Gusareva Elena S, Gaultier Nicolas E, Uchida Akira, Premkrishnan Balakrishnan N V, Heinle Cassie E, Phung Wen J, Wong Anthony, Lau Kenny J X, Yap Zhei H, Koh Yanqing, Ang Poh N, Putra Alexander, Panicker Deepa, Lee Jessica G H, Neves Luis C, Drautz-Moses Daniela I, Schuster Stephan C
Singapore Center for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.
The Asian School of the Environment, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore.
PNAS Nexus. 2022 Apr 14;1(2):pgac043. doi: 10.1093/pnasnexus/pgac043. eCollection 2022 May.
Recent developments in aerobiology have enabled the investigation of airborne biomass with high temporal and taxonomic resolution. In this study, we assess the contributions of local sources to ambient air within a 160,000 m tropical avian park (AP). We sequenced and analyzed 120 air samples from seven locations situated 160 to 400 m apart, representing distinct microhabitats. Each microhabitat contained a characteristic air microbiome, defined by the abundance and richness of its airborne microbial community members, supported by both, PCoA and Random Forest analysis. Each outdoor microhabitat contained 1% to 18.6% location-specific taxa, while a core microbiome of 27.1% of the total taxa was shared. To identify and assess local sources, we compared the AP dataset with a DVE reference dataset from a location 2 km away, collected during a year-round sampling campaign. Intersection of data from the two sites demonstrated 61.6% of airborne species originated from local sources of the AP, 34.5% from ambient air background, and only 3.9% of species were specific to the DVE reference site. In-depth taxonomic analysis demonstrated association of bacteria-dominated air microbiomes with indoor spaces, while fungi-dominated airborne microbial biomass was predominant in outdoor settings with ample vegetation. The approach presented here demonstrates an ability to identify local source contributions against an ambient air background, despite the prevailing mixing of air masses caused by atmospheric turbulences.
空气生物学的最新进展使得能够以高时间分辨率和分类分辨率对空气中的生物量进行研究。在本研究中，我们评估了一个面积为16万平方米的热带鸟类公园（AP）内本地源对周围空气的贡献。我们对来自七个相距160至400米的地点的120个空气样本进行了测序和分析，这些地点代表了不同的微生境。每个微生境都包含一个独特的空气微生物群落，由其空气传播微生物群落成员的丰度和丰富度定义，这一结果得到了主坐标分析（PCoA）和随机森林分析的支持。每个室外微生境包含1%至18.6%的特定地点分类群，而共有27.1%的总分类群构成了核心微生物群落。为了识别和评估本地源，我们将AP数据集与来自2公里外一个地点的DVE参考数据集进行了比较，该参考数据集是在全年采样活动中收集的。两个地点数据的交集表明，61.6%的空气传播物种源自AP的本地源，34.5%源自周围空气背景，只有3.9%的物种是DVE参考地点特有的。深入的分类分析表明，以细菌为主的空气微生物群落与室内空间有关，而以真菌为主的空气传播微生物生物量在植被丰富的室外环境中占主导地位。尽管大气湍流导致气团普遍混合，但本文提出的方法展示了在周围空气背景下识别本地源贡献的能力。
