Computational Modeling Framework Research Articles

ECG analysis of ventricular fibrillation dynamics reflects ischaemic progression subject to variability in patient anatomy and electrode location.

Ventricular fibrillation (VF) is the deadliest arrhythmia, often caused by myocardial ischaemia. VF patients require urgent intervention planned quickly and non-invasively. However, the accuracy with which electrocardiographic (ECG) markers reflect the underlying arrhythmic substrate is unknown. We analysed how ECG metrics reflect the fibrillatory dynamics of electrical excitation and ischaemic substrate. For this, we developed a human-based computational modelling and simulation framework for the quantification of ECG metrics, namely, frequency, slope, and amplitude spectrum area (AMSA) during VF in acute ischaemia for several electrode configurations. Simulations reproduced experimental and clinical findings in 21 scenarios presenting variability in the location and transmural extent of regional ischaemia, and severity of ischaemia in the remote myocardium secondary to VF. Regional acute myocardial ischaemia facilitated re-entries, potentially breaking up into VF. Ischaemia in the remote myocardium modulated fibrillation dynamics. Cases presenting a mildly ischaemic remote myocardium yielded sustained VF, enabled by the high proliferation of phase singularities (PS, 11-22) causing remarkably disorganised activation patterns. Conversely, global acute ischaemia induced stable rotors (3-12 PS). Changes in frequency and morphology of the ECG during VF reproduced clinical findings but did not show a direct correlation with the underlying wave dynamics. AMSA allowed the precise stratification of VF according to ischaemic severity in the remote myocardium (healthy: 23.62-24.45 mV Hz; mild ischaemia: 10.58-21.47 mV Hz; moderate ischaemia: 4.82-11.12 mV Hz). Within the context of clinical reference values, apex-anterior and apex-posterior electrode configurations were the most discriminatory in stratifying VF based on the underlying ischaemic substrate. This in silico study provides further insights into non-invasive patient-specific strategies for assessing acute ventricular arrhythmias. The use of reliable ECG markers to characterise VF is critical for developing tailored resuscitation strategies.

Adaptive empathic response selection is sensitive to multiple dimensions of social interaction

During empathic response selection, individuals draw from both past experiences and social cues, including the distressed person’s identity, their emotional state, and the cause of distress. To study how these social dimensions influence empathic-response learning we integrated a multidimensional learning paradigm, computational modelling, and adaptive empathy framework. Participants identified effective empathic responses across two blocks of distress scenarios, with one social dimension altered between blocks. We anticipated two learning patterns: dimension-sensitive, treating each change as a new learning experience, and dimension-insensitive, relying on previous experience as a baseline. We found that participants were sensitive to changes in person, emotional state, and distress cause, but to different degree. The person dimension was the most salient, suggesting that the distressed person’s identity is the primary reference point when interacting with others. Our findings provide a quantitative evaluation of the weight given to different dimensions of social interactions, which may help understand how people perceive and react in such scenarios.

Nanozyme-enhanced enzyme control: Computational strategies for precise and sustained enzymatic activity in tissue engineering

Nanomaterials are revolutionizing tissue engineering by significantly enhancing enzyme-mediated chemical processes and cellular activities, thereby advancing the development of more effective therapeutic strategies. This research delves into the intricate dynamics of enzyme diffusion within tissue matrices, providing a comprehensive computational modeling framework aimed at optimizing enzyme concentration control in tissue scaffolds. Through advanced numerical algorithms and computational analysis, the study simulates optimal control strategies for regulating nanozyme levels, ensuring precise and sustained enzymatic activity within the scaffolds. A novel aspect of this work is the integration of videographic records, which offers an enriched understanding of the complex interactions between nanomaterials and biological tissues, providing detailed insights into the system’s operational dynamics. The study bridges the gap between experimental methodologies and theoretical models, aligning with the cutting-edge research in chemical physics and physical chemistry by offering novel approaches to tissue engineering challenges. The findings underscore the critical role of nanomaterials in achieving precise enzymatic control, which is pivotal for the development of advanced biomimetic systems and improved therapeutic outcomes. This work contributes to the ongoing frontier research in physical phenomena within chemistry, biology, and materials science, by providing significant new insights into enzyme delivery mechanisms and their application in biomedical contexts. The research not only highlights the importance of innovative methodologies in tissue engineering but also sets the stage for future experimental validations and potential clinical applications

