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Zhukov V, Moldon I, Zagustina N, Mironov V. Removal of terpenes in the presence of easily degradable compounds during biofiltration of gas emissions from composting of municipal solid waste. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 372:123162. [PMID: 39550942 DOI: 10.1016/j.jenvman.2024.123162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 10/10/2024] [Accepted: 10/31/2024] [Indexed: 11/19/2024]
Abstract
Composting of the organic fraction of municipal solid waste (OFMSW) is accompanied by the emission of large volumes of harmful, hazardous and foul-smelling volatile organic compounds (VOCs). To improve the efficiency of terpenes removal, which constitute a significant part of VOCs, pure cultures of microorganisms dominating in its microbiota were isolated from the microbial community of the biofilter, which has been cleaning such emissions for a long time. Seven pure cultures were isolated and then tested for being able to grow on a mineral medium in the presence of terpene vapor as the only source of carbon and energy. Three of the most actively growing cultures were selected, characterized and identified by the 16S rRNA gene as Rhodococcus erythropolis CA1, Rhodococcus pyridinivorans CA3 and Gordonia sp. CA6. Three identical laboratory biofilters (BF) were inoculated with a mix of these cultures to test the possibility of more complete removal of terpenes. Biofilters were adapting to clearing the model mix of terpineols and geraniol vapors for 45 days. During 45 days the purification efficiency of the model mix reached an average of 91.5% with a contact time (CT) of 3.7 ± 0.2 s and the terpene vapors concentration of 14 ± 2 mg m-3. Then the biofilters number BF2.1 and BF3.1 were connected to real emission from composting OFMSW. The biofilter BF2.1 purified the emission directly, whereas BF3.1 purified similar discharge after the intermediate biofilter of the 1st stage of purification (BF0.0). The BF1.0 was left connected to purification of the model mix as a control. The effectiveness of biofiltration of hard-to-remove terpenes was evaluated by gas chromatography of samples taken at the inlet and outlet of biofilters. The average efficiency of removing terpenes from real emissions by BF2.1 was 93.1 % (CT = 5.5 s). The total efficiency of removing terpenes by (BF0.0 + BF3.1) complex was 93.2 % (total CT = 7.4 s). A study of the microbiota of inoculated biofilters after 60 and 90 days of purification the real emission by cultivation from dilutions, identification by the 16S rRNA gene and fingerprinting showed that in BF2.1 and BF3.1 Rhodococcus erythropolis CA1 and Rhodococcus pyridinivorans CA3 were preserved among living cells at a level of 6.5-12.4 %, and genetically fully corresponded to the original cultures. These results could have a positive impact on improving the results of deodorization of emissions from OFMSW composting by biofiltration, simplifying the design of the biofiltration facility (one stage instead of two) and reducing the total time for effective biofiltration. This, in turn, would contribute to the wider introduction of highly efficient emission purification methods at OFMSW composting facilities in order to create more comfortable and ecologically clean environmental conditions around them.
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Affiliation(s)
- Vitaly Zhukov
- Winogradsky Institute of Microbiology, Federal Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia
| | - Ivan Moldon
- Winogradsky Institute of Microbiology, Federal Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia
| | - Nataliya Zagustina
- Bach Institute of Biochemistry, Federal Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia
| | - Vladimir Mironov
- Winogradsky Institute of Microbiology, Federal Research Center of Biotechnology, Russian Academy of Sciences, Moscow, 119071, Russia.
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Sakhaei A, Zamir SM, Rene ER, Veiga MC, Kennes C. Neural network-based performance assessment of one- and two-liquid phase biotrickling filters for the removal of a waste-gas mixture containing methanol, α-pinene, and hydrogen sulfide. ENVIRONMENTAL RESEARCH 2023; 237:116978. [PMID: 37633629 DOI: 10.1016/j.envres.2023.116978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
The performance of one- and two-liquid phase biotrickling filters (OLP/TLP-BTFs) treating a mixture of gas-phase methanol (M), α-pinene (P), and hydrogen sulfide (H) was assessed using artificial neural network (ANN) modeling. The best ANN models with the topologies 3-9-3 and 3-10-3 demonstrated an exceptional capacity for predicting the performance of O/TLP-BTFs, with R2 > 99%. The analysis of causal index (CI) values for the model of OLP-BTF revealed a negative impact of M on P removal (CI = -2.367), a positive influence of P and H on M removal (CI = +7.536 and CI = +3.931) and a negative effect of H on P removal (CI = -1.640). The addition of silicone oil in TLP-BTF reduced the negative impact of M and H on P degradation (CI = -1.261 and CI = -1.310, respectively) compared to the OLP-BTF. These findings suggested that silicone oil had the potential to improve P availability to the biofilm by increasing the concentration gradient of P between the air/gas and aqueous phases. Multi-objective particle swarm optimization (MOPSO) suggested an optimum operational condition, i.e. inlet M, P, and H concentrations of 1.0, 1.1, and 0.3 g m-3, respectively, with elimination capacities (ECs) of 172.1, 26.5, and 0.025 g m-3 h-1 for OLP-BTF. Likewise, one of the optimum operational conditions for TLP-BTF is achievable at inlet concentrations of 4.9, 1.7, and 0.8 g m-3, leading to the optimum ECs of 299.7, 52.9, and 0.072 g m-3 h-1 for M, P, and H, respectively. These results provide important insights into the treatment of complex waste gas mixtures, addressing the interactions between the pollutant removal characteristics in OLP/TLP-BTFs and providing novel approaches in the field of biological waste gas treatment.
