Machine-learning-assisted prediction and optimized kinetic modelling of residual chlorine decay for enhanced water quality management.

The quality of water changes from source to tap, presenting challenges in maintaining consistent water quality across the system. Predicting water quality in distribution systems, including disinfectant residual loss and by-product formation, has been the subject of research since the early 1990s. Although numerous models have been proposed to predict residual chlorine decay, disputes exist among researchers and experts over the superiority of certain models. Accordingly, this study modified the existing process-based bulk decay models by replacing the initial Total Residual Chlorine (TRC) concentration parameter with TRC demand, leading to an improvement in the models' performance. The modification resulted in a 38.03%, 28.02%, 23.11%, and 33.29% average improvement in Mean Squared Error (MSE) values for the First Order Model (FOM), Parallel First Order Model (PFOM), Second Order Model (SOM), and Parallel Second Order Model (PSOM), respectively. The study also introduced an online predictive method based on a Machine Learning (ML) algorithm that predicts the first-order TRC bulk decay rate by using water quality parameters as inputs. A Gaussian Process Regression (GPR) model was used to predict the kinetic parameters in FOM, which accurately predicted the test sets for most of the cases. In addition, a new methodology was proposed in this study for predicting TRC in water distribution systems that incorporates the variability of source natural organic matter, operational actions, and water demands. This method seeks to develop high-fidelity and robust water quality predictions that provide operational decision support for optimized distribution system management. In conclusion, this study emphasizes the importance of understanding water quality changes from source to tap and the challenges of maintaining consistent water quality across the system. The study suggests modifying existing models and introducing a novel methodology for predicting residual chlorine in water distribution systems that can improve water quality management and, ultimately, better public health outcomes.

Visible Light Driven Heterojunction Photocatalyst of CuO-Cu2O Thin Films for Photocatalytic Degradation of Organic Pollutants.

A high recombination rate and low charge collection are the main limiting factors of copper oxides (cupric and cuprous oxide) for the photocatalytic degradation of organic pollutants. In this paper, a high performance copper oxide photocatalyst was developed by integrating cupric oxide (CuO) and cuprous oxide (Cu2O) thin films, which showed superior performance for the photocatalytic degradation of methylene blue (MB) compared to the control CuO and Cu2O photocatalyst. Our results show that a heterojunction photocatalyst of CuO-Cu2O thin films could significantly increase the charge collection, reduce the recombination rate, and improve the photocatalytic activity.

Relationship between organic precursors and N-nitrosodimethylamine (NDMA) formation in tropical water sources.

The presence of organic compounds in water sources is one of the concerns in water treatment. They are potential precursors of disinfection byproducts (DBPs) and thus induce health problems in humans. Among the emerging DBPs, carcinogenic compound N-nitrosodimethylamine (NDMA) has been receiving attention during the last decade. This study examined the characteristics of organic components in various water sources and investigated their relationships with NDMA formation. Experiments were carried out on selected water samples from both natural water and wastewater. Results showed similar NDMA formation kinetics for both water sources. However, more contribution of NDMA precursors was found to be from the wastewater due to its higher organic nitrogen content. NDMA formation potential (NDMAFP) of secondary effluent ranged from 264 to 530 ng/L. A correlation study between organic compound characteristics and NDMAFP indicated that the majority of NDMA precursors came from dissolved organic nitrogen (DON) compound with small molecular weight (smaller than 500 Da), with correlation R(2) = 0.898. Although secondary treatment removed more than 90% of NDMA precursors, the remaining precursors in secondary effluent would still pose a challenge for water quality.

Ferrates (iron(VI) and iron(V)): environmentally friendly oxidants and disinfectants.

Iron(VI) and iron(v), known as ferrates, are powerful oxidants and their reactions with pollutants are typically fast with the formation of non-toxic by-products. Oxidations performed by Fe(VI) and Fe(V) show pH dependence; faster rates are observed at lower pH. Fe(VI) shows excellent disinfectant properties and can inactivate a wide variety of microorganisms at low Fe(VI) doses. Fe(VI) also possesses efficient coagulation properties and enhanced coagulation can also be achieved using Fe(VI) as a preoxidant. The reactivity of Fe(V) with pollutants is approximately 3-5 orders of magnitude faster than that of Fe(VI). Fe(V) can thus be used to oxidize pollutants and inactivate microorganisms that have resistance to Fe(VI). The final product of Fe(VI) and Fe(V) reduction is Fe(III), a non-toxic compound. Moreover, treatments by Fe(VI) do not give any mutagenic/carcinogenic by-products, which make ferrates environmentally friendly ions. This paper reviews the potential role of iron(VI) and iron(V) as oxidants and disinfectants in water and wastewater treatment processes. Examples are given to demonstrate the multifunctional properties of ferrates to purify water and wastewater.