Exploring the mechanisms by which green agricultural production reduces emissions and enhances carbon sequestration in soil can provide a scientific basis for greenhouse gas reduction and sustainable development in farmland. This study uses a combination of meta-analysis and field experiments to evaluate the impact of different agricultural management practices and climatic conditions on soil organic carbon (SOC) and the emissions of CO₂ and CH₄, as well as the role of microorganisms. The results indicate the following: (1) Meta-analysis reveals that the long-term application of organic fertilizers in green agriculture increases SOC at a rate four times higher than that of chemical fertilizers. No-till and straw return practices significantly reduce CO₂ emissions from alkaline soils by 30.7% (p < 0.05). Warm and humid climates in low-altitude regions are more conducive to soil carbon sequestration. (2) Structural equation modeling of plant–microbe–soil carbon interactions shows that plant species diversity (PSD) indirectly affects microbial biomass by influencing organic matter indicators, mineral properties, and physicochemical characteristics, thereby regulating soil carbon sequestration and greenhouse gas emissions. (3) Field experiments conducted in the typical green farming research area of Chenzhuang reveal that soils managed under natural farming absorb CH₄ at a rate three times higher than those under conventional farming, and the stoichiometric ratios of soil enzymes in the former are close to 1. The peak SOC (19.90 g/kg) in the surface soil of Chenzhuang is found near fields cultivated with natural farming measures. This study provides theoretical support and practical guidance for the sustainable development of green agriculture.

Rapid socio-economic development has intensified the conflict between supply and demand for regional water resources, necessitating optimized water resource allocation to enhance water security. This study establishes a multi-objective water resource optimization model by comprehensively considering economic, social, and ecological benefits. Based on the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), we propose the I-NSGA-III algorithm by integrating reference point improvement strategies, dynamic retention of high-quality solutions, and optimized selection strategies to solve the multi-objective optimization model. A multi-system coupling coordination evaluation model is constructed to assess the final allocation schemes. Compared with some commonly used multi-objective algorithms and tested using the DTLZ series functions, the proposed algorithm demonstrates improved overall performance. Specifically, the IGD indicator decreases by 5.17–50.22%, and the HV indicator increases by 2.71–25.51% compared to NSGA-III. The proposed model is applied to Jinzhong City, China, with four scenarios set for the years 2030 and 2035 at P = 50% and P = 75% to derive reasonable water resource allocation schemes. The results show that the economic benefits range from 161.94 × 10⁸ to 212.74 × 10⁸ CNY, the water shortage rate is controlled between 1.38% and 10.86%, and COD emissions are maintained between 6.03 × 10⁴ and 6.91 × 10⁴ tons. Except for the 2030 drought scenario (P = 75%) with a coordination degree of 0.7847, classified as a medium coordination level, all other scenarios have coordination degrees greater than 0.8, indicating a good coordination level. The optimized allocation scheme can serve as a reference for the rational allocation of water resources in Jinzhong City. Moreover, the method proposed in this paper is a general approach that can be extended to other similar water-scarce cities with appropriate parameter adjustments, contributing to the sustainable development of urban water resources.

To thoroughly investigate the distribution of heavy rain disaster risks in the Beijing–Tianjin–Hebei region, this paper analyzes the spatiotemporal evolution characteristics of heavy rain disaster-inducing factors. Based on disaster system theory, we constructed a heavy rain disaster risk assessment framework from four dimensions. We improved the application of model-agnostic meta-learning (MAML) in hyperparameter optimization for the random forest (RF) algorithm, thereby developing the MAML-RF heavy rain disaster risk assessment model. This model was compared with the SCV-RF model, which is based on random search and cross-validation (SCV), to determine which model had higher accuracy. Then we introduced the SHAP (Shapley additive explanations) interpretability algorithm to quantify the impact of each risk factor. The results indicate that (1) the annual characteristics of heavy rain days and rainfall amounts show a significant upward trend over the past 17 years; (2) the MAML-RF model improved the accuracy and precision of heavy rain disaster risk simulation by 4.44% and 3.71%, respectively, and reduced training time by 27.95% compared to the SCV-RF model; and (3) the SHAP interpretability algorithm results show that the top five influential factors are the number of heavy rain days, rainfall amount, slope, drainage pipe density, and impervious surface ratio.

Targeting gut microbiota dysbiosis-a key driver in chronic disease-has become a major research focus. Microalgae-derived bioactive compounds, such as polysaccharides, ω-3 PUFAs, carotenoids, and phenolics, represent promising candidates owing to their structural diversity and multi-functional properties. They operate through a dual mechanism: serving as metabolic substrates for beneficial gut bacteria and directly modulating host signaling pathways. This review first systematically analyzes the epidemiological associations between gut dysbiosis and various chronic diseases. It then focuses on the multi-target regulatory potential of microalgal bioactive compounds in mediating gut microbiota-host interactions. Furthermore, it examines key strategies to enhance the production of these functional metabolites, from optimizing photobioreactor design and cultivation conditions to applying induction techniques. Despite promising prospects, challenges remain in enhancing compound bioavailability, overcoming individual heterogeneity in microbiota responses, and establishing standardized clinical evaluation systems. By integrating multi-omics technologies with synthetic biology tools, this review aims to provide innovative perspectives for developing microalgae-based functional foods and precision therapeutics, thereby bridging microbial ecology with personalized nutrition to offer sustainable solutions for chronic disease prevention and management.

With the rapid growth of plastic production, plastic pollution has become increasingly widespread and persistent in the environment. This persistent pollution poses potential risks to ecosystems and human health. Microbial biodegradation offers a promising approach to plastic waste management because it operates under mild reaction conditions and has potential for sustainable application. This review summarizes recent advances in the major microbial groups involved in plastic biodegradation, methods for assessing degradation efficiency, underlying mechanisms, and strategies for improving degradation performance. Assessment methods are discussed, from physicochemical characterization to biochemical validation. Meanwhile, it emphasizes that real plastic biodegradation and degradation efficiency require multiple complementary lines of evidence to judge in a more complete and objective way. The complete process and molecular mechanisms of microbial plastic degradation are then described, including microbial attachment and biofilm formation, enzymatic degradation, intracellular metabolism, and mineralization of degradation products. In addition, it summarizes various efficiency optimization strategies for degradation, including plastic pretreatment, bioaugmentation, and regulation of environmental parameters. The lack of standardized assessment, limited practical translation, and ecological risks are the identified key challenges. Finally, standardized efficiency evaluation strategies, plastic upcycling, and emerging technologies for subsequent development were discussed. This review aims to provide a reference for understanding microbial plastic degradation.