th
Special Session on Green Building, 4 International Conference on Structural Engineering th th th and Construction Management 2013,Kandy, Sri Lanka, 13 , 14 & 15 December 2013
SECM/13/279 DEVELOPING A DISPERSION MODEL FOR INDOOR VOC FOR ENAMEL PAINTS G.D.V.M. Wijewickrama1, C. Jayasinghe2 and T.M. Perera3 1
Department of Civil Engineering, University of Moratuwa, Sri Lanka E-mail:
[email protected]
2
Department of Civil Engineering, University of Moratuwa, Sri Lanka E-mail:
[email protected]
3
Department of Civil Engineering, University of Moratuwa, Sri Lanka E-mail:
[email protected]
Abstract Volatile Organic Compounds (VOCs) are emitted from various solids and liquids which are used in day to day domestic activities.Some of them are having adverse health effects but still people use certain products which emits such VOCs unconsciously. The most common VOC sources in a house are paints, lacquers, carpets, pesticides, cleaning agents and air fresheners. But finding the respective indoor pollutant effects will have a higher experimental cost. Hence occupants ignore the effects of VOC exposure by neglecting the gravity of the health problems. Therefore suitability of using a computer model to predict the air pollutant dispersion in an indoor environment with a proper validation from an experimental data set has been carried out in the research covered in this paper. Keywords: 1.0
Dispersion Model, VOC, Enamel Paints, Indoor Air Quality
Introduction
Most of the modern day architectural practices in building designing and planning have been changed from the conventional practice. Now both the planners and the clients prefer fast construction and more aesthetically pleasing spaces. Hence the building material manufacturers also adopt artificial and chemical based manufacturing processes in order to cater the needs in the market. Indoor air pollution and its adverse effects in occupational health and productivity has now become a major research area. It has been identified that pollution of indoor air quality causes sicknesses such as respiratory discomfort, headaches, cough, impaired memoryas well as non productive feelings like lethargy [1][2].
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Among most of the building materials, paint has been identified as a major source of indoor air polluting agent. Paint has two main types, water based paints (emulsion) and solvent based paint (enamel). But studies show that emulsion paint have the most adverse effects compared to the other type [3]. Also in practice most of the people go for enamel paints because it has a glossy look after application and a very short drying period. Hence this research has been specifically focused on solvent based enamel paints to derive a dispersion model for general enamel paints so that the building planners can make use of it. 2.0
Objectives
Indoor air quality monitoring is a very expensive procedure.
Therefore building planners and
occupants are reluctant to access the indoor environment very often. Hence in this research an effort has been made to provide a solution to maintain a risk free indoor environment by studying solvent based enamel paint, one of the commonly used indoor air pollutant sources. And in this paper it has been presented a method of predicting a dispersion time using a computer model, validated and contrasted with a proper setup of empirical dispersion model. 3.0
Methodology
3.1 Selecting an Experimental Location In this research the main intention was to develop a dispersion model so that the building planners can make use of the results to assure the building is safe against indoor air pollutants. Therefore the experiments have been setup in such a way that all the other parameters relevant to the deviation of the air pollutant dispersion patterns remain unchanged.
Parameters like number of occupants,
furniture arrangement and air ventilation systems have been controlled. Atest chamber has been selected in the Civil Engineering complex of University of Moratuwa and it has been allocated just for these experiments for a certain period of time with the approval of the department authorities. The room only had minimum furniture in it and the access for students has been restricted for the period of experiment.
