High Background Levels Research Articles

Productivity and SDS-sedimentation of the promising winter bread wheat varieties depending on forecrops

The main purpose of breeding and commercial production is the quantity of grain and its quality. Occupying 74 % in the structure of grain crops, and 80 % in terms of gross yields, wheat grain is the most important source of income for grain producers in the Rostov region. The purpose of the current study was to reveal the effect of forecrops on productivity and SDS-sedimentation of the promising winter bread wheat varieties. In the trial there were studied 12 winter bread wheat varieties developed by the FSBSI “ARC “Donskoy” and sent to the State Variety Testing in recent years. There were estimated productivity and the amount of SDS-sedimentation. On average, over the three years of study (2019–2021), for all the studied forecrops, in terms of productivity there have been identified such winter wheat varieties of the intensive type as ‘Razdolie’ (7.14 t/ha), ‘Razgulay’ (7.67 t/ha) and ‘Vasilich’ (7.51 t/ha), which makes it possible to recommend these genotypes for sowing according to high and medium levels of Agricultural background. Among winter wheat varieties of semi-intensive type there has been identified only one variety ‘Donets’ (7.31 t/ha), which allows us to characterize it as a universal variety and recommend it for sowing in production according to high, medium and low levels of Agricultural background. According to the value of SDS-sedimentation for all the studied forecrops, there have been identified such varieties of intensive type as ‘Razdolie’ (62 ml), ‘Azov Sea’ (61 ml), ‘Razgulay’ (63 ml) and ‘Vasilich’ (63 ml). The varieties of semi-intensive type did not significantly exceed the value of SDS-sedimentation of the standard variety or were at its level, however, the values were high. Such SDS-sedimentation values have indicated that the presented genotypes are able to form high grain quality for all the studied forecrops and regardless of the productivity value.

Can heavy metal pollution induce soil bacterial community resistance to antibiotics in boreal forests?

Abstract The emergence of microbial antibiotic resistance is a central threat to global health, food security, and development. It has been shown that heavy metal pollution can give rise to microbial resistance to antibiotics, but how wide‐spread this phenomenon is remains an open question that urgently needs filling to enable appropriate environmental risk assessments. Here, we determined whether long‐term differences in heavy metal pollution in boreal forests had affected soil microbial communities such that they had increased microbial resistance to antibiotics. First, we assessed variation in metal concentrations in samples collected across a distance trajectory from the pollution source, and also the microbial rates and levels of bacterial community resistance to the heavy metal Cu and the antibiotics tetracycline and vancomycin in those samples. Second, we tested if the exposure to Cu or tetracycline could increase bacterial community resistance to Cu and to antibiotics in soils with high versus low background levels of metal contamination. Metal pollution had affected microbial community structures and suppressed decomposer functioning. Importantly, bacterial community Cu resistance increased with higher metal concentrations, which coincided with an induced bacterial community resistance to tetracycline, but not to vancomycin. Laboratory experiments revealed that bacterial community Cu resistance could be further induced in both the low and high end of the pollution gradient, but also that these short‐term inductions of community metal tolerance did not coincide with enhanced antibiotic resistance. This yielded a surprising negative correlation between long‐term and short‐term effects by metals on microbial metal and antibiotic resistances. One mechanism that could provide protection against both metal cations and tetracycline is the small multidrug resistance (SMR) family, which is an energy demanding physiological mechanism that may take time to confer protection. This may explain the different microbial responses to long‐term gradients and metal addition experiments. Policy implications. We show that metal pollution in boreal forests will promote soil bacterial antibiotic resistance, revealing a so far unrecognized reservoir of microbial antibiotic resistance. We recommend that environmental risk assessments for any activity giving rise to increased soil metal concentrations need to also consider the induction of microbial antibiotic resistance.

