High-valent Mn-oxo species are powerful and selective oxidants in metal-activated advanced oxidation processes (AOPs), yet their efficient and stable generation in Mn(II)/periodate (PI) systems is hindered by the lack of suitable ligand environments. Herein, we report a green and efficient Mn(II)-GLDA/PI system, where the biodegradable chelating agent L-glutamic acid N, N-diacetic acid (GLDA) forms a stable Mn(II)-GLDA complex that activates PI via direct two-electron oxygen atom transfer (OAT), yielding Mn(IV)-oxo as the dominant reactive species. This unique mechanism enables the complete degradation of paracetamol (PCT) within 10 min, and the generation of Mn(IV)-oxo is comprehensively confirmed by PMSO probe tests, 1 8O-isotope labeling, UV-vis spectroscopy, and ESI-MS detection. The Mn(II)-GLDA/PI system exhibits strong substrate selectivity toward electron-rich organic pollutants and excellent resistance to water matrix interference, maintaining over 90% PCT removal efficiency in real water matrices. Notably, PI is selectively reduced to non-toxic IO3-via two-electron OAT, avoiding the formation of toxic iodine byproducts (HIO, I 2 , I 3-). TOC and ICP-MS analyses further verify that GLDA remains structurally intact throughout the reaction, and Mn ions are stably chelated with no Mn-based precipitation. This study elucidates a ligand-enabled strategy for the selective generation of Mn(IV)oxo via direct two-electron OAT, and the developed Mn(II)-GLDA/PI system serves as a sustainable and highperformance AOP platform for the efficient abatement of pharmaceutical contaminants in aquatic environments.

Industrial composting of food waste digestate (FW) and chicken manure (CM) involves distinct dissolved organic matter (DOM) transformation pathways and different microbial interaction mechanisms. This study used Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) and shotgun metagenomics (for microbial community profiling) to compare interactions between DOM and microbial communities in the two composting processes. Results show that FW is dominated by labile organic matter (OM). This dominance increases the degree of DOM oxidation and the relative abundance of CHO. This labile carbon environment selected for a simplified microbial community dominated by key genera, yet facilitated active potential molecular transformations (PMTs) of DOM. These PMTs were characterized by an increase in thermodynamically limited processes (TLPs), indicating a carbon source-oriented pathway. In contrast, PMTs of DOM in CM favor thermodynamically favorable processes (TFPs), exhibiting higher aromaticity and CHOS abundance. The microbial community remains highly diverse, strongly connected, and functionally complementary, forming a synergistic network that supports coupled nitrogen-sulfur transformations. Environmental factors differentially regulate the two systems. This study indicates that the initial chemical properties of the composting feedstock fundamentally shape the PMTs of DOM pathways and the microbial communities they drive, providing an important theoretical basis for optimizing organic solid waste resource recovery processes.

Neonicotinoids (NEOs) are widely used systemic insecticides that persist in the ecosystem, raising concerns over their long-term ecological impact. However, current understanding of neonicotinoid degradation remains fragmented across diverse compounds, regions, and experimental settings, making it difficult to derive generalized conclusions for global pesticide management. Therefore, a systematic meta-analysis based on a global research database is essential to quantitatively integrate these disparate findings and uncover the unifying mechanistic principles governing their environmental fate. This study presents a global meta-analysis synthesizing 157 observations to evaluate degradation enhancement across typical NEOs compounds, different geographic regions, environmental media, and biological treatment strategies, with a focus on uncovering the underlying mechanisms. We found that degradation enhancement is governed by a compound's intrinsic chemical reactivity, with compounds containing electron-withdrawing moieties exhibiting the strongest response to treatment interventions (e.g., THI: SMD (Standardized Mean Difference) = -4.53). Regionally, degradation rates varied substantially, reflecting differences in environmental conditions, with stronger effects observed in Asia (e.g., China: SMD = -2.71; India: SMD = -3.12) compared to Europe (SMD = -1.37). The environmental medium emerged as a key determinant of efficacy, with liquid systems fostering markedly stronger degradation (SMD = -3.38) than soil systems (SMD = -2.39). Among treatment approaches, whole-organism strategies using microalgae (SMD = -4.42) and plants (SMD = -3.43) achieved the most substantial improvements. This study provides a mechanistically-informed assessment framework for sustainable pesticide management. It also offers an evidence-based foundation for designing more targeted and effective remediation strategies for neonicotinoid contamination globally to improve environmentally sustainable management and development.

