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Article

Isotopic Composition (δ15N and δ18O) of Urban Forests in Different Climate Types Indicates the Potential Influences of Traffic Exhaust and Relative Humidity

by
Cheng Gong
,
Chaofan Xian
* and
Zhiyun Ouyang
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(12), 2060; https://doi.org/10.3390/f13122060
Submission received: 2 November 2022 / Revised: 29 November 2022 / Accepted: 1 December 2022 / Published: 4 December 2022
(This article belongs to the Special Issue Stable Isotopes in Dendroecology)
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Abstract

:
Previous studies have shown that climatic and anthropogenic factors influence the variations in the stable isotope signature of tree materials in an urban ecosystem. However, the responses of urban trees to various anthropogenic and precipitation interventions are unclear. To fill the research gap, the isotopic composition (δ15N and δ18O) and total nitrogen content of the soil-plant system of urban forests were measured, focusing on typical urban tree species (Populus tomentosa and Ficus virens). The research was done under various land uses in China’s Beijing and Shenzhen megacities (with different humidity conditions). The linear mixed-effect model revealed that the δ15N values in the leaves of roadside Ficus virens were significantly higher than those in urban park sites, away from the road (δ15N difference = 7.2‰). In contrast, Populus tomentosa leaves exhibited a non-significant difference (δ15N difference = −1.8‰). Further regression analysis revealed that the variations in δ15N in tree leaves could be attributed to the proximity to highways, suggesting the influence of vehicle exhaust on δ15N values in roadside trees. The linear mixed-effect model also revealed δ18O values significantly higher in Populus tomentosa leaves than in Ficus virens leaves. To assess the influence of humidity conditions on δ18O values in the leaves, the ratio of the atmospheric and intercellular vapor pressures (ea/ei) of tree canopies was estimated. The result revealed that the ea/ei of Ficus virens was significantly higher than for Populus tomentosa, ascribed to the variation in the humidity conditions of the two megacities. These results highlight that urban trees can potentially serve as bioindicators for atmospheric pollution and humidity. Our findings highlight the ecophysiological responses of urban forests related to N and O as they vary according to traffic exhaust and relative humidity. Consequently, they are potentially valuable indicators of urban atmospheric contamination, forming a nature-based solution for citizen welfare improvement.

1. Introduction

Megacities in China are confronted with severe environmental problems arising from heavy transportation, industrial activities, and population movement [1,2]. Human-induced primary air pollutants (such as particulate matter (PM), nitrogen oxides (NOx), and ground-level ozone (O3)) are the major contributors to atmospheric pollution in many Chinese cities [3,4,5,6]. Roadside environments have become major hotspots for atmosphere pollution induced by traffic-derived NOx in megacity centers and on the outskirts [7,8,9,10]. Using green infrastructures to mitigate atmospheric pollution is widely recognized as a cost-effective and nature-based solution [11,12]. Usually, urban forests, community green spaces, and roadside trees can absorb anthropogenic nitrogen through stomatal absorption and canopy uptake [13,14]. Therefore, the characteristics of the natural pool of stable N isotopes (15N) in the urban tree canopy can be referred to as trance anthropogenic nitrogen input [14,15]. This is because 15N integrates the stable N isotopic signature with its sources, indicating an anthropic source and its fractionation against the heavier 15N isotope during the N transformation, assimilation, and remobilization [16,17,18,19]. Some studies have focused on several nitrogen sources (i.e., soil nitrogen, air-derived nitrogen) absorbed by urban trees, demonstrating the 15N signal of nitrogen pools (δ15N) in tree canopies as a multi-source mixed system [20,21].
Isotope fractionation effects influence the N isotopic composition of various sources. For example, the NOx or NO3− in a dry deposition is characterized by high δ15N values due to the isotopic exchange between nitric oxide (NO) and nitrogen dioxide (NO2), thereby causing the enrichment of heavier 15N atoms [22,23]. On the contrary, automobile exhaust-derived NOx shows lower and negative δ15N values, owing to the kinetic isotope fractionation that occurs during the NO thermal production in the combustion chambers of vehicular engines [24]. Influenced by air-derived nitrogen sources (such as NOx), the δ15N values of urban tree leaves may shift towards the δ15N signature of air-derived sources [23,25,26]. However, recent studies conducted in several Chinese cities detected fluctuations in δ15N values of roadside leaves [21,27]. Furthermore, studies have found that the climatic factors explained more variations in the δ15N of plants along the transect than the edaphic factors [28]. However, these studies failed to clarify the impact of heterogeneity of local meteorological conditions and the functional traits of tree species on δ15N variation in the various study regions [9,29]. Therefore, more in situ studies and multi-isotope techniques should be conducted in different regions of China to explore the influence of air-derived nitrogen in roadside plant systems.
Measuring the N isotope dynamics with other elements’ isotopes has been a relevant methodology for indicating the main drivers of N enrichment in the plant-soil systems for urban forests. It also demonstrates the effect of ambient air pollution on tree species [30], including the measurement of C and N isotopes [16,31] and O and N isotopes [20,27]. A previous study found that the climatic and edaphic variables explained 52.4% and 31.2% of the variation in plant δ15N, respectively [28]. Moreover, climatic factors can also influence the isotopic compositions of 18O in plants, since the discrimination against heavier 18O during water transportation and stomata movement is influenced by several climatic factors [32,33,34]. If the tree canopies are assumed to be aerodynamic with the ambient environment, the δ18O values of tree samples may signal the relative humidity (RH) [35]. Several researchers have proposed a model that assumes that the isotopic signal of 18O in the leaf water pool is a single and well-mixed system. In this system, the degree of leaf water enrichment is dependent on the ratio of the atmospheric and intercellular vapor pressures (ea/ei). The ea/ei can be approximated by the RH if tree canopies are aerodynamic with an ambient environment [36,37]. Several studies have shown evidence that RH has a significant relationship with δ18O in tree cellulose [36,38]. Therefore, δ18O may potentially indicate the local RH.
Research on urban trees among cities in various climatic zones is currently insufficient. To better understand the influence of climate and traffic conditions on urban trees, this study selected two megacities with different climatic conditions in China: Beijing and Shenzhen. The air quality in these cities is influenced by heavy traffic-related nitrogen emissions, hindering the sustainable improvement of the atmospheric environment [39,40,41]. Previous studies have revealed that roadside plants’ element or isotopic composition is influenced by local transportation [20]. However, it remains unclear whether the N and O isotopic compositions of the tree-soil system of urban trees and their responses to traffic nitrogen pollution would vary in other cities with different climatic conditions. This study aimed to fill the research gap in element isotope measurements across cities, thus contributing to the limited information on δ15N and δ18O measurements in urban forests. This research conducted an in situ study on δ15N and δ18O of tree-soil systems in roadside urban trees in Beijing and Shenzhen. Several hypotheses were formulated and tested by comparing the isotopic composition of the leaves and soils: (1) The isotopic composition of urban trees in Beijing and Shenzhen may differ due to the differences in the local meteorological conditions and functional traits of tree species; (2) The local vehicular fleet significantly affects the isotopic composition and TN content of roadside trees, i.e., the δ value of trees near the highway may exhibit higher isotopic composition of vehicular exhaust. (3) The δ18O value of tree leaves could reflect the RH of the local ambient environment.

