NIST Technical Note 1868
 

Characterizing Indoor Air Quality 

Performance Using a Graphical Approach 

Kevin Teichman 

Cynthia Howard-Reed 

Andrew Persily 

Steve Emmerich 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.TN.1868 
NIST Technical Note 1868 

Characterizing Indoor Air Quality 

Performance Using a Graphical 

Approach 

Kevin Teichman 

Office of Research and Development 

U.S. Environmental Protection Agency 

Washington, DC 

Cynthia Howard-Reed 

Andrew Persily 

Steve Emmerich 

Energy and Environment Division 

Engineering Laboratory 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.TN.1868 

March 2016 

U.S. Department of Commerce 

Penny Pritzker, Secretary 

National Institute of Standards and Technology 

Willie May, Acting Under Secretary of Commerce for Standards and Technology and Acting Director 
Certain commercial entities, equipment, or materials may be identified in this 

document in order to describe an experimental procedure or concept adequately. 

Such identification is not intended to imply recommendation or endorsement by the 

National Institute of Standards and Technology, nor is it intended to imply that the 

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1868 

Natl. Inst. Stand. Technol. Technical Note 1868, 25 pages (March 2016) 

CODEN: NTNOEF 

This publication is available free of charge from 

http://dx.doi.org/10.6028/NIST.TN.1868 
 

 

ABSTRACT 

In this paper, we describe and demonstrate a graphical approach that can be used to illustrate the 

performance of buildings with respect to indoor air quality (IAQ). We start by describing 

previous efforts to establish IAQ and indoor environmental quality (IEQ) metrics, and then, in 

the absence of an adequate or agreed-upon IAQ metric(s), describe a graphical approach to 

presenting IAQ performance. This approach displays measured or predicted levels of indoor 

pollutants relative to health-based guidelines or other appropriate reference values. The 

development of this graphical approach leads to several challenging questions regarding how to 

characterize building IAQ performance, including the determination of relevant contaminant 

concentration limits and the impacts of contaminant mixtures. This paper discusses those 

questions with the intent of promoting future dialog on how to characterize IAQ performance 

using this graphical or any other approach. Lastly, we briefly describe how the approach can be 

extended to illustrate the performance of buildings with respect to IAQ and other building 

parameters (e.g., energy and water consumption). 

Keywords: building performance; contaminants; indoor air quality; indoor environmental 

quality; high-performing buildings; metrics. 

INTRODUCTION 

Defining a “high-performing building” is challenging given the multiple performance goals that 

need to be considered, as well as the lack of accepted performance criteria for many of them. 

These goals include indoor environmental quality, energy consumption, water use, waste 

generation, resource conservation, and transportation impacts. Characterizing indoor 

environmental quality, specifically indoor air quality (IAQ), in buildings is especially difficult 

1
 
 

due to the currently incomplete understanding of the impacts of indoor pollutant exposures on 

human health. Nonetheless, the building community is being challenged to reduce the 

environmental impacts of buildings, while maintaining, if not improving, indoor environments 

that are conducive to occupant health, comfort, and productivity. 

The overarching goal of reducing the environmental impacts of buildings is addressed in 

discussions of green or sustainable buildings, and a number of programs, standards, codes, and 

other efforts are in place to promote, and in some cases require, the design and construction of 

buildings with these attributes [1-4]. These efforts speak to the need for “high performance,” 

which, in addition to energy, generally includes a range of non-energy performance attributes 

such as IAQ, water consumption, the use of recycled and regionally-manufactured materials, and 

the diversion of construction waste from landfills. Many discussions of high-performance and 

sustainable buildings are focused more on energy performance than these other attributes, which 

is a limitation that needs to be overcome as these efforts advance [5]. 

