Abstract:
An Environmental Control System includes a sensor, an air purification subsystem, and a controller in communication with the sensor and air purification subsystem. The sensor detects a contaminant in the air and generates a contaminant signal. The controller compares the contaminant signal to a predicted sensory response threshold. When the contaminant signal reaches the predicted sensory response threshold, the controller commands the air purification subsystem to alter a condition in the air.

Description:
BACKGROUND OF THE INVENTION 
     The present invention generally relates to apparatus and methods for treatment of airstreams in an adaptive Environmental Control System (ECS) to remove contaminants. 
     ECSs of various types and complexity are used in military and civil airplanes, helicopter, and spacecraft applications. In aircraft for example, airflow may be circulated to occupied compartments, cargo compartments, and electronic equipment bays. Air containing many pollutants such as particulate matter, aerosols, and hydrocarbons may range in humidity from dry (&lt;2%) to very humid and may be delivered in a heated condition to the cabin from the ECS. 
     Aircraft occupants are not exposed to a single chemical in isolation, and the effects of co-exposures to multiple chemicals are poorly understood. Exposure duration for crews can be 14+ hours. Crews can routinely be assigned to work a 14 hour duty day without a break. The duty day can be extended if there is a maintenance delay or weather. Some international crews are assigned to work a longer duty day. There are flight safety and security implications for not adequately protecting pilots (who must perform cognitively-demanding safety-sensitive flight duties) and cabin crew (who must maintain cabin safety and security). Specifically, manufacturers are currently required to ensure that aircraft systems are designed to provide—in operation, under normal conditions and during any probable failure—“a sufficient amount of uncontaminated air to enable the crewmembers to perform their duties without undue discomfort or fatigue, and to provide reasonable passenger comfort.” It has been widely recognized by air accident investigators, regulators, and pilot groups that flight safety can be compromised when pilots are exposed to oil-based contaminants in the ventilation air entering from outside the aircraft through the main engine bleeds or APU bleed or other air sources including ground supplies and electric compressors. Requiring pilots to rely on their noses to identify the presence and location of bleed air contaminants prolongs the exposure for the pilots and/or cabin occupants, depending on the location of the contaminant source. 
     The industry accepted approach to verification of acceptability of aircraft cabin air quality has been to gather air samples through various forms of sample media to capture the range of contaminants that might be present. There are three US Environmental Protection Agency (EPA) methods that are accepted as guidance for sample collection and analysis for volatile and semi-volatile compounds and for aldehyde compounds that may create odor and create irritancy. 
     The sample methodology is inadequate to fully characterize all compounds with any given method, thus requiring the use of multiple methods. The analyst must also determine—based on equipment availability and laboratory capability—which methods to use. 
     As can be seen, there may be an ongoing need to interpret real-time air contaminant data and provide an indication of when the levels may increase beyond the range of acceptability to enable corrective action. 
     SUMMARY OF THE INVENTION 
     In one aspect of the present invention, an environmental control system (ECS) having contaminated air therein includes a sensor; an air purification subsystem; and a controller in communication with the sensor and air purification subsystem; wherein the sensor detects a contaminant in the outside air supplied through the engines, APU or other air sources including ground supplies and electric compressors; and generates a contaminant signal; wherein the controller compares the contaminant signal to a predicted sensory response threshold; wherein the predicted sensory response threshold is based on one of odor, irritancy, mass, or a combination thereof; and when the contaminant signal reaches the predicted sensory response threshold, commands the air purification subsystem to alter a condition in the contaminated air. 
     In another aspect of the present invention, a controller for an environmental control system (ECS) having a sensor and an air purification subsystem communicates with the sensor and air purification subsystem; receives a contaminant signal from the sensor; compares the contaminant signal to one of a contaminant concentration look up table and a contaminant mass look up table; and based on the comparison, commands the air purification subsystem to alter a condition of contaminated air in the ECS. 
     In yet another aspect of the present invention, a method of controlling contaminants in air in an environment includes creating a sample database of the contaminants in the air; calculating a contaminant concentration of a contaminant in the sample database; calculating a predicted sensory detection threshold of the contaminant; comparing the predicted sensory detection threshold to one of a contaminant concentration and contaminant mass in the air; and adjusting the air based on the comparison. 
