The Dual Role of Urban Vegetation: Trade-Offs Between Thermal Regulation and Biogenic Volatile Organic Compound Emissions

Abstract

Under the dual pressures of global warming and accelerated urbanization, urban green spaces (UGS) serve as crucial yet paradoxical elements, alleviating urban heat island (UHI) effects while emitting biogenic volatile organic compounds (BVOCs) that exacerbate air pollution; however, their spatial trade-offs remain underexplored. This study bridges this gap by developing an Urban Heat Mitigation Index (HMI) and a BVOC flux accounting framework integrating remote sensing and field observations. The results showed that (1) the cooling effect exhibits significant spatial heterogeneity, with continuous green networks around West Lake and along the Qiantang River forming efficient cooling corridors (HMI > 0.75), while fragmented green spaces in northeastern areas show weaker cooling effects (HMI < 0.35); (2) BVOC emission intensity displays a “high suburbs-low centers” pattern, with suburban areas emitting 1.9–2.3 times more BVOCs than urban centers, while BVOC-induced PM_2.5 (0.02–0.05 μg m⁻³) and O₃ (12–33 μg m⁻³) concentrations in city centers still pose significant health risks; (3) spatial analysis reveals a weak positive correlation between HMI and BVOC emissions (Moran’s I = 0.096, p < 0.05), with four distinct coupling patterns identified: “high cooling-low emissions” (17.5% of area), “low cooling-high emissions” (1.1%), “high cooling-high emissions” (18.7%), and “low cooling-low emissions” (3.9%). This study provides quantitative evidence for optimizing UGS layouts to balance ecological benefits and environmental risks, emphasizing the importance of vegetation screening and spatial allocation in sustainable urban planning.

1. Introduction

Global warming has become the most serious environmental challenge of the 21st century, with the frequency of extreme heat events posing a direct threat to urban systems [1,2,3]. Concurrent with accelerated urbanization and population concentration [4,5], changes in subsurface properties and vegetation cover [6,7], triggered by land-use changes, have not only significantly reshaped the landscape pattern and surface energy balance of cities, but have also resulted in a significant change in the energy balance of urban landscapes [8] causing multiple environmental effects, including the degradation of ecosystem services [9,10], the intensification of atmospheric pollution [11,12], and the urban heat island (UHI) effect [13,14,15].

Notably, the synergistic effect of the UHI with global warming significantly amplifies heat-related mortality risks, posing a critical challenge to global public health [16,17]. As a core element of blue–green infrastructure, urban green space (UGS) is recognized as the most promising nature-based solution for microclimate regulation through shade effect and transpiration cooling mechanism [18,19,20,21], while delivering additional essential ecosystem services [22,23]. Its cooling effectiveness is co-regulated by patch size, spatial configuration, and characteristics of the surrounding built environment [14,15,24], and this multidimensional dependence has led to the significant spatial heterogeneity of cooling effectiveness among global cities [25].

However, the ecological benefits of UGS are accompanied by countervailing effects. Vegetation-released biogenic volatile organic compounds (BVOCs) initiate photochemical processes that generate ground-level ozone (O₃) and secondary organic aerosols (SOA) [26], significantly contributing to urban O₃ and PM_2.5 pollution episodes [27]. These pollutants increase respiratory and cardiovascular disease mortality [28,29]; O₃ also harms ecosystems by reducing photosynthetic rates and even plant yields [30], a risk that is particularly pronounced at high urban background concentrations [31]. Current studies have focused on the characterization of BVOC emissions at different tree species levels [32,33]; the construction of multi-scale BVOC emission inventories [34], and the contribution of BVOCs to regional atmospheric composite pollution [35]. However, there are significant limitations in the refined spatial characterization of the existing methods [34], which seriously constrain the accurate assessment of urban air quality and public health impacts [36,37,38].

In the context of climate change and UHI synergistically exacerbating health risks, how to weigh the cooling effectiveness of UGS and the risk of BVOC emissions has become a key issue to be solved for sustainable urban development. Taking Hangzhou, China, as a case study, this research innovatively integrates 2020 field observation data with remote sensing techniques. We develop an urban heat mitigation index to quantify UGS cooling effectiveness while employing bottom-up BVOC flux accounting through biogenic emission modeling. Spatial overlay analysis and autocorrelation modeling reveal the geographic coupling between thermal regulation and emission patterns. These methodological advances not only elucidate the complex trade-off mechanism between urban greening benefits and air quality risks but also establish a transferable framework for evidence-based urban planning, providing municipalities like Hangzhou with science-driven strategies to simultaneously address climate adaptation and environmental health challenges.

