Printer Friendly

Influence of panel thickness on the release of volatile organic compounds from OSB made of Pinus sylvestris L.


Emissions from building products are considered to be a major source of elevated volatile organic compound (VOC) concentrations in indoor air. To investigate the effect of panel thickness on the release of VOC three oriented strandboards (OSB) of different thicknesses were characterized. The emissions deriving from the panels made of Scots pine were investigated over a period of 125 days using environmental test chambers. According to ISO 16000 parts 6 and 9, the chamber air was actively sampled on adsorbent tubes, which subsequently were analyzed in a gas chromatography (GC) and mass spectrometric (MS) system. Terpenes and aldehydes are the predominant compounds released by the OSB. Since emissions increase with the panel's thickness, it can be concluded that, aside from evaporation, diffusion from the core seems to be a major source of VOCs. This movement of compounds is probably promoted additionally by the porous and capillary structure of wood and in particular wood-based materials. These conclusions are supported by a comparison of two empirical models for estimating terpene emission rates. A double-exponential model, taking into account both evaporation and diffusion-driven emissions, achieved the best quality of fit. Thus, it is crucial to consider sample thickness when measuring and assessing the emissions from wood-based products. Furthermore, it can be concluded that the exclusive utilization of area-related factors (e.g., the area-specific emission rate) is insufficient for describing and evaluating long-term emissions from wood-based composites.

According to numerous studies, construction materials are major sources for elevated volatile organic compound (VOC) concentrations in indoor air (Brown 2002, Hodgson et al. 2002, Jarnstrom and Saarela 2003). Various adverse health effects, such as irritation or allergic diseases, are associated with high VOC concentrations. In order to evaluate the impact of building products on indoor air quality, the release of VOCs is investigated using environmental test chambers. Test specimens are stored inside such chambers in a constant climate (temperature: 23 [degrees]C, relative humidity: 50%) and at a constant air exchange rate. At specific times, generally at least after 3 and 28 days, the VOC concentration of the outgoing chamber air is determined by active sampling on an adsorbent (e.g., Tenax TA) and, subsequently, thermal desorption and gas chromatography (GC) using mass spectrometric (MS) or flame ionization (FI) detectors. All requirements of this method are described in ISO 16000 parts 6 (2004) and 9 (2006).

For improvement of indoor environmental quality and in order to promote building products with low VOC emissions, various environmental product declarations have been developed, including the European Collaborative Action (ECA 1998) and the German Committee for Health-related Evaluation of Building products (AgBB 2005). Both schemes include evaluation procedures as well as concentration limits that should not be exceeded by the tested samples. Limited VOC emissions are additionally required for some voluntary eco-labels, such as "Der Blaue Engel RAL-UZ 38" (1999) or "natureplus" (2003). Most of these schemes only consider the area-specific airflow-rate or, respectively, the area-specific emission rate for transferring the results to a model room and determining resulting model room air concentrations.

Several studies have shown that generally, a high concentration of terpenes and aldehydes characterizes the VOC emissions from pinewood-based panels (Baumann et al. 1999, 2000; Salthammer et al. 2003). Monoterpenes originate in the wood's oleoresin and are easily volatilized due to their high vapor pressures (Fengel and Wegener 1989). As a result of autoxidation, unsaturated fatty acids are degraded and several saturated and unsaturated aldehydes are formed (Back et al. 2000; Makowski et al. 2005; Makowski and Ohlmeyer 2006a, 2006b). However, since both terpenes and aldehydes derive from the wood itself, diffusion-driven emissions from the core layer of pinewood-based composites cannot be neglected. Consequently, it can be assumed that emissions would rise with the panel's thickness.


The objective of this paper is to determine whether the panel's thickness influences the VOC emissions from OSB. For this reason, three panels of different thicknesses but equal surface areas were produced and tested for their VOC emissions. Additionally, two different models for calculating emission rates were applied in order to identify the main drivers for emissions from wood-based panels.

Materials and methods


Industrially flaked and dried Scots pine (Pinus sylvestris L.) was utilized as raw material for the tested boards. One day after flaking and drying, the strandboards were manufactured in a laboratory. The strands were blended with 4 percent pure pMDI (polymeric methylene di(phenyl isocyanate)). Subsequently, a randomized mat was formed and hot pressed with 12 seconds per one millimeter panel thickness at a temperature of 220 [degrees]C. Under these conditions, three panels of different thicknesses (16 mm, 19 mm, and 26 mm) were made. Pressing time and density of the panels are summarized in Table 1.


