Unveiling the optimal regression model for source apportionment of the oxidative potential of PM

The capacity of particulate matter (PM) to generate reactive oxygen species (ROS) in vivo leading to oxidative stress, is thought to be a main pathway for the health effect of PM inhalation. Exogenous ROS from PM can be assessed by acellular oxidative potential (OP) measurements as a proxy of the induction of oxidative stress in the lungs. Here, we investigate the importance of OP apportionment methods on OP repartition by PM sources in different types of environments. PM sources derived from receptor models (e.g. EPA PMF) are coupled with regression models expressing the associations between PM sources and OP measured by ascorbic acid (OPAA) and dithiothreitol assay (OPDTT). These relationships are compared for eight regression techniques: Ordinary Least Squares, Weighted Least Squares, Positive Least Squares, Ridge, Lasso, Generalized Linear Model, Random Forest, and Multilayer Perceptron. The models are evaluated on one year of PM10 samples and chemical analyses at each of six sites of different typologies in France to assess the possible impact of PM source variability on OP apportionment. Source-specific OPDTT and OPAA and out-of-sample apportionment accuracy vary substantially by model, highlighting the importance of model selection depending on the datasets. Recommendations for the selection of the most accurate model are provided, encompassing considerations such as multicollinearity and homoscedasticity.