Birgit Müller
email: birgit.mueller@tu-berlin.de

Arne Dahms

Dirk Müller

Henrik N Knudsen

Alireza Afshari

Pawel Wargocki

Bjarne Olesen

Birgitta Berglund

Olivier Ramalho

Joachim Goschnick

Oliver Jann

Wolfgang Horn

Daniel Nesa

Eric Chanie

Mika Ruponen

A European Project SysPAQ

The European research project Innovative Sensor System for Measuring Perceived Air Quality and Brand Specific Odours (SysPAQ) is started under the VIth framework programme under the work programme "New and Emerging Science and Technology" (NEST PATHFINDER "Measuring the Impossible"). The Kick-off of the project was on the first of September 2006. Ten partners (3 Companies, 4 Universities, 3 research Institutes) from 5 countries are involved.

The main goal of this project is to develop an innovative system to measure indoor air quality as it is perceived by humans to be used as an indicator and a control device for the indoor air quality. The system will also be able to detect brand specific odours and it will serve as a novel interior odour design tool for the vehicle industry.

INTRODUCTION

This innovative sensor system is highly demanded by the European society considering that humans spend about 90% of their time indoors, either at work, at home or when commuting between work and home. Recent data shows that improved indoor air quality will result in fewer complaints, increased comfort, less health problems and higher productivity. Consequently, quality of life will be improved.

Up to now, the indoor air quality has been quantified applying three different measurement methods separately. The three methods are based on the human perception of indoor air quality, chemical measurements and sensors for specific odours. Considering measuring perceived air quality human assessments are still being superior to chemical measurements because of the unmatched sensitivity to many odorous indoor air pollutants. One of the reasons is that in most cases the chemical measurements or signals from chemical sensors designed to detect special odours could not be correlated with the assessments made by humans. They do obviously not measure the relevant indoor air pollutants triggering human sensory response. This project will build upon current knowledge on the perceptual effects of indoor air pollutants and on the experience gained in using chemical measurements and sensors for specific odours. The approach of the project will be to enhance the present state of the art of sensor systems, the perceptual methods and the software tools for modelling human response, and integrate them into one innovative system for measuring indoor air quality as it is perceived by humans. A bridge will consequently be created between the previous works in this area and progress will be achieved by integrating measurements, sensors and modelling by a holistic approach.

The main challenges are the finding of a perceptual space by using different reference odours, the measuring procedure for perceived air quality, and a characterisation method of brand specific odours. This work requires advanced knowledge in perception psychology and technical excellence in sensor system design and indoor environment research, knowledge and experience which the project partners contribute. It is still an open question how to mimic the human perception of odours and air quality. A certain risk of failure is involved in this project's unsolved problem of how to define an adequate system of reference odours for human perception of odours. In any case the project will provide new insight to the human perception of odours and indoor air quality, and will deliver a new approach to assess the perceived air quality and brand specific odours by an advanced sensor system.

PROJECT OBJECTIVES

The main goal of this project is to develop an innovative system to measure indoor air quality as it is perceived by humans based on perception modelling combining measurements of sensors and assessments of perceived air quality by sensory panels. The innovative sensor system can be used as an indicator, monitor and control device for the indoor air quality in buildings and vehicles. Furthermore, the system will be able to detect brand specific odours and it will serve as a novel interior odour design tool for the vehicle industry. The main objectives of the project are:

1. Definition of a method for measuring the perceived air quality and perceived odour intensity in buildings and vehicles. This method will be used by all different labs using sensory panels.

2. To find an advanced perception model for indoor air assessments. The model will be the major input to the software design for the innovative sensor system and it provides new insight to the human reaction to odours.

3. Development of an innovative sensor system for measuring, correspondingly, the perceived air quality and brand specific odours quality.

4. Calibration and test of the innovative sensor system for measuring, correspondingly, the perceived air quality and brand specific odours. The final version of the system is intended for the following applications :

• Monitoring of the ambient air within buildings and vehicles.

• Monitoring of the quality of the inlet air to buildings to ensure health and comfort for occupants. • Labelling of materials based on emissions from buildings and vehicles.

• Control of the production process of building and vehicles materials.

Along with human activities, emissions from building materials, furnishing and equipment are main contributors to air pollution indoors. To reduce indoor air pollution loads it has been recommended to use low-polluting materials and to increase outdoor air supply rates. The new EU Energy Directive requires substantial reduction of energy use which may lead to reduction of ventilation rates and increased indoor air pollution enhancing the need of lowemission materials. In addition to measurements of indoor air quality as perceived by humans the system developed in the project can be used to control the emission rates from building materials already at the production stage. At present, manufacturers generally reduce the emissions from building materials by monitoring the emission rates of a few compounds in practice, but not necessarily the most relevant odour active compounds for perceived air quality. The pollution mixture affects the perceived air quality indoors. It is not taken into account so far. The proposed system can be used by the producers of building materials, furnishing and equipment materials to ensure that the emissions from their products would not negatively affect the perceived air quality indoors. In many countries labelling systems of building materials exist, so that the end-users can select the materials with reduced emissions. The suggested system for measuring the perceived air quality can also be used to quantify whether a material can get a label.

The selection of interior materials is a very important factor for the vehicle and transportation industry (train, car, boat, airplane etc.). The goal of the selection process is to create a highstandard perceived air quality in vehicles combined with a brand specific odour impression. To meet this goal the system measuring the perceived air quality seems indispensable.

The interdisciplinary structure of the project consortium will enable innovative research and it will provide new insights to the human perception of air quality and brand specific odours. The project management will ensure a strong interaction between new perception models, hardware development as well as software design for the innovative sensor system.

APPROACH AND METHODOLOGY

The overall approach of the project and the priorities of the different work-packages are shown in figure 1. The figure illustrates the parallelism of the human perception of air quality and brand specific odours on the left hand side and the sensor system development on the right hand side.

