Characterizing sector-oriented roadside exposure to ultrafine particles (PM0.1) via machine learning models: Implications of covariates influences on sectors variability.

ABSTRACT

Ultrafine particles (UFPs; PM0.1) possess intensified health risk due to their smaller size and unique spatial variability. One of major emission sources for UFPs is vehicle exhaust, which varies based on the traffic composition in each type of roadside sector. The current challenge of epidemiological UFPs study is limited characterization ability due to expensive instruments. This study assessed the UFPs particle number concentrations (UFPs PNC) exposure dose for typical healthy adults and children at three different roadside sectors, including industrial roadside (IN), residential roadside (RS), and urban background (UB). Furthermore, this study also developed and utilized machine learning (ML) algorithms that could accurately characterize the UFPs exposure dose and explain the covariates effects on the model outputs, representing the intra-urban variability of UFPs between sectors. It was found that the average inhaled UFPs dose for healthy adults and children during off-peak season (warm period) were 1.71 ± 0.19 × 1010; 1.28 ± 0.22 × 1010; 1.09 ± 0.18 × 1010 #/hour and 1.33 ± 0.15 × 1010; 0.99 ± 0.17 × 1010; 0.86 ± 0.14 × 1010 #/hour at IN, RS, UB. Inhaled UFPs were mainly deposited in tracheobronchial (TB) respiratory fraction for adults (67.7%) and in alveoli (ALV) fraction for children (67.5%). Among three ML algorithms implemented in this study, XGBoost possessed the highest UFPs PNC exposure dose estimation performances with R2 = 0.965; 0.959; 0.929 & RMSE = 0.79 × 108; 0.54 × 108; 0.15 × 105 #/hour at IN, RS, and UB which then followed by multiple linear regression (MLR), and random forest (RF). Furthermore, SHAP analysis from the XGBoost model has successfully pointed out the spatial variability of each roadside sector by quantifying the approximated contributions of covariates to the model's output. Findings in this study highlighted the potential use of ML models as an alternative for preliminary particle exposure source apportionment.