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Wastewater reuse and other nontraditional water supply options may become necessary for communities facing extended drought due to population shifts and climate change.A community survey and subsequent analyses uncovered misconceptions and gaps in public knowledge surrounding water resources and water reuse.Surveys are useful for understanding public knowledge and opinions but must be supplemented with meaningful education and outreach.Without genuine public engagement and trust-building, water infrastructure projects might not align with a community's values and interests.

Urban centers around the world are grappling with the challenges associated with population increases, drought, and projected water shortages. Potable water reuse (i.e., purification of municipal wastewater for reuse as drinking water) is an option for supplementing existing water supplies. Public perception research on potable water reuse has predominantly employed surveys with multiple-choice questions that constrain survey respondents to describe their concerns by choosing from several response options. This research examines hundreds of write-in responses to a large public survey in Albuquerque, New Mexico, to provide a detailed analysis of residents' questions and concerns about potable water reuse. Findings demonstrate that allowing respondents to voice their actual concerns adds richness and nuance that cannot be obtained from multiple-choice response data alone. Especially with controversial resource considerations, such as potable water reuse, planners would benefit from a full understanding of the problem before engaging with the community.

The ability of bacteriophage (phage), abundant within the gastrointestinal microbiome, to regulate bacterial populations within the same micro-environment offers prophylactic and therapeutic opportunities. Bacteria and phage have both been shown to interact intimately with mucin, and these interactions invariably effect the outcomes of phage predation within the intestine. To better understand the influence of the gastrointestinal micro-environment on phage predation, we employed enclosed, in vitro systems to investigate the roles of mucin concentration and agitation as a function of phage type and number on bacterial killing. Using two lytic coliphage, T4 and PhiX174, bacterial viability was quantified following exposure to phages at different multiplicities of infection (MOI) within increasing, physiological levels of mucin (0–4%) with and without agitation. Comparison of bacterial viability outcomes demonstrated that at low MOI, agitation in combination with higher mucin concentration (>2%) inhibited phage predation by both phages. However, when MOI was increased, PhiX predation was recovered regardless of mucin concentration or agitation. In contrast, only constant agitation of samples containing a high MOI of T4 demonstrated phage predation; briefly agitated samples remained hindered. Our results demonstrate that each phage–bacteria pairing is uniquely influenced by environmental factors, and these should be considered when determining the potential efficacy of phage predation under homeostatic or therapeutic circumstances.

More attention has been given to the computational cost associated with the fitting of an emulator. Substantially less attention is given to the computational cost of using that emulator for prediction. This is primarily because the cost of fitting an emulator is usually far greater than that of obtaining a single prediction, and predictions can often be obtained in parallel. In many settings, especially those requiring Markov Chain Monte Carlo, predictions may arrive sequentially and parallelization is not possible. In this case, using an emulator procedure which can produce accurate predictions efficiently can lead to substantial time savings in practice. In this paper, we propose a global model approximate Gaussian process framework via extension of  a popular local approximate Gaussian process (laGP) framework. Our proposed emulator can be viewed as a treed Gaussian process where the leaf nodes are laGP models, and the tree structure is learned greedily as a function of the prediction stream. The suggested method (called leapGP) has interpretable tuning parameters which control the time‐memory trade‐off. One reasonable choice of settings leads to an emulator with a training cost and makes predictions rapidly with an asymptotic amortized cost of .