scholarly journals A Series of Data-Driven Hypotheses for Inferring Biogeochemical Conditions in Alkaline Lakes and Their Deposits Based on the Behavior of Mg and SiO2

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 106
Author(s):  
Jasmine E. Chase ◽  
Maria L. Arizaleta ◽  
Benjamin M. Tutolo
Keyword(s):  
Water Chemistry ◽  
Data Driven ◽  
Supporting Evidence ◽  
Alkaline Ph ◽  
Alkaline Lakes ◽  
Common Features ◽  
Lake Water Chemistry ◽  
Geologic Record ◽  
Lake Waters ◽  
The Impact

Alkaline (pH > 8.5) lakes have been common features of Earth’s surface environments throughout its history and are currently among the most biologically productive environments on the planet. The chemistry of alkaline lakes favors the deposition of aluminum-poor magnesian clays (e.g., sepiolite, stevensite, and kerolite) whose chemistry and mineralogy may provide a useful record of the biogeochemistry of the lake waters from which they were precipitated. In this forward-looking review, we present six data-driven, testable hypotheses devoted to furthering our understanding of the biogeochemical conditions in paleolake waters based on the geochemical behavior of Mg and SiO2. In the development of these hypotheses, we bring together a compilation of modern lake water chemistry, recently published and new experimental data, and empirical, thermodynamic, and kinetic relationships developed from these data. We subdivide the hypotheses and supporting evidence into three categories: (1) interpreting paleolake chemistry from mineralogy; (2) interpreting the impact of diatoms on alkaline lake sedimentation; and (3) interpreting depositional mineralogy based on water chemistry. We demonstrate the need for further investigation by discussing evidence both for and against each hypothesis, which, in turn, highlights the gaps in our knowledge and the importance of furthering our understanding of the relevant geological and biological systems. The focused testing of these hypotheses against modern occurrences and the geologic record of alkaline lakes can have profound implications for the interpretation of the paleo-biogeochemistry and paleohabitability of these systems on Earth and beyond.

scholarly journals In Situ Formation of Monohydrocalcite in Alkaline Saline Lakes of the Valley of Gobi Lakes: Prediction for Mg, Ca, and Total Dissolved Carbonate Concentrations in Enceladus’ Ocean and Alkaline-Carbonate Ocean Worlds

Minerals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 669 ◽  
Author(s):  
Keisuke Fukushi ◽  
Eigo Imai ◽  
Yasuhito Sekine ◽  
Takuma Kitajima ◽  
Baasansuren Gankhurel ◽  
...  
Keyword(s):  
Water Chemistry ◽  
Suspended Matter ◽  
Freeze Drying ◽  
Soda Lakes ◽  
Alkaline Lakes ◽  
Direct Observations ◽  
The Common ◽  
Lake Waters ◽  
Alkaline Carbonate

The nature of mineral precipitations in terrestrial alkaline soda lakes provides insights into the water chemistry of subsurface oceans on icy bodies in the outer solar system. Saturation analyses of terrestrial alkaline lakes have shown that the solution chemistries of lake waters are generally controlled by the presence of monohydrocalcite (MHC) and amorphous Mg-carbonate (AMC). However, direct observations of the formation of these metastable carbonates in natural alkaline lakes have been limited. This study provides evidence of in situ MHC formation in alkaline lakes, based on the water chemistry and mineralogy of suspended matter in Olgoy, Boon Tsagaan, and Orog Lakes (Valley of Gobi Lakes, Mongolia). The solution chemistries were close to saturation with respect to MHC and AMC, consistent with other alkaline lakes worldwide. Suspended matter was separated by the ultracentrifugation of lake water following freeze-drying. Our results show that MHC is the common mineral phase in the suspended matter. These observations confirm that MHC is the direct authigenic product of evaporation in alkaline lakes. The carbonate fraction in suspended matter from Olgoy Lake has a Mg/Ca ratio of 0.4, suggesting the formation of AMC in association with MHC. Based on the dissolution equilibria of AMC and MHC, we predict the Mg2+, Ca2+, and total dissolved carbonate concentrations in Enceladus’ ocean to be ~1 mmol/kg, ~10 μmol/kg, and 0.06–0.2 mol/kg, respectively, in the presence of AMC and MHC. We propose that the measurements of Mg contents in plumes will be key to constraining the total dissolved carbonate concentrations and chemical affinities of subsurface oceans on Enceladus and other alkaline-carbonate ocean worlds.

scholarly journals Spatial Variation of Water Chemistry in Aries River Catchment Western Romania

