Identification of contaminant sources in enclosed environments by inverse CFD modeling

3D multi-robot olfaction in naturally ventilated indoor environments: Locating a time-varying source at unknown heights

Source localization is significant for mitigating indoor air pollution and safeguarding the well-being and safety of occupants. While most study focuses on mechanical ventilation and static sources, this study explores the less-explored domain of locating time-varying sources in naturally ventilated spaces. We have developed an innovative 3D localization system that adjusts to varying heights, significantly enhancing capabilities beyond traditional fixed-height 2D systems. To ensure consistency in experimental conditions, we conducted comparative analyses of 2D and 3D methods, using a swinging fan to simulate natural ventilation. Our findings reveal a substantial disparity in performance: the 2D method had a success rate below 46.7% in cases of height mismatches, while our 3D methods consistently achieved success rates above 66.7%, demonstrating their superior effectiveness in complex environments. Furthermore, we validated the 3D strategies in real naturally ventilated settings, confirming their wider applicability. This research extends the scope of indoor source localization and offers valuable insights and strategies for more effective pollution control.

Source identification in river pollution incidents using a cellular automata model and Bayesian Markov chain Monte Carlo method

Identification of contaminant sources in rivers is crucial for river protection and emergency response. This study presents an innovative approach for identifying river pollution sources by using Bayesian inference and cellular automata (CA) modeling. A general Bayesian framework is proposed that combines the CA model with observed data to identify unknown sources of river pollution. To reduce the computational burden of the Bayesian inference, a CA contaminant transport model is developed to efficiently simulate pollutant concentration values in the river. These simulated concentration values are then used to calculate the likelihood function of available measurements. The Markov chain Monte Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters, which is a sampling-based method that enables the estimation of complex posterior distributions. The suggested methodology is applied to a real case study of the Fen River in Yuncheng City, Shanxi Province, Northern China, and it estimates the release time, release mass, and source location with relative errors below 19%. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.

Identifying spatiotemporal information of the point pollutant source indoors based on the adjoint-regularization method

Fast and accurate identification of the pollutant source location and release rate is important for improving indoor air quality. From the perspective of public health, identification of the airborne pathogen source in public buildings is particularly important for ensuring people's safety and health. The existing adjoint probability method has difficulty in distinguishing the temporal source, and the optimization algorithm can only analyze a few potential sources in space. This study proposed an algorithm combining the adjoint-pulse and regularization methods to identify the spatiotemporal information of the point pollutant source in an entire room space. We first obtained a series of source-receptor response matrices using the adjoint-pulse method in the room based on the validated CFD model, and then used the regularization method and composite Bayesian inference to identify the release rate and location of the dynamic pollutant source. The results showed that the MAPEs (mean absolute percentage errors) of estimated source intensities were almost less than 15%, and the source localization success rates were above 25/30 in this study. This method has the potential to be used to identify the airborne pathogen source in public buildings combined with sensors for disease-specific biomarkers.

Airborne transmission of COVID-19 virus in enclosed spaces: An overview of research methods

Since the outbreak of COVID-19 in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has spread worldwide. This study summarized the transmission mechanisms of COVID-19 and their main influencing factors, such as airflow patterns, air temperature, relative humidity, and social distancing. The transmission characteristics in existing cases are providing more and more evidence that SARS CoV-2 can be transmitted through the air. This investigation reviewed probabilistic and deterministic research methods, such as the Wells-Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method, and the experimental approach, that have been used for studying the airborne transmission mechanism. The Wells-Riley equation and dose-response model are typically used for the assessment of the average infection risk. Only in combination with the Eulerian method or the Lagrangian method can these two methods obtain the spatial distribution of airborne particles' concentration and infection risk. In contrast with the Eulerian and Lagrangian methods, the Monte-Carlo model is suitable for studying the infection risk when the behavior of individuals is highly random. Although researchers tend to use numerical methods to study the airborne transmission mechanism of COVID-19, an experimental approach could often provide stronger evidence to prove the possibility of airborne transmission than a simple numerical model. All in all, the reviewed methods are helpful in the study of the airborne transmission mechanism of COVID-19 and epidemic prevention and control.

