Exploring the viability of peracetic acid-mediated antibiotic degradation in wastewater through activation with electrogenerated HClO.

Electrochemical advanced oxidation processes (EAOPs) face challenging conditions in chloride media, owing to the co-generation of undesirable Cl-disinfection byproducts (Cl-DBPs). Herein, the synergistic activation between in-situ electrogenerated HClO and peracetic acid (PAA)-based reactive species in actual wastewater is discussed. A metal-free graphene-modified graphite felt (graphene/GF) cathode is used for the first time to achieve the electrochemically-mediated activation of PAA. The PAA/Cl- system allowed a near-complete sulfamethoxazole (SMX) degradation (kobs =0.49 min-1) in only 5 min in a model solution, inducing 32.7- and 8.2-fold rise in kobs as compared to single PAA and Cl- systems, respectively. Such enhancement is attributed to the occurrence of 1O2 (25.5 µmol L-1 after 5 min of electrolysis) from the thermodynamically favored reaction between HClO and PAA-based reactive species. The antibiotic degradation in a complex water matrix was further considered. The SMX removal is slightly susceptible to the coexisting natural organic matter, with both the acute cytotoxicity (ACT) and the yield of 12 DBPs decreasing by 29.4 % and 37.3 %, respectively. According to calculations, HClO accumulation and organic Cl-addition reactions are thermodynamically unfavored. This study provides a scenario-oriented paradigm for PAA-based electrochemical treatment technology, being particularly appealing for treating wastewater rich in Cl- ion, which may derive in toxic Cl-DBPs.

Degradation of tetracyclines via calcium peroxide activation by ultrasonic: Roles of reactive species, oxidation mechanism and toxicity evaluation.

Tetracyclines (TC) frequently detected in the aqueous environment pose threats to humans and ecosystems. The synergistic technology coupling ultrasound (US) and calcium peroxide (CaO2) has a great potential to abate TC in wastewater. However, the degradation efficiency and detailed mechanism of TC removal in the US/CaO2 system is unclear. This work was carried out to assess the performance and mechanism of TC removal in the US/CaO2 system. The results demonstrated that 99.2% of TC was degraded by the combination of 15 mM CaO2 with ultrasonic power of 400 W (20 kHz), but only about 30% and 4.5% of TC was removed by CaO2 (15 mM) or US (400 W) alone process, respectively. Experiments using specific quenchers and electron paramagnetic resonance (EPR) analysis indicated that the generation of hydroxyl radicals (•OH), superoxide radicals (O2-•), and single oxygen (1O2) in the process, whereas •OH and 1O2 were mainly responsible for the degradation of TC. The removal of TC in the US/CaO2 system has a close relationship with the ultrasonic power, the dosage of CaO2 and TC, and the initial pH. The degradation pathway of TC in the US/CaO2 process was proposed based on the detected oxidation products, and it mainly included N,N-dedimethylation, hydroxylation, and ring-opening reactions. The presence of 10 mM common inorganic anions including chloridion (Cl-), nitrate ion (NO3-), sulfate ion (SO42-), and bicarbonate ion (HCO3-) showed negligible influences on the removal of TC in the US/CaO2 system. The US/CaO2 process could efficiently remove TC in real wastewater. Overall, this work firstly demonstrated that •OH and 1O2 mainly contributed to the removal of pollutants in the US/CaO2 system, which was remarkable for understanding the mechanisms of CaO2-based oxidation process and its future application.

Adsorption-Enhanced Ceramic Membrane Filtration Using Fenton Oxidation for Advanced Treatment of Refinery Wastewater: Treatment Efficiency and Membrane-Fouling Control.

With the development of the refining industry, the treatment of refinery wastewater has become an urgent problem. In this study, a ceramic membrane (CM) was combined with Fenton-activated carbon (AC) adsorption to dispose of refinery wastewater. The effect of the combined process was analyzed using excitation-emission matrix (EEM), ultraviolet-visible (UV-vis) and Fourier transform infrared spectroscopies (FTIR). Compared with direct filtration, the combined process could significantly improve the removal of organic pollution, where the removal rate of the COD and TOC could be 70% and the turbidity removal rate was above 97%. It was found that the effluent could meet the local standards. In this study, the membrane fouling was analyzed for the impact of the pretreatment on the membrane direction. The results showed that Fenton-AC absorption could effectively alleviate membrane fouling. The optimal critical flux of the combined process was increased from 60 to 82 L/(m2·h) compared with direct filtration. After running for about 20 d, the flux remained at about 55 L/(m2·h) and the membrane-fouling resistance was only 1.2 × 1012 m-1. The Hermia model revealed that cake filtration was present in the early stages of the combined process. These results could be of great use in improving the treatment efficiency and operation cycle of refinery wastewater.

Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method.

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.