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Figure 1: Experimental Room
Figure 2: Air Quality Monitor (AeroQual IQM 60) 3.2 Equipment Used An Indoor air Quality Monitor (AeroQual IQM 60), data logging equipment used to collect data on indoor air quality with the following parameters. Date and time, Relative humidity (%), Room Temperature (Β°C) and Total VOC concentration (ppm) were recorded and extracted from the data logging information after the end of every experiment. In this particular device βPhoto Ionization Detectorβ is used as the VOC sensor to detect the VOC in a particular environment. 3.3 Experimental Setup Before starting any experiments, dimensions of the room including all openings were taken. Thereafter a suitable paint application area was decided by inspecting the room for any disturbances to the paint application such as beam drops and architectural voids. After deciding the area solvent
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based brilliant white gloss enamel paint and the mixing solvents along with painting tools were prepared. Three volumes of paint were mixed with one volume of solvent and paint mix was prepared. Since all four walls were applied with enamel paint the air quality monitor was placed in the middle of the room assuming the normal dispersion effect from all four walls were captured in the data logger. The air quality meter was placed on a table having a height of about 1 m from the floor level, considering the average working height of a general office space. According to the user manual of IQM 60 machine, it was kept on for 60 minutes to heat up before getting any readings in middle of the room[4]. Paint was then applied all over the selected area leaving 0.6m area from the ceiling (to void any unevenness of the wall geometry caused by beams and air vents). Data logger was kept in side of the room for 7 days, untill it reaches 0 PPM of TVOC concentration inside the room. 3.4 Computer Model for the TVOC Dispersion Developing a dispersion model using a computer is a challenging task, especially when the exact chemical composition is not indicated in the domestic paint containers. There were lot of air quality assessment software such as ATFOX, PARAMS v1.0, SCREEN 3, IAQUEST, etc. But most of these software are having restrictions. For an example the IAQUEST is a very good software but it does not have the ability to change the operational environment of the analysis chamber, because all the materials were tested and stored in a read-only database. Hence it gives results for a controlled environment which is totally different when compared to the Sri Lankan environment. The main purpose of this research is to provide a computer related mathematical model for the indoor air contaminant dispersion (TVOC). Therefore after studyingseveral available software which can assess the dispersion of indoor air pollutants, a software developed by United States Environmental Protection Agency (US EPA) was selected for this experiment. The main program, IAQX version 1.0 has several sub programs specializing in different categories [5]. Among the five sub programs,VBX software was found as a very user-friendly and versatile than other available software.
The
algorithmof it is especially developed with the purpose of predicting VOC concentrations in solvent based indoor coatingsmaterials based on product formulation [6]. This software requires the least amount of parameters in predicting the VOC concentrations, which were found using standard literature accepted internationally.
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3.5 Parameter selection for the dispersion model According to the selected software IAQX version 1.0 VBX, the VOC concentration was calculated for the solvent-based indoor coatings with the basis of product formulation. But in the Sri Lankan context we are not having a national database for material safety like in United States, where they need to provide all relevant data for a particular building material.
Hence to input necessary
parameters values for the software, standard material accepted in international literature has to be used with certain assumptions according to the Sri Lankan context. The first interface of the software asks about the physical parameters like volume and number of sink material in the experimental chamber. The volume was calculated by the dimensions of the chamber (140 m3) and since the room was bounded with heavy wall construction which had been constructed long time ago, the number of sink materials were assumed to be in a negligible margin. In the next input phase the program require details about the ventilation details. Since this is a parameter controlled experiment, the ventilation rate was selected in the form of Air Exchange Rate, because the Air Flow Rate in the room cannot be accurately measured in this particular scenario. Since this particular room has a very low ventilation rate, it was taken as 0.3 air changes per hour with reference to the standard and similar experimental setup [7]. The program also requires details on the VOC contents and possible VOC compounds in the paint with the solvent.
