Application Of Global Sensitivity Analysis Research Articles

Unraveling the complex dynamics of temporal moments for multispecies contaminant transport in saturated porous media: A global sensitivity analysis approach

In this study, we focus on the application of global sensitivity analysis (GSA) to gain insight into the influence of model parameters on the outputs of multispecies contaminant transport model. In this context, employing GSA methods (Morris and FAST) allows us to comprehend the relative significance of each input parameter in the model. We specifically analyzed the effects of model parameters on the concentration breakthrough curves and associated temporal moments of concentration of contaminant species. The spatial variability of sensitivity indexes from Morris and FAST methods for parent and daughter contaminant species is also investigated. We represent uncertainties of longitudinal dispersivity, porosity, first-order degradation constants of parent and daughter contaminant species, and sorption distribution coefficient of parent species into the coupled multispecies contaminant transport model and GSA framework. Our results show a complex interplay between different model parameters and their varying influences on different output metrics. The ranking of parameters based on GSA shows that (i) the contaminant mass recovery of final daughter species of three species chain reaction is governed by the first-order degradation coefficient of parent and first daughter species; (ii) dispersivity and porosity are critical parameters that govern the mean residence time and variance of breakthrough curve of final daughter species; (iii) the coefficient of skewness is more sensitive to dispersivity, sorption distribution coefficient, and first-order degradation coefficient of parent species as compared to other model parameters. The variation in the sensitivity indices among contaminant species and with travel distance is observed. This study highlights the significance of global sensitivity analysis when simulating the multiple species contaminants in saturated porous media.

Development of an Energy Management System for a Renewable Energy Community and Performance Analysis via Global Sensitivity Analysis

This paper presents the development of an energy management system (EMS) for a renewable energy community (REC) with the load-generation balancing objective. In this regard, rule-based and optimization mechanisms are proposed for the REC management in line with the scope of a field trial and considering the scarcity of the measurement and historical data. This typical data scarcity along with the intermittent behaviour of renewable energy resources introduce an unavoidable level of uncertainty— not being adequately addressed in the EMS literature— that might ultimately affect the proper REC management. Hence, a comprehensive performance analysis of the proposed EMS has been conducted via global sensitivity analysis (GSA). Particularly, variance-based sensitivity analysis has been employed to investigate how the variability of a set of selected indicators of the REC performance is apportioned to the different sources of uncertainty specifically related to the forecast and flexibility availability. Results show that the EMS performance is consistent with the EMS objective. The application of GSA reveals though interesting findings that contradict antecedent misconceptions about how different uncertainty sources affect the EMS performance. Although being related to the specific REC under study, the present work specializes GSA method in novel ways that pave the path for its reusability in the context of other EMS applications with different boundary conditions. By highlighting the necessity of GSA and showcasing its suitability to study the EMS performance under an uncertainty framework, the present work offers a precious tool to support system operators in their decision making process.

Response Surface Methodology for Copper Flotation Optimization in Saline Systems

Response surface methodology (RSM) is one of the most effective tools for optimizing processes, and it has been used in conjunction with the Analysis of Variance (ANOVA) test to establish the effect of input factors on output factors. However, when this methodology is used in mineral flotation, its polynomial model usually performs poorly. An alternative is to use artificial neural networks (ANNs) in such situations. Within this context, the ANOVA test is not the best option for these model types; moreover, it requires statistical assumptions that are difficult to satisfy in flotation. This work proposes replacing the polynomial model of the RSM with ANNs and the Sobol methods to determine the influential input factors instead of the ANOVA test. This proposal is applied to two porphyry copper ores with a high content of pyrite, clay, and dilution media. In addition, this study shows how other computational intelligence techniques, such as swarm intelligence, can be incorporated into this type of problem to improve the learning process of ANNs. The results gave an adjustment of over 0.98 for R2 using ANNs, in comparison to values of around 0.5 when the polynomial model of RSM was utilized. On the other hand, the application of Global Sensitivity Analysis (GSA) identified the aeration rate and P80 size as the most influential variables in copper recovery under the conditions studied. Additionally, we identified significant interactions that affect the recovery of copper, with the interactions between the aeration rate, frother concentration, and P80 size being the most important.

