Abstract:
A method for measuring compliance with building air quality attribute set points. The method includes the steps of sensing a first attribute of environmental air quality, setting a range of acceptable values for the first attribute, setting a penalty function for the first attribute and comparing sensed data to the first range of acceptable values. The first penalty function is applied as appropriate to assess a first set of points for the first attribute. An index value is created that is a function of the first set of points. In another aspect, the method includes sensing a second attribute, comparing the data against acceptable values and applying a second penalty function as appropriate. The index value is also a function of the second set of points. In yet another aspect, the method includes the step of sensing a first attribute at a second site.

Description:
This application claims the benefit of U.S. Provisional Application Ser. No. 60/658,449 filed on Mar. 4, 2005. 
    
    
     FIELD OF THE INVENTION 
     The present invention is related to monitoring the environment within the interior of a building or buildings. More specifically, the present invention is related to monitoring certain parameters of air in one or more buildings. 
     BACKGROUND OF THE INVENTION 
     The typical approach to providing satisfactory air quality in work spaces or living spaces is to measure conditions in the space. The measured values are then compared with safe, recommended or desired environmental air quality conditions. Due to the complexity of buildings, including the number of rooms and the types of conditions that might be present, the amount of data that is created may be overwhelming and hard to interpret. What is needed is a way to provide an easy to understand measure of overall air quality and building management performance. 
     SUMMARY OF THE INVENTION 
     The invention is directed toward a method for measuring compliance with building air quality attribute set points. In one aspect of the invention a first attribute of environmental air quality is measured at a first site over a given period of time. A range of acceptable values for the first attribute is determined and the data collected is compared with the range of acceptable values. In the event that the sensed data is out of the acceptable range, a penalty function is invoked to assess a first set of points for the data that is out of acceptable range. An index value is created over time that is a function of the first set of points. 
     In another aspect of the invention a second attribute is measured and is similarly compared against an acceptable range of values. A second penalty function is invoked to assess a second set of points for any data that is out of the acceptable range of values for the second attribute. The index value is also a function of the second set of points. In yet another aspect of the invention, a first attribute is measured at a second site and similarly compared against acceptable ranges, with a penalty function invoked as appropriate. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of an air quality monitoring system measuring air quality of a particular site according to one embodiment of the invention. 
         FIG. 1A  is a schematic diagram illustrating the air quality monitoring system of  FIG. 1  having a plurality of sites from which air quality measurements are provided. 
         FIG. 1B  is a schematic diagram illustrating a site monitoring assembly of an air quality monitoring system according to another embodiment of the invention. 
         FIG. 1C  is a schematic diagram of a communication structure for relaying data collected by the site monitoring assembly of  FIG. 1B  to a remote data analysis system. 
         FIG. 2  is a functional flowchart illustrating a method of sensor data collection by the air quality management system of  FIG. 1  according to one embodiment of the invention. 
         FIG. 3  is a functional flowchart illustrating a method of analyzing an air quality attribute by comparing individual sensor data points previously collected as shown in  FIG. 2  against defined data range values according to one embodiment of the invention. 
         FIG. 3A  is a functional flowchart illustrating a method of analyzing an air quality attribute by comparing individual sensor data points against defined data range values as they are collected according to another embodiment of the invention. 
         FIG. 3B  is a functional flowchart illustrating a method of analyzing an air quality attribute by comparing a block of sensor data points previously collected over time against defined data range values according to another embodiment of the invention. 
         FIG. 3C  is a functional flowchart illustrating a method of analyzing an air quality attribute by comparing a block of sensor data points previously collected over time against defined data range values according to yet another embodiment of the invention. 
         FIGS. 4A-4B  are a functional flowchart illustrating a method of analyzing air quality by comparing individual sensor data points previously collected over time that measure a plurality of attributes against defined data range values according to another embodiment of the invention. 
         FIGS. 4C-4D  are a functional flowchart illustrating a method of analyzing air quality by comparing a block of sensor data points previously collected over time that measure a plurality of attributes against defined data range values according to another embodiment of the invention. 
