Prediction of airborne bacterial concentrations and identification of critical factors in contaminated waste facilities: Insights into interpretable machine learning models

Abstract

The efficient prediction of airborne bacterial concentrations is crucial for better understanding and management of environmental sanitation risks in waste facilities. Traditional linear models have proven inadequate in capturing the complex relationships governing the formation of airborne microorganisms. This study developed four machine learning (ML) models to estimate airborne bacterial concentrations in waste facilities regarding the combined dataset as input features. The results revealed that integrating environmental factors, gaseous pollutants, and microbial datasets as input features yielded an improved testing R² of 0.7369, with a random forest (RF) model identified as the best-performing algorithm. The bacterial populations on the surfaces and handles of waste containers were identified as the most influential parameters in the RF model. The optimal ranges of temperature (32-36 °C) and relative humidity (62%-80%), the optimal concentrations of ammonia (< 0.15 mg/m³) and particulate matter 2.5 (0.01-0.07 mg/m³), and the effective disinfection measures of slightly acidic electrolyzed water were recommended for controlling airborne pollution in waste facilities. Overall, the research demonstrates that ML methods have the potential in the prediction of airborne bacterial concentrations in waste facilities. By identifying critical factors with the interpretability analysis, this study offers valuable insights for targeted airborne microorganisms’ risk management strategies.