The EGU General Assembly最新文献

Indoor radon accumulation is considered the main source of human exposition to ionizing radiation. The main sources of indoor radon are soil gas, the building materials and tap water, especially when they are enriched in ²²⁶Ra and ²³²Th, which are the precursors of main radon isotopes: ²²²Rn and ²²⁰Rn, respectively.

In the frame of RESPIRE (Radon rEal time monitoring System and Proactive Indoor Remediation), a LIFE project funded by European Commission, a scale model-room of 62 cm x 50 cm x 35 cm (inner length x width x height) was manufactured with a very porous and highly radioactive lithoid ignimbrite to evaluate the contribution of building materials to indoor radon accumulation, simulating the effect of a ventilation system to reduce indoor radon levels.

A series of experiments was designed where either outdoor air was introduced in the model room or indoor air was extracted from the room, at different flow rates (from 0.15 to 0.82 liters per minute) to evaluate how air exchange and mixing affect indoor radon level. In the first group of tests, the introduction of outdoor air strongly reduced indoor radon concentration, with radon relative decrease directly proportional to the air flow. In the second set of experiments, the extraction of indoor air very moderately lowered radon levels. Finally, a modified version of Fick’s second law was used to model experimental data, describing how radon diffused through the very porous room walls under different experimental conditions.

Accurate subsurface imaging through geophysics is of prime importance for many geological and hydrogeological applications. Recently, airborne electromagnetic methods have become more popular because of their potential to quickly acquire large data sets at relevant depths for hydrogeological applications. However, the solution of inversion of airborne EM data is not unique, so that many electrical conductivity models can explain the data. Two families of methods can be applied for inversion: deterministic and stochastic methods. Deterministic (or regularized) approaches are limited in terms of uncertainty quantification as they propose one unique solution according to the chosen regularization term. In contrast, stochastic methods are able to generate many models fitting the data. The most common approach is to use Markov chain Monte Carlo (McMC) Methods. However, the application of stochastic methods, even though more informative than deterministic ones, is rare due to a quite high computational cost.

In this research, the newly developed approach named Bayesian Evidential Learning 1D imaging (BEL1D) is used to efficiently and stochastically solve the inverse problem. BEL1D is combined to SimPEG: an open source python package, for solving the electromagnetic forward problem. BEL1D bypasses the inversion step, by generating random samples from the prior distribution with defined ranges for the thickness and electrical conductivity of the different layers, simulating the corresponding data and learning a direct statistical relationship between data and model parameters. From this relationship, BEL1D can generate posterior models fitting the field observed data, without additional forward model computations. The output of BEL1D shows the range of uncertainty for subsurface models. It enables to identify which model parameters are the most sensitive and can be accurately estimated from the electromagnetic data.

The application of BEL1D together with SimPEG for stochastic transient electromagnetic inversion is a very efficient approach, as it allows to estimate the uncertainty at a limited cost. Indeed, only a limited number of training models (typically a few thousands) is required for an accurate prediction. Moreover, the computed training models can be reused for other predictions, considerably reducing the computation cost when dealing with similar data sets. It is thus a promising approach for the inversion of dense data set (such as those collected in airborne surveys). In the future, we plan on relaxing constraints on the model parameters to go towards interpretation of EM data in coastal environment, where transition can be smooth due to salinity variations.

Keywords : EM, Uncertainty, 1D imaging, BEL1D, SimPEG

A large-scale transformation of the heating and cooling sector is needed to achieve the climate neutrality goals by 2050 as outlined in the European Green Deal. One frequently discussed option for reducing the greenhouse gas emissions is the widespread use of ground source heat pumps (GSHPs) for heating and cooling living spaces. Here, the technical potential of GSHPs to supply heat to buildings in the state of Baden-Württemberg, Germany, is analysed. This study is based on the yearly demand for heating energy at a building block scale, geological conditions, mean annual surface temperatures, as well as legal restrictions such as temperature differences at the heat pump, maximum monthly heat extraction rates as well as areas restricted from drilling. It is shown that for many densely populated areas many GSHPs would be needed to supply all the energy needed for heating. However, in less densely populated areas GSHPs can be used for heating. If future heating demand is lower due to wide-spread insulation retrofitting, GSHPs could supply most of the energy needed for heating even in densely populated areas.