The two arrows in the centre of figure 1 indicate the coupling of sensory panel experiments and the sensor system hardware and software development. The innovative sensor system has to detect all relevant odour active substances. Based on the knowledge of the project partners and experiments of the project a list of relevant substances is submitted to the two sensor specialists. The second arrow indicates the input of the new perception model of the perception specialist to the software development. The mathematical model of the new software for the sensor system will apply a reference odour system to reproduce an odour space that covers perceived air quality and brand specific odours in buildings and vehicles.

WORKPLAN OF THE PROJECT

Introduction -general description

The work of the project is divided into two main tasks. The first task is focused on human perception of odours. This part includes the definition of a method to measure perceived air quality and odour intensity by sensory panels. The used assessment methods can have a major influence on the assessment results itself. Besides the assessment method the main challenge of the human perception task is the definition of an odour space using reference odours. The multidimensional odour space has to account for all interior odours in buildings and vehicles. This new odour space is the major input to the second main task of this project. This part is focused on the hard-and software development for the innovative sensor system. The contribution of two sensor specialists enables the application of the most advanced sensor technology. The sensitivity and selectivity of the sensors is adapted to odour active substances and the new odour space. The combination of different sensor types leads to novel sensor system that covers considerable more substances than any present stand alone system. The data processing for this sensor systems combines advanced statistical pattern recognition methods with the novel reference odour systematic. The link between the sensor signals and the perceived air quality consists of a classification process in terms of the reference odours followed by a regression method calibrated by the experimental data of the project. This two main tasks human perception and system development break down to 4 workpackages (human perception and odour space, sensor system, data processing and pattern recognition, system calibration). The management process and the dissemination activities control and link these four work-packages, see figure 2. The human perception task of the project is mainly handled in WP2. Starting point of WP2 is the definition of a method for all sensory panel assessments of perceived air quality and odour intensity. The method is based on the knowledge of the experimentally experienced project partner and the input of new multidimensional psychophysical perception models. This common standard for the assessment method guarantees comparable measurements at all labs for the innovative sensor system calibration data. The second and the most important part of WP2 is the definition of a new odour space based on reference odours. The new odour space has to cover all relevant odours of indoor air related environments. All project partners of WP2 have been working on the characterisation of odours in the past. The work-package leader Karolinska Institute (KI) will provide a human perception model as a theoretical base for the new odour space. First experiments of KI will examine the useability of the new odour space based on reference odours to reproduce indoor air odours from building and vehicle environments. The quality of the odour space is the critical factor for the innovative sensor system and it will influence the software design process. A continuous knowledge transfer between WP2 and WP4 will insure a fast translation of new findings into the software system. WP3 covers all activities related to hardware improvements for the innovative sensor system. This work-package includes the improvement and adaptation of single multi-gas sensors as well as the combination of different multi-gas sensor technology. The adaptation process and the selection of sensors are based on literature review and some analysis of indoor air environment of buildings and vehicles in terms of odour active compounds. The sensor development and sensor combination is handled by two experienced sensor specialist Alpha MOS and Forschungszentrum Karlsruhe (AM, FZK). The two partners can provide gas chromatography/mass spectrometry combined with sniffer experiments to prove the response of the multi gas sensor system to odour relevant substances. The check and minimisation of cross sensitivity (to other chemical compounds, relative humidity and temperature of sample air) of the sensors provides reproducible measurements. The sensor specialists (AM, FZK) manufacture three similar measurement devices for the calibration and validation measurements. The three systems will be jointly used by all partners. Additionally, the partner AM und FZK will provide all necessary hardware information and prototypes to the partner Centre Scientifique et Technique du Bâtiment (CSTB) and Technical University of Berlin (TUB) to insure a parallel development of the data processing and pattern recognition software (WP4).

The set-up of the general software layout for the data processing will be handled in WP4.

The work package leader Technical University of Berlin (TUB) contributes many years of experience in pattern recognition methods and software development to calculate the perceived odour intensity based on multi gas sensor systems. The separation of software and hardware (WP3 and WP4) enables a company independent and extendable mathematical modelling of low-concentration odour mixtures as regards sets of critical odours for building and vehicle materials and products and their combination. The task of the computational data processing is to model the human odour perception of air samples. The method shall consider existing theories of perception and it shall link the measurements with psychophysical aspects of odour sensing. The response values of the sensor device will act as the stimuli and the software shall mimic the human perception and evaluation process. The core of the data processing method will be the "memory", the calibration database. This database will have a major influence on the performance of the method, especially on the classification of the investigated odour sample. Therefore the data shall span the whole odour space which is developed in WP2. The principle components of this odour space will be the basic odours. The data of the database consists on a combination of sensor response values and air quality assessments according to the methods of WP2 for a specific odour sample and concentration. The database shall contain for each odour class data for different stimuli concentrations. The system shall be adaptive which requires the database to be expandable and open for new data of odour investigations. The calculation models will always refer to the current data and therefore the algorithms will be adjusted to the extended data set. The unknown investigated odour shall be positioned inside the odour space which is a classification process for "odour recognition". Unknown odours shall be expressed by a combination of the basic odours. Once the sample is classified the quantitative and qualitative parameters of the perceived air quality shall be estimated. This step of the data processing uses the calibration data of the basic odours, the regression and calculation algorithms for this odours as well as weighing algorithms which consider the position of the unknown odour sample in the odour space. Data of odour samples other than the basic odours is needed to develop, test and optimize the weighing algorithms and to estimate the perceived air quality of different odours. Most of the odour space and combinations of basic odours should be covered. During the validation process of the software the new collected data will be included in the database which may further improve the performance of the data processing method.

