Polycyclic aromatic hydrocarbons (PAHs) in water pose a serious threat to human health due to their toxic effects. This manuscript evaluates various drinking water treatment processes to remove these compounds from drinking water, in order to assure the quality of water intended for human consumption.
Early and accurate anomaly detection in critical infrastructure (CI), such as water treatment plants and electric power grid, is necessary to avoid plant damage and service disruption. Several machine learning techniques have been employed for the design of an effective anomaly detector in such systems. However, threats such as from insiders and state actors, introduce challenges in the design of an effective anomaly detector. This work presents a multi-layer perceptron (MLP) based anomaly detector that uses an unsupervised approach to safeguard CI from the adverse impacts of cyber-attacks.
As emerging contaminants, antibiotic resistance genes (ARGs) have become a public concern. This study aimed to investigate the occurrence and diversity of ARGs, and variation in the composition of bacterial communities in source water, drinking water treatment plants, and tap water in the Pearl River Delta region, South China. Various ARGs were present in the different types of water. Among the 27 target ARGs, floR and sul1 dominated in source water from three large rivers in the region.
