Protecting a whole building from critical indoor contamination with optimal sensor network design and source identification methods

To maintain a healthful and secure indoor environment, it is crucial to design an effective indoor air quality (IAQ) sensor network and interpret sensor outputs for prompt IAQ controls. This paper introduces how a probability concept based inverse modeling method – the adjoint probability method – can be used to assist in designing a high-performance IAQ sensor network and identifying potential contaminant source locations for an entire building based on limited sensor outputs. The study proposes a new IAQ sensor network design and optimization method for buildings with one or more compartments on the basis of the probability calculation. With responses from optimized sensors, a two-stage integrated inverse prediction algorithm is developed that is able to identify a potential IAQ source zone (or room) in a building as well as an exact location within the room. The paper demonstrates the design of sensor networks and the application of the source identification algorithms for a residential dwelling. The case study verifies the feasibility, effectiveness and accuracy of the proposed sensor design method and the two-stage algorithm for indoor contaminant control.     

Prompt tracking of indoor airborne contaminant source location with probability-based inverse multi-zone modeling

Indoor air quality (IAQ) has a significant influence on occupants' comfort, health, productivity, and safety. Existing studies show that the primary causes of many IAQ problems are various airborne contaminants that either are generated indoors or penetrate into indoor environments with passive or active airflows. Accurate and prompt identification of contaminant sources can help determinate appropriate IAQ control solutions, such as, eliminating contaminant sources, isolating and cleaning contaminated spaces. This study develops a fast and effective inverse modeling method for identifying indoor contaminant source characteristics. The paper describes the principles of the probability-based adjoint inverse modeling method and formulates a multi-zone model based inverse prediction algorithm that can rapidly track contaminant source location with known source release time in a building with many compartments. The paper details the inverse modeling procedure with modification of an existing multi-zone airflow and contaminant transport simulation program. The application of the method has been demonstrated with two case studies: contaminant releases in a multi-compartment residential house and in a complex institutional building. The numerical experiments tested the source identification capability of the program for various contaminant sensing scenarios. The investigation verifies the effectiveness and accuracy of the developed method for indoor contaminant source tracking, which will be further explored to identify more complicated indoor contamination episodes.     

Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals

Reduction in indoor environment quality calls for effective control and improvement measures. Accurate and prompt identification of contaminant sources ensures that they can be quickly removed and contaminated spaces isolated and cleaned. This paper discusses the use of inverse modeling to identify potential indoor pollutant sources with limited pollutant sensor data. The study reviews various inverse modeling methods for advection-dispersion problems and summarizes the methods into three major categories: forward, backward, and probability inverse modeling methods. The adjoint probability inverse modeling method is indicated as an appropriate model for indoor air pollutant tracking because it can quickly find source location, strength and release time without prior information. The paper introduces the principles of the adjoint probability method and establishes the corresponding adjoint equations for both multi-zone airflow models and computational fluid dynamics (CFD) models. The study proposes a two-stage inverse modeling approach integrating both multi-zone and CFD models, which can provide a rapid estimate of indoor pollution status and history for a whole building. Preliminary case study results indicate that the adjoint probability method is feasible for indoor pollutant inverse modeling. PRACTICAL IMPLICATIONS: The proposed method can help identify contaminant source characteristics (location and release time) with limited sensor outputs. This will ensure an effective and prompt execution of building management strategies and thus achieve a healthy and safe indoor environment. The method can also help design optimal sensor networks.     

Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Building indoor air quality (IAQ) has received growing attentions lately because of the extended time people spend indoors and the increasing reports of health problems related to poor indoor environments. Recent alarms to potential terrorist attacks with airborne chemical and biological agents (CBA) have further highlighted the research needs on building vulnerability and protection. To maintain a healthful and safe indoor environment, it is crucial to identify contaminant source locations, strengths, and release histories. Accurate and prompt identification of contaminant sources can ensure that the contaminant sources can be quickly removed and contaminated spaces can be effectively isolated and cleaned. This paper introduces a probability concept based prediction method—the adjoint probability method-that can track potential indoor airborne contaminant sources with limited sensor outputs. The paper describes the principles of the method and presents the general modeling algorithm and procedure that can be implemented with current computational fluid dynamics (CFD) or multi-zone airflow models. The study demonstrates the application of the method for identifying airborne pollutant source locations in two realistic indoor environments with few sensor measurement outputs. The numerical simulations verify the feasibility and accuracy of the method for indoor pollutant tracking applications, which forms a good foundation for developing an intelligent and integrated indoor environment management system that can promptly respond to indoor pollution episodes with effective detection, analysis, and control.     

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.     