After hot-pressing and cooling to ambient temperature, the strand boards were cut into samples of 21 by 21 cm size. Aluminum-coated adhesive tape was used to seal the edges, overlapping 1 cm, leading to a sample-surface of 722 [cm.sup.2]. The OSB samples were placed into the environmental testing chamber 24 hours after being manufactured. After 35 days of testing, the samples were removed from the chamber and stored, separated from each other, at 23 [degrees]C and 50 percent relative humidity (RH). Samples were reinserted into the environmental test chambers 4 days prior to the last sampling on day 95 after panel manufacturing.

Sampling and analytical procedures as well as the equipment were in accordance with ISO 16000-6 (2004). Tests were carried out in test chambers made of glass with a volume of 23 L. Jann et al. (1999) demonstrated good comparability of results (for VOC and SVOC) obtained in such chambers with those acquired in larger ones with a volume of 1 [m.sup.3]. Additionally, Makowski and Ohlmeyer (2006c) proofed their general suitability for measuring OSB made of pine wood.

A constant and adjustable airflow (1.2 L min-1) was led through the chamber, resulting in 3.1 air exchanges per hour. The loading factor was 3.1 [m.sup.2] [m.sup.-3], corresponding to an area specific airflow rate of 1.0 [m.sup.3] [m.sup.2] [h.sup.-1]. The airflow was conditioned for a temperature of 23 [+ or -] 0.5 [degrees]C and 50 [+ or -] 3 percent RH, both measured at the inlet port.

Assuming a steady-state condition, the following relation between chamber air concentration and emission rate can be assumed (ECA 1998):

[SER.sub.a](t) = C x q [1]

where [SER.sub.a](?) = area emission rate at time t([micro]g [m.sup.-2] [h.sup.-1]), C = chamber air concentration ([micro]g [m.sup.-3]), and q = area specific airflow rate ([m.sup.3] [h.sup.-1] [m.sup.-2]). To determine q, the following equation is used:

q = n x V/F [2]

where V= volume of test chamber ([m.sup.3]), F = surface area of sample ([m.sup.2]), and n = air exchange rate ([h.sup.-1]).

Air samples were collected on Tenax TA (200 mg, 60 to 80 mesh) using a sampling pump with an electronic flow controller. A sample flow rate of 100 [+ or -] 1 mL [min.sup.-1] was applied for a period of 5 to 40 minutes, leading to a total air volume of 0.5 to 4 L. Before sampling, each tube was mixed with 200 ng [toluene.sub.d8] dissolved in methanol as an internal standard.

After sampling, the tubes were thermally desorbed in a TDS 3 (Gerstel, Muelheim a.d. Ruhr, Germany), characterized and quantified with a GC (Agilent 6895) and MS (Agilent 5973 N). Quantification was achieved by multipoint calibration with reference compounds. Detected peak areas were multiplied with relative response factors of the internal standard. Substances without reference compounds were quantified using the response factor of similar compounds (e.g., [alpha]-pinene for terpenes).

Thermodesorption conditions were: desorption temperature: 280 [degrees]C, desorption time: 10 minutes, cryo focusing at -30 [degrees]C, split-ratio: 1:30. A 30 m analytical column (Zebron 1701, film 0.25 urn, i.d. 0.25 mm) was used. The GC temperature program: 40 [degrees]C hold for 4 minutes, increase 6 [degrees]C [min.sup.-1] to 90 [degrees]C, increase 8 [degrees]C [min.sup.-1] to 200 [degrees]C, increase 12 [degrees]C [min.sup.-1] to 280 [degrees]C, hold for 5 minutes The MS detector was operated in scan modus (29 to 40 amu) with 2.2 scans [s.sup.-1] at 280 [degrees]C interface temperature.

Determination of emission rates

Numerous models can be applied to calculate the emission rate apart from Equation [1], In this work the concentration and time data from the experiments were used to determine the parameters of empirical emission rate models. However, this is restricted to constantly decaying emissions and therefore cannot be applied to aldehydes (see Fig. 1). For verifying whether the emissions' decay is driven by evaporation or by diffusion, a first-order model is compared to a double-exponential model. Since the environmental test chambers were constructed of nonadsorbent and chemically inert materials, such as glass and stainless steel, sink effects are neglected in this investigation.


