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Material emissions and indoor air quality modelling project - an overview
Shaw, C. Y.; Sander, D. M.; Magee, R. J.; Lusztyk, E.; Reardon, J. T.; Bodalal, A.; Nong, G.; Biesenthal, T. A.; Won, D. Y.
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Material emissions and indoor air quality modelling project - an overview
Shaw, C.Y.; Sander, D.M.; Magee, R.J.; Lusztyk, E.; Reardon, J.T.; Bodalal, A.; Nong, G.;
Biesenthal, T.A.; Won, D.Y.
A version of this paper is published in / Une version de ce document se trouve dans :
Proceedings of the 4th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings, v. 2, 2001, pp. 699-706
www.nrc.ca/irc/ircpubs NRCC-44722
Material Emissions and Indoor Air Quality Modelling Project –
An Overview
C.Y. Shaw, D. M. Sander, R.J. Magee1, E. Lusztyk, J.T. Reardon, A. Bodalal, G. Nong, T. A. Biesenthal, and D.Y. Won
Institute for Research in Construction National Research Council Canada
ABSTRACT
This paper presents a brief overview of a three-year project on material
emissions and indoor air quality modelling. It highlights the research plan and sample results. Major deliverables included a database consisting of volatile organic compound (VOC) emission characteristics of 48 commonly used building materials, two mass-transfer-based models for predicting emission rates of total volatile organic compound (TVOC) and individual VOCs, and a single-zone model for use with the database for predicting concentrations of individual VOCs in rooms. Also presented, are the field validation results for the single-zone model.
INTRODUCTION
Provision of acceptable indoor air quality in an energy-efficient manner depends on two factors: contaminant-source control and effective ventilation. Both factors require a thorough understanding of the emission characteristics of building materials and furnishings. The Institute for Research in Construction, National Research Council Canada has initiated a major program on material emissions and indoor air quality modelling. The objective is to develop guidelines for selecting building materials, furnishings, and ventilation strategies for new and existing buildings to meet a specific indoor air quality level. This objective will be achieved through a series of projects, the first of which was initiated in 1995. This paper presents an overview and sample results of this project.
PROJECT OVERVIEW
This is the first of several projects that were initiated to address material emissions and indoor air quality modelling.
To achieve the long-term objective of developing guidelines for selecting building materials, furnishings, and ventilation strategies for new and existing buildings to meet a specific indoor air quality level, it will be necessary to have models and input data to predict indoor air quality levels. This initial project’s workplan, therefore, included three tasks: laboratory testing, modelling, and field validation. The purposes were to develop (a) methods for establishing a material emission database for commonly used building materials, (b) empirical and
mass-transfer-based models for predicting material emission characteristics, and (c) a single-zone model for use with the database to predict indoor air quality levels in rooms. A brief discussion of each task is given below.
Laboratory Testing
Laboratory testing included both small- and full-size chamber testing. One of the main tasks of chamber testing was to produce emission data for commonly used building materials and furnishings. The materials tested under this project
included six wet materials (wood stain, polyurethane, wax, paint, adhesive, and caulk and sealant) and nine dry materials (carpet, underpad, particleboard, plywood, oriented strand board, gypsum board, solid wood, ceiling tile, and vinyl flooring). Based on the recommendations of local architects and designers, three commonly used products of each of the 15 material types were tested. In
addition, five material systems (carpet-adhesive-substrate, vinyl-adhesive-substrate, plywood-subfloor material-plywood, office workstation, and kitchen cabinets) were also tested. The results were included in a database for use with various prediction models which were also developed under this project.
Even though substantial efforts were made to develop the database, much more data are required to make this database useful. Another major task of this project was to develop emission test methods and procedures, so that test results obtained by different laboratories using these methods and procedures would be compatible and suitable for inclusion in the database.
Modelling
Several models were developed under this project, including source, sink, and room simulation models. Source models included both empirical models and mass transfer-based models.
Source models, empirical – These models involve fitting measured
concentrations with an appropriate equation, such as a power-law relationship. The main application of such models is to predict the long-term emission characteristics of a specific sample, using short-term measurement data.