Evaluation of distributed environmental control systems for improving IAQ and reducing energy consumption in office buildings

Conventional heating, ventilation, and air conditioning (HVAC) systems are incapable of providing control over individual environments or adjusting fresh air supply based on the dynamic occupancy of individual rooms in an office building. This paper introduces the concept of distributed environmental control systems (DECS) and shows that improvement in indoor air quality (IAQ) and energy efficiency can be achieved by providing required amounts of fresh air directly to the individual office spaces through distributed demand controlled ventilation (DDCV). In DDCV, fresh air is provided to each micro-environment (room or cubicle) based on input from distributed sensors (CO₂, VOC, occupancy, etc.) or intelligent scheduling techniques to provide acceptable IAQ for each occupant, rather than for groups or populations of occupants. In order to study DECS, a numerical model was developed that incorporates some of the best available models for studying building energy consumption, indoor air flow, contaminant transport and HVAC system performance. The developed model was applied to a DECS in a model office building equipped with a DDCV system. By implementing DECS/DDCV and intelligent scheduling techniques it is possible to achieve an improvement in IAQ along with a reduction in annual energy consumption compared to conventional ventilation systems.     

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling

Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. PRACTICAL IMPLICATIONS: The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.     

The effect of source motion on contaminant distribution in the cleanrooms

In the recent decades, cleanrooms have found growing applications in broad range of industries such as pharmacy and microelectronics. Concerns about negative effects of the contaminant exposure on the human health and product quality motivate many researchers towards understanding of the airflow and contaminant distribution though these environments. With an improvement in computational capacity of the computers, computational fluid dynamics (CFD) technique has become a powerful tool to study the engineering problems including indoor air quality (IAQ). In this research, indoor airflow in a full-scale cleanroom is investigated numerically using Eulerian-Eulerian approach. To evaluate the ventilation system effectiveness, a new index, called final efficiency, is introduced which takes all aspects of the problem into account. The results show that the contaminant source motion and its path have a great influence on the contaminant dispersion through the room. Based on the results, the contaminant distribution indexes, e.g. final efficiency and spreading radius, are improved when the source motion path is in the dominant direction of the ventilation airflow. Consequently, the efficiency of an air distribution system which provides a directional airflow pattern shows the least source path dependency. This study and its results may be useful to gain better understanding of the source motion effects on the indoor air quality (IAQ) and to design more effective ventilation systems.     

Is ventilation duct cleaning useful? A review of the scientific evidence

Ventilation duct cleaning (DC) is widely advocated to provide good indoor air quality (IAQ), health benefits, cost savings, and enhance ventilation system performance. The aim of the present review is to evaluate the scientific evidence as shown in the literature. There is evidence that under normal operating conditions, ventilation ducts can be contaminated with dusts and serve as reservoirs for microbials to proliferate. While controlled experiments noted that contaminants resuspension can elevate exposure levels indoors, no field studies have correlated poor IAQ with duct contamination. Despite high efficiencies of contaminant removal within the ducts during cleaning, reductions for different indoor air pollutants vary widely, where, post-cleaning air pollutants concentrations can be higher than pre-cleaning levels. Further, there are health concerns in the use of biocides, sealants and encapsulants. There is inadequate evidence to show that DC can improve airflow in ducts and reduce energy consumption. Although epidemiological studies indicate suggestive evidence that improperly maintained ducts are associated with higher risks of symptoms among building occupants, this review finds insufficient evidence that DC can alleviate occupant's symptoms. In summary, the need for duct cleanliness has to be properly balanced by the probable generation of indoor pollution resulting from DC and subsequent potential health risks. PRACTICAL IMPLICATIONS: Existing evidence is insufficient to draw solid conclusions regarding positive impact of duct cleaning on IAQ, health benefits, cost savings and HVAC performance. Maintaining duct cleanliness has to be properly balanced by the probable generation of indoor pollution and potential health risks.     

Removal of contaminants released from room surfaces by displacement and mixing ventilation: modeling and validation

This paper presents the experimental and numerical modeling of contaminant dispersion in a full-scale environmental chamber with different room air distribution systems. For the experimental modeling, an area source with uniform emissions of a hypothetical contaminant (SF6) from the entire floor surface is designed and constructed. Two different types of ventilation are studied: displacement and mixing ventilation. A computer model for predicting the contaminant dispersion in indoor spaces was validated with experimental data. The validated model is used to study the effects of airflow and the area-source location on contaminant dispersion. Results show that the global airflow pattern has a strong impact on the distribution of the contaminants. In general, the personal exposure could be estimated by analyzing the relative source positions in the airflow pattern. Accordingly, the location of an exhaust diffuser may not greatly affect the airflow pattern, but can significantly affect the exposure level in the room. PRACTICAL IMPLICATIONS: When designing ventilation in addition to bringing fresh air to occupants, it is important to consider the removal of contaminants released in the off-gassing of building materials. Typical indoor off-gassing examples are emissions of volatile organic compounds from building enclosure surfaces such as flooring and painted walls. In this study, we conducted experimental and numerical modeling of different area sources in a mock-up office setup, with displacement or mixing ventilation. Displacement ventilation was as successful as mixing ventilation in removing the contaminant source from the floor area. Actually, the most important consideration in the removal of these contaminants is the relative position of the area source to the main airflow pattern and the occupied zone.     

