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

Keunje Yoo, Hyunji Yoo, Jae Min Lee, Sudheer Kumar Shukla, Joonhong Park

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Article number11823
JournalScientific reports
Volume8
Issue number1
DOIs
Publication statusPublished - 2018 Dec 1

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hazard
dust
bacterium
prediction
environmental policy
public health
atmospheric pollution
decision making
gene
monitoring
modeling

All Science Journal Classification (ASJC) codes

  • General

Cite this

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title = "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.",
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Classification and Regression Tree Approach for Prediction of Potential Hazards of Urban Airborne Bacteria during Asian Dust Events. / Yoo, Keunje; Yoo, Hyunji; Lee, Jae Min; Shukla, Sudheer Kumar; Park, Joonhong.

In: Scientific reports, Vol. 8, No. 1, 11823, 01.12.2018.

Research output: Contribution to journalArticle

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