Indoor Air Quality Sensors

AIVC 10985 Technical Ne.

INDOOR AIR QUALITY SENSORS

A. J. Martin W. B. Booth

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. . I I Technical Note TN 1/96

INDOOR AIR QUALITY SENSORS

A. J. Martin W. B. Booth

The Building Services Research and Information Association Old Bracknell Lane West Bracknell, Berkshire RG12 ?AH Tel: +44 (0) 1344 426511 Fax: +44 (0) 1344 487575 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means electronic, mechanical, photocopying, recording, or otherwise without prior written permission of the publishers.

ISBN 0 86022 432 5 Printed by Bourne Press Ltd ©BSRIA 77460 December 1996 Indoor Air Quality Sensors Acknowledgements

ACKNOWLEDGEMENTS

BSRIA would like to thank the following for their contribution to the research project:

Department of the Environment The London Stock Exchange J Sainsbury Ove Arup Partnership Staefa Control System Vent-Axia EATe chnology

CONSTRUCTION

SP ONSOR SHI P

DIRECTOR ATE

Department of the Environment

While every opp01tunity has been taken to incorporate the views of the sponsors, final editorial control of this document rests with BSRIA.

©BSRIA Technical Note 1/96

Indoor Air Quality Sensors Summary

SUMMARY

Demand controlled ventilation systems can be used to minimise energy consumption whilst maintaining satisfactory levels of indoor air quality (IAQ). As an alternative to C02 sensors, IAQ sensors (based on Taguchi mixed gas sensors) can be used to infer levels of IAQ.

This Technical Note provides details of a series of laboratory and site tests to determine the performance of a range of IAQ sensors.

As part of laboratory tests the IAQ sensors demonstrated relatively high correlation coefficients when tested against single pollutants; however, coefficients corresponding to test data over periods when the sensors were subj ected to non-controlled conditions (ie a wide range of non-controlled pollutants) demonstrated a poor relationship when there was no single predominant pollutant.

Site monitoringin three buildings demonstrated relatively poor correlation coefficients between the IAQ sensors and monitored gases due to the unquantifiablepresence of other pollutants. The strongest correlations between IAQ sensors and pollutant gases were obtained when a pollutant gas was present in a sufficient concentration to pervade the complete' atmosphere of a zone.

The site monitoring demonstrated that the dilution of pollutants in the indoor environment may be carried out at the expense of introducing polluted air from the outside. A major source of pollution to affect the monitored sites was fromvehicle traffic (indicated by levels of CO).

The use of C02 controlled ventilation techniques is more effective than those using IAQ sensors in applications where IAQ is primarilyinfluenced by varying occupancy.

©BSRIA Technical Note 1/96 .. Indoor Air Quality Sensors Contents

CONTENTS

1. INTRODUCTION ...... 1

2. INDOOR AIR QUALITY SENSORS ...... 2

3. LABORATORY TESTS ...... 3

3.1 SENSOR CHAMBER ...... 3

3.2 MONITORING EQUIPMENT ...... 3

3 .3 SENSORS UNDER TEST ...... 3

3.4 SENSOR TRIALS ...... 4

3.4.1 Methodology ...... 4

3.4.2 Statistical Analysis ...... 4

3.5 SENSOR TRIALS DATA ...... 6

3.6 FORTUITOUS DATA ...... 10

3.7 POWER DOWN TESTS ...... 10

4. SITE MONITORING ...... 15

4. 1 INTRODUCTION ...... 15

4.2 MONITORING AT A RETAIL STORE ...... 15

4.2.1 Site description...... 15

4.2.2 Control strategy ...... 15

4.2.3 Monito1ing strategy ...... 17

4.2.4 Monitoring results ...... 18

4.2.5 Conclusions ...... 24

4.3 MONITORING IN A PHOTOCOPY ROOM ...... 25

4.3.1 Site description...... 25

4.3.2 Monitoring equipment ...... 25

4.3.3 Monitoring results ...... 26

4.3.4 Conclusions ...... 29

4.4 MONITORING IN A NATURALLY VENTILATED OFFICE ...... 32

4.4.1 Site description and results ...... 32

4.4.2 Conclusions ...... 33

5. SENSOR DEVELOPMENTS ...... 35

6. CONCLUSIONS ...... 36

BSRIA ..

Contents Indoor Air Quality Sensors

LIST OF TABLES

Table 3.1 Summary of results: sensor trials ...... 9

Table 3.2 Summary of results: general...... 13

Table 4.1 Food hall analysis ...... 21

Table 4.2 Textile area analysis...... 21

Table 4.3 Sensor output signal correlation with gas concentration ...... 31

Table 4.4 Air quality sensor correlation with TOC concentration ...... 31

Table 4.5 Sensor correlation with TOC (relative to Hexane) concentration ...... 32

LIST OF FIGURES

Figure 3.1 Dependence on hexane levels ...... 7 Figure 3.2 Dependence on octane levels ...... 8

Figure 3.3 Dependence on CO levels ...... 11

Figure 3.4 Dependence on formaldehyde levels ...... 12

Figure 4.1 Retail store AHUs ...... l6 Figure 4.2 Store air system schematic ...... 17 Figure 4.3 Carbon monoxide levels ...... 19 Figure 4.4 Carbon dioxide and occupancy ...... 19

Figure 4.5 Staefa data AHUl (Fri 7/10/94) ...... 22 Figure 4.6 Staefa data AHU2 (Fri 7/10/94) ...... 22 Figure 4.7 TOCs relative to hexane (mg/m3)...... 23 Figure 4.8 Carbon monoxide ...... 23 Figure 4.9 Carbon Dioxide in mg/m3 (Fri 7/ 10/94) ...... 24 Figure 4.10 Photocopy room layout ...... 25 Figure 4.11 Photocopy room: fan on continuously ...... 27 Figure 4.12 Photocopy room: controlled extract...... 28 Figure 4.13 Photocopy room: controlled extract...... 30

Figure 4.14 Office layout ...... 33 Figure 4. 15 BEMS office monitoring ...... 34

©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Introduction

1. INTRODUCTION

Two of the key issues concerning the built environment are improvements in energy conservation and the level of environmental comfort, including the qualityof indoor air. When considering ventilated buildings, whether air-conditioned or mechanically ventilated, these two issues usually conflict. Reductions in freshair rates in the interests of energy conservation result in decreased levels of air quality - an important factor contributing to 'sick building syndrome' . Conversely, increases in fresh air rates result in increased energy consumption.

It is normal practice to set minimum fresh air rates at the design stage based on either the anticipated levels of occupancy or floor area. Unfortunately, where building occupancy patterns vary greatly, as in a large department store, or where the actual levels are significantlydiff erent fromthe original design assumptions, either energy consumption or air quality will suffer.

To help alleviate this problem, C02 sensors can be used which makeit possible to vary the fresh air rates in relation to the implied number of building occupants (used in conjunction with suitable control equipment). However, these sensors can only react to C02 and not the many pollutants which can occur in the indoor environment.