TIP: A trust inference and propagation model in multi-human multi-robot teams

Trust is a crucial factor for effective human–robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human and robotic agents. To fill this important research gap, we present the Trust Inference and Propagation (TIP) model to model and estimate human trust in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=30$$\\end{document}). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of tumor-immune interactions is unlikely in tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen evolve as a mechanism of immune escape, but the exact nature of immune-collagen interactions is poorly understood. Spatial data quantifying collagen fiber alignment in squamous cell carcinomas indicates that late-stage disease is associated with highly aligned fibers. Our computational modeling framework discriminates between two hypotheses: immune cell migration that moves (1) parallel or (2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-extracellular matrix interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. Here, computational modeling provides important mechanistic insights by defining a kernel cell-cell interaction function that considers a spectrum of local (cell-scale) to global (tumor-scale) spatial interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interaction kernels drives tumor growth and infiltration.

A Multiscale Computational Modeling Framework to Quantify Microstructural Effects on Nucleation of Li Metal on Carbon Scaffold Architectures

Carbon materials with scaffold architecture have been shown to mitigate the non-uniform Li plating/stripping issues during cycling, which is critical for achieving significantly enhanced energy densities and improved safety of Li metal batteries. However, optimal design of the architecture and microstructure of the carbon materials requires fundamental understanding of the Li metal nucleation behavior at different length scales. In this poster, we present a multiscale computational modeling framework to quantify the microstructural effects of carbon materials on the tendency of Li metal nucleation during plating by integrating atomic-scale simulation, mesoscale microstructure-based homogenization, continuum-scale electrochemistry modeling, and classical nucleation theory. We modeled the architecture of the carbon scaffold and the electrochemistry of Li transport across the electrode and electrolyte by finite element simulation to determine boundary conditions for our mesoscale models. We then generated different porous microstructures of various carbon materials by stochastic and physics-based simulations informed from experiments and performed multiphysics-based simulations at mesoscale to identify the electro-chemo-mechanical hotspots which are sensitive to the microstructure morphology. We calculated the energy barriers for Li to form clusters on graphene surfaces as a function of local Li concentrations and electric fields by ab initio molecular dynamics simulations. The synergistic deployment of different models across scales allows us to theoretically estimate the Li metal nucleation tendency based on a modified classical nucleation theory for different microstructures under various electroplating conditions. Comparison of the theoretical predictions and experimental results will be briefly discussed. The insights obtained from this multiscale framework offer new understanding and strategies for the design of carbon scaffold architectures to mitigate the non-uniformity of Li plating and issues in Li metal anode deployment in solid-state batteries.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and 22-ERD-043.

A physics-informed deep learning-based urban building thermal comfort modeling and prediction framework for identifying thermally vulnerable building stock

PurposeDue to the increasing frequency of extreme weather and densifying urban landscapes, residences are susceptible to heat-related discomfort, especially those in a naturally ventilated built environment in tropical climates. Indoor thermal comfort is thus paramount to building sustainability and improving occupants' health and well-being. However, to assess indoor thermal comfort considering the urban context, it is conventional to use questionnaire surveys and monitoring units, which are both case-centric and time-intensive. This study presents a dynamic computational thermal comfort modeling framework that can determine indoor thermal comfort at an urban scale to bridge this gap.Design/methodology/approachThe framework culminates in developing a deep learning model for predicting the accurate hourly indoor temperature of urban building stock by the coupling urban scale capabilities of environment modeling with single-building dynamic thermal simulations.FindingsUsing the framework, a surrogate model is created and verified for Dharavi, India's informal urban settlement. The results indicated that the developed surrogate model could predict the building's indoor temperature in several complex new urban scenarios with different building orientations, layouts, building-to-building distances and surrounding building heights, using five different random urban representative scenarios as the training set. The prediction accuracy was reliable, as evidenced by the mean bias error (MBE) and coefficient of (CV) root mean squared error (MSE) falling between 0 and 5%. The findings also showed that if the urban context is ignored, estimates of annual discomfort hours may be inaccurate by as much as 70%.Social implicationsThe developed computational framework could help regulators and policymakers engage in more informed and quantitative decision-making and direct efforts to enhance the thermal comfort of low-income dwellings and informal settlements.Originality/valueUp to this point, majority of literature that has been presented has concentrated on building a body of knowledge about urban-based modeling from an energy management standpoint. In contrast, this study suggests a dynamic computational thermal comfort modeling framework that takes into account the urban context of the neighborhood while examining the indoor thermal comfort of the residential building stock.