Stages of radiation monitoring on the territory of the Tula region affected in consequence of the Chernobyl NPP accident

More than half of the Tula oblast territory was contaminated following the accident at the Chernobyl nuclear power plant. This article describes the stages of radiation hygiene monitoring of objects in the habitat in the Tula region impacted by radioactive contamination. Information is given on changes in radioactively contaminated areas at different periods after the accident. An assessment is made of the radiological situation at the initial “iodic” period of the accident, the subsequent “cesium” period, and at the current stage. A description is given of work done by the oblast’s state sanitary and epidemiological service based on radiation monitoring results for reducing the radiation exposure of the population at different stages of supervision. Information is supplied on high levels of gamma background up to 35 mkSv/h during the “iodic” period. Tabulated data are supplied on exceeding the tentative maximum permissible level of iodine-131 content in dairy products produced in Plavski district, the most contaminated area in the region. Data are given on laboratory tests on food products for total beta activity in 1986-87 and the frequency of exceeding the permissible level of cesium-137 content during 1986. The above permissible content of radionuclides in food products was registered only in 1986 due to surface contamination of plants, while in wild mushrooms this took place as late as 2004. Currently, the proved content of cesium-137 and strontium-90 in food products is determined only by the radio-chemical method. A table is supplied showing results of radio-chemical research on major dose contributing food products from 2010 to 2021. The article supplies a table with numeric values of annual average effective doses of radiation sustained by the population of settlements located in the Chernobyl radioactively contaminated zone during 1986 through 2021. The dose of the population’s exposure to the Chernobyl radiation since 1994 does not exceed 1 mSv/year. In 2021, the population’s annual effective dose in 98.3% of settlements amounted to less than 0.2 mSv/year.

Heavy metal pollution in agricultural soils of a typical volcanic area: Risk assessment and source appointment.

Heavy metals are naturally occurring elements with high natural background levels in the volcanic area. Therefore, it is necessary to conduct a risk assessment and identify potential sources of heavy metals. In this study, 4488 soil samples (0-20cm) were collected in Chengmai County, a typical volcanic area in Hainan Province, and analyzed for eight heavy metals and major oxides. Pollution level, ecological risks, and health risks were evaluated by geo-accumulation index (Igeo), pollution index (PI), potential ecological risk index (RI), hazard index (HI), and carcinogenic risks (CR). The positive matrix factorization (PMF) model was further used to determine the priority source of heavy metals. The average values of heavy metal concentrations in soil were 7.06, 0.07, 156.88, 33.43, 0.05, 72.47, 19.48, and 67.51mgkg-1 for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, respectively. Except for Pb, the average concentrations of all heavy metals exceeded background concentration in Hainan soils, indicating different degrees of heavy metal enrichment. The Igeo and PI showed that the main pollutant element in volcanic soils was Ni, followed by Cr and Cu. The RI shows that the percentage of soil samples with considerable or worse potential ecological risk was 44.4% of the total samples, with Hg, As, Cd, and Ni causing high ecological risks. The estimated average daily doses of heavy metals were below the tolerable limits and the HI values were below one for both adults and children, indicating that the residents had an acceptable potential non-carcinogenic risk. However, the potential carcinogenic risk of exposure to Cr, Ni, and As was unacceptable for residents, with high CR values exceeding 10-4, especially for children. Based on the PMF, five major sources of heavy metals were found in the study area: Ni, Cu, and Zn mainly from parent materials, As and Pb from daily life and vehicle emissions, Cd from agricultural activities, Hg from industrial activities, and Cr from parent materials under different environmental conditions. Significant positive correlations between Al2O3, TFe2O3, Mn, soil organic carbon (SOC), and heavy metals reflect that aluminium-rich minerals, Fe-Mn oxides, and SOC are the most critical factors affecting heavy metal accumulation in volcanic agricultural soils.

Contamination Evaluation and Source Analysis of Heavy Metals in Karst Soil Using UNMIX Model and Pb-Cd Isotopes.

