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Article

Interference Effect of Tree Spacing on Natural Volatile Organic Compound Concentrations Measured Using Passive Samplers

by
Doyun Song
1,
Jae Hyoung Cho
2,
SangTae Lee
3,
Sujin Park
4 and
Geonwoo Kim
5,*
1
Institute of Agriculture & Life Science (IALS), Gyeongsang National University, Jinju 52828, Republic of Korea
2
Forest Disaster and Environmental Research Department, National Institute of Forest Science, Seoul 02455, Republic of Korea
3
Forest Technology and Management Research Center, National Institute of Forest Science, Pocheon-si 11186, Republic of Korea
4
Forest Human Service Division, Future Forest Strategy Department, National Institute of Forest Science, Seoul 02455, Republic of Korea
5
Department of Forest Environmental Resources, College of Agriculture and Life Sciences, Gyeongsang National University, Jinju-Daero, Jinju 52828, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2024, 15(8), 1368; https://doi.org/10.3390/f15081368
Submission received: 22 July 2024 / Revised: 1 August 2024 / Accepted: 3 August 2024 / Published: 6 August 2024
(This article belongs to the Special Issue Advances and Future Prospects in Science-Based Forest Therapy)
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Abstract

:
Research highlights: The increasing rates of mental health disorders during the COVID-19 pandemic have popularized the notion of access to natural environments as a solution, leading to a surge in demand for urban green spaces. The concentration of natural volatile organic compounds (NVOCs) in forests, resulting from plant metabolism, plays a crucial role in forest-based healing and ecosystem health. Background and objectives: This study aimed to investigate how tree spacing influences NVOC concentrations within forest ecosystems using passive samplers, thereby enhancing the understanding of optimal forest management practices to promote human health benefits. Methods: We employed passive samplers to investigate tree spacing effects on NVOC concentrations. We placed passive samplers among trees in the study area to measure NVOC concentrations in individual trees and analyzed the relationship between NVOC concentration and tree spacing and structure. Results: A multiple regression analysis using distance decay models showed that a tree spacing of 2.7–3 m had a significant impact on NVOC concentrations. These findings provide a better understanding of how tree structure, tree spacing, and microclimate within the forest influence NVOC concentration. Conclusion: These findings have important implications for forest management and the design of forest landscapes to promote human health and well-being by considering the spatial distribution of NVOC concentrations.

1. Introduction

For centuries, humans have intuitively understood that engagement with nature is beneficial to health [1,2,3]. Furthermore, green spaces have various positive effects on people, including the promotion of relaxation, comfort, and overall health and well-being [4,5,6,7,8,9]. During the COVID-19 pandemic, rates of mental illnesses, such as depression and insomnia, increased remarkably [10,11], with access to natural environments, such as forests and urban green spaces, being recognized as effective solutions [12,13,14,15]. Consequently, the public demand for access to these green spaces has increased significantly [16], thereby leading to the development of more urban green spaces. However, in expanding the overall area of green space, the systematic consideration of the optimal design and configuration of these spaces to promote health benefits and human utilization has not been sufficiently addressed.
The concentration of natural volatile organic compounds (NVOCs) in plants and trees may influence the therapeutic benefits of green spaces and forests [17,18]. NVOCs are major components of forest ecosystems. They are produced by plant metabolism and are released into the forest air through various processes [19]. NVOCs function as environmental elements in forests and have significant positive effects on human health and ecosystems from the perspective of forest-based healing [4,17,20,21,22]. The representative health-promoting effects of NVOCs include anti-bacterial and anti-allergic effects, stress reduction, and depression relief [6,23,24]. In traditional forest management, trees need to be spaced appropriately for optimal growth [25,26]. Hence, the efficient management of these forest-based healing factors, such as NVOC concentration, also requires the consideration of appropriate inter-tree distances. Appropriate inter-tree spacing can help optimize the spatial distribution and concentration of NVOCs within the forest ecosystem, which may influence the therapeutic benefits of these compounds for human health and well-being.
Various studies have been conducted worldwide examining NVOCs [23,24,27]. Previous research has primarily used an air collection mini-pump to extract samples in an adsorption tube, followed by the derivation of phytoncide concentration via gas chromatography–mass spectrometry (GC-MS) analysis [28,29]. However, these methods are limited as they can only measure the NVOC concentration at a specific point in time and location, rendering it difficult to comprehensively understand the overall NVOC distribution pattern within the forest. Moreover, measuring NVOCs using mini-pumps and GC-MS is laborious, requiring experienced professionals to be constantly present in the forest to replace sorbent tubes every hour. Furthermore, the need for expensive measuring instruments hinders the ability to conduct widespread and concurrent measurements. This makes it challenging to comprehensively assess NVOC concentration variations within a study area.
NVOC concentrations can vary widely depending on factors such as microclimate conditions, the surrounding trees in the forest, and tree characteristics [30,31]. NVOC concentrations increase with higher temperature and humidity, but decrease with stronger wind speeds. Additionally, the concentrations of specific NVOCs, such as α-pinene and β-pinene, peak during sunset hours [29,31]. Furthermore, a higher tree density results in elevated NVOC concentrations [32,33]. However, the precise impact of tree spacing on NVOC concentrations is not yet understood due to the constraints of GC-MS. This warrants different approaches to accurately assess the impact of tree spacing on NVOC concentrations.
Passive sampling techniques have mainly been used in industrial settings to measure volatile organic compounds in the air [34,35]. These techniques are frequently used to monitor volatile organic compounds because they are affordable, straightforward, and provide reliable data without requiring active sampling equipment [34,36]. Passive samplers can assess the temporal and spatial variability of ambient air concentrations of volatile compounds in urban areas [37]. Therefore, passive sampling may address the limitations of traditional active sampling methods in forest environments. It facilitates continuous, on-site monitoring of NVOC concentrations across a large area for prolonged periods without requiring power or extensive labor [34,38]. Therefore, it is well suited for studying the distribution patterns of NVOCs in forests.
The primary aim of our research was to assess the effect of tree spacing on NVOC concentrations using passive samplers, providing insights into the spatial distribution of NVOCs within the forest environment.