In this paper, we describe and demonstrate a graphical approach that can be used to illustrate the 

IAQ performance of buildings. We start by describing previous efforts to establish IAQ and 

indoor environmental quality (IEQ) metrics, and then, in the absence of an adequate or agreedupon IAQ metric(s), describe a graphical approach to presenting IAQ performance. This 

approach displays measured or predicted levels of indoor pollutants relative to health-based 

guidelines or other appropriate reference values. The development of this graphical approach 

leads to several challenging questions regarding how to characterize building IAQ performance, 

including the determination of relevant contaminant concentration limits and the impacts of 

contaminant mixtures. This paper discusses those questions with the intent of promoting future 

2
 
dialog on how to characterize IAQ performance using this graphical or any other approach. This 

approach, once developed further and accepted by the building community, is intended to enable 

building designers, owners, operators, and occupants to more easily understand the performance 

of a building with respect to IAQ. 

THE NEED FOR AN IAQ METRIC 

Part of the reason that IAQ is not always emphasized in descriptions of buildings is the difficulty 

in quantifying IAQ performance due to the lack of accepted metrics. Challenges to quantifying 

IAQ performance using a single, accepted metric(s) include the large number of airborne 

contaminants of interest, a lack of accepted guideline values for the health impacts of many of 

these contaminants, and the need to also consider occupant comfort. The lack of IAQ metrics has 

been identified as a key gap in the IAQ field in many previous discussions [6, 7]. 

Metrics for energy use, e.g., energy use intensity (EUI) expressed as energy consumption either 

per unit floor area or per person, have become commonly accepted to enable building energy 

performance to be expressed and compared [8]. However, while proposals for IAQ metrics have 

been put forward, no such widely accepted metric(s) currently exist.. Nevertheless, IAQ metrics 

are needed for evaluating and comparing indoor environments and for facilitating and 

quantifying IAQ improvements. 

Previously Proposed IAQ Metrics 

Several IAQ metrics addressing contaminant concentrations have been proposed. These metrics 

range from concentrations of single contaminants to indices that incorporate the concentrations 

of multiple pollutants. An example of an existing single contaminant metric is the EPA action 

3
 
 

level for radon [9]. EPA has recommended that homeowners test for elevated levels of radon in 

their homes. If a home’s radon concentration is above 0.148 Bq/L (4 pCi/L), EPA recommends 

implementing a mitigation strategy to lower the radon level. This guideline has been very useful, 

because it provides homeowners, installers of radon control technologies, and local authorities 

with a target concentration and associated actions. 

Another example of a single contaminant metric is the indoor concentration of carbon dioxide 

(CO2). This particular example, however, highlights some of the challenges in setting a 

contaminant benchmark concentration. Indoor CO2 levels have been of interest for many years 

due to their relationship with ventilation rates relative to occupancy levels and the availability of 

affordable measurement instruments. However, indoor CO2 levels are generally do not directly 

impact health and comfort, except in confined spaces that are not relevant to building 

performance discussions. Recent research by Satish et al. [10] suggests indoor CO2 levels may 

directly impact occupant decision making, meriting more research on this topic. Nevertheless, 

while indoor CO2 levels may not be a good indicator of overall IAQ, they can provide a useful 

indication of the acceptability of a space in terms of human body odor, some aspects of 

perceived IAQ, and per person outdoor air ventilation rates. However, indoor CO2 levels relative 

to a concentration of 1800 mg/m3 (equivalent to 1000 ppmv) have been mistakenly viewed as 

indicative of good or bad IAQ, despite well-established limitations in the interpretation of this or 

any other particular reference value [11]. 

A step beyond metrics based on a single airborne contaminant is to combine the concentrations 

of multiple contaminants. For example, Mølhave [12] discusses measuring the total 

concentration of non-reactive volatile organic compounds (TVOC) and comparing this total to an 

4
 
irritation and discomfort scale, where < 0.20 mg/m3 was identified as having “no effect” and 