Low-Cost Air Quality Sensing towards Smart Homes

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

The significance of bioengineered nanoplatforms against SARS-CoV-2: From detection to genome editing

COVID-19 outbreak can impose serious negative impacts on the infrastructures of societies including the healthcare systems. Despite the increasing research efforts, false positive or negative results that may be associated with serologic or even RT-PCR tests, inappropriate or variable immune response, and high rates of mutations in coronavirus may negatively affect virus detection process and effectiveness of the vaccines or drugs in development. Nanotechnology-based research attempts via developing state-of-the-art techniques such as nanomechatronics ones and advanced materials including the sensors for detecting the pathogen loads at very low concentrations or site-specific delivery of therapeutics, and real-time protections against the pandemic outbreaks by nanorobots can provide outstanding biomedical breakthroughs. Considering the unique characteristics of pathogens particularly the newly-emerged ones and avoiding the exaggerated optimism or simplistic views on the prophylactic and therapeutic approaches including the one-size-fits-all ones or presenting multiple medications that may be associated with synergistic toxicities rather than enhanced efficiencies might pave the way towards the development of more appropriate treatment strategies with reduced safety concerns. This paper highlights the significance of nanoplatforms against the viral disorders and their capabilities of genome editing that may facilitate taking more appropriate measures against SARS-CoV-2.

Nanotheranostics against COVID-19: From multivalent to immune-targeted materials

Destructive impacts of COVID-19 pandemic worldwide necessitates taking more appropriate measures for mitigating virus spread and development of the effective theranostic agents. In general, high heterogeneity of viruses is a major challenging issue towards the development of effective antiviral agents. Regarding the coronavirus, its high mutation rates can negatively affect virus detection process or the efficiency of drugs and vaccines in development or induce drug resistance. Bioengineered nanomaterials with suitable physicochemical characteristics for site-specific therapeutic delivery, highly-sensitive nanobiosensors for detection of very low virus concentration, and real-time protections using the nanorobots can provide roadmaps towards the imminent breakthroughs in theranostics of a variety of diseases including the COVID-19. Besides revolutionizing the classical disinfection procedures, state-of-the-art nanotechnology-based approaches enable providing the analytical tools for accelerated monitoring of coronavirus and associated biomarkers or drug delivery towards the pulmonary system or other affected organs. Multivalent nanomaterials capable of interaction with multivalent pathogens including the viruses could be suitable candidates for viral detection and prevention of further infections. Besides the inactivation or destruction of the virus, functionalized nanoparticles capable of modulating patient's immune response might be of great significance for attenuating the exaggerated inflammatory reactions or development of the effective nanovaccines and medications against the virus pandemics including the COVID-19.

The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control

Transmission mechanics of infectious pathogen in various environments are of great complexity and has always been attracting many researchers' attention. As a cost-effective and powerful method, Computational Fluid Dynamics (CFD) plays an important role in numerically solving environmental fluid mechanics. Besides, with the development of computer science, an increasing number of researchers start to analyze pathogen transmission by using CFD methods. Inspired by the impact of COVID-19, this review summarizes research works of pathogen transmission based on CFD methods with different models and algorithms. Defining the pathogen as the particle or gaseous in CFD simulation is a common method and epidemic models are used in some investigations to rise the authenticity of calculation. Although it is not so difficult to describe the physical characteristics of pathogens, how to describe the biological characteristics of it is still a big challenge in the CFD simulation. A series of investigations which analyzed pathogen transmission in different environments (hospital, teaching building, etc) demonstrated the effect of airflow on pathogen transmission and emphasized the importance of reasonable ventilation. Finally, this review presented three advanced methods: LBM method, Porous Media method, and Web-based forecasting method. Although CFD methods mentioned in this review may not alleviate the current pandemic situation, it helps researchers realize the transmission mechanisms of pathogens like viruses and bacteria and provides guidelines for reducing infection risk in epidemic or pandemic situations.

