Indoor air treatment has become a significant concern in recent years. The aim of this study is to investigate the effectiveness of coupling adsorption and photocatalysis for the removal of toluene and formaldehyde, especially in the presence of optical fiber textile. First, we examine the adsorption properties of various commercial activated carbon (AC) filters, as well as different amounts of AC deposited on optical fiber textiles, and assess the impact of titanium dioxide (TiO2) on the adsorption performance. In the second phase, we compare the photocatalytic degradation of toluene and formaldehyde under different irradiance levels. Finally, we analyze the impact of three AC-TiO2 combinations: separate filters, TiO2 deposited on AC-impregnated fiber optic textiles, and TiO2 partially deposited on AC filters. The results led us to test a new photocatalytic and adsorbent material, including heating wires and optical fibers.
The widespread use of antiviral pharmaceuticals during and after the COVID-19 pandemic has raised growing concerns about their role as emerging environmental contaminants. These compounds, including favipiravir, remdesivir, molnupiravir, and oseltamivir carboxylate, are frequently detected in hospital effluents, municipal wastewater, and surface waters. Unlike many previous reviews that treat pharmaceuticals as a broad and undifferentiated class, this article focuses specifically on antiviral drugs as a distinct group of emerging contaminants and provides an integrated perspective that is still largely missing from the literature. As a review article, this work offers a critical and comprehensive synthesis that brings together environmental monitoring data, ecotoxicological and resistance-related risks, and advanced remediation strategies within a single framework. Particular emphasis is placed on recent advances in semiconductor-based photocatalytic degradation (TiO2, ZnO, g-C3N4, and their hybrids) and on mechanistic insights supported by density functional theory (DFT) and machine-learning (ML) approaches, which are used to link molecular-level properties to degradation efficiency and pathway selectivity. By systematically combining occurrence patterns, risk assessment, and DFT/ML-informed photocatalysis—specifically for antiviral pharmaceuticals—this review is among the first to delineate design principles and knowledge gaps unique to this drug class. The article highlights critical research needs and outlines future directions toward reproducible, computationally guided, and scalable treatment technologies for antiviral pollutants.
