Microplastics Fingerprinting: Reflections at the Halfway Point – Part A
The Microplastics Fingerprinting project is currently about halfway through its four-year funding period. At this year’s annual meeting, the team took stock of what it has learned so far. Researchers presented their work to one another and then divided into two groups to synthesize results into two 15-minute presentations. Part A summarizes the first presentation that focused on identifying biases in environmental sampling and extraction methods and highlighting advances in detection and identification techniques. Part B highlights advancements in understanding the fate, transport, and sources of environmental microplastics (MPs).
Part A: Progress in microplastics sampling, detection and identification techniques
By Weijia Cui, Maziar Shafiei Darabi, Una Elizabeth Hogan, Shuhuan Li, Steph Slowinksi, and MoMo Zandieh
Characterizing microplastic pollution in the environment
There are a lot of factors that complicate the ability of researchers to characterize and detect MPs in environmental samples including variation in size, shape and type, and that they can be randomly distributed through the sample in in very low concentrations. Samples often include other micro-sized particles and various chemical additives, which can also impede accurate detection. Our team is testing and evaluating several detection techniques in hopes of finding the best ways for researchers around the world to quickly and accurately identify MPs found in the environment.
Typically, when a researcher wants to understand the abundance and kinds of MPs that are present in the environment, they begin by collecting a sample – usually from soil, sediment, or water. The next step involves separating the plastic from everything else (called the environmental matrix) before they determine what kind of plastic it is (see figure 1). There are numerous techniques for detecting and characterizing MPs, so part of our work involves figuring out which strategies work best in different situations.
Figure 1: Conceptual flow chart showing how the MP characterization and detection process is linked to research and impact goals.
Sampling methods (Lead researchers: Steph Slowinski, Yuba Bhusal)
One aspect of the project involves comparing sampling methods for collecting MPs in river water to see how different techniques influence the results related to abundance, type and size distributions. We collected samples from the Don River in Toronto during a rain event using: (1) net sampling, (2) grab sampling, (3) continuous flow centrifugation combined with inline filtration, and (4) an automated sampler model, a tool commonly used by conservation authorities and other monitoring agencies.
Initial analysis shows that each method produces different results in microplastic abundances. We think that might be because the methods are sampling different parts of the water column profile in the river. This is consistent with what other researchers are finding as well. It is important to understand how sampling techniques influence the results when interpreting findings from any MP research project.
Extraction of microplastics (Lead researchers: Shuhuan Li, Steph Slowinski)
Once samples are collected, the next step is to extract MPs from the environmental matrices. One of the most significant challenges in this process is that we cannot extract every bit of plastic, and this can lead to an inaccurate representation of abundance.
To estimate the number of particles that we are not able to extract from a sample, we add a known number of plastic particles of different sizes to the sample during the extraction process. This allows us to calculate the extraction efficiency, according to the different sizes of MPs. We’ve found that the larger the particle, the easier it is to extract. This suggests that the extraction procedures that most microplastics researchers are using are likely resulting in an underestimation of total MP counts, especially when particles are smaller than 100 µm in diameter.
Detection technology: Laser Direct Infrared Imaging (LDIR) (Lead researcher: Shuhuan Li)
Laser Direct Infrared Imaging (LDIR) is a type of infrared (IR) spectroscopy technology for detecting and characterizing MP sizes, polymer types and abundances. LDIR identifies plastic types by comparing a sample to its built-in IR spectral library. However, the built-in library that came with the instrument is limited, with no spectra available for environmental plastics. Our team has been working to expand the library by acquiring the IR spectra for plastic types including from laboratory contaminants, field contaminants, and MPs we have found in the environment. This will be accessible to any researcher who has access to the library, which we hope will support comparisons across projects.
Expanding the IR library has also improved our ability to accurately identify the type of plastic in the MPs we are extracting from samples. For example, when using the same criteria for spectral match quality, the number of successful MP particles successfully identified increased to almost 100% from below 70% when just using the standard built-in library.
Detection technology: Machine learning (Lead researcher: Una Elizabeth Hogan)
We are also using machine learning to assist in the identification of environmentally degraded plastic particles. We collected and refined a spectroscopic database with over 600 plastic samples using Raman spectroscopy, an analytical technique that is typically used for chemical identification. Using this database, we tested and trained machine learning models to be able to rapidly identify microplastic particles. The use of machine learning speeds up the process (because there is no need for cleaning or other preparations) and allows for very degraded plastic particles to be identified quickly and easily. We are also working on the truncation of Raman spectra to further increase the speed and accuracy of this technique.
Detection technology: DNA aptamers (Lead researcher: MoMo Zandieh)
Another method for detecting MPs is to use DNA aptamers, which are single-stranded DNA sequences engineered to selectively bind to specific target molecules with a high affinity. Our team has been working to find the best aptamers using a process that results in the progressive enrichment of DNA sequences that are most likely to attach to MPs. This process is called Systematic Evolution of Ligands by Exponential Enrichment (SELEX) and produces an aptamer sequence of DNA that is highly specific and capable of tightly binding to microplastics particles.
Recently, we undertook the SELEX process to select DNA aptamers that can bind to polypropylene (PP), one type of plastic. After 9 rounds of SELEX, we found an aptamer sequence that works well for PP plastics. We are continuing this work by thoroughly testing this aptamer and will evaluate how well it works in detecting PP in real-world environmental samples.
Detection technology: Microwave technology (Lead researchers: Maziar ShafieiDarabi and Weijia Cui)
Finally, our team has also been working on applying microwave technology to characterize and detect MPs. To do this, we needed to first understand if it would be feasible to use a microwave-based technique for detecting MP pollutants in water.
In the first round of experiments, we were able to successfully sort samples based on their concentration, but we could not detect MP size and we had trouble detecting smaller particles at low concentrations. Modifying our sensor design and enhancing its sensitivity fixed those problems, but we found that the volume of the reservoir we were using was too small to represent the entire environmental context accurately. Moving forward, we will be implementing a continuous flow into our sensor system to allow us to measure a larger sample volume that is more representative of environmental samples and more statistically meaningful. We hope these changes will help us detect low level concentrations close to those found in the environment. Another promising idea that we are testing involves enhancing sensor sensitivity and performance by integrating nanomaterials like MXene, a 2D nanomaterial, into the sensor. Ultimately, we are hopeful that microwave sensors will be able to be used to monitor MP abundance in water. This could act as an early warning system for MP pollution.
A combination of techniques
Overall, our team is working to advance multiple techniques for detecting and characterizing MPs found in environmental samples. In the future, this will allow researchers to select techniques based on what they want to understand (e.g., MP abundance, polymer type, toxicity, etc.), and the purpose of their research, whether it is to support the ongoing monitoring of MP pollution, enforce MP regulations, or help develop early warning detection systems.