Indoor Air Quality Monitoring Improving Air Quality Perception

Simone Herberger

Heiko Ulmer

AppliedSensor GmbH, Reutlingen,

Germany

Research Article

Indoor Air Quality Monitoring Improving AirQuality Perception

Energy-efficient ventilation strategies relating to good indoor air quality (IAQ) are a

major task for building performance according to the requirements set by the energy

performance of buildings directive (EPBD) in 2010. Applying demand-controlled venti-

lation (DCV) in buildings, using sensors for IAQ control that enables variable airflow

rates adapted to the actual indoor load conditions is one possibility to fulfill the

requirements of adequate IAQ while reducing the energy consumption at the same

time. CO2 concentrations above outdoors are generally used as an indicator for occu-

pancy generated indoor air pollution and corresponding ventilation rates. The objec-

tive of this study is focused on a micromachined metal oxide semiconductor gas sensor

module developed for IAQ control, based on volatile organic compound (VOC) detec-

tion. The sensor output was correlated with measured CO2 concentrations and quan-

tified VOCs in 15 field scenarios. Energy demand and IAQ, applying the sensor module

for DCV in an office, were compared to natural and time-scheduled ventilation in

the office. The study accentuates the need for DCV and proves the functionality of the

sensor module for IAQ control at adequate comfort levels. Compared to time-scheduled

ventilation, 15% heating energy and 70% power consumption were saved with DCV.

Keywords: Demand-controlled ventilation; Metal oxide semiconductor gas sensor;Microelectromechanical-system; Volatile organic compound

Received: July 15, 2010; revised: July 28, 2011; accepted: December 9, 2011

DOI: 10.1002/clen.201000286

1 Introduction

More than 40% of primary energy in Europe and the US is consumed

in buildings, whereof ventilation accounts for 10% [1]. In order to

promote energy-efficient buildings and reduce CO2 emission, the

European Parliament adopted the energy performance of buildings

directive (EPBD) in 2010 and the EU Member States were requested to

set up minimum requirements on the energy performance of new

and renovated buildings [2, 3]. More strict regulations on the air

tightness of buildings led to an increased role of ventilation com-

pared to other elements of the heat balance of a building. However,

sufficient ventilation in today’s airtight buildings is necessary to

remove or reduce indoor generated pollutants and humidity to

acceptable health and comfort levels and to maintain building

integrity.

Ventilation rates <0.5 air changes/h are considered as health risk

in residential buildings [4]. Not only health problems (e.g., asthma or

allergies) but also comfort complaints and a loss in productivity are

attributed to bad indoor air quality (IAQ) due to inadequate venti-

lation [5, 6]. Field experiments showed that measures to improve IAQ

are cost-effective, considering potential benefits of reduced sick

leave, reduced medical costs, and better performance at work gained

at improved IAQ [7]. However, the optimum ventilation rates with

respect to health and comfort aspects are still under discussion and

up till now only few guidelines on IAQ exist in the EU [8, 9].

Energy-efficient ventilation strategies providing adequate IAQ

became a major task for building performance in the recent past

[1]. Applying demand-controlled ventilation (DCV) in buildings, using

sensors for IAQ control that enables variable airflow rates adapted to

the actual load conditions in buildings, is one possibility to fulfill the

requirements of adequate IAQ while reducing the energy consump-

tion at the same time [1, 10]. State-of-the-art is to operate heating,

ventilation, and air conditioning (HVAC) systems on the basis of fixed

duty cycles (time-scheduled) or to apply ventilation on demand based

on CO2 concentrations above outdoors, used as an indicator for IAQ

related to humans and their bio-effluents [11, 12]. CO2 quantification

is thereby mainly realized by optical absorption techniques (non-

dispersive IR (NDIR) sensors) [11, 12]. Ventilation standards and guide-

lines supporting the EPBD, e.g., EN 15251 [13] and CR 1752 [14] as well

as American ASHRAE Standard 62.1-2 (2007) [15] specify ventilation

rates for different IAQ categories commonly based on CO2 concen-

trations above outdoors that can be related to the percentage of

people dissatisfied with the IAQ [14]. Building material emissions

are considered as a constant background level, requiring a minimum

basic ventilation rate and are not decisive for DCV [13].