2. Materials and Methods

2.1. Study Area

This study was conducted in the urban area of two megacities in China: Beijing and Shenzhen (Figure 1A). Beijing is located northwest of the North China Plain (115°25′−117°30′ E, 39°26′−41°03′ N, mean elevation = 44 m). The climate of Beijing is typical continental monsoon, with a monthly average temperature of 21 °C and precipitation of 62 mm in Summer. The major air pollutants in Beijing are particulate matter (PM), ozone (O3), and NOx [42]. Shenzhen is located in southern China (113°46′–114°37′ E, 22°27′–22°52′ N, mean elevation = 70 m). Its climate is humid subtropical, with a monthly average temperature of 28 °C and precipitation of 177 mm in summer. The major air pollutants in Shenzhen are O3 and NOx [43]. Table 1 provides more detailed information about the cities.

2.2. Sampling Design

Sampling was conducted in two types of green lands in summer (September–October 2021) based on the urban forest structure. One site was on roadside green belts, while the other was in the urban parks, far away from local vehicular influence. Following the selection criteria described, seven and four sampling sites were selected in Beijing and Shenzhen, respectively (Table 2). Populus tomentosa, a major urban greening tree species in Beijing [44], was sampled. In Shenzhen, Ficus virens was selected due to its widespread availability [45]. During sampling, three to five healthy trees were selected at each sampling site. 80–200 g of fresh leaves were collected from each tree.
Four to seven soil samples (5–30 cm) were collected at each site using a 10 cm diameter soil drill. Finally, 21 P. tomentosa leaf samples and 32 soil samples were collected in Beijing, while 16 F. virens leaf samples and 22 soil samples were collected in Shenzhen (Table 2). The sampling site distribution is depicted in Figure 1B,C.

