Associations of Cognitive Function Scoreswith Carbon Dioxide, Ventilation, and Volatile
Organic Compound Exposures in Office Workers:
A Controlled Exposure Study of Green andConventional Office Environments
Citation
Allen, Joseph G., Piers MacNaughton, Usha Satish, Suresh Santanam, Jose Vallarino, and John
D. Spengler. 2015. “Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation,
and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study ofGreen and Conventional Office Environments.” Environmental Health Perspectives 124 (6):805-812. doi:10.1289/ehp.1510037. http://dx.doi.org/10.1289/ehp.1510037.

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doi:10.1289/ehp.1510037

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Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation,
and Volatile Organic Compound Exposures in Office Workers: A Controlled
Exposure Study of Green and Conventional Office Environments
Joseph G. Allen,1 Piers MacNaughton,1 Usha Satish,2 Suresh Santanam,3 Jose Vallarino,1 and John D. Spengler1
1Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston,
Massachusetts, USA; 2Psychiatry and Behavioral Sciences, SUNY-Upstate Medical School, Syracuse, New York, USA; 3Industrial
Assessment Center, Center of Excellence, Syracuse University, Syracuse, New York, USA

Background: The indoor built environment plays a critical role in our overall well-being becauseof both the amount of time we spend indoors (~90%) and the ability of buildings to positively
or negatively influence our health. The advent of sustainable design or green building strategies
reinvigorated questions regarding the specific factors in buildings that lead to optimized conditions
for health and productivity.
O bjective : We simulated indoor environmental quality (IEQ) conditions in “Green”
and “Conventional” buildings and evaluated the impacts on an objective measure of humanperformance: higher-order cognitive function.
Methods: Twenty-four participants spent 6 full work days (0900–1700 hours) in an environ­mentally controlled office space, blinded to test conditions. On different days, they were exposed to
IEQ conditions representative of Conventional [high concentrations of volatile organic compounds(VOCs)] and Green (low concentrations of VOCs) office buildings in the United States. Additional
conditions simulated a Green building with a high outdoor air ventilation rate (labeled Green+) andartificially elevated carbon dioxide (CO2) levels independent of ventilation.
Results: On average, cognitive scores were 61% higher on the Green building day and 101%higher on the two Green+ building days than on the Conventional building day (p < 0.0001).
VOCs and CO2 were independently associated with cognitive scores.
Conclusions: Cognitive function scores were significantly better under Green+ building conditions than in the Conventional building conditions for all nine functional domains. These findings
have wide-ranging implications because this study was designed to reflect conditions that arecommonly encountered every day in many indoor environments.
Citation: Allen JG, MacNaughton P, Satish U, Santanam S, Vallarino J, Spengler JD. 2016.
Associations of cognitive function scores with carbon dioxide, ventilation, and volatile organic
compound exposures in office workers: a controlled exposure study of green and conventional officeenvironments. Environ Health Perspect 124:805–812; http://dx.doi.org/10.1289/ehp.1510037

Introduction
The increasing cost of energy in the 1970s led
to a change in building practices throughout
the United States as buildings were increasingly constructed to be airtight and energy
efficient. These changes are reflected indecreasing air exchange rates in homes andoffice buildings. For homes, beginning inthis time period, typical air exchange rates
began decreasing from approximately 1 airchange per hour (ACH) to approximately
0.5 ACH [Chan et al. 2003; Hodgson
et al. 2000; American Society of Heating,
Refrigerating, and Air-Conditioning Engineers(ASHRAE) 2013b].
Homes built since 2000 are designed
to be even more energy efficient and therefore can be even tighter [0.1–0.2 ACH(Allen et al. 2012; ASHRAE 2013b)]. The> 100-year story of ventilation in buildings
is complicated and was neatly summarized
recently by Persily (2015). Persily describes
the original ASHRAE 62 standard, issued in
1973, and the many subsequent iterations(e.g., ASHRAE 62.1 applies to commercial buildings), demonstrating the evolving
nature of our understanding regarding therelationship between ventilation rate andacceptable indoor air quality. Similarly tothe history of home ventilation, commercial
ventilation requirements were lowered in theearly 1980s, largely as an energy-conservation
measure (Persily 2015).
With such design changes comes thepotential for negative consequences toindoor environmental quality (IEQ) because
decreased ventilation can lead to increased
concentration of indoor pollutants. Buildingrelated illnesses and sick building syndrome(SBS) were first reported in the 1980s as
ventilation rates decreased (Riesenberg andArehart-Treichel 1986), with significant
annual costs and productivity losses due tohealth symptoms attributable to the indoor
environment (Fisk and Rosenfeld 1997).
A few factors of the indoor and work environments have been found to be associatedwith occupant health. These factors include
environmental measures, such as humidity;
building factors, such as ventilation rate;workspace factors, such as the presence ofchemical-emitting materials; and personal
factors, such as job stress, allergies, andsex (Mendell 1993; Wargocki et al. 2000;

Environmental Health Perspectives • volume 124 | number 6 | June 2016

Bornehag et al. 2005; Hedge 2009; Hedge
and Gaygen 2010; Nishihara et al. 2014).
The IEQ problems that arose fromconventional buildings with a tight envelope
contributed to the advent of sustainable
design or “green” building rating systems [e.g.,
U.S. Green Building Council’s (USGBC’s)
Leadership in Energy and Environmental
Design (LEED®)]. These rating systems aim
to reduce the environmental footprint ofbuildings and to improve occupant health byproviding design credits to new and existingbuildings for adopting green design, operation,and maintenance. Different levels of ratings
for the building are then awarded based on
the number of acquired credits (e.g., silver,
gold, platinum) (USGBC 2014). Many design
credits are aimed at energy efficiency andenvironmental performance but also include
guidelines for improving ventilation and filtration, using low-emitting materials, controlling indoor chemical and pollutant sources,
improving thermal and lighting conditions,and offering daylight views to building
occupants (USGBC 2014). Comparedwith conventional buildings, environmental
measurements in green buildings show lower
concentrations of several key pollutants
including particles, nitrogen dioxide, volatile
Address correspondence to J.G. Allen, Harvard
T.H. Chan School of Public Health, 401 Park Dr.,
Landmark Center, 404-L, Boston, MA 02215 USA.
Telephone: (617) 384-8475. E-mail: JGAllen@hsph.
harvard.edu
We thank the study participants for volunteeringand the reviewers of this manuscript for their insightsthat helped improve the manuscript.
This research was supported by a gift from United
Technologies to the Center for Health and the Global
Environment at the Harvard T.H. Chan School of
Public Health. J.G.A.’s time was primarily supported
by faculty startup funds, J.D.S.’s time was primarily
funded by his endowed chair, and P.M.’s time was
supported by National Institute of Environmental
Health Sciences (NIEHS) environmental epidemiology training grant 5T32ES007069-35. United
Technologies Research Center provided limited input
during the study design phase (support for adding acontrol day and adding a third CO2 test level).
United Technologies was not involved in the datacollection, data analysis, data interpretation, data
presentation, or drafting of the manuscript.
The authors declare they have no actual or potential
competing financial interests.
Received: 4 April 2015; Accepted: 12 October
2015; Advance Publication: 26 October 2015; Final
Publication: 1 June 2016.