An alternative to C02 sensors are Indoor Air Quality (IAQ) sensors. Based on Taguchi gas sensors, IAQ sensors (sometimes referred toas mixed gas sensors) provide an output in relation to a wide range of pollutants. Therefore, in theory, these sensors have the potential to provide enhanced control of fresh air resulting in improved indoor air quality and/or reduced energy consumption.

This Technical Note reports on research work performed to evaluate the performance of IAQ sensors by means of laboratory tests and site evaluation.

©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Indoor Air Quality Sensors

2. INDOOR AIR QUALITY SENSORS

Commercially available IAQ sensors are based on metal oxide gas sensors (tin oxide semi-conductors). They can respond to a wide range of gases found in typical commercialbuildings.

Claimed charactelistics include varying degrees of sensitivity to many different gases such as hydrogen, carbon monoxide, hydrocarbons, alcohols, esters, benzene etc.

The sensor consists of a tube of porous semi-conducting material fitted over an electrode. A heating element inside the electrode is used to maintain the sensor at a constant temperature. Molecules of certain gases in the air around the sensor are adsorbed by the porous semi-conducting mate1ial and are oxidised (ie give up free electrons to the semi-conducting material). This is manifested as a change in electlical conductivity which is detected and processed by appropriate electronic circuitry. At the same time, other oxidised molecules in the porous matelial recombine with electrons and diffuse back into the air. This prevents the porous matelial becoming saturated. If the concentration of the gas in the surrounding air is constant, a state of dynamic equilibrium is reached in which the rate of adsorption of molecules on to the porous semi-conducting matelial equals the rate at which gas molecules diffuse back into the air. A change in the gas concentration with time will result in a change in electlical conductivity.

Depending on the application, it is possible to produce a sensor either sensitised for one gas or de-sensitised for a particular potential interfeling gas.

Typical sensors comprise a:

• Gas sensor. The solid-state sensor detects gases through an increase in electlical conductivity when the reducing gases are adsorbed on the sensor's surface.

• Sensor Module. The module provides an output voltage which acts as an input to either a dedicated microprocessor or to a BMS type outstation. The sensor module and sensor are calibrated with a specific gas.

2 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Laboratory Tests

3. LABO RA TORY TESTS

3.1 SENSOR CHAMBER

A small test chamber designed to hold the various sensors under test was constructed from aluminium sheet. Two circular spigots each with a small instrument-coolingfan provided a push-pull configuration. A length of metallised flexible ducting was used to connect the two spigots to provide a closed air-circulation path. A tee-junction was fitted into the middle of the flexible ducting with a cap on the side arm. This allowed either gaseous pollutants to be injected directly via a stainless steel tube or for samples to be placed in the main flow of air at the bottom of the tee-junction.

3.2 MONITORING EQUIPMENT

Levels of four gases in the air passing over the IAQ sensors were monitored using a Bruel and Kj aer 1302 multi-gas analyser. The instrument works on the principle of infra-red absorption with a filter (one for each gas) being placed in the path of infra-red light passing through a sample of air drawn into the instrument through a sample tube. A fifthfilt er for water vapour allowed corrections to be made automatically. Corrections for cross-interferences were also made by the analyser. After taking in a sample, a period of three or four minutes elapsed before readings were available. The dynamic measuring range of the analyser was specified by the manufacturer to be 105• The four gases monitored were:

Gas Minimum Detectable Concentration (111g/;m�). (ppm) Carbondioxide 3.Q:6, 1.70

Carbon monoxide •, . , 0.2:3 0.20 Formaldehyde \, @.Q6 0.05 . ' Total Organic Carbon - TOC Oli2 0.005

(relative to hexane) > · .

The minimum detectable concentrations were calculated for gases at 25 °C and a value of 1 atmosphere. The influence of temperature was± 0.3% of measured value per °C change in temperature. The influence of barometric pressure was 0.01% of measured value per mbar change in barometric pressure. In practice, the influence of temperature and barometric pressure is negligible for this application.

3.3 SENSORS UNDER TEST

Simultaneous tests were performed on the following sensors:

Manufacturer Sensor

Staefa Control System FRA-Ql Landis & Gyr Building Control QPA61.1 Sauter Automation ERQl FOOl Datum Controls AQ/S Centra-Burkle LQR-1

©BSRIA Technical Note 1/96 3 Laboratory Tests Indoor Air Quality Sensors

An additional Staefa sensor (referred to as Staefa2) was located outside the test chamber for reference purposes.

Tests were performed with the sensors as supplied by the manufacturers.

3.4 SENSOR TRIALS

3.4.1 Methodology

A series of tests were performed with a range of pollutants (only one pollutant was injected at one time) introduced into the te st chamber. These pollutants were hexane, octane, carbon monoxide and formaldehyde. For hexane, octane and carbon monoxide, a 200 ppm mixture in synthetic air (80% nitrogen and 20% oxygen) was obtained for each gas in a high pressure cylinder. The gaseous pollutants were introduced into the test chamber at different flow rates in order to provide a variety of pollutant concentrations. Formaldehyde was introduced as a 36% solution by weight and allowed to evaporate. Step changes were made to the pollutant flow rate into the test chamber with each flow rate being maintained for approximately one hour. The changes in release rate readilymanif ested themselves as a change inpoll utant concentration and the response of the air quality sensors to these changes was determined (see the upper part of Table 3.1) as described in 3.4.2.

3.4.2 Statistical Analysis

A brief discussion of the strengths and weaknesses of linear correlation is appropriate here. For each selected data set, a linear coITelation was carried out using the conventional formulas (which are sttictly valid fo r nonnal distributions only). The coITelation coefficientr, given in the lower part Table 3 .1, is a measure of the linear relationship between the two variables under study. Low values of r indicate that there is little or no linear relationship between the two vatiables. The sign of r indicates whether the linear correlation, irrespective of strength, is positive or negative. A rule of thumb for interpreting r is to regard a coITelation as significantif there is less than 1 chance in 20 (0.05 confidence limit) that the value will occur by chance.

Standard statistical tables exist to allow appropriate threshold values of r to be determined forsamples of different sizes but the threshold values of r will be larger for a smaller sample size. As an example (using the values in "Statistical Methods In Research And Production" by 0 L Davies) for samples of 30 and 100 pairs of hourly average values, the correlation coefficient r would have to exceed 0.362 and 0.199 respectively for the chance of the variables being unrelated to be less than 1 in 20. The coITesponding values for the chance being less than 1 in 1000 (0.001 confidence limit) are 0.572 and 0.327. In this work, the confidence limits for data sets greater than 100 have been taken to be the same as fo r a data set of 100.

4 ©BSRIA Technical Note 1196 Indoor Air Quality Sensors Laboratory Tests

The failure to find a linear relationship between two variables may be due to one or more of the following:

• the two variables are simply not related.

• more than two variables are involved in a physical process.

• the measured data available does not vary over a sufficiently wide range for a significant statistical assessment to be made.

• the variables are related in a non-linear way.

• the relationship, although inherently linear, is also influenced by factors such as ageing and/or hysteresis.

