Equilibrium moisture content map for Laos

Phonetip, K., Souvansai, D., and Belleville, B. (2022). "Equilibrium moisture content map for Laos," BioResources 17(2), 2428-2442.

Abstract

Providing the equilibrium moisture content values for territories is important for avoiding major problems due to dimensional changes in wooden products. This study evaluated the average equilibrium moisture content values for twelve months (from 1981 to 2020) for Laos and produced an equilibrium moisture content map. A model was used to generate the equilibrium moisture content values based on temperature and relative humidity data obtained from the POWER Data Access Viewer v2.0.0. for 18 provinces (148 districts) based on the geographic coordinates of each district in Laos. The lowest equilibrium moisture content (12% to 13%) values were found in Vientiane Capital City, Vientiane province, Louangprabang, Bokeo, Attapeu, Louang Namtha, Oudomxay, Houaphan, Sayaburi, Savannakhet, Salavan, and Champasak province. The rest of the districts, which had an equilibrium moisture content ranging from 13% to 16%, were mostly found on the eastern part of Laos and a small part of Northern Laos. The monthly swinging average value of the equilibrium moisture content ranged from 10% to 18% across the country. The equilibrium moisture content values for each district in Laos were also defined.

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Equilibrium Moisture Content Map for Laos

Khamtan Phonetip,^a,* Duangphachan Souvansai,^a and Benoit Belleville ^b

DOI: 10.15376/biores.17.2.2428-2442

Keywords: Equilibrium moisture content; Timber moisture content; EMC-Laos map

Contact information: a: Faculty of Forest Sciences, National University of Laos, Vientiane Capital, Lao PDR; b: Faculty of Science, School of Ecosystem and Forest Sciences, The University of Melbourne, Burnley campus, 500 Yarra Boulevard, Richmond VIC 3121 Australia;

* Corresponding author: khamtanfof@gmail.com

INTRODUCTION

The utilization of timber must consider its moisture content (MC) after the timber has been converted to wooden products; it is important to ensure the appropriate moisture content level in the utilized wood has been recommended. Thus, the desired quality of the wood product is met.

As wood absorbs or releases water vapor from the ambient air around it, not only does its MC change, but its dimensions also change. Different species will have different moisture change tolerances based on their own structural make-up (Redman et al. 2016). All wood must be allowed to come to a state of balance with its environment in order to prevent moisture-related damage. This state of balance is called the equilibrium moisture content (EMC) (Waterson 1997). This model was developed by Hailwood and Horrobin (1946).

The most important requirement for most wood products is that “green” or fresh cut wood must be dried before it can be used effectively. Drying makes the final dimensions more predictable and the wood easier to work with. Drying also increases the strength properties of the wood, allows it to bond more fully with adhesives, and even lowers the overall weight for shipping, storing, and handling. For any wood product or project, there is an optimal moisture balance that allows the wood to be machined well, function well, and maintain its natural beauty (Simpson 1991; Simpson 1998).

Wood, like many natural substances, is considered a hygroscopic material; it naturally absorbs and releases water vapor from and into the air around it (Phonetip et al. 2018a). For example, when green lumber is dried, the cellular pathways that served to transport water from the ground to the leaves of the tree remain in place, like tiny tunnels through the wood, to take in additional moisture when the air around it has high humidity or release moisture into drier air. Therefore, the amount of moisture in any particular wood stock or wood product is entirely dependent on the ambient conditions of the air around it. If the humidity is high, the wood will take in moisture; if the environment is very dry, it will release moisture. In uncontrolled environments, these changes can represent potentially damaging conditions for wood, as its natural interaction with its environment can mean rapid or extreme shifts in the moisture content of the wood.

Even in a relatively stable environment, wood is still subject to seasonal movement as humidity levels and temperatures change with the weather. For instance, EMC oscillation between January and December in Vientiane, Laos varies from 10% to 16% (Phonetip et al. 2018b). Controlled indoor environments with functioning heating and air conditioning can help minimize these changes, but they still can present small changes, e.g., the door that sticks in winter or small gaps between the floorboards in summer. For instance, allowing the wood flooring to reach equilibrium before installation and verifying the MC of the wood with an accurate wood moisture meter is the best way to ensure the balance and harmony that EMC can bring to a beautifully satisfying and durable wood floor.

