Proton transfer and esterification reactions in EMIMOAc-based acidic ionic liquids

Acetate-based ionic liquids (such as 1-ethyl-3-methylimidazolium acetate, EMIMOAc) have potential applications for CO2 absorption and electrochemical reduction, chemical separations and extractions, and Fischer esterification of alcohols, amines, and starch. Both strong and weak organic acids can be dissolved in EMIMOAc and yield interesting proton-rich acidic ionic liquid solutions. We have used GCMS vapor pressure measurements, spectroscopic methods, calorimetry, and viscosity/conductivity measurements to investigate the properties and reactions of various acids dissolved in EMIMOAc. Unique proton transfer and esterification reactions are observed in many of these acidic solutions with carboxylic acids or sulfonic acids as solutes. Some acids react with the acetate anion to produce acetic acid, which provides a measure of acid strength in ionic liquid solvents. In addition, we observed an esterification reaction that might involve the imidazolium cation and the acetate anion to yield methyl acetate

WorldFAIR (D7.2) Population health resource library and training package

This project, WorldFAIR – Global Cooperation on FAIR Data Policy and Practice, is funded by the European Commission's WIDERA coordination and support programme under the Grant Agreement no. 101058393. The project consists of 14 work packages, of which work package 7 (WP07) focusses on Population Health. WP07 is led by London School of Hygiene and Tropical Medicine working under the INSPIRE network. The work builds on the delivery of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) which includes funding by Wellcome (formerly Wellcome Trust) and IDRC Canada. The objective of WP07 is to develop a suite of methods and standards to provide the framework for the Go-FAIR principles for population health data. These standards form the basis of an AI-Ready description of data suitable for use by population health scientists, and understandable across domain and institutional boundaries. The first deliverable (D7.1) identified the Implementation Guide that could be used for population health data, and how it can be developed. This deliverable (D7.2) provides a step-by-step guide as to how to achieve the standards. The deliverable is aimed at population health scientists in low-resource settings, who know their own data and want to make those data FAIR. Population health uses many different tools to collect and manage data. One set of tools includes the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and an OHDSI data analysis workbench that runs on top of it. The OMOP common data model has been used to harmonise and share data, and previous work has shown the tools needed to make OMOP data FAIR. Beyond the data themselves, the results from the analyses conducted on OMOP data can be used as indicators for the success of development goals, including the United Nations Sustainable Development Goals (SDGs). At each stage the tools, data, models and activities need to be described in a way that can be understood by other scientists and by computer search algorithms. This deliverable provides an introduction to the processes involved in making population health data FAIR in a pipeline that spans data collection through data analysis into an SDMX indicators database, and gives seven tutorials on what is needed at each step in this pipeline. It outlines the need to describe the study and the study context, how to use DDI Codebook and DDI Lifecycle with study data and how to use repositories like GitHub to make the metadata available. The next tutorials describe the extract-transform-load (ETL) process for putting the data into an OMOP CDM and the role of JSON-LD in preparing the data for machine searching in Schema.org in line with DDI-CDI. Together these tutorials give an overview of the steps in the OMOP processes which are a pipeline for the data, and how these steps can be performed and documented. Finally the tutorials show how predictive and causal analysis can be conducted and documented using the OMOP CDM and the OHDSI data analysis workbench and how the results can be integrated into an SDMX data cube, which would align with UN standards for SDG indicators. The deliverable does not provide detailed training for each step, but rather introduces the topic and clarifies the practical knowledge and skills that are needed to make this type of health data more FAIR

Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.

BACKGROUND: Metadata describe and provide context for other data, playing a pivotal role in enabling findability, accessibility, interoperability, and reusability (FAIR) data principles. By providing comprehensive and machine-readable descriptions of digital resources, metadata empower both machines and human users to seamlessly discover, access, integrate, and reuse data or content across diverse platforms and applications. However, the limited accessibility and machine-interpretability of existing metadata for population health data hinder effective data discovery and reuse. OBJECTIVE: To address these challenges, we propose a comprehensive framework using standardized formats, vocabularies, and protocols to render population health data machine-readable, significantly enhancing their FAIRness and enabling seamless discovery, access, and integration across diverse platforms and research applications. METHODS: The framework implements a 3-stage approach. The first stage is Data Documentation Initiative (DDI) integration, which involves leveraging the DDI Codebook metadata and documentation of detailed information for data and associated assets, while ensuring transparency and comprehensiveness. The second stage is Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardization. In this stage, the data are harmonized and standardized into the OMOP CDM, facilitating unified analysis across heterogeneous data sets. The third stage involves the integration of Schema.org and JavaScript Object Notation for Linked Data (JSON-LD), in which machine-readable metadata are generated using Schema.org entities and embedded within the data using JSON-LD, boosting discoverability and comprehension for both machines and human users. We demonstrated the implementation of these 3 stages using the Integrated Disease Surveillance and Response (IDSR) data from Malawi and Kenya. RESULTS: The implementation of our framework significantly enhanced the FAIRness of population health data, resulting in improved discoverability through seamless integration with platforms such as Google Dataset Search. The adoption of standardized formats and protocols streamlined data accessibility and integration across various research environments, fostering collaboration and knowledge sharing. Additionally, the use of machine-interpretable metadata empowered researchers to efficiently reuse data for targeted analyses and insights, thereby maximizing the overall value of population health resources. The JSON-LD codes are accessible via a GitHub repository and the HTML code integrated with JSON-LD is available on the Implementation Network for Sharing Population Information from Research Entities website. CONCLUSIONS: The adoption of machine-readable metadata standards is essential for ensuring the FAIRness of population health data. By embracing these standards, organizations can enhance diverse resource visibility, accessibility, and utility, leading to a broader impact, particularly in low- and middle-income countries. Machine-readable metadata can accelerate research, improve health care decision-making, and ultimately promote better health outcomes for populations worldwide