2.3. Stable Isotope and Total Nitrogen Content

All leaves were cleaned using an ultrasonic cleaning machine for 10 min before analysis. The leaves were then dried at 70 °C for 24 h. The dry leaves were then ground to pass through a 200-mesh sieve. The soil samples were passed through a 1.7 mm sieve to remove small rocks and debris. These samples were then air-dried at 18 °C for seven days. All of the soil samples were ground and sieved through a 200 mm-mesh sieve.
The samples’ stable isotope composition was determined using an elemental analyzer (FLASH 200, Thermo Fisher Scientific, Waltham, MA, USA) operated in continuous-flow mode and connected to an isotope mass spectrometer (Delta V Advantage, Thermo Fisher Scientific, Waltham, MA, USA). Approximately 1.300 mg of plant samples and 0.255 mg of soil samples were used for the isotopic analysis. The δ value indicates the isotope ratio of each sample relative to the standard:
δ X ‰ = R s a m p l e R s t a n d a r d − 1 × 1000
where R is the molar ratio of heavy or light isotopes, and δX is the isotopic composition (δ15N and δ18O) of the samples. The δ15N values were recorded relative to air (δ15N = 0‰) and calibrated using a laboratory wheat flour standard (δ15N = 2.85 ± 0.17‰). The δ18O values were recorded relative to Vienna standard mean ocean water (VSMOW) and calibrated using the USGS32 (Potassium Nitrate) international standard (δ18O = 25.7 ± 0.4‰). The measuring precision was ±0.2‰ for δ15N and ±0.3‰ for δ18O.
Approximately 7.500 mg of leaf samples and 30.000 mg of soil samples were used to determine the total nitrogen content (TN), measured by an elemental analyzer (Vario EL III, Elementar, Hanau, Germany).

2.4. Estimating ea/ei

The 18O composition of the whole leaf tissue was determined by the isotopic composition of soil water, enrichment effect of leaf water due to transpiration, and the fractionation effect of biochemical synthesis of organic matter [46]. The degree of leaf water enrichment is partly influenced by the ratio of atmospheric and intercellular vapor pressure (ea and ei). ea/ei can be approximated by ambient relative humidity (RH) if tree canopies are assumed as aerodynamically coupled to the ambient environment. Such an assumption hints that the 18O signal of leaf organic matter can reflect the RH of the ambient environment. The δ18O values of whole leaf tissue can be described by the following model [46]:
δ 18 O lb = δ 18 O sw + f ε k + ε e + ( δ 18 O v − δ 18 O sw − ε k ) e a e i + ε c + ε c p
To determine ea/ei, Equation (2) was solved as follows:
e a e i = 1 f δ 18 O lb − δ 18 O sw − ε c − ε c p − ε k − ε e δ 18 O v − δ 18 O sw − ε k
Table 3 summarizes the parameters used in Equations (2) and (3). A correction factor f (0 < f < 1) was added to the equations, reflecting the dampening effect of leaf water δ18O signal due to the heterogeneity of leaf water and the exchange of oxygen atoms of sucrose with stem water [46]. To determine the trend of ea/ei based on changes in f, a group of f values was used in Equation (3).

2.5. Statistical Analysis

To compare the differences in δ values and TN between two types of greenland, several linear mixed-effect models were built to detect the influence of green land types. Then, a pairwise test estimated any significant difference in δ values and TN between the land types. To test the influence of vehicular fleet, the distance of the highway from the sites was extracted from the Gaode online map platform (https://www.amap.com/ (accessed on 1 September 2021)). Then, a linear regression model estimated the coefficients of highway distance. The normality of model residuals and heteroscedasticity of model error were checked before interpreting the models.
All statistical analyses were performed using the R language environment [49]. The R package ‘lme4′ built the linear mixed-effect model [50]. Regression diagnoses were performed by using the ‘performance’ package [51]. All statistical graphs were generated by ‘ggplot2′ and ‘cowplot’ packages [52,53].

3. Results

3.1. Isotopic Composition and Total Nitrogen Content

The mean δ15N of P. tomentosa leaves was 3.05 ± 2.22‰ (Table 4), while that of the soil samples below the canopy was 7.75 ± 1.72‰, which was significantly higher than the leaf samples. However, no significant differences were found for F. virens and the soil samples in Shenzhen. The mean δ18O value of P. tomentosa and F. virens was 33.75 ± 1.45‰ and 24.95 ± 2.09‰, respectively. The δ18O values in all soil samples were significantly lower than those found in the leaves in both megacities. Generally, the TN content of the soils was considerably lower than those of the leaves.
The difference in isotopic compositions (δ15N and δ18O) and TN content of samples located in different green lands were estimated by linear mixed models (Figure 2 and Figure 3). For the difference in δ15N in P. tomentosa leaves and soil samples, the results revealed lower mean δ15N values in the sites close to the urban park (Figure 2A, park) than those adjacent to the highway (Figure 2A, roadside). The δ15N values in urban park site soils were lower than those on the roadside. The δ15N values in leaf and soil samples in Shenzhen showed a similar trend. The δ15N in leaf and soil samples showed significantly lower values in the sites close to the urban park (differences of leaf samples = −7.2‰, p < 0.01, differences of soil samples = −3.2‰, p < 0.01). The different trends in δ18O values in leaves and soils were inconsistent between the cities. For Beijing samples, the δ18O values of P. tomentosa leaves and soil in the urban park were lower than those in the roadside green land. For those samples in Shenzhen, the δ18O values of leaves from urban park sites were higher than those from roadside sites. However, the opposite trend was observed in the soil samples. Generally, the differences in δ18O values in all sample types were insignificant (Figure 2B, p > 0.05).
We observed consistent trends in the differences between the cities’ TN values (Figure 3). For Beijing, the mean TN in P. tomentosa leaves and soil in the urban park sites was lower than that of samples from roadside sites, but the differences were insignificant. The linear mixed model revealed significant differences in TN of F. virens leaves in Shenzhen (Figure 3, differences of leaf samples = −0.83%, p < 0.05), albeit no significant differences with the soil samples.