Of all models the first-order model is the most widely used one for calculating emission rates (Guo and Murray 2000):

[SER.sub.a](t) = [SER.sub.a,0] * [e.sup.-k*t] [3]

where [SER.sub.a](t) = calculated area emission rate at time t([micro]g [m.sup.-2] [h.sup.-1]), [SER.sub.a,0]= initial emission rate ([micro]g [m.sup.-2] [h.sup.-1]), andk= first order decay constant (h). According to Evans (1996), this model is applicable for constant emission rates, with instantaneous termination at the end of a "drying time."

Assuming that VOC emissions from wood-based panels are not only evaporation-driven but also characterized by diffusion from the core layer, a model accounting for this would probably provide a better fit. Therefore, the experimentally obtained data are used with a double-exponential model, which Guo and Murray (2000) and Guo et al. (2000) already proved applicable for various construction materials:


where [SER.sub.a](t) = area specific emission rate at time t([micro]g [m.sup.-2] [h.sup.-1]), [SER.sub.1] = phase 1 (evaporation-dominated) emission rate ([micro]g [m.sup.2] [h.sup.-1]), [SER.sub.2] = phase 2 (diffusion-dominated) emission rate ([micro]g [m.sup.2] [h.sup.1]), [SER.sub.1,0] = phase 1 initial emission rate ([micro]g [m.sup.-2] [h.sup.-1]), [k.sub.1] = phase 1 emission rate decay constant ([h.sup.-1]), SER20 = phase 2 initial emission rate ([micro]g [m.sup.-2] [h.sup.-1]), and [k.sub.2] = phase 2 emission rate decay constant ([h.sup.-1]).

For comparing both models and characterizing the quality of fit, the root mean square deviation (RMSD) was determined. It is a frequently used measure of the difference between values predicted by a model or an estimator, and the values actually observed from the thing being modeled or estimated. In this case, a lower RMSD indicates a better fitting model.


where [y.sub.1] = measured concentrations, y(x,) = calculated concentrations, n = number of observations, and p = number of estimated parameters.

A nonlinear, least squares, best-fit routine (Maple 2006) was used to analyze the chamber air concentrations. Apart from the emission parameters, the software also automatically assessed the quality of the least squares fit.

Results and discussion

VOC emissions from all panels decreased steadily over the time. Concentrations ranging from 3795 to 6353 [micro]g [m.sup.-3] after 3 days declined to 410 to 501 [micro]g [m.sup.-3] at the 111th day, with higher amounts of volatile compounds having been released from thicker samples (see Table 2).

In test chamber air mainly terpenes and aldehydes are detected, with the latter emerging during the testing (see Fig. 1). Thus, the share of aldehydes increased from 3 to 4 percent (third day) to 34 to 56 percent (28th day) while the terpene's share decreased correspondingly. This behavior matches with results published by other authors, e.g., Baumann et al. (1999, 2000) or Salthammer et al. (2003). Increasing aldehyde emissions from OSB made of pinewood were already reported by Makowski et al. (2005) and Makowski and Ohlmeyer (2006a, b).


Initial terpene emissions ranged from 3579 to 6075 [micro]g [m.sup.-3] (third day) and decreased to 76 to 108 [micro]g [m.sup.-3] at 111th day, with [alpha]-pinene, [DELTA]3-carene and [beta]-pinene being the most abundant compounds.

Generally, concentrations increased with the thickness of the board. Compared to the 16-mm sample, 13 or 32 percent and 42 or 130 percent higher emissions arise from the panels of 19 mm or 26 mm, respectively. However, this does not correlate directly with the increase of thickness. In this context, the different conditions of hot-pressing must be taken into account. Thicker mats, for example, have to be pressed for a longer period. During this extra amount of time some compounds might already be volatilized (see Table 1).

From the results, it can be concluded that VOCs are not only evaporating from the surfaces but also source from inner parts of the wood-based composite. This assumption is supported by results of the nonlinear regression approach, illustrated in Figure 2. Initial values and decay constants for terpene emissions for both first-order and double-exponential models were obtained by an iterative nonlinear regression analysis on the experimental data. All curves are clearly separated due to the thickness of the panel. A double exponential model, as shown in Table 3, achieved the best curve fit for all panels. The RMSD of 13 (16 mm and 19 mm) and 26 (26 mm), respectively, proves the double-exponential model to be clearly the better choice than the first-order model with RMSD ranging from 133 to 188 (see Table 4). Predicted emission factors based on first-order decay models significantly underestimate the measured long-term (up to 28 days) terpene concentrations. Zhu et al. (2001) came to a similar finding but proposed the use of a power law model for describing and predicting VOC emissions from wood-based products.