2. Materials and Methods

2.1. Study Area

Hangzhou (29°11′ N–30°33′ N, 118°21′ E–120°30′ E) is located in the northern part of China’s southeastern coastal region (Figure 1) and has a subtropical monsoon climate. There are four distinct seasons and abundant rainfall. The average annual temperature of the city is 17.8 °C, and the annual precipitation is 1454 mm. The main vegetation types here are broad-leaved evergreen forests and mixed forests. In recent years, Hangzhou’s green space has been expanding, and from 2010 to 2021, the area of parks and green spaces increased to 11,124 hectares (https://tjj.hangzhou.gov.cn/art/2023/12/4/art_1229453592_4222689.html, accessed 2 February 2025).

2.2. Urban Thermal Mitigation Analysis

The urban cooling model from the integrated valuation of ecosystem services and trade-offs (InVEST) model (https://naturalcapitalproject.stanford.edu/software/invest, accessed 2 February 2025) was used to calculate thermal mitigation in the city. The model first calculates a cooling capacity (CC) index for each pixel based on local shade, evapotranspiration, and albedo. The shade factor represents the proportion of tree canopy (≥2 m in height) associated with each land use/land cover (LULC) category. Potential evapotranspiration (https://data.tpdc.ac.cn, accessed 5 February 2025) and land use (http://www.resdc.cn, accessed 8 February 2025) were resampled in the calculations.

The evapotranspiration index (ETI) represents a normalized value of potential evapotranspiration [39]:

$$ETI={\displaystyle \frac{K_{c}ET0}{{ET}_{max}}}$$

(1)

where K_c is the evapotranspiration coefficient of the crop, ET0 is the reference evapotranspiration (mm), and ET_max is the maximum raster evapotranspiration value (mm). The cooling index CC_i for each pixel is calculated as follows:

$${CC}_i=0.6shade+0.2albedo+0.2\mathrm{E}\mathrm{T}1$$

(2)

where shade is shade and albedo indicate the percentage of reflected solar radiation for that site type (%).

Urban heat mitigation index HMI:

$$HMI=\left\{\begin{array}{c}{CC}_i\hspace{1em}\hspace{1em}\mathrm{i}\mathrm{f}{CC}_i\ge {CC}_{{park}_i}\mathrm{o}\mathrm{r}{GA}_i<2\mathrm{h}\mathrm{a}\\ {CC}_{{park}_i}\hspace{1em}\hspace{1em}\hspace{1em}\mathrm{O}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{u}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\end{array}\right.$$

(3)

where CC_park is a cooling index for each park [40,41]:

$${\mathrm{G}\mathrm{A}}_{\mathrm{i}}={\mathrm{c}\mathrm{e}\mathrm{l}\mathrm{l}}_{\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}\times {\sum }_{\mathrm{j}\in \mathrm{d}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{u}\mathrm{s} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{i}}{\mathrm{g}}_{\mathrm{i}}$$

(4)

$${\mathrm{C}\mathrm{C}}_{{\mathrm{p}\mathrm{a}\mathrm{r}\mathrm{k}}_{\mathrm{i}}}={\sum }_{\mathrm{j}\in \mathrm{d}\mathrm{r}\mathrm{a}\mathrm{d}\mathrm{i}\mathrm{u}\mathrm{s} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{i}}{\mathrm{g}}_{\mathrm{i}}\times {\mathrm{C}\mathrm{C}}_{\mathrm{j}}\times {\mathrm{e}}^{\left({\displaystyle \frac{-\mathrm{d}\left(\mathrm{i},\mathrm{j}\right)}{{\mathrm{d}}_{\mathrm{c}\mathrm{o}\mathrm{o}\mathrm{l}}}}\right)}$$

(5)

where cell_area represents the grid area in hectares, g_i is pixel j is green space or 0 if it is not, d_(i,j) is the distance between pixels i and j, while d_cool denotes the distance over which the green space exerts a cooling effect (m). The distance-weighted average value of CC_i generated by green spaces is calculated based on these parameters.

