APPLICATIONS OF THE OZONE MONITORING INSTRUMENT IN OBSERVING VOLCANIC SULFUR DIOXIDE PLUMES AND SULFATE DEPOSITION

Simple item page
dc.contributor.advisorDickerson, Russell Ren_US
dc.contributor.authorFedkin, Niko Markovichen_US
dc.contributor.departmentAtmospheric and Oceanic Sciencesen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-06-20T05:33:03Z
dc.date.available2022-06-20T05:33:03Z
dc.date.issued2021en_US
dc.description.abstractSulfur dioxide (SO2), a gas emitted by both volcanoes and anthropogenic activity, is a major pollutant and a precursor to sulfate aerosols. Sulfates can be deposited back to the ground where they have adverse impact on the environment or reside in the stratosphere as aerosols and affect radiative forcing. I investigated two components that stem from SO2: the deposition of sulfate, and the remote sensing of the SO2 layer height, important for aviation safety and chemical modeling. In the first study, I used column SO2 data from the Ozone Monitoring Instrument (OMI), and sulfate wet deposition data from the National Atmospheric Deposition Program to investigate the temporal and spatial relationship between trends in SO2 emissions and the downward sulfate wet deposition over the northeastern U.S. from 2005 to 2015. The results showed that emission reductions are reflected in deposition reductions within this same region. Emission reductions along the Ohio River Valley led to decreases in sulfate deposition not only in eastern OH and western PA, but also further downwind at sites in Delaware and Maryland. The findings suggested that emissions and wet deposition are linked through not only the location of sources relative to the observing sites, but also photochemistry and weather patterns characteristic to the region in winter and summer. The second part of this dissertation focuses on SO2 layer height retrievals and their applications. To this end I applied the Full Physics Inverse Learning Machine (FP-ILM) algorithm to OMI radiances in the spectral range of 310-330 nm. This approach utilized radiative transfer calculations to generate a large dataset of synthetic radiance spectra for a wide range of geophysical parameters. The spectral information was then used to train a neural network to predict the SO2 height. The main advantage of the algorithm is its speed, retrieving plume height in less than 10 min for an entire OMI orbit. I also compared the SO2 height retrievals to other data sources and explored some potential applications, in particular their use in volcanic SO2 plume forecasts and estimating the total mass emitted from volcanic eruptions.en_US
dc.identifierhttps://doi.org/10.13016/akw7-utch
dc.identifier.urihttp://hdl.handle.net/1903/28880
dc.language.isoenen_US
dc.subject.pqcontrolledRemote sensingen_US
dc.subject.pqcontrolledAtmospheric sciencesen_US
dc.subject.pqcontrolledAtmospheric chemistryen_US
dc.subject.pquncontrolledalgorithmen_US
dc.subject.pquncontrolleddepositionen_US
dc.subject.pquncontrolledplumeen_US
dc.subject.pquncontrolledretrievalen_US
dc.subject.pquncontrolledsulfateen_US
dc.subject.pquncontrolledsulfur dioxideen_US
dc.titleAPPLICATIONS OF THE OZONE MONITORING INSTRUMENT IN OBSERVING VOLCANIC SULFUR DIOXIDE PLUMES AND SULFATE DEPOSITIONen_US
dc.typeDissertationen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Fedkin_umd_0117E_22225.pdf
Size:
3.74 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Atmospheric & Oceanic Science Theses and Dissertations