3.2. Effect of the Vehicular Fleet on the Isotopic Composition and TN Content

The linear regression model tested the effect of vehicular fleet on the isotopic values (δ15N and δ18O) and TN content. The δ15N of leaf samples exhibited a significant negative correlation with highway distance (Figure 4A). For the δ15N value in P. tomentosa samples, the δ15N value decreased by 0.08‰ for every 1 m increase in distance from the highway. Road traffic significantly influenced the δ15N value in P. tomentosa leaves, and the linear regression model explained 28% of the variation. A similar negative correlation was found in the δ15N value in F. virens leaves. The linear regression model revealed that the δ15N value decreased by 0.02‰ for every 1 m increase in distance from the highway. The distance explained 59% of variation in δ15N value to the highway. However, no significant correlation existed between highway distance and the δ18O value of the leaves (Figure 4B). The variation of the δ18O value explained by the linear regression models was low (R2P.tomentosa = 0.01, R2F.virens = 0.03).
The vehicular fleet also affected the TN in the leaves (Figure 4C). The linear regression model indicated that TN in F. virens leaves in Shenzhen correlated significantly and negatively with highway distance. The regression model explains that the TN decreased by 0.01% for every 1 m increase in highway distance and 36% variation in TN. We observed a similar negative trend in TN in P. tomentosa leaves (Beijing), but the variation explained by the model was very low (R2P.tomentosa = 0.01).

3.3. ea/ei of Trees in Two Cities

The ea/ei of P. tomentosa and F. virens canopies are obtained using Equation (3) and the parameters in Table 3. The δ18O values for P. tomentosa ranged from 30.5–36.5‰, while that for F. virens was 21.2–28.6‰. To study the effect of varying f, Equation (3) was solved for f = 0.5–1.0 for P. tomentosa and 0.4–1.0 for F. virens. We did not evaluate Equation (3) for f < 0.4 or 0.5 because lower f values would yield a negative ea/ei. Figure 5a,b displays the results obtained after solving Equation (3). The slope and intercept of lines depend on f for the tree species; lower f values tend to yield a lower ea/ei. Under the same f value, the ea/ei of P. tomentosa was always lower than the ea/ei of F. virens. For example, for f = 0.7, the ea/ei for P. tomentosa and F. virens cover the range of 0.07–0.31 and 0.39–0.68, respectively.
To compare the differences in ea/ei between the cities, the ea/ei were solved for the mean δ18O value in leaves and soil water. Figure 5C shows the trend of ea/ei. The ea/ei in Beijing and Shenzhen covers 0.01–0.43 and 0.18–0.67, respectively, showing that that of Beijing was significantly lower than Shenzhen’s (Wilcoxon rank sum test, W = 52, p < 0.001). This observation could be reasonable for the differences in RH and climate between the two megacities.
Figure 6 shows the ea/ei values under the green land types. The ea/ei of P. tomentosa in urban park sites covers 0.01–0.44, while that on the roadside sites covers 0.01–0.41. The difference in the ea/ei of the two green land types was insignificant (Wilcoxon rank sum test, W = 342, p = 0.577). Contrasting ea/ei trends under different types of green land exist in Shenzhen. For urban park sites, the ea/ei covers 0.15–0.66. Similarly, the ea/ei of the roadside samples ranged from 0.23 to 0.69, which is higher than that of the urban park sites. No significant difference in ea/ei exists between the two types of green land (Wilcoxon rank sum test, W = 251, p = 0.239).

4. Discussion

4.1. Variation of Foliar δ15N Values in Two Cities

This study measured the δ15N of the leaves in two China megacities (Table 4). A recent study in a similar climate zone reported that δ15N values in the Populus tremuloides leaves were 2.82 ± 1.76‰, similar to our current result [15]. Elsewhere, another study also reported the δ15N values of the subtropical urban forests in the center of Sao Paulo, Brazil as 2.66 ± 1.27‰, similar to those of F. virens values in Shenzhen’s park sites covered by subtropical urban forests [16].
Table 4 shows that the δ15N in Beijing’s P. tomentosa leaves was lower than that found in Shenzhen’s F. virens. As higher vascular plants, P. tomentosa and F. virens mainly utilize nitrogen from the soil. Therefore, the δ15N values in their leaves display the influence of soil nitrogen [54]. Interestingly, the soil δ15N values in Beijing were significantly higher than in Shenzhen (Student’s t-test, t = 6.517, p < 0.001). One possible reason for this inconsistent trend is the variation in the 15N abundance in the different plant species [55]. Non-environmental factors, such as plant species, can influence trees’ 15N composition. Previous studies have found that the difference in δ15N values of different plant species can be >3.20‰ [56].