Application of global sensitivity analysis in identification of herbicides cocktail effects at environment-related concentrations

<p indent="0mm">The use of herbicides in crop production continues to increase globally. By 2015, China had become one of the most important producers and consumers of herbicides in the world. Many herbicides are persistent organic pollutants and are used continuously for long periods of time, causing the high residual concentrations in the water surrounding farmland and negative effects on aquatic life. The concentrations of herbicides in the environmental water are variable and the types of herbicides are diverse. Herbicide mixtures in surface water may threaten aquatic life. However, in most areas of China, the hazard of herbicide mixtures associated with environmental exposure levels has not been assessed. This paper establishes a hazard identification assessment method for mixtures with environment-related concentrations. Firstly, the hazard of individual pollutant was assessed by hazard quotient (HQ), and the pollutants with high hazards (HQ≥1) to target organisms were further studied. Secondly, the global sensitivity analysis (GSA) based on Morris method was used to study combined effect, and identify the important or hidden drivers in chemical mixtures. In brief, the GSA experimental design consists of four steps: Input factors (target pollutants), Morris sampling, high-throughput screening, and the sensitivity analysis. Thirty-nine herbicides detected in the surface water of Tai Lake and Liao River were screened by HQ with <italic>Scenedesmus obliquus</italic> (<italic>S</italic>.<italic> obliquus</italic>) individually. The herbicides with high hazard were the input factors of GSA. Then, the Morris method was used to generate 150 samples of mixtures. The <italic>S</italic>.<italic> obliquus </italic>was exposed to 150 samples for <sc>24 h</sc> and subjected to high-throughput screening. Finally, the sensitivity analysis was used to identify the important or hidden drivers of the herbicide mixtures. The results showed that 14 herbicides had high hazards (HQ≥1) among 39 herbicides detected in Tai Lake and Liao River basin. Ametryn and diflufenican had higher hazards to algae. At the individual herbicide exposure of <sc>500 μg/L,</sc> most herbicides, except C5 and C10, significantly inhibited the growth of <italic>S</italic>.<italic> obliquus </italic>(<italic>P</italic>&lt;0.05). And the strongest growth inhibition effect caused by individual herbicide was about 40%. The results of 150 samples of mixtures with environment-related concentration showed that most of the mixtures had growth inhibition effects on <italic>S</italic>.<italic> obliquus</italic>. The total concentrations of the mixtures that consist of 14 herbicides are not greater than <sc>79 μg/L.</sc> And the strongest growth inhibition effect caused by mixture samples was over 50%. The toxicity of herbicide mixtures cannot be ignored. The growth inhibition effect of the mixtures that consist of 14 herbicides on <italic>S</italic>.<italic> obliquus</italic> was nonlinear, and there are strong interactions between herbicides. It is difficult to predict the toxic effects of the mixture system from the exposure concentration of individual herbicides. According to the <italic>μ*</italic> value, the most important factors in the combined pollution were terbutryne, bensulfuron methyl, atrazine, and ametryn. Bentazone had the lowest <italic>μ*</italic> and contributed little to the overall mixture system. And bensulfuron methyl was the hidden driver of the mixture system. The important drivers in the herbicide mixtures need to be focused, and their concentrations should be monitored regularly to control emissions and minimize the negative effect on aquatic life. Global sensitivity analysis based on Morris sampling can identify the important factors in cocktail effects of mixtures in limited samples, providing technical support for incorporating the actual toxic effects of mixtures into the risk assessment of pollutants.

Sensitivity analysis in economic evaluation of payments for water and carbon ecosystem services

Payments for ecosystem services (PES) have become a widely accepted tool for mitigating ecosystem decline with hundreds of applications globally. Despite the extensive use of PES, challenges remain in evaluation of feasibility and impact. A key challenge relates to how the multiple, complex ecosystem and market processes determining outcomes are usually only understood with considerable uncertainty. These uncertainties arise throughout the processes to PES evaluation from the conceptual framing and modelling paradigm through to the input data and scenario assumptions. Understanding implications and risks arising requires rigorous and transparent uncertainty treatment and reporting. This article demonstrates best practice in transparent and rigorous PES evaluation with global sensitivity analysis (GSA). The approach systematically identifies uncertain parameters and applies Monte Carlo simulations with stochastic sampling from distributions of uncertain input parameters that are transformed to provide distributions of possible outcomes. The case study considered involves comparing the costs of forest regeneration with the stacked water and carbon value of ecosystem services derived from the vegetation. The results demonstrate the evaluation of multiple ecosystem service process and market uncertainties, quantifies their influence on the overall economic viability of the scheme and the relative impact of different uncertainties on scheme economic viability risks. The results and discussion outline how wider application of GSA could provide better pragmatic and informative assessment of the inevitable uncertainties within ecosystem service payment scheme evaluation.