The innovative sensor system has to identify all reference odours and their combination in indoor environments. Additionally, the system shall link the pattern recognition method for the identification process to the sensory panel results. Due to the multidimensional character of the problem a large effort is needed to calibrate the innovative sensor system. WP 5 handles the collection of the calibration data. It consists of simultaneous measurements of perceived air quality as well as the perceived odour intensity by a sensory panel and measurements with the sensor systems using the most relevant sensory methods selected in WP2 and the most promising innovative system/technical device selected in WP3. All data of WP5 iterates back to the software enhancement of WP4. The odours for calibration measurements of WP5 are produced in emission chambers. The air flow through the chambers is polluted by a series of building and cabin materials. The concentration of air pollutants will be varied within an indoor realistic range to ensure and test if the system works appropriate. The variation in concentration will be achieved by varying the material loading and the ventilation rate and by selecting high and low emitting materials. Measurements will be performed for individual materials in a laboratory setting, for combinations of materials in a full-scale setting, and in real buildings and vehicles. All data of WP5 is stored in a database. A final test run of the innovative sensor system after calibration will show the ability to predict perceived air quality and to characterise brand specific odours. During the sensor development WP5 will provide preliminary test set-ups of building materials in order to secure reasonable sensitivity and discrimination power of the novel sensor system.

The dissemination task of WP6 provides the publication of all project results. The main communication medium of the project is an advanced internet portal that offers a public and a non public information area (www.syspaq.eu). The public area publishes all non confidential findings of the project partner during the project life time. The non public area handles the data exchange of all project partners. All project related findings will be open for the public after the completion of the project. The project will provide a final workshop in order to discuss and disseminate all findings of the project. The consortium will prepare a brief project presentation in English which is written in a style which is accessible to non-specialists, avoiding technical language, mathematical formulae and acronyms as much as possible.

Publication will be done via the NEST www page.

A more detailed information on the major work package interdependencies is given in figure 4.

Figure 1 :

 1 Figure 1: Parallelism of the human perception of air quality and brand specific odours on the left hand side and the sensor system development on the right hand side

Figure 2 :

 2 Figure 2: Work-package structure

Figure 3 :

 3 Figure 3: The software will handle the development/calibration process and it provide the final version for diagnostics of unknown odours

Figure 4 :

 4 Figure 4: Major work-package interdependencies

Proceedings of Clima 2007 WellBeing Indoors

ACKNOWLEDGEMENT

SysPAQ is partly sponsored by the European Community in the Nest programme (NEST-28936) under the management of Mrs. P. Lopez. The co-ordination is done by the Technical University Berlin. Other participants are: Danish Building Research Institute, Aalborg University; Technical University of Denmark; Karolinska Institute; Centre Scientifique et Technique du Bâtiment; Forschungszentrum Karlsruhe; Federal Institute for Materials Research and Testing; REGIENOV; Alpha MOS; Halton Oy.


Birgit Müller
email: birgit.mueller@tu-berlin.de

Arne Dahms

Henrik N Knudsen

Alireza Afshari

Pawel Wargocki

Bjarne Olesen

Birgitta Berglund

Olivier Ramalho

Joachim Goschnick

Daniel Häringer

Oliver Jann

Wolfgang Horn

Daniel Nesa

Eric Chanie

Mika Ruponen

Dirk Müller

Measuring perceived air quality and intensity by a Sensor System, the European Project SysPAQ

Keywords: Perceived air quality, Perceived odour intensity, Profiling, Sensor system

At present, indoor air quality is assessed exclusively by human panels. Because this method is time consuming and cost intensive, little attention is paid to indoor air quality in the planning and operation of buildings. In recent years multi-gas sensor systems have been developed in order to imitate the human sense of smell. These systems comprise an array of gas sensors, with sensors of different sensitivity and selectivity, and a data processing unit. Up to now the sensors have not been sensitive enough to mimic the perception of a human being. Therefore the European research project SysPAQ (Innovative Sensor System for Measuring Perceived Air Quality and Brand Specific Odours) was started in September 2006.

The main goal of this project is to develop an innovative sensor system to measure indoor air quality as it is perceived by humans to be used as a control device for indoor air quality.

INTRODUCTION

An innovative sensor system to measure perceived indoor air quality and odour intensity is in high demand for European society, as humans spend about 90% of their time indoors, either at work, at home or when commuting between work and home. Recent data show that improved indoor air quality results in fewer complaints, increased comfort, fewer health problems and higher productivity. Consequently, the quality of life is improved.

Up to now, indoor air quality has been quantified by applying three different measurement methods separately. The three methods are based on the human perception of indoor air quality, chemical measurements and sensors for specific odours. Regarding the measurement of perceived air quality, human assessments are still superior to chemical measurements Indoor Air 2008, 17-22 August 2008, Copenhagen, Denmark -Paper ID: 530 because of the unmatched sensitivity to many odorous indoor air pollutants. One of the reasons is that in most cases the chemical measurements or signals from chemical sensors designed to detect special odours could not be correlated with the assessments made by humans. Obviously they do not measure the relevant indoor air pollutants that trigger human sensory response. The SysPAQ project builds upon current knowledge of the perceptual effects of indoor air pollutants and on the experience gained in using chemical measurements and sensors for specific odours. The approach of the project is to enhance the present state of the art of sensor systems, perceptual methods and software tools for modelling human response, in order to integrate them into a single innovative sensor system for measuring indoor air quality as it is perceived by humans. This would consequently create a bridge between the previous work in this area, and progress is achieved by integrating measurements, sensors and modelling using a holistic approach.

PROJECT OBJECTIVES

The main goal of this project is to develop an innovative sensor system to measure indoor air quality as it is perceived by humans based on perception modelling combining measurements of sensors and assessments of perceived air quality by sensory panels. The innovative sensor system can be used as an indicator, monitor and control device for the indoor air quality in buildings and vehicles. Furthermore, the system will be able to detect brand-specific odours, and it will serve as a novel interior odour design tool for the vehicle industry. The main objectives of the project are:

1. To define a method for measuring the perceived air quality and perceived odour intensity in buildings and vehicles. This method will be used by all different labs using sensory panels.

