Abstract People living in rural areas of low- and middle-income countries (LMICs) continue to struggle with untreated surface water sources contaminated with microbial, chemical, and physical pollutants. The purpose of this study is to design, model, assess the performance of, and integrate a biosensor-based microbial hybrid detection integrated within an off-grid, solar-powered water treatment system with varied pollutant removal capabilities. The study was conducted in Ishaka municipality, Uganda, where a total of 384 water samples were analyzed using atomic absorption spectrometry, UV-spectrophotometry, membrane filtration, and chromatography to determine the physical, chemical, and biological properties of the water. System design incorporated solar-powered sedimentation, activated carbon filtration, reverse osmosis, and solar thermal treatment. System power requirements were optimized through PV modeling and energy balance calculations. The hydraulic flow was modeled using the Navier–Stokes equations and Darcy’s law, while pollutant removal efficiency was estimated based on first-order kinetics and media-specific filtration models. Biosensor-based BOD detection systems were also modeled using diffusion and Michaelis-Menten kinetics, allowing real-time evaluation of microbial activity. Through simulations, the system demonstrated robust performance, surpassing 95% removal efficiency surpassing retaining removable key E. coli, heavy metals, nitrates, and pharmaceutical contaminants.
Joseph malisaba (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: