Machine Learning: A New Lens on PFAS Environmental Dynamics @WSPGlobal
Machine Learning: A New Lens on PFAS Environmental Dynamics  @WSPGlobal
Uploaded September 2024 | Updated September 2026, 3 weeks ago
In this webinar we explore an inhouse PFAS fingerprinting tool developed by WSP. This innovative technology leverages advanced multivariate algorithms to characterize and manage PFAS contamination at various sites. By employing multiple machine learning methods, the tool helps to differentiate between ambient levels, source signatures, and spatial distributions.

This powerful tool has significant implications for environmental site management, offering a sophisticated approach to source tracking, fate-and-transport pattern assessment, and remediation planning.

Presenters:
Skyler Sorsby, Geologist, WSP in the U.S
Paul Hurst, Senior Principal Environmental Engineer, WSP in Canada

Additional reading:
Decoding PFAS, delivering results: wsp.com/en-us/services/pfas-management-investigation-and-treatment
Machine learning helps fingerprint PFAS to build effective remediation plans: wsp.com/en-us/insights/machine-learning-helps-fingerprint-pfas-to-build-effective-remediation-plans
The Site Remediation Changemaker: wsp.com/en-us/insights/the-site-remediation-changemaker
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Machine Learning: A New Lens on PFAS Environmental Dynamics

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