Automatic adjoint-based inversion schemes for geodynamics: reconstructing the evolution of Earth's mantle in space and time

Abstract. Reconstructing the thermo-chemical evolution of Earth's mantle and its diverse surface manifestations is a widely recognised grand challenge for the geosciences. It requires the creation of a digital twin: a digital representation of Earth's mantle across space and time that is compatible with available observational constraints on the mantle's structure, dynamics and evolution. This has led geodynamicists to explore adjoint-based approaches that reformulate mantle convection modelling as an inverse problem, in which unknown model parameters can be optimised to fit available observational data. Whilst there has been a notable increase in the use of adjoint-based methods in geodynamics, the theoretical and practical challenges of deriving, implementing and validating adjoint systems for large-scale, non-linear, time-dependent problems, such as global mantle flow, has hindered their broader use. Here, we present the Geoscientific ADjoint Optimisation PlaTform (G-ADOPT), an advanced computational modelling framework that overcomes these challenges for coupled, non-linear, time-dependent systems by integrating three main components: (i) Firedrake, an automated system for the solution of partial differential equations using the finite-element method; (ii) Dolfin-Adjoint, which automatically generates discrete adjoint models in a form compatible with Firedrake; and (iii) the Rapid Optimisation Library, ROL, an efficient large-scale optimisation toolkit; G-ADOPT enables the application of adjoint methods across geophysical continua, showcased herein for geodynamics. Through two sets of synthetic experiments, we demonstrate the application of this framework to the initial condition problem of mantle convection, in both square and annular geometries, for both isoviscous and non-linear rheologies. We confirm the validity of the gradient computations underpinning the adjoint approach, for all cases, through second-order Taylor remainder convergence tests and subsequently demonstrate excellent recovery of the unknown initial conditions. Moreover, we show that the framework achieves theoretical computational efficiency. Taken together, this confirms the suitability of G-ADOPT for reconstructing the evolution of Earth's mantle in space and time. The framework overcomes the significant theoretical and practical challenges of generating adjoint models and will allow the community to move from idealised forward models to data-driven simulations that rigorously account for observational constraints and their uncertainties using an inverse approach.

Blind prediction of curved fracture surfaces in gypsum samples under three-point bending using the Discontinuous Galerkin Cohesive Zone method

A computational framework based on a Discontinuous Galerkin (DG)/Cohesive Zone formulation is utilized to simulate the experiments of the Purdue Damage Mechanics Modeling Challenge. The inelastic response of the additively-manufactured gypsum material used in the experimental tests is modeled via a dilatational plasticity model. The constitutive and fracture model parameters are calibrated using the load–displacement curves corresponding to three-point bending tests initially provided by the Challenge organizers. The test samples contained initial notches especially designed to force specific types of mixed fracture modes. The calibrated computational modeling framework is used to blindly simulate the more complex configuration of the Challenge experiments. The numerical predictions of the load–displacement curve and the shape of the curved fracture surface are compared to the experimental data provided a posteriori. It is found that the computational method is able to quantitatively describe the fracture response of the material including crack propagation, plastic wake, and the curved geometry of the fracture surface that results from the evolving fracture mode mixity with significant fidelity.