Scatter diagrams provide a convenient means of establishing the general validity of the assumption of a linear relationship. These are provided for the parameter pairs in Figs 3.1-3.4. It is worth noting that for the situation of a very low value of r then the mean of the distribution of a sample is the best prediction of that variable. The identification of a significantlinear correlation does neither identify a causal relationship nor ensure reliable prediction of values (i.e. interpolation) within the range of the data samples. Extrapolation to predict values outside the range of the data samples may provide useful trend indications but confidence in subsequent ,deductions can be no better than the underlying statistics. As study of the scatter diagrams provided in this Technical Note will illustrate, the existence of a significantcorrelation does not necessarily mean sensible extrapolations can be made. In practice for engineering purposes, a linear regression of data resulting in an r2 value of 0.9 and preferably greater can be construed as being fairly wmthwhile for interpolation and, with due care, extrapolation. A more relaxed criteria has been selected here with an r2 value of better than 0. 81 being highlighted in the presented results.

There are other aspects to be considered in a very comprehensive statistical treatment including establishing that the distribution in data points associated with each variable is normal. However, as with all matters statistical, the perfect experiment is an unrealistic goal. The data discussed here was collected in a scientific manner with as much standardisation during the trials as practical to minimise the influence of external variables.

The data was analysed to establish the degree of correlation between pollutant concentrations and sensor output. The graphs in Figure 3.1 to Figure 3.4 are scatter diagrams showing each point in'the data sets. The number of points in each data set are shown in the column headed "n" and on each graph. The solid line is the best straight line fitand the broken line is the best quadratic (second order) fitto the data. The value of r2 for each fitis shown on each graph.

©BSRIA Technical Note 1/96 5 ... ..

Laboratory Tests Indoor Air Quality Sensors

The statistical significanceof the results obtained in the sensor trials aredenoted using the text attributes listed below for the results of the linear regression (best straight line) correlation exercise presented in Table 3 .1.

2 Style Statistical Confidence r value r value Significance Limit

. . I f t 0 05 t 0 001 �i��i� �QOl.. 1 � 6:9� � white on black s1g:nifican1 >0.00 l better than better than 0.9 0.81

3.5 SENSOR TRIALS DATA

1) Hexane

Refer to Figure 3.1 and Table 3.1. The maximum hexane levels during the trials (valid data) were approximately 17 ppm. All the sensors in the chamber showed a positive correlation with hexane levels. The sensor outside the chamber (Staefa2) showed no response to the levels in the chamber as expected. The response of the Centra-Burkle (ie the gradient of the linear fit) is the lowest. The correlation coefficientfor the Sauter is markedly lower than the others but study of the plot reveals that the sensor was at full scale deflection(fsd) around 8 ppm and above. Consequently, the response figureis an underestimate. The Sauter and the Centra-Burkle represent the two extremes of the response to hexane.

2) Octane

Refer to Figure 3.2 and Table 3.1. The maximum octane levels during the trials (valid data) were approximately 20 ppm. Again, all the sensors in the chamber showed a positive correlation with octane levels. The scatter in the data for the sensor Staefa2 may have been due to a rise in levels of unknown pollutants including octane due to painting (elsewhere in the building) but again shows no significant correlationwith the levels in the test chamber. The linear fitto the Sauter data is again an underestimate and the second order fitover 0-7 ppm describes the data well but clearly the output of the sensor reached its fsd value. The data for the Staefa shows relatively little scatter and the second order fitdescribes the response well at low levels. The response figure (Table 3.1) will again be a slight underestimate at the low levels. The data fromthe Centra-Burkle shows more scatter and a poorer correlation but the scatter appears to be fairly uniform. Although the correlation coefficient (linear fit) is relatively high for the Landis & Gyr and Datum, the data plot for the former suggests some form of hysteresis response at times while the latter shows no real response until levels exceed approximately 3 ppm.

6 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Laboratory Tests

Figure 3.1 Dependence on hexane levels

©BSRIA Technical Note 1/96 7 ... ..

Laboratory Tests Indoor Air Quality Sensors

Figure 3.2 Dependence on octane levels

Datum UcGyr

10 10

1111• 5 i I 0 n= 329 Order n= 329 Order r 0.863 ht 0.908 ht 0.997 2nd 0.991 2nd 10 15 20 5 10 15 20 Ooton• (ppm) Ooton• (ppm)

Staefo Staefa2

0 0

£ • •• ·: • , ..... ·.> 1111 1111 : ...... · :! :! - ·-- 0 ; J - - e > . t"':· .... -.� - � 7 :i-. 5 5 : ,.·::... .. 'S ! I 0 n= 329 Order n= 329 Order 10 0.843 1at 10 0.090 ht 0.890 2nd 0.582 2nd 10 15 20 10 20 Ooton• (ppm) Ooton• {ppm)

Centra. Sauter

...... ' . 10 10 .. ··-- .. - , . . . . ' ' ' ' '

.-· ...... �:·· -, . :...... a 0 0 n= 329 Order n= 329 Order o.•23 1st 0.712 ht 0.997 2nd 0.768 2nd 10 15 20 5 10 15 20 Ooton• (ppm) Ooton• (ppm)

Octane Trlals

8 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Laboratory Tests

Table 3.1 Summary of results: sensor trials

RESPONSE (V/ppm)

Substance n Staefa2* Sauter (room)

Hexane 120

Octane 329 0.037

co 257

203 0.000

Formaldehyde 22 0. I 77> 0. I 19 O.OlJI O.OlJ:'i 0.208

* Staefa IAQ sensor response has been inverted to assist comparison with other sensors.

CORRELATION COEFFICIENT

Substance n Staefa2* Sauter (room)

Hexane 120

Octane 329 0.30

co 257

203 0.32

Formaldehyde 22 0.99 0.% 0.% -0.49 0.98 0.95

* Staefa IAQ sensor response has been inverted to assist comparison with other sensors.

Note: Response is gradient of fitted straight line which equals the first order coefficient in the linear regression.

3) Carbon Monoxide

Refer to Figure 3.3 and Table 3.1. The maximum carbon monoxide levels during the trials (valid data) were approximately 50 ppm. In general, the response was flatter than for either hexane or octane (ie in terms of V /ppm) for the Landis & Gyr, Staefa and Sauter. . The Datum sensor had a very flat response and the correlationcoefficient was much lower. The correlation coefficients for the Centra-Burkle and the Staefa2 sensors were not statistically significant. The response of the Staefa is again slightly better described by a second order fitand the response figure in Table 3 .1will be an underestimate below say 20 ppm. The Landis & Gyr and Sauter had a strong response similar to the Staefa. The latter again had the strongest response (linear fit) with an underestimate at the lower end but scatter in the points is higher than for the previous pollutant and fsd response did not occur in this case.

©BSRIA Technical Note 1/96 9 Laboratory Tests Indoor Air Quality Sensors

4) Formaldehyde

Refer to Figure 3 .4 and Table 3 .1. The maximum levels in the trials (valid data) were approximately 45 ppm. It should be noted that the occupational exposure limit time-weighted average (OEL-TWA) value is 2 ppm. It proved difficultto produce low levels of formaldehyde vapour. The data sub-set for formaldehyde is relatively small and, although the correlation coefficients were statistically significant (ie none below 0.95), the correlation should be viewed with caution. Both the Sauter and the Staefa show evidence of fsd output and thus the quoted response will be underestimated at lower levels. The Staefa2 sensor outside the chamber showed no statistically significantresponse. It should be noted that although there was a strong response, levels above the OEL-TWA should not occur normallyin occupied buildings.

