Bayesian Modeling of Volatile Organic Compound Emissions from Three Softwoods in Hokkaido, Japan Masaki Suzuki*
Total Page：16
File Type：pdf, Size：1020Kb
Suzuki J Wood Sci (2019) 65:8 https://doi.org/10.1186/s10086-019-1790-8 Journal of Wood Science ORIGINAL ARTICLE Open Access Bayesian modeling of volatile organic compound emissions from three softwoods in Hokkaido, Japan Masaki Suzuki* Abstract We used a small chamber method to examine volatile organic compound (VOC) emissions such as α-pinene, β-pinene, and limonene, from three softwood species in Hokkaido, northern Japan. Tests were conducted for 4 weeks to investigate how the rate of VOC emission changed over time. All VOC emission rates rapidly decreased and could be explained by the sum of two exponential functions. The model was rewritten as a hierarchical Bayesian model to estimate the change in emission rates over time to estimate both intraspecies and interspecies variations. The Markov chain Monte Carlo method was then used to estimate the parameters. Posterior distributions over time were also pre- dicted for VOC emission rates. Then VOC concentrations were simulated using the estimated posterior distributions for a typical room size (30 m3). Our results suggest that high VOC concentrations would shortly occur after installa- tion of wood furnishings, even with adequate ventilation, and that those peak values would exhibit large variations. However, the variations would decrease over time to species-specifc VOC concentrations. The model incorporated rather simplifed assumptions due to small amount of data used. More intense investigations are needed to gain more accurate and objective predictions. Keywords: Volatile organic compounds, Hierarchical Bayesian modeling, Markov chain Monte Carlo methods Introduction α-pinene [7] and limonene [8] has been shown to cause Guidelines and standards for indoor air concentra- sensory irritation in mice. Ozone–terpene reactions form tions of volatile organic compounds (VOCs) have been potentially toxic air pollutants [9]. An indoor air quality established in many countries following concerns about survey of 14 houses in Tokyo (1995–1998) showed that indoor air quality. Wood composite materials are iden- the median concentrations were 15.1 μg/m3 for α-pinene tifed as a potential source of indoor air pollutants, and and 10.0 μg/m3 for limonene [10]. A more recent sur- their emission behaviors have been thoroughly inves- vey (2007–2008), conducted by the same survey team in tigated [1, 2]. We had derived a regression model to 13 diferent houses, found that the median concentra- evaluate acetaldehyde emissions over time from wood- tions were 214 μg/m3 for α-pinene and 53.2 μg/m3 for based materials [3]. Solid wood emits terpenes such as limonene, with a maximum of 3140 μg/m3 for limonene. α-pinene, β-pinene, and limonene [4]. Some researchers Te surveying authors suggested that an increased use of have reported that inhalation of wood oils containing ter- solid woods instead of wood composites or use of alter- penes promotes relaxation [5]. Suzuki et al. [6] revealed native solvents instead of regulated solvents is respon- that α-pinene reduced fatigue in car drivers. However, sible for the higher indoor concentrations of α-pinene inhaling large concentrations of terpenes in dwellings and limonene in homes [11]. Furthermore, to encour- may be unhealthy. Exposure to high concentrations of age the consumption of domestic plantation softwoods, the Japanese government has been recently promoting an increased use of wood furnishings in public build- *Correspondence: [email protected] Forest Products Research Institute, Hokkaido Research Organization, 1-10 ings. Terefore, solid softwood furnishings are expected Nishikagura, Asahikawa, Hokkaido 071-0198, Japan © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Suzuki J Wood Sci (2019) 65:8 Page 2 of 9 to become more widespread in both public facilities and Te specifc drying conditions applied to each species to private living spaces. obtain a fnal moisture content of 15%. Terefore, todo- Most previous studies have focused on the emission matsu samples were dried at a maximum temperature of characteristics of adhesive-derived substances, such as 80 °C for 6 days, the karamatsu samples were dried at a formaldehyde from wood composite materials, which maximum temperature of 85 °C for 5 days, and the aka- are highly industrialized products. In contrast, the emis- ezomatsu samples were dried at a maximum temperature sion behavior of solid softwoods used as furnishings has of 80 °C for 6 days. After drying, the sawn samples were not been thoroughly investigated. Solid wood has a com- planed to a thickness of 22 mm and then conditioned plex tissue structure compared with wood composites. for at least 2 weeks under a relative humidity of 50% at In fact, emission properties of