Organic nitrogen (ON) transformation is critical for nitrogen retention during composting. Here, pilot-scale windrow composting experiments were conducted using food waste digestate composting (FW) and chicken manure composting (CM) to investigate associations between microbial functional potential and ON dynamics through glutamate-centered carbon-nitrogen metabolism. The results supported a potential pathway in which α-ketoglutarate from the tricarboxylic acid cycle (TCA) coupled with ammonium nitrogen (NH4+) through glutamate metabolism and was associated with ON dynamics. Core functional genes (FW: e.g., glnA, GDH2, GLUD1_2; CM: e.g., gltB, glnA, ureC) were identified, and their associated microbes (FW: e.g., Novibacillus, Planifilum; CM: e.g., Dietzia, Brevibacterium) were further determined using gene-taxon association analysis. Different regulatory patterns were observed between the two practical composting systems The CM microbial network was more connected than the FW network, with 10,732 versus 9,089 edges and graph densities of 0.194 versus 0.148. Additionally, In FW, functional genes made the largest independent contribution to ON variation (25.3%), and ON dynamics were more closely associated with the glutamate dehydrogenase (GDH) pathway and carbon-skeleton availability. In CM, gene-associated microbes made the largest independent contribution (14.2%). Although the glutamine synthetase/glutamate synthase (GS/GOGAT) pathway and urease-related processes exhibited efficient metabolic coupling, peptide-like DON molecular signatures remained relatively abundant during most composting stages. These findings provide a mechanistic framework for optimizing nitrogen transformation and retention during composting.

The resource utilization of landfill humus soil (LHS) is essential for effective landfill management. However, it poses risks due to the potential spread of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs). This study revealed the mechanisms driving ARG and MGE dissemination, mediated by carbon (C)/nitrogen (N)-cycling microbes and functional genes at different LHS depths. The abundances and diversities of C/N-cycling microbes, functional genes, ARGs, and MGEs declined with increasing depth. C/N-cycling processes were significantly correlated with ARG and MGE dissemination (p < 0.05). Microbes and function genes in the shallow layer (FU, 0-4 m) participated in C fixation, C degradation, ammonia assimilation, and nitrate reduction, predominantly influencing the spread of ARGs. In the intermediate (FM, 5-7 m) and deep (FD, 8-17 m) layers, denitrification-and methanogenesis-related microbes and genes played a dominant role in regulating the spread of ARGs. Network analysis revealed that C/N metabolism-related microbes are more closely connected with ARGs and MGEs and exhibit higher information transmission efficiency. The properties of LHS indirectly control the spread of ARGs by influencing microbial metabolism and functional gene expression, confirming that FU, dominated by N metabolism (NH4+ content), exerted the greatest overall effect on ARGs. In contrast, the horizontal gene transfer of ARGs in the deeper layers increasingly relied on C metabolic pathways, as reflected by dissolved organic C (DOC) content. This study provides a novel theoretical foundation for pollution prevention and the optimized utilization of LHS resources.

