apply Uncertainty (UA) and Sensitivity Analysis (SA) to BSEMs and studies the performance of eleven common UA-SA methods (OAT, SRC, SRRC, FFD, Morris, Sobol', eFAST, FAST-RBD, DMIM, PAWN, DGSM) for three UA-SA targets: screening, ranking and indices. Applying UA and SA to BSEMs requires a two-step input parameter sampling that samples 'across stocks' and 'within stocks'. To make efficient use of computational resources, practitioners should (i) distinguish between three UA-SA targets and (ii) choose a method based on the aimed UA-SA target. The computational cost varies according to the UA-SA target and method; (i) for screening: OAT, SRC, SRRC, FFD and Morris; (ii) for ranking: SRC, SRRC and Morris and (iii) for indices: Sobol' is the most efficient, among the tested UA-SA methods. [ABSTRACT FROM AUTHOR]

Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel SOFC based cogeneration system, consisting of biomass co-gasification, SOFC power generation and ORC waste heat recovery, to achieve the efficient and clean utilization of poultry litter. Based on the conceptual design, the energy, exergy and exergo-economic analysis are conducted, including sensitivity analysis, uncertainty analysis and multi-objective optimization. Results show that when using straw as the co-gasification biomass with poultry litter, the highest energy utilization efficiency of 44.70 % is obtained with the electricity and heat production of 162.02 kW and 52.41 kW, respectively. The biomass gasifier and the SOFC stack have the highest exergy destruction, accounting for 24.43 % and 19.99 % of the total. Sensitivity analysis presents complex results concerning mass fraction of poultry litter, water content, equivalent air ratio and water-carbon ratio, etc. The optimal solution for the current optimization problem is obtained. When poultry litter mass fraction is fixed at 25 %, T SOFC = 852 °C and u f = 0.73, the exergy efficiency is obtained as 41.95 % and the overall exergy cost per unit is obtained as 42.35 $/GJ. This paper investigates a novel cogeneration system relying on the biomass co-gasification of poultry litter, providing a new solution for poultry litter treatment and sustainable development of agriculture, forestry and husbandry. [Display omitted] • Biomass co-gasification, SOFC and ORC are integrated for poultry litter treatment. • Comprehensive energy, exergy and exergo-economic analysis are carried out. • Sensitivity analysis, uncertainty analysis and operational optimization are conducted. • High-value utilization process of the low-calorific poultry litter is proposed and studied. [ABSTRACT FROM AUTHOR]

Nitrate pollution is of considerable global concern as a threat to human health and aquatic ecosystems. Nowadays, δ15N/δ18O-NO3- combined with a Bayesian-based SIAR model are widely used to identify riverine nitrate sources. However, little is known regarding the effect of variations in pollution source isotopic composition on nitrate source contributions. Herein, we used δ15N/δ18O-NO3-, SIAR modeling, probability statistical analysis and a perturbing method to quantify the contributions and uncertainties of riverine nitrate sources in the Wen-Rui Tang River of China and to further investigate the model sensitivity of each nitrate source. The SIAR model confirmed municipal sewage (MS) as the major nitrate source (58.5-75.7%). Nitrogen fertilizer (NF, 8.6-20.9%) and soil nitrogen (SN, 7.8-20.1%) were also identified as secondary nitrate sources, while atmospheric deposition (AD,

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors’ findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources.

Converting ballasted tracks into rigid slab tracks using preplaced aggregate concrete (PAC) technology and injecting sand–cement mortar into the ballast are among the solutions for solving the ballast maintenance problems in railway bridges and tunnels. Since no definite study has been referred to the flexural behavior of this type of slab track considering ballast material property as well as quality and method of mortar injecting into ballast aggregates, so the present research is allocated to developing many three-dimensional numerical models for investigating the flexural behavior of the PAC slab. In this matter a longitudinal slice of slab with 0.6 length with respect to the sleeper-to-sleeper distance is selected for FEM modeling the flexural behavior of combination of sleeper–PAC slab and the achieved results are compared with the experimental ones for validation purposes. To evaluate the effect of the mechanical characteristics of the materials used for preparing this type of slab tracks, a series of parametric analyses is performed on the main characteristics of concrete including modulus of elasticity of PAC (E ), tensile strength of PAC ( σ t ) and ultimate strain of PAC (ε ). The results indicate the modulus of elasticity has the maximum impact of 30% on the deflection of sleeper–PAC slab. Then, a set of probabilistic analyses is performed considering the existing uncertainties in construction conditions of such a composite system. The analysis results obtained using Monte Carlo method indicate the uncertainty governing ballast granulation and conditions of mortar injection and setting could affect the results up to 36.56%. [ABSTRACT FROM AUTHOR]

