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The Problem with Polyethylene

Polyethylene (PE) is the most widely produced plastic due to its ease of synthesis, low cost, and favourable properties, including impermeability, durability, and chemical resistance. Due to its popularity, PE is also one of the microplastics most frequently identified through environmental studies. Some examples of where the average Canadian can find PE in their home are shown below in Figure 1. They are broken down by the specific type of PE, defined by the type and number of side chains on the polymer backbone. Multiple studies have now demonstrated that a more branched polymer results in a more environmentally degradable material; however, the exact pathway of degradation is still debated. Specifically, the question of how enzymes aid in degrading PE in the environment has been posed by numerous research groups over the past 30 years. The Honek lab at the University of Waterloo, led by Dr. John F. Honek (Department of Chemistry), decided to revisit some of these studies and replicate the enzymatic oxidations of PE using commercially available enzymes and standardized PE samples.

Developing effective strategies to reduce future microplastic emissions and manage existing pollution is challenging. These tiny plastic particles come from diverse sources, including clothing fibers, tire wear, packaging, industrial processes, and the breakdown of larger plastics. Their widespread presence allows them to enter the environment through multiple pathways, complicating prevention efforts. Additionally, once released, most microplastics do not biodegrade and tend to accumulate. Removing them from water bodies remains neither cost-effective nor feasible with current technologies.

As part of the Microplastics Fingerprinting project, our team, in collaboration with the University of Waterloo Earth Sciences Museum, has created an interactive outreach activity called “Mitigating Microplastics.”

Urban areas are increasingly recognized as major contributors to microplastics (MP) pollution, with stormwater runoff serving as a primary transport pathway. While many studies have documented the presence of MPs in stormwater, understanding the underlying causes and accurately forecasting contamination levels requires robust modeling efforts.

Researchers in the Smith Group at the University of Waterloo, in collaboration with the Microplastics Fingerprinting project, have developed a new machine learning model to improve microplastic identification. This model is based on a k-Nearest Neighbors (kNN) approach and analyzes a library of Raman spectra from various plastics to enhance accuracy.  

According to United Nations Environment Programme (UNEP), the estimated 11 million metric tons of plastic currently entering the ocean annually will triple by the year 2040. Much of this plastic breaks down into microplastics, less than 5 millimeters in size. These tiny particles are accumulating not only in oceans, but in all the world’s ecosystems, from the highest mountains to the Arctic’s pristine wilderness.

This past November, representatives from at least 170 countries gathered in Busan, South Korea, for the fifth session of the Intergovernmental Negotiating Committee (INC-5). They were tasked with developing an international legally binding instrument on plastic pollution. Although this session was initially expected to conclude the negotiations, it ended without a finalized agreement. Consequently, another meeting will be scheduled for 2025 to continue the discussions.

Microplastics have been found in freshwater ecosystems around the world. Yet, because they are still an emerging contaminant, we lack long-term data about their abundance and sources. One way to fill this gap is by analyzing radiometrically dated lake sediment cores to generate a historical record of microplastic pollution in freshwater environments. These cores act like time capsules, preserving microplastics that have settled over the years.