Enhancing the transformation of carbon and nitrogen organics to humus in composting: Biotic and abiotic synergy mediated by mineral material

Effect of chicken manure and superphosphate on accelerating green waste composting and enhancing nutrient retention

Efficient green waste (GW) management through composting is essential for promoting cleaner production and fostering a green economy. However, traditional GW composting often faces challenges such as extended decomposition times, significant nitrogen losses, and low nutrient content in the final compost product. This research evaluated the effects of incorporating chicken manure (CM: 0, 15, and 30%) and superphosphate (SSP: 0, 5, and 10%) on GW composting. A control treatment without additives was included for comparison. Results demonstrated that the combination of 15% CM and 5% SSP significantly enhanced the composting process, achieving maturity and stability within just 33 days-6 days faster than the control. This treatment also extended the thermophilic phase by 8 days, increased electrical conductivity by 115%, improved organic matter decomposition by 49%, and elevated the germination index by 56%. Furthermore, the final compost showed higher nutrient levels, with total nitrogen, total phosphorus, and total potassium content exceeding the control by 92%、327%、135%, respectively. These findings highlight the synergistic effects of CM and SSP in accelerating GW composting and enhancing compost quality, offering valuable insights for the sustainable and resource-efficient management of GW.

Machine learning-based prediction of compost maturity and identification of key parameters during manure composting

Evaluating compost maturity, e.g. via manual seed germination index (GI) measurement, is both time-consuming and costly during composting. This study employed six machine learning methods, including random forest (RF), extra tree (ET), eXtreme gradient boosting, gradient boosting decision tree, back propagation neural network, and multilayer perceptron, to develop models for predicting GI during manure composting. RF and ET exhibited robust predictive performance for GI, achieving high coefficient of determination (R2) of 0.937 and 0.904, respectively, along with root mean squared error of 7.261 and 8.930. SHapley additive exPlanations identified the duration time of composting, total nitrogen, and electrical conductivity as the key features influencing GI. Validation with actual GI data further confirmed the effectiveness of RF and ET models in predicting GI. This study could facilitate optimizing manure composting strategies, enable efficient parameter regulation, reduce labor costs, assist in anomaly detection, and promote intelligent management in real-world composting practices.

Deciphering the mechanism of microbial metabolic function shift and dissolved organic matter variation in acidogenic fermentation of waste activated sludge induced by antiviral drugs

Antiviral drugs (ATVs), as emerging contaminants enriched in wastewater activated sludge (WAS) in wastewater treatment plants, affect subsequent treatment. ATVs have been shown to have negative influences on anaerobic digestion of WAS, but it is unclear how ATVs affect functional microbial metabolic activity and changes in intermediates. Thus, the effect of the anti-HIV drug ritonavir (RIT) on the period of anaerobic fermentation (AF) and the response of microbial community structure were examined in this study. Results indicated that the production of total volatile fatty acids (VFAs) decreased from 2010.21 mg/L to 372.03 mg/L under 125-1000 μg RIT/kg TSS treatment. Characterization of organic matters revealed that dissolved organic matter in the high-dose RIT groups was less biodegradable, with lower protein content and higher humus content. Mechanistic analyses indicated that RIT exposure reduced the abundance of hydrolyzers and inhibited carbohydrate metabolism, resulting in an increased humification index in the RIT groups. In addition, the expression of genes associated with the synthesis of VFAs was also significantly reduced in the RIT groups, leading to a decrease in both the amount and type of VFAs. This study provides a novel perspective on the effects of emerging contaminants on WAS treatment processes and pollution prevention.

Potassium persulfate enhances humification of chicken manure and straw composting: The perspective of rare and abundant microbial community structure and ecological interactions

Improper disposal of organic solid waste results in serious environmental pollution. Aerobic composting provides an environmentally friendly treatment method, but improving humification of raw materials remains a challenge. This study revealed the effect of different concentrations of potassium persulfate (PP) on humification of chicken manure and straw aerobic composting and the underlying microbial mechanisms. The results showed that when 0.6 % PP was added (PPH group), humus and the degree of polymerization were 80.77 mg/g and 2.52, respectively, which were significantly higher than those in 0.3 % PP (PPL group). As the concentration of PP was increased, the composition of rare taxa (RT) changed and improved in evenness, while abundant taxa (AT) was unaffected. Additionally, the density (0.037), edges (3278), and average degree (15.21) in the co-occurrence network decreased compared to PPL, while the average path (4.021) and modularity increased in PPH. This resulted in facilitating the turnover of matter, information, and energy among the microbes. Interestingly, cooperative behavior between microorganisms during the maturation period (24-60 d) occurred in PPH, but competitive relationships dominated in PPL. Cooperative behavior was positively correlated with humus (p < 0.05). Because the indices, such as higher degree, betweenness centrality, eigenvector centrality, and closeness centrality of the AT, were located in the microbial network center compared to RT, they were unaffected by the concentration of PP. The abundance of carbohydrate and amino acid metabolic pathways, which play an important role in humification, were higher in PPH. These findings contribute to understanding the relative importance of composition, interactions, and metabolic functionality of RT and AT on humification during chicken manure and straw aerobic composting under different concentrations of PP, as well as provide a basic reference for use of various conditioning agents to promote humification of organic solid waste.

Combining citrus waste-derived function microbes with biochar promotes humus formation by enhancing lignocellulose degradation in citrus waste compost

The low degradation rate of lignocellulose limits the humification process of citrus organic waste composting. This study explored the roles of general microbial inoculation (GM), citrus waste-derived function microbial inoculation (CM), and CM combined with biochar (CMB) in citrus waste compost. Results showed microbial inoculations all promoted lignocellulose degradation and humus formation, but the roles of CM and CMB were better than GM, especially CMB. Compared to the control, CMB raised the temperature and duration of thermophilic phase by 2.8 °C and 4 days, and improved lignin degradation rate and humus content by 21.5% and 7.6%. Furthermore, CMB promoted bacterial community succession and cooperation, and decreased network complexity. Moreover, CMB strengthened the correlation between mainly bacterial communities and polysaccharides, reducing sugars as well as carbohydrates metabolic, enhancing the contribution of bacteria such as Bacillus, Flavobacterium and Staphylococcus to humus and its precursors. It concludes that the naturally derived microbes in compost had better effects on promoting humus synthesis than exogenous microbes, which provides a new route for rapid humification of high-lignin organic waste in composting.

Predicting maturity and identifying key factors in organic waste composting using machine learning models

The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.