Temporal Dynamics and Influencing Mechanism of Air Oxygen Content in Different Vegetation Types

Abstract

Air oxygen content, an essential index for measuring air quality, is affected by vegetation and the environment in the forest. However, the scientific understanding of the influential mechanism of air oxygen content in different vegetation types is still not clear. Focusing on four different vegetation types: broad-leaved forest, coniferous forest, coniferous and broad-leaved mixed forest, and non-forest land within Shimen National Forest Park, China, the temporal dynamics of air oxygen content and its relationship with four environmental factors (temperature, relative humidity, wind speed, and negative air ion concentration) in different vegetation types were explored based on path analysis and decision analysis. The results showed that there was a noteworthy impact of vegetation types on air oxygen content, with coniferous and broad-leaved mixed forest (21.33 ± 0.42%) presenting the highest levels. The air oxygen content indicated a fundamentally consistent temporal pattern across different vegetation types, with the highest diurnal variation occurring at noon. It reached its peak in August and hit its nadir in December, with summer > spring > autumn > winter. In broad-leaved forest, the air oxygen content was determined by temperature, wind speed, negative air ion concentration, and relative humidity; in both coniferous forest and coniferous and broad-leaved mixed forest, the air oxygen content was affected by temperature, wind speed, and relative humidity; in non-forest land, the air oxygen content was influenced by temperature and wind speed. Generally, temperature was the dominant factor affecting air oxygen content in different vegetation types, and its positive impact tremendously exceeded other environmental factors. Wind speed had a positive impact on air oxygen content in three forest communities but a negative effect on non-forest land. Relative humidity acted as a limiting factor for air oxygen content within three forest communities. Negative air ion concentration showed a significant positive correlation on air oxygen content in broad-leaved forest. Therefore, when planning urban forests to improve air quality and construct forest oxygen bars, it is recommended that the tree species composition should be given priority to the coniferous and broad-leaved mixed pattern. Meanwhile, make sure the understory space is properly laid out so that the forest microclimates are conducive to the release of oxygen by plants through photosynthesis.

1. Introduction

Human metabolism relies heavily on oxygen, which is one of the most crucial elements for survival and health-keeping. The air we breathe is so vital that a lack of oxygen can negatively impact human health, causing symptoms such as fatigue, memory loss, and concentration difficulties [1,2]. Air oxygen content refers to the ratio of the oxygen volume to the total volume in the air. As a momentous indicator of air quality, it has a decisive impact on people’s health and work performance [2,3]. There has been substantial evidence showing that breathing oxygen-rich air is beneficial to human health, such as improving respiratory diseases [3], delaying facial skin aging [4], improving arterial oxygenation, and enhancing human exercise capacity [5]. Furthermore, air oxygen content is also a critical component of forest therapy resources and must be considered when evaluating the factors and comprehensive benefits of forest therapy [6,7,8]. There is, however, an emphasis on negative air ion concentration, human comfort, forest microclimate, and airborne particle concentration as part of current research on forest therapy resources. There is a scarcity of reports addressing the temporal dynamic, influencing factors, and interaction mechanism associated with oxygen released by forests.

Photosynthesis of terrestrial green vegetation is the paramount source of oxygen in the atmosphere [9,10]. Vegetation notably impacts the air oxygen content, with a contribution rate of 16.7%–24.5%, which has been underestimated in existing research, and the contribution of vegetation should be emphasized [11]. There are, however, differences between stand conditions when it comes to oxygen release by plants. Moreover, only a few scholars have conducted relevant research on the impact of vegetation types on air oxygen content. Zhao et al. [12] believed that the air oxygen content in coniferous and broad-leaved mixed forest is the highest, followed by coniferous forest, and broad-leaved forest is the lowest. Analogously, Tian et al. [13] suggested that coniferous and broad-leaved mixed forest has a higher oxygen content than other vegetation types. In contrast, Feng [14] found that broad-leaved forest has a stronger oxygen-releasing capacity than coniferous forest. Zeng et al. [15] manifested that the oxygen-emitting capacity of a fast-growing broad-leaved forest is stronger than that of coniferous forest, whereas natural secondary near-mature forest releases less oxygen than artificial broad-leaved forest in the fast-growing stage. Clearly, the findings on the impact of vegetation types on air oxygen content are not uniform, and it is extraordinarily meaningful to explore the mechanism of vegetation types affecting air oxygen content.

Generally, the oxygen percentage in the Earth’s atmosphere remains at a level of 20.95% [2,16], but this ratio varies over time and space. Studies have shown that air oxygen content varies significantly at different temporal scales. In terms of diurnal variation, Wen [17] deemed that the diurnal change in air oxygen content in all seasons is characterized by being high at noon, followed by the afternoon, and low in the morning. Wang et al. [18] revealed that the daily changes in air oxygen content show a parabolic curve, with peak values appearing in the afternoon. Gu [19] reported that air oxygen content varies daily in a unimodal pattern throughout the year, with the lowest value at 5:00 am and the highest value at 1:00 pm. There are also significant differences in air oxygen content across seasons. Most scholars reckoned that air oxygen content is highest in summer, followed by spring and autumn, and lowest in winter [17,20,21]. Some scholars held the opposite opinion, discovering that air oxygen content is highest in winter, second in autumn and spring, and lowest in summer [22] or that air oxygen content is highest in spring, followed by summer and autumn, and lowest in winter [23]. At present, fewer studies have been published on the changing patterns of air oxygen content, and relatively unified conclusions have not yet been formed. Exploring the changing characteristics and patterns of air oxygen content in different vegetation types can make up for the lack of multiscale research currently available and provide theoretical support for future related research.