Identification of clandestine groundwater pollution sources using heuristics optimization algorithms: a comparison between simulated annealing and particle swarm optimization

Groundwater pollution is the biggest threat to sustainability of groundwater resources and even more difficult to detect in case of clandestine sources. At the time when pollution is first detected in randomly located sparse wells, very little is known about the pollution sources. Finding the precise locations of clandestine sources of pollution and their release flux history is the biggest challenge and often termed as a problem belonging to the class of environmental forensics. In this study, two linked simulation optimization-based novel techniques are developed to estimate locations and release flux history from clandestine point sources of groundwater pollution. Simulation model is clubbed with optimization solver to determine the locations and release flux histories of groundwater pollution sources by minimizing the residual error between observed and simulated concentration values. Simulated annealing (SA) and particle swarm optimization (PSO) are used as optimization algorithms. A detailed comparative analysis of these two meta-heuristic optimization algorithms in minimizing the residual error is presented in this study. The performance evaluation of both the algorithms in identifying the sources locations and release flux history is carried out for two synthetic cases and a real-life scenario of groundwater pollution in an aquifer in New South Wales, Australia, which has not been attempted in the past. The results of source location identification and release flux history show the selective applicability of each algorithm in solving real-life scenarios of groundwater pollution.

Can Nanotechnology and Materials Science Help the Fight against SARS-CoV-2?

Since 2004, we have been developing nanomaterials with antimicrobial properties, the so-called nanoantimicrobials. When the coronavirus disease 2019 (COVID-19) emerged, we started investigating new and challenging routes to nanoantivirals. The two fields have some important points of contact. We would like to share with the readership our vision of the role a (nano)materials scientist can play in the fight against the COVID-19 pandemic. As researchers specifically working on surfaces and nanomaterials, in this letter we underline the importance of nanomaterial-based technological solutions in several aspects of the fight against the virus. While great resources are understandably being dedicated to treatment and diagnosis, more efforts could be dedicated to limit the virus spread. Increasing the efficacy of personal protection equipment, developing synergistic antiviral coatings, are only two of the cases discussed. This is not the first nor the last pandemic: our nanomaterials community may offer several technological solutions to challenge the ongoing and future global health emergencies. Readers' feedback and suggestions are warmly encouraged.

Experimental study on a comprehensive particle swarm optimization method for locating contaminant sources in dynamic indoor environments with mechanical ventilation

Source localization is critical to ensuring indoor air quality and environmental safety. Although considerable research has been conducted on source localization in steady-state indoor environments, very few studies have dealt with the more challenging source localization problems in dynamic indoor environments. This paper presents a comprehensive particle swarm optimization (CPSO) method to locate a contaminant source in dynamic indoor environments with mechanical ventilation and develops a multi-robot source localization system to experimentally validate the method. Three robots were used to test the presented method in a typical dynamic indoor environment with periodic swinging of the air supply louvers of a cabinet air conditioner. The presented method was validated with two typical source locations, DS (in the downwind zone) and RS (in the recirculation zone). For DS and RS, 15 and 14 experiments out of 15 experiments were successful, with success rates of 100% and 93.3%, and each robot moved an average of 24.4 and 23.6 steps, respectively. The presented method was also compared with the standard particle swarm optimization (SPSO) and wind utilization II (WUII) methods for locating the source at DS. For the SPSO and WUII methods, only 3 and 6 experiments out of 15 experiments were successful, with success rates of 20% and 40% and averages of 33.0 and 38.0 steps, respectively. The experimental results show that the presented method not only has a much higher success rate than the SPSO and WUII methods but also has higher source localization efficiency.

An improved particle swarm optimization method for locating time-varying indoor particle sources

The indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.

A Risk Assessment Approach for Evaluating the Impact of Toxic Contaminants Released Indoors by Considering Various Emergency Ventilation and Evacuation Strategies

The release of toxic airborne contaminants resulting from terrorist attacks on buildings can lead to disastrous consequences. To evaluate and reduce the effects of these emergencies, various methods and models have been developed in the past few years. Such work has provided effective tools for the building management system to do risk assessment of the contaminated areas. Although risk analysis methods to describe the contaminant dispersion scenarios made significant progress, these approaches did not generally consider the releasing scenario occurring in the ventilation system and the effect of human behavior during the developing process of an emergency event. Emergency strategies chosen by the decisionmaker are not always associated with the early-warning system, such as the sensor monitoring network and the source identification system inside the building. This study aims to provide a risk assessment model considering both the variation of contaminant concentration and occupant distribution after the release of toxic agents to obtain the exposure risk for people indoors. The contaminant dispersion is simulated using computational fluid dynamics. The evacuation process for people is modeled using Pathfinder, and the exposure risk for occupants under various emergency strategies is calculated using the efficiency factor of the contaminant source. The results of the exposure risk for 40 basic cases are discussed, and the optimal ventilation mode for these specific cases is recommended. Next, the impact of the variation of human behavior, contaminant detection time needed by sensors, and source identification time needed by inverse modeling on the exposure risk for people indoor is studied. The uncertainty and reproducibility of the numerical simulations are emphatically discussed in the Supporting Information.