> 3 mg/m3 as “expected discomfort.” Since the early proposals of such applications of TVOC 

levels, several questions have been raised regarding the appropriateness of TVOC as an IAQ 

indicator [13]. Part of the problem is that different researchers employ different methodologies 

for measuring TVOC, making it difficult to compare results across studies. Second, summing all 

of the measured volatile organic compounds (VOC) in effect assumes that the toxicities and 

irritancies of the individual compounds are similar and the resulting health and comfort effects 

are additive. Lastly, VOC can react with each other and with other chemicals to sometimes form 

chemicals of greater concern [14]. Therefore, Mølhave and Nielsen [15] concluded that a TVOC 

metric may be best used as a screening tool to identify indoor environments with high levels of 

non-reactive VOC that cause “nonspecific sensory irritation.” Table B-2 of ASHRAE Standard 

62.1 contains a recommendation against setting target values of TVOC, noting a preference 

instead for targeting individual VOC [16]. The ASHRAE IAQ Guide contains additional 

information questioning the use of TVOC as an IAQ metric [17]. 

Tenbrinke et al. [18] expanded the concept of a TVOC metric to six additional metrics 

incorporating multiple VOC. The proposed metrics included the sum of VOC measured with a 

gas chromatograph (GC) flame ionization detector or a GC and mass spectrometer, the sum of 

VOC individually weighted by their relative irritancy or relative odor threshold, the sum of VOC 

according to their chemical class, and two additional metrics defined by principal component 

analysis (air freshener/cleaning product emissions and relative irritancy of VOC). Ten Brinke et 

al. evaluated these metrics using data from the California Healthy Buildings Study, which 

included 12 office buildings in Northern California and measured concentrations of 39 VOC. 

Based on a multivariate logistic regression analysis, relationships between occupant symptoms 

5
 
and VOC metrics were determined. Of the seven exposure metrics tested, the two based on 

principal component analysis were the only ones to be statistically significant in terms of 

symptom prediction. 

A number of other IAQ metrics based on multiple pollutants have also been proposed. For 

example, Sekhar, Tham [19] developed an “indoor pollutant standard index,” which is based on 

eight IAQ parameters including CO2, carbon monoxide (CO), formaldehyde (HCHO), TVOC, 

particulate matter (PM), bacteria, fungi, and thermal comfort. [20] proposed a “total tolerance 

index” that summed the ratio of measured contaminant concentrations to the maximum allowable 

concentration for the individual gaseous contaminants of concern. This index was tested using 41 

“non-problem” buildings from the EPA’s Building Assessment Survey and Evaluation (BASE) 

study [21]. Based on an average of contaminant concentrations measured in the 41 buildings, the 

total tolerance index was 0.88, which was consistent with the occupants’ perceptions in those 

buildings. 

Moschandreas and Sofuoglu [22] proposed an indoor air pollution index that combines a range of 

IAQ contaminants. This index uses a four-tier, tree-structured calculation that yields a single 

indicator of IAQ. The first level includes contaminants (HCHO, TVOC, CO, nitrogen dioxide 

(NO2), total suspended particulate (TSP), respirable suspended particulate (RSP), fungi, and 

bacteria), and the fourth level is a “global” metric that ranges from 0 (lowest pollution level) to 

10 (highest pollution level). The authors further demonstrate the use of this index to choose the 

most appropriate control strategies based on identification of the primary cause of poor IAQ and 

an analysis of cost. 

6
 
 

Recently, Mouradian and Boulanger [23] proposed an IAQ metric based on four groups of 

pollutants representative of similar behavior, use, or effect. The four groupings are: CO2 as a 

marker linked to human occupancy; NO2 and sulfur dioxide (SO2) in dwellings and ozone (O3) in 

offices linked to occupant activities; CO and seven VOC linked to materials, activities, and 

behavior; and particles with diameters both 2.5 um and smaller (PM2.5) and 10 um and smaller 

(PM10) linked to activities. Simulation results for these four groupings were graphed along with 

humidity and energy consumption for different building design alternatives. The authors 

concluded that currently available ventilation systems and required airflow rates are well suited 

for low energy buildings and can provide suitable IAQ as long as the systems are correctly 

designed and installed. 