The generally poorer quality of fit for the data from the 26mm sample is probably caused by elevated and hardly diminishing emissions at the end of the investigation. Most likely this must be ascribed to the conditions of storage between day 35 and 80. For this time, samples were taken out of the test chambers and kept under defined climatic conditions (temperature and RH) that conform to ISO 16000-9. However, the other ambient conditions like air exchange rate, material loading, and air velocity probably differed to a large extent. Even if measuring these deviations was impossible, a lower air exchange rate and air velocity must be assumed during this storage period. Since these factors crucially influence the emission rates (see Eq. [1]), an enrichment of VOCs on the sample's surface during this time cannot be excluded. Furthermore, the comparatively low compression of the 26-mm sample could be responsible for a deviant and slower decaying behavior.

Assuming that the late emissions are mainly diffusiondriven, their movement from the panel's core to the surface might be facilitated by a significantly lower density of the OSB of 26-mm thickness (see Table 1). This could also be a general explanation for the high emissions from the 26-mm sample and for the small differences in the calculated model coefficients. For example, both decay constants, [k.sub.1] and [k.sub.2], are similar for the 16-mm and 19-mm sample, amounting to approximately 0.0093 [hours.sup.-1] (k,) and 0.0008 [hours.sup.-1] ([k.sub.2]). With 0.0074 [hours.sup.-1] (k,) and 0.0010 hours-1 ([k.sub.2]) only slightly differing constants were calculated for the OSB of 26-mm thickness (see Table 3).


Apart from terpenes, which are extractives of the pinewood, other volatiles are formed reactively, such as saturated and unsaturated aldehydes, ketones, alcohols and organic acids (see Table 2). Most of them arise due to an autoxidative, chain-reactive splitting of unsaturated fatty acids contained in pinewood (Back et al. 2000). Hexanal and pentanal are the prevailing VOC formed by scissoring of the double-unsaturated linoleic acid (Chan 1987). With respect to low odor thresholds, numerous of those aldehydes might contribute to the odor of OSB made of pinewood.


Due to their different origins, terpene and aldehyde emissions behave differently. While initially dominating terpene concentrations decay continuously over time, an autoxidatively driven formation of aldehydes takes place (see Figs. 1 and 2). Only when the chain reaction terminates and no more aldehydes are formed, a decline sets in. In this investigation, aldehyde emissions start to decay after a peak concentration (390 to 516 ug irT3) was reached at the 28th day (see Fig. 1). Between day 14 and day 35 of testing, the thickest OSB released the lowest aldehyde concentrations. This is probably due to the elongated press time of the thick panel and the hot platen temperatures at about 220 [degrees]C (see Table 1). Under such conditions, a condensation of fatty acids can occur (Franzke 1996, Belitz et al. 2001). Makowski and Ohlmeyer (2006b) observed a similar effect, with lower aldehyde emissions from OSB pressed at a temperature exceeding 200 [degrees]C than from boards pressed at lower temperatures. The initial difference in aldehyde release from the panels of 16- and 19mm thickness is relatively small and rather ascribable to natural variations of wood than to any influence of thickness. Only after reinserting the panels into the testing chamber, a short increase of aldehyde emissions is observable. From this time on, the greatest emissions arise from the panel of the highest thickness, probably because surface located compounds have evaporated to a great extent, and emissions now predominantly originate in the core. Compared to the terpene emissions, effects of diffusion are observable rather late. Most likely, this is due to a higher availability of oxygen and radicals on a panel's surface compared to its core. Due to its lower density, the thickest OSB probably contains more cavities and therefore more oxygen than the other samples investigated herein. This would facilitate the formation of aldehydes inside the panel and could be an explanation for the strong increase of aldehyde emissions at the end of the investigation. At this stage emissions are mainly driven by diffusion.

The emission-rate models can only be applied to monoterpenes since aldehydes are not constantly decaying but are formed in a complex reaction. For predicting or modeling aldehyde emissions more information is fundamental. Such a model needs to take into account the rate of aldehyde formation, which depends on various factors, e.g., the grade of the fatty acid's unsaturation as well as, according to Makowski and Ohlmeyer (2006b), the structure and size of the strands or particles.


