# Low-Carbon Water Treatment and Energy Recovery
Their finding holds promise for enhancing the catalyst's HER performance. Advances in nanotechnology have paved the way for antimicrobial technology in nanomaterials. The increased bacterial and viral resistance has hindered traditional methods of water purification and disinfection. The review by [PERSON] et al. was an exhaustive summary of recent research advances on antimicrobial ZnO-based nanomaterials [4]. By summarizing previous studies, ZnO-based nanomaterials with excellent antibacterial effects were obtained. In detail, various strategies to enhance the antimicrobial ability of ZnO-based nanomaterials were proposed. Finally, current limitations of the improvement strategies outline future directions for development and potential applications in the field. Here, two papers focus on the degradation of pollutants in water, specifically addressing typical organic pollutants and metal ions. In the first paper authored by [PERSON] et al., the persistence, toxicity, mobility, and widespread pollution of short-chain PFASs in the water cycle are discussed. The study compares how various treatment techniques, such as the adsorption, electrochemical oxidation, and photocatalytic degradation have certain removal effects on short-chain PFASs [5]. Among these techniques, adsorption proves to be the most widely applied technique for the effective removal of short-chain PFASs, suitable for a wide concentration range of pollution and aligning with low-carbon policies. The findings provide a scientific basis for the effective treatment and regulation of short-chain PFAS contaminations in diverse water sources. For metal ions, the coexistence of iron and manganese was common in groundwater. Ways in which to remove iron and manganese efficiently and stably have become the focus of research. [PERSON] et al., [6] developed a three-dimensional response surface to investigate the interaction of three variables (pH, manganese sand dosage, and the initial Fe/Mn concentration ratio) on the removal of iron and manganese ions. The study revealed that pH exerts the most significant influence on the adsorption process, and the optimal conditions for Fe and Mn ion adsorption by modified Mn sand were pH 7.2, 3.54 g/L of sand, and an initial Fe/Mn ratio of 3.8. In addition, the authors confirmed that the relative error between the model predictions and experimental values was close to 1%. Reliable evaluation and accurate prediction of water pollution indicators are crucial for effective water resource management and pollution control. In studies on water quality evaluation and prediction, water quality evaluation and prediction models were established, combining the autoregressive integrated moving average (ARIMA) model and the wavelet neural network (WNN) mode with the bat algorithm to determine the optimal weight of each individual model [7]. The trained T-S fuzzy neural network was applied to the water quality evaluation, achieving total positive water quality grade evaluation rates of 90.38% and 88.46%, respectively. For the prediction of water quality, the combined model (ARIMA-WNN) produced a higher prediction accuracy, with an improvement of up to 68.06%. In the following article authored by [PERSON] et al., the focus was on nitrogen removal through partial nitrification coupled with ANAMMOX technology treating livestock wastewater. The study aimed to evaluate the influences of non-biodegradable organic matter and microbial communities on the performance [8]. Nitrite accumulation efficiencies of 78.4% and 64.7% were obtained in an intermittent aeration sequencing batch reactor and a continuous aeration sequencing batch reactor, respectively, at a loading rate of 0.93 kg ammonium/m\({}^{3}\)/d. With ANAMMOX activity at low loading rates (118 \(\pm\) 13 mg COD/L and 168 \(\pm\) 9 mg COD/L) and an average nitrite removal rate of 87.4%, partial nitrification treating the livestock wastewater was successfully achieved.