A methodology for optimal placement of sensors in enclosed environments: A dynamical systems approach

Air quality has been an important issue in public health for many years. Sensing the level and distributions of impurities help in the control of building systems and mitigate long term health risks. Rapid detection of infectious diseases in large public areas like airports and train stations may help limit exposure and aid in reducing the spread of the disease. Complete coverage by sensors to account for any release scenario of chemical or biological warfare agents may provide the opportunity to develop isolation and evacuation plans that mitigate the impact of the attack. All these scenarios involve strategic placement of sensors to promptly detect and rapidly respond. This paper presents a data driven sensor placement algorithm based on a dynamical systems approach. The approach utilizes the finite dimensional Perron-Frobenius (PF) concept. The PF operator (or the Markov matrix) is used to construct an observability gramian that naturally incorporates sensor accuracy, location constraints, and sensing constraints. The algorithm determines the response times, sensor coverage maps, and the number of sensors needed. The utility of the procedure is illustrated using four examples: a literature example of the flow field inside an aircraft cabin and three air flow fields in different geometries. The effect of the constraints on the response times for different sensor placement scenarios is investigated. Knowledge of the response time and coverage of the multiple sensors aides in the design of mechanical systems and response mechanisms. The methodology provides a simple process for place sensors in a building, analyze the sensor coverage maps and response time necessary during extreme events, as well as evaluate indoor air quality. The theory established in this paper also allows for future work in topics related to construction of classical estimator problems for the sensors, real-time contaminant transport, and development of agent dispersion, contaminant isolation/removal, and evacuation strategies.

Constructing Markov matrices for real-time transient contaminant transport analysis for indoor environments

Predicting the movement of contaminants in the indoor environment has applications in tracking airborne infectious disease, ventilation of gaseous contaminants, and the isolation of spaces during biological attacks. Markov matrices provide a convenient way to perform contaminant transport analysis. However, no standardized method exists for calculating these matrices. A methodology based on set theory is developed for calculating contaminant transport in real-time utilizing Markov matrices from CFD flow data (or discrete flow field data). The methodology provides a rigorous yet simple strategy for determining the number and size of the Markov states, the time step associated with the Markov matrix, and calculation of individual entries of the Markov matrix. The procedure is benchmarked against scalar transport of validated airflow fields in enclosed and ventilated spaces. The approach can be applied to any general airflow field, and is shown to calculate contaminant transport over 3000 times faster than solving the corresponding scalar transport partial differential equation. This near real-time methodology allows for the development of more robust sensing and control procedures of critical care environments (clean rooms and hospital wards), small enclosed spaces (like airplane cabins) and high traffic public areas (train stations and airports).

State-of-the-art methods for inverse design of an enclosed environment

The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. This paper discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, pseudo-reversibility method, and regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the CFD-based adjoint method, CFD-based genetic algorithm method, and proper orthogonal decomposition method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The CFD-based adjoint method is accurate and can handle many design parameters without increasing computing costs, but the method may find a locally optimal design that could meet the design objective with constrains. The CFD-based genetic algorithm method, on the other hand, can provide the global optimal design that can meet the design objective without constraints, but the computing cost can increase dramatically with the number of design parameters. The proper orthogonal decomposition method is a reduced-order method that can significantly lower computing costs, but at the expense of reduced accuracy. This paper also discusses the possibility to reduce the computing costs of CFD-based design methods.

Inverse identification of the release location, temporal rates, and sensor alarming time of an airborne pollutant source

With an accidental release of an airborne pollutant, it is always critical to know where, when, and how the pollutant has been released. Then, emergency measures can be scientifically advised to prevent any possible harm. This investigation proposes an inverse model to identify the release location, the temporal rate profile, and the sensor alarming time from the start of a pollutant release. The first step is to implement the inverse operation to the cause-effect matrix to obtain the release rate profiles for discrete candidate scenarios with concentration information provided by one sensor. The second step is to interpret the occurrence probability of each solution in the first step with the Bayesian model by matching the concentration at the other sensor. The proposed model was applied to identify a single pollutant source in a two-dimensional enclosure using measurement data and in a three-dimensional aircraft cabin with simulated data. The results show that the model is able to correctly determine the pollutant source location, the temporal rate profile, and the sensor alarming time. The known conditions for input into the inverse model include a steady flow field and the valid temporal concentrations at two different locations.