2. To find an advanced perception model for indoor air assessment. The model will be the major input to the software design for the innovative sensor system, and it will provide new insight into the human reaction towards odours.

3. To develop an innovative sensor system for measuring perceived air quality and brandspecific odours.

4. To calibrate and test the innovative sensor system for measuring perceived air quality and brand-specific odours. The final version of the system is intended for the following applications :

• Monitoring the ambient air within buildings and vehicles.

• Monitoring the quality of the inlet air into buildings to ensure the health and comfort of occupants. • Labelling emissions from building and vehicles materials.

• Controlling the production process of building and vehicles materials.

Along with human activities, emissions from building materials, furnishings and equipment are the main contributors to air pollution indoors. Two suggested methods for reducing indoor air pollution are to use low-polluting materials and to increase outdoor air supply rates. The new EU Energy Directive requires substantial reduction of energy use, which may lead to reduction of ventilation rates and increased indoor air pollution, enhancing the need for lowemission materials. In addition to measurements of indoor air quality as perceived by humans, the system developed in the project can be used to control the emission rates from building materials as early as the production stage. At present, manufacturers generally reduce the emissions from building materials by monitoring the emission rates of a few compounds, but these are not necessarily the most relevant odour active compounds for perceived air quality. The pollution mixture affects the perceived air quality indoors. This has not been taken into account so far. The proposed system can be used by the producers of building materials, furnishings and equipment to ensure that the emissions from their products will not negatively affect the perceived air quality indoors. In many countries, labelling systems for building materials exist, so the end-users can select materials that fulfil certain criteria for emissions. The suggested system for measuring the perceived air quality can also be used to assess whether a material can get a label.

The selection of interior materials is a very important factor for the vehicle and transportation industry (trains, cars, boats, airplanes, etc.). The goal of the selection process is to create a high standard of perceived air quality in vehicles combined with a brand-specific odour impression. To meet this goal, a system for measuring perceived air quality seems indispensable.

The interdisciplinary structure of the project consortium will make innovative research possible, and it will provide new insights into the human perception of air quality and brandspecific odours. The project management will ensure a strong interaction between new perception models, hardware development and software design for the innovative sensor system.

APPROACH AND METHODOLOGY

The overall approach of the project and the priorities of the different work packages are shown in Figure 1. The figure illustrates the parallelism of the human perception of air quality and brand-specific odours on the left hand side and the development of the sensor system on the right hand side. The two arrows in the centre of Figure 1 indicate the coupling of sensory panel experiments and the development of the sensor system hardware and software. The innovative sensor system has to detect all relevant odour active substances. Based on the knowledge of the project partners and experiments of the project, a list of relevant substances is submitted to the two sensor specialists. The second arrow indicates the input of the new perception model of the perception specialist into the software development. The mathematical model of the new software for the sensor system will apply a reference odour system to reproduce an odour space that covers perceived air quality and brand-specific odours in buildings and vehicles.

Work planning

The human perception task of the project is mainly handled in WP2. The starting point of WP2 is the definition of a method for all sensory panel assessments of perceived air quality and odour intensity. The method is based on the knowledge of the project partners, who have a great amount of experience with experiments in the field, and the input of new multidimensional psychophysical perception models. This common standard for the assessment method guarantees comparable measurements at all labs for the innovative sensor system calibration data. The second and the most important part of WP2 is the definition of a new odour space based on reference odours. The new odour space has to cover all relevant odours of indoor air environments focusing on materials. All project partners of WP2 have experience with working on the characterisation of odours. The work package leader Karolinska Institute (KI) will provide a human perception model as a theoretical base for the new odour space. Initial experiments by KI will examine the useability of the new odour space based on reference odours to reproduce indoor air odours from building and vehicle environments. The quality of the odour space is the critical factor for the innovative sensor system, and it will influence the software design process. A continuous knowledge transfer between WP2 and WP4 will ensure a fast translation of new findings into the software system.

WP3 covers all activities related to hardware improvements for the innovative sensor system. This work package includes the improvement and adaptation of individual multi-gas sensors as well as the combination of different multi-gas sensor technology. The adaptation process and the selection of sensors are based on a literature review and some analysis of the indoor air environment of buildings and vehicles in terms of odour active compounds. The sensor development and sensor combination are handled by two experienced sensor specialists: Alpha MOS and Forschungszentrum Karlsruhe (AM, FZK). The two partners can provide gas chromatography/mass spectrometry combined with sniffer experiments to check the response of the multi gas sensor system to odour relevant substances. Controlling and the minimisation of cross sensitivity (to other chemical compounds, relative humidity and temperature of sample air) of the sensors provides reproducible measurements. The sensor specialists (AM, FZK) manufacture three similar measurement devices for the calibration and validation measurements. The three systems will be jointly used by all partners. Additionally, partners AM und FZK will provide all necessary hardware information and prototypes to partners Centre Scientifique et Technique du Bâtiment (CSTB) and Technical University of Berlin (TUB) to ensure parallel development of the data processing and pattern recognition software (WP4).

The set-up of the general software layout for the data processing will be handled in WP4.

The work package leader Technical University of Berlin (TUB) contributes many years of experience in pattern recognition methods and software development to calculate the perceived odour intensity based on multi-gas sensor systems. The separation of software and hardware (WP3 and WP4) enables a company to produce independent and extendable mathematical modelling of low-concentration odour mixtures regarding sets of critical odours for building and vehicle materials and products and as well as combinations of the products.

