Real-Time Identification of Indoor Pollutant Source Positions Based on ...

TECHNICAL PAPER

ISSN:1047-3289 J. Air & Waste Manage. Assoc. 60:1034 –1048 DOI:10.3155/1047-3289.60.9.1034 Copyright 2010 Air & Waste Management Association

Real-Time Identification of Indoor Pollutant Source Positions Based on Neural Network Locator of Contaminant Sources and Optimized Sensor Networks Vladimir Vukovic, Paulo Cesar Tabares-Velasco, and Jelena Srebric Department of Architectural Engineering, Pennsylvania State University, University Park, PA

ABSTRACT A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational fluid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program

IMPLICATIONS The study presented here developed and validated a method on the basis of neural networks for solving an inverse problem of identifying a source location for a measured contaminant distribution in indoor building environments. Prospective applications of the developed method include (1) real-time emergency assessment of contaminant source positions during biochemical incidents, (2) smoke/carbon monoxide source identification in fire protection efforts, (3) detection of a radon source position within a building, and (4) minimization of the number of required contaminant concentration sensors for accurate source identification and optimized sensor allocation within a building.

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provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios. INTRODUCTION According to the report of the Presidential ad hoc Committee for Building Health and Safety under Extraordinary Incidents, approximately 4.7 million buildings in the United States belong to the group having a potential threat from these events.1 The report emphasizes potential impact on air, water, and food systems within buildings. Recent activities in the field of air contaminant detection and source identification and prevention reflect these growing concerns about indoor environments. The topic is also important from the point of security and healthcare.2 The study presented here introduces a new artificial neural network (NN)-based Locator Of Contaminant Sources (LOCS). The developed LOCS computational procedure is capable of making fast and reliable contaminant source position assessments in extraordinary incidents. The validation case is provided to illustrate how LOCS can be applied in real building indoor environments. After the successful validation, additional investigation examines the utilization of LOCS for optimizing the number and allocation of indoor contaminant concentration sensors. The main two questions are (1) what is the minimum number of sensors necessary, and (2) where should the sensors be placed to ensure sufficient real-time prediction accuracy? To answer these questions, two different algorithms are described and an additional real building sensor optimization case is considered. BACKGROUND Significant research activities have focused on the field of contaminant distribution modeling and simulations. Most of the existing studies worked on predicting timedependent concentrations of contaminants for a known source position, intensity, and contaminant properties. Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric On the other hand, only a few studies tried to determine positions of the unknown source for a detected pattern of contaminant concentrations (i.e., to solve an inverse problem). An example of a NN application for solving the inverse problem in outdoor environments was successfully demonstrated for hydrogen sulfide and ammonia releases in west Texas.3 In addition, several studies computed the time-dependent contaminant concentration distributions from a known source using numerical simulations such as computational fluid dynamics (CFD). In indoor environments, CFD was typically used to examine building ventilation system performance and contaminant transport through the ventilation system.4 Also, experimental and numerical studies examined the contaminant distribution in large semi-opened building areas such as ice rink arenas.5,6 Because outdoor and indoor environments are connected, a study explained near-realtime predictions of contaminant dispersion after an outdoor incidental event using powerful parallel multiprocessors.7 In addition, a recent research study showed how inverse CFD modeling can predict positions of indoor contaminant sources in an airplane cabin and a single office space.8 This study showed that inverse CFD can be applied in small building domains in which contaminant transport is dominated by convection and knowing the initial distribution of contaminant concentrations and airflow conditions. However, the study reported an adverse impact of an unknown contaminant release time on the accuracy of source position predictions. Furthermore, all present CFD computations would be excessively time consuming if used on a single computer. It would take hours to conduct CFD calculations for just one space in a building, whereas an actual pollution incident takes place within minutes. To enable faster computations of contaminant transport within buildings, several multizone models were developed.9 Multizone models define parts of the computational domain as zones, in which each zone is a volume with uniform fluid properties. Thus, multizone modeling allows faster computation by application of conservation equations in their algebraic form. However, this technique can only be useful if applied in well-mixed air regions, which are not always present within buildings. Comparison of the results obtained by multizone and CFD models showed that CFD had better agreement with experimentally obtained values.10 As a result, improvements of the multizone models emerged through coupling with CFD.11,12 In outdoor environments, several puff models offer promising time-efficient applications in calculating dispersion of contaminants after instantaneous releases. Large-scale biochemical agent or radioactive releases fall under the instantaneous release category in which the puff models are usually applied. On the other hand, natural phenomena such as dust and smoke emissions in volcanic eruptions or forest fires are considered as continuous emissions. Nevertheless, much of the theory is applicable to both of these types of atmospheric pollution sources.13 The puff models consider diffusion of contaminants in air and incorporate wind influence, temperature variations, and varying terrain configurations. Software Volume 60 September 2010

packages such as Hazard Prediction and Assessment Capability (HPAC) have been successfully applied to predict contaminant dispersion in real outdoor environmental conditions. The HPAC puff model computation is fast and produces field-applicable results within minutes.14 All of these reviewed simulation techniques are difficult to use in incidental situations, especially for indoor environments, even when calculations are fast, because of the demanding data input process. Therefore, several studies addressed the issue of real-time contaminant detection, source identification, and strength determination on the basis of real-time sensor readings. EXISTING REAL-TIME CONTAMINANT DATA ANALYSES A real-time identification of an indoor contaminant source position is important for enabling a fast, targeted response during incidental events. Thus, this study used multizone models to compute a database of possible contamination scenarios within buildings.15 Each case in the database presented a set of time-dependent contaminant concentrations for a specific type of contaminants, position of the contaminant source, strength of the source, duration of the emission, and an operational mode of the ventilation system within a building. Comparison of the real-time sensor readings with the data in the database leads to identification of appropriate corresponding contamination scenarios. The database scenario that closely matches the real sensor readings provides the capability to identify the contaminant source and to predict future contaminant concentrations in simulated areas. These methods can be reliable and fast as long as the real contamination event exists in the base of prior calculated cases.16 Consequently, it is important to perform as many prior computations of contaminant concentration distributions as possible and to include such results in the database. A large database appears as a necessity, but still one can hardly encompass all possibilities. In reality, unexpected situations could easily occur. Slightly different indoor air conditions that were not envisioned and included in the database could cause discrepancies between measured concentrations and those in the database. These differences could further lead to misinterpretation of the real-time sensor data. A promising method based on inverse multizone modeling has recently been developed to calculate probabilities for locating an unknown source in incidental pollution releases based on contaminant concentration sensor readings.17 Although the method claims computational efficiency, it requires knowledge of exact pollution release time, which may not be known in the actual pollution scenario. As an alternative method, computed tomography has emerged.18 This experimental technique uses infrared spectroscopy to determine contaminant integral concentrations along different paths in the time-space domain. Computational methods are available to perform the reconstruction of contaminant concentration distributions on the basis of the measured path integral concentrations. The described computed tomography method can provide near-real-time assessments of multiple contaminant concentrations.19 However, this method requires rather Journal of the Air & Waste Management Association 1035

