Dustbins function as critical infrastructures for urban sanitation, creating a distinct breeding ground for microbial assemblages. However, there is no information regarding the dynamics of microbial communities and the underlying mechanism for community assembly on dustbin surfaces. Here, surface samples were collected from three sampling zones (business building, commercial street and residential community) with different types (kitchen waste, harmful waste, recyclables, and others) and materials (metallic and plastic); and distribution pattern and assembly of microbial communities were investigated by high-throughput sequencing. Bacterial and fungal communities showed the distinct community variations across sampling zones and waste sorting. Core community and biomarker species were significantly correlated with the spatial distribution of overall community. The detection of pathogens highlighted the potential risk of surface microbiome. Human skin, human feces and soil biomes were the potential source environments of the surface microbiomes. Neutral model prediction suggested that microbial community assembly was significantly driven by stochastic processes. Co-association patterns varied with sampling zones and waste types, and neutral amplicon sequence variants (ASVs) that fall within the 95 % confidence intervals of neutral model were largely involved in the stability of microbial networks. These findings improve our understanding of the distribution pattern and the underlying assembly of microbial community on the dustbin surface, thus enabling prospective prediction and assessment of urban microbiomes and their impacts on human health.

垃圾箱是城市卫生的重要基础设施，为微生物群落创造了独特的滋生地。然而，没有关于微生物群落动态和垃圾箱表面群落组装的潜在机制的信息。这里，从三个采样区（商务楼、商业街和住宅小区）采集了不同类型（餐厨垃圾、有害垃圾、可回收物等）和材料（金属和塑料）的表面样品；通过高通量测序研究微生物群落的分布模式和组装。细菌和真菌群落在采样区和废物分类之间表现出明显的群落变化。核心群落和生物标志物物种与整个群落的空间分布显着相关。病原体的检测凸显了表面微生物组的潜在风险。人类皮肤、人类粪便和土壤生物群落是表面微生物群落的潜在来源环境。中性模型预测表明微生物群落的组装是由随机过程显着驱动的。共关联模式随采样区域和废物类型的不同而变化，落在中性模型 95% 置信区间内的中性扩增子序列变异 (ASV) 在很大程度上涉及微生物网络的稳定性。这些发现提高了我们对垃圾箱表面微生物群落的分布模式和潜在组装的理解，从而能够对城市微生物组及其对人类健康的影响进行前瞻性预测和评估。