While not an IAQ metric, ANSI/ASHRAE Standard 62.1-2013 Ventilation for Acceptable 

Indoor Air Quality contains a collection of reference values derived from other sources in an 

informative (i.e., not required for compliance with the standard) appendix [16]. While these 

values are not provided as a means of compliance, they are included with a useful discussion of 

the challenges in developing and applying such reference values. 

A New Approach to IAQ Metrics 

As noted above, although there has been some useful work to establish contaminant-based 

metrics, a single IAQ metric is not likely to be accepted and standardized in the near future for 

multiple reasons. Foremost among these is the incomplete understanding of both acute and 

chronic human health effects data on exposures to low-level concentrations of contaminants and 

the effects resulting from exposures to contaminants in combination. Data are also incomplete on 

how contaminant concentrations impact occupant comfort, perceived IAQ, the prevalence of sick 

building syndrome symptoms, and productivity. The incomplete data on human response to 

7
 
 

 

contaminant concentrations in turn leads to a lack of concentration guidelines or other reference 

values for many contaminants, making it difficult to know which contaminants to measure and 

include in an IAQ metric. In addition, pollutant measurements are also often limited to 

concentrations that are above the minimum detection levels of less expensive, and thus more 

common, instruments. Limited standardization of monitoring equipment and measurement 

protocols also makes the repeatability of these measurements challenging. Given the noted 

challenges with a single IAQ metric and the associated need to quantify IAQ performance based 

on multiple parameters, the challenge remains of presenting a multi-dimensional metric in an 

understandable and meaningful fashion. 

A GRAPHICAL APPROACH TO IAQ METRICS 

This section describes and demonstrates a graphical approach to represent IAQ performance in 

buildings. The approach draws upon recent research in computational toxicology, which utilizes 

a similar graphical approach to represent the “toxicity footprint” of a chemical based on in vitro 

assays, chemical properties, and cellular pathways [24]. The approach described below provides 

the “IAQ performance footprint” of a building based on the concentrations of multiple 

contaminants. 

Rather than trying to consolidate the many different dimensions of IAQ into a single metric, we 

propose to compare multiple IAQ-related parameters. In this paper, we have arbitrarily limited 

the discussion to twelve parameters, each of which is represented as an equal angular sector of a 

8
 
circle. More or less than twelve parameters could easily be included, and the angular width of the 

sectors could be varied to differentiate the relative significance of the parameters. The length of 

each sector represents the magnitude of the given parameter, e.g., the concentration of an indoor 

pollutant, and the circumference of the circle represents the upper limit for the parameter, e.g., an 

acceptable health benchmark or other appropriate reference value. If the parameter does not 

exceed its reference value, the length of the sector is proportionately shorter than the sector 

defined by the parameter’s reference value. If the parameter exceeds its reference value, the 

length of the sector is proportionately longer than the sector defined by the parameter’s reference 

value. 

Where a given pollutant has no health benchmark or reference value, an empty sector is 

displayed. To distinguish this situation from one in which the measured or modeled 

concentration is significantly lower than the reference value, we have chosen to graph the latter 

circumstance with a sector that is one quarter of the length of the reference value, thereby 

signifying the measured concentration is well below the reference value. Similarly, when a 

concentration significantly exceeds its corresponding reference value, we have chosen to 

represent this concentration as 1.5 times the length of the reference value, signifying that the 

measured concentration is well above the reference value. These choices can certainly be 

revisited and other approaches employed for displaying low and high concentrations. 

We recognize that in many cases it is very difficult to choose the health benchmark or reference 

value to be used for comparison to a measured pollutant concentration. For some pollutants, 

there are multiple such values from multiple sources; for others, there may be no such value from 

any source. Ideally, the health benchmark or reference value selected will correspond to the 

9
 
 

population to be protected (e.g., adults, children, seniors, asthmatics) from an adverse health 

effect (acute, chronic; cancer, non-cancer; reversible, irreversible) that could potentially result 

from exposure over the relevant period of time (e.g., 8 hours for typical office exposure 

scenarios). Nevertheless, selecting the benchmark or reference value is a key challenge with this 

approach as it reflects the lack of accepted values to characterize IAQ performance. 

