Johnny Kee Hong

Abstract Hierarchical statistical models are important in applied sciences because they capture complex relationships in data, especially when variables are related by space, time, sampling unit, or other shared features. Existing methods for maximum likelihood estimation that rely on Monte Carlo integration over latent variables, such as Monte Carlo Expectation Maximization (MCEM), suffer from drawbacks in efficiency and/or generality.

Microplastics (MPs) and nanoplastics are small plastic particles (1–5000 μm and 1–1000 nm, respectively) that are ubiquitous contaminants in our environment. Their extreme persistence makes them a particularly problematic contaminant of emerging concern (CEC). Studies over the past decade have reported MPs in all environmental compartments: air, water, soil, sediment, and biota.