Department of Civil and Environmental Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
Department of Earth and Environmental Engineering, Columbia University, New York, NY, 10027, USA.
Sci Rep. 2018 Aug 7;8(1):11823. doi: 10.1038/s41598-018-29796-7.
Despite progress in monitoring and modeling Asian dust (AD) events, real-time public hazard prediction based on biological evidence during AD events remains a challenge. Herein, both a classification and regression tree (CART) and multiple linear regression (MLR) were applied to assess the applicability of prediction for potential urban airborne bacterial hazards during AD events using metagenomic analysis and real-time qPCR. In the present work, Bacillus cereus was screened as a potential pathogenic candidate and positively correlated with PM concentration (p < 0.05). Additionally, detection of the bceT gene with qPCR, which codes for an enterotoxin in B. cereus, was significantly increased during AD events (p < 0.05). The CART approach more successfully predicted potential airborne bacterial hazards with a relatively high coefficient of determination (R) and small bias, with the smallest root mean square error (RMSE) and mean absolute error (MAE) compared to the MLR approach. Regression tree analyses from the CART model showed that the PM concentration, from 78.4 µg/m to 92.2 µg/m, is an important atmospheric parameter that significantly affects the potential airborne bacterial hazard during AD events. The results show that the CART approach may be useful to effectively derive a predictive understanding of potential airborne bacterial hazards during AD events and thus has a possible for improving decision-making tools for environmental policies associated with air pollution and public health.
尽管在监测和模拟亚洲沙尘（AD）事件方面取得了进展，但基于 AD 事件期间的生物证据进行实时公共危害预测仍然是一个挑战。在此，使用宏基因组分析和实时 qPCR，应用分类回归树（CART）和多元线性回归（MLR）来评估潜在的城市空气传播细菌危害预测的适用性。在本工作中，筛选出蜡样芽孢杆菌作为潜在的致病候选物，并与 PM 浓度呈正相关（p<0.05）。此外，在 AD 事件期间，检测到编码蜡样芽孢杆菌肠毒素的 bceT 基因的 qPCR 显著增加（p<0.05）。与 MLR 方法相比，CART 方法通过相对较高的确定系数（R）和较小的偏差更成功地预测了潜在的空气传播细菌危害，具有最小的均方根误差（RMSE）和平均绝对误差（MAE）。CART 模型的回归树分析表明，PM 浓度在 78.4µg/m 到 92.2µg/m 之间是影响 AD 事件期间潜在空气传播细菌危害的重要大气参数。结果表明，CART 方法可能有助于有效地了解 AD 事件期间潜在的空气传播细菌危害，从而有可能改进与空气污染和公共卫生相关的环境政策的决策工具。