PRACTICAL IMPLICATIONS

The proposed inverse model can tell where, when, and how a gaseous pollutant has been accidently released based on the monitoring concentrations measured by two sensors. This methodology can be useful for providing emergency protection to indoor occupants.

Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time

The sudden release of airborne hazardous contaminants in an indoor environment can potentially lead to severe disasters, such as the spread of toxic gases, fire, and explosion. To prevent and mitigate these disasters it is critical to rapidly and accurately identify the characteristics of the contaminant sources. Although remarkable achievements have been made in identifying a single indoor contaminant source in recent years, the issues related to multiple contaminant sources are still challenging. This study presents a method for identifying the exact locations, emission rates, and release time of multiple indoor contaminant sources simultaneously released at constant rates, by considering sensor thresholds and measurement errors. The method uses a two-stage procedure for rapid source identification. Before the release of contaminants, only a limited number of time-consuming computational fluid dynamics (CFD) simulations need to be conducted. After the release of contaminants, the method can be executed in real-time. Through case studies in a three-dimensional office the method was numerically demonstrated and validated, and the results show that the method is effective and feasible. The effects of sensor threshold, measurement error and total sampling time on the source identification performance were analysed, and the limitations and applicability of the method were also discussed.

Identifiability and identification of trace continuous pollutant source

Accidental pollution events often threaten people's health and lives, and a pollutant source is very necessary so that prompt remedial actions can be taken. In this paper, a trace continuous pollutant source identification method is developed to identify a sudden continuous emission pollutant source in an enclosed space. The location probability model is set up firstly, and then the identification method is realized by searching a global optimal objective value of the location probability. In order to discuss the identifiability performance of the presented method, a conception of a synergy degree of velocity fields is presented in order to quantitatively analyze the impact of velocity field on the identification performance. Based on this conception, some simulation cases were conducted. The application conditions of this method are obtained according to the simulation studies. In order to verify the presented method, we designed an experiment and identified an unknown source appearing in the experimental space. The result showed that the method can identify a sudden trace continuous source when the studied situation satisfies the application conditions.

Modeling methods for identifying critical source areas of bacteria: recent developments and future perspectives

Identification of critical source areas of bacteria in a watershed is essential to environmental management and restoration. As a result of the nonpoint and distributed nature of bacterial pollution in watersheds, it is often difficult to identify specific source areas of bacteria for remediation because bacteria collected from different sampling sites might display similar fingerprints. Over the past decade, extensive efforts have been made to identify microbial pollution sources, especially in watersheds. The primary objective of this study was to identify effective methods that can be applied to tracking critical source areas of bacteria in a watershed by a review of recent developments in several modeling methods. Comparisons of the models and their applications revealed that comprehensive watershed-scale source area tracking primarily involves two steps-geographical tracking and mathematical tracking. In terms of geographical tracking, bacterial source locations must be identified to prepare structural best management practices or low impact development for site treatments. For mathematical tracking, the quantity (strength) or release history of bacterial sources must be computed to develop total maximum daily loads (TMDLs) for bacterial load reduction and water quality restoration. Mathematically, source tracking is essentially an inverse modeling issue under uncertainty, requiring inverse modeling combined with a geostatistical method or an optimization algorithm. Consequently, combining biological methods, mathematical models, and sensor technologies (including remote sensing and in-situ sensing) provides an effective approach to identifying critical source locations of bacteria at the watershed-scale.

Contaminant source identification within a building: Toward design of immune buildings

The level of protection of a building against the intentional or accidental release of chemical agents is crucial. Both scenarios could endanger life and safety of the buildings occupants. Equipping buildings with appropriate chemical sensors can alert the building occupants about the contaminant release. The readings of these sensors can be employed to trace the location of release, and help to take the appropriate actions to minimize the casualties. However, only a limited number of them can be installed due to their initial and operating cost. Moreover, there is no information about the source strength, release time and possible source location. This paper reports the development of a methodology to identify the source location using sensors reading from limited locations. The methodology uses the artificial neural network (ANN) as a statistical analysis integrated with a multi-zone airborne contaminant transport model, CONTAM. To evaluate the applicability of this method, the contaminant dispersion within a building was modeled and the results were integrated to an ANN for the source identification. The prediction made by the trained ANN was then evaluated by predicting the source of the contaminant in 40 extra cases, which had not been seen by the network during the training session. The model was able to predict the source location in more than 90% of the cases when the building was monitored by three or more sensors. The results show that the method can be used to help building designers decide the optimum configuration of the sensors required for a space based on the accuracy level of the source detection.