805

Allen et al.

organic compounds (VOCs), and allergens(Colton et al. 2014; Jacobs et al. 2015; Noris
et al. 2013). However, these reductions generally did not extend to carbon dioxide (CO2)or air exchange rate, demonstrating the influence of energy efficiency on green building
operation and design. Green buildings wereassociated with improved IEQ and have been
associated with reductions in self-reported
symptoms in people inhabiting the buildings
and with improved productivity in home,school, and office settings (Colton et al. 2014;
NRC 2007; Singh et al. 2010). However, an
important limitation of these studies is theirreliance on subjective outcome measures, suchas surveys, that have the potential for bias
because participants are aware of their status(i.e., green or control). To date, we know of
no studies that have been conducted in green
buildings where participants were blinded totheir building condition (Allen et al. 2015).
We designed this study to objectively
quantify the impact of indoor environment
on higher-order cognitive function, a driver ofreal-world productivity in office workers. We
simulated low-VOC (“Green”) and high-VOC(“Conventional”) building conditions, bothat the ASHRAE standard ventilation rate.
Recognizing that technological advances inmechanical systems open the possibility ofincreasing ventilation rates without sacrificing
energy efficiency, we also tested anotherbuilding condition that introduced higher rates
of ventilation to the Green building condition. This condition was labeled Green+. Last,
we were motivated by the recent findings by
Satish et al. (2012) that CO2 may be a direct
pollutant and not just an indicator of ventilation; therefore, we assessed cognitive function
after a full-workday exposure to CO2 while
holding other variables constant.

Methods
Study Design
This study was undertaken in a controlled
office environment to estimate the effects ofseveral indoor environmental quality parameters on an objective measure of cognitive
function. We used a double-blinded study
design that included repeated measures ofcognitive function on the same individual,
characterization of potential confounding
IEQ variables, and midweek testing to avoid
Monday/Friday effects. All participantsreceived the same exposures on each day, with
exposures varying each day.

Study Population
Twenty-four professional-grade employees(architects, designers, programmers, engineers, creative marketing professionals,
managers) in the Syracuse, New York, area
participated in a 6-day longitudinal study of806

cognitive performance and building conditions(Table 1). Six additional people were originally
recruited as backups but were not enrolled in
the study. Participants were recruited throughemails to local businesses. The study population was restricted to nonsensitive persons by
excluding current smokers and people withasthma (because of testing indoor-air quality),claustrophobia, and schizophrenia (because
this was an experiment where participants wererequired to remain in the laboratory). The
participants were relocated to the Willis H.
Carrier Total Indoor Environmental Quality(TIEQ) Laboratory at the Syracuse Center of
Excellence (CoE) for 6 days over the courseof 2 weeks in November of 2014. The study
protocol was reviewed and approved by theHarvard T.H. Chan School of Public HealthInstitutional Review Board (IRB). SUNY
Upstate Medical and Syracuse University
ceded their review to Harvard’s IRB. All participants signed informed consent documents andwere compensated with $800.
Participants reported to the CoE on
Tuesday, Wednesday, and Thursday, at 0900
hours, for 2 consecutive weeks. The CoE
has two nearly identical office environments
located adjacent to one another as part of the
TIEQ Lab, each with 12 cubicles. The rooms
are similarly constructed and have identicalbuilding materials (e.g., carpeting, cubicles,
painting, computers). Environmental conditions, described in the following sections,were designed to be consistent in the tworooms. On the first day, the participants wererandomly assigned to cubicles in the TIEQ
Lab for the duration of the study. Participants
were requested to spend the entire work
day in the simulated office environments
performing their normal work activities. They
were provided with computers, internet access,and an area for private telephone calls andprinting. A 45-min lunch break was given
between 1200 and 1245 hours (Room 1) or
1215 and 1300 hours (Room 2). A limited
selection of food was provided, served, andeaten in a room adjacent to the two simulated
office environment rooms. Participants thenreturned to the simulated office environment
to continue their work. Cognitive testing was
initiated at 1500 hours each day, after which
the participants completed the daily surveys
and left the TIEQ Lab. Participants wereblinded to test conditions, as were the analysts
performing the cognitive function assessment.
Participants were not given any instructions
regarding how to spend their time in theevenings or on the Mondays before starting
the test period.

Indoor Environment Simulation
The different environmental simulations in
the TIEQ Lab on each day were designed toevaluate commonly encountered conditions