Even though high CO2 levels were correlated with comfort com-

plaints and a loss in productivity [16], the odorless CO2 does not

Correspondence: S. Herberger, AppliedSensor GmbH, Gerhard-Kindler-Str. 8, D-72770 Reutlingen, GermanyE-mail: [email protected]

Abbreviations: AQL, air quality level; DCV, demand-controlledventilation; EPBD, energy performance of buildings directive; IAQ,indoor air quality; MEMS, microelectromechanical system; MOS, metaloxide semiconductor; NDIR, non-dispersive IR; PCB, printed circuitboard; PT-GC/MS, purge and trap GC/MS; VOC, volatile organic compound

Clean – Soil, Air, Water 2012, 00 (0), 1–8 1

� 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com

affect the perception of IAQ directly. DCV based on volatile organic

compound (VOC) detection goes beyond this approach. As metabolic

products, VOCs are present in the human breath and bio-effluents

but also in cooking odors, outdoor pollutants, cleaning supplies, etc.

Ethanol, acetone, isoprene, limonene, decanal, nonanal, a-pinene,

and eucalyptol are statistically confirmed to correlate with occu-

pancy and human activities and therefore rather contribute to the

IAQ perception than CO2 [17, 18].

Metal oxide semiconductor (MOS) gas sensors are suitable sensors

for the detection of VOCs. In the recent past, our work was focused on

the development of a microelectromechanical system (MEMS) MOS

gas sensor module for IAQ monitoring [19]. Implementation of an

empirical algorithm for the prediction of indoor CO2 concentrations

based on the VOC sum concentration as detected by the MOS

gas sensor allows signal-adherence to established ventilation stand-

ards using CO2 as indicator for DCV.

The objective of this study focuses on the reliability of the devel-

oped MOS gas sensor module for IAQ monitoring in different real-life

scenarios (differing in location, number of people, fluctuation

degree, conditioning rates, etc.) and the energy saving potential

applying the sensor module for DCV in an office. Correlations of

the MOS gas sensor output with measured CO2 concentrations and

quantified VOCs were investigated for seven field scenarios where

people are considered to be the main pollution source (meeting

rooms, office, and schools) as well as for eight field scenarios where

human activities are dominating (e.g., kitchens), as listed in Tab. 1.

Energy demand and IAQ for natural, time-scheduled and DCV in the

office were compared.

2 Experimental

2.1 Sensor module

The sensor module used in this study is shown in Fig. 1. The sensing

elements developed for the detection of VOCs are MEMS palladium

doped SnO2 thick film sensors, highly sensitive to ethanol and

acetone [19]. The sensor components are glued directly on the

printed circuit board (PCB) with integrated electronics and housed

in a plastic case covered with a membrane for gas exchange.

2.2 Evaluation algorithm

The predicted CO2 concentration, in the range from 450 to

2000 ppm, is used in this study to evaluate the sensor output. The

empirical evaluation algorithm is transferred to the microcontroller

on the PCB. The raw signal of the MOS gas sensor, the resistance R (V),

Table 1. List of field scenarios

Fieldscenario

Topic Vroom

(m3)Period

(h)Attendees Sampling/sensor

positionsRemarks

1 Meeting 40 1.5 Eight grown-ups, mixedage, and sex

Corner of theroom on table

One window tilted,coffee, and cake

2 Meeting 29 0.5 Six to nine grown-ups,mixed age, and sex


Coffee and cake

3 Meeting 240 3 Thirty-two grown-ups,mixed age, and sex


Temporarily open door

4 Meeting 473 2 Seventy grown-ups, mixedage, and sex


Windows tilted

5 Office 24 2.5 One to three grown-ups,mixed age, and sex

Next to entranceon table

Door open

6 Office 71 4.5 One to five grown-ups,mixed age, and sex


–

7 School 100 1.5 Thirty-nine teenagers, onegrown-up, mixed sex


–

8 Gym 1719 4.5 Seventeen to 21 kids andgrown-ups, mixed sex

Corner of gym ontable

–

9 Restroom 31 5 Ten at all, men, mixed age On table inrestroom area

Vestibule next torestroom area,

connecting door open10 Restroom 31 2.5 Ten at all, men, mixed age Vestibule next to