Karst terrain is the typical area covered with a high background of heavy metals under geochemical anomaly. This research explored the accumulation of geochemical elements and soil sources in karst terrain from rock and soil exposed in carbonate areas. The comprehensive ecological risk and enrichment of heavy metals from parent rock weathered to soil was investigated in 11 formations in the carbonate and clastic areas of the Weining and Hezhang counties in northwest Guizhou. The single factor pollution index, geoaccumulation index, and the potential risk coefficient were used to assess the environmental risk. The results revealed that the heavy metals in an overall geologically high background level of soil in northwest Guizhou is at a slight risk level. However, except for Cd, the heavy metals did not exceed the standard pollution reference. Moreover, the UNMIX model and Cd and Pb isotopes were used to analyze the source of heavy metals, comprising of cadmium (Cd), arsenic (As), lead (Pb), chromium (Cr), copper (Cu), nickel (Ni), and zinc (Zn), and the geochemical elements of silicon (Si), aluminum (Al), iron (Fe), magnesium (Mg), and calcium (Ca). The study showed that most elements in the soil carbonate area exceed the national standard, and the heavy metals in the soil showed a strong enrichment, while the major elements Si and Mg display strong loss. Heavy metal concentrations in soil in the carbonate area were higher than in the clastic area. Geological sources and atmospheric deposition were the main contributors to heavy metal concentrations in both carbonate and clastic areas, and their concentrations differ according to soils developing in different formations.

Rapid determination of thorium in urine by quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS).

Inductively coupled plasma mass spectrometry (ICP-MS) has proven to be an excellent analytical technique with high sensitivity for detecting low levels of long-lived radionuclides, such as thorium. However, the high-sensitivity technique increases the memory effect of thorium. This study developed a rapid, high-throughput, simple method for measuring thorium in urine using quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS). Replacing the commonly used hazardous hydrofluoric acid with a rinse solution of 0.025 mol/L oxalic acid and 5% (v/v) nitric acid eliminated the memory effect of thorium. 233U was used as internal standard in this study. The limit of detection (LOD) for thorium in this study is 0.77 ng/L, which is comparable to those of reported methods using more sophisticated and expensive sector field inductively coupled plasma mass spectrometry (SF-ICP-MS). This proposed method can determine thorium concentrations in urine in both occupationally exposed workers and populations that live in areas with high background levels of thorium.

Elemental composition, mineral characteristics and pedogenesis of serpentine soils in the Western Pacific region

Soil quality is linked to human health and there is a need to evaluate risks, such as high background levels of chromium (Cr) and nickel (Ni) in serpentine soils. Professor Zeng-Yei Hseu is a world-leading soil scientist working on a three-year project to capture and demonstrate mineral composition and transformation in serpentine soils. This work will guide remediation techniques for contaminated soils and, in doing so, contribute to food safety and security. Hseu is based at the Soil Survey and Remediation Lab, Department of Agricultural Chemistry, National Taiwan University. He and his team are seeking to better understand factors such as soil distribution, formation, application and limit. Hseu is interested in phytoremediation, a process through which plants can remove contaminants in the soil and is looking for links between serpentine soils, the plants that grow in them and their ability to facilitate phytoremediation. He investigated the origin and bioavailability of Cr and Ni and their dynamics in serpentine soils, the risk of Cr and Ni to the food chain and identified that Cr and Ni were mainly released from respective chromites and silicates of serpentine soils. Hseu is focused on serpentine soils in the Western Pacific region and he and his team collected and analysed serpentine soils from Japan, Taiwan and the Philippines along the western Pacific island arc, as well as from a control site along the Red River Fault Zone in Vietnam. The researchers looked for the morphology and pedogenesis of different serpentine soils and mineral sources of major and trace elements in the soils.

Multiplexed LNP-mRNA vaccination against pathogenic coronavirus species.