         FIGS. 4E-4F  are a functional flowchart illustrating a method are a functional flowchart illustrating a method of analyzing air quality by comparing a block of sensor data points previously collected over time that measure a plurality of attributes against defined data range values according to yet another embodiment of the invention. 
         FIG. 5  is a functional flowchart illustrating a method of calculating a facility performance index according to one embodiment of the invention. 
         FIG. 6  is a spreadsheet showing a sample set of data and a resulting index number. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to  FIG. 1  thereshown is a schematic diagram of an environmental monitoring system  10  for monitoring the indoor air quality of a particular site  12  within building  40  according to one embodiment of the invention. The system  10  includes a site monitoring assembly  16  located at site  12  and a remote data collection system  18  capable of communication with the site monitoring assembly  16 . The site monitoring assembly  16  is a data acquisition system that includes a site controller  20 , one or more sensors  14  coupled to the site controller, a data storage device  22 , and a remote access device  24 . Each sensor  14  measures one or more air quality parameters and is in electrical communication with the site controller  20 . The site controller  20  is likewise coupled to a data storage device  22 , which is capable of storing data collected from the sensors  14 . 
     The site controller  20  initiates the collection of data from sensors  14  and stores the data in data storage device  22 . The data storage device  22  is an electronic storage medium such as a disk, flash memory, a data logger, or other acceptable media for storing data acquired from sensors  14 . Site controller  20  can request data from the sensors  14  at predefined intervals or in response to an input from an operator provided to site controller  20 . The site controller  20  can be programmed to collect information from the sensors  14  at any interval. For example, site controller  20  may collect information at one given interval continuously. Alternatively, the site controller  20  may collect information at differing intervals, for example, to collect additional data during differing times of the day, week, or month. The time intervals may be programmed either at the site  12  or, alternatively, remotely by providing programming information to the site controller through the remote access device  24 . Data stored in the data storage device  22  from each sensor includes identification information to identify from which sensor the data was collected. For example, data collected from one of the sensors  14  may be designated as data from sensor “a,” data collected from another sensor may be designated as data from sensor “b,” and so forth. In addition, data collected from the sensors  14  would include time stamp information to identify when the data was collected. 
     Remote access device  24  is capable of communication with external devices or systems such as data collection system  18  to provide access to data collected by the site monitoring assembly  16  and stored in the data storage device  22 . Remote access device  24  can be a modem, Internet connected processor, wireless network connection or any other device or communication structure capable of communicating data externally from the site monitoring assembly  16 . 
     The remote data collection system  18  is, in one embodiment, physically separate from the site monitoring assembly  16  and is a facility for systematically collecting, storing, and analyzing data from site monitoring assembly  16 . While remote data collection system  18  is shown as being located outside of the building  40 , it should be appreciated that the remote data collection system can be located within the building without departing from the scope of the invention. The remote data collection system  18  includes a remote access device  26  capable of receiving data from the remote access device  24 , a database  30 , and an output device  32 . The database  30  is a comprehensive, centralized data storage system database for storing data from site monitoring assembly  16 . The remote access device  26  of the remote data collection assembly  18  is designed to electronically communicate with the remote access device  24  of the site monitoring assembly  16  or otherwise access information provided by the site monitoring assembly  16  to download data collected by the sensors  14  of the site monitoring assembly to database  30 . 
     The remote data collection system  18 , in one embodiment, includes a system controller  33  for initiating access to one or more site monitoring assemblies  16 . The environmental monitoring system  10  of  FIG. 1  illustrates a single site monitoring assembly  16 , but the environmental monitoring system can have a plurality of site monitoring assemblies, as shown in  FIG. 1A . The remote data collection system  18  is capable of communication with any number of site monitoring assemblies  16  located at various sites  12  within building  40  to download data collected by each of the site monitoring assemblies. The system controller  33  of the remote data collection system  18  is programmed to access various site monitoring assemblies  16  at defined intervals to download data from the various site monitoring assemblies  16 , which may be stored in each of their respective data storage devices  22 . Alternatively, in another embodiment of the invention, individual site controllers  20  of various site monitoring assemblies  16  may initiate access to the remote data collection system  18  to upload data to the remote central database  30  of the remote data collection system. 
