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The abundance and diversity of amoA genes of ammonia-oxidizing archaea (AOA) and bacteria (AOB) were investigated in ten wastewater treatment systems (WTSs) by polymerase chain reaction (PCR), cloning, sequencing, and quantitative real-time PCR (qPCR). The ten WTSs included four full-scale municipal WTSs, three full-scale industrial WTSs, and three lab-scale WTSs. AOB were present in all the WTSs, whereas AOA were detected in nine WTSs. QPCR data showed that AOB amoA genes (4.625 × 10(4)-9.99 × 10(9) copies g(-1) sludge) outnumbered AOA amoA genes (

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Purpose The purpose of this paper is to promote the application of building information modeling (BIM) in China’s construction industry, key factors and their relationships are explored. Design/methodology/approach Based on a literature review, 28 factors were extracted and their relationships (239 in total) obtained using the Delphi method. A social network model of the factors was constructed and factors were analyzed using social network analysis (SNA). Findings The top 10 key factors and their relationships were obtained using SNA. Among the top 10 critical factors, six were source factors. They were: training for the application of BIM, guidance from experts, proper management modes, efficient BIM teams, specifications and demonstrations and standards for building information communication. The other four factors included as follows: a willingness to accept BIM, knowledge of its value and benefits, the definition of its benefits and the availability of IT software and hardware. These were mediating factors that could further the influence of the source factors. Originality/value The results provide useful information for public agencies and professionals to understand the immediate and mediating influences of the factors on the application of BIM. Solutions and future efforts for different participants are presented to promote the application of BIM-based on the key factors and their relationships.

Abstract. Understanding leaf stoichiometric patterns is crucial for improving predictions of plant responses to environmental changes. Leaf stoichiometry of terrestrial ecosystems has been widely investigated along latitudinal and longitudinal gradients. However, very little is known about the vertical distribution of leaf C : N : P and the relative effects of environmental parameters, especially for shrubs. Here, we analyzed the shrub leaf C, N and P patterns in 125 mountainous sites over an extensive altitudinal gradient (523–4685 m) on the Tibetan Plateau. Results showed that the shrub leaf C and C : N were 7.3–47.5 % higher than those of other regional and global flora, whereas the leaf N and N : P were 10.2–75.8 % lower. Leaf C increased with rising altitude and decreasing temperature, supporting the physiological acclimation mechanism that high leaf C (e.g., alpine or evergreen shrub) could balance the cell osmotic pressure and resist freezing. The largest leaf N and high leaf P occurred in valley region (altitude 1500 m), likely due to the large nutrient leaching from higher elevations, faster litter decomposition and nutrient resorption ability of deciduous broadleaf shrub. Leaf N : P ratio further indicated increasing N limitation at higher altitudes. Interestingly, drought severity was the only climatic factor positively correlated with leaf N and P, which was more appropriate for evaluating the impact of water status than precipitation. Among the shrub ecosystem and functional types (alpine, subalpine, montane, valley, evergreen, deciduous, broadleaf, and conifer), their leaf element contents and responses to environments were remarkably different. Shrub type was the largest contributor to the total variations in leaf stoichiometry, while climate indirectly affected the leaf C : N : P via its interactive effects on shrub type or soil. Collectively, the large heterogeneity in shrub type was the most important factor explaining the overall leaf C : N : P variations, despite the broad climate gradient on the plateau. Temperature and drought induced shifts in shrub type distribution will influence the nutrient accumulation in mountainous shrubs.