Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants

• Main Authors: Xiaoxuan Wei, Miao Li, Yifei Wang, Lingmin Jin, Guangcai Ma, Haiying Yu
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Molecules
• Subjects: microplastic; adsorption partition coefficients (log <i>K</i>_d); predictive model; adsorption mechanism
• Online Access: https://www.mdpi.com/1420-3049/24/9/1784

Microplastics, which have been frequently detected worldwide, are strong adsorbents for organic pollutants and may alter their environmental behavior and toxicity in the environment. To completely state the risk of microplastics and their coexisting organics, the adsorption behavior of microplastics is a critical issue that needs to be clarified. Thus, the microplastic/water partition coefficient (log <i>K</i>_d) of organics was investigated by in silico method here. Five log <i>K</i>_d predictive models were developed for the partition of organics in polyethylene/seawater, polyethylene/freshwater, polyethylene/pure water, polypropylene/seawater, and polystyrene/seawater. The statistical results indicate that the established models have good robustness and predictive ability. Analyzing the descriptors selected by different models finds that hydrophobic interaction is the main adsorption mechanism, and &#960;&#8722;&#960; interaction also plays a crucial role for the microplastics containing benzene rings. Hydrogen bond basicity and cavity formation energy of compounds can determine their partition tendency. The distinct crystallinity and aromaticity make different microplastics exhibit disparate adsorption carrying ability. Environmental medium with high salinity can enhance the adsorption of organics and microplastics by increasing their induced dipole effect. The models developed in this study can not only be used to estimate the log <i>K</i>_d values, but also provide some necessary mechanism information for the further risk studies of microplastics.