Additional complexities arise due to the effects on human response associated with the 

interactions among different indoor environmental parameters, e.g., contaminant concentrations, 

temperature, and lighting. ASHRAE Guideline 10, Interactions Affecting the Achievement of 

Acceptable Indoor Environments, contains useful discussions of these issues [25]. 

One way to address measurement issues is to base an IAQ metric upon predicted contaminant 

concentrations using IAQ simulation models. Modeling has the additional advantage of enabling 

comparisons of the impact of alternative building designs and retrofits on IAQ. However, 

modeling is limited by the adequacy of the model used and its inputs, which themselves can be 

subject to significant uncertainty and variability. While the consistency of predicted 

concentrations among different models can be an issue, this has been improved with the 

development of standardized building scenarios [8], including scenarios specifically intended for 

IAQ analysis [26]. 

To illustrate the graphical approach, let us assume that we have either measured or modeled data 

on radon, PM2.5, and CO levels in a building over the course of a year. For radon, let us assume 

a measured annual average value of 0.111 Bq/L (3 pCi/L); for PM2.5, an annual average of 

15 μg/m3 and a maximum 8-h average concentration of 30 μg/m3; and for CO, a maximum 8-h 

average concentration of 8 mg/m3 and a maximum one-hour average concentration of 50 mg/m3. 

10
 
 

The next step is to identify relevant health benchmarks or reference values for these pollutants. 

For radon, EPA has established an action level of 0.148 Bq/L (4 pCi/L) [9]. For PM2.5, the EPA 

national ambient air quality standard (NAAQS) is an annual average of 12 μg/m3 and a 24-h 

average of 35 μg/m3 [27]. Lastly for CO, the NAAQS is 10 mg/m3 averaged over an 8-h period 

and 40 mg/m3 averaged over a 1-h period [27]. Since one of the measurements is for a time 

period different than the available health benchmark, for illustrative purposes only, we assume 

that the time period of our measured concentration matches the time period of the health 

benchmark. Specifically, we compare the measured 8-h maximum PM2.5 concentration of 30 

μg/m3 to the 24-h NAAQS of 35 μg/m3. These results are displayed in Figure 1. 

In this example, the maximum concentration level of PM2.5 integrated over eight hours is below 

the selected reference value, which may not a concern, but the annual average PM2.5 

concentration is above the reference value and therefore of more interest. Similarly, while the 

maximum concentration level of CO integrated over eight hours is low, there would perhaps be 

more concern about the peak one-hour concentration. 

The proposed approach to displaying IAQ performance data has the following advantages. First, 

rather than questionably and perhaps subjectively combining IAQ parameters into a single 

metric, it preserves the robustness of the data and its ability to inform decision making relative to 

multiple desired health benchmarks or other reference values. Second, it enables users to select 

the IAQ pollutants that are most relevant to them. Lastly, as opposed to a histogram showing 

concentrations of individual pollutants, the approach provides a single graph illustrating the 

“IAQ performance footprint” of a building based on multiple contaminant concentrations. This 

graphical footprint” of IAQ performance enables both comparisons among multiple buildings 

11
 
and the comparison of proposed design alternatives and subsequent mitigation interventions in a 

single building. While the approach offers many advantages, it is important to reiterate that it 

relies on the judicious choice of contaminant concentrations to be measured or modeled and the 

availability of accepted health benchmarks or reference values for these contaminants to enable 

comparison. 

Applying the Approach to Existing Data 

To demonstrate this approach, we have graphed the results from two existing datasets: (1) the 

Small and Medium Sized Commercial Buildings (SMCB) field study [28], and (2) a simulation 

study of residential IAQ control interventions [29]. 