4.2. Influences of Green Land Type and Vehicular Fleet

Our study found that the δ15N value in leaves varies between two types of green land (Figure 2A). The post-hoc comparison of linear mixed models revealed that the δ15N in F. virens leaves collected from urban park sites was significantly lower than that collected from the roadside (Figure 2A, Shenzhen). We also found that TN content in F. virens leaves from urban park sites were significantly lower than in the leaves collected from the roadside (Figure 3, Shenzhen). Several studies have found that the vegetation’ s δ15N and TN contents tend to increase by vehicular exhaust or fossil fuel combustion sources, as found in Shenzhen [15,16,21]. However, we did not observe such results in P. tomentosa leaves in Beijing (Figure 2A, Beijing).
The δ15N values in leaves showed the influence of traffic exhaust in this study, exhibiting a significantly negative correlation with distance to the highway in both cities. However, the correlation between δ18O and highway distance was insignificant in both cities (Figure 4A,B). Several recent studies conducted in near-road environments have found a similar correlation between plant δ15N and highway distance [25,26,57]. This correlation can be explained by the isotopic characteristics of 15N in nitrogen deposition and vehicular exhaust. Figure 4A also displayed that the slope of the regression line in Beijing is smaller than that of Shenzhen. The different RH conditions in the two cities could explain the difference in the decrease trends of δ15N. In Shenzhen, the foliar uptake of NOx by tree leaves under high RH condition is significantly influenced by summer wet deposition characterized with lower isotopic signatures [58]. Therefore, the isotopic signal of leaves may be partially masked by the δ15N characteristics of wet deposition. Besides, the dispersion and deposition of NOx may have potential influences on foliar δ15N. Several studies have demonstrated that the wind speed near tree canopy may influence the deposition velocity and dispersion distance of atmospheric pollutants [59,60]. However, in this case, similar wind speeds during the sampling period were supposed to have limited effects on foliar δ15N, which should be addressed by future works. Figure 7A compares the isotopic signature of potential nitrogen sources and leaves in this study. The measured NOx in the dry deposition in the roadside environment tends to be positive [61,62]. Therefore, trees adjacent to the road were influenced by the positive δ15N air-borne nitrogen source, resulting in an increase of δ15N for the leaves along the roadside.
Although the δ15N values in tree leaves are a useful bioindicator of atmospheric nitrogen pollution in the urban environment, several cautions are needed. First, higher vascular plants mainly absorb nitrogen from soils. Therefore, the isotopic signal of leaves may be masked by the δ15N characteristics of soil and the discrimination effect for heavier 15N during the assimilation of organic nitrogen through the glutamine synthetase-glutamate synthase (GS-GOGAT) pathway [17,66]. In this study, the δ15N in P. tomentosa was significantly lower than in the soil samples, which is explained by the discrimination effect during nitrogen assimilation. Such an effect may mask the influence of anthropogenic nitrogen sources with higher δ15N values [66]. Secondly, the heterogeneity of the isotopic composition of nitrogen sources in different regions may diminish leaves’ reliability as bioindicators. Several studies have reported that trees can absorb nitrogen in wet deposition through the canopy uptake route. Therefore, the isotopic precipitation signal may affect foliar δ15N [14,67]. We collected the leaf samples during the rainy season in Shenzhen. In contrast, there was less precipitation during the sampling period in Beijing. Although a significant influence of vehicle exhaust was detected in this study, the NO3- in the precipitation with similar isotopic composition may partially mask the foliar δ15N signal [61,62].
Some reports have reported correlations between leaves’ TN content and atmospheric nitrogen deposition [25,57,68]. In this study, two plant species’ TN content negatively correlated with highway distance. However, we found only one significant relationship between leaf TN and vehicular fleet in F. virens in Shenzhen (Figure 3 and Figure 4c). The different sampling strategies in the two cities could explain the difference in the response of the two tree species to the vehicular fleet. In Beijing, the distance to the highway was much narrower than in Shenzhen. Therefore, the influence of vehicular fleets may not be significant at this distance. More research is needed to better understand the relationship between leaf TN content and vehicular exhaust.