2. Materials and Methods

2.1. Study Site

This study was conducted in a Seogwipo experimental forest located on Jeju Island, South Korea. The Seogwipo experimental forest spans an altitude of 400 to 1000 m in the central mountainous region near the base of Hallasan Mountain, covering approximately 1550.4 hectares. The forest has a humid subtropical climate (Cfa) with gentle winters and fairly consistent rainfall. The study site is positioned at a height of 550–600 m above sea level. The forest is an 80-year-old, artificially planted, monospecific forest of Chamaecyparis obtuse. Forest thinning operations were carried out in December 2021, and there are currently around 400 trees per hectare. The coordinates of the study site are 33°18′28″ N, 126°32′50″ E (Figure 1). Furthermore, the forested area is far from major roadways and industrial developments, minimizing external pollution influence.

2.2. Data Collection Methods

2.2.1. Forest Environment Investigation

We conducted a thorough examination of the forest to gather data about the research location. The NVOC measurement point and forest survey point were established in 30 m × 65 m quadrants with the forest roads as the boundary. The forest survey items comprised tree species, tree location, crown spread width in four directions, diameter at breast height (DBH), bole height, tree canopy openness, and tree spacing. The inter-tree spacing was calculated using the coordinates of the tree locations. The crown cross-sectional area was calculated based on the crown spread width in four directions. A total of 88 trees were surveyed. Figure 2 displays the forest environment of the study site. Table 1 shows an investigation of the forest environment of the study site.

2.2.2. NVOC Measurement Using Passive Sampler

Passive samplers (Organic Vapor Monitor 3500+, 3M, St. Paul, MN, USA) were placed at a height of 1.5 m on each tree to measure the NVOC concentrations. The trees situated on the perimeter of the study site were omitted to prevent any alterations in the NVOC concentrations because of the proximity to the forest road (considered empty space). A total of 83 passive samplers were installed: 11 on the north side of the trees, corresponding to the vertical midline of the study area, and the remaining 72 on the south side of the trees in the study area. The investigation was conducted during a 20 h period from 16:00 on 29 December 2023 to 12:00 on 30 December 2023. Figure 2b shows the passive sampler setup at the study site.
Samples were collected and analyzed by following the standard IH Sampling Guide (3M. 2022). NVOCs are known to be composed of many different individual components. This study focused specifically on determining their diffusion tendencies. Hence, we targeted only those substances for which accurate diffusion coefficients were available for analysis. Table 2 and Table 3 provides details on the specific NVOC substances that were detected and the corresponding values of the key variables used in the detection process.
The GC–MS analysis of the passive samplers was performed on a gas chromatograph–mass spectrometer (model 7890N-5975; Agilent, Santa Clara, CA, USA), equipped with a thermal desorption device (GC-MSD; Gerstel TDS, Gerstel, Mülheim an der Ruhr, Germany). In this system, the substances adsorbed onto the tube were concentrated using a low-temperature cryofocusing system. This system utilized high-purity helium gas from a thermal desorption device, flowing at a 1 mL/min rate. The gas was desorbed for 3 min at 210 °C, while the cryofocusing maintained a temperature of −30 °C. Subsequently, the compounds underwent a 3 min heating process at 220 °C before being injected into a GC spectrometer and analyzed via mass spectrometry.