and guidance values (Table 2). The three test
parameters that were experimentally controlledwere ventilation with outdoor air, CO2, and
VOCs. We selected two outdoor air ventilation
rates for this study: 20 cfm/person and 40 cfm/
person. LEED® specifies that mechanicallyventilated spaces must meet ventilation rates
under ASHRAE 62.1 or the local equivalent,
whichever is more stringent (USGBC 2014;
ASHRAE 2013a). Many local building codes
use the previous ASHRAE standard of 20 cfm/
person, which corresponds to an indoor CO2
concentration of 945 ppm. Therefore, 20 cfm/
person was the ventilation rate we used for
the Green and Conventional simulation days
because it reflects the minimum required ventilation rate for both green buildings (through
LEED®) and conventional buildings (through
ASHRAE). We also sought to evaluate theimpact of a doubling of that minimum rate to40 cfm/person (labeled Green+ days), whichcorresponds to an approximate steady-state
CO2 concentration of 550 ppm. To ensure
blinding, air movement was maintained
at 40 cfm per person on all study days, with
100% outdoor air ventilation used on Green+days and moderate and high CO2 days, anda mix of 50% outdoor air and 50% recirculated air used on the Green and Conventionaldays to achieve 20 cfm outdoor air ventilation
per person.
For the assessment of the independentassociation of CO2 concentration with cognitive function, the outdoor air ventilation rate
was held constant at 40 cfm/person while
CO 2 was added to the chambers to reach
three steady-state CO2 concentrations. The
first target was 550 ppm (Green+, Days 1 and
6). The second target, 945 ppm, was selected
Table 1. Participant demographics.
Category
Sex
Male
Female
Age
20–30
31–40
41–50
51–60
61–70
Ethnicity
White/CaucasianBlack or African American
LatinoHighest level of schoolingHigh school graduate
Some college
College degree
Graduate degree
Job category
Managerial
Professional
Technical
Secretarial or clerical
Other

volume 124 | number 6 | June 2016

n

%

10
14

42
58

8
3
6
4
3

33
12
25
17
12

22
1
1

92
4
4

1
2
13
8

4
8
54
33

5
15
1
1
2

21
63
4
4
8

• Environmental Health Perspectives

Green buildings and cognitive function

to reflect a level that would be expected at
the previously described ASHRAE minimum
recommended ventilation rate of 20 cfm
outdoor air/person. The third target,
1,400 ppm, was selected to represent a higher,
but not uncommon, concentration of CO2
found in indoor environments [1,400 ppm isthe maximum observed 8-hr time-weightedaverage CO 2 concentration in the U.S.
Environmental Protection Agency (EPA)
BASE data set (U.S. EPA 1998)]. On Days 2
and 3, when the independent effects of CO2
were tested, CO2 was added from a cylinderof ultra-pure CO 2 (≥ 99.9999% pure) to
the TIEQ Lab supply air at a rate needed tomaintain steady-state CO 2 concentrationsof 945 ppm and 1,400 ppm. Because CO2
concentrations are affected by occupancy andmixing impact concentrations, a technician
monitored CO2 in real time and adjusted
the emission rate accordingly to maintain
constant CO2 concentrations. During Days 4
and 5 (Green and Conventional), injectionof pure CO 2 was not needed to reach thetarget CO 2 concentrations because of the
reduced outdoor ventilation rate. A protocol
was established to ensure participant safety inthe event that there were unexpected deviations. CO 2 was monitored in real time ata high spatial resolution in the test rooms
using three different and independently calibrated monitors. A technician seated next to
the CO2 shut-off valves monitored the CO2
concentrations during the entire test period.
The protocol called for immediately cancelingthe testing if CO2 concentrations exceeded
preset thresholds that were well below occupational health limits [2,500 ppm, one-half ofthe threshold limit value set by the American
Conference of Governmental Industrial
Hygienists (ACGIH 2015)]. No deviations
from protocol occurred during the study.

The TIEQ Lab was constructed with
low-VOC materials, and low levels of VOCs
were confirmed by pretesting (Table 3). To
simulate a conventional office space withelevated VOCs, we placed VOC sources inthe diffuser that supplied air to each cubicle
area before the participants arrived on Day 5.
We selected a target total VOC (TVOC) levelof 500 μg/m3 based on the LEED® Indoor Air
Quality Assessment credit limit, as measured
using U.S. EPA method TO-15 (USGBC
2014). The diffusers were built into the floorof the TIEQ Lab, and there were no visible
indicators of these sources for the participants
to observe. We selected a mix of nonodoriferous sources to simulate VOC-emitting
materials that are commonly found in officebuildings and that covered four indoor VOC
source categories including building materials[56 in2 (360 cm2) exposed edge melamine,
56 in 2 (360 cm 2 ) exposed edge particle
board, 64 in2 (415 cm2) vinyl mat], adhesives[80 in2 (520 cm2) duct tape, 80 in2 (520 cm2)
packing tape (exposed)], cleaning products [1oz. (30 mL) multi-surface cleaner, 4 multisurface wipes, 144 in2 (930 cm2) recently drycleaned cloth], and office supplies (4 dry erase
markers, 1 open bottle of correction fluid).

Environmental Monitoring
The study team characterized the TIEQ Lab
on each test day for a wide range of IEQ indicators: CO2, temperature, relative humidity,
barometric pressure, sound levels, VOCs,
aldehydes, nitrogen dioxide (NO 2), ozone(O3), particulate matter ≤ 2.5 μm in diameter(PM2.5), and light. Netatmo Weather Stations
were installed in each cubicle to measuretemperature, humidity, carbon dioxide
concentrations (parts per million), and soundlevels (decibels) every 5 min for each participant. The stations were calibrated to 0 and

3,000 ppm CO2 using calibration gases andwere validated using a calibrated TSI Q-Trak(model 7575). In addition, the Netatmos were
tested with 400 and 1,000 ppm calibration
gas at the end of the study to determine if
the sensors drifted during the 2-week period.
Duplicate measures of CO2 were collected ineach room using a TSI Q-Trak model 7575
and two K-33 data loggers. Summa canisters were used to detect overall levels of 62
common VOCs in a randomly selected work­
station in each room for each of the study days
(Table 3). An additional sample was collected
in a third randomly selected cubicle each day.
Samples were analyzed by ALS Laboratories
according to U.S. EPA method TO-15(U.S. EPA 1999). Thirty-six VOCs were notdetected in any of the samples.
In each room, a monitoring station wasplaced at the far end of the room from theentrance to monitor additional IEQ parameters. The station included a) a TSI SidePak
AM510 personal aerosol monitor to measure
PM 2.5 , b) an integrated filter sample forgravimetric analysis of PM2.5 and elemental
composition, c) an 8-hr integrated active air
sample (0.4 L/min flow rate) analyzed for
14 aldehydes by ALS Analytical Laboratories
using U.S. EPA method TO-11 (U.S.
EPA 1999), d) a passive NO2 badge [8-hrtime-weighted average; model X-595, Assay
Technology; Occupational Safety and HealthAdministration (OSHA) method 182 (OSHA1991)], e) a passive sampling badge for O3[8-hr time-weighted average; model X-586,
Assay Technology; OSHA Method 214(OSHA 2008)], and f) illuminance and irradiance measures using an IL1400 radiometer/
powermeter with SEL-033/Y/W and
SEL-033/F/W detectors. VOC, aldehyde,
NO2, O3, and integrated PM2.5 samples had
at least one blank and one duplicate for every

