Secondary indoor air pollution and passive smoking associated with cannabis smoking using electric cigarette device-demonstrative in silico study.

With electronic (e)-liquids containing cannabis components easily available, many anecdotal examples of cannabis vaping using electronic cigarette devices have been reported. For electronic cigarette cannabis vaping, there are potential risks of secondary indoor air pollution from vapers. However, quantitative and accurate prediction of the inhalation and dermal exposure of a passive smoker in the same room is difficult to achieve due to the ethical constraints on subject experiments. The numerical method, i.e., in silico method, is a powerful tool to complement these experiments with real humans. In this study, we adopted a computer-simulated person that has been validated from multiple perspectives for prediction accuracy. We then conducted an in silico study to elucidate secondary indoor air pollution and passive smoking associated with cannabis vaping using an electronic cigarette device in an indoor environment. The aerosols exhaled by a cannabis vaper were confirmed to be a secondary emission source in an indoor environment; non-smokers were exposed to these aerosols via respiratory and dermal pathways. Tetrahydrocannabinol was used as a model chemical compound for the exposure study. Its uptake by the non-smoker through inhalation and dermal exposure under a worst-case scenario was estimated to be 5.9% and 2.6% of the exhaled quantity from an e-cigarette cannabis user, respectively.

Breathing zone and exhaled air re-inhalation rate under transient conditions assessed with a computer-simulated person.

The breathing zone of an individual indoors is usually defined as a finite region steadily formed in front of a face. Assuming the steady formation of the breathing zone, we propose a procedure for quantitatively identifying a breathing zone formed in front of a human face in the transient condition. This assumption is reasonable considering that the ventilation time scale of human respiration is sufficiently short compared to the ventilation time scale of a room. We used steady-state computational fluid dynamics (CFD) and a computationally simulated person (CSP). We present the probabilistic size of the breathing zone for various postures and breathing conditions. By analyzing unsteady inhalation and exhalation airflow characteristics via a CSP with a respiratory system, we also estimated the direct re-inhalation rate of the exhaled air. The results can be used for developing methods to control the long-term and low-contaminant concentration exposures.

Prediction of exhaled carbon dioxide concentration using a computer-simulated person that included alveolar gas exchange.

Accurate prediction of inhaled CO2 concentration and alveolar gas exchange efficiency would improve the prediction of CO2 concentrations around the human body, which is essential for advanced ventilation design in buildings. We therefore, developed a computer-simulated person (CSP) that included a computational fluid dynamics approach. The CSP simulates metabolic heat production at the skin surface and carbon dioxide (CO2 ) gas exchange at the alveoli during the transient breathing cycle. This makes it possible to predict the CO2 distribution around the human body. The numerical model of the CO2 gas exchange mechanism includes both the upper and lower airways and makes it possible to calculate the alveolar CO2 partial pressure; this improves the prediction accuracy. We used the CSP to predict emission rates of metabolically generated CO2 exhaled by a person and assumed that the tidal volume will be unconsciously reduced as a result of exposure to poor indoor air quality. A reduction in tidal volume resulted in a decrease in CO2 emission rates of the same magnitude as was observed in our published experimental data. We also observed that the predicted inhaled CO2 concentration depended on the flow pattern around the human body, as would be expected.

Stochasticity of disease spreading derived from the microscopic simulation approach for various physical contact networks.

COVID-19 has emphasized that a precise prediction of a disease spreading is one of the most pressing and crucial issues from a social standpoint. Although an ordinary differential equation (ODE) approach has been well established, stochastic spreading features might be hard to capture accurately. Perhaps, the most important factors adding such stochasticity are the effect of the underlying networks indicating physical contacts among individuals. The multi-agent simulation (MAS) approach works effectively to quantify the stochasticity. We systematically investigate the stochastic features of epidemic spreading on homogeneous and heterogeneous networks. The study quantitatively elucidates that a strong microscopic locality observed in one- and two-dimensional regular graphs, such as ring and lattice, leads to wide stochastic deviations in the final epidemic size (FES). The ensemble average of FES observed in this case shows substantial discrepancies with the results of ODE based mean-field approach. Unlike the regular graphs, results on heterogeneous networks, such as Erdos-Rényi random or scale-free, show less stochastic variations in FES. Also, the ensemble average of FES in heterogeneous networks seems closer to that of the mean-field result. Although the use of spatial structure is common in epidemic modeling, such fundamental results have not been well-recognized in literature. The stochastic outcomes brought by our MAS approach may lead to some implications when the authority designs social provisions to mitigate a pandemic of un-experienced infectious disease like COVID-19.

