Modelling plastic additive release in aquatic environments to support plastic environmental risk assessment

Abstract

Plastic litter is reckoned as an issue of global concern due to its prevalent and ubiquitous nature and its detrimental impact on wildlife and ecosystems. One of the ways by which plastic is hypothesized to cause harm to the environment is through the release of potentially toxic additives.

To date, numerous plastic additives are known toxic to human health and the environment. However, most environmental risk assessment (ERA) studies and regulatory frameworks do not consider plastic additives and their release, leading to a biased and incomplete understanding of the ecological implications of plastic pollution. This stems from the lack of accurate and complex mathematical tools that represent the complexity of additive release kinetics and the numerous factors implicated. Addressing this gap, this study introduces an Additive Release Model (ARM), designed to estimate additive losses from plastic litter in aquatic environments. The ARM is intended to implement existing models on plastic accumulation and distribution in the environment, allowing a more complete and realistic Plastic Environmental Risk Assessment.

In the designed ARM, Fick’s second law was used as the core equation of the model and two consecutive steps were considered, namely internal and external additive diffusion. The factors temperature, time, water presence, additive molecular weight (MW), polymer type, and polymer size were included. To overcome limitations in input data availability, the Piringer Equation was included in the model to estimate worst-case scenario additive Diffusion Coefficients (D*p). This model was then used to investigate the release of decabromodiphenyl ether (decaBDE), octabromodiphenyl ether (octaBDE), pentabromodiphenyl ether (pentaBDE), and 1,2-Bis(2,4,6-tribromophenoxy)ethane (BTBPE) from Acrylonitrile Butadiene Styrene (ABS), High Impact Polystyrene (HIPS), Polypropylene (PP), and Polyamide (PA) polymers. These additives and polymers are commonly found in old and recycled Electronic and Electrical Equipment (EEE) devices. To validate the ARM and its output, we introduced a positive control. This control was used to compare the ARM's predictions with empirical measurements of additive release and diffusion coefficients (Dp) reported in the literature.

The ARM proved to be a reliable tool for replicating trends in additive release, particularly under internally-controlled diffusivity and using empirically estimated diffusion coefficients (Dp). In line with previous literature, the ARM modeled cumulative additive release over time and release rates escalating with temperature. Notably, low-molecular-weight additives, such as pentaBDE and BTBPE, exhibited the highest release indices from every polymer, irrespective of conditions. Consistent with other studies, a direct correlation was observed between polymer type, additive diffusivity, and additive release, with more amorphous polymers demonstrating higher release rates. However, the reliability of our input dataset came into question due to the inaccuracy of certain assumptions introduced to overcome the scarcity of input data. Nevertheless, the author was aware of the uncertainties and evaluated the output accordingly.

To conclude, the ARM developed in this study represents a preliminary valuable tool to estimate additive release from amorphous polymers. It marks a significant step towards a more comprehensive plastic environmental risk assessment (ERA). Yet, further research is needed to increase the complexity and reliability of the ARM