The data plotted in Figure 2 are from the SMCB study, with each value in the figure being the 

the 95th percentile values reported in the study, with the exception of PM2.5 which was reported 

at the 75th percentile. These data are also shown in Table 1. The reference values used in the 

figure are as follows: (a) 1800 mg/m3 for CO2 despite the fact that CO2 is not a comprehensive 

indicator of IAQ and there are many problems with its measurement and interpretation [11]; (b) 

the PM2.5 NAAQS [27], and (c) individual VOC chronic reference exposure levels [30]. Note 

that the OEHHA reference exposure value for HCHO is for 8-h, since there is no chronic value. 

Figure 3 shows that for the buildings in the SMCB study, most of the 95th percentile VOC 

concentrations were below their respective OEHHA reference exposure value; however, the 95th 

percentile of HCHO exceeded its reference exposure value. 

12
 
 

 

Figure 3 uses the modeled data from Emmerich et al. [29] to show the impact of different 

interventions to address summertime concentrations of CO and NO2 in a kitchen in Boston with 

a maladjusted gas stove. The resultant concentrations show the impact of each intervention in 

isolation, i.e., one intervention is considered at a time with all other parameters held constant in 

the different simulations. These data are also shown in Table 2. The health benchmarks shown in 

the figure are the EPA one-hour NAAQS for CO, 43.2 mg/m3, and the EPA annual NAAQS for 

NO2, 0.107 mg/m3 [27]. This figure shows that not only does proper adjustment of the stove have 

the greatest impact on lowering both CO and NO2 concentrations, it is the only intervention that 

reduces the NO2 concentration below the EPA NAAQS. 

DISCUSSION 

The effort to develop this graphical approach highlighted many challenges, including identifying 

the most important contaminants to include, selecting the concentration reference values to use 

for comparison, and accounting for the impacts of contaminant mixtures. All of these issues need 

to be addressed if this approach is going to be accepted and useful. 

How do we know which are the most important pollutants needed to characterize IAQ 

performance? Should we select the pollutants that are: (a) the most toxic (e.g., those with 

carcinogenic or irreversible non-carcinogenic health endpoints), (b) those anticipated to lead to 

the greatest exposures, or (c) representative of different indoor pollutant classes (e.g., pollutants 

of outdoor origin, VOC from building materials, indoor products of combustion). Do all of these 

pollutants have at least one accepted health benchmark or reference value that is comparable to 

the anticipated indoor exposure period, and, if so, are our pollutant measurements being 

accurately made over these periods? 

13
 
To illustrate, in Figure 2, we plotted twelve pollutant concentrations drawn from the SMCB 

study (Bennett et al., 2011). While we arbitrarily chose twelve concentrations so that the sectors 

graphed would be easily distinguished, we intentionally chose twelve pollutants for which 

relevant reference values existed for anticipated indoor exposure periods. It is important to note 

that for many of the pollutants measured in the two field studies, there are no recommended 

health benchmarks or reference values. 

Throughout this paper, we have referred to health benchmarks and reference values for 

comparing measured or calculated contaminant concentrations. For health benchmarks, it is 

important to distinguish which benchmarks are enforceable standards and which are 

recommendations or guideline values. For example, the National Institute for Occupational 

Safety and Health develops recommended exposure limits (RELs) for consideration by the 

Occupational Safety and Health Administration during the promulgation of legally enforceable 

permissible exposure limits (PELs) for occupational settings [31, 32]. NIOSH RELs are based on 

the best available science using human or animal health effects data. OSHA PELs, on the other 

hand, are subject to the public rulemaking process, and the interests of all affected parties – 

scientific and otherwise – are taken into consideration. Therefore, caution must be taken when 

comparing health benchmarks for the same pollutant, as the rationale supporting an enforceable 

standard can be very different from that used to develop a guideline value. 

Comparing health benchmarks across pollutants also poses a challenge. While one can compare 

two pollutants that share a common health endpoint, e.g., the potential to develop lung cancer, 

comparing the benchmarks of pollutants with different health endpoints assumes an equal level 

of acceptable risk among health benchmarks. For example, how does one compare the 

benchmarks of pollutants with carcinogenic health endpoints (e.g., an acceptable probability of 

14
 
 

 

getting cancer) with those of pollutants with non-cancer endpoints (e.g., an acceptable number of 

asthma attacks requiring hospitalization)? 

