Inverse modeling of the 2021 spring super dust storms in East Asia

This spring, super dust storms reappeared in East Asia after being absent for a (two) decade(s). The event caused enormous losses both in Mongolia and in China. Accurate simulation of such super sandstorms is valuable for the quantification of health damages, aviation risks, and profound impacts on the Earth system, but also to reveal the driving climate and the process of desertification. However, accurate simulation of dust life cycles is challenging mainly due to imperfect knowledge of emissions. In this study, the emissions that lead to the 2021 spring dust storms are estimated through assimilation of MODIS AOD and ground-based PM10 concentration data. To be able to use the AOD observations to represent the dust load, an Angstrom-based data screening is designed to select only observations that are dominated by dust. In addition, a non-dust AOD bias correction has been designed to remove the part of the AOD that could be attributed to other aerosols than dust. With this, the dust concentrations during the 2021 spring super storms could be reproduced and validated with concentration observations. The emission inversion results reveal that wind blown dust emissions originated from both China and Mongolia during spring 2021. Specifically, 18.3M and 27.2M ton of particles were released in Chinese desert and Mongolia desert respectively during these severe dust events. By source apportionment it has been estimated that 58 % of the dust deposited in the densely populated Fenwei Plain (FWP) in the northern China originate from transnational transport from Mongolia desert. For the North China Plain (NCP), local Chinese desert play a less significant roles in the dust affection; the long-distance transport from Mongolia contributes for about 69 % to the dust deposition in NCP, even if it locates more than 1000 km away from the nearest Mongolian desert.

Supplemental Material: A robust age model for the Cryogenian Pocatello Formation of southeastern Idaho (northwestern USA) from tandem in situ and isotope dilution U-Pb dating of volcanic tuffs and epiclastic detrital zircons

<div>Figure S1. Thin-section photomicrographs for lithologies of the Bannock Volcanic Member and Scout Mountain Member of the Pocatello Formation exposed at Scout Mountain, Idaho. Sample numbers are illustrated on the stratigraphic section of Figure 4. Field of view is 24 mm × 40 mm. Tables S1–S3: LA-ICPMS U-Pb isotope and trace element concentration data. Table S4: CA-IDTIMS U-Pb isotope data.<br></div>

Supplemental Material: A robust age model for the Cryogenian Pocatello Formation of southeastern Idaho (northwestern USA) from tandem in situ and isotope dilution U-Pb dating of volcanic tuffs and epiclastic detrital zircons

<div>Figure S1. Thin-section photomicrographs for lithologies of the Bannock Volcanic Member and Scout Mountain Member of the Pocatello Formation exposed at Scout Mountain, Idaho. Sample numbers are illustrated on the stratigraphic section of Figure 4. Field of view is 24 mm × 40 mm. Tables S1–S3: LA-ICPMS U-Pb isotope and trace element concentration data. Table S4: CA-IDTIMS U-Pb isotope data.<br></div>

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition.

The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean

A feed-forward neural network (FFNN) was used to estimate the monthly climatology of partial pressure of CO2 (pCO2W) at a spatial resolution of 1° latitude by 1° longitude in the continental shelf of the European Arctic Sector (EAS) of the Arctic Ocean (the Greenland, Norwegian, and Barents seas). The predictors of the network were sea surface temperature (SST), sea surface salinity (SSS), the upper ocean mixed-layer depth (MLD), and chlorophyll-a concentration (Chl-a), and as a target, we used 2 853 pCO2W data points from the Surface Ocean CO2 Atlas. We built an FFNN based on three major datasets that differed in the Chl-a concentration data used to choose the best model to reproduce the spatial distribution and temporal variability of pCO2W. Using all physical–biological components improved estimates of the pCO2W and decreased the biases, even though Chl-a values in many grid cells were interpolated values. General features of pCO2W distribution were reproduced with very good accuracy, but the network underestimated pCO2W in the winter and overestimated pCO2W values in the summer. The results show that the model that contains interpolating Chl-a concentration, SST, SSS, and MLD as a target to predict the spatiotemporal distribution of pCO2W in the sea surface gives the best results and best-fitting network to the observational data. The calculation of monthly drivers of the estimated pCO2W change within continental shelf areas of the EAS confirms the major impact of not only the biological effects to the pCO2W distribution and Air-Sea CO2 flux in the EAS, but also the strong impact of the upper ocean mixing. A strong seasonal correlation between predictor and pCO2W seen earlier in the North Atlantic is clearly a yearly correlation in the EAS. The five-year monthly mean CO2 flux distribution shows that all continental shelf areas of the Arctic Ocean were net CO2 sinks. Strong monthly CO2 influx to the Arctic Ocean through the Greenland and Barents Seas (>12 gC m−2 day−1) occurred in the fall and winter, when the pCO2W level at the sea surface was high (>360 µatm) and the strongest wind speed (>12 ms−1) was present.

Extensive primary production promoted the recovery of the Ediacaran Shuram excursion

Member IV of the Ediacaran Doushantuo Formation records the recovery from the most negative carbon isotope excursion in Earth history. However, the main biogeochemical controls that ultimately drove this recovery have yet to be elucidated. Here, we report new carbon and nitrogen isotope and concentration data from the Nanhua Basin (South China), where δ13C values of carbonates (δ13Ccarb) rise from − 7‰ to −1‰ and δ15N values decrease from +5.4‰ to +2.3‰. These trends are proposed to arise from a new equilibrium in the C and N cycles where primary production overcomes secondary production as the main source of organic matter in sediments. The enhanced primary production is supported by the coexisting Raman spectral data, which reveal a systematic difference in kerogen structure between depositional environments. Our new observations point to the variable dominance of distinct microbial communities in the late Ediacaran ecosystems, and suggest that blooms of oxygenic phototrophs modulated the recovery from the most negative δ13Ccarb excursion in Earth history.