In addition, the air oxygen content is greatly affected by the surrounding environment. Most previous studies have focused on environmental factors such as temperature, light intensity, relative humidity, wind speed, air pressure, and airborne particle concentration [17,18,23]. Most of their studies declared that air oxygen content is negatively correlated with relative humidity, particulate matter 2.5 concentration, and particulate matter 10 concentration and has a positive correlation with temperature, light intensity, and air pressure. The current literature on how environmental factors affect air oxygen content does not distinguish between vegetation types and, respectively, discusses their interactions. Therefore, it is difficult to quantitatively evaluate the comprehensive impact of multiple environmental factors on air oxygen content across different vegetation types.

It is comparatively complex to determine the effect of environmental factors on air oxygen content in different vegetation types. Path analysis and decision analysis were introduced to find out the dominant factors affecting air oxygen content in four vegetation types in Shimen National Forest Park to reach a more comprehensive and reliable conclusion. The objectives of this study are: (1) to determine the effect of vegetation types on air oxygen content; (2) to ascertain the changing patterns of air oxygen content in different vegetation types at different temporal scales; (3) to explore the correlation between air oxygen content and environmental factors in different vegetation types, and to clarify its dominant factors. It is of great theoretical and practical importance for this research, which imparts academic support for the temporal variation patterns and influencing factors of air oxygen content in different vegetation types, as well as being instructive for formulating more scientific and rational forest management strategies. It also provides a new perspective for the in-depth understanding of forest oxygen release to maintain ecological balance and ensure the health and safety of residents and tourists in the context of 70% of the population facing sub-health state.

2. Materials and Methods

2.1. Study Area

Shimen National Forest Park (23°36′50″ N~23°39′20″ N, 113°46′16″ E~113°49′17″ E) is situated in the northeast region of Conghua District, Guangzhou City, Guangdong Province, China. It is located adjacent to Guangdong Longmen Nankunshan Provincial Nature Reserve and Guangzhou Baishuizai Scenic Spots, with adjacent forest areas being concentrated and contiguous (Figure 1). Spanning an area of 26.36 km² with a forest coverage rate of 98.91%, its altitude varies from 270 m to 1210 m. Shimen National Forest Park experiences a southern subtropical monsoon climate characterized by an average air temperature fluctuating between 19.5 °C and 21.4 °C. The annual average rainfall amounts to approximately 1800 mm, while the mean annual solar radiation reaches around 440,870 J/cm² [7,8].

2.2. Plot Setting

The coniferous and broad-leaved mixed forest, broad-leaved forest, and coniferous forest are the main vegetation types in subtropical areas [24,25,26]. Selecting the above three typical subtropical vegetation types and setting up a control experiment with non-forest land is aimed at comprehensively understanding the role of different types of vegetation in oxygen production, which helps better protect and utilize forest resources. Therefore, A total of 12 sample points were established in Shimen National Forest Park, including four distinct vegetation types: broad-leaved forest, coniferous forest, coniferous and broad-leaved mixed forest, and non-forest land. The broad-leaved forest and coniferous forest unfolded a comparable vertical structure, each comprising two distinct layers. Within the broad-leaved forest, the tree layer was exclusively dominated by Liquidambar formosana, while its understory vegetation consisted of Lophatherum gracile. Similarly, the coniferous forest solely comprised Cunninghamia lanceolata in its tree layer, with the herb layer being predominantly occupied by Blechnum orientale and Nephrolepis auriculata. The coniferous and broad-leaved mixed forest displayed a three-layer vertical structure encompassing trees, shrubs, and herbs. Specifically, the tree layer included Cunninghamia lanceolata and Liquidambar formosana; the shrub layer featured Melastoma candidu and Rhodomyrtus tomentosa; while Lophatherum gracile and Gleichenia linearis were prominent in the herb layer. In non-forest land, only a herb layer was present, with seasonal flowers primarily constituting ground cover. Detailed information regarding the sample plots can be found in Figure 1 and

Table 1. Overview of sample plots in different vegetation types.

Vegetation Types	Stand Vertical Structure	Dominant Tree Species	Attitude/m	Stand Area/hm2	Canopy Density	Stand Mean Age/Year	Stand Density/N·hm−2	Height of Tree/m	Diameter at Breast Height/cm
Broad-leaved forest	Tree layer and herb layer	Liquidambar formosana	450	15.6	0.6	≥20	637	16.55 ± 3.04	15.36 ± 2.41
Coniferous forest	Tree layer and herb layer	Cunninghamia lanceolata	350	17.9	0.7	≥30	861	30.10 ± 4.83	25.04 ± 3.17
Coniferous and broad-leaved mixed forest	Tree layer, shrub layer, and herb layer	Liquidambar formosana, Cunninghamia lanceolata	290	25.6	0.5	≥20	688	20.38 ± 6.44	20.52 ± 4.78
Non-forest land	Herb layer	None	790	1.7	-	-	-	-	-

.

2.3. Observation Methods

The field experiment was conducted from September 2019 to January 2020 and from May to August 2020, excluding the period from February to April 2020 due to the impact of the COVID-19 epidemic. The experiment took place on sunny or cloudy days during the latter half of each month. It spanned three consecutive days, starting at 9:00 am and concluding at 5:00 pm. Throughout this duration, data on air oxygen content, negative air ion concentration, and forest microclimates were collected every hour from four vegetation types. The air oxygen content was measured using a portable oxygen analyzer called TD6000-SH-O₂ (Beijing Tiandi Shouhe Technology Development Co., Ltd., Beijing, China). The negative air ion concentration was determined using a COM-3200PRO II negative air ions meter (Guangzhou Extreme Technology Co., Ltd., Guangzhou, China) at a sampling height of 1.5 m above ground level. Forest microclimate parameters, including temperature, relative humidity, and wind speed, were recorded with a handheld meteorological instrument known as Kestrel 5500 (Beijing Kestrel Instrument & Meter Co. Ltd, Beijing, China) at a height of 2.0 m. Each data collection process was repeated three times, and their average values were considered for analysis purposes.