restroom area ontable

Ventilation on,vestibule next torestroom area,

connecting door open11 Restroom 22 2.5 Fourteen at all, women,

mixed ageVestibule next torestroom area on

table

Vestibule next torestroom area,

connecting door open12 Kitchen 54 3 One to seven grown-ups,

mixed age, and sexNext to door

frame kitchen/lunch area

Kitchen and lunch areaseparated

13 Kitchen 72 2 Ten to 20 grown-ups,mixed age, and sex

Sensor aboveexhaust hood,

sampling next tocooker

Tilted windows

14 Kitchen 77 4.5 Two to eleven grown-ups,mixed age, and sex

Middle of theroom on table

Door to connectingroom open

15 Cafeteria 240 1.5 One to five grown-ups,mixed age, and sex

Next to cookingactivities

Two windows tilted

2 S. Herberger and H. Ulmer Clean – Soil, Air, Water 2012, 00 (0), 1–8


that is changed due to chemical catalytic reactions of the gas atmos-

phere with the thick film sensor surface, is transformed into con-

ductivity G (mS) followed by the calculation of a new corrected sensor

baseline. The predicted sensor signal in CO2 equivalent units (ppm) is

calculated based on a certain CO2 outdoor concentration in the

range from 350 to 450 ppm indicating good IAQ, i.e., a ventilated

state of the room as well as based on empirical factors. The predicted

sensor signal can be linked to specific air quality levels

(AQLs) according to definite threshold limits for indoor CO2 pollu-

tion. A detailed flow chart explaining data processing can be found

in [19].

2.3 Complementary analytics and

reference sensors

2.3.1 Purge and trap GC/MS

Purge and trap GC/MS (PT-GC/MS) and GC-olfactometry (GC-O) serves

to elucidate the efficiency of monitoring the IAQ with the MOS gas

sensor module in accordance with quantifiable VOCs and odorous

compounds during the field scenarios.

Air sampling has been carried out sequentially (every 20 or 30 min)

during the field scenarios, starting with the room in an empty,

ventilated state for definition of the background VOC profile in

the room (blank value). The blank value includes the sum of all

quantified VOCs released by building materials and furniture.

Sampling, identification, and quantification of VOCs were per-

formed according to the harmonized European standard DIN ISO

16000-6:2004 [20]. Compounds sampled by TENAX-TA1 with lower

retention times than hexane were also included in this study (very

volatile organic compounds, VVOCs). In order to determine VOC

profiles significant for occupancy and human activities, VOCs quan-

tified by the first air sample (blank value attributed to building

material and furniture emissions), were subtracted from VOCs quan-

tified by successive air samples. Identification of odorous compounds

was performed by GC-O. Intensity of the odorous compounds has

been rated by a 3-level intensity scale (weak, significant, and strong).

Detailed information on sampling parameters and instrumentation

can be taken from [18, 21].

2.3.2 Reference sensors

Temperature and relative humidity (r.h.) during the field scenarios

have been monitored using an EL-USB-2 rh/temp data logger manu-

factured by Lascar Electronics Ltd. (www.lascarelectronics.com) [22].

The AMUN 716-USB one channel NDIR CO2 sensor purchased from

Theben (www.theben.de) served to quantify the CO2 concentration

during the field scenarios to evaluate the effectiveness and reliability

of the implemented empirical algorithm for CO2 prediction.

2.4 Field scenarios

In Tab. 1, the field scenarios are listed. Field scenarios #1–7 are

attributed to occupancy, differing in number of attendees, location,

ventilation rate, etc. PT-GC/MS was used for VOC quantification. Field

scenarios #8–15 deal with typical human activities where the IAQ is

mainly affected by odorous compounds. Odorous compounds have

been identified by GC-O. In Fig. 2, an experimental setup comprising

all instrumentation is shown exemplarily. In [18], detailed infor-

mation on sensor position, course of events, etc., can be found.

2.5 Energy demand and IAQ in an office controlled

with the MOS gas sensor module

The energy saving potential and IAQ conditions applying the devel-

oped MOS gas sensor module for DCV was investigated in a 80-m3 test

office located in the ‘‘Versuchsgebaude fur energetische und raum-

klimatische Untersuchungen’’ (VERU) at the Fraunhofer-Institute for

Building Physics in Holzkirchen, Germany, in comparison to natural

and time-scheduled ventilation. The building is equipped with a

computer controlled centralized supply and exhaust air ventilation

system. No temperature or humidity control of the supply air has

been adjusted for the case study. The 0–5 V analog output of the

developed MOS gas sensor module has been connected to the venti-

lation system of the building for DCV. Ventilation rate for time-

scheduled ventilation has been set according to the requirements of

EN 15251 [13]. Four manually operated windows (each 1.5 m2) located

Figure 1. Sensor module – PCB equipped with MEMS MOS gas sensorand plastic housing.

Figure 2. Experimental setup – restroom.

