Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm.

Machine learning (ML) is a novel method of data analysis with potential to overcome limitations of traditional composting experiments. In this study, four ML models (multi-layer perceptron regression, support vector regression, decision tree regression, and gradient boosting regression) were integrated with genetic algorithm to predict and optimize heavy metal immobilization during composting. Gradient boosting regression performed best among the four models for predicting both heavy metal bioavailability variations and immobilization. Gradient boosting regression-based feature importance analysis revealed that the heavy metal initial bioavailability factor, total phosphorus, and composting duration were the determinant factors for heavy metal bioavailability variations (together contributing >75%). After genetic algorithm optimization, the maximum immobilization rates of Cu, Zn, Cd, As, and Cr were 79.53, 31.30, 14.91, 46.25, and 66.27%, respectively, superior to over 90% of the measured data. These findings demonstrate the potential application of ML to risk-control for heavy metals in livestock manure composting.

Probing changes in humus chemical characteristics in response to biochar addition and varying bulking agents during composting: A holistic multi-evidence-based approach.

Despite the various benefits of humus, the changes in its chemical characteristics during composting in response to biochar addition and varying bulking agents remain to be further explored. In this study, three treatments were conducted, in which swine manure, bulking agent, and biochar were mixed at ratios of 4:1:0, 8:1:0, and 8:1:1. Fourier transform infrared spectroscopy (FTIR), carbon nuclear magnetic resonance spectroscopy (13C-NMR), three-dimensional excitation-emission matrix fluorescence spectroscopy (3D-EEM), and near-edge X-ray absorption fine structure (NEXAFS) were employed to characterize the chemical and structural properties of humus from multiple perspectives. The 3D-EEM spectra in this study showed a larger increase in humic acids (HAs) content (56%) and HAs to fulvic acids ratio (128%) during composting, indicating stronger humification in biochar-amended treatment. FTIR, 13C-NMR, and NEXAFS all confirmed the essential properties of HA as the core agronomic functional substance with rich aromatic and carboxyl groups, and that its aromaticity increased gradually during composting. In addition, 13C-NMR demonstrated that biochar addition and a relatively higher bulking agent ratio aided an increase in the carboxyl C proportion in HA after composting. In particular, NEXAFS revealed that biochar addition promoted the diversification of C, N, and O species in HA, with the emergence of quinone C and O-alkyl C as the main representatives. This work suggests that biochar addition and a relatively high bulking agent ratio could enhance humification and improve the agronomic function of humus.

Immobilization pathways of heavy metals in composting: Interactions of microbial community and functional gene under varying C/N ratios and bulking agents.

Excessive heavy metals (HMs) in livestock manure due to additives over-use limits its recycling through composting. However, few studies have investigated the interactive influence of microbial communities, functional genes, and environmental factors in HM immobilization during composting. Therefore, treatments with different C/N ratios (15:1, 20:1, 25:1) and bulking agents (maize straw, green waste, vinasse) were conducted to explore the HMs immobilization pathways using structural equation model (SEM). Results confirmed the promoting effect of C/N ratio of 20:1 and vinasse on microbial diversity, thus leading to greater HMs immobilization rate. Meanwhile, the dominant microbial phyla of Cu/Zn, Cd, As, and Cr were identified as Proteobacteria, Firmicutes, Chytridiomycota, and Bacteroidota, respectively. Moreover, the significant correlation between functional genes (copC, mt, cbh1, aoxB, yieF) and HMs illustrated potential immobilization effects of metal-binding proteins on Cu and Zn, humus complexation on Zn, and oxidase/reductase on As/Cr. Finally, SEM indicated that the redistribution of Cu, Zn, As, and Cr fractions was interactively influenced by environmental factors (organic matter, pH, electrical conductivity, and total phosphorus), microbial communities, and functional genes, while Cd was directly regulated by organic matter and total phosphorus. These results may provide a deeper understanding of HM immobilization pathways during swine manure composting.

Effects of typical sludge treatment on microplastics in China-Characteristics, abundance and micro-morphological evidence.