2021 ◽  
Vol 11 (14) ◽  
pp. 6592
Author(s):  
Ana Moldovan ◽  
Maria-Alexandra Hoaghia ◽  
Anamaria Iulia Török ◽  
Marius Roman ◽  
Ionut Cornel Mirea ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Water Chemistry ◽  
Water Samples ◽  
Mine Drainage ◽  
Pollution Indices ◽  
Mining Activities ◽  
River Catchment ◽  
High Concentrations ◽  
The Impact

This study aims to investigate the quality and vulnerability of surface water (Aries River catchment) in order to identify the impact of past mining activities. For this purpose, the pollution and water quality indices, Piper and Durov plots, as well vulnerability modeling maps were used. The obtained results indicate that the water samples were contaminated with As, Fe, Mn, Pb and have relatively high concentrations of SO42−, HCO3−, TDS, Ca, K, Mg and high values for the electrical conductivity. Possible sources of the high content of chemicals could be the natural processes or the inputs of the mine drainage. Generally, according to the pollution indices, which were correlated to high concentrations of heavy metals, especially with Pb, Fe and Mn, the water samples were characterized by heavy metals pollution. The water quality index classified the studied water samples into five different classes of quality, namely: unsuitable for drinking, poor, medium, good and excellent quality. Similarly, medium, high and very high vulnerability classes were observed. The Durov and Piper plots classified the waters into Mg-HCO3− and Ca-Cl− types. The past and present mining activities clearly change the water chemistry and alter the quality of the Aries River, with the water requiring specific treatments before use.

scholarly journals Impact of tailored water chemistry aqueous ions on foam stability enhancement

2021 ◽  
Author(s):  
Zuhair AlYousef ◽  
Subhash Ayirala ◽  
Majed Almubarak ◽  
Dongkyu Cha
Keyword(s):  
Aqueous Solutions ◽  
Water Chemistry ◽  
Foam Stability ◽  
Chloride Salt ◽  
Foam Generation ◽  
Monovalent Ions ◽  
Injection Water ◽  
Foam Stabilization ◽  
Aqueous Ions ◽  
The Impact

AbstractGenerating strong and stable foam is necessary to achieve in-depth conformance control in the reservoir. Besides other parameters, the chemistry of injection water can significantly impact foam generation and stabilization. The tailored water chemistry was found to have good potential to improve foam stability. The objective of this study is to extensively evaluate the effect of different aqueous ions in the selected tailored water chemistry formulations on foam stabilization. Bulk and dynamic foam experiments were used to evaluate the impact of different tailored water chemistry aqueous ions on foam generation and stabilization. For bulk foam tests, the stability of foams generated using three surfactants and different aqueous ions was analyzed using bottle tests. For dynamic foam experiments, the tests were conducted using a microfluidic device. The results clearly demonstrated that the ionic content of aqueous solutions can significantly affect foam stabilization. The results revealed that the foam stabilization in bulk is different than that in porous media. Depending on the surfactant type, the divalent ions were found to have stronger influence on foam stabilization when compared to monovalent ions. The bulk foam results pointed out that the aqueous solutions containing calcium chloride salt (CaCl2) showed longer foam life with the anionic surfactant and very weak foam with the nonionic surfactant. The solutions with magnesium chloride (MgCl2) and CaCl2 salts displayed higher impact on foam stability in comparison with sodium chloride (NaCl) with the amphoteric alkyl amine surfactant. Less stable foams were generated with aqueous solutions comprising of both magnesium and calcium ions. In the microfluidic model, the solutions containing MgCl2 showed higher resistance to gas flow and subsequently higher mobility reduction factor for the injection gas when compared to those produced using NaCl and CaCl2 salts. This experimental study focusing about the role of different aqueous ions in the injection water on foam could help in better understanding the foam stabilization process. The new knowledge gained can also enable the selection and optimization of the right injection water chemistry and suitable chemicals for foam field applications.

scholarly journals Influenza With an Infiltrate: Investigating the New Community-Acquired Pneumonia Guidelines

2020 ◽  
Vol 41 (S1) ◽  
pp. s302-s302
Author(s):  
Amanda Barner ◽  
Lou Ann Bruno-Murtha
Keyword(s):  
Demographic Data ◽  
Retrospective Chart Review ◽  
Vaccination Rate ◽  
Community Acquired Pneumonia ◽  
Teaching Hospitals ◽  
Antibiotic Prescribing ◽  
Supporting Evidence ◽  
Radiographic Findings ◽  
Significant Difference ◽  
The Impact