CFD and ventilation research

There has been a rapid growth of scientific literature on the application of computational fluid dynamics (CFD) in the research of ventilation and indoor air science. With a 1000-10,000 times increase in computer hardware capability in the past 20 years, CFD has become an integral part of scientific research and engineering development of complex air distribution and ventilation systems in buildings. This review discusses the major and specific challenges of CFD in terms of turbulence modelling, numerical approximation, and boundary conditions relevant to building ventilation. We emphasize the growing need for CFD verification and validation, suggest ongoing needs for analytical and experimental methods to support the numerical solutions, and discuss the growing capacity of CFD in opening up new research areas. We suggest that CFD has not become a replacement for experiment and theoretical analysis in ventilation research, rather it has become an increasingly important partner.

PRACTICAL IMPLICATIONS

We believe that an effective scientific approach for ventilation studies is still to combine experiments, theory, and CFD. We argue that CFD verification and validation are becoming more crucial than ever as more complex ventilation problems are solved. It is anticipated that ventilation problems at the city scale will be tackled by CFD in the next 10 years.

Inhalation of expiratory droplets in aircraft cabins

Airliner cabins have high occupant density and long exposure time, so the risk of airborne infection transmission could be high if one or more passengers are infected with an airborne infectious disease. The droplets exhaled by an infected passenger may contain infectious agents. This study developed a method to predict the amount of expiratory droplets inhaled by the passengers in an airliner cabin for any flight duration. The spatial and temporal distribution of expiratory droplets for the first 3 min after the exhalation from the index passenger was obtained using the computational fluid dynamics simulations. The perfectly mixed model was used for beyond 3 min after the exhalation. For multiple exhalations, the droplet concentration in a zone can be obtained by adding the droplet concentrations for all the exhalations until the current time with a time shift via the superposition method. These methods were used to determine the amount of droplets inhaled by the susceptible passengers over a 4-h flight under three common scenarios. The method, if coupled with information on the viability and the amount of infectious agent in the droplet, can aid in evaluating the infection risk.

PRACTICAL IMPLICATIONS

The distribution of the infectious agents contained in the expiratory droplets of an infected occupant in an indoor environment is transient and non-uniform. The risk of infection can thus vary with time and space. The investigations developed methods to predict the spatial and temporal distribution of expiratory droplets, and the inhalation of these droplets in an aircraft cabin. The methods can be used in other indoor environments to assess the relative risk of infection in different zones, and suitable measures to control the spread of infection can be adopted. Appropriate treatment can be implemented for the zone identified as high-risk zones.

Use of risk assessment and likelihood estimation to analyze spatial distribution pattern of respiratory infection cases

Obvious spatial infection patterns are often observed in cases associated with airborne transmissible diseases. Existing quantitative infection risk assessment models analyze the observed cases by assuming a homogeneous infectious particle concentration and ignore the spatial infection pattern, which may cause errors. This study aims at developing an approach to analyze spatial infection patterns associated with infectious respiratory diseases or other airborne transmissible diseases using infection risk assessment and likelihood estimation. Mathematical likelihood, based on binomial probability, was used to formulate the retrospective component with some additional mathematical treatments. Together with an infection risk assessment model that can address spatial heterogeneity, the method can be used to analyze the spatial infection pattern and retrospectively estimate the influencing parameters causing the cases, such as the infectious source strength of the pathogen. A Varicella outbreak was selected to demonstrate the use of the new approach. The infectious source strength estimated by the Wells-Riley concept using the likelihood estimation was compared with the estimation using the existing method. It was found that the maximum likelihood estimation matches the epidemiological observation of the outbreak case much better than the estimation under the assumption of homogeneous infectious particle concentration. Influencing parameters retrospectively estimated using the new approach can be used as input parameters in quantitative infection risk assessment of the disease under other scenarios. The approach developed in this study can also serve as an epidemiological tool in outbreak investigation. Limitations and further developments are also discussed.