     The data collected from each of the site monitoring assemblies  16  at the various data acquisition sites  12  is stored in the database  30  with identification information that indicates the source of the data. For example, data collected from one of the site monitoring assemblies  16  can be designated as being from site monitoring assembly “A,” data collected from another one of the site monitoring assemblies can be designated as being from site monitoring assembly “B,” and so forth. Thus, each data point collected will, in one embodiment, include sensor identification information (for example, “a”), assembly or site information (for example, “A”), time stamp information as well as the air quality measurement. Thus, any information stored in the database  30  can be accessed by the particular site and sensor from which it was collected. 
     The output device  32  coupled to database  30  of the remote data collection system  18  may be a printer or a removable data storage device (e.g. floppy disk or a CD ROM disk). The remote data collection system  18  may manage data for the sites and provide periodic reports of air quality, in printed form, by disk or via electronic communication (e.g. e-mail, the Internet, radio frequency communication, telephone) for review and analysis. 
     Additionally, the remote database  30  may be connected to a distributed wide area network (e.g. Internet.) so that the database  30  can be directly accessed for review by personnel at individual sites  12 . Preferably, accessibility of the data on a wide area network would be limited by pass codes to protect the confidentiality of the data. Only authorized personnel from each site  12  would be able to access the data from a particular site  12  by using the correct pass code. 
       FIG. 1B  illustrates a site monitoring assembly  116  for an environmental monitoring system  110  according to another embodiment of the invention. Site monitoring assembly  116  includes one or more sensors  114  in electrical communication with a computer  50  having a database  130 . Computer  150  can request data from the sensors  114  at predefined intervals and store that information directly into database  130 . Thus, in this embodiment, no long term storage device such as the data storage device  22  described above is required to store data. Rather, data collected from the sensors  114  is stored directly into the database  130 . The computer  150  may have data analysis software to perform analysis on the database. Alternatively, as is shown in  FIG. 1C , the computer  150  may be connected to a remote analysis center  160 , via a computer network  170  such as the Internet to provide data to the analysis center. In addition, other buildings  42 ,  44 ,  46 , and  48  may have site monitoring assemblies capable of electronic communication via the computer network  170  or other electronic communication to download data to the analysis center  160  for analysis and/or reporting. 
       FIG. 2  is a functional flow chart  200  illustrating operation of the system of  FIG. 1  according to one embodiment of the invention. As illustrated by block  210 , site controller  20  polls one or more sensors  14  to request data from the sensors at selected time intervals to measure air quality and download the measured data to the data storage device  22  as illustrated by block  212 . The rate at which data is polled from the sensors  14  depends upon the configuration of the site controller  20 . As discussed above, the site controller can request data at regular intervals, in response to user inputs, or at varying intervals, depending upon the particular time of day, week, or month. Further, individual sensors  14  within the system may be polled at different times or rates. The polling rates may be preprogrammed into the site controller  20 . Alternatively the site controller  20  may include a user interface for inputting the rate for polling sensors  14  or the polling rates may be remotely programmed by sending polling information to the site controller via the remote communication device  24 . 
     As illustrated by block  214 , data is downloaded or transferred from the data storage device  22  of each site monitoring assembly  16  to the remote database  30 . This data transfer may be initiated by the remote data collection system  18  or the site monitoring assembly  16  (or data acquisition system), either automatically or manually. Either the site controller  20  or controller  33  may be programmed to automatically initiate the transfer of data from the data storage devices  22  of the site monitoring assemblies  16  at each site  12  to the central remote database  30 . The transferred data from the site monitoring assembly  16  is stored in the database  30  with a site identification number. The frequency at which data is downloaded to the central database  30  depends upon the rate at which data is polled from the sensors  14  and preferably is a variable that may be programmed into the system as desired. As illustrated by block  216 , the data can then be outputted to a particular site in the form of a printed report, e-mail, or other electronic communication. 
     As previously explained, the environmental data collected by the sensors  14  may be analyzed for controlling air quality or may be used for maintaining air quality records. For example, the data may be used to set maintenance priorities, help plan and justify capital expenditures, plan and budget predictive maintenance and determine the frequency at which filtering devices, which are used to filter residues from the air, need to be changed. 
