Nasim Pica is an environmental engineer working at CDM Smith Technology Service Unit. She is leading CDM Smith’s pilot-scale electrochemical oxidation system for PFAS destruction. Prior to that she was a postdoctoral researcher in the Department of Civil and Environmental Engineering at Colorado State University working on bioelectrochemical treatment systems for remediation of emerging contaminants. Nasim is a co-PI on a new SERDP PFAS fingerprinting project where she developed a R-code to predict molecular formulas for unknown PFASs. Currently, she is working on understanding PFAS fate and transport in the environment through multiple projects and developing sustainable PFAS treatment systems.
Sustainable and Destructive Remediation Strategy for PFAS Impacted Water via Foam Fractionation and Electrochemical Oxidation
Unique stability of PFAS plus potential transformation of precursors make them one of the most persistent and difficult to treat chemicals in the environment. Cost-effective destructive PFAS treatment technologies are thus needed. Here we demonstrate a sustainable pilot-scale treatment train of 1) foam fractionation (SAFF™) to concentrate dilute PFAS- impacted water, 2) electrochemical oxidation (ECO) of the PFAS concentrate, and 3) biological polishing to treat the oxidation byproduct of perchlorate. Overall, the SAFF™ concentrated PFAS in groundwater by up to 1,800,000x. The SAFF™ was able to remove more than 99% of priority PFAS and generated concentrate stream that was then treated in the ECO pilot system to achieve stringent discharge criteria of 70 ug/l PFOS and PFOA. A full evaluation of this treatment train including fluorine mass balance, transformation products, perchlorate generation and degradation, in addition to cost analysis and potential water quality interferences will be discussed during the presentation.
Datamining as a novel approach for source identification of PFAS impacted water
It is known that there are many sources that can contribute to PFAS contamination such as aqueous film forming foam, landfill leachate, metal plating, etc. While the difference in composition and concentration of a few commonly-found compounds such as PFOA and PFOS might help identify potential contributing sources, given the large number of individual PFAS and ubiquitous use of PFASs, the effectiveness and accuracy of traditional fingerprinting methods is challenging. In this study, an innovative multi-step approach for PFAS source identification is introduced. This novel approach is composed of 1) holistic datamining, 2) multivariant correlation analysis, unsupervised clustering; 3) ML modeling to further help with PFAS source identification, if necessary. This fingerprinting approach is analogous to using AI in image recognition investigations to identify individuals based on an image. Examples are presented to illustrate the application of this newly developed process to identify possible PFAS sources in impacted water bodies.