Classification and Regression Tree Approach for Prediction of Potential Hazards of Urban Airborne Bacteria during Asian Dust Events

Abstract

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 PM10 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 (R2) 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 PM10 concentration, from 78.4 µg/m3 to 92.2 µg/m3, 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.