Tests were performedto determine the sensors' response to levels of carbon dioxide (1500 ppm). No sensor showed any appreciable response to carbon dioxide and the c01relation coefficients were consistently low and were not statistically significant except for the Centra-Burkle.

3.6 FORTUITOUS DATA

Data collected during the background monitoring (eg overnight and at weekends) as well as duting trials represents 'fortuitous' data, ie the levels of the pollutants were not necessarily the result of a single controlled factor. Table 3.2 presents the response factors (linear fit) and c01relation factors for alldata in a similar manner to Table 3 .1. The number of points in each data set is given inside the subscripted brackets in Table 3.2. It was not an objective of the BSRIA investigations to investigate or control the levels of water content, relative humidity or dry bulb temperature but it can be concluded that there was no significant dependence (ie response factors) to these factors over the range occuning during the monitoring period.

3.7 POWER DOWN TESTS

One of the characteristics of air quality sensors is that they require a 'burn in' petiod when initially switched on and similarly if there are any subsequent power interruptions in order to 'bum off' any impurities on the sensing element which have been adsorbed from the surrounding air. Most manufacturers suggest a minimum warming up or bum-in period on switch on. This is specified as several minutes to several days.

10 ©BSRIA Technical Note 1196 Indoor Air Quality Sensors Laboratory Tests

Figure 3.3 Dependence on CO levels

Datum UcGyr 10 10

& 5 i>

..� ��--_.. 01-r- �n= :::±::257:::::::: · �·· ·="�-�·� 257 � Order n= Order 0.331 ht 0.824 1at 0.750 2nd 1.000 2nd 10 20 30 50 10 20 30 50 CO (ppm) CO (ppm)

Stoefo Staefa2

0 0

---- .-.rotl/l/"'T;

. ,

257 n= 257 .� ? on1.. Order 10 0.7« ht [-- 10 0.021 ht - - -- 0.8-40 2nd 0.752 2nd 10 20 30 ..0 50I 10 20 30 50 co (ppm) CO (ppm)

Centro. Sauter 10 10

0 n= 257 Order n= 257 Order 0.095 1at 0.765 ht 0.335 2nd 0.821 2nd 10 20 30 50 10 20 30 50 CO (ppm) CO (ppm)

CO Trials

©BSRIA Technical Note 1/96 11 .. ..

Laboratory Tests Indoor Air Quality Sensors

Figure 3.4 Dependence on formaldehyde levels

Datum l.AcGyr

10 10

• { l n= 22 Order n= 22 Order 0.980 1at 0.922 1at ------0.987 2nd ---- 0.975 2nd 10 20 30 "'° 50 10 20 30 40 50 Formaldehyde (ppm) Formaldehyde (ppm)

Staefo Staefa2

� • • Ill Ill

'!i 15 r---...... c=-: ..... i i ""-=-=--=-=� -=-=--.:-- --�..- } i n= 22 Order n= 22 Order 10 0.912 1at 10 -- 0.239 tat ------0.9.41 2nd ---- 0.764 2nd 10 20 30 "'° 50 10 20 30 "'° Formaldehyde (ppm) Formaldehyde (ppm)

Centro.

10 10

0 0 n= 22 � Order n= 22 r2 0.952 1at 0.911 ------0.990 2nd -- -- 0.938 10' 2d I 30' 40' 150I 10 20' I 30 4" Formaldehyde (ppm) Formaldehyde (ppm) Formaldehyde Trlals I

12 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Laboratory Tests

Table 3.2 Summary of results: general

RESPONSE (V/ppm)

Substance Datum L&Gyr Staefa* Staefa2* Centra. Sauter Controls (room)

Hexane

Octane

co -0.121(15)

C02 0.012(93)

Formaldehyde -0.022(204) 0.052(115) 0.379(24)

Water vapour 0.000c5385J

DB JI -0.310(477)

RH 0.098(477)

* Staefa IAQ sensor response has been inverted to assist comparison with other sensors.

CORRELATION COEFFICIENT

Substance Datum L&Gyr Staefa* Staefa2* Centra. Sauter Controls (room)

Hexane

Octane

co -0.51(15)

C02 0.25(93)

Formaldehyde -0.29(204) 0.23(115) 0.45(24)

Water vapour 0.21(5385) 0

DB -0.26(477)

RH 0.24(477)

* Staefa IAQ sensor response has been inverted to assist comparison with other sensors.

Note: a) Response is gradient of fitted straight line which equals the first order coefficient in the linear regression. 3 b) Units of response are V/ppm except for water vapour (VI(mg/m )) dry bulb (V/°C) and humidity (V/%RH).

©BSRIA Technical Note 1196 13 ......

Laboratory Tests Indoor Air Quality Sensors

A series of tests were conducted in order to evaluate the response of the sensors following the removal and subsequent re-instatement of power. The tests comprised injecting hexane at 200 ppm into the test chamber and monitoring the response of the sensors for a range of power down periods (1 hour to 2 weeks).

The tests demonstrated that theremova l of power has comparatively little effect on the sensor output upon restoration under the conditions tested. The tests indicated that in the event of a power down on site thesensors will quickly recover giving an air quality indication within 90% of the nounal reading within several minutes followingpow er restoration. The effect under different concentrations and combinations of gases however, may be different.

14 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Site Monitoring

4. SITE MONITORING

4.1 INTRODUCTION

The intention of the site monitoring was to determine what improvements in indoor air quality may be obtained by the use of IAQ sensors and the practical implications of using these sensors. Monitoring was carried out in a retail store, a photocopy room (within anoffice building) and a naturally ventilated office environment.

On each of the sites monitoring of four gases (carbon dioxide, carbon monoxide, fmmaldehyde and total organic carbons - relative to hexane) was performed using the Bruel and Kj aer 1302 multi-gas analyser.

4.2 MONITORING AT A RETAIL STORE

4.2.1 Site description

The retail store was completed in 1993 and is situated approximately one mile fromthe M25 motorway. The sales area is on the ground floor with an open floor plan 'divided' into two areas, one retailing food (food hall) and the other area selling clothes (textile area). There is no partition between the two areas. The areas are served by two fixedspeed air handling units (AHUs) of similar design which use a three damper recirculation system. The ductwork arrangement is unusual since each AHU does not extract air fromthe same space that it supplies. The air extracted from the food hall area by AHU 1 is either exhausted to atmosphere or recirculated to supply the textile area with 'free' cooling. (The temperature of the air from the food hall can be low enough to provide cooling to thetextile area.) Similarly, the air extracted from the textile area by AHU 2 is either exhausted to atmosphere or recirculated to supply the food hall area thus providing 'free' heating of the food hall (see Figures 4.1 and 4.2). This implies that poor air quality in one area can be extracted and supplied to the other area thus necessitating the use of air quality monitoring in each of the areas. This function is pe1formed by two air quality sensors mounted in the sales space approximately 2m above the flooron external walls. The sensors are connected to a building management system (BMS) which controls the two air handling units along with the other main plant.