2.3. Estimation of BVOC Emissions and Related Air Pollution

In this study, BVOCs were categorized as isoprene (E_ISP), monoterpenes (E_MN), sesquiterpenes (E_SQT), and other volatile organic compounds (OVOCs) (E_OVP). The BVOC emissions algorithm are as follows [42,43,44]:

$${\mathrm{E}}_{\mathrm{I}\mathrm{S}\mathrm{P}}={\mathsf{\epsilon }}_{\mathrm{I}\mathrm{S}\mathrm{P}}\mathrm{D}{\mathsf{\gamma }}_{\mathrm{T}}{\mathsf{\gamma }}_{\mathrm{S}}{\mathsf{\gamma }}_{\mathrm{P}}{\mathsf{\gamma }}_{\mathrm{C}}$$

(6)

$$E_{MNS}={\epsilon }_{MNS}D{\gamma }_T{\gamma }_S{\gamma }_P$$

(7)

$${\mathrm{E}}_{\mathrm{M}\mathrm{N}\mathrm{P}}={\mathsf{\epsilon }}_{\mathrm{M}\mathrm{N}\mathrm{P}}\mathrm{D}{\mathsf{\gamma }}_{\mathrm{T}}{\mathsf{\gamma }}_{\mathrm{S}}$$

(8)

$${\mathrm{E}}_{\mathrm{S}\mathrm{Q}\mathrm{T}}={\mathsf{\epsilon }}_{\mathrm{S}\mathrm{Q}\mathrm{T}}\mathrm{D}{\mathsf{\gamma }}_{\mathrm{T}}{\mathsf{\gamma }}_{\mathrm{S}}$$

(9)

$${\mathrm{E}}_{\mathrm{O}\mathrm{V}\mathrm{P}}={\mathsf{\epsilon }}_{\mathrm{O}\mathrm{V}\mathrm{P}}\mathrm{D}{\mathsf{\gamma }}_{\mathrm{T}}{\mathsf{\gamma }}_{\mathrm{S}}$$

(10)

$${\mathrm{E}}_{\mathrm{M}\mathrm{N}}=\mathrm{L}\mathrm{D}\mathrm{F}\times {\mathrm{E}}_{\mathrm{M}\mathrm{N}\mathrm{S}}+\left(1-\mathrm{L}\mathrm{D}\mathrm{F}\right)\times {\mathrm{E}}_{\mathrm{M}\mathrm{N}\mathrm{P}}$$

(11)

where ε represents the basal emission rate (μg C g⁻¹ h⁻¹) under standard conditions, and the photosynthetically active radiation (PAR) is 1000 μmol m⁻² s⁻¹. The Peak leaf biomass (D) (g) for each tree species was calculated from diameter at breast height (DBH) and tree height using the anisotropic growth equation [45]. γ_T, γ_P, and γ_S are the environmental correction factors used to calculate the effects of temperature, light intensity, and seasonal variations on the synthetic emissions, respectively. γ_C is the inhibitory factor for isoprene emissions by CO₂. Air temperature was used to calculate γ_T because the difference between leaf temperature and air temperature within the city is minimal [46]. Photosynthetically active radiation (PAR) and leaf area index (LAI) were used to calculate γ_P; atmospheric CO₂ concentration was used to calculate γ_C. Detailed information can be found in Figure S1.

We used the Fractional Aerosol Coefficient (FAC) and Incremental Reactivity (IR) methods to estimate the air quality impacts of BVOC emissions, such as O₃ and Secondary Organic Aerosols (SOA) [47,48]:

$${\mathrm{O}\mathrm{F}\mathrm{P}}_{\mathrm{i},\mathrm{j}}={\mathrm{M}\mathrm{I}\mathrm{R}}_{\mathrm{i}}\times {\displaystyle \frac{{\mathrm{E}}_{\mathrm{i},\mathrm{j}}\times {\mathsf{\tau }}_{{\mathrm{O}}_3}}{{\mathrm{S}}_{\mathrm{i}}\times \mathrm{H}}}$$

(12)

$${\mathrm{S}\mathrm{O}\mathrm{A}}_{\mathrm{i},\mathrm{j}}={\mathrm{F}\mathrm{A}\mathrm{C}}_{\mathrm{i}}\times {\displaystyle \frac{{\mathrm{E}}_{\mathrm{i},\mathrm{j}}\times {\mathsf{\tau }}_{\mathrm{S}\mathrm{O}\mathrm{A}}}{{\mathrm{S}}_{\mathrm{i}}\times \mathrm{H}}}$$

(13)

where E_i,j represents the BVOC emissions estimated; S is the area of grid i (30 m × 30 m); τ is the atmospheric lifetime of SOA or O₃. MIR (g O₃/g VOC) denotes the maximum incremental reactivity of the species i. FAC is the aerosol formation coefficient (g SOA/g VOC) and H is the boundary layer height estimated from hourly surface meteorological observations. Specific detailed information can be found in Table S2.