The task of the computational data processing is to model the human odour perception of the air samples. The method will consider existing theories of perception and link the measurements with psychophysical aspects of odour sensing. The response values of the sensor device will act as the stimuli, and the software will imitate the human perception and evaluation process. The core of the data processing method will be the "memory", the calibration database. This database will have a major influence on the performance of the method, especially on the classification of the investigated odour samples. Therefore the data shall span the whole odour space which is developed in WP2. The principle components of this odour space will be the basic odours. The data of the database consist of a combination of sensor response values and air quality assessments according to the methods of WP2 for a specific odour sample and concentration. The database will contain data for different stimuli concentrations for each odour class. The system will be adaptive, which requires the database to be expandable and open for new data from odour investigations. The calculation models will always refer to the current data, and, therefore, the algorithms will be adjusted to the extended data set.

The unknown odour will be positioned inside the odour space, which is a classification process for "odour recognition". Unknown odours will be expressed as a combination of the basic odours. Once the sample is classified the quantitative and qualitative parameters of the perceived air quality will be estimated. This step of the data processing uses the calibration data of the basic odours, as well as the regression and calculation algorithms for these odours and weighing algorithms which consider the position of the unknown odour sample in the odour space. Data of odour samples other than the basic odours is needed to develop, test and optimise the weighing algorithms and to estimate the perceived air quality of different odours.

Most of the odour space and combinations of basic odours will be covered. During the validation process of the software the new collected data will be included into the database which may furthermore improve the performance of the data processing method.

The innovative sensor system has to identify all reference odours and their combinations in indoor environments. Additionally, the system will link the pattern recognition method for the identification process to the sensory panel results. Due to the multidimensional character of the problem, a large amount of effort is necessary to calibrate the innovative sensor system. WP5 handles the collection of the calibration data. It consists of simultaneous measurements of perceived air quality and the perceived odour intensity by a sensory panel as well as measurements from the sensor system using the most relevant sensory methods selected in WP2 and the most promising innovative system/technical device selected in WP3.

All data from WP5 is employed in the enhancement of the software in WP4. The odours for calibration measurements for WP5 are produced in ventilated emission chambers. The air flow through the chambers is polluted by a series of building and cabin materials. The concentration of air pollutants will be varied within a realistic indoor range to test if the system works properly. The variation in concentration will be achieved by varying the material loading and the ventilation rate and by selecting materials with high and low emisson levels. Measurements will be performed for individual materials in a laboratory setting, for combinations of materials in a full-scale setting, and in real buildings and vehicles. All data from WP5 are stored in a database. A final test run of the innovative sensor system after calibration will show the ability to predict perceived air quality and to characterise brandspecific odours. During the sensor development WP5 will provide preliminary test set-ups of building materials in order to secure reasonable sensitivity and discrimination power of the novel sensor system.

The dissemination task of WP6 provides the publication of all project results. The main communication medium of the project is an advanced internet portal that offers a public and a non public information area (www. syspaq.eu). The public area publishes all non confidential findings of the project partners during the lifetime of the project. The non public area handles the data exchange of all project partners. All project-related findings will be open for the public after the completion of the project. The project will provide a final workshop in order to discuss and disseminate all findings of the project. The consortium will prepare a brief project presentation in English which will be written in a style which is accessible to nonspecialists, avoiding technical language, mathematical formulae and acronyms as much as possible. Publication will be done via the NEST www page.

RESULTS

First results in WP 2 Sensory assessments

In the SysPAQ project, the perceived odour intensity has to be measured with high accuracy and in a way that allows for comparison. This can only be accomplished by using a set of well-controlled reference odour concentrations. The TUB's method of equal-intensity matching to a number scale of reference odour intensities [START_REF] Müller | Neue Systematik zur Bewertung der empfundenen Luftqualität[END_REF], involves a constraint in that the participants are taught and trained to use a series of reference concentrations in the assessment of perceived odour intensity. The SysPAQ group have decided to use the intensity method with a comparison scale of an acetone-air mixture.

For the Perceived Air Quality measurements, it has been decided to use an acceptance scale and a profiling list. Context shall be specified as "Imagine that you, during your daily work, would be exposed to the air in this diffuser/room. How acceptable is the air quality?"

How do you assess the air quality?

Clearly acceptable

Pay attention to the dichotomy between acceptable and not acceptable

Just acceptable Just not acceptable

Clearly not acceptable Ideally, descriptor profiles should be built from 8-10 perceptual-emotional attributes. A sheet with visual analogue scales for each attribute will be prepared. Appropriate context questions have to be developed. The descriptors have to be carefully selected with regard to partner languages, especially Danish and German. The endpoints are marked 0% match and 100% match. The task is formulated as follows:

Instructions: Please consider the attributes one at a time and mark the line that indicates how well the attribute matches the perceived air quality of the exposure (materials emission, indoor air).

Table 1. Possible descriptor list (Berglund et al., 2007).

• Appealing • Refreshing/Fresh • Pleasant • Exciting/Expressive • Subtle • Dryness • Sweet • Irritating • Comfortable • Cool/Cooling • Interesting • Flat • Stale/Stuffy • Sickly/Nauseous
The scale values of attribute matches (0-100%) provide a characteristic profile for attributes.

A correlation matrix is formed from all pairs of profiles. Each profile represents one mixture of emissions (one characteristic indoor air), the correlation matrix can be viewed as a matrix of "similarities".

First results in WP 3 Sensor development

First tests with nano-particle (NP) and nano-wire (NW) sensors have been completed. As shown in Figure 3 the detection limits for an NP -sensor are less than 10 ppb. In contrast, the detection limits for sputtered metal oxide sensors are around 1000 ppb. The main, as yet unsolved, problem of the NP coatings is the instability of the sensor over longer time periods at the operating temperature of about 300°C. Up to now the sensor is only stable for a few weeks.

DISCUSSION AND CONCLUSIONS

The project SysPAQ has reached the mid-term period. First results from the sensor development are available. The first results with nano-particle sensors and nano-wire sensors are good, but the instability over time has not yet been solved. Different types of sensors have been tested, but the most promising sensors for the project seem to be metal oxide sensors.