Applications of Global Sensitivity Analysis to the Optimization of a Dermal PBPK Model of Bromochloromethane

Physiologically based pharmacokinetic (PBPK) models can be used to develop frameworks for risk assessment and predictive toxicology testing routines. However, the predictive power of these models is only as good as the confidence in the parameters within the model itself. Sensitivity analysis, or the study of the effect of propagated error on the predictive power of the model, can be used to determine which model parameters are most likely to affect change in the model. This is important when considering optimization routines, as optimizing non-sensitive parameters may lead to biologically incorrect parameter estimates. This study explores the sensitivity of physiological, metabolic, and chemical-specific parameters for a published dermal exposure PBPK model of bromochloromethane.

Flattening Layer Pruning in Convolutional Neural Networks

The rapid growth of performance in the field of neural networks has also increased their sizes. Pruning methods are getting more and more attention in order to overcome the problem of non-impactful parameters and overgrowth of neurons. In this article, the application of Global Sensitivity Analysis (GSA) methods demonstrates the impact of input variables on the model’s output variables. GSA gives the ability to mark out the least meaningful arguments and build reduction algorithms on these. Using several popular datasets, the study shows how different levels of pruning correlate to network accuracy and how levels of reduction negligibly impact accuracy. In doing so, pre- and post-reduction sizes of neural networks are compared. This paper shows how Sobol and FAST methods with common norms can largely decrease the size of a network, while keeping accuracy relatively high. On the basis of the obtained results, it is possible to create a thesis about the asymmetry between the elements removed from the network topology and the quality of the neural network.

A Neural-Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

Researchers have been investigating data-driven modeling as a key way to achieve ship intelligence for years. This paper presents a novel data analysis approach to data-driven modeling of ship motion. We propose a global sensitivity analysis (GSA) approach combining artificial neural network (ANN) and sparse polynomial chaos expansion (SPCE) techniques to accommodate high-dimensional sensor data collected from ship motion. An ANN is constructed as a surrogate model to associate ship sensor data with a certain type of ship motion. To account for the computational efficiency of GSA, an SPCE is integrated into the GSA to decrease the need for Monte Carlo (MC) samples generated by the ANN. A probe variable is designed to couple with the MC samples, which plays a role in determining the degree of convergence of variable importance. A test on benchmark function demonstrates the efficiency and accuracy of the proposed approach. A case study of ship heading with and without environment effects is conducted. The experimental results show that the proposed approach can identify and rank the most sensitive factors of ship motion. The proposed approach highlights the application of GSA in data-driven modeling for ship intelligence.

Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox

Global Sensitivity Analysis (GSA) is a set of statistical techniques to investigate the effects of the uncertainty in the input factors of a mathematical model on the model’s outputs. The value of GSA for the construction, evaluation, and improvement of earth system models is reviewed in a companion paper by Wagener and Pianosi (2019). The present paper focuses on the implementation of GSA and provides a set of workflow scripts to assess the critical choices that GSA users need to make before and while executing GSA. The workflows proposed here can be adopted by GSA users and easily adjusted to a range of GSA methods. We demonstrate how to interpret the outcomes resulting from these different choices and how to revise the choices to improve GSA quality, using a simple rainfall-runoff model as an example. We implement the workflows in the SAFE toolbox, a widely used open source software for GSA available in MATLAB and R.•The workflows aim to contribute to the dissemination of good practice in GSA applications.•The workflows are well-documented and reusable, as a way to ensure robust and reproducible computational science.