2.2.3. Microclimate

To understand how microclimate conditions can influence NVOC concentrations, physical features of the site environment were recorded at 5 min intervals using a portable multifunction meter (HOBO-U23 V2, Onset Computer Corp., Bourne, MA, USA). A wind- monitoring sensor (wind monitor O5103-45; R. M. Young Company, Traverse City, MI, USA) was used to capture wind direction and velocity data at the designated site. The HOBOware Pro software (Onset, Bourne, MA, USA) was used to analyze the findings. To reduce measurement errors, we excluded data recorded within 10 min before and after each measurement from the study. Table 1 shows the investigation of the microclimate environment of the study site.

2.3. Data Analysis Methods

The analysis was conducted using Python 3.12.3 and the Python packages “matplotlib”, “griddata”, and “statsmodels”. The NVOC concentration data comprised 83 measurements, covering five NVOC compounds (α-pinene, β-pinene, camphene, camphor, and limonene). A total of 11 measurements were recorded on the north side of the tree, and 72 on the south side. The values measured on the north side were not used in other analyses, except to compare the concentrations between the north and south sides.
Using NVOC concentrations from passive sampler and tree survey data, a distribution graph was constructed to display the NVOC concentration values within the forest space. To confirm the influence of the distribution of NVOC concentration due to wind, we analyzed the differences in NVOC concentration measured separately in the north–south direction using a t-test. Previous studies have primarily focused on the relationship between forest structure and NVOC concentration [33]. Consistently, in this study, we conducted multiple regression analyses to examine the correlation between forest structure and the measured NVOC concentrations.
To validate the impact of tree spacing and tree structure on NVOC concentrations, we used multiple regression and distance decay models. Distance decay models are commonly used in spatial ecology research [38,39,40]. Since few NVOC studies have used this method, we examined various approaches, including using the following distance decay models: the negative exponential model, the power law model, and the inverse power model. A multiple regression analysis was performed with NVOC concentration as the dependent variable and tree structural properties (such as diameter and crown width) along with tree spacing as independent variables.

