Activation of indigenous bacteria for rapid degradation of medium-chain and long-chain hydrocarbons in petroleum-contaminated soils

IF 3.7 3区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Jinlan Xu , Jiayi Wang , Chuanyu Liu , Huiwen Guan , Rankang Zhou , Xin Zhai , Qilin Shu
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引用次数: 0

Abstract

To explore the rapid degradation of medium-chain and long-chain alkanes (C20-C40) in petroleum-contaminated soils by activating indigenous bacteria, as well as the degradation mechanism, a 30-day biodegradation experiment was conducted by adding different proportions of activators (glucose, sodium acetate). The results showed that the removal of total petroleum hydrocarbons (TPH), medium-chain, and long-chain alkanes in the activation group reached 9753.06 mg/kg, 4185.85 mg/kg, and 3778.15 mg/kg, respectively, which were significantly higher than those of the inactive group. Furthermore, the microbial community of the activation group was dominated by Mycobacterium (14.55 %) and Pseudarthrobacter (13.95 %). The activation group consumed a large amount of DOC (10,052.64 mg/kg), activating Mycobacterium and Pseudarthrobacter to actively uptake oxygen, and significantly increased the activities of dehydrogenase and lipase,which accelerated the transformation of hydrocarbons. This may be the reason why the activation group achieved rapid TPH degradation. Furthermore, predictive modeling using three machine learning algorithms demonstrated strong concordance with experimental data, validating their utility in optimizing bioremediation strategies. This study presents a cost-effective and sustainable approach for the efficient remediation of petroleum-contaminated soils, supported by data-driven insights.
活化原生细菌快速降解石油污染土壤中长链和中链烃
为探索石油污染土壤中中链和长链烷烃(C20-C40)通过激活本地细菌的快速降解及其降解机理,通过添加不同比例的活化剂(葡萄糖、乙酸钠)进行了为期30天的生物降解实验。结果表明,活化组对总石油烃(TPH)、中链烷烃(4185.85 mg/kg)和长链烷烃(3778.15 mg/kg)的去除率分别达到9753.06 mg/kg、4185.85 mg/kg,显著高于无活性组。激活组微生物群落以分枝杆菌(14.55%)和假关节杆菌(13.95%)为主。激活组大量消耗DOC (10052.64 mg/kg),激活分枝杆菌和假节杆菌主动吸氧,显著提高脱氢酶和脂肪酶活性,加速了烃类转化。这可能是激活组实现快速TPH降解的原因。此外,使用三种机器学习算法的预测建模与实验数据具有很强的一致性,验证了它们在优化生物修复策略方面的实用性。本研究为石油污染土壤的有效修复提供了一种具有成本效益和可持续性的方法,并得到了数据驱动的见解的支持。
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来源期刊
Biochemical Engineering Journal
Biochemical Engineering Journal 工程技术-工程:化工
CiteScore
7.10
自引率
5.10%
发文量
380
审稿时长
34 days
期刊介绍: The Biochemical Engineering Journal aims to promote progress in the crucial chemical engineering aspects of the development of biological processes associated with everything from raw materials preparation to product recovery relevant to industries as diverse as medical/healthcare, industrial biotechnology, and environmental biotechnology. The Journal welcomes full length original research papers, short communications, and review papers* in the following research fields: Biocatalysis (enzyme or microbial) and biotransformations, including immobilized biocatalyst preparation and kinetics Biosensors and Biodevices including biofabrication and novel fuel cell development Bioseparations including scale-up and protein refolding/renaturation Environmental Bioengineering including bioconversion, bioremediation, and microbial fuel cells Bioreactor Systems including characterization, optimization and scale-up Bioresources and Biorefinery Engineering including biomass conversion, biofuels, bioenergy, and optimization Industrial Biotechnology including specialty chemicals, platform chemicals and neutraceuticals Biomaterials and Tissue Engineering including bioartificial organs, cell encapsulation, and controlled release Cell Culture Engineering (plant, animal or insect cells) including viral vectors, monoclonal antibodies, recombinant proteins, vaccines, and secondary metabolites Cell Therapies and Stem Cells including pluripotent, mesenchymal and hematopoietic stem cells; immunotherapies; tissue-specific differentiation; and cryopreservation Metabolic Engineering, Systems and Synthetic Biology including OMICS, bioinformatics, in silico biology, and metabolic flux analysis Protein Engineering including enzyme engineering and directed evolution.
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