     These and other features, aspects and advantages of the present invention will become better understood with reference to the following drawings, description and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a block diagram of an environmental control system according to an exemplary embodiment of the present invention; 
         FIG. 1B  is a block diagram of a controller that can be implemented in the system of  FIG. 1A  according to an exemplary embodiment of the present invention; 
         FIG. 2A  is a flow chart of a method of controlling contaminants in an environmental control system according to an exemplary embodiment of the present invention; 
         FIG. 2B  and  FIG. 2C  is a flow chart of a method that can be implemented as part of the method of  FIG. 2A ; 
         FIG. 3A  and  FIG. 3B  is a table depicting frequency of occurrence of contaminants in air samples according to an exemplary embodiment of the present invention; 
         FIG. 4A  and  FIG. 4B  is a table depicting concentrations, at various percentiles, of contaminants in air samples according to an exemplary embodiment of the present invention; 
         FIG. 4C  and  FIG. 4D  is a table depicting isobutyene equivalent concentrations, at various percentiles, of the contaminants in  FIG. 4A  and  FIG. 4B  according to an exemplary embodiment of the present invention; 
         FIG. 5A  and  FIG. 5B  is a table depicting predicted odor detection thresholds of contaminants in air samples according to an exemplary embodiment of the present invention; 
         FIG. 5C  and  FIG. 5D  is a table depicting odor concentrations, at various percentiles, of contaminants in  FIG. 5A  and  FIG. 5B  according to an exemplary embodiment of the present invention; 
         FIG. 6A  and  FIG. 6B  is a table depicting predicted sensory irritancy detection thresholds of contaminants in air samples according to an exemplary embodiment of the present invention; 
         FIG. 6C  is a table depicting predicted sensory irritancy concentrations, at various percentiles, of contaminants in  FIG. 6A  and  FIG. 6B  according to an exemplary embodiment of the present invention; 
         FIG. 7  is a table summarizing predicted, combined contaminant odor detection thresholds; predicted, combined contaminant sensory irritancy detection thresholds; predicted, combined contaminant sensory thresholds; combined contaminant equivalence concentrations; and combined contaminant mass; 
         FIG. 8  is a graph of probability of detectability, percentile of sample database, and contaminant equivalence concentration; 
         FIG. 9  is a graph of probability of detectability, percentile of sample database, and VOC mass. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following detailed description is of the best currently contemplated modes of carrying out the invention. The description is not to be taken in a limiting sense, but is made merely for the purpose of illustrating the general principles of the invention, since the scope of the invention is best defined by the appended claims. 
     Various inventive features are described below that can each be used independently of one another or in combination with other features. However, any single inventive feature may not address any of the problems discussed above or may address only one of the problems discussed above. Further, one or more of the problems discussed above may not be fully addressed by any of the features described below. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method, or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon. 
     Any combination of one or more computer readable storage media may be utilized. A computer readable storage medium is an electronic, magnetic, optical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium is any tangible medium that can store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the users computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable storage medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable storage medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The present invention generally provides an environmental control system (ECS) that can continuously adapt to changing contaminants—both in kind and degree—in contaminated air in the ECS. The contaminated air may include outside air entering the ECS through engines or APU, or other air sources including ground supplies and electric compressors, as well as recirculating air in the ECS. A controller of the ECS may receive contamination signals from one or more sensors that sense one or more contaminants in the contaminated air. The sensors may also sense total mass of the contaminants—without regard to the specific contaminants being sensed. 
     One or more of these contamination signals can then be compared against a predicted sensory response threshold and/or an average sensory response threshold. If the threshold is exceeded, the controller may send control signals to an air purification subsystem of the ECS to alter, for example, fan speed, air flow rate, or modulating the operating rate of an air purification system, or opening and closing valves to such an air purification system in the outside air and/or recirculating air entering, for example, an environment. The thresholds of the supply air, the recirculated air, and the cabin air may be different. The method of determining thresholds can be the same for each air source or location. The threshold of the outside air divided by its sensory limit times the flow rate, plus the threshold of the recirculated air divided by its sensory limit time the flow rate determines the total threshold of the cabin air. The environment may be a cabin of an aircraft or other vehicle, or other space such as a building intended to be occupied by humans. Once the contamination signal(s) drops below the total threshold of the cabin air, the controller may discontinue commanding the alteration of the contaminated air. 