Although COVID-19 vaccines have been developed, multiple pathogenic coronavirus species exist, urging on development of multispecies coronavirus vaccines. Here we develop prototype lipid nanoparticle (LNP)-mRNA vaccine candidates against SARS-CoV-2 Delta, SARS-CoV, and MERS-CoV, and we test how multiplexing LNP-mRNAs can induce effective immune responses in animal models. Triplex and duplex LNP-mRNA vaccinations induce antigen-specific antibody responses against SARS-CoV-2, SARS-CoV, and MERS-CoV. Single-cell RNA sequencing profiles the global systemic immune repertoires and respective transcriptome signatures of vaccinated animals, revealing a systemic increase in activated B cells and differential gene expression across major adaptive immune cells. Sequential vaccination shows potent antibody responses against all three species, significantly stronger than simultaneous vaccination in mixture. These data demonstrate the feasibility, antibody responses, and single-cell immune profiles of multispecies coronavirus vaccination. The direct comparison between simultaneous and sequential vaccination offers insights into optimization of vaccination schedules to provide broad and potent antibody immunity against three major pathogenic coronavirus species.

Precision digital mapping of endogenous and induced genomic DNA breaks by INDUCE-seq

Understanding how breaks form and are repaired in the genome depends on the accurate measurement of the frequency and position of DNA double strand breaks (DSBs). This is crucial for identification of a chemical’s DNA damage potential and for safe development of therapies, including genome editing technologies. Current DSB sequencing methods suffer from high background levels, the inability to accurately measure low frequency endogenous breaks and high sequencing costs. Here we describe INDUCE-seq, which overcomes these problems, detecting simultaneously the presence of low-level endogenous DSBs caused by physiological processes, and higher-level recurrent breaks induced by restriction enzymes or CRISPR-Cas nucleases. INDUCE-seq exploits an innovative NGS flow cell enrichment method, permitting the digital detection of breaks. It can therefore be used to determine the mechanism of DSB repair and to facilitate safe development of therapeutic genome editing. We further discuss how the method can be adapted to detect other genomic features.

A sensitive and robust bioanalytical assay for pharmacokinetic analysis of ROR1xCD3 bispecific T cell engager (NVG-111) in a first-in-human study.

e19505 Background: Receptor tyrosine kinase-like Orphan Receptor-1 (ROR1) is widely expressed on hematological and solid tumors. NVG-111, a first in class humanized tandem scFv ROR1xCD3 bispecific antibody elicits potent killing of ROR1+ tumor cells in vitro and in vivo. This bispecific T-cell engager (TCE) is being evaluated in a first in human, Phase I trial in patients with relapsed/refractory chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL). The predicted therapeutic dose and steady state serum concentrations of NVG-111 were estimated by allometric scaling using relevant doses from a murine PK study. To assess free drug levels in patients following 21 days of continuous infusion of NVG-111, a bespoke, sensitive pharmacokinetics (PK) assay with high levels of specificity and sensitivity was developed. Methods: Anti-idiotype (anti-ID) antibodies directed to anti-ROR1 (αROR1-ID) and anti-CD3 (αCD3-ID) were generated by mouse immunization or by phage display from customized libraries. A proof-of-concept sandwich ELISA assay was developed using αCD3-ID to capture NVG-111 and detection by biotinylated hROR1-streptavidin-HRP. Gyrolab and Quanterix Simoa high sensitivity ELISA platforms were used to detect NVG-111 by αCD3-ID capture and αROR1-ID detection. The mesoscale discovery electrochemiluminescence assay (MSD-ECLA) was developed using a reversed format; NVG-111 capture with αROR1-ID and detection with αCD3-ID. Results: Allometric scaling predicted a theoretically relevant therapeutic dose and steady state serum concentration of 1ng/mL NVG-111 in humans, which was just at the level of sensitivity of a conventional ELISA under non-matrix conditions. Transferring the format to Quanterix Simoa had limited success due to high background levels in all configurations evaluated. The Gyrolab platform increased sensitivity to 75pg/mL, but suboptimal individual human sera matrix selectivity limited assay validity. Assessment of MSD-ECLA provided the best signal/noise, enhanced human disease and healthy sera selectivity, and a dynamic sensitivity range of 250pg/mL to 32ng/mL, which enabled the development of a GCLP qualified PK assay. The MSD-ECLA assay was employed to measure NVG-111 concentrations in CLL or MCL subjects dosed with 0.3-30µg/day NVG-111. MSD-ECLA detected drug in patients receiving NVG-111, with a range of steady-state serum concentrations (Cavg.ss) of 168-610pg/mL. This was in-line with the predicted drug levels from the single species allometric scaling, albeit with observed levels being marginally lower than expected. Conclusions: Development, custom optimization and validation of a highly sensitive MSD-ECLA PK assay has enabled GCLP-compliant measurement of circulating NVG-111 in CLL or MCL patients treated with at least 10µg/day cIV NVG-111.

