Rhizosphere effects play crucial roles in determining soil carbon (C) and nitrogen (N) cycling. However, the rhizosphere effect on soil gross nitrogen (N) mineralization (N_min) has not been quantitatively assessed on the global scale. Here we performed a meta-analysis of compiled data from 24 publications and 37 species to synthesize the rhizosphere effect on soil gross N_min and its influencing factors. We found that the rhizosphere effect significantly enhanced soil gross N_min by 81% on average. Such rhizosphere effect was significantly higher in woody species than in nonwoody species, and higher in ECM (ectomycorrhizal) associated species than in AM (arbuscular mycorrhizal) associated species. Moreover, the variations of the rhizosphere effect on soil gross N_min were correlated with those on soil C mineralization, phenol oxidase activity and root biomass rather than with other plant (growth form and mycorrhizal association) and climatic (mean annual temperature and precipitation) factors. These results support the ‘microbial activation’ and ‘microbial N mining’ hypotheses of rhizosphere effects and indicate the coupling of soil C and gross N mineralization in the rhizosphere. Overall, these findings provide novel insights into the rhizosphere effect on soil gross N_min among plant growth forms and mycorrhizal associations, and improve our mechanistic understanding of soil N dynamics in the rhizosphere.

More than half of the earth's terrestrial surface currently experiences seasonal snow cover and soil frost. Winter compositional and functional investigations in soil microbial community are frequently conducted in alpine tundra and boreal forest ecosystems. However, little information on winter microbial biogeochemistry is known from seasonally snow-covered temperate ecosystems. As decomposer microbes may differ in their ability/strategy to efficiently use soil organic carbon (SOC) within different phases of the year, understanding seasonal microbial process will increase our knowledge of biogeochemical cycling from the aspect of decomposition rates and corresponding nutrient dynamics. In this study, we measured soil microbial biomass, community composition and potential SOC mineralization rates in winter and summer, from six temperate ecosystems in northern China. Our results showed a clear pattern of increased microbial biomass C to nitrogen (N) ratio in most winter soils. Concurrently, a shift in soil microbial community composition occurred with higher fungal to bacterial biomass ratio and gram negative (G-) to gram positive (G+) bacterial biomass ratio in winter than in summer. Furthermore, potential SOC mineralization rate was higher in winter than in summer. Our study demonstrated a distinct transition of microbial community structure and function from winter to summer in temperate snow-covered ecosystems. Microbial N immobilization in winter may not be the major contributor for plant growth in the following spring.

Housing sustainability is a cornerstone element of sustainable economic and social development. This is particularly true for China, where high-rise residential buildings are the primary form of housing. In recent years, China has experienced frequent housing-related accidents, resulting in a significant loss of life and property damage. This study aims to identify the key factors influencing housing sustainability and provide a basis for the prevention and control of housing-related safety risks. This study has developed a housing sustainability evaluation indicator system comprising three primary indicators and 16 secondary indicators. This system is based on an analysis of the causes of over 500 typical housing accidents that occurred in China over the past 10 years, employing research methods such as literature reviews and expert consultations, and drawing on the analytical frameworks of risk management theory and system safety theory. Subsequently, the NK-SNA model, which significantly outperforms traditional models in terms of adaptive learning and optimization, as well as the explicit modeling of complex nonlinear relationships, was used to identify the key risk factors affecting housing sustainability. The empirical results indicate that the risk coupling value is correlated with the number of risk coupling factors; the greater the number of risk coupling factors, the larger the coupling value. Human misconduct is prone to forming two-factor risk coupling with housing, and the physical risk factors are prone to coupling with other factors. The environmental factors easily trigger ‘physical–environmental’ two-factor risk coupling. The key factors influencing housing sustainability are poor supervision, building facilities, the main structure, the housing height, foundation settlement, and natural disasters. On this basis, recommendations are made to make full use of modern information technologies such as the Internet of Things, big data, and artificial intelligence to strengthen the supervision of housing safety and avoid multi-factor coupling, and to improve upon early warnings of natural disasters and the design of emergency response programs to control the coupling between physical and environmental factors.