The effects of warmth and CO2 concentration, with and without bioeffluents, on the emission of CO2 by occupants and physiological responses.

The emission rate of carbon dioxide (CO2 ) depends on many factors but mainly on the activity level (metabolic rate) of occupants. In this study, we examined two other factors that may influence the CO2 emission rate, namely the background CO2 concentration and the indoor temperature. Six male volunteers sat one by one in a 1.7 m3 chamber for 2.5 h and performed light office-type work under five different conditions with two temperature levels (23 vs. 28°C) and three background concentrations of CO2 (800 vs. 1400 vs. 3000 ppm). Background CO2 levels were increased either by dosing CO2 from a cylinder or by reducing the outdoor air supply rate. Physiological responses to warmth, added CO2 , and bioeffluents were monitored. The rate of CO2 emission was estimated using a mass-balance equation. The results indicate a higher CO2 emission rate at the higher temperature, at which the subjects were warm, and a lower emission rate in all conditions in which the background CO2 concentration increased. Physiological measurements partially explained the present results but more measurements are needed.

Emission rate of carbon dioxide while sleeping.

Humans emit carbon dioxide (CO2 ) as a product of their metabolism. Its concentration in buildings is used as a marker of ventilation rate (VR) and degree of mixing of supply air, and indoor air quality (IAQ). The CO2 emission rate (CER) may be used to estimate the ventilation rate. Many studies have measured CERs from subjects who were awake but little data are available from sleeping subjects and the present publication was intended to reduce this gap in knowledge. Seven females (29 ± 5 years old; BMI: 22.2 ± 0.8 kg/m2 ) and four males (27 ± 1 years old; BMI: 20.5 ± 1.5 kg/m2 ) slept for four consecutive nights in a specially constructed capsule at two temperatures (24 and 28°C) and two VRs that maintained CO2  levels at ca. 800 ppm and 1700 ppm simulating sleeping conditions reported in the literature. The order of exposure was balanced, and the first night was for adaptation. Their physiological responses, including heart rate, pNN50, core body temperature, and skin temperature, were measured as well as sleep quality, and subjective responses were collected each evening and morning. Measured steady-state CO2 concentrations during sleep were used to estimate CERs with a mass-balance equation. The average CER was 11.0 ± 1.4 L/h per person and was 8% higher for males than for females (P < 0.05). Increasing the temperature or decreasing IAQ by decreasing VR had no effects on measured CERs and caused no observable differences in physiological responses. We also calculated CERs for sleeping subjects using the published data on sleep energy expenditure (SEE) and Respiratory Quotient (RQ), and our measured CERs confirmed both these calculations and the CERs predicted using the equations provided by ASHRAE Standard 62.1, ASHRAE Handbook, and ASTM D6245-18. The present results provide a valuable and helpful reference for the design and control of bedroom ventilation but require confirmation and extension to other age groups and populations.

A numerical investigation of the potential effects of e-cigarette smoking on local tissue dosimetry and the deterioration of indoor air quality.

Electronic (e)-cigarette smoking is considered to be less harmful than traditional tobacco smoking because of the lack of a combustion process. However, e-cigarettes have the potential to release harmful chemicals depending on the constituents of the vapor. To date, there has been significant evidence on the adverse health effects of e-cigarette usage. However, what is less known are the impacts of the chemicals contained in exhaled air from an e-cigarette smoker on indoor air quality, the second-hand passive smoking of residents, and the toxicity of the exhaled air. In this study, we develop a comprehensive numerical model and computer-simulated person to investigate the potential effects of e-cigarette smoking on local tissue dosimetry and the deterioration of indoor air quality. We also conducted demonstrative numerical analyses for first-hand and second-hand e-cigarette smoking in an indoor environment. To investigate local tissue dosimetry, we used newly developed physiologically based pharmacokinetic/toxicokinetic models that reproduce inhalation exposure by way of the respiratory tract and dermal exposure through the human skin surface. These models were integrated into the computer-simulated person. Our numerical simulation results quantitatively demonstrated the potential impacts of e-cigarette smoking in enclosed spaces on indoor air quality.

To vaccinate or not to vaccinate: A comprehensive study of vaccination-subsidizing policies with multi-agent simulations and mean-field modeling.