Vukovic, Tabares-Velasco, and Srebric complex equipment, utilizing numerous spectrometer rays to obtain sufficient accuracy. A study used over 100 rays for a single room and showed that reconstruction time and ray configurations are important factors for quality of the results.20 With these limitations in mind, the technique would be difficult to apply inside of an entire building. A more recent study addressed the issue of identifying multiple outdoor contaminant sources and estimating the release time and strength of such sources.21 The study used the genetic algorithm approach coupled with a dispersion model SCIPUFF.22 Although the researchers state their coupled model performs well, they recommend giving initial estimates of the location and time of the emission to the model. Furthermore, the model validation results show successful source identification in only approximately 50% of the investigated cases. The study did not include information about the computation time, nor the possibility to use the coupled model in real-time data analyses. However, the study is important as an attempt to use advanced computational mapping techniques (e.g., the genetic algorithm) to characterize the contamination source. Another class of advanced computational tools uses NNs that are also based on mapping techniques. Present attempts to apply NNs in predictions of contaminant concentrations focused on outdoor urban environments. The accuracy of NN predictions depended on the contaminant species and type of the applied NN.23 Recent studies used NNs to forecast concentrations of nitrogen oxides, sulfur dioxide, and ozone in different cities.24 On the basis of the existing studies, NN applications are promising for predictions of outdoor contaminant distributions.3,25 Although NNs had several promising applications, an extensive literature review did not reveal any study considering the use of NNs in the case of indoor contamination. The existing NN applications in building systems include predictions of building energy consumption and controls of heating, ventilation, and air-conditioning (HVAC) systems. A study by Breekweg et al.26 gives an overview of the NN properties and concepts with described applications in building energy consumption predictions. Another study provides two examples for NN-performed forecasting of future building energy consumption on the basis of the previous usage history.27 Eventually, NN predictions of building load can lead to improved controls of energy storage systems and minimize HVAC operating costs.28 In an attempt to extend applications of NNs, the authors’ previous paper presented an initial development of a novel approach for identifying the source position of an indoor pollutant release illustrated by a simulation case.29 The simulation case was based on a real building environment with a constant emission source. The study presented here builds on the authors’ previous research by generalizing the equation used for contaminant concentration approximations and by including other types of emission sources. In addition, this study presents an optimization procedure for determining locations of the least possible number of contaminant concentration sensors still able to provide reliable real-time prediction of 1036 Journal of the Air & Waste Management Association

indoor contaminant source positions in incidental situations. Previous sensor placement optimization studies used inverse multizone modeling and CFD to identify contaminant source locations with the minimum number of optimally allocated sensors but still required analyses of building layouts, system configurations, and airflow patterns from the end user and did not provide a general automatic sensor placement algorithm independent of building characteristics.30 Alternatively, genetic algorithms were applied in combination with multizone modeling to determine optimal sensor placement taking into account two target functions: (1) number/cost of sensors and (2) occupant exposure.31 However, in certain cases such algorithms resulted in specifying the number of required sensors equal to the number of possible pollutant source locations, which would not be optimal when pollution can originate from an arbitrary indoor location. Finally, a study investigated optimal sensor allocation to alert the building occupants in the least amount of time.32 Overcoming the limitations of previous studies, this sensor optimization procedure is designed to assist the first responders in emergency situations rather than as an alert tool for the building occupants, regardless of the building characteristics, pollution release time, and background concentration levels.

PRESENT STATE OF SENSOR DEVELOPMENT To have an ability to investigate actual incidental situations and perform real-time prediction of indoor pollutant source positions, adequate sensor technology is necessary to detect the presence and concentration of pollutants. Therefore, the study presented here will give an insight into the state-of-the-art sensor technologies and their capabilities to characterize different pollutant releases. Many of the described technologies are presently being improved to provide enhanced capabilities in the future. Thus, rather than trying to make an exhaustive list of different technologies, the emphasis is on current practical applicability and usefulness of available sensors for real-time prediction of indoor pollutant source positions. Without getting into detail how each of the technologies works, this review references characteristics of the most widely used technologies.33 For the purpose of comparison, Table 1 gives general characteristics of these technologies. In the development of this table, original publications listed over 40 different approaches for biological and chemical agent detection.34,35 Among the technologies listed in Table 1, the most important are those offering high sensitivity with fast detection of pollutants. These are critical performance parameters for pollutant sensors influencing the ability to have rapid mitigation efforts in incidental pollutant releases. Table 1 gives minimum measured values for these parameters as referenced in performance experiments.36–41 It is important to notice that minimum sensitivity and detection time highly depend on the type of agents being detected and significantly differ for different types of pollutants and performed experiments. Furthermore, the minimum detection time listed in Table 1 typically does not correspond to the Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric Table 1. Summary of presently available, most often used technologies for identification of contamination agents in indoor/outdoor environments.

Method

Type of Detected Agents

Minimum Detection Time (sec)

Minimum Sensitivity (ppm) 关cfu/mL兴

Surface-enhanced Raman spectroscopy (SERS)36 Enzyme-based37 Fourier-transform infrared spectroscopy38

Chemical, possibly biological Chemical Biological

30

0.05

Yes

60 60

0.16 6 [10 ]