2.4. Path Analysis and Decision Analysis

(1)

Direct and Indirect Path Coefficients

Based on the principle of path analysis [27], the equation for path coefficients is solved using correlation coefficients.

$$\left\{\begin{array}{c}{b}_1+r_{12}{b}_2+\cdots +r_{1n}{b}_n=r_{1y},\\ {r_{21}b}_1+b_2+\cdots +r_{2n}{b}_n=r_{2y},\\ ⋮\\ r_{n1}{b}_1+r_{n2}{b}_2+\cdots +b_n=r_{ny}.\end{array}\right.$$

(1)

where $r_{ij}$ is the correlation coefficients between $x_i$ and $x_j$; $r_{iy}$ is the correlation coefficients between $x_i$ and y; $b_i$ is the direct path coefficients; ${r_{ij}b}_j$ is the indirect path coefficients.

(2)

Decision Coefficients

Decision coefficients are primarily adopted to determine the integrated influence of the respective variables on the dependent variables [28], and the calculation equation is:

$$R_i^2=b_i^2,R_{ij}^2={2b}_i{r}_{ij}{b}_j,$$

(2)

$$R_{\left(i\right)}^2=2b_i{r}_{iy}-b_i^2.$$

(3)

where $R_i^2$ indicates the direct determination coefficients of $x_i$ to y; $R_{ij}^2$ means the indirect determination coefficients of $x_i$ through $x_j$ to y; $b_i{r}_{iy}$ signifies the contribution of $x_i$ to R²; $R_{\left(i\right)}^2$ denotes the decision coefficients of $x_i$ to y. If $R_{\left(i\right)}^2$ > 0, it means that $x_i$ has an enhanced effect on y; if $R_{\left(i\right)}^2$ < 0, it means that $x_i$ has a restrictive effect on y.

(3)

Residual Path Coefficients

Further calculations of the residual path coefficients ($R_e$) are needed to test the strengths and weaknesses of the model [29]. If the value of $R_e$ is small, it reveals that the primary variable has been identified. If the value of $R_e$ is large, it indicates a large error, or there are more important factors that have not yet been considered. The calculation equation as demonstrated:

$$R_e=\sqrt{1-\left(r_{1y}{b}_1+r_{2y}{b}_2+\cdots +r_{ny}{b}_n\right)}.$$

(4)

2.5. Data Processing and Analysis

SPSS 27.0 (SPSS Inc., Chicago, IL, USA) was used to conduct ANOVA and multiple stepwise regression analyses, while R (version 4.2.1, R Development Core Team, Auckland, New Zealand) and RStudio (v2022.12, RStudio Inc., Chicago, IL, USA) were employed to generate the correlation coefficients figure. The diagram was drawn by Origin 2023 (OriginLab, Northampton, MA, USA), and ArcGIS 10.8 (ESRI, Redlands, CA, USA) was utilized to create the sample plots location map.

3. Results

3.1. Temporal Dynamics of Air Oxygen Content in Different Vegetation Types

3.1.1. Diurnal Variation of Air Oxygen Content in Different Seasons

In spring, summer, and winter (Figure 2a,b,d), the air oxygen content presented a consistent diurnal variation pattern across different vegetation types, with the highest levels observed at noon, followed by the afternoon, and the lowest in the morning (p < 0.001). In autumn (Figure 2c), the air oxygen content in four vegetation types exhibited a peak at noon and a trough in the afternoon, with slightly higher levels observed in the morning than in the afternoon (p < 0.001). In spring, the diurnal variability of air oxygen content was relatively stable in broad-leaved forest, while significant fluctuations were observed in the other three vegetation types. Moreover, non-forest land displayed its peak air oxygen content at 14:00, whereas the remaining three vegetation types reached their maximum levels at 13:00. In summer, there was a wide range of diurnal variation in air oxygen content among different vegetation types, reaching its maximum at 12:00 and minimum at 10:00. In autumn, the daily variation range of air oxygen content was relatively mild in different vegetation types, with the coniferous forest and the coniferous and broad-leaved mixed forest showing their highest values at 12:00, broad-leaved forest at 14:00, and non-forest land at 13:00. In winter, the diurnal variation range of air oxygen content in four vegetation types depicted a relatively smooth pattern, with the highest concentration observed at 14:00 and the lowest concentration reached at 9:00, as illustrated in Figure 2 and

Table 2. Comparison of diurnal mean values of air oxygen content in different vegetation types. The different lowercase letters in the same rows indicate significant differences (p < 0.05).