2.4. Spatial Autocorrelation

The autocorrelation model and spatial superposition were used to analyze the spatial relationship between BVOC and HMI to reveal the geographic coupling between the cooling effect of UGS and the risk of BVOC emissions, and to identify the high-risk areas, which will provide a basis for public health interventions [49]. Among them, the autocorrelation model used the bivariate Moran’s I index in geoda [50]:

$$\mathrm{I}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{y}}_{\mathrm{j}}-\overline{\mathrm{y}}\right)}{{\mathrm{S}}^2\left({\sum }_{\mathrm{i}}{\sum }_{\mathrm{j}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}}\right)}}$$

(14)

where I is the bivariate global spatial autocorrelation index, i.e., the correlation between the spatial distributions of spatial variables x and y on the whole; n is the total number of spatial units; W_ij is the spatial weight matrix established by the K-neighborhood method; x_i and y_j are the observed values of the independent variable and the dependent variable in spatial units i and j, respectively; and S² is the variance of all samples.

Spatial superposition uses local indicators of spatial association (LISA), which is used to describe the degree of spatial aggregation of values with a certain degree of similarity in the local area around the spatial unit, and to identify different spatial agglomeration patterns that may exist at different spatial locations.

3. Results

3.1. The Cooling Effect of Green Spaces

The moderating effect of green space on the urban heat link showed significant spatial differentiation (Figure 2). Based on image meta-analysis, vegetation canopy shading, transpiration and surface albedo jointly determined the cooling capacity (Figure 2a), while the cooling effect of each park green space showed a marginal decreasing trend with the expansion of the area (Figure 2b). The HMI composite index (Figure 2c) further indicated that the continuous green space network around West Lake and along the Qiantang River formed efficient cooling corridors (HMI > 0.75), while the northeast part of the city also differed in terms of weaker thermal mitigation due to green space fragmentation (HMI < 0.35), which may be related to the green space area, vegetation type, vegetation cover, and the surrounding environment.

3.2. BVOC Emissions and Air Pollution from Green Spaces

The intensity of BVOC emissions and the air pollution generated were spatially heterogeneous, with high emissions and concentrations concentrated in suburban areas (Figure 3), probably due to the higher vegetation cover in the suburbs, while the concentration of pollutants in urban centers could not be ignored due to the restricted atmospheric diffusion, and the concentrations of PM_2.5 (Figure 3b) and O₃ (Figure 3c) generated by BVOC were not high but the urban areas, with large populations, would cause greater health risks. Although the concentrations of PM_2.5 (Figure 3b) and O₃ (Figure 3c) generated by BVOC are not high, the urban areas are highly populated and cause serious air pollution in the summer, which can lead to greater health risks.

3.3. Spatial Relationships of Greenfield Ecosystem Services

Global spatial autocorrelation analysis showed a weak positive correlation between HMI and BVOC emissions (Moran’s I = 0.096, p < 0.05) (Figure 4a). The Local LISA clustering (Figure 4b) identifies areas that are not correlated (58.8%) as well as four typical types of areas that are correlated: (1) the “high HMI-low BVOC” zone (17.5%), which was concentrated on the southwest side of the West Lake and dominated by transpiration-dominated wetland vegetation; (2) the “low HMI-high BVOC” zone (1.1%), which may be influenced by pine and cypress high-emission tree species; (3) “high HMI-high BVOC” zone (18.7%), which is mostly found in large-scale plantation forests in suburban areas; (4) “low HMI-low BVOC” zone (3.9%), corresponding to hard surfaces in built-up urban areas.

4. Discussion

4.1. Trade-Off Mechanisms and Ecological Regulation of Vegetation Functional Traits

The cooling effects of vegetation and BVOC emissions showed obvious spatial differences, which were closely related to differences in plant functional characteristics [51,52,53]. A clear functional trade-off exists for dominant tree species. For example, although Cinnamomum camphora achieved strong cooling through high transpiration efficiency (average cooling of 0.84 °C per unit area), the intensity of its BVOC emissions was much higher than that of low-emission tree species [54,55,56]. This phenomenon has been related to leaf isoprenoid synthase activity and photosynthetic pathways [32], and C3 plants may mitigate photoinhibition under high-temperature stress by increasing BVOC emissions [33].

In addition, the negative correlation between transpiration efficiency and BVOC emission reveals a dual role of stomatal conductance [57]: large stomatal openings accelerate BVOC volatilization while promoting transpirational cooling. Therefore, tree species with strong stomatal regulation and low BVOC biosynthetic pathways (e.g., Lagerstroemia indica L. and Osmanthus fragrans) and tree species with good cooling effect and low BVOC emissions intensity (e.g., Ilex chinensis and Yulania denudata) were screened as optimization directions [31,58].