This study delineates the intricate dynamics of gas hydrate production in the UBGH2-6 reservoir, located in the Ulleung Basin, by deploying a comprehensive simulation model. By integrating a sensitivity analysis with Latin hypercube sampling-based Monte Carlo simulations, we evaluated the influences on gas and water production and explored the underlying uncertainties within this gas hydrate reservoir. The simulation model revealed significant findings, including the production of approximately 440 t of gas and 34,240 t of water, facilitated by a depressurization strategy at 9 MPa for a year. This highlights the pivotal roles of porosity, permeability, and thermal properties in enhancing production rates and influencing hydrate dissociation processes. Sensitivity analysis of 19 parameters provides insights into their impact on production, identifying the key drivers of increased production rates. Furthermore, uncertainty analysis examined 300 reservoir models, utilizing statistical percentiles to quantify uncertainties, projecting a median gas production of approximately 455 t. This study identifies critical factors affecting gas hydrate production and offers valuable insights for future exploration and exploitation strategies, making a significant contribution to the field of gas hydrate research.

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To overcome computational inefficiency of the traditional subinterval finite element method, a novel adaptive subinterval finite element method based on dynamic sensitivity analysis is proposed to evaluate static response bounds of structures with uncertain-but-bounded parameters. Firstly, the original interval is divided into subintervals, and expansion routes for each displacement component are constructed based on dynamic sensitivity analysis. Then, the structural response bounds are analysed using a modified interval finite element method within each subinterval on the expansion route. Finally, the bounds of all displacement components are obtained via the interval union operation. To determine the appropriate number of subintervals, an adaptive convergence mechanism is proposed. The proposed method has accuracy and efficiency advantages over traditional subinterval finite element methods. The accuracy and effectiveness of this method are verified via numerical calculations of a nine-bar truss and an impeller. [ABSTRACT FROM AUTHOR]

Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes.

For the past two decades, the USA has been embroiled in a growing prescription drug epidemic. The ripples of this epidemic have been especially apparent in the state of Maine, which has fought hard to mitigate the damage caused by addiction to pharmaceutical and illicit opioids. In this study, we construct a mathematical model of the opioid epidemic incorporating novel features important to better understanding opioid abuse dynamics. These features include demographic differences in population susceptibility, general transmission expressions, and combined consideration of pharmaceutical opioid and heroin abuse. We demonstrate the usefulness of this model by calibrating it with data for the state of Maine. Model calibration is accompanied by sensitivity and uncertainty analysis to quantify potential error in parameter estimates and forecasts. The model is analyzed to determine the mechanisms most influential to the number of opioid abusers and to find effective ways of controlling opioid abuse prevalence. We found that the mechanisms most influential to the overall number of abusers in Maine are those involved in illicit pharmaceutical opioid abuse transmission. Consequently, preventative strategies that controlled for illicit transmission were more effective over alternative approaches, such as treatment. These results are presented with the hope of helping to inform public policy as to the most effective means of intervention. [ABSTRACT FROM AUTHOR]

Failure of dams and dikes often leads to devastating consequences in protected areas. Numerical models are instrumental tools to assess flood risk and guide emergency management, but numerous uncertainties affect model outcomes. Identifying uncertain model input parameters that induce high uncertainties in model outputs is essential. This paper focuses on two model outputs: the maximum breach discharge and the time to reach this peak. Using our implementation of a simplified physically based dam breaching model developed by Wu (2016), a global sensitivity analysis was conducted based on Sobol indices of total order. Unlike in most previous studies, many different configurations (twenty‐seven), both at laboratory and field scales, were considered. For each of them, input variables were ranked according to the significance of the contribution of their uncertainty to the output variability, and the dependency between reference configurations and sensitivity analysis results was highlighted. Depending on the considered case study, input parameter uncertainties with the largest impact on output variability were different, and so was the magnitude of output uncertainties. We demonstrate that sensitivity analysis results obtained for a specific configuration cannot be transferred as it to other configurations. Finally, sensitivity and uncertainty quantification results were combined in a decision tree to determine which input parameter uncertainty is the most critical in each configuration and what standard deviation in the output variables is expected. The global sensitivity analysis procedure presented here may apply to a wide variety of models in environmental sciences. Key Points: Based on a simplified physically based dam breaching model, a global sensitivity analysis was conducted using Sobol indices of total orderThe analysis was applied on twenty‐seven configurations at both laboratory and field scalesMost influential parameters and output uncertainty magnitudes were assessed and turned out to strongly depend on the dam configuration [ABSTRACT FROM AUTHOR]

This study is the first to conduct a global sensitivity analysis to identify the crucial variables that have an impact on the energy consumption of substations. The peak cooling and heating energy consumption, as well as the annual cooling and heating energy consumption of a substation in Shandong, are all simulated basing the Monte Carlo method. The simulation outputs are discussed by uncertainty analysis to obtain more accurate energy consumption thresholds. Subsequently, the treed Gaussian process and the standardized rank regression coefficient are used to perform a global sensitivity analysis of the simulation outputs. The results of the two global sensitivity analyses are practically the same, demonstrating that robustness can be ensured by simultaneously using two methods based on different theories. In addition, this study provides an efficient method for the energy-saving retrofitting of the existing substation and the energy-saving design of green substations in the future. [ABSTRACT FROM AUTHOR]