4.2.2 Control strategy

The air quality sensor signals are compared with their associated setpoint. If there is a difference (error) between the two, then a proportional output signal is produced which modulates the mixing dampers on the AHU. In n01mal operation the air quality signal works in conjunction with the following control signals, the greatest signal dictating the AHU positions:

1) free cooling signal 2) freeheating signal 3) minimum supply air temperature signal.

©BSRIA Technical Note 1196 15 ......

Site Monitoring Indoor Air Quality Sensors

Figure 4.1 Retail store AHUs

Extract damper

Food hall ext .. .· ,- Supply Bruel & : Ref.lire�--···· damper ,...... Kjaer ; Gas Monitor f AHIJ1 � JnteJ._cjampers Boiler -House

' ..______....,.... __, Ambient

Extract Sampling tubes ...... � ... -� damper t

AHU2

Rec"irc dapiper --� Supply to ,:�� textile area '------�

Extract from f -.. ile area f ! LJ! l � L

Supply to Extract from food hall food hall

16 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Site Monitoring

Figure 4.2 Store air system schematic

r:;1 AREAr:;;)/ ,-,/TEXT,.- _,!B-SUPPILE�-� IR-- .'2/�-;:

..·< . -�---// r;;;:i 9 r;;;J �J�L:·10. ,;· �.-�-·---�-- '" 7·' FOOD HALL .... / @/ SUPPLY AIR _../ TEXTILE AREA EXTRACT AIR L.. _,,,_,,,_ _ _ ,, , ...... :;;===::===::::::;;------i

TS= TEXTILESUPPLY TE =TEXTILE EXTRAC FS = FOOD HALL SUPPLY FE = FOOD HALL EXTFIACT

TO FOOD HALL EXTRACT FAN

If there is a requirement for cooling, and free cooling is available, then the free cooling signal will be compared with the air quality signal. If the demand for fresh air to achieve good air quality is less than that to achieve free cooling then the latter will take over control of the mixing dampers. A similar control strategy operates for the free heating signal. The control of the dampers is determined by the greatest signal of the air quality demand, the freecooling demand or the free heating demand. This is aimed at achieving a balance between energy and air quality requirements.

4.2.3 Monitoring strategy

The following four points were monitored using the Bruel and Kj aer:

• ambient air

• textile supply duct air (AHU 1)

• textile extract duct air (AHU 2)

• food hall extract air (AHU 1).

©BSRIA Technical Nott:1/96 17 Site Monitoring Indoor Air Quality Sensors

In addition, the fo llowing points were logged using the BMS:

• air quality sensor value

• air quality control output signal

• free cooling control output signal

• free heating control output signal

• AHU fan operation

• damper position control output signal.

4.2.4 Monitoring results

Monitming commenced with the air quality setpoints (Staefa sensors) for both the fo od hall and textile area set to 35% (utilising Staefa IAQ sensors 100% represents very good air quality, 0% equates to poor air quality). Over the initial monitoring period it was found that 35% was a fairly low setpoint with the result that the air quality control loop had little effect. This fact coupled with a very wide proportional band (low gain) meant that the air quality loop rarely took any action. This was further compounded by the free cooling signal making it extremely difficultto observe the effect of the air quality loop alone. Although it was not possible to observe the air quality loop operating effectively the monitming demonstrated effects such as the influence of external carbonmo noxide levels (see Figure 4.3). The monitoring also demonstrated that carbon dioxide is a good indicator of the occupancy levels within the store (see Figure 4.4).

A second monitoring period was performed with the air quality setpoints increased from35% to 45%. In addition, the proportional band for the air quality loop was set to 10% and the freecool ing signal disabled.

18 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Site Monitoring

Figure 4.3 Carbon monoxide levels

i ' : ! � > ,;, E .E 6 UJ c x a � :::;; � 4 Ill "· !i ,.,, , ... . I.)

0 16:00 17:00 18:00 19:00 20:00 TIME

FOOD HALL EXT OUTSIDE TEXTILE SUPPLY TEXTILE EXTRACT

DATA MONITORED ON FRIDAY 7/1 0/94

Figure 4.4 Carbon dioxide and occupancy

1,200

1,100

1,000 "'

�E .E 111 900 c x a c z g 800 a: c( I.)

700

600 o.59 1 .57 2.66 3 54 4 52 5,5 6 46 146 6 45 9 44 10 43 11 42 12.41 13.41 14 4 15 39 16 39 11 38 16.38 19,37 20,36 21,36 22,35 23,34 TIME

FOOD HALL OUTSIDE TEXTILE SUPPLYTEXTILE EXTRACl EXTRACT

FRIDAY 26/8/94 NB THE AIR IS RECIRCULATED FROM THE FOOD HALL EXTRACT INTO THE TEXTILE SUPPLY

©BSRIA Technical Note 1196 19 Site Monitoring Indoor Air Quality Sensors

Data from a twelve day pe1iod demonstrated that the air quality rarely reached the setpoint of 45%, especially in the fo od hall. Con-elation coefficients (see Tables 4.1 and 4.2) were derived relating the air quality sensor readings to the data produced fromthe gas monitor. The tables show that the c01Telation coefficientswere generally low and varied fromday to day, however this is to be expected due to va1iations in different gas concentrations between each day.

It should be recognised that there are some changes in air quality indicated by the air quality sensors that are not detected by the gas monitor and vica versa. This is due to a number of reasons including:

• The gas analyser only monitors four gases (although one of the measuring cells measures TOCs relative to hexane). There will be other pollutants present in the air which the air quality sensors detect.

• It is not known what effect combinations of different pollutants will have on the air quality sensors.

• The air quality sensors are wall mounted and may be remote fromso urces of pollution that may manifest themselves in the return air ducts where the gas samples were taken.

The con-elation exercise was repeated compating two or three hour periods where there was a distinct change in a single gas concentration shown by the gas analyser. The results give a much higher correlation coefficient for carbon monoxide (reaching 0.917) but not for TOC relative to hexane (reaching 0.67). The reason for the latter is that the TOCs are not present in a sufficient concentration to be the wholly dominant gas. It was found that the biggest source of pollution in the building was from the adjacent M25 motorway (undergoing roadworks during the monitored period leading to additional traffic congestion).

A final period of monitoting was perfo1med with the air quality setpoint lowered to 38% with a proportional band of 10% and the free cooling signal disabled. The aim was to see how well the air quality was controlled at this level. Figures 4.5 and 4.6 illustrate a typical petiod on a Friday afternoon. As can be observed from the air quality sensor readings, the setpoint of 38% was maintained dming the early afternoon in both the fo od hall and textile areas (although the textile area air quality sensor still maintained a steadier curve than the fo od hall sensor as desctibed previously). However, fr om approximately 17:30 hours onwards the air quality sensor readings started to fall. Examination of figures 4.7 and 4.8 indicates that high levels of carbon monoxide and total organic carbons (relative to hexane) are responsible. It can be seen that these originate outside and permeate through the retail store. Analysis of figure 4.9 showing the carbon dioxide level in the store, demonstrates that peaks in C02 (eg textile extract between 15:45 and 16:45 hours) have little influence on the air quality sensor reading (the increased textile damper activity at this time being due to an increase in the textile area carbon monoxide level). The increase in C02 concentration at 20:00 is due to the air handling units being turned off.