     Sensors  14  may be positioned at various distributed locations in a particular site  12 . The collection of sensors  14  at any given site may be referred to as a sensor cluster. The number of sensors shown in  FIG. 1  is for illustrative purposes only and the number of sensors can vary per cubic foot depending upon the air quality monitoring precision required. In one embodiment, a fixed system of sensors is used where a plurality of sensors  14  are fixedly mounted to walls or other surfaces at a particular site  12  and are operably wired or are otherwise in communication with the site controller  20  and data storage device  22  for systematically and continuously collecting air quality data at the site  12 . Alternatively, the sensors  14  employed may be mobile sensors. The mobile sensors may be communication with a site controller  20 , similar to fixed sensors, but can be placed in various and changing locations. Communications can be via wired or wireless connections. 
     Various types of sensors  14  may be employed for testing various air quality attributes. In one embodiment, the system  10  employs a particle sensor and a volatile organic compound sensor. Alternatively or in addition, other sensors  14  in a sensor cluster at a particular site  12  can measure smoke, carbon monoxide, temperature, humidity or the presence of foreign substances, such as toxins or other chemicals. This list is for illustrative purposes, and is not intended to be limited to the particular sensors described. Generally, any sensor that can sense environmental quality attributes may be used. 
       FIG. 3  is a functional flowchart  300  of a process for calculating an index number for an individual sensed attribute such as, for example, room temperature, according to one embodiment of the invention. The process of  FIG. 3  assumes that some or all of the data to be analyzed has already been collected (and is thus ready to be analyzed) while allowing for some of the data to be collected in parallel with the process. After starting at block  305 , values are assigned to describe a range over which an attribute can vary under normal conditions. Next a penalty function for a value varying outside of the range in block  310  is set. More than one range may be assigned for each particular attribute and more than one penalty function may apply to data outside a selected range. For example, the range for room temperature may be set to one range during a weekday, another during a weeknight and yet another on a weekend. Penalty functions may vary similarly. As an example, a room temperature range may be set at 70° F.±2° F. The penalty function may be set so that  1  penalty point is assessed for each 1 degree that the actual sensed temperature is outside the range. In a preferred embodiment, only whole points are assessed. As another example, for humidity, an example range would be 32.5% relative humidity±7.5%. A representative penalty function would be one penalty point for each 5% the actual sensed humidity is outside the range. For carbon dioxide (CO 2 ), a possible range would be 825 parts per million±375 parts per million. An example penalty function would be one penalty point for each 200 ppm that the actual sensed CO 2  is outside the range. For carbon monoxide (CO), a possible range would be 25 parts per million+2 parts per million. A possible penalty function would be one penalty point for each 1 part per million that the actual sensed CO is outside the range. 
     Next, at block  320 , a previously collected sensor value is compared to the proper range for that type of value (and for the time at which the data is taken). At decision block  325 , if the sensed value is outside the range for that particular type of value, then the proper penalty function is applied at block  330  and a number of new penalty points for the particular attribute is determined. The new penalty points are added to a running penalty points total in block  335  to create a new running penalty points total for the attribute. The process then advances to block  340 . If, however, the sensed value is within the range, the process moves directly to step  340  and no new penalty points are assigned. At decision block  340 , the process determines whether all of the previously collected data points have been analyzed. If all of the previously collected data points for all of the different sensor attributes have been analyzed, then the process ends at block  355 . If all previously collected data points have not been analyzed, the process continues to block  350 , where a new data point is selected. That data point is then compared to its applicable range in block  320  and the process continues as described above. 
     Referring now to  FIG. 3A , thereshown is a flowchart  300 A of an alternative process for determining an instantaneous penalty point amount for a sensed attribute. This process is similar to the process in  FIG. 3  except that each data point may be analyzed at or near the time that the data is taken. After starting at block  305 A, the process sets a period for taking data in block  308 A. The range and penalty functions described above in relation to flowchart  300  are then set in block  310 A. At block  320 A, the current reading of an attribute value is then compared to the proper range. At decision block  325 A, if the current reading is outside the proper range, then the proper penalty function is applied to generate new points in block  330 A and the running points total is updated in block  335 A before waiting for a new period to begin in block  345 A and looping back to block  320 A. If the current reading is inside the range, then the process moves directly forward to block  345 A. 
