Fossil energy production, processing, flaring, and transmission all can harm climate and air quality by emitting greenhouse gases and air pollutants. Studies now show that onshore oil and gas production emit much more methane than what is inventoried, and that local air quality impacts can be significant, however, natural gas flaring and offshore systems have been largely overlooked.
The F3UEL (Flaring & Fossil Fuels: Uncovering Emissions & Losses) project aims to address these gaps by improving our understanding of offshore emissions, characterizing how flares behave in the real world, identifying what portion of the offshore system is responsible for emissions, and determining how such systems can be monitored.
Spanning three years (2020-2022), the project employed an aircraft platform to measure including both greenhouse gas and air quality measurements. To sample the largest regions of current and potential future offshore production and flaring, airborne measurements targeted the Gulf of Mexico, offshore California and Alaska, the Bakken Formation (North Dakota) and the Permian and Eagle Ford Basins (Texas).
Data provided here includes the airborne measurements collected using Scientific Aviation’s Mooney aircraft platform, equipped with spectroscopic instrumentation to measure methane, carbon dioxide, water vapor, nitrous oxide, and nitrogen oxide, in addition to meteorological variables such as wind speed and direction. Data products from our analysis of these airborne measurements are also provided, including estimated flare destruction removal efficiency for the Bakken, Eagle Ford, and Permian basins.
Each data file is in .csv format and is accompanied by a readme file with further information and descriptors of the variables included. All users should cite the papers and datasets provided in the readme files for each individual dataset.
Website: https://graham.umich.edu/f3uel
This project is funded by the Alfred P. Sloan Foundation with additional support from the Environmental Defense Fund, Scientific Aviation, and University of Michigan (College of Engineering, Climate and Space Sciences and Engineering; Graham Sustainability Institute).
This collection was produced as part of the project, “A ‘Big Data’ Approach to Understanding Neighborhood Effects in Chronic Illness Disparities.” The Investigators for the project are Tiffany Veinot, Veronica Berrocal, Phillipa Clarke, Robert Goodspeed, Daniel Romero, and VG Vinod Vydiswaran from the University of Michigan. The study took place from 2015-2016, with funding from the University of Michigan’s Social Sciences Annual Institute, MCubed, and the Sloan and Moore Foundations.
Contact: Tiffany Veinot, MLS, PhD
Office: 3443 North Quad
Phone: 734/615-8281
Email: tveinot@umich.edu
MCubed project page:
https://mcubed.umich.edu/projects/%E2%80%9Cbig-data%E2%80%9D-approach-understanding-neighborhood-effects-chronic-illness-disparities
Appendix1: Differential expression data for zebrafish regeneration and mouse degeneration models.
Appendix2: Gene ontology data for zebrafish regeneration and mouse degeneration models.
Appendix3: Pathway data for zebrafish regeneration and mouse degeneration models.
Appendix4: Differential expression data and genes within linked peaks for mi2004 mutants.
Appendix5: Gene ontology data for mi2004 mutants.
Appendix6: Pathway data for mi2004 mutants.
Appendix7: Linkage plots for mi2004 mutants.
Appendix8: Inverse PCR and genome-walking data.
Sifuentes, C. J. (2016). Regulation of Müller glial stem cell properties: Insights from a zebrafish model (Doctoral dissertation). Retrieved from http://hdl.handle.net/2027.42/135939
In a broad sense, this project explores morphological and phonological processing in English monolinguals and two bilingual populations, Chinese-English and Spanish-English, using a battery of standardized and self-developed behavioral measures, as well as fNIRS neuroimaging. (T1=NEW PARTICIPANTES -TESTED BEHAVIORAL AND fNIRS-, T2= RETURNING PARTICIPANTS -JUST TESTED WITH BEHAVIORAL ASSESSMENTS)
The outer epithelial layer of zebrafish retinae contains a crystalline array of cone photoreceptors, called the cone mosaic. As this mosaic grows by mitotic addition of new photoreceptors at the rim of the hemispheric retina, topological defects, called “Y-Junctions”, form to maintain approximately constant cell spacing. The generation of topological defects due to growth on a curved surface is a distinct feature of the cone mosaic not seen in other well-studied biological patterns like the R8 photoreceptor array in the _ Drosophila compound eye. Since defects can provide insight into cell-cell interactions responsible for pattern formation, here we characterize the arrangement of cones in individual Y-Junction cores (see Set of images for Figures 1 and 2 and 6 and Supplementary Figure 7) as well as the spatial distribution of Y-junctions across entire retinae (see Dataset for analyzing spatial distribution of Y-junctions in flat-mounted retinae). We find that for individual Y-junctions, the distribution of cones near the core corresponds closely to structures observed in physical crystals (see Set of images for Figures 1 and 2 and 6 and Supplementary Figure 7). In addition, Y-Junctions are organized into lines, called grain boundaries, from the retinal center to the periphery (see Dataset for analyzing spatial distribution of Y-junctions in flat-mounted retinae and Dataset for measuring tendency of Y-junctions to line up into grain boundaries during incorporation into retinae). In physical crystals, regardless of the initial distribution of defects, defects can coalesce into grain boundaries via the mobility of individual particles. By imaging in live fish, we demonstrate that grain boundaries in the cone mosaic instead appear during initial mosaic formation, without requiring defect motion (see Dataset for measuring tendency of Y-junctions to line up into grain boundaries during incorporation into retinae and Dataset for analyzing Y-junction motion in live fish retinae). Motivated by this observation, we show that a computational model of repulsive cell-cell interactions generates a mosaic with grain boundaries (see Code and example simulations of phase-field crystal model (for cone mosaic formation)). In contrast to paradigmatic models of fate specification in mostly motionless cell packings (see Code and accompanying input data for simulating lateral inhibition on motionless cell packing), this finding emphasizes the role of cell motion, guided by cell-cell interactions during differentiation, in forming biological crystals. Such a route to the formation of regular patterns may be especially valuable in situations, like growth on a curved surface, where the resulting long-ranged, elastic, effective interactions between defects can help to group them into grain boundaries.