Dynamic changes of microbial community and moisture ratio during bio-drying of sludge after electro-dewatering

Machine learning modeling of thermally assisted biodrying process for municipal sludge

Preparation of activated carbons is an important way to utilize municipal sludge (MS) resources, while drying is a pretreatment method for making activated carbons from MS. In this study, machine learning techniques were used to develop moisture ratio (MR) and composting temperature (CT) prediction models for the thermally assisted biodrying process of MS. First, six machine learning (ML) models were used to construct the MR and CT prediction models, respectively. Then the hyperparameters of the ML models were optimized using the Bayesian optimization algorithm, and the prediction performances of these models after optimization were compared. Finally, the effect of each input feature on the model was also evaluated using SHapley Additive exPlanations (SHAP) analysis and Partial Dependence Plots (PDPs) analysis. The results showed that Gaussian process regression (GPR) was the best model for predicting MR and CT, with R2 of 0.9967 and 0.9958, respectively, and root mean square errors (RMSE) of 0.0059 and 0.354 ℃. In addition, graphical user interface software was developed to facilitate the use of the GPR model for predicting MR and CT by researchers and engineers. This study contributes to the rapid prediction, improvement, and optimization of MR and CT during thermally assisted biodrying of MS, and also provides valuable guidance for the dynamic regulation of the drying process.

Relationships between arsenic biotransformation genes, antibiotic resistance genes, and microbial function under different arsenic stresses during composting

Although the arsenic contamination and antibiotic resistance genes (ARGs) during composting have been studied separately, there is limited information on their interactions, particularly, the relationship between arsenic biotransformation genes (ABGs) and ARGs. Therefore, the present study used different forms of arsenic stress (organic and inorganic arsenic at 10 and 50 mg/kg) in pig manure and straw co-composting, to evaluate the effects of arsenic stress on microbial community structures, metabolic function, ABGs, and ARGs. The results showed that arsenic stress had different effects on different parameters and promoted the microbial formation of humic acid and the biodegradation of fulvic acid. Inorganic arsenic showed more rapid effects on microbial community structure, visible within about 20 days, while the effects of organic arsenic were later (about 45 days) due to the necessity of transformation. Moreover, the addition of organic roxarsone and inorganic arsenic resulted in higher expression of ABGs and ARGs, respectively. Arsenic addition also caused increased expression of genes associated with replication and repair. A significant relationship was observed between ABG and ARG expression, for instance, genes involved in arsenic reduction and oxidation were influenced by genes involved in aminoglycoside and chloramphenicol resistance genes (p < 0.05). These complex interactions among microorganisms, functional genes, and external parameters contribute to the understanding of the mechanisms underlying cross-contamination.

Effects of complex pollution by microplastics and heavy metals on soil physicochemical properties and microbial communities under alternate wetting and drying conditions

Microplastics (MPs) broadly coexist with heavy metals (HMs) in soil, Cd and Cu are the main types of soil HMs contamination, in addition to polystyrene (PS), which is also widely present in the environment and prone to aging. However, differences in the effects of MPs and HMs on soil properties and microbial characteristics under alternating wetting and drying (AWD) remain unclear. Thus, this study investigated the effects of four conventional (0.2% (w/w)) and aged MPs in indoor incubation experiments on soil properties under desiccation (Dry) and AWD. We found that with the influence of the "enzyme lock" theory, the coexistence of MPs and HMs under Dry had a more pronounced effect on soil physicochemical properties, whereas the effects on soil enzyme activity under AWD were more significant. In addition, MPs decreased the available Cu by 4.27% and, conversely, increased the available Cd by 8.55%. Under Dry, MPs affected microbial function mainly through physicochemical properties, with a contribution of approximately 72.4%, whereas under AWD enzyme activity and HMs were significantly greater, with increases of 28.2% and 7.9%, respectively. These results indicate that the effects of MPs on environmental variation and microbial profiles under AWD conditions differed significantly from those under Dry.

Sodium alginate/sinter gel spheres immobilized lysozyme producing strain SJ25 enhanced sludge reduction: Optimization and mechanism

In order to promote sludge hydrolysis and improve the efficiency of aerobic digestion, the sodium alginate immobilized gel spheres pellet B (SIP B) were prepared using sodium alginate (SA) and sinter as carrier to immobilize lysozyme producing strain SJ25. The optimal conditions for SIP B to promote sludge hydrolysis were 5.6 mg SS^-1 dosage and pH of 9.0. Under the optimal condition compared with the control group, the reduction efficiency of suspended solids (SS) in 24 h was increased by 26.89 %, the release of soluble chemical oxygen demand (SCOD) was increased by 517.79 mg L^-1, polysaccharide (PS) and protein (PN) concentrations were increased by 186.69 and 368.68 mg L^-1, respectively. SIP B enhanced the degradation efficiency of sludge by promote the release of lysozyme, prolonging the action time of the enzyme, enhancing the metabolism and membrane transport of xenobiotics, carbohydrate and amino.