     Referring now to  FIG. 3B , thereshown is a method for analyzing data over a predetermined time period according to another embodiment of the invention. Here, data points may be taken more frequently and larger groups of data may be analyzed together to give a more complete picture of facility performance. For example, data points may be taken every minute and then an hour&#39;s worth of data may be compared to the proper range to determine whether a sensed reading was outside the range for a predefined period of time. This process again assumes that some or all of the data points have already been taken (similar to  FIG. 3 ) while some of the data points may be collected as the process continues. The process begins at block  305 B. At block  310 B, data ranges and penalty functions are set. As above, the data ranges may have a clock time component and the penalty functions may have clock time and duration components in addition to a value component. For example, a room temperature range may be set at 70° F.±2° F. for weekdays and 64±2° F. for other times. The penalty function may be set so that 1 penalty point is assessed for each 1 degree-hour that the actual sensed temperature is outside the range. For humidity, an example range would be 32.5% relative humidity±7.5%. A representative penalty function would then be one penalty point for each 5% per hour the actual sensed humidity is outside the range. For CO 2 , a possible range would be 825 parts per million±375 parts per million for weekdays and 600 parts per million±150 parts per million for other times. An example penalty function would be one penalty point for each 100 parts per million per hour that the actual sensed CO 2  is outside the range. For CO, a possible range would be 25 parts per million+1 part per million. A possible penalty function would be one penalty point for each 1 part per million per hour that the actual sensed CO is outside the range. 
     At block  320 B, sensed values over the selected time period are compared to the proper range for that type of and time value. If the sensed value is outside the range for that type of value for the entire time period, then the proper penalty function is applied at block  330 B and a number of new penalty points is determined. The new penalty points are added to a running points total in block  335 B to create a new running points total for the sensed attribute. If the sensed value is not outside the range for that type of value for the entire time period, then the process moves directly to decision block  340 B to determine whether all of the data points in a data collection have been analyzed. If not, the process selects a next set of data points for analysis at block  350 B and returns to block  320 B. If so, the process ends at block  355 B. 
     Referring to  FIG. 3C , thereshown is flowchart  300 C illustrating a method for analyzing data collected over a predetermined time period according to another embodiment of the invention. This process again assumes that some or all of the data points have already been taken (similar to the method illustrated in  FIG. 3 ) while some of the data points may be collected as the process continues. The process begins at block  305 C and moves to block  310 C where ranges and penalty functions are set. Here, ranges may have a clock time component and the penalty functions may have a clock time and duration component to them in addition to a value component. For example, a room temperature range may be set at 70° F.±2° F. for weekdays and 64±2° F. for other times. The penalty function may be set so that 1 penalty point is assessed for each 1 degree-hour that the actual sensed temperature is outside the range. For humidity, an example range would be 32.5% relative humidity±7.5%. A representative penalty function would then be one penalty point for each 5% per hour the actual sensed humidity is outside the range. For CO 2 , a possible range would be 825 parts per million±375 parts per million for weekdays and 600 parts per million±150 parts per million for other times. An example penalty function would be one penalty point for each 100 parts per million per hour that the actual sensed CO 2  is outside the range. For CO, a possible range would be 25 parts per million+1 parts per million. A possible penalty function would be one penalty point for each 1 parts per million per hour that the actual sensed CO is outside the range. 
     Next, at block  320 C, sensed values over the selected time period are compared to the proper range for that type of and time value. If the sensed value is outside the range for that type of value at decision block  325 C, then the proper penalty function is applied at block  330 C and a number of new penalty points is determined for a given sensed attribute. The amount of time that a parameter is inside or outside of the parameter range is also determined and tracked at block  330 C and an instantaneous percentage determined. The new penalty points are added to a running points total in block  335 C to create a new running points total for the sensed attribute. The instantaneous percentages may also be tracked and a running percentage number generated. 