A computational framework for probabilistic modeling of galvanic corrosion for automotive applications

Abstract To address the need for reduced vehicle weight and improved environmental sustainability, the automotive industry has increasingly turned to mixing lightweight materials and alloys with metal alloys. However, this integration of dissimilar materials has heightened the risk of galvanic corrosion. This study addresses the gap in modeling of galvanic corrosion under dynamic thin film electrolyte by incorporating data derived from real-world weather conditions and finite element simulations. The presented model successfully captures the trend of galvanic corrosion rate for a given atmospheric environmental condition. The model predictions are compared with experimental data in the literature. Good agreements are observed. The model is further used for prediction of galvanic corrosion of two identical vehicles located in two different geographic locations (i.e., Miami Beach in Florida and Wendover in Nevada) in the year 2021 leveraging weather station data. Additionally, a Bayesian estimation method is used to account for uncertainties in the model parameters and estimation of the probability of failure.

Constructing Deep Concepts through Shallow Search

We propose bootstrap learning as a computational account for why human learning is modular and incremental, and identify key components of bootstrap learning that allow artificial systems to learn more like people. Originated from developmental psychology, bootstrap learning refers to people's ability to extend and repurpose existing knowledge to create new and more powerful ideas. We view bootstrap learning as a solution of how cognitively-bounded reasoners grasp complex environmental dynamics that are far beyond their initial capacity, by searching ‘locally’ and recursively to extend their existing knowledge. Drawing from techniques of Bayesian library learning and resource rational analysis, we propose a computational modeling framework that achieves human-like bootstrap learning performance in inductive conceptual inference. In addition, we demonstrate modeling and behavioral evidence that highlights the double-edged sword of bootstrap learning, such that people processing the same information in different batch orders could induce drastically different causal conclusions and generalizations, as a result of the different sub-concepts they construct in earlier stages of learning.

Peer-mediated Social Signals Alter Risk Tolerance in Teenage Boys Depending on their Peers.

During early adolescence, peer influences play a crucial role in shaping learning and decision preferences. When teens observe what their peers are doing, they can learn and change their behavior, especially when they are taking risks. Our study incorporated an economical behavioral task and computational modeling framework to examine whether and how early male adolescents' risk attitudes change when they see information about their peers' choices. We recruited 38 middle school male students aged 12-15 years. The experiment consisted of three sessions: The first and third sessions were designed to evaluate the risk attitude of the participants. In the second session, participants were asked to guess the choices made by their peers, and then, the computer gave them feedback on the correctness of their predictions. Each participant was randomly assigned to risk-taking or risk-averse peers. Our results revealed that teenagers who predicted risk-averse peers exhibited significant declines in their risk attitudes during the last session. On the other hand, participants with risk-seeking peers exhibited a significantly higher level of risk attitudes after predicting their peers. The data showed that these peer-biased changes in risk attitudes are proportional to the gap between teens and their peers' risk perspectives. Results showed that their perspectives aligned closer after receiving the information, and approximately a third of the gap was eliminated. Here, by combining choice data and computational modeling, we demonstrate that risky behavior is contagious among male adolescents. According to our data, peer-biased risk contagion, a socially motivated and deliberate process, is associated with social distance in teens. There's no causal directionality here, but we could speculate that peer influence goes hand-in-hand with social integration as an adaptive process.

A coupled data-physics computational framework for temperature, residual stress, and distortion modeling in autoclave process of composite materials

It is challenging to obtain the full-field temperature profile during autoclave processes to control the temperature uniformity and minimize the residual stress and distortion of cured composite structures. This paper proposes a coupled data-physics computational framework for full-field temperature reconstruction and the subsequent residual stress/distortion modeling by using limited monitoring temperature data. Firstly, a Long Short-Term Memory (LSTM) model is developed for full field temperature reconstruction. In this LSTM model, a cross-recombination method is proposed to maximize the value of monitored temperature data. The method effectively cuts through the bottleneck of neural network training with limited labeled data. The LSTM model’s prediction stability is enhanced based on the mean-teacher and ensemble learning strategy. To train and validate the proposed method, we perform experiments using an autoclave at the National Institute for Aviation Research (NIAR). The LSTM model’s accuracy is assessed by comparing its predicted results with the thermocouple (TC) data from measurements and high-fidelity simulation data from computational fluid dynamics (CFD). The study shows that the proposed LSTM model can effectively reconstruct the full-field temperature using limited monitoring data and significantly improve accuracy and efficiency compared with the CFD-based counterpart. Then, we create a coupled data-physics computational framework by embedding the data-driven LSTM model into a physics-based thermo-mechanical finite element model to predict residual stress and distortion. The simulation results show that the coupled data-physics framework provides an effective way for process-to-performance modeling and simulation of autoclave curing processes.