Background: The Infectious Diseases Society of America released updated community-acquired pneumonia (CAP) guidelines in October 2019. One of the recommendations, with a low quality of supporting evidence, is the standard administration of antibiotics in adult patients with influenza and radiographic evidence of pneumonia. Procalcitonin (PCT) is not endorsed as a strategy to withhold antibiotic therapy, but it could be used to de-escalate appropriate patients after 48–72 hours. Radiographic findings are not indicative of the etiology of pneumonia. Prescribing antibiotics for all influenza-positive patients with an infiltrate has significant implications for stewardship. Therefore, we reviewed hospitalized, influenza-positive patients at our institution during the 2018–2019 season, and we sought to assess the impact of an abnormal chest x-ray (CXR) and PCT on antibiotic prescribing and outcomes. Methods: We conducted a retrospective chart review of all influenza-positive admissions at 2 urban, community-based, teaching hospitals. Demographic data, vaccination status, PCT levels, CXR findings, and treatment regimens were reviewed. The primary outcome was the difference in receipt of antibiotics between patients with a negative (<0.25 ng/mL) and positive PCT. Secondary outcomes included the impact of CXR result on antibiotic prescribing, duration, 30-day readmission, and 90-day mortality. Results: We reviewed the medical records of 117 patients; 43 (36.7%) received antibiotics. The vaccination rate was 36.7%. Also, 11% of patients required intensive care unit (ICU) admission and 84% received antibiotics. Moreover, 109 patients had a CXR: 61 (55.9%) were negative, 29 (26.6%) indeterminate, and 19 (17.4%) positive per radiologist interpretation. Patients with a positive PCT (OR, 12.7; 95% CI, 3.43–60.98; P < .0007) and an abnormal CXR (OR, 7.4; 95% CI, 2.9–20.1; P = .000003) were more likely to receive antibiotics. There was no significant difference in 30-day readmission (11.6% vs 13.5%; OR, 0.89; 95% CI, 0.21–3.08; P = 1) and 90-day mortality (11.6% vs 5.4%; OR, 2.37; 95% CI, 0.48–12.75; P = .28) between those that received antibiotics and those that did not, respectively. Furthermore, 30 patients (62.5%) with an abnormal CXR received antibiotics and 21 (43.7%) had negative PCT. There was no difference in 30-day readmission or 90-day mortality between those that did and did not receive antibiotics. Conclusions: Utilization of PCT allowed selective prescribing of antibiotics without impacting readmission or mortality. Antibiotics should be initiated for critically ill patients and based on clinical judgement, rather than for all influenza-positive patients with CXR abnormalities.Funding: NoneDisclosures: None

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

scholarly journals The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations

2021 ◽  
Vol 11 (7) ◽  
pp. 3110
Author(s):  
Karina Gibert ◽  
Xavier Angerri
Keyword(s):  
Social Vulnerability ◽  
Data Science ◽  
Policy Design ◽  
Data Driven ◽  
Informed Decision Making ◽  
Technological Infrastructure ◽  
Moderate Number ◽  
Reliable Diagnosis ◽  
Multivariate Relationships ◽  
The Impact

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

scholarly journals Data-Driven Localization Mappings in Filtering the Monsoon–Hadley Multicloud Convective Flows

2018 ◽  
Vol 146 (4) ◽  
pp. 1197-1218
Author(s):  
Michèle De La Chevrotière ◽  
John Harlim
Keyword(s):  
Learning Algorithm ◽  
Data Driven ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Errors ◽  
Accurate Analysis ◽  
Perfect Model ◽  
Nonlinear Stochastic Model ◽  
Correlation Statistics ◽  
The Impact

This paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic brightness temperature measurements is simulated using an idealized radiative transfer model and assimilated to the monsoon–Hadley multicloud model, a nonlinear stochastic model containing several thousands of model coordinates. A serial ensemble Kalman filter is implemented in which the empirical correlation statistics are improved using localization maps obtained from a supervised learning algorithm. The impact of the localization mappings is assessed in perfect-model observing system simulation experiments (OSSEs) as well as in the presence of model errors resulting from the misspecification of key convective closure parameters. In perfect-model OSSEs, the localization mappings that use adjacent correlations to improve the correlation estimated from small ensemble sizes produce robust accurate analysis estimates. In the presence of model error, the filter skills of the localization maps trained on perfect- and imperfect-model data are comparable.

Sign in / Sign up

Export Citation Format

Share Document