20 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Site Monitoring

Table 4.1 Food hall analysis

.. Period TOC (rel Fonna.ldehy.de co COz Water Con�elation to hexane) ' 27/8 y 0.603 27/8 y 0.243 ,: 27/8 y 0.555 27/8 y 0.686

27/8 y y y y y 0.750 y y y y 0 ..673 27/8 : 606 27/8 y y y 0. . 27/8 y y 0.604

24/9 y 0.660 24/9 y 0.490 24/9 y 0.358 0.73. '1 24/9 y y y .. y y .

Table 4.2 Textile area analysis

TOO; (rel Formaldehyd co COz Water Correlation to hexane) ' .. .. 19/9 y y y y y ·0.674 20/9 y y y y y 0.380 2119 y y y y y 0.420 22/9 y y y y y 0..430 : 23/9 y y y y y Q.460 o.�. 6.90 24/9 y y y y y : 26/8 y y y y y 0.13:0 27/8 y y y y y 0.410

19/9 y y y 0.520 20/9 y y y 0.260 21/9 y y y 0.420 22/9 y y y 0.080 23/9 y y y o.oao 24/9 y y y 0 ..6 7. 0 26/8 y y y ! 0.47.0 27/8 y y y 0. 100

All correlation analyses are fo r a linear curve. All data values were measured during the occupied hours 08:00 to 20:00 hrs. Y indicates pollutants included in the correlation.

©BSRIA Technical Note 1/96 21 Site Monitoring Indoor Air Quality Sensors

Figure 4.5 Staefa dataAHUl (Fri 7/10/94)

TEXTILE AREA SUPPLY/FOOD HALL EXTRACT

50 r-·-·---··--·-·---·-··--·-

40 ( f 30

0 . 11.21 12.21 13.�1 14.21 15 21 16.21 17.21 18.21 19.21 20.21 21.21 22.21 23.21 TIME

AIR QUALITY AQ SIGNAL FREE CLG FREE HTG DAMPERS

AQ SENSOR IN THE TEXTILE AREA AQ SETPOINT = 38%

Figure 4.6 Staefa data AHU2 (Fri 7/10/94)

TEXTILE AREA EXTRACT/FOOD HALL SUPPLY

0 11.22 12.22 13.22 14.22 15.22 16.22 17.22 18.22 19.22 20.22 21 .22 22.22 23.22 TIME

AIR QUALITY AQ SIGNAL CLG FREE KTG DAMPERS FREE

AQ SENSOR IN THE FOOD HALL AQ SETPOINT = 38%

22 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Site Monitoring

3 Figure 4. 7 TO Cs relative to hexane (mg/m )

•.· .. 4 � . .. ., . . , �. , f� ' � , 3 f

0 11,01 11,34 12.07 12A 13.14 13.48 14 22 14 55 15,29 1603 1637 17.1 17.45 18,18 18.52 19 26 19.59 20.32 21 .06 21 .39 22 12 22 46 23.19 23.53 TIME

FOOD HALL EXT OUTSIDE TEXTILE SUPPLY TEXTILE EXTRACT

Figure 4.8 Carbon monoxide

"' E "' E 6 ·= w c >ii 0 z 0 :::;; z 0 m 4 � 0

2 , '"

11 01 11 34 1207 12.4 13.14 1346 1422 1455 15.29 16.03 1637 17.1 17 45 16 16 1652 1926 19.59 20.32 21-06 21.39 22,12 22.46 23.19 23.53 TIME

FOOD HALL EXT OUTSIDE TEXTILE SUPPLY TEXTILE EXTRACT

DATA MONITORED ON FRIDAY 7110/94

©BSRIA Technical Note l/96 23 ..

Site Monitoring Indoor Air Quality Sensors

Figure 4.9 Carbon Dioxide in mg/m3 (Fri 7/10/94)

1,600

1,400

1,200

1,000

800 :; ,f •' .. � · . ,,

...... '\ ._.,; ... ../ .,/ ..""" ., .J ...... ,.:"'y4 ... """"'\.y···-..,, ,.,..,,,.. ...� · .

11.0t 11.34 1207 124 1314 13.48 1422 14SS 1529 1603 16.37 171 1745 18 18 18.S2 1926 1959 2032 21 06 21 39 2212 22. 46 23�19 ll.63 TIME

FOOD HALL EXT OUTSIDE TEXTILE SUPPLY TEXTILE EXTRACT

4.2.5 Conclusions

The monitoring demonstrated that the air quality sensors respond to certain pollutants within the store, in particular carbon monoxide and total organic carbons relative to hexane. Good correlationcoefficients between the air quality sensors and monitored gases were difficultto achieve due to the unquantifiable presence of other pollutant gases. The response of the air quality sensors to different gases and combinations of gases is not necessarily linear. It is very difficult totr ace the origins of pollution sources.

It was found that the strongest correlations between the air quality sensors and pollutant gases were obtained when the pollutant gas was present in a sufficient concentration to pervade the complete atmosphere of the store. This particularly occurred when carbon monoxide was introduced from outside. Pollution sources local to the air quality sensor were also detected but did not provide a representative indication of the air quality of the complete area.

The ability of the air quality sensors to provide energy savings through the use of air quality demand control was not pursued. This was because the monitoring demonstrated that the air quality within the store was generally at reasonable levels due to the free cooling provision in the control strategy. This, coupled with the fact that the air quality sensors do not recognise carbon dioxide (which can be used to infer occupancy numbers), suggests that the ability of the airqu ality sensor to make truly beneficialenergy savings with the existing control alg01ithm is doubtful in this situation. The use of carbon dioxide for demand controlled ventilation may prove to be more effective in this retail store.

24 ©BSRIA Technical Note 1/96 Indoor Air Quality Sensors Site Monitoring

It was demonstrated that the dilution of a particular pollutant within the store may be canied out at the expense of introducing more heavily polluted air from outside. The main external source of pollution to affect the store was carbon monoxide from the nearby motorway.

4.3 MONITORING IN A PHOTOCOPY ROOM

4.3.l Site description 2 The photocopy room monitored was approximately 27m (see Figure 4.10) and housed an Oce 2450 photocopier together with thermal binding equipment and general paper storage areas. Ventilation is provided by a Vent Axia extract fan (controlled by a Staefa controller) exhausting air fromthe space to the outside.

4.3.2 Monitoring equipment

As in the laboratory tests, the air quality sensor chamber and gas analyser arrnngement was used with the same air quality sensors (including an externally mounted Staefa sensor - referred to as Staefa2). An additional Staefa sensor (FRA-Ql) was positioned on the wall approximately 1 m above the photocopier (referred to as the 'Quality' sensor).

Figure 4.10 Photocopy room layout

3.0 ......

1.4

5.9

l _ _i _

4.5

0.6 1.8 1.1 0.3 3.8

7.6 All dimensionsin metres

4.3.3 Monitoring results

Monitoring commenced with the automatic control of the fan disabled (continuous operation of the fan) and the three doors to the photocopier room left in their nmmal position (two predominantly open during the day, all closed at night). The photocopier was not switched off at night although it went into standby mode if not used for two hours. The thermalbinder was turned off when not in use.

