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Insight into the Impacts and Removal Pathways of Perfluorooctanoic Acid (PFOA) in Anaerobic Digestion. WATER 2022. [DOI: 10.3390/w14142255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Perfluorooctanoic acid (PFOA) that accumulates in wastewater and excess sludge interact with the anaerobes and deteriorate the energy recovery and pollutants removal performance in the anaerobic digestion (AD) system. However, the interaction between PFOA and microbial metabolism in the AD systems remains unclear. This study aimed to clarify the effects and mechanism of PFOA on the AD process as well as the removal pathways of PFOA in an AD system. The results showed that the methane recovery efficiency was inhibited by 7.6–19.7% with the increased PFOA concentration of 0.5–3.0 mg/L, and the specific methanogenesis activity (SMA) was inhibited by 8.6–22.3%. The electron transfer system (ETS) was inhibited by 22.1–37.3% in the PFOA-containing groups. However, extracellular polymeric substance (EPS) gradually increased due to the toxicity of PFOA, and the ratio of protein to polysaccharide shows an upward trend, which led to the formation of sludge aggregates and resistance to the toxic of PFOA. The PFOA mass balance analysis indicated that 64.2–71.6% of PFOA was removed in the AD system, and sludge adsorption was the main removal pathway, accounting for 36.1–61.2% of the removed PFOA. In addition, the anaerobes are proposed to have the potential to reduce PFOA through biochemical degradation since 10.4–28.2% of PFOA was missing in the AD system. This study provides a significant reference for the treatment of high-strength PFOA-containing wastes.
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Li J, Yue Y, Wang Z, Zhou Q, Fan L, Chai Z, Song C, Dong H, Yan S, Gao X, Xu Q, Yao J, Wang Z, Wang X, Hou P, Huang L. Illumination/Darkness-Induced Changes in Leaf Surface Potential Linked With Kinetics of Ion Fluxes. FRONTIERS IN PLANT SCIENCE 2019; 10:1407. [PMID: 31787996 PMCID: PMC6854870 DOI: 10.3389/fpls.2019.01407] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 10/10/2019] [Indexed: 05/21/2023]
Abstract
A highly reproducible plant electrical signal-light-induced bioelectrogenesis (LIB) was obtained by means of periodic illumination/darkness stimulation of broad bean (Vicia faba L.) leaves. By stimulating the same position of the same leaf with different concentrations of NaCl, we observed that the amplitude and waveform of the LIB was correlated with the intensity of stimulation. This method allowed us to link dynamic ion fluxes induced by periodic illumination/darkness to salt stress. The self-referencing ion electrode technique was used to explore the ionic mechanisms of the LIB. Fluxes of H+, Ca2+, K+, and Cl- showed periodic changes under periodic illumination/darkness before and after 50 mM NaCl stimulation. Gray relational analysis was used to analyze correlations between each of these ions and LIB. The results showed that different ions are involved in surface potential changes at different stages under periodic illumination/darkness. The gray relational grade reflected the contribution of each ion to the change in surface potential at a certain time period. The ion fluxes data obtained under periodic illumination/darkness stimulation will contribute to the future development of a dynamic model for interpretation of electrophysiological events in plant cells.
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Affiliation(s)
- Jinhai Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Yang Yue
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Ziyang Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Qiao Zhou
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Lifeng Fan
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Zhiqiang Chai
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Chao Song
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Hongtu Dong
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China
| | - Shixian Yan
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Xinyu Gao
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Qiang Xu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Jiepeng Yao
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Zhongyi Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Xiaodong Wang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China
| | - Peichen Hou
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China
| | - Lan Huang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
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Performance Evaluation of Pilot-scale Hybrid Anaerobic Baffled Reactor (HABR) to Process Dyeing Wastewater Based on Grey Relational Analysis. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9101974] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
A pilot-scale six-compartment hybrid anaerobic baffled reactor (HABR) with effective volume of 18 m3 was used to treat dyeing wastewater. The HABR system was able to treat the wastewater efficiently after FeSO4 pretreatment, as indicated by removal efficiencies of 33.7% for chemical oxygen demand (COD), 39.9% for suspended solid (SS), and 22.5% for sulfate (SO42−) during steadily operational period. Gas chromatography–mass spectrometry (GC-MS) showed that the concentrations of alkanes, amides, organic acids, ketones, phenols, and esters were much lower in the effluent than those in the influent; many high-molecular-weight compounds such as cyclanes, quinolines, and phenols were successfully transformed to low-molecular-weight ones. As illustrated from the results of generalized grey relational analysis (GGRA), COD removal efficiency was more closely associated with flow rate, organic loading rate (OLR), water temperature, and influent SS among the whole selected possible factors. Based on the overall treating effectiveness and the GGRA study, the optimized operation strategy of the dyeing wastewater treatment by HABR was obtained as the hydraulic retention time (HRT) of 12 h for steady-state operation with an up-flow velocity of 1.7 m/h as well as OLR of 1.5–2.0 kg COD/(m3·d).
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