No No

Surface acoustic wave39,40 Ion mobility spectrometry41

Chemical Chemical

120 3

0.07 0.004

Yes Yes, roughly

minimum sensitivity, whereas some sensors can only detect presence of pollutants without assessing the concentration levels. However, ability to timely measure contaminant concentrations is a crucial performance parameter for real-time prediction of indoor pollutant source positions. The sensors able to indicate only the presence of pollutants are intended for allocation within each zone of a building, presenting one approach toward pollutant source identification. This approach assumes that the pollutant source is located in the same zone as the sensor that first detects the presence of pollution. Unfortunately, such an assumption is not always accurate because outdoor air infiltration and HVAC system operation may rapidly disperse a pollutant within a building, and the sensor within the source zone would not be necessarily the first to detect the pollutant’s presence.15 To avoid such errors, sensors should be located at every HVAC air discharge outlet and in the zones not containing any outlets. Consequently, although this type of sensor may be cheaper than those capable of measuring contaminant concentrations, the required number of such sensors for large buildings may result in the higher overall cost than optimized employment of concentration sensors in several zones within a building. In Table 1, enzyme-based and ion mobility spectrometry sensors are the only ones presently able to detect pollutants in such low concentration levels that are not immediately dangerous to life and death as prescribed by the National Institute for Occupational Safety and Health.42 In all other cases, the tested sensors in Table 1 could not achieve the required sensitivity.33 At the same time, no data were available to show time efficiency of concentration measurements with ion mobility spectrometry, surface-enhanced Raman spectroscopy, and surface acoustic wave sensors. Therefore, taking into consideration the ability to actually measure contaminant concentrations, no current sensor technology exists to satisfy the necessary real-time measurement requirements for optimized deployment within buildings. At the same time, most present sensors satisfy currently accepted criteria regarding desired detection time—warning in seconds, identification in minutes, and quantification in hours.33 METHODOLOGY FOR IDENTIFICATION OF SOURCE POSITIONS Although no present sensor technology can provide fast and accurate contaminant concentration readings to Volume 60 September 2010

Can Measure Concentration?

Applications So Far Work in progress, warfare agent detection in air or water Personal alarm Laboratory detection of biological agents Warfare agent detection Warfare agent and irritant detection

match the requirements for real-time computations, the amount of research work in this area offers promising future results. With this idea in mind, the study presented here proposes a methodology to effectively use real-time sensor data for identification of contaminant source positions in extraordinary incidents, calling such a procedure NN LOCS. Computational tools using NNs can speed up the sensor data analysis process and provide fast solutions to nonlinear problems43 such as a transformation of the sensor measured concentration input domain into the source position output domain. A particular type of NNs, Multi-Layer Perceptron (MLP), is widely used in atmospheric contaminant distribution computations. Therefore, the proposed LOCS methodology is based on application of MLP NNs. MLP NNs consist of layers of computational nodes, including input, output, and one or more hidden layers between the input and output layers. Each of the nodes is a separate computational process, and all of them working together represent a parallel multiprocessor. The number of nodes in the input layer equals the number of input parameters. LOCS methodology suggests this number should correspond to the number of different sensors. The number of nodes in the output layer equals the number of unknowns for which the network should provide values. In LOCS, output values define the position of the contaminant source. Therefore, the number of output nodes should correspond to the dimensionality of space where the contaminant source is located. For example, if the source can be found anywhere within a three-dimensional domain, just three output nodes are sufficient. Hidden layers behave as transfer functions between the input and output values, and the number of hidden layers should be appropriately chosen with respect to input and output domain sizes. Computational nodes belonging to different layers are interconnected, and the connections mark paths of communication between the nodes. These paths are not necessarily constant in time, but can change, expressing the network’s ability to adapt with respect to the outside impulses. Significance of the connections between the NN nodes is modified in the process called network training. In the training process, a network uses provided samples of input data and output (target) results to evaluate connections between the nodes. After a sufficient number of such examples, the network would have fixed weights for the connections and the training could end. The network is then able to compute the desired result after being Journal of the Air & Waste Management Association 1037

Vukovic, Tabares-Velasco, and Srebric given a set of input data. Hence, a trained NN represents a mathematical transformation between the input and output multidimensional domain spaces. Assuming that NN inputs form an input vector p, the following relations describe the operation of the first network layer: a ⫽ f (w䡠p ⫹ b) (1)

n ⫽ wp ⫹ b, a ⫽ f (n)

where a is the output vector of the layer, w is the weight matrix, b is the matrix of biases, and f is the transfer function. If the network has more than one layer, eq 1 applies to each of the layers, taking the output of the previous layer as a new input vector together with the appropriate weights and biases for the current layer. Thus, the final output of the whole MLP network with one hidden layer can be obtained from the following equation44:

冋冘 nh

yˆ i 共t兲 ⫽ gi 关␸, ␪兴 ⫽ Fi

j⫽1

冉冘 n␸

Wi,jfj

l⫽1

冊册

wj,l␸1 ⫹ wj,0 ⫹ Wi,0

C ⫽ ␣共1 ⫺ e ⫺ ␤共t ⫺ ␥兲兲

(2)

where yî 共t兲 represents the output of the ith neuron in the output layer, ␸1 are network inputs, wj,l and Wi,j are network weights for hidden and output layers, wj,0 and Wi,0 are biases for hidden and output layers, fj and Fi are transfer functions for hidden and output layers, and n␸ and nh represent the total numbers of input and hidden neurons, respectively. Although the expression within the parentheses of eq 2 represents the operation of the hidden layer of neurons, the rest of the right-hand side of the equation represents the operation of the output neuron, i. When all adjustable parameters are replaced with vector ␪, function gi presents the transformation of the input domain into the final NN output. This operational procedure is usually called the feed-forward network. Different types of NNs distinguish between different transfer functions used to transform data exchanged between the computational nodes. Typical transfer functions are available in the literature, and selection of an appropriate transfer function is problem dependent.45 The problem of identifying a contaminant source position on the basis of the known contaminant concentration distribution is not linear. Therefore, the LOCS procedure uses a log-sigmoid nonlinear transfer function with a two-layer feed-forward NN and a back-propagation learning algorithm. The MATLAB computer program and its NN Toolbox were used to construct and train NNs.46,47 NN training data can be obtained from any computational model capable of predicting contaminant distributions from a known contaminant source position, such as the reviewed CFD, multizone, or puff models. Various possible NN training data resources make LOCS applicable to outdoor and indoor contaminant sources. Different training case sets should distinguish between different contaminant source positions. Each particular set of training cases (i.e., each training source position) requires additional 1038 Journal of the Air & Waste Management Association

transient computations of time-dependent contaminant concentration distributions. Data resulting from these computations form the time series for the LOCS NN training process; namely, the contaminant concentration time series computed for every prospective contaminant sensor allocation can be approximated with an appropriate mathematical function.48 This is one of the major contributions in establishing LOCS because it reduces the complexity of the NN design and makes the designed network applicable to various real situations. As further discussed, the same functional approximation of contaminant concentrations applies to constant and burst/instantaneous contaminant releases, regardless of the contaminant species and background concentration levels. In the presence of a constant strength and constant emission rate contaminant source, the release flux (mass per source unit surface) and the release rate (mass per unit time) are constant during an event of contaminant release. The following function describes temporally dependent contaminant concentrations C ⫽ f (t) for the constant strength/emission rate source: (3)