The KAMINA chip with 38 sensor segments, which is currently in use, was equipped with a sputtered layer of platinum-doped SnO 2 coated with a gas permeable SiO 2 gradient membrane, the thickness of which varied from about 2 nm to 12 nm over the micro array.

Results from the SysPAQ-Box are not yet available but the pre-test of the sensors and the software looks good. We are looking forward to the next milestones of the project; the goals of the project are still approachable.

Figure 1 .

 1 Figure 1. Parallelism of the human perception of air quality and brand specific odours on the left and the sensor system development on the right.

Figure 2 .

 2 Figure 2. Pair of Visual Analogue Scale (VAS) for evaluating indoor air quality as regards degree of acceptability and non acceptability. (Berglund et al 2007, Dlev. 5).

Figure 3 .

 3 Figure 3. Detection limit for different substances of a nano-particle sensor after fabrication.

Figure 4 .

 4 Figure 4. The SysPAQ-Box.

ACKNOWLEDGEMENT SysPAQ is partly sponsored by the European Community in the Nest programme (NEST-28936) under the management of Mrs. P. Lopez. The co-ordination is done by the Technical University Berlin. Other participants are: Danish Building Research Institute, Aalborg University; Technical University of Denmark; Karolinska Institute; Centre Scientifique et Technique du Bâtiment; Forschungszentrum Karlsruhe; Federal Institute for Materials Research and Testing; REGIENOV; Alpha MOS; Halton Oy.


Birgit Müller
email: birgit.mueller@tu-berlin.de

Arne Dahms

Björn Jakob

Henrik N Knudsen

Alireza Afshari

Pawel Wargocki

Bjarne Olesen

Birgitta Berglund

Olivier Ramalho

Daniel Häringer

Oliver Jann

Wolfgang Horn

Daniel Nesa

Francois Loubet

Mika Ruponen

Measuring perceived air quality and intensity by means of sensor systems, the European Project SysPAQ

Keywords: Sensor systems, perceived air quality, perceived intensity

At present, indoor air quality is assessed exclusively by human panels. As this method is time consuming and cost intensive, little attention is paid to indoor air quality in the planning and operation of buildings. In recent years multi-gas sensor systems have been developed in order to mimic the human sense of smell. These systems comprise an array of gas sensors, with sensors of different sensitivity and selectivity, and a data processing unit. Up to now the sensors are not sensitive enough to mimic the perception of a human being. To further the development of these systems, the European research project SysPAQ (Innovative Sensor System for Measuring Perceived Air Quality and Brand Specific Odours) was started in September 2006. It will end in September 2009.

INTRODUCTION

An innovative sensor system to measure perceived indoor air quality and odour intensity is in high demand for European society, as humans spend about 90% of their time indoors, either at work, at home or when commuting between work and home. Recent data show that improved indoor air quality results in fewer complaints, increased comfort, fewer health problems and higher productivity. Consequently, the quality of life is improved.

Up to now, indoor air quality has been quantified by applying three different measurement methods separately. The three methods are based on the human perception of indoor air quality, chemical measurements and sensors for specific odours. Regarding the measurement of perceived air quality, human assessments are still superior to chemical measurements because of the unmatched sensitivity to many odorous indoor air pollutants. One of the reasons is that in most cases the chemical measurements or signals from chemical sensors designed to detect special odours could not be correlated with the assessments made by humans. Obviously they do not measure the relevant indoor air pollutants that trigger human sensory response. The SysPAQ project builds upon current knowledge of the perceptual effects of indoor air pollutants and on the experience gained in using chemical measurements and sensors for specific odours. The aim of the project is to enhance the present state of the art of sensor systems, perceptual methods and software tools for modelling human response, in order to integrate them into a single innovative sensor system for measuring indoor air quality as it is perceived by humans. This would consequently create a bridge between the previous work in this area, and progress is achieved by integrating measurements, sensors and modelling using a holistic approach.

PROJECT OBJECTIVES

The main goal of this project is to develop an innovative sensor system to measure indoor air quality as it is perceived by humans based on perception modelling, combining measurements of sensors and assessments of perceived air quality by sensory panels. The innovative sensor system can be used as an indicator, monitor and control device for the indoor air quality in buildings and vehicles. Furthermore, the system will be able to detect brand-specific odours, and it will serve as a novel interior odour design tool for the vehicle industry. The main objectives of the project are:

1. To define a method for measuring the perceived air quality and perceived odour intensity in buildings and vehicles. This method will be applied by all of the labs involved which are using sensory panels.

2. To find an advanced perception model for indoor air assessment. The model will be the major input to the software design for the innovative sensor system, and it will provide new insight into the human reaction towards odours.

3. To develop an innovative sensor system for measuring perceived air quality and brandspecific odours.

4. To calibrate and test the innovative sensor system for measuring perceived air quality and brand-specific odours. The final version of the system is intended for the following applications :

• Monitoring the ambient air within buildings and vehicles.

• Monitoring the quality of the inlet air into buildings to ensure the health and comfort of occupants. • Labelling emissions from building and vehicles materials.

• Controlling the production process of building and vehicles materials.

Along with human activities, emissions from building materials, furnishings and equipment are the main contributors to air pollution indoors. Two suggested methods for reducing indoor air pollution are using low-polluting materials and increasing outdoor air supply rates. The new EU Energy Directive requires substantial reduction of energy use, which may lead to reduction of ventilation rates and increased indoor air pollution, enhancing the need for lowemission materials. In addition to measurements of indoor air quality as perceived by humans, the system developed in the project can be used to control the emission rates from building materials as early as the production stage. At present, manufacturers generally reduce the emissions from building materials by monitoring the emission rates of a few compounds, but these are not necessarily the most relevant odour active compounds for perceived air quality. The pollution mixture affects the perceived air quality indoors. This has not been taken into account so far. The proposed system can be used by the producers of building materials, furnishings and equipment to ensure that the emissions from their products will not negatively affect the perceived air quality indoors. In many countries, labelling systems for building materials exist, so the end-users can select materials that fulfil certain criteria for emissions. The suggested system for measuring perceived air quality can also be used to assess whether a material can obtain a label.

