Molecular transformations of dissolved organic matter during UV/O3-assisted membrane filtration of UASB-treated real textile wastewater

Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.

Interpretable Machine Learning and Reactomics Assisted Isotopically Labeled FT-ICR-MS for Exploring the Reactivity and Transformation of Natural Organic Matter during Ultraviolet Photolysis

Isotopically labeled FT-ICR-MS combined with multiple post-analyses, including interpretable machine learning (IML) and a paired mass distance (PMD) network, was employed to unravel the reactivity and transformation of natural organic matter (NOM) during ultraviolet (UV) irradiation. FT-ICR-MS analysis was used to assign formulas, which were classified on the basis of their molecular compositions and structural categories. Isotope (deuterium, D) labeling was utilized to unequivocally determine the photochemical products and examine the development of OD radical-mediated NOM transformation. With regard to the reactive molecular formulas, CHOS formulas exhibited the highest reactivity (86.5% of precursors disappeared) followed by CHON (53.4%) and CHO (24.6%) formulas. With regard to structural categories, the degree of reactivity decreased in the following order: tannins > condensed aromatics > lignin/CRAMs. The IML algorithm demonstrated that the crucial features governing the reactivity of formulas were the molecular weight, DBE-O, NOSC, and the presence of heteroatoms (i.e., N and S), suggesting that the large and unsaturated compounds containing S and N are more prone to photodegradation. The reactomics approach using the PMD network further indicated that 11 specific molecular formulas in the CHOS and CHO class served as hubs, implying a higher photoreactivity and participation in a range of transformations. The isotope labeling analyses also found that, among the reactions observed, hydroxylation (i.e., +OD) is dominant for lignin/CRAMs and condensed aromatics, and formulas containing ≤10 D atoms were developed. Overall, this study, by adopting rigorous and interpretable techniques, could provide in-depth insights into the molecular-level dynamics of NOM under UV irradiation.

Release characteristics of hydrochar-derived dissolved organic matter: Effects of hydrothermal temperature and environmental conditions

Much research has been done on the preparation and application of hydrochars, but research on the release characteristics of hydrochar-derived dissolved organic matter (HDOM) is very limited; clarifying the release characteristics of HDOM is important for understanding and adjusting the environmental behaviour of hydrochar. Herein, the potential release of HDOM from rice straw-derived hydrochars prepared at different hydrothermal temperatures was investigated under various potential environmental conditions for the first time. The total release quantity and humification degree of HDOM decreased with increasing hydrothermal temperature. The critical dividing line for various hydrothermal reactions, decomposition and polymerization, was in the range of 240 °C-260 °C. Alkaline condition increased the HDOM release amount (up to 299 mg g^-1), molecular weight (as high as 423 Da) and molecular diversity (8857 compounds) from rice straw-derived hydrochars. The unique substances of HDOM released under alkaline condition were mainly distributed in lipids-like substances, CRAM/lignins-like substances, aromatic structures, and tannins-like substances, while few unique substances were found under acidic condition. Additionally, CRAM/lignins-like substances were the most abundant in all HDOM samples, reaching 82%, which were relatively stable and could achieve carbon sequestration in different environments. The findings provided a new insight on understanding the potential environment behaviors of hydrochar.