Software offers either a Bulk analysis or a MSDS analysis for the VOC
concentration prediction. In bulk analysis option it employs two mass transfer models as shown in Equation 1 and 2 require the user to enter the contents of major VOCs (minimum of 5 numbers) in the solvent paint[5].The software provides the TVOC concentration variation as well as the individual VOC concentration variation. Hence the following two models indicated in equations 1 and 2 were used in the software algorithm[6]. The VBX model for TVOCs οΏ½ ππππ ππ ππ ππππππ
πΈπΈ(π‘π‘) = ππππ οΏ½1.32 ππ0 ππ Where;
β πΆπΆοΏ½
(1)
πΈπΈ(π‘π‘) = Emission Factor (mg/m2/h)
ππππ = Gas-phase mass transfer coefficient (m/h)
ππ0 = Total vapor pressure for TVOCs (mmHg)
ππ οΏ½ = Average molecular weight for TVOCs
ππππ = Mole volume (m3); ππππ = 0.0243 m3at 1atm and 23 Β°C
ππππ = Amount of TVOCs remaining in the source (mg/m2)
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ππππππ = Amount of TVOCs applied (mg/m2) πΆπΆ = Indoor TVOC concentration (mg/m3)
The VBX model for individual VOCs οΏ½ ππππ ππ ππ ππππ
πΈπΈππ (π‘π‘) = ππππ οΏ½1.32 ππππ ππ Where;
(2)
β πΆπΆππ οΏ½
πΈπΈππ (π‘π‘) = Emission Factor for compound i (mg/m2/h)
ππππ = Total vapor pressure for pure compound i (mmHg)
ππππ = Amount of compound i remaining in the source (mg/m2)
πΆπΆππ = Concentration of compound i in indoor air (mg/m3)
Identifying the actual VOCs in the paint mix is a very complicated procedure which has to be done using a Gas Chromatograph (GC). Since there were certain restrictions on the GC analysis, standard literature were cited and general VOCs in a similar paint mix were assumed. First using a research based Canadian database IA-QUEST the probable VOCs were identified [8]. According to the work done by contemporary researches[9], it was found that toluene has a very high governing impact in the TVOC concentration. Work done by Guo and his colleagues reveal that the TVOC content in a Alkyd Paint is around 330 mg/g to 350 mg/g [6].In the VOC contents tab the software also requires VOC contents in products with a proper content. In literature it can be found that the most commonly found VOCs in paints with an approximated level of detail[10][11]. Program then require the source details such as product density, wet film thickness and coated area (as shown in Table 1). And the substances that has to be plotted in the final analysis has to be selected afterwards.Finally the environmental conditions have to be entered to the program. The temperature, air velocity[12] as well as the simulation conditions such as simulation period and output data points can be entered before compiling the input. Table 1: Summary of the parameters in paint details and experimental chamber Parameter Chamber Volume No. of Sink Materials Ventilation Data Analysis Type TVOC content Product Density Wet Film Thickness Coated Area Room Temperature Air velocity Simulation Period Output Data Points
Value 140 m3 0 0.3 Air Changes per hour Bulk Analysis 350 mg/g 1.4 g/cm3 350 ΞΌm 110 m2 30 Β°C (average) 12 cm/s 150 hours 100
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4.0
Results And Analysis
The instrument was switched on and kept for 60 minutes to warm up according to the instrument manual [4] before applying any paint. The TVOC readings in the time span of warm-up time for the instrument was almost null. The Figure 3 will illustrate how the TVOC concentration in the experimental chamber was logged in the instrument. 50 45
TVOC Concentration (ppm)
40 35
Experimental Data
30
Computer Model Data
25 Permissible Limit
20 15 10 5 0 0
20
40
60
80
100
120
140
Time (h)
Figure 3: TVOC Concentration vs. Time After 13 hours of time both the experimental and computer model agree to a certain degree having an overall identical shape factor. This shape also agrees with the outputs of the current research work related to this area [9]. Furthermore the data set was analyzed in such a way that the rising limb was omitted and a attention was given to the dispersion part of the curve. Then by using a curve matching method in Matlab (polyfit), a mathematical equitation was obtained for the dispersion for the both experimental and computer model. Matlab Generated equations; ππ = 854.3 ππ β0.09562 π₯π₯ β 803.9 ππ β0.09542 π₯π₯ for Computer Model
ππ = 2802 ππ β0.0516 π₯π₯ + 41.97 ππ β0.09354 π₯π₯ for Experimental Data
(3) (4)
Y = TVOC Concentration (ppm) X = Time (Hours)
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Figure 4 shows the simplified and approximated fitted curve for both graphs. 50 45