Ecological Health Risk Assessment and Source Identification of Heavy Metals in Surface Soil Based on a High Geochemical Background: A Case Study in Southwest China.

A total of 28,095 surface soil samples were collected in areas with high natural background levels; the potential ecological risk is generally low, and the high-risk area is small and mainly affected by lead–zinc mines. The contribution to the potential ecological risk factor (RI) is as follows: Hg > Cd > As > Pb > Cu > Ni > Cr > Zn, with noncarcinogenic chronic risks of Cr > As > Cd > Pb > Ni > Cu > Hg > Zn; furthermore, dermal contact is the main pathway of exposure causing health risks. The total carcinogenic risks caused by heavy metals were as follows: Cr > Cd > As > Pb; and the risks posed by Cr, Cd, and As were higher than the threshold value (1.0 × 10−4); people face a higher threat to heavy metals in soils in Zhenxiong, Ludian, Huize, Weixin, and Zhaoyang. The evaluation result of the EPA PMF model shows that the soil heavy metals are mainly composed of five sources, of which basalt, Permian, and Triassic carbonate rock parent material constitute the natural background source, while the mining activities of lead–zinc mines and the emissions of coal burning by residents constitute the anthropogenic source. The contribution was ranked in order of lead–zinc mining (26.7%) > Triassic carbonate (23.7%) > basalt (20.9%) > coal burning and automobile emissions (16.1%) > Permian carbonate (12.6%).

Parental Education Moderates the Relation between Physical Activity, Dietary Patterns and Atopic Diseases in Adolescents.

Background: Atopic diseases, particularly asthma, eczema, and rhinitis, are among the most common chronic diseases in childhood, with several factors implicated in their pathogenesis. Our study examined the role of parental education in the association between diet, physical activity, and atopy in adolescents. Methods: 1934 adolescents (47.5% boys) aged 13–14 years old reported information about their diet and physical activity and their parents reported their highest educational level. The moderating role of parental education level (primary/secondary vs. tertiary) in the relation between lifestyle patterns and atopic diseases was examined with logistic regression analyses. Results: High consumption of dairy products was inversely associated to adolescents’ asthma and rhinitis symptoms overall, but this relation was almost 50% stronger for the adolescents with high parental education level background. The same pattern of reduction of the odds was noticed also regarding the association among the high intake of fruits, vegetables, pulses, with all three atopic diseases and the adherence to a physically active lifestyle only with current asthma and eczema (all p < 0.05). Conclusion: Adolescents who are physically active and consume a higher intake of fruits, vegetables, and pulses and a lower intake of fast-food and sweets, and their parents/guardians having higher education, are less likely to have any current symptoms of asthma, eczema, and rhinitis than the ones who have low educated parents.