Seasons	Vegetation Types	Morning (9:00–11:00)	Noon (12:00–14:00)	Afternoon (15:00–17:00)
Spring	Broad-leaved forest	20.97 ± 0.06 c	21.17 ± 0.01 a	21.08 ± 0.02 b
	Coniferous forest	21.02 ± 0.14 c	21.27 ± 0.07 a	21.17 ± 0.05 b
	Coniferous and broad-leaved mixed forest	21.05 ± 0.12 c	21.38 ± 0.04 a	21.20 ± 0.08 b
	Non-forest land	20.78 ± 0.03 c	21.07 ± 0.05 a	20.97 ± 0.01 b
Summer	Broad-leaved forest	21.38 ± 0.25 c	21.82 ± 0.27 a	21.57 ± 0.30 b
	Coniferous forest	21.44 ± 0.24 b	21.92 ± 0.26 a	21.59 ± 0.37 b
	Coniferous and broad-leaved mixed forest	21.62 ± 0.33 b	22.07 ± 0.25 a	21.73 ± 0.37 b
	Non-forest land	21.15 ± 0.25 c	21.55 ± 0.22 a	21.31 ± 0.28 b
Autumn	Broad-leaved forest	20.99 ± 0.12 b	21.12 ± 0.07 a	20.98 ± 0.10 b
	Coniferous forest	21.06 ± 0.11 b	21.16 ± 0.08 a	21.02 ± 0.06 b
	Coniferous and broad-leaved mixed forest	21.15 ± 0.12 b	21.23 ± 0.06 a	21.13 ± 0.07 b
	Non-forest land	20.89 ± 0.09 ab	20.93 ± 0.10 a	20.88 ± 0.06 b
Winter	Broad-leaved forest	20.79 ± 0.05 c	20.92 ± 0.02 a	20.87 ± 0.04 b
	Coniferous forest	20.85 ± 0.08 b	20.95 ± 0.05 a	20.88 ± 0.03 b
	Coniferous and broad-leaved mixed forest	20.87 ± 0.03 c	20.99 ± 0.07 a	20.92 ± 0.04 b
	Non-forest land	20.56 ± 0.05 c	20.70 ± 0.06 a	20.64 ± 0.04 b

.

3.1.2. Monthly Variation of Air Oxygen Content in Different Vegetation Types

The monthly variation of air oxygen content in different vegetation types denoted a significant disparity (p < 0.01), demonstrating a discernible pattern of initial decline followed by an ascent. Specifically, there was a downward trend observed from September to December, followed by an upward trend spanning from December to August. Notably, the peak value occurred in August, while the trough value was recorded in December, as depicted in Figure 3.

3.1.3. Seasonal Variation of Air Oxygen Content in Different Vegetation Types

The air oxygen content of different vegetation types presented seasonal variation as follows: summer > spring > autumn > winter, with significant differences observed among seasons (p < 0.01). Coniferous and broad-leaved mixed forest consistently posted higher oxygen levels compared to other vegetation types in the whole year (Figure 4).

3.2. Dominant Factors of Air Oxygen Content in Different Vegetation Types

3.2.1. Air Oxygen Content and Environmental Factors in Different Vegetation Types

The air oxygen content revealed a significant disparity among different vegetation types (p < 0.001), with the coniferous and broad-leaved mixed forest displaying the highest annual average value, followed by coniferous forest, broad-leaved forest, and non-forest land. Among the four environmental factors examined, the disparities in negative air ion concentration, temperature, and wind speed were extremely significant (p < 0.001), while there was no significant difference observed in relative humidity (p > 0.05). Coniferous forest recorded the highest negative air ion concentration, coniferous and broad-leaved mixed forest had the highest temperature level, and non-forest land exhibited significantly higher wind speed compared to the other three vegetation types at approximately six times their magnitude (

Table 3. The annual average of air oxygen content and environmental factors in different vegetation types. The different lowercase letters in the same row indicate significant differences (p < 0.05).

Indicators	Broad-Leaved Forest	Coniferous Forest	Coniferous and Broad-Leaved Mixed Forest	Non-Forest Land
Air oxygen content	21.18 ± 0.36 b	21.24 ± 0.37 b	21.33 ± 0.42 a	20.99 ± 0.33 c
Negative air ion concentration	1 845 ± 1 375 b	2 264 ± 1 329 a	2 205 ± 1 377 a	1 096 ± 485 c
Temperature	24.60 ± 4.41 b	25.24 ± 4.26 ab	25.59 ± 4.16 a	23.14 ± 4.18 c
Relative humidity	77.63 ± 12.76 a	76.46 ± 13.56 a	76.68 ± 13.26 a	77.06 ± 13.41 a
Wind speed	0.94 ± 0.75 b	0.96 ± 0.82 b	0.91 ± 0.88 b	5.92 ± 2.23 a

).

3.2.2. Correlation Analysis

The Pearson correlation coefficient method was employed to assess the simple correlation between air oxygen content and environmental factors in different vegetation types (Figure 5). In broad-leaved forest (Figure 5a), there was a positive correlation between air oxygen content and negative air ion concentration, temperature, and wind speed. Similarly, in both coniferous forest (Figure 5b) and coniferous and broad-leaved mixed forest (Figure 5c), air oxygen content showed a positive correlation with temperature and wind speed. In non-forest land (Figure 5d), it was observed that air oxygen content depicted a positive correlation with temperature and negative air ion concentration. In addition to the correlation coefficient of non-forest land, in which negative air ion concentration surpasses wind speed, the absolute values of correlation coefficients in other vegetation types were as follows: temperature > wind speed > negative air ion concentration > relative humidity. Among them, temperature indicated a strong correlation; wind speed and negative air ion concentration revealed a moderate correlation; while relative humidity displayed a weak correlation.

3.2.3. Multiple Stepwise Regression Analysis

The independent variables demonstrated a robust correlation and substantial mutual influence across diverse vegetation types (Figure 5). Relying solely on the Pearson correlation coefficient proved inadequate in accurately capturing the impact of environmental factors on air oxygen content, thus necessitating a more comprehensive approach to evaluate their effects. Therefore, multiple linear regression analysis using the stepwise addition method was conducted, with air oxygen content as the dependent variable and negative air ion concentration, temperature, relative humidity, and wind speed as the independent variables (

Table 4. Multiple stepwise regression analysis of air oxygen content and environmental factors in different vegetation types, where T is temperature (°C); RH is relative humidity (%); WS is wind speed (m/s); NAIC is negative air ion concentration (N/cm³).

