Breakthrough method spots pollutants without experimental reference samples, advancing environmental monitoring and analysis.
Breakthrough method spots pollutants without experimental reference samples, advancing environmental monitoring and analysis.
A team of researchers at Rice University and Baylor College of Medicine has developed a new strategy for identifying hazardous pollutants in soil, even ones that have never been isolated or studied in a lab.
The new approach, described in a study published in Proceedings of the National Academy of Sciences, uses light-based imaging, theoretical predictions of compounds’ light signatures and machine learning (ML) algorithms to detect toxic compounds like polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs) in soil. A common by-product of combustion, PAHs and PACs have been linked to cancer, developmental issues and other serious health problems.
Identifying pollutants in soil usually requires advanced laboratories and standard physical reference samples of the suspected contaminants. However, for many environmental pollutants that pose a public health risk, there is no experimental data available that can be used to detect them.
Read more at Rice University
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