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Nanomaterials (NMs) have received tremendous attention as emerging adsorbents for environmental applications. The ever-increasing release into aquatic systems and the potential use in water treatment processes heighten the likelihood of the interactions of NMs with aquatic dissolved organic matter (DOM). Once DOM is adsorbed on NMs, it substantially modifies the surface properties, thus altering the fate and transport of NMs, as well as their toxic effects on (micro)organisms in natural and engineered systems. The environmental consequences of DOM-NMs interaction have been widely studied in the literature. In contrast, a comprehensive understanding of DOM-NM complexes, particularly regarding the controlling factors, is still lacking, and its significance has been largely overlooked. This gap in the knowledge mainly arises from the complex and heterogeneous structures of the DOM, which prompts the urgent need to further characterize the DOM properties to deepen the understanding associated with the adsorption processes on NMs. This review aims to provide in-depth insights into the complex DOM adsorption behavior onto NMs, whether they are metal- or carbon-based materials. First, we summarize the up-to-date analytical methods to characterize the DOM to unravel the underlying adsorption mechanisms. Second, the key DOM characteristics governing the adsorption processes are discussed. Next, the environmental factors, such as the nature of adsorbents and solution chemistry, affecting the DOM-NM interactions, are identified and discussed. Finally, future studies are recommended to fully understand the chemical traits of DOM upon its adsorption onto NMs. Image 1 • Traditional and advanced analyses for DOM adsorption are fully reviewed. • Major factors to control DOM-NMs interactions are identified through literature review. • DOM properties governing DOM adsorption behaviors on NMs are highlighted. • Future studies are recommended of better understanding of DOM adsorption onto NMs. [ABSTRACT FROM AUTHOR]

Photoelectrocatalytic (PEC) process combining the merits of photocatalysis and electrocatalysis is considered as a promising ammonia oxidation technology for water treatment. However, some key issues, such as the limited in situ generation of oxidants on photoanode, slow mass transfer problem and generation of nitrate/nitrite by–products hinder the further application of PEC process in the treatment of ammonia pollutant. In this study, the graphite felt (GF) cathodes modified by different transition metals (Ni, Fe, Mn, Co, Cu) were screened by physicochemical and photoelectrochemical characterizations. The results show that the Ni–GF cathode with more Ni0 uniformly distributed on the GF surface had the best electrocatalytic activity to generate H 2 O 2. The PEC system composed of 10.0 wt% Ni–GF cathode and optimized titania nanotubes (TNTs) photoanode selectively converted about 96.1% ammonia to N 2 within 90 min. Compared with the single TNTs photoanode system, the ammonia oxidation reaction rate constant of the synergistic PEC oxidation system was increased by about two times, which demonstrated the role of the oxidants simultaneously generated on both anode and cathode. The in situ generated reactive oxygen-based oxidants and chlorine-based oxidants interacted together, and ClO• acted a leading role in the ammonia oxidation which were confirmed by quenching and probe experiments. In addition, the contributions of •OH and ClO• were significantly improved in the synergistic PEC oxidation system, compared with the single TNTs photoanode system. Furthermore, the nitrate by–products generated by the ammonia oxidation were further reduced on the Ni–GF cathode. The large amount of active chlorine and active oxygen generated on the electrode diffused into the bulk, effectively overcoming the mass transfer limitation of direct oxidation. Therefore, the developed TNTs photoanode/Ni-GF cathode system can continuously and efficiently convert ammonia to N 2 without the formation of nitrate/nitrite by–products. [Display omitted] • Ni–GF cathode with uniformly distributed Ni0 had the best electrocatalytic activity. • The coupling system presented higher ammonia removal efficiency. • Ammonia was almost completely converted to N 2 without nitrate/nitrite by-products. • Efficient ammonia oxidation was mediated by chlorine-based radicals. [ABSTRACT FROM AUTHOR]

The occurrence, bioaccumulation, and trophic magnification of pharmaceutically active compounds, (PhACs) including antibiotics (roxithromycin and erythromycin), non-steroidal anti-inflammatory drugs (ibuprofen and diclofenac), a non-selective β-adrenoceptor blocker (propranolol), an antiepileptic drug (carbamazepine), and steroid estrogens (17β-estradiol and 17α-ethynylestradiol), were investigated in Taihu Lake, China. All eight PhACs were widely detected in surface water and sediment samples with maximal concentrations in the range of 8.74–118 ng L − 1 and 0.78–42.5 ng g − 1 dry weight (dw), respectively. The investigated organisms in the natural freshwater food web in Taihu Lake included phytoplankton, zooplankton, zoobenthos, and fish, and the maximal concentrations of target compounds in these biota samples ranged from 0.65 to 132 ng g − 1 dw. Bioaccumulation factors (BAFs) for all target PhACs were lower than 1000 L kg − 1 , suggesting their low bioaccumulation potential in aquatic organisms from Taihu Lake. Trophic magnification factors (TMFs) were estimated at 1.11 for roxithromycin, 0.31 for propranolol, and 1.06 for diclofenac, indicating none of these PhACs underwent trophic magnification in this freshwater food web. [ABSTRACT FROM AUTHOR]

The increasing accumulation of pharmaceuticals in aquatic ecosystems could impair freshwater quality and threaten human health. Despite the adsorption of pharmaceuticals on biochars is one of the most cost-effective and eco-friendly removal methods, the wide variation of experimental designs and research aims among previous studies pose significant challenge in selecting biochar for optimal removal. In this work, literature data of 1033 sets with 21 variables collected from 267 papers over ten years (2010–2020) covering 19 pharmaceuticals onto 88 biochars were assessed by different machine learning (ML) algorithms i.e., Linear regression model (LM), Feed-forward neural networks (NNET), Deep neutral networks (DNN), Cubist, K-nearest neighbor (KNN), and Random forest (RF), to predict equilibrium adsorption capacity (Q e) and explore adsorption mechanisms. LM showed the best performance on ranking importance of input variables. Except for initial concentration of pharmaceuticals, Q e was strongly governed by biochars' properties including specific surface area (BET), pore volume (PV), and pore structure (PS) rather than pharmaceuticals' properties and experimental conditions. The most accurate model for estimating Q e was achieved by Cubist, followed by KNN, RF, KNN, NNET and LM. The generalization ability was observed by the tuned Cubist with 26 rules for the prediction of the unseen data. This study not only provides an insightful evidence for data-based adsorption mechanisms of pharmaceuticals on biochars, but also offers a potential method to accurately predict the biochar adsorption performance without conducting any experiments, which will be of high interests in practice in terms of water/wastewater treatment using biochars. [Display omitted] • Comprehensive data of 19 pharmaceuticals onto 88 biochars were examined. • ML models of classification, regression, ensemble and deep learning were compared. • LM outcompeted RF, Cubist, and xgbTree for ranking importance of input variables. • Q e was governed by biochars' structure rather than pharmaceuticals' properties. • Cubist featured the best prediction, followed by KNN, RF, DNN, NNET and LM. [ABSTRACT FROM AUTHOR]