4.3. Influence of Relative Humidity

The δ18O of the leaves was measured in two Chinese megacities (Table 4). The foliar δ18O of P. tomentosa in Beijing was significantly higher than the foliar δ18O of F. virens in Shenzhen (Wilcoxon rank sum test, W = 0, p < 0.001). The significant difference in the foliar δ18O of the two tree species revealed a difference in the cities’ relative humidity. Evaporation enriches heavier 18O atoms at evaporation sites because heavier H218O diffuses slower than normal H216O [35,36]. On the other hand, the degree of 18O enrichment is negatively correlated with the ratio of atmospheric and intercellular vapor pressures (ea/ei) [46]. Therefore, the low RH in the ambient environment would result in higher foliar δ18O due to an increase in 18O enrichment at the evaporation site [16,46].
To further investigate the relationship between δ18O and RH, we evaluated the ea/ei of the two tree species by applying Equation (3). The ea/ei range was determined by a sequence of dampening effect factor f. The model evaluation results showed that the average ea/ei of F. virens in Shenzhen was significantly higher than in Beijing (Figure 5). It is consistent with the prediction of theory model and the RH conditions in the summer of the two cities (RH in Shenzhen = 76%; RH in Beijing = 51%).
The influence of RH on plant’s δ18O has been widely studied [36,38]. However, some other studies also showed that RH does not significantly correlate with δ18O [69,70]. The inconsistency in those results suggests that the assumption of the Craig-Gordon model should be noted in the model evaluation. One of the confounding factors that may mask the RH signal is the dampening effect of leaf water heterogeneity. In some deep root trees that mainly utilize deep soil water or groundwater, the 18O signal in the soil water (δ18Osw) may largely contribute to the variation in foliar δ18O, resulting in a weak correlation between RH and foliar δ18O [71]. Although Equation (3) added a correction factor f to summarize the dampening effect [46], and such a simple correction factor may not accurately reflect the impact of water heterogeneity in leaf water transpiration. The other confounding factor is the variation of δ18Osw in different regions. As Equation (3) describes, the δ18Osw positively relates to the foliar δ18O. Furthermore, comparing the 18O composition of soil water and precipitation in Shenzhen and Beijing showed no significant difference between the cities (Figure 7B). Therefore, the influence of variation in δ18Osw in this study should be assumed to be minimal.

4.4. Limitations and Future Studies

This study measured the isotopic composition and TN content in P. tomentosa and F. virens in two cities. Although this study preliminarily revealed the influence of vehicular fleet and RH condition on the isotopic composition of trees, some limitations still needs to be addressed in future research. Here, we compared the isotopic composition of tree leaves in 11 sampling sites. Therefore, more sampling sites should be included in future studies to better represent the variation of isotopic composition in different green land types. Also, this study evaluated Equation (3) to obtain the ea/ei of the trees and compared the differences in the ea/ei of the two cities under different RH conditions. Moreover, the leaf water heterogeneity and variation of δ18O in source water may mask the RH signal in leaf δ18O (Section 4.3). To improve the model’s accuracy, the isotopic composition of water sources of urban trees should be determined (e.g., precipitation, artificial watering, soil water, etc.). On the other hand, several studies have combined δ18O and δ13C to reveal a more accurate influence of the RH signal on leaves [46], and this should be considered in future research. In addition, regional-specific irrigation methods would be taken to prevent pollution and facilitate tree growth in these two cities with different climate zones [72,73]. In this case, the isotopic signature of irrigation water may vary and influence the foliar δ18O. Thus, the effect of irrigation strategies, as well as isotopic signatures of irrigation water, should be considered in future research.
This study has applied the isotope method to reveal anthropogenic nitrogen (vehicle exhaust)’s impact and RH condition on urban trees. We agree that this method can be used in ecological management in urban areas. For example, measuring the δ15N composition of leaves of urban trees may help monitor local nitrogen deposition. Besides, leaves’ δ18O could reflect historical RH conditions to detect the cooling and humidification effects of urban ecosystem services. This study provides some useful information for future studies to improve the quantitation precision of botanical nitrogen pollution purification. It improves on previous local case studies in Beijing and Shenzhen that relied merely on the i-Tree Eco model [39,74].

5. Conclusions

We applied the isotopic method of 15N and 18O to understand the effects of vehicular fleet and RH conditions on typical urban tree species (P. tomentosa and F. virens) in two megacities, Beijing and Shenzhen. The hypothesis that the isotopic composition of tree leaves varies in two megacities was observed, which addressed that the δ15N and δ18O in the leaves of roadside trees were influenced by vehicle exhaust. Moreover, the isotopic compositions and TN in the leaves near the highway shift towards those of vehicle exhaust, partially confirmed in Shenzhen. Besides, the variations of foliar δ18O and ea/ei were mostly consistent with RH conditions in the cities, supporting the third hypothesis. The new insights on the variations in isotopic compositions in this study will interest urban policymakers involved with the ecological management of the urban ecosystem. Consequently, the foliar isotopic composition (δ15N and δ18O) can be a useful indicator of urban atmospheric contamination for forming a nature-based solution for improved citizen welfare.