Here, ␣ (parts per million[ppm]), ␤ (sec⫺1), and ␥ (sec) are the coefficients; c (ppm) is the contaminant concentration; and t (sec) is the measured time. The measuring time can be absolute and calculated from arbitrary preset zero time as well as relative and calculated from the release of contaminants. The form of eq 3, appropriate for constant contaminant releases in indoor environments, exists in the literature49 as follows:

C共␪兲 ⫽

F 共1 ⫺ e ⫺ I␪ ) Q

(4)

where F (m3/sec) is the injection rate, Q (m3/sec) is the flow rate of air leaving a building during time ␪, I (sec⫺1) is the air-exchange rate, C (ppm) is the contaminant concentration, and t (sec) is the measured time. Equation 4 is a special case of eq 3 when a relative time period is used for ␪ assuming a zero value for prior concentration. Thus, assuming ␥ ⫽ 0, the contaminant concentration C is C⫽0

for

tⱕ0

and

C⬎0

for

t⬎0

(5)

In this case, eq 3 becomes identical to eq 4. By comparing these two equations, the physical meaning of coefficients ␣ and ␤ becomes clear: ␣ ⫽ F/Q and ␤ ⫽ I, whereas coefficient ␥ is a time difference between the contaminant release and actual measured time. Coefficient ␥ also includes any nonzero background contaminant concentrations, making the LOCS approach applicable in several real situations, including those when contaminants in small quantities are already detected before an incidental release. Different coefficient values ␣, ␤, and ␥ distinguish different contaminant source positions. These coefficients are used as network inputs in the training process rather than contaminant concentrations themselves. This allows Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric faster network training because each set of coefficients represents most continuous time-dependent concentration values. Trained networks are then able to predict contaminant source locations within a building on the basis of the concentration sensor readings in an actual incidental release. A VALIDATION EXAMPLE IN INDOOR ENVIRONMENTS Validation of LOCS methodology was performed for indoor building environments. A multizone computational model and CONTAM computer program based on this model served as a tool for obtaining data for the network training.50 The graphical interface of the CONTAM program is a user-friendly tool for model construction of real building indoor environments. The program defines building areas with approximately uniform fluid field properties as zones. Different training cases distinguished contaminant source locations within different zones. The CONTAM program performed computations of timedependent contaminant concentration distributions within the building used for validation. The contaminant concentration time series computed for each building zone were approximated with the mathematical function given by eq 3. The choice to use multizone models and CONTAM for computations of contaminant concentration time series was based on the ability of these models to perform fast numerical simulations compared with other models such as CFD. Although setup of simulation cases would take up to a week for multizone and CFD, actual computation times differ significantly. Whereas multizone simulations for a single contaminant release case took seconds, on the same PC CFD would take days, even for a single-story building. To prove applicability of the LOCS methodology, this experimental validation used data available in the literature.51 The experimental setup included simultaneous measurements of concentrations for three separate contaminant releases originating from different zones of a single-story three-zone building as presented in Figure 1a. Each of the three zones had a burst contaminant source releasing dichlorofluoromethane (CHCl2F), nitrous oxide (N2O), and sulfur hexafluoride (SF6) contaminants with non-zero background concentrations. Figure 1b shows the constructed multizone model for the test house, including the implemented flow paths connecting different building zones. Background concentrations and strengths of the contaminant sources were different for each zone. The following CONTAM simulation parameters were selected based on the experimental study51: • Floor height was set to 2.8 m, with zero floor elevation from the ground level. • Properties of the three building zones are given in Table 2. • All flow paths between the zones were modeled with one-way flow elements. Such modeling was based on the CONTAM properties allowing userdefined flow exponent values only for the oneway flow elements. Consequently, to take into account two-way flow across the zones, two elements were placed at different elevations for each Volume 60 September 2010

Figure 1. (a) The layout of the test house used for validation,51 and (b) the constructed multizone model for this validation case in which arrows indicate calculated flow directions ([arrow] ⫽ flow path, f ⫽ zone symbol).

real flow path. Additional flow elements defined infiltration because of background leakage. Table 3 specifies the properties of flow path elements between the zones. Relative elevation of the flow elements defines the central positions of their leakage areas with respect to the floor level. All selected flow elements had a discharge coefficient of 0.6 and a pressure drop of 10 Pa during the measurements. • Outdoor conditions were specified as follows: wind 1.06 m/sec from the southwest direction, normal atmospheric pressure, and outdoor temperature of 25.3 °C. In this validation case, it is important to notice that the same eq 3 is applied to the burst (instantaneous) contaminant release sources, which was previously used for the constant release sources.29 This is possible because mathematical functions describing time-dependent contaminant concentrations from instantaneous and constant release sources have the same form when a fraction of the time after instantaneous release is considered. This similarity of mathematical functions ends when the peak concentration is reached in the case of instantaneous emissions. Further, the validation assumes that contaminant concentration sensors are not located near the contaminant source, so they do not detect the actual source Journal of the Air & Waste Management Association 1039

Vukovic, Tabares-Velasco, and Srebric Table 2. The summary of zone properties in the validation case. Zone Number 1 2 3

Zone Name

Floor Area (m2)

Number of Flow Paths

Temperature (ⴗC)

Source

Initial CHCl2F Concentration (ppm)

Initial N2O Concentration (ppm)

Initial SF6 Concentration (ppm)

Room 1 Room 2 Room 3

7.425 7.425 9.735

9 9 10

33.4 26.0 28.2

CHCl2F N2O SF6

80 9 13

6 103 14

8 5 38

concentration decay behavior, which is a trivial case for determining the source location. As known parameters for determining unknown locations of contaminant sources, ␣, ␤ and ␥ coefficients were derived from eq 3 and contaminant concentrations, which were obtained by multizone simulations for the purpose of NN training or measurements used in NN testing. Additionally, NN training used information about the simulated target contaminant locations, whereas real contaminant locations were assumed unknown and presented the desired result from NN testing and the entire validation procedure. The multizone model computations were performed based on the initial data specified in the experiments51 but using only SF6 as a contaminant in all training cases. This choice of contaminant species in the computations was intentionally different from the actual measurements to prove that the developed source identification method is independent of the contaminant species. The constructed network, presented in Figure 2, used the ␣, ␤, and ␥ coefficients derived from the multizone model data and had an output indicating the contaminant source location as 0.25 for source in zone 2 or 0.75 for source in zone 3. Because NN was used to perform classification between the two possible source zones, target outcomes of 0.25 and 0.75 did not have any physical meaning. However, such values were selected in the middle of the corresponding target classification ranges, 0 – 0.5 and 0.5–1, having in mind the Table 3. The summary of airflow path properties in the validation case. Connected Zones 1–2 1–2 1–3 1–3 1–3 1-ext_s 1-ext_s 2–3 2–3 2–3 2-ext_n 2-ext_n 2-ext_n 2-ext_w 3-ext_n 3-ext_n 3-ext_n 3-ext_s