AIRCRAFT-BASED STUDIES OF GREENHOUSE GASES AND AEROSOLS

The Earth–atmosphere energy balance is dictated by incoming solar radiation and outgoing thermal radiation with greenhouse gases (GHG) and aerosols playing a major role in this effect. The atmospheric abundance and properties of airborne particles and gases lead to the redistribution of radiative energy, resulting in a warming or cooling effect. However, the extent of this effect remains to be insufficiently constrained. Improved quantification and characterization of GHG and aerosols are important requirements to inform current climate models. High-precision instrumentation and thoughtful experimental strategies are necessary to yield various analytical measurement datasets, despite complex meteorological and environmental conditions. This dissertation focuses on the assessment of CO2 and atmospheric particles from aircraft-based measurements enabling representative and spatially sampling of local regions of interest. Chapter 1 provides introductory discussion on the atmospheric implication of GHG and aerosols on the climate and related uncertainties. Chapter 2 summarizes the employed experimental techniques for quantification of GHG and characterization of atmospheric particles. We relied on an aircraft platform equipped with an air turbulence probe for 3D wind vector calculation and a high-precision cavity ring-down spectrometer for the quantification of ambient CO2, CH4, and H2Ov. Furthermore, the simultaneous composition and morphological information of aerosol samples were assessed using complementary chemical imaging techniques. Chemical composition of elements with Z > 23 was determined using computer-controlled scanning electron microscopy with energy dispersive X-ray spectroscopy (CCSEM/EDX). Scanning transmission X-ray microscopy coupled with near edge X-ray absorption fine structure spectroscopy (STXM/NEXAFS) was used to determined spatially resolved elemental specific molecular information present in atmospheric particles. Chapter 3 presents our study focused on the characterization of mixed mineral dust and biomass burning (BB) aerosols during an intensive burning event. We identified distinct particle types based on individual elemental contribution pre-, syn-, and post-burning event including highly carbonaceous (54–83%) particles, aged mineral dust (1–6%), and sulfur-containing particles (17–41%). X-ray spectromicroscopy techniques were used to characterize the internal chemical heterogeneity of individual BB particles and the morphology of soot inclusions, as well as changes in the particle organic volume fraction (OVF). An estimation method for particle component masses (i.e., organics, elemental carbon, and inorganics) inferred from STXM measurements was used to determine quantitative mixing state metrics based on entropy-derived diversity measures for particles acquired at different periods of the BB event. In general, there was a small difference in the particle-specific diversity among the samples (Dα = 1.3–1.8). However, the disparity from the bulk population diversity observed during the intense periods was found to have high values of Dγ = 2.5–2.9, while particles collected outside of the burning event displayed lower bulk diversity of Dγ = 1.5–2.0. Quantitative methods obtained from chemical imaging measurements presented here will serve to accurately characterize the evolution of mixed BB aerosols within urban environments. Chapter 4 follows the investigation of the physicochemical properties of atmospheric particles collected onboard a research aircraft flown over the Azores using offline spectromicroscopy techniques. Particles were collected within the marine boundary layer (MBL) and free troposphere (FT) comparing samples after long-range atmospheric transport episodes facilitated by dry intrusion (DI) events. The quantification of the OVF of individual particles derived from X-ray spectromicroscopy, which relates to the multi-component internal composition of individual particles, showed a factor of 2.06±0.16 and 1.11±0.04 increase in the MBL and FT, respectively, among DI samples. We show that supplying particle OVF into the κ-Köhler equation can be used as a good approximation of field-measured in situ CCN concentrations. We also report changes in the κ values in the MBL from κMBL, non-DI = 0.48 to κMBL, DI = 0.41, while changes in the FT result in κFT, non-DI = 0.36 to κFT, DI = 0.33, which is consistent with enhancements in OVF followed by the DI episodes. Our observations suggest that the entrainment of particles from long-range continental sources alters the mixing state population and CCN properties of aerosol in the region. Chapter 5 discusses the identification and characterization of fine-mode primary biogenic atmospheric particles (PBAP) from the harvesting of crops. Particle samples were analyzed using complementary chemical imaging techniques to apportion the particle-type population based on their size, morphology, and composition. The contribution of PBAP in the size range of 0.15−1.25 μm is estimated to be 10−12% of ∼39,000 analyzed particles. In addition, particle viscosity and phase state were inferred with X-ray spectromicroscopic analysis has shown that the fine-mode organic particles collected are viscous/semisolid (102−1012 Pa s) while the majority of PBAP fragments are solid (>1012 Pa s). The observation of submicrometer, solid carbonaceous fragments of biogenic origin have implications for the regional CCN and ice nuclei budget. Therefore, the seasonal harvesting of crops may play an important, yet unrecognized, role in regional cloud formation and climate. Chapter 6  explores the measurements and quantification of latent heat, sensible heat, and CO2 fluxes among different land covers in the surrounding area of urban regions using airborne flux techniques. Cities account for the majority of the global CO2 emissions due to the consumption of energy, resources, infrastructure, and transportation demands. Accordingly, the accurate quantification of these emissions, with exceptional precision, is necessary so that progress towards emission reduction can be monitored. However, a major challenge in quantifying urban emission estimates arises from accurate background emission definitions and apportionment of emission sources in complex urban environments. Airborne eddy covariance measurements were performed to quantify the bidirectional exchange of latent heat, sensible heat, and CO2 fluxes in the upwind region of Indianapolis within an active biosphere. Here, we observed differences in fluxes across different days and land covers (e.g., corn, soybean, and forests) allowing us to understand the impact of seasonal variability in urban emissions during the full growing season. These experiments illustrate the capability of a research aircraft to perform technically challenging near-direct measurements of atmosphere–surface exchange over local and regional scales. Chapter 7 presents a new method to spatially allocate airborne mass balance CO2 emissions. We performed seven aircraft measurements downwind of New York City (NYC) quantifying CO2 emissions during the non-growing seasons of 2018–2020. A series of prior inventories and footprint transport models were used to account for flux contribution outside the area of interest and attribute emission sources within policy-relevant boundaries of the five boroughs encompassing NYC and then employ the modeled enhancement fraction (Φ) to the bulk emission observations from the mass balance approach. Here, we calculated a campaign-averaged source apportioned mass balance CO2 emission rate of 56±24 kmol/s. The performance and accuracy of this approach were evaluated against other published works including inventory scaling and inverse modeling, yielding a difference of 5.1% with respect to the average emission rate reported by the two complementary approaches. Utilizing the ensemble of emissions inventories and transport models, we also evaluated the overall sources of variability induced by the prior (1.7%), the transport (4.2%), and the daily variability (42.0%). This approach provides a solution to interpreting aircraft-based mass balance results in complex emission environments. Chapter 8 concludes with a brief discussion of technological advances and research outlooks for X-ray spectromicroscopy analysis on atmospheric particles and the quantification of GHG. Opportunities for future applications and novel development of CCSEM/EDX and STXM/NEXAFS to substantially extend the instrument capabilities and improve our understanding of the physicochemical properties of individual atmospheric particles. Chapter 8 also discusses recent developments in satellite-based CO2 monitoring to complement direct airborne observations. In recent years, significant progress has been made in satellite-based measurements of CO2 to reveal the spatio-temporal variation in atmospheric CO2 concentration. The column-averaged dry air CO2 mole have reached an accuracy of ~1 ppm with a spatial resolution of less than 4 km. Furthermore, column-averaged retrievals can be used to detect and estimate the surface CO2 fluxes in an active biosphere, quantify anthropogenic emissions over megacities, and monitor the transport of fossil fuel plumes across different continents and seasons.</p