Author Contributions

Conceptualization, C.G. and C.X.; Data curation, C.G. and C.X.; Formal analysis, C.G.; Funding acquisition, C.X.; Methodology, C.G. and C.X.; Project administration, C.X.; Resources, C.X. and Z.O.; Supervision, C.X. and Z.O.; Visualization, C.G. and C.X.; Writing—original draft, C.G.; Writing—review & editing, C.G. and C.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant number 42101290.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the copyright of relevant data in the article belonging to the research group rather than to individuals.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Forests 13 02060 g001 550
Figure 1. (A) Location of Beijing and Shenzhen. (B) An aerial view of Beijing showing the sampling sites. (C) An aerial view of Shenzhen showing the sampling sites (Notes: Blue squares represent the sampling sites in greenbelts adjacent to a highway. Red squares represent the sampling sites in urban parks).
Figure 1. (A) Location of Beijing and Shenzhen. (B) An aerial view of Beijing showing the sampling sites. (C) An aerial view of Shenzhen showing the sampling sites (Notes: Blue squares represent the sampling sites in greenbelts adjacent to a highway. Red squares represent the sampling sites in urban parks).
Forests 13 02060 g001
Forests 13 02060 g002 550
Figure 2. Differences in stable isotopic compositions ((A) δ15N and (B) δ18O) of urban trees in the green lands (Note: The numbers and asterisk above the bars indicate the mean value differences. Significance of differences: ** for p < 0.01. Error bars indicate the 95% confidence interval of the means).
Figure 2. Differences in stable isotopic compositions ((A) δ15N and (B) δ18O) of urban trees in the green lands (Note: The numbers and asterisk above the bars indicate the mean value differences. Significance of differences: ** for p < 0.01. Error bars indicate the 95% confidence interval of the means).
Forests 13 02060 g002
Forests 13 02060 g003 550
Figure 3. Differences in TN content of urban trees in the green lands (Note: The numbers and asterisk above the bar indicate the mean value differences. Significance of differences: * for p < 0.05. Error bars indicate the upper 95% confidence interval of the means).
Figure 3. Differences in TN content of urban trees in the green lands (Note: The numbers and asterisk above the bar indicate the mean value differences. Significance of differences: * for p < 0.05. Error bars indicate the upper 95% confidence interval of the means).
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Forests 13 02060 g004 550
Figure 4. Relationship between highway distance and (A) δ15N, (B) δ18O, and (C) TN of urban tree leaves. (Note: The coefficients of the linear regression model are displayed above the regression line. The shaded area represents a 95% confidence interval of the fitted value. The significant coefficients of the linear regression are marked in red. Significance of coefficients: ** for p < 0.01).
Figure 4. Relationship between highway distance and (A) δ15N, (B) δ18O, and (C) TN of urban tree leaves. (Note: The coefficients of the linear regression model are displayed above the regression line. The shaded area represents a 95% confidence interval of the fitted value. The significant coefficients of the linear regression are marked in red. Significance of coefficients: ** for p < 0.01).
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Forests 13 02060 g005 550
Figure 5. The relationship between δ18O in leaves and ea/ei under different values of (A) P. tomentosa leaves in Beijing, (B) F. virens leaves in Shenzhen. (C) Compared ea/ei between the two cities. The text in the figure shows the mean δ18O values, in source water and leaves, used in Equation (3).
Figure 5. The relationship between δ18O in leaves and ea/ei under different values of (A) P. tomentosa leaves in Beijing, (B) F. virens leaves in Shenzhen. (C) Compared ea/ei between the two cities. The text in the figure shows the mean δ18O values, in source water and leaves, used in Equation (3).
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Forests 13 02060 g006 550
Figure 6. Relationship between δ18O in leaves and ea/ei under different green land types (A) P. tomentosa leaves in Beijing (B) F. virens leaves in Shenzhen. The ea/ei was calculated based on mean δ18O values in source water and leaves. The text in the figure shows the mean δ18O values in the leaves under different green land types used in Equation (3).
Figure 6. Relationship between δ18O in leaves and ea/ei under different green land types (A) P. tomentosa leaves in Beijing (B) F. virens leaves in Shenzhen. The ea/ei was calculated based on mean δ18O values in source water and leaves. The text in the figure shows the mean δ18O values in the leaves under different green land types used in Equation (3).