3. Results

Based on the NVOC concentration detected by the passive sampler, we derived the NVOC concentration distribution map (Figure 3). The tree locations are shown as white dots in the lower left corner of Figure 3. The mean NVOC concentration was 0.1858 μg/m3 with a standard deviation of 0.1173 μg/m3. The maximum and minimum values were 0.47 and 0.01 µg/m3, respectively (Table 1).
Due to the limitations of GC-MS in previous studies, these concentration distribution data were obtained for the first time in NVOC investigations. The distribution map indicates that NVOC concentrations varied within the forest space depending on the location of the trees.
The microclimate environment results were used to generate a wind rose diagram. The average wind speed at the surveyed locations was 1.08 m/s with a standard deviation of 0.95 m/s (Table 1 and Figure 3). The predominant wind direction was north, ranging from north–northeast to north–northwest. The percentage of windless time was 23%. Wind direction is a major factor affecting scent dispersion [41]. We hypothesized that the predominant northerly wind direction would lead to differences in NVOC concentrations between the south and north sides of the trees. To confirm this, a t-test was performed between the north and south NVOC measurements (Table 4). The mean NVOC concentration measured in the north was 0.1559 μg/m3, whereas that in the south was 0.1142 μg/m3. A statistical analysis using a t-test showed a t-statistic of 0.9175 and a p-value of 0.3804 (Table 4). While the mean value was higher in the north, no significant difference was noted between the two locations. Hence, despite the predominance of northerly winds during the measurement period, the NVOC concentrations measured from the north and south directions were not significantly different. This result contradicts our initial hypothesis that wind direction would exhibit the greatest impact on NVOC concentration in forests.
We also conducted a stepwise multiple regression analysis to examine the correlation between tree structure and NVOC concentration. The dependent variable was NVOC concentration, and the independent variables were crown cross-sectional area, DBH, bole height, and canopy openness. The results of the stepwise multiple regression are shown in Table 5. The tree structure variables did not significantly contribute to the variance in the NVOC concentrations. Specifically, in Step 1, none of the forest structure variables were significant (p < 0.05). In Step 2, DBH became significant (p < 0.05), whereas crown width remained nonsignificant. In Step 3, when only DBH was included, it was no longer significant (p < 0.05). Furthermore, the overall model fit was consistently low across all steps. These results suggest that the tree structure variables, such as crown spread width, DBH, bole height, and canopy openness, exhibit no consistent relationship with the NVOC concentrations.
As few NVOC studies have used this method of analysis, we used three typical distance decay models, the negative exponential model, power law model, and inverse power model, to analyze the relationship between NVOC concentrations and tree spacing. The results of the distance decay model analysis are shown in Table 6 and Figure 4. We used crown cross-sectional area and DBH as the independent variables in the tree structure analysis. Other tree structure variables, such as bole height and canopy openness, were excluded from the analysis due to high multicollinearity among the predictors. To determine the interference of tree spacing on NVOC concentrations, we restricted the tree spacing as an independent variable. Tree spacing was analyzed in increments of 0.1 m, starting at 2 m and considering the varying distances between the trees. The analysis was conducted up to a separation distance of 60 m at the study site, which was the maximum distance between trees. However, for readability, only the results up to 5 m were recorded.
Table 6 shows the analysis results of the three distance decay models across various tree spacing limits. Figure 4 depicts the graphical results of the model. Across the three distance decay models examined, a consistent pattern emerged when the distance limits between trees varied. In Figure 4, p-values are shown as dashed lines, and a red dashed line corresponding to a p-value of 0.05 has been added to help identify statistical significance. The corresponding range is highlighted in yellow. When the tree spacing was restricted to 2.7–3 m, the statistical significance of the models was confirmed (p < 0.05). The model fit, the adjusted R-squared value, is displayed as a solid line, which indicates how well the model fits the data across different distance limits. The results of this analysis followed a typical pattern, peaking at a certain point and decreasing with an increase in the distance limit. The range 2.7–3 m exhibited statistical significance and a good model fit, suggesting that the impact of tree spacing on NVOC concentrations was most pronounced within this specific range.

4. Discussion

Passive sampling is a new approach to investigating the spatial distribution of NVOCs in forest environments, derived from the work reported in [42,43]. GC-MS analysis, the primary method used in previous studies, is not well suited for long-term measurements (>1 h) and is not suitable for widespread and simultaneous measurements due to its high cost. Using passive samplers to directly measure the NVOC concentrations in each tree allowed us to more accurately assess the diffusion of NVOCs and the impact of tree spacing on NVOC diffusion. Understanding the factors influencing NVOC concentration is important for forest management and enhancing human health benefits in forest environments.
We investigated the influence of tree spacing on NVOC concentrations using passive samplers in a forested environment. We hypothesized that due to the predominant northerly wind direction, the NVOC concentrations measured on the north side of the trees would be higher than those measured on the south side. To test this hypothesis, we compared the NVOC concentrations between the north and south sides of the trees using a t-test. The results showed that the mean NVOC concentration on the north side was 0.1559 mean µg/m3, whereas it was 0.1142 µg/m3 on the south side. As hypothesized, the measured value in the north was high but was not statistically significant compared with that in the south side. Hence, this hypothesis was rejected. Next, we conducted a stepwise multiple regression analysis to investigate whether tree structure variables, such as crown cross-sectional area, DBH, bole height, and canopy openness, were significantly associated with NVOC concentrations. The results revealed that across all steps, these variables were not statistically significantly correlated with the NVOC concentrations.
Previous studies have primarily focused on the relationship between forest structure and NVOC concentration [29,30,31]. Therefore, to investigate the impact of tree spacing and structure on NVOC concentrations, we employed distance decay models that are commonly used in spatial ecology research [38]. Analyzing three distance decay models revealed that the impact of tree spacing on NVOC concentrations was statistically significant within the range of 2.7–3 m. This suggests that the interference effect was more pronounced with closer tree spacing. The interference effect diminished as the tree spacing increased, with NVOC concentrations becoming less dependent on tree spacing.
This study is limited by its relatively small area compared with that used in VOC studies in the field of scent and odor research. Hence, we attempted to incorporate various analytical methods to gain deeper insights. The results of this study represent a case study focused on a single site and may not be directly applicable to all forest environments. However, unlike previous studies, we successfully characterized the spatial variation in the distribution of NVOC concentrations at the study site, highlighting how NVOC distribution varies with tree placement.
Understanding the factors influencing NVOC concentration is important for forest management and realizing human health benefits in forest environments. The results of this study can provide a deeper understanding of the complex relationships between tree structure and spacing and NVOC concentrations within the forest ecosystem. The insights obtained from this study can inform the design of forest environments optimized for healing and therapeutic purposes. They can also help guide the management of forests which are close to human habitation, such as urban forests and parks, in order to create landscapes that promote human health and well-being. By considering the spatial distribution and influential factors of NVOC concentrations, green spaces can be designed and managed to maximize the potential health benefits provided for the visitors and nearby residents.