Clean – Soil, Air, Water 2012, 00 (0), 1–8 Indoor Air Quality Monitoring 3


at the east facade of the building served for natural ventilation of the

office. Details on the ventilation strategies can be found in Tab. 2.

Each ventilation strategy was carried out for 1 wk, reiterating (test

period Dec 2009–April 2010). One to two people for 8 h/day and

5 days/wk, one weekly 1 h meeting with six to eight people and

one artificial event on Friday afternoon (evaporation of 5 mL ethanol

in a Petri dish) represented the normal pollution load in the office.

Attendees had to keep logbook on occupancy and special events,

e.g., window opening. Sensor data, ventilation system parameters

and energy consumption in the office have been logged during the

whole test period. CO2 concentrations have been measured for

comparison.

3 Results and discussion

3.1 VOC versus CO2 approach – occupancy

related field scenarios

Reliable correlations of predicted and measured CO2 concentrations

were obtained for the occupancy related field scenarios.

Quantification of VOCs showed that the best signal accordance is

reached when ethanol and/or acetone concentrations are dominat-

ing isoprene concentration in the course of the respective field

scenario.

In Fig. 3, one representative result as obtained for the occupancy

related field scenarios shows the nice correlation of the sensor

output with measured CO2 concentration in accordance with quan-

tified acetone, ethanol, and isoprene. Significant activities during

the field scenario #4 (see Tab. 1) are plotted in the lower graph.

Large deviations of predicted and measured CO2 concentrations

for the occupancy related field scenarios were found for field

scenario no. 6 in an office (see Fig. 4 and Tab. 1). PT-GC/MS analysis

pointed out isoprene to be the dominating quantified VOC. In the

course of this field scenario ethanol did not increase steadily but

resulted in a steady state concentration as indicated by the GC/MS

results. The MOS gas sensor that is the most sensitive to ethanol

therefore shows a lower predicted CO2 concentration compared to

the measured CO2 concentration.

Slight deviations of measured and predicted CO2 concentrations

detected during the occupancy related field scenarios can be inter-

preted by the metabolic VOC profile differing depending on age,

health, and gender of the test people. In the mornings, human

VOC emission seems to be lower than corresponding CO2 pro-

duction, whereas in the afternoon, metabolic VOC production

seems to be higher resulting in even slightly over-predicted CO2

concentrations.

The concentration course of quantified VOCs (ethanol, acetone,

isoprene, limonene, decanal, nonanal, a-pinene, and eucalyptol),

considered to be significant for occupancy, as well as the estimated

VOC sum content have been found to correlate with the MOS gas

sensor output and CO2 concentration characteristics for all occu-

pancy related field tests.

Table 2. Ventilation strategies

Ventilation strategy Description

Natural ventilation Window opening is up to people working in the office. According to occupancy logbook, peopleventilated the office once or twice a day by manually opening one of the four windows for around5 min

Time-scheduled constantventilation

Ventilation according to EN 15251 (assuming required IAQ level II in the office, low pollutingmaterials, and two attendees), 125 m3/h for working hours (Monday to Friday 8 a.m. to 6 p.m.), 6 m3/hotherwise (5% of the total ventilation rate)

Demand-controlled ventilation,MOS gas sensor module

MOS gas sensor output 0–5 V corresponding to 450–2000 ppm CO2 equivalents serves to triggerventilation rate in linear scale from 6 to 125 m3/h

Figure 3. Field scenario #4, meeting room.Seventy grown-ups, differing in age and sex,some with coffee cups, Vroom¼473 m3, win-dows tilted, a total of six air samples, first airsample in empty, ventilated room. Correlationof predicted MOS gas sensor signal with themeasured CO2 concentration (lower graph)and the concentration course of ethanol, ace-tone, isoprene, and sum VOC content (uppergraph).



3.2 VOC versus CO2 approach – activity related

field scenarios

In Fig. 5, one representative result as obtained for activity related

field scenarios is presented by means of a cooking event (field

scenario #13, see Tab. 1). Occupancy is dominated by activities

and VOCs and odorous compounds that affect perceived air quality

are produced in great quantities. The correlation of the predicted

sensor signal and the concentration course of ethanol, acetone, and

isoprene can be taken from Fig. 5. Concentration course of ethanol

and acetone is in accordance with the intensity course of odorous

compounds (sulfur organic compounds, acids, ketones, roasting

flavors, etc.), determined by GC-O, and indicates times during

the field scenario when unique activities took place and quantifiable

VOCs and odorous compounds have been produced (see Fig. 6). The

NDIR reference sensor used for CO2 quantification however does not

detect odorous events and the measured CO2 concentration can be

only linked to the occupancy in the kitchen.