Complicating things further, our bodies are exposed to pollutants in combination and such 

exposures can have synergistic impacts. For example, smokers exposed to radon are known to be 

at a sub-multiplicative risk of getting lung cancer, i.e., at greater risk than the combined risk 

calculated from exposures to the pollutants individually. Some contaminants, e.g., CO2, may not 

cause health effects at typical indoor concentrations, but are useful indicators for characterizing 

IAQ performance. 

Lastly, it is important to emphasize that even comparisons of pollutant concentrations in 

buildings of the same type, and more so for different types, should be made with caution. 

Important differences include whether a building or owner- or tenant-occupied, the number of 

building occupants and their activities, and how the building was designed and is being operated. 

For example, ventilation system operation, which can be climate dependent, can have a dramatic 

effect on indoor pollutant concentrations. Pollutant concentrations of internally-generated 

contaminants are typically higher in a building operating at its minimum ventilation rate than one 

operating with 100 % outside air, assuming the concentrations are lower in the outdoor air than 

in the building. 

The preceding figures show how different pollutant parameters can be displayed to assess IAQ 

performance by comparing concentrations with respect to their health benchmarks. The 

determination of which parameters are considered can be case-specific depending on the 

objective of the analysis. That said, it would be helpful to arrive at an accepted collection of 

contaminants to allow comparisons among buildings and building datasets. For example, in 

commercial buildings, a user may wish to use the parameters included in the EPA BASE study 

15
 
 

 

[21], as it includes more pollutants than the SMCB study. For residential structures, the list of 

pollutants proposed by Logue et al. [33] may be useful. While the IAQ field is not at the point 

where an accepted list of indoor contaminants exists for either commercial or residential 

buildings, there have been useful discussions of what contaminants merit consideration [5, 34]. 

Extending The Approach To Other Building Parameters 

This graphical approach can also be applied to a range of building performance considerations 

beyond IAQ. These include IEQ, energy and water consumption, the diversion of construction 

waste, and the handling of building waste generated during building use. IEQ includes 

consideration of thermal comfort, acoustics, and lighting. The application of the graphical 

approach to these additional attributes is relatively straightforward, as most of these parameters 

have more well-established health and/or comfort benchmarks or at least ranges of acceptability. 

For example, in the case of thermal comfort, one can use the metric of predicted percentage of 

dissatisfied occupants as defined in ASHRAE Standard 55, where 20 % is considered an 

acceptable reference value [35]. For acoustics and lighting, as well as thermal comfort, there is a 

good discussion of reference values and measurement approaches in the Performance 

Measurement Protocols for Commercial Buildings recently published by ASHRAE [34, 36]. The 

PMP provide objectives, metrics, and benchmarks for each of these parameters at three levels of 

accuracy/cost: Basic, Intermediate, and Advanced. 

Discussions of high performance buildings emphasize minimizing off-site sources of energy and 

water, as well as minimizing on-site waste generation. The graphical approach proposed here 

could also be used to represent these building parameters as well. For example, EUI, expressed 

16
 
in terms of energy per square area or energy per person, is a common benchmark used to 

compare the energy performance of buildings of similar function in the same climatic zone [37]. 

Data to do so in the United States can be found in the Commercial Building Energy 

Consumption Survey [38] and Residential Energy Consumption Survey [39]. In addition, energy 

performance can be simulated and compared to compliance with a given energy standard, e.g., 

ASHRAE Standard 90.1 or 90.2 [40, 41] or an energy rating system e.g., EPA’s ENERGY 

STAR [42]. 