Fire and smoke digital twin – A computational framework for modeling fire incident outcomes

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens. CCS conceptsComputer systems organization → Embedded systems; Real- time systems; • Computing methodologies → Modeling and simu- lation; • Applied computing → Physical sciences and engineering.

Influence of Ductility on the Performance of Lunar Habitat Structures Under Recurrent Disturbances

This research examines how ductility affects the durability of lunar surface structures against recurring disturbances like moonquakes, micrometeorite impacts, and thermal cycles over an extended period. The structural performance at various levels of ductility was determined by adjusting material parameters and the thickness of a reference multilayered dome structure. Moonquake and micrometeorite impact-induced lateral displacements were estimated using a reduced-order model under a control-oriented dynamic computational modeling framework. The study considered the degradation of the metallic dome’s strength properties over time due to thermal cycles. Fragility curves were generated by assessing the likelihood of reaching three predefined damage levels as a result of multiple hazards. Additionally, a discounted cash flow analysis was conducted to incorporate a financial aspect into the performance comparison. The findings revealed that structures with sufficient ductility capacity have a lower probability of sustaining severe damage or collapsing within a shorter time frame. Hence, having ductile structures in lunar environments is advantageous as it allows the postponement of maintenance and repair actions, thereby conserving scarce resources for more urgent tasks. Moreover, the financial analysis demonstrated that lunar habitats with higher ductile capacities result in larger net present values, offering a higher return on the initial investment.

A Generalizable Theory-Driven Agent-Based Framework to Study Conflict-Induced Forced Migration

Large-scale population displacements arising from conflict-induced forced migration generate uncertainty and introduce several policy challenges. Addressing these concerns requires an interdisciplinary approach that integrates knowledge from both computational modeling and social sciences. We propose a generalized computational agent-based modeling framework grounded by Theory of Planned Behavior to model conflict-induced migration outflows within Ukraine during the start of that conflict in 2022. Existing migration modeling frameworks that attempt to address policy implications primarily focus on destination while leaving absent a generalized computational framework grounded by social theory focused on the conflict-induced region. We propose an agent-based framework utilizing a spatiotemporal gravity model and a Bi-threshold model over a Graph Dynamical System to update migration status of agents in conflict-induced regions at fine temporal and spatial granularity. This approach significantly outperforms previous work when examining the case of Russian invasion in Ukraine. Policy implications of the proposed framework are demonstrated by modeling the migration behavior of Ukrainian civilians attempting to flee from regions encircled by Russian forces. We also showcase the generalizability of the model by simulating a past conflict in Burundi, an alternative conflict setting. Results demonstrate the utility of the framework for assessing conflict-induced migration in varied settings as well as identifying vulnerable civilian populations.

Reconstructing microvascular network skeletons from 3D images: What is the ground truth?

Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimer’s disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours such as blood flow or transport processes. Extraction of 3D networks from imaging data broadly consists of two image processing steps: segmentation followed by skeletonisation. Much research effort has been devoted to segmentation field, and there are standard and widely-applied methodologies for creating and assessing gold standards or ground truths produced by manual annotation or automated algorithms.The Skeletonisation field, however, lacks widely applied, simple to compute metrics for the validation or optimisation of the numerous algorithms that exist to extract skeletons from binary images. This is particularly problematic as 3D imaging datasets increase in size and visual inspection becomes an insufficient validation approach. In this work, we first demonstrate the extent of the problem by applying 4 widely-used skeletonisation algorithms to 3 different imaging datasets. In doing so we show significant variability between reconstructed skeletons of the same segmented imaging dataset. Moreover, we show that such a structural variability propagates to simulated metrics such as blood flow. To mitigate this variability we introduce a new, fast and easy to compute super metric that compares the volume, connectivity, medialness, bifurcation point identification and homology of the reconstructed skeletons to the original segmented data. We then show that such a metric can be used to select the best performing skeletonisation algorithm for a given dataset, as well as to optimise its parameters. Finally, we demonstrate that the super metric can also be used to quickly identify how a particular skeletonisation algorithm could be improved, becoming a powerful tool in understanding the complex implication of small structural changes in a network.