Operational experience of the Belle II pixel detector

The Belle II experiment at the SuperKEKB accelerator has started its physics data taking with the full detector setup in March 2019. It aims to collect 40 times more e+e− collision data compared with its predecessor Belle experiment. The Belle II pixel detector (PXD) is based on the Depleted P-channel Field Effect Transistor (DEPFET) technology. The PXD plays an important role in the tracking and vertexing of the Belle II detector. Its two layers are arranged at radii of 14 mm and 22 mm around the interaction point. The sensors are thinned down to 75 μm to minimize multiple scattering, and each module has interconnects and ASICs integrated on the sensor with silicon frames for mechanical support. PXD showed good performance during data taking. It also faces several operational challenges due to the high background level from the SuperKEKB accelerator, such as the damage from beam loss events, the drift in the HV working point due to radiation effect, and the impact of the high background.

A big change

The current period in the development of architectural theory and practice simultaneously demonstrates signs of dynamics and statics. An analysis of reflection of such phenomena as the COVID-19 pandemic and global climate change allows us to conclude that the current mass picture of the world is bifurcated and internally contradictory. A high background level of anxiety suppresses creative tendencies and creates the effect of “stagnation in the era of change”.

Ecological Assessment, Spatial Analysis, and Potential Sources of Heavy Metals (HMs) in Soils with High Background Values in the Lead-Zinc Mine, Hezhang County, Southwestern China

The heavy metals (HMs) usually have high natural background levels in lead-zinc mines. Strengthening the ecological risk assessment and accurate identification sources of HMs is an important component of land resource utilization and food security. A total of 795 soil samples (with a depth of 0~20 cm) were collected in Hezhang County, an area of typical high background levels of HMs with more than 18 large lead-zinc deposits. In this study, inductively coupled plasma emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), atomic fluorescence spectrometry (AFS), and the potentiometric method (POT) were used to test the total concentration of HMs and pH values. The geographic information system was used to model the concentration distribution of HMs, and the ecological risk was assessed according to the source analysis results. The results suggest that: (1) the mean concentration of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 24.55, 2.25, 176.4, 89.6, 0.19, 64.2, 102, and 257 mg·kg−1, respectively, which exceeded the average background value of soils in Guizhou Province by 1.23, 3.41, 1.84, 2.80, 1.73, 1.64, 2.90, and 2.58 times, respectively, and the average concentrations of Cd, Cr, Cu, Ni, Pb and Zn exceed the screening values specified in soil contamination risk in agricultural land; (2) the results of enrichment factor (EF) showed that 29.73% of Cd are moderately polluted, and other HMs were slightly polluted in a small area or near the baseline value (EF ≈ 1); and (3) PMF analysis showed that there are five main sources, pyrite, lead-zinc ore, basalt, carbonate rock, and agricultural production, with the risk contribution ratios of 5.25%, 27.37%, 28.94%, 17.91%, and 20.53%, respectively.

Risk Assessment of Contaminants in Sewage Sludge Applied on Norwegian Soils

Risk Assessment of Contaminants in Sewage Sludge Applied on Norwegian Soils

Use of computational fluid dynamics (CFD) to model observed nasal nitric oxide levels in human subjects.

Upper airway nitric oxide (NO) is physiologically important in airway regulation and defense, and nasal NO (nNO) levels typically exceed those in exhaled breath (fractional exhaled NO [FeNO]). Elevated concentrations of NO sampled from the nose, in turn, reflect even higher concentrations in the paranasal sinuses, suggesting a "reservoir" role for the latter. However, the dynamics of NO flux within the sinonasal compartment are poorly understood. Data from 10 human subjects who had previously undergone both real-time nNO sampling and computed tomography (CT) scanning of the sinuses were analyzed using computational fluid dynamics (CFD) methods. Modeled and observed nNO values during the initial 2-s transient ("spike") during nasal exhalation were then compared. Examining the initial 2-s transient spike for each subject (as well as the pooled group), there was a statistically significant correlation between modeled and observed nNO levels, with r values ranging from 0.43 to 0.89 (p values ranging from<0.05 to<0.0001). Model performance varied between subjects, with weaker correlations evident in those with high background (FeNO) levels. In addition, the CFD simulation suggests that ethmoid sinuses (>60%) and diffusion process (>54%) contributed most to total nasal NO emissions. Analysis of this dataset confirms that CFD is a valuable modeling tool for nNO dynamics, and highlights the importance of the ethmoid sinuses, as well as the role of diffusion as an initiating step in sinonasal NO flux. Future model iterations may apply more generally if baseline FeNO is taken into account.