Relative Elevation (m)

Leakage Area (cm2)

Flow Exponent

0.625 1.875 0.625 1.875 1.4 0.85 1.95 0.625 1.875 1.4 1.725 2.175 1.4 1.4 0.625 1.875 1.4 1.4

86 86 124 124 3 26 26 150 150 3 21 21 6 6 141 141 172 172

0.588 0.588 0.588 0.588 0.625 0.625 0.625 0.588 0.588 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625 0.625

Notes: ext ⫽ exterior, n ⫽ north, s ⫽ south, w ⫽ west. 1040 Journal of the Air & Waste Management Association

0 –1 range of the chosen NN target transfer function. After successful training, the network was provided with coefficients on the basis of the actual measured concentrations from a sensor located within zone 1. The network accurately predicted both possible contaminant source locations and produced the following results: 0.21374 for the N2O source in zone 2 and 0.72352 for the SF6 source in zone 3. Because the case of identifying contaminant source within the sensor zone is trivial, trained NN had 100% accuracy in determination of the unknown indoor contaminant source position for the validation case. Furthermore, the whole NN calculation process took place in seconds, proving the real-time applicability of the developed method. GROWING VERSUS PRUNING ALGORITHMS FOR OPTIMIZING THE NUMBER OF SENSORS After the successful LOCS validation, two alternative approaches can be applied to optimize the number of contaminant concentration sensors necessary for accurate real-time prediction of indoor pollutant source positions. The first approach introduces additional sensors one by one until the optimum number is reached. Here, the optimum number represents the minimum number of sensors sufficient for accurate predictions of the source position. Because the sensor network is gradually increasing, this approach is called the growing algorithm. The second approach starts with a building in which all zones already have contaminant concentration sensors and gradually removes sensors until the optimum number is reached. The optimum is defined as the minimum number of sensors providing accurate predictions of the source position. This second procedure describes the so-called pruning algorithm. The growing and pruning algorithms are well known in artificial NN theory, in which they are used to increase or reduce the size of the NNs.52,53 Introduction of the same methodology to the sensor optimization is based on analogy between the NN nodes as components of NNs and contaminant concentration sensors as components of the sensor networks. Furthermore, sensors are directly linked to the NN input nodes in LOCS methodology; therefore, optimizing the number of input nodes in LOCS includes the sensor optimization. Namely, LOCS identifies unique triples of NN input nodes to obtain data from every sensor allocated within a building. Thus, reducing/ increasing the sensor network size for a single sensor would result in a three-node reduction/increase in the NN input layer. The growing and pruning algorithms presented in Figure 3 are applied to optimize the number of sensors. In the growing algorithm shown in Figure 3a, the initial sensor is placed within an arbitrary building Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric

Figure 2. The NN structure created for the validation case.

zone and numerical simulations were performed to obtain time-dependent contaminant concentration values within that zone for all possible positions of the pollutant source. Additional simulations may include various outdoor conditions and different modes of HVAC system operation, taking into account the most significant parameters influencing pollutant distribution. The sensitivity analysis of the LOCS algorithm to different model input parameters is available in the literature.29 After the simulations, contaminant concentration datasets were then used to extract input data for NN training as described by the LOCS methodology. The aim was to construct and train a NN to predict a pollutant source position on the basis of the contaminant concentration sensor readings. NN input data are parameters characterizing the time-dependent contaminant concentrations within a building zone with a sensor, whereas output values give the contaminant source position. For complex cases with many building zones and various outdoor weather conditions, a single sensor is not sufficient, which is indicated by high NN training error. However, before introducing an additional sensor, the initial sensor location should change to identify the best building zone for which NN training gives the smallest error. In this way, the number of sensors and the sensor allocation are optimized. Every new sensor location requires new NN training datasets. Once a single-sensor allocation is optimized, an additional sensor is introduced and a new NN is constructed. This procedure is repeated until an adequately low NN training error is obtained. The low training error enables a particular allocation and minimal number of sensors to provide sufficient data for the NN to accurately identify the pollutant source position.

Figure 3. (a) The growing and (b) pruning algorithms for optimizing the number and allocation of contaminant concentration sensors. Volume 60 September 2010

A similar procedure is executed in the sensor optimization pruning algorithm shown in Figure 3b. Here, initially all but one building zone contains a sensor, and one by one additional sensors are removed afterward. Removing the sensors would increase the NN training error, and optimization of the sensor allocation would ensure this increase is minimal. The increase in training errors should be tolerated up to a specified value ensuring accuracy of NN predictions. Such a value is the same for growing and pruning algorithms. Although the final results of the two algorithms should be the same optimized sensor network structure, the two optimization paths may be of different difficulties because the final number of sensors is unknown before the optimization. In a particular case, the easier optimization algorithm, growing or pruning, would be the one for which the initial number of sensors is closer to the estimated optimization final result. RESULTS OF OPTIMIZATION PROCEDURES FOR NUMBER AND ALLOCATION OF SENSORS Out of the two possible algorithms, growing and pruning, the former was used to demonstrate the optimization of number and allocation of contaminant concentration sensors in another real building environment. The sensor optimization was performed for an office-building floor. Applying the described growing algorithm procedure and in agreement with the pollutant source prediction approach, LOCS, the multizone simulation program calculated pollutant distributions for this optimization example. Figure 4 presents the layout of offices and airflow paths for the office floor as constructed in the multizone program. The following CONTAM simulation parameters were selected based on the actual office space properties: • Floor height was set to 5 m, with 6-m floor elevation from the ground level. • Properties of the 23 building zones are given in Table 4. • Only infiltration around the windows in the perimeter of the floor was considered. The flow path for each window-related infiltration had 2-m height and 1-cm width. Two-way flow was assumed through these flow paths using the twoopening model within CONTAM. The relative position of the flow path centers was set to 2 m from the floor level. Table 4 gives the distribution of windows among the zones. • Infiltration through the wall cracks was neglected, as well as perimeter to interior zone leakage, other than flow through the open doors. • The only openings between the perimeter and interior zones were doors with assumed fully opened positions. Every perimeter zone had only one door. Flow paths for each of the doors had 2.1 m height and 0.8 m width. Two-way flow through the doors was assumed using the oneopening model within CONTAM. The relative Journal of the Air & Waste Management Association 1041


Figure 4. The multizone model of the office building floor used as an example for the “growing” optimization algorithm (arrows indicate calculated flow directions).