Vegetation Types	Model		B	SE	β	t	Sig.	R2
Broad-leaved forest	1	Constant	19.735	0.091		218.061	0.000	0.523
		T	0.059	0.004	0.723	16.247	0.000
	2	Constant	19.761	0.089		222.265	0.000	0.545
		T	0.055	0.004	0.675	14.758	0.000
		WS	0.074	0.022	0.155	3.394	0.001
	3	Constant	19.723	0.090		219.962	0.000	0.554
		T	0.055	0.004	0.673	14.829	0.000
		WS	0.068	0.022	0.143	3.135	0.002
		NAIC	0.000	0.000	0.099	2.277	0.024
	4	Constant	19.914	0.125		159.678	0.000	0.563
		T	0.055	0.004	0.676	15.013	0.000
		WS	0.073	0.022	0.153	3.357	0.001
		NAIC	0.000	0.000	0.127	2.815	0.005
		RH	−0.003	0.001	−0.098	−2.179	0.030
Coniferous forest	1	Constant	19.699	0.105		187.877	0.000	0.479
		T	0.061	0.004	0.692	14.885	0.000
	2	Constant	19.764	0.103		190.972	0.000	0.508
		T	0.055	0.004	0.627	12.933	0.000
		WS	0.084	0.022	0.184	3.794	0.000
	3	Constant	19.952	0.131		151.775	0.000	0.519
		T	0.056	0.004	0.639	13.220	0.000
		WS	0.089	0.022	0.195	4.043	0.000
		RH	−0.003	0.001	−0.104	−2.285	0.023
Coniferous and broad-leaved mixed forest	1	Constant	19.429	0.113		171.809	0.000	0.547
		T	0.074	0.004	0.740	17.055	0.000
	2	Constant	19.530	0.112		174.169	0.000	0.577
		T	0.067	0.005	0.669	14.750	0.000
		WS	0.090	0.022	0.188	4.149	0.000
	3	Constant	19.745	0.144		137.282	0.000	0.587
		T	0.068	0.005	0.676	15.021	0.000
		WS	0.096	0.022	0.200	4.432	0.000
		RH	−0.003	0.001	−0.099	−2.357	0.019
Non-forest land	1	Constant	19.576	0.074		264.030	0.000	0.609
		T	0.061	0.003	0.780	19.377	0.000
	2	Constant	19.625	0.075		260.653	0.000	0.621
		T	0.063	0.003	0.808	19.743	0.000
		WS	−0.017	0.006	−0.114	−2.782	0.006

). The findings revealed that the air oxygen content in broad-leaved forest was primarily influenced by temperature, wind speed, negative air ion concentration, and relative humidity. The prediction model accounted for 56.3% of the variation in the dependent variable. In both coniferous forest and coniferous and broad-leaved mixed forest, temperature, wind speed, and relative humidity were identified as the principal factors affecting air oxygen content, with these three independent variables explaining 51.9% and 58.7% of the dependent variable variations, respectively. Moreover, in non-forest land, temperature and wind speed emerged as significant determinants, indicating that these two independent variables could explain 62.1% of the variation in the dependent variable.

3.2.4. Path Analysis

The indirect path coefficients were computed using Equation (1) based on the simple correlation coefficients and direct path coefficients obtained from the aforementioned analysis (Figure 5 and Table 4). The findings reported that temperature had the most prominent direct positive impact on air oxygen content, followed by wind speed. Conversely, relative humidity indicated the greatest direct negative influence on air oxygen content within broad-leaved forest. Simultaneously, the cumulative indirect effects of temperature, negative air ion concentration, and relative humidity showed the highest magnitude. It suggested that as temperature and negative air ion concentration increased, the reduction in relative humidity indirectly facilitated an elevation in air oxygen content within broad-leaved forest. Furthermore, it was noteworthy that the direct path coefficient of relative humidity exhibited the smallest magnitude. However, when considering the cumulative effect of indirect path coefficients, it became evident that the indirect negative impact of this factor on air oxygen content held greater significance. In both coniferous forest and coniferous and broad-leaved mixed forest, temperature had the most significant direct influence on air oxygen content, followed by wind speed, which indirectly enhanced air oxygen content through its positive effect on temperature. Conversely, relative humidity posted the smallest direct impact on air oxygen content with a negative effect. In non-forest land, the primary and positive impact on air oxygen content was attributed to temperature, while wind speed exerted the most significant direct negative effect. Additionally, the indirect influence also held considerable importance in this context (

Table 5. The path coefficients of air oxygen content and environmental factors in different vegetation types, where $r_{iy}$ denotes the correlation coefficients; $b_i$ denotes the direct path coefficients; ${r_{ij}b}_j$ denotes the indirect path coefficients; T is temperature (°C); RH is relative humidity (%); WS is wind speed (m/s); NAIC is negative air ion concentration (N/cm³).

Vegetation Types	${\mathit{x}}_{\mathit{i}}$	${\mathit{r}}_{\mathit{i}\mathit{y}}$	${\mathit{b}}_{\mathit{i}}$	${{\mathit{r}}_{\mathit{i}\mathit{j}}\mathit{b}}_{\mathit{j}}$
				T	WS	NAIC	RH	Total
Broad-leaved forest	T	0.723	0.676		0.047	0.008	−0.008	0.047
	WS	0.362	0.153	0.208		0.017	−0.014	0.210
	NAIC	0.159	0.127	0.042	0.020		−0.029	0.033
	RH	0.017	−0.098	0.056	0.022	0.038		0.116
Coniferous forest	T	0.692	0.639		0.070		−0.016	0.053
	WS	0.407	0.195	0.228			−0.016	0.212
	RH	0.025	−0.104	0.100	0.029			0.130
Coniferous and broad-leaved mixed forest	T	0.740	0.676		0.075		−0.012	0.063
	WS	0.440	0.200	0.255			−0.015	0.240
	RH	0.015	−0.099	0.084	0.031			0.114
Non-forest land	T	0.780	0.808		−0.027			−0.027
	WS	0.079	−0.114	0.192				0.192

).

