Department of Medical Laboratory Science, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Department of Infectious Diseases, Kyorin University School of Medicine, Shinkawa, Mitaka, Tokyo, Japan.
PLoS One. 2024 Mar 21;19(3):e0300920. doi: 10.1371/journal.pone.0300920. eCollection 2024.
We previously reported that variations in the number and type of bacteria found in public spaces are influenced by environmental factors. However, based on field survey data alone, whether the dynamics of bacteria in the air change as a result of a single environmental factor or multiple factors working together remains unclear. To address this, mathematical modeling may be applied. We therefore conducted a reanalysis of the previously acquired data using principal component analysis (PCA) in conjunction with a generalized linear model (Glm2) and a statistical analysis of variance (ANOVA) test employing the χ2 distribution. The data used for the analysis were reused from a previous public environmental survey conducted at 8:00-20:00 on May 2, June 1, and July 5, 2016 (regular sampling) and at 5:50-7:50 and 20:15-24:15 on July 17, 2017 (baseline sampling) in the Sapporo underground walking space, a 520-meter-long underground walkway. The dataset consisted of 60 samples (22 samples for "bacterial flora"), including variables such as "temperature (T)," "humidity (H)," "atmospheric pressure (A)," "traffic pedestrians (TP)," "number of inorganic particles (Δ5: 1-5 μm)," "number of live airborne bacteria," and "bacterial flora." Our PCA with these environmental factors (T, H, A, and TP) revealed that the 60 samples could be categorized into four groups (G1 to G4), primarily based on variations in PC1 [Loadings: T(-0.62), H(-0.647), TP(0.399), A(0.196)] and PC2 [Loadings: A(-0.825), TP(0.501), H(0.209), T(-0.155)]. Notably, the number of inorganic particles significantly increased from G4 to G1, but the count of live bacteria was highest in G2, with no other clear pattern. Further analysis with Glm2 indicated that changes in inorganic particles could largely be explained by two variables (H/TP), while live bacteria levels were influenced by all explanatory variables (TP/A/H/T). ANOVA tests confirmed that inorganic particles and live bacteria were influenced by different factors. Moreover, there were minimal changes in bacterial flora observed among the groups (G1-G4). In conclusion, our findings suggest that the dynamics of live bacteria in the underground walkway differ from those of inorganic particles and are regulated in a complex manner by multiple environmental factors. This discovery may contribute to improving public health in urban settings.
我们之前曾报道过，公共空间中细菌数量和类型的变化受环境因素的影响。然而，仅基于实地调查数据，空气中细菌的动态变化是由单一环境因素还是多个因素共同作用导致的尚不清楚。为了解决这个问题，可以应用数学建模。因此，我们使用主成分分析（PCA）结合广义线性模型（Glm2）和基于 χ2 分布的方差分析（ANOVA）检验，对之前获得的数据进行了重新分析。分析中使用的数据是从 2016 年 5 月 2 日 8:00-20:00、6 月 1 日和 7 月 5 日（常规采样）以及 2017 年 7 月 17 日 5:50-7:50 和 20:15-24:15 在札幌地下步行空间（一条 520 米长的地下步行道）进行的先前公共环境调查中重复使用的。数据集包括 60 个样本（22 个样本用于“细菌菌群”），包括“温度（T）”、“湿度（H）”、“大气压（A）”、“交通行人（TP）”、“无机颗粒数（Δ5：1-5μm）”、“活空气中细菌数”和“细菌菌群”等变量。我们使用这些环境因素（T、H、A 和 TP）进行的 PCA 显示，60 个样本可以分为四组（G1 到 G4），主要基于 PC1 的变化[负荷：T（-0.62）、H（-0.647）、TP（0.399）、A（0.196）]和 PC2 [Loadings：A（-0.825）、TP（0.501）、H（0.209）、T（-0.155）]。值得注意的是，无机颗粒的数量从 G4 到 G1 显著增加，但 G2 中的活细菌数量最高，没有其他明显的规律。Glm2 的进一步分析表明，无机颗粒的变化主要可以用两个变量（H/TP）来解释，而活细菌水平受所有解释变量（TP/A/H/T）的影响。方差分析检验证实，无机颗粒和活细菌受不同因素的影响。此外，各组（G1-G4）之间观察到细菌菌群的变化很小。总之，我们的研究结果表明，地下通道中活细菌的动态变化不同于无机颗粒，并且受多种环境因素以复杂的方式调节。这一发现可能有助于改善城市环境中的公共健康。