Table 2. Average indoor environmental conditions simulated in each room of the TIEQ lab.
Variable
Date
Day of the week
Room
Experimental parameters
CO2 (ppm)
Outdoor air ventilation (cfm/person)a
TVOCs (μg/m3)
Other environmental parameters
Temperature (°C)
Relative humidity (%)
NO2 (μg/m3)
O3 (μg/m3)
PM2.5 (μg/m3)
Noise (dB)Illuminance (mV)
Irradiance (mV)

Day 1
Green+
4 November
Tuesday
502
503

Day 2
Moderate CO2
5 November
Wednesday
502
503

Day 3
High CO2
6 November
Thursday
502
503

563
40
43.4

609
40
38.5

906
40
38.2

962
40
28.6

1,400
40
32.2

23.9
31.0
57.9
3.42
2.38
51.3
2.95
9.07

24.5
30.4
58.9
21.2
3.49
49.9
2.70
8.76

22.4
34.2
53.2
14.4
3.35
49.7
2.89
9.45

23.9
31.6
54.1
13.0
2.58
48.8
2.83
9.37

21.3
38.7
60.8
1.37
2.97
52.5
2.31
6.00

Day 4
Green
11 November
Tuesday
502
503

Day 5
Conventional
12 November
Wednesday
502
503

Day 6
Green+
13 November
Thursday
502
503

1,420
40
29.8

761b
20
48.5

726b
20
43.5

969
20
506

921
20
666

486
40
55.8

488
40
14.9

22.0
38.3
58.4
0.00
2.42
48.8
2.04
6.05

22.9
34.3
51.3
6.85
1.26
49.6
3.11
9.90

23.7
33.3
45.6
238
1.83
48.7
2.93
9.60

21.8
39.6
54.6
1.71
1.68
51.1
2.74
8.30

22.5
38.3
50.8
1.37
1.34
48.8
2.51
8.14

20.7
27.8
56.5
4.11
1.26
50.5
2.39
6.70

21.3
26.8
55.5
6.85
1.38
49.2
2.28
6.82

Abbreviations: TIEQ, Total Indoor Environmental Quality; TVOCs, total volatile organic compounds.
aA constant air flow rate of 40 cfm/person was maintained on all study days, with 100% outdoor air used on days 1, 2, 3, and 6 and 50% outdoor air and 50% recirculated air used toachieve an outdoor air ventilation rate of 20 cfm/person on days 4 and 5. bAverage concentration from 1400 to 1700 hours was 926 ppm, but lower CO2 concentrations in the morning
hours during the approach to steady state led to a lower average CO2 concentration.

Environmental Health Perspectives • volume 124 | number 6 | June 2016807

Allen et al.

10 samples. Samples were blank-corrected foranalyses. All duplicate measures were within
15% of each other, and an average of the two
was used for subsequent analyses.
An ambient air monitoring system wasinstalled on the roof of the CoE to measure
PM2.5, O3, and NO2 using the same procedures and equipment as the indoor stations
to establish the potential influence of outdoor
contaminants on the indoor environment.
Outdoor temperature, humidity, solar radiation, and wind speed/direction data wereobtained from the CoE weather station
located on the roof of the building. Baseline(i.e., before occupancy) measurements of all
IEQ parameters were collected in the TIEQ
Lab 1 month before the study was performed.

Cognitive Function Assessment
The cognitive assessment was performed dailyusing the Strategic Management Simulation(SMS) software tool, which is a validated,computer-based test that has been designed
to test the effectiveness of management-level
employees through assessments of higher-orderdecision making (Streufert et al. 1988; Breuer
and Satish 2003; Satish et al. 2004). At thestart of the 1.5-hr test, participants were givena brief, 1-page description of the scenario thatthey were about to participate in during thetest. They were then logged onto a standardized desktop computer station at the TIEQ
Lab using a unique identifier. Participants were
not allowed to use their own computers andwere instructed to turn off all other devices
before the assessment. The simulation was theninitiated. Participants were exposed to diverse
situations based on real-world equivalent challenges (e.g., handling a township in the roleof a mayor or emergency coordinator). These
scenarios are designed to capture participants’standard response pattern. The software allows
flexibility in approach; participants can choose
to make a decision or form a plan at any timein response to any stimulus from the program.
The absence of requirements or stated demands
allows the participant the freedom to strategize
and take initiative in his or her typical cognitive style. Based on the participant’s actions,plans, responses to incoming information, anduse of prior actions and outcomes, the SMS
software computes scores for nine cognitive
factors (Table 4).
A technician trained in administering
this test was present to provide standardized
instructions and periodically answer any questions from participants. Parallel scenarios (i.e.,equivalent scenarios) were used from one day to
the next, which allows individuals to be retested
without potential bias caused by experience andlearning effects (Swezey et al. 1998). Parallel
scenarios have correlation coefficients between
0.68 and 0.94 for the scores on these cognitive
function domains (Streufert et al. 1988).

808

Statistical Analyses

Generalized additive mixed effect models wereused to test associations between environmental exposures and cognitive function while

controlling for the correlated nature of therepeat measures. In the model, the most specificexposure was assigned to each participant,whether it be cubicle-level (CO2), room-level

Table 3. Speciated VOC concentrations (μg/m3) on each study day, averaged across rooms.
Condition
AnalyteVOCs
1,2,4-Trimethylbenzene
2-Butanone
2-Propanol
AcetoneBenzeneCarbon disulfide
Carbon tetrachloride
Chloroform
Chloromethane
Cyclohexane
Dichlorodifluoromethane
Ethyl acetate
Ethylbenzene
Freon 113
HeptaneHexanem,p-Xylene
Methylene chloride
o-Xylene
StyreneTetrachloroethene
Tetrahydrofuran
Toluenetrans-1,2-Dichloroethene
Trichloroethene
TrichlorofluoromethaneGrand total
Aldehydes
2,5-Dimethylbenzaldehyde
Acetaldehyde
Benzaldehyde
Crotonaldehyde
Formaldehyde
Hexanaldehyde
Isovaleraldehyde
m,p-Tolualdehyde
n-Butyraldehyde
o-Tolualdehyde
Propionaldehyde
Valeraldehyde
Glutaraldehyde
o-Phthalaldehyde
Grand total