Many countries have available an EMC map based on geography and climate, so the consumers and producers understand the required level of moisture content for the specific locations. For instance, the EMC zones for Australia (Waterson 1997) and the United States of America (Simpson 1998) have been identified. The EMC data helps producers to carefully consider the level of required environment where the wood products are to be placed based on the oscillation of ambient temperature and relative humidity. The EMC data govern the final moisture content in wood products so that the wood products can minimize dimensional changes. The physical properties of wood and wood anatomical structure are main factors that affect the drying rate to get the green wood to reach the equilibrium (Redman et al. 2016). However, the state of removing moisture in wood have been considered in two periods, i.e., when wood has higher MC than the fiber saturation point (FSP). For example, at 25%, this period the drying rate is faster than when the wood is getting lower than FSP (Phonetip et al. 2018a). Given the fact of the oscillation of EMC through months would help authors to continue further experiments on wood species if they are produced and placed in such conditions, then future researchers would be able to provide recommendation for a better solution to avoid dimensional changes of wood products

However, an EMC map for Laos has not yet been available. To avoid major problems due to dimensional changes in wooden products, the correct final moisture content after drying should be identified based on the particular geography and climatic conditions where the wooden products are going to be placed. This study aims to produce an EMC map for all the territories in Laos and to plot the oscillation of the average EMC that varies throughout the year, i.e., from January until December.

EXPERIMENTAL

A HOBO MX Temp/RH Data Logger (MX1101, Onset Computer Corporation, Bourne, MA) recorded and transmitted the temperature and relative humidity (RH) of the indoor environments with its integrated sensors. This Bluetooth^® low energy-enabled logger is designed for wireless communication with a phone, tablet, or computer using the HOBOconnect^® app. The accuracy for the temperature is ± 0.21 °C from 0 °C to 50 °C and the accuracy for the RH is ± 2% when the RH from 20% to 80%; if the RH is below 20% and above 80% the accuracy is ±6% (Onset Computer Corporation 2021).

The temperature and relative humidity data were obtained from the POWER Data Access Viewer v2.0.0 (NASA 2021) for the geographical locations of interest. This data set was then validated with on-site observation using the HOBO MX Temp/RH Data Logger then plotting a linear regression model to compare the mean temperature and relative humidity from May 2021 to July 2021 at the same geography (Vientiane, Laos (18°02’29.5″ N and 102°37’51.9″ E). The corrections of the EMC values based on the NASA hourly temperature and relative humidity were performed by calculating the absolute difference in the values (∆T) between the temperature of the measured data and temperature that obtained from NASA, as show in Eq. 1,

(1)

where T_mi is the temperature at a specific time (hour) in measured on-site and T_NSi is the temperature measured by NASA (POWER Data Access Viewer v2.0.0). The value of the absolute difference in the values (∆RH) of the relative humidity of the measured data and relative humidity that obtained from NASA, were calculated using Eq. 2,

(2)

where RH_mi is the relative humidity at a specific time (hour) in measured on-site and RH_NSi is the relative humidity measured by NASA (POWER Data Access Viewer v2.0.0).

This method was introduced by Phonetip et al. (2018b). This study used the temperature and relative humidity at 2 m above the ground, based on the available time extent, i.e., from 1981 until 2020 (39 years).

The EMC value was calculated using Eq. 3,

(3)

where h is the relative humidity (%/100); the constant parameters are as follows: W is the temperature dependent coefficient; and k, k₁, and k₂ are dependent on temperature (T, °C), as shown in Eqs. 4 through 7,

(4)

(5)

(6)

(7)

which was developed by (Hailwood and Horrobin 1946).

The EMC values present the range of the means using the ArcGIS modeling process to produce an EMC map for 148 districts at different geographical locations at the district centers for all 18 provinces.

The ArcMap application (Version 10.6, Esri, Redlands, CA) was used to produce a range of EMC maps. The inverse distance weighted (IDW) is held as one of the most common techniques for interpolating (Chen and Liu 2012; Yang et al. 2020). It is used to forecast values for any unmet location by calculating the expected position of the surrounding values. It is a deterministic process type interpolating method with a known distributed collection of samples. The values assigned to the unknown samples are determined by the weighted average of the identified points available (Lam 1983). In this study, the IDW models adopted cross-validation as an appropriate method to assess the accuracy of spatial interpolated EMC data for the center locations of 148 districts that were selected for a cross-validation process.

RESULTS AND DISCUSSION

Temperature And Relative Humidity

The temperature oscillations between the on-site measured data and the data obtained from POWER Data Access Viewer v2.0.0 are presented in Fig. 1a.

Fig. 1. Daily temperature oscillation for May, June and July in Vientiane, Laos (a) and the relationship between the measured temperature on-site and the NASA temperature data (b)

It is shown that the predicted temperature oscillation (according to POWER Data Access Viewer v2.0.0) values were lower than the values of the on-site measured data for the available data of May (21 days), June (28 days), and July (4 days) of 2021. The measured temperature and temperature predicted by POWER Data Access Viewer v2.0.0 were then plotted (as shown in Fig. 1b). A promising confidence was found, i.e., R²= 0.77; this value was 0.03 higher than predicted by the POWER Data Access Viewer for solar radiation (White et al. 2011). However, the corrected temperature is required to be fixed via calculation using Eq. 1. The mean absolute value of difference was found to be 2.7 °C. This value was then added to the temperature measured by POWER Data Access Viewer v2.0.0 at the geography locations of interest.