A combined system of chilled ceiling,displacement ventilation and desiccant dehumidification

Different types of heating, ventilation, and air-conditioning (HVAC) systems consume different amounts of energy yet they deliver similar levels of acceptable indoor air quality (IAQ) and thermal comfort. It is desirable to provide buildings with an optimal HVAC system to create the best IAQ and thermal comfort with minimum energy consumption. In this paper, a combined system of chilled ceiling, displacement ventilation and desiccant dehumidification is designed and applied for space conditioning in a hot and humid climate. IAQ, thermal comfort, and energy saving potential of the combined system are estimated using a mathematical model of the system described in this paper. To confirm the feasibility of the combined system in a hot and humid climate, like China, and to evaluate the system performance, the mathematical model simulates an office building in Beijing and estimates IAQ, thermal comfort and energy consumption. We conclude that in comparison with a conventional all-air system the combined system saves 8.2% of total primary energy consumption in addition to achieving better IAQ and thermal comfort. Chilled ceiling, displacement ventilation and desiccant dehumidification respond consistently to cooling source demand and complement each other on indoor comfort and air quality. It is feasible to combine the three technologies for space conditioning of office building in a hot and humid climate.     

Modeling the resiliency of energy‐efficient retrofits in low‐income multifamily housing

Residential energy efficiency and ventilation retrofits (eg, building weatherization, local exhaust ventilation, HVAC filtration) can influence indoor air quality (IAQ) and occupant health, but these measures’ impact varies by occupant activity. In this study, we used the multizone airflow and IAQ analysis program CONTAM to simulate the impacts of energy retrofits on indoor concentrations of PM_2.5 and NO₂ in a low‐income multifamily housing complex in Boston, Massachusetts (USA). We evaluated the differential impact of residential activities, such as low‐ and high‐emission cooking, cigarette smoking, and window opening, on IAQ across two seasons. We found that a comprehensive package of energy and ventilation retrofits was resilient to a range of occupant activities, while less holistic approaches without ventilation improvements led to increases in indoor PM_2.5 or NO₂ for some populations. In general, homes with simulated concentration increases included those with heavy cooking and no local exhaust ventilation, and smoking homes without HVAC filtration. Our analytical framework can be used to identify energy‐efficient home interventions with indoor retrofit resiliency (ie, those that provide IAQ benefits regardless of occupant activity), as well as less resilient retrofits that can be coupled with behavioral interventions (eg, smoking cessation) to provide cost‐effective, widespread benefits.     

Coupling the multizone airflow and contaminant transport software CONTAM with EnergyPlus using co-simulation

Building modelers need simulation tools capable of simultaneously considering building energy use, airflow and indoor air quality (IAQ) to design and evaluate the ability of buildings and their systems to meet today’s demanding energy efficiency and IAQ performance requirements. CONTAM is a widely-used multizone building airflow and contaminant transport simulation tool that requires indoor temperatures as input values. EnergyPlus is a prominent whole-building energy simulation program capable of performing heat transfer calculations that require interzone and infiltration airflows as input values. On their own, each tool is limited in its ability to account for thermal processes upon which building airflow may be significantly dependent and vice versa. This paper describes the initial phase of coupling of CONTAM with EnergyPlus to capture the interdependencies between airflow and heat transfer using co-simulation that allows for sharing of data between independently executing simulation tools. The coupling is accomplished based on the Functional Mock-up Interface (FMI) for Co-simulation specification that provides for integration between independently developed tools. A three-zone combined heat transfer/airflow analytical BESTEST case was simulated to verify the co-simulation is functioning as expected, and an investigation of a two-zone, natural ventilation case designed to challenge the coupled thermal/airflow solution methods was performed.     

A multi-chamber ventilation model with random parameters

A generalized multi-chamber ventilation model is developed for air contaminant prediction problems where the parameters of the system, such as airflow rates, are described by Gaussian probability distributions. A numerical solution, utilizing stochastic differential equations (SDE's), is provided to facilitate its application. The model is used to calculate contaminant concentration histories described by means and standard deviations. It is also used to show the sensitivity of concentrations to the variation of such parameters as infiltration flows and contaminant source rates. Sample applications of the model are provided.     