Quantitative Assessment of Temporal Changes in Subaqueous Hydrothermal Activity in Active Crater Lakes During Unrest Based on a Time-Series of Lake Water Chemistry

Regular sampling of lake water has been performed at many volcanoes to assess the state of volcanic activity. However, it is not clear whether the absolute concentrations or, instead, rate of changes in concentrations are more suitable for such assessments. In this study, we show that temporal changes in concentrations of an element in lake water are described by a simple differential equation, assuming changes in lake volume and chemical processes are negligible. The time constants (63% response time for changes in the chemical concentration in lake water) have a wide range varying between 20 and 1,000 days for the studied volcanoes in Japan, meaning it takes a long time to assess volcanic activity based on the absolute concentration of an element. In order to assess the volcanic activity in a shorter time period, based on a time-series of lake element concentration data, we developed a numerical model to calculate temporal changes in the steady-state concentration, which is proportional to the elemental concentrations of the bulk hydrothermal fluid injected from subaqueous fumaroles and hot springs. We applied our method to Yugama crater lake at Kusatsu–Shirane volcano, Japan, and quantitatively evaluated temporal changes in the hydrothermal input from 1964 to 2020. As a result, we detected changes in the Cl concentrations of the bulk hydrothermal input that were associated with unrest including the phreatic eruption in 1976 and earthquake swarms in 1989–1992 and 2014–2020. The future concentration in the lake water can be predicted from the most recent steady-state concentrations. Comparing the predicted concentration curve with the concentration obtained from lake water samples, it is possible to quickly assess whether the concentration of the bulk hydrothermal input has increased/decreased or remained constant.

vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

Background As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

Emission Determination by Three Remote Sensing Methods in Two Release Trials

Concentrations of greenhouse gases such as carbon dioxide (CO2), nitrous dioxide (N2O) and methane (CH4) in the atmosphere are rising continuously. The first step to reduce emissions from landfills is to gain better knowledge about the quantities emitted. There are several ways to quantify CH4 emissions at landfills. Comprehensive quality analyses of individual methods for emission rate quantification at landfills are few to date. In the present paper, the authors conducted two field trials with three different remote sensing methods to gain more knowledge about the possibilities and challenges in quantification of CH4 emissions from landfills. One release trial was conducted with released N2O as tracer and CH4 for quality assessment of the methods. In the second trial, the N2O tracer was released on a landfill to gain experience under field conditions. The well-established inverse dispersion modelling method (IDMM) was used based on concentration data of TDLAS (Tunable Diode Laser Absorption Spectroscopy)-instruments and on concentration data of a partly drone based Fourier-Transformation-Infrared-Spectroscopy (FTIR)-instrument. Additionally, a tracer-method with N2O-tracer and FTIR measurements was conducted. In both trials, IDMM based on TDLAS data and FTIR data provided the best results for high emission rates (15% deviation) and low emission rates (47% deviation). However, both methods have advantages, depending on the field of application. IDMM based on TDLAS measurements is the best choice for long-term measurements over several hours with constant wind conditions (8% deviation). The IDMM based on drone based FTIR measurements is the means of choice for measurements under changing wind conditions and where no linear measurement distances are possible.

Study of Atomic Oxygen Airglow Intensities and Air Temperature near Mesopause Obtained by Ground-Based and Satellite Instruments above Baikal Natural Territory

The research studied the comparison of the night air temperatures and the atomic oxygen airglow intensities at the mesopause obtained with satellite and ground-based instruments. Satellite data used in this study were obtained with the SABER limb-scanning radiometer operating aboard the TIMED satellite. Data of ground-based monitoring were obtained using the KEO Scientific “Arinae” Fabry–Pérot interferometer adapted for aeronomic research. Since an interferometer detects parameters of the 557.7 nm line for the entire emission layer, it is not quite appropriate to perform a direct comparison between the upper atmospheric temperature obtained from ground-based observations and that from a satellite at a particular height. To compare temperatures correctly, the effective temperature must be calculated based on satellite data. The effective temperature is a height-averaged temperature profile with the weight factors equal to the 557.7 nm line intensity at relevant heights. The height profile of intensity of this natural green airglow of the upper atmosphere is calculated from the height profile of atomic oxygen concentration. Data on chemical composition and air temperature at the mesopause from SABER were used to calculate the profiles. The night intensity of the 557.7 nm emission obtained from satellite data in this way was in good accordance with the results of ground-based observations, but the temperatures were different. The reason for temperature discrepancy was assumed to lie in the incorrect position of the intensity maximum of the reconstructed emission layer. According to our calculations based on SABER data, the intensity peak was observed at the height of 94–95 km. By shifting it relative to the SABER temperature height profile, we re-calculated the effective temperatures and compared them with the interferometer data. The best coincidence between seasonal temperature variations obtained using the proposed method was achieved when the maximum of the reconstructed 557.7 nm intensity height profile was shifted to 97 km, but it could not eliminate minor local differences in temperature behavior.