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    Benthic invertebrate community composition and sediment properties in Barnegat Bay, New Jersey, 1965-2014
    (Coastal Education and Research Foundation, 2017) Taghon, Gary L.; Fuller, Charlotte M.; Petrecca, Rosemarie F.; Grassle, Judith P.; Belton, Thomas J.; N/A; Balcı, Patricia A. Ramey; Researcher; College of Sciences; N/A
    Extended time series of estuarine benthic community composition and the chemical and physical properties of sediment are necessary for distinguishing natural variation from possible anthropogenic influences, such as EUtrophication. In July 2012, 2013, and 2014, 97 stations, randomly located throughout the Barnegat Bay-Little Egg Harbor estuary, were sampled. Benthic invertebrates were abundant, and the community was, in general, highly diverse. Although there was considerable spatial variability in sediment-particle sizes throughout the estuary, overall the total organic carbon content of the sediments was low (<1%). Comparable historical data from 1965-2010 are spotty in spatial and temporal coverage, limiting comparisons to these recent data. Where comparisons can be made, the abundance and species composition of the benthos and the sediment properties, show few changes in 45 years. Despite high nutrient loading to this coastal bay, its shallow depth and general lack of stratification lead to relatively high dissolved oxygen levels, and it seems likely that heterotrophs in the sediments, both EUkaryotes and prokaryotes, are rapidly metabolizing organic matter as it is produced.
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    Impact of dilution on the transport of poly(acrylic acid) supported magnetite nanoparticles in porous media
    (Elsevier, 2011) Ersenkal, Dila Aksoy; Ziylan, Asu; Ince, Nilsun H.; Copty, Nadim K.; Department of Chemistry; Department of Chemistry; Acar, Havva Funda Yağcı; Demirer, Miray; Faculty Member; Undergraduate Student; Department of Chemistry; College of Sciences; College of Sciences; 178902; N/A
    This paper investigates the impact of dilution on the mobility of magnetite nanoparticles surface coated with poly(acrylic acid) (PM). Transport experiments were conducted in a water-saturated sand-packed column for input nanoparticle solutions with total Fe concentrations ranging from 100 to 600 mg/L Particle size analysis of the synthesized nanoparticle solutions showed that PM provides good size stability for Fe concentrations as low as about 1 mg/L Time-moment analysis of the nanoparticle breakthrough curves, on the other hand, revealed that nanoparticle mass recovery from the column decreased consistently with dilution, with greater attenuation, sharper fronts and longer tails compared to that of the tracer. Particle size analysis of the eluted solutions shows that the nanoparticle size is negatively correlated with nanoparticle concentration. Modeling results suggest that the decrease in nanoparticle mobility with input concentration can be represented using a kinetic time-dependent deposition term with finite deposition capacity and a kinetic detachment term. For field applications, the increase in particle size and detachment resulting from dilution means reduced transport efficiency of nanoparticles and reaction potential with travel distance.
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    Lagrangian prediction and correlation analysis with Eulerian data
    (Scientific and Technological Research Council Turkey, 2011) Piterbarg, Leonid I.; Department of Mathematics; N/A; Çağlar, Mine; Bilal, Taylan; Faculty Member; Master Student; Department of Mathematics; College of Sciences; Graduate School of Sciences and Engineering; 105131; N/A
    A velocity field obtained from the ocean surface by high-frequency radar is used to test Lagrangian prediction algorithms designed to evaluate the position of a particle given its initial position and observations of several other simultaneously released particles. The problem is motivated by oceanographic applications such as search and rescue operations and spreading pollutants, especially in coastal regions. The prediction skill is essentially determined by temporal and spatial covariances of the underlying velocity field. For this reason correlation analysis of both Lagrangian and Eulerian velocities was carried out. Space covariance functions and spectra of the velocity field are also presented to better illustrate statistical environments for the predictability studies. The results show that the regression prediction algorithm performs quite well on scales comparable with and higher than the velocity correlation scales.