     If the sensed values are inside the range for the entire time period, then the process moves directly to step  340 C to determine whether all of the data points in a data collection and percentages have been analyzed. If not, the process selects a next set of data points for analysis at block  350 C and returns to block. If so, the process ends at block  355 C. 
     Referring now to  FIGS. 4A-4B , thereshown is a flowchart  400  of a process for calculating an index where more than sensed attribute is used in formulating the index according to another embodiment of the invention. This process assumes that data will be collected as part of the process. After starting at block  405 , the period for study is set in block  408 . The first and second ranges and first and second penalty functions are then set in block  410 . In block  415 , the data is then collected. In block  420 , a collected value of the first attribute is compared to the proper first range. At decision block  425 , if the collected sensor data is outside the proper first range, the process applies the proper first penalty function to create first new points in block  430  and a new running first points total for the sensed attribute is created in block  435  before the process continues to block  440 . If, however, the data is inside the range, then the process to continues directly from decision block  425  to block  440 . 
     In block  440 , a collected sensor data value of the second attribute is compared to the proper second range. If the value is outside the range then decision block  445  directs the process to apply the proper second penalty function to generate second new points at block  450  and then a new running second points total is generated in block  455  for the second attribute before returning to block  460 . If the value is inside the range, the process proceeds directly from decision block  445  to block  460 . In block  460 , a total points calculation is made for the two attributes by summing the running total of the first points and the running total of the second points. At block  465  the process waits for a new data collection period to begin before returning to block  415 . 
     Referring now to  FIGS. 4C-4D , thereshown is a flowchart  400 A illustrating a process according to another embodiment of the invention where it is assumed (as described above in relation to the process illustrated in  FIG. 3 ) that some or all of the data has been collected prior to the start of the process, while allowing for more data to be collected once the process has begun. After starting at block  405 A, the first and second ranges and first and second penalty functions are then set in block  410 A. In block  420 A, a previously collected value of the first attribute is compared to the proper first range. At decision block  425 A, if the collected sensor data is outside the proper first range, the process applies the proper first penalty function to create first new points in block  430 A and a new running first points total for the first sensed attribute in block  435 A before  440 A. If the data is inside the range, then the process to continues directly from decision block  425 A to block  440 A. 
     In block  440 A, a collected sensor data value of the second attribute is compared to the proper second range. If the value is outside the range then decision block  445 A directs the process to apply the proper second penalty function to generate second new points at block  450 A. Subsequently, a new running second points total is generated in block  455 A before the process continues to block  460 A. If the value is inside the range, however, the process proceeds directly from decision block  445 A to block  460 A. At block  460 A, a total points calculation is made for the two attributes by summing the running total of the first points and the running total of the second points. At decision block  465 A, the process determines whether all of the data points in a data collection have been analyzed. If not, the process selects a next set of data points for analysis at block  470 A and returns to block  420 A to begin the process again as described above. If so, the process ends at block  475 B. 
     Referring now to  FIGS. 4E-4F , thereshown is a flowchart  400 B illustrating a process according to another embodiment of the invention where it is assumed (as described above in relation to the process illustrated in  FIG. 3 ) that some or all of the data has been collected prior to the start of the process, while allowing for more data to be collected once the process has begun. The embodiment illustrated in  FIGS. 4E-4F  is similar to the process described above and illustrated in flowchart  400 A of  FIGS. 4C-4D . This process allows for multiple attributes as in the other process, but also allows for analysis of data over time similar to the process illustrated in  FIG. 3B . After starting at block  405 B, the first and second ranges and first and second penalty functions are then set in block  410 B. In block  420 B, a previously collected value of the first attribute is compared to the proper first range. At decision block  425 B, if the collected sensor data is outside the proper first range for the entire length of time covered by the data analyzed, the process applies the proper first penalty function to create first new points in block  430 B for the first sensed attribute and a new running first points total in block  435 B before returning to block  440 B. Otherwise, the process to continues directly from decision block  425 B to block  440 B. The primary difference between the process illustrated in flowchart  400 B as compared to the process illustrated in flowchart  400 A is that the process illustrated in flowchart  400 B requires that the data block fall outside the proper range for the entire period of interest before the penalty function is applied. 
