Machine learning approaches to predict oxidative potential of fine particulate matter based on chemical constituents

Abstract

Exposure to fine particulate matter (PM_2.5) poses significant health risks, primarily due to its oxidative potential (OP), which induces oxidative stress and related diseases. This study aimed to predict the OP of PM_2.5 based on its chemical constituents using machine learning (ML) models. We collected 119 p.m._2.5 samples from Seoul, Korea, between 2019 and 2021, and analyzed their chemical composition and OP using the dithiothreitol (DTT) assay. Three ML models—k-Nearest Neighbors (kNN), Random Forest (RF), and Fully Connected Deep Neural Network (FCDNN)—were developed to predict OP. Among them, the RF model demonstrated the highest prediction accuracy, with coefficient of determination (R²) values ranging from 0.88 to 0.89 for training data and 0.36 to 0.62 for test data, followed by Extreme Gradient Boosting (XGBoost) and FCDNN with test R² values up to 0.53 and 0.39, respectively. Explainable Artificial Intelligence (AI) techniques, specifically feature importance and SHapley Additive exPlanations (SHAP), were employed to enhance the interpretability of the model, revealing the significant contributions of various chemical constituents. The study underscores the mixed effects of multiple factors on OP and highlights the potential of AI in providing robust predictive tools for environmental health. As OP measurement automation progresses, the availability of large datasets will further improve the accuracy and applicability of AI models, facilitating better health risk assessments and policy-making.