Day 3
Day 2
Med. CO2 High CO2

Day 4
Green

Day 5
Day 6
Conventional Green+

BackgroundDay 1
Green+

0.3
2.5
1.0
12.0
0.5
0.6
ND
ND
1.3
0.2
2.5
ND
0.3
0.3
ND
0.4
0.8
0.5
0.3
0.1
3.7
ND
2.4
19.0
ND
1.3
50.0

0.2
0.7
1.2
14.7
0.8
0.2
0.2
0.1
1.7
0.3
2.6
ND
0.4
0.7
0.3
0.7
1.5
0.3
0.4
ND
0.9
ND
2.1
8.8
ND
1.2
40.1

ND
0.7
1.1
9.6
0.5
ND
0.4
ND
1.5
0.4
2.9
ND
ND
0.8
ND
0.5
0.4
0.6
ND
ND
ND
ND
1.4
12.6
ND
1.6
35.0

0.1
0.8
3.1
8.7
0.9
ND
ND
ND
1.4
0.5
2.7
ND
0.3
0.8
0.3
0.7
1.0
0.5
0.4
ND
ND
ND
1.9
6.2
ND
1.4
31.4

ND
1.1
1.2
20.0
0.7
ND
0.2
ND
1.9
0.1
2.9
1.0
0.2
0.8
ND
0.4
1.0
0.3
0.1
ND
0.9
0.2
2.2
10.3
ND
1.5
46.9

0.5
1.1
312.5
20.0
0.5
ND
ND
0.1
1.5
0.4
2.4
2.0
0.1
0.2
257.5
0.8
0.7
0.4
0.3
ND
0.6
0.1
1.9
21.8
ND
1.1
626.4

0.1
0.6
8.2
8.6
0.5
0.1
ND
ND
1.4
0.3
2.5
ND
0.1
0.4
6.9
1.3
0.7
0.4
0.1
0.1
0.2
0.2
2.9
8.7
0.2
1.2
45.6

ND
1.0
ND
ND
2.4
ND
ND
ND
1.1
ND
ND
ND
ND
ND
4.6

ND
3.7
ND
ND
5.9
0.8
ND
ND
2.7
ND
0.7
ND
0.5
65.1
79.4

ND
3.2
ND
ND
5.5
0.8
ND
ND
1.4
ND
1.2
ND
ND
57.7
69.8

ND
3.1
ND
ND
5.4
ND
ND
ND
2.3
ND
ND
ND
ND
70.0
80.9

ND
5.4
ND
ND
8.9
1.9
ND
ND
2.8
ND
1.4
ND
0.4
41.6
62.4

ND
7.3
1.5
ND
11.7
2.4
ND
ND
2.4
ND
1.6
ND
ND
38.4
65.3

ND
2.1
ND
ND
4.4
ND
ND
ND
2.0
ND
0.6
ND
ND
76.8
85.8

Abbreviations: ND, non-detect; VOC, volatile organic compound.

Table 4. Description of the cognitive domains tested.
Cognitive function domaina
Description
Basic Activity Level
Overall ability to make decisions at all times
Applied Activity Level
Capacity to make decisions that are geared toward overall goals
Focused Activity Level
Capacity to pay attention to situations at hand
Task Orientation
Capacity to make specific decisions that are geared toward completion of tasks at hand
Crisis Response
Ability to plan, stay prepared, and strategize under emergency conditions
Information Seeking
Capacity to gather information as required from different available sources
Information Usage
Capacity to use both provided information and information that has been gathered
toward attaining overall goals
Breadth of Approach
Capacity to make decisions along multiple dimensions and use a variety of options andopportunities to attain goals
Strategy
Complex thinking parameter that reflects the ability to use well-integrated solutions
with the help of optimal use of information and planningaSee Streufert et al. (1986) for detailed descriptions.

volume 124 | number 6 | June 2016

• Environmental Health Perspectives

Green buildings and cognitive function

Results
Green Building and Cognitive
Function
The TVOC levels were constant at< 50 μg/m 3 on all study days except theConventional building day, when levels
increased to 506–666 μg/m3 depending on
the room. The compounds that increased inconcentration included but were not limitedof higher-level cognitive function and decision
making (Streufert and Swezey 1986). For
Crisis Response, scores were 97% higher
during the Green condition than during theConventional condition, and 131% higher
during the Green+ condition than during theConventional condition. For Information
Usage, scores obtained under the Green andGreen+ conditions were 172% and 299%
higher than under the Conventional condition,
respectively. Finally, for Strategy, which tested
the participants’ ability to plan, prioritize, andsequence actions, the Green and Green+ dayscores were 183% and 288% higher than on
the Conventional day, respectively (Table 5).
The raw cognitive scores for each domain
were normalized to the Conventional

to formaldehyde, benzaldehyde, acetaldehyde, heptane, and 2-propanol. Heptane
and 2-propanol had the largest increases ofthe sampled compounds (Table 3). Total
aldehyde concentrations were primarily
driven by o-phthalaldehyde and remained
relatively constant on all study days.
Cognitive function scores were higher
under the Green building condition thanunder the Conventional building condition
for all nine functional domains (Figure 1).
On average, cognitive scores were 61% higher
on the Green building day and 101% higher
on the two Green+ building days than on theConventional building day. The largest effects
were seen for Crisis Response, Information
Usage, and Strategy, all of which are indicators
Conventional

●

Basic
Applied
FocusedActivity Level Activity Level Activity Level

Task
Orientation

●

Green

CrisisResponse●

Green+

Information
Seeking

Information
Usage

1.5

Breadthof Approach

Strategy●●●

Score (normalized to Green)(VOCs), or lab-level (ventilation). Participant
ID was treated as a random intercept to control
for confounding by individual characteristics. The residuals were normally distributedand homoscedastic for all models (data notshown). We used penalized splines to graphically assess linearity in the associations between
environmental exposures and cognitive scores.
SMS scores are often compared with normative data from other uses of the SMS software(e.g., Satish et al. 2012). Because we did nothave access to normative data, we instead used
our study population as the reference group.
Based on the analysis, cognitive scores werenormalized to Conventional (Table 5), Green(Figure 1), or Green+ (Figure 2) scores to allow
for comparisons across cognitive function
domains, each of which has a unique scale inits raw form. The scores were normalized foreach cognitive domain by dividing all scores by
the average score obtained during the normalizing condition. The statistical significance ofour results was not affected by normalization.
Given the multiple comparisons tested in thisanalysis, p-values < 0.001 were considered to
be statistically significant after performing a
Bonferroni correction. Analyses were performed
using the open-source statistical package R
v.3.0.0 (R Core Team 2015).