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Affiliation(s)
- Amirmohammad Sakhaei
- Biochemical Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-114, Iran
| | - Seyed Morteza Zamir
- Biochemical Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-114, Iran.
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, P. O. Box 3015, 2611AX, Delft, the Netherlands
| | - María C Veiga
- Chemical Engineering Laboratory, Faculty of Sciences and Centre for Advanced Scientific Research - Centro de Investigaciones Científicas Avanzadas (CICA), BIOENGIN Group, University of La Coruña, E - 15008, A Coruña, Spain
| | - Christian Kennes
- Chemical Engineering Laboratory, Faculty of Sciences and Centre for Advanced Scientific Research - Centro de Investigaciones Científicas Avanzadas (CICA), BIOENGIN Group, University of La Coruña, E - 15008, A Coruña, Spain
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Wang Z, Hu L, He J, Zhou G, Chen Z, Wang Z, Chen J, Hayat K, Hrynsphan D, Tatsiana S. Mechanisms of N, N-dimethylacetamide-facilitated n-hexane removal in a rotating drum biofilter packed with bamboo charcoal-polyurethane composite. BIORESOURCE TECHNOLOGY 2023; 372:128600. [PMID: 36634880 DOI: 10.1016/j.biortech.2023.128600] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/03/2023] [Accepted: 01/07/2023] [Indexed: 06/17/2023]
Abstract
n-Hexane and N, N-dimethylacetamide (DMAC) are two major volatile organic compounds (VOCs) discharged from the pharmaceutical industry. To enhance DMAC-facilitated n-hexane removal, we investigated the simultaneous removal of multiple pollutants in a rotating drum biofilter packed with bamboo charcoal-polyurethane composite. After adding 800 mg·L-1 DMAC, the n-hexane removal efficiency increased from 59.4 % to 83.1 % under the optimized conditions. The maximum elimination capacity of 10.0 g·m-3·h-1n-hexane and 157 g·m-3·h-1 DMAC were obtained. The biomass of bamboo charcoal-polyurethane and the ratio of protein-to-polysaccharide in extracellular polymeric substances were significantly increased compared with the non-DMAC stage, which is attributed to increased carbon utilization. In addition, Na+ K+-ATPase was positively correlated with increasing electron transport system activity, which was 1.98 and 1.36 times greater. Hydrophilic DMAC improved the bioavailability of hydrophobic n-hexane and benefited bacterial metabolism. Co-degradation of n-hexane and DMAC system can be used for other volatile organic pollutants.
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Affiliation(s)
- Zhaoyun Wang
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Liyong Hu
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Jiamei He
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Gang Zhou
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Zhenghui Chen
- College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
| | - Zeyu Wang
- Key Laboratory of Pollution Exposure and Health Intervention Technology, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou 310021, China
| | - Jun Chen
- Key Laboratory of Pollution Exposure and Health Intervention Technology, Interdisciplinary Research Academy, Zhejiang Shuren University, Hangzhou 310021, China; College of Biological and Environmental Engineering, Zhejiang Shuren University, Hangzhou 310021, China.
| | - Kashif Hayat
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dzmitry Hrynsphan
- Research Institute of Physical and Chemical Problems, Belarusian State University, Minsk 220030, Belarus
| | - Savitskaya Tatsiana
- Research Institute of Physical and Chemical Problems, Belarusian State University, Minsk 220030, Belarus
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Lin K, Zhao Y, Wang L, Shi W, Cui F, Zhou T. MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 2023; 17:77. [PMID: 36628171 PMCID: PMC9815674 DOI: 10.1007/s11783-023-1677-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 12/01/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
UNLABELLED An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions. ELECTRONIC SUPPLEMENTARY MATERIAL Supplementary material is available in the online version of this article at 10.1007/s11783-023-1677-1 and is accessible for authorized users.
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Affiliation(s)
- Kunsen Lin
- The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092 China
| | - Youcai Zhao
- The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092 China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092 China
| | - Lina Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433 China
- Institute of Eco-Chongming (IEC), Shanghai, 202150 China
| | - Wenjie Shi
- The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092 China
| | - Feifei Cui
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Tao Zhou
- The State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092 China
- Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092 China
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Odors Emitted from Biological Waste and Wastewater Treatment Plants: A Mini-Review. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050798] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In recent decades, a new generation of waste treatment plants based on biological treatments (mainly anaerobic digestion and/or composting) has arisen all over the world. These plants have been progressively substituted for incineration facilities and landfills. Although these plants have evident benefits in terms of their environmental impact and higher recovery of material and energy, the release into atmosphere of malodorous compounds and its mitigation is one of the main challenges that these plants face. In this review, the methodology to determine odors, the main causes of having undesirable gaseous emissions, and the characterization of odors are reviewed. Finally, another important topic of odor abatement technologies is treated, especially those related to biological low-impact processes. In conclusion, odor control is the main challenge for a sustainable implementation of modern waste treatment plants.
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