     Generally, the present invention can include determining one or more sensory thresholds for contaminants, such as odor detection thresholds and/or sensory irritancy thresholds and/or mass thresholds. Based on one or more of the thresholds, the present invention may then predict one or more predicted sensory response thresholds. The predicted sensory response threshold can then be used to continuously compare it against actual contaminant levels, and thus enable continuous control of contaminants in the environment. 
       FIG. 1  is a block diagram of an ECS  10  according to an exemplary embodiment of the present invention. The ECS  10  may include a controller  11 , such as a computer having a processor and a memory, in continuous or intermittent communication with an air purification subsystem  13  and one or more sensors  12 . The sensors  12  may be positioned in various points throughout the ECS to sense contaminants in the outside air supplied through engine or APU bleeds, or other air sources including ground supplies and electric compressors, and/or recirculating air in the ECS and/or, in particular, an environment  14 , such an aircraft cabin. The contaminants in the ECS may include, for example, OVCs and/or SVOCs and/or ultrafine particles (UFPs). 
     The sensor  12  can be any sensor capable of sensing the anticipated contaminants in the contaminated air. One or more of the sensors can sense the identity and amount of the individual contaminants in the contaminant air. In addition, one or more sensors can sense the mass of the contaminants without regard to the specific identity of the individual contaminants. For example, to sense individual contaminants, the sensor  12  may be a photoionization detector (PID), such as a PID by RAE Systems of San Jose, Calif. To sense total mass of contaminants, the sensor may also be a PID. These types of sensors and their operation is described in the PID Handbook (Third Edition), by RAE Systems Inc., which is incorporated herein by reference in its entirety. 
     An example configuration of multiple sensors in an ECS that can be employed in the present invention is shown, for example, in US patent application, entitled “Aircraft Environmental Control System That Optimizes the Proportion of Outside Air From Engines, APUs, Ground Air Conditioning Units and the Recirculated Cabin Air to Maintain Occupant Comfort and Maximize Fuel Economy,” filed concurrently with this application and incorporated herein by reference in its entirety. 
     The air purification subsystem  13  may include various coolers, fans, and filters to alter the contaminated air. An example of an air purification subsystem that can be employed in the present invention is shown, for example, in US patent application, entitled “Aircraft Environmental Control System That Optimizes the Proportion of Outside Air From Engines, APUs, Ground Air Conditioning Units and the Recirculated Cabin Air to Maintain Occupant Comfort and Maximize Fuel Economy,” filed concurrently with this application and incorporated herein by reference in its entirety. 
       FIG. 1B  is a functional block diagram of the controller  11  according to an exemplary embodiment of the present invention. The controller  11  include a processor (not shown) and a memory (not shown) that can store instructions to be executed by the processor to implement a method of removing contaminants from a space to be occupied by humans, such as the cabin of an aircraft, according to the present invention. The controller  11  may receive contamination signals from sensor(s)  12   a  which may sense contaminants from, for example, a cabin filter, a filter to a mix manifold, an ECS pack to a mix manifold, and the mix manifold to the cabin. The controller  11  may also receive contamination signals from sensor(s)  12   b  which may sense contaminants from bleed air. 
     The controller  11  may then compare the contamination signals to a contaminant concentration look up table  15   a  that may have information/data of contaminant concentration equivalents versus percentile of a sample database. The controller  11  may, in addition to or in lieu of the foregoing comparison, compare contamination signals to a contaminant mass look up table  15   b  that may have information/data of total mass of contaminants versus percentile of a sample database that may or may not the be same sample database in look up table  15   a.    
     Based on the foregoing comparison(s), the controller  11  may then command a valve  13   a  to alter valve opening/closing and thereby alter a flow of outside air. Also, in addition to or in lieu of the foregoing command, the controller may command a fan  13   b  to alter a fan speed of recirculated air. Alternatively, the response of the controller may be to modulate an air purification device, increasing or decreasing its power to effect the desired change in air contaminant concentrations 
     Either alone or in combination with commanding the valve  13   a  and the fan  13   b , the controller may command an outflow valve  13   c  to open or close. The valve  13   c  may enable combined outside and recirculated air to enter the cabin as cabin air  14   c.    