●
●

●
●

1.0

●

●

●

●

●

●

●

●
●

●

●

●

●

●

●
●

●

●

0.5
●

●

0.0

Cognitive domain

Figure 1. Average cognitive function scores and standard error bars by domain for the Conventional,
Green, and two Green+ conditions, normalized to the Green condition by dividing all scores by the averagescore during the Green condition.

Table 5. Generalized additive mixed effect models testing the effect of IEQ condition and on cognitive scores, normalized to the “Conventional” condition,
treating participant as a random intercept.
Cognitive domain: estimate, [95% confidence interval], (p-value)
Condition
Day 1
Green+
Day 2
Moderate CO2
Day 3High CO2
Day 4
Green
Day 5
Conventional(Reference)
Day 6
Green+
R2

Basic Activity
Level
1.35
[1.28, 1.43](< 0.0001)
1.20[1.13, 1.27](< 0.0001)
0.91[0.84, 0.98](0.015)1.14[1.06, 1.21](0.0003)

AppliedActivity Level
1.39[1.26, 1.52](< 0.0001)
1.08[0.95, 1.21](0.23)
0.88[0.75, 1.01](0.081)1.04[0.91, 1.18](0.51)

FocusedActivity Level
1.44[1.27, 1.62](< 0.0001)
1.68[1.51, 1.85](< 0.0001)
0.85[0.68, 1.02](0.087)1.51[1.34, 1.68](< 0.0001)

Task
Orientation
1.14[1.11, 1.17](< 0.0001)
1.05[1.02, 1.08](0.0009)
1.00[0.97, 1.03](0.76)
1.03[1.00, 1.06](0.065)

CrisisResponse
2.35[1.91, 2.78](< 0.0001)
2.05[1.63, 2.48](< 0.0001)
1.33[0.90, 1.75](0.14)
1.97[1.54, 2.40](< 0.0001)

Information
Seeking
1.10[1.07, 1.14](< 0.0001)
1.11[1.08, 1.15](< 0.0001)
1.08[1.05, 1.12](< 0.0001)
1.09[1.05, 1.12](< 0.0001)

Information
Usage
3.94
[3.47, 4.41](< 0.0001)
2.61[2.15, 3.07](< 0.0001)
1.01[0.55, 1.48](0.95)
2.72[2.26, 3.19](< 0.0001)

Breadth ofApproach
1.43[1.25, 1.60](< 0.0001)
1.29[1.12, 1.46](0.0013)0.98[0.81, 1.15](0.78)
1.21[1.04, 1.38](0.018)Strategy
3.77[3.40, 4.14](< 0.0001)
3.17[2.81, 3.53](< 0.0001)
0.83[0.47, 1.19](0.36)
2.83[2.46, 3.19](< 0.0001)

Average
1.99[1.89, 2.09](< 0.0001)
1.69[1.59, 1.79](< 0.0001)
0.99[0.89, 1.09](0.78)
1.61[1.51, 1.71](< 0.0001)

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.37
[1.30, 1.44](< 0.0001)
0.34

1.33
[1.20, 1.46](< 0.0001)
0.17

1.52
[1.35, 1.69](< 0.0001)
0.33

1.15
[1.12, 1.19](< 0.0001)
0.03

2.27
[1.85, 2.69](< 0.0001)
0.28

1.11
[1.08, 1.15](< 0.0001)
0.06

4.04
[3.58, 4.51](< 0.0001)
0.69

1.50
[1.33, 1.67](< 0.0001)
0.27

3.98
[3.62, 4.34](< 0.0001)
0.79

2.03
[1.93, 2.13](< 0.0001)
0.81

IEQ, indoor environmental quality.

Environmental Health Perspectives • volume 124 | number 6 | June 2016809

Allen et al.

condition and modeled by study day, controlling for participant (Table 5). The repeat
simulation of the Green+ day (Day 6), which
was added to the study as a quality control
measure, showed similar cognitive function
scores: p-values for the null hypothesis of
no difference between the 2 days ranged
from 0.27 for Strategy (normalized scoresof 3.77 and 3.98, respectively) to 0.73 for
Crisis Response (normalized scores of 2.35
and 2.27). Under the Green+ condition,
participants had statistically significantly
higher cognitive function scores than under
the Conventional condition in all domains(p < 0.0001). Under the Green condition,
particpants had higher scores than under theConventional condition in all domains, fiveof which were statistically significant.

Participants scored higher on the Green+days than on the Green day in eight of nine
domains, resulting in a 25% increase in scoreson average when outdoor air ventilation
rates were increased. Cognitive scores were
20% higher on the Green+ days than on themoderate CO2 day when CO2 levels werehigher (p-value < 0.0001), and 5% higher on
the moderate CO2 day than on the Greenday when outdoor air ventilation was reduced(p-value = 0.12). These estimates and p-values
were produced by rerunning the “average”
model in Table 5 with the Green condition as
the reference category (data not shown).
The model of the average scores in Table 5
had a high R2 value of 0.81, indicating thata significant amount of the variability incognitive scores can be explained by these
Medium CO2● Green+

Basic Activity Level

High CO2

Applied Activity Level

Focused Activity Level

2.0
●

1.5

1.0

●

●
●●
●● ●
●● ●
●●● ●
●
●
●
●
●
●
●
● ●
●

●
●●
● ●
●
●
●
●●●
●●
●● ●
●
●●
● ●

0.5

●
●
●
●●
●● ●
●●
●
●
●
●●●
●
●● ●
●●

0.0
500

1000

1500

500

1000

Score (normalized to Green+)