5. Conclusions

This study used passive samplers to directly measure the NVOC concentrations of individual trees, providing a more comprehensive understanding of the spatial variability and impact of tree spacing on NVOC concentrations within a forest environment. The study examined the NVOC concentrations in relation to wind direction and the influence of tree structure variables, such as crown cross-sectional area, DBH, bole height, and canopy openness, on NVOC concentrations. The results revealed that NVOC concentrations are significantly influenced by tree spacing and structure, with the most pronounced impact observed at 2.7–3 m. These findings have important implications for forest management and the design of forest landscapes to promote human health and well-being by considering the spatial distribution of NVOC concentrations. This study was conducted in a stand-alone experimental forest. It is necessary to conduct future studies not only in such experimental forests, but also in other types of forests, such as urban forests. The incidence and diversity of human infectious disease outbreaks have increased significantly in the last 40 years [44], and researchers predict a continued increase in pandemic frequency in the future [45]. This trend highlights the need to develop and maintain high-quality green spaces that can serve as recreational and health-promoting environments for the public. An understanding of how factors such as tree spacing and structure influence NVOC concentrations can inform the development of forest and park environments that improve human health and well-being. The methods and results presented in this study provide a foundation for future research on NVOC concentration distribution patterns in a more diverse range of forest environments, contributing to the development of green spaces, such as urban healing forests, which optimize human health and environmental sustainability.