Source models, mass transfer – These models involve solving fundamental
equations governing the emission process from building materials to the air. Two such models have been developed under this project: one for dry materials and another for wet materials. For dry materials, as the boundary layer effect is negligible, it can be assumed that the VOC concentrations are uniform within the air, including the boundary layer. For simplicity, the emission processes for dry materials were assumed to be governed by the diffusion of a VOC through the source material and the transport of the compound into the surrounding air. The transition between the solid material and the air was accounted for by including a partition coefficient. The partition coefficient is the concentration ratio of the VOC between the material-side and the air-side of the material-air interface. The governing equation for the emission process is the three-dimensional transient
diffusion equation. To solve this equation, it is necessary to know the diffusion coefficient for the compound, the partition coefficient, and the initial VOC mass in the material, in addition to initial and boundary conditions. An experimental method was developed under this project to measure the diffusion and partition coefficients for various VOCs diffused through common building materials. A head space analysis was conducted to determine the equilibrium concentrations of VOCs at the air-side of the material-air interface. The results and the
corresponding partition coefficients were used to estimate the initial VOC mass in the material.
For wet materials applied on a porous substrate , the emission of VOCs includes the diffusion of the VOC through the substrate, the material film, the material-air interface, and the boundary layer.
Both of these mass-transfer-based models give the emission rates of TVOC and individual VOCs. They are useful for researchers to understand the emission processes, but are too complicated for general applications. One main application of such models would be to generate data for developing simple correlations for predicting emission rates.
Sink models – Interior building materials and furnishings can adsorb VOCs from
surrounding air and re-emit them into the air later. This is known as the sink effect. Sink effect can have a significant influence on VOC concentrations in a room and, therefore, it should be considered in indoor air quality predictions. As several sink models are available, the focus of this project was to evaluate these models and select the most suitable one for indoor air quality predictions. Based on tests conducted on vinyl floor tile, painted gypsum wallboard, ceiling tile, and carpet, it was found that the Langmuir isotherm-based first-order reversible adsorption / desorption model (or linear sink model) appears to be suitable for determining the sink effects of building materials, under typical indoor conditions. This model assumes that at a certain temperature, the VOC adsorption rate is proportional to the VOC concentration in the air, and the VOC desorption rate is proportional to the VOC mass adsorbed by the material. It consists of two constants: ka, the adsorption rate constant and Ke, the adsorption/desorption equilibrium constant; both have to be determined by the user. The values of ka and Ke for five VOCs (ethylbenzene, cyclohexanone, 1,4-dichlorobenzene, benzaldehyde, and dodecane) were determined under this project for the four building materials listed above. In addition, a correlation between Ke and VOC vapour pressure was found.
Room simulation model – The computer program MEDB-IAQ (material
emission database and indoor air quality simulation program) was developed to serve several purposes. The user can view material emissions information on the display in tabular or graphical form, print reports, and export data to other
programs for further processing. It also allows the user to browse the database and to formulate queries to search the database for specific information.
Ultimately, this tool will assist building designers in making informed choices of building materials that minimize VOC emissions. MEDB-IAQ has the capability
to predict the concentrations of individual VOCs that occur in a room, based on the emission data for building materials resident in the database. The single zone model employed assumes perfect mixing of the contaminants with indoor air. There is also the further assumption that VOC compounds do not interact with one another. Input data include loading ratio and emission source areas, emitted contaminants and their emission rate – time profiles, sink characteristics of interior surfaces, room or house dimensions, and air change rate. The
database provides two of the key input data: emitted VOCs and their emission rate – time profiles for selected building materials.
Field Validation
Tests were conducted in the NRC two-storey research house to obtain data for checking the accuracy of the prediction results of the single zone model. The VOC source (an oil-based commercial wood stain) was placed on an electronic balance, so that the weight loss rate could be accurately measured. During the test, the forced-air heating system was in operation to assist the mixing of the contaminants emitted from the source with the indoor air. Periodic air samples were taken at four locations within the house beginning immediately after the start of the test. After the initial samples, the sampling intervals varied from four minutes during the first four hours of the test to 60 minutes near the end of the test.