For activity related field scenarios, isoprene, a-pinene, nonanal,

and decanal have been found to correlate with occupancy and there-

fore the CO2 concentration characteristics whereas ethanol and

acetone attributed to unique activities in accordance with the inten-

sity course of odorous compounds correlate with the MOS gas sensor

signal.

The value of IAQ control with sensors became obvious by the field

scenarios. A big advantage compared to offline analytical methods is

the fast response and real-time capability of the MOS gas sensor

module for IAQ monitoring. The sensor module offers the function-

ality to detect in real-time unique VOC activities related to cooking

activities (see Fig. 5), cleaning processes, etc., in the background of

the predicted CO2 concentration and therefore provides additional

information on the IAQ than sole CO2 quantification. Faster response

times for the MOS gas sensor module than for the NDIR sensor were

detected.

Indoor air quality control with sensors may prevent human adap-

tion to indoor air pollution. However, VOC emissions from building

Figure 4. Field scenario #6, office. One to fivegrown-ups, differing in age and sex, some withcoffee cups, Vroom¼ 71 m3, a total of ten air sam-ples, first air sample in empty, ventilated room.Correlation of predicted MOS gas sensor signalwith the measured CO2 concentration (lowergraph) and the concentration course of ethanol,acetone, isoprene, and sum VOC content (uppergraph).

Figure 5. Field scenario #13, kitchen. Ten to20 grown-ups, differing in age and sex,Vroom¼72 m3, a total of six air samples, first airsample in empty, ventilated room. MOS gas sen-sor signal and measured CO2 concentration(lower graph). Correlation of MOS gas sensorsignal with the concentration course of ethanol,acetone, and isoprene as well as sum VOC con-tent (upper graph).



materials and furniture that accumulate over time cannot be

detected by the MOS gas sensor module due to their slow steady

rise characteristics in indoor air.

Contrary to the occupancy related events, sum VOC content of the

activity related events is not in accordance with the concentration

course of the quantified VOCs. Sum VOC course seems to correlate

with occupancy, isoprene, a-pinene, nonanal, and decanal as well as

the CO2 concentration course whereas the MOS gas sensor signal is in

accordance with the intensity course of odorous compounds indi-

cating unique activities and therefore better correlates to the air

quality perception.

3.3 Implementation of the MOS gas sensor module

for DCV in an office

In Fig. 7, the predicted CO2 concentration from the detected VOC

level in the office is shown exemplarily for 1 wk within the test

period in comparison to the measured CO2 concentration. The pre-

dicted and measured CO2 concentrations are almost consistent and

with both sensing technologies, human presence in the office at

daytime can be clearly distinguished by their absence at nighttime.

Unique VOC events can be additionally detected by the MOS gas

sensor module.

Demand-controlled ventilation, using the MOS gas sensor module

for IAQ control and trigger for the supply air rate in linear scale

(from 6 to 125 m3/h) in comparison to time-scheduled ventilation

(125 m3/h for 10 h/day, 6 m3/h otherwise) is shown in the right hand

graph of Fig. 7. Occupancy times of the office are also plotted in the

right hand graph for the demonstrated DCV week. Compared to

time-scheduled ventilation, 60% less supply air rate was delivered

to the office for DCV for the whole test period. For time-scheduled

ventilation, air flow rates, and time schedules have to be adjusted in

the run-up to the operation, depending on the average grade of

occupancy or activity in the room. However, an overlap between

occupancy of the room and operation time of the ventilation system

is not always guaranteed as indicated by the right hand graph of

Fig. 7. In general, as indicated by the IAQ sensors, there is still a

pollution load in the room after the attendees have already left the

Figure 6. Concentration course of quantifiedVOCs and sum VOC content as well as intensitycourse of odorous compounds for field scenario#13.

Figure 7. Left hand side: One week sensor data in the office. The predicted and measured CO2 concentrations are almost consistent but VOC relatedactivities can only be detected by the MOS gas sensor module. Right hand side: Supply air rate (m3/h) using the MOS gas sensor signal for DCV, comparedto time-scheduled ventilation and occupancy of the office.



office that still requires higher ventilation rates than the reduced

ventilation rate (6 m3/h) adjusted by the time-scheduled system.

