Removal of N-Nitrosodiphenylamine from contaminated water: A novel modeling framework using metaheuristic-based ensemble models.

Investigating the complex interactions among physicochemical variables that influence the adsorptive removal of pollutants is a challenge for conventional one-variable-at-a-time (OVAT) batch methods. The adoption of machine learning-based chemometric prediction models is expected to be more accurate than the conventional method. This study proposed a novel modeling framework for predicting and optimizing the adsorptive removal of N-Nitrosodiphenylamine (NDPhA).

Microplastics and Co-pollutants in soil and marine environments: Sorption and desorption dynamics in unveiling invisible danger and key to ecotoxicological risk assessment.

Microplastics (MPs) and their co-pollutants pose significant threats to soil and marine environments, necessitating understanding of their colonization processes to combat the plastic pandemic and protect ecosystems. MPs can act as invisible carriers, concentrating and transporting pollutants, leading to a more widespread and potentially toxic impact than the presence of either MPs or the pollutants alone. Analyzing the sorption and desorption dynamics of MPs is crucial for understanding pollutants amplification and predicting the fate and transport of pollutants in soil and marine environments.