     In block  440 B, a collected sensor data value of the second attribute is compared to the proper second range. If the value is outside the range for the entire period of interest, then decision block  445 B directs the process to apply the proper second penalty function to generate second new points for the second sensed attribute at block  450 B. Subsequently, a new running second points total is generated in block  455 B before the process continues to block  460 B. Otherwise, the process proceeds directly from decision block  445 B to block  460 B. At block  460 B, a total points calculation is made for the two attributes by summing the running total of the first points and the running total of the second points. At decision block  465 B, the process determines whether all of the data points in a data collection have been analyzed. If not, the process selects a next set of data points for analysis at block  475 B and returns to block  420 B to begin the process again as described above. If so, the process ends at block  475 B. 
     Each of the processes described above with respect to flowcharts  400 ,  400 A and  400 B describe a process having two attributes. It is to be understood, however, that a system can have more than two attributes. If additional attributes are to be analyzed using one the processes illustrated in flowcharts  400 ,  400 A and  400 B, additional loops similar to blocks numbered  425 - 430 - 435  for each flowchart can be added for each attribute and block  460  can be modified to sum the points applied for the additional attributes. 
     Referring now to  FIG. 5 , thereshown is a flowchart  500  of a process for calculating a facility performance index where two or more attributes are used and two or more sensors are used to collect data for each attribute. In one example, two different kinds of sensors measuring two different attributes are in each of at least two rooms. After starting at block  505 , the process moves to block  510  where data is collected for each sensor for each room. Penalty points may be assessed per attribute and per sensor (using the any of the processes described above with respect to flowcharts  300 ,  300 A, 300 B,  400 ,  400 A and  400 B) as specified in block  515 . A facility index may then be generated in block  520 . The facility index may be a function of the points assessed per attribute, per room, per sensor, or some combination of these. The facility index provides a single value to describe the air quality in a particular facility and thereby gives an indication of the overall performance of the building. The process then ends in block  525 . 
     Referring now to  FIG. 6 , thereshown is a spreadsheet of data from a data acquisition system that was used to generate a facility performance index according to one embodiment of the invention. As described in  FIG. 2 , after data is transferred to the database as is identified in block  214 , the data is output into a form for use as is identified in block  216 . The data shown in spreadsheet  600  provides output data from a monitoring system that includes measured attributes of temperature, humidity, carbon dioxide and carbon monoxide collected in  25  rooms over a one month time frame. Average values during the selected time frames are shown. The data was divided into weekday, night and weekend collections and penalty points separately applied to each collection of data in accordance with the appropriate penalty function. As can be seen from the bottom of the spreadsheet, the weekday period was 8 am-5 pm Monday-Friday. The Weeknight period was from 5 pm-8 am Monday-Friday. The weekend period was from 5 pm Friday through 8 am Monday. The temperature range varied with the periods: weekday range was 68-72 degrees, night range was 62-66 degrees and the weekend range was 60-64 degrees. Relative humidity and carbon dioxide also had ranges that varied with the period. Carbon monoxide had a fixed range through all periods (although it could have been allowed to vary). The spreadsheet shows points calculated for each individual room for each attribute. The spreadsheet also calculates total points for each room across attributes. 
     One method of calculating the final facility index is to add up all of the points then divide the points by the number of sensor points (e.g. one set of sensors per room) and divide by the number of days of data that are collected. This will give an indication of the amount of deviation of each space on each day from the set ranges. This method allows the direct and accurate comparison of different rooms, times and buildings for meaningful analysis. The spreadsheet shown in  FIG. 6  can be implemented using macros in a spreadsheet program such as Microsoft Excel. The index value along with the Percent in Range number indicate frequency and magnitude of building performance infractions as compared against the desired operating parameters. 
     Various modifications and additions can be made to the embodiments discussed above without departing from the scope of the present invention. For example, while the embodiments described above refer to particular features, the scope of this invention also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Accordingly, the scope of the present invention is intended to embrace all such alternatives, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.