       FIG. 2A  is a flow chart that depicts general exemplary steps of a method  20 , which may be implemented by a controller, such as controller  11 , of removing contaminants from an environment such as an aircraft cabin.  FIG. 2B  is a flow chart that depicts more specific steps that can be employed, according to an exemplary embodiment, to implement at least some of the general steps shown in  FIG. 2A . 
     In  FIG. 2A , the method  20  may generally include a step  21  for determining a frequency of occurrence of individual contaminants in samples of contaminated air that are expected to be present in the environment  14 . In the example of an aircraft cabin, the samples may be outside air and engine bleed air. 
     A step  22  may generally include calculating individual contaminant concentration in the samples at various sample population percentiles, such as the 75th, 90th, 95th, and 99th percentiles, as well as calculating a mean concentration. This may include calculating individual “concentration equivalents” which refer to the concentrations of a calibration or equivalent compound (such as isobutylene) used by a sensor (such as a PID sensor by RAE Systems) to convert concentrations of different contaminants to concentrations of one calibration or equivalent compound, as further described in the PID Handbook (Third Edition), by RAE Systems Inc. 
     A step  23  may generally include calculating predicted, individual contaminant odor detection thresholds (i.e., responses) at various sample population percentiles, which can be the same or different percentiles as the contaminant concentration equivalents above, as well as a mean odor threshold. 
     A step  24  may generally include calculating predicted, individual contaminant sensory irritancy detection thresholds (i.e., responses) at various sample population percentiles, which can be the same or different percentiles as the contaminant concentration equivalents and/or odor thresholds above, as well as a mean irritancy threshold. 
     A step  25  may generally include, using the foregoing predicted sensory detection threshold(s)—i.e., odor thresholds and/or irritancy thresholds—for calculating predicted, combined contaminant sensory thresholds (i.e., responses) at various sample population percentiles, which can be the same or different percentiles as the contaminant concentration equivalents and/or odor thresholds and/or irritancy thresholds above, as well as a mean sensory threshold. The predicted, combined contaminant sensory thresholds may be average sensory responses (i.e., thresholds). 
     As can be appreciated, calculating predicted, combined contaminant sensory thresholds  25  need not be based on only odor and/or irritancy thresholds. Other sensory thresholds, such as contaminant mass thresholds, can be used to calculate the predicted, combined contaminant sensory thresholds. It can also be appreciated that step  25  need not be utilized in method  20  when, for example, a single predicted sensory detection threshold is used for the below described comparison to a contamination signal. 
     A step  26  may generally include comparing one or more predicted, combined contaminant sensory thresholds to one or more contaminant signals from one or more sensors. The contaminant signals can be based on a combination or mixture of the individual contaminant concentration equivalents determined in step  22 . Step  26  may also include a comparison of the foregoing to a probability of contaminant detection by an occupant in an environment, such as environment  14 . 
     A step  27  may generally include adjusting one or more of the contaminant air sources based on the foregoing comparison. The adjusting may occur prior to or when the contaminants reach a threshold of probability of contaminant detection. 
       FIG. 2B  depicts exemplary sub-steps of the general steps  21  to  26 , and such sub-steps are described below. 
     Referring again to  FIG. 2A , step  21  may include collecting samples of contaminated air that may be expected to enter the environment  14 . The samples may be collected at different times and from different sources to create a sample database. For example, in the context of an aircraft cabin, the present inventors used for experimental, exemplary purposes different contaminated air sources that included engine air and APU bleed air. In some instances, the samples came from engine test cells. In other instances, the samples came from engines and APUs installed on aircraft. Further, the experimental samples were acquired between 1996 and 2013; however, the present invention envisions a time frame that is longer or shorter than seventeen years. The total number of experimental samples exceeded two hundred; however, the present invention envisions more or less than two hundred samples. 
