Suchergebnisse

AbstractPer‐ and polyfluoroalkyl substances (PFAS) occur widely in drinking water, and consumption of contaminated drinking water is an important human exposure route. Granular activated carbon (GAC) adsorption can effectively remove PFAS from water. To support the design of GAC treatment systems, a rapid bench‐scale testing procedure and scale‐up approach are needed to assess the effects of GAC type, background water matrix, and empty bed contact time (EBCT) on GAC use rates. The overarching goal of this study was to predict PFAS breakthrough curves obtained at the pilot‐scale from rapid small‐scale column test (RSSCT) data. The scale‐up protocol was developed for pilot data obtained with coagulated/settled surface water (TOC = 2.3 mg/L), three GACs, and two EBCTs. Between 7 and 11 PFAS breakthrough curves were available for each pilot column. RSSCT designs were investigated that assumed intraparticle diffusivity is independent of GAC particle size (i.e., constant diffusivity [CD]) or linearly dependent on GAC particle size (i.e., proportional diffusivity [PD]). CD‐RSSCTs effectively predicted the bed volumes of water that could be treated at the pilot‐scale to reach 50% breakthrough (BV50%) of individual PFAS. In contrast, PD‐RSSCTs overpredicted BV50% obtained at the pilot‐scale by a factor of ~2–3. The shape of PFAS breakthrough curves obtained with CD‐RSSCTs deviated from those obtained at the pilot‐scale, indicating that intraparticle diffusivity was dependent on GAC particle diameter (dp). Using the pore surface diffusion model (PSDM), intraparticle diffusivity was found to be proportional to (dp)0.25 when considering data up to about 70% PFAS breakthrough. This proportionality factor can be used to design RSSCTs or scale up existing CD‐RSSCT data using the PSDM. Using pilot‐scale data obtained with groundwater and wastewater‐impacted groundwater as well as with additional GACs, the developed RSSCT scale‐up approach was validated for PFAS breakthrough percentages up to 70%. The presented methodology permits the rapid prediction of GAC use rates for PFAS removal.