4.2. Spatial Coupling Patterns and Optimization Strategies of Cooling and BVOC Emissions

The spatial analysis revealed a weak positive correlation between thermal mitigation effects and BVOC emissions in the study area (Moran’s I = 0.096, p < 0.05), and identified four typical HMI–BVOC emission coupling patterns, which were closely related to the green space structure and vegetation configuration. This result suggests that the nonlinear relationship between green space cooling and emission risk needs to be regulated by refined spatial management.

In the “high cooling-low emission” hotspot (17.5%), which is mainly distributed in the continuous green space network around West Lake and along the Qiantang River, thanks to the complete ecological corridors and reasonable vegetation hierarchies, it is recommended to implement an ecological control red line system to limit the expansion of hard paving, and to increase the efficiency of cooling per unit area by planting a composite layer of trees, shrubs, and grasses. For the “low cooling-high emission” area (1.1%), which mainly occurs in the northeast part of the region with serious green space fragmentation, it is recommended to introduce pollution-resistant, low-emission tree species (e.g., Nerium oleander) to replace the high-emission coniferous species [31], and at the same time, optimize the spatial layout to enhance the connectivity of the green space. The “high cooling-high emission” areas (18.7%) were mainly located in the suburbs, which had a better cooling effect (HMI > 0.65), but the BVOC emission intensity was 1.9–2.3 times higher than that of the urban center. This is mainly related to the vegetation composition dominated by high-emission tree species such as Cinnamomum camphora and Populus tomentosa, and the risk of BVOC emissions should be reduced by adjusting the vegetation structure. The “low cooling—low emission” area (3.9%) is the most widely distributed, mainly in the sporadic green space in the built-up area. Low-emission cooling tree species should be selected to increase the green space coverage and expand the green space area. In addition, a vertical greening structure should be emphasized to improve the efficiency of emission reduction and ecological benefits. It is recommended that BVOC emission intensity be incorporated into the evaluation system of urban green space, and a dynamic monitoring platform be established to track the coupled changes in vegetation physiological parameters and air quality indicators in real time, so as to provide a scientific basis for the sustainable management of urban green space.

4.3. Research Limitations and Future Prospects

This study has limitations regarding model dependency for BVOC emission impacts on air quality, relying on static emission factors and simplified atmospheric chemistry simulations that inadequately capture BVOC–NOx–O₃ dynamics in complex urban environments. This leads to potential ozone formation miscalculations in both urban centers and vegetated suburban areas under varying meteorological conditions. Future research should develop integrated validation systems combining satellite data, ground monitoring networks, and vertical profile observations, while implementing process-based BVOC emission models that account for canopy micrometeorology and plant physiological responses to environmental variations.

Another limitation is the unquantified offsetting effect of anthropogenic heat emissions on green space cooling. This study inadequately addresses thermal contributions from human activities (air conditioning, traffic, and industrial emissions), potentially overestimating vegetation cooling potential in high-density areas, particularly during heat waves. Future work should integrate urban energy models with microclimate simulations, utilizing high-resolution thermal data from mobile platforms and IoT sensors to identify interactions between anthropogenic heat sources and natural cooling processes, thus providing frameworks for coordinated green–gray infrastructure planning.

Health risk assessments in this study exclude cumulative effects from long-term exposure, focusing primarily on acute scenarios while neglecting BVOC-mediated long-term low-dose exposure, particularly for vulnerable populations. This underestimates health considerations in urban greening decisions. Subsequent research should develop health impact assessment frameworks combining cohort tracking with exposure–response modeling, integrating healthcare data and community surveys to establish vulnerability indices for precision urban greening, while conducting multi-year monitoring to quantify long-term as so quantify long-term associations between BVOC emissions and health outcomes.

5. Conclusions

This study reveals the spatial trade-off relationship between heat mitigation and BVOC emissions in urban green spaces, and the main conclusions are as follows: the cooling effectiveness of green spaces is significantly affected by vegetation type, green space size, and spatial configuration. HMI is positively correlated with BVOC emissions, but also negatively correlated in some areas, and an increase in the cooling efficacy can be achieved through vegetation screening and spatial optimization while reducing the amount of pollution generated. It is recommended that cities establish a multi-dimensional evaluation system of “vegetation function—microclimate—air quality” in green space planning and incorporate BVOC emissions into urban ecological assessment indicators.