Tire wear particle (TWP)-derived compounds may be of high concern to consumers when released in the root zone of edible plants. We exposed lettuce plants to the TWP-derived compounds diphenylguanidine (DPG), hexamethoxymethylmelamine (HMMM), benzothiazole (BTZ), N-phenyl-N′-(1,3-dimethylbutyl)-p-phenylenediamine (6PPD), and its quinone transformation product (6PPD-q) at concentrations of 1 mg L–1 in hydroponic solutions over 14 days to analyze if they are taken up and metabolized by the plants. Assuming that TWP may be a long-term source of TWP-derived compounds to plants, we further investigated the effect of leaching from TWP on the concentration of leachate compounds in lettuce leaves by adding constantly leaching TWP to the hydroponic solutions. Concentrations in leaves, roots, and nutrient solution were quantified by triple quadrupole mass spectrometry, and metabolites in the leaves were identified by Orbitrap high resolution mass spectrometry. This study demonstrates that TWP-derived compounds are readily taken up by lettuce with measured maximum leaf concentrations between ∼0.75 (6PPD) and 20 μg g–1 (HMMM). Although these compounds were metabolized in the plant, we identified several transformation products, most of which proved to be more stable in the lettuce leaves than the parent compounds. Furthermore, continuous leaching from TWP led to a resupply and replenishment of the metabolized compounds in the lettuce leaves. The stability of metabolized TWP-derived compounds with largely unknown toxicities is particularly concerning and is an important new aspect for the impact assessment of TWP in the environment.

<p>As microplastics are being discovered on every corner of the earth, it is imperative to understand how they get there. Modeling capabilities of both the hydrodynamic processes and particle behavior are improving, but it remains expensive to collect and identify microplastics in coastal settings. This highlights the need for and potential of accurate marine debris models. This project compares microplastic deposition field measurements and model predictions on the New Jersey, USA coastline. The objective is to better understand the primary hydrodynamic forcing mechanisms of marine debris. Here, we test the hypothesis that the ability of the model to capture the longshore distribution of microplastic deposition is sensitive to hydrodynamic conditions, particle density, source location(s), and beaching and resuspension rates.</p><p>We created a regional hydrodynamic model in Delft3D of the New Jersey coastline from back bay river mouths to 50km offshore, using tidal, wind, wave, and river discharge conditions from 2016. We ran the model from January 1st, 2016 until December 31st 2016 to capture the seasonality of the flow and wind conditions. We used the Delft3D particle tracking module to insert particles with properties (e.g. particle density, horizontal diffusivity, and beaching probability) defined to best represent the behavior of microplastic particles and monitor their transport and fate. To assess the ability of a regional hydrodynamic model paired with a particle tracking module, 28 beaches were selected from the New Jersey coastline and sampled for microplastics (1-5mm) using methods similar to the US Environmental Protection Agency’s Microplastic Beach Protocol that detail consistent and characteristic microplastic measurement techniques of sandy beaches. The same sites were measured once in the winter of 2020/2021 and again in summer 2021 in an effort to capture the different seasonal flow regimes of the Mid-Atlantic coast. </p><p>Here, we show the comparison between the predicted microplastic deposition on the New Jersey coastline to the measured microplastic distribution for both the summer and winter. We assess the ability of the model to predict transport and deposition of various types and densities of microplastic debris. We illustrate the power that particle tracking models have to capture the transport and fate of microplastic debris and highlight the limitations of such models that need to be addressed. Further, we discuss the importance of predictive microplastic models for targeting specific geographical regions for cleanup and mitigation efforts. </p>