Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection.

For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that automatically classifies nano- and microplastics simultaneously from nonplastic particles within milliseconds in stationary and dynamic natural waters, without sample preparation. AI-assisted nano-DIHM identifies 2 and 1% of waterborne particles as nano/microplastics in Lake Ontario and the Saint Lawrence River, respectively.

In-situ and real-time nano/microplastic coatings and dynamics in water using nano-DIHM: A novel capability for the plastic life cycle research.

A novel nano-digital inline holographic microscope (nano-DIHM) was used to advance in-situ and real-time nano/microplastic physicochemical research, such as particle coatings and dynamic processes in water. Nano-DIHM data provided evidence of distinct coating patterns on nano/microplastic particles by oleic acid, magnetite, and phytoplankton, representing organic, inorganic, and biological coatings widely present in the natural surroundings. A high-resolution scanning transmission electron microscopy confirmed nano-DIHM data, demonstrating its nano/microplastic research capabilities.

A numerical framework for modeling fate and transport of microplastics in inland and coastal waters.

Proliferation of microplastics in rivers, lakes, estuaries, coastal waters and oceans is a major global challenge and threat to the environment, livelihoods and human health. Reliable predictive tools can play an essential role in developing an improved understanding of microplastics behaviour, exposure and risk in water bodies, and facilitate identification of sources and accumulation hot spots, thereby enabling informed decision-making for targeted prevention and clean-up activities. This study presents a new numerical framework (CaMPSim-3D) for predicting microplastics fate and transport in different aquatic settings, which consists of a Lagrangian, three-dimensional (3D) particle-tracking model (PTM) coupled with an Eulerian-based hydrodynamic modeling system (TELEMAC).

Fate and transport of coastal driftwood: A critical review.

Driftwood originating from natural and anthropogenic sources is abundant in coastal regions and plays an important role in ecosystems, providing habitat, structure, nutrients, and carbon storage. Conversely, large accumulations of driftwood can litter coastal zones, negatively impact coastal ecosystems and pose hazards to navigation, infrastructure and communities. Knowledge of the processes underlying the fate and transport of coastal driftwood is therefore needed to inform sustainable management practices.