solid wood might widely 20 °C. We then planed both surfaces of each sample to vary, both within and among tree species. Better insight an additional 1 mm to obtain fresh emission surfaces and into VOC concentrations in newly constructed build- to remove any contamination that might have collected ings could be obtained with more detailed data on VOC on the wood during the conditioning process. Tese emissions of softwoods over time. Such data would also remaining 20 mm sections were cut into dimensions of be useful for isolating the source(s) of terpene concentra- 160 mm × 160 mm × 20 mm. When preparing sections, tions in buildings (i.e., whether they originate from wood we carefully avoided large knots (> 10 mm in diameter), or from transient sources, such as consumer products bark pockets, resin pockets, and reaction wood. After this that contain terpenes as a favoring agent or solvent). fnal preparation of the samples, the samples were tested In this study, we examined emissions over time of immediately (within 1 h) for their emissions properties. α-pinene, β-pinene, limonene, and total volatile organic compounds (TVOC) emanating from three common Small chamber method and industrially important softwood species grown in We measured emissions in small (20 L), stainless steel, Hokkaido, northern Japan. We employed Bayesian hier- cylindrical chambers in accordance with protocol estab- archical modeling to develop robust emission-over-time lished by Japanese Industrial Standard A 1901 [12] and models that could quantify intraspecies and interspecies prior research on gaseous emissions from solid wood variations in emissions. Bayesian modeling describes [13]. We placed the entire measurement system, includ- parameters as probability distributions. Tis approach ing the small chambers, into a thermostatically controlled can describe arbitrary probability distributions and thus room maintained at 28 °C. Clean air of 50% relative can be efcient when skewed distributions are expected, humidity was supplied to each small chamber using an such as the logarithmic normal distributions that are air supplier (AOE3200, GL Sciences, Tokyo, Japan) at an often observed in measurements of chemical concentra- air exchange rate (air volume ventilated per hour divided tions. Hierarchical modeling has a multi-level structure. by volume of the chamber) of 0.5 h−1. Sample wood In this study, we investigated a two-layer model: one layer pieces were inserted to a sealed stainless steel box with describing emission behaviors common to all species, a window of 147 mm × 147 mm to determine the surface using entire data to avoid species-specifc overftting, area of emission and to prevent emissions from cross sec- the other layer describing species-specifc parameters to tions and the back side. A pair of boxed sample pieces model variations in individual species using data from was placed into each chamber to obtain a loading factor individual species. We used the Markov chain Monte (emission surface area divided by chamber volume) of Carlo (MCMC) method to obtain model parameters. Te 2.2 m2/m3. Two replicate measurements were made for Bayesian model and the MCMC method provided both each log sample. Air sampling was performed on 1, 3, 7, point estimates for model for those parameters. To dem- 14, and 28 days after placing the test pieces into the test onstrate the advantages of using Bayesian predictions to chambers. However, some sampling had to be postponed explain variation, we conducted indoor air concentration for 1–2 days due to instrumental shortcomings, such as simulations using the distributions that we derived from gas chromatography (GC) instrument failure and sam- the model. pling tube shortage. Materials and methods Sampling and analysis Materials We used a GC to quantify concentrations of α-pinene, We collected fve logs of todomatsu (Abies sachalinensis), β-pinene, limonene, and TVOC emissions. Using an air fve logs of karamatsu (Larix kaempferi), and two logs of sampling pump (SP208-100 dual, GL Sciences), the air from akaezomatsu (Picea glehnii) from commercial forest plan- chambers was passed through a sampling tube (AERO TD tations; all sample pieces were prepared from heartwood. GL-Tube, GL sciences) packed with 0.1 g of Tenax TA Te logs were sawn to 30 mm in thickness and kiln-dried. adsorbent resin (20/35 mesh, GL sciences). We sampled Suzuki J Wood Sci (2019) 65:8 Page 3 of 9 1000 mL of air at a rate of 33 mL/min or 300 mL at 10 mL/ where R1 is the emission rate during Phase 1, R2 is the min to avoid the exceeding capacity of the absorbent. We emission rate for Phase 2, R01 and R02 are the initial emis- added 100 ng of toluene-d8 (41063-96, Kanto Chemical, sion rates, and k1 and k2 are the coefcients that account Tokyo, Japan) to each sampling tube as an internal stand- for the decrease in emission concentrations during ard.