Emissions from wood-based composites are not only caused by evaporation from the surface but also clearly influenced by a diffusion driven movement of VOCs from the panel's core. This is plausible, taking into account the porous and capillary structure of wood and wood-based materials, which facilitate the flow of compounds. Thus, short-term evaporation-driven (< 35 days) and long-term diffusion-driven emissions (> 80 days) must be differentiated. Panels of greater thickness consequently tend to exhibit higher emissions, probably caused by its lower density. This relationship is of great importance when assessing emissions from different wood-based panels and must be taken into account when applying health-related evaluation schemes.

A double exponential model, including diffusion driven emission, proved to be a good choice for estimating short- and long-term emissions from OSB. However, applying such empirical models is restricted to terpene concentrations, especially as long as aldehyde formation does not terminate at early stages of emission testing.

Literature cited

AgBB. 2005. Health-Related Evaluation for Volatile Organic Compound Emissions (VOC and SVOC) from Building Products, Accessed March 2006.

Back, E.L., I. Johansson, R. Nussbaum, and B. Ostmann. 2000. Effect of wood resin on timber and building products. In: Pitch Control, Wood Resin and Deresination. Back, E.L., and L. Allan, eds., TAPPI Press, Atlanta, Georgia, pp. 376-385.

Baumann, M, A. Battermann, and G.-Z. Zhang. 1999. Terpene emissions from particleboard and medium-density fiberboard products. Forest Prod. J. 49(9):49-56.

--, L. Lorenz, S. Batterman, and G.-Z. Zhang. 2000. Aldehyde

emissions from particleboard and medium fiberboard products. Forest Prod. J. 50(9):75-82.

Belitz, H.-D., W. Grosch, and P. Schieberle. 2001. Lehrbuch der Lebensmittelchemie (Food Chemistry textbook). Springer-Verlag, Berlin-Heidelberg-New York. 213 pp.

Brown, S.K. 2002. Volatile organic pollutants in new and established buildings in Melbourne, Australia. Indoor Air 12(l):55-63.

Chan, H.W.-S. 1987. The mechanisms of autoxidation. In: Autoxidation of Unsaturated Lipids, Chan, H.W.-S., ed.. Academic Press, London, pp. 1-17.

European Collaborative Action (ECA). 1998. Indoor Air Quality and its Impact on Man: VOC-Emissions from Building Products-Solid Flooring Materials. Rept. No. 18, EUR 16284 EN. European Commission, Joint Res. Centre, Environment Inst. pp. 1-48.

Evans, W. 1996. Linear Systems, compartmental modelling, and estimability issuses of IAQ studies. In: STP 1287--Characterizing Sources of Indoor Air Pollution and Related Sink Effects, Tichenor, B., ed. pp. 239-263.

Fengel, D. and G. Wegner. 1989. Wood Chemistry, Ultrastructure, Reactions. Walter de Gruyter. Berlin, New York. pp. 182-222.

Franzke, C. 1996. Allgemeines Lehrbuch der Lebensmittelchemie. Behr's Verlag, Hamburg, Germany, pp. 90-93.

Guo, H. and F. Murray. 2000. Modeling of emissions of total volatile organic compounds in an Australian house. Indoor and Built Environ. 9:171-181.

--, -- and S. Wilkinson. 2000. Evaluation of total volatile organic compound emissions from adhesives based on chamber tests. J. Air Waste Manage. Assoc. 50:199-206.

Hodgson, A.T., D. Beal, and J.E.R. Mclivaine. 2002. Sources of formaldehyde, other aldehydes and terpenes in a new manufactured house. Indoor Air 12(4):235-242.

Inter. Standards Organization (ISO). 2004. Indoor air--Part 6: Determination of volatile organic compounds in indoor air and chamber air by active sampling on TENAX TA sorbent, thermal desorption and gas chromatography using MSD/FID. ISO 16000-6. 2004. ISO, Geneva, Switzerland.

--(ISO). 2006. Indoor air--Part 9: Determination of the emissions of volatile organic compounds from building products and furnishing--Emission test chamber method. ISO 16000-9. ISO, Geneva, Switzerland.