Decision analysis of emergency ventilation and evacuation strategies against suddenly released contaminant indoors by considering the uncertainty of source locations

In case hazardous contaminants are suddenly released indoors, the prompt and proper emergency responses are critical to protect occupants. This paper aims to provide a framework for determining the optimal combination of ventilation and evacuation strategies by considering the uncertainty of source locations. The certainty of source locations is classified as complete certainty, incomplete certainty, and complete uncertainty to cover all the possible situations. According to this classification, three types of decision analysis models are presented. A new concept, efficiency factor of contaminant source (EFCS), is incorporated in these models to evaluate the payoffs of the ventilation and evacuation strategies. A procedure of decision-making based on these models is proposed and demonstrated by numerical studies of one hundred scenarios with ten ventilation modes, two evacuation modes, and five source locations. The results show that the models can be useful to direct the decision analysis of both the ventilation and evacuation strategies. In addition, the certainty of the source locations has an important effect on the outcomes of the decision-making.

U.S. airport entry screening in response to pandemic influenza: modeling and analysis

BACKGROUND

A stochastic discrete event simulation model was developed to assess the effectiveness of passenger screening for Pandemic Influenza (PI) at U.S. airport foreign entry.

METHODS

International passengers arriving at 18 U.S. airports from Asia, Europe, South America, and Canada were assigned to one of three states: not infected, infected with PI, infected with other respiratory illness. Passengers passed through layered screening then exited the model. 80% screening effectiveness was assumed for symptomatic passengers; 6% asymptomatic passengers.

RESULTS

In the first 100 days of a global pandemic, U.S. airport screening would evaluate over 17 M passengers with 800 K secondary screenings. 11,570 PI infected passengers (majority asymptomatic) would enter the U.S. undetected from all 18 airports. Foreign airport departure screening significantly decreased the false negative (infected/undetected) passengers. U.S. attack rates: no screening (26.9%-30.9%); screening (26.4%-30.6%); however airport screening results in 800 K-1.8 M less U.S. PI cases; 16 K-35 K less deaths (2% fatality rate). Antiviral medications for travel contact prophylaxis (10 contacts/PI passenger) were high - 8.8M. False positives from all 18 airports: 100-200/day.

CONCLUSIONS

Foreign shore exit screening greatly reduces numbers of PI infected passengers. U.S. airport screening identifies 50% infected individuals; efficacy is limited by the asymptomatic PI infected. Screening will not significantly delay arrival of PI via international air transport, but will reduce the rate of new US cases and subsequent deaths.

Identification of contaminant sources in enclosed spaces by a single sensor

To protect occupants from infectious diseases or possible chemical/biological agents released by a terrorist in an enclosed space, such as an airliner cabin, it is critical to identify gaseous contaminant source locations and strengths. This paper identified the source locations and strengths by solving inverse contaminant transport with the quasi-reversibility (QR) and pseudo-reversibility (PR) methods. The QR method replaces the second-order diffusion term in the contaminant transport equation with a fourth-order stabilization term. By using the airflow pattern calculated by computational fluid dynamics (CFD) and the time when the peak contaminant concentration was measured by a sensor in downstream, the QR method solves the backward probability density function (PDF) of contaminant source location. The PR method reverses the airflow calculated by CFD and solves the PDF in the same manner as the QR method. The position with the highest PDF is the location of the contaminant source. The source strength can be further determined by scaling the nominal contaminant concentration computed by CFD with the concentration measured by the sensor. By using a two-dimensional and a three-dimensional aircraft cabin as examples of enclosed spaces, the two methods can identify contaminant source locations and strengths in the cabins if the sensors are placed in the downstream location of the sources. The QR method performed slightly better than the PR method but with a longer computing time.

PRACTICAL IMPLICATIONS

The paper presents a method that can be used to find a gaseous contaminant source location and determine its strength in enclosed spaces with the data of contaminant concentration measured by one sensor. The method can be a very useful tool to find where, what, and how the contamination has happened. The method is also useful for optimally placing sensors in enclosed spaces. The results can be applied to develop appropriate measures to protect occupants in enclosed environments from infectious diseases or chemical/biological warfare agents released by a terrorist.