SCCWRP has recently completed a microplastics measurement intercalibration study being used to develop the first monitoring program for environmental microplastics in the world.
One of the challenges for environmental microplastics research is the lack of standardized measurement methods. Currently, a wide variety of methods exist for processing, enumerating, and identifying microplastic particles in environmental media. Comparing data is difficult, as methods development generally do not rigorously evaluate the strengths and weaknesses of methods compared to each other.
In 2018, the State of California passed legislation to require both monitoring of microplastics in drinking water, and developing management plans for microplastic contamination in the State’s coastal waters. In response, SCCWRP ran an intercalibration study with 40 laboratories in 6 countries to determine the strengths and limitations of several candidate monitoring methods.
We created samples to proxy drinking water, surface water, sediment from pre-industrial cores, and fish tissue to represent a biological matrix. Microplastic particles were added as fragments, fibers, and spheres ranging from 1-1000 um in an assortment of colors. Also added were false positive materials that look like synthetic polymers to confound analysis, such as shell fragments, cotton fibers from a dollar-store T-shirt, and even animal fur groomed from two pet bunnies; as well as appropriate matrix materials such as algae, plant detritus, and dirt for surface water simulating a manta trawl. Participating labs did not know the particle composition ahead of time. All laboratories followed draft SOPs for processing and chemical analysis developed at a SCCWRP workshop in April 2019.
Previous intercalibration studies did not delve into particles as small as 1-20 um, suspected to be responsible for human and ecosystem health effects; had no false positives; allowed an assortment of methods leading to extreme variability; and often simply counted particles rather than capturing information useful for monitoring (e.g., time and cost estimates for analysis).