EPA Scientists Test Non-Targeted Analysis Methods Using Drinking Water Filters
Published May 18, 2021
Today, researchers can rapidly search for thousands of never-before studied chemical compounds in a wide variety of environmental, residential, and biological media. This approach is called “non-targeted analysis” (NTA). It differs from targeted analysis because researchers do not have to know what specific chemical they are looking for in a sample. They can use high-resolution mass spectrometry (HRMS) to rapidly identify many of the chemicals present in a sample. The HRMS measures the accurate mass of molecules and can find chemicals that would have gone unnoticed before. This approach is beneficial not just to researchers, but to States, tribes, and local communities who might want to know more about chemical exposure.
NTA approaches largely produce qualitative results and don’t estimate chemical concentrations, which limits their use in risk assessment. Chemical concentration is necessary to assess potential exposure, which looks at how much of a chemical was present and the source of exposure to determine potential health risks.
EPA scientists are working to remedy these limitations by developing a proof-of-concept model for risk-based prioritization that can account for the concentrations of chemicals. To do this they used a mixture of chemicals commonly found in drinking water that were extracted from spiked activated carbon drinking water filters. These mixtures were then analyzed with HRMS and quantitative concentration estimates were determined.
These solution estimates were then statistically analyzed and compared to existing regulatory levels. This research provides a model for future NTA work on larger datasets and priority chemical lists as a means of preliminary prioritization.
NTA is an innovative new approach that can be a valuable tool for protecting human health and the environment. EPA scientists are working hard to develop and validate new applications and capabilities for this powerful new tool.