A rapid and automated computational approach to the design of multistable soft actuators

We develop an automated computational modeling framework for rapid gradient-based design of multistable soft mechanical structures composed of non-identical bistable unit cells with appropriate geometric parameterization. This framework includes a custom isogeometric analysis-based continuum mechanics solver that is robust and end-to-end differentiable, which enables geometric and material optimization to achieve a desired multistability pattern. We apply this numerical modeling approach in two dimensions to design a variety of multistable structures, accounting for various geometric and material constraints. Our framework demonstrates consistent agreement with experimental results, and robust performance in designing for multistability, which facilities soft actuator design with high precision and reliability.

A dislocation density-based meshfree computational framework for solid phase processing

This work proposes a computational modeling framework capable of predicting the behavior of materials when subject to solid phase processing (SPP). A macroscopic dislocation density based constitutive model with internal state variables correlated to microscopic dislocation densities is incorporated into a meshfree smoothed particle hydrodynamics (SPH) framework able to handle large deformations and high temperatures typical of what a material undergoes during SPP. Unlike phenomenological constitutive models that typically neglect material microstructural contributions to macroscale response, the dislocation density based constitutive model improves significantly the predictivity of our modeling framework in a wider range of conditions and allows the multiscale material responses to be simulated simultaneously. With dislocation density based model parameters calibrated to AA1100-O aluminum test data, the generality of the proposed framework is tested on two SPP techniques, namely friction extrusion (FE) and friction stir processing (FSP). Simulation results with different process parameters are systematically validated by experimental data for both cases. The validated models are then used to elucidate the inherent physics associated with SPP by revealing the distributions of temperature, strain, strain rate, and dislocation density, stick-slip contact conditions, and heat generation rates. Model-predicted high dislocation density regions in the processed AA1100-O also agree well with the dynamically recrystallized grain refinement zones observed from experiments.

Modeling clothing as a secondary source of exposure to SVOCs across indoor microenvironments.

Evidence suggests that clothing can influence human exposure to semi-volatile organic compounds (SVOCs) through transdermal uptake and inhalation. The objectives of this study were [1] to investigate the potential for clothing to function as a transport vector and secondary source of gas-phase SVOCs across indoor microenvironments, [2] to elucidate how clothing storage, wear, and laundering can influence the dynamics of transdermal uptake, and [3] to assess the potential for multiple human occupants to influence the multimedia dynamics of SVOCs indoors. A computational modeling framework (ABICAM) was expanded, applied, and evaluated by simulating and augmenting two "real-world" chamber experiments. A primary strength of this framework was its representation of occupants and their clothing as unique entities with the potential for location changes. Estimates of transdermal uptake of diethyl phthalate (DEP) and di(n-butyl) phthalate (DnBP) were generally consistent with those extrapolated from measured concentrations of urinary metabolites, and those predicted by two other mechanistic models. ABICAM predicted that clean clothing (long sleeves, long pants, and socks, 100% cotton, 1 mm thick) readily accumulated DEP (6900-9700 μg) and DnBP (4500-4800 μg) from the surrounding chamber air over 6 h of exposure to average concentrations of 233 (DEP) and 114 (DnBP) μg·m-3. Because of their high capacity, clean clothing also effectively minimized transdermal uptake. In addition, ABICAM predicted that contaminated clothing functioned as a vector for transporting DEP and DnBP across indoor microenvironments and reemitted 13-80% (DEP) and 3-27% (DnBP) of the accumulated masses over 48 h. Though the estimated secondary inhalation exposures from contaminated clothing were low compared to the corresponding primary exposures, these secondary exposures could be accentuated in other contexts, for example, involving longer timeframes of clothing storage, multiple occupants wearing contaminated clothing, and/or repeated instances of clothing-mediated transport of contaminants (e.g., from an occupational setting). This modeling study reaffirms the effectiveness of clean clothing in reducing transdermal uptake of airborne SVOCs and conversely, that contaminated clothing could be a source of SVOC exposure via transdermal uptake and by acting as a vector for transporting those contaminants to other locations.