     In the experimental database, there was significant data scatter. Some of that scatter could be related to environmental conditions around the test area. The scatter could also be due to variation in sampling and laboratory analysis over the seventeen year period. This database indicates that there may be hundreds of different compounds and/or contaminants present in any given sample. 
     Step  21  may include steps  21   a ,  21   b , and  21   c  shown in  FIG. 2B  and  FIG. 2C . In step  21   a , a total sample size for each contaminant may be determined (i.e., total number of samples tested for a contaminant of interest, regardless of whether the contaminant of interest was present). In step  21   b , a non-zero sample size (i.e., number of samples where the contaminant of interest was present) for each contaminant may be determined. In step  21   c , a frequency of occurrence for each contaminant can calculated by taking the ratio of non-zero samples to total samples. These results are shown in  FIG. 3A  and  FIG. 3B . 
     In step  21   d , as shown in  FIG. 3A  and  FIG. 3B , the contaminants can be ordered by importance, starting from the highest frequency of occurrence and ending at the lowest frequency—for purposes of illustration. Other characteristics might be used to determine importance, such as sensory perception. One example is acrolein because it is extremely irritating at very low concentration. 
       FIG. 3  A and  FIG. 3B  lists (for illustration purposes) sixty four of the most frequently occurring compounds in bleed air. It can be seen from  FIG. 3A  and  FIG. 3B  that only ten compounds occur in more than 60% of the samples. However, other contaminants, such as tricresylphosphate isomers (TCP), can be considered significant and included in the database, even if they do not occur frequently, due to concerns about the effects of long term exposure by humans. 
     As further described below, assessing the contaminants as shown in  FIG. 3A  and  FIG. 3B  can be used in the method  20  to interpret real-time air contaminant data from sensors, and provide an indication of when the levels may increase significantly enough to fall outside the range of normal distribution of the data. Identifying the level that falls outside levels normally encountered (i.e., thresholds) can be used to provide an early warning of a pending maintenance action in one instance. In another instance, identifying levels that fall outside a norm can be used for controlling outside air-flow and the air purification subsystem  13 . 
     Referring to  FIG. 2B  and  FIG. 2C  and  FIG. 4A  and  FIG. 4B , in step  22 , for the contaminants of interest determined in step  21 , individual contaminant concentrations can be calculated at various sample population percentiles in step  22   a . In an exemplary embodiment, the individual concentrations for the individual contaminants in  FIG. 3  A and  FIG. 3B  were calculated at the mean of the database, and at the 75th, 90th, 95th, and 99th percentiles. Those contaminant concentrations—in both ug/m 3  and ppmV—are presented in  FIG. 4A  and  FIG. 4B . 
     Referring to  FIG. 2B  and  FIG. 4C  and  FIG. 4D , in step  22   b , individual contaminant “concentration equivalents” can be calculated from the individual contaminant concentrations in step  22   a . In an exemplary embodiment, the individual concentration equivalents for the individual contaminants in  FIG. 3A  and  FIG. 3B  were calculated at the mean of the database, and at the 75th, 90th, 95th, and 99th percentiles. Those contaminant concentration equivalents are presented in  FIG. 4C  and  FIG. 4D . 
     The calculation of contaminant concentration equivalents can be performed in various ways, depending on the specific sensor(s)  12  used. In the illustrative experiments by the inventors herein, isobutylene PID sensors by RAE Systems were used. Therefore, sensors were calibrated to isobutylene, and the actual concentrations of the actual contaminants sensed were all converted to isobutylene equivalent concentrations (ppm-V), as shown in  FIG. 4B . The conversion is obtained by the actual concentration divided by a correction factor for the contaminant as established by RAE Systems and replicated in the “Factor for denominator” column in  FIG. 4C  and  FIG. 4D . 
     Referring to  FIG. 2B  and  FIG. 2C  and  FIG. 5A  and  FIG. 5B , a step  23 —for calculating a predicted odor detection threshold for one or more of the contaminants from step  21 —can include steps  23   a ,  23   b ,  23   c , and  23   d  in an exemplary embodiment. 
     Step  23   a  can include determining odor detection thresholds (ODTs) for one or more of the contaminants of interest from step  21 . In one example, this can be accomplished experimentally. 