Image Matting: A Comprehensive Survey on Techniques, Comparative Analysis, Applications and Future Scope

In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate alpha matte in the unknown region to distinguish foreground from the background region of an image using an input image and the corresponding trimap of an image which represents a foreground and unknown region. Numerous image matting techniques have been proposed recently to extract high-quality matte from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict alpha matte such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level background which tends to produce unwanted artifacts when color is same or semi-transparent in the foreground object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the alpha matte using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper.

Multidisciplinary Constraints on Magma Compressibility, the Pre‐Eruptive Exsolved Volatile Fraction, and the H2O/CO2 Molar Ratio for the 2006 Augustine Eruption, Alaska

AbstractGeodetically modeled reservoir volume changes during volcanic eruptions are commonly much smaller than the observed eruptive volumes. This discrepancy is thought to be partially due to the compressibility of magma, which is largely controlled by the presence of exsolved volatiles. The 2006 eruption of Augustine Volcano, Alaska, produced an eruptive volume that was ∼3 times larger than the geodetically estimated syn‐eruptive subsurface volume change. In this study, we use a multistep methodology that combines constraints from geodetic, volcanic gas, geologic, and petrologic data together with equations relating physical processes to observable parameters. We apply a Monte Carlo approach to quantify uncertainties. Ultimately, we solve for the exsolved volatile volume fraction and the magma compressibility. We estimate Augustine's 2006 pre‐eruptive exsolved volatile phase to be ∼5.5 vol% of the magma at storage depths, yielding a bulk magma compressibility of ∼3.8 × 10−10 Pa−1. We develop a novel approach to estimate the H2O/CO2 ratio of the syn‐eruptive gas emissions in the absence of direct H2O emission measurements which are hard to obtain due to the high background levels in ambient air. We find a best‐fit H2O/CO2 molar ratio of 29. We also investigate the effects of applying different equations of state to our model. We find that the Ideal Gas Law might be used as a first approximation due to its simplicity; however, it overestimates volatile density and compressibility significantly at storage depths. This project capitalizes on the insights that can be gained by integrating multidisciplinary data with models of physical processes.

Statistical Modelling of SPADs for Time-of-Flight LiDAR.

Time-of-Flight (TOF) based Light Detection and Ranging (LiDAR) is a widespread technique for distance measurements in both single-spot depth ranging and 3D mapping. Single Photon Avalanche Diode (SPAD) detectors provide single-photon sensitivity and allow in-pixel integration of a Time-to-Digital Converter (TDC) to measure the TOF of single-photons. From the repetitive acquisition of photons returning from multiple laser shots, it is possible to accumulate a TOF histogram, so as to identify the laser pulse return from unwelcome ambient light and compute the desired distance information. In order to properly predict the TOF histogram distribution and design each component of the LiDAR system, from SPAD to TDC and histogram processing, we present a detailed statistical modelling of the acquisition chain and we show the perfect matching with Monte Carlo simulations in very different operating conditions and very high background levels. We take into consideration SPAD non-idealities such as hold-off time, afterpulsing, and crosstalk, and we show the heavy pile-up distortion in case of high background. Moreover, we also model non-idealities of timing electronics chain, namely, TDC dead-time, limited number of storage cells for TOF data, and TDC sharing. Eventually, we show how the exploit the modelling to reversely extract the original LiDAR return signal from the distorted measured TOF data in different operating conditions.