Multi-modal Chemical Characterization of Highly Viscous Submicrometer Organic Particles.

Distinguishing highly viscous organic particles within complex mixtures of atmospheric aerosol and accurate descriptions of their composition, size distributions, and mixing states are challenges at the forefront of aerosol measurement science and technology. Here, we present results obtained from complementary single-particle measurement techniques employed for the in-depth characterization of highly viscous particles. We demonstrate advantages and synergy of this multi-modal particle characterization approach based on the analysis of individual viscous particles formed in the air-discharged waste produced by a common sewer pipe rehabilitation technology. Using oil immersion flow microscopy, we investigate particle size distributions and morphology of colloidal components present in field-collected aqueous waste condensates. We compare these results with corresponding measurements of viscous particles formed in drying droplets of the aerosolized discharged waste. The colloidal components and viscous particles were found to be approximately 10 µm and 0.5 µm, respectively. The aerosolized viscous particles exhibited a spherical morphology, while the colloidal particles appeared noticeably fractal, resembling fragments of a cured composite material. Chemical imaging of the viscous particles collected on substrates was performed using scanning electron microscopy and soft X-ray spectro-microscopy techniques. Through these methods, comprehensive description of these particles emerged, confirming their high solid-like viscosity, wide-ranging sizes, diverse carbon speciation with high degrees of oxygenation, and high organic volume fractions. The aerosolized viscous particles were further characterized using high-throughput single particle mass spectrometry. This technique provides real-time measurements of composition, size, and morphological metrics for large numbers of individual particles, enabling the identification of their distinct mass spectrometric signatures. </p