Assessing groundwater quality and health risks using machine learning is receiving widespread concern. However, assessment accuracy and cost-effectiveness are key factors in determining the model implementation. Therefore, the main purpose of this study is to develop a convenient, low-cost, and accurate hybrid ensemble model to predict water quality index (WQI) and hazard index (HI). Firstly, Pearson correlation matrix and 'SHAP' value were compared to select the Optimum feature combination. Secondly, base learners were selected from 12 different machine learning candidates. And then select eXtreme Gradient Boosting (XGB) as meta learner to construct stacking and blending ensemble model. The prediction results of the base learners are averaged to obtain the prediction results of averaging ensemble model. Finally, evaluation matrix (R-2 and RMSE), t-test and probabilistic forecasting were integrated to assess models' performance. The results show TDS, HCO3-, Mg2+, SO42- is the best feature combination for WQI prediction, and Na+, Ca2+, Mg2+, HCO3- is the best feature combination for HI prediction. SHAP value perform better than Pearson correlation matrix in reducing the number of input variables and improving model accuracy. The accuracy of stacking ensemble model on test/validation sets (average R-2 = 0.966/0.921 and 0.835/0.714 for WQI and HI respectively) significantly (p < 0.05) higher than the other models. The Stacking ensemble model developed in this study provides supports for governments to assess groundwater quality and formulate rational policies. Meanwhile, the integration of evaluation metrics and statistical analysis also offers new ideas for model evaluation in the environmental field.

The concentration of sulfate in global groundwater has been observed a significant upward trend in recent years. Excessive sulfate levels contribute to increased groundwater salinity and acidification, thereby posing a threat to human health and ecological balance. For effective groundwater pollution management and control, accurately quantifying the sources of sulfate pollution remains a challenge. This research integrates the Self-Organizing Map (SOM) clustering method to enhance the accuracy of the Bayesian isotope mixing model (MixSIAR) in quantifying the contribution rate of groundwater sulfate. During the dry season, sulfate (SO42-) primarily originates from the oxidation of pyrite, whereas SO42- sources include both pyrite oxidation and the co-dissolution of carbonate rocks and gypsum during the normal and wet seasons. Incorporating SOM, the MixSIAR model demonstrates reduced values of Leave-One-Out Information Criterion (LOOIC), and Widely Applicable Information Criterion (WAIC) (LOOIC = 82.5, and WAIC = 82.3). Overall, in the study area, coal mines (accounting for 34.3% - 48.4%) are identified as the primary pollution sources, particularly in Clusters 3, 4 and 5. Clusters 1, 2, and 5 are more significantly affected by other pollution sources, with fertilizers contributing 32.7%, evaporite dissolution contributing 24.1% and 24.2%, respectively. This study supports the development of regional pollution control strategies.

Organic nitrogen (ON) possesses the ability to sustain a stable nitrogen supply fertility during composting. However, research on the biosynthesis and regulation of ON remains limited. The results indicated that despite variations in microbial communities between the chicken manure composting (T group) and kitchen waste digestate composting (F group), their functional genes were remarkably similar, and the microorganisms exhibited similar functions. The microbial community structure of T group was more complex than that of F group. Network analysis identified Saccharomonospora, Corynebacterium, and Thermobifida as the core microorganisms in T group, whereas Oceanobacillus, Staphylococcus, and Fictibacillus were predominant in F group. These microorganisms play a role in the biosynthesis and regulation of various forms of ON (including amino acid nitrogen (AAN), amino sugar nitrogen (ASN), amide nitrogen (AN) and hydrolyzable unknown nitrogen (HUN)) and may contribute to differences in ON production due to the distinct nature of the materials. The core functional genes of the two groups of materials were determined by random forest model. Although differences in functional genes were present between F group and T group, the most crucial genes for ON biosynthesis in both groups were those with ammonia assimilation (such as glnE, gltB, gltD, etc.). The nitrogen transformation processes associated with these core genes can be modulated by managing the activity of multifunctional microorganisms, particularly through the control of ammonia assimilation, nitrate reduction, and ammonification, which are related to NH4+ levels. Notably, electric conductivity (EC), temperature (Tem.), pH, and NH4+ were the pivotal environmental factors influencing the biosynthesis of ON. This investigation enhances our understanding of the previously underexplored mechanisms of ON biosynthesis and regulation.