Sensor based IAQ control ensures that fresh air is supplied to the

room whenever necessary, reducing energy consumption compared

to time-scheduled ventilation (see Fig. 7). DCV strategy clearly out-

matched the time-scheduled ventilation when looking at time-

resolved results in cases where the room occupancy did not match

the design occupancy of two people.

The overall energy consumption in the office was calculated based

on the individual measured power consuming parameters, such as

lighting, office equipment, heating energy, and fan. For each venti-

lation strategy (see Tab. 2), an average determination of the

measured power consuming parameters was conducted for a period

of 3 wk that resulted in an overall energy consumption for the

individual ventilation strategies as shown in the left hand graph

of Fig. 8. Lighting energy and power consumption of the office

equipment was considered independent of the respective ventilation

strategy. The average heating energy consumption is lowest for

natural ventilation. However, the achieved IAQ is rather poor. CO2

concentrations up to 1300 ppm for normal load condition corre-

sponding to IAQ level III, according to EN 15251, were obtained

for natural ventilation (Fig. 8, right hand graph) and can be

explained by human adaption that prevents bad air quality percep-

tion and people to ventilate the office. According to the occupancy

logbook, the office was only ventilated once or twice per day. DCV

saved �15% heating energy and resulted in �70% less power con-

sumption of the fan compared to time-scheduled ventilation. The

IAQ (IAQ level II according to EN 15251) was in overall not as good as

for time-controlled ventilation (lowest indoor CO2 concentrations,

IAQ level I according to EN15251) but still at an acceptable level (see

Fig. 8, right hand side). Moreover, there is still space for improve-

ments of the DCV strategy, e.g., adaptation of the linear scale used to

control the airflow in this study.

4 Concluding remarks

The field scenarios showed that the developed sensor module is well

applicable for real-time IAQ control in locations, where human

activities and therefore VOCs are dominating (kitchens, restrooms,

etc.) as well as in locations where occupancy and therefore CO2 is

dominating (offices, schools, etc.). The approach of the developed

MOS gas sensor module goes beyond CO2 quantification and offers

better correlation to IAQ perception by VOC detection. The combi-

nation of the developed MOS gas sensor element that is highly

sensitive to ethanol and acetone and the implementation of an

empirical algorithm for CO2 prediction based on a sum VOC detec-

tion allows a reliable correlation of predicted and measured CO2

concentrations and additional detection of point source VOCs,

emitted by cleaning agents, chemical, etc., in the background of

the predicted CO2 concentration.

This study also accentuates the need for DCV regarding actual load

conditions using sensor technologies and shows the energy saving

potential of the developed MOS gas sensor module. DCV prevents

human adaption compared to natural ventilation and reduces the

energy if compared to time-scheduled ventilation. Nevertheless, VOC

emissions from building materials and furniture that accumulate

over time are not detected by the developed sensor module due to

their slow steady rise characteristics in indoor air and a basic venti-

lation rate has therefore to be adjusted for unoccupied rooms. In

order to overcome the lack of selectivity and sensitivity of the

developed sensor module for individual indoor gases, pattern recog-

nition is currently under investigation.

Acknowledgments

A. Burdack-Freitag, R. Rampf, and F. Mayer from the Fraunhofer-

Institut fur Bauphysik (IBP), Valley/Oberlaindern, Germany, are

gratefully acknowledged for their support.

The authors have declared no conflict of interest.

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Figure 8. Left hand side: Energy consumption of the individual elements contributing to the overall energy consumption in the office for natural, time-scheduled, and demand-controlled ventilation. Right hand side: Measured CO2 concentrations in the office for normal load condition depending on therespective ventilation state.



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[20] DIN Deutsches Institut fur Normung e.V., DIN ISO 16000-6:2004,Indoor air – Part 6: Determination of volatile organic compounds in indoorand test chamber air by active sampling on Tenax-TA1 sorbent, thermaldesorption and gas chromatography using MS/FID (ISO 16000-6:2004), DINDeutsches Institut fur Normung e.V., Berlin, Germany 2004.

[21] F. Mayer, K. Breuer, E. Mayer, Determination of oderactive volatilesemitted by building materials by a new method using gas chroma-tography-olfactometry, in Proceedings of the 6th International Conferenceon Healthy Buildings 2000, August 6–10, Espoo, Finland 2000, pp. 119–124.

[22] Lascar Electronics Ltd., EL-USB-2, RH/TEMP Data Logger, Salisbury2010.



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Indoor Air Quality Monitoring Improving Air Quality Perception