We combined the elements of evolutionary game theory and mathematical epidemiology to comprehensively evaluate the performance of vaccination-subsidizing policies in the face of a seasonal epidemic. We conducted multi-agent simulations to, among others, find out how the topology of the underlying social networks affects the results. We also devised a mean-field approximation to confirm the simulation results and to better understand the influences of an imperfect vaccine. The main measure of a subsidy' performance was the total social payoff as a sum of vaccination costs, infection costs, and tax burdens due to the subsidy. We find two types of situations in which vaccination-subsidizing policies act counterproductively. The first type arises when the subsidy attempts to increase vaccination among past non-vaccinators, which inadvertently creates a negative incentive for voluntary vaccinators to abstain from vaccination in hope of getting subsidized. The second type is a consequence of overspending at which point the marginal cost of further increasing vaccination coverage is higher than the corresponding marginal cost of infections avoided by this increased coverage. The topology of the underlying social networks considerably worsens the subsidy's performance if connections become random and heterogeneous, as is often the case in human social networks. An imperfect vaccine also worsens the subsidy's performance, thus narrowing or completely closing the window for vaccination-subsidizing policies to beat the no-subsidy policy. These results imply that subsidies should be aimed at voluntary vaccinators while avoiding overspending. Once this is achieved, it makes little difference whether the subsidy fully or partly offsets the vaccination cost.

In silico identification of viral loads in cough-generated droplets - Seamless integrated analysis of CFPD-HCD-EWF.

BACKGROUND AND OBJECTIVE: Respiratory diseases caused by respiratory viruses have significantly threatened public health worldwide. This study presents a comprehensive approach to predict viral dynamics and the generation of stripped droplets within the mucus layer of the respiratory tract during coughing using a larynx-trachea-bifurcation (LTB) model. METHODS: This study integrates computational fluid-particle dynamics (CFPD), host-cell dynamics (HCD), and the Eulerian wall film (EWF) model to propose a potential means for seamless integrated analysis. The verified CFPD-HCD coupling model based on a 3D-shell model was used to characterize the severe acute respiratory syndrome, coronavirus 2 (SARS-CoV-2) dynamics in the LTB mucus layer, whereas the EWF model was employed to account for the interfacial fluid to explore the generation mechanism and trace the origin site of droplets exhaled during a coughing event of an infected host. RESULTS: The results obtained using CFPD delineated the preferential deposition sites for droplets in the laryngeal and tracheal regions. Thus, the analysis of the HCD model showed that the viral load increased rapidly in the laryngeal region during the peak of infection, whereas there was a growth delay in the tracheal region (up to day 8 after infection). After two weeks of infection, the high viral load gradually migrated towards the glottic region. Interestingly, the EWF model demonstrated a high concentration of exhaled droplets originating from the larynx. The coupling technique indicated a concurrent high viral load in the mucus layer and site of origin of the exhaled droplets. CONCLUSIONS: This interdisciplinary research underscores the seamless analysis from initial exposure to virus-laden droplets, the dynamics of viral infection in the LTB mucus layer, and the re-emission from the coughing activities of an infected host. Our efforts aimed to address the complex challenges at the intersection of viral dynamics and respiratory health, which can contribute to a more detailed understanding and targeted prevention of respiratory diseases.

Visual prediction and parameter optimization of viral dynamics in the mucus milieu of the upper airway based on CFPD-HCD analysis.

BACKGROUND AND OBJECTIVE: Respiratory diseases caused by viruses are a major human health problem. To better control the infection and understand the pathogenesis of these diseases, this paper studied SARS-CoV-2, a novel coronavirus outbreak, as an example. METHODS: Based on coupled computational fluid and particle dynamics (CFPD) and host-cell dynamics (HCD) analyses, we studied the viral dynamics in the mucus layer of the human nasal cavity-nasopharynx. To reproduce the effect of mucociliary movement on the diffusive and convective transport of viruses in the mucus layer, a 3D-shell model was constructed using CT data of the upper respiratory tract (URT) of volunteers. Considering the mucus environment, the HCD model was established by coupling the target cell-limited model with the convection-diffusion term. Parameter optimization of the HCD model is the key problem in the simulation. Therefore, this study focused on the parameter optimization of the viral dynamics model, divided the geometric model into multiple compartments, and used Monolix to perform the nonlinear mixed effects (NLME) of pharmacometrics to discuss the influence of factors such as the number of mucus layers, number of compartments, diffusion rate, and mucus flow velocity on the prediction results. RESULTS: The findings showed that sufficient experimental data can be used to estimate the corresponding parameters of the HCD model. The optimized convection-diffusion case with a two-layer multi-compartment low-velocity model could accurately predict the viral dynamics. CONCLUSIONS: Its visualization process could explain the symptoms of the disease in the nose and contribute to the prevention and targeted treatment of respiratory diseases.