Figure 4. 11 shows the monitored gas concentrations and the air quality sensor responses during a 48 hour peliodwith the fan operating continuously. The dotted vertical yellow lines are used to indicate six hour time pe1iods.

The reaction of each of the air quality sensors to changes in the total organic carbon (TOC) relative to hexane concentration can be clearly seen. The high peaks of TOC are piimaiily due to cleaners spraying cleaning fluid on to desks and other surfaces. There are also some TOCs produced fromvehicle emissions. The slow decay of this gas concentration is noticeable. This is due to the fact that the cleaners shut all doors after cleaning. It is interesting to note that with this paiticularly high gas concentration the (continuously running) extract fan configurationappears to be ineffective at extracting air (and consequently inducing outside air) to ameliorate this pollutant.

The carbon monoxide concentration is primalily due to vehicle emissions fromthe main 'A' road approximately 40 m from the front of the building. Rush hour peliods are clearly visible.

Some sensors require higher pollutant gas concentration before they re act, as demonstrated by the Datum and Landis & Gyr sensors. The Sauter sensor exhibits the greatest variance in output signal. The Centra-Burkle sensor reacts in a similar manner to the Sauter sensor but with less variation in the output signal. It should be noted that a high output voltage from these sensors indicates poor (decreasing) air quality, except for the Staefa sensors, which respond in the opposite way.

Figure 4. 12 shows the re sults obtained during a pe1iod when the doors to the photocopy room were kept shut except fo r access to the space thereby significantly reducing the ventilation rate . The extract fan was placed under automatic control according to the air quality sensed within the space. (Fan operation is indicated by the higher orange dotted line on Figure 4. 12.)

Analysis of Figure 4.12 indicates a repeating daily cycle of the monitored gases. The carbon dioxide levels clearly show the occupancy peliods (09:00 - 17:30), and the lunch time breakbetween 12:30 and 13:30 is visible on some days. The reduced ventilation rate compared to that obtained when the photocopy room was in 'free running' mode is demonstrated by the increased concentration of carbon dioxide. The activity of the cleaners between 17:30 and 19:30 is indicated by the concentration of TOC relative to hexane and the c01Tesponding response of the IAQ sensors. The occmTence of concentrated photocopying (>750 copies) was noted and compared with changes in the monitored gas concentration, but no correlation could be found. It is probable that this is due to the fact that most of the organic volatile gases emitted by photocopiers are oxidised due to the elevated temperatures occuning within such devices. In addition, the production of ozone from photocopiers, which is an oxidising agent, reacts with the oxidisable gases which would nmmally affect the air quality sensors' signal.

Figure 4.11 Photocopy room: fan on continuously

--- Datum Sauter

--- L&Gyr ---Quality

--- Staefa - - - - · Staefa2 DB Centra.

10 30 90

25 8 I I i i � t I 20 60 & 6 "" "" � � 0 � e, - > 1 5 ID ::c c ca:

10 30

2 5

Mon:12 Tue:06 Tue:12 Tue:18 w. :00 Day Of Week:Hour

1 --- TOC Quallty

• --- Form. co --- C02 Water

10 20 20

18 18

8 16 16

1 4 ,(" , 4 ,(" E E ...... 12 ';;; 12 r -E - 10,, 10 "b- • - • • & 4 8 8 ... .; 8 31: 6 6

2 4 4

2 2

Mon:12 :12

Figure 4.12 Photocopy room: controlled extract

--- Datum --- Sauter --- L&:Gyr --- Quality --- Staefa ----- FAN ----- Staefa2 --- DB Centra.

------r ·- - - - T --- - - , - - r - - - - - ., --- - 10 30 90 f l I I I I I 1 ------� � -

11:r----.J

,..., � 60 • 6 a � � ...... 0 > m ::c 'S Cl Ill: f 0 0

--- TOC --- Quality --- Form. ----- FAN --- co --- C02 1 --- Water

------1------r ------r ------10 - -- , 20 20 I : I I l I -I I • - I - I 11 I • 11 I II 16

14 1 "" •r E � 12 � 12 a E E ...... 101:i 10� - - • • .. II a .; 8 .J: 6 6

2 4 4

2 2

Tue:OO Tue:06 Tue:12

Figure 4. 13 shows a further monitored period commencing on a Saturday. The repeating daily cycle of the gases during the occupied days contrasts with the comparatively static gas concentrations at the weekend. However, there is some influence from vehicle emissions at the weekend. The sensors' response to TOC and carbon monoxide can be clearly observed.

Tables 4.3, 4.4 and 4.5 present coITelation coefficients for each of the air' quality sensors against three of the monitored gases. The analysis is based on results from one typical day. Statisticalsignificance is marked in accordance with the key in 3.4.2.

Analysis of the data in Table 4.3 demonstrates that there is no clear-cut correlation (with no clipping of data) for the Staefa, Centra-Burkle and Quality sensors. The correlation coefficients are all below 0.9 but are statistically significant. The Datum and Landis & Gyr show very little correlation with carbon monoxide or carbon dioxide, but show a much stronger correlation with TOC relative to hexane.

Table 4.4 (clipped data) shows that there is a clear difference (but not necessarily statistically significant) between the correlationcoefficie nt for TOC relative to hexane and those for carbon monoxide and carbon dioxide (ie there is a much higher correlation coefficient for TOC relative to hexane because this gas was present at a comparatively high concentration for part of the monitored day whereas there was not a significant concentrationof carbon monoxide on the same day). The highest correlation coefficients (and the only ones above 0.9) were obtained for the Staefa sensor (20 months old) and the Quality (ie more recent Staefa) sensor.

Table 4.5 summaiises the effect of raising the clipping threshold. It can be seen that at 6 mg/m3 all six sensors show a comparable statistically significantresponse .

4.3.4 Conclusions

The operation of the extract fan in the photocopy room would be more effectively controlled by a carbon dioxide sensor since this indicates the occupant activity within the space. It was 01iginally envisaged that the photocopier would be a source of pollutants that would be sensed by the air quality sensors. However, in practice the emissions of ozone from the photocopier and the elevated temperatures around the photocopier oxidise any ( oxidisable) pollutants. It is the oxidisable pollutants that the air quality sensors detect.

High concentrations of TOC relative to hexane and of carbon monoxide tend to occur at night when the space is unoccupied. There is generally a natural decay of these concentrations overnight even without the extract fan running.

Site Monitoring Indoor Air Quality Sensors

Figure 4.13 Photocopy room: controlled extract

--- Datum Sauter --- Lc!cGyr Quality

- - --- Staefa --- FAN - - - - · Staefa2 DB I ol � Centra.