A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models

Many applications of global sensitivity analysis (GSA) do not adequately account for the dynamical nature of earth and environmental systems models. Gupta and Razavi (2018) highlight this fact and develop a sensitivity analysis framework from first principles, based on the sensitivity information contained in trajectories of partial derivatives of the dynamical model responses with respect to controlling factors. Here, we extend and generalize that framework to accommodate any GSA philosophy, including derivative-based approaches (such as Morris and DELSA), direct-response-based approaches (such as the variance-based Sobol’, distribution-based PAWN, and higher-moment-based methods), and unifying variogram-based approaches (such as VARS). The framework is implemented within the VARS-TOOL software toolbox and demonstrated using the HBV-SASK model applied to the Oldman Watershed, Canada. This enables a comprehensive multi-variate investigation of the influence of parameters and forcings on different modeled state variables and responses, without the need for observational data regarding those responses.

Application of global sensitivity analysis to statistical energy analysis: Vehicle model development and transmission path contribution

This paper focuses on prediction of the sound pressure level inside a vehicle by using statistical energy analysis (SEA) and control of the noise within limited trim materials for vehicle weight reduction. To this end, a vehicle SEA model was developed considering the effects of the SEA parameter uncertainties on the prediction results. Thereafter, the contributions of the sound transmission losses of several vehicle components and the absorptivity of the vehicle interior to the sound pressure level were identified. Global sensitivity analysis was performed to evaluate both the effects of the SEA parameter uncertainties and the contribution of the acoustical performance. Only the most influential SEA parameters were calculated or measured to reflect the characteristics of the acoustic material. Our results revealed that the sound pressure levels calculated by using the simplified SEA model agreed well with the measured values. Finally, based on the influences of the acoustic performances of the vehicle components on the interior noise, the design of the trim materials on the dash, which was one of the sensitive components, was modified and compared to the initial design and conventional lightweight design in terms of interior noise and weight.

Global sensitivity analysis for high-dimensional problems: How to objectively group factors and measure robustness and convergence while reducing computational cost

Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.

Optimizing the configuration of integrated nutrient and energy recovery treatment trains: A new application of global sensitivity analysis to the generic nutrient recovery model (NRM) library

This paper describes the use of global sensitivity analysis (GSA) for factor prioritization in nutrient recovery model (NRM) applications. The aim was to select the most important factors influencing important NRM model outputs such as biogas production, digestate composition and pH, ammonium sulfate recovery, struvite production, product purity, particle size and density, air and chemical requirements, scaling potential, among others. Factors considered for GSA involve: 1) input waste stream characteristics, 2) process operational factors, and 3) kinetic parameters incorporated in the NRMs. Linear regression analyses on Monte Carlo simulation outputs were performed, and the impact of the standardized regression coefficients on major performance indicators was evaluated. Finally, based on the results, the paper describes the original use of GSA to obtain insight in complex nutrient recovery systems and to propose an optimal nutrient and energy recovery treatment train configuration that maximizes resource recovery and minimizes energy and chemical requirements.

Distribution-based sensitivity analysis from a generic input-output sample

In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost.

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling.

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

Adsorption of thiophene by activated carbon: A global sensitivity analysis

The removal of sulfur compounds in biomass gasification processes is crucial to protect downstream equipment. While conventional sorbent materials can retain H2S, many fail to capture heterocyclic sulfur compounds such as thiophene (C4H4S). This work explored activated carbons (ACs) for the removal of thiophene from the gas phase both experimentally and by employing different models. Experiments were performed in a laboratory packed bed column between 100–200°C at various gas flow-rates and inlet concentrations. Altering the temperature had the strongest effect with breakthrough capacities ranging from 7–138mmolkg−1. The transient adsorption process was described by a 1-D approach, including mass transfer phenomena and axial dispersion. Experimental validation showed good agreement between measured and predicted breakthrough times and capacities. A global sensitivity analysis (GSA) was performed to obtain a ranking of importance of model input factors and gain further process insight. The GSA included 12 model parameters and a selection of axial dispersion correlations and isotherm models. Based on the chosen range of input factors, the GSA indicated that the type of isotherm model, axial dispersion correlation, and temperature have a major effect on the predicted breakthrough time. The calculated slope of the curve is strongly affected by the isotherm model and axial dispersion correlation. Overall, this work showed that ACs are a promising option for thiophene removal and provided a generic approach for the application of GSA to adsorption models. In the given context the analysis demonstrated that the selection of common model assumptions deserves as much attention as the model parameters.

