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The properties of particles in surface waters are especially important for the fate of incident light, with direct ramifications for primary production, habitat and water quality, and optical remote sensing. The rate of absorption and scattering of light depends directly on the particle size, composition, and density (Bowers et al., 2011) and indirectly on the particle settling rate, which controls how long and how high in the water column particles remain in suspension (Friedrichs et al., 2008). In the York River estuary and the adjacent Chesapeake Bay, the period of 1985-2016 was marked by a significant long-term decrease in water clarity as measured by the Secchi depth (Murphy et al., 2019). Gallegos et al. (2011) showed via optical modeling that the systematic decrease in Secchi depth in the Chesapeake Bay since the 1980s was likely due to an increase in the abundance of small, organic-rich suspended particles in the estuarine surface waters. The study of the York River estuary presented in the present paper was motivated in large part by a need to better understand the effects of organic matter content on the properties of particles in surface water due to the key role of organic-rich suspensions in affecting the water clarity in estuarine environments.

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Recent advances in development of in situ video settling columns have significantly contributed toward fine-sediment dynamics research through concurrent measurement of suspended sediment floc size distributions and settling velocities, which together also allow inference of floc density. Along with image resolution and sizing, two additional challenges in video analysis from these devices are the automated tracking of settling particles and accounting for fluid motions within the settling column. A combination of particle tracking velocimetry (PTV) and particle image velocimetry (PIV) image analysis techniques is described, which permits general automation of image analysis collected from video settling columns. In the fixed image plane, large-particle velocities are determined by PTV and small-particle velocities are tracked by PIV and treated as surrogates for fluid velocities. The large-particle settling velocity (relative to the suspending fluid) is determined by the vector difference of the large and small-particle settling velocities. The combined PTV/ PIV image analysis approach is demonstrated for video settling column data collected within a dredge plume in Boston Harbor. The automated PTV/PIV approach significantly reduces uncertainties in measured settling velocity and inferred floc density.

Aggregation state significantly influences the size, density, and transport characteristics of fine sediment. Understanding sediment transport and deposition processes in the nation's navigable waterways is a primary mission for the US Army Corps of Engineers (USACE), particularly when it comes to infilling of navigation channels. In this study, a newly developed camera system was used to evaluate the aggregation state of eroded sediment from cores collected in the tidal James River, VA. Results showed that bed sediments were composed mostly of mud, but that erosion predominately occurred in the form of aggregates with median sizes 50-270 times larger than the disaggregated sediment. Aggregate size weakly correlated to shear stress at levels <2 Pa, as well as sand content and bed density. A numerical simulation demonstrated that mud aggregates were predicted to transport in incipient suspension or bedload, while disaggregated fines were predominately maintained in full suspension. This difference in transport mode has significant implication for channel infilling and sediment transport within the system.

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River deltas are among the world's most imperiled landscapes. Their low elevations, combined with their typically low elevation gradients, make them particularly vulnerable to changes in sediment supply, eustatic sea-level rise (Cazenave & Llovel, 2010) and subsidence (Erickson, 2006), as their sustainability largely hinges on the relative balance between these factors (Syvitski, 2008). With over 500 million people