The selection of interior materials is a very important factor for the vehicle and transportation industry (trains, cars, boats, airplanes, etc.). The goal of the selection process is to create a high standard of perceived air quality in vehicles combined with a brand-specific odour impression. To meet this goal, a system for measuring perceived air quality seems indispensable.

The interdisciplinary structure of the project consortium will make innovative research possible, and it will provide new insights into the human perception of air quality and brandspecific odours. The project management will ensure a strong interaction between new perception models, hardware development and software design for the innovative sensor system.

RESULTS

The first set of calibration data was generated in May 2008. For the calibration and validation of the first sensor system, sets of data consisting of measurements of perceived air quality by a sensory panel and measurements with the sensor system are collected. The data is collected in the form of measurements of polluted air. The pollutions are generated by emissions from a series of building materials. The concentration of air pollutants is varied within a realistic range to ensure and test if the sensor system works appropriately. The variation in concentration is achieved in small-scale test chambers in a laboratory setting by varying the material loading at a fixed ventilation rate and by selecting high and low-emitting individual materials.

A paid, untrained sensory panel comprising 36 panellists [START_REF] Knudsen | SysPAQ Deliverable No. 14: Preliminary calibration data for setting up the innovative system for measuring perceived air quality[END_REF] performed the sensory evaluations. The panellists evaluate different intensity methods (Yaglou Scale, Borg Scale, perceived intensity with a comparison scale), acceptability and some descriptor profiling. For more about this investigation see (Knudsen/Wargocki, in the HB09 proceedings).

• Irritating • Dry • Pleasant • Stuffy • Fresh • Humid • Cool • Warm • Odours •
A first result of several multivariate analysing methods applied to the data is shown in Figure 1. This figure shows the results of a Partial Least Square (PLS) -classifier. The values for perceived intensity (in pi) can be read on the abscissa. The perceived intensity predicted out of the sensor signals by the statistical method can be read on the ordinate. There are remarkable variances between the measured and the calculated results only in a few cases. This mathematical model leads to usable results.

DISCUSSION and CONCLUSIONS

The project SysPAQ is drawing to a close. The main measurement campaign for collecting the calibration and validation data for sensor system will start at the end of April. Some arising difficulties with the sensitivity of the sensor regarding humidity must be solved. As seen in Figure 1, the sensor system is able to predict the perceived intensity relatively well. It is still necessary to make some efforts to find the best mathematical model for the different questions.

Figure 1 :

 1 Figure 1: Results of calibration data for sensor system predicted PI and panel data using PLSclassifier method

Table 1 .

 1 Descriptor list used by panellists

Proceedings of Healthy Buildings 2009Paper 326

ACKNOWLEDGEMENT SysPAQ is partly sponsored by the European Community in the Nest programme (NEST-28936) under the management of Ms. P. Lopez and Ms. A. Cabornero-Marco. The coordination is done by the Technische Universität Berlin. Other participants are: Danish Building Research Institute, Aalborg University; Technical University of Denmark; Karolinska Institute; Centre Scientifique et Technique du Bâtiment; Forschungszentrum Karlsruhe; Federal Institute for Materials Research and Testing; REGIENOV; Alpha MOS; Halton Oy.


Pascal Brault
email: pascal.brault@univ-orleans.fr

Jean-Marc Bauchire
email: marc.bauchire@univ-orleans.fr

François James
email: cfrancois.james@univ-orleans.fr

Christophe Josserand
email: dchristophe.josserand@upmc.fr

Plasma assisted diffusion in porous materials: Experiments, modeling and applications

Keywords: Anomalous diffusion equation, plasma sputtering deposition, AAO template, porous carbon, random stacking, catalytic film, fuel cell

Plasma sputtering deposition of platinum on porous substrates is a relevant way for building high performance catalytic thin films. The knowledge and control of platinum density adsorbed in the porous medium, during deposition, are of primary importance. We show that the experimental concentration depth profiles originate from a superdiffusion phenomenon. Moreover experimental results are well described by a classical porous-fractal diffusion model, for which diffusion coefficient is also time-dependent, even if such a model does not include the proper physical mechanisms.

Introduction

Fuel cell electrodes are typical catalytic layers. It was demonstrated that realizing it using plasma sputtering deposition results in highly efficient fuel cell operation [1,5]. Basically, electrodes are built as randomly stacked conducting carbon spheres with 30 to 100 nm size (Fig. 1a). This staking leads to a porosity of 50-60 %. The layer thickness is 20-50 µm. This layer, called active layer, is supported on a Gas Diffusion Layer made of a carbon cloth or paper. The catalyst, e.g. platinum, is deposited using plasma sputtering deposition. In that case, a biased platinum target is sputtered by the ions (argon, here) created in the plasma. The platinum vapor is thus transported to the porous carbon layer onto which platinum atoms stick after travelling in the pores to some extent. As a comparison we are using another kind of porous medium with a better defined structure: Anodic Aluminum Oxide (AAO) template (Fig 2). While the former case exhibits a disorder medium, this AAO template is an ordered structure made of a 2D quasi-hexagonal array of cylindrical holes. In the present study, the hole diameter is 90 nm with a 4.0 10 9 cm -2 density. This leads to a porosity (void density) of 25 % (Fig. 2). The AA0 are 60 µm thick. The experimental depth profiles are measured using Rutherford Backscattering Spectroscopy (RBS) [1,2,6,7]. As shown in Fig. 3, they take the form of stretched Gaussian functions [1,2,6,7], as:

) ( ) , ( 1 t Z t z   -z 0 (t) < z  0          ) ( exp ) ( ) , ( 2 2 1 t Z z t Z t z   z > 0.
(1) Fig. 3: Concentration profile of deposited platinum into porous carbon of Fig. 1a. The concentration profile in AAO template has a similar shape.