--(ISO). 2006. Indoor air--Part 10: Determination of the

emissions of volatile organic compounds from building products and furnishing--Emission test cell method. ISO 16000-10. ISO, Geneva, Switzerland.

Jann, O., O. Wilke and D. Brodner. 1999. Entwicklung eines Prufverfahrens zur Ermittlung der Emissionen fluchtiger organischer Verbindungen aus beschichteten Holzwerkstoffen und Mobeln. Texte des Umweltbundesamtes Nr. 74. Berlin, pp. 169.

Jarnstrom, H. and K. Saarela. 2003. The development of indoor air quality during the first year in new, residential buildings. Healthy Buildings 2003, Singapore, pp. 259-264.

Makowski, M., M. Ohlmeyer, and D. Meier. 2005. Long-term development of VOC emissions from OSB after hot-pressing. Holzforschung 59(4):519-523.

-- and --. 2006a. Impact of drying temperature and pressing time factor on VOC emissions from OSB made of Scots pine. Holzforschung 60(4):417-422.

-- and --. 2006b. Influences of hot pressing temperature and surface structure on VOC emissions from OSB made of Scots pine. Holzforschung 60(5):533-538.

-- and --. 2006c. Comparison of small and large environmental test chambers for measuring VOC emissions from OSB made of Scots pine (Pinus sylvestris L.). Holz als Roh- und Werkstoff 64(6):469-472.

Maple. 2006. Maple software, Version 10.02. Waterloo Maple Inc. 1981-2005.

Natureplus. 2003. Vergaberichtlinie 0203--OSB-Platten fur das Bauwesen (Guidelines for OSB). natureplus e.V, // Accessed June 2005.

RAL-UZ. 1999. RAL-UZ 38: Grundlage fur Umweltzeichenvergabe. Emissionsarme Produkte aus Holz und Holzwerkstoffen (Standard procedure for low-emission products made of wood and wood-based materials), // Accessed June 2005.

Salthammer, T., C. Boehme, B. Meyer, and N. Siwinski. 2003. Release of primary compounds and reaction products from oriented strand board (OSB). Healthy Buildings 2003, Singapore, pp. 160-165.

Zhu, J.P., J.S. Zhang, and C.Y. Shaw. 2001. Comparison of models for describing measured VOC emissions from wood-based panels under dynamic chamber test condition. Chemosphere 44(5):1253-1257.

Martin Ohlmeyer Mathias Makowski Harald Fried Joachim Hasch Michael Scholer

The authors are, respectively, Senior Researcher, Federal Research Centre for Forestry and Forest Products (BFH), Inst, for Wood Physics and Mechanical Technology of Wood, Hamburg, Germany (; Analyst, Poyry Forest Industry Consulting Ltd., Surrey, United Kingdom (; Area Sales Manager, Pallmann Maschinenfabrik GmbH and Co. KG, Zweibrucken, Germany (; Chief Technology Officer, Swiss Krono-Group, Zary, Poland (; and Head of Division RandD, G. Siempelkamp GmbH and Co. KG, Krefeld, Germany ( The authors gratefully acknowledge FNR e.V. (Fachagentur furNachwachsende Rohstoffe) for financial support. This paper was received for publication in April 2007. Article No. 10348.

[c]Forest Products Society 2008. Forest Prod. J. 58(l/2):65-70.
Table 1.--Properties of the tested panels.

 Panel thickness


Properties and conditions 16 19 26

Press temperature ([degrees]C) 220 220 220
Pressing time factor (s [mm.sup.-1]) 12 12 12
Press time (s) 192 216 300
Density (kg [m.sup.-3]) 692 708 631
Weight (kg) 0.47 0.54 0.74

Table 2.--VOC emissions from OSB of different thicknesses.

 Chamber air concentration
 ([micro]g [m.sup.-3]) after

 3 days 14 days

 Panel thickness (mm)

Compounds 16 19 26 16 19 26

 [alpha]-Pinene 1654 1999 3032 323 364 710
 Camphene 23 28 53 3 3 10
 [beta]-Pinene 413 652 896 60 92 199
 Myrcene 40 52 65 8 10 19
 [DELTA]3-Carene 1270 1574 1790 282 392 629
 Limonene 56 58 63 17 17 28
 Terpinolene 28 30 28 1 ND 1
 Cymenes 34 41 49 13 18 30
 Other terpenes 61 76 99 9 10 15

