| Literature DB >> 35845418 |
Yi Gao1, Chunxue Zhang1, Lu Tan1, Xiaocheng Wei1, Qian Li1, Xiangqun Zheng1, Fang Liu1, Jiarui Wang1, Yan Xu1.
Abstract
Regarding the composting of rural waste, numerous studies either addressed the composting of a single waste component or were conducted at a laboratory/pilot scale. However, far less is known about the mixed composting effect of multi-component rural waste on a large scale. Here, we examined nutrient transformation, maturity degree of decomposition, and succession of microbial communities in large-scale (1,000 kg mixed waste) compost of multi-component wastes previously optimized by response models. The results showed that multi-component compost can achieve the requirement of maturity and exhibit a higher nutritional value in actual compost. It is worth noting that the mixed compost effectively removed pathogenic fungi, in which almost no pathogenic fungi were detected, and only two pathogenic bacteria regrown in the cooling and maturation stages. Structural equation models revealed that the maturity (germination index and the ratio of ammonium to nitrate) of the product was directly influenced by compost properties (electrical conductivity, pH, total organic carbon, moisture, temperature, and total nitrogen) compared with enzymes (cellulase, urease, and polyphenol oxidase) and microbial communities. Moreover, higher contents of total phosphorus, nitrate-nitrogen, and total potassium were conducive to improving compost maturity, whereas relatively lower values of moisture and pH were more advantageous. In addition, compost properties manifested a remarkable indirect effect on maturity by affecting the fungal community (Penicillium and Mycothermus). Collectively, this evidence implies that mixed compost of multi-component rural waste is feasible, and its efficacy can be applied in practical applications. This study provides a solution for the comprehensive treatment and utilization of rural waste.Entities:
Keywords: compost maturity; composting effect; microbial succession; mixed compost; rural waste
Year: 2022 PMID: 35845418 PMCID: PMC9286457 DOI: 10.3389/fbioe.2022.928032
Source DB: PubMed Journal: Front Bioeng Biotechnol ISSN: 2296-4185
Physicochemical characteristics of the composting materials.
| Parameter | Swine manure | Human feces | Rice straw |
|---|---|---|---|
| pH | 8.8 ± 0.86 | 8.1 ± 1.25 | 6.5 ± 0.58 |
| Organic matter (%) | 56.8 ± 2.12 | 10.2 ± 1.36 | 94.3 ± 5.69 |
| Moisture content (%) | 80.52 ± 1.69 | 60.56 ± 4.12 | 60.24 ± 2.58 |
| Total organic carbon (%) | 21.56 ± 2.34 | 6.31 ± 1.28 | 51.28 ± 0.59 |
| Nitrogen content (%) | 2.64 ± 0.69 | 0.59 ± 0.08 | 1.08 ± 0.36 |
FIGURE 1Changes of physicochemical parameters during composting; (A) Temperature during co-compost process; (B) pH and EC, the stripe represents the standard deviation; (C) The concentration of total organic carbon (TOC) and (D) The concentrations of TN, TP and TK.
FIGURE 2The changes of (A) GI and (B) NH4 +-N/NO3 −-N and NI during composting. The stripe represents the standard deviation.
Quality analysis of compost products.
| Substrate | Index | Composting technology | Reference | ||||
|---|---|---|---|---|---|---|---|
| Organic matter (based on dry weight), % | Total nutrient contents (based on dry weight), % | Moisture mass fraction (fresh sample), % | pH | GI (%) | |||
| - | ≥30 | ≥4.0 | ≤30 | 5.5–8.5 | ≥70 | - | NY 525/T-2021 |
| Swine manure, human feces, and rice straw | 37.24 | 5.62 | 28.82 | 7.74 | 95.8 | Windrow composting | This study |
| Swine manure, saw dust, and rice husk | 62.51 | 9.59 | 21.92 | 8.32 | - | A 40-m3 composting reactor |
|
| Blue-green algae sludge, livestock feces, and straw | 57.91 | 6.59 | 28.36 | 8.00 | 114.5 | The device included a main composting vessel, feeding and discharge conveyors, and a gas removal biofilter system |
|
| Fresh swine manure, composted swine manure, and maize straw | 49.48 | 8.4 | 32 | 8.22 | 71 | A patent compost tray |
|
-: unknown.
FIGURE 3The activities of emzymes during composting. (a) cellulase, (b) urease and (c) polyphenol oxidase.
FIGURE 4The microbiome composition during composting. (A) Bacterial and (C) fungal Venn diagram of different phases; (B,D) are compositions of bacterial and fungal communities during composting at the phylum-level, respectively.
FIGURE 5Relative abundance of (A) pathogenic bacteria and (B) pathogenic fungi in top 20 genus and (C) their removal rates.
FIGURE 6Structural equation models (SEMs) showing the pathways (A,B) of the different factors (environmental factors, enzymes and microorganisms) on compost maturity (GI and NI). The path coefficients are adjacent to the arrows. Blue and red lines indicate the positive and negative pathway. Co-occurrence networks between maturity indexes and abiotic factors (C). A connection is observed for strong (Spearman’s ρ>|0.6|) and significant (p < 0.05) correlations among these variables. The green nodes indicate the parameters of maturity indexes while the orange nodes indicate biotic factors. The size of nodes is proportional to the degree of connectivity. Blue links indicate negative interactions between two individual nodes, while red links indicate positive interactions.