CONCLUSIONS 

In this paper, we have reviewed previous attempts to characterize IAQ performance in buildings 

with metrics, noting the many challenges in doing so. To capture the robustness of indoor 

concentration data relative to available health benchmarks and other reference values, we have 

proposed a graphical approach to characterize IAQ performance. The approach was applied to 

selected studies of IAQ performance data to show its usefulness, particularly the facility with 

which one can: (a) identify an issue of potential concern, (b) compare the performance of 

multiple buildings and (c) evaluate the impact of design alternatives and intervention strategies 

in a given building. This graphical approach can be extended to consider additional building 

parameters, such as thermal comfort, lighting, and acoustics, as well as energy, water, and waste 

considerations. 

DISCLAIMER 

The views expressed in this paper are those of the authors and do not necessarily reflect those of 

the U.S. Environmental Protection Agency (EPA) and the National Institute of Standards and 

Technology (NIST). In addition, the full description of the procedures used in this paper requires 

17
 
 

 

the identification of certain commercial products and their suppliers. The inclusion of such 

information should in no way be construed as indicating that such products or suppliers are 

endorsed or recommended by EPA or NIST or that they are necessarily the best materials, 

instruments, software, or suppliers for the purposes described. 

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Table 1 Summary of data from SMCB study illustrated in figure 2 (Bennett et al., 2011) 

Pollutant 

Concentration 

ug/m3 Reference Calculated Sector 

(CO2 in mg/m3) Value Radius 

PM2.5 13.8 35 0.39
 

CO2 817 1000 0.82
 

Acetaldehyde 33.09 140 0.24
 

Benzene 1.53 60 0.03
 

Formaldehyde 53.69 9 5.97
 

Napthalene 0.75 9 0.08
 

Methylene Chloride 4.07 400 0.01
 

Perchloroethylene 1.63 35 0.05
 

Phenol 6.47 200 0.03
 

Trichloroethylene 0.3 600 0.00
 

Toluene 30.83 300 0.10
 

Xylenes 19.22 700 0.03
 

Notes: 

1. Sector radii calculated to be between 0 and 0.25 are graphed as 0.25 to differentiate these 

sectors from those without measurements. 

2. Sector radii calculated to be greater than 1.5 are graphed as 1.5 solely to minimize the graph 

size. 

21
 
Table 2 Summary of data from residential IAQ intervention study in figure 3 (Emmerich 

and Howard-Reed 2005) 

Baseline 

Adjusted Stove 

Kitchen Exh. Fan 

Increased Mech. 

Vent. 

Air Conditioner 

House 

Tightening 

Notes: 

CO CO Calculated NO2 NO2 Calculated 

mg/m3 1-hour Sector mg/m3 Annual Sector 

NAAQS Radius NAAQS Radius 

45 43.2 1.04 0.26 0.107 2.43 

18 43.2 0.42 0.06 0.107 0.56 

31 43.2 0.72 0.15 0.107 1.40 

39 43.2 0.90 0.23 0.107 2.15 

40 43.2 0.93 0.21 0.107 1.96 

100 43.2 2.31 0.45 0.107 4.21 

1. Sector radii calculated to be between 0 and 0.25 are graphed as 0.25 to differentiate these 

sectors from those without measurements. 

2. Sector radii calculated to be greater than 1.5 are graphed as 1.5 solely to minimize the graph 

size. 

22
 
PM 2.5

Annual

PM 2.5

24-h

Carbon Monoxide

1-h

Carbon Monoxide

8-h

Radon

Annual

Figure 1. Hypothetical Example of IAQ Graphic for Three Contaminants 

23
 
Figure 2. Data from Bennett et al. (2011) (95th percentiles, except for PM2.5, which is at the 75th 

percentile) vs. California Chronic Reference Exposure Level (except formaldehyde, which is for 

8 hours) (OEHHA 2013) 

24
 
Figure 3. Data from Emmerich et al. (2005) simulating nitrogen dioxide and carbon monoxide 

concentrations in a summertime Boston kitchen with different gas stove interventions vs. the 

EPA one-hour NAAQS for CO and annual NAAQS for NO2 

25