Task Orientation

1500

500

Crisis Response

1000

1500

Information Seeking

2.0
●

1.5

●●
●
●

1.0

●●
●●
●● ●
●
●
●
●
●
●●
●
● ● ●

0.5

●●

●
●●
●
●
●
●●●
●● ●
●
● ●● ●
● ●
●
●
●
●

●●●
●
●●● ●
●
●
●●
●● ●
●

0.0
500

1000

1500

500

Information Usage

1000

1500

500

Breadth of Approach

1000

1500

Strategy

2.0
●

1.5

1.0

●
●
●
●
● ●
●
●
●●●
● ●
●
●●
●●
●
●
●●

●
●
●
●●
●●●
● ●●
●
●
●● ● ●●●● ●● ●
●●
●
●
●●
●
●
●● ●
● ●● ●
●
●
● ●

0.5

0.0
500

1000

1500

500

1000

1500

500

1000

1500

CO2 concentration (ppm)

Figure 2. Cognitive function scores by domain and participant and the corresponding carbon dioxide
concentration in their cubicles. Each line represents the change in an individual’s CO2 exposure andcognitive scores from one condition to the next, normalized to the average CO2 exposure across all participants during the Green+ conditions.

810

indoor-environment test conditions, leaving
only 19% of the variability to be explainedby all other potential intrapersonal drivers ofcognitive function such as diet, the previous
night’s sleep quality, and mood. For the specificdomains of cognitive function, the R2 ranged
from 0.03 to 0.79.

Carbon Dioxide and Cognitive
Function
The effects of CO2 on cognitive function
scores while all other parameters were held
constant are depicted in Figure 2. Because
the air in each room was not completely
mixed, there was some variability in CO2
levels between cubicles. Each line represents
the change in an individual’s CO2 exposureand cognitive scores from one condition to
the next, normalized to the average CO 2
exposure across all participants during theGreen+ conditions. For seven of the nine
cognitive function domains, average cognitive scores decreased at each higher level of
CO2 (Table 5). Cognitive function scores
were 15% lower for the moderate CO2 day(~ 945 ppm) and 50% lower on the day with
CO2 concentrations of ~1,400 ppm than on
the two Green+ days (Table 5, dividing the
average Green+ estimate by the moderate
CO2 and high CO2 estimates, respectively).
The exposure–response curve between CO2
and cognitive function is approximately linear
across the CO2 concentrations used in thisstudy; however, whether the largest differencein scores is between the Green+ conditions andthe moderate CO2 condition or the moderate
CO2 condition and the high CO2 condition
depends on the domain (Figure 2).
Ventilation rate, CO2, and TVOCs weremodeled separately from study day to capture
the independent effects of each factor on
cognitive function scores, averaged across all
domains. A statistically significant increasein scores was associated with ventilation rate,
CO2, and TVOCs (p < 0.0001 for all threeparameters). On average, a 400-ppm increase
in CO2 was associated with a 21% decrease ina typical participant’s cognitive scores across all
domains after adjusting for participant (datanot shown), a 20-cfm increase in outdoor air
per person was associated with an 18% increasein these scores, and a 500-μg/m3 increase in
TVOCs was associated with a 13% decreasein these scores. Although other environmental
variables were not experimentally modified,some did vary over the course of the study(Table 2). There was a high degree of consistency in IEQ between the two rooms; however,ozone was significantly higher in one of thechambers on the Green day. Cognitive scores
were 4% higher in the room with high ozone
on this day, after accounting for baseline cognitive performance in the two rooms. These IEQ
parameters were added to the model with thevolume 124 | number 6 | June 2016

• Environmental Health Perspectives

Green buildings and cognitive function

experimentally controlled variables and were
not found to be significantly associated withcognitive function at the 0.05 significance level.

Discussion
Green Buildings and Health
We found that when participants spent a fullday in a Green building, there was a significant increase in their cognitive function scorescompared with when they spent a day inan environment that had been designed tosimulate a conventional building by elevating
VOC concentrations. The study was designed
to represent conditions typically observed inmany buildings; we did not include extreme
exposures or choose uncommon VOC sources.
Further, we selected our target levels of VOCs,
ventilation rates and CO2 to be above andbelow the standards in LEED®, ASHRAE, andthe U.S. EPA BASE study in order to evaluate
how these common standards and guidelines
perform (USGBC 2014, ASHRAE 2013a,
U.S. EPA 1998). Our findings indicate that
there may be benefits to meeting the LEED®
VOC guideline of 500 μg/m3 and enhancing
ventilation rates beyond the minimum­requirement under ASHRAE.
The “Conventional” building simulation
parameters in our study were based on conditions described in the U.S. EPA BASE study,
which plausibly represent the upper endof performance for “typical” buildings in the
United States in the 1990s because the owners
were willing to participate in the study, introducing potential self-selection bias, and larger,“non-problem” buildings were preferentially
recruited (Persily and Gorfain 2004). Therefore,
the extent to which BASE buildings represent
typical conventional buildings is unknown. Ourfindings show impacts above the 95th percentile of CO2 (945 ppm) and the mean VOC
concentration in the BASE study (450 μg/m3);
however, a larger proportion of the buildingsin the BASE study would likely have exceeded
these targets if “problem” buildings had been
included in the recruitment process.
The VOC levels on the Conventional
and Green/Green+ days straddled both the
LEED® TVOC guidance concentration of
500 μg/m3 and the BASE mean concentrationof 450 μg/m3. The common VOC sourcesthat were added to the rooms during theConventional building day led to increases ina range of VOCs. Previous testing with the
SMS tool showed that 2 hr of painting, which
exposed participants to VOCs, was associatedwith reductions in three of the five domains
investigated (Satish et al. 2013). The lower
TVOC concentrations (yet larger number ofsources) in the present study were associatedwith statistically significant decrements indecision-making performance in five of thenine domains.