Sample of Results
Examples of the results produced by this project are described below [1].
Small chamber – A total of 48 dry and wet building materials were tested. The
emission process of wet materials exhibited much greater complexity than that of dry materials. Figure 1 shows the emission characteristics of a polyurethane varnish sample, represented by the concentration profiles of TVOC and the four most abundant VOCs in the air of the test chamber. As shown, the
concentrations decayed rapidly during the first 8 hours after the start of the test. The concentrations continued to decrease but at a slower rate for the next 24 hours or so. After these two periods, the decay of the concentrations continued at a rate similar to that of dry materials. A three-equation model was used to fit the measured decay data: the VB (vapour presssure-boundary layer) model [2] for the initial stage, a first-order exponential function for the second stage, and a power law relationship for the third stage. Figure 1 shows a good agreement between the model and the measured data.
Full scale chamber – Tests were carried out on a fully furnished office
workstation with carpet to determine the emission rates of TVOC and individual VOCs, and the sink effect. Figure 2 shows the results of the sink effect test. The sink test started with the injection of a mixture of five VOCs into the chamber and was followed by the monitoring of the concentrations for a period of 80 hours. The underlying assumption was that in the absence of sink effect, the decay of the concentrations of test gases would follow closely the typical exponential
function. To verify this assumption, a small amount of SF6 (which was not
expected to be adsorbed by the test workstation) was also used as a test gas.
As expected, the measured concentrations of SF6 agreed closely with the
theoretical no-sink curve. On the other hand, except for dodecane (which was not detectable during the test period), the decay of the other four VOCs was much slower than the theoretical no-sink curve, suggesting the presence of sink effects.
Figure 2 also shows that the VOC with the highest boiling point appeared to have the slowest decay rate. For the five VOCs used for the sink test, the dodecane has the highest boiling point. One possible reason for its disappearance in the air could be that it was completely adsorbed by the workstation and it would take much longer for the workstation to re-emit it. Further work is needed to
investigate sink effects and, particularly, the relationship between the strength of the sink effect and the physical and chemical properties of individual VOCs (e.g., boiling point, polarity, molecular structure, etc.).
Validation of MEDB-IAQ program - Tests were conducted in the NRC research
house to generate data for validating the single-zone model. The emission source was an oil-based wood stain applied to a 42 cm by 28 cm oak substrate. The dominant VOCs emitted by this stain included nonane, decane, undecane and dodecane. The results indicate that the measured concentrations of nonane and decane agreed reasonably well with the MEDB-IAQ simulation. As an
example, Figure 3 shows the simulation results and the measured concentrations for nonane. However, there was poor agreement for undecane and dodecane. For undecane and dodecane, the measured concentration was much lower than the simulation during the first 96 hours, and did not decay nearly as rapidly as the model over the longer term (see Figure 4 for the dodecane results).
This poor agreement could be explained by sink effect. The pattern shown in Figure 4 is what would be expected from sink effect (see Figure 2). The MEDB-IAQ program can assign a material to be a sink instead of an emission source. The database does contain limited data on the sink effect for the dodecane on painted drywall and on unpainted drywall. An additional simulation was done using that sink effect data. All painted surfaces were represented as painted
drywall (743 m2) and all unpainted surfaces as unpainted drywall (192 m2). The
results of the simulation are shown in Figure 5. Inclusion of sink effect in the model did improve the agreement with measured concentrations. However, the measured concentration was much lower than the simulation during the first 96 hours, indicating that the dodecane was not all adsorded by the painted and unpainted dry walls. The measured concentration did not decay nearly as rapidly as the model during hours 96 to 700.