     In another example, step  23   a  can include looking up previously determined (i.e., published) ODTs for one or more of the contaminants of interest from step  21 . For example, step  23   a  can include obtaining ODTs from Devos et al., (1990) Standardized human olfactory thresholds. Oxford: IRL Press, which is incorporated by reference herein in its entirety. 
     In step  23   b , log 10 values of the reciprocals of the ODTs (for example, those from Devos  1990  above) can be calculated in volume, and in step  23   c  can be calculated in mass. The results are shown in  FIG. 5A  and  FIG. 5B . The volume ODTs in units of ppm can be used to calculate predicted sensory thresholds or average sensory responses and then for eventual comparison with data of concentration equivalents in the contaminated air, as described below. Likewise, the mass ODTs in units of ug/m 3  can be used to calculate predicted sensory thresholds and then for eventual comparison with data of mass of contaminants in the contaminated air, as described below. 
     In step  23   d , for one or more of the contaminants of interest, a predicted, single contaminant odor detection (OD) ratio (e.g., threshold) may be calculated wherein the numerator is the ppm contaminant concentration from  FIG. 4A  and  FIG. 4B  and the denominator is the ppm odor threshold from  FIG. 5A  and  FIG. 5B . These ratios can be calculated at various sample population percentiles, which can be the same or different as the percentiles in  FIG. 4A  and  FIG. 4B , as well as a mean. The results are shown in  FIG. 5C  and  FIG. 5D . It can be noted that the contaminants for which ODTs and/or OD ratios are determined need not be identical to all of the contaminants in step  21 . 
     In step  23   e , the odor detection ratios from step  23   d  can be converted to dose addition ratios, as shown in  FIG. 5A  with the column headings “DA.” The dose addition method is described in Fox, “Assessing Aircraft Supply Air to Recommend Compounds for Early Timely Warning of Contamination”, Dissertation, April 2012 which is incorporated by reference herein in its entirety. 
     Also, in step  23   e , predicted, mixed contaminant odor detection ratios (e.g., thresholds) can then be calculated. These ratios are the sum of the dose addition ratios of the individual contaminants according to the following: 
                         C   1       T   1       +       C   2       T   2       +   …   +       C   n       T   n         =     Qmix   ⁢           ⁢   Dose   ⁢           ⁢   Addition   ⁢           ⁢   Odor             (   1   )               
where C is the individual contaminant concentration in ppmV from  FIG. 4A  and  FIG. 4B  at a selected percentile and T is the odor detection threshold for that contaminant in ppmV for the dose addition (DA) from  FIG. 5C  and  FIG. 5D  at the same selected percentile.
 
     The predicted, mixed contaminant dose addition ratios can be at various ratios, which can be at the same or different percentiles as in the individual dose addition ratios. These mixed ratios are shown at the bottom of  FIG. 5C  and  FIG. 5D . As further described below, the mixed odor ratios are later used provide a predicted combined contaminant sensory threshold (i.e., average sensory response) for the mixture of contaminants of interest. 
     Without intending to limit the scope of the present invention, it is noted that the relationship between the concentrations of contaminants in a mixture to predicted odor is believed to be similar to the relationship of mixture contaminant concentration to the predicted sensory irritancy. This is described in Cometto-Muñiz, J. E., Cain, W. S., Abraham, M. H., &amp; Gola, J. M. R. (1999). Chemosensory detectability of 1-butanol and 2-heptanone singly and in binary mixtures.  Physiology  &amp;  Behaviour,  67, 269-276. doi: 10.1016/S0031-9384(99)00074-8, which is incorporated herein by reference in its entirety. The predicted sensory irritancy of a mixture of contaminants has been found to have an additive effect on sensory irritancy, but not have a hypo-additive effect or a hyper-additive effect (Cometto-Muñiz et al., 1999). A strong correlation between dose additivity of a mixture on predicted odor detection levels half-way between chance and perfect detection has been reported at a probability level of 0.3 (0.00&lt;P&lt;0.35). Cometto-Muñiz, J. E., Cain, W. S., &amp; Abraham, M. H. (2003). Dose-addition of individual odorants in the odor detection of binary mixtures. Behavioural Brain Research, 138, (1), 95-105. doi: 10.1016/S0166-4328(02)00234-6 which is incorporated herein by reference in its entirety. A strong correlation between dose additively at sensory irritancy detection levels half-way between chance and perfect detection has been reported at a probability level of 0.6 (0.55&lt;P&lt;0.65) by Cometto-Muñiz et al. (2003). 