Effect of transient breathing cycle on the deposition of micro and nanoparticles on respiratory walls.

BACKGROUND AND OBJECTIVE: From various perspectives (e.g. inhalation exposure and drug delivery), it is important to provide insights into the behavior of inhaled particles in the human respiratory system. Although most of the experimental and numerical studies have relied on an assumption of steady inhalation, the transient breathing profile is a key factor in particle deposition in the respiratory tract. In this study, particle transportation and deposition were predicted in a realistic human airway model during a breathing cycle and the effects of steady-state and transient flows on the deposition fraction were observed using computational fluid dynamics. METHODS: Two transient breathing cycles with different respiratory durations were considered to evaluate the effects of respiration duration on particle transport and deposition characteristics. Two types of steady breathing conditions with corresponding steady-state respiratory volumes were reproduced. The Lagrangian discrete phase model approach was used to investigate particle transportation and deposition under transient breathing conditions. Additionally, the Eulerian approach was used to analyze the transport of nanoparticles in the gas phase. A total of >50,000 monodispersed particles with aerodynamic diameters ranging between 2 nm and 10 µm were selected for comprehensive deposition predictions for particle sizes ranging from the nano- to microscale. RESULTS: The predicted results were compared with the experimental data. The particle deposition fraction in the nasal cavity and tracheal regions showed differences between the steady and transient simulations. In addition, particle analysis under steady inhalation conditions cannot accurately predict particle transportation and deposition in the lower airway. Furthermore, the breathing cycle had a significant effect on the deposition fraction of the particles and the behavior of the inhaled particles. CONCLUSIONS: Transient simulation mimicking the breathing cycle was observed to be an important factor in accurately predicting the transportation and deposition of particles in the respiratory tract.

An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic.

The COVID-19 pandemic has remarkably heightened concerns regarding the prediction of communicable disease spread. This study introduces an innovative agent-based modeling approach. In this model, the quantification of human-to-human transmission aligns with the dynamic variations in the viral load within an individual, termed "within-host" and adheres to the susceptible-infected-recovered (SIR) process, referred to as "between-host." Variations in the viral load over time affect the infectivity between individual agents. This model diverges from the traditional SIR model, which employs a constant transmission probability, by incorporating a dynamic, time-dependent transmission probability influenced by the viral load in a host agent. The proposed model retains the time-integrated transmission probability characteristic of the conventional SIR model. As observed in this model, the overall epidemic size remains consistent with the predictions of the standard SIR model. Nonetheless, compared to predictions based on the classical SIR process, notable differences existed in the peak number of the infected individuals and the timing of this peak. These nontrivial differences are induced by the direct correlation between the time-evolving transmission probability and the viral load within a host agent. The developed model can inform targeted intervention strategies and public health policies by providing detailed insights into disease spread dynamics, crucial for effectively managing epidemics.

Computational fluid-particle dynamics modeling of ultrafine to coarse particles deposition in the human respiratory system, down to the terminal bronchiole.

BACKGROUND AND OBJECTIVES: Suspended respirable airborne particles are associated with human health risks and especially particles within the range of ultrafine (< 0.1 µm) or fine (< 2.5 µm) have a high possibility of penetrating the lung region, which is concerned to be closely related to the bronchial or alveoli tissue dosimetry. Nature complex structure of the respiratory system requires much effort to explore and comprehend the flow and the inhaled particle dynamics for precise health risk assessment. Therefore, this study applied the computational fluid-particle dynamics (CFPD) method to elucidate the deposition characteristics of ultrafine-to-coarse particles in the human respiratory tract from nostrils to the 16th generation of terminal bronchi. METHODS: The realistic bronchi up to the 8th generation are precisely and perfectly generated from computed tomography (CT) images, and an artificial model compensates for the 9th-16th bronchioles. Herein, the steady airflow is simulated at constant breathing flow rates of 7.5, 15, and 30 L/min, reproducing human resting-intense activity. Then, trajectories of the particle size ranging from 0.002 - 10 µm are tracked using a discrete phase model. RESULTS: Here, we report reliable results of airflow patterns and particle deposition efficiency in the human respiratory system validated against experimental data. The individual-related focal point of ultrafine and fine particles deposition rates was actualized at the 8th generation; whilst the hot-spot of the deposited coarse particles was found in the 6th generation. Lobar deposition characterizes the dominance of coarse particles deposited in the right lower lobe, whereas the left upper-lower and right lower lobes simultaneously occupy high deposition rates for ultrafine particles. Finally, the results indicate a higher deposition in the right lung compared to its counterpart. CONCLUSIONS: From the results, the developed realistic human respiratory system down to the terminal bronchiole in this study, in coupling with the CFPD method, delivers the accurate prediction of a wide range of particles in terms of particle dosimetry and visualization of site-specific in the consecutive respiratory system. In addition, the series of CFPD analyses and their results are to offer in-depth information on particle behavior in human bronchioles, which may benefit health risk assessment or drug delivery studies.

