Chlorinated aliphatic hydrocarbons (CAHs) are some of the most widely distributed organic pollutants in underground environments and have high biological toxicity. This research aims to prepare an effective adsorbent comprising biochar and magnetite (MBC) to remove CAH pollution from soil. Optimization of the preparation and adsorption performance of MBC was investigated. The results of the adsorption experiment, combined with scanning electron microscopy (SEM) observations, show that the best raw material and pyrolysis temperature were coconut shell and 500 °C respectively. The Fourier transform infrared (FTIR) and X-ray diffraction (XRD) pattern characterizations, as well as the adsorption results, demonstrated the successful synthesis and enhancement effect of MBC for CAHs. The adsorption of CAHs on Fe₃O₄-loaded biochar was improved by 34.40–222.25% during pyrolysis at 500–900 °C. Additionally, MBC with a 10% Fe₃O₄ content had the best effect on three types of CAHs at low concentrations. A comparative pore analysis of MBC with different doses of Fe₃O₄ was carried out to reveal the relationship between the pore characteristics and adsorption properties. Furthermore, competitive adsorption experiments demonstrated that 4 wt% MBC addition significantly reduced the soil-bound TCE by 48.6%. Overall, these results indicated that MBC was an effective adsorbent for CAH removal from the polluted underground environment.

Microbial degradation technology presents a sustainable approach to address the environmental persistence of polyethylene (PE). In this study, a consortium of PE-degrading strains was isolated from sludge in the production wastewater of a PE-manufacturing plant. Among these strains, Pseudomonas aeruginosa SG01 demonstrated the highest cellular growth rate in culture medium, indicating its capacity to efficiently degrade PE and utilize it as the sole carbon source. Following treatment with SG01, the PE films exhibited a significant reduction in mass along with a clear decrease in surface contact angle, suggesting an improvement in hydrophilicity. Fourier transform infrared spectroscopy (FTIR) analysis detected the formation of new absorption bands on the treated PE films, corresponding to hydroxyl, carboxyl, and amide functional groups. Scanning electron microscopy (SEM) observations further revealed the presence of erosion pits and network-like cracks on the film surface. This study confirms that Pseudomonas aeruginosa SG01 can effectively degrade PE and modify its surface properties, offering a novel microbial resource for the bioremediation of PE contamination.

This study aimed to explore the microbial communities present in aquifer groundwater at a petrochemical refinery and their relationship with groundwater quality parameters, with a focus on common contaminants such as benzene, toluene, ethylbenzene, and dichloroethane (DCA). Groundwater samples were collected from both the source and plume regions to analyze the spatial diversity of the microbial communities utilizing 16S rRNA analysis. The study demonstrated substantial variations in microbial diversity and composition across the sampled sites. The data showed that the operational taxonomic unit count, Shannon index, and Simpson index initially rose before declining with escalating contaminant concentration, suggesting that the level of contaminants significantly influences the abundance and diversity of microbial communities in the phreatic groundwater. Moreover, through SPSS analysis, the study quantitatively established the correlation between the physiochemical characteristics of the groundwater and the microbial community structure. The study disclosed that geochemical parameters, including total alkalinity, ferrous content, and DCA, play a role in shaping the abundance and diversity of microbial communities at the phylum, class, and genus levels. This research contributes to our comprehension of the intricate interplay between microbial communities, particularly those implicated in the biotransformation of benzene and DCA, and their surrounding physiochemical milieu within contaminated zones.

Time series data of industrial chemical process are typically collected in two ways: distributed control system (DCS) and laboratory test. With the popularity of DCS, a large amount of industrial chemical process monitoring data can be collected, which is commonly feature rich but information poor. Due to the variant design of the processes, some features are still monitored through laboratory tests. Data collected in this way cannot support the training of deep learning models as a result of low monitoring frequency and the lack of historical data. In this study, a reinforcement aided latent temporal feature transfer learning (RALTFTL) method is proposed to predict target features for a data-deficient device (target domain) by learning knowledge from a data-sufficient device (source domain). A transfer learning framework characterized by using latent temporal feature is constructed. Autoencoder is conducted to construct the latent spaces and unify the number of latent features. Reinforcement learning approach is introduced to the framework for feature selection. By taking monitoring features as candidates and taking the improvement of accuracy as reward, it explores a subset of monitoring features that maximizes the accuracy of the prediction model in the target domain. Taking a gas emission NOx concentration prediction task as experiment, the practical data from two industrial chemical devices is utilized to assess the performance of RALTFTL and verify the effectiveness of introduced approaches and mechanisms.

Carbon tetrachloride (CT) is a toxic volatile chlorinated hydrocarbon, posing a serious hazard to ecosystem and human health. This study discussed the bioremediation possibility of groundwater contaminated by CT. Enhanced reductive dechlorination bioremediation (ERD) was used to promote the reductive dechlorination process of CT by adding yeast extract as a supplementary electron donor. The microcosm samples of the Control and Experi group were setup in the experiment, and the CT degradation efficiency and microbial community structure changes over 150 days were monitored. The results showed that the Experi group achieved complete degradation of CT within 40 days, while the control group had no significant change. By analyzing the physical and chemical indexes such as VFAs, sulfate ions, oxidation–reduction potential, pH value and so on, the key changes in the degradation process of CT were revealed. Microbial community analysis showed that specific microorganisms such as Acinetobacter johnsonii, Aeromonas media and Enterobacter mori played a significant role in the degradation of CT. They may produce hydrogen through fermentation to provide electron donors for the reductive dechlorination of CT. In addition, the genes of reductive dehalogenase synthase related to CT degradation were also identified, which provided molecular evidence for understanding the biodegradation mechanism of CT. The results deliver a scientific basis for optimizing the bioremediation strategy of CT-contaminated groundwater.

Owing to their small size and surface hydrophobicity, microplastics (MPs) tend to act as vectors for various organic pollutants. However, in contrast to well-studied pollutants like polycyclic aromatic hydrocarbons, the sorption of benzene-series compounds on MPs has been seldom studied. To investigate the sorption process, the isotherms were determined for the sorption of three benzene-series sorbates by three polymers with different physicochemical properties. The linear sorption isotherms observed for PE indicate that sorbate uptake was dominated by partitioning into the bulk polymer. In contrast, the non-linear isotherms of PP and PVC imply that adsorption onto surfaces was the dominant mechanism. Sorption capacity of m-xylene and ethylbenzene increased in the following order: polyvinyl chloride (PVC) < polyethylene (PE) < polypropylene (PP). This order does not reflect the polarity or the crystallinity of the investigated MPs, suggesting the influence of additional factors (e.g., glass transition temperature, specific surface area) on the sorption of BTEX by MPs. In addition, the particle size and morphology of MPs are also factors affecting sorption capacity. The strong correlation between the sorption coefficients and sorbate hydrophobicity indicates that the hydrophobic interactions played a crucial role. Meanwhile, specific sorbate properties, such as electronic structure and molecular polarizability, are also significant factors that affect the sorption behaviors.