3.2.5. Decision Analysis

As illustrated in Table 5, the alignment between the direct path coefficients and indirect path coefficients lacked complete consistency, posing a challenge to accurately depict the overall impact of various environmental factors on air oxygen content. Nevertheless, the decision coefficients can effectively ascertain the comprehensive influence of these environmental factors on air oxygen content. The decision analysis was conducted based on Equations (2) and (3), with the outcomes presented in

Table 6. The decision coefficients and residual path coefficients of air oxygen content and environmental factors in different vegetation types, where $R_i^2$ denotes the direct determination coefficients; $R_{ij}^2$ denotes the indirect determination coefficients; $R_{\left(i\right)}^2$ denotes the decision coefficients; $R_e$ denotes the residual path coefficients; T is temperature (°C); RH is relative humidity (%); WS is wind speed (m/s); NAIC is negative air ion concentration (N/cm³).

Vegetation Types	${\mathit{x}}_{\mathit{i}}$	Contribution to R2	${\mathit{R}}_{\mathit{i}}^2$	${\mathit{R}}_{\mathit{i}\mathit{j}}^2$				${\mathit{R}}_{\left(\mathit{i}\right)}^2$	${\mathit{R}}_{\mathit{e}}$
				T	WS	NAIC	RH
Broad-leaved forest	T	0.489	0.457		0.064	0.011	−0.011	0.521	0.661
	WS	0.055	0.023	0.064		0.005	−0.004	0.087
	NAIC	0.020	0.016	0.011	0.005		−0.007	0.024
	RH	−0.002	0.010	−0.011	−0.004	−0.007		−0.013
Coniferous forest	T	0.442	0.408		0.089		−0.021	0.476	0.694
	WS	0.079	0.038	0.089			−0.006	0.121
	RH	−0.003	0.011	−0.021	−0.006			−0.016
Coniferous and broad-leaved mixed forest	T	0.500	0.457		0.102		−0.017	0.544	0.643
	WS	0.088	0.040	0.102			−0.006	0.136
	RH	−0.001	0.010	−0.017	−0.006			−0.013
Non-forest land	T	0.630	0.653		−0.044			0.608	0.615
	WS	−0.009	0.013	−0.044				−0.031

.

In broad-leaved forest, the decision coefficients of four environmental factors were ranked as follows: temperature > wind speed > negative air ion concentration > relative humidity. Temperature emerged as the dominant factor impacting air oxygen content through its interaction with other environmental variables. Furthermore, temperature, wind speed, and negative air ion concentration enhanced the air oxygen content, whereas relative humidity acted as a limiting factor by impeding oxygen levels. In both coniferous forest and coniferous and broad-leaved mixed forest, the ranking of the decision coefficients for three factors was as follows: temperature > wind speed > relative humidity. The findings manifested that temperature was the leading determinant of the augmentation of air oxygen content, while wind speed exerted a supplementary influence, and relative humidity acted as a constraining factor. In non-forest land, the decision coefficient of temperature surpassed wind speed, while the decision coefficient of wind speed was negative. It indicated that temperature played a dominant role in determining air oxygen content, and as wind speed increased, it imposed limitations on air oxygen content. In general, temperature was the paramount factor influencing air oxygen content in different vegetation types, with wind speed being the secondary influential factor. As opposed to non-forest land, wind speed had a positive impact on air oxygen content in forest communities. Relative humidity had a negative impact on air oxygen content within the three forest communities.

As indicated by Equation (4), the residual path coefficients of the error term for broad-leaved forest was 0.661, coniferous forest was 0.694, coniferous and broad-leaved mixed forest was 0.643, and non-forest land was 0.615. As a result of these findings, it seemed likely that there were other significant environmental variables having an impact on air oxygen content that had not yet been taken into account in this study.

4. Discussion

4.1. Effect of Vegetation Types on Air Oxygen Content

It is important to note that vegetation types impact the level of oxygen in the air. In accordance with numerous scholars’ conclusions, the air oxygen content in forest communities is significantly higher than that in non-forest land [21,22]. Terrestrial plants produce most of the oxygen in the atmosphere through photosynthesis. Consequently, the forest environment contains a greater number of “natural oxygen generators” than non-forest land, which can release more oxygen through photosynthesis. In the forest community, the air oxygen content in coniferous and broad-leaved mixed forest is the highest, followed by coniferous forest and broad-leaved forest is the lowest (

Table 7. Temporal variations and influencing factors of air oxygen content in different vegetation types, where AOC is air oxygen content (%); T is temperature (°C); RH is relative humidity (%); WS is wind speed (m/s); NAIC is negative air ion concentration (N/cm³). More “+” means greater impact or higher numeric value.

