Carbon dioxide fluxes in Alpine grasslands at the Nivolet Plain, Gran Paradiso National Park, Italy 2017-2023.

We introduce a georeferenced dataset of Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables (air and soil temperature, air relative humidity, soil volumetric water content, pressure, and solar irradiance) collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps, from 2017 to 2023. NEE and ER are derived by measuring the temporal variation of CO2 concentration obtained by the enclosed chamber method. We used a customised portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyser (IRGA) and a portable weather station, measuring CO2 fluxes at a number of points (around 20 per site and per day) within five different sites during the snow-free season (June to October). Sites are located within the same hydrological basin and have different geological substrates: carbonate rocks (site CARB), gneiss (GNE), glacial deposits (GLA, EC), alluvial sediments (AL). This dataset provides relevant and often missing information on high-altitude mountain ecosystems and enables new comparisons with other similar sites, modelling developments and validation of remote sensing data.

Spatial and temporal variability of carbon dioxide fluxes in the Alpine Critical Zone: The case of the Nivolet Plain, Gran Paradiso National Park, Italy.

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.

Microscale drivers of summer CO2 fluxes in the Svalbard High Arctic tundra.

High-Arctic ecosystems are strongly affected by climate change, and it is still unclear whether they will become a carbon source or sink in the next few decades. In turn, such knowledge gaps on the drivers and the processes controlling CO2 fluxes and storage make future projections of the Arctic carbon budget a challenging goal. During summer 2019, we extensively measured CO2 fluxes at the soil-vegetation-atmosphere interface, together with basic meteoclimatic variables and ecological characteristics in the Bayelva river basin near Ny Ålesund, Spitzbergen, Svalbard (NO). By means of multi-regression models, we identified the main small-scale drivers of CO2 emission (Ecosystem Respiration, ER), and uptake (Gross Primary Production, GPP) in this tundra biome, showing that (i) at point scale, the temporal variability of fluxes is controlled by the classical drivers, i.e. air temperature and solar irradiance respectively for ER and GPP, (ii) at site scale, the heterogeneity of fractional vegetation cover, soil moisture and vegetation type acted as additional source of variability for both CO2 emissions and uptake. The assessment of the relative importance of such drivers in the multi-regression model contributes to a better understanding of the terrestrial carbon dioxide exchanges and of Critical Zone processes in the Arctic tundra.

Drivers of carbon fluxes in Alpine tundra: a comparison of three empirical model approaches.

In high mountains, the effects of climate change are manifesting most rapidly. This is especially critical for the high-altitude carbon cycle, for which new feedbacks could be triggered. However, mountain carbon dynamics is only partially known. In particular, models of the processes driving carbon fluxes in high-altitude grasslands and Alpine tundra need to be improved. Here, we propose a comparison of three empirical approaches using systematic statistical analysis, to identify the environmental variables controlling CO2 fluxes. The methods were applied to a complete dataset of simultaneous in situ measurements of the net CO2 exchange, ecosystem respiration and basic environmental variables in three sampling sites in the same catchment. Large year-to-year variations in the Gross Primary Production (GPP) and Ecosystem Respiration (ER) dependences on solar irradiance and temperature were observed. We thus implemented a multi regression model in which additional variables were introduced as perturbations of the standard exponential and rectangular hyperbolic functions for ER and GPP, respectively. A comparison of this model with other common modelling strategies showed the benefits of this approach, resulting in large explained variances (83% to 94%). The optimum ensemble of variables explaining the inter- and intra-annual flux variability included solar irradiance, soil moisture and day of the year for GPP, and air temperature, soil moisture, air pressure and day of the year for ER, in agreement with other studies. The modelling approach discussed here provides a basis for selecting drivers of carbon fluxes and understanding their role in high-altitude Alpine ecosystems, also allowing for future short-range assessments of local trends.

Gas emission into the atmosphere from controlled landfills: an example from Legoli landfill (Tuscany, Italy).

BACKGROUND, AIM AND SCOPE: Landfill gas (LFG) tends to escape from the landfill surface even when LFG collecting systems are installed. Since LFG leaks are generally a noticeable percentage of the total production of LFG, the optimisation of the collection system is a fundamental step for both energy recovery and environmental impact mitigation. In this work, we suggest to take into account the results of direct measurements of gas fluxes at the air-cover interface to achieve this goal. MATERIALS AND METHODS: During the last 5 years (2004-2009), 11 soil gas emission surveys have been carried out at the Municipal Solid Waste landfill of Legoli (Peccioli municipality, Pisa Province, Italy) by means of the accumulation chamber method. Direct and simultaneous measurements of CH(4) and CO(2) fluxes from the landfill cover (about 140,000 m(2)) have been performed to estimate the total output of both gases discharged into the atmosphere. Three different data processing have been applied and compared: Arithmetic mean of raw data (AMRD), sequential Gaussian conditional simulations (SGCS) and turning bands conditional simulations (TBCS). The total amount of LFG (captured and not captured) obtained from processing of direct measurements has been compared with the corresponding outcomes of three different numerical models (LandGEM, IPCC waste model and GasSim). RESULTS: Measured fluxes vary from undetectable values (<0.05 mol m(-2) day(-1) for CH(4) and <0.02 mol m(-2) day(-1) for CO(2)) to 246 mol m(-2) day(-1) for CH(4) and 275 mol m(-2) day(-1) for CO(2). The specific CH(4) and CO(2) fluxes (flux per surface unit) vary from 1.8 to 7.9 mol m(-2) day(-1) and from 2.4 to 7.8 mol m(-2) day(-1), respectively. DISCUSSION: The three different estimation methodologies (AMRD, SGCS and TBCS) used to evaluate the total output of diffused CO(2) and CH(4) fluxes from soil provide similar estimations, whereas there are some mismatches between these results and those of numerical LFG production models. Isoflux maps show a non-uniform spatial distribution, with high-flux zones not always corresponding with high-temperature areas shown by thermographic images. CONCLUSIONS: The average value estimated over the 5-year period for the Legoli landfill is 245 mol min(-1) for CH(4) and 379 mol min(-1) for CO(2), whereas the volume percentage of CH(4) in the total gas discharged into the atmosphere varies from 29% to 51%, with a mean value of 39%. The estimated yearly emissions from the landfill cover is about 1.29 x 10(8) mol annum(-1) (2,100 t year(-1)) of CH(4) and 1.99 x 10(8) mol annum(-1) (8,800 t year(-1)) of CO(2). Considering that the CH(4) global warming potential is 63 times greater than that of CO(2) (20 a time horizon, Lashof and Ahuja 1990), the emission of methane corresponds to 130,000 t annum(-1) of CO(2). RECOMMENDATIONS AND PERSPECTIVES: The importance of these studies is to provide data for the worldwide inventory of CH(4) and CO(2) emissions from landfills, with the ultimate aim of determining the contribution of waste disposal to global warming. This kind of studies could be extended to other gas species, like the volatile organic compounds.