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Forests 13 02060 g007 550
Figure 7. Comparison of the isotopic signal of leaves and potential sources: (A) δ15N in leaves and potential nitrogen sources; (B) δ18O in leaves and potential water sources. (Note: Mean values and standard deviation are shown as error bars. Data sources: precipitation [47,63,64,65], dry deposition [9], soil (current study), leaves (current study), and soil water [47,48]).
Figure 7. Comparison of the isotopic signal of leaves and potential sources: (A) δ15N in leaves and potential nitrogen sources; (B) δ18O in leaves and potential water sources. (Note: Mean values and standard deviation are shown as error bars. Data sources: precipitation [47,63,64,65], dry deposition [9], soil (current study), leaves (current study), and soil water [47,48]).
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Table
Table 1. Detailed information on Beijing and Shenzhen (Note: Data are averages obtained from the local municipal environment bulletins).
Table 1. Detailed information on Beijing and Shenzhen (Note: Data are averages obtained from the local municipal environment bulletins).
CityRelative Humidity (%)Forest Coverage (%)PM10 (μg/m3)PM2.5 (μg/m3)NO2 (μg/m3)O3 (μg/m3)
Beijing5144.4563829149
Shenzhen7639.1371823130
Table
Table 2. Detailed information on the sampling sites.
Table 2. Detailed information on the sampling sites.
CityType of Green Land Number of SitesMean Distance to Highway (m)Soil SamplesLeaf Samples
Beijingurban park4411712
green belt 328159
Shenzhenurban park24301410
green belt214086
TotalAll sites111225437
Table
Table 3. Summary of parameters in Equations (2) and (3). (Note: a: for Beijing samples. b: for Shenzhen samples).
Table 3. Summary of parameters in Equations (2) and (3). (Note: a: for Beijing samples. b: for Shenzhen samples).
ParameterDescriptionValueSources
εeEquilibrium fractionation factor, change of phase from liquid water to vapor at 20 °C9.8‰[46]
εkKinetic fractionation factor, diffusion of vapor into unsaturated air26.5‰[46]
εcBiochemical fractionation factor, discrimination effect between water and the biosynthesis of cellulose27‰[46]
εcpThe difference in enrichment between whole plant tissue and its cellulose–8‰[35]
δ18OswThe δ18O value of soil water, extracted from local studies−5.9‰
−6.0‰		[47,48]
δ18OvThe δ18O value of atmospheric vapor, under the assumption of isotopic equilibrium with soil water, δ18Ov − δ18Osw = −εe−15.7‰ a
−15.8‰ b		Calculated based on equilibrium assumption
δ18OlbThe δ18O value of whole leaf tissue determined in this study30.5~36.5‰ a
21.2~28.6‰ b		A range of values based on samples
fCorrection factor summarize the dampening effect of leaf water δ18O signal0.4~1A range of values between 0.4 and 1
ea/eiThe ratio of atmospheric and intercellular vapor pressure–Unknown value that need to solve the equation
Table
Table 4. Descriptive statistics of the plant and soil samples’ data (Note: a: Beijing samples. b: Shenzhen samples).
Table 4. Descriptive statistics of the plant and soil samples’ data (Note: a: Beijing samples. b: Shenzhen samples).
P. Tomentosa a (n = 21)Soil a (n = 32)F. virens b (n = 16)Soil b (n = 22)
δ15N (‰)All3.05 ± 2.227.75 ± 1.724.34 ± 4.404.06 ± 2.26
Park3.57 ± 2.127.37 ± 1.421.64 ± 2.052.88 ± 1.77
Road2.35 ± 2.278.42 ± 2.038.84 ± 3.416.12 ± 1.36
δ18O (‰)All33.75 ± 1.4520.43 ± 1.5524.95 ± 2.0912.08 ± 1.28
Park33.36 ± 1.0220.2 ± 1.6025.36 ± 2.2011.70 ± 1.10
Road34.27 ± 1.8220.8 ± 1.4424.27 ± 1.8912.70 ± 1.41
TN (%)All1.66 ± 0.150.12 ± 0.052.01 ± 0.700.14 ± 0.10
Park1.63 ± 0.160.11 ± 0.051.69 ± 0.320.11 ± 0.08
Road1.71 ± 0.140.13 ± 0.062.52 ± 0.890.20 ± 0.12
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Gong, C.; Xian, C.; Ouyang, Z. Isotopic Composition (δ15N and δ18O) of Urban Forests in Different Climate Types Indicates the Potential Influences of Traffic Exhaust and Relative Humidity. Forests 2022, 13, 2060. https://doi.org/10.3390/f13122060

AMA Style

Gong C, Xian C, Ouyang Z. Isotopic Composition (δ15N and δ18O) of Urban Forests in Different Climate Types Indicates the Potential Influences of Traffic Exhaust and Relative Humidity. Forests. 2022; 13(12):2060. https://doi.org/10.3390/f13122060

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Gong, Cheng, Chaofan Xian, and Zhiyun Ouyang. 2022. "Isotopic Composition (δ15N and δ18O) of Urban Forests in Different Climate Types Indicates the Potential Influences of Traffic Exhaust and Relative Humidity" Forests 13, no. 12: 2060. https://doi.org/10.3390/f13122060

APA Style

Gong, C., Xian, C., & Ouyang, Z. (2022). Isotopic Composition (δ15N and δ18O) of Urban Forests in Different Climate Types Indicates the Potential Influences of Traffic Exhaust and Relative Humidity. Forests, 13(12), 2060. https://doi.org/10.3390/f13122060

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