Soil organic carbon (OC) levels generally increase with increasing clay and silt content under a similar climatic zone because of increased association of OC to clay minerals and stronger occlusion inside aggregates. Surprisingly though, in Western Europe many silt loam soils actually bear low topsoil OC levels compared to lighter textured soils. Soil texture obviously also strongly controls moisture availability with consequent indirect impact on heterotrophic activity. We hypothesized that with increasingly frequent summer drought: 1) soil microbial activity in sandy soils is more likely impeded due to their limited water holding capacity retention during droughts, while soil OC mineralization in silty soils remain be less drought-limited; 2) capillary rise from sufficiently shallow groundwater would, on the other hand, alleviate the water stress in lighter textures. To test these hypotheses, we established a one-year field trial with manipulation of soil texture, monitoring of soil moisture and maize-C decomposition via ^13/12C-CO₂ emissions. The upper 0.5 m soil layer was replaced by sand, sandy loam and silt loam soil with low soil OC. Another sandy soil treatment with a gravel layer was also included beneath the sand layer to exclude capillary rise. Soil texture did not affect maize-C mineralization (C_maize-min) until April 2019 and thereafter C_maize-min rates were higher in the silt loam than in the sandy soils (P=0.01). θ_v correlated positively with the C_maize-min rate for the sand-textured soils only but not for the finer textures. These results clearly highlight that soil texture controlled C_maize-min indirectly through regulating moisture under the field conditions starting from about May, when soils faced a period of drought. By the end of the experiment, more added C_maize was mineralized in the silt loam soil (81%) (P<0.05) than in the sandy soil (56%). Capillary rise did not result in a significant increase in cumulative C_maize-min in the sandy soil, seemingly because the capillary fringe did not reach the sandy topsoil layer. These results imply that, under future climate scenarios the frequency of drought is expected to increase, the largely unimpeded microbial activity in silty soils might lead to a further stronger difference in soil OC with coarser textured soils under similar management.

Solar occultation flux (SOF) and Mobile extractive FTIR (MeFTIR) are techniques used for over 20 years to quantify industrial emissions of VOCs, CH₄, and others, from refineries in the USA, Europe, and Asia. Here, they were combined to assess methane (CH₄) and ammonia (NH₃) from concentrated animal feeding operations (CAFOs) in the San Joaquin Valley (SJV), California. SOF and MeFTIR were used to measure NH₃ column, and ground concentrations of NH₃ and CH₄, respectively. SOF retrieves the gas column concentration from the solar spectra using a solar track, directing the light to a FTIR spectrometer, while crossing the gas plume. Subsequently, a direct flux approach combines the retrieved columns with wind information to obtain the mass fluxes of ammonia. In this survey, the wind information was acquired by a wind LIDAR, which measures wind speed and direction in the interval of 10 – 300 m. On the other hand, Methane emissions were quantified using a unique indirect flux approach by combining the estimated ammonia fluxes and the NH₃:CH₄ ratios measured from the ground concentration using MeFTIR.

Two field campaigns performed in spring and autumn studied emissions from 14 single dairy CAFOs. The daily emissions from the single farms averaged 96.4 ± 38.4 kg_NH3 h^-1and 411 ± 185.4 kg_CH4h^-1, respectively, for NH₃ and CH₄ with the corresponding emission factors (EF) per animal unit of 11.3 ± 3.8 g_NH3h^-1AU^-1and 50.3 ± 24.1 g_CH4h^-1AU^-1. The uncertainty of ammonia measurements was 17 % in a standard confidence interval (CI) and 37 % in a 95 % CI, with the largest uncertainty associated with the wind measurements. Furthermore, the methane uncertainty estimations averaged 27 % in a standard CI, and 52 % in a 95 % CI, dominated by the ammonia fluxes uncertainty. Comparison between Annual or daily EFs obtained by SOF to other quantification approaches, have to take into consideration the SOF measurement conditions, day-time and sunny weather, due to their effects on the NH₃ emissions. The study contributed to develop the knowledge of dairy CAFOs emission, and to strengthen the role of optical remote sensing techniques, bridging the gap between satellites and stationary measurement approaches.