An integrated modeling tool for simultaneous analysis of thermal performance and indoor air quality in buildings

To analyze the thermal performance and indoor air quality (IAQ) in building simultaneously and quickly, we have developed an integrated modeling tool to simulate the dynamic indoor multi-parameters distributions and concentrations. The tool can take the parameters including indoor temperature, indoor humidity, and pollutant concentrations (e.g., volatile organic compounds (VOC) CO₂, particulate matter (PM)), as well as the heating/cooling load of heating, ventilating, and air-conditioning (HVAC) system into account. It couples a new zonal approach based on room air age. This paper presents the basic concept and flow chart in developing the modeling tool, and demonstrates the tool's application in a hypothetical health care building. The tool could be used for design of HVAC system with IAQ control devices and for the simultaneous analysis of thermal performance and IAQ in buildings.     

Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant

Identifying contaminant sources in a precise and rapid manner is critical to indoor air quality (IAQ) management as disclosed source information can facilitate proper and effective IAQ controls in environments with airborne infection, fire smoke and chemical pollutant release etc. Probability-based inverse modeling method was shown feasible for locating single instantaneous source in IAQ events. To tackle more realistic sources of continuous release, this paper advances the method to identify continuously releasing single contaminant source. The study formulates a suite of inverse modeling algorithms that can promptly locate dynamic source with known release time for IAQ events. Two field experiments are employed to verify the prediction: one in a multi-room apartment and the other in a hospital ward which was involved in a SARS outbreak in Hong Kong in 2003. The developed algorithms promptly and accurately identify the source locations in both cases.     

传感器与 BIM 结合的设施管理研究

现行的设施管理方法下暖通空调系统故障定位过程的耗时低效问题,是由可视化程度差和信息集成化程度低造成的。BIM 技术三维显示和共享参数功能与传感器结合能够提高故障定位过程的可视化和信息集成水平。构建了 BIM 与传感器结合的设施管理系统,论述了该系统的构建思路、方法以及关键技术。对建筑物温度异常状况进行定性分析;综合运用拓扑学原理和最小信息长度理论确定最优故障方案,以推进设施管理自动化的发展。     

Assessing the effects of sliding doors on an operating theatre climate

Surgical site infections (SSIs) are the second to third most common site of health care associated infections (HAIs). It is very important to maintain good indoor air quality (IAQ) and the best ventilation system in the operating theatre (OT) to ensure health and safety for the patient and surgical team, also to reduce the risk of post-operative wound infection. Then a significant consideration in OTs is the control of aerosols, anesthesia gases and smoke. The present paper uses the airflow modeling based on computational fluid dynamics (CFD) to study the indoor climate of a standard ISO5 class OT with an ultraclean air filter system and a total ceiling unidirectional diffuser. The OT has a heating, ventilation, and air conditioning (HVAC) plant with a laminar airflow (LAF) or ultraclean ventilation. A simple method to analyze the effects of different sliding door conditions combined with crossing persons and persons with a stretcher crossing, on the OT climate, airflow patterns and the indoor pressures scheme, is provided. The proposed simulation method, that belongs to the indirect approach for a numerical simulation of solid object movements in a fluid, provides important knowledge on complex flow phenomena combined with multi-physical conditions. Results obtained by transient simulation show disruptions of the airflow inside the OT and different airflow displacement and distribution caused by surgery staff movements and sliding door opening and closing, but in particular static pressure changes in the HVAC plant system with important effects on ventilation system working conditions and its energy performances.     

Inverse identification of multiple outdoor pollutant sources with a mobile sensor

Air pollution is becoming more and more severe in large cities. Accurate and rapid identification of outdoor pollutant sources can facilitate proper and effective air quality management in urban environments. Traditional “trial–error” process is time consuming and is incapacity in distinguishing multiple potential sources, which is common in urban pollution. Inverse prediction methods such as probability based adjoint modelling method have shown viability for locating indoor contaminant sources. This paper advances the adjoint probability method to track outdoor pollutant sources of constant release. The study develops an inverse modelling algorithm that can promptly locate multiple outdoor pollutant sources with limited pollution information detected by a movable sensor. Two numerical field experiments are conducted to illustrate and verify the predictions: one in an open space and the other in an urban environment. The developed algorithm promptly and accurately identifies the source locations in both cases. The requirement of an accurate urban building model is the primary prerequisite of the developed algorithm for urban application.     

室内空气品质与暖通空调

就人们日益关注的室内空气品质问题进行描述,并阐明暖通空调与室内空气品质的关系,指出改善室内空气品质是一项综合工程,其中暖通空调系统是非常重要的影响因素,暖通空调技术的进步可以有效地改善室内空气品质。