For which Z 1 (t) and Z 2 (t) are the power law functions:

m t t Z  ) ( 1 and p t t Z  ) ( 1 .
The parameter θ describe the porous medium, z 0 (t) is the height of a possible growing overlayer.

Modeling

These profile functions are in fact solutions of a porous diffusion equation [8][9][10][11][12], we have modified for including a power law time dependence of the diffusion equation [6], while it is already dependent on the space variable z [11,12]:

               z z t K z t     0 .
(

) 2
For which the space and time dependent diffusion coefficient takes the form:

  z t K t z K 0 ) , (  . This
allows for taking into account the position and time evolutions of transport-diffusion-adsorption processes inside the pores. The time evolution of the overlayer at z ≤ 0 is also taken into account by a time dependent boundary condition on the flux:

    t z z t K    0 , z = 0 . ( 3
)
Moreover the flux at z = - is constant, mimicking the experimental behavior of the sputtering source.

It is advantageous to search for a self-similar solution in the form:

          t z f t t z ) , ( . (4)
And thus exponents are defined by:

      2 1 and α + β = 1 + ε [6].
The parameter θ is a measure of the diffusion ability of the media: if θ > 0, it means that the medium is slowing down the diffusion (subdiffusion) while θ < 0 means the diffusion is facilitated (superdiffusion). In the experiments, θ is ranging from -1.0 and -1.8 depending on plasma deposition conditions and materials. Parameters α, β, θ are rather depending on plasma conditions while ε, γ are depending on the actual porous medium [6]. Solutions of equations ( 2) and ( 3) are:

                    1 0 2 2 ) 2 ( 1 exp ) , ( t K z t t z (5)
Because α = p/2+θ and β = m, when comparing equations ( 1) and ( 5) and taking into account the Z 1 (t) and Z 2 (t) power law expressions, it becomes possible to find the relations between experimental parameters m, p, θ and the "diffusion" exponents γ = p-1 and ε=p/2+θ+m-2. So the concentration profiles in the porous layers are fully determined.

Discussion

Three kinds of deposition have been carried out: 2 on random porous carbon using two different plasma conditions and one using the AAO template, at successive deposition times. Deduced m, p, θ, α, β, γ, ε values are gathered in Table 1. Differences between C1 and C2 only originate from deposition conditions: the kinetic energy of platinum atoms and argon ion flux impinging the porous layer are larger in the case of C1, carbon layers being identical. On the contrary the AAO condition shows a different substrate while deposition condition is identical to the C1 case.

C1 and C2 are leading to negative values of γ and ε, which, on one hand, means that diffusion is decreasing due to pore filling, at least in the upper part. The Pt flow at the boundary z = 0 is decreasing due to a growing overlayer, which in turn, is limiting the Pt access to the pores. On the contrary, for deposition on AAO, the γ and ε becomes positive, which indicates that diffusion is somewhat enhanced despite pore narrowing. This can be explained by the fact the deposited platinum on surface between pores can serves as a reservoir for diffusion inside the pores [7]. Indeed, when considering the time evolution of platinum mass inside the pores (z>0), it evolves following a power law faster than linearity. At the same time, the mass on the topmost surface between pores follows a power law evolution slower than linearity. This two effects lead to a total mass evolving exactly linearly with time, in agreement with measured constant deposition rates.

Differences between porous carbon layer and AAO template are both porosity and tortuosity. For C1 and C2 substrate the porosity is high while the tortuosity is also high due to the sphere stacking. On the contrary, for AAO the porosity is lower and there is no tortuosity for straight holes. So despite lower porosity AAO template favors Pt diffusion along pore wall. This can be explained by the lack of AAO tortuosity favors the platinum transport inside the cylindrical pore whereas in the sphere stacking the access inside the pore is limited despite a greater porosity. This provides a limit for treating transport in porous media using diffusion equations, for which the real geometry of the porous medium and the microscopic processes, as transport-adsorption coupling, are not taken into account. Complementary studies using molecular dynamics simulations [13] and modeling using transport-diffusion equation combined with adsorption laws are currently conducted [14][15][16].

Conclusions

Experimental and modeling studies show that atom diffusion in a porous, tortuous or not, medium leads to reconsidering the diffusion equation with adding a power law time dependence (t γ ) in addition to the usual porous space dependence (z θ ). Concentration profile exponents as well as selfsimilarity exponents are dependent on plasma deposition conditions while "diffusion" exponents are function of the porous geometry. Moreover, it is expected that tortuosity plays a major role in guiding the inside diffusion and thus the maximum reached depth.

The present diffusion model is able to reproduce experimental results of atom diffusion. In all cases, superdiffusion occurs in plasma assisted deposition. The diffusion is slowed down in sphere stacking and enhanced in cylindrical pores. Despite this overall qualitative agreement, it seems necessary to build a model that more precisely relies on microscopic physical processes.

Fig. 1 :Fig. 2 :

 12 Fig.1: Scanning Electron Microscopy picture of porous carbon layer made of random stacking of carbon spheres Fig. 2: Scanning Electron Microscopy picture of a AAO template.

Table 1 :

 1 Exponents of stretched Gaussian functions m,p, self-similar α, β and diffusion ε, γ for three different deposition conditions

	Conditions	m	p	θ	α	β	γ	ε
	C1	0.4	0.2	-1.5	0.4	0.4	-0.6	-0.2
	C2	0.25	0.25	-1.33	0.5	0.25	-0.75	-0.25
	AAO	0.75	1.25	-1.66	3.75	0.75	0.25	4.0