     Therefore, like the calculation of odor detection thresholds for the contaminants of interest, the method  20  includes steps for calculating sensory irritancy thresholds for the contaminants of interest—through steps  24   a ,  24   b ,  24   c ,  24   d ,  24   e  and  24   f  according to an exemplary embodiment. 
     Step  24   a  can include determining sensory irritancy detection thresholds (SIDTs) for one or more of the contaminants of interest from step  21 . For example, this can be accomplished experimentally. It can be noted that the contaminants for which SIDTs are determined need not be identical to all of the contaminants in step  21  and/or all of the contaminants in step  23 . 
     In another example, step  24   a  can include looking up previously determined (i.e., published) SIDTs for at least one of the contaminants of interest from step  21  and/or step  23 . For example, step  24   a  can include obtaining SIDTs from Abraham, M. H., Sanchez-Moreno, R., Gil-Lostes, J., Acree, W. E., Jr., Cometto-Muñiz, J. E., &amp; Cain, W. S. (2010). The biological and toxicological activity of gases and vapors.  Toxicology in Vitro,  24(2), 357-362. doi:10.1016/j.tiv.2009.11.009 which is incorporated herein by reference in its entirety. 
     In step  24   b , log 10 values of the reciprocals of the SIDTs (for example, those from Abraham  2010  above) can be calculated in volume, and in step  24   c  can be converted from volume to mass. The results are shown in  FIG. 6A . For example, the following equation can be used to calculate the conversion:
 
 Y  mg/m 3 =( X  ppm)(molecular weight of contaminant)/24.45  (2)
 
     The volume SIDTs in units of ppm can be used to calculate predicted sensory thresholds or average sensory responses and then for eventual comparison with data of concentration equivalents in the contaminated air, as described below. Likewise, the mass SIDTs in units of ug/m 3  can be used to calculate predicted sensory thresholds and then for eventual comparison with data of mass of contaminants in the contaminated air, as described below. 
     In step  24   d , for one or more of the contaminants of interest, a predicted, single contaminant sensory irritancy detection (SID) ratio (e.g., threshold) may be calculated wherein the numerator is the ppm contaminant concentration from  FIG. 4A  and  FIG. 4B  and the denominator is the ppm odor threshold from  FIG. 6A  and  FIG. 6B . These ratios can be calculated at various sample population percentiles, which can be the same or different as the percentiles in  FIG. 4A  and  FIG. 4B , as well as a mean. It can be noted that the contaminants for which SIDTs and/or SID ratios are determined need not be identical to all of the contaminants in step  21 . 
     In step  24   e , one or more of the odor detection ratios from step  24   d  can be converted to dose addition ratios, as shown in  FIG. 6C . These ratios can be calculated at various percentiles, which can be the same or different as the percentiles in  FIG. 4A  and  FIG. 4B , as well as a mean. The results are shown in  FIG. 6C . 
     Also, in step  24   e , predicted, mixed contaminant, sensory irritancy detection ratios (e.g., thresholds) can then be calculated. These ratios are the sum of the dose addition ratios of the individual contaminants from step  24   e  according to the following: 
                       (         C   1       T   1       +       C   2       T   2       +   …   +       C   n       T   n         )     ⊗   0.0005     =     Qmix   ⁢           ⁢   Dose   ⁢           ⁢   Addition   ⁢           ⁢   Sensory   ⁢           ⁢   Irritancy             (   3   )               
where C is the contaminant concentration in ppmV from  FIG. 4A  and  FIG. 4B  at a selected percentile and T is the sensory irritancy threshold for that contaminant in ppmV from  FIG. 6A  and  FIG. 6B . The results are shown at the bottom of  FIG. 6C .
 
     In step  24   f , the above dose addition equation (3) for sensory irritancy can include an adjustment based on qualitative experience. A factor of 0.0005 is utilized in this exemplary embodiment. 
