Department of Mathematics and Physics, University of Salento, Via per Arnesano, 73100 Lecce, Italy.
Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019, Sesto Fiorentino, Florence, Italy.
Sci Total Environ. 2020 Aug 15;730:138899. doi: 10.1016/j.scitotenv.2020.138899. Epub 2020 Apr 28.
The Redundancy Discrimination Analysis (RDA) and Spearman correlation coefficients were used to investigate relationships between airborne bacteria at the phylum and genus level and chemical species in winter and spring PM10 samples over Southeastern Italy. The identification of main chemical species/pollution sources that were related to and likely affected the bacterial community structure was the main goal of this work. The 16S rRNA gene metabarcoding approach was used to characterize airborne bacteria. Seventeen phyla and seventy-nine genera contributing each by mean within-sample relative abundance percentage > 0.01% were identified in PM10 samples, which were chemically characterized for 33 species, including ions, metals, OC, and EC (organic and elemental carbon, respectively). Chemical species were associated with six different pollution sources. A shift from winter to spring in both bacterial community structure and chemical species mass concentrations/sources and the relationships between them was observed. RDA triplots pointed out significant correlations for all tested bacterial phyla (genera) with other phyla (genera) and/or with chemical species, in contrast to correlation coefficient results, which showed that few phyla (genera) were significantly correlated with chemical species. More specifically, in winter Bacillus and Chryseobacterium were the only genera significantly correlated with chemical species likely associated with particles from soil-dust and anthropogenic pollution source, respectively. In spring, Enterobacter and Sphingomonas were the only genera significantly correlated with chemical species likely associated with particles from the anthropogenic pollution and the marine and soil-dust sources, respectively. The results of this study also showed that the correlation coefficients were the best tool to obtain unequivocal identifications of the correlations of phyla (genera) with chemical species. The seasonal changes of the PM10 chemical composition, the microbial community structure, and their relationships suggested that the seasonal changes of atmospheric particles may have likely contributed to seasonal changes of bacterial community in the atmosphere.
采用冗余判别分析（RDA）和斯皮尔曼相关系数研究了意大利东南部冬、春 PM10 样本中空气中细菌门和属水平与化学物种之间的关系。本研究的主要目的是确定与细菌群落结构相关且可能影响其结构的主要化学物种/污染源。采用 16S rRNA 基因宏条形码方法对空气中细菌进行了表征。在 PM10 样本中鉴定出了 17 个门和 79 个属，每个门和属的平均相对丰度百分比>0.01%。对 33 种化学物种进行了化学特征分析，包括离子、金属、OC 和 EC（有机碳和元素碳）。化学物种与 6 种不同的污染源有关。无论是细菌群落结构还是化学物种质量浓度/污染源，都观察到从冬季到春季的变化，以及它们之间的关系。RDA 三平面图指出，所有测试的细菌门（属）与其他门（属）和/或与化学物种之间存在显著相关性，这与相关系数结果形成对比，相关系数结果表明，少数细菌门（属）与化学物种显著相关。更具体地说，在冬季，芽孢杆菌和黄杆菌是唯一与化学物种显著相关的属，这些化学物种可能与土壤尘埃和人为污染源的颗粒有关。在春季，肠杆菌属和鞘氨醇单胞菌是唯一与化学物种显著相关的属，这些化学物种可能与人为污染源以及海洋和土壤尘埃源的颗粒有关。本研究的结果还表明，相关系数是确定门（属）与化学物种相关性的最佳工具。PM10 化学成分、微生物群落结构及其相互关系的季节性变化表明，大气颗粒物的季节性变化可能导致大气中细菌群落的季节性变化。