LCA of emerging technologies: addressing high uncertainty on inputs' variability when performing global sensitivity analysis

In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models.When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed.We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties.We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.

Understanding the time‐varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis

AbstractSimulations from hydrological models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of such sources of uncertainty is essential to support model calibration, validation and diagnostic evaluation and to prioritize efforts for uncertainty reduction. It can also support the identification of ‘disinformative data’ whose values are the consequence of measurement errors or inadequate observations. Sensitivity analysis (SA) provides the theoretical framework and the numerical tools to quantify the relative contribution of different sources of uncertainty to the variability of the model outputs. In traditional applications of global SA (GSA), model outputs are aggregations of the full set of a simulated variable. For example, many GSA applications use a performance metric (e.g. the root mean squared error) as model output that aggregates the distances of a simulated time series to available observations. This aggregation of propagated uncertainties prior to GSA may lead to a significant loss of information and may cover up local behaviour that could be of great interest. Time‐varying sensitivity analysis (TVSA), where the aggregation and SA are repeated at different time steps, is a viable option to reduce this loss of information. In this work, we use TVSA to address two questions: (1) Can we distinguish between the relative importance of parameter uncertainty versus data uncertainty in time? (2) Do these influences change in catchments with different characteristics? To our knowledge, the results present one of the first quantitative investigations on the relative importance of parameter and data uncertainty across time. We find that the approach is capable of separating influential periods across data and parameter uncertainties, while also highlighting significant differences between the catchments analysed. Copyright © 2016 The Authors. Hydrological Processes. Published by John Wiley &amp; Sons Ltd.

Method for Adaptive Optimization of EOR Performance Under Uncertainty

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173295, “Method for Adaptive Optimization of EOR Performance Under Uncertainty,” by N. Chugunov, T.S. Ramakrishnan, A. Lukyanov, and W.J. Bailey, Schlumberger, and D. Wilkinson, Efficient Solutions, prepared for the 2015 SPE Reservoir Simulation Symposium, Houston, 23–25 February. The paper has not been peer reviewed. Enhanced-oil-recovery (EOR) performance may be assessed quantitatively through a variety of metrics. Because formation and fluid properties are often poorly characterized, however, performance predictions of EOR processes are uncertain. The authors present a method to reduce such uncertainty in EOR performance adaptively while identifying an optimal operational strategy for a given tolerance to risk. The proposed approach allows one to reduce uncertainty progressively in the predicted performance of an iteratively optimized EOR strategy by systematic reduction of uncertainty in identified properties of the reservoir. Introduction Classic approaches to optimization under uncertainty use a mean-variance approach. However, they do not provide necessary insight into the underlying nature of the uncertainties inherent in the optimized model. More importantly, they do not provide any specific quantitative guidance on reducing such uncertainty, which is necessary from an operational point of view. This issue is addressed in this study directly by combining global sensitivity analysis (GSA) with optimization under uncertainty, in an adaptive work flow, for systematic uncertainty reduction of the optimized model prediction. Application of GSA to address various problems arising in the industry has been discussed in a number of studies. However, these studies focused mainly on quantifying uncertainty for specific physical quantities and using that analysis to gain insight into the subsequent measurement-program design and interpretation.

The application of global sensitivity analysis in the development of a physiologically based pharmacokinetic model for m-xylene and ethanol co-exposure in humans.

Global sensitivity analysis (SA) was used during the development phase of a binary chemical physiologically based pharmacokinetic (PBPK) model used for the analysis of m-xylene and ethanol co-exposure in humans. SA was used to identify those parameters which had the most significant impact on variability of venous blood and exhaled m-xylene and urinary excretion of the major metabolite of m-xylene metabolism, 3-methyl hippuric acid. This analysis informed the selection of parameters for estimation/calibration by fitting to measured biological monitoring (BM) data in a Bayesian framework using Markov chain Monte Carlo (MCMC) simulation. Data generated in controlled human studies were shown to be useful for investigating the structure and quantitative outputs of PBPK models as well as the biological plausibility and variability of parameters for which measured values were not available. This approach ensured that a priori knowledge in the form of prior distributions was ascribed only to those parameters that were identified as having the greatest impact on variability. This is an efficient approach which helps reduce computational cost.