Microplastics (MPs) are emerging pollutants that are enriched in sludge. They enter soil through sludge soil amendment, landfill, and discard, which will cause inescapable environmental pollution risks. Sludge treatment technology commonly used in China include anaerobic digestion (AD), thermal drying (TD), thermal hydrolysis (TH) and aerobic composting (AC). In this study, characteristics of MPs in sewage sludge from four representative large cities in China (Zhengzhou, Chongqing, Guangzhou, and Guilin) were analyzed. Effects of four representative sludge treatment technology on sludge MPs were also studied. In addition, the amount of MPs input to soil from sludge in China was estimated. The abundance range of sludge MPs of representative cities in China was 1448-11,125 n∙kg-1 DW. Previous studies indicate that this abundance range is low among other domestic cities and is close to that of European countries. MPs were predominantly fiber-shaped, accounting for 46.66%; 56.5% MPs were white and transparent, and 62.5% were polypropylene and polyethylene. The abundance of MPs in the sludge increased after TH, indicating that MPs broke into smaller particles. However, the other three treatment methods had no significant influence on the abundance of MPs. Scanning electron microscopy analysis showed that the micro-morphology of sludge MPs surface were rougher after AD, and MPs cracked following TD and TH. Furthermore, broken edges were more blurred after TH, and surfaces of MPs were damaged and eroded after AC. The input quantities of MPs in sludge to soil was deduced to be 1013 particles per year. These results are important for controlling the potential risk of sludge MPs in China.

Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review.

Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

Salinity derived from sludge compost amendment is a crucial influencing factor of qualitative performance of sports-field turf.

Treated sewage sludge (or biosolid) is a suitable alternative substrate for use in turf production, but the potential of this resource remains to be explored in higher value-added field. In this study, the negative effects of sludge compost and biochar-amended sludge compost amendment on the qualitative performance of sports-field turf, including vegetation stolon index, grade for turf, and sports performance index, were investigated. It was found that sludge compost and biochar-amended sludge compost amendment induced a significant increase in organic matter, total nitrogen, total potassium, and electrical conductivity. In addition, biochar-amended sludge compost also resulted in a significant increase in turf chlorophyll content and the rate of turf weed emergence. Interestingly, electrical conductivity explained 55.4% of the qualitative changes, and electrical conductivity and pH together explained 68% of the qualitative changes. In summary, salinity was the main factor responsible for the negative effect of sludge compost amendment on qualitative performance of sports-field turf.

Potential mechanisms involving the immobilization of Cd, As and Cr during swine manure composting.

This study aims to investigate the relationship between key physicochemical parameters related to composting process and bioavailability of Cd, As and Cr during swine manure composting through regulating different initial carbon to nitrogen (C/N) ratios (15:1, 20:1, 25:1) and bulking agent types (straw, green waste). Results showed that higher initial C/N ratio of 20:1 or 25:1 and straw as bulking agent were optimal to reduce the bioavailability of Cd, As and Cr (62.4%, 20.6% and 32.2% reduction, respectively). Redundancy analysis implied that the bioavailability of Cd was significantly associated with total phosphorus and total nitrogen, deducing the formation of phosphate precipitation and biosorption might participated in the reaction process, while that of As and Cr were mainly influenced by organic matter (OM), cation exchange capacity (CEC) and OM, CEC, electric conductivity, respectively. A total of 48.5%, 64.6% and 62.2% of Cd, As and Cr redistribution information could be explained by the above parameters. Further correlation analysis revealed that bioavailable As and Cr were negatively correlated with humic acid to fulvic acid ratio. In summary, this study confirms that the mechanisms of phosphate precipitation, biosorption and humification played critical role in reducing Cd, As and Cr bioavailability during swine manure composting.

Dissecting Efficacy and Metabolic Characteristic Mechanism of Taxifolin on Renal Fibrosis by Multivariate Approach and Ultra-Performance Liquid Chromatography Coupled With Mass Spectrometry-Based Metabolomics Strategy.

Taxifolin (TFN) is an important natural compound with antifibrotic activity; however, its pharmacological mechanism is not clear. In this study, our aim is to gain insight into the effects of TFN and its potential mechanisms in unilateral ureteral obstruction (UUO) animal model using metabolomics approach to identify the metabolic biomarkers and perturbed pathways. Serum metabolomics analysis by UPLC-Q-TOF/MS was carried out to discover the changes in the metabolic profile. It showed that TFN has a significant protective effect on UUO-induced renal fibrosis and a total of 32 potential biomarkers were identified and related to RF progression. Of note, 27 biomarkers were regulated by TFN treatment, which participate in eight metabolic pathways, including phenylalanine, tyrosine and tryptophan biosynthesis, and phenylalanine metabolism. It also showed that metabolomics was a promising strategy to better dissect metabolic characteristics and pharmacological mechanisms of natural compounds by multivariate approach and ultra-performance liquid chromatography coupled with mass spectrometry.