Assessing the Health Impact of Disinfection Byproducts in Drinking Water.

This study provides a comprehensive investigation of the impact of disinfection byproducts (DBPs) on human health, with a particular focus on DBPs present in chlorinated drinking water, concentrating on three primary DBP categories (aliphatic, alicyclic, and aromatic). Additionally, it explores pivotal factors influencing DBP formation, encompassing disinfectant types, water source characteristics, and environmental conditions, such as the presence of natural materials in water. The main objective is to discern the most hazardous DBPs, considering criteria such as regulation standards, potential health impacts, and chemical diversity. It provides a catalog of 63 key DBPs alongside their corresponding parameters. From this set, 28 compounds are meticulously chosen for in-depth analysis based on the above criteria. The findings strive to guide the advancement of water treatment technologies and intelligent sensory systems for the efficient water quality surveillance. This, in turn, enables reliable DBP detection within water distribution networks. By enriching the understanding of DBP-associated health hazards and offering valuable insights, this research is aimed to contribute to influencing policy-making in regulations and treatment strategies, thereby protecting public health and improving safety related to chlorinated drinking water quality.

Class-Wise Subspace Alignment-Based Unsupervised Adaptive Land Cover Classification in Scene-Level Using Deep Siamese Network.

In this article, an unsupervised domain adaptation strategy has been investigated using a deep Siamese neural network in scene-level land cover classification using remotely sensed images. At the onset, the soft class label and probability scores of each target sample have been obtained using a pretrained model of a deep convolutional neural network. Thereafter, a semiautomatic threshold selection algorithm along with a graph-based approach has been explored to obtain the "most-confident" target samples. Furthermore, the deep Siamese network has been incorporated by training the source and "most-confident" target samples to generate the classwise cross domain common subspace. To assess the effectiveness of the proposed framework, experiments are carried out using three aerial image datasets. The results are found to be encouraging for the proposed scheme in comparison with the other state-of-art techniques.