Browsing by Author "Casas, Ana"

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    Comparative sanitation data from high-frequency phone surveys across 3 countries
    (Elsevier, 2024-06-26) Lewis, Amy R.; Bell, Andrew R.; Casas, Ana; Kupiec-Teahan, Beata; Mendoza Sanchez, José; Willcock, Simon; Anciano, Fiona; Barrington, Dani J.; Dube, Mmeli; Hutchings, Paul; Karani, Caroline; Llaxacondor, Arturo; López, Hellen; Mdee, Anna L.; Ofori, Alesia D.; Riungu, Joy N.; Russel, Kory C.; Parker, Alison H.
    With less than half of the worldʼs urban population having safely managed sanitation due to the high cost and difficulty of building sewers and treatment plants, many rely on off-grid options like pit latrines and septic tanks, which are hard to empty and often lead to illegal waste dumping; this research focuses on container-based sanitation (CBS) as an emerging off-grid solution. Off-grid sanitation refers to waste management systems that operate independently of centralized infrastructure and CBS is a service providing toilets that collect human waste in sealable containers, which are regularly emptied and safely disposed of. These data relate to a project investigating CBS in Kenya, Peru, and South Africa, focusing on how different user groups access and utilize sanitation – contrasting CBS with other types. Participants, acting as citizen scientists, collected confidential data through a dedicated smartphone app designed by the authors and external contractors. This project aimed to explore the effective scaling, management, and regulation of off-grid sanitation systems, relevant to academics in urban planning, water and sanitation services, institutional capability, policy and governance, and those addressing inequality and poverty reduction. The 12-month data collection period offered participants small incentives for weekly engagement, in a micro payment for micro tasks approach. Participants were randomly selected, attended a training workshop, and (where needed) were given a smartphone which they could keep at the end of the project. We conducted weekly smartphone surveys in over 300 households across informal settlements. These surveys aimed to understand human-environment interactions by capturing daily life, wellbeing, income, infrastructural service use, and socioeconomic variables at a weekly resolution, contributing to more informed analyses and decision-making. The smartphone-based approach offers efficient, cost-effective, and flexible data collection, enabling extensive geographical coverage, broad subject areas, and frequent engagement. The Open Data Kit (ODK) tools were used to support data collection in the resource-constrained environment with limited or intermittent connectivity.
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    Machine learning screening tools for the prediction of extraction yields of pharmaceutical compounds from wastewaters
    (Elsevier, 2024-04-30) Casas, Ana; Rodríguez-Llorente, Diego; Rodríguez-Llorente, Guillermo; García, Juan; Larriba, Marcos
    Pharmaceutical compounds have become an increasingly important source of pollutants in wastewaters being conventional treatments ineffective in removing them, so they are commonly discharged into the environment. Pharmaceuticals can be successfully removed using liquid-liquid extraction, and COSMO-RS can be used to predict interactions and identify the most promising solvents. However, COSMOtherm models cannot account for key process parameters, which reduces the accuracy of these computational models. Therefore, there is a need for alternative computational approaches to accurately predict the extraction yields of pharmaceuticals which can incorporate both processing and interaction variables. This work used machine learning to predict the extraction yield of eleven pharmaceuticals using eight solvents. Six regression models and two classification models were explored. The best performance was obtained with ANN regressor (test MAE: 4.510, test R2: 0.884) and RF classifier (test accuracy: 0.938, test recall: 0.974). The RF regression analysis and classification also showed key extraction yield features: solvent-to-feed ratio, n–octanol–water partition coefficient, hydrogen bond and Van der Waals contributions to excess enthalpy, and pH distance to nearest pKa. Machine learning showed as an excellent tool for screening and selecting the most promising solvents and process conditions to remove pharmaceuticals from wastewater.