Fluorocarbons are an important category of greenhouse gas emissions, and currently, their use is prohibited due to their significant contribution to the global ozone depletion potential (ODP). During this century, they will continue to emit greenhouse gases into the environment since they are present in the thermal insulation foam and HVAC systems in existing buildings; however, proper disposal of these banks of CFCs/HFCs from existing buildings can limit their effects on the environment. However, there are no studies that have investigated quantifying the achievable environmental savings in this case. In this study, a comparative life cycle assessment (LCA) is conducted to evaluate, for the first time in the literature, the environmental savings achievable through the removal and disposal of CFC/HFC banks from buildings including damage-related emissions. To cope with the scarcity of data, uncertainty and sensitivity analysis techniques are applied. The results show that, for the selected archetype building, the largest annual emissions of CFCs/HFCs come from the external thermal insulation of the envelope. The removal of this material can lead to an additional significant reduction in the GWP (up to 569 kgCO2eq/m2) and the ODP (up to 117 × 10−3 kgCFC-11eq/m2), i.e., higher than that achievable by reducing energy consumption through energy retrofit measures (276 and 0, respectively). Thus, CFC/HFC banks should not be neglected in LCA studies of existing buildings due to their possible significant impact on a building's ecoprofile. [ABSTRACT FROM AUTHOR]

This paper proposes a reliability analysis methodology of an electrical power system (EPS) in a French nuclear power plant model based on the fault tree analysis method. The RiskSpectrum PSA software is used for fault tree development, minimal cut set (MCS) identification, sensitivity and uncertainty analyses. A detailed tested reliability model of standby diesel generators (DGs) is proposed considering various testing parameters. Furthermore, a reliability goal problem is formulated as an optimization problem to eliminate the aging impact on DGs reliability using the genetic algorithms (GAs). The obtained results show that the EPS failure probability is 1.39E–06 with an error factor of 3.58. The importance and the sensitivity analyses provide the most important and sensitive components, respectively. The proposed standby reliability model provides a good insight on the impact of testing parameters on the EPS reliability. Furthermore, the proposed optimization problem shows a high capability to eliminate the aging impact on DGs reliability by TI optimization. The obtained results are compared with those given by the reliability block diagram (RBD) method where it is shown that they are very close in terms of failure probability, MCS identification and importance factors. [ABSTRACT FROM AUTHOR]

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China's typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios.

Adjustment strategies including window ventilation and shading have important improvements in energy consumption, thermal and light environments, furthermore, the upper limit for improvement is affected by design parameters. However, studies incorporating adjustment strategies in the building design process are very limited. To address this research gap, we explore the effects of window ventilation and shading on building design performance from uncertainty analysis, sensitivity analysis, and multi-objective optimization. Furthermore, China's typical climate zones are compared given climate effects. Results indicate that (1) the uncertainty of total energy demand in the severe cold climate is most affected with the uncertainty increase rate being 32.0%, the uncertainty of thermal comfort ratio in the hot summer and cold winter climate and the hot summer and warm winter climate is most affected with the uncertainty increase rate being 16.3% and 14.0%, respectively. (2) the sensitivity analysis of the thermal comfort ratio is more sensitive to adjustment strategies than to total energy demand. The severe cold climate is more vulnerable than in other climates. (3) when multi-objective optimization is performed with maximum thermal comfort and minimum total energy demand when considering adjustment strategies, the severe cold climate has the greatest energy-saving potential (38.1%) and the hot summer and cold winter climate has the largest potential to improve thermal comfort (17.6%). More importantly, the light environment is within the comfort range from the daylight glare index, the illuminance, and illuminance uniformity ratios.

Given the fast growth of available computational capacities and the increasing complexity of simulation models addressing agro-environmental issues, uncertainty analysis using stochastic techniques has become a standard modeling practice. However, conventional uncertainty/sensitivity analysis methods are either computationally demanding (Monte Carlo-based methods) or produce results with varying quality (Gaussian quadratures). In this article, we present a computationally inexpensive and reliable uncertainty analysis method for simulation models called informed rotations of Gaussian quadratures (IRGQ). We also provide an R script that generates IRGQ points based on the required input data. The results demonstrate that this method is able to produce approximations that are close to the estimated benchmarks at low computational costs. The method is tested in three different simulation models using different input data in order to demonstrate the independence of the proposed method on specific model types and data structures. This is a methodological paper for practitioners rather than theorists. [ABSTRACT FROM AUTHOR]