Vegetation Types	Seasons	Topics
		Effect of Vegetation Types on AOC	Temporal Variation Pattern of AOC		Effect of Environmental Factors on AOC
			Diurnal Variation	Seasonal Variation
Broad-leaved forest	Spring	++	noon > afternoon > morning	+++	T > WS > NAIC > RH
	Summer		noon > afternoon > morning	++++
	Autumn		noon > morning > afternoon	++
	Winter		noon > afternoon > morning	+
Coniferous forest	Spring	+++	noon > afternoon > morning	+++	T > WS > RH
	Summer		noon > afternoon > morning	++++
	Autumn		noon > morning > afternoon	++
	Winter		noon > afternoon > morning	+
Coniferous and broad-leaved mixed forest	Spring	++++	noon > afternoon > morning	+++	T > WS > RH
	Summer		noon > afternoon > morning	++++
	Autumn		noon > morning > afternoon	++
	Winter		noon > afternoon > morning	+
Non-forest land	Spring	+	noon > afternoon > morning	+++	T > WS
	Summer		noon > afternoon > morning	++++
	Autumn		noon > morning > afternoon	++
	Winter		noon > afternoon > morning	+

), as reported by some researchers [12,13]. This may be related to altitude, stand area, stand vertical structure, and stand mean age of coniferous and broad-leaved mixed forest. Compared to other vegetation types, the coniferous and broad-leaved mixed forest has a lower altitude. It has been found that air oxygen content decreases with altitude, showing an inverse relationship with altitude [17,30]. Moreover, it can be seen from Table 1 that among the three forest communities, the coniferous and broad-leaved mixed forest possesses the largest stand area and the most complex vertical structure, consisting of three layers of trees, shrubs, and herbs. Therefore, its unit biomass and plant coverage rate are higher than those of the other vegetation types. Studies have shown that as the vertical structure of forest communities increases, the higher the vegetation coverage, the larger the unit biomass [31], and the greater the vegetation oxygen production [10]. Research has also shown that air oxygen content correlates positively with stand age during its middle and young stages but negatively as the trees mature [12]. Therefore, the oxygen-releasing capacity of coniferous and broad-leaved mixed forest in the young forest state is stronger than that of coniferous forest and broad-leaved forest. Furthermore, coniferous forest is found to have higher levels of air oxygen content than broad-leaved forest. There has been evidence that the oxygen release of a tree is significantly positively correlated with its height, diameter at breast height, and stand density [32]. Based on Table 1, coniferous forest has remarkably higher tree heights, diameters at breast height, and stand densities than broad-leaved forest, so coniferous forest has a higher air oxygen content.

4.2. Temporal Variation Pattern of Air Oxygen Content in Different Vegetation Types

There is a similar pattern of diurnal variation in air oxygen content throughout all seasons in different vegetation types, with the peak value occurring at noon (Table 7). Aside from autumn, the other three seasons show that air oxygen content in the afternoon is slightly higher than that in the morning, supporting the conclusion reached by most scholars [17,19,23]. It is extremely associated with light intensity, which is a crucial environmental factor influencing the photosynthesis rate [33]. Studies have demonstrated that air oxygen content in the forest is determined by the strength of plant photosynthesis [23]. The diurnal changes in air oxygen content are similar to those in light intensity, except that the peak time lags slightly because it takes time for oxygen to accumulate [19]. In the morning, there is a lower level of air oxygen content. Usually, this may be caused by insufficient light, which inhibits plant photosynthesis and limits oxygen release. A gradual increase in light intensity allows plant photosynthesis to be further enhanced at noon (12:00–14:00) whenever the light intensity is at its maximum and oxygen release is strongest. Afterward, the light intensity gradually weakens, and the plants’ ability to release oxygen decreases. Even so, due to the accumulation during the day, the net accumulation of oxygen content remains higher in the afternoon than in the morning.

Across different vegetation types, monthly changes in air oxygen content show a downward trend from September to December and an upward trend from December to August, peaking in August and reaching a trough in December. Light intensity has obvious periodic changes throughout the year, and the monthly distribution of light radiation is uneven, as well as certain regularities in the photosynthetic characteristics of plants [34]. High temperatures, long sunshine durations, and high light intensities in August promote the photosynthetic rate of plant leaves and enhance the plant’s ability to convert carbon dioxide into oxygen and organic matter through photosynthesis. As a result, plants release more oxygen during the growth period, which accumulates throughout the growing season and peaks in August. It is believed that the drop in temperature, light intensity, and sunshine duration from September to December resulted in a decline in photosynthetically active radiation, a decrease in net photosynthetic rates and plant growth rates, and a reduction in oxygen release, bringing about the accumulation of air oxygen content reaching its lowest value until December.

There is a consistent seasonal pattern in air oxygen content of different vegetation types, namely summer > spring > autumn > winter, in line with most scholars’ observations [14,17,20,21]. Studies have found that the overall change pattern of plant oxygen release capacity is consistent with the net photosynthetic rate and transpiration rate, which are shown as summer > spring > autumn > winter [35]. Seasonal changes in air oxygen content are not only affected by meteorological factors, such as temperature, light intensity, and relative humidity but also by growth cycles and physiological activities of plants. As a result of the favorable light and temperature conditions in spring, plant photosynthesis can take place effectively. Furthermore, since plants have younger leaves and weaker physiological and metabolic functions in spring, their photosynthetic rate is lower than in summer. As the summer begins, with the increasing light intensity and sufficient sunshine, the forest communities enter the vigorous growing season. The vegetation coverage and the plant leaf area index increase, and the light energy utilization rate of plants increases, resulting in more biomass in the vegetation. By increasing vegetation coverage and plant leaf area index, as well as utilizing more light energy, vegetation with more biomass can have stronger photosynthesis and release more oxygen into the atmosphere. Therefore, oxygen content in different vegetation types is highest in summer when plant growth is vigorous. As temperature and sunshine duration drop in autumn and winter, plant physiological activity is weakened, and key enzymes for photosynthesis are reduced as well. These factors make the air oxygen content in autumn and winter lower than in summer and spring, and the air oxygen content in winter with lower temperatures and shorter sunshine duration is lower than in autumn.