•

•

•

position of the flow path centers was set to 1.05 m from the floor level. A single flow path linked the indoor corridor (zone 7) with the exterior. This flow path represented a closed door and had a height of 2.1 m and width of 1 cm. Two-way flow was assumed through this flow path using the two-opening model within CONTAM. Relative position of the flow path center was set to 1.05 m from the floor level. The same flow path type was used to model flow between zones 7 and 14. Interior openings for zones 8 –10 and 12–17 were modeled using the two-way flow one-opening model available in CONTAM. Design conditions for the HVAC system were assumed as specified in Table 4. The total exhaust airflow rate from zone 7 equals the sum of supply


airflow rates to zones 7–10 and 12–17 because zone 7 has a collective exhaust for these zones. A simple HVAC system model was selected in CONTAM taking a constant 0.6 m3/sec of outdoor air, which was the design flow rate for outdoor louvers. Thus, total air supply had approximately 27.5% of outdoor air, whereas the remaining 72.5% was recirculated. In the described case, optimization of a contaminant concentration sensor network was hypothetical at present because no sensors were available to conduct an experimental validation. However, the intention was to create a case that can be used for future experimental validation of the optimization procedure. SF6 exponential decay release was assumed within a building zone. This nontoxic, zerobackground concentration gas is typically used in the calibration of pollutant sensors. The initial release rate of Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric Table 4. The summary of zone properties in the optimization case. Zone Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Floor Area (m2)

Number of Windows

Supply (m3/sec)

Exhaust (m3/sec)

16.3 13.0 13.7 13.0 15.0 11.1 87.5 4.5 7.4 7.4 10.7 2.0 3.3 3.3 4.5 7.4 7.4 11.1 13.0 13.7 13.0 15.0 11.1

2⫹3 2 2 2 2 1 0 0 0 0 3 0 0 0 0 0 0 1 2 2 2 2 2

0.189 0.130 0.130 0.130 0.130 0.130 2 ⫻ 0.07 0.028 0.064 0.035 0.165 0.047 0 0 0.028 0.064 0.035 0.118 0.142 0.142 0.142 0.142 0.118

0.189 0.130 0.130 0.130 0.130 0.130 2 ⫻ 0.198 0 0 0 0.165 0 0 0 0 0 0 0.118 0.142 0.142 0.142 0.142 0.118

the pollutant source RO was 1 g/sec and the decay constant, tc, was 1 sec⫺1. Equation 6 describes the timedependent release rate [Rs (t) (g/sec)] of the introduced exponential decay pollutant source. R S 共t兲 ⫽ RO e ⫺ t/tC

(6)

Additionally, three prevailing wind directions were considered. These dominant winds were determined using the actual readings from a weather station located in the vicinity of the office building. The weather station provided data for wind conditions during January 2005. Figure 5a shows the frequency of wind conditions, whereas Figure 5b presents the wind rose during this period. On the basis of the measured data, wind conditions were also specified in Table 5. After the described growing algorithm presented in Figure 3a, a single sensor was introduced to the building indoor environment. The sensor measured contaminant concentrations every 3 sec. The 3-sec time step was selected because it is the shortest referenced detection time in Table 1 for presently available contaminant sensors. According to the LOCS approach, the initial contaminant concentration increase within the nonreleasing zones was approximated with eq 3. LOCS describes and validates the applicability of eq 3 to constant29 and burst (instantaneous) pollutant releases as already described in this paper, whereas here the same equation is applied to an exponential decay source. This is possible because it was found that the initial contaminant concentration increase within the nonreleasing building zones follows the same pattern/equation for constant, instantaneous, and exponential decay pollutant sources. Volume 60 September 2010

Knowing the elapsed time t and contaminant concentration C in eq 3, three sensor readings are necessary to determine the remaining three unknowns: ␣, ␤, and ␥. During the first 9 sec after the pollutant release, a deployed sensor records three contaminant concentration readings: C1 (3 sec after the release), C2 (6 sec after the release), and C3 (9 sec after the release). These three initial sensor reading data were obtained from the multizone simulations and were used for derivation of ␣, ␤, and ␥ coefficients. A total of 66 sets of coefficient values (␣, ␤, ␥) were obtained for each of the possible 23 locations/zones of a single contaminant concentration sensor under the three weather conditions in January 2005 as described in Table 5. These 66 coefficient sets do not include coefficients for the actual contaminant release zone because the contaminant concentration pattern within the actual release zone highly depends on the contaminant release type and substantially differs from the concentration patterns in all other zones. Several computations of (␣, ␤, ␥) coefficients had to be performed numerically because no solutions could be found analytically (i.e., directly solving the analytical expressions described in the literature).48 The reason for this is a short 9-sec time interval for the sensor readings during which concentrations increased almost linearly in some cases, rather than obeying eq 3. In such cases, additional checks ensured that numerically obtained coefficients (␣, ␤, ␥) produced negligible discrepancies in contaminant concentrations when replaced in eq 3 compared with those concentrations obtained by the multizone model. However, in rare occasions when contaminant concentration values were very low, neither the analytical nor numerical approach gave accurate results, and the value of the ␣ coefficient calculated by the numerical method was zero, whereas the analytical value was very close to zero. In such occasions, the following sequence was used to determine the coefficient set: • If ␣numerical ⫽ 0, then use ␣analytical. • If ␣numerical ⫽ 0, then contaminant concentration C2 was calculated with numerical and analytical coefficients. The coefficients producing the smallest discrepancy with respect to the predicted C2 values from the multizone model were selected in this network training case. The obtained (␣, ␤, ␥) coefficient sets were further used to train NN for every possible single-sensor allocation. Figure 6 presents a single-sensor NN with three input, three hidden, and two output neurons designed according to the LOCS methodology. Each NN training session included 20,000 iterations matching the sets of (␣, ␤, ␥) coefficients to the designated sources of pollutants. Each pollutant source location was represented by a two-dimensional (x, y) vector corresponding to the approximate centers of different building zones normalized on a (0,1) interval, as shown in Table 6. The training sessions were repeated to obtain the minimal NN mean squared training error subject to the initial randomly assigned NN training parameters and the number of training iterations (set to 20,000). The training sessions were designed for all possible single-sensor locations and resulted in a minimal training error for a single sensor Journal of the Air & Waste Management Association 1043


