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

Fine particulate matter (PM) affects visibility, climate and public health. Organic matter (OM), which is hard to characterize due to its complex chemical composition, can constitute more than half of the PM. Biomass burning from residential wood burning, wildfires, and prescribed burning is a major source of OM with an ever-increasing importance.

    Aerosol mass spectrometry (AMS) and Fourier transform infrared spectroscopy (FTIR) are two complementary methods of identifying the chemical composition of OM. AMS measures the bulk composition of OM with relatively high temporal resolution but provides limited parent compound information. FTIR, carried out on samples collected on Teflon filters, provides detailed functional groupinformation at the expense of relatively low temporal resolution.

    In this study, we used these two methods to better understand the evolution of biomass burning OM in the atmosphere with aging. For this purpose, primary emissions from wood and pellet stoves were injected into the Center for Studies of Air Qualities and Climate Change (C-STACC) environmental chamber at ICE-HT/FORTH. Primary emissions were aged using hydroxyl and nitrate radicals (with atmospherically relevant exposures) simulating atmospheric day-time and night-time oxidation.  A time-of-flight (ToF) AMS reported the composition of non-refractory PM₁ every three minutes and PM₁ was collected on PTFE filters over 20-minute periods before and after aging for off-line FTIR analysis.

    We found that AMS and FTIR measurements agreed well in terms of measured OM mass concentration, the OM:OC ratio, and concentration of biomass burning tracers – lignin and levoglucosan. AMS OM concentration was used to estimate chamber wall loss rates which were then used separate the contribution of primary and secondary organic aerosols (POA and SOA) to the aged OM. AMS mass spectra and FTIR spectra of biomass burning SOA and estimates of bulk composition were obtained by this procedure. FTIR and AMS spectra of SOA produced by OH oxidation of biomass burning volatile organic compounds (VOCs) were dominated by acid signatures. Organonitrates, on the other hand, appeared to be important in the SOA aged by the nitrate radical. The spectra from the two instruments also indicated that the signatures of certain compounds such as levoglucosan, lignin and hydrocarbons, which are abundant in biomass burning POA, diminish with aging significantly more than what can be attributed to chamber wall losses. The latter suggests biomass burning POA chemical composition might change noticeably due to heterogeneous reactions or partitioning in the atmosphere. Therefore, the common assumption of stable POA composition is only partially true. In addition, more stable biomass burning tracers should be used to be able to identify highly aged biomass burning aerosols in the atmosphere.

The ESA-JAXA Comet Interceptor mission is expected to flyby a dynamically new comet (or an interstellar one) and better reveal the properties of its dust particles and nucleus surface. We therefore tentatively compare polarimetric properties of dust released by some comets, as well as present on surfaces of some small bodies.

Phase curves of the linear polarization of cometary dust particles (observed in equivalent wavelength ranges) show analogous trends. Some unique dynamically new comets or fragmenting comets (e.g. C/1995 O1 Hale-Bopp, C/1999 S4 LINEAR) may nevertheless present a higher positive branch than Halley-type or Jupiter-family comets (e.g. 1P/Halley, 67P/Churyumov-Gerasimenko). Such differences are clues to differences in the properties (sizes, morphologies, complex optical indices) of the dust particles. Dust particles, ejected by nuclei frequently plunging in the inner Solar System, might indeed partly come from quite dense a surface layer, as detected on the small lobe of comet 67P by Rosetta [1].

Although polarimetric observations of surfaces of cometary nuclei are almost impossible, observations of the rather quiescent nucleus of 1P/Encke have been obtained [2].  Similarities between polarimetric properties of 1P/Encke and atypical small bodies (e.g. Phaeton and particularly Bennu [3]), and of dust in cometary comae may be pointed out. Numerical and laboratory simulations could represent a unique tool to better understand such similarities. It may also be added that dust particles originating from comets, with emphasis on those of Jupiter-family, may survive atmospheric entry, as CP-IDPs collected in the Earth’s stratosphere, and that dust found in debris disks of stellar systems shows levels of polarization similar to those of highly-polarized comets [4].

[1] Kofman et al., MNRAS, 497, 2616-2622, 2020, [2] Boehnhardt et al., A&A, 489, 1337-1343, 2008. [3] Cellino et al., MNRAS, 481, L49-L53, 2018. [4] Levasseur-Regourd et al., PSS, 186, 104896, 2020,

Long-term perspectives on disturbance dynamics are important for the conservation of protected areas, yet restoration and conservation strategies in the Bohemian-Bavarian Forest Mountains do not consider the long-term role and patterns of forest fire, which is still deemed a negligible ecosystem disturbance in Central Europe. The scarcity of macroscopic charcoal studies in this area has likely hampered a complete understanding of local fire regime dynamics and its legacies in the present forest structure and composition. Here we used macroscopic charcoal (number, area and morphology of charred particles) and pollen analysis to investigate high resolution spatial and temporal patterns in Holocene fire regimes in the Bavarian-Bohemian Forest. We explored the relationship between changing forest composition dynamics and the influence topography had on spatial patterns of biomass burning. For this, we selected three lacustrine sites (two new, one published), located along a 30 km longitudinal transect within the studied area, at similar elevations in the mixed forest belt, with opposite (north vs. south) aspects. Results showed similar changes in biomass burning, fire frequency and peak magnitude at all sites, with a maximum during the early Holocene when fire resistant taxa (Pinus and Betula) dominated. Fire frequency decreased by half with the expansion of more fire-sensitive taxa (e.g., Picea and Fagus) during the mid-Holocene and reached a second maximum in the late Holocene, parallel with sustained increases in anthropogenic pollen indicators. We found a close north-south correspondence in the succession of fire patterns, i.e., fine-scale changes in biomass burning in the Bavarian Forest site (south-facing catchment) occurred around the same time with those observed at the Bohemian Forest sites (predominantly north-facing catchments), and these changes mirrored the Holocene dynamics of the main forest taxa. For example, the lowest biomass burning and peak magnitude intervals marked the beginning of Picea abies expansion at ~ 9 ka BP, Fagus sylvatica expansion at ~6 ka and Abies alba expansion at ~5 ka BP. Furthermore, we found a direct relationship between the abundance of charred morphotypes of conifer needles and deciduous leaves and the dominance of pine and birch in our pollen records, and a close correspondence between the abundance of non-woody charcoal morphotypes and pollen-derived landscape openness. Non-woody charcoal morphotypes dominated the charcoal records in the Early Holocene at the peak of biomass burning, whereas the abundance of woody morphotypes peaked around 6-8 ka BP and over the last millennium and their proportion in total charcoal influx increased starting 4 ka BP. Our study enables a better understanding of past and present fire regimes in the Bavarian-Bohemian Forest Mountains and highlights the need to consider the effects of fire as part of climate-cha

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.