Environmental and physiological controls on diurnal and seasonal patterns of biogenic volatile organic compound emissions from five dominant woody species under field conditions

Highlights

• Biogenic volatile organic compound (BVOC) emissions from five dominant tree species were investigated in northern China.

• Isoprene and monoterpene emissions from five woody plant species showed strong diurnal and seasonal variations.

• Individual compounds within the monoterpene and sesquiterpene groups were correlated with each other for all species.

• Monoterpene emissions were nonlinearly correlated to stomatal conductance or intercellular CO₂ concentration.

Abstract

Biogenic volatile organic compounds (BVOCs) play essential roles in tropospheric chemistry, on both regional and global scales. The emissions of large quantities of species-specific BVOC depend not only on environmental (temperature, T; photosynthetically active radiation, PAR), but also physiological parameters (i.e. net photosynthetic rate, P_n; transpiration rate, T_r; stomatal conductance, g_s and intercellular CO₂ concentration, C_i). Here, isoprene, monoterpene and sesquiterpene emissions were determined from five dominant mature woody tree species in northern China, which are two evergreen conifers (Pinus tabuliformis and Platycladus orientalis) and three broad-leaved deciduous trees (Quercus variabilis, Populus tomentosa and Robinia pseudoacacia). A dynamic enclosure technique combined with GC-MS was used to sample BVOCs and analyse their fractional composition at daily and annual scales. The diurnal data showed that both isoprene and monoterpene emissions increased with increasing temperature, and reached their maximum emission rates in the peak of growing season for both coniferous and broad-leaved species. The emissions of individual compound within the monoterpenes and sesquiterpenes were statistically correlated with each other for all species. Furthermore, some oxygenated monoterpene emissions were highly correlated to sesquiterpenes in all tree species. Linking BVOC emissions to environmental and leaf physiological parameters exhibited that monoterpene emissions were linearly and positively correlated to the variation of T, PAR, P_n and T_r, while their relationship to g_s and C_i is more complex. Collectively, these findings provided important information for improving current model estimations in terms of the linkage between BVOC emissions and plant physiological traits. The data presented in this study can be used to update emission capacity used in models, as this is the first time of reporting BVOC emissions from five dominant species in this region. The whole-year measurement of leaf-level BVOCs can also advance our understanding of seasonal variation in BVOC emissions.

Introduction

Biogenic volatile organic compounds (BVOCs), with high reactivity in the atmosphere, have a larger impact on atmospheric chemistry, compared to anthropogenic volatile organic compounds (AVOCs) (Guenther et al., 2006; Peñuelas and Staudt, 2010). The global emissions of BVOC from terrestrial ecosystems have been estimated to range from 700 to 1000 Tg C yr⁻¹ (Sindelarova et al., 2014). The largest emitter of BVOCs is thought to be forest ecosystems (Guenther et al., 1995). BVOCs are composed of a wide variety of chemical species, with isoprenoids (e.g., isoprene, monoterpenes, sesquiterpenes) and oxygenated VOCs dominated in general (Arneth et al., 2008). Previous studies have shown that the composition and emission magnitudes of BVOC can exhibit distinct variations among different species and ecosystems (Winters et al., 2009; Préndez et al., 2013; Aydin et al., 2014). The variations were mainly explained by different environmental conditions and also plant-specific characteristics.

It has been widely reported that conifer tree species such as Pinus pinea, Pinus sylvestris and Picea abies can emit significant amounts of monoterpenes (Keenan et al., 2009; Aydin et al., 2014), while the broad-leaved trees such as Populus deltoids, Populus alba, Eucalyptus sp., Quercus cerris and Acacia cyanophylla emit large amounts of isoprene (Padhy and Varshney, 2005; Loreto et al., 2007; Aydin et al., 2014; Baraldi et al., 2019). However, recent studies have shown that several broad-leaved tree species such as Quercus ilex, Quercus coccifera and Fagus sylvatica can also emit large quantity of monoterpenes (Keenan et al., 2009; Llusia et al., 2012, 2013; Sicard et al., 2018). It seems that the tree species growing in different forest ecosystems might have different emission patterns, potentially influenced by the local growing conditions. It is therefore important to assess the emission differences in BVOCs from species growing in different regions, potentially enriching the emission inventory we have for large scale modelling.

Temperature and light intensity are found to be the major environmental factors that can affect the emissions of isoprenoids. Temperature is considered to affect enzyme activity (i.e., for synthase) and influence both the volatility and diffusivity of mono- and sesquiterpenes stored in plant structures such as resin duct and glandular cells (Monson et al., 1995; Staudt and Bertin, 1998; Niinemets et al., 2002a). The light dependence of BVOCs is linked to the compound synthesis through photosynthesis (Niinemets et al., 1999). It has been previously thought that monoterpene emissions from conifers are light-independent, as monoterpenes mainly originate from compounds stored in storage organs such as resin ducts (Holzke et al., 2006; Lim et al., 2008). However, more and more studies have suggested that monoterpene emissions are a combination of emissions from stored and immediately synthesized compounds (Staudt et al., 1997; Guenther et al., 2012).

Many studies have previously investigated the relationship between BVOC emissions and leaf physiological parameters (Kesselmeier et al., 1997; Niinemets et al., 1999; Owen and Peñuelas, 2013; Ashworth et al., 2016; Pazouki and Niinemets, 2016; Li et al., 2017). For example, a recent study by Yuan et al. (2016) found that isoprene emissions were positively correlated to stomatal conductance. However, insignificant and/or negative relationships have also been reported for some of monoterpene compounds and stomatal conductance (Staudt et al., 1997; Niinemets et al., 2002a; Chen et al., 2019). Transpiration rate, as a substitute for stomatal resistance, has also been linked to the exchange rate of BVOCs (Jardine et al., 2008). Although a strong relationship between BVOC emission and transpiration rate has been often reported in the literature (Jardine et al., 2008; Winters et al., 2009), the relationship could vary among species and plant water conditions. Therefore, it is essential to explore the physiological control mechanism for BVOC emissions, which could be of importance for process-based modelling.

Several BVOC emission modelling approaches have been applied at regional and global scales for estimating temporal and spatial variations of BVOC emissions (Guenther et al., 2012; Grote et al., 2014; Tang et al., 2016). The modelling work is based on lab/field-based emission measurements, vegetation land-cover and the derived emission responses to environmental variables. The models often use the averaged emission capacity (standardized emission rates at 30 °C and 1000 μmol m⁻² s⁻¹) from published values. The levels of representativeness of these measured species and conditions in literature could lead to different levels of uncertainties in modelling results, as for the same region, local acclimation/adaptation might occur and cause different emission patterns. Therefore, considering within-species variations in emissions across different ecosystems can potentially improve the accuracy of model estimations.

In this study, we measured BVOC emissions from five dominant woody tree species (i.e., two coniferous and three broad-leaved species) in a temperate forest in the northern China. The objectives of this study are: 1) to evaluate if the temporal emission patterns differed within individuals and compared the measured results with those previously reported; 2) to get a better understanding of the driving factors (e.g., environmental variables and physiological parameters) for the observed BVOC variations.