Fig. 2. Relative humidity oscillation for May, June, and July 2021 (a) and the relationship between the measured relative humidity on-site and the NASA relative humidity data (b)

The relative humidity oscillation between the on-site measured data and the data obtained from POWER Data Access Viewer v2.0.0 are presented in Fig. 2a. It is shown that the predicted relative humidity oscillation (according to POWER Data Access Viewer v2.0.0) values were lower than the values of the on-site measured data of May, June, and July of 2021. The measured relative humidity and relative humidity predicted by POWER Data Access Viewer v2.0.0 were then plotted (as shown in Fig. 2b). The promising confidence was relatively high (R² = 0.74). However, a corrected relative humidity is required to be fixed via calculation using Eq. 2. The mean absolute value of the difference was found to be 1.9%. This value was then deducted from the relative humidity measured by POWER Data Access Viewer v2.0.0 at the geography locations of interest.

Fig. 3. The recommended average moisture contents for interior use wood products in different parts of Laos based on NASA (2021) with EMC correction

Equilibrium Moisture Content Map for Laos

After the temperature and relative humidity values were corrected for the measurement of the POWER Data Access Viewer v2.0.0, the EMC values were then calculated using Eq. 3. The results of the average EMC values for entire districts of Laos are presented in Fig. 3. The mean EMC for the entirety of Laos was 14.2% ± 2.5%. Four districts out of 148 districts had the highest EMC values, i.e., 16.1% to 16.7%. These locations included the districts that lie to the East of Borikhamxay province (Saychampone district), Savannakhet province (Vilabury district), Salavan province (Samuoi district), and Xekong province (Dakchueng district).

The lowest EMC (12% to 13%) values were found in Vientiane, Vientiane province, Louangprabang Bokeo province, Attapeu province, LouangNamtha province (Long, Viengphoukha districts), Oudomxay (Hoon, Xay, Beng, Pakbeng and Nga districts), Houaphan province (Huim and Sone districts), Sayaburi province (Sayaburi, Paklai and Botene districts), Savannakhet province (Songkhone and Thapangthong district), Salavan province (Khongxedone, Vapy, Lao Ngam, Thateng and Lakhonepheng district), and Champasak province (except Paksong and Pathoumphone districts). The rest of the districts had an EMC value ranging from 13% to 16%.

Monthly Equilibrium Moisture Content in Laos

Figure 4 shows the oscillation of the average EMC values with the standard deviation from January until December. It indicated that the lowest EMC was in March (10.4% ± 1.2%), while the maximum EMC was in August (17.6% ± 0.9%). The results also clearly demonstrated the EMC values differ during the dry and wet seasons. For instance, the heavy rain starts from May until October (Phonetip 2018); this period has high humidity and low temperature in the region. The overall mean EMC for Laos is 14.2% ± 2.5%. However, producing high quality wooden products means avoiding timber defects from cracking and distortion that could be affected by different geographical locations with climates. Thus, the map of the EMC values provided in Fig. 3 is the comprehensive option for considering what moisture content level wood needs to be dried to. In addition, Table 1 can help with comprehensive information for the monthly variations in the EMC values for each district in Laos. The EMC range (10.4% to 17.6%) was 1.6% higher than the previous study, which found an EMC range of 10% to 16% for a particular location in Vientiane, Laos (Phonetip et al. 2018b).

Fig 4. The average equilibrium moisture content with standard deviation from January to December in Laos

Table 1. The Equilibrium Moisture Content for Outside Conditions According to Districts in Laos

It is recommended that further study on the dimensional changes of different timber species should be investigated when the timber products are placed in such a wide EMC value range, i.e., 10% to 18%. The results can be a good database for carpenters in production design.

CONCLUSIONS

The mean EMC for the entirety of Laos was 14.2% ± 2.5%. The EMC ranged from 13% to 16%. The high values mostly were associated with the eastern part of Laos. The EMC map is available in Fig. 3.
A monthly swinging average EMC value for the entirety of Laos ranged from 10% to 18%; each district in Laos is available in Table 1.

ACKNOWLEDGMENTS

The authors would like to thank the VALTIP3 project under the Australian Center for International Agricultural Research (ACIAR) for providing funds for this publication. Additional thanks are given to the National Aeronautics and Space Administration (NASA) who made worldwide energy resources publicly available; without this data, this study would have taken years to complete.

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Article submitted: December 20, 2021; Peer review completed: February 26, 2022; Revised version received and accepted: March 2, 2022; Published: March 7, 2022.

DOI: 10.15376/biores.17.2.2428-2442