A Bayesian hierarchical model for estimating national PFAS drinking water occurrence. Issue 3 (25th May 2022)

• Record Type: Journal Article
• Authors: Cadwallader, Adam; Greene, Ashley; Holsinger, Hannah; Lan, Alexis; Messner, Michael; Simic, Melissa; Albert, Ryan
• Is Part Of: AWWA water science. Volume 4:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-25
• Subjects: Bayesian -- drinking water -- hierarchical model -- PFAS; Water -- Analysis -- Periodicals; Water-supply engineering -- Periodicals; Water -- Social aspects -- Periodicals; Water -- Purification -- Periodicals; Water-supply engineering; Water -- Social aspects; Water -- Analysis; Electronic journals; Periodicals; 628.162
• DOI: 10.1002/aws2.1284
• Languages: English
• ISSNs: 2577-8161

Abstract

Abstract: Per‐ and polyfluoroalkyl substances (PFAS) in U.S. drinking water are currently a significant topic of public health concern. Data collection efforts have been undertaken to better understand PFAS occurrence, though limited data observed above reporting limits leaves considerable uncertainty. This work presents a hierarchical Bayesian model developed to estimate national PFAS occurrence in drinking water with a simple model structure and assumptions. Here the model is limited to the occurrence of perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), perfluorohexanesulfonic acid (PFHxS), and perfluoroheptanoic acid (PFHpA). This model estimates national PFAS exposure while capturing uncertainty, provides information on system‐level PFAS co‐occurrence, and creates an expandable foundation for generating future national estimates of PFAS occurrence. National estimates based on currently available data and model assumptions indicated population‐weighted mean exposure to the sum of mean PFOS, PFOA, PFHpA, and PFHxS around 4.7–5.2 ppt while all four chemicals generally had moderate‐to‐strong correlations among system‐level means.