VBX fitted curve
Concentration (ppm)
40
y=
48.03e-0.09x
Experimental Fitted Curve
35 30 25 20 -0.1x 15 y = 52.17e
10 5 0 0
10
20
30
40
50
60
Time (h)
Figure 4: Dispersion model with Simplified Mathematical Equations The empirical and the computer generated graphs are coinciding after 13 hours of application of enamel paint with a 15 ppm concentration. The VOC variation before 13 hours gives extremely high Toxicity index which is above 20 [3]. 70
Concentration (ppm)
60
VBX fitted curve
50 40
Experimental Fitted Curve
30
Interpolated Curve
20 10 0 0
10
20
30
40
50
60
70
80
90 100 110 120 130 140 150
Time (h)
Figure 5: Interpolated Curve between experimental and Computer model
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However, a mathematical relationship for the converged curves; both empirical and computational have been developed as indicated in the equation 3 and 4. It is proposed to introduce the curve as most appropriate dispersion model for enamel paints commonly used in Sri Lanka by the interpolated curve shown in figure 5. And Equation 5 shows the most appropriate mathematical equation for the dispersion curve shown in the figure 5 obtained by using MATLAB. ππ = 1.244 ππ β0.01784 π₯π₯ + 87.03 ππ β0.136 π₯π₯ 5.0
Approximated Dispersion
(4)
Concluding Remarks
It has been revealed that the fact this model can be used to predict a safe building occupying time after painting a room with a solvent based enamel paint. The safe time of occupying a space painted with such a paint is around 2 days (45 hours). Furthermore if a gas chromatographic data is available, this plot can be further optimized and the accuracy can be further increased. Also the same procedure can be adopted even for an air freshener also to predict the dispersion in the form of a mathematical equation. But most of the air freshener producers are not revealing a document with an accepted standard to investigate the health issues related to the Indoor Air Quality. 6.0
Acknowledgement
The authors gratefully acknowledge the funds allocated by University of Moratuwa for purchasingresearch equipment and materials and maintaining them. Also we would like to thank the administration division of the department of Civil Engineering for giving us the necessary clearance and help to conduct the experiments as we have planned initially to the very end. 7.0
References
[1] F. Haghighat &L. De Bellis,'Material Emission Rates: Literature Review, and the impact of indoor air temperature and Relative Humidity',Building and Environment , 1998, pp. 261-277. [2] H.I. Zeliger,'Sick Building Syndrome',Human Toxicology of Chemical Mixtures (Second Edition) , 2011,pp. 143-158. [3] C. Jayasinghe, S. Perera,S. Rajapaksa & T. Perera, "Measurement and analysis of Concentrations of Volatile Organic Compounds in a newly painted room", International Conference on Sustainable Built Environment. Kandy: ICSBE. [4] Aeroqual Limited,Kanomax. Retrieved July 02, 2013, from IQM 60 Indoor Air Quality Monitor, User Guide V 5.0: http://www.kanomax-usa.com/manuals/IQM60_Manual.pdf, 2010, October.
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[5] National Risk Managment Research Laboratory, USEPA,Simulation Tool Kit for Indoor Air Quality and Inhalation Exposure (IAQX) Version 1.0 User's Guide. North Carolina: USEPA, 2000. [6] Z. Guo, J.C. Chang, L.E. Sparks & R.C. Fortmann, Estimation of the rater of VOC emissions from solvent-based indoor coating materials based on product formulation. Atmospheric Environment , 1999, pp.1205-1215. J.C. Chang &Z. Guo,'Emissions of odorous aldehydes from alkyd paint'. Atmospheric [7] Environment , 1998, pp. 3581-3586. [8] National Research Council of Canada,'Indoor Air Quality Emission Simulation Tool', 1998, Canada. [9] J. Xoing, L. Wang, Y. Bai &Y. Zhang,'Measuring the characteristic parameters of VOC emission from paints'. Building and Environment , 2013, pp. 65-71. Air Quality Science,'Interior Paints and Indoor Air Pollution'. Retrieved November 02, 2013, [10] from Abbey Newsletter: http://cool.conservation-us.org/byorg/abbey/an/an26/an26-5/an26-511.html [11] P. Wolkoff & G.D. Nielsen,'Organic compounds in indoor air - their relevance for perceived indoor air quality?'Atmospheric Environment , 2001, pp. 4407-4417. [12] H. Knudsen, U. Kjaer, P. Nielsen &P. Wolkoff,βSensory and chemical characterization of VOC emissions from building products: impact of concentration and air velocityβ. Atmospheric Environment , 1999, pp. 1217-1230.
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