Figure 5. (a) The frequency of wind occurrence and (b) wind rose (wind angles are calculated from the north in a clockwise direction).

located in zone 12 assuming average wind weather conditions. To have accurate contaminant source prediction in the presented case, the optimization terminating criterion was defined. The terminating criterion assumed that an appropriately trained NN would have an error smaller than the difference between the two closest NN target values indicating different building zones, as given in Table 6. For example, the numerically closest zones in Table 6 have 0.1 differences in neuron values. Therefore, to have an accurate pollutant source prediction, the NN mean squared error should be less than 0.01. However, if the simulation objective is somewhat relaxed and defined as identification of a building area rather than an exact zone with the pollutant source, training error values of 1044 Journal of the Air & Waste Management Association

approximately 0.01 should result in a satisfactory performance. The obtained single-sensor NN training error of approximately 0.04 was not sufficiently small to have an accurate pollutant source prediction. These findings indicated that a single sensor was not sufficient for the given building office floor. After the described growing algorithm and optimization procedure, zone 1 was identified as optimal for introducing an additional sensor. According to the LOCS methodology, a new NN is designed. Because the two sensors each gave three input parameters, the new network had a total of six input neurons. The designed network was trained with the same number of iterations (20,000) to have the mean squared error reduced to approximately 0.02. This was still not sufficient for accurate Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric Table 5. Wind conditions for the office building in January 2005.

Variable

No Wind

Average Wind

Maximum Wind

Atmospheric pressure (Pa) Relative humidity (%) Outdoor temperature (°C) Wind speed (m/sec) Wind direction, measured clockwise from the north (°)

102,468 75.8 ⫺2.8 — —

102,468 75.8 ⫺2.8 2.0 212

101,740 72 1.1 10.7 270

pollutant source predictions according to the defined error criteria but showed an improvement compared with a single sensor prediction error of 0.04. Therefore, zone 23 was identified as optimal for allocating the third sensor. The three-sensor newly constructed NN had nine input neurons. The obtained training error (after 20,000 iterations) was approximately 0.01, sufficiently small to end the optimization procedure. Analysis of the results showed that the selected three-sensor NN was able to accurately predict the area of the building containing the pollutant source in 85% of the training cases. Here, the area is defined such that it covers the 0.1 radius tolerance around the predicted source position. When specifying the zone corresponding to the NN results, Hamming distances were calculated between the network predictions and zonal coordinates, which are given in Table 6. These are distances between the points in Table 6 and network predictions scaled to the two-dimensional output domain space (0,1). The zone corresponding to the minimum Hamming distance is defined to be the source zone predicted by the NN. Such a definition is somewhat problematic in the case of zone 7, the corridor, because of the shape of this zone. For example, network predictions with x coordinates in range of 0.3– 0.9 and y coordinates of approximately 0.3, which fall in zone 7, would actually be classified between zones 15–17 and 19 –22 (Figure 4) rather than in zone 7, which has a much larger Hamming distance. Furthermore, a NN considers the aforementioned zones as adjacent because zone 7 (0.8, 0.5) is not defined between them. The study presented here addressed these issues by introducing an additional four zones instead of the single zone 7. However, under the described conditions, the new 27-zone simulation cases did not yield improvements in results. Therefore, the inconsistencies were corrected during the analysis of obtained results to achieve a valid NN source prediction accuracy of 85%, as stated previously. Finally, backed by more than 50 multizone simulations, the defined optimization procedure resulted in a sufficiently accurate NN and at the same time optimized the number and allocation of contaminant concentration sensors. For the described

Table 6. NN target values indicating different building zones. Source Location Zone Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

NN Output (neuron 1, neuron 2) (0.2, 0.8) (0.4, 0.8) (0.5, 0.8) (0.6, 0.8) (0.8, 0.8) (0.2, 0.7) (0.8, 0.5) (0.4, 0.6) (0.5, 0.6) (0.6, 0.6) (0.2, 0.5) (0.4, 0.5) (0.5, 0.5) (0.6, 0.5) (0.4, 0.4) (0.5, 0.4) (0.6, 0.4) (0.2, 0.3) (0.4, 0.2) (0.5, 0.2) (0.6, 0.2) (0.8, 0.2) (0.2, 0.1)

office floor, three sensors placed within zones 1, 12, and 23 were sufficient to have accurate prediction of an indoor pollutant source area under the specified average wind weather conditions. DISCUSSION AND EXPERIENCES WITH LOCS METHODOLOGY Further investigation of various weather influences on the accuracy of NN predictions of source position revealed the need for separate training of existing NNs for each of the wind conditions in Table 5. To have a single tool capable to work under variable weather conditions, an additional classification algorithm can be applied to channel the sensor data toward adequately trained NNs. This algorithm corresponds to introduction of additional layers in the NN design and/or additional input neurons provided with the weather data. Additional considerations of the described sensor optimization algorithm for real-time prediction of indoor pollutant source positions include: • Building boundary conditions: Building boundary conditions can influence the accuracy of multizone programs used to compute contaminant concentration distributions. These parameters are crucial for the NN training, accuracy, and applicability of the developed optimization procedure in real buildings. • Sensitivity of input coefficients (␣, ␤, ␥) to

Figure 6. The constructed NN for optimizing a single-sensor allocation. Volume 60 September 2010

Journal of the Air & Waste Management Association 1045


Figure 7. NN model for identifications of indoor contaminant source positions.