The intensity and the spatial distribution of precipitation depths are known to be highly dependent on relief and geomorphological parameters. Complex environments like mountainous regions are prone to intense and frequent precipitation events, especially if located near the coastline. Although the link between the mean annual rainfall and geomorphological parameters has received substantial attention, few literature studies investigate the relationship between the sub-daily maximum annual rainfall depth and geographical or morphological landscape features.
In this study, the mean of the rainfall extremes in Italy, recently revised in the so-called I²-RED dataset, are investigated in their spatial variability in comparison with some landscape and also some broad climatic characteristics. The database includes all sub-daily rainfall extremes recorded in Italy from 1916 until 2019 and this analysis considers their mean values (from 1 to 24 hours) in stations with at least 10 years of records, involving more than 3700 stations.
The geo-morpho-climatic factors considered range from latitude, longitude and minimum distance from the coastline on the geographic side, to elevation, slope, openness and obstruction morphological indices, and also include an often-neglected robust climatological information, as the local mean annual rainfall.
Obtained results highlight that the relationship between the annual maximum rainfall depths and the hydro-geomorphological parameters is not univocal over the entire Italian territory and over different time intervals. Considering the whole of Italy, the highest correlation is reached between the mean values of the 24-hours records and the mean annual precipitation (correlation coefficient greater than 0.75). This predominance remains also in sub-areas of the Italian territory (i.e., the Alpine region, the Apennines or the coastal areas) but correlation decreases as the time interval decreases, except for the Alpine region (0.73 for the 1-hour maximum). The other geomorphological parameters seem to act in conjunction, making it difficult to evaluate, with a simple linear regression analysis, their impact. As an example, the absolute value of the correlation coefficient between the elevation and the 1-hour extremes is greater than 0.35 for the Italian and the Alpine regions, while for the 24-hours interval it is greater than 0.35 over the coastal areas.
To further investigate the spatial variability of the relationship between rainfall and elevation, a spatial linear regression analysis has been undertaken. Local linear relationships have been fitted in circles centered on any of the 0.5-km size pixels in Italy, with 1 to 30 km radius and at least 5 stations included. Results indicate the need of more comprehensive terrain analysis to better understand the causes of local increasing or decreasing relations, poorly described in the available literature.

Nitrous oxide (N2O) is a major greenhouse gas whose presence in atmosphere is continuously increasing. Hence it’s important to understand its production and consumption mechanisms. During the summer of 2020, we conducted lab experiments using heavy nitrogen tracers of Potassium Nitrate 15N 98% atom (Sigma Aldrich) and Ammonium Chloride 15N 98% atom (Sigma Aldrich) under different moisture conditions to get an insight into N2O production mechanisms and on their dependence on soil moisture. We applied the tracer to peat samples (Kärevere, Estonia) placed in 36 (12 control, 12 nitrate treatment & 12 ammonia treatment) plastic buckets (radius-10cm, height-20cm) with soil height of 10 cm and a 10 cm head space left for gas collection. We installed oxygen sensors, water table indicators and temperature sensors on all buckets. We focused on studying physical conditions (soil oxygen, temperature, water table and soil moisture), gas (N2O) emission data, soil chemistry, gas isotope 15N, soil isotope and soil microbiology to get a complete picture of the processes involved in production of N2O gas. Under the ammonia treatment, emissions increased more than ten-fold which could be due to multiple processes of the nitrogen cycle in play. N2O emissions increased as the oxygen conditions shifted from anoxic (Omg/L=0) to sub-oxic (Omg/L=0.5–6) and then decreased as oxygen conditions reached the oxic (Omg/L>6) state. Furthermore, we witnessed negative site preference and 18O values during the nitrate treatment indicating nitrifier-denitrification. Under the ammonia treatment, we recorded both negative as well as high positive site preference values indicating presence of multiple production mechanisms. This was expected as ammonia triggers multiple processes in the nitrogen cycle. In some samples, we observed N2O consumption with little change in site preference as compared to the N2O producing samples. This indicates some bacterial-denitrification along with the prevailing nitrifier-denitrification. We also observed that under both treatments, heavy oxygen increased with increasing site preference. This indicates reduction of N2O (Ostrom et al, 2007) as redox supports 15N and 18O enrichments. After these lab experiments, we conducted the same experiment at a large scale in a drained peatland forest in Agali, Estonia. In this experiment, we established 1m2 triangle-shape mesocosms using experimental draining and flooding to achieve varying oxygen conditions. Preliminary results of qPCR analysis of N-cycle control genes support the domination of ammonia oxidation and denitrification as sources of N2O.