The dynamics of carbon dioxide fluxes in the high-altitude Alpine Critical Zone is only partially understood. The complex geomorphology induces significant spatial heterogeneity, and a strong interannual variability is present in the often-extreme climatic and environmental conditions of Alpine ecosystems. To explore the relative importance of the spatial and temporal variability of CO₂ fluxes, we analysed a set of in-situ measurements obtained during the summers from 2018 to 2021 in four sampling plots, characterised by soils with different underlying bedrock within the same watershed in the Nivolet plain, in the Gran Paradiso National Park, western Italian Alps. Multi-regression models of CO₂ emission and uptake were built using measured meteo-climatic and environmental variables considering either individual years (aggregating over plots) or individual plots (aggregating over years). We observed a significant variability of the model parameters across the different years, while such variability was much smaller across different plots. Significant changes between the different years mainly concerned the temperature dependence of respiration (CO₂ emission) and the light dependence of photosynthesis (CO₂ uptake). These results suggest that spatial upscaling can be obtained from site measurements, but long-term flux monitoring is required to properly capture the temporal variability at interannual scales.

Due to the globalization of coffee trade, ensuring the safety and traceability of coffee has become a critical challenge, prompting global authorities to implement new traceability systems to enhance quality identification and protect consumers from fraud. Aroma is a crucial parameter in the evaluation and differentiation of coffees, influenced by factors such as genetics, origin, post harvesting process, roast level, and brewing method. This paper provides, for the first time, a comprehensive overview of the volatile fingerprints of specialty coffees, categorized by their respective quality levels. In particular, this study aimed to evaluate the potential of volatile compounds monitored through Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS) as objective, fast, reliable and repeatable tool for tracking the quality and genetic lineage of Arabica specialty coffees. The spectra of volatile organic compounds (VOCs) were acquired from 1132 coffee samples (both specialty and non-specialty) from various varieties, origins, and processing methods. Results clearly indicate that the volatile composition of specialty coffee is predominantly influenced by its genetic lineage. Arabica coffee species belonging to Bourbon, Typica, and Ethiopian landraces showed higher total VOCs emission, while varieties related to Robusta, which are related to the Canephora one, emit less. Finally, by employing a complex network analysis approach based on headspace VOC analysis, it was possible to accurately distinguish between different categories of specialty Arabica coffee. Notably, our analysis shows that the quality of specialty coffee is not linked to the number of VOCs emitted, but rather to the level emission of some pleasant aroma compounds. These findings open new perspectives for the development of aroma profiling techniques and demonstrate the unique aroma release characteristics of specialty coffees.

Apricot (Prunus armeniacaL.) fruit qualities are affected by harvest date and rapid off-tree ripening progression that limits their shelf life and marketability. Nowadays, consumers are generally dissatisfied with the sensory quality of apricot fruit sold on large-scale retail channels. Pomological analysis (color, firmness, total soluble solids, and total acidity), volatile organic compounds fingerprint and sensory evaluation were performed during the postharvest in two different apricot genotypes with the objective to: (1) characterize and compare two different apricot cultivars at harvest and at different postharvest times; (2) understand how the sensory quality changes during the postharvest process; (3) elucidate which sensory quality parameters drive the consumer's choice. For this purpose, ‘Portici’ (traditional cultivar) and ‘Lady Cot’ (modern one) grown in the same environment, were evaluated at commercial maturity harvest and for the following 15 days. Results indicate that the sensory quality of the selected cultivars was different, with ‘Portici’ showing better performances in terms of aroma, sweetness and juiciness, while ‘Lady Cot’ stood out in terms of firmness. Results also indicate a different trend of the traits analyzed and of the shelf-life, that was reduced for the traditional cultivar.Graphical

Karst aquifers are highly sensitive to climate change due to their complex internal structure, which makes them both reactive and difficult to model. This study proposes a transdisciplinary decision-support analytical framework to support long-term groundwater management in a dynamic karst system in northwestern Tuscany (Italy). The approach combines time- and frequency-domains techniques – multivariate regression models and singular spectrum analysis, respectively – to characterize both short-term system memory and the low-frequency variability of the spring discharge. The methodology was applied to the Cartaro spring (Apuan Alps) using a 18-year dataset of discharge and meteorological variables (precipitation and temperature). Time-domain analysis showed that meteorological variables alone cannot fully explain the long-term variability of spring discharge, and that accounting for the short memory of the karst system (about five days) significantly improved the performance of the classical models. Furthermore, spectral decomposition allowed us to extract the significant annual and semi-annual oscillatory components with modulated amplitude, highlighting the role of low-frequency climatic variability in controlling spring behavior. By combining the results from the time and frequency domains, we fine‑tuned a forecasting procedure that enables statistically reliable annual predictions of long‑term average discharge trends, while limiting noise propagation and without relying on physical governing equations. The research is transdisciplinary, as it was developed from its preliminary phases in collaboration with stakeholders and local managers. The methodological workflow can be applied to other karst aquifers, provided that it is recalibrated using the site-specific hydro-climatic and discharge data.

Climate change significantly influences natural and semi‐natural ecosystems at global scale. This impact is further amplified in alpine environments, owing to a set of climate feedback mechanisms (i.e., elevation‐dependent warming). Due to the short snow‐free season and the consequent adaptation of alpine plants to fast growth, high‐elevation grasslands mainly act as carbon sinks during the vegetative season, with primary productivity usually exceeding ecosystem respiration. We combined timeseries of field data (CO ₂ fluxes and environmental variables), with weather, vegetation and remotely‐sensed land‐cover data from the Nivolet area (Gran Paradiso National Park, western Italian Alps), to identify the factors controlling diurnal ecosystem respiration and primary productivity in such environments. We found that growing‐degree‐days‐since‐snowmelt (GDD ₀ —a measure of cumulative heat available for plant growth since the snow has melted) exerted a strong control on both respiration and primary productivity. The effect of GDD ₀ on productivity is both indirect, mediated by the seasonal trend in greening (proxied by vegetation indices), and direct, possibly reflecting the importance of cumulated heat on vegetation height. While greening had limited effect on respiration, GDD ₀ had a strong effect, supporting the view that ecosystem respiration was mainly temperature‐related and microbial‐driven. After accounting for the effect of greening, we found that scarce winter cumulative precipitation had positive impacts on productivity and respiration, suggesting a possible role of reduced snowpack on the cycling of organic matter. Other variables (i.e., solar irradiance, air temperature, soil moisture, vegetation composition and topography) either supported our expectations or showed no significant effect. Our results contribute to identify the causal relationships between climate and diurnal carbon cycling, improving our understanding of how climate change affects alpine ecosystems in these high‐elevation environments.