4.3. Effect of Environmental Factors on Air Oxygen Content in Different Vegetation Types

Vegetation peculiarly ameliorates microclimatic conditions in forests and considerably contributes to the increase of oxygen in forests. It is of vital interest to pay attention to its microclimatic environment in forest areas [36]. Based on path analysis and decision analysis, Temperature is the most directly dominant factor affecting air oxygen content in different vegetation types (Table 7). Compared with other environmental factors, it has a much greater positive impact on different types of vegetation types, which is consistent with most scholars’ conclusions [17,19,21,23,30]. A higher temperature and more solar radiation are conducive to the progress of plant photosynthesis to a certain extent. When light intensity and temperature are higher, the stomata open more easily, increasing net photosynthetic rates and transpiration rates of leaves [37]. A great deal of studies have also reported that temperature can directly affect the activity of enzymes involved in plant physiological functions [38]. Consequently, within a certain range, the higher the temperature, the greater the opening of stomata, the stronger the activity of enzymes, and the more oxygen released by plants.

It is also found that wind speed has the second greatest effect on air oxygen content after temperature across different vegetation types. Wind speed has a positive impact on three forest communities but a negative effect on non-forest land. Combining the wind speeds of different vegetation types, it can be seen that the wind speed in non-forest land is about six times greater than in the other three vegetation types. It has been reported that the airflow under the influence of wind speed also has a certain impact on the air oxygen content [18]. There is a possibility that the increase in wind speed within a certain range can promote the circulation of ambient air in the forest, reduce particulate matter concentrations, and increase oxygen levels. It is also important to note that when wind speeds are too high, oxygen molecules will move at a faster rate as well. According to the kinetic theory of gas molecules, an increase in molecular speed will lead to an increase in diffusion, resulting in a lower measured oxygen content.

Air oxygen content is impacted negatively by relative humidity in three forest communities. A trade-off relationship exists between relative humidity and air oxygen content, in which the proportion of oxygen decreases with increasing water vapor content [18,39]. In the presence of increasing relative humidity, the pressure of water vapor in the air increases, causing the partial pressure of oxygen in the air to decrease, therefore reducing air oxygen content. Furthermore, relative humidity and stomatal conductance play a major role in the net photosynthetic rate [40]. The net photosynthetic rate reflects the ability of the plant to release oxygen into the atmosphere [33]. Through its influence on the stomatal conductance and the stomatal limit of plant leaves, relative humidity affects the release of oxygen by plants. Plant leaves close their stomata under high relative humidity, causing plants to absorb carbon dioxide less efficiently [40]. In turn, photosynthesis becomes less effective at releasing oxygen.

The air oxygen content in broad-leaved forest is also affected by negative air ion concentration, as indicated by a significant positive correlation, in accordance with some scholars’ findings [17,19]. The ability of the leaves of broad-leaved tree species to absorb air particles may be responsible for this. It has been observed that blade surface roughness correlates positively with their ability to absorb particulate matter [41,42]. Broad-leaved forest has higher surface roughness, and pollutants are absorbed by the surface of the leaves, generating a good filtering effect for pollutants in the air and raising negative air ion concentration accordingly. Therefore, air oxygen content will increase in broad-leaved forest with high negative air ion concentration.

Finally, based on the residual path coefficients, it is found that there are some environmental factors that still have a greater impact that have not been taken into consideration in this study. Air oxygen content in the forest environment is affected by a variety of factors, including seasonal changes, climate change, geographic location, forest site conditions, plant species, growth conditions, etc. It is necessary to conduct in-depth research on the impact of these factors on air oxygen content in the future.

5. Conclusions

The air oxygen content is mainly affected by both stand conditions and the forest atmospheric environment. Vegetation types and atmospheric environmental factors greatly impact the content of oxygen released by plants. However, our knowledge of this topic is limited, as fewer scholars have concentrated on this theme. Currently, although some scholars have discovered differences in air oxygen content among different vegetation types and the correlation between air oxygen content and environmental factors, most scholars have not conducted further research on the dominant factors that affect the changes in air oxygen content among different vegetation types. Therefore, this study analyzed the differences and temporal patterns of oxygen content released by different vegetation types and introduced path analysis and decision analysis methods to eliminate the collinear relationship between environmental factors to probe into the comprehensive determinants of environmental factors on air oxygen content and clarify the dominant influencing factors of air oxygen content in different vegetation types.

The results proved that there were significant differences in air oxygen content and environmental factors, and the driving factors of air oxygen content were not completely consistent across different vegetation types. The air oxygen content was the highest in coniferous and broad-leaved mixed forest and the lowest in non-forest land. At different temporal scales, different vegetation types showed basically consistent changes in air oxygen content. More importantly, temperature was the most momentous environmental factor affecting air oxygen content in different vegetation types, followed by wind speed. This research may provide new insights into the temporal dynamics of air oxygen content and its driving factors in different vegetation types. Meanwhile, the air oxygen content is a paramount component of forest therapy resources, so the selection of vegetation types should be considered when improving air quality and enhancing the oxygen-releasing function of urban forests, giving priority to coniferous and broad-leaved mixed forest. Simultaneously, rationally combining resources such as temperature, relative humidity, light, and understory space can boost the scale effect of oxygen released from forests. Furthermore, the air oxygen content in different vegetation types reaches its peak during the summer. When the summer heat sets in, the suburban forest environment, known as the forest oxygen bar, is an excellent place for people to escape the heat, absorb oxygen, and rejuvenate their bodies and minds.

Admittedly, there were some limitations to this study. The environmental factors in this study were limited to four indicators: temperature, relative humidity, wind speed, and negative air ion concentration. Further studies can supplement the synchronous monitoring experiments of light intensity, ultraviolet radiation intensity, and other metrics. In addition to environmental factors, the influencing factors of air oxygen content should also take geographic location, forest site conditions, plant species, and growth conditions into account.