Substituting chemical fertilizers with compost is anticipated to facilitate the disposal of organic waste and mitigate nonpoint source pollution. However, research investigating the impact of diverse-compost utilization on the chemical reactivity of soil at the molecular-level remains lacking. Herein, the quantification and identification of molecular-scale redox sites and intermolecular interactions of soil dissolved organic matter (DOM) using diverse composts during a crop rotation cycle were investigated using the unified theoretical modeling approach VSOMM2 and Schrodinger. Results showed that compost use considerably altered the molecular weight and composition of soil DOM. In particular, we successfully optimized the validity coefficient of the unit model's molecular number to construct 38 molecular models of DOM molecules to identify and quantify the distribution of redox sites and intermolecular interactions within soil DOM molecules. Moreover, the distinct roles of different composts in modulating redox molecules within the soil DOM were determined during a crop rotation cycle. The application of cow manure compost considerably increased the quinone, Ar-COOH, and Ar-SH contents in Model(EAC+), while application of food waste compost enhanced the Ar-OH and Ar-NH2 in Model(EDC+). Finally, rotatable bonds, cation-pi interactions, aromatic H-bonds, pi-stacking, and salt bridges were identified to facilitate electron transfer within the redox molecules of soil DOM, which can be further enhanced via compost use. The findings of this study provide insights into the environmental biochemical reactions involving microcatalysts, metal reduction fate, pollution fate, and molecular composition of soil, providing a theoretical basis for enhancing soil reactivity using organic fertilizers instead of chemical fertilizers.

The persistent and increasing levels of sulfate due to a variety of human activities over the last decades present a widely concerning environmental issue. Understanding the controlling factors of groundwater sulfate and predicting sulfate concentration is critical for governments or managers to provide information on groundwater protection. In this study, the integration of self-organizing map (SOM) approach and machine learning (ML) modeling offers the potential to determine the factors and predict sulfate concentrations in the Huaibei Plain, where groundwater is enriched with sulfate and the areas have complex hydrogeological conditions. The SOM calculation was used to illustrate groundwater hydrochemistry and analyze the correlations among the hydrochemical parameters. Three ML algorithms including random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN) were adopted to predict sulfate levels in groundwater by using 501 groundwater samples and 8 predictor variables. The prediction performance was evaluated through statistical metrics (R-2, MSE and MAE). Mine drainage mainly facilitated increase in groundwater SO42- while gypsum dissolution and pyrite oxidation were found another two potential sources. The major water chemistry type was Ca-HCO3. The dominant cation was Na+ while the dominant anion was HCO3-. There was an intuitive correlation between groundwater sulfate and total dissolved solids (TDS), Cl-, and Na+. By using input variables identified by the SOM method, the evaluation results of ML algorithms showed that the R-2, MSE and MAE of RF, SVM, BPNN were 0.43-0.70, 0.16-0.49 and 0.25-0.44. Overall, BPNN showed the best prediction performance and had higher R-2 values and lower error indices. TDS and Na+ had a high contribution to the prediction accuracy. These findings are crucial for developing groundwater protection and remediation policies, enabling more sustainable management.

Groundwater is one of the chief water sources for agricultural activities in an aggregation of coal mines surrounded by agricultural areas in the Huaibei Plain. However, there have been few reports on whether mining-affected groundwater can be adopted for agricultural irrigation. We attempted to address this question through collecting 71 shallow groundwater samples from 12 coal mining locations. The Piper trilinear chart, the Gibbs diagram, the proportional coefficient of major ions, and principal component analysis were examined to characterize the source, origin, and formation process of groundwater chemical composition. The suitability for agricultural irrigation was evaluated by a final zonation map that establishes a comprehensive weighting model based on analytic hierarchy process and criteria importance though the intercriteria correlation (AHP-CRITIC). The results revealed that the groundwater was classified as marginally alkaline water with a predominant cation of HCO3- and anion of Na+. Total hardness, total dissolved solids, sulfate (SO42-), sodium (Na+), and fluoride (F-) were the primary ions that exceeded the standard. The results also indicated that the dominant hydrochemical facies were Ca-HCO3 and Na-Cl. The dissolution of carbonate, silicate, sulfate minerals, along with cation exchange, were the main natural drivers controlling the hydrogeochemical process of groundwater. The zonation map suggested that 43.17%, 18.85%, and 37.98% of the study area were high, mediate, and low suitability zones, respectively. These results from this study can support policymakers for better managing groundwater associated with a concentration of underground coal mines.

