Quantifying the emissions of per- and polyfluoroalkyl substances (PFAS) from Australian wastewater treatment plants (WWTP) is of high importance due to potential impacts on receiving aquatic ecosystems. The new Australian PFAS National Environmental Management Plan recommends 0.23 ng L −1 of PFOS as the guideline value for 99% species protection for aquatic systems. In this study, 21 PFAS from four classes were measured in WWTP solid and aqueous samples from 19 Australian WWTPs. The mean ∑ 21 PFAS was 110 ng L −1 (median: 80 ng L −1 ; range: 9.3–520 ng L −1 ) in aqueous samples and 34 ng g −1 dw (median: 12 ng g −1 dw; range: 2.0–130 ng g −1 dw) in WWTP solids. Similar to WWTPs worldwide, perfluorocarboxylic acids were generally higher in effluent, compared to influent. Partitioning to solids within WWTPs increased with increasing fluoroalkyl chain length from 0.05 to 1.22 log units. Many PFAS were highly correlated, and PCA analysis showed strong associations between two groups: odd chained PFCAs, PFHxA and PFSAs; and 6:2 FTS with daily inflow volume and the proportion of trade waste accepted by WWTPs (as % of typical dry inflow). The compounds PFPeA, PFHxA, PFHpA, PFOA, PFNA, and PFDA increased significantly between influent and final effluent. The compounds 6:2 FTS and 8:2 FTS were quantified and F–53B detected and reported in Australian WWTP matrices. The compound 6:2 FTS was an important contributor to PFAS emissions in the studied Australian WWTPs, supporting the need for future research on its sources (including precursor degradation), environmental fate and impact in Australian aquatic environments receiving WWTP effluent.

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This paper seeks an efficient way to screen a population of patients at risk for hepatocellular carcinoma when (1) each patient’s disease evolves stochastically and (2) there are limited screening resources shared by the population. Recent medical discoveries have shown that biological information can be learned at each screening to differentiate patients into varying levels of risk for cancer. We investigate how to exploit this knowledge to choose which patients to screen to maximize early-stage cancer detections while limiting resource usage. We model the problem as a family of restless bandits, with each patient’s disease progression evolving as a partially observable Markov decision process. We derive an optimal policy for this problem and discuss managerial insights into what characterizes more effective screening. To provide numerical evidence, we use two independent data sets of over 800 patients, one to train the optimal policy, and the other to build a computer simulation to act as a test bed for said policy. We are able to show that our policy detects 22% more early-stage cancers than current practice, while using the same amount of resource expenditure. We provide insights into the structure underlying our policy and discuss the implications of our findings. The e-companion is available at https://doi.org/10.1287/msom.2017.0697 .

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