 Butanal 7 7 7 ND ND 12
 Pentanal 12 14 21 53 54 46
 Hexanal 123 131 148 398 413 319
 Heptanal 1 1 2 4 3 3
 Benzaldehyde 8 9 10 7 7 5
 Octanal 1 1 2 3 3 2
 Nonanal 1 1 2 2 2 1
 2-Octanal 4 6 10 15 16 10
 2-Heptanal ND ND 7 9 9 7
 2-Decenal ND ND ND 1 1 ND

 2-Heptanone ND ND ND 1 1 1
 1-Pentanal 1 1 3 5 5 6
 1-Octen-3-ol ND ND ND 4 4 3
 Octane ND ND ND 2 2 3
 Pentyloxirane ND ND ND 3 3 2
 Pentylfurane 3 3 1 ND ND ND
 Acetic acid 55 62 65 ND ND ND
 Hexanoic acid ND ND ND ND ND ND
[SIGMA]Terpenes 3579 4510 6075 716 906 1641
[SIGMA]Aldehydes 157 170 209 492 508 405
[SIGMA]VOC 3795 4746 6353 1223 1429 2061

 Chamber air concentration
 ([micro]g [m.sup.-3]) after

 28 days 111 days

 Panel thickness (mm)

Compounds 16 19 26 16 19 26

 [alpha]-Pinene 178 206 327 36 36 49
 Camphene 2 2 3 ND ND ND
 [beta]-Pinene 27 40 70 5 6 8
 Myrcene 4 5 9 1 1 1
 [DELTA]3-Carene 131 198 290 29 38 43
 Limonene 7 7 13 1 1 1
 Terpinolene l 1 1 1 1 1
 Cymenes 5 8 21 2 1 3
 Other terpenes 4 6 6 1 2 2

 Butanal 12 19 13 2 4 3
 Pentanal 51 55 46 33 38 48
 Hexanal 385 405 310 181 204 247
 Heptanal 4 4 2 6 7 6
 Benzaldehyde 6 6 4 4 4 2
 Octanal 4 5 3 7 7 5
 Nonanal 2 2 1 5 5 3
 2-Octanal 11 12 7 5 5 4
 2-Heptanal 7 7 4 3 3 2
 2-Decenal 1 1 ND ND ND ND

 2-Heptanone 1 1 1 2 2 2
 1-Pentanal 5 5 5 ND 1 ND
 1-Octen-3-ol 2 2 1 ND ND ND
 Octane 1 1 2 ND ND ND
 Pentyloxirane 2 2 2 ND 1 1
 Pentylfurane 1 2 3 2 2 2
 Acetic acid ND ND ND 62 46 45
 Hexanoic acid ND ND ND 22 24 23
[SIGMA]Terpenes 359 473 740 76 86 108
[SIGMA]Aldehydes 483 516 390 246 277 320
[SIGMA]VOC 854 1002 1144 410 439 501

ND = not detectable

Table 3.--Calculated area-specific emission parameters from OSB of
different thicknesses using a double exponential model.


Thickness ([micro]g [m.sup.-2]
(mm) [h.sup.-1]) [k.sub.1] ([h.sup.-1])

16 5903 0.0093
19 7470 0.0096
26 8166 0.0074


Thickness ([micro]g [m.sup.-2]
(mm) [h.sup.-1]) [k.sub.2] ([h.sup.1]) RMSD

16 589 0.0008 13.2
19 802 0.0009 13.1
26 1371 0.0010 26.6

Table 4.--Calculated area-specific emission parameters from OSB of
different thicknesses using a first order model.

Thickness ([micro]g [m.sup-2]
(MM) [h.sup.-1]) k ([h.sup.-1]) RMSD

16 5068 0.005 133.4
19 6360 0.005 165.2
26 8030 0.004 188.3
COPYRIGHT 2008 Forest Products Society
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2008 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ohlmeyer, Martin; Makowski, Mathias; Fried, Harald; Hasch, Joachim; Scholer, Michael
Publication:Forest Products Journal
Geographic Code:1USA
Date:Jan 1, 2008
Previous Article:Effects of furnish parameters on concentrated static load (CSL) and related properties of oriented strandboard.
Next Article:Evaluation of surface roughness of laminated veneer lumber (LVL) made from beech veneers treated with various fire retardants and dried at different...

Terms of use | Copyright © 2017 Farlex, Inc. | Feedback | For webmasters