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On-site analysis of volatile organic compounds (VOCs) with miniaturized gas chromatography–mass spectrometry (GC–MS) systems is a very rapidly developing field of application. While, on the one hand, major technological advances are improving the availability of these systems on the market, on the other hand, systematic studies to assess the performance of such instruments are still lacking. To fill this gap, we compared three portable GC–MS devices to a state-of-the-art benchtop (stationary) system for analysis of a standard mixture of 18 VOCs. We systematically compared analytical parameters such as the sensitivity and similarity of the signal response pattern and the quality of the obtained mass spectra. We found that the investigated mobile instruments (i) showed different response profiles with a generally lower number of identified analytes. Also, (ii) mass spectral reproducibility (% relative standard deviation (RSD) of the relative abundance of selective fragments) was generally worse in the mobile devices (mean RSD for all targeted fragments ~9.7% vs. ~3.5% in the stationary system). Furthermore, mobile devices (iii) showed a poorer mass spectral similarity to commercial reference library spectra (>20% deviation of fragment ion relative intensity vs. ~10% in the stationary GC–MS), suggesting a less reliable identification of analytes by library search. Indeed, (iv) the performance was better with higher-mass and/or more abundant fragments, which should be considered to improve the results of library searches for substance identification. Finally, (v) the estimation of the signal-to-noise ratio (S/N) in mobile instruments as a measure of sensitivity revealed a significantly lower performance compared to the benchtop lab equipment (with a ratio among medians of ~8 times lower). Overall, our study reveals not only a poor signal-to-noise ratio and poor reproducibility of the data obtained from mobile instruments, but also unfavorable results with respect to a reliable identification of substances when they are applied for complex mixtures of volatiles. Graphical Abstract

· The 18O composition of plant cellulose is often used to reconstruct past climate and plant function. However, uncertainty remains regarding the estimation of the leaf sucrose 18O signal and its subsequent attenuation by 18O exchange with source water during cellulose synthesis.· We grew Lolium perenne at three CO2 concentrations (200, 400 or 800 mmol mol-1) and two relative humidity (RH) levels (50% or 75%), and determined 18O enrichment of leaf sucrose (Δ18OSucrose), bulk leaf water (Δ18OLW), leaf cellulose (Δ18OCellulose) and water at the site of cellulose synthesis (Δ18OCelSynW). · Δ18OCellulose correlated with Δ18OSucrose (R2=0.87) but not with Δ18OLW (R2=0.04), due to a variable 18O discrepancy (range 2.0-9.0‰) between sucrose synthesis water (Δ18OSucSynW, estimated from Δ18OSucrose) and bulk leaf water. The discrepancy resulted mainly from an RH effect. The proportion of oxygen in cellulose that exchanged with medium water during cellulose formation (pex), was near-constant when referenced to Δ18OSucSynW (pex-SucSynW = 0.52±0.02 SE), but varied when related to bulk leaf water (pex-LW = –0.01 to 0.46). · We conclude that previously reported RH-dependent variations of pex-LW in grasses are related to a discrepancy between Δ18OSucSynW and Δ18OLW that may result from spatial heterogeneity in 18O gradients of leaf water and photosynthetic sucrose synthesis.

We explore here our mechanistic understanding of the environmental and physiological processes that determine the oxygen isotope composition of leaf cellulose (δ

We explored the effects of atmospheric CO

We explored the effects of atmospheric CO2 concentration (Ca ) and vapor pressure deficit (VPD) on putative mechanisms controlling leaf elongation in perennial ryegrass. Plants were grown in stands at a Ca of 200, 400 or 800 μmol mol-1 combined with high (1.17 kPa) or low (0.59 kPa) VPD during the 16 h-day in well-watered conditions with reduced nitrogen supply. We measured day : night-variation of leaf elongation rate (LERday : LERnight ), final leaf length and width, epidermal cell number and length, stomatal conductance, transpiration, leaf water potential and water-soluble carbohydrates and osmotic potential in the leaf growth-and-differentiation zone (LGDZ). Daily mean LER or morphometric parameters did not differ between treatments, but LERnight strongly exceeded LERday , particularly at low Ca and high VPD. Across treatments LERday was negatively related to transpiration (R2 = 0.75) and leaf water potential (R2 = 0.81), while LERnight was independent of leaf water potential or turgor. Enhancement of LERnight over LERday was proportional to the turgor-change between day and night (R2 = 0.93). LGDZ sugar concentration was high throughout diel cycles, providing no evidence of source limitation in any treatment. Our data indicate a mechanism of diel cycling between daytime hydraulic and night-time stored-growth controls of LER, buffering Ca and daytime VPD effects on leaf elongation.

We explore here our mechanistic understanding of the environmental and physiological processes that determine the oxygen isotope composition of leaf cellulose (δ$^{18}$O$_{cellulose}$) in a drought‐prone, temperate grassland ecosystem. A new allocation‐and‐growth model was designed and added to an $^{18}$O‐enabled soil–vegetation–atmosphere transfer model (MuSICA) to predict seasonal (April–October) and multi‐annual (2007–2012) variation of δ$^{18}$O$_{cellulose}$ and $^{18}$O‐enrichment of leaf cellulose (Δ$^{18}$O$_{cellulose}$) based on the Barbour–Farquhar model. Modelled δ$^{18}$O$_{cellulose}$ agreed best with observations when integrated over c. 400 growing‐degree‐days, similar to the average leaf lifespan observed at the site. Over the integration time, air temperature ranged from 7 to 22°C and midday relative humidity from 47 to 73%. Model agreement with observations of δ$^{18}$O$_{cellulose}$ (R$^{2}$ = 0.57) and Δ$^{18}$O$_{cellulose}$ (R$^{2}$ = 0.74), and their negative relationship with canopy conductance, was improved significantly when both the biochemical $^{18}$O‐fractionation between water and substrate for cellulose synthesis (ε$_{bio}$, range 26–30‰) was temperature‐sensitive, as previously reported for aquatic plants and heterotrophically grown wheat seedlings, and the proportion of oxygen in cellulose reflecting leaf water $^{18}$O‐enrichment (1 – p$_{ex}$p$_{x}$, range 0.23–0.63) was dependent on air relative humidity, as observed in independent controlled experiments with grasses. Understanding physiological information in δ$^{18}$O$_{cellulose}$ requires quantitative knowledge of climatic effects on p$_{ex}$p$_{x}$ and ε$_{bio}$.

The oxygen isotope composition (δ18O) of leaf water δ18O>sub>leaf) is an important determinant of environmental and physiological information found in biological archives, but the system-scale understanding of the propagation of the δ18O of rain through soil and xylem water to δ18Oleaf has not been verified for grassland. Here we report a unique and comprehensive dataset of biweekly δ18O observations in soil, stem and leaf waters made over seven growing seasons in a temperate, drought-prone, mixed-species grassland. Using an 18O-enabled soil–plant–atmosphere transfer model, we evaluated our ability to predict the dynamics of δ18O in soil water, the depth of water uptake, and the effects of soil and atmospheric moisture on 18O-enrichment of leaf water (Δ18Oleaf) in this ecosystem. The model accurately predicted the δ18O dynamics of the different ecosystem water pools. Water uptake occurred from shallow soil depths throughout dry and wet periods in all years, presumably because of the high grazing pressure. Δ18Oleaf responded to both soil and atmospheric moisture and was best described when leaf water was separated into two non-mixing water pools. The close agreement between model predictions and observations is remarkable (and promising) as model parameters describing the relevant physical features or functional relationships of soil and vegetation were held constant with one single value for the entire mixed-species ecosystem.