BACKGROUND:Epidemiological and experimental evidences have implicated chronic inflammation in the association with allergic rhinitis (AR). However, it remains unclear whether specific circulating cytokines are the cause of AR or the consequence of bias. To examine whether genetic-predicted changes in circulating cytokine concentrations are related to the occurrence of AR, we conducted a two-sample Mendelian randomization (MR) analysis.METHODS:We investigated the causal effects of 26 circulating inflammatory cytokines on AR through MR analysis. The primary method employed in this study was the inverse variance-weighted (IVW) method. Sensitivity analyses were conducted using simple median, weighted median, penalized weighted median, and MR-Egger regression.RESULTS:Our study revealed suggestive evidence that higher levels of circulating IL-18 (OR per one standard deviation [SD] increase: 1.006; 95 % CI, 1.002 to 1.011; P = 0.006, PFDR = 0.067, random-effects IVW method) and Macrophage inflammatory protein-1α (MIP-1α) (OR per one SD increase: 1.015; 95 % CI, 1.004 to 1.026; P = 0.009, PFDR = 0.048, random-effects IVW method) were associated with an increased risk of AR. Conversely, higher levels of circulating TRAIL were associated with a decreased risk of AR (OR per one SD increase: 0.993; 95 % CI, 0.989 to 0.997; P = 4.58E-4, PFDR = 0.004, random-effects IVW method). Only the results of TRAIL exist after Bonferroni-correction (the p-value < 0.0019). Sensitivity analysis yielded directionally consistent results. No significant associations were observed between other circulating inflammatory cytokines and AR.CONCLUSION:Genetically predicted levels of IL-18, and MIP-1α are likely to associated with an increased risk of AR occurrence. Genetically predicted levels of TRAIL are statistically significant in reducing the risk of AR occurrence. However, the current research evidence does not support an impact of other inflammatory cytokines on the risk of AR. Future studies are needed to provide additional evidence to support the current conclusions.

Understanding the pathophysiology of sudden sensorineural hearing loss (SSNHL) and identifying its clinical symptoms and associated risk factors are crucial for doctors in order to create effective prevention and therapeutic methods for this prevalent otolaryngologic emergency. This study focuses on investigating the correlation between the C-reactive protein/albumin ratio (CAR) and SSNHL complicated by hypertension. In this study, 120 patients diagnosed with SSNHL were divided into groups with and without hypertension, and propensity score matching was used to compare and analyze the severity, type, prognosis, and CAR levels in SSNHL. The results showed that the SSNHL group with hypertension had significantly higher CAR levels, age, hearing curve abnormalities, and more severe hearing loss compared to the control group with isolated SSNHL. These differences were statistically significant (p < 0.001). Among different subtypes of SSNHL, CAR levels increased progressively with the advancement of the condition, and these differences were also statistically significant (p < 0.001). In summary, in patients with SSNHL, those with hypertension had higher CAR levels than those without a history of hypertension, and they experienced more severe hearing loss. Moreover, there was a clear correlation between CAR levels and the extent of SSNHL, indicating that greater CAR levels in patients with SSNHL are connected to more severe hearing loss in various hearing patterns and perhaps indicative of a poorer prognosis.

