{"@context":"http://iiif.io/api/presentation/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/manifest.json","@type":"sc:Manifest","label":"Using geochemical tracers and mathematical models to estimate sinking particle interaction rate constants","metadata":[{"label":"dc.description.sponsorship","value":"This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree."},{"label":"dc.format","value":"Monograph"},{"label":"dc.format.medium","value":"Electronic Resource"},{"label":"dc.identifier.uri","value":"http://hdl.handle.net/11401/77782"},{"label":"dc.language.iso","value":"en_US"},{"label":"dc.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.abstract","value":"This dissertation has three parts; all concern the behavior of sinking particles in the ocean; all data come from the MedFlux program. Sinking particles play an important role in the oceanic biogeochemical cycle, because they are in the core position of the " biological pump" that is responsible for transporting photosynthesized organic matter and energy into the deep ocean. On the one hand, this transportation fuels benthic organisms; on the other hand, it reduces carbon dioxide partial pressure in the surface water and promotes absorption of elevated atmospheric carbon dioxide. Particle sinking velocity, which controls particle residence time in the water column as well as the efficiency of the biological pump. Particle aggregation and disaggregation can influence particle sinking velocity, because large particles sink faster than small particles according to Stokes' law.  Thorium data collected using Indented Rotating Sphere-Settling Velocity (IRS-SV) sediment traps during the MedFlux program were analyzed using two contrasting models. In both cases, the 11 settling velocity categories collected by these traps were optimally divided into two settling velocity classes (" slow" versus " fast" ) using maximum likelihood estimation. In the first analysis, particle aggregation, disaggregation, remineralization, thorium adsorption, and desorption rate constants were estimated using likelihood methods; these methods do not require any prior estimates of parameter values. Estimated adsorption and desorption rate constants of both slow- and fast-sinking particles were found to increase with depth, while aggregation and disaggregation rate constants were found to decrease with depth. Process contribution results showed that radioactive decay loss of slow-sinking particulate 234Th was compensated by continuous adsorption of dissolved thorium, resulting in that measured flux densities at different depths were approximately the same. Continuous supply from adsorption and aggregation, and negligible radioactive decay loss, explained why 234Th flux densities of fast-sinking particles at deeper depths were higher than those at shallower depths.  In contrast, a widely used mathematical method, the total inverse method, which requires specifying prior estimates of parameter values, was examined by seeding it with different priors to examine the extent to which its results depend on prior information. The results indicate that adsorption, remineralization, and slow-sinking particle desorption rate constants can be relatively well constrained by the total inverse method, but that disaggregation rate constants are highly dependent on prior parameter estimates, suggesting that the latter method should be replaced by the simpler likelihood method. A conceptual model describing pigment cycling was built. The settling velocity (5 m/d) dividing slow from fast settling particles was much slower than that estimated (98 m/d) using thorium data. Compared with rate constants estimated using thorium, aggregation rate constants estimated using pigment data were lower, and disaggregation rate constants were higher."},{"label":"dcterms.available","value":"2017-09-20T16:53:34Z"},{"label":"dcterms.contributor","value":"Lee, Cindy"},{"label":"dcterms.creator","value":"Wang, Weilei"},{"label":"dcterms.dateAccepted","value":"2017-09-20T16:53:34Z"},{"label":"dcterms.dateSubmitted","value":"2017-09-20T16:53:34Z"},{"label":"dcterms.description","value":"Department of Marine and Atmospheric Science."},{"label":"dcterms.extent","value":"150 pg."},{"label":"dcterms.format","value":"Monograph"},{"label":"dcterms.identifier","value":"http://hdl.handle.net/11401/77782"},{"label":"dcterms.issued","value":"2015-05-01"},{"label":"dcterms.language","value":"en_US"},{"label":"dcterms.provenance","value":"Made available in DSpace on 2017-09-20T16:53:34Z (GMT). No. of bitstreams: 1\nWang_grad.sunysb_0771E_12258.pdf: 1923947 bytes, checksum: 3c2939d462686ff5fbb36d032e21ff93 (MD5)\n  Previous issue date: 2015"},{"label":"dcterms.publisher","value":"The Graduate School, Stony Brook University: Stony Brook, NY."},{"label":"dcterms.subject","value":"Likelihood Estimation, Model, Pigment, Sinking Particles, Thorium, Total Inverse Method"},{"label":"dcterms.title","value":"Using geochemical tracers and mathematical models to estimate sinking particle interaction rate constants"},{"label":"dcterms.type","value":"Dissertation"},{"label":"dc.type","value":"Dissertation"}],"description":"This manifest was generated dynamically","viewingDirection":"left-to-right","sequences":[{"@type":"sc:Sequence","canvases":[{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json","@type":"sc:Canvas","label":"Page 1","height":1650,"width":1275,"images":[{"@type":"oa:Annotation","motivation":"sc:painting","resource":{"@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/16%2F78%2F88%2F167888728647474403277595285078606769338/full/full/0/default.jpg","@type":"dctypes:Image","format":"image/jpeg","height":1650,"width":1275,"service":{"@context":"http://iiif.io/api/image/2/context.json","@id":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/16%2F78%2F88%2F167888728647474403277595285078606769338","profile":"http://iiif.io/api/image/2/level2.json"}},"on":"https://repo.library.stonybrook.edu/cantaloupe/iiif/2/canvas/page-1.json"}]}]}]}