     The method  20  continues to a step  25  wherein one or more predicted sensory thresholds, such as the predicted odor detection threshold from step  23  and/or the predicted sensory irritancy detection threshold from step  24 , can but need not be combined to provide a predicted, combined contaminant sensory threshold or average sensory threshold (i.e., response) for the mixture of contaminants of interest. That average sensory threshold can be at various sample population percentiles, such as the same percentiles used in step  22  and/or step  23  and/or step  24 . 
     The average sensory threshold can be calculated in step  25  by summing one-half of the mixed predicted odor ratio (from  FIG. 5C  and  FIG. 5D ) and one-half of the mixed irritancy ratio (from  FIG. 6C ), at the same percentile for each ratio. The results are shown in  FIG. 7 . As can be appreciated, the average sensory threshold or response can be based on thresholds other than odor and/or irritancy. 
     In step  26 , there can be sub-steps  26   a ,  26   b , and  26   c  according to an exemplary embodiment. 
     In step  26   a , a comparison can be made between probability of contaminant detection versus contaminant percentile in database. The probability of contaminant detection can include one or more predicted sensory thresholds or ratios—for a single contaminant or a mixture of contaminants. For example, they can be one or more of the predicted mixed odor ratio, the predicted mixed irritancy ratio, and the average sensory response. A probability of one (1) can be considered equivalent to a 100% probability of detection by an occupant in an environment. The contaminant percentile may be the 75th, 90th, 95th, and 99th percentiles, as well as the mean, for example. 
     Step  26   a  may further include a comparison of contaminant concentration equivalents versus contaminant percentile in database. For example, as in the experiments described above, the concentration equivalents can be isobutylene equivalents. 
     The comparisons in step  26   a  can be depicted in tabular form, as in  FIG. 7 . They may also be shown graphically, as in  FIG. 8 . 
     In step  26   b , and in reference to  FIG. 8 , a determination can be made of the sample population percentile at which one or more of the predicted mixed odor ratio, the predicted mixed irritancy ratio, the average sensory response, and the concentration equivalents has a probability of detection of one (1). This can also include the contaminant mass described below. 
     In step  26   c , comparisons can be made as in step  26   a , except that total contaminant mass can be compared instead of concentration equivalents. The comparisons in step  26   c  can be depicted in tabular form, as in  FIG. 7 . They may also be shown graphically, as in  FIG. 9 . 
     In step  27 , adjustments to the outside air and/or recirculating air may be made based on the comparisons made in step  26 . In some embodiments, the controller  11  can command adjustments to the air purification subsystem  13 . 
     These adjustments may, in various embodiments be based on the sensed concentration equivalents reaching or exceeding a level that is below a 100% probability of contaminant detection—or, in other words, reaching or exceeding a predicted sensory response threshold. 
     For example, based on the comparisons depicted in  FIG. 8 —and in particular the mixed predicted odor ratio, the mixed predicted irritancy ratio, and average sensory response—adjustments may be made at about the 0.85 percentile or less. This is because the probability of detection is less than about 1.0 (or 100%) up to about the 0.80 percentile for the predicted sensory response threshold—e.g., the mixed predicted odor ratio, the mixed predicted irritancy ratio, and/or average sensory response. Thus, in the example of  FIG. 8 , the controller  11  may command adjustments to the air purification subsystem  13  when the controller receives a contamination signal that a predicted sensory threshold has been reached—e.g., an isobutylene concentration equivalent for the mixed contaminants has reached about the 0.80 percentile—about 0.009 ppm. 
     In another example, based on the comparisons depicted in  FIG. 9 , adjustments may be made at about the 0.825 percentile or less than the 0.825 percentile. This is because the probability of detection is less than about 1.0 (or 100%) up to about the 0.825 percentile. In the example of  FIG. 9 , the controller  11  may command adjustments to the air purification subsystem  13  when the controller receives a contamination signal that total mass for the mixed contaminants has reached about the 0.825 percentile—about 450 ug/m 3 . 
     It should be understood, of course, that the foregoing relates to exemplary embodiments of the invention and that modifications may be made without departing from the spirit and scope of the invention as set forth in the following claims.