Carbon Dioxide and Ventilation

Strengths and Limitations

Carbon dioxide concentration in indoor environments has long been used as an indicatorof ventilation and as a proxy for indoor air
quality (ASHRAE 2013b). However, thisconventional thinking is being challenged
as the evidence mounts for CO2 as a direct
pollutant, not just a marker for other pollutants (Satish et al. 2012). We found statisticallysignificant declines in cognitive function scores
when CO 2 concentrations were increased
to levels that are common in indoor spaces(approximately 950 ppm). In fact, this level of
CO2 is considered acceptable because it would
satisfy ASHRAE’s ventilation rate guidance for
acceptable indoor air quality. Larger differenceswere seen when CO2 was raised to 1,400 ppm.
Satish et al. used the SMS tool to test theeffects of CO2 exposures on the cognitive
function of 22 participants, using a controlledchamber and injection of ultra-pure CO2(Satish et al. 2012). The authors reported effects
on seven of nine cognitive function domains
with increasing CO2 concentration. The SMS
tool was also used to test the relationshipbetween ventilation rate and cognitive function
among 16 participants (Maddalena et al. 2015).
Participants scored significantly lower oneight of nine domains at low ventilation rates(12.5 cfm of outdoor air/person). In contrast
to the present study, these other studies hada) a single experimental parameter; b) half-day
or shorter exposures; c) multiple experimental
conditions per day; d) atypical exposure targets(2,500 ppm of CO2 and 12.5 cfm outdoor air/person); and e) primarily students and collegeage adults. Despite these differences, our study
found similar changes in cognitive scores froma unit change in CO2 or outdoor air ventilation. Associations were consistent a) in all threestudy populations, indicating that knowledge
workers and students were equally affected by
CO2 and outdoor air ventilation, and b) at
different exposure durations, indicating thateven short exposures are associated with cognitive function. Given the similarities in findings,
there may not be a desensitization or compensatory response from prolonged exposure. Moreresearch is necessary to investigate the presenceof these responses or the lack thereof.
The CO 2 exposure levels used in thisstudy are comparable to those in a variety ofindoor locations. Assessment of public housing
units in Boston found median CO2 levels to
be 809 ppm in conventional apartments and1,204 ppm in the newly constructed LEED®
platinum apartments (Colton et al. 2014).
Corsi et al. (2002) reported CO2 concentrations > 1,000 ppm in 66% of 120 classrooms
in Texas, and Shendell et al. (2004) measured
CO2 concentrations > 1,000 ppm in 45% of
435 classrooms in Washington and Idaho andreported that elevated CO2 concentrations wereassociated with increases in student absences.

The study design has several notable strengths.
These strengths include repeated measures ofcognitive function on the same individual forcontrol of between-subject variability, characterization of the TIEQ Lab for potential environmental confounders, repeated testing of
the same condition 9 days apart on differentdays of the week, mid-week testing to avoid
potential Monday/Friday bias, participants
and cognitive function analysts blinded totest conditions, and the use of an objective
measure of cognitive function.
The SMS tool is an objective assessment
tool, unlike self-reported metrics, and thus isless susceptible to the participant’s environmental perceptions. Extensive work has been
dedicated to testing the validity of the SMS
software; correlations between scores on thesetests and other measures of productivity suchas income at age and job level at age exceed
0.6 (Streufert et al. 1988). The correlations
are stronger for the more strategic domains,such as strategy, information usage, and crisis
response, than for domains pertaining toactivity, such as information search and activity
level. The domains that were most affectedby the exposures in this study are the same
domains that are most closely related to othermeasures of productivity (Streufert et al.
1988). Lastly, the concordance of the scoresfor the two Green+ conditions suggests thata) the study was internally valid, b) there wereno learning effects associated with the test, andc) day of the week (Tuesday vs. Thursday) was
not a potential confounding variable.
The potential for confounding or effectmodification by parameters measured or otherwise was reduced by the use of the controlledenvironment and by repeated measures on eachparticipant. By testing on subsequent days,
it is possible that effects from one condition
were reflected in the scores obtained on thenext day. The environmental factors that were
not experimentally modified exhibited somevariability owing to changes in outdoor conditions and participant behavior. In particular,ozone levels fluctuated significantly between
some IEQ conditions (Table 2). Environmental
factors other than outdoor air ventilation, CO2,and VOCs were not statistically significant
predictors of cognitive scores, but the possibility of uncontrolled confounding by thesefactors cannot be excluded. The environmental
conditions on each of the study days met design
criteria. On one day (Day 4), CO2 levels werelower in the morning than in the afternoon,
which influenced the reported mean concentration. The CO2 levels on this day were similar
to the moderate CO2 and Conventional conditions (Day 5) during the time leading up to andduring the cognitive test (926 ppm from 1400
to 1700 hours). This study used a controlledenvironment to individually control certain

Environmental Health Perspectives • volume 124 | number 6 | June 2016811

Allen et al.

contaminants. Assessments performed in actual
office environments are important to confirm
the findings in a noncontrolled setting.

Conclusion
Office workers had significantly improved
cognitive function scores when working inGreen and Green+ environments comparedwith scores obtained when working in a
Conventional environment. Exposure to
CO2 and VOCs at levels found in conventional office buildings was associated withlower cognitive scores than those associatedwith levels of these compounds found in a
Green building. Using low-emitting materials, which is common practice in Greenbuildings, reduces in-office VOC exposures.
Increasing the supply of outdoor air lowers
exposures to not only CO2 and VOCs but also
to other indoor contaminants. Green building
design that optimizes employee productivity and energy usage will require adopting­energy-­efficient systems and informed operating practices to maximize benefits to humanhealth while minimizing energy consumption.
This study was designed to reflect indoor
office environments in which large numbers ofpeople work every day. These exposures should
be investigated in other indoor environments,
such as homes, schools, and airplanes, where
decrements in cognitive function and decision
making could have significant impacts on
productivity, learning, and safety.
Editor’s Note: In the Information Seeking
column in Table 5, the p-values for Day 2(Moderate CO 2), Day 3 (High CO 2), andDay 4 (Green) have been changed to < 0.0001
from 0.61, 0.35, and 0.45, respectively. In
the Information Usage column in Table 5,
the p-value for Day 3 (High CO2) has beenchanged from < 0.0001 to 0.95. The previous
p-values were from a different reference categorythat was subsequently changed during the peerreview process. The new p-values are consistent
with the current reference category, and theconclusions of the manuscript are unaffected bythese changes.
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volume 124 | number 6 | June 2016

• Environmental Health Perspectives