Humic acids (HAs) coupled with humic-reducing microorganisms (HRMs) can facilitate contaminants reduction. Molecular-weight (MW) of HA governs the chemical and HRMs behavior. However, MW of HAs with chemical characteristics linking to HRMs in different wastes composting have never been investigated. Results from the HPSEC-UV analysis showed that composting significantly increased weight-average molecular weight (Mw) of HA with a broad range from 675 Da to 27983 Da, and governing heterogeneous chemical characteristics. In proteinaceous composts, MW< 4000 Da of HAs were greatly related to alkyl and carbonyl, while MW> 20000 Da of HAs were presented by oxygen-nitrogenous functional groups, methyl, and alkyl groups. For cellulosic composts, MW< 1500 Da and 4000-10000 Da of HAs were characterized by aliphatic ethers and aromatic groups. MW> 20000 Da of HAs were constructed by phenols, methoxy and nitrogen functional groups. In lignocellulosic composts, MW> 20000 Da of HAs were only characterized by aromatic groups. Furthermore, seven groups of HRMs adapted to the heterogeneous chemical characteristics within HAs ranked by MW were recognized. Consequently, the possible routes that composting properties response to the connections of HRMs-chemical structures-MW of HAs in proteinaceous, cellulosic and lignocellulosic composts were constructed, respectively. Our results can help to develop the fine classification-oriented approach for recycling utilization of organic wastes.

Landfill are persistent sources of nitrogen (N) pollution even in the decades after closure. However, the biological pathways of N-pollution, particularly N2O and NH4+, at different landfill depths have received little attention. In this study, metagenomic analysis was conducted on landfill refuse from vertical reservoir profiles in two closed landfills named XT and MT. NH4+ concentrations were found to be higher in deeper layers of MT, while greater potential for N2O emissions occurred in XT and the shallow layers of MT. Furthermore, the community structure and function of N-metabolizing microbes were more strongly defined by landfill depth than landfill type. Denitrification, involving abundant nirK and norB genes, was identified as the major pathway for N2O production in both XT and MT-shallow, while dissimilatory nitrate reduction with abundant nirBD genes was identified as the major pathway for NH4+ accumulation. Microbes of norB-type and nirBD-type were positively affected by NO3- in XT, whereas negatively affected by contents of organic material and moisture in MT-shallow. The mechanism by which nitrogen fixation, with abundant nifH genes, contributes to NH4+ accumulation in MT-deep should be further elucidated. These findings can provide a theoretical basis for governing scientific N-pollution control strategies throughout the entire landfill process.

以提取鸡粪未添加菌剂(CM)、鸡粪添加菌剂(CMB)及餐厨垃圾(FW)堆肥过程中提取的溶解性有机质(DOM)和腐殖酸(HA)为研究对象,利用荧光光谱平行因子分析及二维相关光谱分析对DOM和HA的物质结构及转化时序进行表征.结果表明:DOM的腐殖化指数(HIX)表现为CMB>CM>FW,FW堆肥有机质的自生源特性更强,CMB堆肥有机质腐殖化程度最高.与堆肥时间相比,DOM和HA的光谱结构特性对堆肥种类的响应更强烈;3 种堆肥的DOM中,类蛋白组分相对浓度整体表现为CM>FW>CMB,类腐殖质组分表现为CMB>FW>CM;HA中,类蛋白组分相对浓度为CM>CMB(FW),类腐殖质组分表现为CMB(FW)>CM.二维相关光谱表明:鸡粪堆肥中,DOM各组分演化时序为类胡敏酸>类酪氨酸>类色氨酸>类富里酸,HA各组分表现为类酪氨酸>类富里酸>类色氨酸;餐厨垃圾堆肥中,DOM各组分演化时序为类色氨酸>类酪氨酸>类富里酸,HA各组分表现为类胡敏酸>类酪氨酸>类色氨酸>类富里酸.适当的含水率有利于DOM中类腐殖质物质的生成,氨氮(NH4+-N)浓度的降低与鸡粪堆肥DOM中类蛋白物质的降解以及腐殖化程度的增强有关,硝氮(NO3--N)浓度与类腐殖质组分呈显著正相关;水溶性有机碳(DOC)浓度和含水率的降低可能标志着堆肥过程中HA的腐殖化程度增强.