Effects of void nodes on epidemic spreads in networks.

We present the pair approximation models for susceptible-infected-recovered (SIR) epidemic dynamics in a sparse network based on a regular network. Two processes are considered, namely, a Markovian process with a constant recovery rate and a non-Markovian process with a fixed recovery time. We derive the implicit analytical expression for the final epidemic size and explicitly show the epidemic threshold in both Markovian and non-Markovian processes. As the connection rate decreases from the original network connection, the epidemic threshold in which epidemic phase transits from disease-free to endemic increases, and the final epidemic size decreases. Additionally, for comparison with sparse and heterogeneous networks, the pair approximation models were applied to a heterogeneous network with a degree distribution. The obtained phase diagram reveals that, upon increasing the degree of the original random regular networks and decreasing the effective connections by introducing void nodes accordingly, the final epidemic size of the sparse network is close to that of the random network with average degree of 4. Thus, introducing the void nodes in the network leads to more heterogeneous network and reduces the final epidemic size.

Pair approximation model for the vaccination game: predicting the dynamic process of epidemic spread and individual actions against contagion.

We successfully establish a theoretical framework of pairwise approximation for the vaccination game in which both the dynamic process of epidemic spread and individual actions in helping prevent social behaviours are quantitatively evaluated. In contrast with mean-field approximation, our model captures higher-order effects from neighbours by using an underlying network that shows how the disease spreads and how individual decisions evolve over time. This model considers not only imperfect vaccination but also intermediate protective measures other than vaccines. Our analytical predictions are validated by multi-agent simulation results that estimate random regular graphs at varying degrees.

Can the inhalation exposure of a specific worker in a cross-ventilated factory be evaluated by time- and spatial-averaged contaminant concentration?

Industry implies economic growth; however, outdoor and indoor air pollution generated by industrial activities represents a widespread problem for the environment and human beings. In terms of human health, indoor air quality assessment has become essential in a society where people spend most of their time in indoor dwellings, as in the case of industry workers. Because indoor air quality is strongly affected by the outdoor environment, especially under natural ventilation conditions (e.g., cross-ventilation), a comprehensive analysis that includes outdoor atmospheric-urban environment is needed to reproduce realistic scenarios. In this context, computational fluid dynamics (CFD) is a useful tool. To perform a precise analysis of the inhalation exposure of factory workers to potential gas-phase contaminants in the working environment (i.e., inhaled dose of contaminants and potential effects), the human body and respiratory tract need to be integrated in the analysis. Therefore, in this study, we performed an integrated occupational inhalation exposure/toxicology assessment in a factory building that applies a computer simulated person (CSP), a virtual human respiratory tract and integrated physiologically-based toxicokinetic (PBTK) model to predict tissue dosimetry distribution. Outdoor airflow variation was transported into the enclosure through an hourly change in wind pressure coefficient to calculate transient ventilation rate and indoor contaminant concentration between 08:00 and 17:00 h. Thereafter, the time-averaged contaminant concentration calculated at the nares of the human body was employed in a steady state calculation of airflow and contaminant distribution inside the virtual respiratory tract. Subsequently, we predicted adsorbed contaminant in the first layer of tissue of the human airways; highest adsorption took place in the nasal cavity. Finally, based on the results of the comprehensive coupled numerical analysis performed using the CFD-CSP-PBTK model, we quantitatively discussed differences between the inhalation exposure concentration and representative contaminant concentration in the factory space (e.g., time and volume-averaged concentration).