Author Contributions

Conceptualization, G.K.; Methodology, G.K.; Software, D.S. and G.K.; Validation, G.K.; Formal analysis, D.S., J.H.C., S.P. and G.K.; Investigation, D.S., J.H.C., S.L., S.P. and G.K.; Resources, G.K.; Data curation, G.K.; Writing—original draft, D.S.; Writing—review & editing, D.S., J.H.C., S.P. and G.K.; Visualization, G.K.; Supervision, G.K.; Project administration, G.K.; Funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study (GNU-2024-240044) was supported by the research grant of the new professor of Gyeongsang National University in 2024.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 15 01368 g001
Figure 1. Study site (33°18′28″ N, 126°32′50″ E).
Figure 1. Study site (33°18′28″ N, 126°32′50″ E).
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Forests 15 01368 g002
Figure 2. Images of the study site: (a) forest environment; (b) NVOC measurement using passive sampler.
Figure 2. Images of the study site: (a) forest environment; (b) NVOC measurement using passive sampler.
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Forests 15 01368 g003
Figure 3. NVOC concentration distribution with wind directions and tree location.
Figure 3. NVOC concentration distribution with wind directions and tree location.
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Forests 15 01368 g004aForests 15 01368 g004b
Figure 4. Distance decay model analysis with tree spacing limits: (a) negative exponential model; (b) power law model; (c) inverse power model.
Figure 4. Distance decay model analysis with tree spacing limits: (a) negative exponential model; (b) power law model; (c) inverse power model.
Forests 15 01368 g004aForests 15 01368 g004b
Table 1. Investigation of the forest environment, microclimate, and NVOC measurements.
Table 1. Investigation of the forest environment, microclimate, and NVOC measurements.
Forest EnvironmentMicroclimate EnvironmentsNVOC
(μg/m3)
DBH
(m)		Bole Height
(m)		Canopy Openness
(%)		Crown Cross Sectional Area
(m2)		Temperature
(°C)		Relative Humidity
(%)		Wind Speed
(m/s)
Mean0.4010.4050.1937.025.7781.971.080.1858
SD0.093.4916.7225.422.904.560.950.1173
n = 66.
Table 2. Desorption efficiency and sampling rates of selected NVOCs.
Table 2. Desorption efficiency and sampling rates of selected NVOCs.
CASSampling Rate
(mL/min)		Desorption Efficiency
(%)		Molecular Mass
(g/mole)
α-pinene80-56-87.8698136.23
β-pinene127-91-37.8697136.23
camphene79-92-557.2100136.24
camphor76-22-26.4395152.23
limonene5989-27-57.8699136.23
Table 3. Operating parameter conditions for NVOC detection.
Table 3. Operating parameter conditions for NVOC detection.
ParametersConditions
ColumnHP-INNOWAX (60 m × 0.25 mm I.D × 0.25 μm, film thickness)
Carrier gas flowHelium at 1 mL/min
Injection modePulsed splitless
Injection port temp.210 °C
Transfer line temp.210 °C
Oven temp. programInitialRateFinal
3 min40 °C8 °C/min220 °C3 min40 °C
Post run220 °C, 5 min
Table 4. Comparison of NVOC concentration results between north side and south side (t-test).
Table 4. Comparison of NVOC concentration results between north side and south side (t-test).
ComparisonMean(SD)t(df)p
north side0.1559(0.0467)0.9175100.3804
south side0.1142(0.0142)
n = 11.
Table 5. Stepwise multiple regression analysis examining forest structure and NVOC concentration.
Table 5. Stepwise multiple regression analysis examining forest structure and NVOC concentration.
VariableβSETpRAdj. RF
Step 1DBH−0.30300.1567−1.93500.05800.07400.02801.6180
canopy openness0.04100.12800.32000.7500
crown width0.25100.15561.61300.1120
Step 2DBH−0.31600.1504−2.10000.0398 *0.07200.04202.4110
crown width0.26200.15041.74500.0860
Step 3DBH−0.1630.1243−1.3120.19420.0270.0111.722
* p < 0.05, n = 66; Bole height was removed due to VIF.
Table 6. Analysis of the distance decay models with tree spacing limits.
Table 6. Analysis of the distance decay models with tree spacing limits.
Negative ExponentialPower LawInverse Power
Distance
Limit (m)		pAdj. RpAdj. RpAdj. R
2.50.19350.80240.19350.80240.19350.8024
2.60.13510.70050.13510.70050.13510.7005
2.70.0237 *0.68130.0237 *0.68130.0237 *0.6813
2.80.0416 *0.56630.0416 *0.56630.0416 *0.5663
2.90.0416 *0.56630.0416 *0.56630.0416 *0.5663
30.0416 *0.56630.0416 *0.56630.0416 *0.5663
3.10.12890.38000.12890.38000.12890.3800
3.20.14350.34980.14350.34980.14350.3498
3.30.20830.26680.20830.26680.20830.2668
3.40.12710.30200.12710.30200.12710.3020
3.50.12710.30200.12710.30200.12710.3020
3.60.29400.19340.29400.19340.29400.1934
3.70.18990.21770.18990.21770.18990.2177
3.80.18900.21060.18900.21060.18900.2106
3.90.22380.18400.22380.18400.22380.1840
40.22380.18400.22380.18400.22380.1840
4.10.20440.18510.20440.18510.20440.1851
4.20.21910.17440.21910.17440.21910.1744
4.30.12620.20720.12620.20720.12620.2072
4.40.21210.16670.21210.16670.21210.1667
4.50.21210.16670.21210.16670.21210.1667
4.60.19650.16730.19650.16730.19650.1673
4.70.17770.16930.17770.16930.17770.1693
4.80.17770.16930.17770.16930.17770.1693
4.90.17350.16640.17350.16640.17350.1664
50.16300.16600.16900.17200.17500.1780
* p < 0.05, n = 66.
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Song, D.; Cho, J.H.; Lee, S.; Park, S.; Kim, G. Interference Effect of Tree Spacing on Natural Volatile Organic Compound Concentrations Measured Using Passive Samplers. Forests 2024, 15, 1368. https://doi.org/10.3390/f15081368

AMA Style

Song D, Cho JH, Lee S, Park S, Kim G. Interference Effect of Tree Spacing on Natural Volatile Organic Compound Concentrations Measured Using Passive Samplers. Forests. 2024; 15(8):1368. https://doi.org/10.3390/f15081368

Chicago/Turabian Style

Song, Doyun, Jae Hyoung Cho, SangTae Lee, Sujin Park, and Geonwoo Kim. 2024. "Interference Effect of Tree Spacing on Natural Volatile Organic Compound Concentrations Measured Using Passive Samplers" Forests 15, no. 8: 1368. https://doi.org/10.3390/f15081368

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