SUMMARY AND FUTURE PLANS
This paper presents a brief overview of a recently completed three-year project on material emissions and indoor air quality modelling. Its main output was a
computer program, consisting of a material emission database and a single-zone indoor air quality prediction model. The database currently includes emission data for 48 commonly used building materials. With this program building designers can make informed choices of building materials and ventilation strategies to strike an acceptable balance between indoor air quality and energy efficiency. Manufacturers can use it to assess how their products affect indoor air quality. MEIAQ II another three-year project, supported mainly by federal government agencies including Public Works & Government Services Canada, Natural Resources Canada, Canada Mortgage and Housing Corporation, Health Canada, and Environment Canada, started in April 2001 and will focus on the environmental factors that influence VOC emissions and health impact issues. It includes a new Health Advisory Committee that will guide the health focus.
REFERENCES
1. Zhang, J.S, Zhu, J.P., Magee, R.J., Lusztyk, E, Yan, A, and Shaw, C.Y. 1999. A database of VOC emissions from building materials. Proceedings, Indoor Air ’99, Vol. 4, pp.634-639, August 1999, Edinburgh, Scotland.
2. Guo Z. And Tichenor B.A. 1992. Fundamental mass transfer models applied to evaluating the emissions of vapor-phase organics from interior architectural coatings. EPA/AWMA Symposium, Durham, NC.
0.00001 0.0001 0.001 0.01 0.1 1 1 0 100 1000 10000 0 50 100 150 200 250 300 Time: t, h Concentration: C, mg/m^3
Ethyl CHX Nonane Propyl CHX Decane TVOC
Ethyl CHX-M Nonane-M Propyl CHX-M Decane-M TVOC-M
Figure 1, Measured and modeled (“-M”) VOC concentrations in a polyurethane varnish test
0.01 0.1 1 0 20 40 60 80 No sink: C(t)/C(0)=e-0.5t SF6 1-octanol (196 oC b.p.) dichlorobenzene (180 oC b.p.) decane (174 b.p.) ethylbenzene (136 b.p.) Elapsed time: t, hr N o rm a li z ed c o n c en tr a ti on s : C (t )/ C (0 ), in l o g sc a le
Figure 2, Results of the sink effect test (Full Scale Chamber test)
C o n c e n t r a t i o n o f N o n a n e 1 1 0 1 0 0 1 0 0 0 0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 E l a s p e d t i m e ( h ) Concentration (ug/m3) F a m i l y R o o m D i n i n g R o o m M a s t e r B e d r o o m B a s e m e n t S i m u l a t i o n
C o n c e n t r a t i o n o f D o d e c a n e 0 . 1 1 1 0 1 0 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 E l a s p e d t i m e ( h ) Concentration (ug/m3) F a m i l y R o o m D i n i n g R o o m M a s t e r B e d r o o m B a s e m e n t S i m u l a t i o n
Figure 4, Measured and predicted concentrations (Field test)
C o n c e n t r a t i o n o f D o d e c a n e 0 . 1 1 1 0 1 0 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0 4 5 0 5 0 0 5 5 0 6 0 0 6 5 0 7 0 0 7 5 0 E l a s p e d t i m e ( h ) Concentration (ug/m3) F a m i l y R o o m D i n i n g R o o m M a s t e r B e d r o o m B a s e m e n t S i m u l a t i o n - n o s i n k e f f e c t S i m u l a t i o n w i t h s i n k e f f e c t
Figure 5, Measured and predicted concentrations (Field test)
ACKNOWLEDGEMENT
This work is part of the MEIAQ project sponsored by a consortium consisting representatives of government departments, universities, and building industry. Members of the consortium included: Canada Mortgage and Housing
Corporation, Natural Resources Canada, Canadian Wood Council, Chemical Manufacturers Association, Gypsum Association, The Building Center of Japan, and USG Corporation. In addition, the following organizations have made significant contributions to the project: Carleton University, Massachusetts Institute of Technology, U.S. Environmental Protection Agency, U.S. National Institute of Standards and Technology, and Australia Commonwealth Scientific and Industrial Research Organization. The authors highly appreciate the valuable guidance and contributions of the members of these organizations. The authors also wish to acknowledge the contributions of our previous colleagues, Drs. J.S. Zhang, J.P. Zhu, Jie Zeng, and Yan An.