------T1 ,- 10 , I ------, I 30 90 I ; 1 I I I I I l _____ J _ ___ _ � • , _ ,

25 a

20 60

(;" 'ii' t.., ...... 1 5 ID ::c Q a:

10 30

Sat:OO

--- TOC Quality •--- Form. - - ---FAN --- co C02 Water

- , - ---r - -- - 10 I " ' , -- 20 20 1 I I I I I I I I I I ------I -I .. - , - I 18 18 I

a 16 16

14 14r """ E E ...... 12 � 12 e ...... E ...... 10 10,, "b - .. - • • .. 8 8 8 • "6 8 � 6 6

4 4

2 2

Table 4.3 Sensor output signal correlation with gas concentration

SENSOR TOC (rel to co hexane)

Datum

La ndis &G yr

Staefa

Centra-B urkle

Sauter

Quality

No data clipping * 183 points Datum/TOC **254 points QualityffOC

Table 4.4 Air quality sensor correlation with TOC concentration

SENSOR TOC (rel to co hexane)

Data clipped >3 >1 >800

Datum*

Landis &Gyr

Staefa -0.40

Centra-Burkle

Sauter

Quality**

Data clipping applied at threshold levels shown (in mg!m3)

* 183 points Datum/TOC 103 points Datum/CO 138 points Datum/C02 **38 points Quality/TOC 117 points Quality/CO 143 points Quality/C02

Table 4.5 Sensor correlation with TOC (relative to Hexane) concentration

CLIPPING THRESHOLD (m

SENSOR

Datum

Landis & G r Staefa Centra Sauter Qualit

No . of points in 180 35 11 data set

Data clipping al threshold levels shown (in mg!m3)

The ventilation of a space is an important consideration when selecting automatic control. Although the fan in the photocopy room operated correctly, the fan operation did not allow sufficient air flow for thedilution of the pollutants within the space. During weekdays the fan operated continuously and was only turned off for a few hours at night. At the weekend the control system turned the fan off. It is therefore important to correctly size the fan for the application. The provision of a supply fan (or an air inlet vent) may help improve the ventilation in this particular case. However, it should be noted that air drawn from outside is often likely to be polluted, therefore not permitting sufficient dilution of the internal pollutant to disable the fan.

Further problems relate to the commissioning of the air quality setpoint. There is no definitivesetpoint, and a subjective method must be used to select the setpoint. This is based on monitoring the air quality sensor output signal when the quality is believed to be reasonable and making the setpoint a few percentage points below this reading. This has its drawbacks in that it is dependent upon the person's sense of well-being when establishing the setpoint. In order to maintain the functionality of the control system, regular checking of the sensors is required. These are not 'fit and forget' devices.

4.4 MONITORING IN A NATURALLY VENTILATED OFFICE

4.4.1 Site description and results

The monitored officehas a floor area of approximately 47 m2 (see Figure 4.14) and is naturally ventilated with three opening windows and is normally occupied by four people. The monitoring procedure made use of the Bruel and Kj aer gas analyser and the range of indoor air quality sensors.

Figure 4. 15 illustrates the gas concentrations monitored, together with the air quality sensor signals. The repeating daily cycle of the gases (TOC relative to hexane, carbon monoxide and formaldehyde) can be seen. The carbon dioxide concentration indicates the occupancy periods. The 'dips' in the carbon dioxide level are due to windows within the office area being opened for short periods (shock ventilation).

The carbon monoxide concentration primarily varies with the vehicle concen!ration on the nearby road despite the fact that this officeis towards the rear of the building.

4.4.2 Conclusions

The output signals from the air quality sensors in the office react primarily to the variation in total organic carbons (relative to hexane) and carbon monoxide. Although there is some coincidence of the requirement for a fan to operate (if it were fitted) and high C02 concentrations this was not always the case (ie the fan would be enabled at the same time as a high C02 concentration on some days but not on others). It is believed that in this application a C02 sensor would provide the most effective 'demand based' ventilation since it is the C02 concentration, indicative of occupancy levels, that primarily requires dilution in this space.

Figure 4.14 Office layout

0.6 1.8 1.2 1.8 1.2 1.8 0.6 4 ......

Window Window

Filing 2m El 8 cabinets Mo,;iomg Sm D equipment 4 ... EJ 2m

"E � J:l Q. ::i (.) B 2m "E � J:l Q. ::i (.)

...... 4m All dimensions in metres

Figure 4.15 BEMS office monitoring

Mon:OO

- --- roe --- Quality --- Form. _, ._ -- ·- · FAN --- co co1 --- Water

- 1 • I 10 � I t I -- ' I 20 20 I 11 I I I • I I I I 1 �:.______- 1- --- i ---' - · ------;- - -� 18 18 : 1 I I II I I lI ,,,.. -· E ... � E ' - --..-;..r--� 12" ...� 12 E' .....E ..... 10,, 10 "b ... • • & "' ' 8 g g

Mon:OO Tue:OO

5. SENSOR DEVELOPl\IBNTS

Due to a combination of increasing awareness of the importance of indoor air quality issues and the development of new or improved sensing techniques, considerable R&D is being perlormed in this area.

Claimed developments include low cost sensors for the detection of:

• relative humidity

• carbon monoxide

• toxic gases (CO, methane, HC, ozone, S02 and NOx)

• fo1maldehyde

• particulate matters

• airborne bacterial and viral organisms.

Sensor technologies include the following:

• catalytic gas sensors

• MOS-PET sensors (metal-oxide semiconductors - field effect transistor)

• organic/conducting polymers

• surface acoustic wave sensors

• nondispersive infrared (NDIR).

Developments also include what is refeITedto as an 'artificial nose'. This device uses an array of gas sensors, each with a different response to at least one of the gases to be detected. By using pattern recognition techniques (such as Neural Networks), specific gas concentrations can be detennined. To date, airnys have been developed using metal oxide sensors and conducting polymers. Applications have included batch analysis of beer, perfume analysis and tmftledetection.

The use of ar tificial noses for building services applications, with the control of ventilation rates in paiticular, is unclear at present.

6. CONCLUSIONS

1) Laboratory tests on a range of indoor air quality sensors demonstrated relatively high correlation coefficients when tested against single pollutants. However, correlation coefficients corresponding to test data collected over periods when the sensors were subjected to non-controlled conditions (ie a wide range of non-controlled pollutants) demonstrated a poor relationship when there was no predominant polJutant.

2) Site monitoring in a range of applications demonstrated the fo llowing:

• Good correlation coefficients between the air quality sensors and monitored gases were difficultto achieve due to the unquantifiable presence of other pollutants.

• The strongest correlations between air quality sensors and pollutant gases were obtained when the pollutant gas was present in a sufficient concentration to pervade the complete atmosphere of a zone.

• The dilution of pollutants in the indoor environment may be carried out at the expense of introducing polluted air from the outside. The main external source of pollution to affect the monitored sites was fr om vehicle traffic (indicated by levels of CO).

• A subjective method is used to define an IAQ sensor setpoint. This is based on monitoring the air quality sensor output signal when the quality is believed to be reasonable.

• The use of C02 sensors for demand controlled ventilation techniques is more effective than IAQ sensors in applications where IAQ is primarily influenced by varying occupancy.

3) Power down tests (based on hexane) demonstrated that the sensors will quickly recover, giving an air quality within 90% of the normal reading within several minutes following power restoration.

4) Sensor developments include the development of artificial noses. However, it is at present unclear how these devices can be effectively applied to demand controlled ventilation techniques given the wide range of 'pollutants' present in the indoor environment.