Composting can decrease petroleum hydrocarbons in petroleum contaminated soils, however the microbial degradation mechanisms and regulating method for biodegradation of petroleum hydrocarbons with different carbon chain structures in the composting system have not yet been investigated. This study analyzed variations of total petroleum hydrocarbon concentrations with C ≤ 16 and C > 16, Random Forest model was applied to identify the key microorganisms for degrading the petroleum hydrocarbon components with specific structure in biomass-amended composting. Regulating method for biodegradation of petroleum hydrocarbons with different carbon chain structures was proposed by constructing the influence paths of "environmental factors-key microorganisms- total petroleum hydrocarbons". The results showed that composting improved the degradation rate of C ≤ 16 fraction and C > 16 fraction of petroleum hydrocarbons by 67.88 % and 61.87 %, respectively. Analysis of the microbial results showed that the degrading bacteria of the C ≤ 16 fraction had degradation advantages in the heating phase of the compost, while the C > 16 fraction degraded better in the cooling phase. Moreover, microorganisms that specifically degraded C > 16 fractions were significantly associated with total nitrogen and nitrate nitrogen. The biodegradation of C ≤ 16 fraction was regulated by organic matter, moisture content, and temperature. The composting system modified by biogas slurry was effective in removing of petroleum hydrocarbons with different carbon chain structures in soil by regulating the metabolic potential of the 46 key microorganisms. This study given their expected importance to achieve the purpose of treating waste with waste and contributing to soil utilization as well as pollution remediation.

Phosphorus bioavailability is essential for assessing compost quality. However, the effects of microbial and environmental factors on potentially active phosphorus (H2O-P + NaHCO3-Pi) in factory compost have not been investigated. The findings indicated that chicken manure had significantly higher available phosphorus (AP) and H2O-P + NaHCO3-Pi throughout the composting process than kitchen waste (P < 0.05). Chicken manure compost also exhibited higher a-microbial diversity. Novibacillus, Marinococcaceae and Bacillales were the core bacteria involved in bioavailable phosphorus conversion in both composts. The core bacteria in kitchen waste compost had a broader range of phosphorus metabolism functions. Moreover, moisture and pH were the key environmental factors that significantly influenced the bioavailable phosphorus (P < 0.05). These findings provide a scientific foundation for regulating the composting process and improving phosphorus utilization efficiency.

The refined classification and subtle transformation order of dissolved organic matter (DOM) components may govern the fate of metal ions (MIs) during composting. However, the classification of DOM components is still rough and the fate of MIs in response to the refined transformation order of DOM during municipal solid waste composting (MSWC) has not been studied. Here, the refined classification and evolution order of DOM components were redefined by two-dimensional correlation spectroscopy (2DCOS) analysis. Eight DOM components were redefined and their evolution order was: tyrosine-like (peak B)>humic acid-like (peak C1>peak C2)>terrestrial humic-like with small molecular size (peak A)>UVA humic-like with medium molecular size (peak D2)>UVC humic-like with medium molecular size (peak D1)>UVA humic-like with large molecular size (peak E2)>UVC humic-like with large molecular size (peak E1). Na and As were releasing in the whole process of DOM transformation. Cu and Al showed strong affinity with humic-like fraction, the anabolism of which leading to storage of Cu and Al in compost. Si, Fe, Mn, Co, Zn, Ni, Sr, Mg and Cr tend to combine with humic-like fraction with small molecular size. These responses were influenced by synergistic effect of key microorganisms (two bacterial groups and three fungal groups), in which the contribution of bacteria was greater than fungus. Finally, partial least-square path models of “environmental factors-key microorganisms-transformation order of DOM-MIs” were constructed. The combination of humic-like fractions continuously produced during MSWC and MIs made compost product with potential environmental risks. It is of great significance to develop abiotic factors regulation approach based on refined classification and transformation of organic components for reducing environmental risks of compost product.