Sonophotocatalysis with Photoactive Nanomaterials for Wastewater Treatment and Bacteria Disinfection.

Sonophotocatalysis is described as a combination of two individual processes of photocatalysis and sonocatalysis. It has proven to be highly promising in degrading dissolved contaminants in wastewaters as well as bacteria disinfection applications. It eliminates some of the main disadvantages observed in each individual technique such as high costs, sluggish activity, and prolonged reaction times.

Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO, FeCl, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV).

Intestinal colonization with multidrug-resistant Enterobacterales: screening, epidemiology, clinical impact, and strategies to decolonize carriers.

The clinical impact of infections due to extended-spectrum β-lactamase (ESBL)- and/or carbapenemase-producing Enterobacterales (Ent) has reached dramatic levels worldwide. Infections due to these multidrug-resistant (MDR) pathogens-especially Escherichia coli and Klebsiella pneumoniae-may originate from a prior asymptomatic intestinal colonization that could also favor transmission to other subjects. It is therefore desirable that gut carriers are rapidly identified to try preventing both the occurrence of serious endogenous infections and potential transmission.

Biotechnological advancements towards water, food and medical healthcare: A review.

The global health status is highly affected by the growing pace of urbanization, new lifestyles, climate changes, and resource exploitation. Modern technologies pave a promising way to deal with severe concerns toward sustainable development. Herein, we provided a comprehensive review of some popular biotechnological advancements regarding the progress achieved in water, food, and medicine, as the most substantial fields related to public health.

A novel, Z-scheme ZnO@AC@FeO photocatalyst, suitable for the intensification of photo-mediated peroxymonosulfate activation: Performance, reactivity and bisphenol A degradation pathways.

In this study, the intensification of a UVC-based PMS activation treatment is performed by a novel photocatalyst. Using ZnO nanoparticles coupled with activated carbon (AC), impregnated by ferroferric oxides (FO, magnetite), as an effective Z-scheme photocatalyst (ZACFO), the effective Bisphenol A (BP-A) removal was attained. Several techniques were applied for the characterization of the as-prepared catalyst and proved the successful preparation of ZACFO.

Developing a chloramine decay index to understand nitrification: A case study of two chloraminated drinking water distribution systems.

The management of chloramine decay and the prevention of nitrification are some of the critical issues faced by water utilities that use chloramine as a disinfectant. In this study, potential association between high performance size exclusion chromatography (HPSEC) data obtained with multiple wavelength Ultraviolet (UV) detection from two drinking water distribution systems in Australia and nitrification occurrence was investigated. An increase in the absorbance signal of HPSEC profiles with UV detection at λ=230nm between apparent molecular weights of 200 to 1000Da was observed at sampling sites that experienced rapid chloramine decay and nitrification while its absorbance signal at λ=254nm decreased.

Chloramine demand estimation using surrogate chemical and microbiological parameters.

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (F) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data.