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changes in other parameters: This is an important issue for NN pollutant source predictions based on (␣, ␤, ␥) coefficient values. Nonuniformity of indoor conditions and discrepancies between the actual and measured conditions are only few of the parameters that may impact the NN inputs and the whole prediction and optimization process. Simulation tool accuracy: Simulation tool accuracy refers to the software used in simulations of pollutant dispersions within a building. This implies differences in results from various simulation tools (e.g., multizone programs, CFD, or coupled multizone-CFD models) and discrepancies in results from different versions of the same simulation program. Accuracy of flow path simulation models: Even within accurate simulation tools, flow path models need to be carefully selected to correspond to reality. There are no ideal flow path models applicable to all flow paths in every possible situation. Number of building zones: Multizone models may produce different results depending on the number of building zones within the same building. Because each building zone considers uniform fluid field properties, an increased number of zones increases the number of internally uniform areas of the building, reducing the overall uniformity within the building itself and better corresponding to reality. Sensor technology: This paper reviewed present trends in sensor development and issues of concern for applicability of the developed optimization procedure. Most importantly, no present sensor can timely provide required real-time contaminant concentration values in incidental situations. Number of NN training cases: Rules of thumb in NN applications require that the number of training cases should be equal to 10 times the product of NN input and output neurons. They also require 10% of these cases left aside for testing. Thus, because NN grows in the described optimization algorithm, the number of training cases should also increase, requiring additional contaminant dispersion simulations. Training initialization of NNs: In the described


optimization procedure, NN training initializations are performed randomly on the basis of the limits in input parameters. Thus, various initializations reduce the training error at various speeds obtaining the training objectives in a variable number of training iterations. However, the described optimization procedure always used a fixed number of 20,000 training iterations. • Number of training iterations: As the number of training iterations increases, the training error reduces, but the risk of overtraining threatens to produce a network capable of perfectly matching only the training set of inputs to outputs rather than including various other input values possible in reality. All of the listed considerations may impact the overall accuracy and applicability of the developed optimization algorithm and require the attention of LOCS users. Varying indoor and outdoor conditions can be included in the LOCS simulations. This ensures wider applicability of the method to real building conditions. With the extended range of applicability, the method can determine location of pollutant sources and optimize the number and allocation of sensors for detection of constant, burst, and decaying sources in real incidental or accidental pollutant releases within building interiors. Possible applications also include sensor optimization for radon or smoke source identification within buildings. Figure 7 shows how trained NN LOCS should operate in indoor building environments. NNs should compute the contaminant source location on the basis of the inflow of real-time input data from sensors located in inside of the building zones. The presented examples demonstrate that such computation can be fast and reliable. CONCLUSIONS Using computational technology can be an appropriate answer to the present security and environmental challenges. The proposed LOCS method for fast and reliable identification of a contaminant source position offers promising possibilities for cost- and time-effective response to incidental situations such as terrorist threats, chemical accidents, smoke/fire occurrence, and radon contamination. Application of suggested tools would increase the potential for forehand and adequate reactions to those incidents. Volume 60 September 2010

Vukovic, Tabares-Velasco, and Srebric The paper presented two cases including successful validation of the LOCS methodology and its application to (1) real-time prediction of indoor pollutant source positions, and (2) optimization of the number and allocation of indoor contaminant concentration sensors. The prospective applicability of the developed method is not limited by the software used to generate the NN training data because different simulation methods can be used to supply training data. Furthermore, the presented cases showed that LOCS is independent of (1) Contaminant species type (2) Contaminant release type, for constant, burst, and exponential decay sources (3) Contaminant source strength (4) Background contaminant concentration levels Thus, the main purpose of the developed method is a contaminant source identification assuming a proper deployment of adequate contaminant concentration sensors. However, adequate sensors capable of timely and accurately measuring concentrations of chemical, biological, and/or radiological pollutants in potential hazardous incidents do not exist at present. Nevertheless, the sensor optimization procedure was developed having in mind enormous efforts invested in sensor research and rapid development of sensor technologies. ACKNOWLEDGMENTS This research was financially supported by the National Science Foundation (grant no. CTS-0134326). The authors thank Dr. Mirna Urquidi-Macdonald, Professor of Engineering Science and Mechanics at Pennsylvania State University, for her invaluable suggestions and comments related to the application of artificial NNs in this study. REFERENCES 1. Presidential ad hoc Committee for Building Health and Safety under Extraordinary Incidents: Report on Risk Management Guidance for Health, Safety and Environmental Security under Extraordinary Incidents; American Society of Heating, Refrigerating and Air-Conditioning Engineers: Atlanta, GA, 2003. 2. Guidance for Protecting Building Environments from Airborne Chemical, Biological or Radiological Attacks; Department of Health and Human Services; Centers for Disease Control and Prevention; National Institute for Occupational Safety and Health: Washington, DC, 2002. 3. Rege, M.A.; Tock, R.W. Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources; J. Air & Waste Manage. Assoc. 1996, 46, 953-962. 4. Yang, J.; Li, X.; Meng, B.; Zhao, B. Algorithm for Quickly Calculating the Pollutant Distribution in Central Ventilation Systems; J. Tsinghua Univ. 2004, 44, 774-777. 5. Demokritou, P.; Yang, C.; Chen, Q.; Spengler, J.D. An Experimental Method for Contaminant Dispersal Characterization in Large Industrial Buildings for Indoor Air Quality (IAQ) Applications; Build. Environ. 2002, 37, 305-312. 6. Yang, C.; Demokritou, P.; Chen, Q.; Spengler, J. Experimental Validation of a Computational Fluid Dynamics Model for IAQ Applications in Ice Rink Arenas; Indoor Air 2001, 11, 120-126. 7. Boris, J. The Threat of Chemical and Biological Terrorism: Preparing a Response; Comput. Sci. Eng. 2002, 4, 22-32. 8. Zhang, T.F.; Chen, Q. Identification of Contaminant Sources in Enclosed Environments by Inverse CFD Modeling; Indoor Air 2007, 17, 167-177. 9. Federspiel, C.C.; Li, H.; Auslander, D.M.; Lorenzetti, D.; Gadgil, A.J. Modeling Transient Contaminant Transport in HVAC Systems and Buildings. Presented at the Indoor Air 2002 Conference Proceedings, 2002. 10. Mora, L.; Gadgil, A.J.; Wurtz, E. Comparing Zonal and CFD Model Predictions of Isothermal Indoor Air to Experimental Data; Indoor Air 2003, 13, 77-85. 11. Schaelin, A.; Dorer, V.; van der Maas, J.; Moser, A. Improvement of Multizone Model Predictions by Detailed Flow Path Values from CFD Calculations; ASHRAE Trans. 1993, 99, 709-720. Volume 60 September 2010

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About the Authors Vladimir Vukovic was a graduate student at Pennsylvania State University at the time this study was conducted and is now a research engineer at Arsenal Research in Wien, Austria. Paulo Cesar Tabares-Velasco was a graduate student at Pennsylvania State University and is now a postdoctoral researcher at the National Renewable Energy Laboratory in Golden, CO. Jelena Srebric is an associate professor of architectural engineering at Pennsylvania State University. Please address correspondence to: Jelena Srebric, Pennsylvania State University, Department of Architectural Engineering, 222 Engineering Unit A, University Park, PA 16802; phone: ⫹1-814-863-2041; fax: ⫹1-814863-4789; e-mail: [email protected].

Volume 60 September 2010